Liang Hua | Computational Methods | Innovative Research Award

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Prof. Liang Hua | Computational Methods | Innovative Research Award

Prof. Liang Hua | Nantong University | China

Liang Hua is a Professor at Nantong University, holding a Ph.D. and serving as a doctoral supervisor and Vice President of the university. He achieved an accelerated promotion to full professor in 2016. With over 60 technical publications more than 30 indexed by SCI or EI. He is recognized for applying machine learning to industrial automation and control systems. As principal or co-investigator, he has led more than ten national and provincial-level projects, including a Key Project of the Joint Funds of the National Natural Science Foundation of China, contributions to the National Key R&D Program “Science and Technology Winter Olympics”, and the General Program of the National Natural Science Foundation. He holds over 50 granted Chinese invention patents (18 licensed or transferred) and 7 PCT patents (including 6 US patents). His leadership and scholarly excellence have earned him over 10 prestigious provincial and ministerial-level awards. He also holds leadership roles in national research committees related to transportation education and automation.

Author Profile

Scopus

Education

Liang Hua earned his Ph.D. presumably in control engineering, automation, or machine learning from a well-recognized institution in China. His doctoral research likely focused on advanced control systems for industrial applications, blending signal processing, servo systems, and machine learning methodologies. After completing his doctoral program, he rose through academic ranks at Nantong University, where he became a full professor in 2016 via an accelerated promotion track. Along the way, he deepened his expertise in intelligent control, robotics, and automation, augmented by exposure to national-level research funding and research collaboration. Participation in high-level training projects such as Jiangsu Province’s “333 High‑level Personnel Training Project” and Nantong city’s “226 High‑level Personnel Training Project” provided advanced professional development in both technical and leadership dimensions, positioning him as a recognized educator and researcher in intelligent systems and machine learning applications within industrial contexts.

Professional Experience

Professor Liang Hua has a robust academic and leadership career at Nantong University, where he serves as a doctoral supervisor and Vice President. He has led and participated in over ten national and provincial research initiatives including the National Natural Science Foundation key program and the Winter Olympics R&D program directing teams focused on industrial automation and control system innovation. Liang has supervised numerous postgraduate students, guiding them in research areas of servo control, robotics, and machine learning. In parallel, he has engaged with industry through patented technology transfer, overseeing more than 18 licensed inventions. He actively contributes to professional communities as Deputy Director of the Standardization Technical Committee of China Transportation Education Research Association and as Member of the Youth Working Committee of the Chinese Association of Automation. His dual roles in academic leadership and industry collaboration demonstrate deep experience in entrepreneurship, education management, and cross-sector research innovation.

Awards and Honors

Liang Hua’s leadership in both research and teaching has garnered over 10 provincial and ministerial awards. In 2021, he received the First Prize in the China Industry‑University‑Research Cooperation Innovation Achievement Award for the development and industrialization of industrial robot equipment based on high-performance servo control systems. The same year, he was awarded the Second Prize by the China Business Federation for precision intelligent servo control systems. Additional honors include the Special Prize of Jiangsu Education Department for innovation in electrical talent training and the First Prize in the Textile Higher Education Teaching Achievement Award. Earlier, in 2019, he earned the Technology Progress Award (Second Prize) from the China Electrical Technology Society and multiple First Prizes in textile–electrical innovation teaching. In 2018, he captured First Prize at the China International Industry Expo for a welding robot innovation, and another First Prize for energy-saving servo-driven motor systems at an industry‑university‑research collaboration award. Recognitions also include local titles such as ‘Outstanding Educator’, ‘Top Ten Outstanding Young Persons Skilled Positions’, and inclusion in Jiangsu’s “333” high‑level talent project.

Research Focus

Professor Liang Hua’s research centers on machine learning and its application to industrial automation, servo control systems, robotics, and smart machinery. He develops learning-based models to optimize performance, precision, and efficiency in high-performance servo-driven industrial robots and motion systems. His work integrates data-driven techniques, control theory, and hardware implementation resulting in over 50 Chinese invention patents and multiple PCT filings. Liang also explores interpretability and safety in AI-driven control contexts. Application domains include energy-saving industrial motors, stress‑aware robotic welding control, and servo actuation systems designed to improve reliability and productivity. His projects have practical impact: they have reached industrial deployment and technology transfer stages, typically in collaboration with enterprise partners. Through his dual focus on theoretical machine learning and practical robotics systems, Liang advances both algorithmic innovation and real-world engineering solutions.

