A technique to improve the empirical mode decomposition in the Hilbert-Huang transform

The Hilbert-based time-frequency analysis has promising capacity to reveal the time-variant behaviors of a sys- tem.To admit well-behaved Hilbert transforms,component decomposition of signals must be performed beforehand.This was first systematically implemented by the empirical mode decomposition(EMD)in the Hilbert-Huang transform,which can provide a time-frequency representation of the signals.The EMD,however,has limitations in distinguishing different components in narrowband signals commonly found in free-decay vibration signals.In this study,a technique for decompo- sing components in narrowband signals based on waves' beating phenomena is proposed to improve the EMD,in which the time scale structure of the signal is unveiled by the Hilbert transform as a result of wave beating,the order of component ex- traction is reversed from that in the EMD and the end effect is confined.The proposed technique is verified by performing the component decomposition of a simulated signal and a free decay signal actually measured in an instrumented bridge structure.In addition,the adaptability of the technique to time-variant dynamic systems is demonstrated with a simulated time-variant MDOF system.

Cavity Ring-Down Spectroscopy Measurements of Trace H_(2)S in SF_(6) and SF_(6)/N_(2) Mixture

H_(2)S is one of the most important characteristic decomposition components of SF_(6)insulated gas,and the detection of trace H_(2)S is significant for early fault diagnosis of gas insulated electrical equipment.A 1578 nm wavelength distributed feedback diode laser(DFB-DL)based cavity ring-down spectroscopy(CRDS)experimental platform is developed to monitor the concentrations of H_(2)S in SF_(6)and SF_(6)/N_(2)mixture carrier gas.The detection sensitivity is higher than 1×10^(-6).The absorption cross section parameterσis vital for calculating the concentration.With repeated experiments using standard gas samples,parameterσof H_(2)S in pure SF_(6)and SF_(6)/N_(2)mixture carrier with different mixing ratios is calibrated.Compared with the simulatedσvalues,the influence of carrier gas on the broadening of spectral profile is discussed.The variation of absorption cross sectionσwith different carrier gas mixing ratios is studied as well,so that the calculation of the concentration in the carrier gas of any mixing ratio is possible.Thus,the application of CRDS in trace component detection of gas insulated electrical equipment is promising.

Parametric identification of time-varying systems from free vibration using intrinsic chirp component decomposition

Time-varying systems are applied extensively in practical applications,and their related parameter identification techniques are of great significance for structural health monitoring of time-varying systems.To improve the identification accuracy for time-varying systems,this study puts forward a novel parameter identification approach in the time-frequency domain using intrinsic chirp component decomposition(ICCD).ICCD is a powerful tool for signal decomposition and parameter extraction,with good signal reconstruction capability in a high-noise environment.To maintain good identification effects for the time-varying system in a noisy environment,the proposed method introduces a redundant Fourier model for the non-stationary signal,including instantaneous frequency(IF)and instantaneous amplitude(IA).The accuracy and effectiveness of the proposed approach are demonstrated by a single-degree-of-freedom system with three types of time-varying parameters,as well as an example of a multi-degree-of-freedom system.The effects of different levels of measured noise on the identified results are also discussed in detail.Numerical results show that the proposed method is very effective in tracking the smooth,periodical,and non-smooth variations of time-varying systems over the entire identification time period even when the response signal is contaminated by intense noise.

Image-based Virtual Exhibit and Its Extension to 3D

In this paper we introduce an image-based virtual exhibition system especially for clothing product. It can provide a powerful material substitution function, which is very useful for customization clothing-built. A novel color substitution algorithm and two texture morphing methods are designed to ensure realistic substitution result. To extend it to 3D, we need to do the model reconstruction based on photos. Thus we present an improved method for modeling human body. It deforms a generic model with shape details extracted from pictures to generate a new model. Our method begins with model image generation followed by silhouette extraction and segmentation. Then it builds a mapping between pixels inside every pair of silhouette segments in the model image and in the picture. Our mapping algorithm is based on a slice space representation that conforms to the natural features of human body.

