Conflict Analysis of Multi-source SST Distribution

Xu, Ling; Zhang, Pingen; Xu, Jun; Wu, Shaochun; Xu, Dongfeng

doi:10.1007/978-3-642-11842-5_66

Cited by 5 publications

(4 citation statements)

References 3 publications

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“…The width of an interface, W , which is referred to as its roughness, grows according to a scaling law with respect to time and the size of the interface. Many authors have sought to explain the scaling behavior of interface growth [17][18][19][20][21][22][23][24][25][26][27][28]. A celebrated model for interface growth is the Kardar-Parisi-Zhang (KPZ) equation [17], which can predict roughening behavior.…”

Section: Introductionmentioning

confidence: 99%

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Anomalous roughening of curvature-driven growth with a variable interface window

et al. 2010

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We studied the curvature-driven roughening of a disk domain pattern with a variable interface window. The relaxation of interface is driven by negative "surface tension." When a domain boundary propagates radially at a constant rate, we found that evolution of interface roughness follows scaling dynamic behavior. The local growth exponents are substantially different from the global exponents. Curvature-driven roughening belongs to a new class of anomalous roughening dynamics. However, a different "surface tension" leads to different global exponents. This is different from that of interface evolution with a fixed-size window, which has universal exponent. The variable growth window leads to a class of anomalous roughening dynamics.

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Section: Introductionmentioning

confidence: 99%

“…Previous observations have particularly focused on the roughening process with a fixed-size window [3][4][5][6][7][8][9][10][17][18][19][20][21][22][23][24][25][26][27][28]. Recently, it has been shown that dynamic scaling behavior is important for tumor therapy [29][30][31].…”

Section: Introductionmentioning

confidence: 99%

Anomalous roughening of curvature-driven growth with a variable interface window

et al. 2010

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“…Brant in 1990 stated that the combination ESD rules and boxplot provide comparable performance [12]. On the other hand, it was shown that the standard deviation and mean are affected by two or more outliers; Grubbs test does not detect outliers [13] correctly. Also, if the standard deviation of the data set is too large or too small, the test will tend to detect false outliers and vice versa.…”

Section: Introductionmentioning

confidence: 99%

“…standard deviations for the specific considered data domain [13]. Meanwhile, some publications show that Grubbs test is robust against the effect of intraclass correlation structure [14] and data that have Baldessari's structure [15,16].…”

Section: Introductionmentioning

confidence: 99%

Data Transformation Technique to Improve the Outlier Detection Power of Grubbs’ Test for Data Expected to Follow Linear Relation

Adikaram

Hussein

Effenberger

et al. 2015

Journal of Applied Mathematics

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Grubbs test (extreme studentized deviate test, maximum normed residual test) is used in various fields to identify outliers in a data set, which are ranked in the order of 1 ≤ 2 ≤ 3 ≤ ⋅ ⋅ ⋅ ≤ ( = 1, 2, 3, . . . , ). However, ranking of data eliminates the actual sequence of a data series, which is an important factor for determining outliers in some cases (e.g., time series). Thus in such a data set, Grubbs test will not identify outliers correctly. This paper introduces a technique for transforming data from sequence bound linear form to sequence unbound form ( = ). Applying Grubbs test to the new transformed data set detects outliers more accurately. In addition, the new technique improves the outlier detection capability of Grubbs test. Results show that, Grubbs test was capable of identifing outliers at significance level 0.01 after transformation, while it was unable to identify those prior to transforming at significance level 0.05.

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Online sequential extreme studentized deviate tests for anomaly detection in streaming data with varying patterns

Ryu

Lee

2021

Cluster Comput

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Conflict Analysis of Multi-source SST Distribution

Cited by 5 publications

References 3 publications

Anomalous roughening of curvature-driven growth with a variable interface window

Anomalous roughening of curvature-driven growth with a variable interface window

Data Transformation Technique to Improve the Outlier Detection Power of Grubbs’ Test for Data Expected to Follow Linear Relation

Online sequential extreme studentized deviate tests for anomaly detection in streaming data with varying patterns

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