Siritaratiwat, Apirat

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Siritaratiwat, Apirat
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Siritaratiwat, A.
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apirat.si@kmitl.ac.th

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    Item type:Publication,
    Simulation of magnetic footprints for heat assisted magnetic recording
    (2017-05-01)
    Pituso, Kotchakorn
    ;
    Khunkitti, Pirat
    ;
    Kruesubthaworn, Anan
    ;
    Chooruang, Komkrit
    ;
    Tongsomporn, Damrongsak
    The heat assisted magnetic recording (HAMR) technology has been the promising candidate to overcome the thermal stability limitation at higher capacities of the hard disk drive. In this work, the characteristics of magnetic footprint of the medium written by HAMR were investigated through the micromagnetic simulations. The Voronoi granular media was firstly modeled, then the magnetic footprint technique was performed to observe the media behaviors at various linear densities. The results indicated that the pattern of magnetic footprint can be perverted at higher densities, which essentially causes a reduction of readback signal power. Also, the dependence of media grain size on the signal power shows that the larger grain size media can provide higher signal power at low linear density, while the smaller grain size media gives higher signal at high density. Thus, the magnetic footprint regarding the HAMR technology needs to be optimized to achieve the efficient recording system.
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    Item type:Publication,
    Automatic classification of spread‐F types in ionogram images using support vector machine and convolutional neural network
    (2024-12-01)
    Benchawattananon, Phongsachot
    ;
    Siritaratiwat, Apirat
    ;
    Supnithi, Pornchai
    ;
    Nishioka, Michi
    ;
    Perwitasari, Septi
    An ionogram image serves as a valuable data for examining the ionospheric bottom side characteristics and variabilities. Spread-F is indicated or identified by plasma irregularity in the ionospheric region. Diffused echo in the ionogram images particularly pose challenges for efficient interpretation required in further applications. An automatic classification of spread-F is presented in this study. Ionogram images are automatically classified using preprocessing techniques to improve the classification performance. In this study, the classification is designed by two machine learning algorithms, including support vector machine (SVM) and convolutional neural network (CNN). The CNN model with preprocessing technique outperforms the SVM alternative based on 4,692 labelled ionogram images from the FMCW-type ionosonde at Chumphon station, Thailand. The model successfully classified clear, frequency spread-F (FSF), range spread-F (RSF), strong spread-F (SSF), and unidentified class with an accuracy of 98.0%, 85.1%, 90.7%, 66.7%, and 99.2%, respectively. The proposed automatic classification models achieved to classify classes of ionogram images. In addition, the image filtering and data preprocessing are useful with ionogram images for improving the model classification performance. Graphical Abstract: (Figure presented.)
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    Improving One-Day-Ahead Forecasting of Low-Latitude Amplitude Scintillation Using an Upsampling-Enhanced LSTM
    (2026-01-01)
    Muangkammuen, Patinya
    ;
    Suthisopapan, Puripong
    ;
    Tongkasem, Napat
    ;
    Supnithi, Pornchai
    ;
    Kruesubthaworn, Anan
    The scintillation in radio wave propagation, particularly in regions near the magnetic equator, is found to be introduced by the ionospheric irregularities causing unsatisfactory performance in satellite-based applications. In order to mitigate this effect, we design a long short-term memory (LSTM) model to forecast amplitude scintillation at 1-min resolution. In addition, the upsampling-based feature preprocessing is introduced to improve forecasting performance, especially for short-term severe scintillation events. In terms of R$^{2}$, which is a popular forecast evaluation metric, our proposed model exhibits about 20% improvement over the same LSTM model without upsampling. Furthermore, although existing studies achieve good forecasting accuracy up to 4 h ahead, the proposed model sets a benchmark with one-day-ahead forecasting, but at the cost of longer training time due to upsampling.