Academic literature on the topic 'Time-Varying Impulse Responses'

Abstract Effective algorithms of constructing models of generalized parametric sections of the n-th order consisting of cascade and parallel elementary LTV section connections are presented in this paper. Moreover, the methods of impulse responses determination have been shown

SummaryAn input shaping technique using a time-varying impulse sequence is presented to reduce the motion-induced vibration of flexible systems in a feedforward way.The decoupled modal responses for a general linear time-varying system are firstly approximated. Upon this approximation, the time-varying impulse sequences to suppress the vibrational modes are found. The reference inputs to the systems are shaped by convolving with the time-varying impulse sequence to suppress the multimode vibrations. This technique can be also applied to suppress the vibration of nonlinear time-varying systems.The performance of this method is demonstrated with two practical examples: a moving overhead crane and a two-link robot manipulator. Consequently, this study provides an input shaping technique applicable to the vibration suppression of broader classes of flexible systems.

Modal identification methods based on the Hilbert Transform are applied to identify modal parameters of time-varying system using impulse response time histories. First, the response data are decomposed into modal responses through the empirical mode decomposition (EMD). Then, the Hilbert transform is applied to each modal response to obtain the instantaneous frequencies. The applications of the proposed methods are illustrated using a 3 degree-of-freedom (DOF) systems with time-varying dynamic characteristics. Numerical simulation results demonstrate that the proposed system identification methods yield good results.

This study is aimed at investigating what has happened to the dynamic linkages between foreign direct investment (FDI) and tourism development in China since the emergence of the so-called new normal economy. A time-varying parameter vector autoregressive (TVP-VAR) model is used for the first time to analyze the equi-spaced and time-point impulse responses between FDI, foreign exchange earnings from international tourism (FEE), and gross domestic product using annual data taken from 1983 to 2017. The results for the equi-spaced impulse response show that a difference in intensity for the interaction effect between FDI and FEE will change with different intervals. In addition, impulse response diagrams for FDI and FEE based on changes in economic development at three significant points in time reveal that the effect FDI in the new normal period has had the greatest impact on FEE in 2012, followed in decreasing impact by 2003 and then 1997.

This paper investigates the impact of fiscal policy on private consumption and labor supply in the UK economy using time-varying parameter vector autoregression (TVP-VAR) with stochastic volatility for the period Q2 1987 to Q2 2017. It considers fiscal variables such as government expenditure and net tax revenue and evaluates their impact on private consumption and average hours worked per week. Three sample periods were selected and two approaches were used to identify impulse responses, first taking the average of stochastic volatility over the sample period, and then allowing for sign restrictions based on contemporaneous relationships among the selected variables. The study found a negative wealth effect of public spending on private consumption and a positive effect on hours worked, as people tend to work more hours to maintain the same standard of living. Similarly, a tax shock generates negative effects on consumption but the impact on worked hours remains unclear over a three-year time horizon. These findings are almost consistent across sample periods and alternative specifications of impulse responses. This is one of only a few studies to determine the linkages between fiscal policy and the labor market using a macroeconomic framework.

Most often, virtual acoustic rendering employs real-time updated room acoustic simulations to accomplish auralization for a variable listener perspective. As an alternative, we propose and test a technique to interpolate room impulse responses, specifically Ambisonic room impulse responses (ARIRs) available at a grid of spatially distributed receiver perspectives, measured or simulated in a desired acoustic environment. In particular, we extrapolate a triplet of neighboring ARIRs to the variable listener perspective, preceding their linear interpolation. The extrapolation is achieved by decomposing each ARIR into localized sound events and re-assigning their direction, time, and level to what could be observed at the listener perspective, with as much temporal, directional, and perspective context as possible. We propose to undertake this decomposition in two levels: Peaks in the early ARIRs are decomposed into jointly localized sound events, based on time differences of arrival observed in either an ARIR triplet, or all ARIRs observing the direct sound. Sound events that could not be jointly localized are treated as residuals whose less precise localization utilizes direction-of-arrival detection and the estimated time of arrival. For the interpolated rendering, suitable parameter settings are found by evaluating the proposed method in a listening experiment, using both measured and simulated ARIR data sets, under static and time-varying conditions.

