Research

18 pages, 1342 KiB

Open AccessArticle

Distributed Cubature Information Filtering Method for State Estimation in Bearing-Only Sensor Network

by Zhan Chen, Wenxing Fu, Ruitao Zhang, Yangwang Fang and Zhun Xiao

Entropy 2024, 26(3), 236; https://doi.org/10.3390/e26030236 - 07 Mar 2024

Viewed by 699

The problem of state estimation based on bearing-only sensors is increasingly important while existing research on distributed filtering solutions is rather limited. Therefore, this paper proposed the novel distributed cubature information filtering (DCIF) method for addressing the state estimation challenge in bearing-only sensor [...] Read more.

The problem of state estimation based on bearing-only sensors is increasingly important while existing research on distributed filtering solutions is rather limited. Therefore, this paper proposed the novel distributed cubature information filtering (DCIF) method for addressing the state estimation challenge in bearing-only sensor networks. Firstly, the system model of the bearing-only sensor network was constructed, and the observability of the system was analyzed. The sensor nodes are paired to measure relative angle information. Subsequently, the coordinated consistency theory is employed to achieve a unified state estimation of the maneuvering target. The DCIF method enhances the observability of the system, addressing the issues of large accuracy errors and divergence in traditional nonlinear filtering algorithms. Building upon the theoretical proof of consistency convergence in DCIF, four simulation experiments were conducted for comparison. These experiments validate the effectiveness and superiority of the DCIF method in bearing-only sensor networks. Full article

(This article belongs to the Special Issue Application of Information Theory to Physical Modeling and State Awareness in Complex Systems)

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16 pages, 1935 KiB

Open AccessArticle

Identification of Critical Links Based on Electrical Betweenness and Neighborhood Similarity in Cyber-Physical Power Systems

by Jiuling Dong, Zilong Song, Yuanshuo Zheng, Jingtang Luo, Min Zhang, Xiaolong Yang and Hongbing Ma

Entropy 2024, 26(1), 85; https://doi.org/10.3390/e26010085 - 19 Jan 2024

Viewed by 773

Abstract

Identifying critical links is of great importance for ensuring the safety of the cyber-physical power system. Traditional electrical betweenness only considers power flow distribution on the link itself, while ignoring the local influence of neighborhood links and the coupled reaction of information flow [...] Read more.

Identifying critical links is of great importance for ensuring the safety of the cyber-physical power system. Traditional electrical betweenness only considers power flow distribution on the link itself, while ignoring the local influence of neighborhood links and the coupled reaction of information flow on energy flow. An identification method based on electrical betweenness centrality and neighborhood similarity is proposed to consider the internal power flow dynamic influence existing in multi-neighborhood nodes and the topological structure interdependence between power nodes and communication nodes. Firstly, for the power network, the electrical topological overlap is proposed to quantify the vulnerability of the links. This approach comprehensively considers the local contribution of neighborhood nodes, power transmission characteristics, generator capacity, and load. Secondly, in communication networks, effective distance closeness centrality is defined to evaluate the importance of communication links, simultaneously taking into account factors such as the information equipment function and spatial relationships. Next, under the influence of coupled factors, a comprehensive model is constructed based on the dependency relationships between information flow and energy flow to more accurately assess the critical links in the power network. Finally, the simulation results show the effectiveness of the proposed method under dynamic and static attacks. Full article

(This article belongs to the Special Issue Application of Information Theory to Physical Modeling and State Awareness in Complex Systems)

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17 pages, 3562 KiB

Open AccessArticle

TLFND: A Multimodal Fusion Model Based on Three-Level Feature Matching Distance for Fake News Detection

by Junda Wang, Jeffrey Zheng, Shaowen Yao, Rui Wang and Hong Du

Entropy 2023, 25(11), 1533; https://doi.org/10.3390/e25111533 - 10 Nov 2023

Viewed by 1000

Abstract

In the rapidly evolving information era, the dissemination of information has become swifter and more extensive. Fake news, in particular, spreads more rapidly and is produced at a lower cost compared to genuine news. While researchers have developed various methods for the automated [...] Read more.

