"Niyom Thai" represents health-centric footwear adorned with traditional Thai patterns, embodying an innovative approach to sustainable development tailored to the current needs of local communities. These shoes utilize natural materials to mitigate fatigue and integrate safety technologies, including location tracking via a mobile application and heart rate monitoring. This addresses the aspects of convenience and well-being in both daily life and travel
เนื่องจากปัจจัยผู้คนให้ความสนใจเรื่องสุขภาพมากขึ้นเเละรองเท้านับเป็นอีกหนึ่งเทรนด์สุขภาพที่กำลังได้รับความสนใจในยุคนี้ อีกทั้งผ้าไทยจัดเป็นศิลปะ ที่มีเอกลักษณ์เเละความสวยงาม คณะผู้จัดทำจึงมีเเนวคิดที่จะออกแบบลวดลายไทยให้เข้ากับยุคสมัยเเต่ยังคงความเป็นเป็นไทยและนำเทคโนโลยีมาผสมผสานเข้าด้วยกันให้เกิดนวัตกรรมรองเท้าเพื่อสุขภาพลายไทย
คณะวิทยาศาสตร์
Currently, climate change and human activities are causing rapid deterioration of coral reefs worldwide. Monitoring coral health is essential for marine ecosystem conservation. This project focuses on developing an Artificial Intelligence (AI) model to classify coral health into four categories: Healthy, Bleached, Pale, and Dead using Deep Learning techniques. With pre-trained convolutional neural network (CNN) for image classification. To improve accuracy and mitigate overfitting, 5-fold Cross-Validation is employed during training, and the best-performing model is saved. The results of this project can be applied to monitor coral reef conditions and assist marine scientists in analyzing coral health more efficiently and accurately. This contributes to better conservation planning for marine ecosystems in the future.
คณะอุตสาหกรรมอาหาร
The Mahachanok mango sauce is crafted from low-grade mangoes sourced from Ban Nong Bua Chum in Kalasin Province. Utilizing advanced food science technology, it effectively reduces agricultural waste and enhances product quality. This sauce is enriched with prebiotic fiber that supports the growth of beneficial gut microorganisms. With low sugar content, it is a healthy choice free from artificial colors and flavors. Its rich, natural taste makes it versatile, perfect for enhancing a wide variety of dishes, both savory and sweet.
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