2021 |
Yan, Ruobing; Take, Andy W; Hoult, Neil A; Meehan, Jonathan; Levesque, Christiane Evaluation of Shape Array sensors to quantify the spatial distribution and seasonal rate of track settlement Journal Article Transportation Geotechnics, 27 , pp. 100487, 2021, ISSN: 2214-3912. Abstract | Links | BibTeX | Tags: Field Test, LIDAR, Rail settlement monitoring, Shape Array (SAA), Track geometry irregularities @article{YAN2021100487, title = {Evaluation of Shape Array sensors to quantify the spatial distribution and seasonal rate of track settlement}, author = {Ruobing Yan and Andy W Take and Neil A Hoult and Jonathan Meehan and Christiane Levesque}, url = {http://www.sciencedirect.com/science/article/pii/S2214391220303755}, doi = {https://doi.org/10.1016/j.trgeo.2020.100487}, issn = {2214-3912}, year = {2021}, date = {2021-03-01}, journal = {Transportation Geotechnics}, volume = {27}, pages = {100487}, abstract = {Rail track geometry irregularities can lead to ride discomfort for passengers and redistribution of wheel loads potentially causing derailments. Current techniques for monitoring track settlements involve the use of discrete sensors or vehicle mounted sensors, which make it difficult to capture either spatial or temporal variations in settlement. Shape Array sensors (SAA) can potentially be used to capture temporal variations in distributed track settlement profiles to monitor and investigate potential track geometry irregularities and inform track maintenance programs. In this study, a site with known ground deformation issues (i.e. soft spots) was monitored with both an SAA and intermittent LIDAR scans. The objectives of the research were to investigate the accuracy of the SAA for measuring track settlements based on a comparison with LIDAR data, use those measurements to assess track irregularities at the site, evaluate temporal and seasonal changes in rail deformation, and to gain insight into the underlying causes of the ground deformation issues. The SAA was found to provide comparable settlement measurements to those from the LIDAR with the added advantage that the data could be used to assess settlement rates. At this particular site, long-term rail settlements were found to be a function of seasonal/climatic conditions with slower settlement rates in the winter and higher rates in the summer. In addition, the measurements indicated that the deformation issues are potentially caused by an asymmetric bearing failure.}, keywords = {Field Test, LIDAR, Rail settlement monitoring, Shape Array (SAA), Track geometry irregularities}, pubstate = {published}, tppubtype = {article} } Rail track geometry irregularities can lead to ride discomfort for passengers and redistribution of wheel loads potentially causing derailments. Current techniques for monitoring track settlements involve the use of discrete sensors or vehicle mounted sensors, which make it difficult to capture either spatial or temporal variations in settlement. Shape Array sensors (SAA) can potentially be used to capture temporal variations in distributed track settlement profiles to monitor and investigate potential track geometry irregularities and inform track maintenance programs. In this study, a site with known ground deformation issues (i.e. soft spots) was monitored with both an SAA and intermittent LIDAR scans. The objectives of the research were to investigate the accuracy of the SAA for measuring track settlements based on a comparison with LIDAR data, use those measurements to assess track irregularities at the site, evaluate temporal and seasonal changes in rail deformation, and to gain insight into the underlying causes of the ground deformation issues. The SAA was found to provide comparable settlement measurements to those from the LIDAR with the added advantage that the data could be used to assess settlement rates. At this particular site, long-term rail settlements were found to be a function of seasonal/climatic conditions with slower settlement rates in the winter and higher rates in the summer. In addition, the measurements indicated that the deformation issues are potentially caused by an asymmetric bearing failure. |
Ahmed, Mirza Tahir; Ziauddin, Sheikh; Marshall, Joshua; Greenspan, Michael Point Cloud Registration Using Virtual Interest Points from Macaulay’s Resultant of Quadric Surfaces Journal Article Journal of Mathematical Imaging and Vision, 2021. @article{article, title = {Point Cloud Registration Using Virtual Interest Points from Macaulay’s Resultant of Quadric Surfaces}, author = {Mirza Tahir Ahmed and Sheikh Ziauddin and Joshua Marshall and Michael Greenspan}, doi = {10.1007/s10851-020-01013-z}, year = {2021}, date = {2021-01-07}, journal = {Journal of Mathematical Imaging and Vision}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
Hui, Binyuan; Geng, Ruiying; Ren, Qiyu; Li, Binhua; Li, Yongbin; Sun, Jian; Huang, Fei; Si, Luo; Zhu, Pengfei; Zhu, Xiaodan Dynamic Hybrid Relation Network for Cross-Domain Context-Dependent Semantic Parsing Miscellaneous Forthcoming Forthcoming. BibTeX | Tags: @misc{hui2021dynamic, title = {Dynamic Hybrid Relation Network for Cross-Domain Context-Dependent Semantic Parsing}, author = {Binyuan Hui and Ruiying Geng and Qiyu Ren and Binhua Li and Yongbin Li and Jian Sun and Fei Huang and Luo Si and Pengfei Zhu and Xiaodan Zhu}, year = {2021}, date = {2021-01-05}, keywords = {}, pubstate = {forthcoming}, tppubtype = {misc} } |
2020 |
Tan, Chao-Hong; Yang, Xiaoyu; Zheng, Zióu; Li, Tianda; Feng, Yufei; Gu, Jia-Chen; Liu, Quan; Liu, Dan; Ling, Zhen-Hua; Zhu, Xiaodan Learning to Retrieve Entity-Aware Knowledge and Generate Responses with Copy Mechanism for Task-Oriented Dialogue Systems Miscellaneous 2020. BibTeX | Tags: @misc{tan2020learning, title = {Learning to Retrieve Entity-Aware Knowledge and Generate Responses with Copy Mechanism for Task-Oriented Dialogue Systems}, author = {Chao-Hong Tan and Xiaoyu Yang and Zióu Zheng and Tianda Li and Yufei Feng and Jia-Chen Gu and Quan Liu and Dan Liu and Zhen-Hua Ling and Xiaodan Zhu}, year = {2020}, date = {2020-12-22}, keywords = {}, pubstate = {published}, tppubtype = {misc} } |
Vuuren, Jansen-van R D; Nunzi, J -M; Givigi, S N Frontiers in Photosensor Materials and Designs for New Image Sensor Applications Journal Article IEEE Sensors Journal, pp. 1-1, 2020. @article{9286498, title = {Frontiers in Photosensor Materials and Designs for New Image Sensor Applications}, author = {R D Jansen-van Vuuren and J -M Nunzi and S N Givigi}, doi = {10.1109/JSEN.2020.3043288}, year = {2020}, date = {2020-12-08}, journal = {IEEE Sensors Journal}, pages = {1-1}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
Woods, Joshua E; Lau, David T; Erochko, Jeffrey Evaluation by Hybrid Simulation of Earthquake-Damaged RC Walls Repaired for In-Plane Bending with Single-Sided CFRP Sheets Journal Article Journal of Composites for Construction, 24 (6), pp. 04020073, 2020. Abstract | Links | BibTeX | Tags: @article{doi:10.1061/(ASCE)CC.1943-5614.0001085, title = {Evaluation by Hybrid Simulation of Earthquake-Damaged RC Walls Repaired for In-Plane Bending with Single-Sided CFRP Sheets}, author = {Joshua E Woods and David T Lau and Jeffrey Erochko}, url = {https://ascelibrary.org/doi/abs/10.1061/%28ASCE%29CC.1943-5614.0001085}, doi = {10.1061/(ASCE)CC.1943-5614.0001085}, year = {2020}, date = {2020-12-01}, journal = {Journal of Composites for Construction}, volume = {24}, number = {6}, pages = {04020073}, abstract = {The realistic seismic response of two damaged reinforced concrete (RC) shear walls repaired using externally bonded carbon fiber-reinforced polymer (CFRP) sheets was evaluated using hybrid simulation. The CFRP repair used horizontal and vertical CFRP layers applied to a single side of the wall that were anchored with a steel tube anchor system and CFRP fan anchors. The objective of the CFRP repair was to restore the initial stiffness and restore or increase the strength, ductility, and energy dissipation capacity of the damaged walls. Hybrid simulation was used to evaluate the effectiveness of the repair strategy under real earthquake ground motion records with realistic boundary conditions, including the effects of axial load, shear force, and overturning moment. The results show that the single-sided application of the CFRP sheets restored the seismic performance of the damaged RC shear walls tested in this study. Hybrid simulation is shown to be an efficient test method to experimentally study the seismic response of a structural component with realistic boundary conditions over a range of earthquake hazard levels.}, keywords = {}, pubstate = {published}, tppubtype = {article} } The realistic seismic response of two damaged reinforced concrete (RC) shear walls repaired using externally bonded carbon fiber-reinforced polymer (CFRP) sheets was evaluated using hybrid simulation. The CFRP repair used horizontal and vertical CFRP layers applied to a single side of the wall that were anchored with a steel tube anchor system and CFRP fan anchors. The objective of the CFRP repair was to restore the initial stiffness and restore or increase the strength, ductility, and energy dissipation capacity of the damaged walls. Hybrid simulation was used to evaluate the effectiveness of the repair strategy under real earthquake ground motion records with realistic boundary conditions, including the effects of axial load, shear force, and overturning moment. The results show that the single-sided application of the CFRP sheets restored the seismic performance of the damaged RC shear walls tested in this study. Hybrid simulation is shown to be an efficient test method to experimentally study the seismic response of a structural component with realistic boundary conditions over a range of earthquake hazard levels. |
Jing, Guoqing; Siahkouhi, Mohammad; Edwards, Riley J; Dersch, Marcus S; Hoult, N A Smart railway sleepers - a review of recent developments, challenges, and future prospects Journal Article Construction and Building Materials, pp. 121533, 2020, ISSN: 0950-0618. Abstract | Links | BibTeX | Tags: Concrete sleeper, High speed railway, Self-sensing, Smart railway sleeper, Structural health monitoring, Sustainability design @article{JING2020121533, title = {Smart railway sleepers - a review of recent developments, challenges, and future prospects}, author = {Guoqing Jing and Mohammad Siahkouhi and J Riley Edwards and Marcus S Dersch and N A Hoult}, url = {http://www.sciencedirect.com/science/article/pii/S0950061820335376}, doi = {https://doi.org/10.1016/j.conbuildmat.2020.121533}, issn = {0950-0618}, year = {2020}, date = {2020-11-20}, journal = {Construction and Building Materials}, pages = {121533}, abstract = {To date, the application of self-sensing or other structural health monitoring (SHM) technologies in railway sleepers is limited. Development and deployment of smart sleepers can provide value to rail infrastructure owners and maintainers by informing maintenance and replacement decisions, collecting support condition data providing insight into the health of the track substructure, supporting numerical model development and validation, and providing energy harvesting capabilities. These result in extending the service life of sleepers and the surrounding railway infrastructure providing a key element of sustainable design for the track systems. This paper provides a comprehensive review of recent smart sleeper developments and describes challenges to more widespread adoption. Limitations to using smart sleepers include their production cost and limited data on long term performance, which can be incompatible with the sleeper’s service life. Potential solutions for overcoming the challenges associated with the application of smart sleeper technologies include the use of intrinsic self-sensing concrete, adding self-healing features, taking advantage of recent wireless sensing developments, and connecting with the emerging use of Internet of Things (IoT) technology. Opportunities abound to expand smart sleeper deployment for condition monitoring and real-time management of track assets, to decrease the life cycle cost (LCC) of track components and increase infrastructure availability whereby increasing track capacity.}, keywords = {Concrete sleeper, High speed railway, Self-sensing, Smart railway sleeper, Structural health monitoring, Sustainability design}, pubstate = {published}, tppubtype = {article} } To date, the application of self-sensing or other structural health monitoring (SHM) technologies in railway sleepers is limited. Development and deployment of smart sleepers can provide value to rail infrastructure owners and maintainers by informing maintenance and replacement decisions, collecting support condition data providing insight into the health of the track substructure, supporting numerical model development and validation, and providing energy harvesting capabilities. These result in extending the service life of sleepers and the surrounding railway infrastructure providing a key element of sustainable design for the track systems. This paper provides a comprehensive review of recent smart sleeper developments and describes challenges to more widespread adoption. Limitations to using smart sleepers include their production cost and limited data on long term performance, which can be incompatible with the sleeper’s service life. Potential solutions for overcoming the challenges associated with the application of smart sleeper technologies include the use of intrinsic self-sensing concrete, adding self-healing features, taking advantage of recent wireless sensing developments, and connecting with the emerging use of Internet of Things (IoT) technology. Opportunities abound to expand smart sleeper deployment for condition monitoring and real-time management of track assets, to decrease the life cycle cost (LCC) of track components and increase infrastructure availability whereby increasing track capacity. |
Yang, Xiaoyu; Nie, Feng; Feng, Yufei; Liu, Quan; Chen, Zhigang; Zhu, Xiaodan Program Enhanced Fact Verification with Verbalization and Graph Attention Network Miscellaneous 2020, (arXiv.org > cs > arXiv:2010.03084v4 ). Abstract | Links | BibTeX | Tags: @misc{yang2020program, title = {Program Enhanced Fact Verification with Verbalization and Graph Attention Network}, author = {Xiaoyu Yang and Feng Nie and Yufei Feng and Quan Liu and Zhigang Chen and Xiaodan Zhu}, url = {https://arxiv.org/pdf/2010.03084.pdf}, year = {2020}, date = {2020-11-04}, abstract = {Performing fact verification based on structured data is important for many real-life applications and is a challenging research problem, particularly when it involves both symbolic operations and informal inference based on language understanding. In this paper, we present a Program-enhanced Verbalization and Graph Attention Network (ProgVGAT) to integrate programs and execution into textual inference models. Specifically, a verbalization with program execution model is proposed to accumulate evidences that are embedded in operations over the tables. Built on that, we construct the graph attention verification networks, which are designed to fuse different sources of evidences from verbalized program execution, program structures, and the original statements and tables, to make the final verification decision. To support the above framework, we propose a program selection module optimized with a new training strategy based on margin loss, to produce more accurate programs, which is shown to be effective in enhancing the final verification results. Experimental results show that the proposed framework achieves the new state-of-the-art performance, a 74.4% accuracy, on the benchmark dataset TABFACT. }, note = {arXiv.org > cs > arXiv:2010.03084v4 }, keywords = {}, pubstate = {published}, tppubtype = {misc} } Performing fact verification based on structured data is important for many real-life applications and is a challenging research problem, particularly when it involves both symbolic operations and informal inference based on language understanding. In this paper, we present a Program-enhanced Verbalization and Graph Attention Network (ProgVGAT) to integrate programs and execution into textual inference models. Specifically, a verbalization with program execution model is proposed to accumulate evidences that are embedded in operations over the tables. Built on that, we construct the graph attention verification networks, which are designed to fuse different sources of evidences from verbalized program execution, program structures, and the original statements and tables, to make the final verification decision. To support the above framework, we propose a program selection module optimized with a new training strategy based on margin loss, to produce more accurate programs, which is shown to be effective in enhancing the final verification results. Experimental results show that the proposed framework achieves the new state-of-the-art performance, a 74.4% accuracy, on the benchmark dataset TABFACT. |
