朱

朱涛 (Tao Zhu)

University of South China副教授邮箱:tzhu@usc.edu.cn

H指数:21

被引数:1,411

个人简介

"朱涛，男，博士，副教授，硕士生导师，IEEE高级会员。于中南大学和中国科学技术大学获得计算机科学与技术专业学士、博士学位。曾任微博算法设计工程师，北京科技大学教师、博士后。主讲大数据技术、机器学习、深度学习和全栈开发等课程。研究兴趣包括：智能感知，物联网和大语言模型等技术在健康领域，尤其是运动康复领域的应用。发表SCI/EI检索论文七十余篇，引用千余次。主持国家级、省部级科研课题、横向课题多项，曾获北京市科学技术二等奖。联系方式：tzhu{at}usc.edu.cn ({at}换成@\n实验室主页：https://diheal.github.io/\n个人谷歌学术主页: https://scholar.google.com/citations?user=36lrywUAAAAJ\n研究生培养和招生\n近三年指导的研究生多人获得国家奖学金、院学术之星，被大连理工大学、北京科技大学和英国奥斯特大学（全奖）录取攻读博士。研究生要求：专业成绩排名前列，编码能力较好，喜欢学习和科研；有机器学习、物联网和竞赛背景优先，联系请附本科成绩单。\n近5年部分期刊论文\n1. Hu, S., Qiu Qi., Xiao D., Deng X., Xu X., Fan P., Dai L, Hu Z., Zhu T., and Zhou Q.,. Physics-Informed Neural Network with Equation Adaption for Predicting the 220Rn Progeny Concentration,, Nuclear Science and Technique, 2025.（中科院SCI 一区）\n2. Li, S., Zhu, T., Nie, M., Ning, H., Liu, Z., & Chen, L. P2LHAP:Wearable sensor-based human activity recognition, segmentation and forecast through Patch-to-Label Seq2Seq Transformer. IEEE Internet of Things Journal, PP(8), 2024 (中科院SCI一区)\n3. Han, W., Zhu, T., Chen, L., Ning, H., Luo, Y., & Wan, Y.. MCformer: Multivariate Time Series Forecasting with Mixed-Channels Transformer. IEEE Internet of Things Journal, 11(17), 28320–28329, 2024. (中科院SCI一区)\n4. Li, S., Zhu, T., Duan, F., Chen, L., Ning, H., & Wan, Y.. HARMamba: Efficient Wearable Sensor Human Activity Recognition Based on Bidirectional Selective SSM, 2024. IEEE Internet of Things Journal, PP(8), 1. (中科院SCI一区)\n5. Negative selection by clustering for contrastive learning in human activity recognition,J Wang, T Zhu, L Chen, H Ning, Y Wan, IEEE Internet of Things Journal, 2023 (中科院SCI一区)\n6. CASL: Capturing Activity Semantics through Location Information for enhanced activity recognition, X Zhang, C Zhang, T Zhu, L Chen, F Zhou, H Ning, IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2023\n7. Parameter identification and state estimation for nuclear reactor operation digital twin,H Gong, T Zhu, Z Chen, Y Wan, Q Li, Annals of Nuclear Energy, 2023 (中科院SCI小类一区)\n8. Federated Markov Logic Network for indoor activity recognition in Internet of Things,C Zhang, X Ren, T Zhu, F Zhou, H Liu, Q Lu, H Ning, Knowledge-Based Systems 253, 109553, 2022 (中科院SCI一区)\n9. MCLA: Research on cumulative learning of Markov Logic Network, S Cui, T Zhu, X Zhang, H Ning, Knowledge-Based Systems, 108352, 2022, (中科院SCI一区)\n10. Few-shot activity learning by dual Markov logic networks, Z Zhang, T Zhu, D Gao, J Xu, H Liu, H Ning, Knowledge-Based Systems, 108158, 2022 (中科院SCI一区)\n11. Generating Markov Logic Networks Rulebase Based on Probabilistic Latent Semantics Analysis, S Cui, T Zhu, X Zhang, L Chen, L Mao, H Ning, Tsinghua Science and Technology 28 (5), 952-964, 2023 (中科院SCI一区)\n12. EvoDCMMO: Benchmarking and solving dynamic constrained multimodal optimization problems,X Lin, W Luo, Y Qiao, P Xu, T Zhu, Swarm and Evolutionary Computation 75, 101184, 2022 (中科院SCI一区)\n13. Making use of observable parameters in evolutionary dynamic optimization, T Zhu, W Luo, C Bu, H