Frank Liou | AI/ML-based Distributed Manufacturing | Innovative Research Award

Innovative Research Award

Frank Liou
Missouri University of Science and Technology
Frank Liou
Affiliation Missouri University of Science and Technology
Country United States
Scopus ID 7005258863
Documents 286
Citations 5,921 citations by 4,623 documents
h-index 41
Subject Area AI/ML-based Distributed Manufacturing
Event International Invention Awards
ORCID 0000-0001-9505-0841

Frank Liou is a researcher affiliated with Missouri University of Science and Technology whose scholarly activities are associated with advanced manufacturing systems, artificial intelligence applications in manufacturing, machine learning integration, and distributed manufacturing technologies. His academic profile reflects extensive contributions to engineering research and interdisciplinary industrial innovation, particularly in the development of intelligent manufacturing frameworks and adaptive production methodologies.[1]

The recognition associated with the Innovative Research Award acknowledges sustained scholarly productivity, citation influence, and contributions to AI/ML-based distributed manufacturing research. The researcher’s documented publication output, citation metrics, and participation in advanced engineering studies indicate notable engagement within the international scientific and technological research community.[2]

Abstract

The Innovative Research Award article examines the academic and scientific profile of Frank Liou in relation to contemporary developments in AI/ML-based distributed manufacturing. The researcher’s scholarly record demonstrates consistent engagement with manufacturing automation, additive manufacturing systems, intelligent process optimization, and industrial digitalization. Through peer-reviewed publications, interdisciplinary engineering research, and citation influence, the academic contributions align with emerging technological priorities within advanced manufacturing ecosystems.[1]

The documented publication activity and citation performance provide evidence of ongoing participation in engineering innovation and applied manufacturing research. Recognition through the International Invention Awards framework reflects the relevance of these contributions to industrial transformation, smart manufacturing strategies, and global engineering research initiatives.[3]

Keywords

  • AI/ML-based Distributed Manufacturing
  • Advanced Manufacturing Systems
  • Additive Manufacturing
  • Industrial Automation
  • Machine Learning Applications
  • Smart Manufacturing
  • Engineering Innovation
  • Distributed Production Systems

Introduction

Modern manufacturing research increasingly integrates artificial intelligence, machine learning, robotics, and distributed production methodologies to address industrial efficiency and adaptability challenges. Within this context, Frank Liou’s research activities contribute to the advancement of intelligent manufacturing environments capable of supporting data-driven production processes and industrial automation strategies.[2]

The development of AI-enhanced distributed manufacturing systems has become a significant research area due to the growing demand for flexible production architectures and digitally integrated industrial platforms. Research contributions in this field support predictive analytics, process optimization, and scalable manufacturing operations, which are increasingly relevant to Industry 4.0 frameworks and smart factory initiatives.[4]

Research Profile

Frank Liou’s academic profile is associated with Missouri University of Science and Technology and reflects substantial involvement in manufacturing engineering and intelligent systems research. The publication record indexed through Scopus includes numerous peer-reviewed articles, conference papers, and collaborative engineering studies focused on advanced manufacturing technologies and automation methodologies.[1]

The researcher’s documented h-index and citation metrics indicate sustained scholarly visibility and influence across engineering and manufacturing-related disciplines. Areas of research emphasis include additive manufacturing, machine learning-assisted manufacturing control, industrial robotics integration, and distributed manufacturing optimization systems.[5]

  • Research affiliation with Missouri University of Science and Technology
  • Extensive Scopus-indexed publication portfolio
  • Research focus on AI/ML-driven manufacturing technologies
  • Contributions to additive and distributed manufacturing systems
  • Interdisciplinary collaboration in industrial engineering research

Research Contributions

Research contributions attributed to Frank Liou include the advancement of intelligent production systems capable of integrating automation, machine learning algorithms, and adaptive manufacturing techniques. The work supports the broader transition toward digitally coordinated manufacturing infrastructures and smart industrial operations.[6]

The integration of AI methodologies into distributed manufacturing systems has contributed to research efforts focused on predictive maintenance, process optimization, manufacturing scalability, and production quality monitoring. Such developments align with global engineering objectives concerning sustainability, operational efficiency, and industrial digital transformation.[4]

  • Development of intelligent manufacturing frameworks
  • Application of machine learning in manufacturing analytics
  • Research on additive manufacturing process optimization
  • Distributed manufacturing architecture studies
  • Industrial automation and robotics integration
  • Research collaboration in smart production systems

Publications

The researcher’s publication portfolio includes journal articles and conference proceedings addressing manufacturing technologies, additive manufacturing systems, automation engineering, and AI-supported industrial applications. Several studies have contributed to discussions on intelligent process control, digital manufacturing ecosystems, and machine learning integration within engineering systems.[1]

  1. Research on additive manufacturing optimization and intelligent production systems.
  2. Studies involving AI-assisted manufacturing process monitoring and predictive analytics.
  3. Collaborative engineering publications focused on distributed manufacturing methodologies.
  4. Peer-reviewed contributions addressing smart factory and Industry 4.0 technologies.
  5. Engineering investigations involving machine learning integration in industrial applications.

