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Author: search.filters.author.Kasemsiri, Watjanapong

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    Vehicle tracking using fuzzy-based vehicle detection window with adaptive parameters
    (2018-01-01)
    Chitsobhuk, Orachat
    ;
    Kasemsiri, Watjanapong
    ;
    Glomglome, Sorayut
    ;
    Lapamonpinyo, Pipatphon
    In this paper, fuzzy-based vehicle tracking system is proposed. The proposed system consists of two main processes: vehicle detection and vehicle tracking. In the first process, the Gradient-based Adaptive Threshold Estimation (GATE) algorithm is adopted to provide the suitable threshold value for the sobel edge detection. The estimated threshold can be adapted to the changes of diverse illumination conditions throughout the day. This leads to greater vehicle detection performance compared to a fixed user's defined threshold. In the second process, this paper proposes the novel vehicle tracking algorithms namely Fuzzy-based Vehicle Analysis (FBA) in order to reduce the false estimation of the vehicle tracking caused by uneven edges of the large vehicles and vehicle changing lanes. The proposed FBA algorithm employs the average edge density and the Horizontal Moving Edge Detection (HMED) algorithm to alleviate those problems by adopting fuzzy rule-based algorithms to rectify the vehicle tracking. The experimental results demonstrate that the proposed system provides the high accuracy of vehicle detection about 98.22%. In addition, it also offers the low false detection rates about 3.92%.
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    Door lock system via web application
    (2017-10-19)
    Vongchumyen, Charoen
    ;
    Watanachaturaporn, Pakorn
    ;
    Jinjakam, Chompoonuch
    ;
    Watcharapupong, Akkradach
    ;
    Kasemsiri, Watjanapong
    This research proposes the smart way to solve the common problem of the conventional door-locking system. The important problems of the ordinary door-locking systems are key lost, forgetting to lock the system and cannot check the lock status from remote. To solve these problems, we proposed a method to lock-unlock the system which utilizes the web application and passcode alongside the usual method of using the ordinary keys. The proposed system can remotely lock, remotely unlock, remotely check door state, remotely check door locking state, send the email to the owner in case of someone knocking on the door and generate the temporary passcode to use in specific date and time.
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    Thai characters recognition based on tolerant rough sets with fuzzy c-mean
    (2009-12-01)
    Kasemsiri, Watjanapong
    ;
    Shi, Yan
    This paper presents a method of using fuzzy c-mean in combination withrough sets for recognizing Thai Characters. The clusters made by fuzzy c-meanalgorithm alone may compose of many types of data, which is the problem forrecognition system. By coupling fuzzy c-mean with rough sets, the status oflower and upper approximation will be associated to all created clusters. We cansplit the clusters which have upper approximation status until they becomelower approximation set. With most clusters as lower approximation set, we canclearly state the type of members of each cluster and that will help improve theaccuracy of the Thai characters recognition system. ICIC International © 2009.
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    Printed thai character recognition using Fuzzy-Rough Sets
    (2001-12-01)
    Kasemsiri, Watjanapong
    ;
    Kimpan, Chom
    This paper proposes the method of using the Fuzzy-Rough Sets for the recognition of Thai Characters. In our work we divide the classification process into 2 levels, coarse and fine classification. Both levels of classification have the same processes, applying Rough Set's lower approximation and then using Fuzzy-Rough sets. The different point between those two levels is the features of inputted data used for classifying. There are 40 coarse groups and some of them need not to pass through the second level of classification. We trained this system with 2816 training samples, which composed of 4 fonts and 4 sizes characters. The system is tested with unknown sample, which composed of 7 fonts and 7 sizes characters, 4 fonts and 4 sizes of the training sample are inclusive. The accuracy of this proposed system is as high as 89%.