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  • Open Access

    ARTICLE

    Olive Leaf Disease Detection via Wavelet Transform and Feature Fusion of Pre-Trained Deep Learning Models

    Mahmood A. Mahmood1,2,*, Khalaf Alsalem1

    CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3431-3448, 2024, DOI:10.32604/cmc.2024.047604

    Abstract Olive trees are susceptible to a variety of diseases that can cause significant crop damage and economic losses. Early detection of these diseases is essential for effective management. We propose a novel transformed wavelet, feature-fused, pre-trained deep learning model for detecting olive leaf diseases. The proposed model combines wavelet transforms with pre-trained deep-learning models to extract discriminative features from olive leaf images. The model has four main phases: preprocessing using data augmentation, three-level wavelet transformation, learning using pre-trained deep learning models, and a fused deep learning model. In the preprocessing phase, the image dataset is augmented using techniques such as… More >

  • Open Access

    ARTICLE

    Research on Sleeve Grouting Density Detection Based on the Impact Echo Method

    Pu Zhang1, Yingjun Li1, Xinyu Zhu1, Shizhan Xu1, Pinwu Guan1,*, Wei Liu2, Yanwei Guo2, Haibo Wang2

    Structural Durability & Health Monitoring, Vol.18, No.2, pp. 143-159, 2024, DOI:10.32604/sdhm.2024.046986

    Abstract Grouting defects are an inherent challenge in construction practices, exerting a considerable impact on the operational structural integrity of connections. This investigation employed the impact-echo technique for the detection of grouting anomalies within connections, enhancing its precision through the integration of wavelet packet energy principles for damage identification purposes. A series of grouting completeness assessments were meticulously conducted, taking into account variables such as the divergent material properties of the sleeves and the configuration of adjacent reinforcement. The findings revealed that: (i) the energy distribution for the high-strength concrete cohort predominantly occupied the frequency bands 42, 44, 45, and 47,… More >

  • Open Access

    ARTICLE

    Wavelet Multi-Resolution Interpolation Galerkin Method for Linear Singularly Perturbed Boundary Value Problems

    Jiaqun Wang1,2, Guanxu Pan2, Youhe Zhou2, Xiaojing Liu2,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 297-318, 2024, DOI:10.32604/cmes.2023.030622

    Abstract In this study, a wavelet multi-resolution interpolation Galerkin method (WMIGM) is proposed to solve linear singularly perturbed boundary value problems. Unlike conventional wavelet schemes, the proposed algorithm can be readily extended to special node generation techniques, such as the Shishkin node. Such a wavelet method allows a high degree of local refinement of the nodal distribution to efficiently capture localized steep gradients. All the shape functions possess the Kronecker delta property, making the imposition of boundary conditions as easy as that in the finite element method. Four numerical examples are studied to demonstrate the validity and accuracy of the proposed… More >

  • Open Access

    ARTICLE

    A Monitoring Method for Transmission Tower Foots Displacement Based on Wind-Induced Vibration Response

    Zhicheng Liu1, Long Zhao1,*, Guanru Wen1, Peng Yuan2, Qiu Jin1

    Structural Durability & Health Monitoring, Vol.17, No.6, pp. 541-555, 2023, DOI:10.32604/sdhm.2023.029760

    Abstract The displacement of transmission tower feet can seriously affect the safe operation of the tower, and the accuracy of structural health monitoring methods is limited at the present stage. The application of deep learning method provides new ideas for structural health monitoring of towers, but the current amount of tower vibration fault data is restricted to provide adequate training data for Deep Learning (DL). In this paper, we propose a DT-DL based tower foot displacement monitoring method, which firstly simulates the wind-induced vibration response data of the tower under each fault condition by finite element method. Then the vibration signal… More > Graphic Abstract

    A Monitoring Method for Transmission Tower Foots Displacement Based on Wind-Induced Vibration Response

  • Open Access

    ARTICLE

    Recognizing Breast Cancer Using Edge-Weighted Texture Features of Histopathology Images

    Arslan Akram1,2, Javed Rashid2,3,4, Fahima Hajjej5, Sobia Yaqoob1,6, Muhammad Hamid7, Asma Irshad8, Nadeem Sarwar9,*

    CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 1081-1101, 2023, DOI:10.32604/cmc.2023.041558

    Abstract Around one in eight women will be diagnosed with breast cancer at some time. Improved patient outcomes necessitate both early detection and an accurate diagnosis. Histological images are routinely utilized in the process of diagnosing breast cancer. Methods proposed in recent research only focus on classifying breast cancer on specific magnification levels. No study has focused on using a combined dataset with multiple magnification levels to classify breast cancer. A strategy for detecting breast cancer is provided in the context of this investigation. Histopathology image texture data is used with the wavelet transform in this technique. The proposed method comprises… More >

  • Open Access

    PROCEEDINGS

    A Directional Fast Algorithm for Oscillatory Kernels with Curvelet-Like Functions