Representative DOI-linked research themes associated with manufacturing engineering and intelligent systems research include studies indexed through international publication databases and engineering repositories.[7]

Research Impact

The research impact associated with Frank Liou is reflected in citation activity, publication visibility, and sustained scholarly engagement within manufacturing engineering and intelligent systems disciplines. Citation metrics demonstrate recognition by the academic and industrial research communities, particularly in fields connected to manufacturing innovation and industrial automation.[1]

The integration of AI and machine learning technologies into distributed manufacturing systems continues to influence industrial engineering research agendas. Contributions in this domain support the evolution of adaptive manufacturing environments and digitally coordinated industrial infrastructures capable of improving operational efficiency and production flexibility.[6]

  • More than 5,900 citations across indexed documents
  • Broad visibility in engineering and manufacturing research literature
  • Influence on AI-integrated manufacturing studies
  • Recognition in smart manufacturing and automation research
  • Contribution to industrial digital transformation discussions

Award Suitability

The Innovative Research Award suitability assessment is based on documented scholarly productivity, publication influence, interdisciplinary engineering research, and relevance to emerging industrial technologies. Frank Liou’s research profile demonstrates alignment with award criteria associated with technological innovation, industrial applicability, and scientific contribution within advanced manufacturing disciplines.[3]

Research contributions involving AI/ML-based distributed manufacturing systems are particularly relevant to contemporary engineering innovation priorities. The integration of intelligent technologies into manufacturing processes reflects ongoing developments within smart production ecosystems and Industry 4.0 research initiatives.[4]

Conclusion

Frank Liou’s academic and research profile reflects sustained contributions to advanced manufacturing engineering, AI-integrated industrial systems, and distributed manufacturing technologies. The combination of publication activity, citation influence, and interdisciplinary engineering engagement demonstrates continued participation in the advancement of intelligent manufacturing research.[1]

Recognition through the Innovative Research Award framework corresponds with the broader significance of AI/ML-based manufacturing research and its relevance to global industrial innovation initiatives. The documented research activities support ongoing developments in smart manufacturing, industrial automation, and intelligent engineering systems.[3]

References

  1. Elsevier. (n.d.). Scopus author details: Frank Liou, Author ID 7005258863. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=7005258863
  2. Additive manufacturing of Ti-Ni based ternary shape memory alloys.
    https://www.sciencedirect.com/science/article/pii/S2949822826000316
  3. In-situ Transmission Electron Microscopy Investigation of Grain Size and Temperature Dependent Irradiation Behavior of 304L Stainless Steel.
    https://link.springer.com/article/10.1007/s11837-025-07894-y
  4. Effects of heat treatment on Ti–Ni–Cu/TiNi shape memory bimetal fabricated by directed energy deposition.
    https://www.sciencedirect.com/science/article/abs/pii/S1044580325010812
  5. Bending Fatigue in Additively Manufactured Metals: A Review of Current Research and Future Directions.
    https://scholarsmine.mst.edu/mec_aereng_facwork/6325/
  6. DED printing process modeling using metal matrix composites: in-situ feedstock mixing with variable compositions and empirical validation.
    https://link.springer.com/article/10.1007/s00170-025-16828-6
  7. Digital Twins, AI, and Cybersecurity in Additive Manufacturing: A Comprehensive Review of Current Trends and Challenges.
    https://www.preprints.org/manuscript/202506.2516

Maikel Leon | Artificial Intelligence | Research Excellence Award

Assoc. Prof. Dr. Maikel Leon | Artificial Intelligence | Research Excellence Award

University of Miami, United States

Assoc. Prof. Dr. Maikel Leon is an accomplished academic and AI specialist with a Ph.D. in Computer Science focused on artificial intelligence applied to transportation from Hasselt University, Belgium, and summa cum laude degrees from the Central University of Las Villas, Cuba. Since 2015, he has been a faculty member at the Department of Business Technology, Miami Herbert Business School, University of Miami, teaching and coordinating a wide range of courses in business analytics, programming, machine learning, databases, and artificial intelligence for business. His academic career spans institutions in the United States and Cuba, reflecting strong international teaching and research experience. Dr. Leon is an active reviewer and program committee member for leading journals and conferences, including IEEE Transactions on Fuzzy Systems and FLAIRS. He has received prestigious honors such as the Best Paper Award at the IEEE ICTAI Conference and the Cuban National Academy of Sciences Award for outstanding research. Beyond academia, he is a frequent media commentator on AI, a certified professional in generative AI and cloud technologies, and a leader in innovative teaching, entrepreneurship, and international collaboration initiatives.

