Feng Xiang | Mechanical Engineering | Best Researcher Award

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Prof. Dr. Feng Xiang | Mechanical Engineering | Best Researcher Award

Professor | Wuhan University of Science and Technology | China

Prof. Dr. Xiang Feng, Vice Dean, Professor, and Doctoral Supervisor in the School of Mechanical Engineering at Wuhan University of Science and Technology, is a distinguished scholar in manufacturing intelligent services, digital twins, and green manufacturing, holding advanced degrees in mechanical engineering with a specialization aligned to intelligent manufacturing systems. With extensive professional experience, he has led major national, provincial, and industrial research initiatives as principal investigator of multiple NSFC projects as well as key programs supported by leading government and industry bodies, while contributing significant academic leadership as a co-initiator of the National Conference on Digital Twins and Intelligent Services. His research has produced influential advancements in digital-twin-enabled intelligent service systems and sustainable manufacturing, reflected in more than thirty publications in top-tier journals including IEEE Transactions on Industrial Informatics, Journal of Manufacturing Systems, International Journal of Advanced Manufacturing Technology, Advanced Intelligent Systems, CIMS, China Mechanical Engineering, and Scientia Sinica Informationis, alongside six granted invention patents and co-editorship of three English monographs on digital twins. Prof. Feng’s academic service is equally distinguished: he serves on national expert committees for digital-twin and manufacturing-service standards, acts as an Advisory Board Member of the high-impact journal Digital Twin, contributes as an Editorial Board Member of Digital Engineering, and holds leadership roles such as Deputy Secretary-General of the Digital Twin Application Branch of the China Productivity Promotion Center Association. Recognized for his scientific contributions, he is a recipient of the First Prize of the Mechanical Industry Science and Technology Award, underscoring his sustained impact on advancing intelligent manufacturing and digital-twin technologies.

Profiles: Scopus | ORCID

Featured Publications

1. Inverse kinematics solution for demolition robot manipulators based on improved Newton–Raphson algorithm. (2025). Concurrency and Computation: Practice and Experience.

2. A federated learning-based method for personalized manufacturing service recommendation with collaborative relationships. (2025). Applied Soft Computing.

3. Human-centric smart manufacturing: Analysis and prospects of human activity recognition. (2025). Journal of Mechanical Engineering (Jixie Gongcheng Xuebao).

4. Analysis and control of manufacturing service collaboration networks failure under intentional attacks. (2025). Knowledge-Based Systems.

5. Modeling and optimization of manufacturing service collaboration based on digital twins. (2025). International Journal of Advanced Manufacturing Technology.

Prof. Dr. Xiang Feng’s pioneering research in digital twins and intelligent manufacturing enhances the scientific foundations of smart industrial systems while driving practical advancements in sustainable, efficient, and human-centric production. His innovations bridge academic discovery and industrial application, shaping next-generation manufacturing technologies that strengthen global competitiveness and accelerate digital transformation.

Chang Soo Kim | Engineering | Best Researcher Award

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Prof. Chang Soo Kim | Engineering | Best Researcher Award

Professor | Pukyong National University | South Korea

Professor. Chang Soo Kim is a distinguished Full Professor in the Division of Computer and AI Engineering at PuKyong National University, recognized for his expertise in intelligent manufacturing systems, artificial intelligence, and computational optimization. He holds advanced degrees in computer science with specialization in AI-driven optimization and machine learning, forming the foundation for his multidisciplinary research career. Throughout his long-standing academic tenure, he has served in key leadership roles including department chair, graduate program administrator, research center director, and executive leader for university–industry cooperation, successfully guiding large-scale projects, fostering collaborative innovation, and advancing strategic academic initiatives. His research focuses on flexible job shop scheduling, deep learning–based fault diagnosis, time-series forecasting, metaheuristic optimization, and smart industrial systems. He has produced an extensive portfolio of influential publications in high-impact SCI-indexed journals, contributing novel hybrid algorithms, trainable fusion strategies, adaptive scheduling frameworks, lightweight diagnostic models, and intelligent computational methods that support the evolution of smart manufacturing and data-driven engineering. His scholarly achievements have earned him multiple recognitions, including awards for research excellence, and he actively contributes to the global academic community through editorial service, participation in professional societies, and engagement in scientific committees. With a sustained record of innovative research, academic leadership, and impactful contributions to computer and AI engineering, Professor Chang Soo Kim exemplifies the qualities of a leading researcher whose work continues to influence both industry and academia.

Profiles:  Scopus

Featured Publications

1. Kim, C. S., et al. (2025). Flexible job shop scheduling optimization with multiple criteria using a hybrid metaheuristic framework. Processes.

2. Kim, C. S., et al. (2025). Multi-branch global Transformer-assisted network for fault diagnosis. Applied Soft Computing.

3. Kim, C. S., et al. (2025). DL-MSCNN: A general and lightweight framework for fault diagnosis with limited training samples. Journal of Intelligent Manufacturing.

4. Kim, C. S., et al. (2025). Enhanced quantum-based DNA sequence alignment with noise handling and error detection. IEEE Access.

5. Kim, C. S., et al. (2024). GAILS: An effective multi-object job shop scheduler based on genetic algorithm and iterative local search. Scientific Reports.

Professor Chang Soo Kim’s pioneering research in intelligent manufacturing, AI-driven optimization, and fault diagnosis advances the scientific foundations of smart industry while enabling more efficient, reliable, and data-driven production systems. His innovative computational frameworks and adaptive algorithms contribute directly to industrial digital transformation, fostering technological competitiveness and sustainable global innovation.