Self-adaptive Wavelet Research Articles

Wavelet convolutional neural network for robust and fast temperature measurements in Brillouin optical time domain reflectometry.

In this paper, a wavelet convolutional neural network (WNN) consisting of a one-dimensional (1D) convolutional neural network and a self-adaptive wavelet neural network has been proposed and demonstrated experimentally for temperature measurement in a Brillouin optical time domain reflectometry (BOTDR) system. Based on the analysis of the system noise, it follows the Gaussian white noise distribution along the time-related sensing distance. The impact of the noise in time-domain on the measured Brillouin gain spectra (BGSs) could be neglected, so that the BGSs in the fiber can be regarded as a series of 1D input data of the proposed WNN. Different self-adaptive wavelet activation functions connected to each output of the full-connection network are adopted to realize the multi-scaled analysis and the scale translation, which can obtain more local characteristics in frequency-domain. The output extracted by the WNN is Brillouin frequency shift (BFS), which presents linearity correlation to the actual temperature. Considering the multi-parameters including different frequency ranges, signal-to-noise-ratios (SNRs), BFSs and spectral widths (SWs), a general model of the proposed WNN is trained to handle more extreme cases, in which it doesn't require retraining for different single-mode (SM) optical fibers in BOTDR sensing system. The performances of the WNN are compared with other two techniques, the Lorentzian curve fitting based on Levenberg-Marquardt (LM) algorithm and the basic neural network (NN) containing input and output layers together with two hidden layers. Both the simulated and measured results show that the WNN has better robustness and flexibility than the LM and the NN. Besides, the computational accuracy of the WNN is improved and the fluctuation of that is slighter, especially when the SNR is less than 11 dB. Moreover, the WNN takes approximately 0.54 s to measure the temperature from the 18,000 collected BGSs transmitted through the 18 km SM optical fiber. The calculating time of the WNN is greatly reduced by three orders of magnitude in comparison with that of the LM, and is comparable to that of the NN. It proves that the proposed WNN may provide a feasible or even better scheme for the robust and fast temperature measurement in BOTDR system.

Early Detection of Alzheimer’s Disease Using Polar Harmonic Transforms and Optimized Wavelet Neural Network

Effective and accurate diagnosis of Alzheimer’s disease (AD), as well as early-stage detection, has gained more and more attention in recent years. For AD classification, we propose a new hybrid method for early detection of Alzheimer’s disease (AD) using Polar Harmonic Transforms (PHT) and Self-adaptive Differential Evolution Wavelet Neural Network (SaDE-WNN). The orthogonal moments are used for feature extraction from the grey matter tissues of structural Magnetic Resonance Imaging (MRI) data. Irrelevant features are removed by the feature selection process through evaluating the in-class and among-class variance. In recent years, WNNs have gained attention in classification tasks; however, they suffer from the problem of initial parameter tuning, parameter setting. We proposed a WNN with the self-adaptation technique for controlling the Differential Evolution (DE) parameters, i.e., the mutation scale factor (F) and the cross-over rate (CR). Experimental results on the Alzheimer’s disease Neuroimaging Initiative (ADNI) database indicate that the proposed method yields the best overall classification results between AD and mild cognitive impairment (MCI) (93.7% accuracy, 86.0% sensitivity, 98.0% specificity, and 0.97 area under the curve (AUC)), MCI and healthy control (HC) (92.9% accuracy, 95.2% sensitivity, 88.9% specificity, and 0.98 AUC), and AD and HC (94.4% accuracy, 88.7% sensitivity, 98.9% specificity and 0.99 AUC).

A New Method of Denoising of Vibration Signal and Its Application

In order to improve the performance of the denoising method for vibration signals of rotating machinery, a new method of signal denoising based on the improved median filter and wavelet packet technology is proposed through analysing the characteristics of noise components and relevant denoising methods. Firstly, the window width of the median filter is calculated according to the sampling frequency so that the impulse noise and part of the white noise can be effectively filtered out. Secondly, an improved self-adaptive wavelet packet denoising technique is used to remove the residual white noise. Finally, useful vibration signals are obtained after the previous processing. Simulation signals and rotor experimental vibration signals were used to verify the performance of the method. Experiment results show that the method can not only effectively eliminate the mixed complex noises but also preserve the fault character details, which demonstrates that the proposed method outperforms the method based on the wavelet-domain median filter.

