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  • Open Access


    Shadow Extraction and Elimination of Moving Vehicles for Tracking Vehicles

    Kalpesh Jadav1, Vishal Sorathiya1,*, Walid El-Shafai2, Torki Altameem3, Moustafa H. Aly4, Vipul Vekariya5, Kawsar Ahmed6, Francis M. Bui6

    CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 2009-2030, 2023, DOI:10.32604/cmc.2023.043168

    Abstract Shadow extraction and elimination is essential for intelligent transportation systems (ITS) in vehicle tracking application. The shadow is the source of error for vehicle detection, which causes misclassification of vehicles and a high false alarm rate in the research of vehicle counting, vehicle detection, vehicle tracking, and classification. Most of the existing research is on shadow extraction of moving vehicles in high intensity and on standard datasets, but the process of extracting shadows from moving vehicles in low light of real scenes is difficult. The real scenes of vehicles dataset are generated by self on the Vadodara–Mumbai highway during periods… More >

  • Open Access


    Paradigm of Numerical Simulation of Spatial Wind Field for Disaster Prevention of Transmission Tower Lines

    Yongxin Liu1, Puyu Zhao2, Jianxin Xu2, Xiaokai Meng1, Hong Yang1, Bo He2,*

    Structural Durability & Health Monitoring, Vol.17, No.6, pp. 521-539, 2023, DOI:10.32604/sdhm.2023.029850

    Abstract Numerical simulation of the spatial wind field plays a very important role in the study of wind-induced response law of transmission tower structures. A reasonable construction of a numerical simulation method of the wind field is conducive to the study of wind-induced response law under the action of an actual wind field. Currently, many research studies rely on simulating spatial wind fields as Gaussian wind, often overlooking the basic non-Gaussian characteristics. This paper aims to provide a comprehensive overview of the historical development and current state of spatial wind field simulations, along with a detailed introduction to standard simulation methods.… More > Graphic Abstract

    Paradigm of Numerical Simulation of Spatial Wind Field for Disaster Prevention of Transmission Tower Lines

  • Open Access


    A Differential Privacy Federated Learning Scheme Based on Adaptive Gaussian Noise

    Sanxiu Jiao1, Lecai Cai2,*, Xinjie Wang1, Kui Cheng2, Xiang Gao3

    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.2, pp. 1679-1694, 2024, DOI:10.32604/cmes.2023.030512

    Abstract As a distributed machine learning method, federated learning (FL) has the advantage of naturally protecting data privacy. It keeps data locally and trains local models through local data to protect the privacy of local data. The federated learning method effectively solves the problem of artificial Smart data islands and privacy protection issues. However, existing research shows that attackers may still steal user information by analyzing the parameters in the federated learning training process and the aggregation parameters on the server side. To solve this problem, differential privacy (DP) techniques are widely used for privacy protection in federated learning. However, adding… More > Graphic Abstract

    A Differential Privacy Federated Learning Scheme Based on Adaptive Gaussian Noise

  • Open Access


    GMLP-IDS: A Novel Deep Learning-Based Intrusion Detection System for Smart Agriculture

    Abdelwahed Berguiga1,2,*, Ahlem Harchay1,2, Ayman Massaoudi1,2, Mossaad Ben Ayed3, Hafedh Belmabrouk4

    CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 379-402, 2023, DOI:10.32604/cmc.2023.041667

    Abstract Smart Agriculture, also known as Agricultural 5.0, is expected to be an integral part of our human lives to reduce the cost of agricultural inputs, increasing productivity and improving the quality of the final product. Indeed, the safety and ongoing maintenance of Smart Agriculture from cyber-attacks are vitally important. To provide more comprehensive protection against potential cyber-attacks, this paper proposes a new deep learning-based intrusion detection system for securing Smart Agriculture. The proposed Intrusion Detection System IDS, namely GMLP-IDS, combines the feedforward neural network Multilayer Perceptron (MLP) and the Gaussian Mixture Model (GMM) that can better protect the Smart Agriculture… More >

  • Open Access


    Numerical Simulation of Non-Gaussian Winds and Application on Floating Offshore Wind Turbines

    Shu Dai1,*, Bert Sweetman2

    The International Conference on Computational & Experimental Engineering and Sciences, Vol.27, No.2, pp. 1-1, 2023, DOI:10.32604/icces.2023.09687

