论文

Google Scholar Profile

Brain-Computer Interfaces:

Journal Papers:

  1. L. Meng, X. Jiang and D. Wu*,  “Adversarial Robustness Benchmark for EEG-Based Brain-Computer Interfaces,” Future Generation Computer Systems, 2022, submitted.

  2. W. Zhang, Z. Wang and D. Wu*, “Multi-Source Decentralized Transfer for Privacy-Preserving BCIs,” IEEE Trans. on Neural Systems and Rehabilitation Engineering, 2022, submitted.

  3. L. Meng, J. Huang, Z. Zeng, X. Jiang, S. Yu, T-P Jung, C-T Lin, R. Chavarriaga and D. Wu*, “EEG-Based Brain-Computer Interfaces Are Vulnerable to Backdoor Attacks,”  2022, to be submitted (Python on Github; CodeOcean Capsule).

  4. S. Li, H. Wu, D. Wu* and L. Ding*, “Meta-Learning for Fast and Privacy-Preserving Calibration of EEG-based BCIs,” IEEE Computational Intelligence Magazine, 2022, submitted.

  5. W. Zhang and D. Wu*, “Lightweight Source Free Transfer for Privacy-Preserving Motor Imagery Classification,” IEEE Trans. on Cognitive and Devemental Systems, 2022, in press.

  6. X. Jiang, L. Meng, S. Li and D. Wu*, “Active Poisoning: Efficient Backdoor Attacks to Transfer Learning Based BCIs,” Science China Information Sciences, 2022, in press.

  7. D. Wu*, X. Jiang, R. Peng, “Transfer Learning for Motor Imagery Based Brain-Computer Interfaces: A Tutorial,” Neural Networks, 153:235-253, 2022. (Matlab)

  8. D. Wu, J. Xu, W. Fang, Y. Zhang, L. Yang, X. Xu*, H. Luo* and X. Yu*, “Adversarial Attacks and Defenses in Physiological Computing: A Systematic Review,” National Science Open, 2022, in press.

  9. K. Xia, L. Deng, W. Duch and D. Wu*, “Privacy-Preserving Domain Adaptation for Motor Imagery-based Brain-Computer Interfaces,” IEEE Trans. on Biomedical Engineering, 2022, in press. (Github)

  10. K. Xia, W. Duch, Y. Sun, K. Xu, W. Fang, H. Luo, Y. Zhang, D. Sang, X. Xu*, F-Y Wang* and D. Wu*, “Privacy-Preserving Brain-Computer Interfaces: A Systematic Review,” IEEE Trans. on Computational Social Systems, 2022, in press.

  11. H. Wu and D. Wu*, “Review of training-free event-related potential classification approaches in the World Robot Contest 2021,” Brain Science Advances, 8(2):82-98, 2022.

  12. R. Bian and D. Wu*, “Overview of the winning approaches in BCI Controlled Robot Contest in World Robot Contest 2021: Calibration-free SSVEP,” Brain Science Advances, 8(2):99-110, 2022.

  13. R. Bian, L. Meng and D. Wu*, “SSVEP-Based Brain-Computer Interfaces Are Vulnerable to Square Wave Attacks,” Science China Information Sciences, 65(4):140406, 2022.

  14. D. Wu*, Y. Xu and B.-L. Lu, “Transfer Learning for EEG-Based Brain-Computer Interfaces: A Review of Progress Made Since 2016,” IEEE Trans. on Cognitive and Developmental Systems, 14(1):4-19, 2022. (ESI Highly Cited Paper)

  15. Y. Zhang, K. Xia*, Y. Jiang, P. Qian, W. Cai, C. Qiu, L. Khin Wee and D. Wu, “Multi-modality Fusion & Inductive Knowledge Transfer Underlying Non-Sparse Multi-Kernel Learning and Distribution Adaption,” IEEE/ACM Trans. on Computational Biology and Bioinformatics, 2022, in press.

  16. Y. Ming, D. Wu, Y-K Wang, Y. Shi and C-T Lin*, “EEG-based Drowsiness Estimation for Driving Safety using Deep Q-Learning,” IEEE Trans. on Emerging Topics in Computational Intelligence, 5(4):583-594, 2021.

  17. X. Gu, Z. Cao*, A. Jolfaei, P. Xu, D. Wu, T-P Jung and C-T Lin, “EEG-based Brain-Computer Interfaces (BCI): A Survey of Recent Studies on Signal Sensing Technologies and Computational Intelligence Approaches and Their Applications,” IEEE/ACM Trans. on Computational Biology and Bioinformatics, 18(5):1645-1666, 2021.

  18. C-T Lin, C-H Chuang*, Y-C Hung, C-N Fang, D. Wu, Y-K Wang*,  “A Driving Performance Forecasting System Based on Brain Dynamic State Analysis using 4-D Convolutional Neural Networks,” IEEE Trans. on Cybernetics, 51(10):4959-4967, 2021.

  19. Z. Liu, L. Meng, X. Zhang, W. Fang, D. Wu*, “Universal Adversarial Perturbations for CNN Classifiers in EEG-Based BCIs,” Journal of Neural Engineering, 8:0460a4, 2021. (Python)

  20. X. Zhang, D. Wu*, L. Ding*, H. Luo, C-T Lin, T-P Jung and R. Chavarriaga, “Tiny noise, big mistakes: Adversarial perturbations induce errors in Brain-Computer Interface spellers,” National Science Review, 8(4), 2021. (Python; TechXplore; TechXplore2)

  21. Y. Jiang, Y. Zhang, C. Lin, D. Wu and C-T Lin*, “EEG-based Driver Drowsiness Estimation Using an Online Multi-View and Transfer TSK Fuzzy System,” IEEE Trans. on Intelligent Transportation Systems, 22(3):1752-1764, 2021. (ESI Highly Cited Paper) 

  22. Z. Shi, X. Chen, C. Zhao, H. He, D. Wu* and V. Stuphorn*, “Multi-View Broad Learning System for Primate Oculomotor Decision Decoding“, IEEE Trans. on Neural Systems and Rehabilitation Engineering, 28(9):1908-1920, 2020.

  23. W. Zhang and D. Wu*, “Manifold Embedded Knowledge Transfer for Brain-Computer Interfaces,” IEEE Trans. on Neural Systems and Rehabilitation Engineering, 28(5):1117-1127, 2020. (Python; 世界机器人大会)

  24. H. He and D. Wu*, “Different Set Domain Adaptation for Brain-Computer Interfaces: A Label Alignment Approach,” IEEE Trans. on Neural Systems and Rehabilitation Engineering, 28(5):1091-1108, 2020. (Matlab; IEEE Brain)

  25. H. He and D. Wu*, “Transfer Learning for Brain-Computer Interfaces: A Euclidean Space Data Alignment Approach,” IEEE Trans. on Biomedical Engineering, 67(2):399-410, 2020. (ESI Highly Cited Paper; Matlab; IEEE Brain)

  26. Y. Cui, Y. Xu and D. Wu*, “EEG-Based Driver Drowsiness Estimation Using Feature Weighted Episodic Training,” IEEE Trans. on Neural Systems and Rehabilitation Engineering, 27(11), pp. 2263-2273, 2019. (IEEE TNSRE Cover Article; Python) 

  27. Y. Ming, W. Ding, D. Pelusi, D. Wu, Y-K Wang, M. Prasad and C-T Lin*, “Subject Adaptation Network for EEG Data Analysis“, Applied Soft Computing, vol. 84, 2019. 

