Publications

More details can be found at  Google Citation Page.

*Note:  Bold  indicates the students supervised by Prof. Tang.

Peer-Reviewed Journal Papers:

1. A. Hasankhani, Y. Tang, and J. VanZwieten, “Ocean Current Turbine Power Maximization: A Spatiotemporal Optimization Approach,”  Sustainable Energy, IEEE Transactions on, 2020. (Under review)
2. 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)
3. 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)
4. 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)
5. 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)
6. 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)
7. 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)
8. Y. Huang, Y. Tang, and J. VanZwieten, “Prognostics with Variational Autoencoder by Generative Adversarial Learning,”  Industrial Electronics, IEEE Transactions on, 2020. (In press)
9. M. Shi, Y. Tang, X. Zhu, and J. Liu, “Multi-Label Graph Convolutional Network Representation Learning,”  Big Data, IEEE Transactions on, 2020. (In press)
10. 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)
11. 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.
12. 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)
13. M. Shi, Y. Tang, and X. Zhu, “MLNE: Multi-Label Network Embedding,”  Neural Networks and Learning Systems, IEEE Transactions on, 2019. (Accepted)
14. M. Shi, Y. Tang, and X. Zhu, “Topical Network Embedding,”  Data Mining and Knowledge Discovery, 2019. (Accepted)
15. 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.
16. 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.
17. 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.
18. 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.
19. 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.
20. 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.
21. 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.
22. 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.
23. 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.
24. 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.
25. 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.
26. 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.
27. 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.
28. 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.
29. 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.
30. 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.
31. 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.
32. 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.
33. 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.
34. 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.
35. 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.
36. 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)
37. 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)
38. 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.
39. 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.
40. 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.
41. 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.
42. 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.
43. 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.
44. 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:

1. 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)
2. 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)
3. 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)
4. 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)
5. 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)
6. 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)
7. 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)
8. 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.
9. 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.
10. 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)
11. 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.
12. 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.
13. 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.
14. 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.
15. 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)
16. 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.
17. 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.
18. 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.
19. 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.
20. 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)
21. 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.
22. 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.
23. 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.
24. 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.
25. 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.
26. 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.
27. 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.
28. 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.
29. 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.
30. 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.
31. 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.
32. 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.
33. 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.
34. 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)
35. 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.
36. 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.
37. 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.
38. 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.
39. 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.
40. 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.
41. 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.

Openings

Ph.D. Research Assistant Positions Available:

Fully-funded Ph.D. positions are always available. We are particularly interested in hiring creative, energetic, and persistent students with a focus on conducting cutting-edge research. Master’s degree in electrical/mechanical/computer engineering, computer science, or related department is required.

Send your CV to Prof. Tang at tangy@fau.edu and specify in the subject line “Application for Ph.D. position”.