scholarly journals A New Approach Method of Crossover Process Based On Genetic Algorithm Using High Dimensional Benchmark Functions

2021 ◽  
Vol 2 (2) ◽  
Keyword(s):  
Genetic Algorithm ◽  
High Dimensional ◽  
New Approach ◽  
Approach Method

scholarly journals Fault Detection of Diesel Engine Air and after-Treatment Systems with High-Dimensional Data: A Novel Fault-Relevant Feature Selection Method

Processes ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 259
Author(s):  
Qilan Ran ◽  
Yedong Song ◽  
Wenli Du ◽  
Wei Du ◽  
Xin Peng
Keyword(s):  
Genetic Algorithm ◽  
Diesel Engine ◽  
Fault Detection ◽  
Correlation Analysis ◽  
Canonical Correlation Analysis ◽  
Diesel Engines ◽  
Canonical Correlation ◽  
High Dimensional Data ◽  
High Dimensional ◽  
Relevant Variables

In order to reduce pollutants of the emission from diesel vehicles, complex after-treatment technologies have been proposed, which make the fault detection of diesel engines become increasingly difficult. Thus, this paper proposes a canonical correlation analysis detection method based on fault-relevant variables selected by an elitist genetic algorithm to realize high-dimensional data-driven faults detection of diesel engines. The method proposed establishes a fault detection model by the actual operation data to overcome the limitations of the traditional methods, merely based on benchmark. Moreover, the canonical correlation analysis is used to extract the strong correlation between variables, which constructs the residual vector to realize the fault detection of the diesel engine air and after-treatment system. In particular, the elitist genetic algorithm is used to optimize the fault-relevant variables to reduce detection redundancy, eliminate additional noise interference, and improve the detection rate of the specific fault. The experiments are carried out by implementing the practical state data of a diesel engine, which show the feasibility and efficiency of the proposed approach.

A new approach method for characterizing inter-species toxicodynamic variability

2021 ◽  
pp. 1-20
Author(s):  
Sarah D. Burnett ◽  
Moumita Karmakar ◽  
William J. Murphy ◽  
Weihsueh A. Chiu ◽  
Ivan Rusyn
Keyword(s):  
New Approach ◽  
Approach Method

scholarly journals Some Studies on Forming Optimization with Genetic Algorithm

2012 ◽  
Vol 2 (2) ◽  
pp. 105-112
Author(s):  
Ganesh Marotrao KAKANDIKAR ◽  
Vilas M. NANDEDKAR
Keyword(s):  
Genetic Algorithm ◽  
Process Parameters ◽  
Wide Spectrum ◽  
Simulation Software ◽  
Flow Conditions ◽  
Element Analysis ◽  
Blank Holder ◽  
New Approach ◽  
Finite Element Analysis Simulation ◽  
Selection Of

Forming is a compression-tension process involving wide spectrum of operations and flow conditions. The result of the process depends on the large number of parameters and their interdependence. The selection of various parameters is still based on trial and error methods. In this paper the authors presents a new approach to optimize the geometry parameters of circular components, process parameters such as blank holder pressure and coefficient of friction etc. The optimization problem has been formulated with the objective of optimizing the maximum forming load required in Forming. Genetic algorithm is used for the optimization purpose to minimize the drawing load and to optimize the process parameters. A finite element analysis simulation software Fast Form Advanced is used for the validations of the results after optimization.

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