Smart Agriculture has rapidly developed in recent years, particularly with the integration of robotics and automation technologies to improve production efficiency and reduce costs, thereby enhancing the quality of current agricultural practices. A key innovation in this area is the rail-based robotic arm, designed to enhance work efficiency using a rail system with high precision and effectiveness. The application of this robotic arm covers various processes, such as planting, sorting, maintenance, harvesting, and resource management, allowing continuous operation and reducing human labor in repetitive and high-risk tasks. Studies have shown that the use of rail-based robotic arms in agriculture can significantly improve work efficiency, reduce production costs, and effectively mitigate environmental impact. By using robots in agricultural processes, it is possible to reduce contamination, lower the risk of crop damage, and make agriculture more sustainable. Additionally, it can increase accuracy in operations on limited spaces or farms with diverse crops. From these findings, it can be concluded that adopting rail-based robotic arm technology in agriculture not only enhances long-term production efficiency but also promotes sustainable agriculture and maximizes resource use, meeting future agricultural demands
ประเทศไทยเป็นประเทศเกษตรกรรม การที่จะนำเทคโนโลยีเข้ามาช่วยพัฒนาระบบการเกษตรกรรม ให้มีความทันสมัย มีประสิทธิภาพ เพื่อช่วยยกระดับอุตสาหกรรมการเกษตรของไทยนั้น เป็นสิ่งที่มีความสำคัญมาก เราจึงได้พัฒนา “เเขนกลระบบรางเพื่อการเกษตรอัฉริยะ” โดยเทคโนโลยีนี้ช่วยลดการใช้แรงงานคน เพิ่มความแม่นยำในการทำงาน และสามารถทำงานได้ตลอดเวลาไม่หยุดพัก นอกจากนี้ยังช่วยลดต้นทุนการผลิตและเพิ่มผลผลิต ทำให้เกษตรกรสามารถแข่งขันในตลาดโลกได้ดียิ่งขึ้น และช่วยพัฒนาความยั่งยืนในภาคการเกษตรของประเทศไทย
คณะเทคโนโลยีการเกษตร
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คณะวิศวกรรมศาสตร์
The railway brake system usually uses compressed air brake system which uses high-pressure air to press the brake shoe on the surface of the wheel to reduce the speed of the train. Repeated friction generates heat at the contact surface, increasing thermal stress on the cast iron brake shoe. The purpose of this study is to investigate the thermal stress on a prototype of cast iron brake shoe using the finite element method compare the analytical results to the actual brake shoe and redesign a brake shoe prototype to reduce thermal stress. Based on the results of the thermal stress study using the finite element method, it has shown that the location of the thermal stress on the prototype brake shoe according to the location of the crack on the real brake shoe. The brake shoe's design which includes single notch in the center of brake shoe which is can help to reduce thermal stress. The results from this study should be validated with the results from the field test to evaluate both of thermal distribution and braking efficiency in term of braking distances as well.
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.