scholarly journals Extreme geomagnetic activities: a statistical study

2020 ◽  
Vol 72 (1) ◽  
Author(s):  
Ryuho Kataoka
Keyword(s):  
Magnetic Storms ◽  
Magnetic Observatory ◽  
Statistical Distributions ◽  
Limited Data ◽  
Data Set ◽  
Sudden Commencements ◽  
The One ◽  
Log Normal ◽  
Geomagnetic Activities ◽  
Log Normal Distribution

Abstract Statistical distributions are investigated for magnetic storms, sudden commencements (SCs), and substorms to identify the possible amplitude of the one in 100-year and 1000-year events from a limited data set of less than 100 years. The lists of magnetic storms and SCs are provided from Kakioka Magnetic Observatory, while the lists of substorms are obtained from SuperMAG. It is found that majorities of events essentially follow the log-normal distribution, as expected from the random output from a complex system. However, it is uncertain that large-amplitude events follow the same log-normal distributions, and rather follow the power-law distributions. Based on the statistical distributions, the probable amplitudes of the 100-year (1000-year) events can be estimated for magnetic storms, SCs, and substorms as approximately 750 nT (1100 nT), 230 nT (450 nT), and 5000 nT (6200 nT), respectively. The possible origin to cause the statistical distributions is also discussed, consulting the other space weather phenomena such as solar flares, coronal mass ejections, and solar energetic particles.

scholarly journals Extreme geomagnetic activities: A statistical study

2020 ◽  
Author(s):  
Ryuho Kataoka
Keyword(s):  
Magnetic Storms ◽  
Magnetic Observatory ◽  
Statistical Distributions ◽  
Limited Data ◽  
Data Set ◽  
Sudden Commencements ◽  
The One ◽  
Log Normal ◽  
Geomagnetic Activities ◽  
Log Normal Distribution

Abstract Statistical distributions are investigated for magnetic storms, sudden commencements (SCs), and substorms to identify the possible amplitude of the one in 100-year and 1000-year events from a limited data set of less than 100 years. The lists of magnetic storms and SCs are provided from Kakioka Magnetic Observatory, while the list of substorms are obtained from SuperMAG. It is found that majorities of events essentially follow the log-normal distribution, as expected from the random output from a complex system. However, it is uncertain that largest-amplitude events follow the same log-normal distributions, and rather follow the power-law distributions. Based on the statistical distributions, the probable amplitudes of the 100-year (1000-year) events can be estimated for magnetic storms, SCs, and substorms as approximately 750 nT (1100 nT), 230 nT (450 nT), and 5000 nT (6200 nT), respectively. The possible origin to cause the statistical distributions are also discussed, consulting the other space weather phenomena such as solar flares, coronal mass ejections, and solar energetic particles.

scholarly journals Extreme geomagnetic activities: A statistical study

2020 ◽  
Author(s):  
Ryuho Kataoka
Keyword(s):  
Magnetic Storms ◽  
Statistical Distributions ◽  
Limited Data ◽  
Data Set ◽  
Sudden Commencements ◽  
The One ◽  
Log Normal ◽  
Geomagnetic Activities ◽  
Log Normal Distribution ◽  
Power Law Distributions

Abstract Statistical distributions are investigated for magnetic storms, sudden commencements (SCs), and substorms to identify the possible amplitude of the one in 100-year and 1000-year events from a limited data set of less than 100 years. It is found that majorities of events essentially follow the log-normal distribution, as expected from the random output from a complex system. However, it is uncertain that rare events follow the same log-normal distributions, and rather follow the power-law distributions. Based on the statistical distributions, the probable amplitudes of the 100-year (1000-year) events can be estimated for magnetic storms, SCs, and substorms as approximately 750 nT (1100 nT), 230 nT (450 nT), and 5000 nT (6200 nT), respectively. The possible origin to cause the statistical distributions are also discussed, consulting the other space weather phenomena such as solar flares, coronal mass ejections, and solar energetic particles.

scholarly journals Comparative Study of Statistical Distributions in Electromigration-Induced Failures of Al/Cu Thin-Film Interconnects

