Discover the ultimate innovations of the future developed by Thai researchers! Meet the latest technology from KMITL that will transform our way of life and industry.
คณะวิศวกรรมศาสตร์
In this project, we introduce Power Grid Analyzer (PGAz), an open-source software package based on MATLAB, specifically designed for analyzing and controlling future power grids. Initially, PGAz is equipped with four fundamental features: power flow (PF), optimal power flow (OPF), small-signal stability analysis (SSSA), and time-domain simulation (TS). At this stage, Part I concentrates on the development of PF and OPF. The formats of our developed tool are presented, along with its command prompts. In this part, we have developed several conventional yet effective methods in the PGAz package to address PF and OPF problems, including techniques such as the Newton-Raphson method, Gauss-Seidel method, Interior Point Method, Iwamoto’s method, Fast Decoupled Load Flow, Genetic Algorithm, and Particle Swarm Optimization. Additionally, it emphasizes important aspects, algorithms, and various case studies that have been tested against IEEE benchmarks ranging from the IEEE 5-bus to the IEEE 300-bus test systems. The results demonstrate the capabilities of PGAz for future educational and research applications in PF and OPF. Finally, we outline a plan for developing Part II, which will mainly focus on SSSA and TS.
คณะวิทยาศาสตร์
Photocatalytic materials decorated with bi-metallic nanoparticles (Bi-Metallic NPs/ photocatalyst) was synthesized for the degradation of aflatoxin B1. Bi-metallic NPs/ photocatalyst were synthesized by ultrasonic irradiation. The as-synthesized was characterized the chemical characteristics by the transmission electron microscope (TEM), X-ray photoelectron spectroscopy (XPS), X-ray diffraction analysis (XRD), Fourier transform infrared spectrometer (FT-IR), zeta potential analyzer, and UV-visible spectrophotometer. Bi-metallic NPs/photocatalyst was used to evaluate the degradation efficiency of AFB1 in household wastewater under visible light. The degradation process was analyzed using high-performance liquid chromatography (HPLC) at a wavelength of 365 nm, revealing that AFB1 was completely degraded 100% within 2 minutes. This superior performance is attributed to its highly porous structure, increased specific surface area, and reduced electron-hole recombination rate, which demonstrate that the developed nanomaterial has successfully achieved AFB1 degradation.
คณะวิศวกรรมศาสตร์
Our project seek to create an Al-powered tarot card reader that bridges the gap between traditional fortune-telling and modern technology. By leveraging a combination of 3D modeling, natural language processing, text-to-speech (TTS), and speech-to-text (STT) systems, the service will deliver an interactive and culturally sensitive experience in Thai and English. Users will input their queries through voice, which will be processed via STT, and receive engaging Al-generated tarot readings through TTS. Additionally, a 3D animated avatar will mimic a real-life fortune teller, adding a visual dimension to the experience. Hosted on a user-friendly website, this platform will redefine fortune-telling by blending tradition with innovation, making it both accessible and engaging for modern users.
คณะวิทยาศาสตร์
This project aims to investigate and develop an energy storage system utilizing solar energy sources through the integration of solar cell technology and Graphene Quantum Dot Battery, representing a novel approach to enhancing energy storage efficiency and prolonging the lifespan of renewable energy systems. The selection of graphene and quantum dots as materials for battery development is attributed to their exceptional properties, including high electrical conductivity, charge storage capacity, efficient energy transfer, and enhanced stability.
คณะสถาปัตยกรรม ศิลปะและการออกแบบ
Interior Architecture Design Project: A Halal Restaurant Integrating the Culture of Songkhla, Thailand
วิทยาลัยเทคโนโลยีและนวัตกรรมวัสดุ
The photoelectrochemical detection of salbutamol, which is illicitly used as a lean meat promoter in pigs, is investigated using a molecularly imprinted polymer (MIP)-based sensor with a CuO/g-C₃N₄ nanocomposite to enhance detection performance, leveraging nanomaterials and molecular imprinting for high selectivity and sensitivity. This approach offers a promising strategy for the precise and efficient analysis of salbutamol in food samples.
