Hamin Chong is a skilled researcher specializing in computer vision models with a focus on anomaly detection and object detection. With a master’s degree in Industrial Data Engineering from Hanyang University and a bachelor’s degree in Mechanical Engineering from Sungkyunkwan University, Chong brings a unique blend of technical expertise to his work. He is known for his passion for creating value through the development of lightweight models and increasing accuracy with limited data. His professional journey spans various roles, including Research Engineer at LS Mtron and expertise at the Advanced Manufacturing Laboratory. Chong has earned recognition for his contributions, including honors and awards, and has published impactful research papers in renowned journals.
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Education:
Hamin Chong holds a Master’s degree in Industrial Data Engineering from Hanyang University and a Bachelor’s degree in Mechanical Engineering from Sungkyunkwan University. This academic background equips him with a solid foundation in both theoretical principles and practical applications, essential for his research endeavors in computer vision and smart manufacturing systems.
Research Focus:
Chong’s research primarily focuses on computer vision models, particularly in the domains of anomaly detection and object detection. He specializes in developing advanced technology solutions to enhance manufacturing processes, leveraging AI-based approaches. His work involves the development of real-time systems for quality improvement, lightweight models for efficiency, and the fusion of collaborative experiences from mechanical engineering with computer vision techniques.
Professional Journey:
Hamin Chong’s professional journey has been characterized by impactful roles in both industry and academia. As a Research Engineer at LS Mtron, he led projects focusing on AI-based technology research and system development, contributing significantly to real-time leakage inspection support systems and lightweight engine exterior inspection models. Additionally, his expertise at the Advanced Manufacturing Laboratory involved standardizing shared data in continuous process industries and developing unsupervised learning algorithms for anomaly detection.
Honors & Awards:
Throughout his career, Hamin Chong has received recognition for his outstanding contributions. His work has been honored with awards for excellence in research, innovation, and technical achievements. These accolades reflect his dedication and proficiency in advancing the field of computer vision and smart manufacturing systems.
Publications Top Noted & Contributions:
Chong has made significant contributions to the academic community through his publications and research contributions. Notably, his papers have been published in reputable journals, covering topics such as anomaly detection, automatic labeling algorithms, and ensemble learning approaches. These publications demonstrate his expertise and the impact of his research on advancing knowledge in the field.
Title: Data-fused and concatenated-ensemble learning for in-situ anomaly detection in wire and arc-based direct energy deposition
Authors: D.B. Kim, H. Chong, M.M. Mahdi, S.-J. Shin
Journal: Journal of Manufacturing Processes, 2024, 112, pp. 273–289
Abstract: In-situ anomaly detection in wire and arc-based direct energy deposition (WA-DED) processes is crucial for ensuring product quality and process reliability. However, the complex nature of sensor data collected during WA-DED makes anomaly detection challenging. In this study, we propose a novel approach for in-situ anomaly detection by employing data-fused and concatenated-ensemble learning techniques. The proposed method combines multiple data sources and leverages ensemble learning to enhance anomaly detection performance. Experimental results demonstrate the effectiveness of the proposed approach in detecting anomalies in WA-DED processes, thereby contributing to improved quality control and manufacturing efficiency.
Research Timeline:
Chong’s research timeline showcases the progression of his work and accomplishments over the years. From his academic pursuits to his professional roles, each phase represents a milestone in his journey of advancing knowledge and innovation in computer vision, smart manufacturing, and related domains.
