Wavelet Analysis Technique Research Articles

Roughness scale dependence of the relationship between tracer longitudinal dispersion and Peclet number in variable‐aperture fractures

AbstractThe relationship between the longitudinal dispersion (DL) and Peclet number (Pe) is crucial for predicting and simulating tracer through the variable‐aperture fracture. In this study, the roughness of the self‐affine fracture wall was decomposed into primary roughness (relatively large‐scale waviness) and secondary roughness (relatively small‐scale waviness) by a multiscaled wavelet analysis technique. Based on the complete dispersion mechanism (diffusion, macrodispersion, and Taylor dispersion) in the variable‐aperture fracture, three relationships (second‐order, power‐law, and linear relationships) between the DL and Pe were investigated at large and small scales, respectively. Our results showed that the primary roughness mostly controlled the Taylor dispersion mechanism, whereas the secondary roughness was a dominant factor for the macrodispersion mechanism. Increasing the Hurst exponent and removing the secondary roughness led to the decreasing range of Pe where macrodispersion mechanism dominated the solute transport. It was found that estimating the DL from the power‐law relationship based on Taylor dispersion theory resulted in considerable errors, even in the range of Pe where the Taylor dispersion mechanism dominated. The exponent of the power‐law relationship increased as the secondary roughness was removed. Analysing the linear relationship between the DL and Pe revealed that the longitudinal dispersivity αL increased linearly. However, this linear increase became weak as the Taylor dispersion mechanism dominated. In the range of Pe where the macrodispersion mechanism dominated, increasing the Hurst exponent caused the increase of αL and the secondary roughness played a significant role in enhancing the αL. As the Taylor dispersion mechanism dominated, the αL was insensitive to the influence of multiscale roughness in variable‐aperture fractures.

Geomagnetic field variations observed by INTERMAGNET during 4 total solar eclipses

We investigate variations of the geomagnetic field observed by INTERMAGNET geomagnetic observatories over which the totality path passed during a total solar eclipse. As a result, we have presented results of examining the geomagnetic field data observed by 6 geomagnetic observatories during the 4 total solar eclipses (11 August 1999, 1 August 2008, 11 July 2010, and 20 March 2015). These total solar eclipses are the only total solar eclipse, since 1991 when the INTERMAGNET began to work, during which the umbra of the Moon swept an INTERMAGNET geomagnetic observatory and simultaneously variations of the geomagnetic field are recorded at the geomagnetic observatory. We have confirmed previous reports that, at least on average, during the solar eclipse characteristic increase of Y component of the geomagnetic field Y and decreases of X,Z and F are conspicuous. Interestingly, we have noted that variations of X,Y,Z and F observed during the total solar eclipse at Isla de Pascua Mataveri (Easter Island) in Chile (IPM) in the southern hemisphere show distinct decrease of Y and increases of X and Z on the contrary. We have found, however, that variations of X,Y,Z and F observed at Hornsund in Norway (HRN) at 77°N in latitude seem to be dominated by other geomagnetic occurrence since the solar activity is near maximum, and the geomagnetic field is disturbed on the day of eclipse. We further discuss a possibility of exploring an effect of the solar eclipse on the amplitude of harmonic components of the geomagnetic field using the wavelet analysis technique, which has revealed suppression in the amplitude during the middle of a solar eclipse. Finally, we conclude by discussing implications of what we have found.

Evaluation of the Effect of Molybdenum on the Pitting Corrosion Behavior of Austenitic Stainless Steels Using Electrochemical Noise Technique

Electrochemical noise (EN) studies were conducted on three austenitic stainless steels (SSs) with different molybdenum contents, 0.02 wt% Mo (Type 304LN SS), 2.53 wt% Mo (Type 316LN SS), and 3.58 wt% Mo (Type 317LN SS), in 0.01 M FeCl3 solution at the corrosion potential (Ecorr) and at a sampling frequency of 1 Hz. The EN data were analyzed using shot noise and wavelet analysis techniques. Current transient analysis showed that the total number of current transients, as well as transients with high current amplitude, decreased with increase in Mo content indicating increased resistance to pitting corrosion. Shot noise analysis revealed higher normalized characteristic charge (q)N at low frequency in Type 304LN SS as compared to Type 316LN SS and Type 317LN SS, implying increase in pitting corrosion resulting from the absence of Mo in this steel. Pit current decreased substantially with increase in Mo content. These results were supported by the standard deviation of partial signal (SDPS) values generated ...