Citation Metrics (Scopus)

400
300
200
100
50
0

Citations
355

Documents
38

h-index
11

Citations

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h-index

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Featured Publications

 

Anna Wolak-Tuzimek | Digital Technologies | Research Excellence Award

Assist. Prof. Dr. Anna Wolak-Tuzimek | Digital Technologies | Research Excellence Award

Faculty of Law and Social Sciences, Jan Kochanowski University, Poland

Assist. Prof. Dr. Anna Wolak-Tuzimek has made significant contributions to the fields of management and quality sciences, with a strong focus on sustainable development, corporate social responsibility, business competitiveness, and digitalisation. Her research integrates theoretical and practical perspectives on enterprise management, particularly in small and medium-sized enterprises operating in competitive and sustainable economic environments. She is the author of over 160 scientific publications, including 9 influential monographs published in Poland and internationally, addressing innovation, CSR, financial tools, and modern management trends. As an active grant project coordinator and deputy coordinator, she has led and supported international cooperation projects related to entrepreneurship, education, and lifelong learning across the European Union. She has presented her research at more than 60 national and international conferences and actively collaborates with leading European universities, contributing to the internationalisation of management research and academic practics.

Citation Metrics (Scopus)

200
150
100
50
0

Citations
105

Documents
11

h-index
4

Citations

Documents

h-index

View Scopus Profile

Featured Publications

 

Dinesh Babu M | Engineering | Best Researcher Award

Dr. Dinesh Babu M | Engineering | Best Researcher Award

Rajalakshmi Institute of technology | India 

Dr. M. Dinesh Babu, B.E., M.Tech., Ph.D., is a distinguished academic and researcher recognized among the Top 2% Scientists Worldwide in the subfield of Energy for the year 2023 by Elsevier and Stanford University. He holds a Ph.D. in Energy Systems Engineering from the College of Engineering, Anna University, Chennai, where his doctoral research focused on “Studies on the Effect of Internal Longitudinal Fins and Nanoparticles on the Performance of Solar Flat Plate Collectors.” He also holds an M.Tech. in Energy Systems Engineering from Vellore Institute of Technology (VIT), Vellore, and a B.E. in Mechanical Engineering from Sriram Engineering College, University of Madras, both with First Class distinction. With over 21 years of teaching and research experience, Dr. Dinesh Babu has served in reputed institutions such as Dr. M.G.R. University, Sathyabama University, R.M.K. Engineering College, Panimalar Engineering College, and currently, as a Professor at Rajalakshmi Institute of Technology, Chennai. His academic contributions encompass teaching core subjects like Heat and Mass Transfer, Thermodynamics, Thermal Engineering, Power Plant Engineering, Machine Design, Manufacturing Technology, Environmental Science, and Entrepreneurship Development. Dr. Babu has an outstanding research profile with 93 publications in Scopus, SCI, and Web of Science-indexed journals, achieving a cumulative impact factor of 302.54. His research has garnered over 3,500 citations on Google Scholar (h-index: 32, i10-index: 52), 3,177 citations on Scopus (h-index: 31), and 2,978 citations with 15,220 reads on ResearchGate. He has also published two patents and has four ongoing research papers under review. He currently supervises four Ph.D. research scholars registered under Anna University (Supervisor ID: 3120042). His research interests include renewable energy systems, solar thermal engineering, nanofluids, biofuels, combustion and emission analysis, and sustainable manufacturing. Dr. Babu has designed innovative projects such as a 50 LPD copper solar water heater with a ladder-type heat exchanger and has secured funding through initiatives like the RIT-FIT Seed Money Fund and a SERB project proposal worth ₹16.1 lakhs. An active academic contributor, Dr. Babu serves as a Doctoral Committee Member at Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai, and frequently participates as a resource person and reviewer for journals and research programs. He has completed multiple Elsevier Research Academy certifications on topics such as producing highly visible research, academia–industry collaboration, journal impact metrics, and open hardware innovation. Dr. M. Dinesh Babu’s exemplary academic dedication, prolific research output, and consistent pursuit of innovation in the field of energy systems engineering have earned him a reputation as one of India’s leading scholars in sustainable and renewable energy technologies.

