[Book/Edited Books]                [Journal Papers]             [Conference Papers]

Book/Edited Books/Book Chapters

• Z. Ni, H. He, and X. Zhong, "Experimental Studies on Data-Driven Heuristic Dynamic Programming for POMDP," in Frontiers of Intelligent Control and Information Processing, Editors: D. Liu, C. Alippi, D. Zhao, and H. Zhang, ch.3, pp 88-105, World Scientic Publishing, 2014.
• H. He and Y. Ma, Editors, Imbalanced Learning: Foundations, Algorithms, and Applications, Wiley-IEEE, ISBN: 978-1-118-07462-6, Hardcover, 216 pages, Wiley-IEEE, July 2013. [Links: Wiley or Amazon].

• H. He, Self-Adaptive Systems for Machine Intelligence, ISBN: 978-0-470-34396-8, Hardcover, 248 pages, Wiley, August, 2011. [Links: Wiley or Amazon].

• H. He, Z. Ni, D. Zhao, "Learning and Optimization in Hierarchical Adaptive Critic Design," in Reinforcement Learning and Approximate Dynamic Programming for Feedback Control, Editors: F. Lewis and D. Liu, Wiley-IEEE, December, 2012. [Wiley]

• Advances in Neural Networks, Proceedings of the 6th International Symposium on Neural Networks, ISNN 2009,  W. Yu, H. He, and N. Zhang, (Eds.), Lecture Notes in Computer Science, vol. 5551, 5552, and 5553, ISBN: 978-3-642-01506-9 (Part I), 978-3-642-01509-0 (Part II), 978-3-642-01512-0 (Part III), Springer, May 2009. [Part I] [Part II] [Part III].

• Advances in Neural Networks, Proceedings of the 8th International Symposium on Neural Networks, ISNN 2011, D. Liu, H. Zhang, M. Polycarpou, C. Alippi, and H. He, (Eds.), Lecture Notes in Computer Science, vol. 6675, 6676 and 6677, ISBN: 978-3-642-21104-1 (Part I), 978-3-642-21089-1 (Part II), 978-3-642-21110-2 (Part III), Springer, 2011. [Part I]  [Part II] [Part III]

Refereed Journal Papers

 H. He and E. A. Garcia, "Learning from Imbalanced Data," IEEE Trans. Knowledge and Data Engineering, vol. 21, issue 9, pp. 1263-1284, 2009. [pdf]. (22 pages) *** The most-cited paper in the IEEE TKDE since 2009 to date according to the Scopus database -- Thanks to Jin for letting us know this news! *New*: [Lecture Notes on in PDF]  [Lecture Notes in PPT] Imbalanced learning could play a key role in the Big Data era. This survey paper provides a comprehensive and critical review of learning from imbalacnd data, including the foundation/nature of the problem, state-of-the-art technologies, assessment metrics, as well as future research opportunities and directions on Imbalanced Learning. H. He and J. Yan, "Cyber-physical Attacks and Defences in the Smart Grid: A Survey", IET Cyber-Physical Systems: Theory & Applications, vol. 1, issue 1, pp. 13 - 27, 2016. [pdf] (15 pages) This survey paper provides a comprehensive and systematic review of the critical attack threats and defence strategies in the smart grid. We start this survey with an overview of the smart grid security from the cyber-physical (CP) perspective, and then focuses on prominent CP attack schemes with significant impact on the smart grid operation and corresponding defense solutions. We hope this paper raises awareness of the CP attack threats and defence strategies in complex CPS-based infrastructures such as the smart grid and inspires research effort toward the development of secure and resilient CP infrastructures. S. Fu, H. He, and Z. Hou, "Learning Race from Face: A Survey," IEEE Trans. on Pattern Analysis and Machine Intelligence (TPAMI), vol. 36, issue 12, pp. 2483-2509, 2014. [pdf] (27 pages) Faces convey a wealth of social signals, including race, expression, identity, age and gender. Therefore, racial face analysis has became a critical topic for its significant potential and broader impacts in extensive real-world applications. This survey provides a comprehensive and critical review of the state-of-the-art advances in face-race perception, principles, algorithms, models, databases, tools, and applications. D. Wang, H. He, and D. Liu, "Adaptive Critic Nonlinear Robust Control: A Survey," IEEE Trans. on Cybernetics, vol. 47, issue 10, pp. 3429-3451, 2017 [pdf] (23 page) This survey paper provides a comprehensive and critical review of the recent research development on adaptive-critic based robust control design for nonlinear systems.

[142] S. Li, L. Li, J. Yan, and H. He, "SDE: A Novel Clustering Framework Based on Sparsity-Density Entropy," IEEE Trans. on Knowledge and Data Engineering, 2018 (in press).

[141] C. Yuan, H. He, and C. Wang, "Cooperative Deterministic Learning-Based Formation Control for A Group of Nonlinear Uncertain Mechanical Systems,"IEEE Transactions on Industrial Informatics, 2018 (in press).

