Frequency weighting filter design for automotive ride comfort evaluation

Few study gives guidance to design weighting filters according to the frequency weighting factors,and the additional evaluation method of automotive ride comfort is not made good use of in some countries.Based on the regularities of the weighting factors,a method is proposed and the vertical and horizontal weighting filters are developed.The whole frequency range is divided several times into two parts with respective regularity.For each division,a parallel filter constituted by a low-and a high-pass filter with the same cutoff frequency and the quality factor is utilized to achieve section factors.The cascading of these parallel filters obtains entire factors.These filters own a high order.But,low order filters are preferred in some applications.The bilinear transformation method and the least P-norm optimal infinite impulse response(IIR) filter design method are employed to develop low order filters to approximate the weightings in the standard.In addition,with the window method,the linear phase finite impulse response(FIR) filter is designed to keep the signal from distorting and to obtain the staircase weighting.For the same case,the traditional method produces 0.330 7 m · s~(–2) weighted root mean square(r.m.s.) acceleration and the filtering method gives 0.311 9 m · s~(–2) r.m.s.The fourth order filter for approximation of vertical weighting obtains 0.313 9 m · s~(–2) r.m.s.Crest factors of the acceleration signal weighted by the weighting filter and the fourth order filter are 3.002 7 and 3.011 1,respectively.This paper proposes several methods to design frequency weighting filters for automotive ride comfort evaluation,and these developed weighting filters are effective.     

数字带阻滤波器的设计及畸变矫正

在研究数字信号处理的过程中,可以采用数字带阻滤波器方法来进行消除交流电产生的工频干扰,但是传统的数字滤波器在起始位置存在畸变。利用双线性变换的方法将模拟带阻滤波器转换为数字带阻滤波器,借助离散时间系统中Z变换后的系统函数设计相应的数字带阻滤波器,以此来去除被测信号中的干扰信号,并通过两种简单算法来消除数字滤波器在起始位置的畸变。对一仿真混合正弦信号进行测试,试验表明,该滤波器可以很好地消除50 Hz的工频干扰,且该方法具有设计简单、应用方便、实用性强等特点。     

体外CFRP筋预应力混凝土箱梁长期受力性能试验研究

碳纤维(CFRP)筋具有优异的物理力学性能,可用于替代传统的预应力钢筋。制作了体外配置碳纤维筋预应力混凝土箱梁模型,对持续均布荷载作用箱梁的截面应力重分布、长期挠曲变形及裂缝发展等规律进行了1001 d的试验观测。基于素混凝土柱体的实测徐变系数,运用双线性法和曲率法分别对试验箱梁的长期挠曲变形进行预测。试验结果表明:受压钢筋应变较初值增长225%～268%,受拉钢筋的应变较初值增长36.2～38.6%,混凝土表面压应变较初值增长164%～224%。按现行设计规范计算长期荷载作用特征裂缝宽度较实测值偏小11.8%～55.5%。跨中长期挠曲变形实测值为初始变形的2.32～2.42倍,较现行设计规范取值偏大18.5%。     

Lyapunov equation for infinite-dimensional discrete bilinear systems

Mean-square stability for discrete systems requires that uniform convergence is preserved between input and state correlation sequences. Such a convergence preserving property holds for an infinite-dimensional bilinear system if and only if the associate Lyapunov equation has a unique strictly positive solution.     

Bilinear optimal control of a Kirchhoff plate

We consider the problem of optimal control of a Kirchhoff plate. A bilinear control is used as a force to make the plate close to a desired profile taking into the account, a quadratic cost of control. We prove the existence of an optimal control and characterize it uniquely through the solution of an optimality system.     

Identification environment and robust forecasting for nonlinear time series

In this paper, the methods of time series for nonlinearity are briefly surveyed, with particular attention paid to a new test design based on a neural network specification. The proposed integrated expert system contains two main components: an identification environment and a robust forecasting design. The identification environment can be viewed as a integrated dynamic design in which cognitive capabilities arise as a direct consequence of their self-organizational properties. The integrated framework used for discussing the similarities and differences in the nonlinear time series behavior is presented. Moreover, its performance in prediction proves to be superior than the former work. For the investigation of robust forecasting, we perform a simulation study to demonstrate the applicability and the forecasting performance.     

RANDOM COEFFICIENT AUTOREGRESSIVE PROCESSES:A MARKOV CHAIN ANALYSIS OF STATIONARITY AND FINITENESS OF MOMENTS

Abstract. Simple yet practically efficient conditions for the ergodicity of a Markov chain on a general state space have recently been developed. We illustrate their application to non-linear time series models and, in particular, to random coefficient autoregressive models.
As well as ensuring the existence of a unique stationary distribution, geometric rates of convergence to stationarity are ensured. Moreover, sufficient conditions for the existence and convergence of moments can be determined by a closely related method. The latter conditions, in particular, are new.     

Observation of bilinear systems with application to biological control

In this paper a system of bilinear equations is derived which under appropriate conditions model the microbial cellgrowth and product formation of various waste treatment and fermentation systems. Given the available measurements, the problem of obtaining an asymptotic estimate of the state of the system is considered. Necessary and sufficient conditions for the observability of a general system of bilinear equations are also derived. As accurate biological sensors do not often exist the results are significant for the on-line control of these biological processes.     

A STUDY OF THE APPLICATION OF STATE-DEPENDENT MODELS IN NON-LINEAR TIME SERIES ANALYSIS

Abstract. The theory of state-dependent models was developed by Priestley (1980), and a few simple applications were given in Priestley (1981). In this paper, an extensive study of the application of state-dependent models to a wide variety of non-linear time series data is carried out. Both real and simulated data are used in the study, and the problems encountered are highlighted. The method is demonstrated to be successful in practice in many cases, and suggestions for the further development of the algorithm are also given.     

A DIAGNOSTIC TEST FOR NONLINEAR SERIAL DEPENDENCE IN TIME SERIES FITTING ERRORS

Abstract. Time series analysts have begun to consider the applicability of nonlinear models. In order for nonlinear models to be accepted by practitioners, practicai tests must be avilable to test for the presence of nonlinearity in both raw time series and in the residuals from fitted models. A diagnostic test, based on the bispectrum, for the presence of nonlinear serial dependence in these time series is investigated here using artificial data. Detection of such nonlinear dependence is taken to indicate that nonlinear modelling methods are necessary. The theory behind the test is reviewed and simulations driven by pseudorandom numbers are presented for a variety of models and sample sizes. The simulations indicate that the test has substantial power for many models. In addition, theoretical and empirical results are presented which show that the bispectral diagnostic test is equally powerful for both the source series and for the fitting errors from a line& model. Thus, while the test is suitable for use as a diagnostic test on the fitting errors of linear time series models, prior linear modeling of the time series is not required.