Wavelet Analysis Method Research Articles

Spillover Network Features from the Industry Chain View in Multi-Time Scales.

Financial stocks in the industry chain interact notably because of close economic and technical relationships. Some participants pay particular attention to one industry chain and are concerned with different investment horizons. The motivation for this study is to offer more targeted information to various market participants who focus on different time scales in one industry chain from a systematic perspective by combining the GARCH-BEKK, heterogeneous network, and wavelet analysis methods. The findings are as follows: (1) For parties who prefer to take more risks to gain higher returns, scale 2 (4–8 days) is a good option, while long-term investment (32–128 days) is suitable for conservative investors. (2) In most cases, some links in the industry chain are particularly sensitive to changes in stocks in other links. (3) The influence, sensitivity, and intermediary of stocks in the industry chain on different time scales were explored, and participants could use the resulting information to monitor the market or select stocks. (4) The structures, key players, and industry chain attributes of the main transmission paths differ on multi-time scales. Risk transmission can be controlled by intercepting important spillover relations within the paths.

A Recognition Algorithm of Seismic Signals Based on Wavelet Analysis

In order to meet the requirements of mobile marine seismometers to observe and record seismic signals, a study of fast and accurate seismic signal recognition was carried out. This paper introduces the use of the wavelet analysis method for seismic signal processing and recognition, and compares and analyzes the abilities of different wavelet basis functions to detect the seismic signal. By denoising and reconstructing the signal, the distribution law of the wavelet coefficients of seismic signal at different scales was obtained. On this basis, this paper proposes an identification model of seismic signals based on wavelet analysis and thereby solves the conflict between high speed and high accuracy of seismic signal recognition methods. In this study, the simulation was carried out in the Matlab2020b environment, and the feasibility of wavelet recognition algorithm was proven by applying this algorithm to the seismic signal database for experimental verification.

Characterisation of compound dry and hot events in Inner Mongolia and their relationship with large-scale circulation patterns

In recent decades, frequent compound dry and hot events (CDHEs) have posed a great threat to humans and the ecological environment, especially in Inner Mongolia, which has typical arid and semi-arid characteristics. Therefore, while exploring the characteristics of the spatial and temporal evolution of the CDHEs in Inner Mongolia, and elucidating their teleconnections with large-scale circulation indexes, it can not only provide useful enlightenment for ecological protection, but also provide more detailed information for understanding the corresponding details of CDHEs with large-scale circulation indexes. In this study, monthly temperature and precipitation data from 115 meteorological stations in Inner Mongolia for each summer from 1981 to 2020 were used to establish a standardised dry and hot index (SDHI), characterising the severity of extreme compound events of drought and high temperature in Inner Mongolia. This data was also used to analyse the spatial and temporal characteristics of such compound events over the past 40 years. Thereafter, Pearson’s correlation, stepwise multiple regression models, and multivariate wavelet analysis methods were used to assess the relationship between the atmospheric circulation index and SDHI in terms of linear, non-linear, and different combinations of the four large-scale circulation indexes. The results show that SDHI performs well in the assessment of CDHEs in Inner Mongolia and shows great similarities with historical records. The overall trend of CDHEs in Inner Mongolia over the past 40 years has intensified over time. Spatially, abnormal and moderate dry and hot events mainly occurred in Inner Mongolia before the 21st century, and the frequency of occurrence was higher in the eastern region than in the western region. Furthermore, the severity of CDHEs has increased significantly since the 21st century, with severe and extreme dry and hot events occurring more frequently in the western region than in the eastern region. The results of the correlation analysis show that, among the four large-scale circulation indexes, the Atlantic Multidecadal Oscillation (AMO) has the greatest influence on the CDHEs in Inner Mongolia when it is positive, with the highest significance, and when the Pacific Decadal Oscillation (PDO) and North Atlantic Oscillation (NAO) show negative phases, they are more likely to cause dry and hot events in Inner Mongolia. Further, where the number of stations negatively influenced by NAO is higher, the effect of El Niño Southern Oscillation (ENSO) on dry and hot events is usually insignificant. Partial wavelet coherence (PWC) analysis suggests that the independent effects of circulation factors may be weaker but appear to be stronger due to their interdependence with other circulation patterns. Finally, the multiple wavelet coherence (MWC) results show that the changes in SDHI values can be best explained by a combination of two or more factors.