  28. A. Agarwal, R. Dowsley, N.D. McKinney, D. Wu, C-T Lin, M. De Cock* and A. Nascimento, “Protecting Privacy of Users in Brain-Computer Interface Applications,” IEEE Trans. on Neural Systems and Rehabilitation Engineering, 27(8), pp. 1546-1555, 2019.

  29. X. Zhang and D. Wu*, “On the Vulnerability of CNN Classifiers in EEG-Based BCIs,” IEEE Trans. on Neural Systems and Rehabilitation Engineering, 27(5):814-825, 2019. (Python)

  30. Y. Ming, C-N Fang, M. Prasad, Y-K Wang, D. Wu and C-T Lin*, “EEG Data Analysis with Stacked Differentiable Neural Computers,” Neural Computing and Applications, 2018.

  31. D. Wu*, J-T King, C-C Chuang, C-T Lin and T-P Jung, “Spatial Filtering for EEG-Based Regression Problems in Brain-Computer Interface (BCI),” IEEE Trans. on Fuzzy Systems, 26(2):771-781, 2018.

  32. D. Wu*, B. J. Lance, V. J. Lawhern, S. Gordon, T-P Jung and C-T Lin, “EEG-Based User Reaction Time Estimation Using Riemannian Geometry Features”, IEEE Trans. on Neural Systems and Rehabilitation Engineering, 25(11):2157-2168, 2017. 

  33. D. Wu, V. Lawhern, S. Gordon, B. Lance and C-T Lin, “Driver Drowsiness Estimation from EEG Signals Using Online Weighted Adaptation Regularization for Regression (OwARR),” IEEE Trans. on Fuzzy Systems, 25(6):1522-1535, 2017.

  34. D. Wu, “Online and Offline Domain Adaptation for Reducing BCI Calibration Effort,” IEEE Trans. on Human-Machine Systems, 47(4): 550-563, 2017. (ESI Highly Cited Paper)

  35. D. Wu, V. Lawhern, D. Hairston and B. Lance, “Switching EEG Headsets Made Easy: Reducing Offline Calibration Effort Using Active Weighted Adaptation Regularization,” IEEE Trans. on Neural Systems and Rehabilitation Engineering, 24(11):1125-1137, 2016.

  36. A. Marathe, V. Lawhern, D. Wu, D. Slayback and B. Lance, “Improved Neural Signal Classification in a Rapid Serial Visual Presentation Task using Active Learning,” IEEE Trans. on Neural Systems and Rehabilitation Engineering, 24(3), pp. 333-343, 2016. 

Conference Papers:

  1. W. Wang, J. Huang*, D. Wu and P. Zhang, “Neural Decoding Based on Active Learning for Intracortical Brain-Machine Interfaces,” 17th IEEE Int’l Conf. on Mechatronics and Automation, Beijing, China, October 2020. (Best Conference Paper Award)

  2. X. Jiang, X. Zhang and D. Wu, “Active Learning for Black-Box Adversarial Attacks in EEG-Based Brain-Computer Interfaces,” IEEE Symposium Series on Computational Intelligence, Xiamen, China, December 2019.

  3. Y. Xu and D. Wu, “EEG-based Driver Drowsiness Estimation Using Self-Paced Learning with Label Diversity,” IEEE Symposium Series on Computational Intelligence, Xiamen, China, December 2019.

  4. L. Meng, C-T Lin, T-P Jung and D. Wu, “White-Box Target Attacks for EEG-Based BCI Regression Problems,” Int’l Conf. on Neural Information Processing, Sydney, Australia, December 2019.

  5. H. H and D. Wu, “Channel and Trial Selection for Reducing Covariate Shift in EEG-based Brain-Computer Interfaces,” IEEE Int’l Conf. on Systems, Man and Cybernetics, Bari, Italy, 2019. 

  6. A. Agarwal, R. Dowsley, N. D. McKinney, D. Wu, C-T Lin, M. De Cock and A. Nascimento, “Privacy-Preserving Linear Regression for Brain-Computer Interface Applications,” IEEE Int’l Conf. on Big Data, Seattle, WA, 2018.

  7. Y. Ming, Y-K Wang, M. Prasad, D. Wu and C-T Lin, “Sustained Attention Driving Task Analysis based on Recurrent Residual Neural Network using EEG Data,” IEEE World Congress on Computational Intelligence, Rio, Brazil, 2018.

  8. H. He and D. Wu, “Spatial Filtering for Brain Computer Interfaces: A Comparison between the Common Spatial Pattern and Its Variant,” IEEE Int’l Conf. on Signal Processing, Communications and Computing, Qingdao, China, 2018.

  9. H. He and D. Wu, “Transfer learning enhanced common spatial pattern filtering for brain computer interfaces (BCIs): Overview and a New Approach,” 24th Int’l Conf. on Neural Information Processing, Guangzhou, China, 2017. (Best Student Paper Award Finalist)

  10. Y. Cui and D. Wu, “EEG-based driver drowsiness estimation using convolutional neural networks,” 24th Int’ Conf. on Neural Information Processing, Guangzhou, China, 2017.

  11. Y. Wang and D. Wu, “Real-time fMRI-based brain computer interface: A review,” 24th Int’l Conf. on Neural Information Processing, Guangzhou, China, 2017.

  12. D. Wu, “Active Semi-supervised Transfer Learning (ASTL) for Offline BCI Calibration,” IEEE Int’l. Conf. on Systems, Man and Cybernetics, Banff, Canada, 2017.

  13. Y-C Chang, Y-K Wang, D. Wu and C-T Lin, “Generating a Fuzzy rule-based Brain-state-drift Detector by Riemann-Metric-based Clustering,” IEEE Int’l. Conf. on Systems, Man and Cybernetics, Banff, Canada, 2017.

  14. M. De Cock, R. Dowsley, N. McKinney, A. C. A. Nascimento and D. Wu, “Privacy Preserving Machine Learning with EEG Data,” Private and Secure Machine Learning Workshop, ICML, Sydney, Australia, 2017.

  15. D. Wu, V. Lawhern, S. Gordon, B. Lance and C-T Lin, “Offline EEG-Based Driver Drowsiness Estimation Using Enhanced Batch-Mode Active Learning (EBMAL) for Regression,” IEEE Int’l. Conf. on Systems, Man and Cybernetics, pp. 730-736, Budapest, Hungary, 2016. 

