This project has been developed to address medical challenges related to the process of counting and classifying blood cells from samples, a task that requires both time and high precision. To reduce the workload of medical personnel, the developers have created a platform and an artificial intelligence (AI) system capable of automatically classifying and counting cells from sample images. This system is designed to assist medical laboratory technicians by enabling them to work more efficiently and accurately, reducing the time required for analysis. Furthermore, it promotes the advancement of medical technology, ensuring effective usability from classrooms and laboratories to hospitals.
เนื่องจากคณะผู้จัดทำมีโอกาสได้พูดคุยกับบุคลากรที่ทำงานในสายอาชีพเทคนิคการแพทย์ จึงเล็งเห็นถึงปัญหาด้านระยะเวลาการทำงาน โดยเฉพาะในกระบวนการแยกและนับจำนวนเซลล์เม็ดเลือด ซึ่งเป็นขั้นตอนที่ใช้เวลาค่อนข้างมาก คณะผู้จัดทำจึงเล็งเห็นโอกาสในการนำความรู้ที่มีมาพัฒนาแนวทางใหม่เพื่อช่วยลดภาระงานของบุคลากรทางการแพทย์ หากสามารถลดระยะเวลาในกระบวนการดังกล่าวได้ ด้วยเหตุนี้เอง โครงการนี้จึงถูกริเริ่มขึ้นมา โดยมีเป้าหมายเพื่อพัฒนาเทคโนโลยีที่สามารถสนับสนุนการทำงานของบุคลากรทางการแพทย์ และยกระดับคุณภาพของกระบวนการตรวจวินิจฉัยให้มีความรวดเร็วและแม่นยำยิ่งขึ้น
วิทยาลัยอุตสาหกรรมการบินนานาชาติ
The capture of a target spacecraft by a chaser is an on-orbit docking operation that requires an accurate, reliable, and robust object recognition algorithm. Vision-based guided spacecraft relative motion during close-proximity maneuvers has been consecutively applied using dynamic modeling as a spacecraft on-orbit service system. This research constructs a vision-based pose estimation model that performs image processing via a deep convolutional neural network. The pose estimation model was constructed by repurposing a modified pretrained GoogLeNet model with the available Unreal Engine 4 rendered dataset of the Soyuz spacecraft. In the implementation, the convolutional neural network learns from the data samples to create correlations between the images and the spacecraft’s six degrees-of-freedom parameters. The experiment has compared an exponential-based loss function and a weighted Euclidean-based loss function. Using the weighted Euclidean-based loss function, the implemented pose estimation model achieved moderately high performance with a position accuracy of 92.53 percent and an error of 1.2 m. The in-attitude prediction accuracy can reach 87.93 percent, and the errors in the three Euler angles do not exceed 7.6 degrees. This research can contribute to spacecraft detection and tracking problems. Although the finished vision-based model is specific to the environment of synthetic dataset, the model could be trained further to address actual docking operations in the future.
คณะวิศวกรรมศาสตร์
Under The National Broadcasting and Telecommunications Commission (NBTC), the Telecommunication Enforcement Bureau collects a lot of data on service quality by monitoring and controlling the quality of telecommunications services, mainly by assessing mobile network infrastructure. The NBTC used Microsoft Excel for data analysis but became ineffective and slow. We used Python programming for preparation, analysis, and data processing to address this. Raw data was obtained from the Syberiz program in CSV format, processed in Python, and displayed on a dashboard. The dashboard, developed using Power BI, meets NBTC's telecommunications quality standards. It features maps, test results, and graphical representations. This method enhances the dashboard's appearance and usability and speeds up data processing and visualization compared to Microsoft Excel. This project is primarily designed to help the Telecommunication Enforcement Bureau's operations by making data processing and display for telecommunications quality monitoring faster, more effective, and easier to use.
คณะเทคโนโลยีสารสนเทศ
The process of treating cancer patients in the chemotherapy department at Chonburi Cancer Hospital is complicated and inconvenient due to the procedure of submitting blood test results through the personal LINE application of medical staff, which hinders workflow efficiency. Therefore, the researcher has developed a cancer patient management and tracking program in the form of a web-based application and LINE LIFF (LINE Front-end Framework) application to facilitate both medical personnel and patients. The web-based application is designed for medical personnel to monitor, schedule, and collect patient data, while the LINE application is designed for patients to submit blood test results, view appointment schedules, record symptoms after chemotherapy, log their weekly weight, and access a chatbot for consultation. This system is developed based on client-server technology, which enhances data analysis efficiency and supports automated treatment planning. As a result, the cancer treatment process becomes faster, more modern, and more efficient.