Home / Advanced Search

  • Title/Keywords

  • Author/Affliations

  • Journal

  • Article Type

  • Start Year

  • End Year

Update SearchingClear
  • Articles
  • Online
Search Results (127)
  • Open Access

    ARTICLE

    State-Based Control Feature Extraction for Effective Anomaly Detection in Process Industries

    Ming Wan1, Jinfang Li1, Jiangyuan Yao2, *, Rongbing Wang1, 3, Hao Luo1

    CMC-Computers, Materials & Continua, Vol.63, No.3, pp. 1415-1431, 2020, DOI:10.32604/cmc.2020.09692

    Abstract In process industries, the characteristics of industrial activities focus on the integrality and continuity of production process, which can contribute to excavating the appropriate features for industrial anomaly detection. From this perspective, this paper proposes a novel state-based control feature extraction approach, which regards the finite control operations as different states. Furthermore, the procedure of state transition can adequately express the change of successive control operations, and the statistical information between different states can be used to calculate the feature values. Additionally, OCSVM (One Class Support Vector Machine) and BPNN (BP Neural Network), which are optimized by PSO (Particle Swarm… More >

  • Open Access

    ARTICLE

    Implementation of PSOANN Optimized PI Control Algorithm for Shunt Active Filter

    M. Sujith1, *, S. Padma2

    CMES-Computer Modeling in Engineering & Sciences, Vol.122, No.3, pp. 863-888, 2020, DOI:10.32604/cmes.2020.08908

    Abstract This paper proposes the optimum controller for shunt active filter (SAF) to mitigate the harmonics and maintain the power quality in the distribution system. It consists of shunt active filter, Voltage Source Inverter (VSI), series inductor and DC bus and nonlinear load. The proposed hybrid approach is a combination of Particle Swarm Optimization (PSO) and Artificial Neural Network (ANN) termed as PSOANN. The PI controller gain parameters of kp and ki are optimized with the help of PSOANN. The PSOANN improves the accuracy of tuning the gain parameters under steady and dynamic load conditions; thereby it reduces the values of… More >

  • Open Access

    ARTICLE

    Seasonal testicular changes in Dendropsophus minutus Peters, 1872 (Anura, Hylidae)

    ADELINA FERREIRA1,* AND MAHMOUD MEHANNA2

    BIOCELL, Vol.36, No.2, pp. 57-62, 2012, DOI:10.32604/biocell.2012.36.057

    Abstract The reproductive cycle in anurans may be either continuous or discontinuous. These differences may be connected to seasonal climate changes and/or to anthropic activity. Forty adult male individuals of the Dendropsophus minutus species were collected during one year, in the municipality of Chapada dos Guimarães (Mato Grosso, Brazil). The testicles were studied under light and transmission electron microscopy. No variations were observed when the diameter of the seminiferous tubules and the thickness of the interstitial tissue were studied. However, changes in spermatogenesis were conspicuous and indicated that the reproductive cycle of D. minutus in Chapada dos Guimarães is discontinuous and… More >

  • Open Access

    ARTICLE

    SVM Model Selection Using PSO for Learning Handwritten Arabic Characters

    Mamouni El Mamoun1,*, Zennaki Mahmoud1, Sadouni Kaddour1

    CMC-Computers, Materials & Continua, Vol.61, No.3, pp. 995-1008, 2019, DOI:10.32604/cmc.2019.08081

    Abstract Using Support Vector Machine (SVM) requires the selection of several parameters such as multi-class strategy type (one-against-all or one-against-one), the regularization parameter C, kernel function and their parameters. The choice of these parameters has a great influence on the performance of the final classifier. This paper considers the grid search method and the particle swarm optimization (PSO) technique that have allowed to quickly select and scan a large space of SVM parameters. A comparative study of the SVM models is also presented to examine the convergence speed and the results of each model. SVM is applied to handwritten Arabic characters… More >

  • Open Access

    ARTICLE

    Analysis of OSA Syndrome from PPG Signal Using CART-PSO Classifier with Time Domain and Frequency Domain Features

    N. Kins Burk Sunil1, *, R. Ganesan2, B. Sankaragomathi3

    CMES-Computer Modeling in Engineering & Sciences, Vol.118, No.2, pp. 351-375, 2019, DOI:10.31614/cmes.2018.04484

    Abstract Obstructive Sleep Apnea (OSA) is a respiratory syndrome that occurs due to insufficient airflow through the respiratory or respiratory arrest while sleeping and sometimes due to the reduced oxygen saturation. The aim of this paper is to analyze the respiratory signal of a person to detect the Normal Breathing Activity and the Sleep Apnea (SA) activity. In the proposed method, the time domain and frequency domain features of respiration signal obtained from the PPG device are extracted. These features are applied to the Classification and Regression Tree (CART)-Particle Swarm Optimization (PSO) classifier which classifies the signal into normal breathing signal… More >

  • Open Access

    ARTICLE

    Homogenization Analysis for Particulate Composite Materials using the Boundary Element Method

