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  • Open Access


    Defocus Blur Segmentation Using Genetic Programming and Adaptive Threshold

    Muhammad Tariq Mahmood*

    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 4867-4882, 2022, DOI:10.32604/cmc.2022.019544

    Abstract Detection and classification of the blurred and the non-blurred regions in images is a challenging task due to the limited available information about blur type, scenarios and level of blurriness. In this paper, we propose an effective method for blur detection and segmentation based on transfer learning concept. The proposed method consists of two separate steps. In the first step, genetic programming (GP) model is developed that quantify the amount of blur for each pixel in the image. The GP model method uses the multi-resolution features of the image and it provides an improved blur More >

  • Open Access


    A Self-Learning Data-Driven Development of Failure Criteria of Unknown Anisotropic Ductile Materials with Deep Learning Neural Network

    Kyungsuk Jang1, Gun Jin Yun2,*

    CMC-Computers, Materials & Continua, Vol.66, No.2, pp. 1091-1120, 2021, DOI:10.32604/cmc.2020.012911

    Abstract This paper first proposes a new self-learning data-driven methodology that can develop the failure criteria of unknown anisotropic ductile materials from the minimal number of experimental tests. Establishing failure criteria of anisotropic ductile materials requires time-consuming tests and manual data evaluation. The proposed method can overcome such practical challenges. The methodology is formalized by combining four ideas: 1) The deep learning neural network (DLNN)-based material constitutive model, 2) Self-learning inverse finite element (SELIFE) simulation, 3) Algorithmic identification of failure points from the self-learned stress-strain curves and 4) Derivation of the failure criteria through symbolic regression More >

  • Open Access


    An Intelligent Predictive Model-Based Multi-Response Optimization of EDM Process

    N. Ganesh1, R. K. Ghadai2, A. K. Bhoi3, K. Kalita4,*, Xiao-Zhi Gao5

    CMES-Computer Modeling in Engineering & Sciences, Vol.124, No.2, pp. 459-476, 2020, DOI:10.32604/cmes.2020.09645

    Abstract Electrical Discharge Machining (EDM) is a popular non-traditional machining process that is widely used due to its ability to machine hard and brittle materials. It does not require a cutting tool and can machine complex geometries easily. However, it suffers from drawbacks like a poor rate of machining and excessive tool wear. In this research, an attempt is made to address these issues by using an intelligent predictive model coupled global optimization approach to predict suitable combinations of input parameters (current, pulse on-time and pulse off-time) that would effectively increase the material removal rate and More >

  • Open Access


    Genetic Approaches to Iteration-free Local Contact Search

    Atsuya Oishi1, Shinobu Yoshimura2

    CMES-Computer Modeling in Engineering & Sciences, Vol.28, No.2, pp. 127-146, 2008, DOI:10.3970/cmes.2008.028.127

    Abstract This paper describes new methods based on genetic approaches for finding approximating expressions of local coordinates of a contact point in a local contact search process. A contact search process generally consists of the following 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. The local contact search can be regarded as the mapping from the coordinates of nodes to the local coordinates of contact points. In this paper, two methods are proposed to… More >

  • Open Access


    Genetic Programming Metamodel for Rotating Beams

    Anuj Pratap Singh, V. Mani, Ranjan Ganguli1

    CMES-Computer Modeling in Engineering & Sciences, Vol.21, No.2, pp. 133-148, 2007, DOI:10.3970/cmes.2007.021.133

    Abstract This paper investigates the use of Genetic Programming (GP) to create an approximate model for the non-linear relationship between flexural stiffness, length, mass per unit length and rotation speed associated with rotating beams and their natural frequencies. GP, a relatively new form of artificial intelligence, is derived from the Darwinian concept of evolution and genetics and it creates computer programs to solve problems by manipulating their tree structures. GP predicts the size and structural complexity of the empirical model by minimizing the mean square error at the specified points of input-output relationship dataset. This dataset… More >

  • Open Access


    Comparison of New Formulations for Martensite Start Temperature of Fe-Mn-Si Shape Memory Alloys Using Geneting Programming and Neural Networks

    CMC-Computers, Materials & Continua, Vol.10, No.1, pp. 65-96, 2009, DOI:10.3970/cmc.2009.010.065

    Abstract This work proposed an alternative formulation for the prediction of martensite start temperature (Ms) of Fe-Mn-Si shape memory alloys (SMAs) depending on the various compositions and heat treatment techniques by using Neural Network (NN) and genetic programming (GP) soft computing techniques. The training and testing patterns of the proposed NN and GP formulations are based on well established experimental results from the literature. The NN and GP based formulation results are compared with experimental results and found to be quite reliable with a very high correlation (R2=0.955 for GEP and 0.999 for NN). More >

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