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Search Results (1,238)
  • Open Access

    ARTICLE

    Estimation of thermo-elasto-plastic properties of thin-film mechanical properties using MD nanoindentation simulations and an inverse FEM/ANN computational scheme

    D. S. Liu1, C.Y. Tsai1

    CMES-Computer Modeling in Engineering & Sciences, Vol.39, No.1, pp. 29-48, 2009, DOI:10.3970/cmes.2009.039.029

    Abstract Utilizing a thin copper substrate for illustration purposes, this study presents a novel numerical method for extracting the thermo-mechanical properties of a thin-film. In the proposed approach, molecular dynamics (MD) simulations are performed to establish the load-displacement response of a thin copper substrate nanoindented at temperatures ranging from 300~1400 K. The load data are then input to an artificial neural network (ANN), trained using a finite element model (FEM), in order to extract the material constants of the copper substrate. The material constants are then used to construct the corresponding stress-strain curve, from which the elastic modulus and the plastic… More >

  • Open Access

    ARTICLE

    Evaluation of Seismic Design Values in the Taiwan Building Code by Using Artificial Neural Network

    Tienfuan Kerh1,2, J.S. Lai1, D. Gunaratnam2, R. Saunders2

    CMES-Computer Modeling in Engineering & Sciences, Vol.26, No.1, pp. 1-12, 2008, DOI:10.3970/cmes.2008.026.001

    Abstract Taiwan frequently suffers from strong ground motion, and the current building code is essentially based on two seismic zones, A and B. The design value of horizontal acceleration for zone A is 0.33g, and the value for zone B is 0.23g. To check the suitability of these values, a series of actual earthquake records are considered for evaluating peak ground acceleration (PGA) for each of the zones by using neural network models. The input parameters are magnitude, epicenter distance, and focal depth for each of the checking stations, and the peak ground acceleration is calculated as the output with the… More >

  • Open Access

    ARTICLE

    A New Local Contact Search Method Using a Multi-Layer Neural Network

    Atsuya Oishi1, Shinobu Yoshimura2

    CMES-Computer Modeling in Engineering & Sciences, Vol.21, No.2, pp. 93-104, 2007, DOI:10.3970/cmes.2007.021.093

    Abstract This paper describes a new local contact search method using a multi-layer neural network and its application to smoothed contact surface consisting of Gregory patches. A contact search process consists of two phases: a global search phase for finding the nearest node-segment pair and a local search phase for finding an exact local coordinate of the contact point within the segment. In the present method, the multi-layer neural network is utilized in the latter phase. The fundamental formulation of the proposed local contact search method is described in detail, and it is applied to smoothed contact surfaces consisting of Gregory… More >

  • Open Access

    ARTICLE

    A Study on the Estimation of Prefabricated Glass Fiber Reinforced Concrete Panel Strength Values with an Artificial Neural Network Model

    S.A. Yıldızel1,2, A.U. Öztürk1

    CMC-Computers, Materials & Continua, Vol.52, No.1, pp. 41-52, 2016, DOI:10.3970/cmc.2016.052.041

    Abstract In this study, artificial neural networks trained with swarm based artificial bee colony optimization algorithm was implemented for prediction of the modulus of rapture values of the fabricated glass fiber reinforced concrete panels. For the application of the ANN models, 143 different four-point bending test results of glass fiber reinforced concrete mixes with the varied parameters of temperature, fiber content and slump values were introduced the artificial bee colony optimization and conventional back propagation algorithms. Training and the testing results of the corresponding models showed that artificial neural networks trained with the artificial bee colony optimization algorithm have remarkable potential… More >

  • Open Access

    ARTICLE

    Identification of Materials Properties with the Help of Miniature Shear Punch Test Using Finite Element Method and Neural Networks

    Asif Husain1, M. Guniganti2, D. K. Sehgal2, R. K. Pandey2

    CMC-Computers, Materials & Continua, Vol.8, No.3, pp. 133-150, 2008, DOI:10.3970/cmc.2008.008.133

    Abstract This paper describes an approach to identify the mechanical properties i.e. fracture and yield strength of steels. The study involves the FE simulation of shear punch test for various miniature specimens thickness ranging from 0.20mm to 0.80mm for four different steels using ABAQUS code. The experimental method of the miniature shear punch test is used to determine the material response under quasi-static loading. The load vs. displacement curves obtained from the FE simulation miniature disk specimens are compared with the experimental data obtained and found in good agreement. The resulting data from the load vs. displacement diagrams of different steels… More >

  • Open Access

    ARTICLE

    Neural Network Mapping of Corrosion Induced Chemical Elements Degradation in Aircraft Aluminum

