Chuck Chung Yin Albert

City University of Macau

Author Statistics

Papers

Citation

H-Index

i-10 index

Research Trends

Author Order

Document Type

Co-Authors

Guang Li

Northeastern University

Ran Feng

Shanghai Academy of Educational Sciences

Jun Peng

Institute of High Energy Physics

Yuqi Sun

Macao Polytechnic University

Xujia Li

Shijiazhuang Tiedao University

Jie Zhou

Tsinghua University

Ruijie Luo

University College London

Liukai Zhu

City University of Macau

Yongde Guo

City University of Macau

Zheng Wang

Northwest A&F University

Cooperative Institutions

City University of Macau

Tsinghua University

National Engineering Research Center for Information Technology in Agriculture

Shanghai Academy of Educational Sciences

Shanghai University

Institute of High Energy Physics

Chinese Academy of Agricultural Sciences

First People's Hospital of Yunnan Province

Cotton Research Institute

Lanzhou University of Technology

Author Statistics

Papers

Citation

H-Index

i-10 index

Research Field

Identifying and Monitoring Students’ Classroom Learning Behavior Based on Multisource Information

Mobile Information Systems (2022)

Chuck Chung Yin Albert Yuqi Sun Guang Li Jun Peng Ran Feng

Understanding human activity and behavior, particularly real-time understanding in video feeds, is one of the most active areas of research in Computer Vision (CV) and Artificial Intelligence (AI) nowadays. To advance the topic of integrating learning engagement research with university teaching practice, accurate and efficient assessment, and analysis of students’ classroom learning behavior engagement is very important. The recently proposed classroom behavior recognition algorithms have some limitations, such as the inability to quickly and accurately identify students’ classroom behaviors because they do not consider the motion information of students between consecutive frames. In recent years, action recognition algorithms based on Convolutional Neural Networks (CNN) have improved significantly. To address the limitations of existing algorithms, in this study, a 3D-CNN is selected as a network model for classroom student behavior recognition, which increases information multisourcing and classroom student localization with high accuracy and robustness. For better analysis of human behavior in videos, the 3D convolution extends the 2D convolution to the spatial–temporal domain. In the proposed system, first of all, a real-time picture stream of each student is obtained by combining real-time target detection and tracking. Then, a deep spatiotemporal residual CNN is used to learn the spatiotemporal features of each student’s behavior, so, as to achieve real-time recognition of classroom behaviors for multistudent targets in classroom teaching scenarios. To verify the effectiveness of the proposed model, different experiments are conducted using the labeled classroom behavior dataset. The experimental results demonstrate that the proposed model exhibits better performance in classroom behavior recognition. The accurate recognition of classroom behaviors can assist the teachers and students to understand the classroom learning situation and help to promote the development of smart classroom.

Robustness

Action Recognition

Convolution (computer science)

10.1155/2022/9903342

Cite

Citations (15)

Research on hotel energy intelligent monitoring system and service improvement based on data mining and cloud computing

2022 IEEE 2nd International Conference on Power, Electronics and Computer Applications (ICPECA) (2022)

Chuck Chung Yin Albert Liukai Zhu Guang Li Yongde Guo Xujia Li

Many modern hotels have multiple functions such as accommodation, catering, entertainment, conferences, exhibitions, etc., and consume a lot of energy. At present, most of the predictions of building energy consumption are predictions of building cooling and heating loads and lighting energy consumption. The consideration is lack of building cooling (heating) area ratios, building cooling (heating) methods, meteorological parameters, and personnel activity patterns for large public buildings. By analyzing the data mining status of hotel energy consumption, sorting out concepts and technical support conditions, etc., the article has established a more comprehensive energy monitoring, forecasting, and evaluation system, which has enriched the research scope of the current hotel energy management research system, and has a strong Theoretical significance.

Scope (computer science)

Consumption

Building management system

Building Automation

10.1109/icpeca53709.2022.9719090

Cite

Citations (0)