This paper covers a novel method named wavelet packet transform based Elman recurrent neural network(WPTERNN) for the simultaneous kinetic determination of periodate and iodate. The wavelet packet representations of s...This paper covers a novel method named wavelet packet transform based Elman recurrent neural network(WPTERNN) for the simultaneous kinetic determination of periodate and iodate. The wavelet packet representations of signals provide a local time-frequency description, thus in the wavelet packet domain, the quality of the noise removal can be improved. The Elman recurrent network was applied to non-linear multivariate calibration. In this case, by means of optimization, the wavelet function, decomposition level and number of hidden nodes for WPTERNN method were selected as D4, 5 and 5 respectively. A program PWPTERNN was designed to perform multicomponent kinetic determination. The relative standard error of prediction(RSEP) for all the components with WPTERNN, Elman RNN and PLS were 3.23%, 11.8% and 10.9% respectively. The experimental results show that the method is better than the others.展开更多
This study concerns with fault diagnosis of urban rail vehicle auxiliary inverter using wavelet packet and RBF neural network. Four statistical features are selected: standard voltage signal, voltage fluctuation signa...This study concerns with fault diagnosis of urban rail vehicle auxiliary inverter using wavelet packet and RBF neural network. Four statistical features are selected: standard voltage signal, voltage fluctuation signal, impulsive transient signal and frequency variation signal. In this article, the original signals are decomposed into different frequency subbands by wavelet packet. Next, an automatic feature extraction algorithm is constructed. Finally, those wavelet packet energy eigenvectors are taken as fault samples to train RBF neural network. The result shows that the RBF neural network is effective in the detection and diagnosis of various urban rail vehicle auxiliary inverter faults.展开更多
In this study, performances comparison to discriminate five mental states of five artificial neural network (ANN) training methods were investigated. Wavelet Packet Transform (WPT) was used for feature extraction of t...In this study, performances comparison to discriminate five mental states of five artificial neural network (ANN) training methods were investigated. Wavelet Packet Transform (WPT) was used for feature extraction of the relevant frequency bands from raw electroencephalogram (EEG) signals. The five ANN training methods used were (a) Gradient Descent Back Propagation (b) Levenberg-Marquardt (c) Resilient Back Propagation (d) Conjugate Learning Gradient Back Propagation and (e) Gradient Descent Back Propagation with movementum.展开更多
An efficient face recognition system with face image representation using averaged wavelet packet coefficients, compact and meaningful feature vectors dimensional reduction and recognition using radial basis function ...An efficient face recognition system with face image representation using averaged wavelet packet coefficients, compact and meaningful feature vectors dimensional reduction and recognition using radial basis function (RBF) neural network is presented. The face images are decomposed by 2-level two-dimensional (2-D) wavelet packet transformation. The wavelet packet coefficients obtained from the wavelet packet transformation are averaged using two different proposed methods. In the first method, wavelet packet coefficients of individual samples of a class are averaged then decomposed. The wavelet packet coefficients of all the samples of a class are averaged in the second method. The averaged wavelet packet coefficients are recognized by a RBF network. The proposed work tested on three face databases such as Olivetti-Oracle Research Lab (ORL), Japanese Female Facial Expression (JAFFE) and Essexface database. The proposed methods result in dimensionality reduction, low computational complexity and provide better recognition rates. The computational complexity is low as the dimensionality of the input pattern is reduced.展开更多
This paper advances a new approach based on wavelet and wavelet packet transforms in tandem with a fuzzy cluster neural network,abbreviated WPFCNN.Wavelets and wavelet packets decompose a vibration signal into differe...This paper advances a new approach based on wavelet and wavelet packet transforms in tandem with a fuzzy cluster neural network,abbreviated WPFCNN.Wavelets and wavelet packets decompose a vibration signal into different bands at different levels and provides multiresolution or multiscale views of a signal which is stationary or nonstationary. Fuzzy mathematics processes uncertain problems in engineering and converts the attributes extracted by wavelet packets to fuzzy membership degree.To achieve self-organizing classification,the MAXNET neural network is employed.WPFCNN integrates the advantages of wavelet packets and fuzzy cluster with MAXNET.The approach is adopted to process and classify vibration signal of a NH_3 compressor in a petrochemical plant.The results indicate that it is a useful and effective intelligence classification in the field of condition monitoring and fault diagnosis.展开更多
