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  • Open Access

    ARTICLE

    A Fault Detection Method for Electric Vehicle Battery System Based on Bayesian Optimization SVDD Considering a Few Faulty Samples

    Miao Li, Fanyong Cheng*, Jiong Yang, Maxwell Mensah Duodu, Hao Tu

    Energy Engineering, Vol.121, No.9, pp. 2543-2568, 2024, DOI:10.32604/ee.2024.051231 - 19 August 2024

    Abstract Accurate and reliable fault detection is essential for the safe operation of electric vehicles. Support vector data description (SVDD) has been widely used in the field of fault detection. However, constructing the hypersphere boundary only describes the distribution of unlabeled samples, while the distribution of faulty samples cannot be effectively described and easily misses detecting faulty data due to the imbalance of sample distribution. Meanwhile, selecting parameters is critical to the detection performance, and empirical parameterization is generally time-consuming and laborious and may not result in finding the optimal parameters. Therefore, this paper proposes a… More >

  • Open Access

    ARTICLE

    Explainable Anomaly Detection Using Vision Transformer Based SVDD

    Ji-Won Baek1, Kyungyong Chung2,*

    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 6573-6586, 2023, DOI:10.32604/cmc.2023.035246 - 28 December 2022

    Abstract Explainable AI extracts a variety of patterns of data in the learning process and draws hidden information through the discovery of semantic relationships. It is possible to offer the explainable basis of decision-making for inference results. Through the causality of risk factors that have an ambiguous association in big medical data, it is possible to increase transparency and reliability of explainable decision-making that helps to diagnose disease status. In addition, the technique makes it possible to accurately predict disease risk for anomaly detection. Vision transformer for anomaly detection from image data makes classification through MLP.… More >

  • Open Access

    ARTICLE

    Residual Attention Deep SVDD for COVID-19 Diagnosis Using CT Scans

    Akram Ali Alhadad1,2,*, Omar Tarawneh3, Reham R. Mostafa1, Hazem M. El-Bakry1

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 3333-3350, 2023, DOI:10.32604/cmc.2023.033413 - 31 October 2022

    Abstract COVID-19 is the common name of the disease caused by the novel coronavirus (2019-nCoV) that appeared in Wuhan, China in 2019. Discovering the infected people is the most important factor in the fight against the disease. The gold-standard test to diagnose COVID-19 is polymerase chain reaction (PCR), but it takes 5–6 h and, in the early stages of infection, may produce false-negative results. Examining Computed Tomography (CT) images to diagnose patients infected with COVID-19 has become an urgent necessity. In this study, we propose a residual attention deep support vector data description SVDD (RADSVDD) approach… More >

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