    Yanchuang Cao1, Jun Liu1, Dawei Chen1,*

    The International Conference on Computational & Experimental Engineering and Sciences, Vol.25, No.4, pp. 1-1, 2023, DOI:10.32604/icces.2023.09272

    Abstract Interactions of multiple points with oscillatory kernels are widely encountered in wave analysis. For large scale problems, its direct evaluation is prohibitive since the computational cost increases quadratically with the number of points.
    Various fast algorithms have been constructed by exploiting specific properties of the kernel function. Early fast algorithms, such as the fast multipole method (FMM) and its variants, H2-matrix, adaptive cross approximation (ACA), wavelet-based method, etc., are generally developed for kernels that are asymptotically smooth when source points and target points are well separated. For oscillatory kernels, however, the asymptotic smoothness criteria is only satisfied when the oscillation… More >

  • Open Access

    ARTICLE

    An Efficient Numerical Scheme for Biological Models in the Frame of Bernoulli Wavelets

    Fei Li1, Haci Mehmet Baskonus2,*, S. Kumbinarasaiah3, G. Manohara3, Wei Gao4, Esin Ilhan5

    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.3, pp. 2381-2408, 2023, DOI:10.32604/cmes.2023.028069

    Abstract This article considers three types of biological systems: the dengue fever disease model, the COVID-19 virus model, and the transmission of Tuberculosis model. The new technique of creating the integration matrix for the Bernoulli wavelets is applied. Also, the novel method proposed in this paper is called the Bernoulli wavelet collocation scheme (BWCM). All three models are in the form system of coupled ordinary differential equations without an exact solution. These systems are converted into a system of algebraic equations using the Bernoulli wavelet collocation scheme. The numerical wave distributions of these governing models are obtained by solving the algebraic… More >

  • Open Access

    ARTICLE

    Sparsity-Enhanced Model-Based Method for Intelligent Fault Detection of Mechanical Transmission Chain in Electrical Vehicle

    Wangpeng He1,*, Yue Zhou1, Xiaoya Guo2, Deshun Hu1, Junjie Ye3

    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.3, pp. 2495-2511, 2023, DOI:10.32604/cmes.2023.027896

    Abstract In today’s world, smart electric vehicles are deeply integrated with smart energy, smart transportation and smart cities. In electric vehicles (EVs), owing to the harsh working conditions, mechanical parts are prone to fatigue damages, which endanger the driving safety of EVs. The practice has proved that the identification of periodic impact characteristics (PICs) can effectively indicate mechanical faults. This paper proposes a novel model-based approach for intelligent fault diagnosis of mechanical transmission train in EVs. The essential idea of this approach lies in the fusion of statistical information and model information from a dynamic process. In the algorithm, a novel… More >

  • Open Access

    ARTICLE

    Development of Features for Early Detection of Defects and Assessment of Bridge Decks

    Ahmed Silik1,2,7, Xiaodong Wang3, Chenyue Mei3, Xiaolei Jin3, Xudong Zhou4, Wei Zhou4, Congning Chen4, Weixing Hong1,2, Jiawei Li1,2, Mingjie Mao1,2, Yuhan Liu1,2, Mohammad Noori5,6,*, Wael A. Altabey8,*

    Structural Durability & Health Monitoring, Vol.17, No.4, pp. 257-281, 2023, DOI:10.32604/sdhm.2023.023617

    Abstract Damage detection is an important area with growing interest in mechanical and structural engineering. One of the critical issues in damage detection is how to determine indices sensitive to the structural damage and insensitive to the surrounding environmental variations. Current damage identification indices commonly focus on structural dynamic characteristics such as natural frequencies, mode shapes, and frequency responses. This study aimed at developing a technique based on energy Curvature Difference, power spectrum density, correlation-based index, load distribution factor, and neutral axis shift to assess the bridge deck condition. In addition to tracking energy and frequency over time using wavelet packet… More > Graphic Abstract

    Development of Features for Early Detection of Defects and Assessment of Bridge Decks

  • Open Access

    ARTICLE

    Short-Term Wind Power Prediction Based on Combinatorial Neural Networks

    Tusongjiang Kari1, Sun Guoliang2, Lei Kesong1, Ma Xiaojing1,*, Wu Xian1

    Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 1437-1452, 2023, DOI:10.32604/iasc.2023.037012

    Abstract Wind power volatility not only limits the large-scale grid connection but also poses many challenges to safe grid operation. Accurate wind power prediction can mitigate the adverse effects of wind power volatility on wind power grid connections. For the characteristics of wind power antecedent data and precedent data jointly to determine the prediction accuracy of the prediction model, the short-term prediction of wind power based on a combined neural network is proposed. First, the Bi-directional Long Short Term Memory (BiLSTM) network prediction model is constructed, and the bi-directional nature of the BiLSTM network is used to deeply mine the wind… More >

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