Epileptiform events detection using a two-stage approach based on multiscale edge detection and artificial neural networks

2011 ◽  
Author(s):  
Meena AbdelMaseeh ◽  
Ahmed Gaber ◽  
Ahmed Morsy
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Edge Detection ◽  
Two Stage ◽  
Artificial Neural ◽  
Events Detection ◽  
Epileptiform Events

A two-stage model based on artificial neural networks to determine pitting corrosion status of 316L stainless steel

2016 ◽  
Vol 34 (1-2) ◽  
pp. 113-125 ◽  
Author(s):  
Maria Jesus Jiménez-Come ◽  
Ignacio J. Turias ◽  
Juan Jesus Ruiz-Aguilar
Keyword(s):  
Neural Networks ◽  
Stainless Steel ◽  
Artificial Neural Networks ◽  
Austenitic Stainless Steel ◽  
Pitting Corrosion ◽  
Area Under The Curve ◽  
Two Stage ◽  
Corrosion Failure ◽  
Model Based ◽  
Artificial Neural

AbstractMotivated to reduce the costs incurred by corrosion in material science, this article presents a combined model based on artificial neural networks (ANNs) to predict pitting corrosion status of 316L austenitic stainless steel. This model offers the advantage of automatically determining the pitting corrosion status of the material. In this work, the pitting corrosion status was predicted, with the environmental conditions considered, in addition to the values of the breakdown potential estimated by the model previously, but without having to use polarization tests. The generalization ability of the model was verified by the evaluation using the experimental data obtained from the European project called “Avoiding Catastrophic Corrosion Failure of Stainless Steel”. Receiver operating characteristic space, in addition to area under the curve (AUC) values, was presented to measure the prediction performance of the model. Based on the results (0.994 for AUC, 0.980 for sensitivity, and 0.956 for specificity), it can be concluded that ANNs become an efficient tool to predict pitting corrosion status of austenitic stainless steel automatically using this two-stage procedure approach.

scholarly journals Experimental applications of artificial neural networks in Engineering Processing System

2014 ◽  
Vol 8 (2) ◽  
pp. 28-33
Author(s):  
Samad Dadvandipour
Keyword(s):  
Neural Networks ◽  
Image Processing ◽  
Artificial Neural Networks ◽  
Edge Detection ◽  
Moving Objects ◽  
Processing System ◽  
Conveyor Belt ◽  
Proposed Model ◽  
Artificial Neural ◽  
Scientific Environment

Artificial Neural Networks along with Image Processing Systems have proven to be successful, particularly in the domains of mathematics, science and technology. They have gained quite notable advantages beyond classical learning, as their usable engagement are observable in many fields of scientific environment related to the relevant systems. This paper presents a model for identifying the small components parts. The model may be significant in various industries mainly in engineering processing system areas. The objective of the study is to apply Artificial Neural Networks (ANN) in Image Processing System (IPS) with feed forward structure to detect, and recognize different parts or any other environment products on a moving conveyor bel. In the proposed model, we have used appropriate method of edge detection. The edge detection realizes artificial neural network with noise. The paper emphasizes the implementation of the model considering functionality, parts images, accurate detection and identifying the different components. The result shows that the model can detect moving objects (products of many kinds) accurately on the conveyor belt with very high success rate and sort them accordingly for further processes.

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