Qing Xia¹, Yonghua Li^2,*, 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 computation, a reliability modeling method… More >

Changting Shi^{1, *}

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. Just as people’s unique fingerprinting,… More >

Kehua Yang^1,2,*, 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 different discount factors are adopted… More >