Thanakulketsarat, Thananphat

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Thanakulketsarat, Thananphat
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Thanakulketsarat, T.
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thananphat.th@kmitl.ac.th

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    Equatorial Plasma Bubble Detection by Support Vector Machine at Chumphon Station, Thailand
    (2022-01-01)
    Thanakulketsarat, Thananphat
    ;
    Supnithi, Pornchai
    ;
    Myint, Lin Min Min
    ;
    Hozumi, Kornyanat
    Equatorial Plasma Bubble (EPB) is a phenomenon in which depletion of plasma density occurs in the ionosphere particularly in the equatorial region. It can degrade the performances of the navigation system and satellite communication. In this work, we analyze EPB based on the very-high frequency (VHF) radar images at Chumphon station, Thailand. Then an EPB detection system using the support vector machine (SVM) technique is developed, and the accuracies of the systems using different kernels: linear kernel, the polynomial kernel, the radial basic functions kernel (RBF), and the sigmoid kernel are compared. Among the different kernels, we find that the RBF kernel gives the highest accuracy in prediction at 86.67 percent.
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    Ionograms Scaling by Using the Convolutional Neural Network
    (2021-03-10)
    Thanakulketsarat, Thananphat
    ;
    Sopon, Thanomsak
    ;
    Phakphisut, Watid
    ;
    Hozumi, Kornyanat
    ;
    Wongtrairat, Wannaree
    Ionosphere in F layer has the most irregularity for phenomenon occurrence of amplitude scintillation which leads to the problem in the satellite signals. Ionosphere can be observed by Ionosonde to study F2 layer critical frequency (foF2) parameter and height of F layer (h'F) parameter from the ionogram. This paper presents the convolutional neural network (CNN) to determine foF2 and h'F parameters. The simulation start from passing the ionogram images to the proposed CNN model with 2,000 epoch training. The simulated accuracies of both foF2 and h'F parameters are equal to 92.8% and 98.4%, respectively.
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    Classification of the equatorial plasma bubbles using convolutional neural network and support vector machine techniques
    (2023-12-01)
    Thanakulketsarat, Thananphat
    ;
    Supnithi, Pornchai
    ;
    Myint, Lin Min Min
    ;
    Hozumi, Kornyanat
    ;
    Nishioka, Michi
    Equatorial plasma bubble (EPB) is a phenomenon characterized by depletions in ionospheric plasma density being formed during post-sunset hours. The ionospheric irregularities can lead to disruptions in trans-ionospheric radio systems, navigation systems and satellite communications. Real-time detection and classification of EPBs are crucial for the space weather community. Since 2020, the Prachomklao radar station, a very high frequency (VHF) radar station, has been installed at Chumphon station (Geographic: 10.72° N, 99.73° E and Geomagnetic: 1.33° N) and started to produce radar images ever since. In this work, we propose two real-time plasma bubble detection systems based on support vector machine techniques. Two designs are made with the convolutional neural network (CNN) and singular value decomposition (SVD) used for feature extraction, the connected to the support vector machine (SVM) for EPB classification. The proposed models are trained using quick look (QL) plot images from the VHF radar system at the Chumphon station, Thailand, in 2017. The experimental results show that the combined CNN-SVM model, using the RBF kernel, achieves the highest accuracy of 93.08% while the model using the polynomial kernel achieved an accuracy of 92.14%. On the other hand, the combined SVD-SVM models yield the accuracies of 88.37% and 85.00% for RBF and polynomial kernels of SVM, respectively. Graphical Abstract: [Figure not available: see fulltext.].
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    Equatorial Plasma Bubble Detection using the Convolutional Neural Network (CNN) and Support Vector Machine (SVM)
    (2023-01-01)
    Thanakulketsarat, Thananphat
    ;
    Supnithi, Pornchai
    ;
    Myint, Lin Min Min
    ;
    Hozumi, Kornyanat
    Equatorial plasma bubbles (EPB) refer to the area of low electron density in the Earth's ionosphere near the equator during post sunset and post-midnight. They influence the radio communications and GPS signals. In this work, we study the EPB occurrences and characteristics using the VHF radar images observed at the Chumphon station, Thailand, near the magnetic equator.. We develop an EPB image detection system using a hybrid learning technique with convolutional neural network (CNN) and support vector machine (SVM) and evaluate the accuracy of the proposed CNN-SVM model using two kernels: polynomial kernel and radial basis function (RBF) kernel.
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