[140] 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," IEEE Trans. on Smart Grid, 2018 (in press)

[139] H. Jiang and H. He, "Data-Driven Distributed Output Consensus Control for Partially Observable Multi-Agent Systems," IEEE Trans. on Cybernetics, 2018 (in press)

[138] H. Su, Y. Ye, X. Chen, and H. He, "A Geometric Approach to Second-order Consensus of Heterogeneous Multi-agent Systems," IEEE Trans. on Cybernetics, 2018 (in press)

[137] J. Yi, J. Bai, W. Zhou, H. He, and L. Yao, "Operating Parameters Optimization for the Aluminum Electrolysis Process Using an Improved Quantum-behaved Particle Swarm Algorithm," IEEE Transactions on Industrial Informatics, 2017 (in press)

[136] X. Yang, H. He, and X. Zhong, "Adaptive Dynamic Programming for Robust Regulation and Its Application to Power Systems," IEEE Trans. on Industrial Electronics, 2017 (in process).

[135] X. Yang and H. He, "Self-learning robust optimal control for continuous-time nonlinear systems with mismatched disturbances," Neural Networks, vol. 99, pp. 19 - 30, 2018.

[134] X. Chen, M. Shi, H. Sun, Y. Li, and H. He, "Distributed Cooperative Control and Stability Analysis of Multiple DC Electric Springs in DC Microgrid,"IEEE Trans. on Industrial Electronics, 2017 (in process).

[133] X. Yang, H. He, and D. Liu, "Event-Triggered Optimal Neuro-Controller Design with Reinforcement Learning for Unknown Nonlinear Systems," IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2017 (in press).

[132] D. Wang, H. He, and D. Liu, "Intelligent Optimal ControlWith Critic Learning for a Nonlinear Overhead Crane System," IEEE Trans. on Industrial Informatics, 2017 (in press).

[131] C. Yuan and H. He, "Cooperative Output Regulation of Heterogeneous Multi-Agent Systems with A Leader of Bounded Inputs," IET Control Theory & Applications, vol. 12, issue 2, pp. 233 - 242, 2018.

[130] C. Mu and H. He, "Dynamic Behavior of Terminal Sliding Mode Control," IEEE Trans. on Industrial Electronics, vol. 65, issue 4, pp. 3480 - 3490, 2018.

[129] G. Jiang, P. Xie, H. He, and J. Yan, "Wind Turbine Fault Detection Using Denoising Autoencoder with Temporal Information," IEEE/ASME Transactions on Mechatronics (TMECH), 2017 (in press)

[128] C. Yuan, S. Licht, and H. He, "Formation Learning Control of Multiple Autonomous Underwater Vehicles with Heterogeneous Nonlinear Uncertain Dynamics,"IEEE Trans. on Cybernetics, 2017 (in press)

[128] C. Mu, D. Wang, and H. He, "Data-Driven Finite-Horizon Approximate Optimal Control for Discrete-Time Nonlinear Systems Using Iterative HDP Approach," IEEE Trans. on Cybernetics, 2017 (in press)

[127] C. Jiang, Z. Ni, Y. Guo, and H. He, "Learning Human-Robot Interaction for Robot-Assisted Pedestrian Flow Optimization, " IEEE Transactions on Systems, Man and Cybernetics: Systems, 2017 (in press).

[126] D. Wang, H. He, and D. Liu, "Adaptive Critic Nonlinear Robust Control: A Survey," IEEE Trans. on Cybernetics, vol. 47, issue 10, pp. 3429 - 3451, 2017. (Survey Paper)

[125] X. Hu, L. Chen, B. Tang, D. Cao, and H. He, "Dynamic Path Planning for Autonomous Driving on Various Roads with Avoidance of Static and Moving Obstacles," Mechanical Systems and Signal Processing, vol. 100, pp. 481 - 500, 2018.

[124] Y. Shen, W. Yao, J. Wen, H. He, and W. Chen, "Adaptive Supplementary Damping Control of VSC-HVDC for Interarea Oscillation Using GrHDP," IEEE Transactions on Power Systems, 2017, (in press).

[123] C. Yang, H. He, T. Huang, A. Zhang, J. Qiu, J. Cao, and X. Li, "Consensus for nonlinear multi-agent systems modeled by PDEs based on spatial boundary communication," IET Control Theory & Applications, vol. 11, issue 17, pp. 3196 - 3200, 2017.

[122] Z. Huang, X. Xu, H. He, J. Tan, and Z. Sun, "Parameterized Batch Reinforcement Learning for Longitudinal Control of Autonomous Land Vehicles, " IEEE Transactions on Systems, Man and Cybernetics: Systems, 2017 (in press).

[121] X. Zhong, H. He, D. Wang, and Z. Ni, "Model-Free Adaptive Control for Unknown Nonlinear Zero-Sum Differential Game," IEEE Trans. on Cybernetics, 2017 (in press)

[120] X. Yang, H. He, D. Liu, Y. Zhu, "Adaptive dynamic programming for robust neural control of unknown continuous-time non-linear systems," IET Control Theory & Applications, vol. 11, issue 14, pp. 2307- 2316, 2017.

[119] B. Tang and H. He, "GIR-based Ensemble Sampling Approaches for Imbalanced Learning," Pattern Recognition, vol. 71, pp. 306 - 319, 2017.

