scholarly journals An analysis of manual and autoanalysis for submicrosecond parameters in the typical first lightning return stroke

2021 ◽  
Vol 23 (3) ◽  
pp. 1451
Author(s):  
Muhammad Akmal Bahari ◽  
Zikri Abadi Baharudin ◽  
Tole Sutikno ◽  
Ahmad Idil Abdul Rahman ◽  
Mohd Ariff Mat Hanafiah ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Detection System ◽  
Programming Technique ◽  
Return Stroke ◽  
Zero Crossing ◽  
Manual Analysis ◽  
Lightning Detection ◽  
Crossing Time ◽  
Rising Time ◽  
Initial Peak

The mechanism on how lightning detection system (LDS) operated never been exposed by manufacturer since it was confidential. This scenario motivated the authors to explore the issue above by using MATLAB to develop autoanalysis software based on the feature extraction. This extraction is intended for recognizing the parameters in the first return stroke, and compare the measurement between the autoanalysis software and the manual analysis. This paper is a modification based on a previous work regarding autoanalysis of zero-crossing time and initial peak of return stroke using features extraction programming technique. Further, the parameter on rising time of initial peak is added in this autoanalysis programming technique. Finally, the manual analysis using WaveStudio (LeCroy product) of those two lightning parameters is compared with autoanalysis software. This study found that the autoanalysis produce similar result with the manual analysis, hence proved the reliability of this software.

Review and Evaluation of Characterization First Return Stroke in the Measured Lightning Electric Fields on Malaysia Data

2015 ◽  
Vol 793 ◽  
pp. 44-48
Author(s):  
S.N.M. Arshad ◽  
Mohd Zainal Abidin Ab Kadir ◽  
Mahdi Izadi ◽  
A.M. Ariffen ◽  
M.N. Hamzah ◽  
...  
Keyword(s):  
Electric Fields ◽  
Rise Time ◽  
Statistical Correlation ◽  
Peak Time ◽  
Return Stroke ◽  
Zero Crossing ◽  
Time To Peak ◽  
Lightning Channel ◽  
Crossing Time

In this paper, the characterization of measured electric fields on first return stroke due to lightning channel was studied done. Likewise, previous studies on this case were discussed and reviewed accordingly. Furthermore, the first return stroke was analyzed done in detailed and was indicated on the real measured electric fields. Later the results were discussed appropriately. The behaviorsof first return stroke signal has beencharacterized from previous researchers. This study shows themeasured data in detailed, which include there are slow front time, first return stroke peak, time to peak, zero crossing time and 10% to 90% rise time. The characteristic of first return stroke signal data in Malaysia was compared with data gathered in Sweden. Moreover In addition, the statistical correlation between electric field zero times and corresponding rise times was also been studied.

scholarly journals Comparing the Wave Characteristics of Breakdown Pulses of the Lightning Waveforms in the Himalayan Region

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Pitri Bhakta Adhikari ◽  
Aashutosh Adhikari
Keyword(s):  
Rise Time ◽  
Pulse Train ◽  
Amplitude Ratio ◽  
Pulse Amplitude ◽  
Himalayan Region ◽  
Time Interval ◽  
Return Stroke ◽  
Zero Crossing ◽  
Wave Characteristics ◽  
Crossing Time

We have analyzed the breakdown pulse train with or without the main event in this paper. Among the selected 81 flashes, 36 flashes are starting positively, and 45 are starting negatively. Also, 58 flashes contain positive pulses, and 67 flashes contain negative pulses, whereas 44 flashes contain both positive and negative pulses. Among these 81 flashes, 22 flashes follow the main events, and the rest are isolated events. In this study, we got the main duration of PB pulses as 1.94 ms and the time interval between the breakdown pulse and return stroke as 61.49 ms. On taking each pulse train, we found the rise time to be 2.6 μs, zero-crossing time 14.95 μs, and the time interval between pulses 199.3 μs. The largest pulse amplitude ratio in the preliminary breakdown pulse to the main event return stroke was 0.43.

scholarly journals The Energy, Momentum, and Peak Power Radiated by Negative Lightning Return Strokes

Atmosphere ◽  
2020 ◽  
Vol 11 (12) ◽  
pp. 1288
Author(s):  
Vernon Cooray ◽  
Andre Lobato
Keyword(s):  
Radiation Field ◽  
Peak Power ◽  
Outer Space ◽  
Cylindrical Symmetry ◽  
Return Stroke ◽  
Zero Crossing ◽  
Radiation Fields ◽  
Radiated Energy ◽  
Crossing Time ◽  
Energy And Momentum

Electromagnetic radiation fields generated by return strokes transport both energy and momentum from the return stroke to outer space. The momentum transported by the radiation field has only a vertical or z component due to azimuthal symmetry (cylindrical symmetry) associated with a vertical return stroke. In this paper, the energy, momentum, and peak power radiated by return strokes as a function of the return stroke current, return stroke speed, and the zero-crossing time of the radiation fields are studied. The results obtained by numerical simulations for the energy, vertical momentum, and the peak power radiated by lightning return strokes (all parameters normalized by dividing them by the square of the radiation field peak at 100 km) are the following: A typical first return stroke generating a radiation field having a 50 μs zero-crossing time will dissipate field normalized energy of about (1.7–2.5) × 103 J/(V/m)2 and field-normalized vertical momentum of approximately (2.3–3.1) × 10−6 Kg m/s/(V/m)2. A radiation field with a zero-crossing time of 70 μs will dissipate about (2.6–3.4) × 103 J/(V/m)2 in field-normalized energy and (3.2–4.3) × 10−6 Kg m/s/(V/m)2 in field-normalized vertical momentum. The results show that, for a given peak radiation field, the radiated energy and momentum increase with increasing zero-crossing time of the radiation field. The normalized peak power generated by a first return stroke radiation field is about 1.2 × 108 W/(V/m)2 and the peak power is generated within about 5–6 μs from the beginning of the return stroke. Conversely, a typical subsequent return stroke generating a radiation field having a 40 μs zero-crossing time will dissipate field-normalized energy of about (6–9) × 102 J/(V/m)2 and field-normalized vertical momentum of approximately (7.5–11) × 10−7 Kg m/s/(V/m)2. The field-normalized peak power generated by a subsequent return stroke radiation field is about 1.26 × 108 W/(V/m)2 and the peak power is generated within about 0.7–0.8 μs from the beginning of the return stroke. In addition to these parameters, the possible upper bounds for the energy and momentum radiated by return strokes are also presented.

