Earticle

The hydraulic acoustic attenuator fur an automotive active suspension system is so highly nonlinear and of high order that the analysis in time-domain has been performed quite little. In this paper, a state-space representation of the dynamics for a hydraulic attenuator was presented utilizing the electrical analogy. And the results of experiment were compared with those of simulation to validate the state-space model proposed. The comparison revealed that the state-space model proposed is practically applicable to estimate the dynamic responses of the hydraulic attenuator in time-domain.

본 연구에서는 신흥국가의 채권수익률 스프레드의 변화에 대한 동태적 과정을 분석한다. 한 국가의 채권수익률은 그 나라 고유의 경제상황과 글로벌 충격요인들에 의하여 결정되며 이때 이 채권수 익률의 변동은 경제적 요인에 의해 결정되는 내재가치(fundamental value) 부분과 투자자들의 심리적 요인에 따라 변동하는 일시적 가치(transient value) 부분의 합으로 구성된다고 볼 수 있다. 본 연구에서는 신흥시장에서의 채권수익률과 미국 국채수익률을 대용치로 하는 무위험수익률과의 차이 로 추정하는 신흥시장 스프레드에 대한 변동을 상태공간모형(state space model)을 사용하여 내재가치와 일시가치로 분해하여 살펴봄으로써 신흥시장 스프레드의 변동이 경제적 요인에 부합되는지 여부를 판별 해 보고자 한다. 신흥시장의 채권수익률로는 JP Morgan Chase에서 제공하는 각 신흥국별 주별 EMBIGS (Emerging market global bond index spread) 자료를 사용하며 상태공간모형은 Kalman filter 기법을 사 용한다. 이를 위하여 EMBIGS 자료에 대한 시계열적 자기상관관계의 검정과 단위근 검정을 사전적으로 실시하여 상태공간모형 사용상의 정당성을 확보하고 상태공간모형으로부터 도출된 수익률 스프레드 격 차(Yield Spread Difference: YSD)에 대한 내재가치와 관측된 YSD를 상호 비교함으로써 신흥국의 YSD 가 과잉반응으로 유발되는지를 평가해 보기로 한다. 또한 이러한 과잉반응의 강건성 검정을 위하여 별도 로 T-GARCH(Threshold Generalized AutoRegressive Conditional Heteroskedasticity) 모형에서 도출되는 YSD의 변동성을 비교해 보기로 한다. 본 연구에서 채택한 신흥국가는 라틴아메리카 6개국, 유럽 4개국, 아시아 5개국 등 총 15개국을 대상으로 선정하였으며 표본기간은 주별 스프레드를 기준으로 1998년 4월~2008년 12월까지의 559주를 선택하였다. 스프레드 도출을 위한 범세계적 기준 수익률로는 미국의 10년 만기 재무성증권(treasury bond: TB) 유통 수익률을 사용하였다. 본 연구에서 행한 두 가지 추정모형에 대한 결과를 요약하면 다음과 같다. 첫째, 신흥국 스프레드의 변화는 내재가치보다는 심리적 가치인 일시가치에 의하여 주로 변화과정을 겪 는 것으로 분석되었다. 이는 내재가치가 지속적 성격을 갖는다는 가정 하에 설정한 상태공간모형에 따른 것으로 분석 결과 내재가치는 장기적으로 거의 확정적(deterministic) 추세를 따르는 반면, 일시가치는 시계열의 안정성이 확보된 상태에서도 변동성의 주요인으로 나타났다. 둘째, 이러한 내재가치의 변동은 T-GARCH 모형에 의하여 분석한 수익률 변동의 비대칭적 변화에 대하 여 거의 영향을 받지 않는 것으로 나타난 반면, 수익률 관측치에 대한 변동성은 비대칭적 변화에 대하여 두드러진 반응을 가져오는 것으로 분석되어 신흥시장에서의 수익률 변동이 주로 투자자들의 심리적 요 인이 반영된 일시가치에 의하여 이루어지는 과잉반응의 존재를 확인하였다. 셋째, 이러한 과잉반응은 글로벌 충격요인들과 국가고유의 충격요인들을 동시에 감안한 다중회귀분석에 서도 지속적으로 유효한 것으로 나타나 신흥시장에서의 채권수익률의 변동이 과도한 것으로 분석됨으로 써 과잉반응을 사전적으로 억제할 수 있는 정책적 필요성이 제기되었다. 이와 같은 결과는 내재가치에 의한 채권수익률 변동보다는 심리적 요인이 반영된 일시가치에 의한 채권 수익률 변동이 신흥시장에서의 주요한 채권시장 변동성을 설명한다는 기존의 연구결과들과 부합되는 것 으로 볼 수 있다. 한편, 본 연구의 표본대상 기간에는 1998년 러시아 모라토리엄, 2002년 남미 외환위기, 2008년 전 세계적 금융위기 등 세 차례의 충격국면이 포함된 관계로 이러한 내재가치에 의한 변동이 과소 추정되었을 가능 성도 제기될 수 있다. 향후로는 표본 시계열을 위기국면에 따라 재편성하거나 구조적 전환(structural break)을 탐지할 수 있는 확장된 칼만필터(extended Kalman filter) 기법에 의한 보다 정밀한 분석도 요 구된다고 하겠다. 이는 추후의 연구과제로 남겨 둔다.