Stochastic differential equations (SDEs) are used to model ultrawideband (UWB) indoor wireless channels. We show that the impulse responses for time-varying indoor wireless channels can be approximated in a mean-square sense as close as desired by impulse responses that can be realized by SDEs. The state variables represent the inphase and quadrature components of the UWB channel. The expected maximization and extended Kalman filter are employed to recursively identify and estimate the channel parameters and states, respectively, from online received signal strength measured data. Both resolvable and nonresolvable multipath received signals are considered and represented as small-scaled Nakagami fading. The proposed models together with the estimation algorithm are tested using UWB indoor measurement data demonstrating the method’s viability and the results are presented.

A time-varying-parameter VAR for real output growth and inflation is estimated with annual U.S. series dating back to 1870. Volatility for both variables rises quickly with World War I and its aftermath, stays high until the end of World War II, and drops rapidly until the 1960s. This Postwar Moderation yields the largest decline in volatilities, surpassing the Great Moderation. Conditional on temporary shocks, inflation and output growth are positively correlated. Our model implies that aggregate demand played a key role in inflation volatility fluctuations. Conversely, the two variables are negatively correlated conditional on permanent shocks. Our model suggests that aggregate supply played an important role in output volatility fluctuations. Most impulse responses support an aggregate supply interpretation for permanent shocks. However, before World War I, a permanent increase in output raised the price level at longer horizons, and these responses are frequently statistically significant. This evidence supports the hypothesis that aggregate demand had a long-run positive effect on output during the pre–World War I period.

The constant phase element (CPE) is a capacitive element with a frequency-independent negative phase between current and voltage which interpolates between a capacitor and a resistor. It is used extensively to model the complexity of the physics in e.g. the bioimpedance and electrochemistry fields. There is also a similar element with a positive phase angle, and both the capacitive and inductive CPEs are members of the family of fractional circuit elements or fractance. The physical meaning of the CPE is only partially understood and many consider it an idealized circuit element. The goal here is to provide alternative equivalent circuits, which may give rise to better interpretations of the fractance. Both the capacitive and the inductive CPEs can be interpreted in the time-domain, where the impulse and step responses are temporal power laws. Here we show that the current impulse responses of the capacitive CPE is the same as that of a simple time-varying series RL-circuit where the inductor’s value increases linearly with time. Similarly, the voltage response of the inductive CPE corresponds to that of a simple parallel RC circuit where the capacitor’s value increases linearly with time. We use the Micro-Cap circuit simulation program, which can handle time-varying circuits, for independent verification. The simulation corresponds exactly to the expected response from the proposed equivalents within 0.1% error. The realization with time-varying components correlates with known time-varying properties in applications, and may lead to a better understanding of the link between CPE and applications.

The aim of this master’s thesis is to create a multiband stop derived from Lynn filters for suppressing mains hum and baseline variation (drift). The first part of the thesis is focused on brief theoretical introduction to the distortion types affecting ECG signal and twelve lead connection. The following practical part describes free realizations of ECG filter and ECG signal filtration. The filter has been tested both on distorted and on non-distorted signal. Finally filters’ error rate was computed from CSE database signals.

碩士
國立交通大學
電信工程系
90
In this paper, an advanced vector channel sounder as employed to perform the time-varying vector radio channel impulse response measurement in urban and suburban environments. By analyzing measurement data, characteristics of radio channel parameters are studied, which includes the cluster factor, the mean arrival rate of multipath, the time-varying parameter, the spatial parameter and a series of amplitudes, phase shifted replicas of the transmitted signal. Then a time-varying vector radio channel impulse response simulator based on Turin’s D-K model is proposed, where concepts of Hashemi’s time-varying channel impulse response model and COST 259 channel model are applied. This simulator is validated by comparing the computed r.m.s. delay spread with the measured one and has been proven that which can accurately model the time-varying channel impulse response.

