Kangming Chen | Fatigue performence of intersection joint | Best Researcher Award

Published on by Mechanics Awards

Mr. Kangming Chen |  Fatigue performence of intersection joint | Best Researcher Award

No designation at College of Civil Eng, Fuzhou University Fuzhou, China

🛠️ Dr. Chen Kangming, PhD in Engineering, is a researcher and doctoral supervisor at Fuzhou University’s School of Civil Engineering. Specializing in steel-concrete composite structures and bridge fatigue, he has led 5 vertical and 40+ horizontal projects. Dr. Chen has published over 60 academic papers, with 40+ indexed in SCI/EI, obtained 30+ patents, and received Fujian Science and Technology Progress Awards (2nd and 3rd prizes).

Profile

scopus

Education🎓

PhD in Bridge Engineering (2010–2013): Nagasaki University, Japan.Master’s in Bridge and Tunnel Engineering (2007–2010): Fuzhou University.Bachelor’s in Civil Engineering (2003–2007): Fuzhou University.

Experience👨‍🏫

Researcher, Fuzhou University (2023–present). Associate Researcher, Fuzhou University (2017–2023). Assistant Researcher, Fuzhou University (2013–2017).

Awards and Honors🏆

2020: Fujian Provincial Science and Technology Progress Award (2nd Prize) 2018: Fujian Provincial Science and Technology Progress Award (3rd Prize). 2021: National Postdoctoral Innovation Competition (4th place). 2023: Fuzhou University “Outstanding Young Teacher” Inspirational Award.

Research Focus🔬

Steel-concrete composite structures. Fatigue resistance and design of bridge structures. Durability improvement in prefabricated bridges. Optimization of steel box girders and anchorage designs.

Publication  Top Notes

Calculation method of out-of-plane elastic stability bearing capacity for concrete-filled steel tubular arch bridges with circular tube ribs

Journal: Journal of Jilin University (Engineering and Technology Edition), 2024, 54(10), pp. 2930–2940.

Co-authors: Q.-W. Huang, Q.-X. Wu, B.-C. Chen, Z.-W. Ye.

Focus: Analytical method for elastic stability in CFST arch bridges.

Simplified calculation method for suspension bridge deck system under safety limit conditions of suspender fracture

Journal: China Civil Engineering Journal, 2024, 57(10), pp. 57–70.

Co-authors: Q. Wu, J. Luo, J. Lin.

Focus: Suspension bridge deck safety under suspender failure.

Flexural behavior of composite continuous girders with concrete-filled steel tubular truss chords

Journal: Journal of Jilin University (Engineering and Technology Edition), 2024, 54(6), pp. 1665–1676.

Co-authors: H.-H. Huang, Q.-X. Wu.

Focus: Investigates bending performance in CFST girders.

Bending Performance of a Prestressed Concrete Composite Girder Bridge with Steel Truss Webs

Journal: Applied Sciences (Switzerland), 2024, 14(11), 4822.

Co-authors: W. Wang, Y. Liu.

Focus: Experimental and theoretical studies on composite girder bridges.

Fatigue performance experiment of concrete-filled steel tubular-KK joint

Journal: Journal of Traffic and Transportation Engineering, 2024, 24(1), pp. 100–116.

Co-authors: Q.-X. Wu, J.-P. Luo, Y.-L. Yang, C.-Y. Miao, S. Nakamura.

Focus: Fatigue resistance in KK joints.

Research on the torsional behavior of composite girders with CSW-CFST truss chords

Journal: China Civil Engineering Journal, 2023, 56(10), pp. 93–126.

Co-authors: H. Huang, Q. Wu, S. Nakamura, J. Dong.

Focus: Examines torsion resistance in truss chord girders.

Equivalent static calculation method for CFST arch bridges considering hanger fracture dynamics

Journal: China Civil Engineering Journal, 2023, 56(6), pp. 63–74.

Co-authors: Q. Wu, J. Luo, H. Wang.

Focus: Static response analysis for hanger-damage scenarios.

Experimental investigation on composite girders with CSW-CFST truss chords subjected to combined flexure and torsion

Journal: Advances in Structural Engineering, 2023, 26(8), pp. 1468–1485.