[118] Y. Shen, W. Yao, J. Wen, and H. He, "Adaptive Wide-area Power Oscillation Damper Design for Photovoltaic Plant Considering Delay Compensation," IET Generation, Transmission & Distribution, vol. 11, issue 18, pp. 4511 - 4519, 2017.

[117] D. Wang, H. He, X. Zhong, and D. Liu, "Event-Driven Nonlinear Discounted Optimal Regulation Involving A Power System Application," IEEE Trans. on Industrial Electronics, vol. 64, issue 10, pp. 8177 - 8186, 2017.

[116] Z. Wan, H. He, and B. Tang, "A Generative Model for Sparse Hyperparameter Determination," IEEE Trans. on Big Data, 2017 (in press).

[115] C. Mu, Y. Tang, and H. He, "Improved Sliding Mode Design for Load Frequency Control of Power System Integrated an Adaptive Learning Strategy,"IEEE Trans. on Industrial Electronics, vol. 64, issue 8, pp. 6742 - 6751, 2017.

[114] G. Jiang, H. He, P. Xie, Y. Tang, "Stacked Multi-Level-Denoising Autoencoders: A New Representation Learning Approach for Wind Turbine Gearbox Fault Diagnosis," IEEE Transactions on Instrumentation & Measurement, vol. 66, issue 9, pp. 2391 -2402, 2017.

[113] C. Mu, D. Wang, and H. He, "Novel Iterative Neural Dynamic Programming for Data-Based Approximate Optimal Control Design," Automatica, vol. 81, pp. 240 - 252, July 2017.

[112] D. Wang, H. He, C. Mu, and D. Liu, "Intelligent Critic Control With Disturbance Attenuation for Affine Dynamics Including an Application to a Micro-Grid System, " IEEE Trans. on Industrial Electronics, vol. 64, issue 6, pp. 4935 - 4944, 2017.

[111] B. Tang, Z. Chen, G. Hefferman, S. Pei, T. Wei, H. He, and Q. Yang, "Incorporating Intelligence in Fog Computing for Big Data Analysis in Smart Cities," IEEE Transactions on Industrial Informatics, vol. 13, issue 5, pp. 2140 - 2150, 2017.

[110] L. Chen, J. Wang, S. Yang, and H. He, "A Finger Vein Image-Based Personal Identification System With Self-Adaptive Illuminance Control," IEEE Trans. on Instrumentation and Measurement, vol. 66, no. 2, pp. 294-304, 2017.

[109] B. Tang and H. He, "A Local Density-Based Approach for Outlier Detection," Neurocomputing, vol. 241, pp. 171 -180, 2017.

[108] J. Yan, H. He, X. Zhong, and Y. Tang, "Q-learning Based Vulnerability Analysis of Smart Grid against Sequential Topology Attacks," IEEE Trans. on Information Forensics and Security (TIFS), vol. 12, no. 1, pp. 200-210, Jan. 2017.

[107] H. He and J. Yan, "Cyber-physical Attacks and Defences in the Smart Grid: A Survey", IET Cyber-Physical Systems: Theory & Applications, vol. 1, issue 1, pp. 13 - 27, 2016. [pdf] (Survey Paper)

[106] J. Yi, D. Huang, H. He, W. Zhou, Q. Han, and T. Li, "A Novel Framework for Fault Diagnosis Using Kernel Partial Least Squares Based on Optimal Preference Matrix, "IEEE Trans. on Industrial Electronics, vol. 64, issue 5, pp. 4315 - 4324, 2017.

[105]  D. Wang, H. He, B. Zhao, and D. Liu, "Adaptive Near-optimal Controllers for Nonlinear Decentralized Feedback Stabilization Problems," IET Control Theory & Applications, vol. 11, issue 6, pp. 799 - 806, 2017.

[104] Y. Zhu, D. Zhao, H. He, and J. Ji, "Event-Triggered Optimal Control for Partially-Unknown Constrained-Input Systems via Adaptive Dynamic Programming,"IEEE Trans. on Industrial Electronics, vol. 64, issue 5, pp. 4101 - 4109, 2017.

[103] B. Tang, C. Jiang, H. He, and Y. Guo, "Human Mobility Modeling for Robot-Assisted Evacuation in Complex Indoor Environments," IEEE Trans. on Human-Machine Systems, vol. 46, no. 5, pp. 694-707, 2016.

[102] X. Zhong, Z. Ni, and H. He, "Gr-GDHP: A New Architecture for Globalized Dual Heuristic Dynamic Programming," IEEE Transactions on Cybernetics, vol. 47, issue 10, pp. 3318 - 3330, 2017.

[101] D. Wang, H. He, and D. Liu, "Improving the Critic Learning for Event-Based Nonlinear $H_{\infty}$ Control Design, " IEEE Transactions on Cybernetics, vol. 47, issue 10, pp. 3417 - 3428, 2017.