Identify temporal trend of air temperature and its impact on forest stream flow in Lower Mississippi River Alluvial Valley using wavelet analysis

Characterization of stream flow is essential to water resource management, water supply planning, environmental protection, and ecological restoration; while air temperature variation due to climate change can exacerbate stream flow and add instability to the flow. In this study, the wavelet analysis technique was employed to identify temporal trend of air temperature and its impact upon forest stream flows in Lower Mississippi River Alluvial Valley (LMRAV). Four surface water monitoring stations, which locate near the headwater areas with very few land use disturbances and the long-term data records (60–90 years) in the LMRAV, were selected to obtain stream discharge and air temperature data. The wavelet analysis showed that air temperature had an increasing temporal trend around its mean value during the past several decades in the LMRAV, whereas stream flow had a decreasing temporal trend around its average value at the same time period in the same region. Results of this study demonstrated that the climate in the LMRAV did get warmer as time elapsed and the streams were drier as a result of warmer air temperature. This study further revealed that the best way to estimate the temporal trends of air temperature and stream flow was to perform the wavelet transformation around their mean values.

Study on the evaluation of erosion damage by using laser ultrasonic integrated with a wavelet analysis technique

Spallation targets are the key components of accelerator driven systems (ADSs) that are being developed in the world. Erosion damages on the target vessels are anticipated. To prevent accidents occurrence due to erosion of spallation target vessel, the damage evaluation technique is desirable. The excited vibration of LBE target vessel will be monitored remotely to establish the technique. In this study, the basic researches were carried out through experiments and numerical simulations to investigate the interaction between ultrasonic waves and damage to understand the correlation between structural vibration and damage degree. Specimens with distributed erosion damage was irradiated by laser shots, and the vibration was detected by a laser vibrometer subsequently. A technique, Wavelet Differential Analysis (WDA), was developed to quantitatively and clearly indicate the differences caused by damage in the vibration signals. The results illustrated that the developed technique is sensitive to erosion damage with small size and is capable of quantitatively evaluating erosion damage. It is expected that the developed techniques can be applied to monitor the real spallation targets in the future.

Evaluation of interaction dynamics of concurrent processes

Abstract The purpose of this paper is to present the wavelet tools that enable the detection of temporal interactions of concurrent processes. In particular, the determination of interaction coherence of time-varying signals is achieved using a complex continuous wavelet transform. This paper has used electrocardiogram (ECG) and seismocardiogram (SCG) data set to show multiple continuous wavelet analysis techniques based on Morlet wavelet transform. MATLAB Graphical User Interface (GUI), developed in the reported research to assist in quick and simple data analysis, is presented. These software tools can discover the interaction dynamics of time-varying signals, hence they can reveal their correlation in phase and amplitude, as well as their non-linear interconnections. The user-friendly MATLAB GUI enables effective use of the developed software what enables to load two processes under investigation, make choice of the required processing parameters, and then perform the analysis. The software developed is a useful tool for researchers who have a need for investigation of interaction dynamics of concurrent processes.

Tidal propagation and its non-linear characteristics in the Head Bay of Bengal

The Head Bay of Bengal is highly vulnerable to flooding events caused due to monsoons, cyclones and sea-level rise, owing to its funnel-like shape, high tidal range, presence of numerous river drainage systems, low-lying topography, and shallow bathymetry. Tides dominate the hydrodynamic behaviour and coastal processes in this region and its propagation is quite distinct. The present study uses ADCIRC hydrodynamic model customized for the Head Bay of Bengal, discretized using unstructured finite elements and validated against limited available observations in this data sparse region. The water-level elevations derived from ADCIRC simulation was used to understand the pattern of non-linear tidal propagation with respect to complex coastal geomorphology prevalent in this region. The study finds a marginal amplification of diurnal tide, nearly double amplification of semi-diurnal components, and existence of a degenerate amphidromic point near Meghna delta consistent with previous studies. The spatial and temporal variability of tidal spectral components were examined by applying the techniques of wavelet, harmonic, and time-series analysis at various locations. Maximum amplification of tides occurs at the head of the bay, along a zone enclosing the mouth of tidal inlets; and for regions northward, the tides decay with progressively increasing phase lags. The study signifies dominance of a forced fortnightly tide and tidal asymmetry leading to flood-dominance in the rivers Hooghly, Meghna, and Tetulia. The non-linear properties of tides have been elucidated, and their origin and spatio-temporal variability in these riverine systems were further investigated. Shallow depth and sharp depth gradients were discerned to be the important conditions for the origin of non-linear components. It has been deduced that non-linear tides are generated in regions where propagating tides are accumulated, and amplified in regions where they are funneled. A study of tidal energetics show that maximum rate of tidal energy dissipation is deep inside the rivers at head of the bay. The spatial variability analysis of tidal form factor shows an overall semi-diurnal dominance in the Head Bay.

