scholarly journals An Improved Nesting Algorithm for Irregular Patterns

2021 ◽  
Author(s):  
German Martinez-Martinez ◽  
Jose-Luis Sanchez-Romero ◽  
Antonio Jimeno-Morenilla ◽  
Higinio Mora-Mora
Keyword(s):  
Genetic Algorithm ◽  
Computational Time ◽  
Greedy Method ◽  
Representation System ◽  
Acceptable Solution ◽  
Optimal Position ◽  
Robust Solutions ◽  
Different Types ◽  
Industrial Environments ◽  
Nesting Algorithm

In industrial environments, nesting consists in cutting or extracting pieces from a material sheet, with the purpose of minimizing the surface of the sheet used. This problem is present in different types of industries, such as shipping, aeronautics, woodworking, footwear, and so on. In this work, the aim is to find an acceptable solution to solve complex nesting problems. The research developed is oriented to sacrifice accuracy for speed so as to obtain robust solutions in less computational time. To achieve this, a greedy method and a genetic algorithm have been implemented, being the latter responsible for generating a sequence for the placement of the pieces, where each piece is placed in its current optimal position with the help of a representation system for both the pieces and the material sheet.

3D-Multi Constrained Heterogeneous Packing Using Genetic Approach

2012 ◽  
Vol 479-481 ◽  
pp. 1825-1830 ◽  
Author(s):  
Rajesh Kanna ◽  
P. Malliga ◽  
K. Sarukesi
Keyword(s):  
Genetic Algorithm ◽  
Bin Packing ◽  
Three Dimensional ◽  
Empty Space ◽  
Computational Time ◽  
Orientation Tuning ◽  
Optimal Position ◽  
Different Shapes ◽  
Practical Constraints ◽  
Better Than

This paper presents a combination of Genetic Algorithm (GA) and Tuning algorithm, for optimizing Three Dimensional (3D) arbitrary sized heterogeneous box packing into a container, by considering practical constraints facing in the logistics industries. Objective of this research is to pack four different shapes of boxes of varying sizes into a container of standard dimension, without violating various practical constraints. Inorder to obtain a real time feasible packing pattern, Genetic Algorithm is developed to maximize the container volume utilization and inturn profit [9]. It significantly improves the search efficiency with less computational time and loads most of the heterogeneous boxes into the container by considering its optimal position and orientation. Tuning algorithm is used to decode the genetic output into user understandable sequential packing pattern and to fill the left-out empty space inside the container. In general, GA in conjunction with the tuning algorithm is substantially better than those obtained by applying heuristics to the bin packing directly.

A Comparative Study of Ant and Genetic Algorithms in Digital Mammography

2018 ◽  
pp. 194-200
Author(s):  
J. Magelin Mary ◽  
Chitra K. ◽  
Y. Arockia Suganthi
Keyword(s):  
Genetic Algorithm ◽  
Image Processing ◽  
Medical Image ◽  
Medical Image Processing ◽  
Processing Technique ◽  
Input Image ◽  
Image Processing Technique ◽  
Computational Time ◽  
Image Mining ◽  
The Individual

Image processing technique in general, involves the application of signal processing on the input image for isolating the individual color plane of an image. It plays an important role in the image analysis and computer version. This paper compares the efficiency of two approaches in the area of finding breast cancer in medical image processing. The fundamental target is to apply an image mining in the area of medical image handling utilizing grouping guideline created by genetic algorithm. The parameter using extracted border, the border pixels are considered as population strings to genetic algorithm and Ant Colony Optimization, to find out the optimum value from the border pixels. We likewise look at cost of ACO and GA also, endeavors to discover which one gives the better solution to identify an affected area in medical image based on computational time.

Minimization of total trim loss occurring in pipe cutting in shipbuilding with genetic algorithm

2021 ◽  
pp. 147509022199169
Author(s):  
Abdullah Türk ◽  
Dursun Saral ◽  
Murat Özkök ◽  
Ercan Köse
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Search Space ◽  
Cutting Process ◽  
Genetic Operators ◽  
Critical Stage ◽  
Different Types ◽  
Trim Loss ◽  
Pipe Systems

Outfitting is a critical stage in the shipbuilding process. Within the outfitting, the construction of pipe systems is a phase that has a significant effect on time and cost. While cutting the pipes required for the pipe systems in shipyards, the cutting process is usually performed randomly. This can result in large amounts of trim losses. In this paper, we present an approach to minimize these losses. With the proposed method it is aimed to base the pipe cutting process on a specific systematic. To solve this problem, Genetic Algorithms (GA), which gives successful results in solving many problems in the literature, have been used. Different types of genetic operators have been used to investigate the search space of the problem well. The results obtained have proven the effectiveness of the proposed approach.