  16. D. Wu, V. Lawhern, S. Gordon, B. Lance and C-T Lin, “Agreement Rate Initialized Maximum Likelihood Estimator (ARIMLE) for Ensemble Classifier Aggregation and Its Application in Brain-Computer Interface,” IEEE Int’l. Conf. on Systems, Man and Cybernetics, pp. 724-729, Budapest, Hungary, 2016.

  17. D. Wu, V. Lawhern, S. Gordon, B. Lance and C-T Lin, “Spectral Meta-Learner for Regression (SMLR) Model Aggregation: Towards Calibrationless Brain-Computer Interface (BCI),” IEEE Int’l. Conf. on Systems, Man and Cybernetics, pp. 743-749, Budapest, Hungary, 2016. (IEEE Brain Initiative Best Paper Award Finalist)

  18. D. Wu, V. Lawhern and B. Lance, “Reducing BCI calibration effort in RSVP tasks using online weighted adaptation regularization with source domain selection,” Int’l Conf. on Affective Computing and Intelligent Interaction (ACII), Xi’an, China, September 2015.

  19. D. Wu, C-H Chuang and C-T Lin, “Online driver’s drowsiness estimation using domain adaptation with model fusion,” Int’l Conf. on Affective Computing and Intelligent Interaction (ACII), Xi’an, China, September 2015.

  20. D. Wu, V. Lawhern and B. Lance, “Reducing Offline BCI Calibration Effort Using Weighted Adaptation Regularization with Source Domain Selection,” IEEE Int’l Conf. on Systems, Man, and Cybernetics (SMC), Hong Kong, October 2015.

  21. D. Wu, B. Lance, and V. Lawhern, “Transfer Learning and Active Transfer Learning for Reducing Calibration Data in Single-Trial Classification of Visually-Evoked Potentials,” IEEE Int’l Conf. on Systems, Man, and Cybernetics (SMC), San Diego, CA, October 2014.

Machine Learning:

Journal Papers:

  1. Z. Shi, C. Zhao and D. Wu*, “Linear Dimensionality Reduction of TSK Fuzzy Systems in High-Dimensional Regression Tasks,” Engineering Applications of Artificial Intelligence, 2022, submitted.

  2. L. Deng, C. Zhao, K. Xia and D. Wu*, “Semi-Supervised Transfer Boosting (SS-TrBoosting),” IEEE Transactions on Cybernetics, 2022, submitted.

  3. W. Zhang, L. Deng, L. Zhang and D. Wu*, “A Survey on Negative Transfer,” IEEE/CAA Journal of Automatica Sinica, 2022, in press.

  4. C. Zhao, D. Wu* and R. Peng, “Bagging and Boosting Fine-tuning (BBF) for Ensemble Learning,” IEEE Trans. on Neural Networks and Learning Systems, 2021, submitted.

  5. Y.Cui, D. Wu*, Y. Xu and R. Peng, “Layer Normalization for TSK Fuzzy System Optimization in Regression Problems,” IEEE Trans. on Fuzzy Systems, 2022, in press.

  6. C. Zhao, D. Wu*, J. Huang, Y. Yuan, H-T Zhang, R. Peng and Z. Shi, “BoostTree and BoostForest for Ensemble Learning,” IEEE Trans. on Pattern Analysis and Machine Intelligence, 2021, submitted.

  7. J. Xie, H. Wang, D. Wu*, “Adaptive Image Steganography Using Fuzzy Enhancement and Grey Wolf Optimizer,” IEEE Trans. on Fuzzy Systems, 2022, in press.

  8. J. Xie, H. Wang, J. Garibaldi and D. Wu*, “Network Intrusion Detection Based on Dynamic Intuitionistic Fuzzy Sets,” IEEE Trans. on Fuzzy Systems, 2022, in press.

  9. H. Liu, H. Xiong*, Y. Wang, H. An, D. Dou and D. Wu, “Exploring the Common Principal Subspace of Deep Features in Neural Networks“, Machine Learning, 2022, in press.

  10. 刘子昂, 蒋雪, 伍冬睿*, “基于池的无监督线性回归主动学习,” 自动化学报, 47(12):2771-2783, 2021. (Matlab)

  11. 汪雷, 黄剑*, 段涛, 伍冬睿, 熊蔡华, 崔雨琦, 基于气压肌动图和改进神经模糊推理系统的手势识别研究, 自动化学报, 2021, in press.

  12. Z. Shi, D. Wu*, C. Zhao, Y. Cui and F-Y Wang*, “FCM-RDpA: TSK Fuzzy Regression Model Construction Using Fuzzy C-Means Clustering, Regularization, DropRule, and Powerball AdaBelief“, Information Sciences, 574: 490-504, 2021. (Matlab)

  13. L. Tang, D. Wu, H. Wang, M. Chen and J. Xie*, “An Adaptive Fuzzy Inference Approach for Color Image Steganography,” Soft Computing, 25:10987–11004, 2021.

  14. H. Jiang, H. Xiong*, D. Wu, J. Liu, D. Dou, “AgFlow: fast model selection of penalized PCA via implicit regularization effects of gradient flow,” Machine Learning, 110:2131-2150, 2021.

  15. Z. Liu, X. Jiang, H. Luo, W. Fang, J. Liu and D. Wu*, “Pool-Based Unsupervised Active Learning for Regression Using Iterative Representativeness-Diversity Maximization (iRDM),” Pattern Recognition Letters, 142:11-19, 2021.

  16. Y. Cui, D. Wu* and J. Huang*, “Optimize TSK Fuzzy Systems for Classification Problems: Mini-Batch Gradient Descent with Uniform Regularization and Batch Normalization,” IEEE Trans. on Fuzzy Systems, 28(12):3065-3075, 2020. (Matlab; Python)

  17. D. Wu, C-T Lin, J. Huang* and Z. Zeng*, “On the Functional Equivalence of TSK Fuzzy Systems to Neural Networks, Mixture of Experts, CART, and Stacking Ensemble Regression,” IEEE Trans. on Fuzzy Systems, 28(10):2570-2580, 2020.

  18. D. Wu* and J.M. Mendel, “Patch Learning,” IEEE Trans. on Fuzzy Systems, 28(9):1996-2008, 2020. (Matlab; IEEE CIS Publication Spotlight)

  19. C. Cheng, B. Zhou, G. Ma, D. Wu and Y. Yuan*, “Wasserstein Distance based Deep Adversarial Transfer Learning for Intelligent Fault Diagnosis with Unlabeled or Insufficient Labeled Data,” Neurocomputing, 409, pp. 35-45, 2020.

