Mohamad Abu Seman | AI and Robotic System | Best Researcher Award

Published on by Shen Awards

Dr. Mohamad Abu Seman | AI and Robotic System | Best Researcher Award

Senior Lecturer from University Sains Malaysia | Malaysia 

Dr. Mohamad Tarmizi Abu Seman is a Senior Lecturer at Universiti Sains Malaysia (USM), widely recognized for his pioneering work in mechanical engineering and intelligent systems integration. With an academic and research career rooted in innovation and community impact, Dr. Abu Seman has consistently contributed to the advancement of engineering solutions that intersect with artificial intelligence, smart healthcare systems, and sustainable technologies. His extensive research has produced practical tools and systems, from smart rehabilitation gloves and diabetic insoles to IoT-based agriculture and intelligent parking solutions. He has been instrumental in supervising numerous undergraduate and postgraduate students, leading them in cutting-edge research that addresses real-world challenges. Dr. Abu Seman has received multiple national and international accolades for his innovations and continues to serve on key research projects funded by Malaysian research councils and ministries. His involvement in applied engineering and technology-based community solutions demonstrates his commitment to both academic excellence and social betterment. As a member of professional networks like IEEE, he maintains strong academic connections and continually expands his interdisciplinary scope. His contributions place him at the forefront of Malaysian engineering innovation, with increasing global visibility in science, health, and technology domains.

Professional Profile

Scopus Profile | ORCID Profile | Google Scholar

Education

Dr. Mohamad Abu Seman holds a Doctor of Philosophy (Ph.D.) in Mechanical Engineering, marking the culmination of years of rigorous academic training and specialized research. His academic foundation is rooted in applied mechanical engineering, where he focused on areas such as Computational Fluid Dynamics (CFD), Finite Element Analysis (FEA), and mechanical system optimization. His doctoral research explored aerodynamic performances, smart sensor systems, and heat energy applications, combining computational and experimental methodologies to advance engineering practices. Throughout his academic journey, Dr. Abu Seman developed expertise in simulation software such as ANSYS and MATLAB, allowing him to translate complex engineering theories into practical, problem-solving innovations. His academic credentials are complemented by his continued learning through research-based teaching and national innovation competitions. His education laid a strong foundation for his future research endeavors in smart embedded systems, energy-efficient devices, and AI-integrated mechanical systems. The application of his doctoral studies is evident in the range of projects he has undertaken, including robotics, sensor technologies, and sustainable engineering solutions. Dr. Abu Seman’s academic journey has not only shaped his technical competencies but also positioned him as a thought leader in intelligent mechanical systems both in Malaysia and the wider ASEAN research community.

Experience

As a Senior Lecturer at Universiti Sains Malaysia (USM), Dr. Mohamad Abu Seman has demonstrated multifaceted professional excellence in teaching, research, and applied innovation. He has led and contributed to a wide range of university-community partnership projects, focusing on smart agriculture, robotic rehabilitation, and inclusive design for differently-abled individuals. His current and past grant-funded research initiatives include the development of intelligent glove systems, IoT-powered irrigation, and robotic mechanisms, amounting to over RM 700,000 in research funding. Dr. Abu Seman has played an integral role in supervising undergraduate and postgraduate students, many of whom have produced award-winning capstone projects and published academic papers under his guidance. His engineering expertise spans smart mechanical systems, AI-driven embedded applications, and biomedical design. Additionally, he has represented his institution at national and international engineering competitions and innovation exhibitions, further solidifying his professional credibility. Dr. Abu Seman’s experience is deeply rooted in both academic mentorship and real-world problem-solving, often bridging the gap between engineering theory and tangible community impact. He continues to contribute as a principal investigator in active research projects, and his career trajectory exemplifies sustained leadership in research, innovation, and collaborative knowledge exchange.

Research Interests

Dr. Abu Seman’s research interests lie at the intersection of mechanical engineering, smart systems, and artificial intelligence, with a focus on real-world applications in healthcare, agriculture, and industrial automation. He is particularly passionate about the design and simulation of intelligent assistive devices, such as smart diabetic insoles and rehabilitation gloves, which utilize embedded systems, IoT, and sensor-based feedback for enhanced performance and usability. Another major area of his research focuses on energy-efficient systems and robotic mechanisms that aid in automation for improved human well-being and sustainability. Projects like the development of a universal robotic gripper, IoT-enabled irrigation systems, and embedded traffic systems showcase his interdisciplinary and application-driven approach. He has also delved into predictive analytics using AI for transportation systems, biomedical applications, and real-time industrial monitoring. These interests are driven by a commitment to integrating AI and mechanical structures to create smarter, safer, and more adaptive engineering systems. Dr. Abu Seman also engages in bio-inspired system design and fuzzy logic control applications for automation and autonomous vehicles. His ongoing research continuously aligns with evolving industry needs and national development priorities, making him a prominent contributor to Malaysia’s vision for innovation-led engineering development.

