Multiscale limited penetrable horizontal visibility graph for analyzing nonlinear time series

Gao, Zhong-Ke; Cai, Qing; Yang, Yuxuan; Dang, Weidong; Zhang, Shanshan

doi:10.1038/srep35622

Cited by 151 publications

(87 citation statements)

References 58 publications

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“…Complex network theory [8][9][10][11][12][13][14][15][16][17][18] has undergone an explosive growth in recent years. In particular, complex network analysis of time series 19 has been well developed and it contributes greatly to solve challenging problems in different research fields.…”

Section: Introductionmentioning

confidence: 99%

Multiplex multivariate recurrence network from multi-channel signals for revealing oil-water spatial flow behavior

Gao

Dang

Yang

et al. 2017

Chaos: An Interdisciplinary Journal of Nonlinear Science

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The exploration of the spatial dynamical flow behaviors of oil-water flows has attracted increasing interests on account of its challenging complexity and great significance. We first technically design a double-layer distributed-sector conductance sensor and systematically carry out oil-water flow experiments to capture the spatial flow information. Based on the well-established recurrence network theory, we develop a novel multiplex multivariate recurrence network (MMRN) to fully and comprehensively fuse our double-layer multi-channel signals. Then we derive the projection networks from the inferred MMRNs and exploit the average clustering coefficient and the spectral radius to quantitatively characterize the nonlinear recurrent behaviors related to the distinct flow patterns. We find that these two network measures are very sensitive to the change of flow states and the distributions of network measures enable to uncover the spatial dynamical flow behaviors underlying different oil-water flow patterns. Our method paves the way for efficiently analyzing multi-channel signals from multi-layer sensor measurement system.

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Section: Introductionmentioning

confidence: 99%

Multiplex multivariate recurrence network from multi-channel signals for revealing oil-water spatial flow behavior

Gao

Dang

Yang

et al. 2017

Chaos: An Interdisciplinary Journal of Nonlinear Science

Self Cite

View full text Add to dashboard Cite

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“…As one of the most popular research domains, time series forecasting has received particular concern from researchers [1–5]. Many comparative studies have been conducted with the aim of identifying the most accurate methods for time series forecasting [6].…”

Section: Introductionmentioning

confidence: 99%

A new accuracy measure based on bounded relative error for time series forecasting

2017

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Many accuracy measures have been proposed in the past for time series forecasting comparisons. However, many of these measures suffer from one or more issues such as poor resistance to outliers and scale dependence. In this paper, while summarising commonly used accuracy measures, a special review is made on the symmetric mean absolute percentage error. Moreover, a new accuracy measure called the Unscaled Mean Bounded Relative Absolute Error (UMBRAE), which combines the best features of various alternative measures, is proposed to address the common issues of existing measures. A comparative evaluation on the proposed and related measures has been made with both synthetic and real-world data. The results indicate that the proposed measure, with user selectable benchmark, performs as well as or better than other measures on selected criteria. Though it has been commonly accepted that there is no single best accuracy measure, we suggest that UMBRAE could be a good choice to evaluate forecasting methods, especially for cases where measures based on geometric mean of relative errors, such as the geometric mean relative absolute error, are preferred.

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“…On the other hand, multivariate pseudo Wigner distribution allows uncovering local flow behavior revealing different oil—water flow patterns [41]. Gao et al proposed a multiscale limited penetrable horizontal visibility graph to analyze nonlinear time series [42] and then developed a novel AOK-TFR based visibility graph to classify epileptic EEG signals [43]. …”

Section: Introductionmentioning

confidence: 99%

Classification of Partial Discharge Measured under Different Levels of Noise Contamination

2017

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Cable joint insulation breakdown may cause a huge loss to power companies. Therefore, it is vital to diagnose the insulation quality to detect early signs of insulation failure. It is well known that there is a correlation between Partial discharge (PD) and the insulation quality. Although many works have been done on PD pattern recognition, it is usually performed in a noise free environment. Also, works on PD pattern recognition in actual cable joint are less likely to be found in literature. Therefore, in this work, classifications of actual cable joint defect types from partial discharge data contaminated by noise were performed. Five cross-linked polyethylene (XLPE) cable joints with artificially created defects were prepared based on the defects commonly encountered on site. Three different types of input feature were extracted from the PD pattern under artificially created noisy environment. These include statistical features, fractal features and principal component analysis (PCA) features. These input features were used to train the classifiers to classify each PD defect types. Classifications were performed using three different artificial intelligence classifiers, which include Artificial Neural Networks (ANN), Adaptive Neuro-Fuzzy Inference System (ANFIS) and Support Vector Machine (SVM). It was found that the classification accuracy decreases with higher noise level but PCA features used in SVM and ANN showed the strongest tolerance against noise contamination.

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Multiscale limited penetrable horizontal visibility graph for analyzing nonlinear time series

Cited by 151 publications

References 58 publications

Multiplex multivariate recurrence network from multi-channel signals for revealing oil-water spatial flow behavior

Multiplex multivariate recurrence network from multi-channel signals for revealing oil-water spatial flow behavior

A new accuracy measure based on bounded relative error for time series forecasting

Classification of Partial Discharge Measured under Different Levels of Noise Contamination

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