scholarly journals A NOVEL ALGORITHM BASED ON CASCADING OF NEURAL NETWORK MODELS AND WAVELET TRANSFORM FOR IMAGE ENHANCEMENT

2013 ◽  
pp. 30-34
Author(s):  
SHOBHIT VERMA ◽  
HITESH GUPTA
Keyword(s):  
Neural Network ◽  
Wavelet Transform ◽  
Image Enhancement ◽  
Network Models ◽  
Histogram Equalization ◽  
Conventional Technique ◽  
Neural Network Models ◽  
Self Organized ◽  
Artificial Neural Network Method ◽  
Unsupervised Training

Image enhancement and restoration is pre-request of computer vision. The distortion and degradation of image suffered the process of pattern matching and quality of image. Wavelet is very important transform function play a role in image enhancement and image de-noising. The concept of wavelet used as soft thresholding and hard thresholding. A processing of data through wavelet is very efficient in process of neural network. In this paper we discuss the proposed algorithm for image enhancement based on self organized map network and wavelet transform. Basically self organized map network is unsupervised training mechanisms of pattern, due to this reason the processing of network is very fast in compression of another artificial neural network method. And the combination of wavelet and self organized map network have great advantage over conventional method such as histogram equalization and multi-point histogram equalization and another conventional technique of image enhancement.

scholarly journals Data transformation for neural network models in water resources applications

2003 ◽  
Vol 5 (4) ◽  
pp. 245-258 ◽  
Author(s):  
Gavin J. Bowden ◽  
Graeme C. Dandy ◽  
Holger R. Maier
Keyword(s):  
Neural Network ◽  
Water Resources ◽  
Linear Transformation ◽  
South Australia ◽  
Network Models ◽  
Data Transformation ◽  
Histogram Equalization ◽  
Neural Network Models ◽  
Ann Models ◽  
Input Variables

A step that should be considered when developing artificial neural network (ANN) models for water resources applications is the selection of an appropriate transformation of the data. In general, the primary motivations for data transformation are: (1) to scale the data so as to be commensurate with the transfer function in the output layer; (2) to standardise each of the variables; (3) to provide a suitable initialization of the ANN; and (4) to modify the distribution of the input variables to provide a better mapping to the outputs. In this paper, five different transformations are investigated in an attempt to improve the ANN's forecasting ability. These are: linear transformation, logarithmic transformation, histogram equalization, seasonal transformation and a transformation to normality. A case study is presented in which each of the ANN models developed using the different transformation techniques is used to forecast salinity in the River Murray at Murray Bridge (South Australia) 14 days in advance. When tested on a validation set from July 1992 to March 1998, the model developed using the linear transformation resulted in the lowest root mean squared forecasting error. This finding further strengthens the claim that the probability distribution of the data does not need to be known to develop effective ANN models. No improvement in the ANN model's forecasting ability was made using the logarithmic, seasonal and normality transformations. The model developed using histogram equalization produced good results for data within the training domain but was not robust on new patterns outside of the calibration range.

CNN: A SPEAKER RECOGNITION SYSTEM USING A CASCADED NEURAL NETWORK

1996 ◽  
Vol 07 (02) ◽  
pp. 203-212 ◽  
Author(s):  
M. ZAKI ◽  
A. GHALWASH ◽  
A.A. ELKOUNY
Keyword(s):  
Neural Network ◽  
Speaker Recognition ◽  
Network Models ◽  
Recognition System ◽  
Conventional Technique ◽  
Neural Network Models ◽  
Neural Network Approach ◽  
Unsupervised Neural Network ◽  
And Performance ◽  
Filtration Stage

