Combining Wavelet Analysis Research Articles

Analysis about Effect of Hollow on Time-Frequency Characteristic of Surface Vibration Signal

In blasting excavation of shallow tunnel, the surface vibration of excavated tunnel can be amplified due to effect of hollow. This effect is an important factor for safety of surface buildings. Based on the measured data of one tunnel excavation project, combining wavelet analysis and AOK time-frequency distribution method, the surface vibration signals in front and rear position of working face are processed into different frequency bands. Taking PPV, dominant frequency, d7 (7.8125-15.625 Hz) band energy ratio and d7 (7.8125-15.625 Hz) band energy duration as indexes, the effect of hollow on time-frequency characteristics of surface vibration signal is studied in this article. The results show that, affected by the hollow in excavated region, the PPV and dominant frequency increase, and the d7 (7.8125-15.625 Hz) band energy shows fluctuant ratio of total energy and an increase of band energy duration. The results show that the hollow influence on the frequency characteristics of the surface vibration signals comprehensively, and also provide an analytical basis for anti-vibration and vibration reduction study from the angle of energy.

Segmentation and detection of breast cancer in mammograms combining wavelet analysis and genetic algorithm

In Brazil, the National Cancer Institute (INCA) reports more than 50,000 new cases of the disease, with risk of 51 cases per 100,000 women. Radiographic images obtained from mammography equipments are one of the most frequently used techniques for helping in early diagnosis. Due to factors related to cost and professional experience, in the last two decades computer systems to support detection (Computer-Aided Detection – CADe) and diagnosis (Computer-Aided Diagnosis – CADx) have been developed in order to assist experts in detection of abnormalities in their initial stages. Despite the large number of researches on CADe and CADx systems, there is still a need for improved computerized methods. Nowadays, there is a growing concern with the sensitivity and reliability of abnormalities diagnosis in both views of breast mammographic images, namely cranio-caudal (CC) and medio-lateral oblique (MLO). This paper presents a set of computational tools to aid segmentation and detection of mammograms that contained mass or masses in CC and MLO views. An artifact removal algorithm is first implemented followed by an image denoising and gray-level enhancement method based on wavelet transform and Wiener filter. Finally, a method for detection and segmentation of masses using multiple thresholding, wavelet transform and genetic algorithm is employed in mammograms which were randomly selected from the Digital Database for Screening Mammography (DDSM). The developed computer method was quantitatively evaluated using the area overlap metric (AOM). The mean±standard deviation value of AOM for the proposed method was 79.2±8%. The experiments demonstrate that the proposed method has a strong potential to be used as the basis for mammogram mass segmentation in CC and MLO views. Another important aspect is that the method overcomes the limitation of analyzing only CC and MLO views.

Ultrasonic Excitation and Fiber Bragg Grating Sensing Based on Wavelet Analysis

Plate metallic structures are widely used in various engineering equipments, in which the structure damage will be potential cause of failure, or even destroy the whole equipment. To ensure structural and operational safety, it is important to research structural health monitoring (SHM) techniques. This paper puts forward a kind of detection method based on the ultrasonic extraction and Fiber Bragg Grating(FBG) sensing, taking the aluminum plate as the research object, and discusses a denoising method based on wavelet transform. In the signal processing of the wavelet analysis, the appropriate wavelet function is selected in the MATLAB environment under different conditions, and the damage location is analyzed according to the denoised signals on the aluminum plate. On these bases, the detection system was used to research the damage location of a hole in the aluminum alloy plate. Test results show that the combination of wavelet analysis makes the damage information more easily identified and facilitates subsequent analysis of damage detection.

A new approach to fault location in three-phase underground distribution system using combination of wavelet analysis with ANN and FLS

This paper presents the results of investigation into a new fault classification and location technique, using EMTP software. The simulated data is then analyzed using advanced signal processing technique based on wavelet analysis to extract useful information from the signals. The artificial neural network (ANN) and the fuzzy logic system (FLS) is then used to detect the type and the location of the ground high impedance, ungrounded series, ungrounded and ground shunt faults in a practical underground distribution system (UDS). The results indicate that the fault location technique has an acceptable accuracy (error<1.5%) under the whole variety of different systems and fault conditions. Therefore, the proposed approach can have high performance for the evaluation of the fault location and classification.

