Assoc. Prof. Dr. Jing Chen | Forecasting model | Best Researcher Award

Associate Professor at Fuzhou University, China

Dr. Chen Jing is an Associate Professor at the School of Electrical Engineering and Automation, Fuzhou University. With a focused research trajectory in advanced sensing technologies and intelligent systems, Dr. Chen has built a strong reputation in the fields of Internet of Things (IoT), fault detection, and human activity recognition. Through a multidisciplinary lens that merges machine learning, signal processing, and sensor networks, Dr. Chen has contributed significantly to both theoretical advancements and practical innovations. Her academic and project work reflects a balance between fundamental research and application-driven development, particularly in nuclear safety, smart environments, and optical communication systems.

Profile

Scopus

Education

Dr. Chen Jing has pursued comprehensive academic training in electrical engineering and automation, culminating in a doctoral degree that laid the foundation for her research in intelligent detection systems. Although her detailed educational background is not provided, her publication record and project involvement indicate a strong foundation in computational intelligence, systems modeling, and photonic engineering. Her academic journey is underscored by her consistent contributions to top-tier journals, reflecting a rigorous engagement with both foundational theory and experimental practice.

Experience

Dr. Chen has accumulated a wealth of academic and practical experience through her role at Fuzhou University and extensive involvement in multiple high-impact projects. She played a central role in the PowerSkel Project, which developed a device-free human skeleton estimation system using channel state information signals—an innovative step forward in the application of IoT for human-centric monitoring. Her work on fault detection and tolerance for self-powered neutron detectors contributed to nuclear power plant safety, while her collaboration on projects involving infrared sensors and anomaly detection in control systems highlighted her applied engineering capabilities. Over the years, she has also designed learning-based systems for human activity recognition and wavelength detection, integrating advanced AI with optical sensing technologies.

Research Interests

Dr. Chen Jing’s research interests are centered around Internet of Things applications, fault detection methodologies, human activity recognition, anomaly detection algorithms, and fiber optic sensor networks. She has delved deeply into the use of neural networks and machine learning approaches such as long short-term memory (LSTM) and gated recurrent unit (GRU) networks for signal processing and pattern recognition. Her studies also include Raman fiber amplifiers and multi-objective optimization, indicating a keen interest in both computational models and real-world engineering systems. This wide array of interests positions her at the intersection of intelligent sensing, data-driven diagnostics, and robust system design.

Awards

While no explicit awards are mentioned in the resume, Dr. Chen’s inclusion in Q1 journals such as the IEEE Internet of Things Journal and Nuclear Science and Techniques is a testament to her high-impact research and recognition within the scientific community. The quality and consistency of her publications suggest a commendable academic reputation, likely accompanied by institutional and research-based accolades.

Publications

Dr. Chen Jing has authored several high-impact publications, among which the following seven are most notable:

1. Chen Jing*, et al. “PowerSkel: A Device-Free Framework Using CSI Signal for Human Skeleton Estimation in Power Station,” IEEE Internet of Things Journal, 2024.

2. Chen Jing*, et al. “Human Activity Recognition with Infrared Sensor Using Semi-supervised Neural Networks,” IEEE Internet of Things Journal, 2023.

3. Chen Jing, et al. “Twin Model-based Fault Detection for Neutron Detectors,” Nuclear Science and Techniques, 2023.

4. Chen Jing, et al. “Anomaly Detection Using LSTM-Autoencoder and XGBoost,” Nuclear Science and Techniques, 2022.

5. Chen Jing, et al. “Human Activity Recognition Based on Hierarchical Learning,” Sensors, 2021.

6. Chen Jing*, et al. “Device-Free Recognition with Low-Resolution Infrared Sensor,” Sensors, 2021.

7. Chen Jing*, et al. “Wavelength Detection Using GRU in FBG Networks,” Guangxue Xuebao/Acta Optica Sinica, 2020.

Conclusion

In light of his prolific publication record, practical contributions to safety and automation, and sustained impact across multiple high-priority research areas, Dr. Chen Jing clearly stands out as a deserving recipient of the Best Researcher Award. His work exemplifies the integration of academic rigor with societal relevance, making a compelling case for recognition at the highest level.