A stochastic mid-term scheduling for integrated wind-thermal systems using self-adaptive optimization approach: A comparative study

In the optimal and economic operation of the power system, generation scheduling is an essential task. Conventional short-term generation scheduling does not regard the huge important operational issues related to the generators, such as initial enterprise costs, maintenance costs, fuel availability, monthly load, etc. Hence, due to the time horizon scheduling of the daily short-term generation scheduling, it is not optimal in the long-term operation while considering the mentioned effects. In this context, this paper proposes a stochastic higher level of scheduling named Stochastic Mid-Term Generation Scheduling of Wind-Thermal systems by considering fixed and variable maintenance costs of generators units. In the proposed model, the 2m + 1 Point Estimate Method is applied to accurately evaluate the uncertainty related to the operation cost wind power and the load uncertainties for the proposed problem. To effectively solve it, a heuristic algorithm named Adaptive Modified Cuckoo Search Algorithm is employed with a novel self-adaptive Wavelet mutation tactic. To assess the performance of the proposed algorithm on solving the problem, the results are compared with the latest algorithms presented in the literature. Numerical results confirm the efficiency and superiority of the 2m + 1 point estimate method model and stability of the novel adaptive modified cuckoo search algorithm on solving the stochastic mid-term generation scheduling of wind-thermal systems problem.

A Novel Control Strategy of Hybrid Energy Storage System for Wind Power Smoothing

For the purpose of smoothing wind power fluctuations by using a battery–supercapacitor hybrid energy storage system (HESS), this paper designs a novel control strategy based on a self-adaptive wavelet packet decomposition technique and a two-level power reference signal distribution method. According to the fluctuation characteristics of wind power in different output scenarios, and considering the grid regulations on wind power volatility, the proposed technique can yield wind power grid-connected values adaptively. Then, the HESS power reference signals will be given by a two-stage reasonable distribution method based on the performance characteristics and the state of charge of the HESS. Simulation results show that the proposed control strategy can effectively improve the performance of wind power fluctuation smoothing and ensure that the HESS works within a reasonable range to guarantee the long-term stable operation of the HESS.

Noise reduction for lidar returns using self-adaptive wavelet neural network

Lidar has been widely applied in many fields, such as meteorology and environment. However, because lidar returns are very weak, the influence of noise on useful signal is very serious. To obtain useful lidar return signals from raw data, a self-adaptive method combining wavelet analysis and a neural network that suppresses noise is proposed, in which the orthogonal Daubechies wavelet family serves as node functions in the hidden layer of the neural network, a search algorithm is selected to optimize the parameters and thresholds, and the Levenberg–Marquardt algorithm is adopted in the neural network gradient algorithm. Some comparative experiments were carried out to verify the feasibility of the noise reduction method and the results showed that the signal-to-noise ratio (SNR) of the common wavelet threshold denoising method is about 10, while that of the self-adaptive wavelet neural network denoising method is more than 20. From the experimental results, it can be seen that the wavelet neural network denoising method has less distortion and a higher SNR value than other methods, giving it superior performance.

Incipient Fault Diagnosis of Roller Bearing Using Optimized Wavelet Transform Based Multi-Speed Vibration Signatures

Condition monitoring and incipient fault diagnosis of rolling bearing is of great importance to detect failures and ensure reliable operations in rotating machinery. In this paper, a new multi-speed fault diagnostic approach is presented by using self-adaptive wavelet transform components generated from bearing vibration signals. The proposed approach is capable of discriminating signatures from four conditions of rolling bearing, i.e., normal bearing and three different types of defected bearings on outer race, inner race, and roller separately. Particle swarm optimization and Broyden-Fletche—Goldfarb-Shanno-based quasi-Newton minimization algorithms are applied to seek optimal parameters of Impulse Modeling-based continuous wavelet transform model. Then, a 3-D feature space of the statistical parameters and a nearest neighbor classifier are, respectively, applied for fault signature extraction and fault classification. Effectiveness of this approach is then evaluated, and the results have achieved an overall accuracy of 100%. Moreover, the generated discriminatory fault signatures are suitable for multi-speed fault data sets. This technique will be further implemented and tested in a real industrial environment.

