From November 11 to 16, 2024, two teams from the Faculty of Information and Communication Technology, Mahidol University (ICT Mahidol) — “Baan Lae Suan 2024” and “Baan Lae Suan 2024, Branch 2” — participated in the 18th TESA Top Gun Rally 2024, Thailand Embedded Systems Skill Championship. The competition, held under the theme “Acoustic and AI-Based Predictive Maintenance with Edge Computing,” was organized to compete for the Royal Trophy graciously bestowed by Her Royal Highness Princess Maha Chakri Sirindhorn. The teams were supervised by Asst. Prof. Dr. Thitinan Tantidham, and co-advised by Dr. Sirawich Vachmanus, Instructors of the Computer Science Academic Group. The event took place at the Faculty of Engineering, Kasetsart University, Sriracha Campus.
The “Baan Lae Suan 2024” serves as the main team comprising:
- Ongsa Raksalam, a second-year student from the B.Sc. in Information and Communication Technology (ICT International Program)
- Pichamanchu Onkwimongkon, Mr. Chakrit Tansue, Mr. Pakphan Permwanichkul, Miss Pichamon Ongvimolkarn, Mr. Jakkrit Tansue, and Mr. Pakkaphan Permvanitkul, third-year students from B.Sc. in Digital Science and Technology (DST Thai Program)
- Sirichet Nontichan, a fourth-year student from the DST Thai Program
Additionally, the “Baan Lae Suan 2024, Branch 2” participated as the observation team, including:
- Miss Wanlida Suphasri-Itsara, Ms. Napasorn Lapprakobkit, Mr. Pichitchai Paecharoenchai, second-year students from the DST Thai Program
- Kunapoom Oprik, and Mr. Thanaphat Boonleang, third-year students from the DST Thai Program
The 18th TESA Top Gun Rally 2024 brought together 50 teams from 26 educational institutions across the country. Organized by the Thai Embedded Systems Association (TESA) in partnership with Kasetsart University, Sriracha Campus, and the National Innovation Agency (NIA), the competition aims to foster innovation and talent in embedded systems.
Participants were challenged to integrate creative problem-solving with cutting-edge predictive maintenance techniques, utilizing acoustic technology, artificial intelligence (AI), and edge computing. These technologies were applied to analyze and predict abnormalities in machinery and equipment, helping to prevent potential failures and minimize downtime.