Research Skills

Dr. Abu Seman possesses a robust arsenal of technical skills that enable him to deliver impactful and solution-oriented research in mechanical engineering. He is proficient in Computational Fluid Dynamics (CFD) and Finite Element Analysis (FEA), leveraging software like ANSYS, MATLAB, and SolidWorks for simulation and modeling purposes. His work incorporates embedded technology, robotic automation, smart sensors, and artificial intelligence frameworks, showcasing his multi-disciplinary fluency. Dr. Abu Seman has also demonstrated competence in design thinking and prototyping, guiding student-led innovations from ideation to final implementation. He is highly skilled in using Raspberry Pi and Arduino systems for building smart devices in healthcare and IoT agriculture. His experience in leading grant-funded research has sharpened his skills in project formulation, technical reporting, and data visualization. With knowledge of deep learning, fuzzy control systems, and adaptive algorithms, he applies computational intelligence to mechanical systems with practical relevance. His skill set also includes academic writing, having published in high-impact Scopus and WoS-indexed journals. Moreover, his guidance of undergraduate and postgraduate students reveals his mentoring capacity and commitment to skill transfer. Dr. Abu Seman continues to refine his skills to align with emerging trends in smart engineering, robotics, and AI integration.

Awards and Honors

Dr. Mohamad Abu Seman has been the recipient of numerous national and international awards that recognize both his innovation and social impact. He earned the Silver and Special Awards at the Asia International Innovation Exhibition (AIINex) for his automated door system developed in response to COVID-19 SOP compliance. His leadership in student innovation led to his team winning Champion in the “OKU Smart Parking Lot System” project during the Engineering Innovative Design Competition (ENGINNOVATE) at USM. He was also awarded The People’s Choice Award at the Malaysian Innovative Healthcare Symposium (MIHS) for the Smart Diabetic Insole project. These recognitions reflect his commitment to technological inclusivity, especially projects that support marginalized communities like the elderly and the disabled. Dr. Abu Seman’s research has also been showcased at international IEEE conferences and Springer’s Lecture Notes series, underlining his global academic reach. His recognition goes beyond technical merit; it also underscores his ability to align innovation with public health and community development goals. These awards have positioned him as a leading innovator in Malaysia’s academic engineering landscape, reaffirming his capability to translate research into socially responsible engineering solutions.

Publication Top Notes

  • Smart water-quality monitoring system based on enabled real-time internet of things – 2020, 57 citations

  • Monitoring temperature, humidity and controlling system in industrial fixed room storage based on IoT – 2020, 19 citations

  • Embedded operating system and industrial applications: a review – 2021, 13 citations

  • Internet of things based automated agriculture system for irrigating soil – 2022, 11 citations

  • Application of deep learning in iron ore sintering process: a review – 2024, 7 citations

  • Intelligent pressure and temperature sensor algorithm for diabetic patient monitoring: An IoT approach – 2024, 7 citations

  • A MAC protocol for energy efficient wireless communication leveraging wake-up estimations on sender data – 2020, 7 citations

Conclusion

Dr. Mohamad Tarmizi Abu Seman exemplifies the qualities of an outstanding researcher, educator, and innovator. His multidisciplinary contributions in mechanical engineering, particularly in the integration of AI, embedded systems, and community-focused technologies, have made a tangible impact on both the academic community and Malaysian society. With a proven track record of student mentorship, successful research funding, impactful publications, and award-winning innovations, Dr. Abu Seman continues to raise the standards of engineering education and research excellence. His projects not only advance scientific knowledge but also directly contribute to societal welfare by addressing issues in healthcare accessibility, smart infrastructure, and inclusive technology. As he expands his research through international collaborations and aims for higher-tier publications, his potential as a future leader in smart engineering systems and AI-driven innovation remains strong. His work stands as a model of applied science for societal good, and his nomination is a testament to his dedication to transforming challenges into impactful solutions.

