A maximum extreme-value distribution model for switching conductance of oxide-RRAM in memory applications

2020 ◽  
Vol 116 (8) ◽  
pp. 082901 ◽  
Author(s):  
Ernest Wu ◽  
Takashi Ando ◽  
Youngseok Kim ◽  
Ramachandran Muralidhar ◽  
Eduard Cartier ◽  
...  
Keyword(s):  
Extreme Value Distribution ◽  
Extreme Value ◽  
Value Distribution ◽  
Distribution Model ◽  
Memory Applications

scholarly journals Introduction of Confidence Interval Based on Probability Limit Method Test into Non-Stationary Hydrological Frequency Analysis

Water ◽  
2020 ◽  
Vol 12 (10) ◽  
pp. 2727
Author(s):  
Keita Shimizu ◽  
Tadashi Yamada ◽  
Tomohito J. Yamada
Keyword(s):  
Confidence Interval ◽  
Frequency Analysis ◽  
Extreme Value Distribution ◽  
Extreme Rainfall ◽  
Extreme Value ◽  
Value Distribution ◽  
Distribution Model ◽  
Probability Limit ◽  
Time Variations ◽  
Hydrological Frequency Analysis

Nonstationarity in hydrological variables has been identified throughout Japan in recent years. As a result, the reliability of designs derived from using method based on the assumption of stationary might deteriorate. Non-stationary hydrological frequency analysis is among the measures to counter this possibility. Using this method, time variations in the probable hydrological quantity can be estimated using a non-stationary extreme value distribution model with time as an explanatory variable. In this study, we build a new method for constructing the confidence interval regarding the non-stationary extreme value distribution by applying a theory of probability limit method test. Furthermore, by introducing a confidence interval based on probability limit method test into the non-stationary hydrological frequency analysis, uncertainty in design rainfall because of lack of observation information was quantified, and it is shown that assessment pertaining to both the occurrence risk of extremely heavy rainfall and changes in the trend of extreme rainfall accompanied with climate change is possible.

scholarly journals Bayesian Updates for an Extreme Value Distribution Model of Bridge Traffic Load Effect Based on SHM Data

2021 ◽  
Vol 13 (15) ◽  
pp. 8631
Author(s):  
Xin Gao ◽  
Gengxin Duan ◽  
Chunguang Lan
Keyword(s):  
Probability Model ◽  
Extreme Value Distribution ◽  
Value Theory ◽  
Extreme Value ◽  
Value Distribution ◽  
Traffic Load ◽  
Monitoring Data ◽  
Distribution Model ◽  
Load Effect ◽  
Tail Distribution

As the distribution function of traffic load effect on bridge structures has always been unknown or very complicated, a probability model of extreme traffic load effect during service periods has not yet been perfectly predicted by the traditional extreme value theory. Here, we focus on this problem and introduce a novel method based on the bridge structural health monitoring data. The method was based on the fact that the tails of the probability distribution governed the behavior of extreme values. The generalized Pareto distribution was applied to model the tail distribution of traffic load effect using the peak-over-threshold method, while the filtered Poisson process was used to model the traffic load effect stochastic process. The parameters of the extreme value distribution of traffic load effect during a service period could be determined by theoretical derivation if the parameters of tail distribution were estimated. Moreover, Bayes’ theorem was applied to update the distribution model to reduce the statistical uncertainty. Finally, the rationality of the proposed method was applied to analyze the monitoring data of concrete-filled steel tube arch bridge suspenders. The results proved that the approach was convenient and found that the extreme value distribution type III might be more suitable as the traffic load effect probability model.

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