Saenthon, Anakkapon

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Saenthon, Anakkapon
Alternative Name
Saenthon, A.
Saenthon, Anakapon
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Email
anakkapon.sa@kmitl.ac.th

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    Clamp Dot Image Classification Using Neural Network
    (2024-01-01)
    Srikam, Krittapak
    ;
    Saenthon, Anakkapon
    In this paper, we discuss the classification of images captured by a machine camera while assembling components. To crop out specific points of interest, we employ image processing. Additionally, we utilize deep learning techniques, specifically convolutional neural networks, to identify the type of equipment being assembled. This approach allows us to determine and record specific parts within a device. However, the main challenge of this project is to achieve both high accuracy and the shortest possible prediction time.
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    The Development of Adaptive Gray Level Mapping Combined Partical Wwarm Optimization for Measuring the Dimeter Size of Automotive Nut
    (2020-04-01)
    Pondech, Wichai
    ;
    Saenthon, Anakkapon
    ;
    Konghuayrob, Poom
    In line assembly process, it is necessary to use the accurate tools to inspect the work piece. In order to measure the width, thickness or depth of the nut used in the commercial car, previously in Thai Steel Cable Company used the caliper to measure size of the automotive nut operated by human as an sample test inspection. Even the result on this technique is high accuracy, however; there are some disadvantages based on this method such as human error, idle time and also fatigue by the human. Therefore, this research focus on the development of the new measurement technique that utilized industrial camera together with the image processing algorithm to measure the entire nut with 100% inspection test. Circular Hough Transform (CHT) is applied to be the basic concept used for finding the circle position and measuring nut diameter. Although the CHT technique can measure diameter of the interested nut, but the result's accuracy is not acceptable due to measuring error from the various range of light condition. This research proposed the new technique, Adaptive Gray Level Mapping algorithm (AGLM) to increase the quality of the input picture before measuring the radius by CHT technique. Moreover, the beta in AGLM is optimized by particle swarm optimization (PSO) technique that applies 50% of nut data and other is used for validate. The results show the effectiveness of the proposed AGLM combined PSO that increase the accuracy of the visual measuring method via compare to the conventional threshold with CHT technique.
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    Development of artificial-intelligence vision system for measurement on-service train for train-track inspection
    (2020-01-01)
    Saenthon, Anakkapon
    ;
    Kiatwanidvilai, Somyot
    ;
    Pumyoy, Suparat
    We studied and inspected the evaluation of safety during the operation of a rail system. In addition, in order to find patterns in the interaction between wheels and rails, the interaction is investigated using an automatic vision system to analyze the patterns that occur. The model of a faster region with a convolutional neural network (R-CNN) can be used to design artificial intelligence (AI) models with vision systems for detecting abnormal operations that occur, causing rail accidents, and to monitor the measurement of tracks and interactions between wheels and tracks in real time.
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    Localization for Outdoor Mobile Robot Using LiDAR and RTK-GNSS/INS
    (2024-01-01)
    Thepsit, Thitipong
    ;
    Konghuayrob, Poom
    ;
    Saenthon, Anakkapon
    ;
    Yanyong, Sarucha
    Two types of sensors, light detection and ranging (LiDAR) and real-time kinematic of global navigation satellite system with inertial navigation system (RTK-GNSS/INS), are used for the localization of outdoor mobile robots. However, using LiDAR and RTK-GNSS/INS independently was found to be insufficient for achieving precise positioning. Therefore, a sensor fusion approach based on an adaptive-network-based fuzzy inference system (ANFIS) was implemented to enhance reliability. In this research, data from both sensors were collected to create a dataset for training with ANFIS. The findings indicated that the model derived from the fusion of these two sensors provided results that were much closer to the actual values obtained using each sensor independently. The result demonstrated the effectiveness of the ANFIS-based fusion method in terms of improving the accuracy and reliability of the positioning system for outdoor mobile robots.