Avec les avancements de la technologie de l'information, les données temporelles économiques et financières sont de plus en plus disponibles. Par contre, si les techniques standard de l'analyse des séries temporelles sont utilisées, une grande quantité d'information est accompagnée du problème de dimensionnalité. Puisque la majorité des séries d'intérêt sont hautement corrélées, leur dimension peut être réduite en utilisant l'analyse factorielle. Cette technique est de plus en plus populaire en sciences économiques depuis les années 90. Étant donnée la disponibilité des données et des avancements computationnels, plusieurs nouvelles questions se posent. Quels sont les effets et la transmission des chocs structurels dans un environnement riche en données? Est-ce que l'information contenue dans un grand ensemble d'indicateurs économiques peut aider à mieux identifier les chocs de politique monétaire, à l'égard des problèmes rencontrés dans les applications utilisant des modèles standards? Peut-on identifier les chocs financiers et mesurer leurs effets sur l'économie réelle? Peut-on améliorer la méthode factorielle existante et y incorporer une autre technique de réduction de dimension comme l'analyse VARMA? Est-ce que cela produit de meilleures prévisions des grands agrégats macroéconomiques et aide au niveau de l'analyse par fonctions de réponse impulsionnelles? Finalement, est-ce qu'on peut appliquer l'analyse factorielle au niveau des paramètres aléatoires? Par exemple, est-ce qu'il existe seulement un petit nombre de sources de l'instabilité temporelle des coefficients dans les modèles macroéconomiques empiriques? Ma thèse, en utilisant l'analyse factorielle structurelle et la modélisation VARMA, répond à ces questions à travers cinq articles. Les deux premiers chapitres étudient les effets des chocs monétaire et financier dans un environnement riche en données. Le troisième article propose une nouvelle méthode en combinant les modèles à facteurs et VARMA. Cette approche est appliquée dans le quatrième article pour mesurer les effets des chocs de crédit au Canada. La contribution du dernier chapitre est d'imposer la structure à facteurs sur les paramètres variant dans le temps et de montrer qu'il existe un petit nombre de sources de cette instabilité. Le premier article analyse la transmission de la politique monétaire au Canada en utilisant le modèle vectoriel autorégressif augmenté par facteurs (FAVAR). Les études antérieures basées sur les modèles VAR ont trouvé plusieurs anomalies empiriques suite à un choc de la politique monétaire. Nous estimons le modèle FAVAR en utilisant un grand nombre de séries macroéconomiques mensuelles et trimestrielles. Nous trouvons que l'information contenue dans les facteurs est importante pour bien identifier la transmission de la politique monétaire et elle aide à corriger les anomalies empiriques standards. Finalement, le cadre d'analyse FAVAR permet d'obtenir les fonctions de réponse impulsionnelles pour tous les indicateurs dans l'ensemble de données, produisant ainsi l'analyse la plus complète à ce jour des effets de la politique monétaire au Canada. Motivée par la dernière crise économique, la recherche sur le rôle du secteur financier a repris de l'importance. Dans le deuxième article nous examinons les effets et la propagation des chocs de crédit sur l'économie réelle en utilisant un grand ensemble d'indicateurs économiques et financiers dans le cadre d'un modèle à facteurs structurel. Nous trouvons qu'un choc de crédit augmente immédiatement les diffusions de crédit (credit spreads), diminue la valeur des bons de Trésor et cause une récession. Ces chocs ont un effet important sur des mesures d'activité réelle, indices de prix, indicateurs avancés et financiers. Contrairement aux autres études, notre procédure d'identification du choc structurel ne requiert pas de restrictions temporelles entre facteurs financiers et macroéconomiques. De plus, elle donne une interprétation des facteurs sans restreindre l'estimation de ceux-ci. Dans le troisième article nous étudions la relation entre les représentations VARMA et factorielle des processus vectoriels stochastiques, et proposons une nouvelle classe de modèles VARMA augmentés par facteurs (FAVARMA). Notre point de départ est de constater qu'en général les séries multivariées et facteurs associés ne peuvent simultanément suivre un processus VAR d'ordre fini. Nous montrons que le processus dynamique des facteurs, extraits comme combinaison linéaire des variables observées, est en général un VARMA et non pas un VAR comme c'est supposé ailleurs dans la littérature. Deuxièmement, nous montrons que même si les facteurs suivent un VAR d'ordre fini, cela implique une représentation VARMA pour les séries observées. Alors, nous proposons le cadre d'analyse FAVARMA combinant ces deux méthodes de réduction du nombre de paramètres. Le modèle est appliqué dans deux exercices de prévision en utilisant des données américaines et canadiennes de Boivin, Giannoni et Stevanovic (2010, 2009) respectivement. Les résultats montrent que la partie VARMA aide à mieux prévoir les importants agrégats macroéconomiques relativement aux modèles standards. Finalement, nous estimons les effets de choc monétaire en utilisant les données et le schéma d'identification de Bernanke, Boivin et Eliasz (2005). Notre modèle FAVARMA(2,1) avec six facteurs donne les résultats cohérents et précis des effets et de la transmission monétaire aux États-Unis. Contrairement au modèle FAVAR employé dans l'étude ultérieure où 510 coefficients VAR devaient être estimés, nous produisons les résultats semblables avec seulement 84 paramètres du processus dynamique des facteurs. L'objectif du quatrième article est