Kearns, Oliver; Moore, Ian D; Hoult, Neil A Measured Responses of a Corrugated Steel Ellipse Culvert at Different Cover Depths Journal Article Journal of Bridge Engineering, 25 (11), pp. 04020096, 2020. Abstract | Links | BibTeX | Tags: @article{doi:10.1061/(ASCE)BE.1943-5592.0001635, title = {Measured Responses of a Corrugated Steel Ellipse Culvert at Different Cover Depths}, author = {Oliver Kearns and Ian D Moore and Neil A Hoult}, url = {https://ascelibrary.org/doi/abs/10.1061/%28ASCE%29BE.1943-5592.0001635}, doi = {10.1061/(ASCE)BE.1943-5592.0001635}, year = {2020}, date = {2020-11-01}, journal = {Journal of Bridge Engineering}, volume = {25}, number = {11}, pages = {04020096}, abstract = {Typical design and installation methods for corrugated steel culverts involve consideration of a minimum burial depth and most current North American design codes consider failure only due to excessive circumferential force in the conduit walls (i.e., hoop thrust). However, recent studies have shown that the bending moment is often the more dominant behavior for corrugated steel culverts at shallow cover. To address this issue, an elliptical corrugated steel culvert was tested under simulated vehicle loading at depths ranging from 0.1 to 1.2 m. The results show that, under a wheel pair load, a peak negative bending moment, and thrust, force is consistently developed at the crown with positive bending moments adjacent to the crown and near the shoulders. When the flexural and circumferential force results are extrapolated to the yield point and compared, the bending moment values are up to five times larger than the yield limit while thrust values are only 60% of the limit. The test results suggest that bending moments should be considered during the design and installation of corrugated steel culverts at shallow cover.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Typical design and installation methods for corrugated steel culverts involve consideration of a minimum burial depth and most current North American design codes consider failure only due to excessive circumferential force in the conduit walls (i.e., hoop thrust). However, recent studies have shown that the bending moment is often the more dominant behavior for corrugated steel culverts at shallow cover. To address this issue, an elliptical corrugated steel culvert was tested under simulated vehicle loading at depths ranging from 0.1 to 1.2 m. The results show that, under a wheel pair load, a peak negative bending moment, and thrust, force is consistently developed at the crown with positive bending moments adjacent to the crown and near the shoulders. When the flexural and circumferential force results are extrapolated to the yield point and compared, the bending moment values are up to five times larger than the yield limit while thrust values are only 60% of the limit. The test results suggest that bending moments should be considered during the design and installation of corrugated steel culverts at shallow cover. |
Wolfe, S; Givigi, S; Rabbath, C -A Distributed Multiple Model MPC for Target Tracking UAVs Inproceedings 2020 International Conference on Unmanned Aircraft Systems (ICUAS), pp. 123-130, 2020. Abstract | Links | BibTeX | Tags: @inproceedings{9213852, title = {Distributed Multiple Model MPC for Target Tracking UAVs}, author = {S Wolfe and S Givigi and C -A Rabbath}, doi = {10.1109/ICUAS48674.2020.9213852}, year = {2020}, date = {2020-10-06}, booktitle = {2020 International Conference on Unmanned Aircraft Systems (ICUAS)}, pages = {123-130}, abstract = {In this paper, the idea of using teams of Unmanned Aerial Vehicles (UAVs) to track a ground vehicle and exploiting the benefits of multiple UAVs is considered. The design and testing of a Distributed Multiple Model MPC (DMMMPC) controller for tracking in formation flight is investigated. Using information from state estimation about which target model is performing best, the DMMMPC changes its target motion model accordingly to match the target. This MPC controller is first implemented for a single UAV, then tested in both a real-time simulation environment and indoor flight. The MPC is then expanded to the multi-UAV scenario, which is tested in the same real-time simulation environment, demonstrating effective target tracking in formation flight for the team of UAVs. Lastly, the strength of the distributed topology is shown by exposing specific agents in the formation to measurement occlusions and observing the degradation of the tracking performance of the occluded UAVs.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } In this paper, the idea of using teams of Unmanned Aerial Vehicles (UAVs) to track a ground vehicle and exploiting the benefits of multiple UAVs is considered. The design and testing of a Distributed Multiple Model MPC (DMMMPC) controller for tracking in formation flight is investigated. Using information from state estimation about which target model is performing best, the DMMMPC changes its target motion model accordingly to match the target. This MPC controller is first implemented for a single UAV, then tested in both a real-time simulation environment and indoor flight. The MPC is then expanded to the multi-UAV scenario, which is tested in the same real-time simulation environment, demonstrating effective target tracking in formation flight for the team of UAVs. Lastly, the strength of the distributed topology is shown by exposing specific agents in the formation to measurement occlusions and observing the degradation of the tracking performance of the occluded UAVs. |
Ruan, Yu-Ping; Ling, Zhen-Hua; Zhu, Xiaodan Condition-Transforming Variational Autoencoder for Generating Diverse Short Text Conversations Journal Article ACM Trans. Asian Low-Resour. Lang. Inf. Process., 19 (6), 2020, ISSN: 2375-4699. Abstract | Links | BibTeX | Tags: conversation, Neural network, text generation, variational autoencoder @article{10.1145/3402884, title = {Condition-Transforming Variational Autoencoder for Generating Diverse Short Text Conversations}, author = {Yu-Ping Ruan and Zhen-Hua Ling and Xiaodan Zhu}, url = {https://doi.org/10.1145/3402884}, doi = {10.1145/3402884}, issn = {2375-4699}, year = {2020}, date = {2020-10-01}, journal = {ACM Trans. Asian Low-Resour. Lang. Inf. Process.}, volume = {19}, number = {6}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, abstract = {In this article, conditional-transforming variational autoencoders (CTVAEs) are proposed for generating diverse short text conversations. In conditional variational autoencoders (CVAEs), the prior distribution of latent variable z follows a multivariate Gaussian distribution with mean and variance modulated by the input conditions. Previous work found that this distribution tended to become condition-independent in practical applications. Thus, this article designs CTVAEs to enhance the influence of conditions in CVAEs. In a CTVAE model, the latent variable z is sampled by performing a non-linear transformation on the combination of the input conditions and the samples from a condition-independent prior distribution N (0, I). In our experiments using a Chinese Sina Weibo dataset, the CTVAE model derives z samples for decoding with better condition-dependency than that of the CVAE model. The earth mover’s distance (EMD) between the distributions of the latent variable z at the training stage, and the testing stage is also reduced by using the CTVAE model. In subjective preference tests, our proposed CTVAE model performs significantly better than CVAE and sequence-to-sequence (Seq2Seq) models on generating diverse, informative, and topic-relevant responses.}, keywords = {conversation, Neural network, text generation, variational autoencoder}, pubstate = {published}, tppubtype = {article} } In this article, conditional-transforming variational autoencoders (CTVAEs) are proposed for generating diverse short text conversations. In conditional variational autoencoders (CVAEs), the prior distribution of latent variable z follows a multivariate Gaussian distribution with mean and variance modulated by the input conditions. Previous work found that this distribution tended to become condition-independent in practical applications. Thus, this article designs CTVAEs to enhance the influence of conditions in CVAEs. In a CTVAE model, the latent variable z is sampled by performing a non-linear transformation on the combination of the input conditions and the samples from a condition-independent prior distribution N (0, I). In our experiments using a Chinese Sina Weibo dataset, the CTVAE model derives z samples for decoding with better condition-dependency than that of the CVAE model. The earth mover’s distance (EMD) between the distributions of the latent variable z at the training stage, and the testing stage is also reduced by using the CTVAE model. In subjective preference tests, our proposed CTVAE model performs significantly better than CVAE and sequence-to-sequence (Seq2Seq) models on generating diverse, informative, and topic-relevant responses. |
Delamer, J -A; Givigi, S Trust in Multi-Vehicle Systems Using MDP Control Strategies Inproceedings 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 2377-2382, 2020, ISSN: 2577-1655. Abstract | Links | BibTeX | Tags: Privacy;Protocols;Process control;Markov processes;Unmanned aerial vehicles;Cryptography;Cybernetics @inproceedings{9283302, title = {Trust in Multi-Vehicle Systems Using MDP Control Strategies}, author = {J -A Delamer and S Givigi}, doi = {10.1109/SMC42975.2020.9283302}, issn = {2577-1655}, year = {2020}, date = {2020-10-01}, booktitle = {2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC)}, pages = {2377-2382}, abstract = {This paper proposes a protocol that ensures trust between two vehicles in a multi-vehicle system. Trust is the implicit assessment that another vehicle will follow a predetermined strategy. The communication is done through a channel and the quantity of information transferred is guaranteed to be small. For privacy, the channel can be encrypted, but the message can only be decoded if the vehicles know the control strategy being followed. The protocol is implemented for a problem of two Unmanned Aerial Vehicles (UAVs) trying to find a target in a maze. The control strategy is implemented using Markov Decision Processes (MDPs). Simulations of the protocol demonstrate that communication is received and decoded by the teammates without explicitly revealing the tactics being used.}, keywords = {Privacy;Protocols;Process control;Markov processes;Unmanned aerial vehicles;Cryptography;Cybernetics}, pubstate = {published}, tppubtype = {inproceedings} } This paper proposes a protocol that ensures trust between two vehicles in a multi-vehicle system. Trust is the implicit assessment that another vehicle will follow a predetermined strategy. The communication is done through a channel and the quantity of information transferred is guaranteed to be small. For privacy, the channel can be encrypted, but the message can only be decoded if the vehicles know the control strategy being followed. The protocol is implemented for a problem of two Unmanned Aerial Vehicles (UAVs) trying to find a target in a maze. The control strategy is implemented using Markov Decision Processes (MDPs). Simulations of the protocol demonstrate that communication is received and decoded by the teammates without explicitly revealing the tactics being used. |
Mokogwu, C N; Hashtrudi-Zaad, K Vehicle Platoon String Stability: Network Passivity Approach Inproceedings 2020 IEEE Conference on Control Technology and Applications (CCTA), pp. 648-653, 2020. @inproceedings{9206345, title = {Vehicle Platoon String Stability: Network Passivity Approach}, author = {C N Mokogwu and K Hashtrudi-Zaad}, doi = {10.1109/CCTA41146.2020.9206345}, year = {2020}, date = {2020-09-28}, booktitle = {2020 IEEE Conference on Control Technology and Applications (CCTA)}, pages = {648-653}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } |
Moemen, M Y; Elghamrawy, H; Givigi, S; Noureldin, A M 3-D Reconstruction and Measurement System Based on Multi-Mobile Robot Machine Vision Journal Article IEEE Transactions on Instrumentation and Measurement, pp. 1-1, 2020. @article{9205875, title = {3-D Reconstruction and Measurement System Based on Multi-Mobile Robot Machine Vision}, author = {M Y Moemen and H Elghamrawy and S Givigi and A M Noureldin}, doi = {10.1109/TIM.2020.3026719}, year = {2020}, date = {2020-09-25}, journal = {IEEE Transactions on Instrumentation and Measurement}, pages = {1-1}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
Artan, U; Marshall, J A Towards Automatic Classification of Fragmented Rock Piles via Proprioceptive Sensing and Wavelet Analysis Inproceedings 2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI), pp. 348-353, 2020. Abstract | Links | BibTeX | Tags: Machine Learning, Mining @inproceedings{9235261, title = {Towards Automatic Classification of Fragmented Rock Piles via Proprioceptive Sensing and Wavelet Analysis}, author = {U Artan and J A Marshall}, doi = {10.1109/MFI49285.2020.9235261}, year = {2020}, date = {2020-09-14}, booktitle = {2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)}, pages = {348-353}, abstract = {In this paper, we describe a method for classifying rock piles characterized by different size distributions by using accelerometer data and wavelet analysis. Size distribution (frag-mentation) estimates are used in the mining and aggregates industries to ensure the rock that enters the crushing and grinding circuits meet input design specifications. Current technologies use exteroceptive sensing to estimate size distributions from, for example, camera images. Our approach instead proposes the use of signals acquired from the process of loading equipment that are used to transport fragmented rock. The experimental setup used a laboratory-sized mock up of a haul truck with two inertial measurement units (IMUs) for data collection. Results utilizing wavelet analysis are provided that show how accelerometers could be used to distinguish between piles with different size distributions.}, keywords = {Machine Learning, Mining}, pubstate = {published}, tppubtype = {inproceedings} } In this paper, we describe a method for classifying rock piles characterized by different size distributions by using accelerometer data and wavelet analysis. Size distribution (frag-mentation) estimates are used in the mining and aggregates industries to ensure the rock that enters the crushing and grinding circuits meet input design specifications. Current technologies use exteroceptive sensing to estimate size distributions from, for example, camera images. Our approach instead proposes the use of signals acquired from the process of loading equipment that are used to transport fragmented rock. The experimental setup used a laboratory-sized mock up of a haul truck with two inertial measurement units (IMUs) for data collection. Results utilizing wavelet analysis are provided that show how accelerometers could be used to distinguish between piles with different size distributions. |
Fernando, Heshan; Marshall, Joshua What lies beneath: Material classification for autonomous excavators using proprioceptive force sensing and machine learning Journal Article 119 , pp. 103374, 2020, ISSN: 0926-5805. Abstract | Links | BibTeX | Tags: Force sensing, Loader automation, Machine Learning, Material classification, Robotic excavation @article{FERNANDO2020103374, title = {What lies beneath: Material classification for autonomous excavators using proprioceptive force sensing and machine learning}, author = {Heshan Fernando and Joshua Marshall}, url = {http://www.sciencedirect.com/science/article/pii/S0926580520309547}, doi = {https://doi.org/10.1016/j.autcon.2020.103374}, issn = {0926-5805}, year = {2020}, date = {2020-09-04}, volume = {119}, pages = {103374}, abstract = {The ability of robotic excavators to acquire meaningful knowledge about materials during digging can augment their autonomous functionality, as well as optimize downstream operations in construction and mining. Some material properties, such as rock sizes, can be determined visually, but these methods cannot see what lies beneath. In this work, a classification methodology that utilizes only proprioceptive force data acquired from an autonomous digging system and machine learning algorithms is proposed for excavation material identification. The consistent performance synonymous with autonomous digging systems allows for the use of basic features extracted from the force data for classification. A proof of concept of this novel approach to excavation material classification is demonstrated through a binary classification of rock and gravel materials. Force data were obtained from full-scale autonomous loading trials with a 14-tonne capacity load-haul-dump machine at a mining and construction test facility. Preliminary results achieved a classification accuracy of 90%.}, keywords = {Force sensing, Loader automation, Machine Learning, Material classification, Robotic excavation}, pubstate = {published}, tppubtype = {article} } The ability of robotic excavators to acquire meaningful knowledge about materials during digging can augment their autonomous functionality, as well as optimize downstream operations in construction and mining. Some material properties, such as rock sizes, can be determined visually, but these methods cannot see what lies beneath. In this work, a classification methodology that utilizes only proprioceptive force data acquired from an autonomous digging system and machine learning algorithms is proposed for excavation material identification. The consistent performance synonymous with autonomous digging systems allows for the use of basic features extracted from the force data for classification. A proof of concept of this novel approach to excavation material classification is demonstrated through a binary classification of rock and gravel materials. Force data were obtained from full-scale autonomous loading trials with a 14-tonne capacity load-haul-dump machine at a mining and construction test facility. Preliminary results achieved a classification accuracy of 90%. |
