The development of a fruit spoilage detection system originates from the need to reduce agricultural product losses, a global issue affecting both the agricultural and food distribution industries. Spoiled fruit can negatively impact product quality and result in significant economic losses. The primary goal of this system is to assist in screening and removing unsuitable fruit from the supply chain, thereby preserving product quality and meeting consumer demands for fresh produce. The system was designed to simulate the sorting process by utilizing images as a key factor in detecting spoiled fruit. Experimental results demonstrated high efficiency and rapid prediction capabilities, highlighting the system’s potential for practical applications.
ระบบตรวจจับผลไม้เน่ามีที่มาจากความต้องการในการลดการสูญเสียผลผลิตทางการเกษตร ซึ่งเป็นปัญหาที่เกิดขึ้นทั่วโลกโดยเฉพาะในอุตสาหกรรมการเกษตรและการจัดจําหน่ายอาหาร ผลไม้ที่เน่าเสียจะส่งผลกระทบต่อคุณภาพของผลิตภัณฑ์และสามารถก่อให้เกิดความสูญเสียทางเศรษฐกิจได้อย่างมาก การพัฒนาระบบตรวจจับผลไม้เน่าจึงมีเป้าหมายเพื่อช่วยในการคัดกรองและแยกผลไม้ที่ไม่เหมาะสมออกจากกระบวนการจัดส่ง เพื่อรักษาคุณภาพของสินค้าและตอบสนองต่อความต้องการของผู้บริโภคที่ต้องการผลไม้สดใหม่
คณะครุศาสตร์อุตสาหกรรมและเทคโนโลยี
This project presents the development of a single-frequency GPS-based total electron content measurement tool. It applies theories related to total electron content in the ionospheric layer and the measurement of total electron content using GPS time delay to design the single-frequency GPS total electron content measurement tool. The tool consists of an antenna, a single-frequency GPS satellite receiver, a data processing unit for evaluating and calculating total electron content, and a display unit for showing total electron content data. The performance of the single-frequency GPS total electron content measurement tool is tested by comparing it with total electron content data obtained from the International Reference Ionosphere (IRI) model, which is a global reference model for electron content. The tool is also put to practical use. The results of the comparison and practical applications conclude that the single-frequency GPS-based total electron content measurement tool can be effectively utilized, with the difference from the IRI model being 50 TECU
คณะวิทยาศาสตร์
This special problem aims to compare the performance of machine learning methods in time series forecasting using lagged time periods as independent variables. The lagged periods are categorized into three groups: lagged by 10 units, lagged by 15 units, and lagged by 20 units. The study employs four machine learning methods: Decision Tree (DT), Random Forest (RF), K-Nearest Neighbors (KNN), and Support Vector Machine (SVM). The time series data simulated as independent variables diverse including characteristics: Random Walk data, Trending data, and Non-Linear data, with sample sizes of 100, 300, 500, and 700. The research methodology involves splitting the data into 90% for training and 10% for testing. Simulations and analysis are performed using the R programming language, with 1,000 iterations conducted. The results are evaluated based on the average mean squared error (AMSE) and the average mean absolute percentage error (AMAPE) are calculated to identify the best performing method. The research findings revealed that for Random Walk data, the best performing methods are Random Forest and Support Vector Machine. For Trend data, the best performing methods are Random Forest. For Non-Linear data, the best performing methods are Support Vector Machine. When tested with real-world data, the results show that for the Euro-to-Thai Baht exchange rate, the best methods are Random Forest and Support Vector Machine. For the S&P 500 Index in USD, the best performing methods are Random Forest. For the Bank of America Corp Index in USD, the best performing methods are Support Vector Machine.
คณะสถาปัตยกรรม ศิลปะและการออกแบบ
Interior Architecture Design Project: A Halal Restaurant Integrating the Culture of Songkhla, Thailand