Characterization of Turbulence in Terms of Probability Density Function

2005 ◽  
pp. 257-272
Author(s):  
C. Hidalgo ◽  
B. Gonçalves ◽  
M. A. Pedrosa
Keyword(s):  
Probability Density Function ◽  
Probability Density ◽  
Density Function

Study on Determining Safety-Monitoring Index for Concrete Dam Based on Cloud Model

2012 ◽  
Vol 226-228 ◽  
pp. 1106-1110 ◽  
Author(s):  
Dong Qin ◽  
Xue Qin Zheng ◽  
Song Lin Wang
Keyword(s):  
Probability Density Function ◽  
Probability Density ◽  
Density Function ◽  
Cloud Model ◽  
Safety Monitoring ◽  
Monitoring Data ◽  
Concrete Dam ◽  
Deterministic Function ◽  
Monitoring Index

The paper, based on analyzing original monitoring data, employs forward and backward cloud algorithm in studying determining safety-monitoring index for concrete dam ,which integrates randomness and fuzziness into of qualitative concept of digital features. By means of above monitoring data, its digital characteristics can be easily transformed to the “quantitative-qualitative- quantitative” change. The final generated quantitative value constitutes the cloud diagram where each droplet demonstrates the characterization of raw monitoring data. At the same time, it also shows the randomness and fuzziness of monitored value. we can study out the safety monitoring indexes according to different remarkable levels by using the probability density function and deterministic function which completed by cloud algorithm. In the end, it is obtained with practice that this method is more suitable and reliability.

A note on the characterization of a U-shaped probability density function

1972 ◽  
Vol 9 (03) ◽  
pp. 684-685
Author(s):  
P. Ghosh ◽  
D. N. Shanbhag
Keyword(s):  
Probability Density Function ◽  
Probability Density ◽  
Density Function ◽  
Characteristic Property

In this note a characterization for a U-shaped probability density function is given using a well-known result for unimodal distributions due to Khinchine. As a corollary of this result, a characteristic property based on moments is found.

Application of the compound probability density function for characterization of breast masses in ultrasound B scans

2005 ◽  
Vol 50 (10) ◽  
pp. 2241-2248 ◽  
Author(s):  
P M Shankar ◽  
C W Piccoli ◽  
J M Reid ◽  
F Forsberg ◽  
B B Goldberg
Keyword(s):  
Probability Density Function ◽  
Probability Density ◽  
Density Function ◽  
Breast Masses ◽  
Compound Probability

scholarly journals A Characterization of Power Method Transformations throughL-Moments

2011 ◽  
Vol 2011 ◽  
pp. 1-22 ◽  
Author(s):  
Todd C. Headrick
Keyword(s):  
Probability Density Function ◽  
Probability Density ◽  
Density Function ◽  
Power Method ◽  
Systems Of Equations ◽  
Nonnormal Distributions ◽  
Empirical Distributions ◽  
Standard Normal ◽  
Polynomial Transformations

Power method polynomial transformations are commonly used for simulating continuous nonnormal distributions with specified moments. However, conventional moment-based estimators can (a) be substantially biased, (b) have high variance, or (c) be influenced by outliers. In view of these concerns, a characterization of power method transformations byL-moments is introduced. Specifically, systems of equations are derived for determining coefficients for specifiedL-moment ratios, which are associated with standard normal and standard logistic-based polynomials of order five and three. Boundaries forL-moment ratios are also derived, and closed-formed formulae are provided for determining if a power method distribution has a valid probability density function. It is demonstrated thatL-moment estimators are nearly unbiased and have relatively small variance in the context of the power method. Examples of fitting power method distributions to theoretical and empirical distributions based on the method ofL-moments are also provided.

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