1994 ◽  
Vol 16 (2) ◽  
pp. 119-126 ◽  
Author(s):  
M. I. Loupis ◽  
J. N. Avaritsiotis ◽  
G. D. Tziallas
Keyword(s):  
Thin Film ◽  
Grain Size ◽  
Extreme Value Distribution ◽  
Statistical Distributions ◽  
Post Processing ◽  
Failure Times ◽  
Life Data ◽  
Cu Thin Film ◽  
Log Normal ◽  
Log Normal Distribution

In electromigration failure studies, it is in general assumed that electromigration-induced failures may be adequately modelled by a log-normal distribution. Further to this, it has been argued that a lognormal distribution of failure times is indicative of electromigration mechanisms. We have combined post processing of existing life-data from Al/Cu + TiW bilayer interconnects with our own results from Al/Cu interconnects to show that the Log Extreme Value distribution is an equally good statistical model for electromigration failures, even in cases where grain size exceeds the linewidth. The significance of such a modelling is particularly apparent in electromigration failure rate prediction.

scholarly journals THE NETWORK OF INTER-REGIONAL DIRECT INVESTMENT STOCKS ACROSS EUROPE

2007 ◽  
Vol 10 (01) ◽  
pp. 29-51 ◽  
Author(s):  
STEFANO BATTISTON ◽  
JOAO F. RODRIGUES ◽  
HAMZA ZEYTINOGLU
Keyword(s):  
Power Law ◽  
Direct Investment ◽  
Policy Making ◽  
Scaling Laws ◽  
Statistical Distributions ◽  
Regional Investment ◽  
Log Normal ◽  
Geographical Space ◽  
Log Normal Distribution ◽  
Different Levels

We present an analysis of inter-regional investment stocks within Europe from a complex networks perspective. We consider two different levels: first, we compute the inward–outward investment stocks at the level of firms, based on ownership shares and number of employees; then we estimate the inward–outward investment stock at the level of regions in Europe, by aggregating the ownership network of firms, based on their headquarter location. To our knowledge, there is no similar approach in the literature so far, and we believe that it may lead to important applications for policy making. In the present paper, we focus on the statistical distributions and the scaling laws, while in further studies we will analyze the structure of the network and its relation to geographical space. We find that while outward investment and activity of firms are power law distributed with a similar exponent, for regions these quantities are better described by a log-normal distribution. At both levels we also find scaling laws relating investment to activity and connectivity. In particular, we find that investment stock scales as a power law of the connectivity, as previously found for stock market data.

scholarly journals The Statistical Distributions of PM2.5 in Rayong and Chonburi Provinces, Thailand

2020 ◽  
Vol 8 (3) ◽  
Author(s):  
Vanida Pongsakchat ◽  
Pattaraporn Kidpholjaroen
Keyword(s):  
Fine Particulate Matter ◽  
Statistical Distribution ◽  
Distribution Model ◽  
Statistical Distributions ◽  
Average Data ◽  
Fine Particulate ◽  
Pearson Type ◽  
Anderson Darling ◽  
Log Normal ◽  
Log Normal Distribution

The fine particulate matter (PM2.5) concentrations is one of the most important issues that are often discussed since it has a greater impact on human health. Statistical distribution modeling plays an important role in predicting PM2.5 concentrations. This research aims to find the optimum statistical distribution model of PM2.5 in Rayong Province and Chonburi Province. The daily average data from 2014 – 2019 for Rayong and from 2015 – 2019 for Chonburi were using. Five statistical distributions were compared. A proper statistical distribution that represents PM2.5 concentrations has been chosen based on three criteria include Anderson-Darling statistic and RMSE. The results show that Pearson type VI distribution performs better compared to other distributions for PM2.5 concentrations in Rayong. For Chonburi, the proper statistical distribution is Log normal distribution.  

scholarly journals Statistical Modeling of Energy Harvesting in Hybrid PLC-WLC Channels

2021 ◽  
Vol 14 (1) ◽  
pp. 442
Author(s):  
Victor Fernandes ◽  
Thiago F. A. Nogueira ◽  
H. Vincent Poor ◽  
Moisés V. Ribeiro
Keyword(s):  
Maximum Likelihood ◽  
Statistical Models ◽  
Energy Efficient ◽  
Additive Noise ◽  
Wireless Channel ◽  
Independent Random Variables ◽  
Data Set ◽  
Hybrid Power ◽  
Log Normal ◽  
Log Normal Distribution