Research on Fault Diagnosis Method of Wind Turbine Based on Wavelet Analysis and LS-SVM

Aiming at the fault characteristics of high-speed gearbox fault diagnosis of wind turbine, a fault diagnosis method of combining wavelet analysis with least square-support vector machine (LS-SVM) is proposed. According to the method, the energy of frequency bands generated by wavelet decomposition and reconstruction of the high-speed gearbox's vibration signals in different fault states is normalized as eigenvectors, forming training and testing samples of LS-SVM fault classifier. Train the LS-SVM fault diagnosis model with the training samples and test the accuracy with the testing samples. The result of research shows that the fault diagnosis method based on the wavelet analysis and LS-SVM has good diagnostics effect.

Research of Denoising Technology about Wavelet Analysis with Wiener Filter

Interfering noise of power line is one of the important factors which affects the quality of power line communication (PLC). Its frequency spectrum has the character of the 1/f process and the great autocorrelation. The wavelet analysis is an important signal-processing tool. Selecting suitable wavelet analysis can turn non-white noise to white noise, followed by wiener filtering, we can achieve the purpose of denoising. This paper introduces a denoising method of combining wavelet analysis with wiener filtering. Experiment proves this method has a strong feasibility and practical value.

Improved Wavelet Modeling Framework for Hydrologic Time Series Forecasting

The combination of wavelet analysis with black-box models presently is a prevalent approach to conduct hydrologic time series forecasting, but the results are impacted by wavelet decomposition of series, and uncertainty cannot be evaluated. In this paper, the method for discrete wavelet decomposition of series was developed, and an improved wavelet modeling framework, WMF for short, was proposed for hydrologic time series forecasting. It is to first separate different deterministic components and remove noise in original series by discrete wavelet decomposition; then, forecast the former and quantitatively describe noise's random characters; at last, add them up and obtain the final forecasting result. Forecasting of deterministic components is to obtain deterministic forecasting results, and noise analysis is to estimate uncertainty. Results of four hydrologic cases indicate the better performance of the proposed WMF compared with those black-box models without series decomposition. Because of having reliable hydrologic basis, showing high effectiveness in accuracy, eligible rate and forecasting period, and being capable of uncertainty evaluation, the proposed WMF can improve the results of hydrologic time series forecasting.

A study on the dynamic six-component force measurement with wind loads

Dynamic six-component force measurement is of great significance in the area of aerodynamics. Although a large number of previous experiments had been carried out to study the static characteristics of wind tunnel balance, these test methods are difficult to apply to the dynamic measurement. In this article, a new mechanical device is developed to simulate the hatch open motion with wind loads, and then the dynamic loads is measured by the novel piezoelectric balance. Owing to the dynamic motion characteristics of nonlinearity, nonstationarity, and internal coupling, an effective control system, measuring principle and signal processing method have been proposed. A combined wavelet analysis and EMD adaptive algorithm with multi-sensor data fusion method is studied to process the complicated force signal. In addition, the inertial force of the hatch is eliminated and a force curve changing with angle and time has been obtained. Furthermore, dynamic experimental results demonstrate that the mechanical system and signal processing method can effectively control the hatch open motion and extract the feature of six-component force.

Wavelet analysis in determination of reservoir fluid contacts

One of the significant issues in each petrophysical evaluation is the determination of reservoir fluid contacts. Since each fluid with an exclusive disturbance contributes to the intensity of the recorded well-log signals, it is expectant to conceive disturbance imparted by each fluid would be endowed with a specific span of energy. Therefore, disturbance variations in recorded signals can be considered as a significant criterion in determining the boundary between reservoir fluids. In this context, the wavelet transform (WT) has been employed on new well logs generated by principle component analysis (PCA) method to decompose them into a series of components revealing reservoir features by which the intensity and variable characteristics of reservoir fluid energy can be closely scrutinized. In this study, the principle component analysis has been used to tackle unraveling the correlation of selected well logs. The results obtained in this investigation are indicative of using the combination of wavelet analysis and principle component analysis as an efficient method in the determination of reservoir fluid contacts.

