Wavelet Packet Decomposition

Accurate rainfall forecasting in watersheds is of indispensable importance for predicting streamflow and flash floods. This paper investigates the accuracy of several forecasting technologies based on Wavelet Packet Decomposition (WPD) in monthly rainfall forecasting. First, WPD decomposes the observed monthly rainfall data into several subcomponents. Then, three data-based models, namely Back-propagation Neural Network (BPNN) model, group method of data handing (GMDH) model, and autoregressive integrated moving average (ARIMA) model, are utilized to complete the prediction of the decomposed monthly rainfall series, respectively. Finally, the ensemble prediction result of the model is formulated by summing the outputs of all submodules. Meanwhile, these six models are employed for benchmark comparison to study the prediction performance of these conjunction methods, which are BPNN, WPD-BPNN, GMDH, WPD-GMDH, ARIMA, and WPD-ARIMA models. The paper takes monthly data from Luoning and Zuoyu stations in Luoyang city of China as the case study. The performance of these conjunction methods is tested by four quantitative indexes. Results show that WPD can efficiently improve the forecasting accuracy and the proposed WPD-BPNN model can achieve better prediction results. It is concluded that the hybrid forecast model is a very efficient tool to improve the accuracy of mid-and long-term rainfall forecasting.

Fingerprint analysis plays a crucial role in crucial legal matters such as investigation of crime and security system. Due to the large number and size of fingerprint images, data compression has to be applied to reduce the storage and communication bandwidth requirements of those images. To do this, there are many types of wavelet has been used for fingerprint image compression. In this paper we have used Coiflet-Type wavelets and our aim is to determine the most appropriate Coiflet-Type wavelet for better compression of a digitized fingerprint image and to achieve our goal Retain Energy (RE) and Number of Zeros (NZ) in percentage is determined for different oiflet-Type wavelets at different threshold values at the fixed decomposition level 3 using wavelet and wavelet packet transform. We have used 8-bit grayscale left thumb digitized fingerprint image of size 480×400 as a test image.

Fingerprint analysis plays a crucial role in crucial legal matters such as investigation of crime and security system. Due to the large number and size of fingerprint images, data compression has to be applied to reduce the storage and communication bandwidth requirements of those images. To do this, there are many types of wavelet has been used for fingerprint image compression. In this paper we have used Coiflet-Type wavelets and our aim is to determine the most appropriate Coiflet-Type wavelet for better compression of a digitized fingerprint image and to achieve our goal Retain Energy (RE) and Number of Zeros (NZ) in percentage is determined for different oiflet-Type wavelets at different threshold values at the fixed decomposition level 3 using wavelet and wavelet packet transform. We have used 8-bit grayscale left thumb digitized fingerprint image of size 480×400 as a test image.

Bearings are common and vital elements in rotating machinery. By tracking the condition of a bearing, unscheduled machinery outages and costly damage caused by a bearing failure can be avoided. In this paper, we developed a new scheme based on wavelet packet decomposition and hidden Markov modeling (HMM) for tracking the severity of bearing faults. In this scheme, vibration signals were decomposed into wavelet packets and the node energies of the decomposition tree were used as features. Based on the features extracted from normal bearing vibration signals, an HMM was trained to model the normal bearing operating condition. The probabilities of this HMM were then used to track the condition of the bearing. Experimental data collected from a bearing accelerated life test showed that unlike many of the other commonly used trend parameters whose distinguishing features diminished to normal bearing-like levels as the damage grew, the probabilities of the normal bearing HMM kept decreasing as the bearing damage progressed toward bearing failure. As the bearing approached the end of its life (10% of remaining life), the HMM probabilities dropped dramatically signaling severe damage and imminent bearing failure.

We present an algorithm for the online detection of rotor bar breakage in induction motors through the use of wavelet packet decomposition (WPD) and neural networks. The system provides a feature representation of multiple frequency resolutions for faulty modes and accurately differentiates between healthy and faulty conditions, and its main applicability is to dynamic time-variant signals experienced in induction motors

The present work introduces an innovative method for measuring particle size distribution of an airborne powder, based on the application of signal processing techniques to the acoustic emission signals produced by the impacts of the powder with specific metallic surfaces. The basic idea of the proposed methodology lies on the identification of the unknown relation between the acquired acoustic emission signals and the powder particle size distribution, by means of a multi-step procedure. In the first step, wavelet packet decomposition is used to extract useful features from the acoustic emission signals; the dimensionality of feature space is further reduced through multivariate data analysis techniques. As a final step, a neural network is properly trained to map the feature vector into the particle size distribution.The proposed solution has several advantages, such as low cost and low invasiveness which allow the system based on this technique to be easily integrated in pre-existing plants. It has been successfully applied to the PSD measurement of coal powder produced by grinding mills in a coal-fired power station, and the experimental results are reported in the paper. The measurement principle can also be applied to different particle sizing applications, whenever a solid powder is carried in air or in other gases.

