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
คณะเทคโนโลยีสารสนเทศ
Facial Expression Recognition (FER) has attracted considerable attention in fields such as healthcare, customer service, and behavior analysis. However, challenges remain in developing a robust system capable of adapting to various environments and dynamic situations. In this study, the researchers introduced an Ensemble Learning approach to merge outputs from multiple models trained in specific conditions, allowing the system to retain old information while efficiently learning new data. This technique is advantageous in terms of training time and resource usage, as it reduces the need to retrain a new model entirely when faced with new conditions. Instead, new specialized models can be added to the Ensemble system with minimal resource requirements. The study explores two main approaches to Ensemble Learning: averaging outputs from dedicated models trained under specific scenarios and using Mixture of Experts (MoE), a technique that combines multiple models each specialized in different situations. Experimental results showed that Mixture of Experts (MoE) performs more effectively than the Averaging Ensemble method for emotion classification in all scenarios. The MoE system achieved an average accuracy of 84.41% on the CK+ dataset, 54.20% on Oulu-CASIA, and 61.66% on RAVDESS, surpassing the 71.64%, 44.99%, and 57.60% achieved by Averaging Ensemble in these datasets, respectively. These results demonstrate MoE’s ability to accurately select the model specialized for each specific scenario, enhancing the system’s capacity to handle more complex environments.
คณะวิศวกรรมศาสตร์
The Thai Sign Language Generation System aims to create a comprehensive 3D modeling and animation platform that translates Thai sentences into dynamic and accurate representations of Thai Sign Language (TSL) gestures. This project enhances communication for the Thai deaf community by leveraging a landmark-based approach using a Vector Quantized Variational Autoencoder (VQVAE) and a Large Language Model (LLM) for sign language generation. The system first trains a VQVAE encoder using landmark data extracted from sign videos, allowing it to learn compact latent representations of TSL gestures. These encoded representations are then used to generate additional landmark-based sign sequences, effectively expanding the training dataset using the BigSign ThaiPBS dataset. Once the dataset is augmented, an LLM is trained to output accurate landmark sequences from Thai text inputs, which are then used to animate a 3D model in Blender, ensuring fluid and natural TSL gestures. The project is implemented using Python, incorporating MediaPipe for landmark extraction, OpenCV for real-time image processing, and Blender’s Python API for 3D animation. By integrating AI, VQVAE-based encoding, and LLM-driven landmark generation, this system aspires to bridge the communication gap between written Thai text and expressive TSL gestures, providing the Thai deaf community with an interactive, real-time sign language animation platform.
คณะครุศาสตร์อุตสาหกรรมและเทคโนโลยี
This study aims to develop a board game for teaching Integrated Farming System and to examine the learning achievement of third-year vocational certificate students at Ratchaburi College of Agriculture and Technology who used the board game as a learning tool. The research instruments included a board game developed using the Educational Boardgame Design Canvas. The board game is a strategic planning game consisting of five game boards, and 166 cards categorized into four types: 30 event cards, 60 special action cards, 16 character cards, and 60 production cards. It also includes 180 resource tokens of six kinds: 60 water tokens, 60 soil tokens, 45 plant product tokens, 45 animal product tokens, 45 aquatic product tokens, and 45 currency tokens. Additional components include one die and five player aid sheets. The game emphasizes planning and decision-making in integrated farming to maximize production and achievement points under game conditions and simulated scenarios. Research tools also included pre-and post-tests and a satisfaction questionnaire. The results indicated that students’ average scores significantly increased at the .05 level after using the board game, with the average pre-test score at 6.54 and the post-test score rising to 17.71. Additionally, an analysis of student satisfaction with board game-based learning revealed a high level of satisfaction (mean score = 4.45). The highest-rated aspects were the teacher’s implementation of post-tests (mean score = 4.69) and the engaging and diverse teaching methods used (mean score = 4.66).