Publications
*Note: Blue indicates the students supervised by Prof. Tang.
Peer-Reviewed Journal Papers:
- A. Hasankhani, Y. Tang, and J. VanZwieten, “Ocean Current Turbine Power Maximization: A Spatiotemporal Optimization Approach,” Sustainable Energy, IEEE Transactions on, 2020. (Under review)
- A. Hasankhani, J. VanZwieten, and Y. Tang, “Modeling and Numerical Simulation of a Buoyancy Controlled Ocean Current Turbine,” International Marine Energy Journal, 2020. (Under review)
- B. Freeman, Y. Tang, Y. Huang, and J. VanZwieten, “Rotor Blade Imbalance Fault Detection for Variable-Speed Marine Current Turbines via Generator Power Signal Analysis,” Ocean Engineering, 2020. (In press)
- M. Shi, D. Wilson, X. Zhu, Y. Huang, Y. Zhuang, J. Liu, and Y. Tang, “Evolutionary Architecture Search for Graph Neural Networks,” IEEE Computational Intelligence Magazine, Special Issue on Evolutionary Neural Architecture Search and Applications, 2020. (Under review)
- M. Shi, Y. Tang, and X. Zhu, “Topology and Content Co-Alignment Graph Convolutional Learning,” Neural Networks and Learning Systems, IEEE Transactions on, 2020. (Under revision)
- B. Ouyang, P. Wills, Y. Tang, J. Hallstrom, T. Su, J. Rodriguez-Labra, Y. Li, and C. Den Ouden, “Initial Development of the Hybrid Aerial Underwater Robotic System (HAUCS): Internet of Things (IoT) for Aquaculture Farms,” IEEE Internet of Things Journal, 2020. (Under revision)
- M. Shi, Y. Tang, X. Zhu, and J. Liu, “Feature-Attention Graph Convolutional Networks for Noise Resilient Learning,” Cybernetics, IEEE Transactions on, 2020. (Under review)
- Y. Huang, Y. Tang, and J. VanZwieten, “Prognostics with Variational Autoencoder by Generative Adversarial Learning,” Industrial Electronics, IEEE Transactions on, 2020. (In press)
- M. Shi, Y. Tang, X. Zhu, and J. Liu, “Multi-Label Graph Convolutional Network Representation Learning,” Big Data, IEEE Transactions on, 2020. (In press)
- Y. Huang, Y. Tang, and J. VanZwieten, “Reliable Machine Prognostic Health Management in the Presence of Missing Data,” Computation Practice and Experience (CCPE), 2020. (In press)
- M. Shi, J. Liu, Y. Tang, and X. Zhu, “Topic-aware Web Service Representation Learning,” ACM Transactions on the Web (TWEB), vol. 14, no. 2, pp. 1-23, 2020.
- Y. Tang, Y. Huang, E. Lindbeck, S. Lizza, James VanZwieten, Nathan Tom, and Wei Yao, “WEC Fault Modeling and Condition Monitoring: A Graph-Theoretic Approach,” IET Electric Power Applications, 2020. (Accepted)
- M. Shi, Y. Tang, and X. Zhu, “MLNE: Multi-Label Network Embedding,” Neural Networks and Learning Systems, IEEE Transactions on, 2019. (Accepted)
- M. Shi, Y. Tang, and X. Zhu, “Topical Network Embedding,” Data Mining and Knowledge Discovery, 2019. (Accepted)
- M. Shi, J. Liu, D. Zhou and Y. Tang, “A Topic-Sensitive Method for Mashup Tag Recommendation Utilizing Multi-Relational Service Data,” Services Computing, IEEE Transactions on, vol. 30, no. 5, pp. 1077-1090, 1 May 2019.
- M. Shi, Y. Tang, and J. Liu, “Functional and Contextual Attention-based LSTM for Service Recommendation in Mashup Creation,” Parallel and Distributed Systems, IEEE Transactions on, vol. 30, no. 5, pp. 1077-1090, May 2019.
- Z. Lu, M. Wei, Y. Tang, and X. Lu, “Cyber and Physical Interactions to Combat Failure Propagation in Smart Grid: Characterization, Analysis, and Evaluation,” Computer Networks, vol. 158, pp. 184-192, 2019.
- Y. Liu, J. Yang, Y. Tang, J. Xu, Y. Sun, Y. Chen, X. Peng, and S. Liao, “Bi-level fuzzy stochastic expectation modeling and optimization for energy storage systems planning in virtual power plants,” Journal of Renewable and Sustainable Energy, vol. 11, no. 1, pp. 014-026, 2019.
