Dr. Jinchao Chen is an Associate Professor in School of Computer Science at Northwestern Polytechnical University, Xi’an, China. He has received his Ph.D. degree in Computer Science from the same institution in 2016. He focuses on the multi-processor scheduling, embedded and real-time systems, simulation and verification, decision-making and intelligent control of unmanned aerial vehicles, human-computer interaction systems. He has over 50 papers and 4 ESI highly-cited papers published in international conferences and journals (e.g., IEEE Transactions on Intelligent Transportation Systems, IEEE Transactions on Industrial Electronics, IEEE Transactions on Cybernetics, IEEE Real-Time Systems Symposium). He is the Editor-in-Chief of ASP Transactions on Computers and ASP Transactions on Computers, and the Academic Editor of International Journal of Aerospace Engineering. He is a TCP member of many conferences and regular reviewer of IEEE Transactions on Industrial Informatics, IEEE Transactions on Intelligent Transportation Systems, IEEE Transactions on Transportation Electrification, IEEE Transactions on Vehicular Technology, and Concurrency and Computation Practice and Experience. He is a member of IEEE and CCF.
This research presents a novel 3D fire escape educational system for children, addressing the urgent need for more engaging and interactive fire safety training methods. Traditional approaches to fire safety education fall short in capturing the attention of primary and middle school students, who are crucial yet less responsive audiences for such critical knowledge. Leveraging 3D modeling and human-computer interaction technologies, our system offers an immersive learning experience that allows children to actively participate in self-rescue drills. Through autonomous navigation within a simulated environment, learners are empowered to make decisions during fire scenarios, including choosin... More >
This paper reviews advancements in assistive technology for deaf and hard of hearing individuals, highlighting the Internet of Things' (IoTs) pivotal role in enhancing their daily lives. Despite progress in sign language technologies, communication barriers persist. To address these gaps, we developed a video-based American Sign Language (ASL) identification system. Our approach utilizes MediaPipe for hand tracking, OpenCV for image normalization, and Gesture Control Convolutional Neural Network (CNN) for gesture localization. Implemented in Python, the system records video streams, filters hand regions, and recognizes ASL letter gestures with high accuracy. Leveraging computer vision and ma... More >
With the rapid development of the new energy vehicle (NEV) industry, supply chain finance (SCF) has become one of the key factors promoting the prosperity of this industry. Based on in total 884 samples, this study investigates the impact of SCF on the integrated business performance of NEV enterprises. Through empirical analysis, it is confirmed that SCF significantly and positively affects the integrated business performance. In addition, the results of the mediation effect test indicate that by alleviating financing constraints of small and medium-sized enterprises, SCF enables the improvement of firms' overall business performance. These findings provide new insights to NEV enterprises'... More >
Graphical Abstract
Free Access
|
Research Article
| 22 September 2024
Monitoring the health condition of wind turbines is crucial to ensure the safety and efficient operation of wind farms. Wireless sensor networks (WSNs) provide an economical and effective solution for such monitoring. However, when sensors detect faults in wind turbines, traditional WSN routing protocols often lead to redundant data transmission, resulting in energy waste. To address this issue, an event-triggered energy-efficient wireless routing protocol (EEWRP) is proposed specifically in this paper for wind turbine fault monitoring. The protocol improves the distributed energy-efficient clustering algorithm (DEEC) by first identifying the type of event and then using an adaptive dynamic... More >
With the rapid development of artificial intelligence, extracting latent information from medical data has become increasingly critical. Cardiovascular disease is now a major threat to human health, being one of the leading causes of mortality. Therefore, developing effective prediction methods for cardiovascular diseases is urgently needed. Current medical approaches primarily focus on disease detection rather than prediction, which limits early intervention. By leveraging computational methods, it is possible to predict cardiovascular disease in advance, enabling timely treatment and potentially reducing the disease’s impact. This study employs machine learning techniques, including Supp... More >
Our website uses cookies that are essential for its operation and additional cookies to track performance, or to improve and personalize our services. By clicking "Allow all cookies", you agree to the storing of cookies on your device. For more information on how we use cookies, please see our Cookie Policy.