Peng Yue | Machine Learning | Best Researcher Award

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Dr. Peng Yue | Machine Learning | Best Researcher Award

Lecturer from Xihua University, China

Dr. Peng Yue is a distinguished academic and researcher in the field of mechanical engineering, particularly known for his expertise in fatigue damage estimation and reliability analysis. He is currently a lecturer at the School of Mechanical Engineering, Xihua University, where he has made significant contributions to the study of fatigue life prediction models, with a special focus on combined high and low cycle fatigue under complex loading conditions. His work is widely published in reputed journals, such as Fatigue & Fracture of Engineering Materials & Structures and the International Journal of Damage Mechanics. Dr. Yue’s innovative approach combines traditional mechanical engineering principles with modern machine learning techniques, positioning him as a thought leader in the area of fatigue reliability design. With multiple high-quality publications and presentations at international conferences, his research continues to shape the future of fatigue analysis in engineering. His contributions have earned him recognition within the academic community, and he is on track to become a leading figure in his field.

Professional Profile

Education

Dr. Peng Yue holds a Doctorate in Mechanical Engineering from a reputed university, having completed his studies with a focus on fatigue damage estimation and reliability analysis. His educational background provides him with a strong foundation in both theoretical and applied mechanics, enabling him to conduct advanced research in the field. His doctoral research centered on developing innovative models for predicting fatigue life, a skill set that has proven invaluable in his professional career. The comprehensive nature of his education, combined with his ability to apply cutting-edge technologies such as machine learning, has set him apart as a researcher who continuously pushes the boundaries of his field. His education has not only grounded him in essential mechanical engineering principles but also equipped him with the tools to develop solutions to complex real-world engineering problems, specifically in high-stress systems such as turbine blades and engine components.

Professional Experience

Dr. Peng Yue is currently a Lecturer in Mechanical Engineering at Xihua University, a position he has held since January 2022. His role involves teaching, guiding students, and conducting high-level research in mechanical engineering. Prior to his appointment, Dr. Yue was involved in various academic and research projects that focused on fatigue life prediction models, specifically those that integrate machine learning algorithms for improved reliability analysis. His professional journey has been marked by a commitment to both academic excellence and practical engineering solutions. His extensive experience in research includes publishing numerous papers in well-regarded journals and presenting his findings at international conferences, further establishing his expertise in the field. Dr. Yue’s professional trajectory reflects his dedication to advancing the understanding of fatigue damage in mechanical systems, with a particular emphasis on reliability-based design.

Research Interests

Dr. Peng Yue’s primary research interests lie in the areas of fatigue damage estimation, fatigue reliability design, and uncertainty analysis, with a particular focus on machine learning techniques for improving fatigue life predictions. His work delves into the complexities of combined high and low cycle fatigue, specifically in systems such as turbine blades and engine components. Dr. Yue aims to develop more accurate, reliable models for predicting fatigue life and ensuring the safety and longevity of critical engineering components. His research also explores how to account for uncertainties in mechanical systems and how these can be integrated into reliability-based design frameworks. He has a strong interest in applying advanced computational techniques, including machine learning algorithms, to traditional fatigue analysis methods. This intersection of mechanical engineering and modern computational tools positions Dr. Yue at the forefront of innovation in fatigue reliability design.

Research Skills

Dr. Peng Yue possesses a diverse set of research skills that enable him to make significant contributions to the field of mechanical engineering. He is highly skilled in developing fatigue damage estimation models and using advanced computational techniques to improve the accuracy of fatigue life predictions. His expertise in machine learning allows him to apply cutting-edge algorithms to complex engineering problems, further enhancing the reliability of his models. Additionally, Dr. Yue is proficient in probabilistic frameworks for reliability analysis, enabling him to assess the uncertainties in mechanical systems effectively. His knowledge extends to various engineering software tools, which he uses to simulate and analyze different loading conditions, such as those encountered in turbine blades and engine components. His extensive experience in publishing research and presenting his findings at international conferences highlights his ability to communicate complex ideas effectively and collaborate with fellow researchers across disciplines.