This study analyzes the dynamic process of yield spread differences in 15 emerging countries’ bond markets by using alternately the state-space model and the threshold-GARCH (T-GARCH) model. Using the discrete Kalman filter technique, we decompose yield spread differences into fundamental and transient components in the adoption of the first order state-space model estimation. We then further decide whether spread changes coincide with both global and country-specific explanatory factors by adoption of T-GARCH estimation after extracting the fundamental values from the yield spread observations. This procedure can be taken by comparing the T-GARCH estimation results of the observed and the derived fundamental values from the state-space model. The existence of overreaction by investors’ psychological situation and also its magnitude can be checked in the emerging bond markets if the conditional volatility of observed spreads turned out to be significantly positive upon arrival of unanticipated negative shocks, whereas that of the fundamental one turned out not to be significant. In this analysis, EMBIGS (emerging bond market global index spread) from JP Morgan Chase are used as data for country yield spreads. For weekly data in use, the sample period covers the April 1998 to December 2008 period and the sample emerging countries cover 6 from Latin America, 4 from Europe and 5 from Asia. Among them, Korea and Thailand have data only up to Aril 2004 and March 2005, respectively, at which points they were excluded from the emerging market status. Major findings of this study are as follows. First, yield spread differences in emerging markets are mainly altered by their transient values rather than by their fundamental values, as the empirical results show that the fundamental values are almost deterministic in the long run and the transient values explain most of changes in spread. This is because three different crises such as the Russian moratorium in 1998, the Argentinean currency crisis in 2002 and the global financial crisis in late 2008 which all occurred during the sample period can be absorbed into the transient value process, whereas the fundamental values remained stable. This can be a meaningful evidence in that real spread changes reveal in the state of overreaction, especially in the bearish trend of the emerging bond markets during the sample period.. Second, in the procedure of T-GARCH estimation, clear evidence of overreaction in observed yield spread differences is found such that the impact on the conditional variance of the unexpected rise in observed spreads is significantly positive, whereas that for the unexpected rise in fundamental spreads does not show any significance. This evidence can be interpreted as that investors’ psychological attitude result from their on-going noise trading.. Third, in the estimation of multiple regressions in the T-GARCH model where two global factors, US T-bond rate and VIX, and two country-specific factors, domestic stock index return and US dollar based foreign exchange rate, are added as explanatory variables for the changes of spread, the same evidence of overreaction as in the above simple T-GARCH model is found. All the four estimators appear to coincide with the theoretical hypotheses in estimating the observed yield spreads. However, in the estimation of the fundamental yield spreads, most of the coefficients on the explanatory variables turn out to be insignificant. This may be partly because our model could not catch the structural break such as the October Crisis of 1987 in the US, using only a first order state-space model with a discrete Kalman filter technique. A more precise estimation technique such as the extended Kalman filter in the nonlinear estimation model need to be used to overcome this weakness, which is left for future research.