This work introduces statistical models for the energy harvested from the in-home hybrid power line-wireless channel in the frequency band from 0 to 100 MHz. Based on numerical analyses carried out over the data set obtained from a measurement campaign together with the use of the maximum likelihood value criterion and the adoption of five distinct power masks for power allocation, it is shown that the log-normal distribution yields the best model for the energies harvested from the free-of-noise received signal and from the additive noise in this setting. Additionally, the total harvested energy can be modeled as the sum of these two statistically independent random variables. Thus, it is shown that the energies harvested from this kind of hybrid channel is an easy-to-simulate phenomenon when carrying out research related to energy-efficient and self-sustainable networks.

Parametric Uncertainty and Fat Tails

2012 ◽  
Author(s):  
Christian Gollier
Keyword(s):  
Economic Growth ◽  
Extreme Events ◽  
Growth Process ◽  
Parametric Uncertainty ◽  
Large Data ◽  
Limited Data ◽  
Data Set ◽  
Low Probability ◽  
The One

This chapter shows how the probability distribution for economic growth is subject to some parametric uncertainty. There is a limited data set for the dynamics of economic growth, and the absence of a sufficiently large data set to estimate the long-term growth process of the economy implies that its parameters are uncertain and subject to learning in the future. This problem is particularly crucial when its parameters are unstable, or when the dynamic process entails low-probability extreme events. Thus, the rarer the event, the less precise the estimate of its likelihood. This builds a bridge between the problem of parametric uncertainty, and the one of extreme events.

scholarly journals Is Mean Time to Failure (MTTF) Equal to Mean Time of Life for Unrepairable Systems?

2021 ◽  
Vol 51 (1) ◽  
pp. 255-264
Author(s):  
Leszek Opyrchał
Keyword(s):  
Wigner Distribution ◽  
Time To Failure ◽  
Statistical Distributions ◽  
Chi Square ◽  
Mean Time To Failure ◽  
Reliability Parameters ◽  
The Mean ◽  
Log Normal ◽  
Mean Time ◽  
Log Normal Distribution

Abstract One of the most important reliability parameters is the mean time to failure (MTTF). It is widely accepted that the MTTF is equal to the mean time of life ET. This article shows that this is not necessarily true. Although for the most commonly used statistical distributions (such as exponential, Gaussian, chi-square, Fisher-Tippett distributions) the values of MTTF and ET are equal, this is not the case for the log-normal distribution. Similarity, some less commonly used distributions (such as Breit-Wigner distribution) may also require calculation adjustments resulting from MTTF ≠ ET. Ignoring this discrepancy, an erroneous MTTF value can be obtained.

scholarly journals Comparative Study of Failure Rate of Bank’s ATM: Log Normal Distribution Approach

2019 ◽  
pp. 1-19
Author(s):  
Orumie, Ukamaka Cynthia ◽  
E. O. Biu
Keyword(s):  
Normal Distribution ◽  
Failure Rate ◽  
Service Time ◽  
Time To Failure ◽  
Normal Model ◽  
Significant Difference ◽  
The One ◽  
Log Normal ◽  
Log Normal Distribution ◽  
One Way Anova

This research determined time to failure rate and number of successful transaction of selected banks in Nigeria, using Log normal distribution. Transformation technique was applied to the log-normal model to obtain a quadratic equation or polynomial regression that assisted in determining the parameters of the log-normal model. In addition, one-way ANOVA was used to test for equality of the average (or mean) time to failure rate and average number of successful service time of the banks. The research fitted the log-normal models of the banks with the help of SPSS 21 statistical software and the result showed that GT-Bank model has the highest variation of 90.3% for number of successful service time (t), while Fidelity bank model has the highest variation of 56.6% for time of failure rate. The one-way ANOVA result of the number of successful service time (min) showed a significant difference. The Tukey comparison tests showed that GT bank is significant at 5% and 10% from other banks. Hence, the number of successful service time (min) were not the same for all the five banks. However, the one-way ANOVA result of the banks in term of number of Time to Failure (t) (min) showed no significant difference among the five banks.

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