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  • Open Access

    ARTICLE

    A Transmission and Transformation Fault Detection Algorithm Based on Improved YOLOv5

    Xinliang Tang1, Xiaotong Ru1, Jingfang Su1,*, Gabriel Adonis2

    CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 2997-3011, 2023, DOI:10.32604/cmc.2023.038923 - 08 October 2023

    Abstract On the transmission line, the invasion of foreign objects such as kites, plastic bags, and balloons and the damage to electronic components are common transmission line faults. Detecting these faults is of great significance for the safe operation of power systems. Therefore, a YOLOv5 target detection method based on a deep convolution neural network is proposed. In this paper, Mobilenetv2 is used to replace Cross Stage Partial (CSP)-Darknet53 as the backbone. The structure uses depth-wise separable convolution toreduce the amount of calculation and parameters; improve the detection rate. At the same time, to compensate for… More >

  • Open Access

    PROCEEDINGS

    Multi-Scale Topology Optimization Method Considering Multiple Structural Performances

    Wenjun Chen1, Yingjun Wang1,*

    The International Conference on Computational & Experimental Engineering and Sciences, Vol.26, No.2, pp. 1-1, 2023, DOI:10.32604/icces.2023.09095

    Abstract The rapid development of topology optimization has given birth to a large amount of different topology optimization methods, and each of them can manage a class of corresponding engineering problems. However, structures need to meet a variety of requirements in engineering application, such as lightweight and multiple load-bearing performance. To design composite structures that have multiple structural properties, a new multi-scale topology optimization method considering multiple structural performances is proposed in this paper. Based on the fitting functions of the result set and the bisection method, a new method to determine the weight coefficient is… More >

  • Open Access

    PROCEEDINGS

    Characterization of Mechanical Properties of CNFs and the Assembled Microfibers Through a Multi-scale Optimization-Based Inversion Method

    Shuaijun Wang1, Wenqiong Tu1,*

    The International Conference on Computational & Experimental Engineering and Sciences, Vol.25, No.2, pp. 1-1, 2023, DOI:10.32604/icces.2023.09926

    Abstract Cellulose nanofibrils (CNFs) and the continuously assembled microfibers have shown transversely isotropic behavior in many studies. Due to fact that the size of CNFs and the assembled microfibers is at the nano and micro scale, respectively, the characterization of their mechanical properties is extremely challenge. That greatly hinders the accurate multi-scale modeling and design of CNFs-based materials. In our study, we have characterized the elastic constants of both CNFs microfibers and CNFs through a Multi-scale Optimization Inversion technology. Through the tensile test of CNFs microfibers reinforced resin with different volume fractions and the micromechanics model More >

  • Open Access

    ARTICLE

    Rockburst Intensity Grade Prediction Model Based on Batch Gradient Descent and Multi-Scale Residual Deep Neural Network

    Yu Zhang1,2,3, Mingkui Zhang1,2,*, Jitao Li1,2, Guangshu Chen1,2

    Computer Systems Science and Engineering, Vol.47, No.2, pp. 1987-2006, 2023, DOI:10.32604/csse.2023.040381 - 28 July 2023

    Abstract Rockburst is a phenomenon in which free surfaces are formed during excavation, which subsequently causes the sudden release of energy in the construction of mines and tunnels. Light rockburst only peels off rock slices without ejection, while severe rockburst causes casualties and property loss. The frequency and degree of rockburst damage increases with the excavation depth. Moreover, rockburst is the leading engineering geological hazard in the excavation process, and thus the prediction of its intensity grade is of great significance to the development of geotechnical engineering. Therefore, the prediction of rockburst intensity grade is one… More >

  • Open Access

    ARTICLE

    Theoretical Analysis of the Galloping Energy Harvesters under Bounded Random Parameter Excitation

    Hang Deng, Jimin Ye*, Wei Li*, Dongmei Huang

    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.2, pp. 1731-1747, 2023, DOI:10.32604/cmes.2023.028334 - 26 June 2023

    Abstract In this paper, the response properties of galloping energy harvesters under bounded random parameter excitation are studied theoretically. The first-order approximate solution of the galloping energy harvester is derived by applying the multi-scales method. The expression for the largest Lyapunov exponent that determines the trivial solution is derived, and the corresponding simulation diagrams, including the largest Lyapunov exponent diagrams and time domain diagrams, verify our results. Then the steady-state response moments of the nontrivial solution are studied using the moment method, and the analytical expressions for the first-order and second-order moments of the voltage amplitude… More >

  • Open Access

    ARTICLE

    MSCNN-LSTM Model for Predicting Return Loss of the UHF Antenna in HF-UHF RFID Tag Antenna

    Zhao Yang1, Yuan Zhang1, Lei Zhu2,*, Lei Huang1, Fangyu Hu3, Yanping Du1, Xiaowei Li1