Time-frequency dynamics, co-movement and causality among returns of global carbon emissions trading schemes (ETSs): A tale of four markets

This study aims to explore the co-movement effect of returns among the four most representative global carbon emissions trading schemes (ETSs) and the correlations and time lag characteristics of returns in four ETSs. Specifically, this study examines the price volatility and return coherence of ETS of the European Union (EU), New Zealand, South Korea, and Shenzhen, China from 2013 to 2019 using the wavelet analysis method. Firstly, from the temporal perspective, the trading prices of the four ETSs demonstrate certain volatility characteristics, that is, more medium- and long-term (over five trading days) but less short-term fluctuations. Secondly, because of the different starting stages and maturity levels of the four major ETSs, the wavelet analysis indicates that the linkage between ETS trading rates varies across frequencies and evolves and shows dynamic characteristics from two angles of time and frequency domains. The findings also show the principle of mutual influence or linkage between global ETSs, which is helpful for the establishment of ETS and can improve the awareness of ETS risk prevention among market participants to avoid losses. • This study explores the linkage effect of returns among global major ETSs. • Correlations and time lag of returns in four ETS markets were explored. • Co-movement between ETSs' returns varies across frequencies and evolves over time. • The four ETSs have large price fluctuations in the medium- and long-term. • Shenzhen, China ETS has no significant influence on EU, New Zealand and Korea ETSs.

Application of the complementary ensemble empirical mode decomposition for the identification of simulation model parameters and groundwater contaminant sources

Identification of groundwater contaminant sources (GCSs) relies on actual observed data, and the observed data directly affects the accuracy of the identification results. However, the observed data inevitably contains noise due to accidental and systematic errors, and the identification results of GCSs based on the observed data containing noise are usually not reliable. This seriously restricts the rational design of contamination remediation plans and contamination risk assessment. To solve this problem, complementary ensemble empirical mode decomposition (CEEMD) was applied to the joint identification of simulation model parameters and GCSs information in this study for the first time. The wavelet analysis and CEEMD methods were used to reduce the noise in observed concentrations respectively, and the noise reduction effect of the two methods was compared and analyzed. Then the simulated concentrations, observed concentrations and noise-reduced observed concentrations with the best noise reduction effect were then applied to identify simulation model parameters and GCSs information, respectively, after which the corresponding identification results were compared and analyzed. The results showed that CEEMD could reduce the noise contained in the observed concentrations more effectively and make it closer to the actual values when compared with the wavelet analysis method. The accuracy of identification results based on the noise-reduced observed concentrations by was improved. As the noise contained in the observed concentrations increased, the noise reduction effect of the CEEMD decreased. Nevertheless, the accuracy of the identification results based on the noise-reduced observed concentrations was still higher than that of those based on the observed concentrations with the same intensity noise without denoising.

Research on Bridge Damage Identification Based on WPE-MDS and HTF-SAPSO

In recent decades, structural damage identification based on the wavelet analysis method has been widely developed, but it is still confronted with many difficulties, such as large decomposition error and complex data. In order to overcome the shortcomings of analysis based on wavelet, the wavelet packet analysis method is adopted to decompose the acceleration data into wavelet packets, and the frequency band energy value after wavelet packet decomposition (WPE) is taken as the different dimensions of the Mahalanobis distance squared (MDS) in this study, where the MDS value of the same element between different samples is calculated, and the mean value of 30 groups of MDS values for each element is processed. The change rate between the MDS value of the element that exceeds the MDS value in the healthy state and the MDS mean value in the healthy state as the objective function. The combination of weight coefficient and hyperbolic tangent function is used to improve the simulated annealing particle swarm optimization (SAPSO) algorithm, and the improved hyperbolic tangent function-simulated annealing particle swarm optimization (HTF-SAPSO) is used to iteratively calculate the damage severity. The numerical simulation and vibration testing of a steel beam are conducted to verify the identification performance of damage location and the analysis of damage severity by this method, respectively. The numerical model of the experimental I-beam is established based on the MATLAB modeling platform, and the different damage cases are utilized to illustrate the correctness of this study. The different proportions of noise effects are adopted to the numerical simulation analysis, where the correlations between noise effects and MDS value and damage severity are analyzed. In the numerical simulation, although the MDS value increases to different degrees with the increase of the noise ratio, the damage identification result of the damaged element remains mostly constant, which indicates that the influence is negligible. In conclusion, it is feasible to construct the damage index via the combination of WPE and MDS values, the damage location can be judged from whether the MDS value of the element exceeds the threshold, and the HTF-SAPSO algorithm is more efficient and accurate to be adopted in the quantification of the damage severity.