Profiles: Scopus | Orcid | Google Scholar

Featured Publications

Yuvarajan, D., Babu, M. D., Beem Kumar, N., & Kishore, P. A. (2018). Experimental investigation on the influence of titanium dioxide nanofluid on emission pattern of biodiesel in a diesel engine. Atmospheric Pollution Research, 9(1), 47–52.

Radhakrishnan, S., Munuswamy, D. B., Devarajan, Y., T., A., & Mahalingam, A. (2018). Effect of nanoparticle on emission and performance characteristics of a diesel engine fueled with cashew nut shell biodiesel. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 40, 1–10.

Sathiyamoorthi, R., Sankaranarayanan, G., Munuswamy, D. B., & Devarajan, Y. (2021). Experimental study of spray analysis for Palmarosa biodiesel‐diesel blends in a constant volume chamber. Environmental Progress & Sustainable Energy, 40(6), e13696.

Devarajan, Y., Munuswamy, D. B., & Mahalingam, A. (2018). Influence of nano-additive on performance and emission characteristics of a diesel engine running on neat neem oil biodiesel. Environmental Science and Pollution Research, 25(26), 26167–26172.

Devarajan, Y., Munuswamy, D. B., Nagappan, B., & Pandian, A. K. (2018). Performance, combustion and emission analysis of mustard oil biodiesel and octanol blends in diesel engine. Heat and Mass Transfer, 54(6), 1803–1811.

Devarajan, Y., Munuswamy, D. B., & Mahalingam, A. (2019). Investigation on behavior of diesel engine performance, emission, and combustion characteristics using nano-additive in neat biodiesel. Heat and Mass Transfer, 55(6), 1641–1650.

Pandian, A. K., Munuswamy, D. B., Radhakrishnan, S., & Devarajan, Y. (2018). Emission and performance analysis of a diesel engine burning cashew nut shell oil biodiesel mixed with hexanol. Petroleum Science, 15(1), 176–184.

Devarajan, Y., Mahalingam, A., Munuswamy, D. B., & Arunkumar, T. (2018). Combustion, performance, and emission study of a research diesel engine fueled with palm oil biodiesel and its additive. Energy & Fuels, 32(8), 8447–8452.

Nirmal Varghese Babu | Artificial Intelligence | Best Researcher Award 

Dr. Nirmal Varghese Babu | Artificial Intelligence | Best Researcher Award 

Dr. Nirmal Varghese Babu | Karunya Institute of Technology and Sciences | India

Author Profiles

Scopus

Orcid

Google Scholar

Early Academic Pursuits

Dr. Nirmal Varghese Babu began his academic journey with a strong inclination toward computer science and technology. He completed his B.Tech in Information Technology from Karunya Institute of Technology and Sciences, Coimbatore, in 2017 with a CGPA of 8.0. His passion for research and innovation in computing led him to pursue an M.Tech in Computer Science & Engineering from Amal Jyothi College of Engineering, Kanjirappally (2017–2019), graduating with an impressive CGPA of 8.72. Currently, he is pursuing a Ph.D. in Computer Science & Engineering from Karunya Institute of Technology and Sciences, expected to be completed in 2025. His academic excellence was rooted in his formative years at Mathews Mar Athanasius Residential School, Chengannur, where he built a strong foundation in analytical and computational thinking.

Professional Endeavors

Dr. Nirmal Varghese Babu is currently serving as an Assistant Professor at the School of Computer Science and Technology, Karunya Institute of Technology and Science, Coimbatore (since July 26, 2022). His professional endeavors include teaching, research, and mentoring in various areas of computer science and Artificial Intelligence. He has delivered lectures on Artificial Intelligence: Principles and Techniques, Cloud Computing for Data Analytics, AI for Games, AI for Food Processing Engineering, AI for Biotechnology, and MLOps. Alongside teaching, he mentors undergraduate students, coordinates final-year projects, and supervises academic research initiatives. His teaching methodology emphasizes experiential learning, guiding students to bridge theoretical knowledge with real-world technological applications.

Contributions and Research Focus

Dr. Nirmal’s research contributions revolve around Artificial Intelligence, machine learning, data analytics, and real-time systems. His M.Tech project, Multiclass Sentiment Analysis of Social Media Data using Neural Networks, explored advanced deep learning algorithms like CNN and RNN for classifying sentiment across social media platforms, specifically Twitter. This study integrated text and emoticon data for multiclass classification using one-hot encoding and neural networks. His earlier project, Real-Time Traffic Incident Detection using Social Media Data, demonstrated innovative use of Natural Language Processing to detect and analyze traffic incidents using Twitter data, integrating AI for real-time decision-making. His work exemplifies how Artificial Intelligence can transform data into actionable insights for societal and industrial benefit.

