This artwork was created based on the universal concepts of global warming and post-apocalyptic world, which has caused disturbances and chaos in ecosystems, leading to the extinction of many living beings on Earth due to human actions. Repairing and restoring this world may therefore be a false hope, connected to my personal experience of losing loved ones and the sorrow from setting high hope, through the artistic process using Animation Art and Sound Art.
ข้าพเจ้าต้องการแสดงถึงปัญหาและสิ่งที่มนุษย์ได้ทิ้งเศษซากไว้ให้ผู้อื่นรับผลกระทบแทนตน เพื่อให้ตระหนักรู้ถึงความสำคัญในการเปลี่ยนแปลงการดำรงชีวิต เพื่อการดำรงอยู่ของสิ่งมีชีวิตทั่วโลก และข้าพเจ้าได้เชื่อมโยงประสบการณ์ส่วนตัวลงไปเพื่อเป็นการแสดงความรู้สึกร่วมซึ่งล้วนเป็นความทุกข์ออกไปให้ผู้อื่นได้รับทราบไม่มากก็น้อยเพื่อแบ่งเบาความทุกข์นี้ไปจากข้าพเจ้า
คณะเทคโนโลยีการเกษตร
This project involves the development of a plant care system for dormitories using IoT (Internet of Things). The system is implemented through programming on an ESP-32 board and controlled via sensors for automated watering. The commands are operated through smartphones, supporting both iOS and Android. It is expected that this project will make plant care in dormitories easier and more convenient.
คณะเทคโนโลยีสารสนเทศ
This research presents a deep learning method for generating automatic captions from the segmentation of car part damage. It analyzes car images using a Unified Framework to accurately and quickly identify and describe the damage. The development is based on the research "GRiT: A Generative Region-to-text Transformer for Object Understanding," which has been adapted for car image analysis. The improvement aims to make the model generate precise descriptions for different areas of the car, from damaged parts to identifying various components. The researchers focuses on developing deep learning techniques for automatic caption generation and damage segmentation in car damage analysis. The aim is to enable precise identification and description of damages on vehicles, there by increasing speed and reducing the work load of experts in damage assessment. Traditionally, damage assessment relies solely on expert evaluations, which are costly and time-consuming. To address this issue, we propose utilizing data generation for training, automatic caption creation, and damage segmentation using an integrated framework. The researchers created a new dataset from CarDD, which is specifically designed for cardamage detection. This dataset includes labeled damages on vehicles, and the researchers have used it to feed into models for segmenting car parts and accurately labeling each part and damage category. Preliminary results from the model demonstrate its capability in automatic caption generation and damage segmentation for car damage analysis to be satisfactory. With these results, the model serves as an essential foundation for future development. This advancement aims not only to enhance performance in damage segmentation and caption generation but also to improve the model’s adaptability to a diversity of damages occurring on various surfaces and parts of vehicles. This will allow the system to be applied more broadly to different vehicle types and conditions of damage inthe future
วิทยาลัยนวัตกรรมการผลิตขั้นสูง
The offline evaluation system for Thai-language large language models (LLMs) is designed to enable experts to efficiently test and assess various LLMs without relying on external services. This enhances the flexibility in selecting LLMs that best suit organizational needs or expert systems (ES). The system operates on personal computers, ensuring data security by eliminating concerns about external data storage. Additionally, it supports model testing and development using Retrieval-Augmented Generation (RAG), allowing access to domain-specific knowledge for accurate, energy-efficient processing. This ensures that the models can perform optimally and effectively meet the demands of organizations and expert systems.