    Abstract Short-term wind process is normally assumed to be a Gaussian distribution, such as TurbSim, the widely used 3D wind field tool. Nowadays, newest researches indicate that non-Gaussian wind model is believed to be more accurate according to the field observation data. A new numerical method is proposed to generate non-Gaussian wind filed using translation process theory and spectral representation method. This study presents a comprehensive investigation on power production and blades fatigue damage of floating offshore wind turbines (FOWTs) to the non-Gaussian wind field. The comparisons of Gaussian and non-Gaussian simulation results indicate that the non-Gaussian wind fields will cost… More >

  • Open Access


    Deep Learning Applied to Computational Mechanics: A Comprehensive Review, State of the Art, and the Classics

    Loc Vu-Quoc1,*, Alexander Humer2

    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.2, pp. 1069-1343, 2023, DOI:10.32604/cmes.2023.028130

    Abstract Three recent breakthroughs due to AI in arts and science serve as motivation: An award winning digital image, protein folding, fast matrix multiplication. Many recent developments in artificial neural networks, particularly deep learning (DL), applied and relevant to computational mechanics (solid, fluids, finite-element technology) are reviewed in detail. Both hybrid and pure machine learning (ML) methods are discussed. Hybrid methods combine traditional PDE discretizations with ML methods either (1) to help model complex nonlinear constitutive relations, (2) to nonlinearly reduce the model order for efficient simulation (turbulence), or (3) to accelerate the simulation by predicting certain components in the traditional… More >

  • Open Access


    Classification-Detection of Metal Surfaces under Lower Edge Sharpness Using a Deep Learning-Based Approach Combined with an Enhanced LoG Operator

    Hong Zhang1,*, Jiaming Zhou1, Qi Wang1, Chengxi Zhu1, Haijian Shao2

    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.2, pp. 1551-1572, 2023, DOI:10.32604/cmes.2023.027035

    Abstract Metal flat surface in-line surface defect detection is notoriously difficult due to obstacles such as high surface reflectivity, pseudo-defect interference, and random elastic deformation. This study evaluates the approach for detecting scratches on a metal surface in order to address a problem in the detection process. This paper proposes an improved Gauss-Laplace (LoG) operator combined with a deep learning technique for metal surface scratch identification in order to solve the difficulties that it is challenging to reduce noise and that the edges are unclear when utilizing existing edge detection algorithms. In the process of scratch identification, it is challenging to… More >

  • Open Access


    Cancer Regions in Mammogram Images Using ANFIS Classifier Based Probability Histogram Segmentation Algorithm

    V. Swetha*, G. Vadivu

    Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 707-726, 2023, DOI:10.32604/iasc.2023.035483

    Abstract Every year, the number of women affected by breast tumors is increasing worldwide. Hence, detecting and segmenting the cancer regions in mammogram images is important to prevent death in women patients due to breast cancer. The conventional methods obtained low sensitivity and specificity with cancer region segmentation accuracy. The high-resolution standard mammogram images were supported by conventional methods as one of the main drawbacks. The conventional methods mostly segmented the cancer regions in mammogram images concerning their exterior pixel boundaries. These drawbacks are resolved by the proposed cancer region detection methods stated in this paper. The mammogram images are classified… More >

  • Open Access


    Designing Pair of Nonlinear Components of a Block Cipher over Gaussian Integers

    Muhammad Sajjad1,*, Tariq Shah1, Robinson Julian Serna2

    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5287-5305, 2023, DOI:10.32604/cmc.2023.035347

    Abstract In block ciphers, the nonlinear components, also known as substitution boxes (S-boxes), are used with the purpose of inducing confusion in cryptosystems. For the last decade, most of the work on designing S-boxes over the points of elliptic curves has been published. The main purpose of these studies is to hide data and improve the security levels of crypto algorithms. In this work, we design pair of nonlinear components of a block cipher over the residue class of Gaussian integers (GI). The fascinating features of this structure provide S-boxes pair at a time by fixing three parameters. But the prime… More >

  • Open Access


    A Network Traffic Prediction Algorithm Based on Prophet-EALSTM-GPR

    Guoqing Xu1, Changsen Xia1, Jun Qian1, Guo Ran3, Zilong Jin1,2,*

    Journal on Internet of Things, Vol.4, No.2, pp. 113-125, 2022, DOI:10.32604/jiot.2022.036066

    Abstract Huge networks and increasing network traffic will consume more and more resources. It is critical to predict network traffic accurately and timely for network planning, and resource allocation, etc. In this paper, a combined network traffic prediction model is proposed, which is based on Prophet, evolutionary attention-based LSTM (EALSTM) network, and Gaussian process regression (GPR). According to the non-smooth, sudden, periodic, and long correlation characteristics of network traffic, the prediction procedure is divided into three steps to predict network traffic accurately. In the first step, the Prophet model decomposes network traffic data into periodic and non-periodic parts. The periodic term… More >

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