Htet Myat Aung

Incoming Master's Student (Sept 2026)

Department of Mechanical Engineering

National Chung Cheng University

Minhsiung, Chiayi County, Taiwan

Professional Summary

Incoming Master of Mechanical Engineering (Automation & Control) at National Chung Cheng University on a fully funded scholarship, and a Machine Learning Researcher with hands-on experience across computer vision, 3D deep learning, SLAM, and data-pipeline engineering. First author of the Best Paper Award-winning study at IEEE ICoICT 2025, which introduced an optimised 3D-CNN reaching 99.75% accuracy in real-time infrared gas-leak classification, and co-author of a Q1 (Web of Science) journal paper on motion-induced eddy current testing for high-speed railway inspection. Proficient in Python and C/C++ for perception applications, with practical experience in camera calibration, sensor integration, ROS2-based autonomous navigation, and model optimisation for resource-constrained environments. Focused on perception, robotic autonomy, and ML-driven automation.

Until May 2026 I worked as a Full Time Machine Learning Researcher at the Maintenance Technology Center (MTC), KMUTT, collaborating with Prof. Cherdpong Jomdecha on detection technology research for non-destructive testing applications — developing machine learning solutions for industrial maintenance technology challenges.

From 1 September 2026 I am joining the Department of Mechanical Engineering at National Chung Cheng University (CCU) in Chiayi, Taiwan, as a fully funded Master of Mechanical Engineering (Automation & Control) student, where I will continue my research at the intersection of machine learning, perception, and mechanical systems.

Research Interests

Machine Learning & Deep Learning: Supervised, unsupervised, and semi-supervised learning algorithms
Computer Vision & Image Processing: 3D CNN for real-time classification, SLAM algorithms
Robotics: Autonomous navigation, control systems, robot kinematics
AI Applications: Natural gas leak detection, acoustic detection systems, LLM integration

Recent Achievements

Fully Funded Scholarship for the Master of Mechanical Engineering (Automation & Control) program at National Chung Cheng University (CCU), Chiayi, Taiwan — starting September 2026
Q1 Journal Publication (Web of Science) in Nondestructive Testing and Evaluation (Taylor & Francis) for “Development of high-speed inspection technique based on motion-induced eddy current testing for surface railway defects” — recognized by KMUTT (DOI: 10.1080/10589759.2026.2638558)
Best Paper Award Winner at 13th International Conference on Information and Communication Technology (ICoICT 2025, IEEE Xplore) for “Optimized 3D-CNN for Real-Time Infrared Natural Gas Leak Classification” with 99.75% accuracy
First Runner-Up in “Smart Infrastructure for Smart City” competition by Metropolitan Waterworks Authority
KMUTT Hall of Fame recognition for outstanding achievement in Smart Infrastructure development
Best Presentation Award at ICA-SP Con 2025 for Natural Gas Methane Leak Detection research

I am passionate about applying innovative AI/ML technologies to solve real-world problems and contributing to the advancement of robotics and automation engineering.

news

Aug 01, 2026	Awarded a Fully Funded Scholarship for the Master of Mechanical Engineering (Automation & Control) program at the Department of Mechanical Engineering, National Chung Cheng University, Chiayi, Taiwan — starting 1 September 2026!
Mar 15, 2026	Our NDT&E paper recognized as a Q1 journal on Web of Science
Sep 01, 2025	Started as a Full Time Machine Learning Researcher at Maintenance Technology Center (MTC), KMUTT, collaborating with Prof. Cherdpong Jomdecha on non-destructive detection technology research.
Jul 31, 2025	Listed in KMUTT Hall of Fame for outstanding achievement in Smart Infrastructure development!
Jul 30, 2025	Won Best Paper Award at 13th International Conference on Information and Communication Technology (ICoICT 2025, IEEE Xplore) for “Optimized 3D-CNN for Real-Time Infrared Natural Gas Leak Classification” with Asst. Prof. Dr. Sarawan Wongsa!

