Application of Sliding Nest Window Control Chart in Data Stream Anomaly Detection

Li, Guang; Wang, Jie; Liang, Jiye; Yue, Caitong

doi:10.3390/sym10040113

Cited by 9 publications

(9 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the future, work will be done in the following areas to further improve SNR and increase transmission distance: (1) research on signal enhancement from the perspective of array signals or MIMO by adding distributed antennas; (2) study the communication relay problem of drill pipe relay transmission; and (3) multi-channel data fusion analysis (using neural network algorithms [23,24], etc. ).…”

Section: Discussionmentioning

confidence: 99%

Application of a New Combination Algorithm in ELF-EM Processing

Li¹,

Yang²,

Fan³

et al. 2020

Symmetry

Self Cite

View full text Add to dashboard Cite

With regards to the electromagnetic measurement while drilling (EM-MWD), the extremely-low frequency electromagnetic wave signal (ELF-EM) below 20 Hz is usually used as the carrier of downhole measurement data due to the transmission characteristics of the electromagnetic wave (EM). However, influenced by the low frequency noise of drilling, the ELF-EM signal will be inevitably interfered by field noise, which ultimately impedes decoding. The Fourier band-pass filter can effectively remove out-of-band noise but is incapable of handling in-band noise. Therefore, based on the traditional method, a hybrid algorithm of adaptive Wiener algorithm and correlation detection (AWCD) is designed, so as to enhance the in-band noise processing capability, and the effectiveness of such algorithm is well verified through coding and decoding simulation as well as experimental data. The proposed algorithm, as indicated by theoretical analysis and test data, can effectively solve actual engineering issues, providing methodological references to engineers and technicians.

show abstract

Section: Discussionmentioning

confidence: 99%

Application of a New Combination Algorithm in ELF-EM Processing

Li¹,

Yang²,

Fan³

et al. 2020

Symmetry

Self Cite

View full text Add to dashboard Cite

show abstract

“…In the wider context, an alternate window-based strategy has been proposed in which outliers are detected in each window by the Tukey method and labeled so that they can be excluded from the realization of the process points to be used for model identification [42]. A contingency-based strategy proposes maximization of true positive (TP) values and minimization of false negative (FN) and false positive (FP) values [43]. Finally, another distribution testing procedure has been proposed in [44].…”

Section: Further Discussionmentioning

confidence: 99%

A Test Detecting the Outliers for Continuous Distributions Based on the Cumulative Distribution Function of the Data Being Tested

Jäntschi

2019

Symmetry

View full text Add to dashboard Cite

One of the pillars of experimental science is sampling. Based on the analysis of samples, estimations for populations are made. There is an entire science based on sampling. Distribution of the population, of the sample, and the connection among those two (including sampling distribution) provides rich information for any estimation to be made. Distributions are split into two main groups: continuous and discrete. The present study applies to continuous distributions. One of the challenges of sampling is its accuracy, or, in other words, how representative the sample is of the population from which it was drawn. To answer this question, a series of statistics have been developed to measure the agreement between the theoretical (the population) and observed (the sample) distributions. Another challenge, connected to this, is the presence of outliers - regarded here as observations wrongly collected, that is, not belonging to the population subjected to study. To detect outliers, a series of tests have been proposed, but mainly for normal (Gauss) distributions—the most frequently encountered distribution. The present study proposes a statistic (and a test) intended to be used for any continuous distribution to detect outliers by constructing the confidence interval for the extreme value in the sample, at a certain (preselected) risk of being in error, and depending on the sample size. The proposed statistic is operational for known distributions (with a known probability density function) and is also dependent on the statistical parameters of the population—here it is discussed in connection with estimating those parameters by the maximum likelihood estimation method operating on a uniform U(0,1) continuous symmetrical distribution.

show abstract

“…The detection of statistical outliers/extremes is an important subject in statistical data analysis. The detection of outliers/extremes and anomalies has diverse real-life applications, including: detection for plant-wide processes with outlier multisampling rates [55], industrial data stream anomaly detection [56], and sliding nest window control chart in the task of data stream anomaly detection [57].…”

Section: Algorithm 1 Mpsi Measurementmentioning

confidence: 99%

ExtrIntDetect—A New Universal Method for the Identification of Intelligent Cooperative Multiagent Systems with Extreme Intelligence

2019

View full text Add to dashboard Cite

In this research, we define a specific type of performance of the intelligent agent-based systems (IABSs) in terms of a difficult problem-solving intelligence measure. Many studies present the successful application of intelligent cooperative multiagent systems (ICMASs) for efficient, flexible and robust solving of difficult real-life problems. Based on a comprehensive study of the scientific literature, we conclude that there is no unanimous view in the scientific literature on machine intelligence, or on what an intelligence metric must measure. Metrics presented in the scientific literature are based on diverse paradigms. In our approach, we assume that the measurement of intelligence is based on the ability to solve difficult problems. In our opinion, the measurement of intelligence in this context is important, as it allows the differentiation between ICMASs based on the degree of intelligence in problem-solving. The recent OutIntSys method presented in the scientific literature can identify systems with outlier high and outlier low intelligence from a set of studied ICMASs. In this paper, a novel universal method called ExtrIntDetect, defined on the basis of a specific series of computing processes and analyses, is proposed for the detection of the ICMASs with statistical outlier low and high problem-solving intelligence from a given set of studied ICMASs. ExtrIntDetect eliminates the disadvantage of the OutIntSys method with respect to its limited robustness. The recent symmetric MetrIntSimil metric presented in the literature is capable of measuring and comparing the intelligence of large numbers of ICMASs and based on their respective problem-solving intelligences in order to classify them into intelligence classes. Systems whose intelligence does not statistically differ are classified as belonging to the same class of intelligent systems. Systems classified in the same intelligence class are therefore able to solve difficult problems using similar levels of intelligence. One disadvantage of the symmetric MetrIntSimil lies in the fact that it is not able to detect outlier intelligence. Based on this fact, the ExtrIntDetect method could be used as an extension of the MetrIntSimil metric. To validate and evaluate the ExtrIntDetect method, an experimental evaluation study on six ICMASs is presented and discussed.

show abstract

Application of Sliding Nest Window Control Chart in Data Stream Anomaly Detection

Cited by 9 publications

References 32 publications

Application of a New Combination Algorithm in ELF-EM Processing

Application of a New Combination Algorithm in ELF-EM Processing

A Test Detecting the Outliers for Continuous Distributions Based on the Cumulative Distribution Function of the Data Being Tested

ExtrIntDetect—A New Universal Method for the Identification of Intelligent Cooperative Multiagent Systems with Extreme Intelligence

Contact Info

Product

Resources

About