- B. Tan, J. Yang, Y. Tang, S. Jiang, P. Xie and W. Yuan, “A Deep Imbalanced Learning Framework for Transient Stability Assessment of Power System,” IEEE Access, vol. 7, pp. 81759-81769, 2019.
- H. Li, P. Ju, C. Gan, and Y. Tang, “Analytic Estimation Method of Forced Oscillation Amplitude Under Stochastic Continuous Disturbances,” Smart Grid, IEEE Transactions on, vol. 10, no. 4, pp. 4026-4036, July 2019.
- H. Shuai, J. Fang, X. Ai, Y. Tang, J. Wen, and H. He, “Stochastic Optimization of Economic Dispatch for Microgrid Based on Approximate Dynamic Programming,” Smart Grid, IEEE Transactions on, vol. 10, no. 3, pp. 2440-2452, May 2019.
- C. Mu, Y. Tang, and H. He, “Improved Sliding Mode Design for Load Frequency Control of Power System Integrated an Adaptive Learning Strategy,” Industrial Electronics, IEEE Transactions on, vol. 64, no. 8, pp. 6742-6751, Aug. 2017.
- G. Jiang, H. He, P. Xie, and Y. Tang, “Stacked Multi-Level-Denoising Autoencoders: A New Representation Learning Approach for Wind Turbine Gearbox Fault Diagnosis,” Instrumentation & Measurement, IEEE Transactions on, vol. 66, no. 9, pp. 2391-2402, Sept. 2017.
- Y. Tang, C. Luo, J. Yang, and H. He, “A Chance Constrained Optimal Reserve Scheduling Approach for Economic Dispatch Considering Wind Penetration,” IEEE/CAA Journal of Automatica Sinica, vol. 4, no. 2, pp. 186-194, April 2017.
- Y. Guo, X. Li, Y. Tang, and J. Li, “Heuristic Artificial Bee Colony Algorithm for Uncovering Community in Complex Networks,” Mathematical Problems in Engineering Journal, vol. 2017, Article ID 4143638, 12 pages, 2017.
- G. Weng, F. Huang, Y. Tang, J. Yan, and H. He, “Fault-tolerant Location of Transient Voltage Disturbance Source for DG Integrated Smart Grid,” Electric Power Systems Research Journal, Volume 144, Pages 13-22, March 2017.
- J. Yan, H. He, X. Zhong, and Y. Tang, “Q-learning Based Vulnerability Analysis of Smart Grid against Sequential Topology Attacks,” Information Forensics and Security, IEEE Transactions on, vol. 12, no. 1, pp. 200-210, Jan. 2017.
- L. Dong, Y. Tang, C. Sun, and H. He, “An Event-Triggered Approach for Load Frequency Control with Supplementary ADP,” Power Systems, IEEE Transactions on, vol. 32, no. 1, pp. 581-589, Jan. 2017.
- Y. Tang, C. Mu, and H. He, “SMES Based Damping Controller Design Using Fuzzy-GrHDP Considering Transmission Delay,” Applied Superconductivity, IEEE Transactions on, vol. 26, no. 7, pp. 1-6, Oct. 2016.
- L. He, J. Yang, J. Yan, Y. Tang, and H. He, “A bi-layer optimization based temporal and spatial scheduling for large-scale electric vehicles,” Applied Energy, Volume 168, Pages 179-192, 15 April 2016.
- Y. Tang, Z. Ni, X. Zhong, H. He, D. Zhao, and X. Xu, “Fuzzy-based goal representation adaptive dynamic programming,” Fuzzy Systems, IEEE Transactions on, vol. 24, no. 5, pp. 1159-1175, Oct. 2016.
- Y. Tang, H. He, Z. Ni, and J. Wen, “Adaptive dynamic modulation for DFIG and STATCOM with HVDC transmission” Neural Networks and Learning Systems, IEEE Transactions on, vol. 27, no. 8, pp. 1762-1772, Aug. 2016.
- Z. Ni, Y. Tang, X. Sui, H. He, and J. Wen, “An adaptive neuro-control approach for multi-machine power systems,” International Journal of Electrical Power Energy Systems, vol. 75, pp. 108-116, Feb. 2016.
- Y. Tang, J. Yang, J. Yan, and H. He, “Intelligent load frequency controller using GrADP for island smart grid with electric vehicles and renewable resources,” Neurocomputing, vol. 170, no. 1, Dec. 2015.