  20. D. Wu*, Y. Yuan, J. Huang and Y. Tan*, “Optimize TSK Fuzzy Systems for Regression Problems: Mini-Batch Gradient Descent with Regularization, DropRule and AdaBound (MBGD-RDA),” IEEE Trans. on Fuzzy Systems, 28(5), pp. 1003-1015, 2020. (Matlab)

  21. D. Wu* and X. Tan, “Multitasking Genetic Algorithm (MTGA) for Fuzzy System Optimization,” IEEE Trans. on Fuzzy Systems, 28(6), pp. 1050-1061, 2020. (Matlab; IEEE CIS Publication Spotlight)

  22. T. Zhang, Z. Deng*, D. Wu and S. Wang, “Multiview fuzzy logic system with the cooperation between visible and hidden views,” IEEE Trans. on Fuzzy Systems, 27(6), pp. 1162-1173, 2019.

  23. D. Wu*, C-T Lin and J. Huang*, “Active Learning for Regression Using Greedy Sampling,” Information Sciences, 474:90-105, 2019. (Matlab)

  24. D. Wu, “Pool-based sequential active learning for regression,” IEEE Trans. on Neural Networks and Learning Systems, 30(5): 1348-1359, 2019. (Matlab)

Conference Papers:

  1. X. Zhang, H. Xiong and D. Wu, “Rethink the Connections among Generalization, Memorization, and the Spectral Bias of DNNs,” Int’l Joint. Conf. on Artificial Intelligence (IJCAI), Montreal, Canada, August 2021.

  2. X. Quan, D. Wu*, M. Zhu, K. Xia and L. Deng, “Manifold Discriminative Transfer Learning for Unsupervised Domain Adaptation,” ICONIP, Bali, Indonisia, December 2021.

  3. Y. Cui, D. Wu and Y. Xu, “Curse of Dimensionality for TSK Fuzzy Neural Networks: Explanation and Solutions,” Int’l Joint Conf. on Neural Networks (IJCNN), Shenzhen, China, July 2021.

  4. X. Zhang and D. Wu*, “Empirical Studies on the Properties of Linear Regions in Deep Neural Networks,” Int’l. Conf. on Learning Representations (ICLR), Addis Ababa, Ethiopia, April 2020.

  5. W. Zhang and D. Wu*, “Discriminative Joint Probability Maximum Mean Discrepancy (DJP-MMD) for Domain Adaptation,” Int’l Joint Conf. on Neural Networks (IJCNN), Glasgow, UK, July 2020. (Python; PaperWeekly)

  6. Z. Shi, D. Wu*, J. Huang, Y-K Wang and C-T Lin, “Supervised Discriminative Sparse PCA with Adaptive Neighbors for Dimensionality Reduction,” Int’l Joint Conf. on Neural Networks (IJCNN), Glasgow, UK, July 2020.

  7. Z. Liu and D. Wu, “Integrating Informativeness, Representativeness and Diversity in Pool-Based Sequential Active Learning for Regression,” Int’l Joint Conf. on Neural Networks (IJCNN), Glasgow, UK, July 2020.

  8. Z. Liu and D. Wu, “Unsupervised Ensemble Learning for Class Imbalance Problems,” Chinese Automation Conference, Xian, Shaanxi, 2018.

Smart Healthcare:

Journal Papers:

  1. R. Peng, C. Zhao, J. Jiang, G. Kuang, Y. Cui, Y. Xu, H. Du, J. Shao*, and D. Wu*, “TIE-EEGNet: Temporal Information Enhanced EEGNet for Seizure Subtype Classification,” IEEE Trans. on Neural Systems and Rehabilitation Engineering, 2022, submitted.

  2. 伍冬睿*, 彭睿旻, 孟璐斌, 蒋雪, 江军, 邵剑波*, “基于EEG的癫痫自动病灶定位研究进展”, 中国生物医学工程学报, 2021, submitted.

  3. 彭睿旻, 江军, 匡光涛, 杜浩, 伍冬睿*, 邵剑波. 基于EEG的癫痫自动检测: 综述与展望. 自动化学报, 48(2):335-350, 2022.

  4. X. Yang, Y. Zhang*, B. Lo, D. Wu, H. Liao an Y-T Zhang, “DBAN: Adversarial Network with Multi-Scale Features for Cardiac MRI Segmentation,” IEEE Journal of Biomedical and Health Informatics, 25(6):2018-2028, 2021.

  5. X. Song, P. Qian*, J. Zheng, Y. Jiang, K. Xia, B. Traughber, D. Wu and R. F. Muzic, “mDixon-Based Synthetic CT Generation via Transfer and Patch Learning,” Pattern Recognition Letters, 138: 51-59, 2020.

  6. Y. Jiang, X. Gu*, D. Wu, W. Hang, J. Xue, S. Qiu and C-T Lin, “A Novel Negative-Transfer-Resistant Fuzzy Clustering Model with a Shared Cross-Domain Transfer Latent Space and its Application to Brain CT Image Segmentation,” IEEE/ACM Trans. on Computational Biology and Bioinformatics, 18(1): 40-52, 2021. (ESI Highly Cited Paper) 

  7. X. Tian, Z. Deng*, K-S Choi, D. Wu, B. Qin, J. Wan, H. Shen and S. Wang, “Deep multi-view feature learning for epileptic seizure detection,” IEEE Trans. on Neural Systems and Rehabilitation Engineering, 27(10):1962-1972, 2019.

  8. Y. Jiang, D. Wu, Z. Deng*, P. Qian, J. Wang, G. Wang, F-L Chung, K-S Choi and S. Wang, “Seizure Classification from EEG Signals Using Transfer Learning, Semi-Supervised Learning and TSK Fuzzy System,” IEEE Trans. on Neural Systems and Rehabilitation Engineering, 25(12):2270-2284, 2017. (ESI Highly Cited Paper; IEEE TNSRE Best Paper Award) 

Conference Papers:

  1. D. Wu*, F. Liu and C. Liu, “Active Stacking for Heart Rate Estimation,” Int’l Joint Conf. on Neural Networks (IJCNN), Glasgow, UK, July 2020.

  2. B. Wang, W. Li, W. Fan, X. Chen and D. Wu, “Alzheimer’s Disease Brain Network Classification Using Improved Transfer Feature Learning with Joint Distribution Adaptation,” 41st Annual Int’l Conf. of the IEEE Engineering in Medicine & Biology Society, Berlin, Germany, 2019.

  3. Y. Wang and D. Wu, “Deep Learning for Sleep Stage Classification,” Chinese Automation Conference, Xian, Shaanxi, 2018.

Others:

  1. Y. Wang, H. Ma, Y. Xu, X. Deng, C. Deng and D. Wu*, “Deep Learning for Fully Automatic MRI-Based Nasopharyngeal Carcinoma Diagnosis,” 2020.

Affective Computing:

Book Chapters:

  1. X. Quan and D. Wu*, “EEG-based Affective Computing,” in Handbook of Research on Human-Machine Systems: State-of-the-art and Research Challenges, G. Fortino and A. Nurnberger and D. Kaber and D. Mendonca, Ed., 2022.

