scholarly journals Activation Anomaly Analysis

2021 ◽  
pp. 69-84
Author(s):  
Philip Sperl ◽  
Jan-Philipp Schulze ◽  
Konstantin Böttinger
Keyword(s):  
Anomaly Analysis

Application of geometric moving average martingale algorithm in anomaly analysis before earthquake based on sliding window

2013 ◽  
Vol 33 (12) ◽  
pp. 3608-3610 ◽  
Author(s):  
Liping CHEN ◽  
Xiangzen KONG ◽  
Zhi ZHENG ◽  
Xinqi LIN ◽  
Xiaoshan ZHAN
Keyword(s):  
Moving Average ◽  
Sliding Window ◽  
Anomaly Analysis

scholarly journals Outlier Detection Transilience-Probabilistic Model for Wind Tunnels Based on Sensor Data

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2532
Author(s):  
Encarna Quesada ◽  
Juan J. Cuadrado-Gallego ◽  
Miguel Ángel Patricio ◽  
Luis Usero
Keyword(s):  
Anomaly Detection ◽  
Outlier Detection ◽  
Research Work ◽  
Industrial Sector ◽  
Sensor Data ◽  
Correct Selection ◽  
Wind Tunnels ◽  
Data Set ◽  
First Case ◽  
Anomaly Analysis

Anomaly Detection research is focused on the development and application of methods that allow for the identification of data that are different enough—compared with the rest of the data set that is being analyzed—and considered anomalies (or, as they are more commonly called, outliers). These values mainly originate from two sources: they may be errors introduced during the collection or handling of the data, or they can be correct, but very different from the rest of the values. It is essential to correctly identify each type as, in the first case, they must be removed from the data set but, in the second case, they must be carefully analyzed and taken into account. The correct selection and use of the model to be applied to a specific problem is fundamental for the success of the anomaly detection study and, in many cases, the use of only one model cannot provide sufficient results, which can be only reached by using a mixture model resulting from the integration of existing and/or ad hoc-developed models. This is the kind of model that is developed and applied to solve the problem presented in this paper. This study deals with the definition and application of an anomaly detection model that combines statistical models and a new method defined by the authors, the Local Transilience Outlier Identification Method, in order to improve the identification of outliers in the sensor-obtained values of variables that affect the operations of wind tunnels. The correct detection of outliers for the variables involved in wind tunnel operations is very important for the industrial ventilation systems industry, especially for vertical wind tunnels, which are used as training facilities for indoor skydiving, as the incorrect performance of such devices may put human lives at risk. In consequence, the use of the presented model for outlier detection may have a high impact in this industrial sector. In this research work, a proof-of-concept is carried out using data from a real installation, in order to test the proposed anomaly analysis method and its application to control the correct performance of wind tunnels.

Identifying and Evaluating Anomalous Structural Change-based Nodes in Generalized Dynamic Social Networks

2021 ◽  
Vol 15 (4) ◽  
pp. 1-22
Author(s):  
Huan Wang ◽  
Chunming Qiao ◽  
Xuan Guo ◽  
Lei Fang ◽  
Ying Sha ◽  
...  
Keyword(s):  
Social Networks ◽  
Structural Change ◽  
Structural Changes ◽  
Structural Information ◽  
Similarity Index ◽  
Dynamic Social Network ◽  
Dynamic Social Networks ◽  
Micro Level ◽  
Anomaly Analysis ◽  
Similarity Method

Recently, dynamic social network research has attracted a great amount of attention, especially in the area of anomaly analysis that analyzes the anomalous change in the evolution of dynamic social networks. However, most of the current research focused on anomaly analysis of the macro representation of dynamic social networks and failed to analyze the nodes that have anomalous structural changes at a micro level. To identify and evaluate anomalous structural change-based nodes in generalized dynamic social networks that only have limited structural information, this research considers undirected and unweighted graphs and develops a multiple-neighbor superposition similarity method ( ), which mainly consists of a multiple-neighbor range algorithm ( ) and a superposition similarity fluctuation algorithm ( ). introduces observation nodes, characterizes the structural similarities of nodes within multiple-neighbor ranges, and proposes a new multiple-neighbor similarity index on the basis of extensional similarity indices. Subsequently, maximally reflects the structural change of each node, using a new superposition similarity fluctuation index from the perspective of diverse multiple-neighbor similarities. As a result, based on and , not only identifies anomalous structural change-based nodes by detecting the anomalous structural changes of nodes but also evaluates their anomalous degrees by quantifying these changes. Results obtained by comparing with state-of-the-art methods via extensive experiments show that can accurately identify anomalous structural change-based nodes and evaluate their anomalous degrees well.

