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数字健康与辅助生活实验室
DIgital Health and Assisted Living @ USC
Selected recent papers
1. 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)
2. 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
3. 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)
4. 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
5. 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
6. 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)
7. 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)
8. 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)
9. NURBS Interpolator with Minimum Feedrate Fluctuation based on Two-level Parameter Compensation, sensors, 2023(SCI)
10. Exploring LoRa and Deep Learning-Based Wireless Activity Recognition, electronics, 2023(SCI)
11. Jerk-continuous Feedrate Optimization method for NURBS Interpolation, IEEE access, 2023(SCI)
12. Real-Time NURBS Interpolation under Multiple Constraints, Computational Intelligence and Neuroscience, 2022(SCI)
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Our Research
Machine learning
Machine Learning (ML) is a fascinating field of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. It focuses on the development of algorithms that can process large amounts of data and make predictions or decisions, mimicking the way humans learn.
Deep learning
Deep Learning is a subset of machine learning that employs artificial neural networks with many layers of processing units, taking inspiration from the human brain's structure and function. These deep neural networks have the capability to learn complex patterns from large amounts of data, making them particularly powerful for tasks involving high-dimensional data such as images, sound, and text.
Human Activity Recognition
HAR is a technology that involves identifying the actions or behaviors of individuals based on data derived from sensors. It has become an increasingly important area of research due to its vast potential applications in healthcare, sports, security, and human-computer interaction.
Python
TensorFlow
Pytorch
Numpy