Co-authors: H. Huang, Q. Wu, S. Nakamura.

Focus: Studies combined structural load effects.

Calculation Method for Flexural Bearing Capacity of Composite Girders with CFST Truss Chords

Journal: Journal of Bridge Engineering, 2023, 28(5), 04023019.

Co-authors: H. Huang, Q. Wu, S. Nakamura.

Focus: Provides a simplified flexural capacity model for girders.

Fatigue Performance Test and Finite-Element Analysis of CFST K-Joints

Journal: Journal of Bridge Engineering, 2023, 28(3), 04023003.

Co-authors: Q. Wu, H. Huang, Q. Zheng, S. Nakamura.

Focus: Combines experimental and computational methods to study joint fatigue.

Conclusion

Dr. Chen Kangming’s remarkable achievements in bridge engineering, steel-concrete composite structures, and fatigue resistance research make him an excellent candidate for the Best Researcher Award. His extensive research portfolio, innovative contributions, and academic leadership set him apart. Strengthening his global presence, interdisciplinary efforts, and public outreach could elevate his profile further, aligning with the award’s standards for exemplary researchers.

Jinxia Zhang | Defect detection | Best Researcher Award

Published on by Mechanics Awards

Assoc Prof Dr. Jinxia Zhang | Defect detection | Best Researcher Award

 Associate Professor at Southeast University, China

Assoc Prof Dr. Jinxia Zhang is an Associate Professor at Southeast University, Nanjing, China, specializing in saliency detection, visual attention, computer vision, and deep learning. With a Ph.D. in Pattern Recognition and Intelligent Systems from Nanjing University of Science and Technology, he has extensive experience in artificial intelligence research. His journey includes time as a visiting scholar at Harvard Medical School and numerous prestigious research projects funded by national foundations. Assoc Prof Dr. Jinxia Zhang leads key AI initiatives, driving innovations in multimodal understanding, defect analysis, and object detection. His academic and professional contributions have positioned him as a prominent researcher in visual computing and AI.

Publication Profile

scholar

Education 🎓

Assoc Prof Dr. Jinxia Zhang  earned his M.Sc. and Ph.D. in Pattern Recognition and Intelligent Systems from Nanjing University of Science and Technology in 2015. His doctoral research laid a foundation for his interest in artificial intelligence, particularly in areas like visual attention and computer vision. Prior to his postgraduate work, he completed his B.Sc. in Computer Science and Technology at the same institution in 2009, where he developed a solid understanding of computational theories and applications. His education has provided him with both theoretical knowledge and practical skills that are central to his current research on AI and deep learning.Assoc Prof Dr. Jinxia Zhang  is currently an Associate Professor at Southeast University, Nanjing, a role he has held since 2019. From 2016 to 2019, he served as a Lecturer at the same university, where he significantly contributed to AI teaching and research. His early career included a prestigious stint as a Visiting Scholar at Harvard Medical School, USA, between 2012 and 2014, where he collaborated on cutting-edge AI-driven healthcare projects. His international exposure and academic roles have enriched his teaching and research, particularly in computer vision and AI, making him a key figure in the field.

Awards and Honors  🏆

Assoc Prof Dr. Jinxia Zhang  has received numerous accolades for his research excellence and contributions to the field of AI. He was awarded the National Natural Science Foundation of China grant in 2018 for his project on salient object detection. In 2017, he secured the Jiangsu Natural Science Foundation Grant for his innovative research on visual cognitive characteristics. Additionally, his work in defect diagnosis for photovoltaic modules was recognized as part of the National Key Research and Development Plan. These prestigious awards underscore his pioneering contributions in artificial intelligence and computer vision research.

Research Focus  🔬

Assoc Prof Dr. Jinxia Zhang ‘s research focuses on the intersection of visual attention, saliency detection, and deep learning within artificial intelligence. He leads projects on multimodal understanding and e-commerce applications, and is a Principal Investigator for research into AI-based fruit and vegetable recognition. His earlier work in defect diagnosis for photovoltaic modules and salient object detection in complex scenes has been supported by prominent grants. His innovative approach combines perceptual grouping and visual attention to develop cutting-edge solutions in computer vision, making significant advancements in how machines perceive and interact with visual data.

