Anomaly analysis on an open DNS dataset

The increasing availability of open data and the demand to understand better the nature of anomalies and the causes underlying them in modern systems is encouraging researchers to analyse open datasets in various ways. These include both quantitative and qualitative methods. We show here how quantitative methods, such as timeline, local averages and exponentially weighted moving average analyses, led in this work to the discovery of three anomalies in a large open DNS dataset published by the Los Alamos National Laboratory.

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Anomaly analysis on an open DNS dataset

10.7287/peerj.preprints.27116 ◽

2018 ◽

Author(s):

Benjamin Aziz ◽

Nikolaos Menychtas ◽

Ammar Al-Bazi

Keyword(s):

Quantitative Methods ◽

Moving Average ◽

National Laboratory ◽

Open Data ◽

Los Alamos National Laboratory ◽

Anomaly Analysis ◽

Open Datasets ◽

Local Averages ◽

Quantitative And Qualitative Methods ◽

Weighted Moving Average

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Structural Nonlinear Damage Detection Based on ARMA-GARCH Model

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.204-208.2891 ◽

2012 ◽

Vol 204-208 ◽

pp. 2891-2896

Author(s):

Cong Cheng ◽

Ling Yu ◽

Liu Jie Chen

Keyword(s):

Time Series ◽

Damage Detection ◽

Moving Average ◽

National Laboratory ◽

Garch Model ◽

Los Alamos National Laboratory ◽

Practical Applications ◽

Moving Average Model ◽

Auto Regressive ◽

Story Building

Two economic models, i.e. auto-regressive and moving average model (ARMA) and generalized auto-regressive conditional heteroscedasticity model (GARCH), are adopted to assess the conditions of structures and to detect structural nonlinear damage based on time series analysis in this study. To improve the reliability of the method for nonlinear damage detection, a new damage sensitive feature (DSF) for the ARMA-GARCH model is defined as a ratio of the standard deviation of the variance time series of ARMA-GARCH model residual errors in test condition to ones in reference condition. Compared to the traditional DSF defined as the ratio between the deviations of ARMA-GARCH model residual error in two conditions, the successful outcomes of the new DSF can give obvious explanation for the current states of structures and can detect the nonlinear damage exactly, which enhance the worth of structural health monitoring as well as condition-based maintenance in practical applications. This method is finally verified by a series of experimental data of three-story building structure made in Los Alamos National Laboratory USA.

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Automated mineral analysis in TASK8

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100134661 ◽

1990 ◽

Vol 48 (2) ◽

pp. 212-213

Author(s):

William F. Chambers ◽

Arthur A. Chodos ◽

Roland C. Hagan

Keyword(s):

National Laboratory ◽

Control Program ◽

Mineral Analysis ◽

Alamos National Laboratory ◽

Mineral Phase ◽

Los Alamos National Laboratory ◽

California Institute Of Technology ◽

Mineral Phases ◽

Letter Code ◽

Institute Of Technology

TASK8 was designed as an electron microprobe control program with maximum flexibility and versatility, lending itself to a wide variety of applications. While using TASKS in the microprobe laboratory of the Los Alamos National Laboratory, we decided to incorporate the capability of using subroutines which perform specific end-member calculations for nearly any type of mineral phase that might be analyzed in the laboratory. This procedure minimizes the need for post-processing of the data to perform such calculations as element ratios or end-member or formula proportions. It also allows real time assessment of each data point.The use of unique “mineral codes” to specify the list of elements to be measured and the type of calculation to perform on the results was first used in the microprobe laboratory at the California Institute of Technology to optimize the analysis of mineral phases. This approach was used to create a series of subroutines in TASK8 which are called by a three letter code.

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