This research aims to study the guidelines and develop a prototype of an application for public transport users to plan their journey and increase safety in using different types of public transport to travel to King Mongkut's Institute of Technology Ladkrabang. The objectives are as follows: 1) To study the factors of user experience (UX) and user interface (UI) design that affect the users of the application for using public transport. 2) To study the needs of users of public transport applications who must travel to King Mongkut's Institute of Technology Ladkrabang. 3) To present the guidelines for designing the user experience (UX) and user interface (UI) and to produce a prototype of the application for using public transport to travel to King Mongkut's Institute of Technology Ladkrabang. The research includes a review of the literature on User Experience (UX) and User Interface (UI) design, as well as a look at examples of public transportation applications and pick-up sites near King Mongkut's Institute of Technology Ladkrabang. This study is based on qualitative research. There are examples of employing relevant applications during the interview. The target audience is students aged 18 to 35 who will give prototypes for application development to satisfy their requirements. Provide information that is actually useful to users. The research results found that the public transport vehicles that the target group used the most were the Songthaew (the pick-up truck), train, airport rail link, motorcycle taxi, taxi, and bus, respectively. Users were concerned about various safety issues and wanted to design features to increase safety and confidence in using public transport vehicles for students, such as sending locations to relevant officials in the event of an emergency or when assistance was needed, and important information about public transport vehicles that students needed, such as calculating prices, calculating travel times, bus schedules, official and clear pick-up and drop-off points, bus routes, driver registration, suggestions or route recommendations, and the time of public transport vehicles arriving at the point where users were waiting, etc. The guidelines for designing the User Experience (UX) were presented from the analysis of the target group's data, which was a prioritization of the features of the menu for recording frequently used routes, a menu showing nearby pick-up points, a menu for searching for routes and selecting using various user constraints, such as calculating travel prices or travel times, and a menu that could set fonts and color modes to support a variety of users. This was because the study of user needs for fonts found an equal demand for Thai fonts with looped (Looped font) and without looped (Loopless font), as well as a study of the application's color requirements, which required both light and dark colors to be displayed in approximately equal amounts. This includes the design of the user interface (User Interface) by designing symbols that allow users to access the desired information quickly without confusion.
1. ยังไม่มีแอปพลิเคชันสำหรับรถโดยสารสาธารณะเพื่อการเดินทางมาสจล.โดยตรง (www.condonewb.com ,2564) 2. จากการสำรวจบางแอปพลิเคชันนั้นไม่ ได้รับการเพิ่มเติมข้อมูลที่เป็ นปั จจุบันและไม่ ได้รับการพัฒนา อย่างต่อเนื่องนั้นก่อให้เกิดความสับสนในผู ้ใช้งานแอปพลิเคชัน (เว็บไซต์ rottuthai.com อัปเดทปี ล่าสุด 2562) 3. กลุ่มผู้โดยสารมีทัศนคติที่ ไม่ปลอดภัยในการโดยสารรถตู้สาธารณะ (ณิชา สุขวัฒนากรณ์ ,2562)
คณะวิศวกรรมศาสตร์
The railway brake system usually uses compressed air brake system which uses high-pressure air to press the brake shoe on the surface of the wheel to reduce the speed of the train. Repeated friction generates heat at the contact surface, increasing thermal stress on the cast iron brake shoe. The purpose of this study is to investigate the thermal stress on a prototype of cast iron brake shoe using the finite element method compare the analytical results to the actual brake shoe and redesign a brake shoe prototype to reduce thermal stress. Based on the results of the thermal stress study using the finite element method, it has shown that the location of the thermal stress on the prototype brake shoe according to the location of the crack on the real brake shoe. The brake shoe's design which includes single notch in the center of brake shoe which is can help to reduce thermal stress. The results from this study should be validated with the results from the field test to evaluate both of thermal distribution and braking efficiency in term of braking distances as well.
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.
วิทยาเขตชุมพรเขตรอุดมศักดิ์
This research focuses on the design and development of a prototype Artificial Intelligence of Things (AIoT) system for monitoring and controlling irrigation using weather information. The system consists of four main components: 1) Weather Station – This component includes various sensors such as air temperature, relative humidity, wind speed, and sunlight duration, among others, to collect real-time weather data. 2) Controller Unit – This unit is equipped with machine learning algorithms or models to estimate the reference evapotranspiration (ETo) and calculate the plant’s water requirement by integrating the crop coefficient (Kc) with other plant-related data. This enables the system to determine the optimal irrigation amount based on plant needs automatically. 3) User Interface (UI) and Display – This section allows farmers or users to input relevant information, such as plant type, soil type, irrigation system type, number of water emitters, planting distance, and growth stages. It also provides a display for monitoring and interaction with the system. 4) Irrigation Unit – This component is responsible for controlling the water supply and managing the irrigation emitters to ensure efficient water distribution based on the calculated requirements.