The Experts below are selected from a list of 309 Experts worldwide ranked by ideXlab platform
Jianping Zhang - One of the best experts on this subject based on the ideXlab platform.
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life prediction for white oled based on lsm under Lognormal Distribution
Solid-state Electronics, 2012Co-Authors: Jianping Zhang, Helen Wu, Wenli Wu, Liang WuAbstract:Abstract In order to acquire the reliability information of White Organic Light Emitting Display (OLED), three groups of OLED constant stress accelerated life tests (CSALTs) were carried out to obtain failure data of samples. Lognormal Distribution function was applied to describe OLED life Distribution, and the accelerated life equation was determined by Least square method (LSM). The Kolmogorov–Smirnov test was performed to verify whether the white OLED life meets Lognormal Distribution or not. Author-developed software was employed to predict the average life and the median life. The numerical results indicate that the white OLED life submits to Lognormal Distribution, and that the accelerated life equation meets inverse power law completely. The estimated life information of the white OLED provides manufacturers and customers with important guidelines.
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A Study of Accelerated Life Test of White OLED Based on Maximum Likelihood Estimation Using Lognormal Distribution
IEEE Transactions on Electron Devices, 2012Co-Authors: Jianping Zhang, Helen Wu, Wenli Wu, Aixi ZhouAbstract:In this paper, accelerated life tests of white organic light-emitting diodes (WOLEDs) are conducted to obtain failure data at normal operation conditions. The Lognormal Distribution function was applied to describe WOLED life Distribution. Log mean and log standard deviation were determined by maximum likelihood estimation. The Kolmogorov-Smirnov test was performed, and the results further confirmed that WOLED life met the Lognormal Distribution. Numerical results indicated that WOLED life followed the Lognormal Distribution. It was also found that the acceleration model was consistent with inverse power law.
Giuseppe Toscani - One of the best experts on this subject based on the ideXlab platform.
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human behavior and Lognormal Distribution a kinetic description
Mathematical Models and Methods in Applied Sciences, 2019Co-Authors: Stefano Gualandi, Giuseppe ToscaniAbstract:In recent years, it has been increasing evidence that Lognormal Distributions are widespread in physical and biological sciences, as well as in various phenomena of economics and social sciences. In social sciences, the appearance of Lognormal Distribution has been noticed, among others, when looking at body weight, and at women’s age at first marriage. Likewise, in economics, Lognormal Distribution appears when looking at consumption in a western society, at call-center service times, and others. The common feature of these situations, which describe the Distribution of a certain people’s hallmark, is the presence of a desired target to be reached by repeated choices. In this paper, we discuss a possible explanation of Lognormal Distribution forming in human activities by resorting to classical methods of statistical mechanics of multi-agent systems. The microscopic variation of the hallmark around its target value, leading to a linear Fokker–Planck-type equation with Lognormal equilibrium density, is bu...
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human behavior and Lognormal Distribution a kinetic description
arXiv: Physics and Society, 2018Co-Authors: Stefano Gualandi, Giuseppe ToscaniAbstract:In recent years it has been increasing evidence that Lognormal Distributions are widespread in physical and biological sciences, as well as in various phenomena of economics and social sciences. In social sciences, the appearance of Lognormal Distribution has been noticed, among others, when looking at body weight, and at women's age at first marriage. Likewise, in economics, Lognormal Distribution appears when looking at consumption in a western society, at call-center service times, and others. The common feature of these situations, which describe the Distribution of a certain people's hallmark, is the presence of a desired target to be reached by repeated choices. In this paper we discuss a possible explanation of Lognormal Distribution forming in human activities by resorting to classical methods of statistical mechanics of multi-agent systems. The microscopic variation of the hallmark around its target value, leading to a linear Fokker--Planck type equation with Lognormal equilibrium density, is built up introducing as main criterion for decision a suitable value function in the spirit of the prospect theory of Kahneman and Twersky.
Eric D Ebel - One of the best experts on this subject based on the ideXlab platform.