[100] D. Wang, C. Mu, H. He, and D. Liu, "Event-Driven Adaptive Robust Control of Nonlinear Systems With Uncertainties Through NDP Strategy," IEEE Trans. on Systems, Man, adn Cybernetics: Systems, vol. 47, issue 7, pp. 1358 - 1370, 2017.

[99] G. Weng, F. Huang, J. Yan, X. Yang, Y. Zhang, and H. He, "A Fault-Tolerant Location Approach for Transient Voltage Disturbance Source Based on Information Fusion, " Energies, 9(12), 1092, doi:10.3390/en9121092, 2016.

[98] G. Weng, F. Huang, Y. Tang, J. Yan, Y. Nan, and H. He, "Fault-tolerant location of transient voltage disturbance source for DG integrated smart grid,"Electric Power Systems Research, vol. 144, pp. 13-22, March 2017.

[97] L. Dong, X. Zhong, C. Sun, and H. He, "Event-Triggered Adaptive Dynamic Programming for Continuous-Time Systems with Control Constraints," IEEE Transactions on Neural Networks and Learning Systems (TNNLS), vol. 28, issue 8, pp. 1941 - 1952, 2017.

[96] X. Wang, J. Yang, P. Liu, W. Liu, J. Yan, Y. Tang, and H. He, "Online Calculation for the Optimal Reclosing Time of Transmission Lines,"  Electric Power Components and Systems, 44:17, pp. 1904-1916, 2016.

[95] Y. Tang, C. Mu, and H. He, "SMES Based Damping Controller Design Using Fuzzy-GrHDP Considering Transmission Delay," IEEE Trans. on Applied Superconductivity, vol. 26, issue 7, Article Sequence Number: 5701206, 2016.

[94] B. Tang, S. Kay, H. He, and P. M. Baggenstoss, "EEF: Exponentially Embedded Families with Class-Specific Features for Classification," IEEE Signal Processing Letters, vol. 23, issue 7, pp. 969 - 973, 2016.

[93] B. Tang, S. Kay, and H. He, "Toward Optimal Feature Selection in Naive Bayes for Text Categorization," IEEE Trans. on Knowledge and Data Engineering (TKDE), vol. 28, issue 9, pp. 2508 - 2521, 2016.

[92] J. Xu, B. Tang, H. He, and H. Man, "Semi-Supervised Feature Selection Based on Relevance and Redundancy Criteria," IEEE Trans. on Neural Networks and Learning Systems (TNNLS), vol. 28, issue 9, pp. 1974 - 1984, 2017.

[91] C. Mu, Z. Ni, C. Sun, and H. He, "Data-Driven Tracking Control with Adaptive Dynamic Programming for a Class of Continuous-time Nonlinear Systems," IEEE Trans. on Cybernetics, vol. 47, issue 6, pp. 1460 - 1470, 2017.

[90] S. Li, B. Tang, and H. He, "An Imbalanced Learning based MDR-TB Early Warning System," Journal of Medical Systems, vol. 40, issue 7, pp. 1-9, 2016.

[89] D. Lu, X. Zhong, C. Sun, and H. He, "Adaptive Event-Triggered Control based on Heuristic Dynamic Programming for Nonlinear Discrete-time Systems,"IEEE Trans. on Neural Networks and Learning Systems (TNNLS), vol. 28, issue 7, pp. 1594 - 1605, 2017.

[88] 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, issue 2, pp. 186 - 194, 2017.

[87] L. Dong, Y. Tang, H. He, and C. Sun, "An Event-Triggered Approach for Load Frequency Control with Supplementary ADP," IEEE Trans on Power Systems, vol. 32, issue 1, pp. 581 - 589, 2017.

[86] 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, vol. 168, pp. 179-192, Apr. 2016.

[85] X. Zhong and H. He, "An Event-Triggered ADP Control Approach for Continuous-time System with Unknown Internal States," IEEE Trans. on Cybernetics, vol. 47, issue 3, pp. 683 - 694, 2017

[84] B. Tang, H. He, P. M. Baggenstoss, and S. Kay, "A Bayesian Classification Approach Using Class-Specific Features for Text Categorization," IEEE Trans. on Knowledge and Data Engineering (TKDE), vol. 28, issue 6, pp. 1602 - 1606, 2016.

[83] C. Mu, Z. Ni, C. Sun, and H. He, "Air-breathing Hypersonic Vehicle Tracking Control based on Adaptive Dynamic Programming," IEEE Trans. on Neural Networks and Learning Systems (TNNLS), vol. 28, issue 3, pp. 584 - 598, 2017.

[82] J. Yi, D. Huang, S. Fu, H. He, T. Li, "Optimized Relative Transformation Matrix Using Bacterial Foraging Algorithm for Process Fault Detection, "IEEE Trans. on Industrial Electronics, vol. 63, issue 4, pp. 2595 - 2605, 2016.

[81] X. Xu, Z. Huang, L. Zuo, and H. He, "Manifold-based Reinforcement Learning via Locally Linear Reconstruction," IEEE Trans. on Neural Networks and Learning Systems (TNNLS), vol. 28, issue 4, pp. 934 - 947, 2017.