Longitude correction method for the field magnetic surveyed diurnal-variation correction

Solar quiet daily variation(Sq)are dependent on local time.Herein,we applied the moving superposition method to separate the Sq component of correction observatory data and performed a time diff erence correction on the Sq component according to the longitudinal diff erence between the correction observatory and the field station while maintaining the time of other data components.The data were then reconstructed and used for diurnal-variation correction to improve the accuracy of the daily variations correction resu;lts The moving superposition method employs data of“nonmagnetic disturbance days”obtained 15 d before and after to perform the superposing average calculation on a daily basis,aiming to obtain the Sq of continuous morphological changes.The effect of longitude correction was tested using the observatory record and field survey data.The average correction distance of the test observatories was 2114 km,and the correction accuracies of the H(horizontal component of geomagnetic field),D(geomagnetic declination),and Z(vertical component of geomagnetic field)were improved by 28.4%,45.0%,and 21.7%,respectively;the average correction distance of the field stations was 2130 km,and the correction accuracies of the F(geomagnetic total intensity),D,I(geomagnetic inclination)components were improved by 35.2%,26.7%,and 13.9%,respectively.The test results also demonstrated that the longitude correction eff ect was greater with an increased correction distance.

Sub-band ICA with selection criterion for BBS of dependent mages

Because of the correlation of images,the efficiency of the standard ICA is not satisfied in the blind source separation (BSS) of image.Therefore,a new method of sub-band ICA with selection criterion is proposed for this problem.Firstly,the sub-bands of the new method are made up of the wavelet packets (WP) coefficients.Secondly,the selection criterion of the new method is a combination of the mutual information (MI),kurtosis and sparsity.One sub-band or a sub-bands group obtained from the new method are more suitable as the inputs parameters of the algorithm of ICA than mixed images.The new method has been applied into the BSS of partially dependent images and highly dependent images successfully.According to the separation experiments,it is shown that the separation efficacy of the new method is more accurate and robust.

Trends in per capita ecological footprint and biocapacity in China 1949-2013

This paper proposes two concepts: the ecological footprint component index(EFCI) and the biocapacity component index(BCCI), based on the ecological footprint(EF) and Shannon entropy approaches. Per capita EFCI and BCCI in China 1949-2013 are analyzed using empirical mode decomposition(EMD). Nonlinear models of per capita EFCI and BCCI in China 1949-2013 are presented and their cycles and predictions from 2014 to 2023 are analyzed. The results over the last 65 years show:(1) EFCI in China has increased constantly with fluctuations, while BCCI has slowly decreased. Their annual change rates are 2.81% and-1.26%, respectively. The increasing EFCI indicates a gradual improvement in China's sustainable development potential; the decreasing BCCI indicates severe environmental and population challenges.(2) The cycles of per capita EFCI have periods of 5.4 and 16.3 years, while cycles of per capita BCCI have periods of 3.6, 13,and 21.7 years. The predictive models indicate that EFCI will first decrease, reaching 0.02725 in2014, and will subsequently increase to 0.03261 in 2021. BCCI will increase, reaching 0.01365 in2014 and 0.01541 in 2022. EFCI and BCCI will reach 0.03037 and 0.01537, respectively, in 2023.Policymakers should ensure that the EFCI and BCCI increase in 2023.

Decomposition Analysis of Poverty Reduction in Rural China:2007-2018

Using the last three waves of the rural household surveys conducted by the Chinese Household Income Project in 2007,2013,and 2018,this paper focuses on changes in poverty in rural China.The paper decomposes poverty change into the growth effect and the inequality effect,and also decomposes the contributions of income components,concentrating particularly on income from public transfers.Economic growth had a very significant poverty reduction effect for both absolute and relative poverty,but the inequality effect mostly offset it;in total,absolute poverty reduced significantly,and relative poverty increased from 2007 to 2018.Local wage income became the main contributor to both absolute and relative poverty reduction,replacing household agricultural operational income,and the contribution of wage income from migration declined.Public transfers effectively reduced absolute poverty but not relative poverty.

Intraday Volume Percentages Forecasting Using a Dynamic SVM-Based Approach

This paper proposes a dynamic model to forecast intraday volume percentages by decomposing the trade volume into two parts: The average part as the intraday volume pattern and the residual term as the abnormal changes. An empirical test on data spanning half-a-year gold futures and S&P 500 futures reveals that a rolling average of the previous days' volume percentages shows great predictive ability for the average part. An SVM approach with the input pattern consisting of two categories is employed to forecast the residual term. One is the previous days' volume percentages in the same time interval and the other is the most recent volume percentages. The study shows that this dynamic SVM-based forecasting approach outperforms the other commonly used statistical methods and enhances the tracking performance of a VWAP strategy greatly.