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  • Open Access

    EDITORIAL

    Key Issues for Modelling, Operation, Management and Diagnosis of Lithium Batteries: Current States and Prospects

    Bo Yang1,*, Yucun Qian1, Jianzhong Xu2, Yaxing Ren3, Yixuan Chen4

    Energy Engineering, Vol.121, No.8, pp. 2085-2091, 2024, DOI:10.32604/ee.2024.050083

    Abstract This article has no abstract. More >

  • Open Access

    ARTICLE

    The Lightweight Edge-Side Fault Diagnosis Approach Based on Spiking Neural Network

    Jingting Mei, Yang Yang*, Zhipeng Gao, Lanlan Rui, Yijing Lin

    CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4883-4904, 2024, DOI:10.32604/cmc.2024.051860

    Abstract Network fault diagnosis methods play a vital role in maintaining network service quality and enhancing user experience as an integral component of intelligent network management. Considering the unique characteristics of edge networks, such as limited resources, complex network faults, and the need for high real-time performance, enhancing and optimizing existing network fault diagnosis methods is necessary. Therefore, this paper proposes the lightweight edge-side fault diagnosis approach based on a spiking neural network (LSNN). Firstly, we use the Izhikevich neurons model to replace the Leaky Integrate and Fire (LIF) neurons model in the LSNN model. Izhikevich… More >

  • Open Access

    ARTICLE

    Empowering Diagnosis: Cutting-Edge Segmentation and Classification in Lung Cancer Analysis

    Iftikhar Naseer1,2, Tehreem Masood1,2, Sheeraz Akram3,*, Zulfiqar Ali4, Awais Ahmad3, Shafiq Ur Rehman3, Arfan Jaffar1,2

    CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4963-4977, 2024, DOI:10.32604/cmc.2024.050204

    Abstract Lung cancer is a leading cause of global mortality rates. Early detection of pulmonary tumors can significantly enhance the survival rate of patients. Recently, various Computer-Aided Diagnostic (CAD) methods have been developed to enhance the detection of pulmonary nodules with high accuracy. Nevertheless, the existing methodologies cannot obtain a high level of specificity and sensitivity. The present study introduces a novel model for Lung Cancer Segmentation and Classification (LCSC), which incorporates two improved architectures, namely the improved U-Net architecture and the improved AlexNet architecture. The LCSC model comprises two distinct stages. The first stage involves… More >

  • Open Access

    ARTICLE

    Fault Diagnosis Method of Rolling Bearing Based on MSCNN-LSTM

    Chunming Wu1, Shupeng Zheng2,*

    CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4395-4411, 2024, DOI:10.32604/cmc.2024.049665

    Abstract Deep neural networks have been widely applied to bearing fault diagnosis systems and achieved impressive success recently. To address the problem that the insufficient fault feature extraction ability of traditional fault diagnosis methods results in poor diagnosis effect under variable load and noise interference scenarios, a rolling bearing fault diagnosis model combining Multi-Scale Convolutional Neural Network (MSCNN) and Long Short-Term Memory (LSTM) fused with attention mechanism is proposed. To adaptively extract the essential spatial feature information of various sizes, the model creates a multi-scale feature extraction module using the convolutional neural network (CNN) learning process.… More >

  • Open Access

    ARTICLE

    Fault Diagnosis Scheme for Railway Switch Machine Using Multi-Sensor Fusion Tensor Machine

    Chen Chen1,2, Zhongwei Xu1, Meng Mei1,*, Kai Huang3, Siu Ming Lo2

    CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4533-4549, 2024, DOI:10.32604/cmc.2024.048995

    Abstract Railway switch machine is essential for maintaining the safety and punctuality of train operations. A data-driven fault diagnosis scheme for railway switch machine using tensor machine and multi-representation monitoring data is developed herein. Unlike existing methods, this approach takes into account the spatial information of the time series monitoring data, aligning with the domain expertise of on-site manual monitoring. Besides, a multi-sensor fusion tensor machine is designed to improve single signal data’s limitations in insufficient information. First, one-dimensional signal data is preprocessed and transformed into two-dimensional images. Afterward, the fusion feature tensor is created by More >

