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  • Open Access

    ARTICLE

    Aircraft Structural Integrity Assessment through Computational Intelligence Techniques

    RamanaM. Pidaparti1

    Structural Durability & Health Monitoring, Vol.2, No.3, pp. 131-148, 2006, DOI:10.3970/sdhm.2006.002.131

    Abstract This paper provides an overview of the computational intelligence methods developed for the structural integrity assessment of aging aircraft structures. Computational intelligence techniques reviewed include artificial neural networks, inverse neural network mapping, wavelet based image processing methods, genetic algorithms, spectral element methods, and particle swarm optimization. Multi-site damage, corrosion, and corrosion-fatigue damage in aging aircraft is specifically discussed. Results obtained from selected computational intelligence methods are presented and compared to the existing alternate solutions and experimental data. The results presented illustrate the applicability of computational intelligence methods for assessing the structural integrity of aging aircraft structures and materials. More >

  • Open Access

    ARTICLE

    CPAC: Energy-Efficient Algorithm for IoT Sensor Networks Based on Enhanced Hybrid Intelligent Swarm

    Qi Wang1,*, Wei Liu1, Hualong Yu1, Shang Zheng1, Shang Gao1, Fabrizio Granelli2

    CMES-Computer Modeling in Engineering & Sciences, Vol.121, No.1, pp. 83-103, 2019, DOI:10.32604/cmes.2019.06897

    Abstract The wireless sensor network (WSN) is widely employed in the application scenarios of the Internet of Things (IoT) in recent years. Extending the lifetime of the entire system had become a significant challenge due to the energy-constrained fundamental limits of sensor nodes on the perceptual layer of IoT. The clustering routing structures are currently the most popular solution, which can effectively reduce the energy consumption of the entire network and improve its reliability. This paper introduces an enhanced hybrid intelligential algorithm based on particle swarm optimization (PSO) and ant colony optimization (ACO) method. The enhanced PSO is deployed to select… 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

    Evolved Algorithm and Vibration Stability for Nonlinear Disturbed Security Systems

    Tcw Chen1, Wray Marriott2, Ann Nicholson3, Tim Chen4, Mars Kmieckowiak5, Jcy Chen6,*

    Sound & Vibration, Vol.53, No.2, pp. 29-37, 2019, DOI:10.32604/sv.2019.04224

    Abstract In this paper, a method sustaining system stability after decomposition is proposed. Based on the stability criterion derived from the energy function, a set of intelligent controllers is synthesized which is used to maintain the stability of the system. The sustainable stability problem can be reformulated as a Linear Matrix Inequalities (LMI) problem. The key to guaranteeing the stability of the system as a whole is to find a common symmetrically positive definite matrix for all subsystems. Furthermore, the Evolved Bat Algorithm (EBA) is employed to replace the pole assignment method and the conventional mathematical methods for solving the LMI.… More >

  • Open Access

    ARTICLE

    Suppression of Ambipolar Conduction in Schottky Barrier Carbon Nanotube Field Effect Transistors: Modeling, Optimization Using Particle Swarm Intelligence, and Fabrication

    P. Reena Monica1,*, V. T. Sreedevi2

    CMES-Computer Modeling in Engineering & Sciences, Vol.119, No.3, pp. 577-591, 2019, DOI:10.32604/cmes.2019.04718

    Abstract A mathematical model and experimental analysis of the impact of oxide thickness on the ambipolar conduction in Schottky Barrier Carbon Nanotubes (CNTs) Field Effect Transistor (SB CNTFETs) is presented. Suppression of ambipolar conduction in SB CNTFETs is imperative in order to establish them as the future of IC technology. The ambipolar nature of SB CNTFETs leads to a great amount of leakage current. Employing a gate oxide dielectric of thickness, tox~50 nm suppresses the ambipolar behavior. In an SB CNTFET, it is the electric field at the source/drain contacts that control the conductance and the band bending length at the… More >

  • Open Access

    ARTICLE

    A Novel Image Categorization Strategy Based on Salp Swarm Algorithm to Enhance Efficiency of MRI Images

    Mohammad Behrouzian Nejad1, Mohammad Ebrahim Shiri Ahmadabadi1, 2, *

    CMES-Computer Modeling in Engineering & Sciences, Vol.119, No.1, pp. 185-205, 2019, DOI:10.32604/cmes.2019.01838

    Abstract The main target of this paper is presentation of an efficient method for MRI images classification so that it can be used to diagnose patients and non-patients. Image classification is one of the prominent subset topics of machine learning and data mining that the most important image technique is the auto-categorization of images. MRI images with high resolution and appropriate accuracy allow physicians to decide on the diagnosis of various diseases and treat them. The auto categorization of MRI images toward diagnosing brain diseases has been being used to accurately diagnose hospitals, clinics, physicians and medical research centers. In this… 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 >

  • Open Access

    ARTICLE

    Hybrid Adaptive Particle Swarm Optimized Particle Filter for Integrated Navigation System

    Zhimin Chen1,2, Yuanxin Qu1, Tongshuang Zhang1, Xiaoshu Bai1, Xiaohong Tao1, Yong Liu1

    CMES-Computer Modeling in Engineering & Sciences, Vol.106, No.6, pp. 379-393, 2015, DOI:10.3970/cmes.2015.106.379

    Abstract Particle swarm optimization algorithm based particle filter is trapping in local optimum easily, it is not able to satisfy the requirement of modern integrated navigation system. In order to solve the problem, A novel particle filter algorithm based on hybrid adaptive particle swarm optimization(HPSO-PF) is presented in this paper. This improved particle filter will conduce to finding the ideal solution domain by making use of the global convergence of artificial fish swarm and enhancement of fusion precision by guiding particles to move toward the high likelihood area through particle swarm optimization. Finally different models are used for simulation and the… More >

  • Open Access

    ARTICLE

    A set-based method for structural eigenvalue analysis using Kriging model and PSO algorithm

    Zichun Yang1,2,3, Wencai Sun2

    CMES-Computer Modeling in Engineering & Sciences, Vol.92, No.2, pp. 193-212, 2013, DOI:10.3970/cmes.2013.092.193

    Abstract The set-based structural eigenvalue problem is defined, by expressing the uncertainties of the structural parameters in terms of various convex sets. A new method based on Kriging model and Particle Swarm Optimization (PSO) is proposed for solving this problem. The introduction of the Kriging model into this approach can effectively reduce the computational burden especially for largescale structures. The solutions of the non-linear and non-monotonic problems are more accurate than those obtained by other methods in the literature with the PSO algorithm. The experimental points for Kriging model are sampled according to Latin hypercube sampling method. Two approaches of imposing… More >

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