본 연구는 칼만필터 앨고리즘의 상태공간모형을 이용하여 1995년부터 2011년까지 미국, 일본, 중국의 월별 관광객수, 소비자물가지수, 국내총생산, 환율을 통해 관광객의 수요탄력성을 파악하고자 한다. 본 연구의 결과를 요약하면, 첫째, 관광수요모형의 칼만필터 추정결과는 3개의 소득탄력성이 통계적으로 유의하나, 자체가격과 대체가격탄력성은 모든 수요모형에서 유의하지 않았다. 수요탄력성의 기대부호는 3개의 소득탄력성의 경우만이 양(+)의 부호로 일치하여 소득탄력성 변수만이 한국관광수요의 결정요인이었다. 관광수요는 3개의 자체가격탄력성이 모두 탄력적이었고 양(+)의 값을 보여주어 대체재의 관계가 존재하여 한국관광가격은 비경쟁적이며 가격경쟁에 목표를 둔 전략들로 추가적인 수익을 창출할 것이다. 미국의 가격탄력성은 매우 크고 일본의 소득탄력성은 탄력적으로 사치재의 관계를 나타내고 대체가격탄력성은 모두 비탄력적이고 미국은 대체재, 일본과 중국은 보완재의 관계가 존재하였다. 둘째, 상태공간을 이용한 미국, 일본 및 중국의 수요탄력성의 칼만필터 추정결과는 소득탄력성에 있어서 글로벌 금융위기기간인 2007년과 2008년에 미국은 이전과 거의 동일하고, 일본은 약간 높고 중국은 조금 낮아졌다. 2011년의 소득탄력성이 미국은 증가, 일본은 대폭 증가, 중국은 이전과 거의 동일하여 한국으로의 여행은 미국과 중국의 관광객들에게는 필수재, 일본의 관광객들에게 사치재로 인식되었다. 가격탄력성에 있어서 글로벌 금융위기기간인 2007년과 2008년에 미국은 이전보다 조금 높고, 일본은 대폭 높고 중국은 약간 높아졌다. 2011년의 가격탄력성이 미국은 대폭 증가, 일본은 거의 동일, 중국은 대폭 감소하였다. 따라서 본 연구에서 이용된 상태공간모형이 일정한 모수를 가정하는 전통적인 회귀분석방법보다 모수의 불안정을 추정하는 관광수요모형의 칼만필터 앨고리즘과 더불어 관광객들의 시간가변 수요탄력성을 더욱 정확히 파악하는데 적절하다는 것을 확인하였고, 또한 관광수요모형의 수요탄력성은 관광정책과 관광제도의 변경, 소비자의 취향변화, 기대감 등과 같은 비관측요인들로 인하여 시간가변적일 가능성이 존재하였다.

The purpose of this study was to examine the elasticity in the demand for tourists through the relation between tourists’s arrivals of source country and consumer price index, gross domestic products, the exchange rates in Korea, the United States, Japan, China by using a state space model with Kalman filter algorism from January 1995 to December 2011. First, in the Kalman filter estimates of tourism demand models, three income elasticities were statistically significant, while the own price elasticities and cross price elasticities were not significant in all demand models. The expected sign of demand elasticities were consistently positive with three income elasticities as the determining factor of Korea tourism demand. According to the tourism demand, three own price elasticities were positively elastic and related to substitutes and the tourism price was non-competitive in Korea. This indicated that the elastic tourism price will create additional revenues in strategy based on the goal to price competition. The price elasticity was very large in the U.S. and the income elasticity was elastic as the luxury goods in Japan, but the cross price elasticity was inelastic. Thus they showed that there were the flexible relations to the United States as substitutes, Japan and China as complements. Second, by utilizing the State Space, the results of the Kalman filter estimates of demand elasticities showed that three income elasticities increased from 1995 to 2000 and the United States and China were slightly higher in 2003, but Japan was much lower. And the United States were about the same as before in 2007 and 2008 (during the Global financial crisis) and Japan increased slightly, but China was a little lower. In terms of the income elasticity in 2011, it increased in the United States and China, but Japan increased significantly. Thus the travel to Korea was recognized as necessities for the tourists in China and the United States, but as luxuries for tourists in Japan. In the price elasticity, the United States and Japan decreased from 1995 to 2000, but China increased, following the United States and Japan were slightly higher in 2003, but China was almost the same. Thus the United States was a little higher than before and Japan was significantly higher, but China rose slightly in 2007 and 2008. In the price elasticity in 2011, the United States increased significantly and Japan was about the same, but China decreased significantly. Third, the research potential applications need to be further investigated. First, the equation of state of demand models in this study was based on empirical study of the demand for consumer goods such as home, food and general including durable or not durable goods (Song, Romilly and Liu, 1998; Brown, Song and McGillivray, 1997), which were assumed to follow the walk process. However, the decision-making process of tourists was not necessarily the same as consumers and the tourism products and services consumer demand in general can not relate at all. Therefore, in a great deal of research, the equation of state, setting such as autoregressive (AR), moving average (MA) or autoregressive moving average (ARMA), needs to be done in order to grasp whether it is more appropriate than the course of random walk. This can be done by examining the predictability of the state space model and the state equation of another. Second, this study was focused on investigating whether that could be used to analyze the elasticity of tourist demand related to long term determinants using the state space model. If this results could be compared with the alternative tourism demand model through the prediction performance of the state space model, they were possible to become more help with practitioners and policy makers of tourism. Third, the legitimate process theoretically should be developed for the instability structure and the state space model need to determine more desirable models than the fixed parameter models. The aspects should be detailed in analysis of future research. Therefore, this study confirmed that the state space models used were appropriate than the traditional regression analysis approaches to examine the Kalman filter estimates of tourism demand models and the time-varying demand elasticities. And in the future, this models will be considered to be particularly useful in simulating the structural changes of tourism demand models that have been frequently altered by unobserved factors such as consumer taste, expectations and policy and regime changes.

