Bond Graph Binary Encoding Method for Genetic Algorithms Applications

2003 ◽  
Author(s):  
Germa´n L. Di´az-Cuevas ◽  
Roger F. Ngwompo
Keyword(s):  
Genetic Algorithms ◽  
Dynamic Systems ◽  
Binary Code ◽  
Worked Examples ◽  
Bond Graphs ◽  
Binary Encoding ◽  
Coding Method ◽  
Performance Specifications ◽  
Model Equations ◽  
Encoding Method

A binary encoding method for bond graphs that can be used for genetic algorithms (GAs) applications is presented. The originality of the proposed coding is that it encompasses causal information. This ensures that causal analysis is taken into account in assessing the fitness of topologies generated in GA operations and the suitability of design candidates to meet performance specifications can be tested directly from the binary code as the model equations can be derived from it. The code is suitable for GAs applications on bond graphs (BG) for topology and parameter optimisation in automated synthesis of dynamic systems. The coding method and its possible applications are illustrated through worked examples.

scholarly journals Three methods of coding nominal variables in regression analysis

2021 ◽  
Vol 4 ◽  
pp. 35-45
Author(s):  
S.M. Lapach ◽  
Keyword(s):  
Regression Analysis ◽  
Orthogonal Polynomials ◽  
Semantic Analysis ◽  
Binary Code ◽  
Detailed Comparison ◽  
Separate Variable ◽  
Coding Method ◽  
Long Time ◽  
Encoding Method ◽  
Nominal Variables

The paper compares three methods of coding nominal variables in regression analysis: coding of each level as a separate variable, coding with binary code, numbering of factor levels. Although these methods have existed for a long time and even have a theoretical justification (except for encoding with binary code), there were no recommendations and comparisons for their practical application. The features of the application of each method and the existing limitations are analyzed. In the article, there are considered two examples that provide a detailed comparison of these three methods. Comparative analysis has been carried out in the following areas: the presence of restrictions in use; statistical properties of plans; labour intensity and difficulty of obtaining mathematical models and the final result of their building; convenience of semantic analysis and use. Additionally, there have been made comparisons with models based on Chebyshev orthogonal polynomials. It has been established that different methods of coding nominal variables, when used correctly, lead to regression models that are approximately identical in their properties. Moreover, the method of encoding each level as a separate variable is possible only if there are experiments in which there is no nominal variable as an influence effect. The binary coding method is inconvenient to use with a large number of levels of variation of the nominal variable and inconvenient to analyze. When coding by level numbering, it is necessary that the average response values, according to the dispersion diagram of this factor, are sorted by value in accordance with the assigned numbers. With this encoding method, a natural number of factors is preserved. Sharply distinguishable best results are achieved with this coding method using Chebyshev orthogonal polynomials. The highest accuracy and uniformity of approximation are ensured.

scholarly journals Hiding a Covert Digital Image by Assembling the RSA Encryption Method and the Binary Encoding Method

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Kuang Tsan Lin ◽  
Sheng Lih Yeh
Keyword(s):  
Digital Image ◽  
Binary Data ◽  
Holographic Image ◽  
Binary Encoding ◽  
Image Dimension ◽  
Encoding Method ◽  
Encryption Method

The Rivest-Shamir-Adleman (RSA) encryption method and the binary encoding method are assembled to form a hybrid hiding method to hide a covert digital image into a dot-matrix holographic image. First, the RSA encryption method is used to transform the covert image to form a RSA encryption data string. Then, all the elements of the RSA encryption data string are transferred into binary data. Finally, the binary data are encoded into the dot-matrix holographic image. The pixels of the dot-matrix holographic image contain seven groups of codes used for reconstructing the covert image. The seven groups of codes are identification codes, covert-image dimension codes, covert-image graylevel codes, pre-RSA bit number codes, RSA key codes, post-RSA bit number codes, and information codes. The reconstructed covert image derived from the dot-matrix holographic image and the original covert image are exactly the same.

scholarly journals Event Processing and Variable Part of Sample Period Determining in Combined Systems Using GA

2011 ◽  
Vol 19 (30) ◽  
pp. 99-104
Author(s):  
Maximilián Strémy ◽  
Pavol Závacký ◽  
Martin Jedlička
Keyword(s):  
Genetic Algorithms ◽  
Dynamic Systems ◽  
Sampling Period ◽  
Management Systems ◽  
Event Processing ◽  
Sample Period ◽  
Event Management ◽  
Variable Part ◽  
Processing Algorithms

Event Processing and Variable Part of Sample Period Determining in Combined Systems Using GA This article deals with combined dynamic systems and usage of modern techniques in dealing with these systems, focusing particularly on sampling period design, cyclic processing tasks and related processing algorithms in the combined event management systems using genetic algorithms.

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