- Y. Zhu, J. Yan, Y. Tang, Y. Sun, and H. He, “Joint substation-transmission line vulnerability assessment against the smart grid,” Information Forensics and Security, IEEE Transactions on, vol. 10, no. 5, pp. 1010-1024, May 2015.
- Y. Tang, H. He, J. Wen, and J. Liu, “Power system stability control for a wind farm based on adaptive dynamic programming,” Smart Grid, IEEE Transactions on, vol. 6, no. 1, pp. 166-177, Jan. 2015. (Ranked No. 8 in citation from Web of Science for all the papers published in IEEE TSG since 2015)
- J. Yan, Y. Tang, H. He, and Y. Sun, “Cascading failure risk assessment with DC power flow model and transient stability analysis,” Power Systems, IEEE Transactions on, vol. 30, no. 1, pp. 285-297, Jan. 2015. (Ranked No. 13 in citation from Web of Science for all the papers published in IEEE TPS since 2015)
- J. Yang, F. Xin, Y. Tang, J. Yan, H. He, and C. Luo, “A power system optimal dispatch strategy considering the flow of carbon emissions and large consumers,” Energies, vol. 8, no. 9, pp. 9087-9106, 2015.
- J. Yang, L. Gong, Y. Tang, J. Yan, H. He, L. Zhang, and G. Li, “An improved SVM-based cognitive diagnosis algorithm for operation states of distribution grid,” Cognitive Computation, vol. 7, no. 5, pp. 582-593, 2015.
- J. Yang, Z. Zeng, Y. Tang, J. Yan, H. He, and Y. Wu, “Load frequency control in isolated micro-grids with electrical vehicles based on multi-variable generalized predictive theory,” Energies, vol. 8, no. 3, pp. 2145-2164, 2015.
- Y. Tang, H. He, Z. Ni, J. Wen, and X. Sui, “Reactive power control of grid-connected wind farm based on adaptive dynamic programming,” Neurocomputing, vol. 125, no. 1, pp. 125-133, 2014.
- X. Sui, Y. Tang, H. He, and J. Wen, “Energy-storage-based low-frequency oscillation damping control using particle swarm optimization and heuristic dynamic programming,” Power Systems, IEEE Transactions on, vol. 29, no. 5, pp. 2539-2548, Sept. 2014.
- Y. Zhu, J. Yan, Y. Tang, Y. Sun, and H. He, “Resilience analysis of power grids under the sequential attack,” Information Forensics and Security, IEEE Transactions on, vol. 9, no. 12, pp. 2340-2354, Dec. 2014.
- Y. Tang, P. Ju, H. He, C. Qin, and F. Wu, “Optimized control of DFIG-based wind generation using sensitivity analysis and particle swarm optimization,” Smart Grid, IEEE Transactions on, vol. 4, no. 1, pp. 509-520, March 2013.
Peer-Reviewed Conference Papers and Poster Presentations:
- A. Hasankhani, J. VanZwieten, and Y. Tang, “Modeling and Numerical Simulation of a Lifting Surface Controlled Ocean Current Turbine,” 2021 American Control Conference (ACC), New Orleans, Louisiana, USA. (Submitted)
- Y. Huang, Y. Tang, H. Zhuang, J. VanZwieten, and L. Cherubin, “Physics-informed Tensor-train ConvLSTM for Volumetric Velocity Forecasting,” 2020 Conference on Neural Information Processing Systems (NIPS), Virtual-only Conference, 2020. (Submitted)
- Y. Tang, Y. Huang, David Wilson, “A Spatiotemporal Seq2Seq Learning Algorithm for Loop Current Forecasting in GoM,” Gulf of Mexico Oil Spill & Ecosystem Science Conference, Tampa, FL, 2020. (Accepted)
- Y. Huang, Y. Tang, J. VanZwieten, and F. Wu, “Prognostic and Health Management in Ocean Energy System: A Self-healing Framework based on Reinforcement Learning,” 2020 International Conference on Ocean Energy (ICOE), Washington, DC., US, 2020. (Accepted)
- A. Hasankhani, Y. Tang, J. VanZwieten, and C. Sultan, “Ocean Current Turbine Active Depth Optimization for Maximum Power Production,” 2020 International Conference on Ocean Energy (ICOE), Washington, DC., US, 2020. (Accepted)
- A. De Luera, J. VanZwieten, B. Dunlap, Y. Tang, C. Sultan, and N. Xiros “Numerical Simulation of a Buoyancy Controlled Ocean Current Turbine,” 2020 International Conference on Ocean Energy (ICOE), Washington, DC., US, 2020. (Accepted)
- Y. Tang, A. Hasankhani, Y. Zhang, and J. VanZwieten, “Adaptive Super-Twisting Sliding Mode Control for Ocean Current Turbine-Driven Permanent Magnet Synchronous Generator,” 2020 American Control Conference (ACC), Denver, Colorado, USA. (Accepted)
- Y. Tang, J. VanZwieten, B. Dunlap, D. Wilson, C. Sultan and N. Xiros, “In-Stream Hydrokinetic Turbine Fault Detection and Fault Tolerant Control – A Benchmark Model,” 2019 American Control Conference (ACC), Philadelphia, PA, USA, 2019, pp. 4442-4447.