This paper presents the modeling and analysis of dynamic characteristics of hydraulic snubber in electric power plant. The nonlinear state-space model of 14th order to describe the dynamics of the snubber was established by Simulink. The simulation results show that the hydraulic snubber reacts as like the conventional shock absorbers against the high pulse shock load. The snubber also shows the peculiar characteristics to the small step load, which temporarily lock the control valves up, however maintain same steady-state pressures of all internal chambers in the long run. Two case studies for the analysis of the snubber were addressed. Practical pulse testing method was also proposed to identify the frequency response characteristics of the snubber.

본 논문에서는 파킨슨병의 분자병리적 기작 중 하나인 미토콘드리아 기능 장애로 인한 신경세포 사멸과정을 유전자 제어 네트워크 관점에서 분석하고자 하였다. 유전자 상호 간의 미치는 영향을 추정하기 위해 상태변수와 관측변 수로 구성된 상태공간 모델을 이용하였으며, 상태공간 모델의 파라미터는 파킨슨병 실험모델로부터 얻어진 시계열 마이 크로어레이 데이터를 통해 계산하였다. 유전자 제어 네트워크는 상태공간 모델의 파라미터를 통해 계산된 유전자-유전 자 상호작용 행렬로부터 구축되었으며, 지속적인 발현 유지를 가능하게 하는 양의 self-loop 구조를 보이는 15개 유전 자가 관측되었으며, 이 가운데 8개는 허브 유전자였다. 이들 유전자는 세포 사멸과정에서 지속적인 발현을 유지하면서 중요한 역활을 감당할 것으로 판단되며 미토콘드리아 기능 장애로 발병되는 파킨슨병에 대한 치료전략 개발에 실마리를 제공할 수 있을 것으로 기대된다.

In this paper, we aimed to analyze the neuronal cell death process resulting from mitochondrial dysfunction, which is one of the molecular pathological mechanisms of Parkinson's disease from the perspective of a gene regulatory network. To estimate the influence between genes, a state space model consisting of state variables and observation variables was utilized, and the parameters of the state-space model were calculated through time-series microarray data obtained from a Parkinson's disease experimental model. The gene regulatory network was constructed from the gene-gene interaction matrix calculated through the parameters of the state-space model. Fifteen genes showing a positive self-loop structure that enables continuous gene expression were observed, among which eight were hub genes. These genes are judged to play a critical role while maintaining continuous expression during the cell death process, and it is expected that they can provide insights for developing therapeutic strategies for Parkinson's disease caused by mitochondrial dysfunction.

