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The purpose of this paper is to establish the damage alarming indexes for ancient wood structures and study the damage sensitivity and noise robustness of these indexes under random excitation.

Xi’an Bell Tower is taken as a case in this paper to simulate the damage of ancient wood structures through finite element (FE) simulation and determine the satisfactory damage alarming indexes with wavelet packet energy spectrum.

The results of this paper show that: 1) the damage alarming indexes can effectively identify the damage of ancient wood structures, each index with a different damage sensitivity; 2) the energy ratio deviation is greater than the energy ratio variance and is close to the maximum variation of energy ratio; 3) the energy ratio deviation has a better alarming effect than the energy ratio variance during the initial period of the damage. With the accumulation of the damage, the energy ratio variance outperforms the energy ratio deviation; 4) the sensitivity of the energy ratio deviation and variance varies from positions, changing from the highest to lowest at the mortise-and-tenon joints, the beam mid-span and the plinth; 5) if signal to noise ratio (SNR) is 40db or larger, the indexes can accurately identify the damage of ancient wood structures. As SNR increases, the indexes will have an increasingly higher sensitivity and certain ability to resist noise.

The FE model is simpiy, it does not completely reflect Xi’an Bell Tower.

It will provide a theoretical basis for the damage alarming of Xi’an Bell Tower.

It makes structural health monitoring through structural vibration response under ambient excitation a new research field in damage detection as well as a positive way of ancient architecture protection.

This paper studies the damage alarming effect on ancient wood structures from different wavelet functions and wavelet packet decomposition levels. To study the effect under white noise environment, this paper adds Gaussian white noise with a SNR of 10, 20, 30, 40 and 50 db to the acceleration response signal of intact structure and damaged structure.

This study aims to explore the linkage between fluctuations in the global crude oil price and equity market in fast emerging economies of India and China.

The present research uses wavelet decomposition and maximal overlap discrete wavelet transform (MODWT), which decompose the time series into various frequencies of short, medium and long-term nature. The paper further uses continuous and cross wavelet transform to analyze the variance among the variables and wavelet coherence analysis and wavelet-based Granger causality analysis to examine the direction of causality between the variables.

The continuous wavelet transform indicates strong variance in WTIR (return series of West Texas Instrument crude oil price) in short, medium and long run at various time periods. The variance in CNX Nifty is observed in the short and medium run at various time periods. The Chinese stock index, i.e. SCIR, experiences very little variance in short run and significant variance in the long and medium run. The causality between the changes in crude oil price and CNX Nifty is insignificant and there exists a bi-directional causality between global crude oil price fluctuations and the Chinese equity market.

To the best of the authors’ knowledge, very limited work has been done where the researchers have analyzed the linkage between the equity market and crude oil price fluctuations under the framework of discrete wavelet transform, which overlooks the bottleneck of non-stationarity nature of the time series. To bridge this gap, the present research uses wavelet decomposition and MODWT, which decompose the time series into various frequencies of short, medium and long-term nature.

The purpose of this paper is to reduce the destructive effects of existing unavoidable noises contaminating temperature data in inverse heat conduction problems (IHCP) utilizing the wavelets.

For noise reduction, sensor data were treated as input to the filter bank used for signal decomposition and implementation of discrete wavelet transform. This is followed by the application of wavelet denoising algorithm that is applied on the wavelet coefficients of signal components at different resolution levels. Both noisy and de‐noised measurement temperatures are then used as input data to a numerical experiment of IHCP. The inverse problem deals with an estimation of unknown surface heat flux in a 2D slab and is solved by the variable metric method.

Comparison of estimated heat fluxes obtained using denoised data with those using original sensor data indicates that noise reduction by wavelet has a potential to be a powerful tool for improvement of IHCP results.

Noise reduction using wavelets, while it can be implemented very easily, may also significantly relegate (or even eliminate) conventional regularization schemes commonly used in IHCP.

The purpose of this paper is to derive crucial insights from multi-scale analysis to detect equity return co-movements between Korean and emerging Asian markets.

Wavelet correlation, wavelet coherence and wavelet clustering measures are used to uncover Korean equity market interactions which are hard to see using any other modern econometric method and which would otherwise had remained hidden.

The authors observed that Korean equity market is strongly integrated with Asian equity markets at lower frequency scales and has a relatively weak correlation at higher frequencies. Further this correlation eventually grows strong in the interim of crises period at lower frequency scales. The authors, however, do not found any significant deviation in dendrograms generated in data clustering process from wavelet scale 2 to 6 which are associated with four and 64 weeks period, respectively. Overall the findings are relevant and have strong policy and practical implications.

The unique contribution of this paper is that it introduces wavelet clustering analysis to produce a nested hierarchy of similar markets at each frequency level for the first time in finance literature

The purpose of this paper is to present forecasts of fossil fuels prices until 2030 with spectral analysis to provide a clearer picture of this energy sector.

Fossil fuels prices time series are decomposed in simpler signals called approximations and details in the framework of the one-dimensional discrete wavelet analysis. The simplified signals are recomposed after Burg extension.

In 2019-2030 average price forecasts of: West Texas intermediate (WTI) oil ($58.67) is above its 1986-2030 long-term mean of $47.83; and coal ($81.01) is above its 1980-2030 long-term mean of $60.98. On the contrary, 2019-2030 average of price forecasts of: Henry Hub natural gas ($3.66) is below its 1997-2030 long-term mean of $4; heating oil ($0.64) is below its 1986-2030 long-term mean of $1.16; propane ($0.26) is below its 1992-2030 long-term mean of $0.66; and regular gasoline ($1.45) is below its 2003-2030 long-term mean of $1.87.

