Wen Zhou
This is Wen Zhou’s personal homepage.
A short introduction
Since 2021, I have been an associate professor at the School of Computer and Information, Anhui Normal University, China. Meantime, between July and January 2024, as a visiting scholar, I have come and carried out related collaborative research at the School of Computing, the National University of Singapore (NUS). In particular, my research interests mainly focus on virtual reality and Artificial intelligence, including deep reinforcement methods in 3D shape analysis, shape segmentation, and a virtual emergency scenario, as well as Visualization, and 3D retrieval based on hand-sketch.
Profile:
- Highly self-motivated researcher with demonstrated research expertise in virtual scenario processes.
- Strong interpersonal skills with a good sense of teamwork.
- Programming Skills: Python, C/C++, and Java in both Linux and Windows environments.
- Rich experience in 3D modeling and retrieval.
Selected journal publications
Novel learning framework for optimal multi-object video trajectory tracking
Siyuan Chen, Xiaowu Hu, Wenying Jiang, Wen Zhou* , Xintao Ding, Novel learning framework for optimal multi-object video trajectory tracking. Virtual Reality & Intelligent Hardware, 2023,5(5):422-438.
Dual deep Q-learning network guiding a multiagent path planning approach for virtual fire emergency scenarios
Wen Zhou , C. Zhang, S. Chen, Dual deep Q-learning network guiding a multiagent path planning approach for virtual fire emergency scenarios. Applied Intelligence, 2023,53:21858–21874.
An Improved Federated Learning Approach Enhanced Internet of Health Things Framework for Private Decentralized Distributed Data
Huang, C., Xu, G., Chen, S., Zhou, Wen*, NG, E. Y., & de Albuquerque, Victor Hugo C. de Albuquerque*. An Improved Federated Learning Approach Enhanced Internet of Health Things Framework for Private Decentralized Distributed Data. Information Sciences, 2022, 614:138-152
Multiagent Evacuation Framework for a Virtual Fire Emergency Scenario Based on Generative Adversary Imitation Learning
Wen Zhou,W. Jiang, B. Jie, W. Bian. "Multiagent Evacuation Framework for a Virtual Fire Emergency Scenario Based on Generative Adversary Imitation Learning." Computer Animation and Virtual Worlds. 2022, 33(1): e2035.
A robust approach for privacy data protection: IoT security assurance using generative adversarial imitation learning
C. Huang, S. Chen, Y. Zhang, Wen Zhou*, etc. "A robust approach for privacy data protection: IoT security assurance using generative adversarial imitation learning." IEEE Internet of Things Journal. 2021,9(18):17089-17097.
Sketch Augmentation-driven Shape Retrieval Learning Framework Based on Convolutional Neural Networks
Wen Zhou, J. Jia, W. Jiang, C. Huang, Sketch Augmentation-driven Shape Retrieval Learning Framework Based on Convolutional Neural Networks. IEEE Transactions on Visualization and Computer Graphics, 2021,27(8):3558–3570.
Training Deep Convolutional Neural Networks to Acquire the Best View of a 3D Shape
Wen Zhou, J. Jia "Training Deep Convolutional Neural Networks to Acquire the Best View of a 3D Shape"[J]. Multimedia Tools and Applications, 2020, 79(1):581-601.
Training convolutional Neural Network for sketch recognition on large-scale dataset
Wen Zhou, J. JIA, Training convolutional Neural Network for sketch recognition on large-scale dataset.The International Arab Journal of Information Technology，2020,17(1):82-89.
Web3D learning framework for 3D Shape retrieval based on hybrid Convolutional Neural Networks
Wen Zhou, J. JIA, C. HUANG, Y. CHENG, Web3D learning framework for 3D Shape retrieval based on hybrid Convolutional Neural Networks. Tsinghua Science and Technology, 2019, 25(1): 93-102
WebVR Human-centered Indoor Layout Design Framework using a Convolutional Neural Network and Deep Q-learning
Wen Zhou, W. JIANG, W. BIAN, B. JIE, WebVR Human-centered Indoor Layout Design Framework using a Convolutional Neural Network and Deep Q-learning. IEEE Access, 2019, 7(1):185773-185785.
Projects
Patents & Software Copyright
For more information
More info about me can be found in CV.