d'identifier et mesurer les effets des chocs de crédit au Canada dans un environnement riche en données et en utilisant le modèle FAVARMA structurel. Dans le cadre théorique de l'accélérateur financier développé par Bernanke, Gertler et Gilchrist (1999), nous approximons la prime de financement extérieur par les credit spreads. D'un côté, nous trouvons qu'une augmentation non-anticipée de la prime de financement extérieur aux États-Unis génère une récession significative et persistante au Canada, accompagnée d'une hausse immédiate des credit spreads et taux d'intérêt canadiens. La composante commune semble capturer les dimensions importantes des fluctuations cycliques de l'économie canadienne. L'analyse par décomposition de la variance révèle que ce choc de crédit a un effet important sur différents secteurs d'activité réelle, indices de prix, indicateurs avancés et credit spreads. De l'autre côté, une hausse inattendue de la prime canadienne de financement extérieur ne cause pas d'effet significatif au Canada. Nous montrons que les effets des chocs de crédit au Canada sont essentiellement causés par les conditions globales, approximées ici par le marché américain. Finalement, étant donnée la procédure d'identification des chocs structurels, nous trouvons des facteurs interprétables économiquement. Le comportement des agents et de l'environnement économiques peut varier à travers le temps (ex. changements de stratégies de la politique monétaire, volatilité de chocs) induisant de l'instabilité des paramètres dans les modèles en forme réduite. Les modèles à paramètres variant dans le temps (TVP) standards supposent traditionnellement les processus stochastiques indépendants pour tous les TVPs. Dans cet article nous montrons que le nombre de sources de variabilité temporelle des coefficients est probablement très petit, et nous produisons la première évidence empirique connue dans les modèles macroéconomiques empiriques. L'approche Factor-TVP, proposée dans Stevanovic (2010), est appliquée dans le cadre d'un modèle VAR standard avec coefficients aléatoires (TVP-VAR). Nous trouvons qu'un seul facteur explique la majorité de la variabilité des coefficients VAR, tandis que les paramètres de la volatilité des chocs varient d'une façon indépendante. Le facteur commun est positivement corrélé avec le taux de chômage. La même analyse est faite avec les données incluant la récente crise financière. La procédure suggère maintenant deux facteurs et le comportement des coefficients présente un changement important depuis 2007. Finalement, la méthode est appliquée à un modèle TVP-FAVAR. Nous trouvons que seulement 5 facteurs dynamiques gouvernent l'instabilité temporelle dans presque 700 coefficients.
As information technology improves, the availability of economic and finance time series grows in terms of both time and cross-section sizes. However, a large amount of information can lead to the curse of dimensionality problem when standard time series tools are used. Since most of these series are highly correlated, at least within some categories, their co-variability pattern and informational content can be approximated by a smaller number of (constructed) variables. A popular way to address this issue is the factor analysis. This framework has received a lot of attention since late 90's and is known today as the large dimensional approximate factor analysis. Given the availability of data and computational improvements, a number of empirical and theoretical questions arises. What are the effects and transmission of structural shocks in a data-rich environment? Does the information from a large number of economic indicators help in properly identifying the monetary policy shocks with respect to a number of empirical puzzles found using traditional small-scale models? Motivated by the recent financial turmoil, can we identify the financial market shocks and measure their effect on real economy? Can we improve the existing method and incorporate another reduction dimension approach such as the VARMA modeling? Does it help in forecasting macroeconomic aggregates and impulse response analysis? Finally, can we apply the same factor analysis reasoning to the time varying parameters? Is there only a small number of common sources of time instability in the coefficients of empirical macroeconomic models? This thesis concentrates on the structural factor analysis and VARMA modeling and answers these questions through five articles. The first two articles study the effects of monetary policy and credit shocks in a data-rich environment. The third article proposes a new framework that combines the factor analysis and VARMA modeling, while the fourth article applies this method to measure the effects of credit shocks in Canada. The contribution of the final chapter is to impose the factor structure on the time varying parameters in popular macroeconomic models, and show that there are few sources of this time instability. The first article analyzes the monetary transmission mechanism in Canada using a factor-augmented vector autoregression (FAVAR) model. For small open economies like Canada, uncovering the transmission mechanism of monetary policy using VARs has proven to be an especially challenging task. Such studies on Canadian data have often documented the presence of anomalies such as a price, exchange rate, delayed overshooting and uncovered interest rate parity puzzles. We estimate a FAVAR model using large sets of monthly and quarterly macroeconomic time series. We find that the information summarized by the factors is important to properly identify the monetary transmission mechanism and contributes to mitigate the puzzles mentioned above, suggesting that more information