Accurate and consistent annual runoff prediction in a region is a hot topic in management, optimization, and monitoring of water resources. A novel prediction model (ESMD-SE-WPD-LSTM) is presented in this study. Firstly, extreme-point symmetric mode decomposition (ESMD) is used to produce several intrinsic mode functions (IMF) and a residual (Res) by decomposing the original runoff series. Secondly, sample entropy (SE) method is employed to measure the complexity of each IMF. Thirdly, wavelet packet decomposition (WPD) is adopted to further decompose the IMF with the maximum SE into several appropriate components. Then long short-term memory (LSTM) model, a deep learning algorithm based recurrent approach, is employed to predict all components. Finally, forecasting results of all components are aggregated to generate the final prediction. The proposed model, which is applied to seven annual series from different areas in China, is evaluated based on four evaluation indexes (R, MAE, MAPE and RMSE). Results indicate that ESMD-SE-WPD-LSTM outperforms other benchmark models in terms of four evaluation indexes. Hence the proposed model can provide higher accuracy and consistency for annual runoff prediction, rendering it an efficient instrument for scientific management and planning of water resources.

Recently, the impact of colors on the brain signals has become one of the leading researches in BCI systems. These researches are based on studying the brain behavior after color stimulus, and finding a way to classify its signals offline without considering the real time. Moving to the next step, we present a real time classification model (online) for EEG signals evoked by RGB colors stimuli, which is not presented in previous studies. In this research, EEG signals were recorded from 7 subjects through BCI2000 toolbox. The Empirical Mode Decomposition (EMD) technique was used at the signal analysis stage. Various feature extraction methods were investigated to find the best and reliable set, including Event-related spectral perturbations (ERSP), Target mean with Feast Fourier Transform (FFT), Wavelet Packet Decomposition (WPD), Auto Regressive model (AR) and EMD residual. A new feature selection method was created based on the peak's time of EEG signal when red and blue colors...

Medical experts often examine hundreds of spine x-rays to determine existence of diseases like osteoarthritis, osteoporoses, and osteophites. Accurate vertebrae segmentation plays a great role in the proper assessment of various vertebral abnormalities. Manual segmentation methods are both time consuming and non-reproducible, hence, developing efficient computer-assisted segmentation methods has been a long standing goal. Over the past decade, segmentation methods

This paper proposes a novel transmission line fault location scheme, combining wavelet packet decomposition (WPD) and support vector regression (SVR). Various types of faults at different locations, fault resistance and fault inception angles on a series compensated 400 kV–285.65 km power system transmission line are investigated. The system only utilizes a single-end measurements. WPD is used to extract distinctive fault features from 1/2 cycle of post fault signals after noises have been eliminated by a low pass filter, and SVR is trained with features obtained from WPD. After training, SVR was then used in precise location of fault on the transmission line. The result shows that fault location on transmission line can be determined rapidly and correctly irrespective of fault impedance. © 2010 Elsevier B.V. All rights reserved.

ABSTRACT Chemical process installations are exposed to aggressive chemicals and conditions leading to corrosion. The damage from corrosion can lead to an unexpected plant shutdown and to the exposure of people and the environment to chemicals. Due to changes within and on the surface of materials subjected to corrosion, energy is released in the form of acoustic waves. This acoustic activity can be captured and used for corrosion monitoring in chemical process installations. Wavelet packet coefficients extracted from the acoustic activity have been considered to determine whether corrosion occurs, and to identify the type of corrosion process, at least for the most important corrosion processes in the chemical process industry. Feature subset selection is then applied to these wavelet coefficients to achieve a much higher accuracy in the identification of different corrosion processes than when no feature subset selection is applied to the acoustic waves. However, due to the statistical dependencies that potentially exist between the wavelet coefficients, the latter should not be selected independently from each other. Local discriminant basis selection algorithms do not take the statistical dependencies between wavelet coefficients into account. In this paper, we have used several mutual information-based approaches that take these dependencies into account and compared them to the wavelet-specific local discriminant basis selection algorithm. Furthermore, a hybrid filter–wrapper genetic algorithm, which uses a relevance–redundancy approach as a local search procedure, was designed. The highest classification accuracies are obtained with the hybrid filter–wrapper genetic algorithm, for all classifiers used in this paper. Furthermore, the proposed algorithm easily outperformed one of the most commonly used classifiers in chemometrics: partial least squares discriminant analysis (PLS-DA). A naïve Bayes classifier that uses the features selected by the hybrid filter–wrapper genetic algorithm was able to identify the absence of corrosion, uniform corrosion, pitting and stress corrosion cracking, with an accuracy of up to 87.20%.

Recordings of spontaneous activity of in vitro neuronal networks reveal various phenomena on different time scales. These include synchronized firing of neurons, bursting events of firing on both cell and network levels, hierarchies of bursting events, etc. These ...

Three methods for automatic detection of mesoscale coherent structures are applied to Sea Level Anomaly (SLA) fields in the South Atlantic. The first method is based on the wavelet packet decomposition of the SLA data, the second on the estimation of the Okubo-Weiss parameter and the third on a geometric criterion using the winding-angle approach. The results provide a comprehensive

The important thing to do after we detect the existence of partial discharges inside an isolation system is signal processing. One form of signal processing that needs to be carried out on the generated waveform is denoising or noise removal from the wave obtained. In this study, an experiment will be conducted to denounce partial discharge waves using Wavelet and Threshold transformations. Wavelet transformation is applied in various levels then we compare the best denomination while denoisation using Threshold is applied using Soft Threshold and Hard Threshold.

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