An investigation of rolling bearing early diagnosis based on high-frequency characteristics and self-adaptive wavelet de-noising

Rolling bearings are necessary parts in rotary machines. However, the problem of early fault diagnosis for rolling bearings is difficult to solve due to its low signal-to-noise ratio and non-linear and non-stationary signal. Based on a detailed investigation of rolling bearing vibration signals, this paper proposes a method for determining whether a fault occurs by comparing the high-frequency band power. If a fault occurs, we first de-noise the vibration signals using wavelet de-noising and then extract the fault characteristics in both the time domain and the time-frequency domain to avoid the limitations of using only one domain. Finally, the fault location is identified using the grey correlation method. According to the method application results, the recognition accuracy using the method proposed in this paper is satisfactory, proving that the method has superior performance.

Texture orientation-based algorithm for detecting infrared maritime targets.

Infrared maritime target detection is a key technology for maritime target searching systems. However, in infrared maritime images (IMIs) taken under complicated sea conditions, background clutters, such as ocean waves, clouds or sea fog, usually have high intensity that can easily overwhelm the brightness of real targets, which is difficult for traditional target detection algorithms to deal with. To mitigate this problem, this paper proposes a novel target detection algorithm based on texture orientation. This algorithm first extracts suspected targets by analyzing the intersubband correlation between horizontal and vertical wavelet subbands of the original IMI on the first scale. Then the self-adaptive wavelet threshold denoising and local singularity analysis of the original IMI is combined to remove false alarms further. Experiments show that compared with traditional algorithms, this algorithm can suppress background clutter much better and realize better single-frame detection for infrared maritime targets. Besides, in order to guarantee accurate target extraction further, the pipeline-filtering algorithm is adopted to eliminate residual false alarms. The high practical value and applicability of this proposed strategy is backed strongly by experimental data acquired under different environmental conditions.

Application of Self-Adaptive Wavelet Ridge Demodulation Method Based on LCD to Incipient Fault Diagnosis

When a local defect occurs in gearbox, the vibration signals present as the form of multicomponent amplitude modulation and frequency modulation (AM-FM). Demodulation analysis is an effective way for this kind of signal. A self-adaptive wavelet ridge demodulation method based on LCD is proposed in this paper. Firstly, multicomponent AM-FM signal is decomposed into series of intrinsic scale components (ISCs) and the special intrinsic scale component is selected in order to decrease the lower frequency background noise. Secondly, the genetic algorithm is employed to optimize wavelet parameters according to the inherent characteristics of signal; thirdly, self-adaptive wavelet ridge demodulation wavelet for the selected ISC component is performed to get instantaneous amplitude (IA) or instantaneous frequency (IF). Lastly, the characteristics frequency can be obtained to identify the working state or failure information from its spectrum. By two simulation signals, the proposed method was compared with various existing demodulation methods; the simulation results show that it has higher accuracy and higher noise tolerant performance than others. Furthermore, the proposed method was applied to incipient fault diagnosis for gearbox and the results show that it is simple and effective.