The most common reason for blindness among human beings is Glaucoma.The increase of fluid pressure damages the optic nerve which gradually leads to irreversible loss of vision.A technique for automated screening of Gl...The most common reason for blindness among human beings is Glaucoma.The increase of fluid pressure damages the optic nerve which gradually leads to irreversible loss of vision.A technique for automated screening of Glaucoma from the fundal retinal images is presented in this paper.This paper intends to explore the significance of both the approximate and detail coefficients through wavelet packet decomposition(WPD).Decomposition is done with "db3" wavelet function and the images are decomposed up to level-3producing 84 sub-bands.Two features,the energy and the entropy are calculated for each sub-band producing two feature matrices(158 images × 84 features).The above step is purely a statistical measure based on WPD.To enhance the diagnostic accuracy,the second phase considers the structural(biological) region of interest(ROI) in the image and then extracts the same features.It is worthy to note that direct biological features are not extracted to eliminate the drawbacks of segmentation whereas the biologically significant region is taken as biological-ROI.Interestingly,the detailed coefficient sub-bands(prominent edges) show more significance in the biological-ROI phase.Apart from enhancing the diagnostic accuracy by feature reduction,the paper intends to mark the significance indices,uniqueness and discrimination capability of the significant features(sub-bands) in both the phases.Then,the crisp inputs are fed to the classifier ANN.Finally,from the significant features of the biological-ROI feature matrices,the accuracy is raised to 85%which is notable than the accuracy of 79%achieved without considering the ROI.展开更多
In last few years, several recent developments concern a new proposed techniques of communication for WSN (Wireless Sensors Network) using a complex methods and technics. This network is considered a future platform f...In last few years, several recent developments concern a new proposed techniques of communication for WSN (Wireless Sensors Network) using a complex methods and technics. This network is considered a future platform for many applications like: medical, agriculture, industrial, monitoring and others. The challenge of this work consists in proposing a new design of transceiver for WSN based on IDWPT (Inverse Discrete Wavelet Packet Transform) in emitter and DWPT (Discrete Wavelet Packet Transform) in receiver for mono and multi users using AWGN Channel. We will propose in this paper, a new concept of impulse radio communication for multiband orthogonal communication for UWB (Ultra-wideband) applications. The main objective of this work is to present a new form of pulse communication adapted to low through-put short-range applications and is scalable according to the type of use but also the number of sensors.展开更多
基金National Natural Science Foundation of China(No.2 996 5 0 0 1) and Natural Science Foundation of InnerMongolia(No.2 0 0 2 2 0 80 2 0 115 )
文摘This paper covers a novel method named wavelet packet transform based Elman recurrent neural network(WPTERNN) for the simultaneous kinetic determination of periodate and iodate. The wavelet packet representations of signals provide a local time-frequency description, thus in the wavelet packet domain, the quality of the noise removal can be improved. The Elman recurrent network was applied to non-linear multivariate calibration. In this case, by means of optimization, the wavelet function, decomposition level and number of hidden nodes for WPTERNN method were selected as D4, 5 and 5 respectively. A program PWPTERNN was designed to perform multicomponent kinetic determination. The relative standard error of prediction(RSEP) for all the components with WPTERNN, Elman RNN and PLS were 3.23%, 11.8% and 10.9% respectively. The experimental results show that the method is better than the others.
文摘This study concerns with fault diagnosis of urban rail vehicle auxiliary inverter using wavelet packet and RBF neural network. Four statistical features are selected: standard voltage signal, voltage fluctuation signal, impulsive transient signal and frequency variation signal. In this article, the original signals are decomposed into different frequency subbands by wavelet packet. Next, an automatic feature extraction algorithm is constructed. Finally, those wavelet packet energy eigenvectors are taken as fault samples to train RBF neural network. The result shows that the RBF neural network is effective in the detection and diagnosis of various urban rail vehicle auxiliary inverter faults.
