Aung Ye Thway

Bachelor of Engineering in Electrical Engineering (International Program)

King Mongkut's Institute of Technology Ladkrabang (KMITL), Bangkok, Thailand

Expected Graduation: August 2026 | GPA: 3.83/4.0

Relevant Coursework: High Voltage Engineering, Electrical Machines, Power System Protection, Control Systems, Circuit Analysis, Electronics, Machine Learning Applications.
Academic Note: Transferred in 2023 after independent study period (2021–2023) due to COVID‑19 impact.

Undergraduate Researcher – High Voltage Engineering Laboratory

School of Engineering, KMITL | September 2023 – Present | Bangkok, Thailand

• Developed and deployed AI‑based partial discharge (PD) classification system achieving near‑100% accuracy using 1D Convolutional Neural Networks.
• Implemented production model on Raspberry Pi with online accessibility for real‑time testing and remote diagnostics.
• Conducted systematic data acquisition using IEC 60270‑based measurement circuits and OMICRON MPD 600 tester.
• Designed and evaluated Transformer Neural Network architectures for PD pattern recognition.
• Published 2 peer‑reviewed conference papers at IEEE ICPADM 2024.
• Performed extensive model optimization including hyperparameter tuning and false‑positive reduction.
• Key Technologies: Python, TensorFlow, PyTorch, MATLAB, 1D CNN, Transformer Networks, PRPD Analysis.

Research Engineer (Internship)

PD Solutions Co., Ltd. | April 2025 – June 2025 | Bangkok, Thailand

• Built production‑grade PD classification models for commercial deployment in power system condition monitoring.
• Accompanied field engineering teams for on‑site maintenance and real‑time data acquisition.
• Validated model performance in real‑world operational environments with diverse noise conditions.

Systems Administrator & Data Infrastructure Developer

Independent Projects | 2024 – Present | Bangkok, Thailand

• Designed and deployed self‑hosted Nextcloud and Gokapi server infrastructure on Raspberry Pi 5.
• Implemented Cloudflare tunneling for secure remote access and established personal portfolio website.
• Maintained MySQL databases and Apache web server with 99%+ uptime.
• Established Personal Jupyter Lab with Cloudflare tunneling for remote access and AI model training.

Online‑Accessible PD Classification System

Personal Research Project & Live API | 2024 – Present

• Deployed 1D CNN‑based partial discharge classification model on Raspberry Pi with web interface for remote testing.
• Achieved near‑100% classification accuracy across corona, surface, internal discharge, and no‑PD conditions.
• Implemented RESTful API for real‑time model inference and result visualization.

Self‑Hosted Research Infrastructure

Personal Infrastructure Project | 2024 – Present

• Built complete home server environment on Raspberry Pi 5 running Nextcloud, Gokapi, Jupyter Notebook, MySQL, and Apache.
• Configured Cloudflare tunneling for secure external access without port forwarding.
• Manages 100GB+ research data including experimental datasets and automated backup systems.

Systematic Review on AI in Condition Monitoring

Ongoing Research Project | 2025 – Present

• Conducting comprehensive systematic literature review on machine learning applications in power system diagnostics.
• Utilizing Zotero for reference management across 100+ research papers.
• Synthesizing findings on CNN, LSTM, SVM, and Transformer architectures for time‑series signal classification.

1. T. Cheypoca, W. Promphanich, A. Y. Thway, A. P. Hankae, S. Jeenmuang, and N. Pattanadech, “Partial Discharge Classification with Transformer Neural Networks,” 2024 IEEE 14th International Conference on the Properties and Applications of Dielectric Materials (ICPADM), Phuket, Thailand, 2024, pp. 93–96.
   DOI: 10.1109/ICPADM61663.2024.10750584
2. T. Cheypoca, W. Promphanich, A. Y. Thway, A. P. Hankae, S. Jeenmuang, and N. Pattanadech, “Partial Discharge Classification With 1D Convolutional Neural Network,” 2024 IEEE 14th International Conference on the Properties and Applications of Dielectric Materials (ICPADM), Phuket, Thailand, 2024, pp. 89–92.
   DOI: 10.1109/ICPADM61663.2024.10750640
3. Wiangtong, T., Boonlaksananusorn, C., Ye Thway, A., Jeenmuang, S., & Pattanadech, N. “Embedded Partial Discharge Classification System Using Transformer Neural Networks on Raspberry Pi,” ECTI Transactions on Electrical Engineering, Electronics, and Communications, 24(2). (2026)
   DOI: 10.37936/ecti-eec.2026242.262835

I'm currently open to research collaborations, internships, and engineering roles in AI‑driven power system diagnostics and data infrastructure.