Kudrinko, K; Flavin, E; Zhu, X; Li, Q Wearable Sensor-Based Sign Language Recognition: A Comprehensive Review Journal Article IEEE Reviews in Biomedical Engineering, pp. 1-1, 2020, ISSN: 1941-1189. Abstract | Links | BibTeX | Tags: Gesture recognition;Assistive technology;Cameras;Auditory system;Three-dimensional displays;Sociology;Statistics;wearable sensors;sign language;gesture recognition;machine learning;sensor systems @article{9178440, title = {Wearable Sensor-Based Sign Language Recognition: A Comprehensive Review}, author = {K Kudrinko and E Flavin and X Zhu and Q Li}, doi = {10.1109/RBME.2020.3019769}, issn = {1941-1189}, year = {2020}, date = {2020-08-26}, journal = {IEEE Reviews in Biomedical Engineering}, pages = {1-1}, abstract = {Sign language is used as a primary form of communication by many people who are Deaf, deafened, hard of hearing, and non-verbal. Communication barriers exist for members of these populations during daily interactions with those who are unable to understand or use sign language. Advancements in technology and machine learning techniques have led to the development of innovative approaches for gesture recognition. This literature review focuses on analyzing studies that use wearable sensor-based systems to classify sign language gestures. A review of 72 studies from 1991 to 2019 was performed to identify trends, best practices, and common challenges. Attributes including sign language variation, sensor configuration, classification method, study design, and performance metrics were analyzed and compared. Results from this literature review could aid in the development of user-centred and robust wearable sensor-based systems for sign language recognition.}, keywords = {Gesture recognition;Assistive technology;Cameras;Auditory system;Three-dimensional displays;Sociology;Statistics;wearable sensors;sign language;gesture recognition;machine learning;sensor systems}, pubstate = {published}, tppubtype = {article} } Sign language is used as a primary form of communication by many people who are Deaf, deafened, hard of hearing, and non-verbal. Communication barriers exist for members of these populations during daily interactions with those who are unable to understand or use sign language. Advancements in technology and machine learning techniques have led to the development of innovative approaches for gesture recognition. This literature review focuses on analyzing studies that use wearable sensor-based systems to classify sign language gestures. A review of 72 studies from 1991 to 2019 was performed to identify trends, best practices, and common challenges. Attributes including sign language variation, sensor configuration, classification method, study design, and performance metrics were analyzed and compared. Results from this literature review could aid in the development of user-centred and robust wearable sensor-based systems for sign language recognition. |
Mokogwu, C N; Hashtrudi-Zaad, K Vehicle Platoon String Stability: Network Passivity Approach Inproceedings 2020 IEEE Conference on Control Technology and Applications (CCTA), pp. 648-653, 2020. @inproceedings{9206345b, title = {Vehicle Platoon String Stability: Network Passivity Approach}, author = {C N Mokogwu and K Hashtrudi-Zaad}, doi = {10.1109/CCTA41146.2020.9206345}, year = {2020}, date = {2020-08-24}, booktitle = {2020 IEEE Conference on Control Technology and Applications (CCTA)}, pages = {648-653}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } |
Miller, Sean; Woods, Joshua E; Erochko, Jeff; Lau, David T; Gilbert, Colin F Experimental and analytical fragility assessment of a combined heavy timber–steel-braced frame through hybrid simulation Journal Article Earthquake Engineering & Structural Dynamics, n/a (n/a), 2020. Abstract | Links | BibTeX | Tags: fragility curves, friction-braced frame, heavy timber, hybrid simulation, incremental dynamic analysis, steel @article{https://doi.org/10.1002/eqe.3329, title = {Experimental and analytical fragility assessment of a combined heavy timber–steel-braced frame through hybrid simulation}, author = {Sean Miller and Joshua E Woods and Jeff Erochko and David T Lau and Colin F Gilbert}, url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/eqe.3329}, doi = {https://doi.org/10.1002/eqe.3329}, year = {2020}, date = {2020-08-09}, journal = {Earthquake Engineering & Structural Dynamics}, volume = {n/a}, number = {n/a}, abstract = {Summary A combined heavy timber–steel structural system has recently been developed for the adaptation of advanced structural braces into tall timber structures. This study investigates the system-level performance of a seven-story combined timber–steel friction-braced frame using hybrid simulation and incremental dynamic analysis (IDA). The IDA results from the hybrid simulations are compared with a numerical model to assess its ability to capture the nonlinear seismic performance of the timber–steel friction-braced frame. Experimental and numerical IDA results are used to derive fragility curves for interstory and residual drift and assess its collapse probability under different earthquake hazard levels. The hybrid test results show that the timber–steel friction-braced frame demonstrates excellent seismic performance under a range of ground motion intensities. The experimental results from the hybrid simulations and the experimental fragility curves correlate well with the numerical modeling results.}, keywords = {fragility curves, friction-braced frame, heavy timber, hybrid simulation, incremental dynamic analysis, steel}, pubstate = {published}, tppubtype = {article} } Summary A combined heavy timber–steel structural system has recently been developed for the adaptation of advanced structural braces into tall timber structures. This study investigates the system-level performance of a seven-story combined timber–steel friction-braced frame using hybrid simulation and incremental dynamic analysis (IDA). The IDA results from the hybrid simulations are compared with a numerical model to assess its ability to capture the nonlinear seismic performance of the timber–steel friction-braced frame. Experimental and numerical IDA results are used to derive fragility curves for interstory and residual drift and assess its collapse probability under different earthquake hazard levels. The hybrid test results show that the timber–steel friction-braced frame demonstrates excellent seismic performance under a range of ground motion intensities. The experimental results from the hybrid simulations and the experimental fragility curves correlate well with the numerical modeling results. |
Magueta, M P B; d. Santos, S R B; Cappabianco, F A M; Givigi, S N Designing Collective Behavior for Construction of Containment Structures using Actuated Blocks Inproceedings 2020 IEEE International Systems Conference (SysCon), pp. 1-8, 2020, ISSN: 2472-9647. Abstract | Links | BibTeX | Tags: Training;Robots;Robot kinematics;Self-assembly;Robotic assembly;Dispersion;Collision avoidance @inproceedings{9275866, title = {Designing Collective Behavior for Construction of Containment Structures using Actuated Blocks}, author = {M P B Magueta and S R B d. Santos and F A M Cappabianco and S N Givigi}, doi = {10.1109/SysCon47679.2020.9275866}, issn = {2472-9647}, year = {2020}, date = {2020-08-01}, booktitle = {2020 IEEE International Systems Conference (SysCon)}, pages = {1-8}, abstract = {This paper presents a decentralized learning algorithm for learning how to coordinate an automated team of actuated parts designed to build several types of structures specified by a user on a plane surface. The algorithm learns from the environment feedback and agent behavior. This problem is defined as a Markov decision process where agents (actuated parts) are modeled as small cube-shaped robots subject to the Bellman’s equation (Q-learning). The Q-learning algorithm considers the communication and conflict resolution models between the agents that lead to the emergence of intelligent global behavior (in a non-stationary stochastic environment). The main contribution of this paper is to propose a self-assembly approach capable of randomly generating the navigation routes of the multiple agents while learning the structure shape according to the hazardous dispersion area that must be isolated in the environment. Simulation trials show the feasibility of merging between the multi-agent coordination process and anti-collision strategy where different case studies are analysed and discussed.}, keywords = {Training;Robots;Robot kinematics;Self-assembly;Robotic assembly;Dispersion;Collision avoidance}, pubstate = {published}, tppubtype = {inproceedings} } This paper presents a decentralized learning algorithm for learning how to coordinate an automated team of actuated parts designed to build several types of structures specified by a user on a plane surface. The algorithm learns from the environment feedback and agent behavior. This problem is defined as a Markov decision process where agents (actuated parts) are modeled as small cube-shaped robots subject to the Bellman’s equation (Q-learning). The Q-learning algorithm considers the communication and conflict resolution models between the agents that lead to the emergence of intelligent global behavior (in a non-stationary stochastic environment). The main contribution of this paper is to propose a self-assembly approach capable of randomly generating the navigation routes of the multiple agents while learning the structure shape according to the hazardous dispersion area that must be isolated in the environment. Simulation trials show the feasibility of merging between the multi-agent coordination process and anti-collision strategy where different case studies are analysed and discussed. |
Silveira, J; Givigi, S N; Freire, E O; Molina, L; Carvalho, E Aggressive Motion Planning for a Quadrotor System with Slung Load Based on RRT Inproceedings 2020 IEEE International Systems Conference (SysCon), pp. 1-7, 2020, ISSN: 2472-9647. Abstract | Links | BibTeX | Tags: Task analysis;Attitude control;Planning;Payloads;Trajectory;Heuristic algorithms;Rotors;Motion Planning;Kinodynamic RRT;Quadrotor with slung load @inproceedings{9275664, title = {Aggressive Motion Planning for a Quadrotor System with Slung Load Based on RRT}, author = {J Silveira and S N Givigi and E O Freire and L Molina and E Carvalho}, doi = {10.1109/SysCon47679.2020.9275664}, issn = {2472-9647}, year = {2020}, date = {2020-08-01}, booktitle = {2020 IEEE International Systems Conference (SysCon)}, pages = {1-7}, abstract = {Recent advances in technologies related to Un-manned Aerial Vehicles have led to applications in challenging tasks such as carrying a payload through a small window. Common approaches to solve this task are based on Reinforcement Learning, Quadratic Programming, and Model Predictive Control. Although they provide optimal motion planning, they rely on the necessity of a quadratic cost function or linear constraints for the obstacles. We propose to use a variant of the Rapidly-exploring Random Tree algorithm to solve the task without relying on convex constraints, tuning weights for cost functions or linear constraints. We show that our approach was able to solve the task of crossing a small window with a slung load, and we suggest future modifications to accelerate the convergence rate of the algorithm. The main advantage of the proposed approach is the capability finding a collision-free trajectory for the whole system that is not restricted to linear constraints, and does not depend on cost functions.}, keywords = {Task analysis;Attitude control;Planning;Payloads;Trajectory;Heuristic algorithms;Rotors;Motion Planning;Kinodynamic RRT;Quadrotor with slung load}, pubstate = {published}, tppubtype = {inproceedings} } Recent advances in technologies related to Un-manned Aerial Vehicles have led to applications in challenging tasks such as carrying a payload through a small window. Common approaches to solve this task are based on Reinforcement Learning, Quadratic Programming, and Model Predictive Control. Although they provide optimal motion planning, they rely on the necessity of a quadratic cost function or linear constraints for the obstacles. We propose to use a variant of the Rapidly-exploring Random Tree algorithm to solve the task without relying on convex constraints, tuning weights for cost functions or linear constraints. We show that our approach was able to solve the task of crossing a small window with a slung load, and we suggest future modifications to accelerate the convergence rate of the algorithm. The main advantage of the proposed approach is the capability finding a collision-free trajectory for the whole system that is not restricted to linear constraints, and does not depend on cost functions. |
Ramos, A; Hashtrudi-Zaad, K Estimation of Energy Absorption Capability of Arm Using Force Myography for Stable Human-Machine Interaction Inproceedings 2020 42nd Annual International Conference of the IEEE Engineering in Medicine Biology Society (EMBC), pp. 4758-4761, 2020. @inproceedings{9175410, title = {Estimation of Energy Absorption Capability of Arm Using Force Myography for Stable Human-Machine Interaction}, author = {A Ramos and K Hashtrudi-Zaad}, doi = {10.1109/EMBC44109.2020.9175410}, year = {2020}, date = {2020-07-20}, booktitle = {2020 42nd Annual International Conference of the IEEE Engineering in Medicine Biology Society (EMBC)}, pages = {4758-4761}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } |
Davoodnia, Vandad; Slinowsky, Monet; Etemad, Ali Deep multitask learning for pervasive BMI estimation and identity recognition in smart beds Journal Article Journal of Ambient Intelligence and Humanized Computing, pp. 1–15, 2020. Abstract | Links | BibTeX | Tags: Artificial Intelligence, Healthcare, Internet of Things @article{davoodnia2020deep, title = {Deep multitask learning for pervasive BMI estimation and identity recognition in smart beds}, author = { Vandad Davoodnia and Monet Slinowsky and Ali Etemad}, doi = {10.1007/s12652-020-02210-9}, year = {2020}, date = {2020-06-26}, journal = {Journal of Ambient Intelligence and Humanized Computing}, pages = {1--15}, publisher = {Springer}, abstract = {Smart devices in the Internet of Things (IoT) paradigm provide a variety of unobtrusive and pervasive means for continuous monitoring of bio-metrics and health information. Furthermore, automated personalization and authentication through such smart systems can enable better user experience and security. In this paper, simultaneous estimation and monitoring of body mass index (BMI) and user identity recognition through a unified machine learning framework using smart beds is explored. To this end, we utilize pressure data collected from textile-based sensor arrays integrated onto a mattress to estimate the BMI values of subjects and classify their identities in different positions by using a deep multitask neural network. First, we filter and extract 14 features from the data and subsequently employ deep neural networks for BMI estimation and subject identification on two different public datasets. Finally, we demonstrate that our proposed solution outperforms prior works and several machine learning benchmarks by a considerable margin, while also estimating users’ BMI in a 10-fold cross-validation scheme.}, keywords = {Artificial Intelligence, Healthcare, Internet of Things}, pubstate = {published}, tppubtype = {article} } Smart devices in the Internet of Things (IoT) paradigm provide a variety of unobtrusive and pervasive means for continuous monitoring of bio-metrics and health information. Furthermore, automated personalization and authentication through such smart systems can enable better user experience and security. In this paper, simultaneous estimation and monitoring of body mass index (BMI) and user identity recognition through a unified machine learning framework using smart beds is explored. To this end, we utilize pressure data collected from textile-based sensor arrays integrated onto a mattress to estimate the BMI values of subjects and classify their identities in different positions by using a deep multitask neural network. First, we filter and extract 14 features from the data and subsequently employ deep neural networks for BMI estimation and subject identification on two different public datasets. Finally, we demonstrate that our proposed solution outperforms prior works and several machine learning benchmarks by a considerable margin, while also estimating users’ BMI in a 10-fold cross-validation scheme. |
