Thi Huynh Anh Le | Smart Grid | Editorial Board Member

Published on by Applied Scientist

Dr. Thi Huynh Anh Le | Smart Grid | Editorial Board Member

The university of Danang, University of Science and Technology | Vietnam

Dr. Le Thi Huynh Anh is a rising researcher in renewable energy systems whose work has gained notable scholarly impact, reflected in 104 citations, an h-index of 5, and an i10-index of 4. Her research centers on sustainable microgrid design, peer-to-peer (P2P) energy trading, uncertainty modeling, and computational intelligence for optimizing modern power systems. She has published influential studies in leading journals such as Renewable and Sustainable Energy Reviews, Renewable Energy, Expert Systems with Applications, and Applied Sciences. Dr. Anh’s contributions emphasize advanced multi-microgrid planning frameworks that integrate seasonal demand patterns, government subsidies, stochastic uncertainties, and digital technologies to enhance economic, operational, and environmental performance. Her collaborative works on blockchain-enabled P2P energy trading propose innovative pathways for decentralized and consumer-centric energy markets. Alongside system optimization, she has developed intelligent methodologies for improving data quality and decision-making in energy systems, including dynamic-programming-based time-series anomaly detection using the longest common subsequence approach, as well as advanced clustering techniques that blend fuzzy logic, possibilistic modeling, and genetic algorithms for handling mixed data types. Her research also extends to maintenance optimization for offshore wind systems, demonstrating a broader commitment to the renewable energy ecosystem. Across her body of work, Dr. Anh integrates sustainability principles, AI-driven modeling, and robust optimization techniques to support the development of next-generation, resilient, and intelligent energy infrastructures. Her recent studies continue advancing sustainable multi-microgrid systems by addressing the challenges of uncertainty, seasonality, and evolving energy trading mechanisms.

Profile: Google Scholar

Featured Publications

  • Vincent, F. Y., Le, T. H. A., & Gupta, J. N. D. (2022). Sustainable microgrid design with multiple demand areas and peer-to-peer energy trading involving seasonal factors and uncertainties. Renewable and Sustainable Energy Reviews, 161, 112342.

  • Nguyen, T. P. Q., Phuc, P. N. K., Yang, C. L., Sutrisno, H., Luong, B. H., Le, T. H. A., … (2023). Time-series anomaly detection using dynamic programming based longest common subsequence on sensor data. Expert Systems with Applications, 213, 118902.

  • Yu, V. F., Chiang, F. Y., Le, T. H. A., & Lin, S. W. (2022). Using the ISM method to analyze the relationships between various contractor prequalification criteria. Applied Sciences, 12(8), 3726.

  • Vincent, F. Y., Le, T. H. A., & Gupta, J. N. D. (2023). Sustainable microgrid design with peer-to-peer energy trading involving government subsidies and uncertainties. Renewable Energy, 206, 658–675.

  • Nguyen, T. P. Q., Kuo, R. J., Le, M. D., Nguyen, T. C., & Le, T. H. A. (2022). Local search genetic algorithm-based possibilistic weighted fuzzy c-means for clustering mixed numerical and categorical data. Neural Computing and Applications, 34(20), 18059–18074.

Shengjie Li | Internet of Things | Editorial Board Member

Published on by Applied Scientist

Assist. Prof. Dr . Shengjie Li | Internet of Things | Editorial Board Member

Beijing University of Posts and Telecommunications | China

Assist. Prof. Dr. Li Shengjie is a researcher specializing in robust visual object tracking, focusing on resolving key challenges such as occlusion, noise, scale variation, drift, and real-time computational constraints in dynamic visual environments. His body of work integrates particle filter methodologies, siamese network models, and correlation filter (CF)-based approaches to develop adaptive and resilient tracking frameworks. He has introduced several influential models, including a part-aware tracking framework that leverages local target structures, a dynamic particle filter architecture for improved failure correction, a noise-aware similarity measurement framework, and an efficient particle scale-space strategy designed to more accurately estimate target size under challenging scale variations. Assist. Prof. Dr. Li’s research also advances lightweight and accelerated particle-filter solutions that significantly reduce computational cost while preserving tracking accuracy, supporting real-time implementation. Additionally, his work integrates ensemble deep features and decision fusion mechanisms to combat model drift and enhance robustness during sudden appearance changes or out-of-view scenarios. His earlier contributions include physical modeling studies, reflecting a progression toward highly refined hybrid deep-learning and filter-based tracking systems. With eight publications, more than 100 citations, and nearly 300 reads, Assist. Prof. Dr. Li Shengjie’s research is recognized for systematically improving the reliability and efficiency of visual tracking algorithms. His innovations consistently address the limitations of traditional tracking methods by introducing adaptive, context-sensitive, and computationally optimized approaches that advance the field toward more effective real-world application.

Profile: Research Gate

Featured Publications

  • Li, S., Zhao, S., Cheng, B., & Chen, J. (2023). Part-aware framework for robust object tracking.

  • Li, S., Zhao, S., Cheng, B., & Chen, J. (2021). Dynamic particle filter framework for robust object tracking.

  • Li, S., Zhao, S., Cheng, B., & Chen, J. (2020). Noise-aware framework for robust visual tracking.

  • Li, S., Zhao, S., Cheng, B., & Chen, J. (2020). Efficient particle scale space for robust tracking.

  • Li, S., Zhao, S., Cheng, B., & Chen, J. (2018). Robust visual tracking via hierarchical particle filter and ensemble deep features.