Statistical analysis of complex weighted network for seismicity

He, Xuan; Wang, Luyang; Zhu, Hongbo; Liu, Zheng

doi:10.1016/j.physa.2020.125468

Cited by 5 publications

(3 citation statements)

References 31 publications

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“…For the evolution of the average nodal strength, we can see from Figure 8 that all the networks present a high correlation with the dynamics of seismic activities, which is ρ TS � 0.97, ρ TW � 0.96, and ρ STID � 0.94. is can explain 6 Complexity that the weighted form of networks is more helpful for earthquake network research than the unweighted one, which is also mentioned in their study [31,32].…”

Section: Weighted Propertiesmentioning

confidence: 83%

Comparison and Analysis of Network Construction Methods for Seismicity Based on Complex Networks

Shah

Wei

et al. 2021

Complexity

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The approach of the complex network has well described seismic complex systems. In this paper, this is the first time three classical network construction methods for seismicity are compared. By using the same dataset from the Southern California Seismic Network, three networks are constructed. They all present the scale-free, small-world properties, a strength-degree correlation, and an assortative mixing feature. However, they show some differences in the hierarchical clustering feature. On observing the evolution results, three measures show a similar correlation with seismicity dynamics, but one measure shows a different result. These results show that different network construction methods will present some similarities and differences in network properties. This situation needs to be considered, especially when discussing a predictive indicator of seismicity.

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Section: Weighted Propertiesmentioning

confidence: 83%

Comparison and Analysis of Network Construction Methods for Seismicity Based on Complex Networks

Shah

Wei

et al. 2021

Complexity

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“…Complex networks have been recently successfully used to study real systems such as brain networks, seismic systems, climate networks and social systems. [16][17][18][19] Motivated from this, the present article adopts complex network approach to model the highly nonlinear and nonstationary rotating machine system. In general, a complex network is characterized by a graph composed of nodes or vertices connected by a set of edges that represent the interactions within the system.…”

Section: Introductionmentioning

confidence: 99%

Gear fault diagnosis based on complex network theory and error-correcting output codes: Multi class support vector machine

Rai,

Kapu,

Balaji

et al. 2024

Proceedings of the Institution of Mechanical Engineers, Part I:

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Gearbox failures have a detrimental effect on the machine performance that affects the production capacity and economic benefits of a manufacturing industry in an adverse manner. Early detection of faults in gears helps to prevent sudden machine failures. Previously, many signal processing and artificial intelligence techniques have been successfully applied to diagnose gear faults by analyzing the machine vibrations. These techniques have repeatedly emphasized on the need to extract quality features from gear vibration signals and using artificial intelligence techniques that can identify multiple types of faults simultaneously. As such, in this article, new features called as visibility graph features based on complex network theory have been proposed to extract fault information from vibration signals. In addition, a new artificial intelligence framework for multifault classification known as error-correcting output codes-multiclass support vector machine is proposed to detect gear faults. To the best of authors’ knowledge, these features along with the error-correcting output codes-artificial intelligence framework have been rarely utilized for fault diagnosis purpose. The proposed approach has been applied on experimental gear vibration data to validate its effectiveness. First, the vibration signals are transformed to graphs, and various graph properties are computed to serve as fault features. Then, the fault features are supplied to train the error-correcting output codes-multiclass support vector machine model and learn the fault patterns. Finally, the gear fault classification accuracies are determined for various gear test conditions and compared with those of existing methods. It is observed that the proposed approach provides an average improvement of 4.22%, 9.93% and 11.8% over the time-domain features, time–frequency domain features and voting-based multiclass support vector machine classifier, respectively, for different operating conditions. Furthermore, the suggested technique augments the classification accuracies by 10%, 7.5% and 6.7%, respectively, as compared with the deep-learning models, namely standard stacked sparse autoencoder, deep neural network and convolution neural network.

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“…Recently, researchers have described and explained the complex interactions among components in the complex systems, such as economics [ 19 ], biology [ 20 ], social sciences [ 21 ], and the earthquake network [ 22 , 23 ], from a novel perspective. By analyzing graphs, the insights of the structure and the corresponding network topological characteristics, such as average shortest path, clustering coefficient, degree distribution, have been studied widely in various complex networks [ 24 , 25 ]. In addition, the complex network is a low complexity and effective method to deal with large-scale data and their relations based on graph theory.…”

Section: Introductionmentioning

confidence: 99%

Graph-Based Motion Artifacts Detection Method from Head Computed Tomography Images

Liu

Wen

Sun

et al. 2022

Sensors

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Computed tomography (CT) images play an important role due to effectiveness and accessibility, however, motion artifacts may obscure or simulate pathology and dramatically degrade the diagnosis accuracy. In recent years, convolutional neural networks (CNNs) have achieved state-of-the-art performance in medical imaging due to the powerful learning ability with the help of the advanced hardware technology. Unfortunately, CNNs have significant overhead on memory usage and computational resources and are labeled ‘black-box’ by scholars for their complex underlying structures. To this end, an interpretable graph-based method has been proposed for motion artifacts detection from head CT images in this paper. From a topological perspective, the artifacts detection problem has been reformulated as a complex network classification problem based on the network topological characteristics of the corresponding complex networks. A motion artifacts detection method based on complex networks (MADM-CN) has been proposed. Firstly, the graph of each CT image is constructed based on the theory of complex networks. Secondly, slice-to-slice relationship has been explored by multiple graph construction. In addition, network topological characteristics are investigated locally and globally, consistent topological characteristics including average degree, average clustering coefficient have been utilized for classification. The experimental results have demonstrated that the proposed MADM-CN has achieved better performance over conventional machine learning and deep learning methods on a real CT dataset, reaching up to 98% of the accuracy and 97% of the sensitivity.

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Statistical analysis of complex weighted network for seismicity

Cited by 5 publications

References 31 publications

Comparison and Analysis of Network Construction Methods for Seismicity Based on Complex Networks

Comparison and Analysis of Network Construction Methods for Seismicity Based on Complex Networks

Gear fault diagnosis based on complex network theory and error-correcting output codes: Multi class support vector machine

Graph-Based Motion Artifacts Detection Method from Head Computed Tomography Images

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