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
คณะวิศวกรรมศาสตร์
The Thai Sign Language Generation System aims to create a comprehensive 3D modeling and animation platform that translates Thai sentences into dynamic and accurate representations of Thai Sign Language (TSL) gestures. This project enhances communication for the Thai deaf community by leveraging a landmark-based approach using a Vector Quantized Variational Autoencoder (VQVAE) and a Large Language Model (LLM) for sign language generation. The system first trains a VQVAE encoder using landmark data extracted from sign videos, allowing it to learn compact latent representations of TSL gestures. These encoded representations are then used to generate additional landmark-based sign sequences, effectively expanding the training dataset using the BigSign ThaiPBS dataset. Once the dataset is augmented, an LLM is trained to output accurate landmark sequences from Thai text inputs, which are then used to animate a 3D model in Blender, ensuring fluid and natural TSL gestures. The project is implemented using Python, incorporating MediaPipe for landmark extraction, OpenCV for real-time image processing, and Blender’s Python API for 3D animation. By integrating AI, VQVAE-based encoding, and LLM-driven landmark generation, this system aspires to bridge the communication gap between written Thai text and expressive TSL gestures, providing the Thai deaf community with an interactive, real-time sign language animation platform.
คณะเทคโนโลยีการเกษตร
Durian is a crucial economic crop of Thailand and one of the most exported agricultural products in the world. However, producing high-quality durian requires maintaining the health of durian trees, ensuring they remain strong and disease-free to optimize productivity and minimize potential damage to both the tree and its fruit. Among the various diseases affecting durian, foliar diseases are among the most common and rapidly spreading, directly impacting tree growth and fruit quality. Therefore, monitoring and controlling leaf diseases is essential for preserving durian quality. This study aims to apply image analysis technology combined with artificial intelligence (AI) to classify diseases in durian leaves, enabling farmers to diagnose diseases independently without relying on experts. The classification includes three categories: healthy leaves (H), leaves infected with anthracnose (A), and leaves affected by algal spot (S). To develop the classification model, convolutional neural network (CNN) algorithms—ResNet-50, GoogleNet, and AlexNet—were employed. Experimental results indicate that the classification accuracy of ResNet-50, GoogleNet, and AlexNet is 93.57%, 93.95%, and 68.69%, respectively.
คณะวิศวกรรมศาสตร์
This research focuses on the design and development of a high-power converter to regulate energy supply from solar cells (Photovoltaic: PV) to a hydrogen production unit (Electrolyzer), which is a crucial component in advancing renewable energy in alignment with the RE100 initiative. Specifically, this study targets Green Hydrogen, which is generated through the water electrolysis process using clean energy from solar cells, ensuring zero emissions and environmental sustainability. The proposed converter includes of a Three-Level NPC Inverter, transformer, Full-Bridge Rectifier, and LC filter to enhance the power quality supplied to the electrolyzer. The system's design and simulation were conducted using MATLAB and Simulink to evaluate circuit performance and analyze operational efficiency. Simulation was conducted using MATLAB and Simulink to evaluate circuit performance and analyze operational efficiency. Additionally, a microcontroller-based control system is integrated with a gate driver circuit to optimize the electrolysis process by reducing power losses. This proposed converter effectively converts PV energy into suitable voltage and current levels for the electrolyzer while maintaining high hydrogen production efficiency.