High gravity‐wave activity observed in Patagonia, Southern America: generation by a cyclone passage over the Andes mountain range

AbstractThe Antarctic peninsula and Patagonia region (the south of South America) have recently been identified as the regions with the highest gravity‐wave activity in the world. In this work, the generation and propagation of gravity waves in the Patagonia region in an event of strong wave activity from 30 October 1995–1 November 1995 is examined by means of radiosonde measurements and simulations with the Weather Research and Forecasting (WRF) model. The waves are generated by strong surface winds found near the Andes mountains at a latitude of 49–51°S. The strong low‐level winds are related to an extratropical cyclone that propagates southeastward in the South Pacific ocean and approaches the western coast of the continent. The waves propagate southeast toward Tierra del Fuego and they continue their propagation over the Drake Passage. They are found to propagate long meridional (lateral) distances due to the shear background conditions. This fact is corroborated with WRF simulations and a novel technique that combines wavelet analysis and backward ray‐tracing. Therefore, this work provides further evidence that high gravity‐wave activity found by several studies over Drake Passage may have an orographic origin. During the orographic wave event, which lasts about 72 hours, the horizontal wavelength is unexpectedly found to change day‐to‐day. The analysis shows that changes in the near‐surface meteorological conditions produced by the cyclone passage may trigger different components of the forcing orography. The orographic waves propagate toward their critical levels, which are found at 25 km and above. The radiosonde measurements show that the wave is breaking continuously along a wide altitude range; this finding from measurements supports the picture of continuous wave erosion along the ray path instead of abrupt wave‐breaking for the examined wave event. Copyright © 2012 Royal Meteorological Society

Ship Rolling Motion Prediction Based on Wavelet Neural Network

The traditional time series predictive models are not able to achieve a satisfying prediction effect in the problem of a non-linear system and nonstationary time series. To solve these problems, ship course time series prediction, which is based on back propagation wavelet neural network structure and algorithm, was proposed. It combined wavelet analysis and neural network characteristics, and employed the nonlinear Morlet wavelet radices as the activation function. This method was applied to ship rolling motion prediction, and simulation results showed the validity to improving the prediction accuracy.

Simultaneous velocity and density measurements for an energy-based approach to internal waves generated over a ridge

We present experimental results of internal wave generation by the oscillation of a two-dimensional topography in a linearly stratified fluid. Simultaneous synthetic schlieren and particle image velocimetry high-resolution measurements are made in a series of experiments with different forcing frequencies, all other parameters being kept constant. This setup allows us to obtain the potential and kinetic components of the mechanical energy transported by the internal wave beam for different relative values of the maximum topographic slope to the slope of internal wave phase lines, in a quasi-linear regime. Measurements are carefully validated and a combined wavelet and principal component analysis are carried out to extract the most energetic physical processes associated with the internal waves. The duration of the transient regime is evaluated in order to consider only results during the steady regime. We discuss the evolution of the radiated mechanical energy with respect to the forcing frequency, and we show that it reaches a maximum in the near-critical regime, in good agreement with recent numerical and theoretical works. New insights are provided about the role played by the relative values of the maximum topographic slope and the internal wave beam slope in the efficiency of energy transfers from barotropic tide to radiated internal waves. This study is a step toward a better quantification of the energy transported away by internal waves and available for mixing the ocean.

Forecasting stock indices with wavelet domain kernel partial least square regressions

Abstract Financial time series are nonlinear and non-stationary. Most financial phenomena cannot be clearly characterized in time domain. Therefore, traditional time domain models are not very effective in financial forecasting. To address the problem, this study combines wavelet analysis with kernel partial least square (PLS) regressions for stock index forecasting. Wavelet transformation maps time domain inputs to time-frequency (or wavelet) domain, where financial characteristics can be clearly identified. Because of the high dimensionality and heavy multi-collinearity of the input data, a wavelet domain kernel PLS regressor is employed to create the most efficient subspace that maintains maximum covariance between inputs and outputs, and to perform final forecasting. Empirical results demonstrate that the proposed model outperforms traditional neural networks, support vector machines, GARCH models, and has significantly reduced the forecasting errors.