does help. Finally, the FAVAR framework allows us to check impulse responses for all series in the informational data set, and thus provides the most comprehensive picture to date of the effect of Canadian monetary policy. As the recent financial crisis and the ensuing global economic have illustrated, the financial sector plays an important role in generating and propagating shocks to the real economy. Financial variables thus contain information that can predict future economic conditions. In this paper we examine the dynamic effects and the propagation of credit shocks using a large data set of U.S. economic and financial indicators in a structural factor model. Identified credit shocks, interpreted as unexpected deteriorations of the credit market conditions, immediately increase credit spreads, decrease rates on Treasury securities and cause large and persistent downturns in the activity of many economic sectors. Such shocks are found to have important effects on real activity measures, aggregate prices, leading indicators and credit spreads. In contrast to other recent papers, our structural shock identification procedure does not require any timing restrictions between the financial and macroeconomic factors, and yields an interpretation of the estimated factors without relying on a constructed measure of credit market conditions from a large set of individual bond prices and financial series. In third article, we study the relationship between VARMA and factor representations of a vector stochastic process, and propose a new class of factor-augmented VARMA (FAVARMA) models. We start by observing that in general multivariate series and associated factors do not both follow a finite order VAR process. Indeed, we show that when the factors are obtained as linear combinations of observable series, their dynamic process is generally a VARMA and not a finite-order VAR as usually assumed in the literature. Second, we show that even if the factors follow a finite-order VAR process, this implies a VARMA representation for the observable series. As result, we propose the FAVARMA framework that combines two parsimonious methods to represent the dynamic interactions between a large number of time series: factor analysis and VARMA modeling. We apply our approach in two pseudo-out-of-sample forecasting exercises using large U.S. and Canadian monthly panels taken from Boivin, Giannoni and Stevanovic (2010, 2009) respectively. The results show that VARMA factors help in predicting several key macroeconomic aggregates relative to standard factor forecasting models. Finally, we estimate the effect of monetary policy using the data and the identification scheme as in Bernanke, Boivin and Eliasz (2005). We find that impulse responses from a parsimonious 6-factor FAVARMA(2,1) model give an accurate and comprehensive picture of the effect and the transmission of monetary policy in U.S.. To get similar responses from a standard FAVAR model, Akaike information criterion estimates the lag order of 14. Hence, only 84 coefficients governing the factors dynamics need to be estimated in the FAVARMA framework, compared to FAVAR model with 510 VAR parameters. In fourth article we are interested in identifying and measuring the effects of credit shocks in Canada in a data-rich environment. In order to incorporate information from a large number of economic and financial indicators, we use the structural factor-augmented VARMA model. In the theoretical framework of the financial accelerator, we approximate the external finance premium by credit spreads. On one hand, we find that an unanticipated increase in US external finance premium generates a significant and persistent economic slowdown in Canada; the Canadian external finance premium rises immediately while interest rates and credit measures decline. From the variance decomposition analysis, we observe that the credit shock has an important effect on several real activity measures, price indicators, leading indicators, and credit spreads. On the other hand, an unexpected increase in Canadian external finance premium shows no significant effect in Canada. Indeed, our results suggest that the effects of credit shocks in Canada are essentially caused by the unexpected changes in foreign credit market conditions. Finally, given the identification procedure, we find that our structural factors do have an economic interpretation. The behavior of economic agents and environment may vary over time (monetary policy strategy shifts, stochastic volatility) implying parameters' instability in reduced-form models. Standard time varying parameter (TVP) models usually assume independent stochastic processes for all TVPs. In the final article, I show that the number of underlying sources of parameters' time variation is likely to be small, and provide empirical evidence on factor structure among TVPs of popular macroeconomic models. To test for the presence of, and estimate low dimension sources of time variation in parameters, I apply the factor time varying parameter (Factor-TVP) model, proposed by Stevanovic (2010), to a standard monetary TVP-VAR model. I find that one factor explains most of the variability in VAR coefficients, while the stochastic volatility parameters vary in the idiosyncratic way. The common factor is highly and positively correlated to the unemployment rate. To incorporate the recent financial crisis, the same exercise is conducted with data updated to 2010Q3. The VAR parameters present an important change after 2007, and the procedure suggests two factors. When applied to a large-dimensional structural factor model, I find that four dynamic factors govern the time instability in almost 700 coefficients.

