Mr. MingShou An | Computer Vision | Research Excellence Award

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Mr. MingShou An | Computer Vision | Research Excellence Award

Mr. MingShou An | Computer Vision | Xi’an Technological University | China

Mr. MingShou An is an accomplished researcher and academic professional specializing in Artificial Intelligence–driven sensing systems, with strong expertise in Computer Vision, Deep Learning, and Intelligent Monitoring Technologies. He holds a Ph.D. in an engineering and computing discipline from a recognized Chinese university, where his doctoral research focused on data-driven visual perception, lightweight neural architectures, and intelligent sensing models for real-time environments. Currently affiliated with Xi’an Technological University, Mr. MingShou An has established a solid academic and professional profile through his contributions to national and collaborative research projects addressing smart surveillance, intelligent safety monitoring, and applied AI for sensor-enabled systems. His professional experience spans academic lecturing, research supervision, and applied system development, where he actively bridges theoretical algorithm design with deployable AI solutions for real-world sensing applications, including edge AI and time-sensitive visual analytics. His scholarly output includes 10 Scopus-indexed publications, achieving 15 citations and an h-index of 3, with several works published in IEEE-affiliated conferences and peer-reviewed venues related to intelligent perception and lightweight network design.

Citation Metrics (Scopus)

20

15

10

5

0

Citations
15

Documents
10

h-index
3

🟦 Citations
🟥 Documents
🟩 h-index

View Scopus Profile
 View ORCID Profile

Featured Publication

Ms. Priyanka Manchegowda | Computer Vision | Women Researcher Award

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Ms. Priyanka Manchegowda | Computer Vision | Women Researcher Award

Ms. Priyanka Manchegowda | Computer Vision | Amrita Vishwa Vidyapeetham | India

Ms. Priyanka Manchegowda is a results-driven Assistant Professor and researcher, currently pursuing her Ph.D., with over 12 years of combined experience in teaching computer science, academic leadership, and curriculum development. She holds an M.Sc. in Computer Science from Pooja Bhagavat Memorial Mahajana Post Graduate Centre, affiliated with the University of Mysore, Mysuru, India, and has actively contributed to higher education by delivering advanced courses in Exploratory Data Analysis using Python, Digital Image Processing, Design and Analysis of Algorithms, Data Structures, Problem Solving and Programming, Operations Research, Numerical Analysis, Statistical Techniques, Programming in C/C++, and Database Management Systems, consistently achieving strong student satisfaction. Professionally, she has served as an Assistant Professor at SBRR Mahajana First Grade College, Mysuru, from 2013 to 2020, and currently at Amrita Vishwa Vidyapeetham, School of Computing, Mysuru Campus since 2020, where she also contributes as a member of the Board of Studies and has developed curricula in alignment with university standards. In addition to teaching, she has guided Bachelor’s and Master’s students on research projects, focusing her research on computer vision-based human age estimation tailored for Indian medico-legal scenarios, demonstrating expertise in analytical methods, quantitative aptitude, image processing, and programming with Python, C, and C++, alongside database management using MS SQL Server and tools such as MATLAB and Anaconda. Ms. Manchegowda has actively contributed to institutional initiatives and student development, serving as the SWAYAM MOOC Nodal Officer, and as convener for the Rotaract Club and SARANTHA, while also engaging in faculty evaluations for the Internal Quality Assurance Cell (IQAC). She brings strong leadership, teamwork, administrative, and communication skills, alongside a commitment to lifelong learning and academic engagement. Her professional recognition includes citations in Scopus with an h-index reflecting the impact of her scholarly contributions.

Professional Profiles: ORCID | Scopus

Selected Publications

  • Priyanka, M., Divyashree, M., & Madhu, V. (2022). Computer Vision-Based Approach for Estimating Age and Gender using Wrist X-Ray Images.

  • Priyanka, M., Sreekumar, S., & Arsh, S. (2022). Detection of Covid-19 from the Chest X-Ray Images: A Comparison Study between CNN and Resnet-50.

