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    Modern Manufacturing for Alloy Wheel Defect Detection using Image Processing and Application
    (2024-01-01)
    Archevapanich, Tuaniai
    ;
    Krungseanmuang, Woranidtha
    ;
    Chaowalittawin, Vasutorn
    ;
    Sathaporn, Posathip
    ;
    Chaowalittawin, Punyisa
    This paper presents an innovative approach to identifying defects in alloy wheel production by integrating image processing techniques with a mobile application platform. The system receives X-ray alloy images from the factory via mobile phone, processes them using image processing techniques to enhance clarity and readiness for defect detection, and then transmits the processed images to a Django framework via a uniform resource locator (URL). Subsequently, the system detects defects in the images, encodes them in Base64 format, and sends them to the mobile application through an API (Application Program Interface) for display on the user interface. This well-designed system architecture offers manufacturers a comprehensive solution to ensure product quality, reduce costs, and enhance customer satisfaction.
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    Inventory Management System based on IoT and Microservices Architecture Design
    (2023-01-01)
    Sithiyopasakul, Paisan
    ;
    Piyatananugoon, Chavinkorn
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    Chaowalittawin, Vasutorn
    ;
    Krungseanmuang, Woranidtha
    ;
    Sathaporn, Posathip
    This paper presents the inventory management system based on IoT and microservices architecture that synced between IoT (Internet of Things) and web application. There are two main parts consisting of IoT forklift vehicle and data transmitted system by MQTT protocol. Microservice can utilize data to process business logic and provides API. Inventory area includes a zone, a subzone and parking area for forklifts to scan QR code on each subzone. Data of forklifts is published to back-end service. The results of system microservice publish information events and processes business data to the admin client with a delay under a second.
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    Automated Data Digitization System for Vehicle Registration Certificates Using Google Cloud Vision API
    (2022-07-01)
    Thammarak, Karanrat
    ;
    Sirisathitkul, Yaowarat
    ;
    Kongkla, Prateep
    ;
    Intakosum, Sarun
    This study aims to develop an automated data digitization system for the Thai vehicle registration certificate. It is the first system developed as a web service Application Programming Interface (API), which is essential for any enterprise to increase its business value. Currently, this system is available on “www.carjaidee.com”. The system involves four steps: 1) an embedded frame aligns a document to be correctly recognised in the image acquisition step; 2) sharpening and brightness filtering techniques to enhance image quality are applied in the pre-processing step; 3) the Google Cloud Vision API receives a prompt to proceed in the recognition step; 4) a specific domain dictionary to improve accuracy rate is developed for the post-processing step. This study defines 92 images for the experiment by counting the correct words and terms from the output. The findings suggest that the proposed method, which had an average accuracy of 93.28%, was significantly more accurate than the original method using only the Google Cloud Vision API. However, the system is limited because the dictionaries cannot automatically recognise a new word. In the future, we will explore solutions to this problem using natural language processing techniques.
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    Automated attribute inference for IOT data visualization service
    (2019-01-01)
    Sangpetch, Orathai
    ;
    Sangpetch, Akkarit
    ;
    Nartnorakij, Jittinat
    ;
    Vejprasitthikul, Narawan
    As data becomes vital to urban development of modern cities, Thailand has initiated a smart city project on pilot cities around the country. We have implemented an interoperable data platform for smart city to enable Internet of Things (IoT) data exchanges among organizations through APIs. One of the key success is that people can access and visual the data. However, data can have various attributes since standard has not completely established and adopted. Therefore, it is difficult to automate the process to achieve comprehensive visualization. Traditionally, we require developers to manually examine data streams to determine which data attribute should be presented. This process can be very time consuming. The visualization system must be manually updated whenever a source stream modifies its data attributes. This problem becomes an impediment to implement a scalable cloud-based visualization service. To mitigate this challenge, we propose an automated attribute inference approach to automatically select key visualizable attribute from heterogeneous streams of data sources. We have experimented with different data attribute selection algorithms, namely an empirical rule-based system and the chosen machine learning algorithms. We implement and evaluate the proposed selection algorithms through our 3D visualization program in order to get the feedback from users.