scholarly journals Levels of detail analysis of microwave scattering from human head models for brain stroke detection

PeerJ ◽  
2017 ◽  
Vol 5 ◽  
pp. e4061 ◽  
Author(s):  
Awais Munawar Qureshi ◽  
Zartasha Mustansar
Keyword(s):  
Scattering Problem ◽  
Human Head ◽  
Computational Time ◽  
Head Model ◽  
Microwave Scattering ◽  
Levels Of Detail ◽  
Realistic Head Model ◽  
Different Types ◽  
Brain Stroke ◽  
Brain Tissues

In this paper, we have presented a microwave scattering analysis from multiple human head models. This study incorporates different levels of detail in the human head models and its effect on microwave scattering phenomenon. Two levels of detail are taken into account; (i) Simplified ellipse shaped head model (ii) Anatomically realistic head model, implemented using 2-D geometry. In addition, heterogenic and frequency-dispersive behavior of the brain tissues has also been incorporated in our head models. It is identified during this study that the microwave scattering phenomenon changes significantly once the complexity of head model is increased by incorporating more details using magnetic resonance imaging database. It is also found out that the microwave scattering results match in both types of head model (i.e., geometrically simple and anatomically realistic), once the measurements are made in the structurally simplified regions. However, the results diverge considerably in the complex areas of brain due to the arbitrary shape interface of tissue layers in the anatomically realistic head model.After incorporating various levels of detail, the solution of subject microwave scattering problem and the measurement of transmitted and backscattered signals were obtained using finite element method. Mesh convergence analysis was also performed to achieve error free results with a minimum number of mesh elements and a lesser degree of freedom in the fast computational time. The results were promising and the E-Field values converged for both simple and complex geometrical models. However, the E-Field difference between both types of head model at the same reference point differentiated a lot in terms of magnitude. At complex location, a high difference value of 0.04236 V/m was measured compared to the simple location, where it turned out to be 0.00197 V/m. This study also contributes to provide a comparison analysis between the direct and iterative solvers so as to find out the solution of subject microwave scattering problem in a minimum computational time along with memory resources requirement.It is seen from this study that the microwave imaging may effectively be utilized for the detection, localization and differentiation of different types of brain stroke. The simulation results verified that the microwave imaging can be efficiently exploited to study the significant contrast between electric field values of the normal and abnormal brain tissues for the investigation of brain anomalies. In the end, a specific absorption rate analysis was carried out to compare the ionizing effects of microwave signals to different types of head model using a factor of safety for brain tissues. It is also suggested after careful study of various inversion methods in practice for microwave head imaging, that the contrast source inversion method may be more suitable and computationally efficient for such problems.

Cazacu and Barlat Criterion Identification Using the Cylindrical Cup Deep Drawing Test and the Coupled Artificial Neural Networks – Genetic Algorithm Method

2012 ◽  
Vol 504-506 ◽  
pp. 637-642 ◽  
Author(s):  
Hamdi Aguir ◽  
J.L. Alves ◽  
M.C. Oliveira ◽  
L.F. Menezes ◽  
Hedi BelHadjSalah
Keyword(s):  
Genetic Algorithm ◽  
Finite Element ◽  
Deep Drawing ◽  
Inverse Analysis ◽  
Computational Time ◽  
Ann Model ◽  
Drawing Test ◽  
Artificial Neural ◽  
Deep Drawing Test ◽  
Cylindrical Cup

This paper deals with the identification of the anisotropic parameters using an inverse strategy. In the classical inverse methods, the inverse analysis is generally coupled with a finite element code, which leads to a long computational time. In this work an inverse analysis strategy coupled with an artificial neural network (ANN) model is proposed. This method has the advantage of being faster than the classical one. To test and validate the proposed approach an experimental cylindrical cup deep drawing test is used in order to identify the orthotropic material behaviour. The ANN model is trained by finite element simulations of this experimental test. To reduce the gap between the experimental responses and the numerical ones, the proposed method is coupled with an optimization procedure based on the genetic algorithm (GA) to identify the Cazacu and Barlat’2001 material parameters of a standard mild steel DC06.