scholarly journals Anomaly analysis of Hawking radiation from acoustic black hole

2007 ◽  
Vol 2007 (07) ◽  
pp. 070-070 ◽  
Author(s):  
Wontae Kim ◽  
Hyeonjoon Shin
Keyword(s):  
Black Hole ◽  
Hawking Radiation ◽  
Anomaly Analysis

From Finding to Explaining

2018 ◽  
pp. 1535-1551
Author(s):  
Valentina Dragos
Keyword(s):  
Information Retrieval ◽  
Semantic Integration ◽  
Challenging Problem ◽  
Domain Experts ◽  
User Interests ◽  
Maritime Surveillance ◽  
Novel Approach ◽  
Basic Facts ◽  
Anomaly Analysis ◽  
Definition Of

Supporting anomaly analysis in the maritime field is a challenging problem because of the dynamic nature of the task: the definition of abnormal or suspicious behaviour is subject to change and depends on user interests. This paper provides a novel approach to support anomaly analysis in the maritime domain through the exploration of large collections of interpretation reports. Based on observables or more sophisticated patterns, the approach provides information retrieval strategies going from basic facts retrieval that guide short-term corrective actions to more complex networks of related concepts that help domain experts to understand or to explain abnormal vessel behaviours. Semantic integration is used to link various information sources, by using a commonly adopted standard. The paper seeks to explore different aspects of using information retrieval to support the analysis and interpretation of abnormal vessel behaviours for maritime surveillance.

Improved inversion of geopotential field anomalies for lithospheric investigations

Geophysics ◽  
1988 ◽  
Vol 53 (3) ◽  
pp. 375-385 ◽  
Author(s):  
R. R. B. von Frese ◽  
D. N. Ravat ◽  
W. J. Hinze ◽  
C. A. McGue
Keyword(s):  
Large Scale ◽  
Superposition Principle ◽  
Gravity Anomalies ◽  
Damping Factor ◽  
Stable Model ◽  
Memory Integration ◽  
Unstable Solutions ◽  
Ill Posed ◽  
Anomaly Analysis ◽  
Magnetic And Gravity Anomalies

Instabilities and the large matrices which are common to inversions of regional magnetic and gravity anomalies often complicate the use of efficient least‐squares matrix procedures. Inversion stability profoundly affects anomaly analysis, and hence it must be considered in any application. Wildly varying or unstable solutions are the products of errors in the anomaly observations and the integrated effects of observation spacing, source spacing, elevation differences between sources and observations, geographic coordinate attributes, geomagnetic field attitudes, and other factors which influence the conditioning of inversion. Solution instabilities caused by ill‐posed parameters can be efficiently minimized by ridge regression with a damping factor large enough to stabilize the inversion, but small enough to produce an analytically useful solution. An effective choice for the damping factor is facilitated by plotting damping factors against residuals between observed and modeled anomalies and by then comparing this curve to curves of damping factors plotted against solution variance or the residuals between predicted anomaly maps representing the processing objective (e.g., downward continuation, differential reduction to the radial pole, etc.). To obtain accurate and efficient large‐scale inversions of anomaly data, a procedure based on the superposition principle of potential fields may be used. This method involves successive inversions of residuals between the observations and various stable model fields which can be readily accommodated by available computer memory. Integration of the model fields yields a well‐resolved representation of the observed anomalies corresponding to an integrated model which normally could not be obtained by direct inversion because the memory requirements would be excessive. MAGSAT magnetic anomaly inversions over India demonstrate the utility of these procedures for improving the geologic analysis of potential field anomalies.

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