Wavelet twin support vector machines based on glowworm swarm optimization

Twin support vector machine is a machine learning algorithm developing from standard support vector machine. The performance of twin support vector machine is always better than support vector machine on datasets that have cross regions. Recently proposed wavelet twin support vector machine introduces the wavelet kernel function into twin support vector machine to make the combination of wavelet analysis techniques and twin support vector machine come true. Wavelet twin support vector machine not only expands the range of the kernel function selection, but also greatly improves the generalization ability of twin support vector machine. However, similar with twin support vector machine, wavelet twin support vector machine cannot deal with the parameter selection problem well. Unsuitable parameters reduce the classification capability of the algorithm. In order to solve the parameter selection problem in wavelet twin support vector machine, in this paper, we use glowworm swarm optimization method to optimize the parameters of wavelet twin support vector machine and propose wavelet twin support vector machine based on glowworm swarm optimization. Wavelet twin support vector machine based on glowworm swarm optimization takes the parameters of wavelet twin support vector machine as the position information of glowworms, regards the function to calculate the wavelet twin support vector machine classification accuracy as objective function and starts glowworm swarm optimization algorithm to update the glowworms. The optimal parameters are the position information of glowworms that we get when the glowworm swarm optimal algorithm stops. Wavelet twin support vector machine based on glowworm swarm optimization determines the parameters in wavelet twin support vector machine automatically before the training process to avoid difficulty of parameter selection. Reasonable parameters promote the performance of wavelet twin support vector machine and improve the accuracy. The experimental results on benchmark datasets indicate that the proposed approach is efficient and has high classification accuracy.

Improving daily streamflow forecasts in mountainous Upper Euphrates basin by multi-layer perceptron model with satellite snow products

This paper investigates the contribution of Moderate Resolution Imaging Spectroradiometer (MODIS) satellite Snow Cover Area (SCA) product and in-situ snow depth measurements to Artificial Neural Network model (ANN) based daily streamflow forecasting in a mountainous river basin. In order to represent non-linear structure of the snowmelt process, Multi-Layer Perceptron (MLP) Feed-Forward Backpropagation (FFBP) architecture is developed and applied in Upper Euphrates River Basin (10,275km2) of Turkey where snowmelt constitutes approximately 2/3 of total annual volume of runoff during spring and early summer months. Snowmelt season is evaluated between March and July; 7years (2002–2008) seasonal daily data are used during training while 3years (2009–2011) seasonal daily data are split for forecasting. One of the fastest ANN training algorithms, the Levenberg-Marquardt, is used for optimization of the network weights and biases. The consistency of the network is checked with four performance criteria: coefficient of determination (R2), Nash-Sutcliffe model efficiency (ME), root mean square error (RMSE) and mean absolute error (MAE). According to the results, SCA observations provide useful information for developing of a neural network model to predict snowmelt runoff, whereas snow depth data alone are not sufficient. The highest performance is experienced when total daily precipitation, average air temperature data are combined with satellite snow cover data. The data preprocessing technique of Discrete Wavelet Analysis (DWA) is coupled with MLP modeling to further improve the runoff peak estimates. As a result, Nash-Sutcliffe model efficiency is increased from 0.52 to 0.81 for training and from 0.51 to 0.75 for forecasting. Moreover, the results are compared with that of a conceptual model, Snowmelt Runoff Model (SRM), application using SCA as an input. The importance and the main contribution of this study is to use of satellite snow products and data preprocessing in ANN to improve the streamflow forecasts for ungauged or data sparse mountainous basins.