Journal Papers:

  1. Y. Xu, Y. Cui, X. Jiang and D. Wu*, “Inconsistency-Based Multi-Task Cooperative Learning for Emotion Recognition,” IEEE Trans. on Affective Computing, 2022, submitted.

  2. D. Wu* and J. Huang, “Affect Estimation in 3D Space Using Multi-Task Active Learning for Regression,” IEEE Trans. on Affective Computing, 13(1):16-27, 2022.

  3. 权学良, 曾志刚, 蒋建华, 张亚倩, 吕宝粮, 伍冬睿*, 基于生理信号的情感计算研究综述. 自动化学报, 47(8):1769−1784, 2021.

  4. D. Wu, C. Courtney, B. Lance, S. Narayanan, M. Dawson, K. Oie, and T.D. Parsons, “Optimal Arousal Identification and Classification for Affective Computing: Virtual Reality Stroop Task,” IEEE Trans. on Affective Computing, 1(2):109-118, 2010. (Top Accessed Article; IEEE Trans. on Affective Computing Most Influential Paper Award Finalist)

Conference Papers:

  1. X. Jiang, L. Meng, J. Huang and D. Wu, “Multi-Task Active Learning for Simultaneous Emotion Classification and Regression,” IEEE Int’l Conf. on Systems, Man and Cybernetics, virtual, October 2021.

  2. D. Wu, “Genetic Algorithm based feature selection for speaker trait classification,” InterSpeech, Portland, OR, September 2012.

  3. D. Wu and T.D. Parsons, “Customized Cognitive State Recognition Using Minimal User-Specific Data,” Military Health Systems Research Symposium, Fort Lauderdale, FL, August 2012. (Plenary Presentation; 2.5%)

  4. D. Wu, “Fuzzy sets and systems in building closed-loop affective computing systems for human-computer interaction: Advances and new directions,” IEEE World Congress on Computational Intelligence, Brisbane, Australia, June 2012.

  5. D. Wu and T.D. Parsons, “Active Class Selection for Arousal Classification,” Affective Computing and Intelligent Interaction Conference, Memphis, TN, October 2011.

  6. D. Wu and T.D. Parsons, “Inductive Transfer Learning for Handling Individual Differences in Affective Computing,” Affective Computing and Intelligent Interaction Conference, Memphis, TN, October 2011.

  7. D. Wu, T.D. Parsons, and S. Narayanan, “Acoustic feature analysis in speech emotion primitives estimation,” InterSpeech, Makuhari, Japan, September 2010.

  8. D. Wu, T. Parsons, E. Mower and S. Narayanan, “Speech Emotion Estimation in 3D Space,” IEEE International Conference on Multimedia & Expo, Singapore, July 2010. (Oral Presentation; 15%)

Intelligent Control:

Book Chapters:

  1. W. W. Tan, D. Wu and M. Nie, “Analysis of Simplified Interval Type-2 Fuzzy PI and PD Controllers,” Introduction to Type-2 Fuzzy Logic Control: Theory and Applications, J.M. Mendel et al, Ed.,  Jon Wiley & Sons, 2014.

  2. D. Wu, ‘‘Two differences between interval type-2 and type-1 fuzzy logic controllers: Adaptiveness and novelty,” in Advances in Type-2 Fuzzy Sets: Theory and Applications, A. Sadeghian, J. M. Mendel and H. Tahayori, Ed., 2013.

  3. W. W. Tan and D. Wu, “Design of type-reduction strategies for type-2 fuzzy logic systems using genetic algorithms,” in Advances in Evolutionary Computing for System Design, L.C. Jain, V. Palade and D. Srinivasan, Ed. pp. 169-188, Springer, 2007.

Journal Papers:

  1. C. Chen, J. Huang*, D. Wu* and X. Tu, “Interval Type-2 Fuzzy Disturbance Observer-Based T–S Fuzzy Control for a Pneumatic Flexible Joint,” IEEE Trans. on Industrial Electronics, 2022, in press.

  2. H. Mo, Y. Meng, F-Y Wang and D. Wu, “Interval Type-2 Fuzzy Hierarchical Adaptive Cruise Following-Control for Intelligent Vehicles,” IEEE/CAA Journal of Automatica Sinica, 2022, in press.

  3. 汪雷, 黄剑, 段涛, 伍冬睿, 熊蔡华, 崔雨琦, “基于气压肌动图和改进神经模糊推理系统的手势识别研究,” 自动化学报, 48(5):1220-1233, 2022.

  4. 陈诚, 黄剑*, 刘磊, 伍冬睿, 基于数据驱动的气动柔性关节Takagi–Sugeno模糊系统建模与预测控制, 控制理论与应用, 39(4):633-642, 2022.

  5. J. Huang, S. Yan, D. Yang, D. Wu*, L. Wang, Z. Yang and S. Mohammed, “Proxy-Based Control of an Intelligent Assistive Walker for Sit-to-Stand Transfer,” IEEE Trans. on Automation Science and Engineering, 27(2):904-915, 2022.

  6. C. Chen, D. Wu, J. M. Garibaldi, R. John, J. Twycross and J. Mendel, “A Comprehensive Study of the Efficiency of Type-Reduction Algorithms,” IEEE Trans. on Fuzzy Systems, 29(6):1556-1566, 2021.

  7. P. M. Kebria, A. Khosravi, S. Nahavandi, D. Wu and F. Bello, “Adaptive Type-2 Fuzzy Neural-Network Control for Teleoperation Systems with Delay and Uncertainties,” IEEE Trans. on Fuzzy Systems, 28(10):2543-2554, 2020. 

  8. G-P Ren, Z. Chen, H-T Zhang, Y. Wu, H. Meng, D. Wu and H. Ding, “Design of Interval Type-2 Fuzzy Controllers for Active Magnetic Bearing Systems,” IEEE/ASME Trans. on Mechatronics, 25(5):2449-2459, 2020.

  9. 伍冬睿*，曾志刚，莫红，王飞跃，“区间二型模糊集和模糊系统: 综述与展望,” 自动化学报, 46(8): 1539-1556, 2020.

  10. Y. Cao, J. Huang, C. Xiong, D. Wu, M. Zhang, Z. Li and Y. Hasegawa, “Adaptive Proxy-based Robust Control Integrated with Nonlinear Disturbance Observer for Pneumatic Muscle Actuators,” IEEE/ASME Transactions on Mechatronics, 25(4): 1756-1764, 2020.

  11. J. Huang, J. Wang, Y. Tan*, D. Wu and Y. Cao, “An Automatic Analog Instrument Reading System Using Computer Vision and Inspection Robot,” IEEE Trans. on Instrumentation & Measurement, 69(9), pp. 6322-6335, 2020.

  12. Y. Wang, J. Huang*, D. Wu*, Z-H Guan, Y-W Wang, “Set-Membership Filtering with Incomplete Observations,” Information Sciences, vol. 517, pp. 37-51, 2020.

