scholarly journals Moisture Estimation in Cabinet Dryers with Thin-Layer Relationships Using a Genetic Algorithm and Neural Network

Mathematics ◽  
2019 ◽  
Vol 7 (11) ◽  
pp. 1042 ◽  
Author(s):  
Maleki ◽  
Ghazvini ◽  
Ahmadi ◽  
Maddah ◽  
Shamshirband
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Thin Layer ◽  
Network Models ◽  
Experimental Models ◽  
Moisture Loss ◽  
Neural Network Models ◽  
The Neural Network ◽  
Neural Network Structure ◽  
Artificial Neural Network Models

Nowadays, industrial dryers are used instead of traditional methods for drying. When designing dryers suitable for controlling the process of drying and reaching a high-quality product, it is necessary to predict the gradual moisture loss during drying. Few studies have been conducted to compare thin-layer models and artificial neural network models on the kinetics of pistachio drying in a cabinet dryer. For this purpose, ten mathematical-experimental models with a neural network model based on the kinetic data of pistachio drying were studied. The data obtained was from a cabinet dryer evaluated at four temperatures of inlet air and different air velocities. The pistachio seeds were placed in a thin layer on an aluminum sheet on a drying tray and weighed by a scale attached to the computer at different times. In the neural network, data was divided into three parts: Educational (60%), validation (20%) and testing (20%). Finally, the best mathematical-experimental model using a genetic algorithm and the best neural network structure for predicting instantaneous moisture were selected based on the least squared error and the highest correlation coefficient.

scholarly journals Moisture Estimation in Cabinet Dryer with Thin-Layer Relationships Using Genetic Algorithm and Neural Network

2019 ◽  
Author(s):  
Behzad Maleki ◽  
Mahyar Ghazvini ◽  
Mohammad Hossein Ahmadi ◽  
Heydar Maddah ◽  
Shahab Shamshirband
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Thin Layer ◽  
Experimental Models ◽  
Moisture Loss ◽  
Squared Error ◽  
The Neural Network ◽  
Quality Product ◽  
Neural Network Structure ◽  
Moisture Estimation

Nowadays industrial dryers are used instead of traditional methods for drying. In designing dryers suitable for controlling the process of drying and reaching a high quality product, it is necessary to predict the instantaneous moisture loss during drying. For this purpose, ten mathematical-experimental models with a neural network model based on the kinetic data of pistachio drying are studied. The data obtained from the cabinet dryer will be evaluated at four temperatures of inlet air and different air velocities. The pistachio seeds will be placed in a thin layer on an aluminum sheet on a drying tray and weighed by a scale attached to the computer at different times. In the neural network, data are divided into three parts: educational (60%), validation (20%) and test (20%). Finally, the best mathematical-experimental model using genetic algorithm and the best neural network structure for predicting instantaneous moisture are selected based on the least squared error and the highest correlation coefficient.

An explicit methodology for manufacturing cost–tolerance modeling and optimization using the neural network integrated with the genetic algorithm

2020 ◽  
Vol 34 (3) ◽  
pp. 430-443
Author(s):  
A. Saravanan ◽  
J. Jerald ◽  
A. Delphin Carolina Rani
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Network Model ◽  
Neural Network Model ◽  
Network Models ◽  
Manufacturing Cost ◽  
Genetic Operators ◽  
Neural Network Models ◽  
Data Set ◽  
The Neural Network

AbstractThe objective of the paper is to develop a new method to model the manufacturing cost–tolerance and to optimize the tolerance values along with its manufacturing cost. A cost–tolerance relation has a complex nonlinear correlation among them. The property of a neural network makes it possible to model the complex correlation, and the genetic algorithm (GA) is integrated with the best neural network model to optimize the tolerance values. The proposed method used three types of neural network models (multilayer perceptron, backpropagation network, and radial basis function). These network models were developed separately for prismatic and rotational parts. For the construction of network models, part size and tolerance values were used as input neurons. The reference manufacturing cost was assigned as the output neuron. The qualitative production data set was gathered in a workshop and partitioned into three files for training, testing, and validation, respectively. The architecture of the network model was identified based on the best regression coefficient and the root-mean-square-error value. The best network model was integrated into the GA, and the role of genetic operators was also studied. Finally, two case studies from the literature were demonstrated in order to validate the proposed method. A new methodology based on the neural network model enables the design and process planning engineers to propose an intelligent decision irrespective of their experience.

scholarly journals Adversarial Image Perturbation with a Genetic Algorithm

2021 ◽  
Author(s):  
Rok Kukovec ◽  
Špela Pečnik ◽  
Iztok Fister Jr. ◽  
Sašo Karakatič
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Prior Knowledge ◽  
Network Models ◽  
Visual Assessment ◽  
Training Process ◽  
Neural Network Models ◽  
The Neural Network ◽  
Statistical Metrics

