This project aims to study the load transfer in timber building structures by analyzing weight distribution across key structural components such as beams, columns, and floors, as well as the load-bearing behavior of wood under different conditions. The research incorporates structural calculations and modeling to examine load transfer patterns. Additionally, it enhances skills in design, analysis, and teamwork, providing practical knowledge applicable to real-world construction projects.
การออกแบบอาคารสถาปัตยกรรม ต้องเข้าใจในส่วนของโครงสร้างของตัวอาคาร จึงเริ่มจากโครงสร้างขั้นพื้นฐานที่เป็นอาคารโครงสร้างไม้ก่อน
คณะอุตสาหกรรมอาหาร
The consumption of plant-based products has been gaining popularity as consumers become more health-conscious and aware of environmental impacts. The food industry has been developing meat analogs with properties similar to conventional meat. This study investigates the chemical and physical properties of chickpea-based meat analog burgers and hybrid burgers containing both chickpeas and pork, using the sous-vide cooking method. This technique helps maintain food quality in terms of texture, moisture retention, and nutritional value. The experiment examined various properties of both types of burgers, including cooking loss, water holding capacity, shear force, pH value, and color analysis. Additionally, sensory evaluation was conducted to assess taste, texture, and overall consumer preference. The findings will provide insights into the optimal sous-vide conditions for producing plant-based and hybrid burgers with desirable quality characteristics that meet the needs of health-conscious consumers. This study serves as a valuable guideline for the food industry in developing high-nutritional-value alternative protein products while reducing meat consumption. By incorporating plant-based ingredients, it helps minimize environmental impact and promotes sustainability in food production. The research is significant in both food science and the development of healthier, competitive food products for the future market.
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.
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.