This project is a carbon safe haven of Bangkok, aspiring to be the prototypal gateway of the future's carbon net zero ambitions. The project aims to answer the fundamental "flaw" of the existing urban fabric, still being extremely inefficient and highly polluting. Conversely, Carbon Oasis would not only create its own energy, but look to provide its excess energy and water surplus' back to the city and its surroundings. Taking parts of the existing city and implementing new concepts to inspire a change in the urban fabric and its people.
โครงการนี้เริ่มต้นจากความสนใจส่วนตัวในด้านสิ่งแวดล้อมและ Green Design โดยณปัจจุบันโลกกำลังเผชิญกับวิกฤตสภาพภูมิอากาศ ซึ่งตอนนี้อุณหภูมิโลกได้เพิ่มสูงขึ้นถึงระดับ 1.5°C และกำลังเข้าใกล้ขีดจำกัด 2°C เพื่อรับมือกับสถานการณ์นี้องค์กร United Nations ได้กำหนดเป้าหมายใว้ในปี 2015 ในการบรรลุ Carbon Net Zero ภายในปี 2050 โครงการนี้ทำขึ้นเพื่อเป็นจุดเริ่มต้นของการเปลี่ยนแปลงในสถาปัตยกรรมสู่อนาคต
วิทยาลัยนวัตกรรมการผลิตขั้นสูง
The research on improving the strength of solid electrolytes aims to enhance the properties of solid electrolyte materials produced from cement and additives that help develop the cement structure to generate electricity. The main components include sodium chloride (NaCl) and graphite, which contribute to the material’s ability to generate a weak electrical current. The objective is to develop an electricity-generating flooring material. This study involves preparing a mixture of cement, water, sodium chloride (NaCl), and graphite to enhance the material’s electrical conductivity. It is highly anticipated that this research will lead to the development of concrete flooring capable of generating electricity and can be further expanded for future applications.
วิทยาลัยนวัตกรรมการผลิตขั้นสูง
This research aims to develop an automatic gemstone color sorting machine to overcome the limitations of manual color sorting, which can be restricted by speed and accuracy. This study applies deep learning technology to analyze and classify gemstone colors precisely, developing an algorithm capable of accurately detecting and categorizing color shades. An automated conveyor system was also designed to efficiently transport gemstones through the color sorting process, allowing for continuous operation. The sorting machine works by capturing high-resolution images of the gemstones, processing them with software to classify color shades, and directing each gemstone to its designated position on the automated conveyor. Experimental results demonstrate that the automated color sorting machine, integrated with the conveyor system, achieves high speed and accuracy, significantly reducing labor costs and enhancing the efficiency of gemstone color sorting.
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.