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
คณะอุตสาหกรรมอาหาร
The Ginbanirose project aims to develop herbal extracts for alleviating menstrual pain using key ingredients: roselle, banana inflorescence, and ginger. These ingredients contain bioactive compounds with anti-inflammatory, antioxidant, and pain-relieving properties. The extracts are enhanced through liposome encapsulation technology, which improves absorption and stability. The production process involves herbal extraction, freeze-drying, and liposome formulation using lecithin and stabilizers. Experimental results demonstrate high phenolic content and antioxidant activity via the DPPH method. Ginbanirose addresses women’s quality of life concerns while offering significant business opportunities in the rapidly growing herbal market, particularly in the Asia-Pacific region.
คณะวิศวกรรมศาสตร์
Stirling engine is the external heated engine that heat is sup-plied externally to the heater part of the engine. Thus, Stirling cycle engine can be employed with various sources of renewable energy such as biomass, biofuel, solar energy, geothermal energy, recovery heat, and waste. The integration of gasifier, burner, and heat engine as a power system offers more fuel choices of each local area with potential resources resulting independent from shortage and cost fluctuation of fossil fuel. This research aims to investigate the integration of the Stirling engine with a wood pellet gasifier for electric power generation. Biomass can be controlled to have continuously combustion with ultra-low toxic emission. Stirling engine, therefore, is a promising alternative in small-scale-electricity production. Even though many biomass-powered Stirling engines were successfully constructed and marketed but these engines and the use of biomass resources as fuel for power generation are quite new concepts in some developing countries. Especially, the capital cost of this engine is high and unaffordable for installation compared to other power systems. Therefore, this research aims to the study attractive and feasibility of the compact Stirling engine with green energy.
คณะเทคโนโลยีสารสนเทศ
Facial Expression Recognition (FER) has attracted considerable attention in fields such as healthcare, customer service, and behavior analysis. However, challenges remain in developing a robust system capable of adapting to various environments and dynamic situations. In this study, the researchers introduced an Ensemble Learning approach to merge outputs from multiple models trained in specific conditions, allowing the system to retain old information while efficiently learning new data. This technique is advantageous in terms of training time and resource usage, as it reduces the need to retrain a new model entirely when faced with new conditions. Instead, new specialized models can be added to the Ensemble system with minimal resource requirements. The study explores two main approaches to Ensemble Learning: averaging outputs from dedicated models trained under specific scenarios and using Mixture of Experts (MoE), a technique that combines multiple models each specialized in different situations. Experimental results showed that Mixture of Experts (MoE) performs more effectively than the Averaging Ensemble method for emotion classification in all scenarios. The MoE system achieved an average accuracy of 84.41% on the CK+ dataset, 54.20% on Oulu-CASIA, and 61.66% on RAVDESS, surpassing the 71.64%, 44.99%, and 57.60% achieved by Averaging Ensemble in these datasets, respectively. These results demonstrate MoE’s ability to accurately select the model specialized for each specific scenario, enhancing the system’s capacity to handle more complex environments.