Impact and Influence

Dr. Nirmal Varghese Babu’s impact as an educator and researcher extends across academia and applied technology. At Karunya Institute, he plays a vital role in shaping the next generation of AI-driven engineers and data scientists. As a mentor and coordinator, he has successfully guided numerous B.Tech projects, fostering innovation in the domains of Artificial Intelligence, MLOps, and machine learning. His pedagogical style emphasizes research-based learning, promoting creative problem-solving and real-world application of AI. Through his leadership in academic project coordination and curriculum development, he has significantly influenced the integration of AI-based methodologies into modern engineering education.

Academic Cites

Dr. Nirmal’s academic contributions are recognized through his published works, research projects, and student-guided studies. His projects on sentiment analysis and traffic incident detection have been well-cited and appreciated within the AI and data analytics community. The relevance of his research is reflected in growing academic references to his work in areas such as neural networks, data mining, and sentiment classification. His scholarly achievements continue to inspire students and researchers pursuing advanced studies in Artificial Intelligence and computational learning.

Legacy and Future Contributions

Looking ahead, Dr. Nirmal Varghese Babu aims to expand his research in Artificial Intelligence, focusing on its integration with real-time analytics, smart systems, and cognitive computing. His future contributions are expected to advance the use of AI in multidisciplinary fields such as biotechnology, healthcare, and environmental systems. As an educator, his legacy lies in his ability to inspire and mentor young researchers, promoting a culture of innovation and ethical AI development. His ongoing research and academic leadership will undoubtedly continue to shape the evolution of AI-driven solutions and their transformative potential across industries.

Artificial Intelligence

Dr. Nirmal Varghese Babu’s expertise in Artificial Intelligence is evident through his teaching, research, and innovation in deep learning, neural networks, and data analytics. His projects and mentorship highlight the transformative role of Artificial Intelligence in addressing real-world challenges. The continued advancement of Artificial Intelligence under his guidance promises to create meaningful impact in both academic and applied technological domains.

Featured Publications

Babu, N. V., & Kanaga, E. G. M. (2022). Sentiment analysis in social media data for depression detection using artificial intelligence: A review. SN Computer Science, 3(1), 1–15. https://doi.org/10.1007/s42979-021-00921-2

Babu, D. E. G. M. K. N. V. (2022). Sentiment analysis in social media data for depression detection using artificial intelligence: A review. SN Computer Science, 3, 350.

Babu, N. V., & Rawther, F. A. (2021). Multiclass sentiment analysis in text and emoticons of Twitter data: A review. Proceedings of the Second International Conference on Networks and Advances in Computational Technologies (NetACT).

Prince, S. C., & Babu, N. V. (2024). Advancing multiclass emotion recognition with CNN-RNN architecture and illuminating module for real-time precision using facial expressions. Proceedings of the 2024 International Conference on Advances in Modern Age Technologies for Sustainable Development (AMATS).

Babu, N. V., Kanaga, E. G. M., Kattappuram, J. T., & Benny, R. V. (2023). AI-based EEG analysis for depression detection: A critical evaluation of current approaches and future directions. Proceedings of the 2023 International Conference on Computational Intelligence and Sustainable Technologies (CIST).

Babu, D. E. G. M. K. N. V. (2022). Depression analysis using electroencephalography signals and machine learning algorithms. Proceedings of the Third International Conference on Intelligent Computing Instrumentation and Control Technologies (ICICICT).

Adityasai, B., & Babu, N. V. (2024). Advancing Alzheimer’s diagnosis through transfer learning with deep MRI analysis. Proceedings of the 2024 International Conference on Advances in Modern Age Technologies for Sustainable Development (AMATS).

Babu, N. V., & Kanaga, E. G. M. (2023). Multiclass text emotion recognition in social media data. In Machine Intelligence Techniques for Data Analysis and Signal Processing (pp. 123–135). Springer.

Rawther, F. A., & Babu, N. V. (2019). User behavior analysis on social media data using sentiment analysis or opinion mining. International Research Journal of Engineering and Technology (IRJET), 6(6), 3081–3085.

Assoc. Prof. Dr. Lei Wang | Automatic Control Systems | Best Researcher Award

Assoc. Prof. Dr. Lei Wang | Automatic Control Systems | Best Researcher Award

Wuxi University, China.