  13. D. Wu* and J.M. Mendel, “Recommendations on Designing Practical Interval Type-2 Fuzzy Systems“, Engineering Applications of Artificial Intelligence, 95:182-193, 2019.

  14. H-T Zhang, B. Hu, L. Li, Z. Chen, D. Wu, B. Xu, X. Huang, G. Gu and Y. Yuan, “Distributed Hammerstein Modeling for Cross-Coupling Effect of Multiaxis Piezoelectric Micropositioning Stages,” IEEE/ASME Trans. on Mechatronics, 23(6), pp. 2794-2804, 2018.

  15. C. Chen, D. Wu*, J.M. Garibaldi, R. John, J. Twycross and J. Mendel, “A Comment on “A Direct Approach for Determining the Switch Points in the Karnik-Mendel Algorithm,” IEEE Trans. on Fuzzy Systems, 26(6), pp. 3905-3907, 2018.

  16. J. Huang, M. Ri, D. Wu* and S. Ri, “Interval Type-2 Fuzzy Logic Modeling and Control of a Mobile Two-Wheeled Inverted Pendulum,” IEEE Trans. on Fuzzy Systems, 26(4):2030-2036, 2018.

  17. J.M Mendel and D. Wu, “Critique of ‘A New Look at Type-2 Fuzzy Sets and Type-2 Fuzzy Logic Systems’,” IEEE Trans. on Fuzzy Systems, 25(3), pp. 725-727, 2017.

  18. S.M. Salaken, A. Khosravi, S. Nahavandi, and D. Wu, “Approximation of centroid end-points and switch points for replacing type reduction algorithms,” International Journal of Approximate Reasoning, 66, pp. 39-52, 2015. 

  19. D. Wu*, “Approaches for Reducing the Computational Cost of Interval Type-2 Fuzzy Logic Controllers: Overview and Comparison,” IEEE Trans. on Fuzzy Systems, 21(1):80-99, 2013. (ESI Highly Cited Paper)

  20. D. Wu*, “On the Fundamental Differences between Interval Type-2 and Type-1 Fuzzy Logic Controllers,” IEEE Trans. on Fuzzy Systems, 20(5):832-848, 2012. (IEEE CIS Publication Spotlight)

  21. X. Liu, J.M. Mendel and D. Wu, “Study on Enhanced Karnik-Mendel algorithms: Initialization explanations and computation improvements,” Information Sciences, 184(1), pp. 75-91, 2012.

  22. D. Wu and J. M. Mendel, “On the Continuity of Type-1 and Interval Type-2 Fuzzy Logic Systems,” IEEE Trans. on Fuzzy Systems, 19(1):179-192, 2011. (2014 IEEE TFS Outstanding Paper Award; IEEE CIS Publication Spotlight)

  23. D. Wu and J. M. Mendel, “Enhance Karnik-Mendel Algorithms,” IEEE Trans. on Fuzzy Systems, 17, pp. 923-934, 2009. (ESI Highly Cited Paper; Ranked 12th among all 1,288 SCI papers published worldwide on type-2 fuzzy systems in 1997-2017, according to “A Bibliometric Overview of the Field of Type-2 Fuzzy Sets and Systems,” IEEE Computational Intelligence Magazine, 15(1), pp. 89-98, 2020; Available in Matlab Fuzzy Logic Toolbox)

  24. D. Wu and W. W. Tan, “Genetic Learning and Performance Evaluation of Type-2 Fuzzy Logic Controllers,” Engineering Applications of Artificial Intelligence, 19(8):829-841, 2006. (Ranked 13th among all 2,960 papers published in EAAI in 1988-2018, according to “Engineering applications of artificial intelligence: A bibliometric analysis of 30 years (1988–2018),” EAAI, 85, pp. 517–532, 2019)

  25. D. Wu and W. W. Tan, “A Simplified Type-2 Fuzzy Controller for Real-Time Control,” ISA Trans., 15(4):503-516, 2006.

Conference Papers:

  1. L. Wang, J. Huang and D. Wu, “Hand Gesture Recognition Based on Multi-Classification Adaptive Neuro-Fuzzy Inference System and pMMG,” IEEE Int’l Conf. on Advanced Robotics and Mechatronics, Shenzhen, China, December 2020.

  2. C. Chen, J. Huang and D. Wu, “Nonlinear Disturbance Observer Based T-S Fuzzy Logic Control of Pneumatic Artificial Muscles,” IEEE Int’l Conf. on Advanced Robotics and Mechetronics, Osaka, 2019.

  3. X. Huang, H-T Zhang, D. Wu and L. Zhu, “Interval Type-2 Fuzzy Control of Pneumatic Muscle Actuator,” International Conference on Intelligent Robotics and Applications, pp. 423-431, Newcastle, NSW, Australia, 2018.

  4. Z. Song and D. Wu, “Performance comparison of efficient type-reduction approaches for interval type-2 fuzzy logic control,” Chinese Automation Conference, Jinan, Shandong, 2017.

  5. D. Wu and J. M. Mendel, “Designing Practical Interval Type-2 Fuzzy Logic Systems Made Simple,” IEEE World Congress on Computational Intelligence, Beijing, China, July 2014.

  6. D. Wu, “An Overview of Alternative Type-reduction Approaches for Reducing the Computational Cost of Interval Type-2 Fuzzy Logic Controllers,” IEEE World Congress on Computational Intelligence, Brisbane, Australia, June 2012.

  7. D. Wu, “Twelve Considerations in Choosing between Gaussian and Trapezoidal Membership Functions in Interval Type-2 Fuzzy Logic Controllers,” IEEE World Congress on Computational Intelligence, Brisbane, Australia, June 2012.

  8. D. Wu, “P-Map: An Intuitive Plot to Visualize, Understand, and Compare Variable-Gain PI Controllers,” IEEE International Conference on Autonomous and Intelligence Systems, Burnaby, BC, Canada, June 2011.

  9. D. Wu and M. Nie, “Comparison and Practical Implementation of Type-Reduction Algorithms for Type-2 Fuzzy Sets and Systems,” IEEE International Conference on Fuzzy Systems, Taipei, Taiwan, June 2011. (Available in Matlab Fuzzy Logic Toolbox)

  10. D. Wu, “An Interval Type-2 Fuzzy Logic System Cannot Be Implemented by Traditional Type-1 Fuzzy Logic Systems,” World Conference on Soft Computing, San Francisco, CA, May 2011.

  11. D. Wu and W.W. Tan, “Interval type-2 fuzzy PI controllers: Why they are more robust,” IEEE International Conference on Granular Computing, Silicon Valley, August 2010.

  12. D. Wu and J. M. Mendel, “Examining the Continuity of Type-1 and Interval Type-2 Fuzzy Logic Systems,” IEEE World Congress on Computational Intelligence, Barcelona, Spain, July 2010.