คณะวิทยาศาสตร์
Otitis Media is an infection of the middle ear that can occur in individuals of all ages. Diagnosis typically involves analyzing images taken with an otoscope by specialized physicians, which relies heavily on medical experience to expedite the process. This research introduces computer vision technology to assist in the preliminary diagnosis, aiding expert decision-making. By utilizing deep learning techniques and convolutional neural networks, specifically the YOLOv8 and Inception v3 architectures, the study aims to classify the disease and its five characteristics used by physicians: color, transparency, fluid, retraction, and perforation. Additionally, image segmentation and classification methods were employed to analyze and predict the types of Otitis Media, which are categorized into four types: Otitis Media with Effusion, Acute Otitis Media with Effusion, Perforation, and Normal. Experimental results indicate that the classification model performs moderately well in directly classifying Otitis Media, with an accuracy of 65.7%, a recall of 65.7%, and a precision of 67.6%. Moreover, the model provides the best results for classifying the perforation characteristic, with an accuracy of 91.8%, a recall of 91.8%, and a precision of 92.1%. In contrast, the classification model that incorporates image segmentation techniques achieved the best overall performance, with an mAP50-95 of 79.63%, a recall of 100%, and a precision of 99.8%. However, this model has not yet been tested for classifying the different types of Otitis Media.
คณะวิทยาศาสตร์
The purpose of this study was to examine and analyze the factors influencing household energy expenditures in Thailand. With sample group of 57,600 households. The findings reveal that the majority of the sample population is male, with an average age of 54.31 years, and most are married. The majority have an education level of primary or secondary school and are primarily Own-account worker (without employee), Private company employee or engaged in other job. In terms of social characteristics, the average household size is 2.71 people. Most residences are located in the Central, Northeastern, and Northern regions with similar proportions, followed by the Southern region and Bangkok, respectively. Most type of dwelling in detached houses, with materials of construction being cement or brick, followed by half concrete and wood. Regarding tenure, almost own dwelling and land, with an average of 2.88 rooms per household. Electricity is available in all households, with an average of 2.30 vehicles per household and an average of 22 electrical appliances per household. Regarding economic characteristics, most respondents have government/state enterprise welfare and receive benefits from the government programs. The majority have never borrow money from government funds. The average communication services of respondents amount to 788.46 THB, while the average household debt stands at 4,760.74 THB. At a significance level of 0.05, the factors influencing household energy expenditures in Thailand include gender, education level, marital status, job, household size, residential region, type of dwelling, material of construction, tenure, number of rooms, number of vehicles, number of electrical appliances, welfare of medical services, receive benefits from the government programs, borrow money from government funds, communication services, and household debt. However, age does not affect household energy expenditures in Thailand. The results of multiple linear regression analysis indicate that six quantitative independent variables—communication services, number of household electrical appliances, number of vehicles in the household, household debt, number of rooms, and household size—explain variations in household energy expenditures, with an Adjusted R Square value of 0.561.
คณะเทคโนโลยีสารสนเทศ
This report is part of applying the knowledge gained from studying machine learning models and methods for developing a predictive model to identify customers likely to cancel their credit card services with a bank. The project was carried out during an internship at a financial institution, where the creator developed a model to predict customers likely to churn from their credit card services using real customer data through the organization's system. The focus was on building a model that can accurately predict customer churn by selecting features that are appropriate for the prediction model and the unique characteristics of the credit card industry data to ensure the highest possible accuracy and efficiency. This report also covers the integration of the model into the development of a website, which allows related departments to conveniently use the prediction model. Users can upload data for prediction and receive model results instantly. In addition, a dashboard has been created to present insights from the model's predictions, such as identifying high-risk customers likely to cancel services, as well as other important analytical information for strategic decision-making. This will help support more efficient marketing planning and customer retention efforts within the organization.