IDENTIFICATION OF TIME-VARYING MODAL PARAMETERS USING LINEAR TIME-FREQUENCY REPRESENTATION

A new method of parameter identification based on linear time-frequencyrepresentation and Hubert transform is proposed to identity modal parameters of linear time-varyingsystems from measured vibration responses. Using Gabor expansion and synthesis theory, measuredresponses are represented in the time-frequency domain and modal components are reconstructed bytime-frequency filtering. The Hilbert transform is applied to obtain time histories of the amplitudeand phase angle of each modal component, from which time-varying frequencies and damping ratios areidentified. The proposed method has been demonstrated with a numerical example in which a lineartime-varying system of two degrees of freedom is used to validate the identification scheme based ontime-frequency representation. Simulation results have indicated that time-frequency representationpresents an effective tool for modal parameter identification of time-varying systems.

A Survey of Output Feedback Robust MPC for Linear Parameter Varying Systems

For constrained linear parameter varying(LPV)systems,this survey comprehensively reviews the literatures on output feedback robust model predictive control(OFRMPC)over the past two decades from the aspects on motivations,main contributions,and the related techniques.According to the types of state observer systems and scheduling parameters of LPV systems,different kinds of OFRMPC approaches are summarized and compared.The extensions of OFRMPC for LPV systems to other related uncertain systems are also investigated.The methods of dealing with system uncertainties and constraints in different kinds of OFRMPC optimizations are given.Key issues on OFRMPC optimizations for LPV systems are discussed.Furthermore,the future research directions on OFRMPC for LPV systems are suggested.

TECHNICAL STABILITY OF NONLINEAR TIME-VARYING SYSTEMS WITH SMALL PARAMETERS

Technical stability:allowing quantitative estimation of trajectory behavior of a dynamical system over a given time interval was considered. Based on a differential comparison principle and a basic monotonicity condition, technical stability relative to certain prescribed state constraint sets of a class of nonlinear time-varying systems with small parameters was analyzed by means of vector Liapunov function method. Explicit criteria of technical stability are established in terms of coefficients of the system under consideration. Conditions under which the technical stability of the system can be derived from its reduced linear time-varying (LTV) system were further examined, as well as a condition for linearization approach to technical stability of general nonlinear systems. Also, a simple algebraic condition of exponential asymptotic stability of LTV systems is presented. Two illustrative examples are given to demonstrate the availability of the presently proposed method.

Support vector classification algorithm based on variable parameter linear programming

To solve the problems of SVM in dealing with large sample size and asymmetric distributed samples, a support vector classification algorithm based on variable parameter linear programming is proposed. In the proposed algorithm, linear programming is employed to solve the optimization problem of classification to decrease the computation time and to reduce its complexity when compared with the original model. The adjusted punishment parameter greatly reduced the classification error resulting from asymmetric distributed samples and the detailed procedure of the proposed algorithm is given. An experiment is conducted to verify whether the proposed algorithm is suitable for asymmetric distributed samples.

Probabilistic Robust Linear Parameter-varying Control of a Small Helicopter Using Iterative Scenario Approach

In this paper, we present an iterative scenario approach (ISA) to design robust controllers for complex linear parameter-varying (LPV) systems with uncertainties. The robust controller synthesis problem is transformed to a scenario design problem, with the scenarios generated by identically extracting random samples on both uncertainty parameters and scheduling parameters. An iterative scheme based on the maximum volume ellipsoid cutting-plane method is used to solve the problem. Heuristic logic based on relevance ratio ranking is used to prune the redundant constraints, and thus, to improve the numerical stability of the algorithm. And further, a batching technique is presented to remarkably enhance the computational efficiency. The proposed method is applied to design an output-feedback controller for a small helicopter. Multiple uncertain physical parameters are considered, and simulation studies show that the closed-loop performance is quite good in both aspects of model tracking and dynamic decoupling. For robust LPV control problems, the proposed method is more computationally efficient than the popular stochastic ellipsoid methods. © 2014 Chinese Association of Automation.