文摘In this study, performances comparison to discriminate five mental states of five artificial neural network (ANN) training methods were investigated. Wavelet Packet Transform (WPT) was used for feature extraction of the relevant frequency bands from raw electroencephalogram (EEG) signals. The five ANN training methods used were (a) Gradient Descent Back Propagation (b) Levenberg-Marquardt (c) Resilient Back Propagation (d) Conjugate Learning Gradient Back Propagation and (e) Gradient Descent Back Propagation with movementum.
文摘An efficient face recognition system with face image representation using averaged wavelet packet coefficients, compact and meaningful feature vectors dimensional reduction and recognition using radial basis function (RBF) neural network is presented. The face images are decomposed by 2-level two-dimensional (2-D) wavelet packet transformation. The wavelet packet coefficients obtained from the wavelet packet transformation are averaged using two different proposed methods. In the first method, wavelet packet coefficients of individual samples of a class are averaged then decomposed. The wavelet packet coefficients of all the samples of a class are averaged in the second method. The averaged wavelet packet coefficients are recognized by a RBF network. The proposed work tested on three face databases such as Olivetti-Oracle Research Lab (ORL), Japanese Female Facial Expression (JAFFE) and Essexface database. The proposed methods result in dimensionality reduction, low computational complexity and provide better recognition rates. The computational complexity is low as the dimensionality of the input pattern is reduced.
基金This project was supported by National Natural Science Foundation of China
文摘This paper advances a new approach based on wavelet and wavelet packet transforms in tandem with a fuzzy cluster neural network,abbreviated WPFCNN.Wavelets and wavelet packets decompose a vibration signal into different bands at different levels and provides multiresolution or multiscale views of a signal which is stationary or nonstationary. Fuzzy mathematics processes uncertain problems in engineering and converts the attributes extracted by wavelet packets to fuzzy membership degree.To achieve self-organizing classification,the MAXNET neural network is employed.WPFCNN integrates the advantages of wavelet packets and fuzzy cluster with MAXNET.The approach is adopted to process and classify vibration signal of a NH_3 compressor in a petrochemical plant.The results indicate that it is a useful and effective intelligence classification in the field of condition monitoring and fault diagnosis.
文摘The most common reason for blindness among human beings is Glaucoma.The increase of fluid pressure damages the optic nerve which gradually leads to irreversible loss of vision.A technique for automated screening of Glaucoma from the fundal retinal images is presented in this paper.This paper intends to explore the significance of both the approximate and detail coefficients through wavelet packet decomposition(WPD).Decomposition is done with "db3" wavelet function and the images are decomposed up to level-3producing 84 sub-bands.Two features,the energy and the entropy are calculated for each sub-band producing two feature matrices(158 images × 84 features).The above step is purely a statistical measure based on WPD.To enhance the diagnostic accuracy,the second phase considers the structural(biological) region of interest(ROI) in the image and then extracts the same features.It is worthy to note that direct biological features are not extracted to eliminate the drawbacks of segmentation whereas the biologically significant region is taken as biological-ROI.Interestingly,the detailed coefficient sub-bands(prominent edges) show more significance in the biological-ROI phase.Apart from enhancing the diagnostic accuracy by feature reduction,the paper intends to mark the significance indices,uniqueness and discrimination capability of the significant features(sub-bands) in both the phases.Then,the crisp inputs are fed to the classifier ANN.Finally,from the significant features of the biological-ROI feature matrices,the accuracy is raised to 85%which is notable than the accuracy of 79%achieved without considering the ROI.
文摘In last few years, several recent developments concern a new proposed techniques of communication for WSN (Wireless Sensors Network) using a complex methods and technics. This network is considered a future platform for many applications like: medical, agriculture, industrial, monitoring and others. The challenge of this work consists in proposing a new design of transceiver for WSN based on IDWPT (Inverse Discrete Wavelet Packet Transform) in emitter and DWPT (Discrete Wavelet Packet Transform) in receiver for mono and multi users using AWGN Channel. We will propose in this paper, a new concept of impulse radio communication for multiband orthogonal communication for UWB (Ultra-wideband) applications. The main objective of this work is to present a new form of pulse communication adapted to low through-put short-range applications and is scalable according to the type of use but also the number of sensors.