In this paper, the state space model of high speed train is established to describe its nonlinear dynamic characteristics, whose parameters are disturbed by noise with an arbitrary distribution, and an online state and parameter identification method is proposed based on a parameters set by employing Bayesian theory and Particle Filter (PF). Firstly, the priori probabilities of all of the possible parameters are set to be equal, and the predicted states with different parameters are estimated using PF. Then the posterior probabilities of the parameters are updated by analyzing the characteristic of the measurement noise using Bayesian theory. Finally, the system sate and parameter are estimated by weighted summing all of the parameters and predicted states. The simulation results indicate that the proposed method can estimate the states and parameters of high speed train online and adaptively.

To solve the security problem in the supervisor control and data acquisition (SCADA), a new factor network model of SCADA network attack and defense based on factor state space is presented. Combining with factor space theory, formal descriptions of factor neurons based on factor state space are developed. On the basis of analysis and expression of network attack and defense factors, factor neuron model based on variable weight is proposed and a FNN-based security defense architecture model for SCADA network is put forward. For illustration, by introducing factor space canes, an attack simulation experiment is utilized to show the feasibility of the proposed method in solving network attack and defense knowledge reasoning. Experimental results indicate that the proposed method can effectively improve recognition rate of different attacks. Factor neuron network based on factor state space can effectively solve complexity of knowledge reasoning and expression in network attack and defense system and provides a new method for solving similar application.

The state space model is effective on analyzing non-stationary time series data, especially in adapting better to the dynamic variation analysis of the time series data and forecasting demand,by replacing fixed parameters with the variable ones.This article elaborates the constructing process of state space model by the measurement equation and state equation. This article also selects M0 money supply, M1 money supply, M2 money supply as the characterize variables of monetary policy, selects the national housing climate Index as characterize variables of real estate development,status regression model with stronger dynamic analysis capabilities as empirical analysis tool, with 2005 to 2012 monthly data of relevant variables as empirical analysis object, carry out the empirical study of relationship between the development of China's real estate industry and the amount of the three currencies. The empirical results show that the amount of three currencies elastic influence for real estate development are positive, M2 money supply impact of greater intensity. Among, M0 money supply influence gradually weakened, M1 and M2 money supply influence gradually increased.

We have applied the state-space model to the Korean stock market under restrictions imposed by the present-value relation. Our main findings are (i) expected stock returns vary over time and have persistent and predictable component, (ii) expected dividend growth rates do not contain persistent and predictable component, (iii) expected stock returns play relatively more important role in explaining variations in the price-dividend ratio, (iv) shocks to expected stock returns are also more crucial in understanding unexpected stock return shocks, and (v) the state-space model does not appear to perform better than the predictive regression in terms of the ability in forecasting stock returns or dividend growth rates.

A well-performed core power control to track load changes is crucial in pressurized water reactor (PWR) nuclear power stations. It is challenging to keep the core power stable at the desired value within acceptable error bands for the safety demands of the PWR due to the sensitivity of nuclear reactors. In this paper, a state-space model predictive control (MPC) method was applied to the control of the core power. The model for core power control was based on mathematical models of the reactor core, the MPC model, and quadratic programming (QP). The mathematical models of the reactor core were based on neutron dynamic models, thermal hydraulic models, and reactivity models. The MPC model was presented in state-space model form, and QP was introduced for optimization solution under system constraints. Simulations of the proposed state-space MPC control system in PWR were designed for control performance analysis, and the simulation results manifest the effectiveness and the good performance of the proposed control method for core power control.

In a standard Metropolis-type Monte Carlo simulation, the proposal distribution cannot be easily adapted to "local dynamics" of the target distribution. To overcome some of these difficulties, Duane et al. (1987) introduced the method of hybrid Monte Carlo(HMC) which combines the basic idea of molecular dynamics and the Metropolis acceptance-rejection rule to produce Monte Carlo samples from a given target distribution. In this paper, using the HMC within Gibbs sampler, an asymptotical estimate of the smoothing mean and a general solution to state space modeling in Bayesian framework is obtaineds obtained.

The in-cabinet response spectrum is used to define the input motion in the seismic qualification of instruments and devices mounted inside an electrical cabinet. This paper presents a procedure for generating the in-cabinet response spectrum for electrical equipment based on in-situ testing by an impact hammer. The proposed procedure includes an algorithm to build the relationship between the impact forces and the measured acceleration responses of cabinet structures by estimating the state-space model. This model is used to predict seismic responses to the equivalent earthquake forces. Three types of structural model are analyzed for numerical verification of the proposed method. A comparison of predicted and simulated response spectra shows good convergence, demonstrating the potential of the proposed method to predict the response spectra for real cabinet structures using vibration tests. The presented procedure eliminates the uncertainty associated with constructing an analytical model of the electrical cabinet, which has complex mass distribution and stiffness.