Stochastic mid-term generation scheduling incorporated with wind power

Abstract A challenge now facing system operator is how to schedule optimally the generation units in a wind integrated power system over a one year time horizon considering the effects of wind forecasting and variability; also, regarding the effects of load uncertainty. By the same token, this paper first develops a new formulation for Stochastic Mid-term Generation Scheduling (SMGS). In the formulation, 2m + 1 point estimate method is developed to accurately estimate the output variables of Mid-term Generation Scheduling (MGS) problem. Then, the formulation is combined with adaptive modified gravitational search algorithm and a novel self-adaptive wavelet mutation strategy for the establishment of new robust algorithm for the present problem. It is noteworthy to say that the classical methods considered certain wind information in the deterministic solution of the MGS problem which is not the realistic approach. However, this study improves modeling of wind–thermal system in the MGS problem by considering possible uncertainties when scheduling the generators of power system. The proposed model is capable of taking uncertainty of load and wind into account. The proposed method is applied on two test cases and the numerical results confirmed the efficiency and stability of the proposed algorithm.

A new stochastic framework for optimal generation scheduling considering wind power sources

This paper suggests a new stochastic framework based on 2 m+1 point estimate method PEM to solve the mid-term generation scheduling SMGS problem. The new formulation makes use of an adaptive modified bat algorithm and a novel self-adaptive wavelet mutation strategy for the establishment of new robust algorithm for the present problem. In addition, this work improves the modeling process of wind-thermal system in the MGS problem by considering the possible uncertainties when scheduling the generators of power system of the problem. The proposed model can concurrently capture the uncertainty effect of load and wind speed variations. The feasibility and efficiency of the proposed method is examined using two test systems.

Short-term scheduling of thermal power systems using hybrid gradient based modified teaching–learning optimizer with black hole algorithm

Nowadays, a challengeable subject facing the electric power system operator is how to manage optimally the power generating units over a scheduling horizon of one day considering all of the practical equality, inequality and dynamic constraints. These constraints are comprised of load plus transmission losses balance, valve-point effects, prohibited operating zones, multi-fuel options, line flow constraints, operating reserve and minimum on/off time. In this regard, the proposed framework first presents a practical formulation for the short-term thermal generation scheduling (STGS). It has high-dimensional, high-constraints, non-convex, non-smooth and non-linear nature and needs an efficient algorithm to be solved. Then, a new optimization approach, known as gradient-based modified teaching–learning-based optimization combined with black hole (MTLBO–BH) algorithm, has been proposed to seek the optimum operational cost. Despite the superior characteristics of the MTLBO and BH algorithms, both of them suffer from the problem of entrapping in local optima. Consequently, the powerful combination of MTLBO and BH and a novel self-adaptive wavelet mutation operator for the organization of the new robust algorithm are proposed in this work. Although the MTLBO–BH algorithm has many properties, some problems still remain to be solved pleasantly. One of these problems is the produced further robust solution that the classical gradient-based technique can overcome it. Finally, performance of the suggested technique is tested on different thermal power systems.

A Novel Self-adaptive Discrete Wavelet Transform Digital Watermarking Algorithm

On the basis of the research of wavelet transform and digital watermarking technology,this paper proposed a self-adaptive discrete wavelet transform(DWT) digital watermarking algorithm, which can achieve the purpose of embedding hidden watermarks by decompose three-level wavelet of image and decompose bit-plane of watermarking gray scale image by Arnold scrambling transformation. Layer adaptive threshold and quantizer were referenced in this algorithm, and which adaptive selected coefficient of detail subbands of embedded watermarking to improve the robustness of the watermarking. In testing of semi-blind watermarking, renewing of watermarking based on the embedding sequence of point locations and the quantizer sequence without participation of the original image. Experimental results show that the algorithm is effective to improve the robustness of the cut, adding noise, filtering, and compression image attack treatment. DOI: http://dx.doi.org/10.11591/telkomnika.v11i11.2761

The Application of Self-Adaptive Wavelet Neural Network in Multi-Layered Structure Ultrasonic Testing of the Solid Rocket-Motor

t is important to remove the noise signal effectively in non-destructive ultrasonic testing. Use the wavelet and neural network algorithm in multi-layered structure ultrasonic testing system of the solid rocket-motor, and construct self-adaptive wavelet neural network in the ultrasonic testing in order to restrain the noise. Better fitting signal is achieved by choosing Orthogonal Daubechies wavelet group and optimize the scale parameter by a searching algorithm. The simulation result shows that the wavelet neural network can make the testing system less distortion and better noise cancellation, and the method can be widely applied to ultrasonic detecting.

