Xu Yang^1,2, Ling Zhuang¹, Yuqiang Sun³, Wenjie Zhang^4,5,*

Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 1637-1654, 2023, DOI:10.32604/iasc.2023.039879 - 21 June 2023

Abstract The loss of three-dimensional atmospheric electric field (3DAEF) data has a negative impact on thunderstorm detection. This paper proposes a method for thunderstorm point charge path recovery. Based on the relationship between a point charge and 3DAEF, we derive corresponding localization formulae by establishing a point charge localization model. Generally, point charge movement paths are obtained after fitting time series localization results. However, AEF data losses make it difficult to fit and visualize paths. Therefore, using available AEF data without loss as input, we design a hybrid model combining the convolutional neural network (CNN) and… More >

Xiaopeng Li¹, Ziyuan Xu^3,4, Jin Han^1,*, Xingming Sun^1,2, Yi Cao⁵

Intelligent Automation & Soft Computing, Vol.33, No.2, pp. 1153-1166, 2022, DOI:10.32604/iasc.2022.023261 - 08 February 2022

Abstract Lightning has been one of the most talked-about natural disasters worldwide in recent years, as it poses a great threat to all industries and can cause huge economic losses. Thunderstorms are often accompanied by natural phenomena such as lightning strikes and lightning, and many scholars have studied deeply the regulations of thunderstorm generation, movement and dissipation to reduce the risk of lightning damage. Most of the current methods for studying thunderstorms focus on using more complex algorithms based on radar or lightning data, which increases the computational burden and reduces the computational efficiency to some… More >

Lili He^1,2, Dantong Ouyang^1,2, Meng Wang^1,2, Hongtao Bai^1,2, Qianlong Yang^1,2, Yaqing Liu^3,4, Yu Jiang^1,2,*

CMC-Computers, Materials & Continua, Vol.57, No.3, pp. 549-570, 2018, DOI:10.32604/cmc.2018.03840

Abstract In this paper, the clustering analysis is applied to the satellite image segmentation, and a cloud-based thunderstorm cloud recognition method is proposed in combination with the strong cloud computing power. The method firstly adopts the fuzzy C-means clustering (FCM) to obtain the satellite cloud image segmentation. Secondly, in the cloud image, we dispose the ‘high-density connected’ pixels in the same cloud clusters and the ‘low-density connected’ pixels in different cloud clusters. Therefore, we apply the DBSCAN algorithm to the cloud image obtained in the first step to realize cloud cluster knowledge. Finally, using the method More >