Fossil fuels prices projections may relieve participants of WTI oil and coal markets but worry participants of Henry Hub, heating oil, propane and regular gasoline markets including countries whose economy is tied to energy prices.

This paper aims to explore a new wavelet adaptive threshold de-noising method to resolve the shortcomings of wavelet hard-threshold method and wavelet soft-threshold method, which are usually used in gear fault diagnosis.

A new threshold function and a new determined method of threshold for each layer are proposed. The principle and the implementation of the algorithm are given. The simulated signal and the measured gear fault signal are analyzed, and the obtained results are compared with those from wavelet soft-threshold method, wavelet hard-threshold method and wavelet modulus maximum method.

The presented wavelet adaptive threshold method overcomes the defects of the traditional wavelet threshold method, and it can effectively eliminate the noise hidden in the gear fault signal at different decomposition scales. It provides more accurate information for the further fault diagnosis.

A new threshold function is adopted and the multi-resolution unbiased risk estimation is used to determine the adaptive threshold, which overcomes the defect of the traditional wavelet method.

Fabric defects detection is vital in the automation of textile industry. The purpose of this paper is to develop and implement a new fabric defects detection method based on adaptive wavelet.

Fabric defects can be regarded as the abrupt features of textile images with uniform background textures. Wavelets have compact support and can represent these textures. When there is an abrupt feature existed, the response is totally different with the response of the background textures, so wavelets can detect these abrupt features. This method designs the appropriate wavelet bases for different fabric images adaptively. The defects can be detected accurately.

The proposed method achieves accurate detection of fabric defects. The experimental results suggest that the approach is effective.

This paper develops an appropriate method to design wavelet filter coefficients for detecting fabric defects, which is called adaptive wavelet. And it is helpful to realize the automation of textile industry.

The purpose of this paper is to identify significant factors of environmental variables and pollutants that have an effect on PM_2.5 through wavelet and regression analysis.

In order to provide stable data set for regression analysis, multiresolution analysis using wavelets is conducted. For the sampled data, multicollinearity among the independent variables is removed by using principal component analysis and multiple linear regression analysis is conducted using PM_2.5 as a dependent variable.

It is found that few pollutants such as NO₂, NO_x, SO₂, benzene and environmental factors such as ambient temperature, solar radiation and wind direction affect PM_2.5. The regression model developed has high R² value of 91.9 percent, and the residues are stationary and not correlated indicating a sound model.

The research provides a framework for extracting stationary data and other important features such as change points in mean and variance, using the sample data for regression analysis. The work needs to be extended across all areas in India and for various other stationary data sets there can be different factors affecting PM_2.5.

Control measures such as control charts can be implemented for significant factors.

Rules and regulations can be made more stringent on the factors.

The originality of this paper lies in the integration of wavelets with regression analysis for air pollution data.

The purpose of this paper is to revisit the dynamic linkages between the US and the national securitized real estate markets of each of the nine Asian-Pacific (APAC) economies in time-frequency domain.

Wavelet decomposition via multi-resolution analysis is employed as an empirical methodology to consider time-scale issue in studying the dynamic changes of the US–APAC cross-real estate interdependence.

The strength and direction of return correlation, return exogeneity, shock impulse response, market connectivity and causality interactions change when specific time-scales are involved. The US market correlates with the APAC markets weakly or moderately in the three investment horizons with increasing strength of lead-lag interdependence in the long-run. Moreover, there are shifts in the net total directional volatility connectivity effects at the five scales among the markets.

Given the focus of the five approaches and associated indicators, the picture that emerges from the empirical results may not completely uniform. However, long-term investors and financial institutions should evaluate the time-scale based dynamics to derive a well-informed portfolio decision.

Future research is needed to ascertain whether the time-frequency findings can be generalizable to the regional and global context. Additional studies are required to identify the factors that contribute to the changes in the global and regional connectivity across the markets over the three investment horizons.

This study has successfully decomposed the various market linkage indicators into scale-dependent sub-components. As such, market integration in the Asia-Pacific real estate markets is a “multi-scale” phenomenon.

The purpose of this paper is to classify different conditions of the rolling element bearing (REB) using vibration signals acquired from a customized bearing test rig.

An effort has been made to develop health index (HI) based on singular values of the statistical features to classify different conditions of the REB. The vibration signals from the normal bearing (N), bearing with defect on ball (B), bearing with defect on inner race (IR) and bearing with defect on outer race (OR) have been acquired from a customized bearing test rig under variable load and speed conditions. These signals were subjected to “modified kurtosis hybrid thresholding rule” (MKHTR)-based denoising. The denoised signals were decomposed using discrete wavelet transform. A total of 17 statistical features have been extracted from the wavelet coefficients of the decomposed signal.

Singular values of the statistical features can be effectively used for REB classification.

REB are critical components of rotary machinery right across the industrial sectors. It is a well-known fact that critical bearing failures causes major breakdowns resulting in untold and most expensive downtimes that should be avoided at all costs. Hence, intelligently based bearing failure diagnosis and prognosis should be an integral part of the asset maintenance and management activity in any industry using rotary machines.

It is found that singular values of the statistical features exhibit a constant value and accordingly can be assigned to each type of bearing fault and can be used for fault characterization in practical applications. The effectiveness of this index has been established by applying this to data from Case Western Reserve University data base which is a standard bench mark data for this application. HIs minimizes the computation time when compared to fault diagnosis using soft computing techniques.