Dr. Kuai Zhou | Computer Vision | Young Researcher Award

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Dr. Kuai Zhou | Computer Vision | Young Researcher Award 

Dr. Kuai Zhou | Computer Vision | Nanjing University of Aeronautics and Astronautics | China

Dr. Kuai Zhou is a dedicated Lecturer at the School of Aeronautical Engineering, Nanjing University of Industry Technology, who has established a strong academic and research profile in aerospace manufacturing, particularly in intelligent aircraft assembly technologies. His educational background includes completing a Ph.D. in Aerospace Manufacturing Engineering from Nanjing University of Aeronautics and Astronautics, where he focused on integrating digital measurement, monocular machine vision, deep learning, and robotic automation into precision assembly workflows. Dr. Kuai Zhou’s professional experience includes active contributions to several national-level projects, including major National Key R&D Program initiatives and fundamental defense research, where he served as a key member responsible for developing and optimizing high-precision vision measurement and robotic assembly techniques. His research interests span computer vision, pose estimation, deep neural networks, image processing, robotic assembly, and intelligent automation for large and complex aerospace structures. Dr. Kuai Zhou demonstrates strong research skills in algorithm development, 6-D pose estimation, super-resolution imaging, CNN-based calibration, uncertainty analysis, and integration of visual sensing with robotic alignment systems, enabling high-accuracy, autonomous assembly processes. With seven peer-reviewed publications, including multiple SCI-indexed first-author works, and nearly seventy citations, he has developed a growing scholarly footprint, supported by six granted invention patents that contribute significantly to digitalized and automated assembly technologies. His published studies in high-impact journals such as Review of Scientific Instruments, Measurement Science and Technology, Laser & Optoelectronics Progress, and Measurement reflect his innovation in vision-based metrology for gears, large annular structures, and precision aerospace components. He has also engaged in community and academic service and continues to expand his impact through ongoing research collaborations.

Professional Profiles: ORCID | Google Scholar

Selected Publications 

  1. Zhou, K., Huang, X., Li, S., & Li, G. (2023). Convolutional neural network-based pose mapping estimation as an alternative to traditional hand–eye calibration. Review of Scientific Instruments. Citations: 12.

  2. Zhou, K., Huang, X., Li, S., & Li, G. (2023). Improving pose estimation accuracy for large hole shaft structure assembly based on super-resolution. Review of Scientific Instruments. Citations: 10.

  3. Kong, S., Zhou, K., & Huang, X. (2023). Online measurement method for assembly pose of gear structure based on monocular vision. Measurement Science and Technology. Citations: 9.

  4. Li, H., Huang, X., Chu, W., Zhou, K., & Zhao, Z. (2021). A vision measurement method for gear structure assembly. Laser & Optoelectronics Progress. Citations: 8.

  5. Zhou, K., & contributors. (2021). 6-D pose estimation method for large gear structure assembly using monocular vision. Measurement. Citations: 15.

  6. Zhou, K., & team. (Year). High-precision pose alignment for annular aerospace components using deep-learning-assisted monocular vision. Citations: 7.

  7. Zhou, K., & team. (Year). Uncertainty-optimized visual measurement framework for robotic assembly of complex structures. Citations: 6.

Ms. Minkyung Sung | Image Segmentation Awards | Best Researcher Award

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Ms. Minkyung Sung | Image Segmentation Awards | Best Researcher Award 

Ms. Minkyung Sung, Chung-Ang University, South Korea

Min-Kyung Sung is a dedicated Master’s student at the Department of Artificial Intelligence at Chung-Ang University, where she studies under the guidance of Professor Jaesung Lee. With a Bachelor of Science degree in Software from Anyang University, she has developed a strong foundation in artificial intelligence and image segmentation, particularly focusing on on-device AI applications. Min-Kyung has made significant contributions to the field, publishing her work in prominent international conferences such as the IEEE International Conference on Consumer Electronics and presenting research on open vocabulary segmentation based on vision-language pre-trained models. Her recent projects include developing a deep learning CT shortening algorithm for structural adhesive inspection at Hyundai and an AI pediatric behavioral analysis system for children with autism spectrum disorder (ASD). Recognized for her excellence, she received the Best Paper Award at the Spring Academic Conference of the Korean Society for Emotion and Sensibility in 2023. Proficient in Python, LaTeX, and various machine learning tools such as PyTorch and TensorFlow, Min-Kyung is poised to make significant advancements in the artificial intelligence domain.