[80] Y. Tang, H. He, Z. Ni, D. Zhao, and X. Xu, "Fuzzy-Based Goal Representation Adaptive Dynamic Programming," IEEE Trans. on Fuzzy Systems, vol. 24, issue. 5, pp. 1159 - 1174, 2016.

[79] J. Yi, D. Huang, S. Fu, H. He, and T. Li, "Multi-objective Bacterial Foraging Optimization Algorithm Based on Parallel Cell Entropy for Aluminum Electrolysis Production Process," IEEE Trans. on Industrial Electronics, vol. 63, issue 4, pp. 2488 - 2500, 2016.

[78] Y. Tang, H. He, Z. Ni, J. Wen, and T. Huang, "Adaptive Modulation for DFIG and STATCOM with HVDC Transmission," IEEE Trans. on Neural Networks and Learning Systems (TNNLS), vol. 27, issue. 8, pp. 1762 - 1772, 2016.

[77] J. Qiu, L. Cheng, X. Chen, J. Lu, H. He, "Semi-periodically intermittent control for synchronization of switched complex networks: a mode-dependent average dwell time approach," Nonlinear Dynamics (Springer), vol. 83, issue 3, pp. 1757 - 1771, 2016.

[76] X. Zhong, Z. Ni, and H. He, "A Theoretical Foundation of Goal Representation Heuristic Dynamic Programming," IEEE Trans. on Neural Networks and Learning Systems (TNNLS), vol. 27, issue 12, pp. 2513 - 2525, 2016.

[75] Q. Sun, Y. Zhang, H. He, D. Ma, and H. Zhang, "A Novel Energy Function-based Stability Evaluation and Nonlinear Control Approach for Energy Internet," IEEE Trans. on Smart Grid, vol. 8, issue 3, pp. 1195 - 1210, 2017.

[74] A. Zhang, J. Qiu, C. Yang, H. He, "Stabilization of Under Actuated Four-link Gymnast Robot using Torque-coupled Method," International Journal of Non-Linear Mechanics, 77, pp. 299-306, 2015.

[73] S. Kay, Q. Ding, B. Tang, and H. He, "Probability Density Function Estimation using the EEF with Application to Subset/Feature Selection," IEEE Trans. on Signal Processing, vol. 64, issue. 3, pp. 641 - 651, 2016.

[72] C. Lian, X. Xu, H. Chen, and H. He, "Near-optimal Tracking Control of Mobile Robots via Receding-horizon Dual Heuristic Programming," IEEE Trans on Cybernetics, vol. 46, issue 11, pp. 2484 - 2496, 2015.

[71] X. Chen, J. Qiu, H. He, J. Cao, "Hybrid Synchronization Behavior in An Array of Coupled Chaotic Systems with Ring Connection," Neurocomputing, vol. 173, part 3, pp. 1299 - 1309, 2015.

[70] J. Yang, X. Feng, 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, issue. 9, pp. 9087-9106, Aug. 2015.

[69] 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 and Energy Systems, vol. 75, pp. 108 - 116, 2016.

[68] Y. Zhu, D. Zhao, H. He, and J. Ji, "Convergence Proof of Approximate Policy Iteration for Undiscounted Optimal Control of Discrete-Time Systems," Cognitive Computation, vol. 7, issue 6, pp. 763 - 771, 2015.

[67] J. Li, S. Fu, H. He, H. Jia, Y. Li, and Y. Guo, "Simulating large-scale pedestrian movement using CA and event driven model: Methodology and case study," Physica A, 437, pp. 304-321, 2015. [pdf]

[66] Z. Ni, H. He, X. Zhong, and D. V. Prokhorov, "Model-Free Dual Heuristic Dynamic Programming," IEEE Trans. on Neural Networks and Learning Systems (TNNLS), vo. 26, issue. 8, pp. 1834 - 1839, 2015.

[65] 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, pp. 406-416, 2015.

[64] X. Fang, D. Zheng, H. He, and Z. Ni, "Data-Driven Heuristic Dynamic Programming with Virtual Reality," Neurocomputing, vol. 166, pp. 244-255, 2015.

[63] B. Tang and H. He, "ENN: Extended Nearest Neighbor Method for Pattern Recognition," IEEE Computational Intelligene Magazine, vol.10, issue 3, pp.52 - 60, Aug. 2015.

[62] J. Yang, L. Gong, Y. Tang, J. Yan, H. He, L. Zhang, G. Li, "An Improved SVM-based Cognitive Diagnosis Algorithm for Operation States of Distribution Grid," Cognitive Computation, vol. 7, issue 5, pp 582-593, Oct. 2015.