selected publications

NDT&E

Development of high-speed inspection technique based on motion-induced eddy current testing for surface railway defects

N. Brienza, N. Khunsombutcharoen, B. Poopat, and 5 more authors

Nondestructive Testing and Evaluation, 2026

Q1 (Web of Science) Abs DOI Bib HTML

Q1 Journal - Web of Science

@article{brienza2026development,
  title = {Development of high-speed inspection technique based on motion-induced eddy current testing for surface railway defects},
  author = {Brienza, N. and Khunsombutcharoen, N. and Poopat, B. and Janya-anurak, C. and Jirarunsatian, C. and Mutthanu, R. and Aung, Htet Myat and Jomdecha, C.},
  journal = {Nondestructive Testing and Evaluation},
  pages = {1--22},
  year = {2026},
  publisher = {Taylor \& Francis},
  doi = {10.1080/10589759.2026.2638558},
  url = {https://www.tandfonline.com/doi/abs/10.1080/10589759.2026.2638558},
}

ICoICT
Optimised 3D-CNN for Real-Time Infrared Natural Gas Leak Classification: Balancing Accuracy and Computational Cost

Htet Myat Aung and Sarawan Wongsa

In 2025 International Conference on Information and Communication Technology (ICoICT), Bandung, Indonesia (Hybrid), 2025

Best Paper Award Abs DOI Bib HTML Cert

Best Paper Award

This paper presents an optimized 3D Convolutional Neural Network (CNN) for real-time classification of natural gas leaks using infrared imaging. The proposed model achieves a high accuracy of 99.75% through the application of advanced preprocessing techniques including Running Average, Gaussian Mixture Models (GMM), Moving Average, and Background Subtraction. To enable efficient deployment on edge devices, the model was optimized using smaller image sizes (Half Size and Quarter Size), maintaining above 99% accuracy while significantly reducing computational requirements. The system demonstrates robust performance for industrial methane gas leak detection applications, providing a practical solution for real-time monitoring and safety applications.
@inproceedings{11192973, author = {Aung, Htet Myat and Wongsa, Sarawan}, booktitle = {2025 International Conference on Information and Communication Technology (ICoICT)}, title = {Optimised 3D-CNN for Real-Time Infrared Natural Gas Leak Classification: Balancing Accuracy and Computational Cost}, year = {2025}, volume = {}, number = {}, pages = {1-6}, keywords = {Performance evaluation;Methane;Accuracy;Image resolution;Heuristic algorithms;Infrared imaging;Real-time systems;Computational efficiency;Leak detection;Optimization;Infrared Imaging;Methane Emissions Detection;Running Average Background Subtraction;Deep Learning Optimisation;Computational Efficiency}, doi = {10.1109/ICoICT66265.2025.11192973}, url = {https://ieeexplore.ieee.org/document/11192973}, location = {Bandung, Indonesia (Hybrid)}, redirect = {https://ieeexplore.ieee.org/document/11192973} }
ICA-SP
Natural Gas Methane Leak Detection Using an Infrared Camera

Htet Myat Aung

In 16th Instrumentation, Control and Automation Senior Project Conference (ICA-SP Con 2025), Apr 2025

Best Presentation Award Abs Bib

Best Presentation Award

This research presents a comprehensive approach to detecting natural gas methane leaks using infrared camera technology. The system leverages thermal imaging capabilities to identify methane gas leaks in real-time, providing a non-contact detection method suitable for industrial safety applications. The research demonstrates the effectiveness of infrared imaging combined with advanced image processing techniques for reliable gas leak detection.
@inproceedings{aung2025methane, title = {Natural Gas Methane Leak Detection Using an Infrared Camera}, author = {Aung, Htet Myat}, booktitle = {16th Instrumentation, Control and Automation Senior Project Conference (ICA-SP Con 2025)}, year = {2025}, month = apr, }