Pressure response using wavelet analysis in the process of hydraulic fracturing: Numerical simulation and field case

Diagnosis of hydraulic fracturing is of great significance for field treatment guidance. The real-time analysis of the pressure curve is helpful to evaluate the hydraulic fracture status, and provide guidance for the adjustment of the fracturing treatment. However, the response pattern of hydraulic fracture propagation and fluid flow in the pressure curve is still unclear and difficult to capture. In this paper, the 2D hydraulic fracturing model was built to investigate the effects of different stress differences, elastic modulus, and variable rate. Simultaneously, the wavelet analysis method was introduced to observe its response characteristics. Taking the peak energy of the wavelet as the standard, the db5 wavelet basis is selected as the optimal wavelet basis and the first decomposition level is the optimal recognition level. There is a positive correlation between the pressure drop value and the wavelet peak energy under different stress difference and elastic modulus. In addition, we compared the pressure drop ratio and the wavelet energy ratio found that the wavelet energy can greatly amplify the impact of pressure fluctuations. Comparing the wavelet energy of pressure and rate can eliminate the influence of rate fluctuation on pressure. Finally, the field treatment curve was decomposed and explained by wavelet analysis, then it is compared with the results of microseismic interpretation for the first time. This paper can provide further reference for the interpretation of hydraulic fracturing curves.

Estimating sea level, wind direction, significant wave height, and wave peak period using a geodetic GNSS receiver

Monitoring oceanographic parameters is essential for marine engineering construction, coastal safety, marine analysis, and climatic analysis. Traditionally, oceanographic parameters have been monitored using different sensors. With the development of global navigation satellite systems (GNSS), signal-to-noise ratio (SNR) observations have been found to oscillate due to the interference between direct and reflected signals. GNSS-interferometry reflectometry (GNSS-IR) utilizes the characteristics of these oscillations to retrieve the parameters of a reflecting surface. For GNSS marine application, GNSS-IR sea-level retrieval technology has considerably improved and is undergoing rapid development; however, only a few studies have estimated significant wave height (SWH) and wind direction using GNSS-IR. The cut-off elevation, an SNR parameter, is the elevation angle at which the dominant power of one SNR arc switches between coherent and incoherent energy and is related to SWH and wind direction. Methods to estimate SWH and wind direction were presented based on the cut-off elevation. Moreover, we introduced the Generalized Shallow Water Wave Growth (GSWWG) model to estimate the wave peak period based on retrieved SWH. The sea level, wind direction, SWH, and wave peak period were estimated using a geodetic GNSS receiver. Data were collected before and after Hurricane Delta using the GNSS site CALC, Louisiana, USA. First, the classical GNSS-IR method was used to retrieve sea levels. Second, the wavelet analysis method was processed to SNR arcs to form wavelet spectra showing the nonstationary power and estimate their cut-off elevations. Third, the lower cut-off elevation was used to estimate the wind direction, and the upper cut-off elevation was used to estimate the SWH. Finally, the wave peak period was estimated from the retrieved SWH based on the GSWWG model. The results showed a root mean square error of 4.72 cm between sea-level measurements and sea-level retrievals, and a correlation coefficient > 80% between wind speed measurements and SWH retrievals estimated from GPS L5 and GLONASS R2. The points of SWH retrievals vs. wind speeds of GPS L5 and GLONASS R2 corresponded well to the wind–SWH relationship deduced from the GSWWG model. These results indicated that GNSS-IR technology could estimate the wave peak period in addition to its previously known applications for retrieving sea level, wind direction, and SWH, providing additional oceanographic services.

Multi-agent cooperative structural vibration control of three coupled flexible beams based on value decomposition network

Multi-coupled flexible body (MCFB) structure has the characteristics of small damping, close modes and coupled vibration, and the structural vibration is difficult to freely attenuate, which will lead to rapid fatigue failure of the structure. In order to suppress the vibration of the MCFB system quickly, a system identification scheme and a multi-agent reinforcement learning (MARL) scheme are proposed to obtain the cooperative controller. The mathematical model of the three-coupled flexible beam (TCFB) system is identified by wavelet analysis method, particle swarm optimization (PSO) and sinusoidal excitation response method. Based on value decomposition network (VDN) and deterministic policy gradient (DPG), a cooperative MARL framework is constructed to obtain the controller (VDN-DPG controller). The cooperative VDN-DPG controller is applied to the vibration control simulation and experiments of the TCFB system, and compared with proportional–derivative (PD) control without cooperation. The results show that the performance of the VDN-DPG controller is better than that of the PD controller, which verifies the effectiveness of the multi-agent cooperative control scheme based on VDN-DPG.