[61] J. Yang, Z. Zeng, Y. Tang, J. Yan, H. He, Y. Wu, "Load Frequency Control in Isolated Micro-Grid with Electrical Vehicle Based on Multivariable Generalized Predictive Theory," Energies, 8(3), pp. 2145-2164, 2015. [pdf]

[60] B. Tang, H. He, Q. Ding, and S. Kay, "A Parametric Classification Rule Based on the Exponentially Embedded Family," IEEE Trans. Neural Networks and Learning Systems (TNNLS), vol. 26, issue 2, pp. 367-377, 2015. [pdf] (IEEE CIM Publication Spotlight paper)

[59] Y. Tang, H. He, J. Wen, J. Liu, "Power System Stability Control for a Wind Farm Based on Adaptive Dynamic Programming," IEEE Trans. on Smart Grid, vol. 6, issue 1, pp. 166-177, 2015. [pdf]

[58] C. Yang, J. Qiu, and H. He, "Exponential Synchronization for a Class of Complex Spatio-temporal Networks with Space-varying Coefficients," Neurocomputing, vol. 151, part 1, pp. 401-407, 2015. [pdf]

[57] Y. Zhu, J. Yan, Y. Tang, Y. Sun, and H. He, "Resilience Analysis of Power Grids under the Sequential Attack," IEEE Trans. Information Forensics and Security, vol. 9, issue 12, pp. 2340-2354, 2014. [pdf]

[56] Z. Ni, H. He, D. Zhao, X. Xu, and D. Prokhorov, "GrDHP: A General Utility Function Representation for Dual Heuristic Dynamic Programming," IEEE Trans. Neural Networks and Learning Systems (TNNLS), vol. 26, issue 3, pp. 614-627, 2015. [pdf]

[55] J. Liu, W. Yao, J. Wen, H. He, X. Zheng, "Active Power Oscillation Property Classification of Electric Power Systems Based on SVM," Journal of Applied Mathematics, volume 2014, Article ID 218647, 2014. [pdf]

[54] S. Fu, H. He, and Z. Hou, "Learning Race from Face: A Survey," IEEE Trans. on Pattern Analysis and Machine Intelligence (TPAMI), vol. 36, issue 12, pp. 2483-2509, 2014. [pdf]

[53] J. Yan, Y. Tang, H. He, and Y. Sun, "Cascading Failure Analysis with DC Power Flow Model and Transient Stability Analysis," IEEE Trans. on Power Systems (TPS), vol. 30, issue 1, pp. 285-297, 2015. [pdf]

[52] X. Zhong, H. He, H. Zhang, and Z. Wang, "A Neural Network based Online Learning and Control Approach for Markov Jump Systems," Neurocomputing, vol. 149, Part A, pp. 116-123, 2015. [pdf]

[51] J. Xu, G. Yang, Y. Yin, H. Man, and H. He, "Sparse Representation Based Classification with Structure Preserving Dimension Reduction," Cognitive Computation, Volume 6, Issue 3, pp 608-621, 2014. [pdf]

[50] X. Zhong, H. He, H. Zhang, and Z. Wang, "Optimal Control for Unknown Discrete-Time Nonlinear Markov Jump Systems Using Adaptive Dynamic Programming," IEEE Trans. Neural Networks and Learning Systems (TNNLS), vol. 25, issue 12, pp. 2141-2155, 2014 [pdf]

[49] X. Sui, Y. Tang, H. He, J. Wen, "Energy Storage Based Low Frequency Oscillation Damping Control Using Particle Swarm Optimization and Heuristic Dynamic Programming," IEEE Trans. on Power Systems (TPS), vol. 29, issue 5, pp.  2539-2548, 2014. [pdf]

[48] J. Yan, H. He, and Y. Sun, "Integrated Security Analysis on Cascading Failure in Complex Networks," IEEE Trans. on Information Forensics and Security (TIFS), vol. 9, issue 3, pp. 451-463, 2014. [pdf]

[47] Q. Cai, H. He, and H. Man, "Imbalanced evolvingself-organizinglearning," Neurocomputing, vol. 133, 10 June 2014, pp. 258-270, 2014. [pdf]

[46] Y. Zhu, J. Yan, Y. Sun, and H. He, "Revealing Cascading Failure Vulnerability in Power Grids using Risk-Graph," IEEE Trans. on Parallel and Distributed Systems (TPDS), vol. 25, issue 12, pp. 3274-3284, 2014. [pdf]

[45] K. Li, Y. Guo, D. Laverty, H. He, M. Fei, "Distributed Adaptive Learning Framework for Wide Area Monitoring of Power Systems Integrated with Distributed Generations," Energy and Power Engineering, Vol.5, No.4B, pp. 962-969, July 2013. [pdf]

[44] Z. Ni and H. He, "Heuristic Dynamic Programming with Internal Goal Representation," Soft Computing, vol. 17, issue 11, pp. 2101-2108, November, 2013. [pdf]

[43] Z. Ni, H. He, J. Wen, and X. Xu, "Goal Representation Heuristic Dynamic Programming on Maze Navigation," IEEE Trans. Neural Networks and Learning Systems, vol. 24, issue 12, pp. 2038-2050, 2013. [pdf]

[42] L. Zhang, K. Li, H. He and G. W. Irwin, "A New Discrete-Continuous Algorithm for Radial Basis Function (RBF) Networks Construction," IEEE Trans. Neural Networks and Learning Systems, vol. 24, issue 11, pp. 1785-1798, 2013. [pdf]

