Differences of Electric Field Parameters for Lightning Strikes on Tall Towers and Nonelevated Objects

Li, Cai; Liu, Wei; Zhou, Mi; Wang, Jianguo; Yan, Ruisheng; Tian, Ruixin; Fan, Yadong

doi:10.1109/temc.2022.3207237

Cited by 3 publications

(2 citation statements)

References 35 publications

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“…Based on the test set data, this section compares the classification performance of classification methods based on waveform time-domain parameter features and convolutional encoder features. As for the identification method of waveform time-domain parameter characteristics, this paper refers to the method proposed by Cai et al [21] to extract time-domain characteristic parameters such as rise time, fall time, pulse width, pulse interval, pulse amplitude ratio, the ratio of the maximum waveform fluctuation amplitude before pulse to its peak to peak value, the ratio of the difference between the maximum and minimum values in the data, and the ratio of the negative and positive amplitudes of the pulse; we then use a random forest classifier for multi-classification testing. Table 6 shows the accuracy of two methods for each type of lightning waveform.…”

Section: Comparison Of Classification Methods Based On Different Feat...mentioning

confidence: 99%

A Lightning Classification Method Based on Convolutional Encoding Features

Zhu,

Zhang,

Fan

et al. 2024

Remote Sensing

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At present, for business lightning positioning systems, the classification of lightning discharge types is mostly based on lightning pulse signal features, and there is still a lot of room for improvement. We propose a lightning discharge classification method based on convolutional encoding features. This method utilizes convolutional neural networks to extract encoding features, and uses random forests to classify the extracted encoding features, achieving high accuracy discrimination for various lightning discharge events. Compared with traditional multi-parameter-based methods, the new method proposed in this paper has the ability to identify multiple lightning discharge events and does not require precise detailed feature engineering to extract individual pulse parameters. The accuracy of this method for identifying lightning discharge types in intra-cloud flash (IC), cloud-to-ground flash (CG), and narrow bipolar events (NBEs) is 97%, which is higher than that of multi-parameter methods. Moreover, our method can complete the classification task of lightning signals at a faster speed. Under the same conditions, the new method only requires 28.2 µs to identify one pulse, while deep learning-based methods require 300 µs. This method has faster recognition speed and higher accuracy in identifying multiple discharge types, which can better meet the needs of real-time business positioning.

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Section: Comparison Of Classification Methods Based On Different Feat...mentioning

confidence: 99%

A Lightning Classification Method Based on Convolutional Encoding Features

Zhu,

Zhang,

Fan

et al. 2024

Remote Sensing

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“…Waveforms of four examples are shown in Figures 5a–5d, all of which are misclassified as IC discharges and whose source heights have been confirmed to be close to the ground. First, it is well known that RSs striking tall grounded objects usually produce very narrow pulses (Araki et al., 2018; Cai et al., 2022; Pavanello et al., 2007; Zhu et al., 2018), making it easy to misclassify them as IC discharges. One example is shown in Figure 5a.…”

Section: Atypical Intracloud and Return Stroke Waveformsmentioning

confidence: 99%

High‐Accuracy Classification of Radiation Waveforms of Lightning Return Strokes

Wang

Takagi

2023

JGR Atmospheres

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Ground-based lightning location systems (LLSs) are widely used to monitor lightning activities. A prominent feature of ground-based LLSs is that lightning activities in a wide area can be monitored in real time with only a limited number of sensors. Some famous national and continental LLSs include the National Lightning Detection Network (NLDN) covering the continental United States (e.g., Cummins & Murphy, 2009), the European Cooperation for Lightning Detection network (EUCLID) covering the European continent (e.g., Schulz et al., 2016), and the Earth Networks Total Lightning Network (ENTLN) (e.g., Zhu et al., 2022) with the aim of a global coverage.It is a basic requirement for LLSs to automatically and efficiently classify cloud-to-ground (CG) lightning flashes from intracloud (IC) flashes as the former consist of discharges with direct connections to the ground and thus pose a much larger threat to the human society. The fundamental difference between a CG flash and an IC flash is that a CG flash contains one or more return strokes (RSs), so the classification of CG flashes is basically realized by classifying RSs. Further, it is well known that RSs produce characteristic electric field radiation waveforms that are largely different from those of IC discharges (e.g., Lin et al., 1979), so most LLSs classify RSs based on their waveform characteristics.

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