Mitchell, Jordan; Marshall, Joshua Autorotating unmanned aerial vehicle surveying platform Patent 2020, (US Patent App. 16/046,436). Abstract | Links | BibTeX | Tags: Mapping, Signal Processing, Unmanned Aerial Vehicles @patent{mitchell2019autorotating, title = {Autorotating unmanned aerial vehicle surveying platform}, author = { Jordan Mitchell and Joshua Marshall}, url = {https://patents.google.com/patent/US20190031342A1/en}, year = {2020}, date = {2020-06-09}, abstract = {An autorotating unmanned aerial vehicle (UAV) has a data acquisition system and a rotor assembly including a hub that couples the rotor assembly to the UAV. Although not limited thereto, the UAV is suitable for collecting data about the inside of a cavity. The data acquisition system includes a processor and one or more sensors that obtain data about motion of the UAV and at least one parameter of the cavity as the UAV descends though the cavity. Features of the cavity may be mapped by generating a 3D point cloud from the data. The cavity may be natural or man-made, such as a mine.}, note = {US Patent App. 16/046,436}, keywords = {Mapping, Signal Processing, Unmanned Aerial Vehicles}, pubstate = {published}, tppubtype = {patent} } An autorotating unmanned aerial vehicle (UAV) has a data acquisition system and a rotor assembly including a hub that couples the rotor assembly to the UAV. Although not limited thereto, the UAV is suitable for collecting data about the inside of a cavity. The data acquisition system includes a processor and one or more sensors that obtain data about motion of the UAV and at least one parameter of the cavity as the UAV descends though the cavity. Features of the cavity may be mapped by generating a 3D point cloud from the data. The cavity may be natural or man-made, such as a mine. |
Sreedharan, Sarath; Chakraborti, Tathagata; Muise, Christian; Khazaeni, Yasaman; Kambhampati, Subbarao --D3WA+--A Case Study of XAIP in a Model Acquisition Task for Dialogue Planning Inproceedings Proceedings of the International Conference on Automated Planning and Scheduling, pp. 488–497, 2020. Abstract | Links | BibTeX | Tags: Artificial Intelligence, Human Machine Interaction, Natural Language Processing, Planning @inproceedings{sreedharan2020d3wa+, title = {--D3WA+--A Case Study of XAIP in a Model Acquisition Task for Dialogue Planning}, author = { Sarath Sreedharan and Tathagata Chakraborti and Christian Muise and Yasaman Khazaeni and Subbarao Kambhampati}, url = {https://www.aaai.org/ojs/index.php/ICAPS/article/view/6744}, year = {2020}, date = {2020-06-01}, booktitle = {Proceedings of the International Conference on Automated Planning and Scheduling}, volume = {30}, pages = {488--497}, abstract = {Recently, the D3WA system was proposed as a paradigm shift in how complex goal-oriented dialogue agents can be specified by taking a declarative view of design. However, it turns out actual users of the system have a hard time evolving their mental model and grasping the imperative consequences of declarative design. In this paper, we adopt ideas from existing works in the field of Explainable AI Planning (XAIP) to provide guidance to the dialogue designer during the model acquisition process. We will highlight in the course of this discussion how the setting presents unique challenges to the XAIP setting, including having to deal with the user persona of a domain modeler rather than the end-user of the system, and consequently having to deal with the unsolvability of models in addition to explaining generated plans.}, keywords = {Artificial Intelligence, Human Machine Interaction, Natural Language Processing, Planning}, pubstate = {published}, tppubtype = {inproceedings} } Recently, the D3WA system was proposed as a paradigm shift in how complex goal-oriented dialogue agents can be specified by taking a declarative view of design. However, it turns out actual users of the system have a hard time evolving their mental model and grasping the imperative consequences of declarative design. In this paper, we adopt ideas from existing works in the field of Explainable AI Planning (XAIP) to provide guidance to the dialogue designer during the model acquisition process. We will highlight in the course of this discussion how the setting presents unique challenges to the XAIP setting, including having to deal with the user persona of a domain modeler rather than the end-user of the system, and consequently having to deal with the unsolvability of models in addition to explaining generated plans. |
von Tiessenhausen, Johann; Artan, Unal; Marshall, Joshua; Li, Qingguo Hand gesture-based control of a front-end loader Journal Article 2020. Abstract | Links | BibTeX | Tags: Human Machine Interaction, Mining, Wearables @article{vonhand, title = {Hand gesture-based control of a front-end loader}, author = { Johann von Tiessenhausen and Unal Artan and Joshua Marshall and Qingguo Li}, url = {http://hdl.handle.net/1974/27726}, year = {2020}, date = {2020-05-01}, publisher = {IEEE}, abstract = {In this paper, we present the design and use of an instrumented glove consisting of a 9-DOF inertial measurement unit (IMU) and resistive flex sensors. The glove is used as a unique human-machine interface to control a Kubota R520s front-end loader, through input gestures, for the excavation of a fragmented rock pile. Raw sensor data from the glove is recorded and transmitted to a computer for gesture recognition. Recognized gestures are then used to command the loader to switch between dig states and control the excavation process. The system allows an operator to observe the entire process from beside the loader, providing them with valuable information about interactions between the loader bucket and rock pile not usually available when seated in the vehicle's cab. Preliminary experiments show that a novice operator was able to improve their performance by using the proposed system, evaluated based on metrics of total and dig completion times, as well as payload.}, keywords = {Human Machine Interaction, Mining, Wearables}, pubstate = {published}, tppubtype = {article} } In this paper, we present the design and use of an instrumented glove consisting of a 9-DOF inertial measurement unit (IMU) and resistive flex sensors. The glove is used as a unique human-machine interface to control a Kubota R520s front-end loader, through input gestures, for the excavation of a fragmented rock pile. Raw sensor data from the glove is recorded and transmitted to a computer for gesture recognition. Recognized gestures are then used to command the loader to switch between dig states and control the excavation process. The system allows an operator to observe the entire process from beside the loader, providing them with valuable information about interactions between the loader bucket and rock pile not usually available when seated in the vehicle's cab. Preliminary experiments show that a novice operator was able to improve their performance by using the proposed system, evaluated based on metrics of total and dig completion times, as well as payload. |
Houmanfar, Roshanak; Etemad, Seyed Ali; MacEachern, Leonard; Klibanov, Mark Systems, methods and devices for activity recognition Patent 2020, (US Patent 10,575,760). Abstract | Links | BibTeX | Tags: Artificial Intelligence, Predictive Model, Wearables @patent{houmanfar2020systems, title = {Systems, methods and devices for activity recognition}, author = { Roshanak Houmanfar and Seyed Ali Etemad and Leonard MacEachern and Mark Klibanov}, url = {https://patents.google.com/patent/US10575760B2/en}, year = {2020}, date = {2020-03-03}, publisher = {Google Patents}, abstract = {Systems, methods and devices for recognizing user activity using data from accelerometer or other sensors. “Feature-based” approach and “model-based” approaches are described. In a feature-based approach, various values are extracted from input signals and projected onto a space that is selected to facilitate better segregation of data points. Classifiers identify the regions in this projected space in which the data points fall to distinguish between the different activity types. In a “model-based” approach, a generative model is trained for each activity type. Different activity types can be distinguished by identifying similarities between the input data with the generative models.}, note = {US Patent 10,575,760}, keywords = {Artificial Intelligence, Predictive Model, Wearables}, pubstate = {published}, tppubtype = {patent} } Systems, methods and devices for recognizing user activity using data from accelerometer or other sensors. “Feature-based” approach and “model-based” approaches are described. In a feature-based approach, various values are extracted from input signals and projected onto a space that is selected to facilitate better segregation of data points. Classifiers identify the regions in this projected space in which the data points fall to distinguish between the different activity types. In a “model-based” approach, a generative model is trained for each activity type. Different activity types can be distinguished by identifying similarities between the input data with the generative models. |
Salari, Omid; Zaad, Keyvan Hashtrudi; Bakhshai, Alireza; Jain, Praveen Reconfigurable Hybrid Energy Storage System for an Electric Vehicle DC/AC Inverter Journal Article IEEE Transactions on Power Electronics, 2020, ISSN: 1941-0107. Abstract | Links | BibTeX | Tags: Controls, Energy Storage @article{salari2020reconfigurable, title = {Reconfigurable Hybrid Energy Storage System for an Electric Vehicle DC/AC Inverter}, author = { Omid Salari and Keyvan Hashtrudi Zaad and Alireza Bakhshai and Praveen Jain}, doi = {10.1109/TPEL.2020.2993783}, issn = {1941-0107}, year = {2020}, date = {2020-01-01}, journal = {IEEE Transactions on Power Electronics}, publisher = {IEEE}, abstract = {Hybrid energy storage systems using battery packs and super capacitor (SC) banks are gaining considerable attraction in Electric Vehicle (EV) applications. Conventionally, such hybrid systems have been exploited using massive high-power DC/DC converters with bulk magnetic elements. Recently, switch-capacitor Multi-Source Inverters (MSIs) have been introduced for such applications. In this paper a new modular reconfigurable MSI is proposed for active control of energy storage systems in EV applications. Utilizing MSIs, reduces the weight and the volume of the power electronics interface and offers simple control. Along with the proposed MSI, a Space Vector Modulation (SVM) technique and a deterministic State of Charge (SOC) controller are also introduced for control of the switching actions and the operation of the SC bank. Simulations using MATLAB/Simulink and experimental results on a scaled down lab prototype are studied to assess the concepts.}, keywords = {Controls, Energy Storage}, pubstate = {published}, tppubtype = {article} } Hybrid energy storage systems using battery packs and super capacitor (SC) banks are gaining considerable attraction in Electric Vehicle (EV) applications. Conventionally, such hybrid systems have been exploited using massive high-power DC/DC converters with bulk magnetic elements. Recently, switch-capacitor Multi-Source Inverters (MSIs) have been introduced for such applications. In this paper a new modular reconfigurable MSI is proposed for active control of energy storage systems in EV applications. Utilizing MSIs, reduces the weight and the volume of the power electronics interface and offers simple control. Along with the proposed MSI, a Space Vector Modulation (SVM) technique and a deterministic State of Charge (SOC) controller are also introduced for control of the switching actions and the operation of the SC bank. Simulations using MATLAB/Simulink and experimental results on a scaled down lab prototype are studied to assess the concepts. |
Agarwal, Shubham; Muise, Christian; Agarwal, Mayank; Upadhyay, Sohini; Tang, Zilu; Zeng, Zhongshen; Khazaeni, Yasaman TraceHub-A Platform to Bridge the Gap between State-of-the-Art Time-Series Analytics and Datasets. Inproceedings AAAI, pp. 13600–13601, 2020. Links | BibTeX | Tags: Artificial Intelligence, Planning, Signal Processing @inproceedings{agarwal2020tracehub, title = {TraceHub-A Platform to Bridge the Gap between State-of-the-Art Time-Series Analytics and Datasets.}, author = { Shubham Agarwal and Christian Muise and Mayank Agarwal and Sohini Upadhyay and Zilu Tang and Zhongshen Zeng and Yasaman Khazaeni}, url = {https://www.aaai.org/Papers/AAAI/2020GB/DEMO-AgarwalS.556.pdf}, year = {2020}, date = {2020-01-01}, booktitle = {AAAI}, pages = {13600--13601}, keywords = {Artificial Intelligence, Planning, Signal Processing}, pubstate = {published}, tppubtype = {inproceedings} } |
Asai, Masataro; Muise, Christian Learning Neural-Symbolic Descriptive Planning Models via Cube-Space Priors: The Voyage Home (to STRIPS) Journal Article arXiv preprint arXiv:2004.12850, 2020. Abstract | Links | BibTeX | Tags: Artificial Intelligence, Planning @article{asai2020learning, title = {Learning Neural-Symbolic Descriptive Planning Models via Cube-Space Priors: The Voyage Home (to STRIPS)}, author = { Masataro Asai and Christian Muise}, url = {https://arxiv.org/abs/2004.12850}, year = {2020}, date = {2020-01-01}, journal = {arXiv preprint arXiv:2004.12850}, abstract = {We achieved a new milestone in the difficult task of enabling agents to learn about their environment autonomously. Our neuro-symbolic architecture is trained end-to-end to produce a succinct and effective discrete state transition model from images alone. Our target representation (the Planning Domain Definition Language) is already in a form that off-the-shelf solvers can consume, and opens the door to the rich array of modern heuristic search capabilities. We demonstrate how the sophisticated innate prior we place on the learning process significantly reduces the complexity of the learned representation, and reveals a connection to the graph-theoretic notion of "cube-like graphs", thus opening the door to a deeper understanding of the ideal properties for learned symbolic representations. We show that the powerful domain-independent heuristics allow our system to solve visual 15-Puzzle instances which are beyond the reach of blind search, without resorting to the Reinforcement Learning approach that requires a huge amount of training on the domain-dependent reward information. }, keywords = {Artificial Intelligence, Planning}, pubstate = {published}, tppubtype = {article} } We achieved a new milestone in the difficult task of enabling agents to learn about their environment autonomously. Our neuro-symbolic architecture is trained end-to-end to produce a succinct and effective discrete state transition model from images alone. Our target representation (the Planning Domain Definition Language) is already in a form that off-the-shelf solvers can consume, and opens the door to the rich array of modern heuristic search capabilities. We demonstrate how the sophisticated innate prior we place on the learning process significantly reduces the complexity of the learned representation, and reveals a connection to the graph-theoretic notion of "cube-like graphs", thus opening the door to a deeper understanding of the ideal properties for learned symbolic representations. We show that the powerful domain-independent heuristics allow our system to solve visual 15-Puzzle instances which are beyond the reach of blind search, without resorting to the Reinforcement Learning approach that requires a huge amount of training on the domain-dependent reward information. |
