Interior Architecture Design Project: A Halal Restaurant Integrating the Culture of Songkhla, Thailand
สร้างเอกลักษณ์เด่นเเก่สงขลาทางด้านอาหาร
คณะวิศวกรรมศาสตร์
The Diabetes Meal Management Application is a digital health tool designed to empower Type 2 diabetic patients in managing their diet and blood sugar levels more effectively. With features like personalized meal recommendations, nutrient tracking, and seamless integration with wearable blood glucose monitors via Blood sugar measuring device (CGM), the application enables users to monitor glucose fluctuations in real time and adjust dietary choices accordingly. Built with the Flutter framework and supported by a backend of Express.js and MongoDB, the application prioritizes a user-friendly interface, ensuring easy navigation and encouraging consistent engagement with meal planning and health tracking. Preliminary user trials show that the application contributes to more stable blood sugar levels and improved adherence to dietary recommendations, helping users reduce health risks associated with diabetes complications. By offering a proactive approach to diabetes management, the application reduces the need for frequent clinical interventions, thus potentially lowering medical costs over time. This project highlights the promising role of digital health solutions in supporting personalized diabetes care, emphasizing the potential for scalable, user-centered interventions that foster long-term health improvements for diabetic patients.
คณะสถาปัตยกรรม ศิลปะและการออกแบบ
The Kachatthai Project in Surin Province has been developed as a space to promote and generate income for farmers, incorporating a design concept that reflects the unique identity of Surin Province as its main guideline.
คณะวิศวกรรมศาสตร์
Currently, lithium batteries are widely used in electronic devices and electric vehicles, making the estimation of their State of Health (SOH) crucial. Accurate SOH estimation helps extend battery lifespan, reduce maintenance costs, and prevent safety issues such as overheating or explosions. This project aims to study and analyze mathematical models of batteries and develop SOH estimation techniques using Neural Networks to enhance accuracy and evaluation speed. The experiment involved collecting charge and discharge data from three lithium battery cells under controlled temperature conditions while maintaining a constant current. The current, voltage, and time data were recorded and analyzed to determine the battery capacity for each cycle. These data were then used to train a Neural Network model. The results demonstrated an effective method for predicting battery health status. The outcomes of this project can contribute to the development of a Battery Management System (BMS) that improves battery efficiency and longevity. Additionally, it provides a foundation for applying artificial intelligence techniques in the energy sector effectively.