This chapter provides a System-of-Systems (SoS) perspective on a study of frequency estimation of signals with a focus on Linear Frequency Modulation (LFM) signals. This chapter describes an SoS approach for frequency estimation using Chirplet Transform (CT), Hough Transform (HT), and the Short Time Fourier Transform (STFT) with filtering viewpoint. The filtering viewpoint employs the filter impulse response length to obtain the best time-frequency concentration for accurate estimation of a signal frequency. The optimum impulse response length can be found by varying the length of the filter impulse response and observe the changing in the time-frequency distribution (TFD). The chapter shows that when the length of the impulse response becomes longer, the time-frequency concentration in TFD increases first and then decreases.

The main aim of ECG signal enhancement is to separate the required signal components from the unwanted artifacts. Adaptive filter-based ECG enhancement helps in detecting time varying potentials and also helps to track the dynamic variations of the signals. LMS-based adaptive recurrent filter is used to obtain the impulse response of normal QRS complexes. It is also used for arrhythmia detection in ambulatory ECG recordings. Adaptive filters self-modify its frequency response to change the behavior in time. This property of adaptive filter allows it to adapt its response to change in the input signal characteristics. A major problem in adaptive filtering is the computational complexity of adaptive algorithm when the unknown system has a long impulse response and therefore requires a large number of taps. The wavelet transform is a time-scale representation method with a basis function called mother wavelet. In wavelet transform, the input signal is subsequently decomposed into subbands. Wavelet transform thresholding in the subband gives better performance of denoising.