Wollenstein-Betech, Salomón; Muise, Christian; Cassandras, Christos G; Paschalidis, Ioannis Ch; Khazaeni, Yasaman Explainability of Intelligent Transportation Systems using Knowledge Compilation: a Traffic Light Controller Case Journal Article arXiv preprint arXiv:2007.04916, 2020. Abstract | Links | BibTeX | Tags: Artificial Intelligence, Civil Infrastructure, Planning, Transportation @article{wollenstein2020explainability, title = {Explainability of Intelligent Transportation Systems using Knowledge Compilation: a Traffic Light Controller Case}, author = { Salomón Wollenstein-Betech and Christian Muise and Christos G Cassandras and Ioannis Ch Paschalidis and Yasaman Khazaeni}, url = {https://arxiv.org/abs/2007.04916}, year = {2020}, date = {2020-01-01}, journal = {arXiv preprint arXiv:2007.04916}, abstract = {sage of automated controllers which make decisions on an environment are widespread and are often based on black-box models. We use Knowledge Compilation theory to bring explainability to the controller's decision given the state of the system. For this, we use simulated historical state-action data as input and build a compact and structured representation which relates states with actions. We implement this method in a Traffic Light Control scenario where the controller selects the light cycle by observing the presence (or absence) of vehicles in different regions of the incoming roads. }, keywords = {Artificial Intelligence, Civil Infrastructure, Planning, Transportation}, pubstate = {published}, tppubtype = {article} } sage of automated controllers which make decisions on an environment are widespread and are often based on black-box models. We use Knowledge Compilation theory to bring explainability to the controller's decision given the state of the system. For this, we use simulated historical state-action data as input and build a compact and structured representation which relates states with actions. We implement this method in a Traffic Light Control scenario where the controller selects the light cycle by observing the presence (or absence) of vehicles in different regions of the incoming roads. |
Joshi, Keyur D; Chauhan, Vedang; Surgenor, Brian A flexible machine vision system for small part inspection based on a hybrid SVM/ANN approach Journal Article Journal of Intelligent Manufacturing, 31 (1), pp. 103–125, 2020. Abstract | Links | BibTeX | Tags: Computer Vision @article{joshi2020flexible, title = {A flexible machine vision system for small part inspection based on a hybrid SVM/ANN approach}, author = { Keyur D Joshi and Vedang Chauhan and Brian Surgenor}, doi = {10.1007/s10845-018-1438-3}, year = {2020}, date = {2020-01-01}, journal = {Journal of Intelligent Manufacturing}, volume = {31}, number = {1}, pages = {103--125}, publisher = {Springer}, abstract = {Machine vision inspection systems are often used for part classification applications to confirm that correct parts are available in manufacturing or assembly operations. Support vector machines (SVMs) and artificial neural networks (ANNs) are popular choices for classifiers. These supervised classifiers perform well when developed for specific applications and trained with known class images. Their drawback is that they cannot be easily applied to different applications without extensive retuning. Moreover, for the same application, they do not perform well if there are unknown class images. This paper proposes a novel solution to the above limitations of SVMs and ANNs, with the development of a hybrid approach that combines supervised and semi-supervised layers. To illustrate its performance, the system is applied to three different small part identification and sorting applications: (1) solid plastic gears, (2) clear plastic wire connectors and (3) metallic Indian coins. The ability of the system to work with different applications with minimal tuning and user inputs illustrates its flexibility. The robustness of the system is demonstrated by its ability to reject unknown class images. Four hybrid classification methods were developed and tested: (1) SSVM–USVM, (2) USVM–SSVM, (3) USVM–SANN and (4) SANN–USVM. It was found that SANN–USVM gave the best results with an accuracy of over 95% for all three applications. A software package known as FlexMVS for flexible machine vision system was written to illustrate the hybrid approach that enabled easy execution of the image conditioning, feature extraction and classification steps. The image library and database used in this study is available at http://my.me.queensu.ca/People/Surgenor/Laboratory/Database.html.}, keywords = {Computer Vision}, pubstate = {published}, tppubtype = {article} } Machine vision inspection systems are often used for part classification applications to confirm that correct parts are available in manufacturing or assembly operations. Support vector machines (SVMs) and artificial neural networks (ANNs) are popular choices for classifiers. These supervised classifiers perform well when developed for specific applications and trained with known class images. Their drawback is that they cannot be easily applied to different applications without extensive retuning. Moreover, for the same application, they do not perform well if there are unknown class images. This paper proposes a novel solution to the above limitations of SVMs and ANNs, with the development of a hybrid approach that combines supervised and semi-supervised layers. To illustrate its performance, the system is applied to three different small part identification and sorting applications: (1) solid plastic gears, (2) clear plastic wire connectors and (3) metallic Indian coins. The ability of the system to work with different applications with minimal tuning and user inputs illustrates its flexibility. The robustness of the system is demonstrated by its ability to reject unknown class images. Four hybrid classification methods were developed and tested: (1) SSVM–USVM, (2) USVM–SSVM, (3) USVM–SANN and (4) SANN–USVM. It was found that SANN–USVM gave the best results with an accuracy of over 95% for all three applications. A software package known as FlexMVS for flexible machine vision system was written to illustrate the hybrid approach that enabled easy execution of the image conditioning, feature extraction and classification steps. The image library and database used in this study is available at http://my.me.queensu.ca/People/Surgenor/Laboratory/Database.html. |
Jardine, Peter T; Givigi, Sidney N; Yousefi, Shahram Leveraging Data Engineering to Improve Unmanned Aerial Vehicle Control Design Journal Article IEEE Systems Journal, 2020, ISSN: 1937-9234 . Abstract | Links | BibTeX | Tags: Controls, Predictive Model, Robotics, Unmanned Aerial Vehicles @article{jardine2020leveraging, title = {Leveraging Data Engineering to Improve Unmanned Aerial Vehicle Control Design}, author = { Peter T Jardine and Sidney N Givigi and Shahram Yousefi}, doi = {10.1109/JSYST.2020.3003203}, issn = {1937-9234 }, year = {2020}, date = {2020-01-01}, journal = {IEEE Systems Journal}, publisher = {IEEE}, abstract = {The potential benefits of big data and machine learning techniques are yet to be fully realized in real-time, safety-critical applications like unmanned aerial vehicle control. This is because of challenges related to interpretation, error susceptibility, and resources requirements. Due to their robustness and reliability, traditional model-based design techniques still dominate this landscape. However, a growing body of research in adaptive control has demonstrated the potential benefits of merging these two distinct design philosophies. This article investigates the benefits of using a combination of machine learning techniques to automatically tune parameters within a strictly defined model predictive control architecture. Fast orthogonal search and finite action-set learning automata are used to tune model coefficients and objective function weights, respectively. The strategy is validated experimentally on an actual Quanser Qball2 quadcopter and through several simulations of a Parrot AR.drone. Results demonstrate that the proposed approach improves performance while reducing design effort.}, keywords = {Controls, Predictive Model, Robotics, Unmanned Aerial Vehicles}, pubstate = {published}, tppubtype = {article} } The potential benefits of big data and machine learning techniques are yet to be fully realized in real-time, safety-critical applications like unmanned aerial vehicle control. This is because of challenges related to interpretation, error susceptibility, and resources requirements. Due to their robustness and reliability, traditional model-based design techniques still dominate this landscape. However, a growing body of research in adaptive control has demonstrated the potential benefits of merging these two distinct design philosophies. This article investigates the benefits of using a combination of machine learning techniques to automatically tune parameters within a strictly defined model predictive control architecture. Fast orthogonal search and finite action-set learning automata are used to tune model coefficients and objective function weights, respectively. The strategy is validated experimentally on an actual Quanser Qball2 quadcopter and through several simulations of a Parrot AR.drone. Results demonstrate that the proposed approach improves performance while reducing design effort. |
Lins, Romulo Gonçalves; Givigi, Sidney N FPGA-Based Design Optimization in Autonomous Robot Systems for Inspection of Civil Infrastructure Journal Article IEEE Systems Journal, 14 (2), pp. 2961–2964, 2020, ISSN: 1937-9234. Abstract | Links | BibTeX | Tags: Autonomous Vehicles, Civil Infrastructure, Robotics @article{lins2020fpga, title = {FPGA-Based Design Optimization in Autonomous Robot Systems for Inspection of Civil Infrastructure}, author = { Romulo Gonçalves Lins and Sidney N Givigi}, doi = {10.1109/JSYST.2019.2960309}, issn = {1937-9234}, year = {2020}, date = {2020-01-01}, journal = {IEEE Systems Journal}, volume = {14}, number = {2}, pages = {2961--2964}, publisher = {IEEE}, abstract = {Recently, Lins et al. have developed an autonomous robot system for inspection of defects in civil infrastructure. The proposed autonomous system is a unique design because it is comprised of commercial off-the-shelf products that integrate into one fully automated system. The design can be easily deployed to different environments, such as nuclear power plants and bridges, while also being less expensive to create and maintain. However, a crucial point to be improved is the system performance, which is related to the low image processing frequency that largely increases the time needed to autonomously inspect a real structure. A new embedded and more efficient crack detection and crack measurement apparatus based on a field-programmable gate array was developed to optimize the on-board image processing. Thereby, the hardware architecture, software design, and database schematic were redesigned in order to update the autonomous robot system for inspection. Finally, some discussion and data are provided to validate the technical feasibility of the proposed optimization.}, keywords = {Autonomous Vehicles, Civil Infrastructure, Robotics}, pubstate = {published}, tppubtype = {article} } Recently, Lins et al. have developed an autonomous robot system for inspection of defects in civil infrastructure. The proposed autonomous system is a unique design because it is comprised of commercial off-the-shelf products that integrate into one fully automated system. The design can be easily deployed to different environments, such as nuclear power plants and bridges, while also being less expensive to create and maintain. However, a crucial point to be improved is the system performance, which is related to the low image processing frequency that largely increases the time needed to autonomously inspect a real structure. A new embedded and more efficient crack detection and crack measurement apparatus based on a field-programmable gate array was developed to optimize the on-board image processing. Thereby, the hardware architecture, software design, and database schematic were redesigned in order to update the autonomous robot system for inspection. Finally, some discussion and data are provided to validate the technical feasibility of the proposed optimization. |
Ragab, Hany; Elhabiby, Mohamed; Givigi, Sidney; Noureldin, Aboelmagd 2020 IEEE/ION Position, Location and Navigation Symposium (PLANS), pp. 960–966, IEEE 2020, ISSN: 2153-3598. Abstract | Links | BibTeX | Tags: Artificial Intelligence, Autonomous Vehicles, Computer Vision, Navigation @inproceedings{ragab2020utilization, title = {The Utilization of DNN-based Semantic Segmentation for Improving Low-Cost Integrated Stereo Visual Odometry in Challenging Urban Environments}, author = { Hany Ragab and Mohamed Elhabiby and Sidney Givigi and Aboelmagd Noureldin}, doi = {10.1109/PLANS46316.2020.9110144}, issn = {2153-3598}, year = {2020}, date = {2020-01-01}, booktitle = {2020 IEEE/ION Position, Location and Navigation Symposium (PLANS)}, pages = {960--966}, organization = {IEEE}, abstract = {Positioning and Navigation (PN) is one of the most important topics in the world of Autonomous Vehicles (AVs). Being equipped with a suite of sensors and high-performance computers, self-driving cars are designed to perceive its surrounding environment prior to planning and control. Among the observations are the semantics of the objects appearing in the scene. While PN is very challenging for extended GNSS outages, vision sensors can also exhibit failures in the case of highly dynamic scenes and lack of texture between consecutive image frames. To overcome this problem, we propose a stereo visual odometry scheme and advocate the use of a pretrained state-of-the-art Semantic Segmentation (SS) Deep Convolutional Neural Networks (CNN) model to forcefully remove features belonging to objects that most likely behave dynamically in the scene prior to egomotion estimation and integration with inertial sensors. When loosely coupled with inertial sensors, the proposed method was able to outperform the integrated algorithm without SS-based outlier rejection during natural GNSS outages.}, keywords = {Artificial Intelligence, Autonomous Vehicles, Computer Vision, Navigation}, pubstate = {published}, tppubtype = {inproceedings} } Positioning and Navigation (PN) is one of the most important topics in the world of Autonomous Vehicles (AVs). Being equipped with a suite of sensors and high-performance computers, self-driving cars are designed to perceive its surrounding environment prior to planning and control. Among the observations are the semantics of the objects appearing in the scene. While PN is very challenging for extended GNSS outages, vision sensors can also exhibit failures in the case of highly dynamic scenes and lack of texture between consecutive image frames. To overcome this problem, we propose a stereo visual odometry scheme and advocate the use of a pretrained state-of-the-art Semantic Segmentation (SS) Deep Convolutional Neural Networks (CNN) model to forcefully remove features belonging to objects that most likely behave dynamically in the scene prior to egomotion estimation and integration with inertial sensors. When loosely coupled with inertial sensors, the proposed method was able to outperform the integrated algorithm without SS-based outlier rejection during natural GNSS outages. |
Wang, Cunxiang; Liang, Shuailong; Jin, Yili; Wang, Yilong; Zhu, Xiaodan; Zhang, Yue SemEval-2020 task 4: Commonsense validation and explanation Journal Article arXiv preprint arXiv:2007.00236, 2020. Abstract | Links | BibTeX | Tags: Artificial Intelligence, Natural Language Processing @article{wang2020semeval, title = {SemEval-2020 task 4: Commonsense validation and explanation}, author = { Cunxiang Wang and Shuailong Liang and Yili Jin and Yilong Wang and Xiaodan Zhu and Yue Zhang}, url = {https://arxiv.org/abs/2007.00236}, year = {2020}, date = {2020-01-01}, journal = {arXiv preprint arXiv:2007.00236}, abstract = {In this paper, we present SemEval-2020 Task 4, Commonsense Validation and Explanation (ComVE), which includes three subtasks, aiming to evaluate whether a system can distinguish a natural language statement that makes sense to human from one that does not, and provide the reasons. Specifically, in our first subtask, the participating systems are required to choose from two natural language statements of similar wording the one that makes sense and the one does not. The second subtask additionally asks a system to select the key reason from three options why a given statement does not make sense. In the third subtask, a participating system needs to generate the reason automatically. }, keywords = {Artificial Intelligence, Natural Language Processing}, pubstate = {published}, tppubtype = {article} } In this paper, we present SemEval-2020 Task 4, Commonsense Validation and Explanation (ComVE), which includes three subtasks, aiming to evaluate whether a system can distinguish a natural language statement that makes sense to human from one that does not, and provide the reasons. Specifically, in our first subtask, the participating systems are required to choose from two natural language statements of similar wording the one that makes sense and the one does not. The second subtask additionally asks a system to select the key reason from three options why a given statement does not make sense. In the third subtask, a participating system needs to generate the reason automatically. |