Linear numerical calculation method for obtaining critical point,pore fluid,and framework parameters of gas-bearing media

Up to now, the primary method for studying critical porosity and porous media are experimental measurements and data analysis. There are few references on how to numerically calculate porosity at the critical point, pore fluid-related parameters, or framework-related parameters. So in this article, we provide a method for calculating these elastic parameters and use this method to analyze gas-bearing samples. We first derive three linear equations for numerical calculations. They are the equation of density p versus porosity Ф, density times the square of compressional wave velocity p Vp^2 versus porosity, and density times the square of shear wave velocity pVs^2 versus porosity. Here porosity is viewed as an independent variable and the other parameters are dependent variables. We elaborate on the calculation steps and provide some notes. Then we use our method to analyze gas-bearing sandstone samples. In the calculations, density and P- and S-velocities are input data and we calculate eleven relative parameters for porous fluid, framework, and critical point. In the end, by comparing our results with experiment measurements, we prove the viability of the method.

Selection of the Linear Regression Model According to the Parameter Estimation

In this paper, based on the theory of parameter estimation, we give a selection method and, in a sense of a good character of the parameter estimation, we think that it is very reasonable. Moreover, we offer a calculation method of selection statistic and an applied example.

Estimation of intra-operator variability in perfusion parameter measurements using DCE-US

AIM:To investigate intra-operator variability of semiquantitative perfusion parameters using dynamic contrast-enhanced ultrasonography(DCE-US),following bolus injections of SonoVue.METHODS:The in vitro experiments were conducted using three in-house sets up based on pumping a fluid through a phantom placed in a water tank.In the in vivo experiments,B16F10 melanoma cells were xenografted to five nude mice.Both in vitro and in vivo,images were acquired following bolus injections of the ultrasound contrast agent SonoVue(Bracco,Milan,Italy) and using a Toshiba Aplio ultrasound scanner connected to a 2.9-5.8 MHz linear transducer(PZT,PLT 604AT probe)(Toshiba,Japan) allowing harmonic imaging("Vascular Recognition Imaging") involving linear raw data.A mathematical model based on the dye-dilution theory was developed by the Gustave Roussy Institute,Villejuif,France and used to evaluate seven perfusion parameters from time-intensity curves.Intra-operator variability analyses were based on determining perfusion parameter coefficients of variation(CV).RESULTS:In vitro,different volumes of SonoVue were tested with the three phantoms:intra-operator variability was found to range from 2.33% to 23.72%.In vivo,experiments were performed on tumor tissues and perfusion parameters exhibited values ranging from 1.48% to 29.97%.In addition,the area under the curve(AUC) and the area under the wash-out(AUWO) were two of the parameters of great interest since throughout in vitro and in vivo experiments their variability was lower than 15.79%.CONCLUSION:AUC and AUWO appear to be the most reliable parameters for assessing tumor perfusion using DCE-US as they exhibited the lowest CV values.

RIEMANN-HILBERT CHARACTERIZATION FOR MAIN BESSEL POLYNOMIALS WITH VARYING LARGE NEGATIVE PARAMETERS

In this article, we establish the Bessel polynomials with varying large negative parameters and discuss their orthogonality based on the generalized Bessel polynomials. By using the Riemann-Hilbert boundary value problem on the positive real axis, we get the Riemann-Hilbert characterization of the main Bessel polynomials with varying large negative parameters.

Geometric interpretation of several classical iterative methods for linear system of equations and diverse relaxation parameter of the SOR method

Two kinds of iterative methods are designed to solve the linear system of equations, we obtain a new interpretation in terms of a geometric concept. Therefore, we have a better insight into the essence of the iterative methods and provide a reference for further study and design. Finally, a new iterative method is designed named as the diverse relaxation parameter of the SOR method which, in particular, demonstrates the geometric characteristics. Many examples prove that the method is quite effective.