The quality of image recognition with neural network models relies heavily on filters and parameters optimized through the training process. These filters are di˙erent compared to how humans see and recognize objects around them. The di˙erence in machine and human recognition yields a noticeable gap, which is prone to exploitation. The workings of these algorithms can be compromised with adversarial perturbations of images. This is where images are seemingly modified imperceptibly, such that humans see little to no di˙erence, but the neural network classifies t he m otif i ncorrectly. This paper explores the adversarial image modifica-tion with an evolutionary algorithm, so that the AlexNet convolutional neural network cannot recognize previously clear motifs while preserving the human perceptibility of the image. The ex-periment was implemented in Python and tested on the ILSVRC dataset. Original images and their recreated counterparts were compared and contrasted using visual assessment and statistical metrics. The findings s uggest t hat t he human eye, without prior knowledge, will hardly spot the di˙erence compared to the original images.

Comparative Analysis of Regression and Artificial Neural Network Models for Wind Turbine Power Curve Estimation

2001 ◽  
Vol 123 (4) ◽  
pp. 327-332 ◽  
Author(s):  
Shuhui Li ◽  
Donald C. Wunsch ◽  
Edgar O’Hair ◽  
Michael G. Giesselmann
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Regression Model ◽  
Wind Turbine ◽  
Network Models ◽  
Power Curve ◽  
Neural Network Models ◽  
Curve Estimation ◽  
The Neural Network ◽  
Artificial Neural Network Models

This paper examines and compares regression and artificial neural network models used for the estimation of wind turbine power curves. First, characteristics of wind turbine power generation are investigated. Then, models for turbine power curve estimation using both regression and neural network methods are presented and compared. The parameter estimates for the regression model and training of the neural network are completed with the wind farm data, and the performances of the two models are studied. The regression model is shown to be function dependent, and the neural network model obtains its power curve estimation through learning. The neural network model is found to possess better performance than the regression model for turbine power curve estimation under complicated influence factors.

scholarly journals The Influence of Changes in Active Binder Content on the Control System of the Moulding Sand Quality

2012 ◽  
Vol 12 (4) ◽  
pp. 71-74 ◽  
Author(s):  
J. Jakubski ◽  
St. M. Dobosz ◽  
K. Major-Gabryś
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Models ◽  
Assessment Method ◽  
Neural Network Models ◽  
The Neural Network ◽  
Network Sensitivity ◽  
Artificial Neural ◽  
Moulding Sand ◽  
Artificial Neural Network Models

Abstract Artificial neural networks are one of the modern methods of the production optimisation. An attempt to apply neural networks for controlling the quality of bentonite moulding sands is presented in this paper. This is the assessment method of sands suitability by means of detecting correlations between their individual parameters. The presented investigations were aimed at the selection of the neural network able to predict the active bentonite content in the moulding sand on the basis of this sand properties such as: permeability, compactibility and the compressive strength. Then, the data of selected parameters of new moulding sand were set to selected artificial neural network models. This was made to test the universality of the model in relation to other moulding sands. An application of the Statistica program allowed to select automatically the type of network proper for the representation of dependencies occurring in between the proposed moulding sand parameters. The most advantageous conditions were obtained for the uni-directional multi-layer perception (MLP) network. Knowledge of the neural network sensitivity to individual moulding sand parameters, allowed to eliminate not essential ones.

scholarly journals Application of Artificial Neural Network and Genetic Algorithm Based Artificial Neural Network Models for River Flow Prediction

2020 ◽  
Vol 34 (6) ◽  
pp. 745-751
Author(s):  
Arvind Yadav ◽  
Boggavarapu Bhanu Venkata Satya Vara Prasad ◽  
Ramesh Kumar Mojjada ◽  
Kiran Kumar Kothamasu ◽  
Devendra Joshi
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
River Flow ◽  
Network Models ◽  
Neural Network Models ◽  
Flow Prediction ◽  
Artificial Neural ◽  
Artificial Neural Network Models

Digital twin of equipment as a basis for the consumer in digital production

2020 ◽  
Keyword(s):  
Neural Network ◽  
Tool Wear ◽  
Chip Formation ◽  
Network Models ◽  
Machining Accuracy ◽  
Neural Network Models ◽  
Digital Twin ◽  
The Neural Network ◽  
Digital Production ◽  
Cyberphysical System

The neural network models series used in the development of an aggregated digital twin of equipment as a cyber-physical system are presented. The twins of machining accuracy, chip formation and tool wear are examined in detail. On their basis, systems for stabilization of the chip formation process during cutting and diagnose of the cutting too wear are developed. Keywords cyberphysical system; neural network model of equipment; big data, digital twin of the chip formation; digital twin of the tool wear; digital twin of nanostructured coating choice

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