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

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 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.

Thin bed identification using zero-crossing-time stratal slices

2015 ◽  
Author(s):  
Guofa Li ◽  
Mauricio D. Sacchi ◽  
Shanshan Zhu ◽  
Yajing Wang
Keyword(s):  
Zero Crossing ◽  
Crossing Time

scholarly journals Average crossing time: An alternative characterization of mean aversion and reversion

2021 ◽  
Vol 12 (3) ◽  
pp. 903-944 ◽  
Author(s):  
John B. Donaldson ◽  
Rajnish Mehra
Keyword(s):  
Time Series ◽  
Financial Time Series ◽  
Mean Reversion ◽  
Statistical Characteristics ◽  
Alternative Measure ◽  
Financial Time ◽  
Asset Pricing Models ◽  
Crossing Time ◽  
Alternative Characterization

This study compares and contrasts the multiple characterizations of mean reversion in financial time series as regards the restrictions they imply. This is accomplished by translating them into statements about an alternative measure, the “Average Crossing Time” or ACT. We argue that the ACT measure, per se, provides not only a useful benchmark for the degree of mean reversion/aversion, but also an intuitive, and easily quantified sense of one time series being “more strongly mean‐reverting/averting” than another. We conclude our discussion by deriving the ACT measure for a wide class of stochastic processes and detailing its statistical characteristics. Our analysis is principally undertaken within a class of well‐understood production based asset pricing models.

scholarly journals Short communication: A new tool to define multiscale bedform characteristics from bed elevation data

2021 ◽  
Author(s):  
Judith Zomer ◽  
Suleyman Naqshband ◽  
Ton Hoitink
Keyword(s):  
Multiple Scales ◽  
Sigmoid Function ◽  
Zero Crossing ◽  
Bathymetric Data ◽  
Side Slope ◽  
Bed Elevation ◽  
Elevation Data ◽  
Systematic Identification ◽  
Identification And Characterization

Abstract. Systematic identification and characterization of bedforms from bathymetric data are crucial in many studies focused on fluvial processes. Automated and accurate processing of bed elevation data is challenging where dune fields are complex, irregular and, especially, where multiple scales co-exist. Here, we introduce a new tool to quantify dune properties from bathymetric data representing multiple dune scales. A first step in the procedure is to decompose the bathymetric data based on a LOESS algorithm. Steep dune lee side slopes are accounted for by implementing objective breaks in the algorithm, accounting for discontinuities in the bed level profiles, often occurring at the toe of the lee side slope of dunes. The steep lee slopes are then approximated by fitting a sigmoid function. Following the decomposition of the bathymetric data, bedforms are identified based on zero-crossing, and the relevant properties are calculated. The approach to decompose bedforms adopted in the presented tool is particularly applicable where secondary dunes are large and thus filtering could easily lead to undesired smoothing of the primary morphology. Application of the tool to two bathymetric maps demonstrates that the decomposition and identification are successful, as the lee side slopes are better preserved.

The universal nature of zero-crossing time and velocity scales in turbulent shear flows

AIAA Journal ◽  
1985 ◽  
Vol 23 (2) ◽  
pp. 161-162
Author(s):  
Promode Bandyopadhyay ◽  
A. K. M. F. Hussain
Keyword(s):  
Shear Flows ◽  
Turbulent Shear ◽  
Zero Crossing ◽  
Crossing Time ◽  
Turbulent Shear Flows ◽  
Universal Nature

scholarly journals Characterization of Odin-OSIRIS ozone profiles with the SAGE II dataset

2013 ◽  
Vol 6 (5) ◽  
pp. 1447-1459 ◽  
Author(s):  
C. Adams ◽  
A. E. Bourassa ◽  
A. F. Bathgate ◽  
C. A. McLinden ◽  
N. D. Lloyd ◽  
...  
Keyword(s):  
Infrared Imaging ◽  
Imaging System ◽  
Correlation Coefficients ◽  
Relative Difference ◽  
Stratospheric Aerosol ◽  
High Latitudes ◽  
Crossing Time ◽  
Ozone Trends ◽  
Relative Differences

Abstract. The Optical Spectrograph and InfraRed Imaging System (OSIRIS) on board the Odin spacecraft has been taking limb-scattered measurements of ozone number density profiles from 2001–present. The Stratospheric Aerosol and Gas Experiment II (SAGE II) took solar occultation measurements of ozone number densities from 1984–2005 and has been used in many studies of long-term ozone trends. We present the characterization of OSIRIS SaskMART v5.0× against the new SAGE II v7.00 ozone profiles for 2001–2005, the period over which these two missions had overlap. This information can be used to merge OSIRIS with SAGE II into a single ozone record from 1984 to the present, if other satellite ozone measurements are included to account for gaps in the OSIRIS dataset in the winter hemisphere. Coincident measurement pairs were selected for ±1 h, ±1° latitude, and ±500 km. The absolute value of the resulting mean relative difference profile is <5% for 13.5–54.5 km and <3% for 24.5–53.5 km. Correlation coefficients R > 0.9 were calculated for 13.5–49.5 km, demonstrating excellent overall agreement between the two datasets. Coincidence criteria were relaxed to maximize the number of measurement pairs and the conditions under which measurements were taken. With the broad coincidence criteria, good agreement (< 5%) was observed under most conditions for 20.5–40.5 km. However, mean relative differences do exceed 5% for several cases. Above 50 km, differences between OSIRIS and SAGE II are partly attributed to the diurnal variation of ozone. OSIRIS data are biased high compared with SAGE II at 22.5 km, particularly at high latitudes. Dynamical coincidence criteria, using derived meteorological products, were also tested and yielded similar overall results, with slight improvements to the correlation at high latitudes. The OSIRIS optics temperature is low (<16 °C) during May–July, when the satellite enters the Earth's shadow for part of its orbit. During this period, OSIRIS measurements are biased low by 5–12% for 27.5–38.5 km. Biases between OSIRIS ascending node (northward equatorial crossing time ~18:00 LT – local time) and descending node (southward equatorial crossing time ~06:00 LT) measurements are also noted under some conditions. This work demonstrates that OSIRIS and SAGE II have excellent overall agreement and characterizes the biases between these datasets.

A pantograph arcing model for electrified railways with different speeds

2017 ◽  
Vol 232 (6) ◽  
pp. 1731-1740 ◽  
Author(s):  
Guoqiang Gao ◽  
Tingting Zhang ◽  
Wenfu Wei ◽  
Yi Hu ◽  
Guangning Wu ◽  
...  
Keyword(s):  
Electromagnetic Interference ◽  
Average Power ◽  
Field Tests ◽  
Field Testing ◽  
Electrical Characteristics ◽  
Zero Crossing ◽  
Crossing Time ◽  
Testing Data ◽  
Traction Power ◽  
Electrified Railways

Pantograph arcing is an unavoidable phenomenon in electrified railways, which not only causes damage to the carbon strip and the catenary contact line but also results in voltage surge and electromagnetic interference. In recent years, there are more cases of pantograph arcing due to the increase in speed of trains. Therefore, it is essential to understand the basic electrical characteristics of pantograph arcing at different running speeds of trains. In this work, a pantograph arcing model was proposed, which considers the effects of the speed of a train on the arc-dissipated power. An overall electrical model, concerning the traction power system and the traction drive system, was further established. The results indicated that the running speed of trains significantly influenced the arcing voltage, duration of arcing, and the zero-crossing time. A qualitative relation between the average power of the arc and the speed of the train was also presented. Finally, field tests were carried out, and comparisons between the field testing data and the calculated results were made, which validate the accuracy of the developed model.

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