Dr. Wang Lei is an Associate Professor at Wuxi University, specializing in intelligent control systems. With a strong background in artificial intelligence applications in automation, he has led over 10 major research projects, published more than 30 peer-reviewed papers, and holds 20+ patents and 11 software copyrights. He has international training experience in Thailand, Taiwan, Poland, and the UK, enriching his global academic perspective.

Profile

Scopus
Orcid

🎓 Education

Dr. Wang Lei pursued extensive academic training, including joint doctoral programs funded by China’s national “111 Plan”, conducting research in institutions like Green Mountain University (Poland) and the University of Southampton (UK). His master's studies included training at Prince Songkhla University (Thailand) and Yunlin University of Science and Technology (Taiwan).

💼 Experience

Currently an Associate Professor at Wuxi University, Wang Lei has spearheaded numerous provincial and national research projects, including collaborations with the Ministry of Education, Wuxi Science and Technology Bureau, and the National Natural Science Foundation of China. His editorial roles include reviewing for journals like International Journal of Robust and Nonlinear Control and Security and Communication Networks.

🔬 Research Interests

His research focuses on the application of artificial intelligence in automatic control systems, covering areas such as iterative learning control, dynamic observers, fuzzy systems, and actuator fault tolerance.

🏆 Awards & Patents

Principal investigator of 10+ funded projects including Jiangsu Provincial Natural Science and Wuxi “Light of Taihu Lake” programs.

Holder of 20+ patents, including “A trajectory tracking method for non-repetitive time-varying systems”.

Recognized with support from national initiatives such as the “111 Plan”.

📚 Notable Publications

🆕 2025

Output feedback based PD-type iterative learning fault-tolerant control for uncertain discrete systems with actuator faults
📘 Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering
🔗 DOI: 10.1177/09596518241263003
👥 Yanxia Shen, Wei Zou, Lei Wang

🔬 2024

An innovative dynamic observer for nonlinear interconnected systems with uncertainties
📘 Transactions of the Institute of Measurement and Control
🗓 Published: 2024-10-23
🔗 DOI: 10.1177/01423312241274007
👥 Nan Ji, Lei Wang, Xinggang Yan, Dezhi Xu

Iterative learning control with parameter estimation for non-repetitive time-varying systems
📘 Journal of the Franklin Institute
🗓 Published: 2024-02
🔗 DOI: 10.1016/j.jfranklin.2024.01.011
👥 Lei Wang, Ziwei Huangfu, Ruiwen Li, Xiewen (Sitman) Wen, Yuan Sun, Yiyang Chen

📊 2023

Design of robust fuzzy iterative learning control for nonlinear batch processes
📘 Mathematical Biosciences and Engineering
🔗 DOI: 10.3934/mbe.2023897
👥 Wei Zou, Yanxia Shen, Lei Wang

A Soft Actor-Critic Approach for a Blind Walking Hexapod Robot with Obstacle Avoidance
📘 Actuators
🗓 Published: 2023-10-21
🔗 DOI: 10.3390/act12100393
👥 Lei Wang, Li Ruiwen, Ziwei Huangfu, Yishan Feng, Yiyang Chen

Fully Distributed, Event-Triggered Containment Control of Multi-Agent Systems
📘 Applied Sciences
🗓 Published: 2023-10-07
🔗 DOI: 10.3390/app131911039
👥 Lei Wang, Guanwen Chen, Tai Li, Ruitian Yang

Slowness or Autocorrelation? A serial correlation feature analysis method
📘 Journal of Process Control
🗓 Published: 2023-01
🔗 DOI: 10.1016/j.jprocont.2022.11.010
👥 Qinghua Li, Zhonggai Zhao, Lei Wang

Mr. Chibuzo Nwabufo Okwuosa | Fault Detection | Best Researcher Award

Mr. Chibuzo Nwabufo Okwuosa | Fault Detection | Best Researcher Award

Kumoh National Institute of Technology, South Korea.

Okwuosa Chibuzo Nwabufo is a Research Ph.D. Scholar at Kumoh National Institute of Technology 🇰🇷, South Korea, specializing in Mechanical Engineering. With a strong foundation in machine learning, deep learning, and real-time fault diagnostics, his work emphasizes bridging theoretical innovation with industrial application. Chibuzo is passionate about Prognostics and Health Management (PHM), Explainable AI (XAI), and digital twin technologies, aiming to create smart, AI-driven maintenance systems for next-generation industries.

Profile

Scopus
Orcid
Google Scholar

🎓 Education

Chibuzo earned both his Master’s and is currently pursuing his Ph.D. in Mechanical Engineering from Kumoh National Institute of Technology, South Korea. His academic focus has been consistently rooted in intelligent fault diagnostics, predictive maintenance, and real-time monitoring technologies.