The effects of windscreen flow on noise in motorcycle helmets.

Vortex shedding from a motorcycle windscreen results in three flow regions into which the helmet of the rider may be immersed. First, the helmet may be fully in the free stream. Second, the helmet may be directly in the path of vortex shedding from the windscreen. Third, the helmet may be beneath the vortex shedding and shielded from the free stream by the windscreen. On-track tests were conducted and show a difference in sound pressure level of over 10 dB and a change in spectral content for different riding positions and helmet angle. Similar tests were then conducted in a wind tunnel, where simultaneous microphone and flow visualisation measurements allowed the identification and investigation of each flow region under controlled conditions. The contribution of vortex shedding to the noise was assessed using a combination of wavelet analysis and conditional averaging to identify intermittent structures.

GC Wave Analysis in Promoter Regions via Wavelet Analysis and Support Vector Machine

A large number of genomes have been sequenced and the number is growing rapidly. It is crucial to improve sequence annotation, including promoter prediction. Many aspects of DNA sequences have been examined and used in promoter prediction. In particular, the physical instability correlating GC content in the promoter region has been focus of many studies. To extract the GC signals of a promoter region in a genome sequence, we adopt a scheme combining wavelet analysis and a support vector machine (SVM). In this scheme, we take a simplified way to quantize and extract chemo-physical properties of a DNA sequence. Four types of DNA are converted to binary form with respect to G and C or not. The sequences are expanded to two dimensional spaces, frequency and location, by discrete wavelet transformation (DWT). The fixed length of the promoter and randomly selected DNA segments are prepared as the positive and negative training data, respectively. The two types of data are converted by DWT and learned by a SVM. Then, previously unknown DNA segments are classified as promoter or nonpromoter by the trained SVM.

Random Process Model for Urban Traffic Flow Using a Wavelet-Bayesian Hierarchical Technique

The existing well-known short-term traffic forecasting algorithms require large traffic flow data sets, including information on current traffic scenarios to pre- dict the future traffic conditions. This article proposes a random process traffic volume model that enables esti- mation and prediction of traffic volume at sites where such large and continuous data sets of traffic condi- tion related information are unavailable. The proposed model is based on a combination of wavelet analysis (WA) and Bayesian hierarchical methodology (BHM). The average daily trend of urban traffic flow obser- vations can be reliably modeled using discrete WA. The remaining fluctuating parts of the traffic volume observa- tions are modeled using BHM. This BHM modeling con- siders that the variance of the urban traffic flow observa- tions from an intersection vary with the time-of-the-day. A case study has been performed at two busy junctions at the city-centre of Dublin to validate the effectiveness of the strategy.

Synchrosqueezed wavelet transforms: An empirical mode decomposition-like tool

The EMD algorithm is a technique that aims to decompose into their building blocks functions that are the superposition of a (reasonably) small number of components, well separated in the time–frequency plane, each of which can be viewed as approximately harmonic locally, with slowly varying amplitudes and frequencies. The EMD has already shown its usefulness in a wide range of applications including meteorology, structural stability analysis, medical studies. On the other hand, the EMD algorithm contains heuristic and ad hoc elements that make it hard to analyze mathematically. In this paper we describe a method that captures the flavor and philosophy of the EMD approach, albeit using a different approach in constructing the components. The proposed method is a combination of wavelet analysis and reallocation method. We introduce a precise mathematical definition for a class of functions that can be viewed as a superposition of a reasonably small number of approximately harmonic components, and we prove that our method does indeed succeed in decomposing arbitrary functions in this class. We provide several examples, for simulated as well as real data.

