This conceptual model, titled "DeHome", incorporates the principles of Deconstructivism in architectural design. It deconstructs the fundamental elements of a house—roof, columns, doors, windows, and bricks—separating them and reassembling them in a way that conveys fragmentation, contradiction, and movement. This design challenges the traditional concept of structural stability by enlarging key elements such as doors, windows, and columns, emphasizing distortion and the dynamic force of transformation. Beyond merely dismantling the physical structure of a house, this project reinterprets the very concept of "home" within the context of contemporary architecture.
ต้องการประยุกต์ความรู้ที่ได้เรียนเข้ากับการออกแบบ และท้าทายความคิดโดยการตีความแนวคิดของ "บ้าน" ใหม่ในบริบทของสถาปัตยกรรมร่วมสมัย
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.
คณะวิศวกรรมศาสตร์
The presented project topic is Garbage Sorting Systems. The purpose is to study the operation and develop a waste sorting system that can automatically detect the type of waste using a proximity sensor to separate the types of metal and non-metal waste, as well as an ultrasonic sensor to check the amount of waste in the bin. If the amount of waste exceeds the specified amount, the system will send a notification to the communication device connected to the system, such as a smartphone or computer. The operation of the system is designed to increase the efficiency of waste management, reduce the burden of manual waste sorting, and promote recycling. This system can be applied in various places, such as educational institutions or public places, to help reduce the amount of waste that is not properly separated and increase the opportunity to reuse waste.
คณะสถาปัตยกรรม ศิลปะและการออกแบบ
This work got the idea of bringing the car culture of Thai teenagers to present in a new way through our perspective. Create characters and bring various elements within the culture to combine with what we like. Whether it's stickers, posters and band shirts with acrylic paint techniques.