The project of game development to educate about Egyptian civilization This is intended to be a game to promote learning about ancient Egyptian civilization. The original learning may feel boring. Do not attract the attention of learners Therefore saw the presentation in the form of a game on virtual reality technology that inserts knowledge history With the aim of making learning more interesting The organizers can choose to use Unreal Engine 5.1 (Unreal Engine 5.1) and Oculus Quest II (Oculus Quest2) to develop. Within the game, players must find a way to escape from this room within the specified period of solving puzzles in various forms such as statues, traps, etc. In order to solve puzzles and leave this room, players must collect all the information inside the room. In addition, the game shows the life of the player and when the player cannot solve the puzzle correctly.
อารยธรรมอียิปต์โบราณเป็นหนึ่งในอารยธรรมอันยิ่งใหญ่ของโลกยุคโบราณที่เป็นต้นกำเนิดของศาสตร์หลายๆแขนง ชาวอียิปต์โบราณถือเป็นนักสร้างสรรค์อารยธรรมทั้งด้านคณิตศาสตร์ และสถาปัตยกรรมอันโดดเด่น เพื่อศึกษาประวัติศาสตร์ซึ่งเป็นรากเหง้าของศาสตร์หลากหลายแขนงนั้นอาจต้องใช้เวลาและแหล่งข้อมูลจากหลายแหล่ง และมักจะเกิดความเบื่อหน่ายขึ้นได้ง่าย เพื่อที่ทำให้การศึกษาประวัติศาตร์เหล่านั้นไม่น่าเบื่อจนเกินไป ผู้จัดทำจึงได้นำเกมแนวไขปริศนาซึ่งสร้างขึ้นออกมาเป็นเสมือนกลไกในห้องลับเพื่อที่จะเสริมสร้างวิธีการแก้ไขปัญหาและได้ค้นหา เรียนรู้ประวัติศาสตร์ไปพร้อมๆกัน ผู้จัดทำเล็งเห็นว่าเทคโนโลยีความเป็นจริงเสมือน หรือ VR (Virtual Reality) สามารถดึงความสนุก และเพิ่มความน่าสนใจของตัวเกมจากผู้เล่นในการแก้ไขปริศนาเพื่อหาวัตถุโบราณและนำข้อมูลที่ได้พบนั้นมาตอบคำถามเพื่อเรียนรู้ประวัติศาสตร์ของอารยธรรมอียิปต์ได้เป็นอย่างดี
คณะวิศวกรรมศาสตร์
The project uses artificial intelligence (AI) and deep learning to develop a smart police system (Smart Police) to analyze the identity of individuals and vehicles suspected of involvement in crimes. The system uses CCTV cameras to detect people with concealed weapons and track vehicles involved in crimes. The system also sends alerts to the police when a crime is detected. The Smart Police system is a collaboration between the Faculty of Engineering, King Mongkut's Institute of Technology Ladkrabang, the Provincial Police Region 2, the Chachoengsao Foundation for Development, and the Smart City Office of Chachoengsao Province. The system is designed to prevent and deter crime, increase public safety and order, and build a network of cooperation between the government, the private sector, and the community. The system is currently under development, but it has the potential to be a valuable tool for law enforcement. The system could help to reduce crime and improve public safety in Chachoengsao Province and other parts of Thailand.
วิทยาลัยอุตสาหกรรมการบินนานาชาติ
The capture of a target spacecraft by a chaser is an on-orbit docking operation that requires an accurate, reliable, and robust object recognition algorithm. Vision-based guided spacecraft relative motion during close-proximity maneuvers has been consecutively applied using dynamic modeling as a spacecraft on-orbit service system. This research constructs a vision-based pose estimation model that performs image processing via a deep convolutional neural network. The pose estimation model was constructed by repurposing a modified pretrained GoogLeNet model with the available Unreal Engine 4 rendered dataset of the Soyuz spacecraft. In the implementation, the convolutional neural network learns from the data samples to create correlations between the images and the spacecraft’s six degrees-of-freedom parameters. The experiment has compared an exponential-based loss function and a weighted Euclidean-based loss function. Using the weighted Euclidean-based loss function, the implemented pose estimation model achieved moderately high performance with a position accuracy of 92.53 percent and an error of 1.2 m. The in-attitude prediction accuracy can reach 87.93 percent, and the errors in the three Euler angles do not exceed 7.6 degrees. This research can contribute to spacecraft detection and tracking problems. Although the finished vision-based model is specific to the environment of synthetic dataset, the model could be trained further to address actual docking operations in the future.
คณะวิศวกรรมศาสตร์
Nowadays, rail transportation has a significant impact on people's lives and economic growth. Consequently, the number of rail systems being built around our country has dramatically increased. This process causes various types of pollution, such as noise and rail-way vibration, which can badly affect the life of citizens who live nearby. The most popular way to solve this problem recently is to decrease the noise from the sound source or to adjust the vibration by attaching a Track Damper to the railway. This technique is being used in many countries especially in Europe and Australia because it is cheap and has high efficiency. The key piece called Track Dampers are made by AUT company’s Thailand for a period of time. The company produces Track Dampers for the owner of the technology so as to sell more than 300,000 pieces of it overseas. Furthermore, the demand of Track Dampers grows as the railway systems expand. Unfortunately, the imported synthetic materials, which are used to create Track Dampers, are made from environmentally unfriendly sources. As a result, this research aims to develop the product to be environmentally-safe by replacing some imported materials with Thai’s local content; which are natural rubber and rubber crumbs. Furthermore, the product will be added value by mounting with embedded sensors for real-time monitoring of track vibration, noise, and rail temperature. All embedded devices developed will sense, collect, and automatically send to cloud by wireless technology platform. The AI and IOT platform will also be developed for safety, security, and maintenance proposed of railway track system. However, in conducting research, there will be close collaboration with AUT company through design, production, and testing. The outcome of this research is to upgrade AUT company from tier 2 manufacturer (TRL 8-9) to tier 1 manufacturer (TRL 7-8) which will be served the Thailand competitiveness enhancing strategic goal.