Sarkar, Pritam; Etemad, Ali Self-supervised learning for ecg-based emotion recognition Inproceedings ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 3217–3221, IEEE 2020, ISBN: 2379-190X . Abstract | Links | BibTeX | Tags: Affective Computing, Artificial Intelligence, Human Machine Interaction, Neural Networks @inproceedings{sarkar2020self, title = {Self-supervised learning for ecg-based emotion recognition}, author = { Pritam Sarkar and Ali Etemad}, doi = {10.1109/ICASSP40776.2020.9053985}, isbn = {2379-190X }, year = {2020}, date = {2020-01-01}, booktitle = {ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, pages = {3217--3221}, organization = {IEEE}, abstract = {We present an electrocardiogram (ECG) -based emotion recognition system using self-supervised learning. Our proposed architecture consists of two main networks, a signal transformation recognition network and an emotion recognition network. First, unlabelled data are used to successfully train the former network to detect specific pre-determined signal transformations in the self-supervised learning step. Next, the weights of the convolutional layers of this network are transferred to the emotion recognition network, and two dense layers are trained in order to classify arousal and valence scores. We show that our self-supervised approach helps the model learn the ECG feature manifold required for emotion recognition, performing equal or better than the fully-supervised version of the model. Our proposed method outperforms the state-of-the-art in ECG-based emotion recognition with two publicly available datasets, SWELL and AMIGOS. Further analysis highlights the advantage of our self-supervised approach in requiring significantly less data to achieve acceptable results.}, keywords = {Affective Computing, Artificial Intelligence, Human Machine Interaction, Neural Networks}, pubstate = {published}, tppubtype = {inproceedings} } We present an electrocardiogram (ECG) -based emotion recognition system using self-supervised learning. Our proposed architecture consists of two main networks, a signal transformation recognition network and an emotion recognition network. First, unlabelled data are used to successfully train the former network to detect specific pre-determined signal transformations in the self-supervised learning step. Next, the weights of the convolutional layers of this network are transferred to the emotion recognition network, and two dense layers are trained in order to classify arousal and valence scores. We show that our self-supervised approach helps the model learn the ECG feature manifold required for emotion recognition, performing equal or better than the fully-supervised version of the model. Our proposed method outperforms the state-of-the-art in ECG-based emotion recognition with two publicly available datasets, SWELL and AMIGOS. Further analysis highlights the advantage of our self-supervised approach in requiring significantly less data to achieve acceptable results. |
Best, Aaron N; Wu, Amy R Upper body and ankle strategies compensate for reduced lateral stability at very slow walking speeds Journal Article bioRxiv, 2020. Abstract | Links | BibTeX | Tags: Biomechanics @article{best2020upper, title = {Upper body and ankle strategies compensate for reduced lateral stability at very slow walking speeds}, author = { Aaron N Best and Amy R Wu}, doi = {10.1101/2020.07.16.207092 }, year = {2020}, date = {2020-01-01}, journal = {bioRxiv}, publisher = {Cold Spring Harbor Laboratory}, abstract = {At the typical walking speeds of healthy humans, step placement seems to be the primary strategy to maintain gait stability, with ankle torques and upper body momentum providing additional compensation. The average walking speeds of populations with an increased risk of falling, however, are much slower and may require differing control strategies. The purpose of this study was to analyze mediolateral gait stability and the contributions of the different control strategies at very slow walking speeds. We analyzed an open dataset including kinematics and kinetics from eight healthy subjects walking at speeds from 0.1 to 0.6 m/s as well as a self-selected speed. As gait speed slowed, we found that the margin of stability decreased linearly. Increased lateral excursions of the extrapolated centre of mass, caused by increased lateral excursions of the trunk, were not compensated for by an equivalent increase in the lateral centre of pressure, leading to decreased margin of stability. Additionally, both the ankle eversion torque and hip abduction torque at the minimum margin of stability event increased at the same rate as gait speed slowed. These results suggest that the contributions of both the ankle and the upper body to stability are more crucial than stepping at slow speeds, which have important implications for populations with slow gait and limited motor function.}, keywords = {Biomechanics}, pubstate = {published}, tppubtype = {article} } At the typical walking speeds of healthy humans, step placement seems to be the primary strategy to maintain gait stability, with ankle torques and upper body momentum providing additional compensation. The average walking speeds of populations with an increased risk of falling, however, are much slower and may require differing control strategies. The purpose of this study was to analyze mediolateral gait stability and the contributions of the different control strategies at very slow walking speeds. We analyzed an open dataset including kinematics and kinetics from eight healthy subjects walking at speeds from 0.1 to 0.6 m/s as well as a self-selected speed. As gait speed slowed, we found that the margin of stability decreased linearly. Increased lateral excursions of the extrapolated centre of mass, caused by increased lateral excursions of the trunk, were not compensated for by an equivalent increase in the lateral centre of pressure, leading to decreased margin of stability. Additionally, both the ankle eversion torque and hip abduction torque at the minimum margin of stability event increased at the same rate as gait speed slowed. These results suggest that the contributions of both the ankle and the upper body to stability are more crucial than stepping at slow speeds, which have important implications for populations with slow gait and limited motor function. |
Dai, Yinpei; Li, Hangyu; Tang, Chengguang; Li, Yongbin; Sun, Jian; Zhu, Xiaodan Learning Low-Resource End-To-End Goal-Oriented Dialog for Fast and Reliable System Deployment Inproceedings Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 609–618, 2020. Abstract | Links | BibTeX | Tags: Natural Language Processing @inproceedings{dai2020learning, title = {Learning Low-Resource End-To-End Goal-Oriented Dialog for Fast and Reliable System Deployment}, author = { Yinpei Dai and Hangyu Li and Chengguang Tang and Yongbin Li and Jian Sun and Xiaodan Zhu}, doi = {10.18653/v1/2020.acl-main.57}, year = {2020}, date = {2020-01-01}, booktitle = {Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics}, pages = {609--618}, abstract = {Existing end-to-end dialog systems perform less effectively when data is scarce. To obtain an acceptable success in real-life online services with only a handful of training examples, both fast adaptability and reliable performance are highly desirable for dialog systems. In this paper, we propose the Meta-Dialog System (MDS), which combines the advantages of both meta-learning approaches and human-machine collaboration. We evaluate our methods on a new extended-bAbI dataset and a transformed MultiWOZ dataset for low-resource goal-oriented dialog learning. Experimental results show that MDS significantly outperforms non-meta-learning baselines and can achieve more than 90% per-turn accuracies with only 10 dialogs on the extended-bAbI dataset.}, keywords = {Natural Language Processing}, pubstate = {published}, tppubtype = {inproceedings} } Existing end-to-end dialog systems perform less effectively when data is scarce. To obtain an acceptable success in real-life online services with only a handful of training examples, both fast adaptability and reliable performance are highly desirable for dialog systems. In this paper, we propose the Meta-Dialog System (MDS), which combines the advantages of both meta-learning approaches and human-machine collaboration. We evaluate our methods on a new extended-bAbI dataset and a transformed MultiWOZ dataset for low-resource goal-oriented dialog learning. Experimental results show that MDS significantly outperforms non-meta-learning baselines and can achieve more than 90% per-turn accuracies with only 10 dialogs on the extended-bAbI dataset. |
Ruan, Yu-Ping; Ling, Zhen-Hua; Zhu, Xiaodan; Liu, Quan; Gu, Jia-Chen Generating diverse conversation responses by creating and ranking multiple candidates Journal Article Computer Speech & Language, 62 , pp. 101071, 2020. Abstract | Links | BibTeX | Tags: Natural Language Processing @article{ruan2020generating, title = {Generating diverse conversation responses by creating and ranking multiple candidates}, author = { Yu-Ping Ruan and Zhen-Hua Ling and Xiaodan Zhu and Quan Liu and Jia-Chen Gu}, doi = {10.1016/j.csl.2020.101071}, year = {2020}, date = {2020-01-01}, journal = {Computer Speech & Language}, volume = {62}, pages = {101071}, publisher = {Elsevier}, abstract = {This paper introduces our systems built for Track 2 of Dialog System Technology Challenge 7 (DSTC7). This challenge track aimed to evaluate the response generation methods using fully data-driven conversation models in a knowledge-grounded setting, where textual facts were provided as the knowledge for each context-response pair. The sequence-to-sequence models have achieved impressive results in machine translation and have also been widely used for end-to-end generative conversation modelling. However, they tended to output dull and repeated responses in previous studies. Our work aims to promote the diversity of end-to-end conversation response generation by adopting a two-stage pipeline. 1) Create multiple responses for an input context together with its textual facts. At this stage, two different models are designed, i.e., a variational generative (VariGen) model and a retrieval-based (Retrieval) model. 2) Rank and return the most relevant response by training a topic coherence discrimination (TCD) model for calculating ranking scores. In our experiments, we demonstrated the effectiveness of the response ranking strategy and the external textual knowledge for generating better responses. According to the official evaluation results, our Retrieval and VariGen systems ranked first and second respectively among all participant systems on Entropy metrics which measured the objective diversity of generated responses. Besides, the VariGen system ranked second on NIST and METEOR metrics which measured the objective quality of generated responses.}, keywords = {Natural Language Processing}, pubstate = {published}, tppubtype = {article} } This paper introduces our systems built for Track 2 of Dialog System Technology Challenge 7 (DSTC7). This challenge track aimed to evaluate the response generation methods using fully data-driven conversation models in a knowledge-grounded setting, where textual facts were provided as the knowledge for each context-response pair. The sequence-to-sequence models have achieved impressive results in machine translation and have also been widely used for end-to-end generative conversation modelling. However, they tended to output dull and repeated responses in previous studies. Our work aims to promote the diversity of end-to-end conversation response generation by adopting a two-stage pipeline. 1) Create multiple responses for an input context together with its textual facts. At this stage, two different models are designed, i.e., a variational generative (VariGen) model and a retrieval-based (Retrieval) model. 2) Rank and return the most relevant response by training a topic coherence discrimination (TCD) model for calculating ranking scores. In our experiments, we demonstrated the effectiveness of the response ranking strategy and the external textual knowledge for generating better responses. According to the official evaluation results, our Retrieval and VariGen systems ranked first and second respectively among all participant systems on Entropy metrics which measured the objective diversity of generated responses. Besides, the VariGen system ranked second on NIST and METEOR metrics which measured the objective quality of generated responses. |
Li, Xiaoyan; Kiringa, Iluju; Yeap, Tet; Zhu, Xiaodan; Li, Yifeng Exploring deep anomaly detection methods based on capsule net Inproceedings Canadian Conference on Artificial Intelligence, pp. 375–387, Springer 2020, ISBN: 978-3-030-47358-7. Abstract | Links | BibTeX | Tags: Artificial Intelligence, Natural Language Processing @inproceedings{li2020exploring, title = {Exploring deep anomaly detection methods based on capsule net}, author = { Xiaoyan Li and Iluju Kiringa and Tet Yeap and Xiaodan Zhu and Yifeng Li}, doi = {10.1007/978-3-030-47358-7_39}, isbn = {978-3-030-47358-7}, year = {2020}, date = {2020-01-01}, booktitle = {Canadian Conference on Artificial Intelligence}, pages = {375--387}, organization = {Springer}, abstract = {In this paper, we develop and explore deep anomaly detection techniques based on capsule network (named AnoCapsNet) for image data. Being able to encode intrinsic spatial relationship between parts and a whole, CapsNet has been applied as both a classifier and deep autoencoder. This inspires us to design three normality score functions: prediction-probability-based (PP-based), reconstruction-error-based (RE-based), and combination of both (PP+RE-based) for evaluating the “outlierness” of unseen images. Our results on four datasets demonstrate that PP-based and RE-based methods outperform the principled benchmark methods in many cases and the pp-based method performs consistently well, while the RE-based approach is relatively sensitive to the similarity between labeled and unlabeled images. The PP+RE-based approach effectively takes advantages of both methods and achieves state-of-the-art results. }, keywords = {Artificial Intelligence, Natural Language Processing}, pubstate = {published}, tppubtype = {inproceedings} } In this paper, we develop and explore deep anomaly detection techniques based on capsule network (named AnoCapsNet) for image data. Being able to encode intrinsic spatial relationship between parts and a whole, CapsNet has been applied as both a classifier and deep autoencoder. This inspires us to design three normality score functions: prediction-probability-based (PP-based), reconstruction-error-based (RE-based), and combination of both (PP+RE-based) for evaluating the “outlierness” of unseen images. Our results on four datasets demonstrate that PP-based and RE-based methods outperform the principled benchmark methods in many cases and the pp-based method performs consistently well, while the RE-based approach is relatively sensitive to the similarity between labeled and unlabeled images. The PP+RE-based approach effectively takes advantages of both methods and achieves state-of-the-art results. |
Gu, Jia-Chen; Li, Tianda; Liu, Quan; Zhu, Xiaodan; Ling, Zhen-Hua; Ruan, Yu-Ping Pre-Trained and Attention-Based Neural Networks for Building Noetic Task-Oriented Dialogue Systems Journal Article arXiv preprint arXiv:2004.01940, 2020. Abstract | Links | BibTeX | Tags: Artificial Intelligence, Natural Language Processing @article{gu2020pre, title = {Pre-Trained and Attention-Based Neural Networks for Building Noetic Task-Oriented Dialogue Systems}, author = { Jia-Chen Gu and Tianda Li and Quan Liu and Xiaodan Zhu and Zhen-Hua Ling and Yu-Ping Ruan}, url = {https://arxiv.org/abs/2004.01940}, year = {2020}, date = {2020-01-01}, journal = {arXiv preprint arXiv:2004.01940}, abstract = {The NOESIS II challenge, as the Track 2 of the 8th Dialogue System Technology Challenges (DSTC 8), is the extension of DSTC 7. This track incorporates new elements that are vital for the creation of a deployed task-oriented dialogue system. This paper describes our systems that are evaluated on all subtasks under this challenge. We study the problem of employing pre-trained attention-based network for multi-turn dialogue systems. Meanwhile, several adaptation methods are proposed to adapt the pre-trained language models for multi-turn dialogue systems, in order to keep the intrinsic property of dialogue systems. In the released evaluation results of Track 2 of DSTC 8, our proposed models ranked fourth in subtask 1, third in subtask 2, and first in subtask 3 and subtask 4 respectively. }, keywords = {Artificial Intelligence, Natural Language Processing}, pubstate = {published}, tppubtype = {article} } The NOESIS II challenge, as the Track 2 of the 8th Dialogue System Technology Challenges (DSTC 8), is the extension of DSTC 7. This track incorporates new elements that are vital for the creation of a deployed task-oriented dialogue system. This paper describes our systems that are evaluated on all subtasks under this challenge. We study the problem of employing pre-trained attention-based network for multi-turn dialogue systems. Meanwhile, several adaptation methods are proposed to adapt the pre-trained language models for multi-turn dialogue systems, in order to keep the intrinsic property of dialogue systems. In the released evaluation results of Track 2 of DSTC 8, our proposed models ranked fourth in subtask 1, third in subtask 2, and first in subtask 3 and subtask 4 respectively. |