💼 Experience

With over four completed and two ongoing research projects, Chibuzo has hands-on experience in both academia and industry. Notable projects include real-time diagnostics for diaphragm pumps, fault analysis in induction motors, and zinc phosphating coating processes. He has collaborated on industry-sponsored projects and led initiatives involving advanced data-driven solutions for predictive maintenance.

🔬 Research Interests

His key research domains include:

🔧 Prognostics and Health Management (PHM)

🤖 Machine Learning & Deep Learning

🧠 Explainable AI (XAI)

🌐 Digital Twin Technologies

⚙️ Real-time Fault Diagnostics

🏆 Awards & Grants

Chibuzo’s research has been supported by prestigious Korean government grants:

IITP Innovative Human Resource Development for Local Intellectualization

ITRC Program (MSIT, Korea)
These grants facilitated collaborations with industry leaders and funded cutting-edge research in diagnostics and manufacturing innovation.

📚 Selected Publications

🆕 Optimizing Defect Detection on Glossy and Curved Surfaces Using Deep Learning and Advanced Imaging Systems

📅 2025-04-13 | Sensors
🔗 DOI: 10.3390/s25082449
👨‍🔬 Contributors: Joung-Hwan Yoon, Chibuzo Nwabufo Okwuosa, Nnamdi Chukwunweike Aronwora, Jang-Wook Hur
📌 Application of deep learning and high-resolution imaging for defect detection on challenging industrial surfaces.


⚙️ A Spectral-Based Blade Fault Detection in Shot Blast Machines with XGBoost and Feature Importance

📅 2024-10-09 | Journal of Sensor and Actuator Networks
🔗 DOI: 10.3390/jsan13050064
👨‍🔬 Contributors: Joon-Hyuk Lee, Chibuzo Nwabufo Okwuosa, Baek Cheon Shin, Jang-Wook Hur
📌 Fault detection in mechanical components using spectral features and XGBoost.


🔍 Transformer Core Fault Diagnosis via Current Signal Analysis with Pearson Correlation Feature Selection

📅 2024-02-29 | Electronics
🔗 DOI: 10.3390/electronics13050926
👨‍🔬 Contributors: Daryl Domingo, Akeem Bayo Kareem, Chibuzo Nwabufo Okwuosa, Paul Michael Custodio, Jang-Wook Hur
📌 Intelligent transformer fault diagnosis using statistical signal analysis and feature engineering.


Enhancing Transformer Core Fault Diagnosis and Classification through Hilbert Transform Analysis of Electric Current Signals

📅 2024-01-18 | Preprint
🔗 DOI: 10.20944/preprints202401.1371.v1
👨‍🔬 Contributors: Daryl Domingo, Akeem Bayo Kareem, Chibuzo Nwabufo Okwuosa, Paul Michael Custodio, Jang-Wook Hur
📌 Preprint focusing on enhanced signal processing for electrical fault classification.


🧠 An Intelligent Hybrid Feature Selection Approach for SCIM Inter-Turn Fault Classification at Minor Load Conditions Using Supervised Learning

📅 2023 | IEEE Access
🔗 DOI: 10.1109/ACCESS.2023.3266865
👨‍🔬 Contributors: Chibuzo Nwabufo Okwuosa, Jang-Wook Hur
📌 Machine learning-based fault classification in squirrel cage induction motors under low-load conditions.

 

 

 

Dr. Zhe Wang | Wireless Network | Best Researcher Award

Dr. Zhe Wang | Wireless Network | Best Researcher Award

Guangxi Minzu University, China.

Dr. Zhe Wang is an Assistant Professor at the School of Artificial Intelligence, Guangxi Minzu University. He earned his PhD in Electric Power and Intelligent Information from Guangxi University in 2019. His research focuses on Simultaneous Wireless Information and Power Transfer (SWIPT), wireless power transfer, optimization, and AI applications. Dr. Wang has contributed significantly to the field of federated learning and privacy-preserving AI techniques, with publications in high-impact journals.

Profile

Scopus

🎓 Education

Dr. Zhe Wang holds a PhD in Electric Power and Intelligent Information from Guangxi University, China, which he obtained in 2019. His academic journey has been centered on advancing research in wireless power transfer, optimization techniques, and AI applications in energy systems. With a strong foundation in electrical engineering and intelligent systems, Dr. Wang has contributed to cutting-edge innovations in Simultaneous Wireless Information and Power Transfer (SWIPT). His expertise bridges the gap between power systems and artificial intelligence, driving new methodologies for efficient and intelligent energy solutions.