Notable Publication

APG‑DPNet: A dual‑path network with anatomical priors for perigastric veins segmentation and varicosity quantification

  • Journal: Neurocomputing

  • Year: 2025

Fusion method of multi‑layer perceptron and multi‑innovation adaptive unscented Kalman filter for power battery state of charge estimation

  • Journal: Journal of Energy Storage

  • Year: 2025

Maneuver strategy recognition technology for enemy combat aircraft based on Bayesian deep learning

  • Journal: Journal of Shenzhen University Science and Engineering (Shenzhen Daxue Xuebao Ligong Ban)

  • Year: 2025

Stability analysis of inertial delayed neural network with delayed impulses via dynamic event‑triggered impulsive control

  • Journal: Neurocomputing

  • Year: 2025

Modal acoustic emission‑based circumferential crack feature extractions for pipeline welds with L‑shaped flexible sensor array

  • Journal: Nondestructive Testing and Evaluation

  • Year: 2025

Nonsingular Terminal Sliding Mode Control of the Yarn Winding Process Based on a Finite‑Time Extended State Observer

  • Journal: IEEE Access

  • Year: 2025

Conclusion

Liang Hua exemplifies a leader at the intersection of machine learning, automation, and engineering innovation. With robust experience managing national R&D projects and translating patented research into real-world industrial systems, he serves as both educator and executive at Nantong University. His honors span national awards in control technology development and educational innovation, underscoring his impact on talent development and technical excellence. Looking ahead, Liang’s work promises to advance machine learning–driven automation in sustainable manufacturing and smart infrastructures, further bridging academic research with industry advancement and enhancing the strategic competitiveness of Chinese engineering.

Qingguo Lü | Computational Methods | Best Researcher Award

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Assoc. Prof. Dr. Qingguo Lü | Computational Methods | Best Researcher Award

Chongqing University | China

Dr. Qingguo Lü is currently an Associate Professor at the College of Computer Science, Chongqing University, China. With a Ph.D. in Computational Intelligence and Information Processing from Southwest University, his academic journey has been marked by excellence. His work primarily focuses on distributed control and optimization in networked systems, especially in areas involving machine learning, cooperative control, and smart grids.

👨‍🎓Profile

Scopus

🎓 Early Academic Pursuits

Dr. Lü began his academic journey with a Bachelor’s degree in Measurement Control Technology and Instrument from Anhui University of Technology, before advancing to a Master’s degree in Signal and Information Processing at Southwest University. His early academic years were dedicated to mastering core concepts of computational intelligence and information processing, laying the foundation for his later groundbreaking research.

💼 Professional Endeavors

Throughout his career, Dr. Lü has held significant positions, including being a Research Assistant at the Texas A&M University Science Program, Qatar, where he contributed to the research in networked control systems, distributed computing, and smart grids. Following this, he transitioned to his postdoctoral research at Chongqing University, collaborating with Prof. Shaojiang Deng on topics like cooperative control, distributed optimization, and machine learning. His role as an Associate Professor has enabled him to further deepen his research and lead academic projects.

🔬 Contributions and Research Focus

Dr. Lü’s research is deeply embedded in solving real-world problems using distributed optimization algorithms across networked systems. Notable contributions include the development of asynchronous algorithms for decentralized resource allocation, privacy protection algorithms, and the design of algorithms for economic dispatch in smart grids. His research focus is centered on improving distributed optimization through stochastic algorithms, cooperative control, and networked machine learning.

📚 Academic Cites

Dr. Lü’s research has been extensively cited in major journals, indicating the high impact of his work. For example, his paper in IEEE Transactions on Cybernetics (2021) has garnered attention for its privacy-masking stochastic algorithms, highlighting his role in advancing the field of privacy in decentralized systems. His consistent contributions to top-tier journals underscore his prominence as a thought leader in computational intelligence and information processing.

🛠 Research Skills

Dr. Lü possesses advanced skills in developing decentralized algorithms, with expertise in distributed optimization, privacy protection, and machine learning for networked systems. His ability to design efficient algorithms that are not only theoretically sound but also computationally feasible has enabled the practical deployment of these methods in diverse real-world applications, including energy optimization and economic dispatch in smart grids.

🏫 Teaching Experience

As an Associate Professor, Dr. Lü plays an active role in shaping the next generation of researchers and engineers. His teaching focuses on distributed control systems, networked optimization, and machine learning, ensuring that students are well-versed in the latest techniques and applications of computational intelligence. His involvement in academic mentorship and research supervision is highly regarded, helping foster a collaborative and innovative research environment.

🏆 Legacy and Future Contributions

Dr. Lü’s career is already distinguished by his extensive research publications, patents, and contributions to academic growth. His research continues to shape the development of distributed algorithms for complex networks, offering solutions that are highly relevant in today’s rapidly evolving technological landscape. Looking ahead, he aims to expand his work on energy optimization, privacy protection, and networked control systems to tackle emerging challenges in fields like smart cities and autonomous systems.

Publications Top Notes