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Self Doubt

Abstract

A Photographic series that expresses the abstract states of myself, towards the question of existence that results from being surrounded by expectations of both surrender and freedom of expression, this series focuses on my own subjectivities in order to bring back memories of almost forgotten feelings and make them clear once more.

Objective

การที่เติบโตมาจากครอบครัวที่คาดหวังในตัวเรา ที่สมาชิกคาดหวังในตัวเราไม่เหมือนกัน ถ้าเราทำแบบใดแบบหนึ่งที่คนใดคนหนึ่งต้องการอีกคนจะไม่พอใจ จนเราเกิดสงสัยว่าเราต้องเป็นแบบไหน เมื่อเข้ามาอยู่ในสังคมใหม่ทำให้เราตั้งคำถามกับตนเองเมื่อเข้าหาผู้คนว่าเราต้องเป็นไปแบบที่เขาต้องการหรือเปล่าเราถึงจะเข้าถึงเขาได้ ทำให้เราสับสนกับตัวเองและต้องสร้างตัวตนใหม่ไปตามที่คนคนนั้นพอใจ จนเราเองเริ่มเกิดคำถามว่าจริงๆแล้วตัวตนของเราจริงๆเป็นแบบไหน

Other Innovations

Testing of Electric Vehicle Supply Equipment (EVSE) based on  IEC 61851-1 Annex A

คณะวิศวกรรมศาสตร์

Testing of Electric Vehicle Supply Equipment (EVSE) based on IEC 61851-1 Annex A

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.

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Vision-Based Spacecraft Pose Estimation

วิทยาลัยอุตสาหกรรมการบินนานาชาติ

Vision-Based Spacecraft Pose Estimation

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.

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Web Application System Prototype for Hand Dental Instruments Identifying and Counting using Deep Learning

คณะเทคโนโลยีสารสนเทศ

Web Application System Prototype for Hand Dental Instruments Identifying and Counting using Deep Learning

This research presents the development of an AI-powered system designed to automate the identification and quantification of dental surgical instruments. By leveraging deep learning-based object detection, the system ensures the completeness of instrument sets post-procedure. The system's ability to process multiple images simultaneously streamlines the inventory process, reducing manual effort and potential errors. The extracted data on instrument quantity and type can be seamlessly integrated into a database for various downstream applications.

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