Mr. Zhongwen Hao, Cranfield University, China
Zhongwen Hao is a Master’s candidate in Aerospace Manufacturing at Cranfield University, UK, and concurrently pursuing a Master of Mechanical Engineering at Nanjing University of Aeronautics and Astronautics, China. He completed his Bachelor’s degree in Electronic Information with a focus on Image Processing from China University of Mining and Technology. His research interests include robot control, visual servoing, image processing, and deep learning. Zhongwen has led notable projects such as visual servoing of robotic arms using deep learning techniques and galaxy image classification. His proficiency in programming with C++, Python, and MATLAB, coupled with his skills in deep learning and image processing, underscores his technical expertise. He has published research on motion prediction and object detection in visual servoing systems. Zhongwen is known for his strong project execution abilities, team spirit, and resilience.
Professional Profile:
Summary of Suitability:
Hao’s research direction aligns well with cutting-edge fields such as robot control, visual servoing, image processing, and deep learning. These areas are highly relevant and significant in contemporary technological advancements. Hao has a solid educational foundation with advanced studies in Aerospace Manufacturing and Mechanical Engineering, complemented by a bachelor’s degree in Electronic Information with a focus on Image Processing. This diverse yet interconnected educational background enhances his research capabilities.
Education
- Cranfield University, Bedford, UK
Master’s Candidate of Aerospace Manufacturing
Major: Deep Learning and Image Processing
September 2023 – September 2024
- Nanjing University of Aeronautics and Astronautics, Nanjing, China
Master of Mechanical Engineering
Major: Mechanical
September 2022 – June 2025 (Expected)
- China University of Mining and Technology, Xuzhou, China
Bachelor of Electronic Information
Major: Image Processing
September 2017 – June 2021
Work Experience
- Project Leader
Research on Visual Servoing of Robotic Arms Based on Deep Learning
June 2024 – September 2024
- Led research on target detection using the DETR model, trajectory planning with the PSO algorithm, and motion prediction using BiLSTM and KAN neural networks.
- Integrated and simulated algorithms in ROS using Gazebo to validate their effectiveness.
- Participator
Galaxy Image Classification Based on Deep Learning
February 2024 – March 2024
- Handled image preprocessing and reconstruction, and implemented galaxy image classification using the VIT model, achieving a classification accuracy of 90%.
Publication top Notes:
Motion Prediction and Object Detection for Image-Based Visual Servoing Systems Using Deep Learning