This project aims to develop an AI-powered system for detecting and classifying wall cracks using image processing. It identifies different crack types, assesses severity, and ensures accuracy across various image conditions. The goal is to support preventive maintenance by enabling early detection of structural issues, reducing repair costs, and improving safety.
รอยร้าวบนผนังอาจส่งผลต่อ ความปลอดภัยของอาคาร แต่การตรวจสอบยังต้องพึ่ง ผู้เชี่ยวชาญ โครงงานนี้จึงพัฒนา ระบบ AI เพื่อตรวจจับและจำแนก รอยร้าวอัตโนมัติ ผ่าน การประมวลผลภาพ ช่วยให้ ประเมินเบื้องต้นได้ง่าย ลดค่าใช้จ่าย และส่งเสริม การบำรุงรักษาเชิงป้องกัน
คณะวิศวกรรมศาสตร์
This research focuses on the development of a 3D-printed spectrophotometer. The device was designed using AutoCAD, ensuring durability, low cost, and portability. It is intended for measuring the amount of formaldehyde in fresh seafood.
คณะวิศวกรรมศาสตร์
A high-pressure gas storage tank made from composite materials, including carbon fiber, resin, and plastic, is designed for storing compressed natural gas (CNG) or hydrogen. This type of tank is classified as a Type IV high-pressure vessel. In this research, it is designed to operate at a pressure of 250 bar for the transportation of compressed natural gas.
คณะวิทยาศาสตร์
With the development of space technology, wide-field sky surveys using telescopes have expanded the range of new data available for time-domain astronomical research. Traditional data analysis methods can no longer respond quickly and accurately enough to the growing volume of data. Thus, classifying time-series data, such as light curves, has become a significant challenge in the era of big data. In modern times, analyzing light curves has become essential for using machine learning techniques to handle and filter through massive amounts of data. Machine learning algorithms can be divided into two categories: shallow learning and deep learning. Numerous researchers have proposed and developed a variety of algorithms for light curve classification. In this study, we experimented with Support Vector Machine (SVM) and XGBoost, which are shallow machine learning algorithms, as well as 1D-CNN and Long Short-Term Memory (LSTM), which are deep learning algorithms, which are branches of deep machine learning, to classify variable stars. The training and testing data used in this study were from the Optical Gravitational Lensing Experiment-III (OGLE-III), consisting of variable star data from the Large Magellanic Cloud (LMC), categorized into five main classes: Classical Cepheids, δ Scutis, eclipsing binaries, RR Lyrae stars, and Long-period variables. The results demonstrate the performance analysis of each machine learning algorithm type applied to light curve data, while also highlighting the accuracy and statistical metrics of the algorithms used in the experiments.