In the rapidly evolving information era, the dissemination of information has become swifter and more extensive. Fake news, in particular, spreads more rapidly and is produced at a lower cost compared to genuine news. While researchers have developed various methods for the automated detection of fake news, challenges such as the presence of multimodal information in news articles or insufficient multimodal data have hindered their detection efficacy. To address these challenges, we introduce a novel multimodal fusion model (TLFND) based on a three-level feature matching distance approach for fake news detection. TLFND comprises four core components: a two-level text feature extraction module, an image extraction and fusion module, a three-level feature matching score module, and a multimodal integrated recognition module. This model seamlessly combines two levels of text information (headline and body) and image data (multi-image fusion) within news articles. Notably, we introduce the Chebyshev distance metric for the first time to calculate matching scores among these three modalities. Additionally, we design an adaptive evolutionary algorithm for computing the loss functions of the four model components. Our comprehensive experiments on three real-world publicly available datasets validate the effectiveness of our proposed model, with remarkable improvements demonstrated across all four evaluation metrics for the PolitiFact, GossipCop, and Twitter datasets, resulting in an F1 score increase of 6.6%, 2.9%, and 2.3%, respectively. Full article

(This article belongs to the Special Issue Application of Information Theory to Physical Modeling and State Awareness in Complex Systems)

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25 pages, 16359 KiB

Open AccessArticle

A Novel Intelligent Fault Diagnosis Method for Self-Priming Centrifugal Pumps

by Bo Zhang, Zhenya Wang, Ligang Yao and Biaolin Luo

Entropy 2023, 25(11), 1501; https://doi.org/10.3390/e25111501 - 30 Oct 2023

Viewed by 993

Abstract

The real-time diagnostic monitoring of self-priming centrifugal pumps is essential to ensure their safe operation. Nevertheless, owing to the intricate structure and complex operational conditions inherent in such pumps, existing fault diagnosis methods encounter challenges in effectively extracting crucial fault feature information and [...] Read more.

The real-time diagnostic monitoring of self-priming centrifugal pumps is essential to ensure their safe operation. Nevertheless, owing to the intricate structure and complex operational conditions inherent in such pumps, existing fault diagnosis methods encounter challenges in effectively extracting crucial fault feature information and accurately identifying fault types. Consequently, this paper introduces an intelligent fault diagnosis method tailored for self-priming centrifugal pumps. The approach amalgamates refined time-shift multiscale fluctuation dispersion entropy, cosine pairwise-constrained supervised manifold mapping, and adaptive chaotic Aquila optimization support vector machine techniques. To begin with, refined time-shift multiscale fluctuation dispersion entropy is employed to extract fault-related features, adeptly mitigating concerns related to entropy domain deviations and instability. Subsequently, the application of cosine pairwise-constrained supervised manifold mapping serves to reduce the dimensionality of the extracted fault features, thereby bolstering the efficiency and precision of the ensuing identification process. Ultimately, the utilization of an adaptive chaotic Aquila optimization support vector machine facilitates intelligent fault classification, leading to enhanced accuracy in fault identification. The experimental findings unequivocally affirm the efficacy of the proposed method in accurately discerning among various fault types in self-priming centrifugal pumps, achieving an exceptional recognition rate of 100%. Moreover, it is noteworthy that the average correct recognition rate achieved by the proposed method surpasses that of five existing intelligent fault diagnosis techniques by a significant margin, registering a notable increase of 15.97%. Full article

(This article belongs to the Special Issue Application of Information Theory to Physical Modeling and State Awareness in Complex Systems)

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27 pages, 7199 KiB

Open AccessArticle

Distributed Diagnoses Based on Constructing a Private Chain via a Public Network

by Bing Li and Choujun Zhan

Entropy 2023, 25(9), 1305; https://doi.org/10.3390/e25091305 - 07 Sep 2023

Viewed by 704

Abstract

Secure online consultations can provide convenient medical services to patients who require experts from different regions. Moreover, this process can save time, which is critical in emergency cases, and cut medical costs. However, medical services need a high level of privacy protection that [...] Read more.

Secure online consultations can provide convenient medical services to patients who require experts from different regions. Moreover, this process can save time, which is critical in emergency cases, and cut medical costs. However, medical services need a high level of privacy protection that advances the difficulty of a construction method. It is a good idea to construct a virtual private chain through public networks by means of cryptology and identity verification. For this purpose, novel protocols are proposed to finish the package layout, secure transmission, and authorization. By mining the special characteristics of this application, two different kinds of encryption channels were designed to support the proposed protocol to ensure the secure transmission of data. And Hash values and multiple checking were employed in the transmission package to find the incompleteness of data related to network errors or attacks. Besides the secure communication of medical information, the Extended Chinese Remainder Theorem was utilized to finish the approval during a change in committee in emergency situations. Finally, example case was used to verify the effectiveness of the total methods. Full article

(This article belongs to the Special Issue Application of Information Theory to Physical Modeling and State Awareness in Complex Systems)

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16 pages, 6115 KiB

Open AccessArticle

HE-YOLOv5s: Efficient Road Defect Detection Network

by Yonghao Liu, Minglei Duan, Guangen Ding, Hongwei Ding, Peng Hu and Hongzhi Zhao

Entropy 2023, 25(9), 1280; https://doi.org/10.3390/e25091280 - 31 Aug 2023

Cited by 1 | Viewed by 1321

Abstract

In recent years, the number of traffic accidents caused by road defects has increased dramatically all over the world, and the repair and prevention of road defects is an urgent task. Researchers in different countries have proposed many models to deal with this [...] Read more.