Investigation of Zero Crossing Detection for First Return Stroke in Negative Cloud-to-Ground Lightning Flash

2019 ◽  
Vol 11 (3) ◽  
Author(s):  
Ahmad Idil Abd Rahman ◽  
◽  
Muhammad Akmal Bahari ◽  
Zikri Abadi Baharudin ◽  
◽  
...  
Keyword(s):  
Lightning Flash ◽  
Return Stroke ◽  
Zero Crossing ◽  
Cloud To Ground Lightning

Deep convolutional neural networks for human movement detection using wireless signals

2021 ◽  
pp. 1-10
Author(s):  
Chien-Cheng Leea ◽  
Zhongjian Gao ◽  
Xiu-Chi Huanga
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Convolutional Neural Network ◽  
Detection System ◽  
Deep Convolutional Neural Network ◽  
Human Detection ◽  
Two Dimensional ◽  
Dimensional Matrix ◽  
State Classification ◽  
Propagation Paths

This paper proposes a Wi-Fi-based indoor human detection system using a deep convolutional neural network. The system detects different human states in various situations, including different environments and propagation paths. The main improvements proposed by the system is that there is no cameras overhead and no sensors are mounted. This system captures useful amplitude information from the channel state information and converts this information into an image-like two-dimensional matrix. Next, the two-dimensional matrix is used as an input to a deep convolutional neural network (CNN) to distinguish human states. In this work, a deep residual network (ResNet) architecture is used to perform human state classification with hierarchical topological feature extraction. Several combinations of datasets for different environments and propagation paths are used in this study. ResNet’s powerful inference simplifies feature extraction and improves the accuracy of human state classification. The experimental results show that the fine-tuned ResNet-18 model has good performance in indoor human detection, including people not present, people still, and people moving. Compared with traditional machine learning using handcrafted features, this method is simple and effective.

System of Warning about Dangerous Atmospheric Phenomena in the North Caucasus for Objects of Economic Activity

2018 ◽  
Vol 931 ◽  
pp. 1019-1024
Author(s):  
Vitaliy A. Shapovalov
Keyword(s):  
Land Reclamation ◽  
Detection System ◽  
Radar Data ◽  
Automated System ◽  
Insurance Companies ◽  
North Caucasus ◽  
Weather Radars ◽  
The North ◽  
Lightning Detection ◽  
Atmospheric Phenomena

This paper presents the developed program-mathematical software for receiving, archiving, analysis and display of radar, lightning and satellite data on clouds and precipitation, interfacing of meteorological information. The program of processing of meteorological information "GIMET-2010" is established on a network of weather radars DMRL-C of the Russian Federation. An automated system combining radar and lightning detection system information applies to the command posts of the uniformed services on the fight against hail and centers of severe storm warning. Following items are provided: a receiving and transmitting to consumers the operational radar data on the actual weather; the detection, identification, and warning of hazardous weather phenomena for airports and populated areas; measurement of the intensity and amount of precipitation for agriculture, hydrological forecasts and land reclamation; obtaining precipitation map for agriculture and insurance companies.

scholarly journals Cost-Sensitive Approach to Improve the HTTP Traffic Detection Performance on Imbalanced Data

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Wenmin Li ◽  
Sanqi Sun ◽  
Shuo Zhang ◽  
Hua Zhang ◽  
Yijie Shi
Keyword(s):  
Feature Extraction ◽  
Cost Function ◽  
Network Traffic ◽  
Detection System ◽  
Imbalanced Data ◽  
Detection Performance ◽  
Learning Technology ◽  
The Cost ◽  
Decision Boundaries ◽  
Traffic Detection

Aim. The purpose of this study is how to better detect attack traffic in imbalance datasets. The deep learning technology has played an important role in detecting malicious network traffic in recent years. However, it suffers serious imbalance distribution of data if the traffic model skews towards the modeling in the benign direction, because only a small portion of traffic is malicious, while most network traffic is benign. That is the reason why the authors wrote this manuscript. Methods. We propose a cost-sensitive approach to improve the HTTP traffic detection performance with imbalanced data and also present a character-level abstract feature extraction approach that can provide features with clear decision boundaries in addition. Finally, we design a spark-based HTTP traffic detection system based on these two approaches. Results. The methods proposed in this paper work well in imbalanced datasets. Compared to other methods, the experiment results indicate that our system has F1-score in a high precision. Conclusion. For imbalanced HTTP traffic detection, we confirmed that the method of feature extraction and the cost function is very effective. In the future, we may focus on how to use the cost function to further improve detection performance.

Characterization of interbedded thin beds using zero-crossing-time amplitude stratal slices

Geophysics ◽  
2015 ◽  
Vol 80 (5) ◽  
pp. N23-N35 ◽  
Author(s):  
Guofa Li ◽  
Mauricio D. Sacchi ◽  
Yajing Wang ◽  
Hao Zheng
Keyword(s):  
Zero Crossing ◽  
Crossing Time
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