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  • Open Access


    Database development for alfalfa (Medicago sativa L.) characterization in an artificial vision system

    Martínez-Corral1 L, E Martínez-Rubín2, F Flores-García1, GC Castellanos2, AR Juárez2, MJD López3

    Phyton-International Journal of Experimental Botany, Vol.78, pp. 43-47, 2009, DOI:10.32604/phyton.2009.78.043

    Abstract The increasing demand of alfalfa crop production in the Lagunera Region has caused the search of new alternatives to the conventional methods of nutritional and hydric evaluation of alfalfa, where costs and time are optimized. The use of a machine vision system for computerized visual recognition of the crop hydric and/or nutritional stress implies the analysis and processing of certain characteristics, such as color, shape and object dimensions from a digital image. Due to the fact that identification parameters are closely related, it is necessary to compile information from specialists, foliar analysis, mathematical morphology and alfalfa crop deficiency photographs. The… More >

  • Open Access


    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


    A Deep Learning-Based Computational Algorithm for Identifying Damage Load Condition: An Artificial Intelligence Inverse Problem Solution for Failure Analysis

    Shaofei Ren1,2, Guorong Chen2 , Tiange Li2 , Qijun Chen2, Shaofan Li2, *

    CMES-Computer Modeling in Engineering & Sciences, Vol.117, No.3, pp. 287-307, 2018, DOI:10.31614/cmes.2018.04697

    Abstract In this work, we have developed a novel machine (deep) learning computational framework to determine and identify damage loading parameters (conditions) for structures and materials based on the permanent or residual plastic deformation distribution or damage state of the structure. We have shown that the developed machine learning algorithm can accurately and (practically) uniquely identify both prior static as well as impact loading conditions in an inverse manner, based on the residual plastic strain and plastic deformation as forensic signatures. The paper presents the detailed machine learning algorithm, data acquisition and learning processes, and validation/verification examples. This development may have… More >

  • Open Access


    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


    Research on Artificial Intelligence Method for Identification of Transformer Fault

    Ryuji Shioya and Hongjie Zheng

    The International Conference on Computational & Experimental Engineering and Sciences, Vol.22, No.4, pp. 195-195, 2019, DOI:10.32604/icces.2019.05008

    Abstract Oil-filled power transformers play an important role in the modern network system. Stability of power supply can be achieved by early detection of power transformer fault and continuous monitoring of equipment status. Transformers in operation are constantly affected by various types of stresses such as electrical, thermal and mechanical stress. Much attention is needed on maintenance of transformers in order to have fault free electric supply and to maximize the lifetime of a transformer. In recent years, Dissolved gas analysis (DGA) has been widely used for diagnostic fault of power transformers. Although DGA is an easier and simpler method for… More >

  • Open Access


    Surface reconstrucion by means of AI

    T. Podoba1, L. Tomsu1, K. Vlcek1, M. Heczko

    The International Conference on Computational & Experimental Engineering and Sciences, Vol.15, No.4, pp. 111-122, 2010, DOI:10.3970/icces.2010.015.111

    Abstract Surface reconstruction based on chaotic systems or exactly given point clouds is very difficult area. Current algorithms such as Marching Cube or Voronoi Filtering do not use methods based on artificial intelligence. In this paper, we investigate solution of polygonal surface construction based on AI. The main purpose is to generate complex polygonal mesh structures based on strange attractors with fractal structure. Attractors have to be created as 4D objects using quaternion algebra or using methods of AI. Polygonal mesh can have different numbers of polygons because of iterative application of this system. Our main goal is to develop new… More >

  • Open Access


    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


    Exploring Urban Population Forecasting and Spatial Distribution Modeling with Artificial Intelligence Technology

    Yan Zou1,2,3,*, Shaoliang Zhang1, Yanhai Min1

    CMES-Computer Modeling in Engineering & Sciences, Vol.119, No.2, pp. 295-310, 2019, DOI:10.32604/cmes.2019.03873

    Abstract The high precision population forecasting and spatial distribution modeling are very important for the theory and application of population sociology, city planning and Geo-Informatics. However, the two problems need to be solved for providing the high precision population information. One is how to improve the population forecasting precision of small area (e.g., street scale); another is how to improve the spatial resolution of urban population distribution model. To solve the two problems, some new methods are proposed in this contribution. (1) To improve the precision of small area population forecasting, a new method is developed based on the fade factor… More >

  • Open Access


    Application of Artificial Neural Networks in Design of Steel Production Path

    Igor Grešovnik1,2, Tadej Kodelja1, Robert Vertnik2,3, Bojan Senčič3,2,3, Božidar Šarler1,2,4

    CMC-Computers, Materials & Continua, Vol.30, No.1, pp. 19-38, 2012, DOI:10.3970/cmc.2012.030.019

    Abstract Artificial neural networks (ANNs) are employed as an alternative to physical modeling for calculation of the relations between the production path process parameters (melting of scrap steel and alloying, continuous casting, hydrogen removal, reheating, rolling, and cooling on a cooling bed) and the final product mechanical properties (elongation, tensile strength, yield stress, hardness after rolling, necking) of steel semi products. They provide a much faster technique of response evaluation complementary to physical modeling. The Štore Steel company process path for production of steel bars is used as an example for demonstrating the approach. The applied ANN is of a multilayer… More >

  • Open Access


    Network Security Situation Awareness Framework based on Threat Intelligence

    Hongbin Zhang1, 2, Yuzi Yi1, *, Junshe Wang1, Ning Cao3, *, Qiang Duan4

    CMC-Computers, Materials & Continua, Vol.56, No.3, pp. 381-399, 2018, DOI: 10.3970/cmc.2018.03787

    Abstract Network security situation awareness is an important foundation for network security management, which presents the target system security status by analyzing existing or potential cyber threats in the target system. In network offense and defense, the network security state of the target system will be affected by both offensive and defensive strategies. According to this feature, this paper proposes a network security situation awareness method using stochastic game in cloud computing environment, uses the utility of both sides of the game to quantify the network security situation value. This method analyzes the nodes based on the network security state of… More >

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