[41] J. Yan, Y. Zhu, H. He, and Y. Sun, "Multi-Contingency Cascading Analysis of Smart Grid Based on Self-Organizing Map," IEEE Trans. Information Forensics and Security, vol. 8, no. 4, pp. 646-656, 2013. [pdf].  (IEEE TIFS Cover Page highlighted paper & IEEE Communications Society Best Readings on Communications and Information Systems Security)

[40] Z. Ni, H. He, and J. Wen, "Adaptive Learning in Tracking Control Based on the Dual Critic Network Design, IEEE Trans. Neural Networks and Learning Systems, vol. 24, no. 6, pp. 913-928, 2013. [pdf]. (IEEE CIM Publication Spotlight paper)

[39] Q. Cai, H. Man, and H. He, "Spatial Outlier Detection Based on Iterative Self-Organizing Learning Model," Neurocomputing, 117 (2013): 161-172, 2013. [pdf]

[38] X. Xu, C. Lian, L. Zuo, and H. He, "Kernel-Based Approximate Dynamic Programming for Real-Time Online Learning Control: An Experimental Study," IEEE Trans. Control Systems Technology, vol. 22, issue 1, pp. 146-156, 2014.  [pdf]

[37] Y. Tang, P. Ju, H. He, C. Qin, and W. Feng, "Optimized Control of DFIG-Based Wind Generation Using Sensitivity Analysis and Particle Swarm Optimization," IEEE Trans Smart Grid, vol. 4, no1., pp. 509-520, 2013. [pdf]

[36] X. Xu, Z. Hou, C. Lian, and H. He, "Online Learning Control Using Adaptive Critic Designs with Sparse Kernel Machines," IEEE Trans. Neural Networks and Learning Systems, vol. 24, no.5, pp. 762-775, 2013. [pdf]

[35] H. Li, H. Sun, J. Wen, S. Cheng, and H. He, "A Fully Decentralized Multi-Agent System for Intelligent Restoration of Power Distribution Network Incorporating Distributed Generations," IEEE Computational Intelligence Magazine, pp. 66-76, November, 2012 [pdf].

[34] 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, pp. 125-133, February 2014. [pdf]

[33] Y. Cao, H. He, and M. Hong, "SOMKE: Kernel Density Estimation Over Data Streams by Sequences of Self-Organizing Maps," IEEE Trans. Neural Networks and Learning Systems, vol. 23, issue 8, pp. 1254-1268, 2012. [pdf]

[32] H. He and Y. Cao, "SSC: A Classifier Combination Method Based on Signal Strength," IEEE Trans. Neural Networks and Learning Systems, vol. 23, issue 7, pp. 1100-1117, 2012. [pdf]

[SSC source code and examples are available from here...]

[31] H. He, S. Chen, K. Li, and X. Xu, "Incremental Learning from Stream Data,"  IEEE Trans. Neural Networks and Learning Systems, vol. 22, issue 22, pp. 1901-1914, 2012. [pdf]

[30] J. Xu, H. He, and H. Man, "DCPE co-training for classification," Neurocomputing, vol. 86, pp. 75-85, 2012. [pdf].

[29] H. He, Z. Ni, and J. Fu, "A Three-network Architecture for On-line Learning and Optimization based on Adaptive Dynamic Programming," Neurocomputing, vol. 78, issue 1, pp. 3-13, 2012. [pdf].

[28] G. Weng, Y. Zhang, and H. He, "A Novel Location Algorithm for Power Quality Disturbance Source Using Chain Table and Matrix Operation," International Review of Electrical Engineering (IREE), 2011.

[27] H. He, Yuan Cao, Yi Cao, J. Wen, "Ensemble Learning for Wind Profile Prediction with Missing Values," Neural Computing & Applications, DOI 10.1007/s00521-011-0708-1, 2011. [pdf]

[26] J. Fu, H. He, and X. Zhou, "Adaptive Learning and Control for MIMO System Based On Adaptive Dynamic Programming," IEEE Trans. Neural Networks, vol. 22, issue 7, pp. 1133-1148, 2011. [pdf]

[25] Y. Cao, H. He, and H. Huang, "LIFT: A New Framework of Learning From Testing Data for Face Recognition," Neurocomputing, vol. 74, issue 6, pp. 916-929, February, 2011. [pdf]

[24] S. Chen and H. He, "Towards Incremental Learning of Nonstationary Imbalanced Data Stream: A Multiple Selectively Recursive Approach," Evolving Systems, vol. 2, no. 1, pp. 35-50, 2011.  [pdf]

[23] J. Qiu, J. Lu, J. Cao, and H. He, "Tracking Analysis for General Linearly Coupled Dynamical Systems," Communications in Nonlinear Science and Numerical Simulation, vol. 16, issue 4, pp. 2072-2085, 2011.  [pdf]

[22] H. Huang, F. Zhang, Y. Sun, and H He, "Design of a Robust EMG Sensing Interface for Pattern Classification," Journal of Neural Engineering, 7(5), 056005 (10pp), 2010. [pdf]

[21] K. Li, J. Deng, H. He, and D. Du, "Compact Extreme Learning Machines for biological systems, " Int. Journal of Computational Biology and Drug Design, vol. 3, no. 2, pp.112-132, 2010.  [pdf].