Hedayati, Mohammadali; Kim, Il-Min CoMP-NOMA in the SWIPT Networks Journal Article IEEE Transactions on Wireless Communications, 2020, ISBN: 1558-2248. Abstract | Links | BibTeX | Tags: Telecommunication @article{hedayati2020comp, title = {CoMP-NOMA in the SWIPT Networks}, author = { Mohammadali Hedayati and Il-Min Kim}, doi = {10.1109/TWC.2020.2985038}, isbn = {1558-2248}, year = {2020}, date = {2020-01-01}, journal = {IEEE Transactions on Wireless Communications}, publisher = {IEEE}, abstract = {This paper studies coordinated multipoint (CoMP) transmission with non-orthogonal multiple access (NOMA) for a simultaneous wireless information and power transfer (SWIPT) network. We investigate two different CoMP-NOMA schemes, namely joint transmission-NOMA (JT-NOMA) and Alamouti NOMA (A-NOMA). Also, as the benchmark, joint transmission orthogonal multiple access (JT-OMA) is considered. Expressions are derived for the outage probabilities achieved by JT-NOMA, A-NOMA, and JT-OMA for cell edge users with quality of service (QoS) requirements. To compare these schemes in terms of spectral efficiency, we use the ϵ -outage rate region that is defined to be the set of all achievable rate pairs at which the outage probabilities are equal to ϵ . It is shown that A-NOMA is superior to both JT-NOMA and JT-OMA in high SNR. Also, it is analytically proved that JT-NOMA outperforms JT-OMA, when the circuit energy consumption is negligible. A particularly interesting observation is that when the circuit energy consumption is non-negligible, JT-NOMA is not always better than JT-OMA. It is shown that when the difference between the expectations of effective channel power gains is not sufficiently large, JT-OMA is superior to JT-NOMA for the same circuit energy consumption.}, keywords = {Telecommunication}, pubstate = {published}, tppubtype = {article} } This paper studies coordinated multipoint (CoMP) transmission with non-orthogonal multiple access (NOMA) for a simultaneous wireless information and power transfer (SWIPT) network. We investigate two different CoMP-NOMA schemes, namely joint transmission-NOMA (JT-NOMA) and Alamouti NOMA (A-NOMA). Also, as the benchmark, joint transmission orthogonal multiple access (JT-OMA) is considered. Expressions are derived for the outage probabilities achieved by JT-NOMA, A-NOMA, and JT-OMA for cell edge users with quality of service (QoS) requirements. To compare these schemes in terms of spectral efficiency, we use the ϵ -outage rate region that is defined to be the set of all achievable rate pairs at which the outage probabilities are equal to ϵ . It is shown that A-NOMA is superior to both JT-NOMA and JT-OMA in high SNR. Also, it is analytically proved that JT-NOMA outperforms JT-OMA, when the circuit energy consumption is negligible. A particularly interesting observation is that when the circuit energy consumption is non-negligible, JT-NOMA is not always better than JT-OMA. It is shown that when the difference between the expectations of effective channel power gains is not sufficiently large, JT-OMA is superior to JT-NOMA for the same circuit energy consumption. |
Kang, Jae-Mo; Chun, Chang-Jae; Kim, Il-Min; Kim, Dong In Dynamic Power Splitting for SWIPT With Nonlinear Energy Harvesting in Ergodic Fading Channel Journal Article IEEE Internet of Things Journal, 2020, ISSN: 2327-4662. Abstract | Links | BibTeX | Tags: Energy Harvesting, Telecommunication @article{kang2020dynamic, title = {Dynamic Power Splitting for SWIPT With Nonlinear Energy Harvesting in Ergodic Fading Channel}, author = { Jae-Mo Kang and Chang-Jae Chun and Il-Min Kim and Dong In Kim}, doi = { 10.1109/JIOT.2020.2980328}, issn = {2327-4662}, year = {2020}, date = {2020-01-01}, journal = {IEEE Internet of Things Journal}, publisher = {IEEE}, abstract = {Simultaneous wireless information and power transfer (SWIPT) is very promising for various applications with the Internet of Things (IoT). In this article, we study dynamic power splitting for the SWIPT in an ergodic fading channel. Considering nonlinearity of practical energy harvesting (EH) circuits, we adopt the realistic nonlinear EH model rather than the idealistic linear EH model. To characterize the optimal rate-energy (R-E) tradeoff, we consider the problem of maximizing the R-E region, which is nonconvex. We solve this challenging problem for two different cases of the channel state information (CSI): 1) when the CSI is known only at the receiver (the CSIR case) and 2) when the CSI is known at both the transmitter and the receiver (the CSI case). For these two cases, we develop the corresponding optimal dynamic power-splitting schemes. To address the complexity issue, we also propose the suboptimal schemes with low complexities. Comparing the proposed schemes to the existing schemes, we provide various useful insights into the dynamic power splitting with nonlinear EH. Furthermore, we extend the analysis to the scenarios of the partial CSI at the transmitter and the harvested energy maximization. The numerical results demonstrate that the proposed schemes significantly outperform the existing schemes and the proposed suboptimal scheme works very close to the optimal scheme at a much lower complexity.}, keywords = {Energy Harvesting, Telecommunication}, pubstate = {published}, tppubtype = {article} } Simultaneous wireless information and power transfer (SWIPT) is very promising for various applications with the Internet of Things (IoT). In this article, we study dynamic power splitting for the SWIPT in an ergodic fading channel. Considering nonlinearity of practical energy harvesting (EH) circuits, we adopt the realistic nonlinear EH model rather than the idealistic linear EH model. To characterize the optimal rate-energy (R-E) tradeoff, we consider the problem of maximizing the R-E region, which is nonconvex. We solve this challenging problem for two different cases of the channel state information (CSI): 1) when the CSI is known only at the receiver (the CSIR case) and 2) when the CSI is known at both the transmitter and the receiver (the CSI case). For these two cases, we develop the corresponding optimal dynamic power-splitting schemes. To address the complexity issue, we also propose the suboptimal schemes with low complexities. Comparing the proposed schemes to the existing schemes, we provide various useful insights into the dynamic power splitting with nonlinear EH. Furthermore, we extend the analysis to the scenarios of the partial CSI at the transmitter and the harvested energy maximization. The numerical results demonstrate that the proposed schemes significantly outperform the existing schemes and the proposed suboptimal scheme works very close to the optimal scheme at a much lower complexity. |
Kang, Jae-Mo; Chun, Chang-Jae; Kim, Il-Min; Kim, Dong In Deep RNN-Based Channel Tracking for Wireless Energy Transfer System Journal Article IEEE Systems Journal, 2020, ISSN: 1937-9234. Abstract | Links | BibTeX | Tags: Neural Networks, Telecommunication @article{kang2020deep, title = {Deep RNN-Based Channel Tracking for Wireless Energy Transfer System}, author = { Jae-Mo Kang and Chang-Jae Chun and Il-Min Kim and Dong In Kim}, doi = {10.1109/JSYST.2020.2975188}, issn = {1937-9234}, year = {2020}, date = {2020-01-01}, journal = {IEEE Systems Journal}, publisher = {IEEE}, abstract = {In this article, we study channel tracking for a wireless energy transfer (WET) system. This problem is practically very important, but challenging. Regarding time-varying channels as a sequence to be predicted, we exploit the deep learning technique for channel tracking. Particularly, by constructing a recurrent neural network (RNN) architecture based on long short-term memory (LSTM) and feedforward neural network (FNN), we develop a novel channel tracking scheme for the WET system. This scheme sequentially estimates the channel state information (CSI) at the ET based on the previous CSI estimates and the harvested energy feedback information from the ER. Numerical results demonstrate the superior performance and effectiveness of the proposed scheme.}, keywords = {Neural Networks, Telecommunication}, pubstate = {published}, tppubtype = {article} } In this article, we study channel tracking for a wireless energy transfer (WET) system. This problem is practically very important, but challenging. Regarding time-varying channels as a sequence to be predicted, we exploit the deep learning technique for channel tracking. Particularly, by constructing a recurrent neural network (RNN) architecture based on long short-term memory (LSTM) and feedforward neural network (FNN), we develop a novel channel tracking scheme for the WET system. This scheme sequentially estimates the channel state information (CSI) at the ET based on the previous CSI estimates and the harvested energy feedback information from the ER. Numerical results demonstrate the superior performance and effectiveness of the proposed scheme. |
Yun, Sangseok; Kang, Jae-Mo; Kim, Il-Min; Ha, Jeongseok Deep Artificial Noise: Deep Learning-Based Precoding Optimization for Artificial Noise Scheme Journal Article IEEE Transactions on Vehicular Technology, 69 (3), pp. 3465–3469, 2020, ISSN: 1939-9359. Abstract | Links | BibTeX | Tags: Deep Learning, Signal Processing, Telecommunication @article{yun2020deep, title = {Deep Artificial Noise: Deep Learning-Based Precoding Optimization for Artificial Noise Scheme}, author = { Sangseok Yun and Jae-Mo Kang and Il-Min Kim and Jeongseok Ha}, doi = {10.1109/TVT.2020.2965959}, issn = {1939-9359}, year = {2020}, date = {2020-01-01}, journal = {IEEE Transactions on Vehicular Technology}, volume = {69}, number = {3}, pages = {3465--3469}, publisher = {IEEE}, abstract = {In this work, we consider a secure precoding optimization problem for the artificial noise (AN) scheme in multiple-input single-output (MISO) wiretap channels. In previous researches (Lin et al., 2013), it was proved that the generalized AN scheme which allows some portion of AN signal to be injected to the legitimate receiver's channel is the optimal precoding scheme for MISO wiretap channels. However, the optimality is valid only under some ideal assumptions such as perfect channel estimation and spatially uncorrelated channels. To break through this limitation, in this paper, we propose a novel deep neural network (DNN)-based secure precoding scheme, called the deep AN scheme. To the best of the authors' knowledge, the deep AN scheme is the first secure precoding scheme which exploits a DNN to jointly design and optimize the precoders for the information signal and the AN signal. From the numerical experiments, it is demonstrated that the proposed deep AN scheme outperforms the generalized AN scheme under various practical wireless environments.}, keywords = {Deep Learning, Signal Processing, Telecommunication}, pubstate = {published}, tppubtype = {article} } In this work, we consider a secure precoding optimization problem for the artificial noise (AN) scheme in multiple-input single-output (MISO) wiretap channels. In previous researches (Lin et al., 2013), it was proved that the generalized AN scheme which allows some portion of AN signal to be injected to the legitimate receiver's channel is the optimal precoding scheme for MISO wiretap channels. However, the optimality is valid only under some ideal assumptions such as perfect channel estimation and spatially uncorrelated channels. To break through this limitation, in this paper, we propose a novel deep neural network (DNN)-based secure precoding scheme, called the deep AN scheme. To the best of the authors' knowledge, the deep AN scheme is the first secure precoding scheme which exploits a DNN to jointly design and optimize the precoders for the information signal and the AN signal. From the numerical experiments, it is demonstrated that the proposed deep AN scheme outperforms the generalized AN scheme under various practical wireless environments. |
Schneider, Adrian Jessup L; Keiver, Kathy; Orr, Alison Pritchard; Reynolds, James N; Golubovich, Neven; Graham, TC Nicholas Toward the Design of Enjoyable Games for Children with Fetal Alcohol Spectrum Disorder Inproceedings Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–13, 2020. Abstract | Links | BibTeX | Tags: Game Design, Healthcare @inproceedings{schneider2020toward, title = {Toward the Design of Enjoyable Games for Children with Fetal Alcohol Spectrum Disorder}, author = { Adrian L Jessup Schneider and Kathy Keiver and Alison Pritchard Orr and James N Reynolds and Neven Golubovich and TC Nicholas Graham}, doi = {10.1145/3313831.3376480}, year = {2020}, date = {2020-01-01}, booktitle = {Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems}, pages = {1--13}, abstract = {Fetal Alcohol Spectrum Disorder (FASD) is a heterogeneous and complex set of disorders caused by prenatal alcohol exposure, estimated to affect 2-5% of the North American population. Deficits associated with FASD affect social skill development and executive function, including emotional regulation and impulse control. These deficits can increase the difficulty of playing digital games. While considerable research has been performed in understanding how to design games for people with neurodevelopmental disorders in general, there is little data on how to design engaging games for children with FASD. We conducted a ten-week in-school gaming trial with eleven elementary-aged children with diagnosed or suspected FASD. Participants enjoyed playing together and responded well to the in-game reward system, while some game elements caused unexpected frustration. Based on our observations, we advise that games for FASD be designed to have low cost of failure, avoid retracting options, account for taking breaks when needed, show progression in rewards, and enable cooperative play.}, keywords = {Game Design, Healthcare}, pubstate = {published}, tppubtype = {inproceedings} } Fetal Alcohol Spectrum Disorder (FASD) is a heterogeneous and complex set of disorders caused by prenatal alcohol exposure, estimated to affect 2-5% of the North American population. Deficits associated with FASD affect social skill development and executive function, including emotional regulation and impulse control. These deficits can increase the difficulty of playing digital games. While considerable research has been performed in understanding how to design games for people with neurodevelopmental disorders in general, there is little data on how to design engaging games for children with FASD. We conducted a ten-week in-school gaming trial with eleven elementary-aged children with diagnosed or suspected FASD. Participants enjoyed playing together and responded well to the in-game reward system, while some game elements caused unexpected frustration. Based on our observations, we advise that games for FASD be designed to have low cost of failure, avoid retracting options, account for taking breaks when needed, show progression in rewards, and enable cooperative play. |