  13. D. Wu and J.M. Mendel, “Enhanced Karnik-Mendel Algorithms for Interval Type-2 Fuzzy Sets and Systems,” NAFIPS Annual Conference, pp. 18489, San Diego, CA, June 2007.

  14. D. Wu and W. W. Tan, “Type-2 FLS Modeling Capability Analysis,” IEEE Int’l Conf. on Fuzzy Systems, Reno, USA, May 2005. (Best Student Paper Award)

  15. D. Wu and W. W. Tan, “Computationally Efficient Type-Reduction Strategies for a Type-2 Fuzzy Logic Controller,” IEEE International Conference on Fuzzy Systems, pp. 353–358, Reno, USA, May 2005.

  16. D. Wu and W. W. Tan, “A Simplified Architecture for Type-2 FLSs and Its Application to Nonlinear Control,” IEEE Conference on Cybernetics and Intelligent Systems, pp. 485–490, Singapore, Dec. 2004.

  17. D. Wu and W.W. Tan, “A Type-2 Fuzzy Logic Controller for the Liquid-Level Process,” IEEE International Conference on Fuzzy Systems, vol. 2, pp. 953–958, Budapest, July 2004.

Tutorial:

  1. D. Wu, A brief tutorial on interval type-2 fuzzy sets and systems.

Perceptual Computing & Decision Making:

Book:

  1. J. M. Mendel and D. Wu, “Perceptual Computing: Aiding People in Making Subjective Judgments,” Wiley-IEEE Press, April 2010. (Matlab code) (Book Review)(Google Books)

Book Chapter:

  1. D. Wu and J. Huang, “Ordered novel weighted averages,” in Type-2 Fuzzy Logic and Systems, Springer, pp. 25-47, 2018.

  2. J. M. Mendel and D. Wu, “Computing with words for hierarchical and distributed decision making,” in Computational Intelligence in Complex Decision Systems, D. Ruan, Ed. Paris, France: Atlantis Press, 2009.

Journal Papers:

  1. N. Yue, D. Wu, J. Xie and S. Chen, “Probabilistic linguistic multi-criteria decision-making based on double information under imperfect conditions,” Fuzzy Optimization and Decision Making, 2020. 

  2. D. Wu, H-T Zhang* and J. Huang*, “A Constrained Representation Theorem for Well-Shaped Interval Type-2 Fuzzy Sets, and the Corresponding Constrained Uncertainty Measures,” IEEE Trans. on Fuzzy Systems, 27(6):1237-1251, 2019. (IEEE CIS Publication Spotlight)

  3. D. Wu* and J. M. Mendel, “Similarity Measures for Closed General Type-2 Fuzzy Sets: Overview, Comparisons, and a Geometric Approach,” IEEE Trans. on Fuzzy Systems, 27(3):515-526, 2019.

  4. D. Wu*, “A Reconstruction Decoder for Computing with Words,” Information Sciences, 255:1-15, 2014.

  5. J.M. Mendel and D. Wu, “Challenges for Perceptual Computer applications and how they were overcome,” IEEE Computational Intelligence Magazine, 7(3), pp 36-47, 2012.

  6. D. Wu, S. Coupland and J.M. Mendel, “Enhanced Interval Approach for Encoding Words into Interval Type-2 Fuzzy Sets and Its Convergence Analysis,” IEEE Trans. on Fuzzy Systems, 20(3), pp. 499-513, 2012. (Matlab code)

  7. X. Liu, J.M. Mendel and D. Wu, “Analytical solution methods for the fuzzy weighted average,” Information Sciences, vol. 187, pp. 151-170, 2012.

  8. D. Wu and J. M. Mendel, “Linguistic Summarization Using IF-THEN Rules and Interval Type-2 Fuzzy Sets,” IEEE Trans. on Fuzzy Systems, 19(1):136-151, 2011.

  9. D. Wu and J.M. Mendel, “Computing With Words for Hierarchical Decision Making Applied to Evaluating a Weapon System,” IEEE Trans. on Fuzzy Systems, 18(3):441-460, 2010.

  10. H. Acosta, D. Wu and B. M. Forrest, “Fuzzy experts on recreational vessels, a risk modelling approach for marine invasions,” Ecological Modelling, 221(5), pp. 850-863, 2010.

  11. D. Wu and J. M. Mendel, “Perceptual reasoning for perceptual computing: A similarity-based approach,” IEEE Trans. on Fuzzy Systems, 17(6):1397-1411, 2009.

  12. D. Wu and J. M. Mendel, “A Comparative Study of Ranking Methods, Similarity Measures and Uncertainty Measures for Interval Type-2 Fuzzy Sets,” Information Sciences, 179(8):1169-1192, 2009. (ESI Highly Cited Paper; Top 25 Hottest Article)

  13. J. M. Mendel and D. Wu, “Perceptual Reasoning for Perceptual Computing,” IEEE Trans. on Fuzzy Systems, 16(6), pp. 1550-1564, 2008.

  14. D. Wu and J. M. Mendel, “A Vector Similarity Measure for Linguistic Approximation: Interval Type-2 Fuzzy Sets and Type-1 Fuzzy Sets,” Information Sciences, 178, pp. 381-402, 2008.

  15. D. Wu and J. M. Mendel, “Aggregation Using the Linguistic Weighted Average and Interval Type-2 Fuzzy Sets,” IEEE Trans. on Fuzzy Systems, 15(6):1145-1161, 2007.

  16. D. Wu and J. M. Mendel, “Uncertainty Measures for Interval Type-2 Fuzzy Sets,” Information Sciences, 177:5378-5393, 2007.

Conference Papers:

  1. J. M. Mendel and D. Wu, “Determining Interval Type-2 Fuzzy Set Models for Words Using Data Collected From One Subject: Person FOUs,” IEEE World Congress on Computational Intelligence, Beijing, China, July 2014.

  2. D. Wu, “A Reconstruction Decoder for the Perceptual Computer,” IEEE World Congress on Computational Intelligence, Brisbane, Australia, June 2012.

  3. D. Wu, “A Constrained Representation Theorem for Interval Type-2 Fuzzy Sets Using Convex and Normal Embedded Type Fuzzy Sets, and Its Application to Centroid Computation,” World Conference on Soft Computing, San Francisco, CA, May 2011.

  4. M.R. Rajati, D. Wu, and J.M. Mendel, “On Solving Zadeh’s Tall Swedes Problem,” World Conference on Soft Computing, San Francisco, CA, May 2011.

  5. M.R. Rajati, J.M. Mendel, and D. Wu, “Solving Zadeh’s Magnus Challenge Problem on Linguistic Probabilities via Linguistic Weighted Averages,” IEEE International Conference on Fuzzy Systems, Taipei, Taiwan, June 2011.

  6. D. Wu, J. M. Mendel and J. Joo, “Linguistic Summarization Using IF-THEN Rules,” IEEE World Congress on Computational Intelligence, Barcelona, Spain, July 2010.

