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  • Open Access

    RETRACTION

    Retraction: A Hybrid Modified Sine CosineAlgorithm Using Inverse Filtering andClipping Methods forLow AutocorrelationBinary Sequences

    Siti Julia Rosli1,2, Hasliza A Rahim1,2,*, Khairul Najmy Abdul Rani1,2, Ruzelita Ngadiran2,3, Wan Azani Mustafa3,4, Muzammil Jusoh1,2, Mohd Najib Mohd Yasin1,2, Thennarasan Sabapathy1,2, Mohamedfareq Abdulmalek5, Wan Suryani Firuz Wan Ariffin2, Ahmed Alkhayyat6

    CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 2571-2571, 2023, DOI:10.32604/cmc.2023.045533

    Abstract This article has no abstract. More >

  • Open Access

    ARTICLE

    A Hybrid Modified Sine Cosine Algorithm Using Inverse Filtering and Clipping Methods for Low Autocorrelation Binary Sequences

    Siti Julia Rosli1,2, Hasliza A Rahim1,2,*, Khairul Najmy Abdul Rani1,2, Ruzelita Ngadiran2,3, Wan Azani Mustafa3,4, Muzammil Jusoh1,2, Mohd Najib Mohd Yasin1,2, Thennarasan Sabapathy1,2, Mohamedfareq Abdulmalek5, Wan Suryani Firuz Wan Ariffin2, Ahmed Alkhayyat6

    CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 3533-3556, 2022, DOI:10.32604/cmc.2022.021719

    Abstract The essential purpose of radar is to detect a target of interest and provide information concerning the target's location, motion, size, and other parameters. The knowledge about the pulse trains’ properties shows that a class of signals is mainly well suited to digital processing of increasing practical importance. A low autocorrelation binary sequence (LABS) is a complex combinatorial problem. The main problems of LABS are low Merit Factor (MF) and shorter length sequences. Besides, the maximum possible MF equals 12.3248 as infinity length is unable to be achieved. Therefore, this study implemented two techniques to propose a new metaheuristic algorithm… More >

  • Open Access

    ARTICLE

    Identification of Abnormal Patterns in AR (1) Process Using CS-SVM

    Hongshuo Zhang1, Bo Zhu1,*, Kaimin Pang1, Chunmei Chen1, Yuwei Wan2

    Intelligent Automation & Soft Computing, Vol.28, No.3, pp. 797-810, 2021, DOI:10.32604/iasc.2021.017232

    Abstract Using machine learning method to recognize abnormal patterns covers the shortage of traditional control charts for autocorrelation processes, which violate the applicable conditions of the control chart, i.e., the independent identically distributed (IID) assumption. In this study, we propose a recognition model based on support vector machine (SVM) for the AR (1) type of autocorrelation process. For achieving a higher recognition performance, the cuckoo search algorithm (CS) is used to optimize the two hyper-parameters of SVM, namely the penalty parameter c and the radial basis kernel parameter g. By using Monte Carlo simulation methods, the data sets containing samples of… More >

  • Open Access

    ARTICLE

    Numerical Simulation of 3D Rough Surfaces and Analysis of Interfacial Contact Characteristics

    Guoqing Yang1, Baotong Li2,3, Yang Wang2, Jun Hong2

    CMES-Computer Modeling in Engineering & Sciences, Vol.103, No.4, pp. 251-279, 2014, DOI:10.3970/cmes.2014.103.251

    Abstract Mechanical behaviors arising at the contact interface largely depend on its surface topographies, particularly when it comes to rough surfaces. A numerical simulation based on an appropriate characterization of rough surfaces especially in terms of three dimensional can be of great significance when it comes to capturing the deformation patterns of micro-scale contacts. In this paper, a simple and practical scheme is developed to generate 3D rough surfaces and to analyze and evaluate the contact characteristics. Firstly amplitude and spatial statistical characterizations of asperities are introduced to avert from the redundancy of topography data caused by traditional measuring methods. A… More >

  • Open Access

    ARTICLE

    Modelling Fruit Microstructure Using Novel Ellipse Tessellation Algorithm

    H.K. Mebatsion1, P. Verboven1, Q. T. Ho1, F. Mendoza1, B. E. Verlinden2, T. A. Nguyen1, B. M. Nicolaï1,2

    CMES-Computer Modeling in Engineering & Sciences, Vol.14, No.1, pp. 1-14, 2006, DOI:10.3970/cmes.2006.014.001

    Abstract Modeling plant microstructure is of great interest to food engineers to study and explain material properties related to mass transfer and mechanical deformation. In this paper, a novel ellipse tessellation algorithm to generate a 2D geometrical model of apple tissue is presented. Ellipses were used to quantify the orientation and aspect ratio of cells on a microscopic image. The cell areas and centroids of each cell were also determined by means of a numerical procedure. These characteristic quantities were then described by means of probability density functions. The model tissue geometry was generated from the ellipses, which were truncated when… More >

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