This research aims to investigate the adulteration of Khao Dawk Mali 105 rice based on storage age using Near-Infrared Spectroscopy (NIRS) with Fourier Transform Near-Infrared Spectroscopy (FT-NIR) in the wavenumber range of 12,500 – 4,000 cm-1 (800 – 2,500 nm). Storage duration significantly impacts the quality of cooked rice. This research is divided into two parts: 1) to investigate the feasibility of separating rice according to storage age (1, 2, and 3 years) using the best model created by an Ensemble method combined with Second Derivative, which achieved an accuracy of 96.3%. 2) To investigate adulteration based on storage age by adulterating at 0% (all 2- and 3-year-old rice), 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, and 100% (all 1-year-old rice). The best model was created using Gaussian Process Regression (GPR) combined with Smoothing + Multiplicative Scatter Correction (MSC), with coefficients of determination (r²), root mean square error of prediction (RMSEP), bias, and prediction ability (RPD) values of 0.92, 8.6%, 0.9%, and 3.6 respectively. This demonstrates that the adulteration model can be applied to separate rice by storage age (1, 2, and 3 years). Additionally, the color values of rice with different storage ages show differences in L* and b* values.
โรงงานผู้ผลิตข้าวพบปัญหาการปลอมปนของข้าวสารที่มีอายุการเก็บรักษาต่างกัน โดยทั่วไปการคัดแยกการปลอมปนจะใช้วิธีมาตรฐานโดยการหุงข้าว จากนั้นนำข้าวหุงสุกไปวัดเนื้อสัมผัสเพื่อแยกอายุของข้าว ซึ่งใช้เวลาและเป็นการทำลายตัวอย่างและเกิดความล่าช้าในการตรวจสอบคุณภาพข้าวสาร งานวิจัยนี้ใช้เทคนิคเนียร์อินฟราเรดสเปกโทรสโกปี (Near-Infrared Spectroscopy, NIRS) ในการตรวจสอบการปลอมปนของข้าวสารพันธุ์ขาวดอกมะลิ 105 (KDML 105) ที่อายุการเก็บรักษาต่างกันเพื่อแก้ไขปัญหาดังกล่าว
วิทยาลัยอุตสาหกรรมการบินนานาชาติ
The capture of a target spacecraft by a chaser is an on-orbit docking operation that requires an accurate, reliable, and robust object recognition algorithm. Vision-based guided spacecraft relative motion during close-proximity maneuvers has been consecutively applied using dynamic modeling as a spacecraft on-orbit service system. This research constructs a vision-based pose estimation model that performs image processing via a deep convolutional neural network. The pose estimation model was constructed by repurposing a modified pretrained GoogLeNet model with the available Unreal Engine 4 rendered dataset of the Soyuz spacecraft. In the implementation, the convolutional neural network learns from the data samples to create correlations between the images and the spacecraft’s six degrees-of-freedom parameters. The experiment has compared an exponential-based loss function and a weighted Euclidean-based loss function. Using the weighted Euclidean-based loss function, the implemented pose estimation model achieved moderately high performance with a position accuracy of 92.53 percent and an error of 1.2 m. The in-attitude prediction accuracy can reach 87.93 percent, and the errors in the three Euler angles do not exceed 7.6 degrees. This research can contribute to spacecraft detection and tracking problems. Although the finished vision-based model is specific to the environment of synthetic dataset, the model could be trained further to address actual docking operations in the future.
คณะวิทยาศาสตร์
This project focuses on the study and development of a short-term investment framework via gold trading in the foreign exchange market. Machine learning techniques are applied to analyze and forecast pricing trends. Moreover, we develop the system using a stochastic process to determine optimal stop-loss points, with the aim of maximizing expected returns. Additionally, we apply game theory to guide the decision-making process regarding order holding or closure. The system is implemented and tested on the MetaTrader 5 (MT5) platform. This project outlined the clear process that includes data preparation, machine learning model training, probabilistic modeling of gold price movements, stop-loss strategy formulation, strategic decision modeling based on game theory, the development of an automated trading program, and backtesting to evaluate system performance.
คณะวิศวกรรมศาสตร์
The design and development of an electric locomotive for the TRRN Railway Challenge 2025 aims to enhance learning potential and apply various theories in practical settings. The focus is on developing the locomotive to pass various tests, including acceleration, automatic braking system, noise, vibration, energy consumption, and durability throughout the competition. Additionally, the project helps improve skills in writing engineering design reports, which enables students to develop their analytical and discussion abilities in order to successfully complete each test according to the competition rules.