💼 Experience

Dr. Zhe Wang is an Assistant Professor at the School of Artificial Intelligence, Guangxi Minzu University, a position he has held since 2021. Prior to this, he served as a Lecturer at the School of Information Engineering at the same university from 2019 to 2020. His academic contributions focus on advancing research and education in artificial intelligence and information engineering, fostering innovation in these rapidly evolving fields.

🔬 Research Interests

Simultaneous Wireless Information and Power Transfer (SWIPT)

Wireless Power Transfer

Optimization Techniques

AI Applications in Power Systems

Privacy-Preserving AI & Federated Learning

🏆 Awards & Recognitions

Outstanding Research Contribution Award – Guangxi Minzu University

Best Paper Award – International Conference on Artificial Intelligence Applications

Innovation Excellence Honor – SWIPT & Wireless Power Transfer Research

📚 Publications

1️⃣ "A Review of Privacy-Preserving Research on Federated Graph Neural Networks"

Journal: Neurocomputing (2024)

Cited by: 2 articles

2️⃣ "A Review of Secure Federated Learning: Privacy Leakage Threats, Protection Technologies, Challenges, and Future Directions"

Journal: Neurocomputing (2023).

Cited by: 22 articles

 

 

Prof. Wen Jiang | Artificial Intelligence | Best Researcher Award

Prof. Wen Jiang | Artificial Intelligence | Best Researcher Award

Northwestern Polytechnical University, China.

Prof. Wen Jiang is a distinguished researcher and academic with a Ph.D. from Northwestern Polytechnical University, Xi’an, China (2009). She currently serves as a professor in the School of Electronics and Information at Northwestern Polytechnical University. Her work focuses on cutting-edge areas like information fusion, artificial intelligence, remote sensing image processing, and intelligent algorithm security, making her a leader in her field.

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Education 🎓

Prof. Wen Jiang has an impressive academic background in information systems and engineering. She earned her Ph.D. in Information Systems from Northwestern Polytechnical University, Xi’an, China, in 2009, where her research focused on innovative data systems and intelligent technologies. Prior to that, she completed her Master’s degree in Information Engineering at Information Engineering University, Zhengzhou, China, in 1997, gaining in-depth knowledge of advanced engineering concepts. She began her academic journey with a Bachelor’s degree in Information Engineering from the same university in 1994, building a strong foundation for her pioneering contributions to the field.

Experience 🏫

Prof. Wen Jiang is a Professor at the School of Electronics and Information, Northwestern Polytechnical University, where she has made a significant impact in academia. She is highly regarded for mentoring aspiring researchers and leading innovative projects in advanced technologies. Her leadership and expertise have been instrumental in driving forward research in areas like artificial intelligence, information fusion, and algorithm security..

Research Interests 🔍

Information Fusion:
Integrating data from diverse sources to enable smarter and more efficient decision-making processes, crucial for applications in defense, healthcare, and industry.

Artificial Intelligence:
Advancing machine learning and intelligent systems to solve complex problems and enhance automation across various domains.

Remote Sensing Image Processing:
Developing cutting-edge tools for environmental monitoring, urban planning, disaster management, and mapping applications.

Intelligent Algorithm Security:
Ensuring the robustness, reliability, and safety of AI-driven solutions to address vulnerabilities in critical systems.

Publications Top Notes 📚

A New Data Augmentation Method Based on Mixup and Dempster-Shafer Theory IEEE Transactions on Multimedia, 2024
Contributors: Zhuo Zhang, Hongfei Wang, Jie Geng, Xinyang Deng, Wen Jiang. Link

A Novel Air Target Intention Recognition Method Based on Sample Reweighting and Attention-Bi-GRU IEEE Systems Journal, 2024
Contributors: Yu Zhang, Weichen Ma, Fanghui Huang, Xinyang Deng, Wen Jiang. Link

Causal Intervention and Parameter-Free Reasoning for Few-Shot SAR Target Recognition IEEE Transactions on Circuits and Systems for Video Technology, 2024, Contributors: Jie Geng, Weichen Ma, Wen Jiang. Link

CMSE: Cross-Modal Semantic Enhancement Network for Classification of Hyperspectral and LiDAR Data IEEE Transactions on Geoscience and Remote Sensing, 2024, Contributors: Wenqi Han, Wang Miao, Jie Geng, Wen Jiang. Link

Dual-Path Feature Aware Network for Remote Sensing Image Semantic Segmentation IEEE Transactions on Circuits and Systems for Video Technology, 2024, Contributors: Jie Geng, Shuai Song, Wen Jiang. Link