Ning, Information Sciences 512, 708-725, 2020 (中科院SCI一区)\n14. A clonal selection algorithm for dynamic multimodal function optimization, W Luo, X Lin, T Zhu, P Xu, Swarm and Evolutionary Computation 50, 100459, 2019 (中科院SCI一区)\n15. A Multi-Task Deep Learning Approach for Sensor-based Human Activity Recognition and Segmentation, F Duan, T Zhu, J Wang, L Chen, H Ning, Y Wan, IEEE Transactions on Instrumentation and Measurement, 2023 (中科院SCI二区 Top)\n16. Duan, F., Zhu, T., Chen, L., Member, S., Ning, H., Liu, C., & Wan, Y.. SCAGOT : Semi-supervised Disentangling Context and Activity Features without Target Data for Sensor-based Human Activity Recognition. IEEE Sensors Journal, 1–15. 2025 (中科院SCI二区)\n17. A Two-Step Channel Mask Method for Data Augmentation in Contrastive Learning-Based Human Activity Recognition,Qi Xie, Tao Zhu, Jinqiang Wang, Liming Luke Chen, Chao Liu, Yaping Wan, Human-centric Computing and Information Sciences, 2024 (中科院SCI二区)\n18. An Improved Masking Strategy for Self-supervised Masked Reconstruction in Human Activity Recognition, J Wang, W Cui, T Zhu, H Ning, Z Liu, IEEE Sensors Journal, 2024 (中科院SCI二区)\n19. Skin Conductance-Based Acupoint and Non-Acupoint Recognition Using Machine Learning, F Shi, H Ning, R Xiao, T Zhu, N Li, IEEE Journal of Biomedical and Health Informatics, 2024 (中科院SCI二区)\n20. PerMl-Fed: enabling personalized multi-level federated learning within heterogenous IoT environments for activity recognition, C Zhang, T Zhu, H Wu, H Ning, Cluster Computing, 1-16, 2024 (SCI)\n21. A Parallel Multiobjective PSO Weighted Average Clustering Algorithm Based on Apache Spark, H Ling, X Zhu, T Zhu, M Nie, Z Liu, Z Liu, Entropy 25 (2), 259, 2023\n22. Exploring LoRa and Deep Learning-Based Wireless Activity Recognition,Y Xiao, Y Chen, M Nie, T Zhu, Z Liu, C Liu, Electronics 12 (3), 629, 2023\n23. Sensor Data Augmentation by Resampling in Contrastive Learning for Human Activity Recognition, J Wang, T Zhu, J Gan, LL Chen, H Ning, Y Wan, IEEE Sensors Journal 22 (23), 22994-23008 10, 2022 (中科院SCI二区 TOP)\n24. Classroom student posture recognition based on an improved high-resolution network, Y Zhang, T Zhu, H Ning, Z Liu, EURASIP Journal on Wireless Communications and Networking 2021 (1), 1-15, 2021\n25. On Followers Search, L Ni, W Luo, T Zhu, P Xu, IEEE Transactions on Computational Social Systems 8 (5), 1068-1082, 2021 (中科院SCI二区)\n26. Multi-resident type recognition based on ambient sensors activity, Q Li, W Huangfu, F Farha, T Zhu, S Yang, L Chen, H Ning, Future Generation Computer Systems 112, 108-115, 2020 (中科院SCI二区，TOP)\n27. Guest editorial: Special issue on data intelligence in sustainable computing, T Zhu, W Luo, J Cao, H Lei, Sustainable Computing: Informatics and Systems 25, 100375, 2020\n28. A hybrid approach to inferring the Internet of Things for complex activity recognition, Q Li, H Ning, T Zhu, S Cui, L Chen, EURASIP Journal on Wireless Communications and Networking 2019 (1), 1-11, 2019\n29. Species and memory enhanced differential evolution for optimal power flow under d"