- Y. Huang, Y. Tang, J. VanZwieten, J. Liu and X. Xiao, “An Adversarial Learning Approach for Machine Prognostic Health Management,” 2019 International Conference on High Performance Big Data and Intelligent Systems (HPBD&IS), Shenzhen, China, 2019, pp. 163-168.
- E. Lindbeck, Y. Tang, and J. VanZwieten, “Advanced Signal Processing for Marine Hydrokinetic Turbine Fault Detection,” Marine Energy Technology Symposium (METS), DC, 2019. (Poster only)
- M. Shi, Y. Tang, J. Liu, and B. Cao, “TA-BLSTM: Tag Attention-Based Bidirectional Long Short-Term Memory for Service Recommendation in Mashup Creation,” International Joint Conference on Neural Networks (IJCNN), Budapest, Hungary, 2019.
- B. Freeman, Y. Tang, and J. VanZwieten, “Morlet Continuous Time Wavelet Transform for MHK Rotor Blade Fault Detection,” IEEE Power and Energy Society General Meeting (PESGM), Atlanta, Georgia, 2019.
- Y. Huang, Y. Tang, and J. VanZwieten “Remaining Useful Life Estimation of Hydrokinetic Turbine Blades Using Power Signals,” IEEE Power and Energy Society General Meeting (PESGM), Atlanta, Georgia, 2019.
- D. Wilson, S. Passmore, Y. Tang and J. VanZwieten, “Bidirectional Long Short-Term Memory Networks for Rapid Fault Detection in Marine Hydrokinetic Turbines,” The 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando, FL, 2018, pp. 495-500.
- Y. Tang, B. Freeman, D. Wilson, and J. VanZwieten, “FAST-Based In-Stream Hydrokinetic Generation System Modeling for MCM and PHM,” Marine Energy Technology Symposium (METS), DC, 2018. (Poster only)
- D. Wilson, Y. Tang, J. Yan, and Z. Lu, “Deep Learning-Aided Cyber-Attack Detection in Power Transmission Systems,” IEEE Power and Energy Society General Meeting (PESGM), Portland, OR, 2018.
- M. Wei, Z. Lu, Y. Tang and X. Lu, “How Can Cyber-Physical Interdependence Affect the Mitigation of Cascading Power Failure?” IEEE INFOCOM 2018 – IEEE Conference on Computer Communications, Honolulu, HI, 2018, pp. 2501-2509.
- Y. Tang, and J. Yang, “Dynamic Event Monitoring Using Unsupervised Feature Learning Towards Smart Grid Big Data,” International Joint Conference on Neural Networks (IJCNN), 2017.
- Y. Tang, C. Mu, and H. He, “Near-Space Aerospace Vehicles Attitude Control Based on Adaptive Dynamic Programming and Sliding Mode Control,” International Joint Conference on Neural Networks (IJCNN), 2017.
- Y. Tang and H. He, “Inter-Connected Power System Frequency Stability with Wind Penetration by Using Fuzzy-GrHDP,” Power and Energy Society General Meeting (PESGM), 2017. (Nominated for Best Paper Award)
- J. Yan, Y. Tang, B. Tang, H. He, and Y. Sun “Power Grid Resilience Against False Data Injection Attacks,” Power and Energy Society General Meeting (PESGM), 2016 IEEE, 2016.
- C. Mu, Y. Tang, and H. He, “Observer-Based Sliding Mode Frequency Control for Micro-Grid with Photovoltaic Energy Integration,” Power and Energy Society General Meeting (PESGM), 2016 IEEE, 2016.
- C. Luo, J. Yang, Y. Tang, H. He, and M. Liu, “Chance constraint based risk-aware optimal power flow for cascading failure prevention,” Power and Energy Society Transmission & Distribution Conference and Exposition (PES T&D), 2016.
