Yazar "Xing, Hongyan" seçeneğine göre listele

Listeleniyor 1 - 3 / 3
  • Küçük Resim Yok
    Öğe
    3DAEF-Based Thunderstorm Multipath Imaging System
    (Ieee-Inst Electrical Electronics Engineers Inc, 2023) Yang, Xu; Xing, Hongyan; Ji, Xinyuan; Huang, Ting; Zhou, Chen; Yin, Wenjie; Su, Xin
    This article presents a multipath imaging system for thunderstorm developments, wherein data are three-dimensional atmospheric electric-field signals (3DAEFSs) collected with a self-made 3DAEF apparatus (3DAEFA). In this way, thunderstorms are presented in a staged and visual form. To start with, entropy-based intervals are constructed from historical AEF data, to classify denoised AEFS components, according to the entropy value of each component. Furthermore, AEFS time sequences are reconstructed with a reference to whether components within the same entropy-based interval are sequential or not, providing time information for the subsequent clustering-based spatial denoising to thunderstorm point charge coordinates. Finally, predicted value (PV) intervals, which are used to divide and then reconstruct AEFS time periods, are acquired to realize the point charge multipath imaging corresponding to periods, based on the established stacked autoencoder and the extreme gradient boosting (SAE-XGBoost) model. Empirical results demonstrate that the multipath better visualizes the whole process of thunderstorm activities. Comparisons with radar charts further confirm that the proposed system effectively images charge multipaths and provides a valid reference for visual thunderstorm monitoring.
  • Küçük Resim Yok
    Öğe
    Multifeature Fusion-Based Thunderstorm Prediction System With Switchable Patterns
    (Ieee-Inst Electrical Electronics Engineers Inc, 2023) Yang, Xu; Xing, Hongyan; Ji, Xinyuan; Zhao, Di; Su, Xin; Pedrycz, Witold
    Atmospheric electric field signal (AEFS) features can be characterized by their average value (AV), standard deviation (SD), and entropy value (EV). How to mine and fully utilize AEFS features to ensure reliable and efficient thunderstorm detection has not been considered so far. In this article, based on the stacked autoencoder (SAE) and extreme gradient boosting (XGBoost) model, extracted deep-seated features of AEFS are used to obtain its predicted value (PV). It fuses three regular features plus one PV feature and proposes a thunderstorm moving path (TMP) prediction system with switchable patterns among the applied three AEFS prediction models based on the convolutional neural network (CNN) and bidirectional long short-term memory (BiLSTM). This fully considers that a single model is difficult to meet AEFS predictions with different weather attributes. Specifically, AEF data measured by a self-made AEF apparatus are adopted to determine feature values (FVs). According to FV intervals (FVIs) in sunny and thunderstorm weathers, the proportion of each feature satisfying FVIs is taken as the weighting factors of corresponding feature terms. A switchable pattern function with different switching conditions is formed by combining weightings and feature variables. Optimal AEFS prediction models are fixed under the same switching condition and applied to corresponding patterns. Empirical results confirm that the proposed system effectively predicts TMPs, with an average determination coefficient of 95.58%. This is the first study to design switchable patterns to detect thunderstorms from a new perspective of multiple AEFS feature fusion, which provides promising solutions to the refinement and intelligent prediction of thunderstorms.
  • Küçük Resim Yok
    Öğe
    Multitime Scale Thunderstorm Monitoring System With Real-Time Warning and Imaging
    (Ieee-Inst Electrical Electronics Engineers Inc, 2024) Yang, Xu; Xing, Hongyan; Ji, Xinyuan; Su, Xin; Pedrycz, Witold
    As an important factor in fine thunderstorm detections, a multitime scale thunderstorm monitoring, warning, and imaging system is proposed in this article. The first computing phase involves a decomposition, classification, denoising, and reconstruction of the atmospheric electric field signals (AEFSs), collected by a self-made 3-D AEF apparatus, based on autocorrelation characteristics and fuzzy $C$-means (FCM). Second, FCM classifies the equally divided AEFS components. A scale reconstruction rule is put forward and applied to obtain multitime scale AEF branch data, according to the component temporal continuity in the same class. A corresponding scale correction strategy is then proposed. Thunderstorm point charge coordinate results are calculated by using branch data, and noise points contained in these results are removed. Finally, the curve fitting of denoised coordinate results is performed to image the point charge moving path. Empirical results confirm that the proposed system effectively warns and images thunderstorms, as well as provides a valid reference for multiscale thunderstorm monitoring.