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  • Open Access

    ARTICLE

    Synthesized AI LMI-based Criterion for Mechanical Systems

    Jcy Chen1,*, Wc Chen1, Tim Chen1, Alex Wilson2, N. Fadilah Jamaludin3, Nertrand Kapron1, Tim Chen4,5, John Burno5

    Sound & Vibration, Vol.53, No.6, pp. 245-250, 2019, DOI:10.32604/sv.2019.04233

    Abstract This paper proposes a novel artificial intelligence sythethized controller in the mechanical system which has high speed computation because of the LMI type criterion. The proposed membership functions are adopted and stabilization criterion of the closed-loop T-S fuzzy systems are obtained through a new parametrized LMI (linear matrix) inequality which is rearranged by machine learning membership functions. More >

  • Open Access

    REVIEW

    Review on Application of Artificial Intelligence in Civil Engineering

    Youqin Huang1, Jiayong Li1, Jiyang Fu1,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.121, No.3, pp. 845-875, 2019, DOI:10.32604/cmes.2019.07653

    Abstract In last few years, big data and deep learning technologies have been successfully applied in various fields of civil engineering with the great progress of machine learning techniques. However, until now, there has been no comprehensive review on its applications in civil engineering. To fill this gap, this paper reviews the application and development of artificial intelligence in civil engineering in recent years, including intelligent algorithms, big data and deep learning. Through the work of this paper, the research direction and difficulties of artificial intelligence in civil engineering for the past few years can be More >

  • Open Access

    ARTICLE

    Identification of axillary buds of potato seedlings based on a vision system with fuzzy logic

    Martínez Corral L1, E Martínez-Rubin2, F F lores-García3, M Vázquez-Rueda3, J Frías-Ramírez2, MA Segura-Castruita2

    Phyton-International Journal of Experimental Botany, Vol.80, pp. 79-84, 2011, DOI:10.32604/phyton.2011.80.079

    Abstract Potato (Solanum tuberosum L.) is a crop whose production yield at national level is very low compared with that in the most productive countries. This is because it is a partially automated crop with deficient and inadequate agronomic practices, low technification levels and great quantity of work wages required per hectare of cultivation. The necessity to generate technical and modern procedures that increase crop production, quality and yield has fostered development of projects leading to obtain seedlings free of pathogens with material of high genetic, physiological and sanitary quality. Utilization of a vision system for the… More >

  • Open Access

    ARTICLE

    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 More >

  • Open Access

    ARTICLE

    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 More >

  • Open Access

    ABSTRACT

    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… More >

  • Open Access

    ABSTRACT

    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 More >

  • Open Access

    ARTICLE

    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… More >

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