AI Talk in Conjunction with the A3I Short Mobility 2023 Campus Asia Plus Short Mobility Program
21.02.2024

AI TALKS IN CONJUNCTION WITH THE A3I SHORT MOBILITY 2023 A3I CAMPUS ASIA PLUS SHORT MOBILITY PROGRAM 

Speaker 1: Professor Dr Jongnam Kim from Pukyong National University
Speaker 2: Assistant Professor Dr Chun-Kwon Lee
Speaker 3: Associate Professor Dr Ammar Zakaria

In 2013, Ammar Zakaria earned his Ph.D. in Electrical Engineering from Universiti Malaysia Perlis, and since then, he has served as a principal researcher at the Centre of Excellence for Advanced Sensor Technology. Demonstrating a commitment to bridging academia and entrepreneurship, Zakaria co-founded IDERIA Sdn Bhd in 2017 with two university colleagues. His research expertise encompasses Smart Tracking, Sport Analytics, and Vision AI, resulting in approximately 10 filed intellectual property projects. Zakaria's work reflects a dedication to leveraging cutting-edge technologies to address real-world challenges. The startup's innovative approach focuses on automating sports performance measurement through multimodal sensors, including pose detection, video analysis, and wearables. This framework, presented in their talk, not only provides real-time insights lacking in traditional manual sports statistics but also enhances injury prevention, skill development, and health feedback for customized training plans. The research underscores the benefits of improved prediction accuracy and training efficiency, contributing to a nuanced understanding of the relationship between physical fitness and athletic success. The overarching goal is to integrate advanced technologies into sports analytics for a more informed and innovative approach to athlete development.

Professor Jongnam Kim from Pukyong National University presented his recent research on signal and image processing, specifically addressing a notable advancement in marketing tools—three-dimensional pattern films for enhancing sales by applying captivating 3D patterns to product surfaces. Assessing the quality of these films is challenging due to issues like low contrast and unclear layout, and the scarcity of research on effective evaluation methods. To tackle this, Kim proposes an algorithm classifying 3D pattern films as 'good' or 'bad' based on width information at specific heights of image histograms. Experimental results reveal a significant disparity in histogram shapes between good and bad patterns, achieving 100% accuracy by comparing widths at the quintile of histogram height. In comparative experiments, Kim's algorithm consistently outperformed other methods, demonstrating its effectiveness in classifying 3D pattern film.

In 2019, Chun-Kwon Lee earned his Ph.D. in the Department of Electrical and Electronic Engineering at Yonsei University, Korea, before joining the Department of Smart Distribution Laboratory at Korea Electric Power Corporation Research Institute as a researcher. In 2021, he became part of the Department of Control and Instrumentation Engineering at Pukyong National University. Specializing in IoT-based electric equipment diagnosis for condition-based maintenance and fault detection, Lee's research focuses on addressing the risks associated with the aging of power systems. His presentation, titled "Intelligent Prognostics and Diagnostics of Power System," underscores the challenges posed by aging power systems and the need for accurate diagnostic methods to prevent failures and disruptions. Lee advocates a multidisciplinary approach, incorporating signal processing, data science, and machine learning to enhance fault diagnosis and condition monitoring across various power facilities, such as cables, transformers, poles, and breakers, ultimately ensuring the reliable operation of the power system.