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    Comparison of logistic regression and artificial neural network model for apron allocation assignment
    (2023-01-01)
    Purahong, B.
    ;
    Teerapanpong, S.
    ;
    Satayarak, N.
    ;
    Benjangkaprasert, C.
    Management of the parking apron is one of the most essential airport ground service operations for flight operations to run smoothly. Effective airport ground service management will have a direct effect on the cost and duration of flights. Therefore, in this paper, we address the issue of using machine learning techniques, such as logistic regression analysis and artificial neural network (ANNs) models, for classified targets of stand locations assignment of an arriving flight. Also, this could assist ground controllers to assign apron allocation and improve the efficiency and predictability of airport operations which reduce the time required for airport ground processing to increase flight capacity. In order to evaluate the performance of the proposed method, simulation results reveal that ANN has the lowest error rate and the highest accuracy. Therefore, ANN is the effective classification technique for this data set.
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    On the Study of Thai Music Emotion Recognition Based on Western Music Model
    (2022-01-01)
    Satayarak, N.
    ;
    Benjangkaprasert, C.
    The mood of the song could be identified by tracking the listener's emotion. The research in this area is growing significantly at the present. There are many research studies in western music, but a few in Thai music. Therefore, in this research, Thai songs were chosen because the Thai is a native language and Thai songs are quite popular in the region of research. This research is divided into 2 parts. First, Thai music was evaluated by the set of a system based on western music training settings. By using valence-arousal values, multiple linear regression, and k-nearest neighbors to represent the emotional annotations from the music. As a result, the highest f-measure of Thai music from multiple linear regression by ALL model was 41% and the f-measure of western music from multiple linear regression by No Tempo model was 51%, which was very different because ALL model in western music has lower efficiency than other models. Second, we measured the mood of 125 Thai popular songs and used valence-arousal (energy) values from Spotify API to investigate the results. In this research we used multiple linear regression (MLR) and support vector regression (SVR). Experimental results show that the multiple linear regression provides the highest accuracy of 61.29% with the precision of 65%, recall of 61%, and f-measure of 60% which is more than support vector regression.
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    Adaptive lattice structure filters using variable step-size algorithm for echo cancellation
    (2007-12-01)
    Sukhumalwong, S.
    ;
    Satayarak, N.
    ;
    Benjangkaprasert, C.
    In this paper, we propose a novel variable step-size algorithm for the adaptive lattice form structure filter for the echo canceller in telephone network. By the proposed techniques, it is demonstrated that the proposed algorithm yields the performances over the previous one. Computer simulation results in terms of Echo Return Loss Enhancement (ERLE) are provided to confirm the performances of the proposed algorithm. ©ICROS.