Since organic rice storage silos were faced with an insect problem, an owner solved this problem using the expert system (ES) in the controlled atmosphere process (CAP) under the required standard, fumigating insects with an N2, reducing O2 concentration to less than 2% for 21 days. This article presents the computational fluid dynamics (CFD) assisted ES successfully solved this problem. First, CFD was employed to determine the gas flow pattern, O2 concentration, proper operating conditions, and a correction factor (K) of silos. As expected, CFD results were consistent with the experimental results and theory, assuring the CFD’s credibility. Significantly, CFD results revealed that the ES controlled N2 distribution throughout the silos and effectively reduced O2 concentration to meet the requirement. Next, the ES was developed based on the inference engine assisted by CFD results and the sweep-through purging principle, and it was implemented in the CAP. Last, the experiments evaluated CAP’s efficacy in controlling O2 concentration and insect extermination in the actual silos. The experimental results and owner’s feedback confirmed the excellent efficacy of ES implementation; therefore, the CAP is effective and practical. The novel aspect of this research is a CFD methodology to create the inference engine and the ES.
การเก็บรักษาข้าวใน ไซโลเก็บข้าวอินทรีย์ เป็นแนวทางสำคัญในการรักษาคุณภาพข้าวและลดการสูญเสียหลังการเก็บเกี่ยว อย่างไรก็ตาม ปัญหาการปนเปื้อนของแมลงศัตรูข้าว เป็นอุปสรรคสำคัญที่ส่งผลกระทบต่อคุณภาพและความปลอดภัยของข้าว โดยทั่วไป การกำจัดแมลงในไซโลมักใช้สารรมยาเคมี เช่น ฟอสฟีน (PH₃) หรือ เมทิลโบรไมด์ (CH₃Br) ซึ่งอาจก่อให้เกิดสารตกค้าง ส่งผลต่อสุขภาพผู้บริโภค และสร้างผลกระทบต่อสิ่งแวดล้อม
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.
คณะสถาปัตยกรรม ศิลปะและการออกแบบ
This project aims to study the load transfer in timber building structures by analyzing weight distribution across key structural components such as beams, columns, and floors, as well as the load-bearing behavior of wood under different conditions. The research incorporates structural calculations and modeling to examine load transfer patterns. Additionally, it enhances skills in design, analysis, and teamwork, providing practical knowledge applicable to real-world construction projects.
คณะเทคโนโลยีการเกษตร
Mangosteen peel (Garcinia mangostana Linn.) extract using hot water (MPE) has been shown to have antibacterial potential in freshwater sea bass (Lates calcarifer) larvae infected with Aeromonas hydrophila. In vitro studies showed that MPE has a minimum inhibitory concentration (MIC) of 25 ppm and a minimum bactericidal concentration (MBC) of 25 ppm. In vivo, sea bass larvae were immersed in various concentrations of MPE at 0 ppm (control), 20 ppm, 40 ppm and 60 ppm, respectively, for 7 days with A. hydrophila. The results showed that the MPE-treated group had a higher survival rate compared to the control group. Hematological parameters showed that the MPE-treated group had significantly increased red blood cell (RBC), white blood cell (WBC) and hemoglobin (Hb) concentrations compared to the control group. In addition, the water quality parameters were not significantly different, except for ammonia concentration, with MPE having an ammonia concentration of 60 ppm being the lowest. All results can indicate that MPE can improve the antibacterial potential and the culture potential of sea bass larvae.