This chapter now turns to the discussion of filters, which extend the notion of spectrum beyond signals into the processes themselves. A gentle introduction to the concept of delaying signals, aided by yet another variant of the Fourier transform, the discrete-time Fourier transform, allows the operation of filters to be dissected. Another analysis tool, in the form of the z-transform, is brought to the fore as a complex-valued version of the discrete-time Fourier transform. A study of the characteristics of filters, introducing the notion of zeros and poles, as well as finite impulse response (FIR) and infinite impulse response (IIR) forms, composes the main body of the text. This is complemented by a discussion of filter design and applications, including ideas related to time-varying filters. The chapter conclusion expands once more the definition of spectrum.

No one doubts that immense variability exists at the neural level. Even when the identical stimulus is presented repeatedly within a single experimental session, the response of an individual neuron varies from trial to trial. Similarly, with lowlevel cognitive processes such as association, there is no disagreement concerning the existence of competing units. Models of associative memory, both symbolic (e.g., Gillilund & Shiffrin, 1984) and subsymbolic (e.g., Seidenberg & McClelland, 1990), are predicated on the assumptions that stimuli have multiple associations and that these varying associations influence the way in which we remember. Higher level cognition, however, has been treated differently. Many models are universalist: Everyone is depicted as proceeding in the same way when relevant stimuli are presented. Other models are comparative; they hypothesize different ways of thinking among groups defined on the basis of such characteristics as age, expertise, or aptitudes, but hypothesize a single consistent kind of reasoning within each group. Thus, 8-year-olds might be depicted as performing in one way and 5-year-olds in another, experts in one way and novices in another, people with high spatial ability in one way and those with low spatial ability in another, and so on. The finest differentiations that are typically made within these comparative approaches examine individual differences within people of a single age; for example, reflective 8-year-olds are described as taking a long time but answering accurately on the Matching Familar Figures Test, and impulsive 8-year-olds are described as answering more quickly but less accurately (Kogan, 1983). The main purpose of this chapter is to summarize the rapidly growing body of research suggesting that variability is actually a pervasive reality in high-level, as well as low-level, cognition. To place this work in context, however, it seems useful first to briefly consider some prominent examples of universalist and comparative models of cognition and then to consider why they might be proposed and widely accepted even if thinking is far more variable than they depict it as being. A great deal of cognitive research has been devoted to identifying the processing approach that people use on a particular task. This universalist approach has led to many influential models and theories.

Abstract The purpose of this paper is to provide a modal expression of a time-varying MDOF system and to develop an identification method for it. The single-input-multi-output relation of a time-varying N-DOF system is expressed as a superposition of N time-varying SDOF subsystems in the time domain, where the expansion coefficients represent the time-varying mode-shapes, and the natural frequency and the damping ratio of each subsystem represent the time-varying modal parameters of each mode. Then we define the SDOF wavelets, which correspond to the time-varying impulse responses of SDOF subsystems and show that the output of the entire system can be expressed by a superposition of SDOF wavelets. Then, the identification problem is reduced to an atomic decomposition problem of choosing the nearly best set of SDOF wavelets and determining the expansion coefficients. We develop a modified matching pursuit algorithm, called modal pursuit, to solve the problem. Basic examples are numerically examined to show that the proposed modal representation and the identification method are applicable to track the modal characteristics of time-varying systems.

A new time-varying multivariate autoregressive (TVMAR) model method for modal parameter identification of linear time-varying (TV) systems with multi-output is introduced. Besides, a modified recursive least square method based on the traditional one is presented to determine the coefficient matrices of the TVMAR model. In the proposed method, multi-dimensional nonstationary response signals of the vibrating system can be processed simultaneously. Not only the TV modal frequency and damping ratio of the system, but also the changing behavior of the mode shape in the course of vibration are identified by the proposed procedure. Numerical simulations, in which a three-degree-of-freedom system with TV stiffness is respectively subjected to impulse excitation and white noise excitation, are presented. The validity and accuracy of the method are demonstrated by the good simulation results.

This paper presents a solution to control system design issues for membrane mirrors used in retinal imaging adaptive optics systems. Such systems allow for the early diagnosis of eye diseases through high resolution imaging of the retina. Optical defects in the eye, known as aberrations, distort the retinal images, hence reducing their resolution. A retinal imaging adaptive optics system makes use of a deformable mirror whose shape is adjusted in real time to cancel the aberration effects. Due to the unknown and time-varying nature of the aberrations in the eye, the main control problem addressed in this paper is the tracking of an unknown and time-varying shape for the membrane mirror. Since the desired shape of the mirror is unknown and time-varying, it is proposed in this paper to design a multivariable controller that is tuned online to converge to the controller needed to achieve regulation. This is done iteratively, by taking advantage of the Q-parameterization of stabilizing controllers, so that the controller will converge to the ideal controller. Most often, finite impulse response (FIR) filters are used to represent the Q-parameter. It is proposed in this paper to represent the Q-parameter using orthonormal infinite impulse response filter basis functions. Such basis functions yield faster convergence rates during parameter estimation, and a Q-parameter representation that is less sensitive to parameter variations from the desired parameters. This is particularly crucial for the proposed application, where small errors in a typical FIR representation for the Q-parameter can lead to significant performance degradation. Simulation results are presented to illustrate the performance of the proposed adaptive controller design approach.

