A platform that aims to connect students from all faculties and departments to promote joint activities and develop effective social and collaborative skills, focusing on: Promoting learning and self-development through reviewing lessons and collaborative learning that are relevant to all faculties and departments in the university, creating a space for negotiation and exchange of knowledge, and supporting joint activities to build relationships and cooperation among students.
จากสถิติโดยเฉลี่ยแล้วในแต่ละวันคนเราจะพบเจอคนแปลกหน้าประมาณ 47 คน นั้นเท่ากับว่าเรา จะพบเจอคนหน้าตาใหม่ๆมากถึง 17,155 คน ต่อปี ซึ่งในการพบเจอกันของผู้คนโดยปกติแล้วเป็นเรื่องที่ เป็นไปไม่ได้เลยที่จะเจอคนที่ชอบหรือมีความต้องการอะไรคล้ายๆกัน เราจึงเกิดแนวคิดที่จะนำพาคนเหล่านั้นให้มาเจอกันได้ง่ายขึ้นจากการสร้างแอปพลิเคชัน ที่จะ ส่งเสริมการทำกิจกรรมร่วมกันของนักศึกษาทั้งจาก ต่างคณะและภายในคณะ เดียวกัน และยังส่งเสริมการ ทบทวนบทเรียน ทำแบบฝึกหัด เพื่อพัฒนาความรู้ในบทเรียนร่วมกัน ที่จะมีรายวิชาจากทุกคณะ และทุก สาขาภายใต้คอนเซ็ปต์ที่ว่า KMITL GO and grow up together
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.
คณะเทคโนโลยีการเกษตร
This project involves the development of a plant care system for dormitories using IoT (Internet of Things). The system is implemented through programming on an ESP-32 board and controlled via sensors for automated watering. The commands are operated through smartphones, supporting both iOS and Android. It is expected that this project will make plant care in dormitories easier and more convenient.
คณะวิศวกรรมศาสตร์
This project aims to introduce an Automated Vertical Metal Sheet Storage System. The project is aimed at teaching how to make an Automation Vertical Metal Sheet Storage System with the integration of microcontroller devices. The project is divided into two main sections, which are the structure and control systems of the Automation Vertical Metal Sheet Storage System that will be designed and drawn through a computer program and constructed using major aluminum structures upon completion of their actual sizes outlined in the programs. Also, a Microcontroller control system using GX Works 2 program from Mitsubishi PLC has been designed for this purpose where it controls up and down movements as well as sideways movement of the pallet. It also has a weighing capability along with touch screen display for displaying information about the steel plates and controlling the Automation Vertical Metal Sheet Storage System with safety light curtain that protect users safety. These tests have shown that the machine operates normally. There are few mistakes whose rates fall within those expected by humans.