The main emphasis of this paper is to present an approach for combining supervised and unsupervised neural network models to the issue of speaker recognition. To enhance the overall operation and performance of recognition, the proposed strategy integrates the two techniques, forming one global model called the cascaded model. We first present a simple conventional technique based on the distance measured between a test vector and a reference vector for different speakers in the population. This particular distance metric has the property of weighting down the components in those directions along which the intraspeaker variance is large. The reason for presenting this method is to clarify the discrepancy in performance between the conventional and neural network approach. We then introduce the idea of using unsupervised learning technique, presented by the winner-take-all model, as a means of recognition. Due to several tests that have been conducted and in order to enhance the performance of this model, dealing with noisy patterns, we have preceded it with a supervised learning model—the pattern association model—which acts as a filtration stage. This work includes both the design and implementation of both conventional and neural network approaches to recognize the speakers templates—which are introduced to the system via a voice master card and preprocessed before extracting the features used in the recognition. The conclusion indicates that the system performance in case of neural network is better than that of the conventional one, achieving a smooth degradation in respect of noisy patterns, and higher performance in respect of noise-free patterns.

Method of complex copper-zinc ore typification using neural network models

2020 ◽  
Vol 5 ◽  
pp. 140-147 ◽  
Author(s):  
T.N. Aleksandrova ◽  
◽  
E.K. Ushakov ◽  
A.V. Orlova ◽  
◽  
...  
Keyword(s):  
Neural Network ◽  
Network Models ◽  
Neural Network Models ◽  
Copper Zinc ◽  
Complex Copper

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

scholarly journals Prediction of Stress in Power Transformer Winding Conductors Using Artificial Neural Networks: Hyperparameter Analysis

Energies ◽  
2021 ◽  
Vol 14 (14) ◽  
pp. 4242
Author(s):  
Fausto Valencia ◽  
Hugo Arcos ◽  
Franklin Quilumba
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Finite Element ◽  
Power Transformer ◽  
Network Models ◽  
Activation Function ◽  
Neural Network Models ◽  
Transformer Winding ◽  
Artificial Neural ◽  
Femm Software

The purpose of this research is the evaluation of artificial neural network models in the prediction of stresses in a 400 MVA power transformer winding conductor caused by the circulation of fault currents. The models were compared considering the training, validation, and test data errors’ behavior. Different combinations of hyperparameters were analyzed based on the variation of architectures, optimizers, and activation functions. The data for the process was created from finite element simulations performed in the FEMM software. The design of the Artificial Neural Network was performed using the Keras framework. As a result, a model with one hidden layer was the best suited architecture for the problem at hand, with the optimizer Adam and the activation function ReLU. The final Artificial Neural Network model predictions were compared with the Finite Element Method results, showing good agreement but with a much shorter solution time.

scholarly journals The Effectiveness of Ensemble-Neural Network Techniques to Predict Peak Uplift Resistance of Buried Pipes in Reinforced Sand

2021 ◽  
Vol 11 (3) ◽  
pp. 908
Author(s):  
Jie Zeng ◽  
Panagiotis G. Asteris ◽  
Anna P. Mamou ◽  
Ahmed Salih Mohammed ◽  
Emmanuil A. Golias ◽  
...  
Keyword(s):  
Neural Network ◽  
Numerical Models ◽  
Correlation Coefficients ◽  
Network Models ◽  
Small Scale ◽  
Offshore Platforms ◽  
Neural Network Models ◽  
Buried Pipes ◽  
Ensemble Techniques ◽  
Uplift Resistance

Buried pipes are extensively used for oil transportation from offshore platforms. Under unfavorable loading combinations, the pipe’s uplift resistance may be exceeded, which may result in excessive deformations and significant disruptions. This paper presents findings from a series of small-scale tests performed on pipes buried in geogrid-reinforced sands, with the measured peak uplift resistance being used to calibrate advanced numerical models employing neural networks. Multilayer perceptron (MLP) and Radial Basis Function (RBF) primary structure types have been used to train two neural network models, which were then further developed using bagging and boosting ensemble techniques. Correlation coefficients in excess of 0.954 between the measured and predicted peak uplift resistance have been achieved. The results show that the design of pipelines can be significantly improved using the proposed novel, reliable and robust soft computing models.

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