Calculation Analysis on Audio Frequency Compression Based on Wavelet Packet Decomposition

This text have further discussed the audio frequency compression based on wavelet packet decomposition and optimize the choice of optimum wavelet packet and proposed a optimized self-adaptive wavelet packet decomposition and adopted zerotree coding based on psycho-acoustic model, which makes this system have a higher compression ratio than audio signal and can keep good real-time and objective recovery signal to noise ratio. Computer simulation test is made for the calculation proposed with Matlab,and this simulation test has proved the point that is mentioned in this text.

The Detection for Image Spread Spectrum Watermarking Based on Self-Adaptive Wavelet Transform by DSP

The relation between the digital watermarking and spread-spectrum is analyzed in this paper at the beginning. Then, a spread-spectrum adaptive digital watermarking scheme based on the wavelet transform is proposed and the system for this digital watermarking algorithm based on DSP is realized. Finally, a testing on the image digital watermarking processing system by DSP is conducted. The result from the testing approves that the system is flexible and can accomplish watermark embedding and detecting.

Self-Adaptive Wavelet Based on Parametric Equation in Manufacturing Engineering

The paper analyzes the difference between useful signal and noise signal in dissemination characteristic inside wavelet space in manufacturing engineering, and then puts forward a search algorithm based on wavelet decorrelation white noise testing and involved with the parameters in a parametric equation. The algorithm can select wavelet transform to realize the best noise reduction effect in a self-adaptive way according to the characteristic of signal containing noise and signal to noise ratio. At last, simulation experiment and engineering application are made, and their results are compared with the decomposition result of Daubechies wavelet. It’s concluded that self-adaptive wavelet basis can more adequately separate useful information from signal.

Inversion of controlled-source seismic tomography and gravity data with the self-adaptive wavelet parametrization of velocities and interfaces

A self-adaptive automated parametrization approach is suggested for the sequential inversion of controlled-source seismic tomography and gravity data. The velocities and interfaces are parametrized by their Haar wavelet expansion coefficients. Only those coefficients that are well constrained by the data, as measured by the number of rays that cross the corresponding wavelet function support area and their angular coverage, are inverted for, others are set to zero. This approach results in a reasonable distribution of resolution throughout the model even in cases of irregular ray coverage and does overcome the trade-off between different types of model parameters. A modified sequential inversion approach is suggested to join the traveltimes and gravity anomalies inversion. An algorithm is developed that inverts for smooth velocity and density variations inside the seismic layer, the position of its bottom interface as well as for optimal values of the velocity-to-density regression coefficients. The algorithm makes use of direct (diving), reflected and head (critically refracted) wave traveltimes. The algorithm workflow is demonstrated on a synthetic data example.

An Adaptive Wavelet–Vaguelette Algorithm for the Solution of PDEs

The paper first describes a fast algorithm for the discrete orthonormal wavelet transform and its inverse without using the scaling function. This approach permits to compute the decomposition of a function into a lacunary wavelet basis, i.e., a basis constituted of a subset of all basis functions up to a certain scale, without modification. The construction is then extended to operator-adapted biorthogonal wavelets. This is relevant for the solution of certain nonlinear evolutionary PDEs where a priori information about the significant coefficients is available. We pursue the approach described in (J. Fröhlich and K. Schneider,Europ. J. Mech. B/Fluids13,439, 1994) which is based on the explicit computation of the scalewise contributions of the approximated function to the values at points of hierarchical grids. Here, we present an improved construction employing the cardinal function of the multiresolution. The new method is applied to the Helmholtz equation and illustrated by comparative numerical results. It is then extended for the solution of a nonlinear parabolic PDE with semi-implicit discretization in time and self-adaptive wavelet discretization in space. Results with full adaptivity of the spatial wavelet discretization are presented for a one-dimensional flame front as well as for a two-dimensional problem.