Krungseanmuang, Woranidtha

Preferred name

Alternative Name

Krungseanmuang, W.

Main Affiliation

School of Engineering

woranidtha.kr@kmitl.ac.th

Now showing 1 - 9 of 9

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Inventory Management System based on IoT and Microservices Architecture Design
(2023-01-01)
Sithiyopasakul, Paisan
;
Piyatananugoon, Chavinkorn
;
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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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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Ensemble of CNN classifiers using Choquet Fuzzy Integral Technique for PCB Defect Classification
(2024-01-01)
Purahong, Boonchana
;
Krungseanmuang, Woranidtha
;
Tenghongsakul, Kasi
;
Archevapanich, Tuanjai
;
Khunthawiwone, Parkpoom
This paper presents a novel method for detecting defects in printed circuit boards (PCBs) using an ensemble of classifiers based on the Choquet fuzzy integral. Our approach employs convolutional neural network (CNN) models, specifically ResNet152, VGG19, and InceptionV3 as base classifiers to identify six types of PCB defects: spurs, mouse bites, short circuits, open circuits, spurious copper, and pinholes. Given the critical role of PCBs in ensuring electronic equipment reliability, effective defect detection methods like ours are essential. We employ pre-trained CNN models for feature extraction and classification of PCB defects. Following this, we combine the prediction scores using the Choquet fuzzy integral to derive more accurate final labels, exceeding the accuracy of standalone models. Our approach is tested on PCB images obtained from public repositories, captured using a linear scan CCD. The evaluation results demonstrate average precision, recall, F-score, and accuracy of 93.0%, 95.2%, 95.1%, and 95.1%, respectively.
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Comparative Study of Machine Learning Models for Soil Fertilizer Classification in Precision Agriculture
(2025-01-01)
Archevapanich, Tuanjai
;
Sirikham, Thanapat
;
Chaowalittawin, Vasutorn
;
Krungseanmuang, Woranidtha
;
Sathaporn, Posathip
This study explores the machine learning techniques compare for fertilizer classification based on soil nutrient dataset aligning with the goals of precision agriculture. Five models include Random Forest, Logistic Regression, SVM, XGBoost and Neural Network(ANN) were tested using precision, accuracy, F1-score, recall and confusion matrices. The highest F1-score is XGBoost model, while the best precision performance delivered by Random Forest model. Results emphasize the significance of model selection in handling imbalanced agricultural data. The approach supports data-driven decision-making for sustainable farming aligned with Thailand's 20-Year Agricultural Strategic Plan.
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Classification of Overlapping Eggs Based on Image Processing
(2022-01-01)
Purahong, B.
;
Krungseanmuang, W.
;
Chaowalittawin, V.
;
Pumee, T.
;
Kanjanasurat, I.
This paper presents a method for classifying the overlapped eggs and counting the number of eggs on the conveyor belt using image processing techniques. The image was acquired by a webcam camera that connected to the computer and then rescaled. The image was then converted to grayscale and noise was reduced using a Gaussian blur filter. Otsu's Binarization is used to convert the image to binary. The binary image is then subjected to morphological operations. Following that, using the Watershed Algorithm, separate the egg's overlapped area. Finally, the prepared image is ready to be counted using the contour matrix method. This method independently classifies each egg segmentation and can count up to 18 eggs per frame with a processing time of less than 1 second.
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Centralizing Data Warehouse Platform for Mining Management Using AWS Computing Instance
(2024-01-01)
Sathaporn, Posathip
;
Chaowalittawin, Vasutorn
;
Krungseanmuang, Woranidtha
;
Benjangkaprasert, Chawalit
;
Purahong, Boonchana
Mining industry is one of significant industry in the world. In order to increase efficiency, safety, sustainability and environmental impacts in mining process. This article presents a centralized data platform for mining process by design based on microservices architecture which can be supported various of input data source such as manual form, excel file and internet of things (IoT) device and notification when the value is abnormal. Our platform implementation on Amazon Web Service (AWS) cloud. For development of productivity in process and controlling quality the mine environment. The results of the platform operations with various source of data acquisition with response time is less than 1.5 seconds per each request.
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Voice over IP Integration Platform Performance Using EC2 AWS Cloud Service
(2022-01-01)
Sathaporn, Posathip
;
Krungseanmuang, Woranidtha
;
Chaowalittawin, Vasutorn
;
Anuwongpinit, Thanavit
;
Purahong, Boonchana
This article describes a method for integrating a mobile application for controlling and transmitting voice data levels in various departments within an organization with Amazon Elastic Compute Cloud (AWS EC2) to reduce hardware location costs and create more convenient in-house management at a single point. To begin, the paper introduces the project objective with a business scenario from an organization. Second, the SIP server implementation method is provided by Asterisk on AWS EC2 Ubuntu operating system and connection with a Mobile application that is used by flutter framework. Finally, the project experiments and discussions will be presented, and the obtained results show that the call setup time for the iOS/Android platforms to PC performed the best, taking less than one second, and was the fastest when compared to other testing metrics. However, there are many more metrics that should be considered, which are presented in the research's results section. With high performance and stability, this article was able to broadcast voice data via mobile applications.
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Egg-Counting System Using Image Processing and a Website for Monitoring
(2021-04-01)
Kanjanasurat, Isoon
;
Krungseanmuang, Woranidtha
;
Chaowalittawin, Vasutorn
;
Purahong, Boonchana
This paper presents an egg-counting system method that uses a morphological operation and shows the results in real time on a website. The method begins with image acquisition from a Raspberry Pi camera and converts it to greyscale. A Gaussian blur filter is applied to reduce noise, and the blurred image is then converted to a binary image. Next, an opening operation is utilized to delete small objects from the foreground. Finally, the eggs in the image are counted using a contoured matrix, and the number of eggs is shown on a website. The system was able to count 100% of the eggs and showed the number of eggs in real time.
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Crack Detection of Eggshell using Image Processing and Computer Vision
(2022-01-01)
Purahong, B.
;
Chaowalittawin, V.
;
Krungseanmuang, W.
;
Sathaporn, P.
;
Anuwongpinit, T.
This article presents an eggshell crack inspection using image processing techniques. This approach uses the concept of industrial 4.0 to reduce manual coordination in the egg industry's manufacturing process. The method started with receiving images from a webcam camera. Then, we rescaled the image to 1147 x 633 for faster computation. Next, divide the image into the red and green channels. The red channel image was converted to grayscale using a Gaussian blur filter with a kernel filter 11 x 11 to reduce noise, followed by turning the image to binary. After that, multiply the binary image with the grayscale of the green channel to remove the background. By that time, a morphological operation was used to enhance the quality of the image. Finally, use the contour matrix to find the area of the object and then build the condition to detect the crack in the eggshell. These techniques of image processing are used to inspect the eggshell crack with a high accuracy of more than 98% as well as the high performance of computing.
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Krungseanmuang, Woranidtha

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