A Novel Method for Real Time Protection of DC Microgrid Using Cumulative Summation and Wavelet Transform

DC microgrid is a compact framework comprising interconnected nearby sources and loads. The renewable energy source used in DC microgrids being intermittent leads to the change in the power availability as well as the fault current levels. In such situations, detecting and clearing the faults is very important to protect the DC microgrid without compromising on fault clearing time and interruption of the load. This paper proposes a hybrid Cumulative Sum (CumSum) and Wavelet transform-based approach to detect the fault. The CumSum value raises the amplitude by averaging the fault current. Wavelet transforms obtain important fault current features by decomposing the current signal. The hybrid method of CumSum and Wavelet analysis proposed here enables the detection of the fault and differentiates the fault condition from sudden load variation. Additionally, it helps to recognize the location of the fault by the wavelet energy difference. The proposed scheme is tested with a developed ring-type low voltage DC (LVDC) microgrid hardware model under various fault conditions. The scheme is implemented using TMS320F28069 digital signal processors (DSP) of Texas Instruments. The hardware results are validated using MATLAB simulation. The proposed method performance is also compared with the existing methods used for DC microgrid protection. The outcome shows that the proposed method has a high accuracy of 98.72%, selectivity of 96.08%, and reliability of 99.01%. The execution time required by the proposed method is also less.

Comparison of flow dynamics and reactor performance in gas-solids circulating turbulent fluidized bed with other fluidized beds across regimes

The hydrodynamics and the reactor performance in the recently proposed gas-solids circulating turbulent fluidized bed (CTFB) were compared with that in the low-velocity conventional fluidized beds and the high-density circulating fluidized bed (HDCFB) riser. The average solids holdup distributions, fluctuations, and probability density distributions of the instantaneous solids holdup signals indicate that the CTFB either more resembles a TFB if the Gs/Ug ratio is large or more resembles an HDCFB if the Gs/Ug ratio is small. The distributions of the bubble occurrence, equivalent bubble size, and bubble phase fraction were extracted from the optical fiber signals of the local solids holdup using the wavelet analysis method. The CTFB was found to have a greater number of smaller bubbles than the bubbling and turbulent fluidized beds and relatively fewer and larger bubbles than that in an HDCFB. The effect of flow dynamics and bubble behaviors on the reaction performance in different types of fluidized beds are compared by analytical calculation based on the K-L reactor model. A superior reactor performance can be achieved using the CTFB reactor for a higher effective reaction rate, higher mass transfer rates, and a lower degree of solids backmixing.

Application of wavelet analysis methods to study the climate of the Arctic region

This paper provides an assessment of the possibilities and advantages of using wavelet analysis methods in the study of regional mechanisms of climate formation in the Arctic region by analyzing the relationship between the values of the air temperature anomalies at the Earth's surface and the North Atlantic Oscillation index. It is shown that the methods of wavelet analysis of climatic indices have certain advantages for identifying cyclical changes in climatic characteristics on local time scales and allow an in-depth spectral assessment. The conducted wavelet analysis of the NAO and SAT index (1950-2020) made it possible to estimate the previously known natural quasi-cyclic oscillations with a typical period of 7-15 years and other dominant components of their spectrum.

Moving structures in ultraviolet bright points: Observations from Solar Orbiter/EUI

