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  • Open Access

    ARTICLE

    Research on Damage Identification of Cable-Stayed Bridges Based on Modal Fingerprint Data Fusion

    Yue Cao1,2, Longsheng Bao1, Xiaowei Zhang1,*, Zhanfei Wang1, Bingqian Li1

    Structural Durability & Health Monitoring, Vol.18, No.4, pp. 485-503, 2024, DOI:10.32604/sdhm.2024.049698

    Abstract This study addresses the issue of inaccurate single damage fingerprint recognition during the process of bridge damage identification. To improve accuracy, the proposed approach involves fusing displacement mode difference and curvature mode difference data for single damage identification, and curvature mode difference and displacement mode wavelet coefficient difference data for two damage identification. The methodology begins by establishing a finite element model of the cable-stayed bridge and obtaining the original damage fingerprints, displacement modes, curvature modes, and wavelet coefficient differences of displacement modes through modal analysis. A fusion program based on the D-S evidence theory… More > Graphic Abstract

    Research on Damage Identification of Cable-Stayed Bridges Based on Modal Fingerprint Data Fusion

  • Open Access

    ARTICLE

    System Reliability Analysis Method Based on T-S FTA and HE-BN

    Qing Xia1, Yonghua Li2,*, Dongxu Zhang2, Yufeng Wang2

    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.2, pp. 1769-1794, 2024, DOI:10.32604/cmes.2023.030724

    Abstract For high-reliability systems in military, aerospace, and railway fields, the challenges of reliability analysis lie in dealing with unclear failure mechanisms, complex fault relationships, lack of fault data, and uncertainty of fault states. To overcome these problems, this paper proposes a reliability analysis method based on T-S fault tree analysis (T-S FTA) and Hyper-ellipsoidal Bayesian network (HE-BN). The method describes the connection between the various system fault events by T-S fuzzy gates and translates them into a Bayesian network (BN) model. Combining the advantages of T-S fault tree modeling with the advantages of Bayesian network… More >

  • Open Access

    ARTICLE

    A Novel Ensemble Learning Algorithm Based on D-S Evidence Theory for IoT Security

    Changting Shi1, *

    CMC-Computers, Materials & Continua, Vol.57, No.3, pp. 635-652, 2018, DOI:10.32604/cmc.2018.03754

    Abstract In the last decade, IoT has been widely used in smart cities, autonomous driving and Industry 4.0, which lead to improve efficiency, reliability, security and economic benefits. However, with the rapid development of new technologies, such as cognitive communication, cloud computing, quantum computing and big data, the IoT security is being confronted with a series of new threats and challenges. IoT device identification via Radio Frequency Fingerprinting (RFF) extracting from radio signals is a physical-layer method for IoT security. In physical-layer, RFF is a unique characteristic of IoT device themselves, which can difficultly be tampered. More >

  • Open Access

    ARTICLE

    An Evidence Combination Method based on DBSCAN Clustering

    Kehua Yang1,2,*, Tian Tan1, Wei Zhang1

    CMC-Computers, Materials & Continua, Vol.57, No.2, pp. 269-281, 2018, DOI:10.32604/cmc.2018.03696

    Abstract Dempster-Shafer (D-S) evidence theory is a key technology for integrating uncertain information from multiple sources. However, the combination rules can be paradoxical when the evidence seriously conflict with each other. In the paper, we propose a novel combination algorithm based on unsupervised Density-Based Spatial Clustering of Applications with Noise (DBSCAN) density clustering. In the proposed mechanism, firstly, the original evidence sets are preprocessed by DBSCAN density clustering, and a successfully focal element similarity criteria is used to mine the potential information between the evidence, and make a correct measure of the conflict evidence. Then, two More >

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