    Hiroshi Okada1, Yasuyoshi Fukui1, Noriyoshi Kumazawa1

    CMES-Computer Modeling in Engineering & Sciences, Vol.5, No.2, pp. 135-150, 2004, DOI:10.3970/cmes.2004.005.135

    Abstract A method to obtain the effective mechanical properties of particulate composite materials is presented in this paper. The methodology is based on the boundary element method (BEM) coupled with analytical solutions for ellipsoidal inclusions such as Eshelby's tensor. There is no numerical integration for the surfaces or the domains of distributed particles, and, therefore, proposed technique is very efficient. Homogenization analysis based on representative volume element (RVE) is carried out considering a unit cell containing many particles (up to 1000). By using a conventional BEM approach (i.e., multi-region BEM), it would be extremely difficult to analyze such a large RVE,… More >

  • Open Access

    ARTICLE

    Emerging Trends in Soft Computing Techniques for Metamaterial Design and Optimization

    Balamati Choudhury11, Sanjana Bisoyi1, R M Jha1

    CMC-Computers, Materials & Continua, Vol.31, No.3, pp. 201-228, 2012, DOI:10.3970/cmc.2012.031.201

    Abstract Soft computing methods play an important role in the design and optimization in diverse engineering disciplines including those in the electromagnetic applications. The aim of these soft computing methods is to tolerate imprecision, uncertainties, and approximations to achieve a quick solution, which is both robust and economically viable. Soft computing methods such as genetic algorithm, neural network and fuzzy logic have been widely used by researchers for microwave design since the last decade. The metamaterial multilayer concept in conjunction with transformation optics leads to exciting applications in the microwave regime with such examples as invisible cloak, frequency selective surfaces (FSS),… More >

  • Open Access

    ARTICLE

    Development of 3D Trefftz Voronoi Cells with Ellipsoidal Voids &/or Elastic/Rigid Inclusions for Micromechanical Modeling of Heterogeneous Materials

    Leiting Dong1, Satya N. Atluri11

    CMC-Computers, Materials & Continua, Vol.30, No.1, pp. 39-82, 2012, DOI:10.3970/cmc.2012.030.039

    Abstract In this paper, as an extension to the authors's work in [Dong and Atluri (2011a,b, 2012a,b,c)], three-dimensional Trefftz Voronoi Cells (TVCs) with ellipsoidal voids/inclusions are developed for micromechanical modeling of heterogeneous materials. Several types of TVCs are developed, depending on the types of heterogeneity in each Voronoi Cell(VC). Each TVC can include alternatively an ellipsoidal void, an ellipsoidal elastic inclusion, or an ellipsoidal rigid inclusion. In all of these cases, an inter-VC compatible displacement field is assumed at each surface of the polyhedral VC, with Barycentric coordinates as nodal shape functions. The Trefftz trial displacement fields in each VC are… More >

  • Open Access

    ARTICLE

    A PSO based Energy Efficient Coverage Control Algorithm for Wireless Sensor Networks

    Jin Wang1,2, Chunwei Ju2, Yu Gao2, Arun Kumar Sangaiah3, Gwang-jun Kim4,*

    CMC-Computers, Materials & Continua, Vol.56, No.3, pp. 433-446, 2018, DOI: 10.3970/cmc.2018.04132

    Abstract Wireless Sensor Networks (WSNs) are large-scale and high-density networks that typically have coverage area overlap. In addition, a random deployment of sensor nodes cannot fully guarantee coverage of the sensing area, which leads to coverage holes in WSNs. Thus, coverage control plays an important role in WSNs. To alleviate unnecessary energy wastage and improve network performance, we consider both energy efficiency and coverage rate for WSNs. In this paper, we present a novel coverage control algorithm based on Particle Swarm Optimization (PSO). Firstly, the sensor nodes are randomly deployed in a target area and remain static after deployment. Then, the… More >

  • Open Access

    ARTICLE

    Improved Particle Swarm Optimization for Selection of Shield Tunneling Parameter Values

    Gongyu Hou1, Zhedong Xu1,*, Xin Liu1, Cong Jin1

    CMES-Computer Modeling in Engineering & Sciences, Vol.118, No.2, pp. 317-337, 2019, DOI:10.31614/cmes.2019.04693

    Abstract This article proposes an exponential adjustment inertia weight immune particle swarm optimization (EAIW-IPSO) to enhance the accuracy and reliability regarding the selection of shield tunneling parameter values. According to the iteration changes and the range of inertia weight in particle swarm optimization algorithm (PSO), the inertia weight is adjusted by the form of exponential function. Meanwhile, the self-regulation mechanism of the immune system is combined with the PSO. 12 benchmark functions and the realistic cases of shield tunneling parameter value selection are utilized to demonstrate the feasibility and accuracy of the proposed EAIW-IPSO algorithm. Comparison with other improved PSO indicates… More >

Displaying 111-120 on page 12 of 127. Per Page