- Y. Tang, H. He, and J. Wen, “Optimal operation for energy storage with wind power generation using adaptive dynamic programming,” in Power and Energy Society General Meeting (PESGM), 2015 IEEE, 2015.
- Y. Tang, C. Mu, and H. He, “Superconducting magnetic energy storage based power system control using adaptive dynamic programming,” Proceedings of 2015 IEEE International Conference on Applied Superconductivity and Electromagnetic Devices, 2015.
- J. Yan, Y. Tang, Y. Zhu, Y. Sun, and H. He, “Smart grid vulnerability under cascade-based sequential line-switching attacks,” in Global Communications Conference (GLOBECOM), 2015 IEEE, Dec. 2015.
- Y. Zhu, J. Yan, Y. Tang, Y. Sun, and H. He, “Diversities of cascading failure processes in electric grids,” in Innovative Smart Grid Technologies Conference (ISGT), 2015 IEEE Power & Energy Society, 18-20 Feb. 2015.
- Y. Tang, X. Zhong, Z. Ni, J. Yan, and H. He, “Impact of signal transmission delays on power system damping control using heuristic dynamic programming,” in Computational Intelligence Applications in Smart Grid (CIASG), 2014 IEEE Symposium on, 9-12 Dec. 2014.
- Z. Ni, Y. Tang, H. He, and J. Wen, “Multi-machine power system control based on dual heuristic dynamic programming,” in Computational Intelligence Applications in Smart Grid (CIASG), 2014 IEEE Symposium on, 9-12 Dec. 2014.
- X. Zhong, Z. Ni, Y. Tang, and H. He, “Data-driven partially observable dynamic processes using adaptive dynamic programming,” in Adaptive Dynamic Programming and Reinforcement Learning (ADPRL), 2014 IEEE Symposium on, 9-12 Dec. 2014.
- Y. Zhu, J. Yan, Y. Tang, Y. Sun, and H. He, “Coordinated attacks against substations and transmission lines in power grids,” in Global Communications Conference (GLOBECOM), 2014 IEEE, pp. 655-661, 8-12 Dec. 2014.
- Y. Su, J. Liu, S. Liao, Y. Tang, J. Fang, J.Wen, and H. He, “Transient over-voltage control for a wind farm based on goal representation adaptive dynamic programming,” in Power System Technology (POWERCON), 2014 International Conference on, pp. 705-712, 20-22 Oct. 2014.
- Y. Tang, J. Yang, J. Yan, Z. Zeng, and H. He, “Frequency control using on-line learning method for island smart grid with EVs and PVs,” in Neural Networks (IJCNN), 2014 International Joint Conference on, pp. 1440-1446, 6-11 July 2014.
- Y. Zhu, J. Yan, Y. Tang, Y. Sun, and H. He, “The sequential attack against power grid networks,” in IEEE International Conference on Communications (ICC), Sydney, Australia, Jun. 10-14, 2014. (Best Paper Award)
- Y. Tang, H. He, and J. Wen, “Optimized control of DFIG based wind generation using swarm intelligence,” Power and Energy Society General Meeting (PES), 2013 IEEE, 21-25 July 2013.
- Y. Tang, H. He, and J. Wen, “Comparative study between HDP and PSS on DFIG damping control,” Computational Intelligence Applications In Smart Grid (CIASG), 2013 IEEE Symposium on, pp.59-65, 16-19 April 2013.
- Y. Tang, H. He, and J. Wen, “Adaptive control for an HVDC transmission link with FACTS and a wind farm,” Innovative Smart Grid Technologies (ISGT), 2013 IEEE PES, 24-27 Feb. 2013.
- Y. Tang, S. Fu, B. Tang, and H. He. “A modified PSO based particle filter algorithm for object tracking,” In SPIE Defense, Security, and Sensing, pp. 87500S-87500S. International Society for Optics and Photonics, 2013.
- B. Tang, S. Fu, Y. Tang and H. He, “Robust multiple objects tracking: particle filter with ePSO,” International Conference on Cognitive and Neural Systems (ICCNS), Boston, 2013.
- X. Fang, H. He, Z. Ni, and Y. Tang, “Learning and control in virtual reality for machine intelligence,” Intelligent Control and Information Processing (ICICIP), 2012 Third International Conference on, pp.63-67, 15-17 July 2012.
- Y. Tang, H. He, and J. Wen, “Power system stabilization with high wind power penetration using hierarchical ADP control,” International Conference on Cognitive and Neural Systems (ICCNS), Boston, 2012.