Prediction method of physical parameters based on linearized rock physics inversion

A linearized rock physics inversion method is proposed to deal with two important issues, rock physical model and inversion algorithm, which restrict the accuracy of rock physics inversion. In this method, first, the complex rock physics model is expanded into Taylor series to get the first-order approximate expression of the inverse problem of rock physics;then the damped least square method is used to solve the linearized rock physics inverse problem directly to get the analytical solution of the rock physics inverse problem. This method does not need global optimization or random sampling, but directly calculates the inverse operation, with high computational efficiency. The theoretical model analysis shows that the linearized rock physical model can be used to approximate the complex rock physics model. The application of actual logging data and seismic data shows that the linearized rock physics inversion method can obtain accurate physical parameters. This method is suitable for linear or slightly non-linear rock physics model, but may not be suitable for highly non-linear rock physics model.

Reliability of linear coupling synchronization of hyperchaotic systems with unknown parameters

Complete synchronization could be reached between some chaotic and/or hyperchaotic systems under linear coupling. More generally, the conditional Lyapunov exponents are often calculated to confirm the stability of synchronization and reliability of linear controllers. In this paper, detailed proof and measurement of the reliability of linear controllers are given by constructing a Lyapunov function in the exponential form. It is confirmed that two hyperchaotic systems can reach complete synchronization when two linear controllers are imposed on the driven system unidirectionally and the unknown parameters in the driving systems are estimated completely. Finally, it gives the general guidance to reach complete synchronization under linear coupling for other chaotic and hyperchaotic systems with unknown parameters.

A Piecewise Linear Isotropic-Kinematic Hardening Model with Semi-Implicit Rules for Cyclic Loading and Its Parameter Identification

A simple constitutive model,called semi-implicit model,for cyclic loading is proposed for steel materials used for structures such as building frames in civil engineering.The constitutive model is implemented in the E-Simulator,which is a software package for large-scale seismic response analysis.The constitutive relation is defined in an algorithmic manner based on the piecewise linear combined isotropic-kinematic hardening.Different rules are used for the first and subsequent loading states to incorporate characteristics such as yield plateau and Bauschinger effect of rolled mild steel materials.An optimization method is also presented for parameter identification from the results of cyclic and monotonic loading tests.Therefore,the proposed model is readily applicable to practical elastoplastic analysis of building frames.Accuracy of the model is demonstrated in an example of a cantilever subjected to various types of cyclic loading.

Robust model-reference control for descriptor linear systems subject to parameter uncertainties

Robust model-reference control for descriptor linear systems with structural parameter uncertainties is investigated. A sufficient condition for existing a model-reference zero-error asymptotic tracking controller is given. It is shown that the robust model reference control problem can be decomposed into two subproblems： a robust state feedback stabilization problem for descriptor systems subject to parameter uncertainties and a robust compensation problem. The latter aims to find three coefficient matrices which satisfy four matrix equations and simultaneously minimize the effect of the uncertainties to the tracking error. Based on a complete parametric solution to a class of generalized Sylvester matrix equations, the robust compensation problem is converted into a minimization problem with quadratic cost and linear constraints. A numerical example shows the effect of the proposed approach.

Model Parameter Transfer for Gear Fault Diagnosis under Varying Working Conditions

Gear fault diagnosis technologies have received rapid development and been effectively implemented in many engineering applications.However,the various working conditions would degrade the diagnostic performance and make gear fault diagnosis(GFD)more and more challenging.In this paper,a novel model parameter transfer(NMPT)is proposed to boost the performance of GFD under varying working conditions.Based on the previous transfer strategy that controls empirical risk of source domain,this method further integrates the superiorities of multi-task learning with the idea of transfer learning(TL)to acquire transferable knowledge by minimizing the discrepancies of separating hyperplanes between one specific working condition(target domain)and another(source domain),and then transferring both commonality and specialty parameters over tasks to make use of source domain samples to assist target GFD task when sufficient labeled samples from target domain are unavailable.For NMPT implementation,insufficient target domain features and abundant source domain features with supervised information are fed into NMPT model to train a robust classifier for target GFD task.Related experiments prove that NMPT is expected to be a valuable technology to boost practical GFD performance under various working conditions.The proposed methods provides a transfer learning-based framework to handle the problem of insufficient training samples in target task caused by variable operation conditions.