The optimal control and filtering problems for the systems with the generalized state space model are considered and the generalized Riccati equation is derived. Also the algorithm for the solution of the generalized algebraic Riccati equation is developed and it is shown that the algotithm can be applied to the case where the matrix R is singular or near singular.

Normally, the artificial intelligence algorithms are widely applied to the optimal controller design. Then, it is expected that the best output performance is achieved. Unfortunately, when resulting controller parameters are implemented by using the practical devices, the output performance cannot be the best as expected. Therefore, the paper presents the optimal controller design using the combination between the state-space averaging model and the adaptive Tabu search algorithm with the new criteria as two penalty conditions to handle the mentioned problem. The buck-boost converter regulated by the cascade PI controllers is used as the example power system. The results show that the output performance is better than those from the conventional design method for both input and load variations. Moreover, it is confirmed that the reported controllers can be implemented using the realistic devices without the limitation and the stable operation is also guaranteed. The results are also validated by the simulation using the topology model of MATLAB and also experimentally verified by the testing rig.

Most studies using state-space models have been conducted under the assumption of independently distributed noises in measurement and state equation without adequate verification of the assumption. To avoid the improper use of state-space model, testing the assumption prior to the parameter estimation of state-space model is very important. The purpose of this paper is to investigate the general relationship between parameters of state-space models and those of ARIMA processes. Under the assumption, we derive restricted parameter spaces of ARIMA(p,0,p-1) models with mutually different AR roots where $p\;{\le}\;5$. In addition, the results of ARIMA(p,0,p-1) case can be expanded to more general ARIMA models, such as ARIMA(p-1,0,p-1), ARIMA(p-1,1,p-1), ARIMA(p,0,p-2) and ARIMA(p-1,1,p-2).

부분 최소자승회귀, 균형 잡힌 realization, 균형 잡힌 truncation을 결합함으로써, MIMO 상태공간 모델의 모델인식을 위한 효과적인 방법이 개발되었다. 개발된 방법에서 MIMO 시스템은 고차 ARX 모델로 표현되는 다중 MISO 시스템으로 분해된다. 이 때, ARX 모델의 파라미터는 부분 최소자승회귀에 의해 추정된다. 그 후, realization을 통해 각각의 MISO ARX 전달함수에 대한 MISO 상태공간 모델이 만들어지며, MIMO 상태공간 모델로 결합된다. 최종적으로, 균형 잡힌 realization과 균형 잡힌 truncation을 통해 최소의 균형 잡힌 MIMO 상태공간 모델이 얻어진다. 제안된 방법은 고압 $CO_2$ 용해도 측정 실험 장치의 온도제어를 위한 모델 예측 제어의 설계에 적용되었다.

An efficient method for identification of MIMO state space model has been developed by combining partial least squares (PLS) regression, balanced realization, and balanced truncation. In the developed method, a MIMO system is decomposed into multiple MISO systems each of which is represented by a high-order ARX model and the parameters of the ARX models are estimated by PLS. Then, MISO state space models for respective MISO ARX transfer function are found through realization and combined to a MIMO state space model. Finally, a minimal balanced MIMO state space model is obtained through balanced realization and truncation. The proposed method was applied to the design of model predictive control for temperature control of a high pressure $CO_2$ solubility measurement system.

The objective of this study is to design a one-stage dynamic prediction model with Kalman state space model. For a model verification, it is compared with EWMA(Exponentially Weighed Moving Average) model. The model designed in this research can be extended to process prevention control and quality monitoring.

In many cases the systems are so complex that it is not possible to obtain reasonable models using physical insight. Also a model based on physical insight contains a number of unknown parameters even if the structure is derived from physical laws. These problems can be solved by system identification. In this paper, Arago's disk system which has both stable and unstable regions is selected as an example for identification and a state-space model is identified using tailor-made model structure of this system. In stable region, a state-space model of Arago's disk system is identified through open loop experiment and a state-space model of unstable region is identified through closed loop experiment after using fuzzy controller to stabilize unstable system.