In recent years, the number of traffic accidents caused by road defects has increased dramatically all over the world, and the repair and prevention of road defects is an urgent task. Researchers in different countries have proposed many models to deal with this task, but most of them are either highly accurate and slow in detection, or the accuracy is low and the detection speed is high. The accuracy and speed have achieved good results, but the generalization of the model to other datasets is poor. Given this, this paper takes YOLOv5s as a benchmark model and proposes an optimization model to solve the problem of road defect detection. First, we significantly reduce the parameters of the model by pruning the model and removing unimportant modules, propose an improved Spatial Pyramid Pooling-Fast (SPPF) module to improve the feature signature fusion ability, and finally add an attention module to focus on the key information. The activation function, sampling method, and other strategies were also replaced in this study. The test results on the Global Road Damage Detection Challenge (GRDDC) dataset show that the FPS of our proposed model is not only faster than the baseline model but also improves the MAP by 2.08%, and the size of this model is also reduced by 6.07 M. Full article

(This article belongs to the Special Issue Application of Information Theory to Physical Modeling and State Awareness in Complex Systems)

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13 pages, 2290 KiB

Open AccessArticle

A Novel Evidence Combination Method Based on Improved Pignistic Probability

by Xin Shi, Fei Liang, Pengjie Qin, Liang Yu and Gaojie He

Entropy 2023, 25(6), 948; https://doi.org/10.3390/e25060948 - 16 Jun 2023

Cited by 2 | Viewed by 1041

Abstract

Evidence theory is widely used to deal with the fusion of uncertain information, but the fusion of conflicting evidence remains an open question. To solve the problem of conflicting evidence fusion in single target recognition, we proposed a novel evidence combination method based [...] Read more.

Evidence theory is widely used to deal with the fusion of uncertain information, but the fusion of conflicting evidence remains an open question. To solve the problem of conflicting evidence fusion in single target recognition, we proposed a novel evidence combination method based on an improved pignistic probability function. Firstly, the improved pignistic probability function could redistribute the probability of multi-subset proposition according to the weight of single subset propositions in a basic probability assignment (BPA), which reduces the computational complexity and information loss in the conversion process. The combination of the Manhattan distance and evidence angle measurements is proposed to extract evidence certainty and obtain mutual support information between each piece of evidence; then, entropy is used to calculate the uncertainty of the evidence and the weighted average method is used to correct and update the original evidence. Finally, the Dempster combination rule is used to fuse the updated evidence. Verified by the analysis results of single-subset proposition and multi-subset proposition highly conflicting evidence examples, compared to the Jousselme distance method, the Lance distance and reliability entropy combination method, and the Jousselme distance and uncertainty measure combination method, our approach achieved better convergence and the average accuracy was improved by 0.51% and 2.43%. Full article

(This article belongs to the Special Issue Application of Information Theory to Physical Modeling and State Awareness in Complex Systems)

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17 pages, 10798 KiB

Open AccessArticle

Attention-Based Spatial–Temporal Convolution Gated Recurrent Unit for Traffic Flow Forecasting

by Qingyong Zhang, Wanfeng Chang, Conghui Yin, Peng Xiao, Kelei Li and Meifang Tan

Entropy 2023, 25(6), 938; https://doi.org/10.3390/e25060938 - 14 Jun 2023

Viewed by 1324

Abstract

Accurate traffic flow forecasting is very important for urban planning and traffic management. However, this is a huge challenge due to the complex spatial–temporal relationships. Although the existing methods have researched spatial–temporal relationships, they neglect the long periodic aspects of traffic flow data, [...] Read more.

Accurate traffic flow forecasting is very important for urban planning and traffic management. However, this is a huge challenge due to the complex spatial–temporal relationships. Although the existing methods have researched spatial–temporal relationships, they neglect the long periodic aspects of traffic flow data, and thus cannot attain a satisfactory result. In this paper, we propose a novel model Attention-Based Spatial–Temporal Convolution Gated Recurrent Unit (ASTCG) to solve the traffic flow forecasting problem. ASTCG has two core components: the multi-input module and the STA-ConvGru module. Based on the cyclical nature of traffic flow data, the data input to the multi-input module are divided into three parts, near-neighbor data, daily-periodic data, and weekly-periodic data, thus enabling the model to better capture the time dependence. The STA-ConvGru module, formed by CNN, GRU, and attention mechanism, can capture both temporal and spatial dependencies of traffic flow. We evaluate our proposed model using real-world datasets and experiments show that the ASTCG model outperforms the state-of-the-art model. Full article

(This article belongs to the Special Issue Application of Information Theory to Physical Modeling and State Awareness in Complex Systems)

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