The designing of mosquitoes counting system instrument is presented in this work. The mosquitoes that were counted died in order not to measure duplicate counting data. As soon as the input source counting machine can detect the mosquito, the single trigger signal is transmitted to the IOT system to interrupt the server immediately. The number of real mosquito is not transmitting to the IOT but only a signal to interrupt the server. The server records the number of the interrupt signal with real-time clock. Then the interrupt information will be further handled. The front end counting machine consist of the high voltage generate with the suitable voltage value and electrode distance for the required mosquitoes size. The low trigger pulse signals of the mosquitoes killed by high voltage are sending to the controller unit. Immediately, interrupt counting signal of the number of mosquitoes is sent to the big stream data collection on IOT system by the time stamp technique. Form the measurement results, 10 live sample mosquitoes in a limited space box to fly though the counting machine show that the count results are 100% correct count.
ประเทศไทยประสบกับปัญหาการแพร่ระบาดของโรคที่มียุงเป็นพาหะนำโรคมานาน เช่น ไข้มาราเลีย โรคไข้เลือดออก โรคเท้าช้าง เป็นต้น โรคไข้เลือดออกถูกพบขึ้นครั้งแรกในประเทศไทยในปี พ.ศ. 2492 ข้อมูลรายงานสถานการณ์โรคไข้เลือดออกตั้งแต่ปี พ.ศ. 2558 ถึงปี พ.ศ. 2563 พบว่ามีผู้ป่วยสูงสุดในปี 2562 ซึ่งพบผู้ป่วยสูงถึง 18,105 รายโดยภาครัฐไม่ได้นิ่งนอนใจเกี่ยวกับปัญหาที่เกิดขึ้นและได้ทำการสนับสนุนนวัตกรรมที่จะเข้ามาช่วยจัดการกับปัญหาดังกล่าว
คณะวิศวกรรมศาสตร์
This cooperative education project aims to enhance the efficiency of Hydrogen Manufacturing Unit 2 (HMU-2) and Pressure Swing Adsorption 3 (PSA-3) by using AVEVA Pro/II process modeling and a Machine Learning model for process simulation. The study found that the AVEVA Pro/II model predicted outcomes with deviations ranging from 0–35%, including a hydrogen flow rate deviation from the PSA unit of 12%, exceeding the company’s acceptable limit of 10%. To address this, a Machine Learning model based on the Random Forest algorithm was developed with hyperparameter tuning. The Machine Learning model demonstrated high accuracy, achieving Mean Squared Errors (MSE) of 8.48 and 0.18 for process and laboratory data, respectively, and R-squared values of 0.98 and 0.88 for the same datasets. It outperformed the AVEVA Pro/II model in predicting all variables and reduced the hydrogen flow rate deviation to 4.75% and 1.35% for production rates of 180 and 220 tons per day, respectively. Optimization using the model provided recommendations for process adjustments, increasing hydrogen production by 7.8 tons per day and generating an additional annual profit of 850,966.23 Baht.
คณะเทคโนโลยีสารสนเทศ
This research presents the development of an AI-powered system designed to automate the identification and quantification of dental surgical instruments. By leveraging deep learning-based object detection, the system ensures the completeness of instrument sets post-procedure. The system's ability to process multiple images simultaneously streamlines the inventory process, reducing manual effort and potential errors. The extracted data on instrument quantity and type can be seamlessly integrated into a database for various downstream applications.
คณะบริหารธุรกิจ
In the digital era, Artificial Intelligence (AI) plays a crucial role in developing smart cities and enhancing business operations. Among AI-driven technologies, AI Vision Analytics has gained significant attention for Access Control Systems (ACS) and Consumer Behavior Analytics. This research focuses on integrating AI Access Control and AI Video Analytics to examine factors influencing Technology Adoption Behavior using the UTAUT2 (Unified Theory of Acceptance and Use of Technology 2) framework. Key factors assessed include Trust in Technology, Effort Expectancy, Social Influence, and Performance Expectancy, which impact users’ willingness to adopt AI-driven security and analytics solutions. The study also includes a real-world implementation of AI Vision Analytics at KMITL EXPO, where an AI-powered Access Control System and AI Video Analytics are deployed. The collected data is analyzed to identify trends in AI adoption for business management and security enhancement. The findings provide valuable insights for businesses and organizations to optimize AI Vision Analytics for enhancing security management and digital marketing strategies.