Kummool, Sart

Loading...
Thumbnail Image
Preferred name
Kummool, Sart
Main Affiliation
Email
sart.ku@kmitl.ac.th

Settings

Now showing 1 - 2 of 2
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    APPLYING MELSOFT GEMINI SOFTWARE FOR DIGITAL TWIN CREATION IN AUTOMATION SYSTEM PROJECT PHASES
    (2026-08-01)
    Thepmanee, Teerawat
    ;
    Sopha, Watcharapon
    ;
    Asadi, Farzin
    ;
    Pongswatd, Sawai
    ;
    Kummool, Sart
    In the era of Industry 4.0, Digital Twin (DT) technology played a critical role in advancing manufacturing systems, particularly in real-time process simulation, performance optimization, and strategic decision-making. This study presents the application of MELSOFT Gemini software to develop DT for simulated industrial automation project phases. The system is designed to enable seamless data access and integration from physical devices into the DT in accordance with ISO 23247. The development process comprises two phases. The pre-deployment phase involves creating 3D models and simulating system behavior within a virtual environment prior to actual implementation. The Synchronized Operation phase enables visualization in MELSOFT Gemini through the transmission of data from physical devices via Operational Technology (OT) systems and Autonomous Mobile Robots (AMRs), using standard protocols such as OPC UA. The developed system accurately replicates the actual production process, provides early safety alerts, supports connectivity with various device types, and allows for future scalability all while maintaining compliance with the ISO 23247 framework. These findings demonstrate the potential of MELSOFT Gemini as a core platform for implementing practical, industry-ready DT systems.
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    Improving Accuracy in Image-Recognition Systems with Techniques for Handling Similar-Looking Items in Inventory Management
    (2025-01-01)
    Smerpitak, Krit
    ;
    Kummool, Sart
    ;
    Asadi, Farzin
    ;
    Ar-Karachaiphong, Chitsanuwit
    ;
    Sopha, Watcharapon
    Inventory management is crucial for businesses dealing with many visually similar items. This paper presents the development of an automated board game detection system utilizing computer vision and deep learning to enhance the accuracy and efficiency of tracking visually similar inventory. The dataset used to train the model was collected using a Pi Camera Module 3 mounted on a Raspberry Pi 5, ensuring consistency in captured images. To improve the model's performance, images were annotated using CVAT, and dataset augmentation was applied using Augmentations, incorporating transformations such as flipping, brightness adjustment, blurring, and noise addition. These augmentations allowed the model to learn and differentiate board games under varying environmental conditions accurately. The selected model was YOLOv8m, chosen for its balance between accuracy and computational efficiency. The model was trained for 20 epochs using a learning rate of 0.0005, a batch size of 12, and augmentation techniques such as Mosaic and MixUp. Additionally, overfitting prevention techniques including early stopping, dropout, and weight decay were applied. After training, the model was converted into the ONNX format to optimize it for deployment on embedded devices and then uploaded to the Raspberry Pi 5 for realworld testing. The system was evaluated on 10 visually similar or small-sized board games, with 10 tests per game, totaling 100 trials. The results showed that the model successfully classified all board games without a single error. Moreover, these findings demonstrate that the proposed system effectively identify board games with high accuracy while maintaining real-Time processing capability. The use of data augmentation and model optimization significantly improved object classification performance, making it a promising approach for inventory management in board game cafes and similar environments.