Context. Moving structures have been detected in coronal bright points and in a solar flare in active regions that are bidirectional, symmetrical, simultaneous, and quasi-periodic. These could be regarded as observational evidence of plasma outflows via magnetic reconnection. Aims. We explored pairs of moving structures in fifteen ultraviolet bright points (UBPs), which were observed in the quiet Sun or inside a small active region on 19 November 2020. Methods. The UBPs were measured by the High Resolution (HRI) Telescopes of the Extreme Ultraviolet Imager (EUI) on board the Solar Orbiter (SolO) in two passbands, HRIEUV 174 Å and HRILyα 1216 Å. The pairs of moving structures are identified in time-distance slices along curved slits of UBPs and their quasi-periods are determined from the fast Fourier transform and wavelet analysis methods. Results. Moving structures observed in ten UBPs, starting from their bright cores and propagating toward two ends, are interpreted as diverging motions of bidirectional moving structures. These moving structures are also characterized by simultaneity and symmetry and in the case of seven UBPs, they exhibit quasi-periodicity. Moving structures seen in another five UBPs, originating from double ends, moving closer, and merging together are manifested as converging motions. A sympathetic UBP induced by the primary UBP is observed at the edge of a small active region and their moving structures also show the converging motion. Conclusions. The diverging motions of bidirectional moving structures could be generated by outflows after magnetic reconnections. The converging motions of two moving structures might be caused by inflows through the magnetic reconnection or could also be interpreted as upflows driven by the chromospheric evaporation.

Co-Movement between Carbon Prices and Energy Prices in Time and Frequency Domains: A Wavelet-Based Analysis for Beijing Carbon Emission Trading System.

This study aims to investigate the co-movement and lead–lag relationship between carbon prices and energy prices in the time–frequency domain in the carbon emission trading system (ETS) of Beijing. Based on wavelet analysis method, this study examines the weekly data on oil and natural gas prices and carbon prices in Beijing ETS from its establishment in November 2013 to April 2019. Empirical results show the following important findings: (1) Carbon and natural gas prices are mainly negatively correlated, with natural gas prices occupying a leading position in the 12–20 weeks frequency band, indicating that the increase (decrease) of natural gas price will lead to the decrease (increase) of carbon price; (2) carbon and oil prices show an unstable dependence relationship, and their leadership position in the market constantly changes. The partial wavelet coherency and partial phase differences vary greatly in different time–frequency domains, indicating that there is no stable coherency between oil prices and carbon prices. The estimation results prove the existence of coherency between the carbon and energy prices in the Beijing ETS. The research findings of this paper provide quantifiable references for investors to achieve risk control in asset allocation and investment portfolio in the ETS market.

Application of Methods of Wavelet and Recurrent Analysis to Studies of Bioelectrical Activity of the Brain in Moderate Cognitive Impairment

Application of Methods of Wavelet and Recurrent Analysis to Studies of Bioelectrical Activity of the Brain in Moderate Cognitive Impairment

Hilbert spectrum analysis method of blast vibration signal based on HHT instantaneous phase optimization

Compared with wavelet analysis method of signal data, HHT (Hilbert-Huang Transform) method is widely used for the local signal data analysis in the time–frequency domain associated with adaptable capacity, which has been recognized as one of the most efficient method to address the non-stationary signals. In this study, Matlab software was used as the calculation code and field millisecond delay blasting data was collected as the research objects, the Hilbert time–frequency spectrum, marginal spectrum, instantaneous energy spectrum and Hilbert energy spectrum scatter were obtained for the signal IMF components under the standard instfreq.m function (as shown in Section 3.3) frequency modulation with the identified bandwidth of 1 Hz. The reasons of negative frequency for digital signals were discussed based on the theoretical analysis and the digitial signals were then summarized as two types of negative frequency according to the instantaneous phase characteristics. The field collected blasting vibration signals were used to illustrate the features of the negative frequency points such as large number, correlated relation between the signal amplitude extent and frequency band, and non-negligible of the total vibration energy. In addition, a theoretical equation was proposed for the instantaneous phase optimization function based on the HHT method, and a triple identification approach of millisecond time was proposed based on instantaneous phase optimization method. The process of Hilbert spectrum analysis based on HHT instantaneous phase optimization was implemented using Matlab, which was used to analyze Hilbert spectrum characteristics. Further, a theoretical comparison and analysis between the standard instfreq.m time–frequency modulation and the proposed phase optimized HHT spectrum were performed, and the results shows that the optimized HHT spectrum analysis improves the accuracy of time–frequency spectrum and Hilbert spectrum calculation, besides, the frequency domain recognition width is about 2∙r times of that from the standard solution. Finally, the phase optimization was used to analyze the amplitude spectrum, instantaneous velocity energy spectrum and 3D time–frequency seismic velocity energy spectrum features, application of HHT-based instantaneous phase optimization method is given for the accurate recognition application of multi-segment detonation millisecond time in underground blasting.

Research on Fitness Movement Monitoring System Based on Internet of Things.