The high sensitivity to impulse-type events previously uncovered for bistable oscillators has motivated recent experimental and numerical studies on the power generation performance of bistable vibration energy harvesters. To lead to an effective and efficient predictive tool and design guide, this research develops a new analytical approach to estimate the vibration response decay and power generation of a bistable energy harvester when excited by an impulse. Enabling the prediction of time-varying snap-through dynamics, this new approach greatly extends the capabilities of the current averaging method when employed with the Jacobian elliptic functions. Comparison with values determined by the direct simulation of the governing equations shows that the analytically predicted average generated power is very accurate for a wide range of impulse strengths and load resistances. The analytical approach represents a great leap forward in the ever-expanding understanding of bistable vibration energy harvesters as implements to effectively capture and convert a wide range of excitation energies.

Modeling the internal combustion engine for air-to-fuel ratio (AFR) control has been widely studied and several methodologies have been adopted toward the end goal of applying model based control schemes. In this paper, an online binary sensor identification (BID) technique using switching sensors is adopted for modeling the response from fuel input to AFR output of a spark-ignited, internal combustion engine, to be used in AFR control. In general terms, the algorithm identifies the impulse response of a linear time invariant (LTI) system by choosing an optimal sequence of inputs. The entire modeling process is done online with a four-cylinder engine in a test cell, using typical production switching sensors. Finite impulse response (FIR) linear time invariant (LTI) models are identified at prescribed operating points of the engine (specified by engine speed and the manifold air pressure). The validity of the resulting model is then tested on separate data streams with AFR measured from a wide-range sensor output. By scheduling the coefficients of the FIR models based on the operating condition, it is possible to identify a linear parameter varying AFR model for the appropriate operating regions of the engine.

A numerical investigation of transient performance of 3D linear micronozzles has been performed. The baseline model for the study is derived from the NASA/Goddard Space Flight Center MEMS-based hydrogen peroxide micro-thruster prototype. The 3D micronozzles investigated here have depths of 25μm, 50μm, 100μm, and 150μm and employ expanders with a 30° half-angle. A hyperbolic-tangent actuation profile is used to model the opening of a microvalve in order to simulate start-up of the thruster. The inlet stagnation pressure when the valve is fully opened is 250kPa and generates a maximum throat Reynolds number of Remax ∼ 800. The complete actuation occurs over 0.55ms and is followed by 0.25ms of steady-state operation. The propulsion scheme employs 85% pure hydrogen peroxide as a monopropellant. Simulation results have been analyzed and thrust production as a function of time has been quantified along with the total impulse delivered. Micronozzle impulse efficiency has also been determined based on a theoretical maximum impulse achieved by a quasi-1D inviscid flow responding instantaneously to the actuation profile. It is found that both the flow and thrust exhibit a response ‘lag’ to the time-varying inlet pressure profile. Simulations are compared to previous 2D results and indicate that thrust per unit nozzle depth, impulse, and efficiency increase with nozzle depth and approach the 2D results for nozzle depths greater than 150μm.

The purpose of the present study is to fundamentally investigate dynamic hull girder response due to slamming load. A series of time domain FE-simulation is carried out using a non-uniform finite element beam model of a 8000 TEU container ship where slamming load is applied at the bottom of the bow. The ship is modeled by elaso-plastic material with equivalent ultimate strength and strain rate effect is considered. Hull-girder vertical bending moment as well as deformation modes, bending stress are investigated by varying the time duration of the slamming load which is modeled by sinusoidal impulse. In order to obtain post vibration after the first slamming load explicit analysis is adopted instead of implicit analysis with considering gravity and buoyancy. Buoyancy is modeled by inelastic spring elements. It is found from the present study hull girder vertical bending moment is dependent on time duration of slamming load. Especially if time duration is smaller than natural period response bending moment may become smaller than applied bending moment. Moreover effect of inertia at fore and aft is also investigated in detail.

Abstract Active control of orthotropic plates subjected to an impulse loading is considered. The dynamic response is minimized using in-plane forces or bending moments induced by piezoelectric stiffeners bonded to the opposite surfaces of the plate and placed symmetrically with respect to the middle plane. The control forces and moments are activated by a piece-wise constant alternating voltage with varying switch-over time intervals. The magnitude of voltage is bounded while the switch-over time intervals are constantly adjusted to achieve an optimum control. Numerical examples presented in the paper demonstrate the effectiveness of the method and the possibility of reducing the vibrations to very small amplitudes within a short time interval which is in the order of a second.