A study on the main periodicities in interplanetary magnetic field Bz component and geomagnetic AE index during HILDCAA events using wavelet analysis

The interplanetary and geomagnetic characteristics of High-Intensity Long-Duration Continuous AE Activity (HILDCAA) events are studied using wavelet analysis technique. The Morlet wavelet transform was applied to the 1min interplanetary magnetic field (IMF) Bz component and the geomagnetic AE index during HILDCAA events. We have analyzed the AE data for the events occurring between 1975 and 2011, and the IMF Bz data (both in GSE and GSM) for the events between 1995 and 2011. We analyzed the scalograms and the global wavelet spectrum of the parameters. For 50% of all HILDCAA events, the main periodicities of the AE index are generally between 4 and 12h. For the Bz component, the main periodicities were found to be less than 8h for ~56% of times in GSM system and for ~54% of times in GSE system. It is conjectured that the periodicities might be associated with the Alfvén waves which have typical periods between 1 and 10h. The results are discussed in the light of self organized criticality theory where the physical events have the capacity of releasing a considerable amount of energy in a short interval of time.

Influence of surface roughness on nonlinear flow behaviors in 3D self-affine rough fractures: Lattice Boltzmann simulations

This study investigates the impacts of surface roughness on the nonlinear fluid flow through three-dimensional (3D) self-affine rock fractures, whose original surface roughness is decomposed into primary roughness (i.e. the large-scale waviness of the fracture morphology) and secondary roughness (i.e. the small-scale unevenness) with a wavelet analysis technique. A 3D Lattice Boltzmann method (LBM) is adopted to predict the flow physics in rock fractures numerically created with and without consideration of the secondary roughness, respectively. The simulation results show that the primary roughness mostly controls the pressure distribution and fracture flow paths at a large scale, whereas the secondary roughness determines the nonlinear properties of the fluid flow at a local scale. As the pressure gradient increases, the secondary roughness enhances the local complexity of velocity distribution by generating and expanding the eddy flow and back flow regions in the vicinity of asperities. It was found that the Forchheimer's law characterizes well the nonlinear flow behavior in fractures of varying roughness. The inertial effects induced by the primary roughness differ only marginally in fractures with the roughness exponent varying from 0.5 to 0.8, and it is the secondary roughness that significantly enhances the nonlinear flow and leads to earlier onset of nonlinearity. Further examined were the effects of surface roughness on the transmissivity, hydraulic aperture and the tortuosity of flow paths, demonstrating again the dominant role of the secondary roughness, especially for the apparent transmissivity and the equivalent hydraulic aperture at high pressure gradient or high Reynolds number. The results may enhance our understanding of the role of surface roughness in the nonlinear flow behaviors in natural rock fractures.

Multi-scale variation of the meridional movement of the western Pacific warm pool and its associated large-scale climate features

In this study, we investigated variation of the meridional movement of the western Pacific warm pool (WPWP). The variation was measured by the central latitude (Clat) of the WPWP on various time scales. Its relationships with global sea surface temperature (SST), precipitation, and atmospheric circulation were examined by applying several advanced statistical methods. First, the techniques of wavelet analysis and least-square adjustment were used to depict the time-frequency features and the mean dominant oscillating time scales of the Clat. Then, a multi-stage filtering technique was applied to illustrate the related dominant oscillating signals. We also examined the time-frequency characteristics of the relationships between Clat and the leading modes of the Indo-Pacific oceans by employing a cross-covariance function analysis and a multiple moving-window method. The physical mechanisms for the relationships between Clat and the patterns of SST, precipitation, and atmospheric circulation were discussed. Results indicated that there is a weakening trend in the oscillation of Clat mainly because the quasi-annual oscillation of Clat increases in January–March and decreases in July–September. The semi-annual oscillation of Clat closely interacts with the westerly wind over the summer hemisphere of the tropical western Pacific Ocean and with the easterly wind over the winter hemisphere of the ocean. The interannual component of Clat corresponds to El Niño-Southern Oscillation and the Indian Ocean basin-wide warming or cooling with strengthened oscillations in the 1970s, and the lower-frequency component of Clat closely corresponds to the central Pacific type of El Niño from the 1990s.