A multiuser motion-monitoring system based on MEMS is proposed for fitness movement, it is used to monitor the three important parameters of movement type, movement times, and movement cycle in the body movement and supports the simultaneous use of multiple users. The specific content of the method: (1) In terms of system design, a motion-monitoring system framework based on the Internet of things is proposed considering the motion-monitoring scene oriented to intelligent fitness. (2) In the aspect of algorithm, the relevant research of motion pattern recognition and cycle calculation method is carried out. For action pattern recognition, SVM-based algorithm to adapt to different computing capabilities of the scene is applied. (3) Experiments on 7 kinds of actions show that the proposed deep neural network has a good learning effect on small datasets, the recognition accuracy of the proposed deep neural network reaches 97.61%, and the recognition accuracy of SVM also reaches over 96%. In the 50 times of operation cycle calculation experiments, the frequency statistics algorithm has reached 100% of the calculation accuracy, and the calculation results of the operation cycle are close to the real value, which proves the validity of the method of cycle calculation. The experiment proves that the zero-crossing detection and wavelet analysis methods have a good overall effect and can accurately count and calculate the period when the number of actions is more, improve fitness efficiency, and provide guarantee for human health.

Prominence oscillations activated by an EUV wave

Prominence oscillations are one of interesting phenomena in the solar atmosphere, which can be utilized to infer the embedded magnetic field magnitude. We present here the transverse oscillations of two different prominences located at the East solar limb on 2011 February 11 using the multi-wavebands data of the Atmospheric Imaging Assembly (AIA) on-board the Solar Dynamics Observatory (SDO) satellite. A prominence eruption was observed towards the east direction with an average speed of ≈ 275 km s−1. The eruption is fitted with the combination of a linear and an exponential functions of time. An extreme ultraviolet (EUV) wave event was associated with the prominence eruption. This EUV wave triggered the oscillations of both prominences on the East limb. We computed the period of each prominence using the wavelet analysis method. The oscillation period varies from 14 to 22 min. The magnetic field of the prominences was derived, which ranges from 14 to 20 G.

Using wavelet analysis to characterize the transition from bubbling to turbulent fluidization

Turbulent fluidized bed (TFB) regime has demonstrated many advantages such as a combination of enhanced gas-solids contact and increased gas throughput, making it an optimal reactor in many industrial chemical and metallurgical processes. While the transition from bubbling to turbulent bed has been extensively investigated, some details still remain unclear such as the local transition velocity and the corresponding flow characteristics during the transition process. In this work, experiments were conducted in a conventional fluidized column with 0.267 m in diameter and 2.46 m in height and solids holdup was measured using an optical fiber probe in a superficial velocity range of 0.1–1.0 m/s. A discrete wavelet analysis method was proposed to extract quantitative information of bubbles/voids including the count and the lasting time. The local transition details and the onset velocity to the TFB regime in the fluidized bed were more clearly identified based on the extracted bubble/void information.

Application of wavelet transform in evaluating the Kaiser effect of rocks in acoustic emission test

The Kaiser Effect is amongst the phenomena that can be detected through the so-called acoustic emission method. This effect can be used to evaluate in-situ stress of the rock. Identification of the point at which the Kaiser Effect is applied is problematic in particular cases where the acoustic parameters increase gradually, and this is where signal analysis methods come into play. In this research, Brazilian test was performed on Phyllite samples and acoustic data was recorded simultaneously. Following an energy-based and count-based parametric approach, the occurrence time and the Kaiser Effect stress were obtained for the test specimens. Then, wavelet transform method was used to estimate the occurrence time of the Kaiser Effect. Continuous wavelet transform analysis was undertaken to analyze the signals. The signal analysis was performed by considering the peak frequency as desired parameter and db5 wavelet as the mother wavelet. In continuous wavelet analysis method, the results were classified into five cluster using the K-means clustering algorithm. Considering the mechanism of the Kaiser Effect, the fifth category was considered for further investigations. The results showed that the Phyllite specimens exhibited good capabilities for recovering the stress memory and evaluating the Kaiser Effect-driven stress, the results further showed a good agreement between the occurrence times obtained from the parametric method and those resulted from continuous wavelet transform technique, so that the corresponding differences in the Kaiser Effect-driven stress are negligible. The level of Kaiser Effect-driven stress obtained from the proposed method were acceptably in agreement with the preloading stress levels.