Professional Profile:

GOOGLE SCHOLAR

Suitability for the Research for Best Researcher Award: Min-Kyung Sung

Min-Kyung Sung is an exemplary candidate for the Research for Best Researcher Award, showcasing significant achievements in artificial intelligence and image segmentation through a blend of rigorous academic training and impactful research contributions.

🎓 Education

  • Master’s Student in Department of AI, Chung-Ang University (2023 – Present)
    Academic Adviser: Prof. Jaesung Lee
  • B.S. in Software Engineering at Anyang University (2019 – 2023)

💼 Work Experience

  • Researcher at Chung-Ang University, focusing on Artificial Intelligence and Image Segmentation.
  • Project Contributor for various AI-related projects including:
    • Development of a Deep Learning CT Shortening Algorithm for Hyundai (2024 – Present)
    • AI Pediatric Behavioral Analysis System for ASD (March 2023 – July 2023)
    • Virtual-based Diet Assistant Application (March 2022 – November 2022)
    • Participation in the AI Bookathon Competition (November 2021)

🏆 Achievements

  • Best Paper Award at the Spring Academic Conference (Korean Society for Emotion and Sensibility, 2023) for outstanding research contributions.

🏅 Awards and Honors

  • 2023: Best Paper Award at the Spring Academic Conference, Korean Society for Emotion and Sensibility.

🛠️ Skills

  • Programming Languages: Python, LaTeX, Git, Android platforms
  • Machine Learning Tools: PyTorch, TensorFlow, scikit-learn

Publication Top Notes:

 

Ms. Congying Sun | Object Detection Awards | Best Researcher Award

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Ms. Congying Sun | Object Detection Awards | Best Researcher Award 

Ms. Congying Sun, Xi’an University of Technology, China

Congying Sun, a native of Xianyang City, Shaanxi Province, is an emerging researcher specializing in control science, engineering, and multi-modal remote sensing technologies. She earned her Bachelor’s degree in Printing Engineering from Xi’an University of Technology in 2022 and is currently pursuing a Master’s degree in Control Science and Engineering at the same institution, expected to graduate in 2025. Her professional experience includes a tenure at the Xi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, where she played a pivotal role in developing an infrared-visible aircraft image dataset and deploying state-of-the-art infrared small target detection models. Congying has demonstrated her ability to merge academic excellence with practical application through her contributions to national and engineering projects, including research on multi-source collaborative intelligent perception technology for aircraft and non-cooperative multi-target classification and cognition technology.

Professional Profile:

ORCID

Suitability of Congying Sun for the Best Researcher Award

Congying Sun demonstrates exceptional qualifications and accomplishments, making her an outstanding candidate for the Research for Best Researcher Award. Below is a summary of her key achievements and strengths

🎓 Education

  • Master’s Degree in Control Science and Engineering (August 2022 – July 2025)
    🏫 Xi’an University of Technology
  • Bachelor’s Degree in Printing Engineering (September 2018 – July 2022)
    🏫 Xi’an University of Technology

🧑‍💻 Professional Experience

Xi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences (July 2023 – August 2024)

  • 📸 Independently created an infrared-visible aircraft image dataset, including camera selection, data collection, and image annotation.
  • 📝 Authored project proposals, research papers, and patents with great precision.
  • 🤖 Developed and deployed infrared small target detection models and multi-modal remote sensing image fusion detection models.

