Partial Discharge Signal Extraction Method Based on EDSSV and Low Rank RBF Neural Network

This paper introduces an efficient method for the recognition of human faces in 2D digital images using a feature extraction technique that combines the global and local information in frontal view of facial images. The proposed feature extraction includes human face localization derived from the shape information. Efficient parameters are defined to eliminate irrelevant data while Pseudo Zernike Moments (PZM) with a new moment orders selection method is introduced as face features. The proposed method while yields better recognition rate, also reduces the classifier complexity. This paper also examines application of various feature domains as face features using the face localization method. These include Principle Component Analysis (PCA) and Discrete Cosine Transform (DCT). The Radial Basis Function (RBF) neural network has been used as the classifier and we have shown that the proposed feature extraction method requires an RBF neural network classifier with a simpler structure and faster training phase that is less sensitive to select training and testing images. Simulation results on the Olivetti Research Laboratory (ORL) database and comparison with other techniques indicate the effectiveness of the proposed method.

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Defects Identification of Partial Discharge Map in GIS

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.448-453.1947 ◽

2013 ◽

Vol 448-453 ◽

pp. 1947-1950

Author(s):

Yi Long Zhang ◽

Yi Hui Zheng ◽

Li Xue Li ◽

Xin Wang ◽

Gang Yao ◽

...

Keyword(s):

Neural Network ◽

Pattern Recognition ◽

Random Function ◽

Rbf Neural Network ◽

Partial Discharge ◽

Rbf Network ◽

Novel Method ◽

Training Error ◽

Defect Pattern ◽

Defects Identification

With GIS being widely used, partial discharge detecting and defect pattern recognition become more and more meaningful and important. To realize defects identification of partial discharge map in GIS, a novel method based on Radical Basis Function (RBF) neural network is proposed. Firstly, a model is constructed to simulate the discharge pattern map by the use of random function randint. Secondly, based on the model above, a lot of data which meet the condition can be collected to provide for pattern recognition. Then, a RBF network is introduced to identify the pattern recognition. It can be trained by using the data above. Finally, through changing training error, high correct rate can be got. These indicate that the method is effective.

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An Optimizing Dynamic Spectrum Differential Extraction Method for Noninvasive Blood Component Analysis

Applied Spectroscopy ◽

10.1177/0003702818815508 ◽

2019 ◽

Vol 74 (1) ◽

pp. 23-33 ◽

Cited By ~ 5

Author(s):

Wei Tang ◽

Qiang Chen ◽

Wenjuan Yan ◽

Guoquan He ◽

Gang Li ◽

...

Keyword(s):

Neural Network ◽

Correlation Coefficient ◽

Extraction Method ◽

Rbf Neural Network ◽

Extraction Methods ◽

Dynamic Spectrum ◽

The Other ◽

Single Trial ◽

Differential Extraction ◽

Better Than

Dynamic spectra (DS) can greatly reduce the influence of individual differences and the measurement environment by extracting the absorbance of pulsating blood at multiple wavelengths, and it is expected to achieve noninvasive detection of blood components. Extracting high-quality DS is the prerequisite for improving detection accuracy. This paper proposed an optimizing differential extraction method in view of the deficiency of existing extraction methods. In the proposed method, the sub-dynamic spectrum (sDS) is composed by sequentially extracting the absolute differences of two sample points corresponding to the height of the half peak on the two sides of the lowest point in each period of the logarithm photoplethysmography signal. The study was based on clinical trial data from 231 volunteers. Single-trial extraction method, original differential extraction method, and optimizing differential extraction method were used to extract DS from the volunteers’ experimental data. Partial least squares regression (PLSR) and radial basis function (RBF) neural network were used for modeling. According to the effect of PLSR modeling, by extracting DS using the proposed method, the correlation coefficient of prediction set ( Rp) has been improved by 17.33% and the root mean square error of prediction set has been reduced by 7.10% compared with the original differential extraction method. Compared with the single-trial extraction method, the correlation coefficient of calibration set ( Rc) has increased from 0.747659 to 0.8244, with an increase of 10.26%, while the correlation coefficient of prediction set ( Rp) decreased slightly by 3.22%, much lower than the increase of correction set. The result of the RBF neural network modeling also shows that the accuracy of the optimizing differential method is better than the other two methods both in calibration set and prediction set. In general, the optimizing differential extraction method improves the data utilization and credibility compared with the existing extraction methods, and the modeling effect is better than the other two methods.

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