A New Nuclear Material Safeguards Method Using Fractal Dimension

Booth, David E.

doi:10.2174/157341109787047853

CAC

2009

DOI: 10.2174/157341109787047853

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A New Nuclear Material Safeguards Method Using Fractal Dimension

David E. Booth¹

Abstract: The detection of losses from nuclear material inventories is an important problem to be solved. The present paper considers the detection of such losses by using the fractal dimension of the material balance time series to detect the outliers that are the loss points. We find the proposed method to be successful.

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Cited by 2 publications

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Effect of Outliers on the Fractal Dimension Estimation

Mohammadi

2011

Fractals

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The effect of outliers on estimation of the fractal dimension of experimental chaotic and stock market stochastic data series is investigated. The results indicate that influential observations of a magnitude of mean ±5 standard deviations can lead to a distortion of fractal dimension estimations by as much as 40% for short (e.g. 500 observations) time series data. Moreover, the box dimension estimation method is more sensitive to outliers than information and correlation dimension estimation methods and the effect of outliers decreases as the number of observations increases. Application of outlier adjustment to the stock prices of 60 companies of the Dow Jones Industrial Index reveals that the effect of outliers is critical in estimating the fractal dimension. The fractal dimension has applications in risk analysis for financial markets that can be affected by outliers.

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Effect of Outliers on the Fractal Dimension Estimation

Mohammadi

2011

Fractals

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A new control chart based on the loess smooth applied to information system quality performance

Tao

Liu

Shen

et al. 2012

IJOR

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A New Nuclear Material Safeguards Method Using Fractal Dimension

Cited by 2 publications

References 11 publications

Effect of Outliers on the Fractal Dimension Estimation

Effect of Outliers on the Fractal Dimension Estimation

A new control chart based on the loess smooth applied to information system quality performance

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