Expanding from a public park design project to a campus design on an area of over 50 rai in Ang Sila Subdistrict, Mueang District, Chonburi Province, to serve as both an educational institution and a place for relaxation and learning for the surrounding people.
การพัฒนาพื้นที่แห่งนี้ให้กลายเป็นCampus Park เพื่อเป็นพื้นที่พักผ่อนและแลกเปลี่ยนความรู้เกี่ยวกับระบบนิเวศชายหาดของจังหวัดชลบุรี
คณะเทคโนโลยีสารสนเทศ
The AfterDay Horizon project is a two-player survival game developed to raise awareness of the impact of climate change. It leverages Virtual Reality (VR) technology and a website as gaming platforms. In the game, players experience a world where civilization has collapsed due to global warming, forcing the remaining population to live in bunkers to avoid environmental dangers. AfterDay Horizon focuses on collaboration between the two players to complete various missions that help the bunker’s inhabitants survive as long as possible. These missions are designed to encourage teamwork and decision-making in challenging scenarios, while also raising awareness of the potential consequences of climate change if left unresolved. Preliminary testing of the game showed that players successfully completed the missions and worked well together. However, some missions were complex and time-consuming, indicating areas for improvement to enhance the overall enjoyment and gameplay experience.
คณะวิทยาศาสตร์
With the development of space technology, wide-field sky surveys using telescopes have expanded the range of new data available for time-domain astronomical research. Traditional data analysis methods can no longer respond quickly and accurately enough to the growing volume of data. Thus, classifying time-series data, such as light curves, has become a significant challenge in the era of big data. In modern times, analyzing light curves has become essential for using machine learning techniques to handle and filter through massive amounts of data. Machine learning algorithms can be divided into two categories: shallow learning and deep learning. Numerous researchers have proposed and developed a variety of algorithms for light curve classification. In this study, we experimented with Support Vector Machine (SVM) and XGBoost, which are shallow machine learning algorithms, as well as 1D-CNN and Long Short-Term Memory (LSTM), which are deep learning algorithms, which are branches of deep machine learning, to classify variable stars. The training and testing data used in this study were from the Optical Gravitational Lensing Experiment-III (OGLE-III), consisting of variable star data from the Large Magellanic Cloud (LMC), categorized into five main classes: Classical Cepheids, δ Scutis, eclipsing binaries, RR Lyrae stars, and Long-period variables. The results demonstrate the performance analysis of each machine learning algorithm type applied to light curve data, while also highlighting the accuracy and statistical metrics of the algorithms used in the experiments.
วิทยาเขตชุมพรเขตรอุดมศักดิ์
This project aims to design and develop a propulsion system for agricultural equipment using RFID technology and evaluate its movement performance on different surfaces, including concrete and grass. The experiment focuses on examining the tag detection range under transmission power levels of 20 dBm, 23 dBm, and 26 dBm, as well as the impact of antenna angles on detection efficiency. Additionally, the system was tested in three movement scenarios: straight path, left turn, and right turn, at distances of 2 meters, 4 meters, and 6 meters. The results indicate that the system achieved the highest average speed of 0.4736 m/s and an average turning angle of 91.6° when moving in a straight path on a concrete surface at a distance of 4 meters. On a grass surface at the same distance, the average speed was 0.4483 m/s, with an average turning angle of 91.1°. For left and right turns, the movement on the concrete surface generally exhibited a higher average speed than on grass, particularly at a distance of 4 meters, where differences in turning angles were observed. This study provides insights into the factors affecting the movement of agricultural mowing equipment and serves as a foundation for enhancing the efficiency of propulsion systems in future developments.