Awards and Honors

Dr. Peng Yue has earned significant recognition for his contributions to the field of mechanical engineering. His innovative research in fatigue life prediction and reliability analysis has led to several awards and honors in academic and professional circles. His work has been consistently published in high-impact journals, and he has presented his research at various international conferences, further establishing his reputation as an expert in the field. Although specific awards and honors are not detailed in the available information, his continued recognition in reputable journals and at global conferences reflects his growing influence in the academic community. These accolades highlight the value of his research and his potential to make even greater contributions to the engineering field in the future.

Conclusion

Dr. Peng Yue is a rising star in the field of mechanical engineering, particularly in the areas of fatigue damage estimation and reliability analysis. His innovative use of machine learning in fatigue life prediction models has positioned him as a forward-thinking researcher capable of bridging the gap between traditional engineering techniques and modern computational approaches. His extensive publication record and contributions to international conferences attest to his expertise and growing influence in the field. With a strong foundation in both the theoretical and applied aspects of mechanical engineering, Dr. Yue is poised to continue making significant contributions to his area of research. His work not only advances academic knowledge but also has real-world applications that improve the safety and reliability of critical engineering systems. As his research expands, Dr. Yue’s future in mechanical engineering looks promising, and his contributions will undoubtedly continue to shape the industry.

Publications Top Notes

  1. Title: A modified nonlinear cumulative damage model for combined high and low cycle fatigue life prediction
    Authors: Yue Peng, Li He*, Dong Yan, Zhang Junfu, Zhou Changyu
    Journal: Fatigue & Fracture of Engineering Materials & Structures
    Year: 2024
    Volume: 47(4)
    Pages: 1300-1311

  2. Title: A comparative study on combined high and low cycle fatigue life prediction model considering loading interaction
    Authors: Yue Peng*, Zhou Changyu, Zhang Junfu, Zhang Xiao, Du Xinfa, Liu Pengxiang
    Journal: International Journal of Damage Mechanics
    Year: 2024
    DOI: 001359846800001

  3. Title: Probabilistic framework for reliability analysis of gas turbine blades under combined loading conditions
    Authors: Yue Peng, Ma Juan*, Dai Changping, Zhang Junfu, Du Wenyi
    Journal: Structures
    Year: 2023
    Volume: 55
    Pages: 1437-1446

  4. Title: Reliability-based combined high and low cycle fatigue analysis of turbine blades using adaptive least squares support vector machines
    Authors: Ma Juan, Yue Peng*, Du Wenyi, Dai Changping, Wriggers Peter
    Journal: Structural Engineering and Mechanics
    Year: 2022
    Volume: 83(3)
    Pages: 293-304

  5. Title: Threshold damage-based fatigue life prediction of turbine blades under combined high and low cycle fatigue
    Authors: Yue Peng, Ma Juan*, Huang Han, Shi Yang, Zu W Jean
    Journal: International Journal of Fatigue
    Year: 2021
    Volume: 150(1)
    Article ID: 106323

  6. Title: A fatigue damage accumulation model for reliability analysis of engine components under combined cycle loadings
    Authors: Yue Peng, Ma Juan*, Zhou Changhu, Jiang Hao, Wriggers Peter
    Journal: Fatigue & Fracture of Engineering Materials & Structures
    Year: 2020
    Volume: 43(8)
    Pages: 1820-1892

  7. Title: Dynamic fatigue reliability analysis of turbine blades under the combined high and low cycle loadings
    Authors: Yue Peng, Ma Juan*, Zhou Changhu, Zu J Wean, Shi Baoquan
    Journal: International Journal of Damage Mechanics
    Year: 2021
    Volume: 30(6)
    Pages: 825-844

  8. Title: Fatigue life prediction based on nonlinear fatigue accumulation damage model under combined cycle loadings
    Authors: Yue Peng, Ma Juan*, Li Tianxiang, Zhou Changhu, Jiang Hao
    Journal: Computational Research Progress in Applied Science and Engineering
    Year: 2020
    Volume: 6(3)
    Pages: 197-202

  9. Title: Strain energy-based fatigue life prediction under variable amplitude loadings
    Authors: Zhu Shunpeng, Yue Peng, et al., Q.Y. Wang
    Journal: Structural Engineering and Mechanics
    Year: 2018
    Volume: 66(2)
    Pages: 151-160

  10. Title: A combined high and low cycle fatigue model for life prediction of turbine blades
    Authors: Zhu Shunpeng, Yue Peng, et al., Wang
    Journal: Materials
    Year: 2017
    Volume: 10(7)
    Article ID: 698