Neural network feature vectors for sonar targets classifications

In order to diagnose the health state of Aircraft effectively, a new method based on ARMA Model and probabilistic neural network(PNN) is proposed in this paper. First, an ARMA model is built using the original acoustic emission signal of aircraft crucial components, then use the autoregressive approximation theory to estimate model parameters, and order of the model is calculated according to Akaike Information Criterion(AIC). Use the autoregressive parameters to build feature vectors, then the probabilistic neural network is used to carry out the recognition of these feature vectors, and the health state of aircraft crucial components is effectively diagnosed. After the application on certain type of real aircraft, this method is proved to be capable of detecting the fatigue crack on crucial structural components. And we can conclude that the method is an effective way to carry out aircraft health diagnosis.

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Semantic object removal with convolutional neural network feature-based inpainting approach

Multimedia Systems ◽

10.1007/s00530-018-0585-x ◽

2018 ◽

Vol 24 (5) ◽

pp. 597-609 ◽

Cited By ~ 1

Author(s):

Xiuxia Cai ◽

Bin Song

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Object Removal ◽

Network Feature ◽

Feature Based ◽

Semantic Object

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Statistical Methods and Artificial Neural Networks Techniques in Electromyography

International Journal of System Dynamics Applications ◽

10.4018/ijsda.2012010103 ◽

2012 ◽

Vol 1 (1) ◽

pp. 39-47 ◽

Cited By ~ 3

Author(s):

Ahmad Taher Azar ◽

Valentina E. Balas

Keyword(s):

Neural Network ◽

Statistical Methods ◽

Muscle Activity ◽

Masseter Muscle ◽

Discrete Wavelet ◽

Neural Network Classifier ◽

Denture Base ◽

Feature Vectors ◽

Significant Difference ◽

Before And After

This work represents a comparative study for the activity of the masseter muscle for patients before trial base denture insertion and the activity of the same muscle after trial denture base insertion for both right and left masseter muscles. The study tried to find if there were significant differences in the activity of the masseter muscle before and after patients wearing their trial denture base using two approaches: parametric statistical methods and a Neural Network Classifier. Statistical analysis was performed on three feature vectors extracted from autoregressive (AR) modeling, Discrete Wavelet Transform (WT), and from Wavelet Packet Transform (WP). The least significant difference test and the student t-test have not proved significant differences in the masseter muscle activity before and after wearing denture. However, using the same feature vectors, a neural network classifier has proved that there are significant differences in the masseter muscle activity before and after patients wearing trial denture base.

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