  7. D. Wu and J. M. Mendel, “Ordered Fuzzy Weighted Averages and Ordered Linguistic Weighted Averages,” IEEE World Congress on Computational Intelligence, Barcelona, Spain, July 2010.

  8. D. Wu and J. M. Mendel, “Efficient Algorithms for Computing a Class of Subsethood and Similarity Measures for Interval Type-2 Fuzzy Sets,” IEEE World Congress on Computational Intelligence, Barcelona, Spain, July 2010.

  9. D. Wu and J. M. Mendel, “Social Judgment Advisor: An Application of the Perceptual Computer,” IEEE World Congress on Computational Intelligence, Barcelona, Spain, July 2010.

  10. S. Coupland, J.M. Mendel and D. Wu, “Enhanced Interval Approach for encoding words into interval type-2 fuzzy sets and convergence of the word FOUs,” IEEE World Congress on Computational Intelligence, Barcelona, Spain, July 2010.

  11. D. Wu and J. M. Mendel, “Similarity-based perceptual reasoning for perceptual computing,” IEEE International Conference on Fuzzy Systems, Jeju Island, South Korea, August 2009.

  12. D. Wu and J. M. Mendel, “Perceptual Reasoning Using Interval Type-2 Fuzzy Sets: Properties,” IEEE World Congress on Computational Intelligence, Hong Kong, June 2008.

  13. J. M. Mendel and D. Wu, “Perceptual Reasoning: A New Computing With Words Engine,” IEEE International Conference on Granular Computing, pp. 446-451, Silicon Valley, CA, November 2007.

  14. D. Wu and J.M. Mendel, “A Vector Similarity Measure for Interval Type-2 Fuzzy Sets,” IEEE International Conference on Fuzzy Systems, pp. 1-6, London, UK, July 2007.

  15. D. Wu and J.M. Mendel, “Enhanced Karnik-Mendel Algorithms for Interval Type-2 Fuzzy Sets and Systems,” NAFIPS Annual Conference, pp. 18489, San Diego, CA, June 2007.

  16. D. Wu and J.M. Mendel, “A Vector Similarity Measure for Type-1 Fuzzy Sets,” IFSA World Congress, pp. 575-583, Cancun, Mexico, June 2007.

  17. J.M. Mendel and D. Wu, “Cardinality, Fuzziness, Variance and Skewness of Interval Type-2 Fuzzy Sets,” IEEE Symposium on Foundations of Computational Intelligence, pp. 375-382, Honolulu, HI, April 2007.

  18. D. Wu and J.M. Mendel, “The Linguistic Weighted Average,” IEEE International Conference on Fuzzy Systems, pp. 566-573, Vancouver, BC, Canada, July 2006.

Smart Oilfield Technologies:

  1. J. Joo, D. Wu, J. M. Mendel and A. Bugacov, “Forecasting the Post Fracturing Response of Oil Wells in a Tight Reservoir,” SPE Western Regional Meeting, San Jose, CA, March 2009.

Theses:

  1. D. Wu, “Intelligent Systems for Decision Support,” PhD Dissertation, Ming Hsieh Department of Electrical Engineering, University of Southern California, Los Angeles, CA, March 2009. (2009 Viterbi Best Dissertation Nomination; 2012 IEEE Computational Intelligence Society Outstanding Dissertation Award)

Society Activities:

  1. A. Y.S. Lam, M. J. Watts, D. Wu and P. A Estevez, “IEEE CIS Social Media: Have You Joined Our Online Community?” IEEE Computational Intelligence Magazine, 7(1), 2012.

Patents:

  1. A. Kumar, B. Ellis, Z. Wan, C. Pierce, M. Dokucu, D. Wu and S. Balram, Dynamic monitoring, diagnosis, and control of cooling tower systems, WO2015012832, 1/29/2015.

  2. S. Gustfason and D. Wu, Influencer analyzer platform for social and traditional media document authors, US20150348216, 12/3/2015.

  3. J. Reimann, C. Johnson, D. Wu, S. Evans, R. Cheinhample and A. Pandey, System and method using generative model to supplement incomplete industrial plant information, US20160004794, 1/7/2016.

  4. A. Can, E. Bas, D. Wu, J. Yu, and L. Wahrmund, Expert guided knowledge acquisition system for analyzing seismic data, WO2017152119, 8/15/2017.

  5. X. Gui, B. Shi, H. Liu and D. Wu, Target Positioning And Tracking System, Device, And Positioning And Tracking Method, WO2017084240, 5/1/2017.

  6. 伍冬睿，石振华，赵昶铭，一种用于恒河猴眼动决策解码的多视图学习方法和系统，201910586165.4， 2020-07-10

  7. 伍冬睿，谭显烽，一种适用于云计算系统的多任务处理方法，201811434588.6， 2020-07-10

  8. 伍冬睿，孟璐斌，一种基于EEG的脑机接口回归系统白盒目标攻击方法，201910896360.7， 2020-08-04

  9. 伍冬睿，张潇，一种针对以卷积神经网络为基础的EEG脑机接口的攻击方法，201811543220.3，2020-11-10

  10. 伍冬睿，何赫，一种用于脑机接口校准的异构标签空间迁移学习方法，201911100099.1，2021-08-04

  11. 伍冬睿，刘子涵，一种胚胎时序图像中的胚胎发育阶段识别方法，201910605282.0, 1/11/2022

  12. 伍冬睿，蒋雪，一种脑机接口系统黑盒攻击方法，201910982682.3, 1/12/2022

  13. 伍冬睿，刘子昂，一种用于语音情感计算的无监督主动学习方法，201910999055.0, 1/29/2022

  14. 伍冬睿，夏坤，基于欧氏对齐和Procrustes分析的EEG分类的迁移学习方法和系统，202010578377.0，2022.2.18

Talks:

1. 脑机接口中的机器学习, 5/12/2020.

2. Affective Computing, 12/6/2011.

毕业论文

  1. 何赫，脑机接口中的迁移学习方法研究，博士论文，华中科技大学，2020.
  2. 崔雨琦，模糊系统训练方法研究及其脑机接口应用，博士论文，华中科技大学，2022.
  3. 石振华，分类与回归任务中强泛化线性降维算法研究，博士论文，华中科技大学，2022.
  4. 宋志康，二型模糊控制器控制性能的理论分析与仿真研究，硕士论文，华中科技大学，2018.
  5. 谭显烽，基于多任务进化算法的连续优化研究，硕士论文，华中科技大学，2019.
  6. 刘子涵，脑机接口分类问题中的通用对抗扰动，硕士论文，华中科技大学，2020.
  7. 王阳，基于深度学习的医学影像分类方法研究，硕士论文，华中科技大学，2020.
  8. 张潇，神经网络训练过程中的泛化误差二次下降研究，硕士论文，华中科技大学，2021.
  9. 权学良，域适应及其在情感脑机接口中的应用研究，硕士论文，华中科技大学，2022.