"Green and Smart City Innovation" is a concrete integration of social innovation and innovation for smart city in Chiang Rai Province with an interdisciplinary collabarative learning approach based on the research and development of learning in the area by the community. Project Title : “APOLE” Cultural Product Design: The Cultural Product Design Beyond. “City development that aims to improve the quality of life By increasing the efficiency of service city management cost reduction and use of resources Emphasis is placed on the participation mechanisms of the public sector, private sector, public sector, and academic sector. Under the concept of developing a livable, modern, sustainable city that provides citizens in the city with a good quality of life. by leveraging technology and innovation as tools” to move towards a Smart City in the future The government sector uses technology as a driving force. Emphasis is placed on creating an infrastructure system. (Infrastructure) to be consistent with the living conditions of local people. By laying down telecommunications infrastructure, smart poles, arranging electrical wires and grounding communication cables. Installation of intelligent CCTV systems, air quality improvement, Internet of Things (IoT) devices, and Internet of Things (IoT) technology control systems, which help improve people's quality of life so that they can live with more quality.
พัฒนาเสาอัจฉริยะcและระบบปรับปรุงคุณภาพอากาศเพื่อใช้ในพื้นที่สาธารณะของเมือง โดยมีอรรถประโยชน์ ในการตรวจประเมินสภาพแวดล้อม การตรวจพื้นที่ด้วยกล้องวงจรปิด CCTV เป็นแหล่งจ่ายพลังงานไฟฟ้า เป็นต้น
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.
คณะวิทยาศาสตร์
Otitis Media is an infection of the middle ear that can occur in individuals of all ages. Diagnosis typically involves analyzing images taken with an otoscope by specialized physicians, which relies heavily on medical experience to expedite the process. This research introduces computer vision technology to assist in the preliminary diagnosis, aiding expert decision-making. By utilizing deep learning techniques and convolutional neural networks, specifically the YOLOv8 and Inception v3 architectures, the study aims to classify the disease and its five characteristics used by physicians: color, transparency, fluid, retraction, and perforation. Additionally, image segmentation and classification methods were employed to analyze and predict the types of Otitis Media, which are categorized into four types: Otitis Media with Effusion, Acute Otitis Media with Effusion, Perforation, and Normal. Experimental results indicate that the classification model performs moderately well in directly classifying Otitis Media, with an accuracy of 65.7%, a recall of 65.7%, and a precision of 67.6%. Moreover, the model provides the best results for classifying the perforation characteristic, with an accuracy of 91.8%, a recall of 91.8%, and a precision of 92.1%. In contrast, the classification model that incorporates image segmentation techniques achieved the best overall performance, with an mAP50-95 of 79.63%, a recall of 100%, and a precision of 99.8%. However, this model has not yet been tested for classifying the different types of Otitis Media.
คณะบริหารธุรกิจ
This project is a part of KMITL business student’s thesis. The topic is business plan about blazers and trousers made by recycled fabric