MOSA/D: Multi-operator evolutionary many-objective algorithm with self-adaptation of parameters based on decomposition

The conception of special finite elements called multi-area elements for the analysis of structures with different stiffness areas has been presented in the paper. A new type of finite element has been determined in order to perform analyses and calculations of heterogeneous, multi-coherent, and layered structures using fewer finite elements and it provides proper accuracy of the results. The main advantage of the presented special multi-area elements is the possibility that areas of the structure with different stiffness and geometrical parameters can be described by single element integrated in subdivisions (sub-areas). The formulation of such elements has been presented with the example of one-dimensional elements. The main idea of developed elements is the assumption that the deformation field inside the element is dependent on its geometry and stiffness distribution. The deformation field can be changed and adjusted during the calculation process that is why such elements can be treated as self-adaptive. The application of the self-adaptation method on strain field should simplify the analysis of complex non-linear problems and increase their accuracy. In order to confirm the correctness of the established assumptions, comparative analyses have been carried out and potential areas of application have been indicated.

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Machine Vision Window Division on Self-Adaptation

Key Engineering Materials ◽

10.4028/www.scientific.net/kem.460-461.617 ◽

2011 ◽

Vol 460-461 ◽

pp. 617-620

Author(s):

Xiu Chen Wang

Keyword(s):

Image Analysis ◽

Machine Vision ◽

Defect Detection ◽

Actual Situation ◽

Adaptive Window ◽

Fabric Defect Detection ◽

Fabric Defect ◽

Self Adaptation ◽

Recognition Efficiency ◽

Division Model

Aiming at time-consuming and ineffective problem of image window division in fabric defect detection, this paper proposes a new adaptive division method after a large number of experiments. This method can quickly and exactly recognize defect feature. Firstly, a division model on adaptive window is established, secondly, the formula to anticipate generally situation of fabric image is given according to the peaks and valleys change in the model, and methods to calculate the division size and position of adaptive window are given. Finally, we conclude that the algorithm in this paper can quickly and simply select the size and position of window division according to actual situation of different fabric images, and the time of image analysis is shortened and the recognition efficiency is improved.

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