Kasemsiri, Watjanapong
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Preferred name
Kasemsiri, Watjanapong
Alternative Name
Kasemsiri, W.
Main Affiliation
Email
watjanapong.ka@kmitl.ac.th
2 results
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Item type:Publication, Door lock system via web application(2017-10-19); ; ; 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. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Vehicle tracking using fuzzy-based vehicle detection window with adaptive parameters(2018-01-01); ; ; Lapamonpinyo, PipatphonIn 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%.