[20] B. Liu, H. He, and S. Chen, "Adaptive Dual Network Design for a Class of SIMO Systems with Nonlinear Time-variant Uncertainties," Acta Automatic Sinca, vol. 36, no. 4, 2010. [pdf]

[19] S. Chen, H. He, and E. A. Garcia, "RAMOBoost: Ranked Minority Over-sampling in Boosting," IEEE Trans. Neural Networks, vol. 21, no. 10, pp. 1624-1642, 2010. [pdf]

[18] R. Machado, H. He, G. Wang, and S. Tekinay, "Redundancy Estimation and Adaptive Density Control in Wireless Sensor Networks," Ad Hoc & Sensor Wireless Networks, Volume 10, Number 2-3, pp. 153-176, 2010. [pdf]

[17] H. He and E. A. Garcia, "Learning from Imbalanced Data," IEEE Trans. Knowledge and Data Engineering, vol. 21, issue 9, pp. 1263-1284, 2009. [pdf]. [Lecture Notes on in PDF]  [Lecture Notes in PPT] (The Most-cited paper in the IEEE TKDE since publishing).

[16] H. He, X. Shen, and J. A. Starzyk, "Power Quality Disturbances Analysis Based on EDMRA Method," International Journal of Electrical Power & Energy Systems, vol. 31, issue 6, pp. 258-268, 2009. [pdf]

[15] J. A. Starzyk and H. He, "Spatio-Temporal Memories for Machine Learning: A Long-Term Memory Organization," IEEE Trans. Neural Networks, vol. 20, no. 5, pp. 768-780, 2009.  [pdf]

[14] H. He and S. Chen, "IMORL: Incremental Multiple Objects Recognition and Localization," IEEE Trans. Neural Networks, vol. 19, no. 10, pp. 1727-178, 2008  [pdf]

[13] J. A. Starzyk and H. He, "Anticipation-Based Temporal Sequences Learning in Hierarchical Structure," IEEE Trans. Neural Networks, vol. 18, pp. 344 - 358, 2007.  [pdf]

[12] J. A. Starzyk and H. He, "A Novel Low Power Logic Circuit Design Scheme," IEEE Trans. Circuits Syst. II, vol. 54, pp. 176 - 180, 2007. [pdf]

[11] Z. Zhu, H. He, J. A. Starzyk, C. Tseng. "Self-Organizing Learning Array and its Application to Economic and Financial Problems," Information Science, vol. 177, pp. 1180 - 1192, 2007. [pdf

[10] H. He and J. A. Starzyk, "A Self Organizing Learning Array System for Power Quality Classification based on Wavelet Transform," IEEE Trans. on Power Delivery, vol.21, pp. 286-295, January, 2006. [pdf]   (Ranked No. 3 in citation for all papers published in IEEE TPD since publishing).

[9] H. He, S. Cheng, Y. Zhang, and J. Nguimbis, "Home Network Power Line Communication Signal Processing Based on Wavelet Packet Analysis," IEEE Trans. on Power Delivery. vol. 20, pp. 1879-1885, July, 2005. [pdf]  (Ranked No. 7 in citation for all papers published in IEEE TPD since publishing).

[8] H. He, S. Cheng, Y. Zhang, and J. Nguimbis, "Analysis of Reflection of Signal Transmitted In Low Voltage Powerline with Complex Wavelet," IEEE Trans. on Power Delivery, vol. 19, pp. 86-91, January, 2004. [pdf

[7] X. Jiang, J. Nguimbis, S. Cheng, and H. He, "A Novel Scheme for Low Voltage Powerline Communication Signal Processing," Int. J. Elect. Power Energy Syst., vol. 25, pp. 269-274, May 2003. [pdf]

[6] H. He, Y. Zhang, and S. Cheng, "Signal Reflection Analysis for Low Voltage Powerline Communication," Proc. Chin. Soc. Elect. Eng., vol. 22, No. 6, pp.11-15, 2002. (in Chinese)

[5] H. He and X. Jiang, "A Novel Scheme for Low Voltage Power Line Communication Signal Processing," Proc. Chin. Soc. Elect. Eng., vol. 21, No.7, pp. 66-71, 2001. (in Chinese)

[4] H. He, Y. Zhou, and X. Wu, "The State of Research and Application of Low Voltage Powerline Communication," Relay, vol. 29, No.7, pp.12-16, 2001. (in Chinese)

[3] Y. Zhang, H. He, and X. Wu, "Modeling of The Channel for Low Voltage Power Line Carrier Communication," Relay, Vol. 30, No. 5, pp.20-24, 2002. (in Chinese)

[2] X. Jiang, J. Wen, and H. He, "The study and realization of real-time power quality supervisory software," Relay, Vol. 28, No.3, pp. 33-36, 2000. (in Chinese)

[1] Y. Zhang, S. Cheng, H. He, X. Lan, J. Nguimbis, "Modeling of The Low Voltage Powerline Used as High Frequency Communication Channel Based on the Experimental Results," Auto. Elect. Power Syst., vol. 26, no. 23, pp. 62-66, 2002. (in Chinese)

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