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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    Development of a new part of casing cap for the parking brake cable using finite element analysis
    (2017-04-01)
    Buthgate, Siwawong
    ;
    Saenthon, Anakkapon
    ;
    Kaitwanidvilai, Somyot
    The brake system is an important safety system in every vehicle. This system is utilized for controlling of the vehicle movement. This paper proposes a new design and development of casing cap for the parking brake cable by applying the finite element technique to analyzing and evaluating the feasibility of the material and the design of the product. The study and analysis of influential factors affecting the parts of casing cap by JASO standard F903-75 control cable for automobiles are described. The results show that the plastic PA GF15 can be used to replace the conventional material that is steel casing cap, and will then be able to reduce the overall cost material by 2.5%. In addition, the results of the analysis illustrate that all features of the new product are complied with JASO F903-75, the control cable standard for vehicles.
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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.
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    Development of new edge-detection filter based on genetic algorithm: An application to a soldering joint inspection
    (2010-02-01)
    Saenthon, A.
    ;
    Kaitwanidvilai, S.
    This paper proposes a new technique of edge detection for inspecting edge and perfection of soldering joint in the flip-chip, which is an important part of a hard disk drive. Summation of error between the actual values and the measured values from the designed system of several data sets is formulated as the objective function. Genetic algorithm is adopted to find the optimal filter mask to enhance the accuracy of the inspection system. As the results indicated, the accuracy of a system with the proposed edge-detection technique is superior to that of a system with conventional filters. © 2009 Springer-Verlag London Limited.