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.

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.

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.

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.

Extracting the interference components of normal modes in shallow water waveguide using singular value decomposition method

The normal mode interference characteristic in shallow water waveguide is a valu- able topic in the fields of underwater acoustic. A method for extracting the interference components of normal modes from broadband acoustic propagation data recorded by a single hy- drophone without any prior information is present in this paper. First, a Hermitian matrix is formed by the power spectral density. Second, a singular value decomposition （SVD） is performed on the Hermitian matrix to obtain the orthonormal eigenvectors, which are proportional to the interference components of normal modes. The fundamental equations of the new extracting method are derived based on normal mode and waveguide invariant theory. And the validity of the present method is verified by the numerical simulation and experimental results. In addition, the extracted results of normal-mode interference components are intended to be used for passive ranging of broadband sources.

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.