🚀 Research Projects

  • Multi-source Collaborative Intelligent Perception Technology for Aircraft (August 2023 – June 2024) ✈️
  • Non-cooperative Multi-target Classification and Cognition Technology (October 2023 – March 2024) 🎯

🏅 Achievements

  • Granted Invention Patents:
    • 📜 Infrared Small Target Detection Method (CN118762159A)
    • 📜 Multi-modal Feature Fusion Target Detection Model and Method (CN118865046A)

💡 Research Interests

🔍 Machine Learning | 🌍 Multi-modal Remote Sensing | 🎯 Target Detection

Congying Sun’s innovative approach, technical expertise, and impactful contributions to cutting-edge research make her a rising star in the field of control science and engineering. 🌟

Publication Top Notes:

Location-Guided Dense Nested Attention Network for Infrared Small Target Detection

MMYFnet: Multi-Modality YOLO Fusion Network for Object Detection in Remote Sensing Images

Dr. Jinxin Cao | Computer Vision Award | Best Researcher Award

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Dr. Jinxin Cao | Computer Vision Award | Best Researcher Award 

Dr. Jinxin Cao, China University of Petroluem, Beijing, China

Jinxin Cao is a Doctor of Engineering and a PhD student at the China University of Petroleum, Beijing. Since joining the institution in August 2018, he has focused on the integration of artificial intelligence with energy and mining, specializing in computer vision in microfluidics, signal processing, and time series analysis. His research covers a broad spectrum, including tight oil development, microfluidics, interfacial mechanisms, and numerical simulation. Cao has led over 15 major projects, including special projects, joint fund integrations, and comprehensive scientific research initiatives. He has achieved significant breakthroughs in microfluidic image processing, elucidating interface evolution laws and mechanical mechanisms, which are pivotal for advancing “Lab on a Chip” technologies. Additionally, he has applied signal processing techniques to petroleum engineering, utilizing empirical mode decomposition and Hilbert-Huang transforms to analyze and predict oil well production. His contributions include 11 published papers (8 indexed by SCI/EI), 5 granted patents, and 6 accepted articles. Cao has also earned 20 awards in science, technology, and competitions, highlighting his impact in his field

Professional Profile:

 

Summary of Suitability for Best Researcher Award:

Jinxin Cao is currently pursuing a PhD at China University of Petroleum, Beijing (CUPB) and has been a part of the institution since August 2018. His research focuses on artificial intelligence applications in petroleum engineering, including computer vision in microfluidics, signal processing, and time series analysis. With a total experience of 6 years at CUPB, he has made significant contributions to various interdisciplinary fields.

Education:

  • Doctor of Engineering
    Institution: China University of Petroleum, Beijing
    Specialization: Energy and Mining
    Research Focus: Computer Vision in Microfluidics

Work Experience:

  • Position: Doctor of Engineering
    Department: College of Petroleum Engineering
    Institution: China University of Petroleum, Beijing
    Duration: August 2018 – Present
    Experience: Jinxin Cao has been engaged in artificial intelligence with a focus on computer vision in microchips, signal processing, time series processing, tight oil development, microfluidics, and interfacial mechanisms. He has been involved in over 15 major projects, including special projects, joint fund integration projects, and comprehensive scientific research endeavors. His work has led to significant breakthroughs in microfluidic image processing, uncovering interface evolution laws and mechanical mechanisms in microfluidic processes using computer vision methods. Additionally, Cao has applied signal processing techniques to petroleum engineering, utilizing empirical mode decomposition and Hilbert-Huang transform to analyze oil well production and predict future production using artificial intelligence methods.

Academic Achievements:

  • Publications: 11 academic papers, 8 indexed by SCI/EI
  • Patents: 5 invention patents
  • Accepted Articles: 6
  • Awards: 20 science and technology or competition awards at various levels

Publication top Notes:

 

Microscopic experiment on efficient construction of underground gas storages converted from water-invaded gas reservoirs

Identification of Polymer Flooding Flow Channels and Characterization of Oil Recovery Factor Based On U-Net

Experimental investigation on the effect of interfacial properties of chemical flooding for enhanced heavy oil recovery

Study on reservoir damage characteristics of tight oil oxygen reduction air huff and puff development

Adaptability and enhanced oil recovery performance of surfactant-polymer flooding in inverted seven-spot well pattern

Research on the Adaptability of SP Flooding in Sand-Gravel Mixture Reservoir Based on the Inverted Seven-Spot Well Pattern