Assessment of CO2 dynamics in subsurface atmospheres using the wavelet approach: from cavity–atmosphere exchange to anthropogenic impacts in Rull cave (Vall d′Ebo, Spain)

Subsurface environments are non-negligible contributors to the net carbon balance because they act as natural sinks of CO2 and are responsible for the efflux to the Earth’s atmosphere during their ventilation states. In this way, the characterization of the CO2 dynamics in these underground environments is essential to determine the gas exchange between both atmospheres. A complete microclimatic analysis and trace gas (CO2 and 222Rn) monitoring of Rull cave (southeast Spain) were conducted to characterize the natural dynamics and anthropogenic influence on the cavity. The analysis was accomplished by implementing wavelet analysis and resemblance techniques. This study enhances wavelet analysis as an efficient tool to analyse microclimatic time series, as it allows for the detection of the main periodicities of signals located in the time domain and the prevailing relationships between them. The analysis indicates that the low-frequency components of the signals were close to the identified annual natural cycles. For a 1-year cycle, the ventilation of the cavity causes the CO2 concentration to decrease from 3569 to 932 ppm in nearly 1 month, highlighting the existence of an output efflux from the cavity. On the contrary, the high-frequency components are linked to human perturbations caused by visitors in the cavity.

A Review on Artificial Intelligence Methodologies for the Forecasting of Crude Oil Price

AbstractWhen crude oil prices began to escalate in the 1970s, conventional methods were the predominant methods used in forecasting oil pricing. These methods can no longer be used to tackle the nonlinear, chaotic, non-stationary, volatile, and complex nature of crude oil prices, because of the methods’ linearity. To address the methodological limitations, computational intelligence techniques and more recently, hybrid intelligent systems have been deployed. In this paper, we present an extensive review of the existing research that has been conducted on applications of computational intelligence algorithms to crude oil price forecasting. Analysis and synthesis of published research in this domain, limitations and strengths of existing studies are provided. This paper finds that conventional methods are still relevant in the domain of crude oil price forecasting and the integration of wavelet analysis and computational intelligence techniques is attracting unprecedented interest from scholars in the domai...

Influence of aging treatment on the pitting behavior associated with the dissolution of active nanoscale β-phase precipitates for an Al–Mg alloy

The pitting characteristics of Al-5wt%Mg microelectrodes after various aging treatment have been investigated in a deaerated 0.6M NaCl solution (pH 3.5). The dissolution current of single and numerous active β-phase precipitates have been detected on the aged alloys under anodic potentiostatic polarizations. The results show that the metastable pitting rates depend on the size and number of β-phase precipitates. The pitting potential decreases and the average size of β-phase increases initially with the increase of aging time, while for long-time aged alloys, the energy distribution plot (EDP) obtained from wavelet analysis technique can distinguish their small microstructural differences.

Identification of critical load for scratch adhesion strength of nitride-based thin films using wavelet analysis and a proposed analytical model

The use of Acoustic Emission (AE) signal of scratch test to evaluate the thin films' adherence led to unavoidable large uncertainties due to the problematic determination of the frequency band and the improper installation of the acoustic sensor, especially for coatings thinner than 100μm. In this paper, thin film–substrate adherence in high-speed steel is evaluated by Wavelet Analysis (WA) of the force–displacement curve in micro-scratch tests performed on the samples. The measured load displacement signals of deposited thin films were used as inputs for the wavelet module. We propose an analytical model combined with wavelet analysis, which is based on experimental data. Scratch adhesion strength could be identified by applying wavelet technique to the measured force displacement data to detect the location of the critical load during micro-scratch testing for TiSiN coating on high-speed steel. An analytical model is also proposed to predict the adhesion stress and to support the wavelet analysis technique where the stress is difficult to be assessed. Thin films were studied by optical microscopy, micro scratch testing machine and X-ray diffraction. The WA results are compared with model predictions in order to establish indication of scratch adhesion strength aimed at improving the manufacturing process.

