Inventing robots for the TPA Robotics Competition Thailand Championship 2024, game “Rice Way, Thai Way to the International Way (HARVEST DAY)”
สมาคมส่งเสริมเทคโนโลยี (ไทย-ญี่ปุ่น) จัดแข่งขันหุ่นยนต์ ชิงถ้วยพระราชทาน ทุกปี
วิทยาลัยวิศวกรรมสังคีต
Some musicians are hearing impaired, some have finished their careers, but some continue to work, and it is much harder for them to be deaf than for musicians who are not. Some use hearing aids in their daily lives and use in-ear monitors in their live performances, which seem normal, but in their in-ears they only hear the metronome and drums. How can we improve the performance of in-ear monitors to near normal?
วิทยาลัยนวัตกรรมการผลิตขั้นสูง
Ultrasonic cleaning tank is a machine that many factories widely used to clean objects. At one factory, a problem occurred in the cleaning process, resulting in the factory not being able to clean objects, but cracks also appeared on some objects. It was anticipated that these were caused by uneven acoustics pressure distribution which resulted in unsuitable cavitation This directly affected cleaning performance within the tank. In order to improve the tank's efficacy, in this research, we use Harmonic Response Analysis in ANSYS simulate simulate the occurrence of acoustic pressure in the tank to find the appropriate conditions of factors affected the intensity and the distribution pattern of acoustic pressure in ultrasonic tank, including the position of object, power, ultrasonic frequency and a suitable type and placing position of the transducer for the tank. Reliability of the simulate results was validate by the actual result from the foil corrosion test and the ultrasonic power probe. We found that objects receive different pattern of corrosion at each location. When temperature increasing the intensity of cavitation was increased. When we increase the ultrasonic frequency, acoustic pressure that is evenly dispersed throughout the tank.
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.