Conclusion

The candidate demonstrates an impressive body of work across several domains of artificial intelligence, particularly in salient object detection, visual cognition, and multimodal learning. Their academic achievements, project leadership, and dedication to advancing AI make them a strong contender for the Best Researcher Award. By continuing to broaden their industry collaborations and expanding the scope of their research impact, they can become a globally recognized leader in AI and computer vision.

Publication  Top Notes

  • Towards the Quantitative Evaluation of Visual Attention Models (2015)
    • Citation: 75
    • Journal: Vision Research
    • Key Contributors: Z. Bylinskii, E.M. DeGennaro, R. Rajalingham, H. Ruda, J. Zhang, J.K. Tsotsos
    • Highlights: Focuses on quantitative approaches to evaluate visual attention models, essential for improving saliency detection.
  • A Novel Graph-Based Optimization Framework for Salient Object Detection (2017)
    • Citation: 63
    • Journal: Pattern Recognition
    • Key Contributors: J. Zhang, K.A. Ehinger, H. Wei, K. Zhang, J. Yang
    • Highlights: Presents a new graph-based optimization method for enhancing the accuracy of salient object detection.
  • Salient Object Detection by Fusing Local and Global Contexts (2020)
    • Citation: 60
    • Journal: IEEE Transactions on Multimedia
    • Key Contributors: Q. Ren, S. Lu, J. Zhang, R. Hu
    • Highlights: This paper integrates both local and global visual contexts to refine salient object detection in multimedia applications.
  • Inter-Hour Direct Normal Irradiance Forecast with Multiple Data Types and Time-Series (2019)
    • Citation: 36
    • Journal: Journal of Modern Power Systems and Clean Energy
    • Key Contributors: T. Zhu, H. Zhou, H. Wei, X. Zhao, K. Zhang, J. Zhang
    • Highlights: Introduces a time-series forecasting model for direct normal irradiance, benefiting renewable energy systems.
  • Winter is Coming: How Humans Forage in a Temporally Structured Environment (2015)
    • Citation: 35
    • Journal: Journal of Vision
    • Key Contributors: D. Fougnie, S.M. Cormiea, J. Zhang, G.A. Alvarez, J.M. Wolfe
    • Highlights: Examines human visual foraging behavior in dynamically changing environments.
  • Domain Adaptation for Epileptic EEG Classification Using Adversarial Learning and Riemannian Manifold (2022)
    • Citation: 25
    • Journal: Biomedical Signal Processing and Control
    • Key Contributors: P. Peng, L. Xie, K. Zhang, J. Zhang, L. Yang, H. Wei
    • Highlights: This paper explores domain adaptation techniques to improve epileptic EEG classification through adversarial learning.
  • A Lightweight Network for Photovoltaic Cell Defect Detection in Electroluminescence Images (2024)
    • Citation: 23
    • Journal: Applied Energy
    • Key Contributors: J. Zhang, X. Chen, H. Wei, K. Zhang
    • Highlights: Develops a lightweight neural network for detecting defects in photovoltaic cells using knowledge distillation.
  • Salient Object Detection via Deformed Smoothness Constraint (2018)
    • Citation: 21
    • Journal: IEEE International Conference on Image Processing (ICIP)
    • Key Contributors: X. Wu, X. Ma, J. Zhang, A. Wang, Z. Jin
    • Highlights: Proposes a deformed smoothness constraint approach for improving salient object detection.
  • Character Recognition via a Compact Convolutional Neural Network (2017)
    • Citation: 20
    • Conference: International Conference on Digital Image Computing
    • Key Contributors: H. Zhao, Y. Hu, J. Zhang
    • Highlights: Develops a compact CNN for robust character recognition in natural scene images.
  • A Prior-Based Graph for Salient Object Detection (2014)
    • Citation: 23
    • Conference: IEEE International Conference on Image Processing (ICIP)
    • Key Contributors: J. Zhang, K.A. Ehinger, J. Ding, J. Yang
    • Highlights: Uses a prior-based graph model to enhance the performance of salient object detection algorithms.