Roughness decomposition and nonlinear fluid flow in a single rock fracture

The objective of this paper is to investigate the effects of wall surface roughness on fluid flow through rock fractures. A wavelet analysis technique was developed to define a mathematical criterion for decomposing the original wall surface roughness profiles of a fracture into a high-frequency (secondary roughness) profile and a low-frequency (primary roughness) profile, in order to examine their impacts on fluid flow, by solving the Navier–Stokes equations (NSE) without linearization, using a self-developed 2D finite volume method (FVM) code. The results indicate that the high-frequency secondary roughness is the main cause for dynamic evolution of Eddy flow regions in the fracture flow field, besides the Reynolds number (Re). In the original fracture model with the high-frequency secondary roughness, our results show that fluid flow fields are not only generally non-linear, but also with non-stop generation and motions of eddies in the boundary layer regions of rough fractures when the Re=1000 in this study, which will affect the solute transport processes in fractured rock masses. The complete NSE were solved without removing acceleration and inertial terms, so that the impacts of surface roughness on the nonlinear and dynamic flow behavior of rock fractures were calculated and visualized more accurately, which is important for modeling mass and energy transport processes in fractures and fractured rock masses.

Wavelet Analysis of Doppler Flow Velocity Waveforms from the Ocular Circulation Detects Abnormalities in Micro-vascular Function in Heart Failure

Aims: Many pathological processes may result in Heart Failure (HF), however, the final clinical phenotype appears to be common to all aetiologies. Microvascular abnormalities in HF have been demonstrated in a variety of vascular beds however the effect of HF on the retrobulbar circulation is not known. We employed a novel method of analysing blood flow velocity waveforms in order to quantify changes in the Doppler signal obtained from the retrobulbar arteries between HF patients and controls. FMD was also assessed. Methods and Results: 32 patients with systolic HF on maximal medical therapy and 10 age and sex-matched controls were studied. Doppler waveforms were obtained from the ophthalmic, central retinal and brachial artery in all subjects. Waveforms were analysed using a wavelet technique which expressed each waveform in constituent frequency bands, allowing direct comparisons to be made. Significant differences were found in ophthalmic and central retinal waveforms but not brachial waveforms between patients and controls. Flow mediated dilatation was also found to be abnormal in patients. Conclusion: This study suggests that abnormalities in microvascular function in the retrobulbar circulation are present in HF and that these changes can be detected by a novel wavelet analysis technique.

Short-term periodicities in interplanetary, geomagnetic and solar phenomena during solar cycle 24

In this paper we study the quasi-periodic variations of sunspot area/number, 10.7 cm solar radio flux, Average Photospheric Magnetic Flux, interplanetary magnetic field (B z ) and the geomagnetic activity index A p during the ascending phase of the current solar cycle 24. We use both Lomb-Scargle periodogram and wavelet analysis technique and find evidence for a multitude of quasi-periodic oscillations in all the data sets. In high frequency range (10 days to 100 days), both methods yield similar significance periodicities around 20–35 days and 45–60 days in all data sets. In the case of intermediate range, the significant periods were around 100–130 days, 140–170 days and 180–240 days The Morlet wavelet power spectrum shows that all of the above-mentioned periods are intermittent in nature. We find that the well-known “Rieger period” of (150–160 days) and near Rieger periods (130–190 days) were significant in both solar, interplanetary magnetic field and geomagnetic activity data sets during cycle 24. The geomagnetic activity is the result of the solar wind-magnetosphere interaction. Thus the variations in the detected periodicity in variety of solar, interplanetary and geomagnetic indices could be helpful to improve our knowledge of the inter-relationship between various processes in the Sun-Earth-Heliosphere system.

A WAVELET ANALYSIS OF BOLD SIGNALS OBTAINED IN MR STUDIES FOR ADHD DIAGNOSIS

A series of wavelet analysis techniques were applied to the Blood Oxygen Level Dependent (BOLD) signals of Functional Magnetic Resonance Images (fMRI) of healthy children and a group diagnosed with Attention deficit hyperactivity disorder (ADHD). Imaging was performed on a total of 15 subjects (ages between 4 – 13 years) divided into Healthy (H) and ADHD (AD) groups. The 1.5T Philips scanner from ``Hospital Infantil de Mexico Federico Gomez´´ was used to obtain data. Statistically significant differences were found for the wavelet scale and the frequency parameters between the H and the AD groups. These results point towards the future use of these techniques as diagnostic tools.