Linear Support Vector Machine Based Inter-harmonic Modeling and Parameter Estimation

There are often system. The high measure many inter-harmonics in power t accuracy of inter-harmonics order, amplitude and initial phase is needed. A new approach is presented for inter-harmonic modeling and parameter estimation based on linear support vector machine （SVM）. Firstly, parameter estimation of linear model is realized based on standard linear SVM. Then, interharmonic model is transformed to a linear model according to trigonometric functions. The approach obtains order of inter-harmonic model with windowed Blackman-Tukey （BT） spectrum analysis, and gets number and frequency of harmonics. Finally, the linear SVM is applied to estimate the inter-harmonic parameters, amplitude and initial phase. The simulation results show that the proposed approach has high precision and good antinoise. The accuracy of three parameters are all higher than 98%.

Resonance analysis of single DOF parameter-varying system of magnetic-liquid double suspension bearing

Magnetic-liquid double suspension bearing(MLDSB)is mainly supported by electromagnetic suspension and supplemented by hydrostatic supporting.Its bearing capacity and stiffness can be greatly improved,and then it is suitable for the occasions of medium speed,heavy load.When the bearing system is excited by periodic force,the flow q and current i regulated by the double-closed-loop control mechanism change periodically.Then the risk of parametric resonance in MLDSB is greatly aggravated by the change of the parameter system,which seriously affects its operation stabil-ity and reliability.Therefore,this paper intends to study the resonance characteristics of the parame-ter system of MLDSB.Firstly,Marshall-Duffing equation of the parametric system is established by taking the flow q and the current i as variables respectively.Then,by using the asymptotic method,the occurrence condition and variation rule of the principal,1/2 Harmonic and 1/3 Harmonic para-metric resonance are solved.The results show that only the 1/2 Harmonic resonance of the flow q parameter varying system occurs accompanied by the resonance condition of high frequency.The principal,1/2 Harmonic and 1/3 Harmonic parametric resonance of the current i occur accompanied by the resonance condition of high frequency.And the 1/2 Harmonic resonance of the current i oc-curs accompanied by the non-single value bifurcation and dynamic bifurcation.The paper can pro-vide theoretical reference for the parameter design and stable operation of MLDSB.

Identification of Modal Parameters with Linear Structure under Non-stationary Ambient Excitation

Empirical mode decomposition (EMD) is proposed to identify linear structure under non-stationary excitation,and non-white noise coefficient is introduced under the assumption of random signals consisting of white noise and non-white noise signals. The cross-correlation function of response signal is decomposed into mode functions and residue by EMD method. The identification technique of the modal parameters of single freedom degree is applied to each mode function to obtain natural frequencies, damping ratios and mode shapes. The results of identification of the five-degree freedom linear system demonstrate that the proposed method is effective in identifying the parameters of linear structures under non-stationary ambient excitation.

A New Discrete-time Adaptive ILC for Nonlinear Systems with Time-varying Parametric Uncertainties

用在分离时间轴和反复的学习轴之间的类比，一条新分离时间的适应反复的学习控制(AILC ) 途径被开发与变化时间的参量的无常探讨非线性的系统的一个班。类似于适应控制，新 AILC 能合并一个设计算法，因此，学习获得能沿着学习的轴反复地被调节。当起始的状态是随机的，参考轨道是变化重复的时，新 AILC 能沿着反复的学习轴 asymptotically 在有限时间间隔上完成 pointwise 集中。

Locally Varying Hubble Parameter in Terms of Reduced Friedmann Equation

Assessment of the Hubble parameter as an indicator of the expansion rate of the universe holds a central position in the field of astronomy. From its initial estimate of about 500 km⋅sec-1⋅parsc-1, this value had been steadily amended as the observational tools became more accurate and precise. Despite this, a gap remains between the value of observations relating to local and nonlocal estimations of the Hubble parameter that gave rise to what became known as the Hubble tension. This tension is addressed here while dealing with space fabric as a cosmological fluid that undergoes transition.