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methods for fitting the poisson Lognormal Distribution to microbial testing data
Food Control, 2012Co-Authors: Michael S Williams, Eric D EbelAbstract:The Poisson Distribution can be used to describe the number of microorganisms in a serving of food, but in most food-safety applications, the variability of the Poisson Distribution is insufficient to describe the heterogeneity in microbial contamination across the population of all servings. To model how contamination varies across the population of all possible servings, the Lognormal Distribution can be paired with the Poisson Distribution to create an over-dispersed Distribution, which is referred to as the Poisson-Lognormal Distribution. An advantage of this Distribution is that random draws from the Distribution are integer-valued. This is beneficial for some food-safety risk assessments because a modeled serving is either contaminated or not. This Distribution is also appropriate when the results of a laboratory test are integer-valued, such as when direct plating is used to enumerate samples. While some surveys perform only absence/presence screenings tests, surveys that are more thorough can pair a screening test with enumeration of screen-test positive samples via direct plating. For this application, statistical methods that accommodate censored data are required to fit the data to a Poisson-Lognormal Distribution. This study compares a Bayesian hierarchical model to a maximum likelihood estimation approach for fitting data to the Poisson-Lognormal Distribution. Across a range of datasets, the Bayesian method demonstrates superior performance. OpenBUGS and R code are provided to implement both methods.
Donald A. Singer - One of the best experts on this subject based on the ideXlab platform.
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A Lognormal Distribution of metal resources
2020Co-Authors: Donald A. SingerAbstract:For national or global resource estimation of frequencies of metals,a Lognormal Distribution has commonly been recommended but not adequately tested.Tests of frequencies of Cu,Zn,Pb,Ag,and Au contents of 1 984 well-explored mineral deposits display a poor fit to the Lognormal Distribution.When the same metals plus Mo,Co,Nb2O3,and REE2O3 are grouped into 19 geologically defined deposit types,only eight of the 73 tests fail to be fit by Lognormal Distribution,and most of those failures are in two deposit types suggesting a problem with those types.Estimates of the mean and standard deviation of each of the metals in each of the deposit types are provided for modeling.
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the Lognormal Distribution of metal resources in mineral deposits
Ore Geology Reviews, 2013Co-Authors: Donald A. SingerAbstract:For national or global resource estimation of frequencies of metals a Lognormal Distribution has sometimes been assumed but never adequately tested. Tests of frequencies of Cu, Zn, Pb, Ag, Au, Mo, Re, Ni, Co, Nb2O3, REE2O3, Cr2O3, Pt, Pd, Ir, Rh, and Ru, contents in over 3000 well-explored mineral deposits display a poor fit to the Lognormal Distribution. Neither a Lognormal Distribution nor a power law is an adequate model of the metal contents across all deposits. When these metals are grouped into 28 geologically defined deposit types, only nine of the over 100 tests fail to be fit by the Lognormal Distribution, and most of those failures are in two deposit types suggesting problems with those types. Significant deviations from Lognormal Distributions of most metals when ignoring deposit types demonstrate that there is not a global Lognormal or power law equation for these metals. Mean and standard deviation estimates of each metal within deposit types provide a basis for modeling undiscovered resources. When tracts of land permissive for specific deposit types are delineated, deposit density estimates and contained metal statistics can be used in Monte Carlo simulations to estimate total amounts of undiscovered metals with associated explicit uncertainties as demonstrated for undiscovered porphyry copper deposits in the Tibetan Plateau of China.
Aixi Zhou - One of the best experts on this subject based on the ideXlab platform.
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A Study of Accelerated Life Test of White OLED Based on Maximum Likelihood Estimation Using Lognormal Distribution
IEEE Transactions on Electron Devices, 2012Co-Authors: Jianping Zhang, Helen Wu, Wenli Wu, Aixi ZhouAbstract:In this paper, accelerated life tests of white organic light-emitting diodes (WOLEDs) are conducted to obtain failure data at normal operation conditions. The Lognormal Distribution function was applied to describe WOLED life Distribution. Log mean and log standard deviation were determined by maximum likelihood estimation. The Kolmogorov-Smirnov test was performed, and the results further confirmed that WOLED life met the Lognormal Distribution. Numerical results indicated that WOLED life followed the Lognormal Distribution. It was also found that the acceleration model was consistent with inverse power law.
