In the era where information technology plays a significant role in various fields, the use of website to support education has become essential. This case study focuses on the development of a website for the Object Oriented Programming (OOP) course at King Mongkut's Institute of Technology Ladkrabang (KMITL) to facilitate instructors and teaching assistants in grading, assessing student work, and tracking student progress. The developed system helps reduce grading errors, ensures accurate and timely assessments, and enables efficient monitoring of students' academic performance. Additionally, the platform allows students to schedule project submissions and track their grades, while providing statistical data on student performance. This development aims to modernize and enhance the quality of teaching and learning.
ในปัจจุบันคณะเทคโนโลยีสารสนเทศ สถาบันเทคโนโลยีพระจอมเกล้าเจ้าคุณทหารลาดกระบัง ได้เปิดการเรียนการสอนวิชา object oriented programming และให้บริการเว็บไซต์ สำหรับรายวิชา เพื่ออำนวยความสะดวกให้แก่ผู้สอน และ ผู้เรียน โดยทางทีมผู้พัฒนาได้เล็งเห็นว่า ขั้นตอนในกรอกคะแนนเก็บของนักศึกษาว่าอาจเกิดปัญหาจากการกรอกคะแนนจากการส่งงานหรือการส่งโปรเจคผิดอาจส่งผลกระทบถึงเกรดของนักศึกษา ปัญหาต่อที่ผู้สอนได้พบเจอคือผู้ช่วยสอนเดินตรวจงานของนักศึกษาได้ลำบากเพราะตำแหน่งการแสดงผลในระบบไม่ชัดเจนจึงทำให้เกิดความล่าช้าในการตรวจงาน
คณะวิศวกรรมศาสตร์
The Thai Sign Language Generation System aims to create a comprehensive 3D modeling and animation platform that translates Thai sentences into dynamic and accurate representations of Thai Sign Language (TSL) gestures. This project enhances communication for the Thai deaf community by leveraging a landmark-based approach using a Vector Quantized Variational Autoencoder (VQVAE) and a Large Language Model (LLM) for sign language generation. The system first trains a VQVAE encoder using landmark data extracted from sign videos, allowing it to learn compact latent representations of TSL gestures. These encoded representations are then used to generate additional landmark-based sign sequences, effectively expanding the training dataset using the BigSign ThaiPBS dataset. Once the dataset is augmented, an LLM is trained to output accurate landmark sequences from Thai text inputs, which are then used to animate a 3D model in Blender, ensuring fluid and natural TSL gestures. The project is implemented using Python, incorporating MediaPipe for landmark extraction, OpenCV for real-time image processing, and Blender’s Python API for 3D animation. By integrating AI, VQVAE-based encoding, and LLM-driven landmark generation, this system aspires to bridge the communication gap between written Thai text and expressive TSL gestures, providing the Thai deaf community with an interactive, real-time sign language animation platform.
คณะวิศวกรรมศาสตร์
This project focuses on developing a test device for an AC charger for electric vehicles according to the IEC 61851-1 Annex A standard by simulating the test circuit inside an electric vehicle according to the standard to test the operation of the AC charger. The test topic is related to the communication between the electric vehicle and the charger via a Pulse Width Modulation (PWM) control circuit system and creating an operation manual (WI) to prepare for testing in accordance with ISO/IEC 17025 standards, which are general requirements for laboratory capabilities in conducting tests and/or calibrations. The overall picture of this project is to develop test equipment and create an operation manual by collecting knowledge and various devices and then comparing the data to meet the abovementioned standards to test the Type II AC charger in each state. The test equipment consists of a communication part between the test equipment and the AC charger using a PLC S7-1200 and an HMI to control the operation of the switches in the test equipment circuit, including controlling parameters and displaying results. The equipment used to measure values is an oscilloscope and a multimeter that have undergone a calibration process to comply with the specified standards.
คณะวิศวกรรมศาสตร์
Currently, lithium batteries are widely used in electronic devices and electric vehicles, making the estimation of their State of Health (SOH) crucial. Accurate SOH estimation helps extend battery lifespan, reduce maintenance costs, and prevent safety issues such as overheating or explosions. This project aims to study and analyze mathematical models of batteries and develop SOH estimation techniques using Neural Networks to enhance accuracy and evaluation speed. The experiment involved collecting charge and discharge data from three lithium battery cells under controlled temperature conditions while maintaining a constant current. The current, voltage, and time data were recorded and analyzed to determine the battery capacity for each cycle. These data were then used to train a Neural Network model. The results demonstrated an effective method for predicting battery health status. The outcomes of this project can contribute to the development of a Battery Management System (BMS) that improves battery efficiency and longevity. Additionally, it provides a foundation for applying artificial intelligence techniques in the energy sector effectively.