COMPUTING AN ADAPTIVE MESH IN FLUID PROBLEMS USING NEURAL NETWORK AND GENETIC ALGORITHM WITH ADAPTIVE RELAXATION

2008 ◽  
Vol 17 (06) ◽  
pp. 1089-1108 ◽  
Author(s):  
NAMEER N. EL. EMAM ◽  
RASHEED ABDUL SHAHEED
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Numerical Experiments ◽  
Back Propagation ◽  
Adaptive Mesh ◽  
Learning System ◽  
Back Propagation Algorithm ◽  
Adaptive Smoothing ◽  
Different Types ◽  
Optimal Values

A method based on neural network with Back-Propagation Algorithm (BPA) and Adaptive Smoothing Errors (ASE), and a Genetic Algorithm (GA) employing a new concept named Adaptive Relaxation (GAAR) is presented in this paper to construct learning system that can find an Adaptive Mesh points (AM) in fluid problems. AM based on reallocation scheme is implemented on different types of two steps channels by using a three layer neural network with GA. Results of numerical experiments using Finite Element Method (FEM) are discussed. Such discussion is intended to validate the process and to demonstrate the performance of the proposed learning system on three types of two steps channels. It appears that training is fast enough and accurate due to the optimal values of weights by using a few numbers of patterns. Results confirm that the presented neural network with the proposed GA consistently finds better solutions than the conventional neural network.

AFM-Based Nanoindentation Process: A Comparative Study

2012 ◽  
Author(s):  
Rapeepan Promyoo ◽  
Hazim El-Mounayri ◽  
Kody Varahramyan
Keyword(s):  
Md Simulation ◽  
Computational Models ◽  
Surface Deformation ◽  
Experimental Results ◽  
Computational Time ◽  
Machined Surface ◽  
Indentation Force ◽  
Subsurface Deformation ◽  
Different Types ◽  
Copper Aluminum

Atomic force microscopy (AFM) has been widely used for nanomachining and fabrication of micro/nanodevices. This paper describes the development and validation of computational models for AFM-based nanomachining. Molecular Dynamics (MD) technique is used to model and simulate mechanical indentation at the nanoscale for different types of materials, including gold, copper, aluminum, and silicon. The simulation allows for the prediction of indentation forces at the interface between an indenter and a substrate. The effects of tip materials on machined surface are investigated. The material deformation and indentation geometry are extracted based on the final locations of the atoms, which have been displaced by the rigid tool. In addition to the modeling, an AFM was used to conduct actual indentation at the nanoscale, and provide measurements to which the MD simulation predictions can be compared. The MD simulation results show that surface and subsurface deformation found in the case of gold, copper and aluminum have the same pattern. However, aluminum has more surface deformation than other materials. Two different types of indenter tips including diamond and silicon tips were used in the model. More surface and subsurface deformation can be observed for the case of nanoindentation with diamond tip. The indentation forces at various depths of indentation were obtained. It can be concluded that indentation force increases as depth of indentation increases. Due to limitations on computational time, the quantitative values of the indentation force obtained from MD simulation are not comparable to the experimental results. However, the increasing trends of indentation force are the same for both simulation and experimental results.

scholarly journals Object recognition using hybrid boosting method

2016 ◽  
Author(s):  
Osama Ashfaq
Keyword(s):  
Object Recognition ◽  
Covariance Matrix ◽  
Local Region ◽  
Computational Time ◽  
Local Descriptor ◽  
Alternative Approach ◽  
Different Types ◽  
Boosting Method ◽  
Mixture Of Gaussian ◽  
Diagonal Covariance Matrix

Li (ICCV, 2005) proposed a novel generative/discriminative way to combine features with different types and use them to learn labels in the images. However, the mixture of Gaussian used in Li’s paper suffers greatly from the curse of dimensionality. Here I propose an alternative approach to generate local region descriptor. I treat GMM with diagonal covariance matrix and PCA as separate features, and combine them as the local descriptor. In this way, we could reduce the computational time for mixture model greatly while score greater 90% accuracies for caltech-4 image sets.

COMPARING MINIMUM NEIGHBORHOOD EVALUATION SCHEMES FOR FINDING SPATIALLY ROBUST SOLUTIONS

2007 ◽  
Vol 17 (03) ◽  
pp. 299-309
Author(s):  
GRZEGORZ DRZADZEWSKI ◽  
MARK WINEBERG
Keyword(s):  
Genetic Algorithm ◽  
Fitness Function ◽  
Operational Definition ◽  
Significant Drop ◽  
Robust Solutions ◽  
Common Definition ◽  
Solution Robustness ◽  
The Common

The common definition for robust solutions considers a solution robust if it remains optimal when the parameters defining the fitness function are perturbed. A second definition that can be found in the literature: robustness occurs when a solution can be varied spatially without a significant drop in fitness. We propose an alternative operational definition for spatial robustness: both the solution and the neighbourhood around the solution has fitness above a given threshold. With this new definition, we created a set of functions with useful properties to allow for the testing of solution robustness. The performance of a Genetic Algorithm (GA) is then evaluated based on its ability to identify multiple robust solutions based on the above robustness definition. Different neighbourhood evaluation schemes are identified from the literature and compared, with the minimum neighbour technique proving to be the most effective.

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