Uppal, Hardik; Sepas-Moghaddam, Alireza; Greenspan, Michael; Etemad, Ali Attention-aware fusion RGB-D face recognition Journal Article arXiv preprint arXiv:2003.00168, 2020. Abstract | Links | BibTeX | Tags: Artificial Intelligence, Computer Vision, Neural Networks @article{uppal2020attention, title = {Attention-aware fusion RGB-D face recognition}, author = { Hardik Uppal and Alireza Sepas-Moghaddam and Michael Greenspan and Ali Etemad}, url = {https://arxiv.org/abs/2003.00168}, year = {2020}, date = {2020-01-01}, journal = {arXiv preprint arXiv:2003.00168}, abstract = {With recent advances in RGB-D sensing technologies as well as improvements in machine learning and fusion techniques, RGB-D facial recognition has become an active area of research. A novel attention aware method is proposed to fuse two image modalities, RGB and depth, for enhanced RGB-D facial recognition. The proposed method first extracts features from both modalities using a convolutional feature extractor. These features are then fused using a two-layer attention mechanism. The first layer focuses on the fused feature maps generated by the feature extractor, exploiting the relationship between feature maps using LSTM recurrent learning. The second layer focuses on the spatial features of those maps using convolution. The training database is preprocessed and augmented through a set of geometric transformations, and the learning process is further aided using transfer learning from a pure 2D RGB image training process. Comparative evaluations demonstrate that the proposed method outperforms other state-of-the-art approaches, including both traditional and deep neural network-based methods, on the challenging CurtinFaces and IIIT-D RGB-D benchmark databases, achieving classification accuracies over 98.2% and 99.3% respectively. The proposed attention mechanism is also compared with other attention mechanisms, demonstrating more accurate results. }, keywords = {Artificial Intelligence, Computer Vision, Neural Networks}, pubstate = {published}, tppubtype = {article} } With recent advances in RGB-D sensing technologies as well as improvements in machine learning and fusion techniques, RGB-D facial recognition has become an active area of research. A novel attention aware method is proposed to fuse two image modalities, RGB and depth, for enhanced RGB-D facial recognition. The proposed method first extracts features from both modalities using a convolutional feature extractor. These features are then fused using a two-layer attention mechanism. The first layer focuses on the fused feature maps generated by the feature extractor, exploiting the relationship between feature maps using LSTM recurrent learning. The second layer focuses on the spatial features of those maps using convolution. The training database is preprocessed and augmented through a set of geometric transformations, and the learning process is further aided using transfer learning from a pure 2D RGB image training process. Comparative evaluations demonstrate that the proposed method outperforms other state-of-the-art approaches, including both traditional and deep neural network-based methods, on the challenging CurtinFaces and IIIT-D RGB-D benchmark databases, achieving classification accuracies over 98.2% and 99.3% respectively. The proposed attention mechanism is also compared with other attention mechanisms, demonstrating more accurate results. |
Sepas-Moghaddam, Alireza; Etemad, Ali; Pereira, Fernando; Correia, Paulo Lobato Facial emotion recognition using light field images with deep attention-based bidirectional LSTM Inproceedings ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 3367–3371, IEEE 2020. Abstract | Links | BibTeX | Tags: Affective Computing, Artificial Intelligence, Computer Vision, Neural Networks @inproceedings{sepas2020facial, title = {Facial emotion recognition using light field images with deep attention-based bidirectional LSTM}, author = { Alireza Sepas-Moghaddam and Ali Etemad and Fernando Pereira and Paulo Lobato Correia}, doi = {10.1109/ICASSP40776.2020.9053919}, year = {2020}, date = {2020-01-01}, booktitle = {ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, pages = {3367--3371}, organization = {IEEE}, abstract = {Light field cameras are able to capture the intensity of light rays coming from multiple directions, thus representing the visual scene from multiple viewpoints. This paper exploits the rich spatio-angular information available in light field images for facial emotion recognition. In this context, a new deep network is proposed that first extracts spatial features using a VGG16 convolutional neural network. Then, a Bidirectional Long Short-Term Memory (Bi-LSTM) recurrent neural network is used to learn spatio-angular features from viewpoint feature sequences, exploring both forward and backward angular relationships. Additionally, an attention mechanism allows our model to selectively focus on the most important spatio-angular features, thus enabling a more effective learning outcome. Finally, a fusion scheme is adopted to obtain the emotion recognition classification results. Comprehensive experiments have been conducted on the IST-EURECOM Light Field Face database using two challenging evaluation protocols, showing the superiority of our method over the state-of-the-art.}, keywords = {Affective Computing, Artificial Intelligence, Computer Vision, Neural Networks}, pubstate = {published}, tppubtype = {inproceedings} } Light field cameras are able to capture the intensity of light rays coming from multiple directions, thus representing the visual scene from multiple viewpoints. This paper exploits the rich spatio-angular information available in light field images for facial emotion recognition. In this context, a new deep network is proposed that first extracts spatial features using a VGG16 convolutional neural network. Then, a Bidirectional Long Short-Term Memory (Bi-LSTM) recurrent neural network is used to learn spatio-angular features from viewpoint feature sequences, exploring both forward and backward angular relationships. Additionally, an attention mechanism allows our model to selectively focus on the most important spatio-angular features, thus enabling a more effective learning outcome. Finally, a fusion scheme is adopted to obtain the emotion recognition classification results. Comprehensive experiments have been conducted on the IST-EURECOM Light Field Face database using two challenging evaluation protocols, showing the superiority of our method over the state-of-the-art. |
Sarkar, Pritam; Etemad, Ali Self-supervised ecg representation learning for emotion recognition Journal Article arXiv preprint arXiv:2002.03898, 2020. Abstract | Links | BibTeX | Tags: Affective Computing, Artificial Intelligence, Neural Networks @article{sarkar2020selfb, title = {Self-supervised ecg representation learning for emotion recognition}, author = { Pritam Sarkar and Ali Etemad}, url = {https://arxiv.org/abs/2002.03898}, year = {2020}, date = {2020-01-01}, journal = {arXiv preprint arXiv:2002.03898}, abstract = {We present a self-supervised deep multi-task learning framework for electrocardiogram (ECG) -based emotion recognition. The proposed framework consists of two stages of learning a) learning ECG representations and b) learning to classify emotions. ECG representations are learned by a signal transformation recognition network. The network learns high-level abstract representations from unlabeled ECG data. Six different signal transformations are applied to the ECG signals, and transformation recognition is performed as pretext tasks. Training the model on pretext tasks helps our network learn spatiotemporal representations that generalize well across different datasets and different emotion categories. We transfer the weights of the self-supervised network to an emotion recognition network, where the convolutional layers are kept frozen and the dense layers are trained with labelled ECG data. We show that our proposed method considerably improves the performance compared to a network trained using fully-supervised learning. New state-of-the-art results are set in classification of arousal, valence, affective states, and stress for the four utilized datasets. Extensive experiments are performed, providing interesting insights into the impact of using a multi-task self-supervised structure instead of a single-task model, as well as the optimum level of difficulty required for the pretext self-supervised tasks. }, keywords = {Affective Computing, Artificial Intelligence, Neural Networks}, pubstate = {published}, tppubtype = {article} } We present a self-supervised deep multi-task learning framework for electrocardiogram (ECG) -based emotion recognition. The proposed framework consists of two stages of learning a) learning ECG representations and b) learning to classify emotions. ECG representations are learned by a signal transformation recognition network. The network learns high-level abstract representations from unlabeled ECG data. Six different signal transformations are applied to the ECG signals, and transformation recognition is performed as pretext tasks. Training the model on pretext tasks helps our network learn spatiotemporal representations that generalize well across different datasets and different emotion categories. We transfer the weights of the self-supervised network to an emotion recognition network, where the convolutional layers are kept frozen and the dense layers are trained with labelled ECG data. We show that our proposed method considerably improves the performance compared to a network trained using fully-supervised learning. New state-of-the-art results are set in classification of arousal, valence, affective states, and stress for the four utilized datasets. Extensive experiments are performed, providing interesting insights into the impact of using a multi-task self-supervised structure instead of a single-task model, as well as the optimum level of difficulty required for the pretext self-supervised tasks. |
2021 |
Evaluation of Shape Array sensors to quantify the spatial distribution and seasonal rate of track settlement Journal Article Transportation Geotechnics, 27 , pp. 100487, 2021, ISSN: 2214-3912. |
Point Cloud Registration Using Virtual Interest Points from Macaulay’s Resultant of Quadric Surfaces Journal Article Journal of Mathematical Imaging and Vision, 2021. |
Dynamic Hybrid Relation Network for Cross-Domain Context-Dependent Semantic Parsing Miscellaneous Forthcoming Forthcoming. |
2020 |
Learning to Retrieve Entity-Aware Knowledge and Generate Responses with Copy Mechanism for Task-Oriented Dialogue Systems Miscellaneous 2020. |
Frontiers in Photosensor Materials and Designs for New Image Sensor Applications Journal Article IEEE Sensors Journal, pp. 1-1, 2020. |
Evaluation by Hybrid Simulation of Earthquake-Damaged RC Walls Repaired for In-Plane Bending with Single-Sided CFRP Sheets Journal Article Journal of Composites for Construction, 24 (6), pp. 04020073, 2020. |
Smart railway sleepers - a review of recent developments, challenges, and future prospects Journal Article Construction and Building Materials, pp. 121533, 2020, ISSN: 0950-0618. |
Program Enhanced Fact Verification with Verbalization and Graph Attention Network Miscellaneous 2020, (arXiv.org > cs > arXiv:2010.03084v4 ). |
Measured Responses of a Corrugated Steel Ellipse Culvert at Different Cover Depths Journal Article Journal of Bridge Engineering, 25 (11), pp. 04020096, 2020. |
Distributed Multiple Model MPC for Target Tracking UAVs Inproceedings 2020 International Conference on Unmanned Aircraft Systems (ICUAS), pp. 123-130, 2020. |
Condition-Transforming Variational Autoencoder for Generating Diverse Short Text Conversations Journal Article ACM Trans. Asian Low-Resour. Lang. Inf. Process., 19 (6), 2020, ISSN: 2375-4699. |
Trust in Multi-Vehicle Systems Using MDP Control Strategies Inproceedings 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 2377-2382, 2020, ISSN: 2577-1655. |
Vehicle Platoon String Stability: Network Passivity Approach Inproceedings 2020 IEEE Conference on Control Technology and Applications (CCTA), pp. 648-653, 2020. |
3-D Reconstruction and Measurement System Based on Multi-Mobile Robot Machine Vision Journal Article IEEE Transactions on Instrumentation and Measurement, pp. 1-1, 2020. |
Towards Automatic Classification of Fragmented Rock Piles via Proprioceptive Sensing and Wavelet Analysis Inproceedings 2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI), pp. 348-353, 2020. |
What lies beneath: Material classification for autonomous excavators using proprioceptive force sensing and machine learning Journal Article 119 , pp. 103374, 2020, ISSN: 0926-5805. |
Wearable Sensor-Based Sign Language Recognition: A Comprehensive Review Journal Article IEEE Reviews in Biomedical Engineering, pp. 1-1, 2020, ISSN: 1941-1189. |
Vehicle Platoon String Stability: Network Passivity Approach Inproceedings 2020 IEEE Conference on Control Technology and Applications (CCTA), pp. 648-653, 2020. |
Experimental and analytical fragility assessment of a combined heavy timber–steel-braced frame through hybrid simulation Journal Article Earthquake Engineering & Structural Dynamics, n/a (n/a), 2020. |
Designing Collective Behavior for Construction of Containment Structures using Actuated Blocks Inproceedings 2020 IEEE International Systems Conference (SysCon), pp. 1-8, 2020, ISSN: 2472-9647. |
Aggressive Motion Planning for a Quadrotor System with Slung Load Based on RRT Inproceedings 2020 IEEE International Systems Conference (SysCon), pp. 1-7, 2020, ISSN: 2472-9647. |
Estimation of Energy Absorption Capability of Arm Using Force Myography for Stable Human-Machine Interaction Inproceedings 2020 42nd Annual International Conference of the IEEE Engineering in Medicine Biology Society (EMBC), pp. 4758-4761, 2020. |
Deep multitask learning for pervasive BMI estimation and identity recognition in smart beds Journal Article Journal of Ambient Intelligence and Humanized Computing, pp. 1–15, 2020. |
Autorotating unmanned aerial vehicle surveying platform Patent 2020, (US Patent App. 16/046,436). |
--D3WA+--A Case Study of XAIP in a Model Acquisition Task for Dialogue Planning Inproceedings Proceedings of the International Conference on Automated Planning and Scheduling, pp. 488–497, 2020. |
Hand gesture-based control of a front-end loader Journal Article 2020. |
Systems, methods and devices for activity recognition Patent 2020, (US Patent 10,575,760). |
Reconfigurable Hybrid Energy Storage System for an Electric Vehicle DC/AC Inverter Journal Article IEEE Transactions on Power Electronics, 2020, ISSN: 1941-0107. |
TraceHub-A Platform to Bridge the Gap between State-of-the-Art Time-Series Analytics and Datasets. Inproceedings AAAI, pp. 13600–13601, 2020. |
Learning Neural-Symbolic Descriptive Planning Models via Cube-Space Priors: The Voyage Home (to STRIPS) Journal Article arXiv preprint arXiv:2004.12850, 2020. |
Explainability of Intelligent Transportation Systems using Knowledge Compilation: a Traffic Light Controller Case Journal Article arXiv preprint arXiv:2007.04916, 2020. |
A flexible machine vision system for small part inspection based on a hybrid SVM/ANN approach Journal Article Journal of Intelligent Manufacturing, 31 (1), pp. 103–125, 2020. |
Leveraging Data Engineering to Improve Unmanned Aerial Vehicle Control Design Journal Article IEEE Systems Journal, 2020, ISSN: 1937-9234 . |
FPGA-Based Design Optimization in Autonomous Robot Systems for Inspection of Civil Infrastructure Journal Article IEEE Systems Journal, 14 (2), pp. 2961–2964, 2020, ISSN: 1937-9234. |
2020 IEEE/ION Position, Location and Navigation Symposium (PLANS), pp. 960–966, IEEE 2020, ISSN: 2153-3598. |
SemEval-2020 task 4: Commonsense validation and explanation Journal Article arXiv preprint arXiv:2007.00236, 2020. |
Self-supervised learning for ecg-based emotion recognition Inproceedings ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 3217–3221, IEEE 2020, ISBN: 2379-190X . |
Upper body and ankle strategies compensate for reduced lateral stability at very slow walking speeds Journal Article bioRxiv, 2020. |
Learning Low-Resource End-To-End Goal-Oriented Dialog for Fast and Reliable System Deployment Inproceedings Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 609–618, 2020. |
Generating diverse conversation responses by creating and ranking multiple candidates Journal Article Computer Speech & Language, 62 , pp. 101071, 2020. |
Exploring deep anomaly detection methods based on capsule net Inproceedings Canadian Conference on Artificial Intelligence, pp. 375–387, Springer 2020, ISBN: 978-3-030-47358-7. |
Pre-Trained and Attention-Based Neural Networks for Building Noetic Task-Oriented Dialogue Systems Journal Article arXiv preprint arXiv:2004.01940, 2020. |
CoMP-NOMA in the SWIPT Networks Journal Article IEEE Transactions on Wireless Communications, 2020, ISBN: 1558-2248. |
Dynamic Power Splitting for SWIPT With Nonlinear Energy Harvesting in Ergodic Fading Channel Journal Article IEEE Internet of Things Journal, 2020, ISSN: 2327-4662. |
Deep RNN-Based Channel Tracking for Wireless Energy Transfer System Journal Article IEEE Systems Journal, 2020, ISSN: 1937-9234. |
Deep Artificial Noise: Deep Learning-Based Precoding Optimization for Artificial Noise Scheme Journal Article IEEE Transactions on Vehicular Technology, 69 (3), pp. 3465–3469, 2020, ISSN: 1939-9359. |
Toward the Design of Enjoyable Games for Children with Fetal Alcohol Spectrum Disorder Inproceedings Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–13, 2020. |
Attention-aware fusion RGB-D face recognition Journal Article arXiv preprint arXiv:2003.00168, 2020. |
Facial emotion recognition using light field images with deep attention-based bidirectional LSTM Inproceedings ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 3367–3371, IEEE 2020. |
Self-supervised ecg representation learning for emotion recognition Journal Article arXiv preprint arXiv:2002.03898, 2020. |