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Author: search.filters.author.Shiokawa, Kazuo

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    A Bootstrapping Convolutional Neural Network Technique for Optimizing Automated Detection of Equatorial Plasma Bubbles by Optical All-Sky Imagers
    (2025-06-01)
    Okoh, Daniel
    ;
    Cesaroni, Claudio
    ;
    Rabiu, Babatunde
    ;
    Shiokawa, Kazuo
    ;
    Otsuka, Yuichi
    Equatorial plasma bubbles (EPBs) disrupt satellite-based communication and navigation systems, particularly in equatorial regions. Reliable detection and classification of EPBs from all-sky imager (ASI) images are essential for accurate space weather monitoring and forecasting. This study presents a novel bootstrapping convolutional neural network (CNN) approach to optimize automated EPB detection on ASI images for operational space weather monitoring applications, and overcoming challenges related to image variability and imbalanced data sets. Data used for CNN training were obtained from the optical mesosphere thermosphere imagers ASI installed at the Space Environment Research Laboratory, National Space Research and Development Agency, Abuja during the period from 2015 to 2020. Our method involved training three sub-models, and aggregating their predictions. The CNN trainings were conducted on three sub-datasets of 3,000 images each, categorized as “EPB,” “Noisy/Cloudy” or “No EPB.” Three corresponding sub-models were developed from the CNN trainings. The three sub-model classifications independently gave prediction accuracies of 98.67%, 98.33%, and 95.83% on a reserved test data set of 600 images. Ensemble models further improved the model prediction accuracies to 99.17% and 99.33% for methods based on the mean of sub-model probabilities and the mode of sub-model classifications respectively. Our results indicate that the bootstrapping CNN technique enhanced the EPB detection accuracy, providing a powerful tool for real-time space weather monitoring applications, and implications for improving operational reliability of satellite-based navigation and communication in the equatorial region.
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    Detecting Equatorial Plasma Bubbles on All-Sky Imager Images Using Convolutional Neural Network
    (2022-01-01)
    Srisamoodkham, Worachai
    ;
    Shiokawa, Kazuo
    ;
    Otsuka, Yuichi
    ;
    Ansari, Kutubuddin
    ;
    Jamjareegulgarn, Punyawi
    This paper proposes initially to apply convolutional neural network (CNN) for detecting the equatorial plasma bubbles on the ASI images. The considered CNN model is the YOLO v3 tiny model under a deep learning API (Keras), running on top of the machine learning platform (TensorFlow). Our program for EPB detection is written in Python that is extended easily to combine into a space weather web site for detecting and notifying EPBs in our next step. The results show that the YOLO v3-based CNN can detect the EPBs in ASI images with different intensities obtained from many countries. The threshold is tested and selected to be 0.40 suitably for detecting the anomaly (EPB existence). The maximum anomalous value is selected to decide the EPB occurrence.