  • Open Access

    ARTICLE

    Fault Diagnosis Method of Energy Storage Unit of Circuit Breakers Based on EWT-ISSA-BP

    Tengfei Li1, Wenhui Zhang1, Ke Mi1, Qingming Lin1, Shuangwei Zhao2,*, Jiayi Song2

    Energy Engineering, Vol.121, No.7, pp. 1991-2007, 2024, DOI:10.32604/ee.2024.049460

    Abstract Aiming at the problem of energy storage unit failure in the spring operating mechanism of low voltage circuit breakers (LVCBs). A fault diagnosis algorithm based on an improved Sparrow Search Algorithm (ISSA) optimized Backpropagation Neural Network (BPNN) is proposed to improve the operational safety of LVCB. Taking the 1.5kV/4000A/75kA LVCB as an example. According to the current operating characteristics of the energy storage motor, fault characteristics are extracted based on Empirical Wavelet Transform (EWT). Traditional BPNN has problems such as difficulty adjusting network weights and thresholds, being sensitive to initial weights, and quickly falling into More >

  • Open Access

    ARTICLE

    Research on the Icing Diagnosis of Wind Turbine Blades Based on FS–XGBoost–EWMA

    Jicai Guo1,2, Xiaowen Song1,2,*, Chang Liu1,2, Yanfeng Zhang1,2, Shijie Guo1,2, Jianxin Wu1,2, Chang Cai3, Qing’an Li3,*

    Energy Engineering, Vol.121, No.7, pp. 1739-1758, 2024, DOI:10.32604/ee.2024.048854

    Abstract In winter, wind turbines are susceptible to blade icing, which results in a series of energy losses and safe operation problems. Therefore, blade icing detection has become a top priority. Conventional methods primarily rely on sensor monitoring, which is expensive and has limited applications. Data-driven blade icing detection methods have become feasible with the development of artificial intelligence. However, the data-driven method is plagued by limited training samples and icing samples; therefore, this paper proposes an icing warning strategy based on the combination of feature selection (FS), eXtreme Gradient Boosting (XGBoost) algorithm, and exponentially weighted… More >

  • Open Access

    ARTICLE

    Bearing Fault Diagnosis Based on Optimized Feature Mode Decomposition and Improved Deep Belief Network

    Guangfei Jia*, Yanchao Meng, Zhiying Qin

    Structural Durability & Health Monitoring, Vol.18, No.4, pp. 445-463, 2024, DOI:10.32604/sdhm.2024.049298

    Abstract The vibration signals of rolling bearings exhibit nonlinear and non-stationary characteristics under the influence of noise. In intelligent fault diagnosis, unprocessed signals will lead to weak fault characteristics and low diagnostic accuracy. To solve the above problem, a fault diagnosis method based on parameter optimization feature mode decomposition and improved deep belief networks is proposed. The feature mode decomposition is used to decompose the vibration signals. The parameter adaptation of feature mode decomposition is implemented by improved whale optimization algorithm including Levy flight strategy and adaptive weight. The selection of activation function and parameters is More > Graphic Abstract

    Bearing Fault Diagnosis Based on Optimized Feature Mode Decomposition and Improved Deep Belief Network

  • Open Access

    CORRECTION

    Correction: Diabetic Retinopathy Diagnosis Using Interval Neutrosophic Segmentation with Deep Learning Model

    V. Thanikachalam1,*, M. G. Kavitha2, V. Sivamurugan1

    Computer Systems Science and Engineering, Vol.48, No.3, pp. 857-858, 2024, DOI:10.32604/csse.2024.052484

    Abstract This article has no abstract. More >

  • Open Access

    CORRECTION

    Correction: An Effective Diagnosis System for Brain Tumor Detection and Classification

    Ahmed A. Alsheikhy1, Ahmad S. Azzahrani1, A. Khuzaim Alzahrani2, Tawfeeq Shawly3

    Computer Systems Science and Engineering, Vol.48, No.3, pp. 853-853, 2024, DOI:10.32604/csse.2024.051630

    Abstract This article has no abstract. More >

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