    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 2889-2904, 2023, DOI:10.32604/cmc.2023.037297 - 31 March 2023

    Abstract High-frequency (HF) and ultrahigh-frequency (UHF) dual-band radio frequency identification (RFID) tags with both near-field and far-field communication can meet different application scenarios. However, it is time-consuming to calculate the return loss of a UHF antenna in a dual-band tag antenna using electromagnetic (EM) simulators. To overcome this, the present work proposes a model of a multi-scale convolutional neural network stacked with long and short-term memory (MSCNN-LSTM) for predicting the return loss of UHF antennas instead of EM simulators. In the proposed MSCNN-LSTM, the MSCNN has three branches, which include three convolution layers with different kernel… More >

  • Open Access

    ARTICLE

    Meta-Learning Multi-Scale Radiology Medical Image Super-Resolution

    Liwei Deng1, Yuanzhi Zhang1, Xin Yang2,*, Sijuan Huang2, Jing Wang3,*

    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 2671-2684, 2023, DOI:10.32604/cmc.2023.036642 - 31 March 2023

    Abstract High-resolution medical images have important medical value, but are difficult to obtain directly. Limited by hardware equipment and patient’s physical condition, the resolution of directly acquired medical images is often not high. Therefore, many researchers have thought of using super-resolution algorithms for secondary processing to obtain high-resolution medical images. However, current super-resolution algorithms only work on a single scale, and multiple networks need to be trained when super-resolution images of different scales are needed. This definitely raises the cost of acquiring high-resolution medical images. Thus, we propose a multi-scale super-resolution algorithm using meta-learning. The algorithm… More >

  • Open Access

    ARTICLE

    Crack Segmentation Based on Fusing Multi-Scale Wavelet and Spatial-Channel Attention

    Peng Geng*, Ji Lu, Hongtao Ma, Guiyi Yang

    Structural Durability & Health Monitoring, Vol.17, No.1, pp. 1-22, 2023, DOI:10.32604/sdhm.2023.018632 - 02 March 2023

    Abstract Accurate and reliable crack segmentation is a challenge and meaningful task. In this article, aiming at the characteristics of cracks on the concrete images, the intensity frequency information of source images which is obtained by Discrete Wavelet Transform (DWT) is fed into deep learning-based networks to enhance the ability of network on crack segmentation. To well integrate frequency information into network an effective and novel DWTA module based on the DWT and scSE attention mechanism is proposed. The semantic information of cracks is enhanced and the irrelevant information is suppressed by DWTA module. And the… More >

  • Open Access

    ARTICLE

    Grid Side Distributed Energy Storage Cloud Group End Region Hierarchical Time-Sharing Configuration Algorithm Based on Multi-Scale and Multi Feature Convolution Neural Network

    Wen Long*, Bin Zhu, Huaizheng Li, Yan Zhu, Zhiqiang Chen, Gang Cheng

    Energy Engineering, Vol.120, No.5, pp. 1253-1269, 2023, DOI:10.32604/ee.2023.026395 - 20 February 2023

    Abstract There is instability in the distributed energy storage cloud group end region on the power grid side. In order to avoid large-scale fluctuating charging and discharging in the power grid environment and make the capacitor components show a continuous and stable charging and discharging state, a hierarchical time-sharing configuration algorithm of distributed energy storage cloud group end region on the power grid side based on multi-scale and multi feature convolution neural network is proposed. Firstly, a voltage stability analysis model based on multi-scale and multi feature convolution neural network is constructed, and the multi-scale and… More >

  • Open Access

    ARTICLE

    Electrical Tree Image Segmentation Using Hybrid Multi Scale Line Tracking Algorithm

    Mohd Annuar Isa1, Mohamad Nur Khairul Hafizi Rohani1,*, Baharuddin Ismail1, Mohamad Kamarol Jamil1, Muzamir Isa1, Afifah Shuhada Rosmi1, Mohd Aminudin Jamlos2, Wan Azani Mustafa1, Nurulbariah Idris3, Abdullahi Abubakar Mas’ud4

    CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 741-760, 2023, DOI:10.32604/cmc.2023.036077 - 06 February 2023

    Abstract Electrical trees are an aging mechanism most associated with partial discharge (PD) activities in crosslinked polyethylene (XLPE) insulation of high-voltage (HV) cables. Characterization of electrical tree structures gained considerable attention from researchers since a deep understanding of the tree morphology is required to develop new insulation material. Two-dimensional (2D) optical microscopy is primarily used to examine tree structures and propagation shapes with image segmentation methods. However, since electrical trees can emerge in different shapes such as bush-type or branch-type, treeing images are complicated to segment due to manifestation of convoluted tree branches, leading to a… More >

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