An Identification Method of Library Identifiers Based on Computer Vision

2013 ◽  
Vol 380-384 ◽  
pp. 3534-3537
Author(s):  
Li Ya Liu
Keyword(s):  
Neural Network ◽  
Computer Vision ◽  
Network Model ◽  
Neural Network Model ◽  
Recognition Rate ◽  
Computation Time ◽  
Principal Component ◽  
Structural Features ◽  
Traditional Methods ◽  
Quantum Neural Network

For traditional methods of library identifies based on the two-dimensional code characteristics, these methods are time consuming and a lot of prior experience is required. A method of library identifies based on computer vision technology is proposed. In this method, a preprocessing, such as image equalization, binarization and wavelet change, is first performed on the acquired library label images. Then on the basis of the structural features of the character, the features of library identifiers are obtained by applying PCA for a principal component analysis. A quantum neural network model is designed to have an optimization analysis and calculation on the extracted features, to avoid the drawbacks which need a lot of prior knowledge for the traditional methods. At the same time, an optimization is carried out for the neural network model saving a large amount of computation time. The experimental results show that a recognition rate, up to 98.13%, is obtained by using this method. With a high recognition speed, the method can meet the actual needs to be applied in a practical system.

A Novel Quantum Neural Network Model with Variable Selection for Short Term Load Forecasting

2010 ◽  
Vol 20-23 ◽  
pp. 612-617 ◽  
Author(s):  
Wei Sun ◽  
Yu Jun He ◽  
Ming Meng
Keyword(s):  
Neural Network ◽  
Variable Selection ◽  
Network Model ◽  
Neural Network Model ◽  
Load Forecasting ◽  
Short Term ◽  
Bp Network ◽  
Quantum Neural Network ◽  
Short Term Load Forecasting ◽  
Selection For

The paper presents a novel quantum neural network (QNN) model with variable selection for short term load forecasting. In the proposed QNN model, first, the combiniation of maximum conditonal entropy theory and principal component analysis method is used to select main influential factors with maximum correlation degree to power load index, thus getting effective input variables set. Then the quantum neural network forecating model is constructed. The proposed QNN forecastig model is tested for certain province load data. The experiments and the performance with QNN neural network model are given, and the results showed the method could provide a satisfactory improvement of the forecasting accuracy compared with traditional BP network model.

Multi-steps degradation process prediction for bearing based on improved back propagation neural network

2012 ◽  
Vol 227 (7) ◽  
pp. 1544-1553 ◽  
Author(s):  
Lin Mi ◽  
Wei Tan ◽  
Ran Chen
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Back Propagation ◽  
Principal Component ◽  
Degradation Process ◽  
Extraction Methods ◽  
Back Propagation Neural Network ◽  
Time Frequency ◽  
Process Prediction

Bearing degradation process prediction is extremely important in industry. This article proposed a new method to achieve multi-steps bearing degradation prediction based on an improved back propagation neural network model. Firstly, time domain and time–frequency domain features extraction methods are employed to extract the original features from the mass vibration signals. However, the extracted original features still with high dimensional and include superfluous information, the multi-features fusion technique principal component analysis is used to merge the original features and reduce the dimension, the typical sensitive features can be extracted. Then, based on the extracted features, the improved three-layer back propagation neural network model is constructed and trained for multi-steps bearing degradation process prediction. The phase space construction method is used to determine the embedding dimension of the back propagation neural network model. An accelerated bearing run-to-failure experiment was carried out, the results proved the effectiveness of the methodology.

scholarly journals Residual Control Chart for Binary Response with Multicollinearity Covariates by Neural Network Model

Symmetry ◽  
2020 ◽  
Vol 12 (3) ◽  
pp. 381 ◽  
Author(s):  
Jong-Min Kim ◽  
Ning Wang ◽  
Yumin Liu ◽  
Kayoung Park
Keyword(s):  
Neural Network ◽  
Control Chart ◽  
Network Model ◽  
Neural Network Model ◽  
Multivariate Data ◽  
Principal Component ◽  
Binary Response ◽  
Link Functions ◽  
The Neural Network ◽  
Input Variables

Quality control studies have dealt with symmetrical data having the same shape with respect to left and right. In this research, we propose the residual (r) control chart for binary asymmetrical (non-symmetric) data with multicollinearity between input variables via combining principal component analysis (PCA), functional PCA (FPCA) and the generalized linear model with probit and logit link functions, and neural network regression model. The motivation in this research is that the proposed control chart method can deal with both high-dimensional correlated multivariate data and high frequency functional multivariate data by neural network model and FPCA. We show that the neural network r control chart is relatively efficient to monitor the simulated and real binary response data with the narrow length of control limits.

A QUANTUM NEURAL NETWORK MODEL

2002 ◽  
Vol 13 (01) ◽  
pp. 75-88 ◽  
Author(s):  
M. ANDRECUT ◽  
M. K. ALI
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
General Problem ◽  
Learning Algorithms ◽  
Quantum Gates ◽  
Quantum Neural Network ◽  
Classification Tasks

We present the algorithms necessary for the implementation of a quantum neural network with learning and classification tasks. A complete implementation for the classification and learning algorithms is given in terms of unitary quantum gates. Such a quantum neural network can be used to perform complex classification tasks or to solve the general problem of binary mapping.

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