"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
วิทยาลัยเทคโนโลยีและนวัตกรรมวัสดุ
The development of skin-on-a-chip models plays a crucial role in research for drug and cosmetic development. Traditional approaches often utilize two-dimensional (2D) methods that rely on culturing cells on flat surfaces, resulting in a lack of complexity in skin structure and realistic cell interactions. Moreover, traditional methods have limitations in mimicking fluid flow and nutrient circulation, which affects the accuracy of pharmaceutical testing and the prediction of drug effects. This has led to the advancement of three-dimensional (3D) skin models using new microfluidic technology, enhancing the realism of skin structure by replicating both the epidermis and dermis layers, as well as simulating fluid flow similar to physiological conditions in the human body. The design of 3D systems allows for more realistic cell arrangement and interactions, enabling better simulation of skin functions and increasing the accuracy in evaluating the effects of various substances on cell responses, including absorption, inflammation, and wound healing. Therefore, the development of three-dimensional (3D) skin models not only addresses the limitations of traditional methods but also represents a significant step forward in creating models that can be effectively applied in drug testing and pharmaceutical product development.
คณะเทคโนโลยีสารสนเทศ
This thesis aims to present the development of a metaverse project for the KMITL Lifelong Learning Center (KLLC) and KMITL Data Management Center (KDMC) for Public Relations at King Mongkut's Institute of Technology Ladkrabang, with the main goal of creating a metaverse prototype to promote learning and public relations through virtual reality technology for students, staff, and external individuals. In this project, the developers have created a metaverse system to simulate a virtual experience for users at the KMITL Lifelong Learning Center (KLLC) and KMITL Data Management Center (KDMC) for Public Relations at King Mongkut's Institute of Technology Ladkrabang. Users will be able to access the system through a web application developed with Unity, which is the tool used to create the metaverse system. The design allows users to visit and interact with various locations within the building to promote public relations in a more widespread virtual format. The developers used Maya and Unity software to create a metaverse system for modeling 3D objects and managing various functions, providing users with a realistic and novel experience. This project is expected to promote learning and the dissemination of information in an easily accessible modern format, creating opportunities for education and learning for those who cannot travel to see the actual locations. This makes metaverse technology an important tool for effectively developing learning and engagement in the digital age.
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