A hybrid FDD strategy for local system of AHU based on artificial neural network and wavelet analysis

This paper presents a self-adaptive sensor fault detection and diagnosis (FDD) strategy for local system of air handing unit (AHU). This hybrid strategy consists of two stages. In the first stage, a fault detection model for the AHU control loop including two back-propagation neural network (BPNN) models is developed. BPNN models are trained by the normal operating data of system. Based on sensitive analysis for the first BPNN model, the second BPNN model is constructed in the same control loop. In the second stage, a fault diagnosis model is developed which combines wavelet analysis method with Elman neural network. The wavelet analysis is employed to process the measurement data by extracting the approximation coefficients of sensor measurement data. The Elman neural network is used to identify sensor faults. A new approach for increasing adaptability of sensor fault diagnosis is presented. This approach gains clustering information of the approximations coefficients by fuzzy c-means (FCM) algorithm. Based on cluster information of the approximation coefficients, the unknown sensor fault can be identified in the control loop. Simulation results in this paper show that this strategy can successfully detect and diagnose fixed biases and drifting fault of sensors for the local system of AHU.

Tibial acceleration variability during consecutive gait cycles is influenced by the menstrual cycle

Background The relationship between the phases of the menstrual cycle and injury risk remains unclear. Neuromuscular function may be compromised during menstruation, which could result in reduced cyclicality of movement patterns. We hypothesize that mediolateral (varus/valgus) knee acceleration during running gait will possess increased variability during menstruation when compared to ≈ ovulation in women who do not take the monophasic oral contraceptive pill (MOCP). Methods Thirty-six women (18 MOCP users: MOCP group and 18 non-pill users: NP group) performed six-minute treadmill running trials at 10 km h − 1 with an accelerometer fixed to the proximal tibia. Trials were performed at menstruation and ≈ ovulation (for the MOCP group at a similar stage of the cycle) in a randomized order. The cyclicality of gross mediolateral tibial acceleration during 15 consecutive strides was assessed using combined wavelet and autocorrelation analysis. Longitudinal and anteroposterior accelerations were also examined. Repeated measures analysis of variance (ANOVA) tests were performed to assess differences at each stage of the menstrual cycle ( α = 0.05). Findings Gross mediolateral acceleration in the NP group had significantly ( P = 0.022) increased variability at the time of menstruation compared to ≈ ovulation, and was also significantly ( P = 0.011) more variable than the MOCP group at the corresponding time point. No significant difference was observed for any measure in the MOCP group. Interpretation Increased variability in the NP users at menstruation may be a result of compromised motor control strategies. This provides further evidence of variability in performance and motor control during menstruation, and may have implications for a female athlete's risk of injury.

Identifying significant scale-specific spatial boundaries using wavelets and null models: spruce budworm defoliation in Ontario, Canada as a case study

We combine wavelet analysis and multiple null models to identify significant spatial scales of pattern and spatial boundaries in historical spruce budworm defoliation in Ontario, Canada. Previous analyses of budworm defoliation in Ontario over the last two outbreaks have suggested three distinct zones of defoliation. We asked the following three questions: (1) is there statistical support for the existence of these three zones? (2) Are the locations of these boundaries consistent between outbreak periods? And (3) how does boundary identification depend on the spatial null model used? Defoliation data for the two outbreak periods (1941–1965 and 1966–2001), and the combined period (1941–2001) were analyzed using a 1D continuous wavelet transform. Boundaries were identified through comparison of wavelet power spectra of each outbreak period to reference distributions based on three different spatial null models: (1) a complete spatial randomness model, (2) an autoregressive model, and (3) a Gaussian random field model. The Gaussian random field model identified coarser scales of pattern than the autoregressive model. Locally, the Gaussian random field model found significant boundaries similar to those previously described, whereas the autoregressive model only did so for the first outbreak. These results indicate that the coarse scale spatial factors that influenced defoliation were more consistent between outbreaks relative to fine scale factors, and that previously described boundaries were strongly driven by the first outbreak. Wavelet analysis combined with spatial null models provides a powerful tool for identifying non-arbitrary scales of structure and significant spatial boundaries in non-stationary ecological data.