    Ramana M. Pidaparti1,2, Evan J. Neblett2

    CMC-Computers, Materials & Continua, Vol.5, No.1, pp. 1-10, 2007, DOI:10.3970/cmc.2007.005.001

    Abstract A neural network (NN) model is developed for the analysis and prediction of the mapping between degradation of chemical elements and electrochemical parameters during the corrosion process. The input parameters to the neural network model are alloy composition, electrochemical parameters, and corrosion time. The output parameters are the degradation of chemical elements in AA 2024-T3 material. The NN is trained with the data obtained from Energy Dispersive X-ray Spectrometry (EDS) on corroded specimens. A very good performance of the neural network is achieved after training and validation with the experimental data. After validating the NN model, simulations were carried out… More >

  • Open Access

    ARTICLE

    A Compensation Controller Based on a Nonlinear Wavelet Neural Network for Continuous Material Processing Operations

    Chen Shen1,*, Youping Chen1, Bing Chen1, Jingming Xie1

    CMC-Computers, Materials & Continua, Vol.61, No.1, pp. 379-397, 2019, DOI:10.32604/cmc.2019.04883

    Abstract Continuous material processing operations like printing and textiles manufacturing are conducted under highly variable conditions due to changes in the environment and/or in the materials being processed. As such, the processing parameters require robust real-time adjustment appropriate to the conditions of a nonlinear system. This paper addresses this issue by presenting a hybrid feedforward-feedback nonlinear model predictive controller for continuous material processing operations. The adaptive feedback control strategy of the controller augments the standard feedforward control to ensure improved robustness and compensation for environmental disturbances and/or parameter uncertainties. Thus, the controller can reduce the need for manual adjustments. The controller… More >

  • Open Access

    ARTICLE

    Text Detection and Recognition for Natural Scene Images Using Deep Convolutional Neural Networks

    Xianyu Wu1, Chao Luo1, Qian Zhang2, Jiliu Zhou1, Hao Yang1, 3, *, Yulian Li1

    CMC-Computers, Materials & Continua, Vol.61, No.1, pp. 289-300, 2019, DOI:10.32604/cmc.2019.05990

    Abstract Words are the most indispensable information in human life. It is very important to analyze and understand the meaning of words. Compared with the general visual elements, the text conveys rich and high-level moral information, which enables the computer to better understand the semantic content of the text. With the rapid development of computer technology, great achievements have been made in text information detection and recognition. However, when dealing with text characters in natural scene images, there are still some limitations in the detection and recognition of natural scene images. Because natural scene image has more interference and complexity than… More >

  • Open Access

    ARTICLE

    Application of Self-Organizing Feature Map Neural Network Based on K-means Clustering in Network Intrusion Detection

    Ling Tan1,*, Chong Li2, Jingming Xia2, Jun Cao3

    CMC-Computers, Materials & Continua, Vol.61, No.1, pp. 275-288, 2019, DOI:10.32604/cmc.2019.03735

    Abstract Due to the widespread use of the Internet, customer information is vulnerable to computer systems attack, which brings urgent need for the intrusion detection technology. Recently, network intrusion detection has been one of the most important technologies in network security detection. The accuracy of network intrusion detection has reached higher accuracy so far. However, these methods have very low efficiency in network intrusion detection, even the most popular SOM neural network method. In this paper, an efficient and fast network intrusion detection method was proposed. Firstly, the fundamental of the two different methods are introduced respectively. Then, the self-organizing feature… More >

  • Open Access

    ARTICLE

    Parkinson’s Disease Detection Using Biogeography-Based Optimization

    Somayeh Hessam1, Shaghayegh Vahdat1, Irvan Masoudi Asl2,*, Mahnaz Kazemipoor3, Atefeh Aghaei4, Shahaboddin Shamshirband,5,6,*, Timon Rabczuk7

    CMC-Computers, Materials & Continua, Vol.61, No.1, pp. 11-26, 2019, DOI:10.32604/cmc.2019.06472

    Abstract In recent years, Parkinson's Disease (PD) as a progressive syndrome of the nervous system has become highly prevalent worldwide. In this study, a novel hybrid technique established by integrating a Multi-layer Perceptron Neural Network (MLP) with the Biogeography-based Optimization (BBO) to classify PD based on a series of biomedical voice measurements. BBO is employed to determine the optimal MLP parameters and boost prediction accuracy. The inputs comprised of 22 biomedical voice measurements. The proposed approach detects two PD statuses: 0-disease status and 1- good control status. The performance of proposed methods compared with PSO, GA, ACO and ES method. The… More >

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