• 대한원격탐사학회지
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• 제7권1호
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• pp.45-67
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• 1991

An overview is given on stochastic techniques with which remotely sensed data may be used together with ground measurements for purposes of state estimation and prediction. They can explicitly account for spatiotemporal differences in measurement characteristics between ground measurements and remotely sensed data, and are suitable for highly variant space or space-time processes, such as atmosperic processes, which may be viewed as (containing) a random process. For state estimation of static ststems, optimal linear estimation is described. As alternatives, various co-kriging estimation techniques are also described, including simple, ordinary, universal, lognormal, disjunctive, indicator, and Bayesian extersion to simple and lognormal. For illustrative purposes, very simple examples of optimal linear estimation and simple co-kriging are given. For state estimation and prediction of dynamic system, distributed-parameter kalman filter is described. Issues concerning actual implemention are given, and with application potential are described.

• 한국지구과학회지
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• 제31권5호
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• pp.437-447
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• 2010

Spatial estimation of environmental variables has been regarded as an important preliminary procedure for decision-making. A minimum variance criterion, which has often been adopted in traditional kriging algorithms, does not always guarantee the optimal estimates for subsequent decision-making processes. In this paper, a geostatistical framework is illustrated that consists of uncertainty modeling via stochastic simulation and risk modeling based on loss functions for the selection of optimal estimates. Loss functions that quantify the impact of choosing any estimate different from the unknown true value are linked to geostatistical simulation. A hybrid loss function is especially presented to account for the different impact of over- and underestimation of different land-use types. The loss function-specific estimates that minimize the expected loss are chosen as optimal estimates. The applicability of the geostatistical framework is demonstrated and discussed through a case study of copper mapping.

• 제어로봇시스템학회논문지
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• 제1권1호
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• pp.20-24
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• 1995

In this paper we analyze the performance bounds of the optimal FIR filter in continuous time systems with modeling uncertainty. The performance bounds are presented by the estimation error convariance and they are here expressed by the upper bounds of the difference of the estimation error covariance between the real and nominal values in case of the system with model uncertainties whose upper bounds are imperfrctly known a priori. The performance bounds of the optimal FIR filter are compared with those of the Kalman filter via a numerical example applied to the estimation of the motion of an aircraft carrier at sea, which shows the former has better performances than the latter.

• 전기의세계
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• 제30권11호
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• pp.728-733
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• 1981

Most of real world systems are highly non-linear. But due to difficulties in analyzing and dealing with it, only the linear system theory is well estabilished. Bilinear system where state and control are linear but not linear jointly is introduced. Here shows that optimal state estimation of stochastic bilinear system requirs infinite dimensional filter, thus onesub-optimal estimator for this system is suggested.

• 제어로봇시스템학회논문지
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• 제20권1호
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• pp.1-5
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• 2014

In this paper, we propose an optimal fixed-lag FIR (Finite-Impulse-Response) smoother for a class of discrete time-varying state-space signal models. The proposed fixed-lag FIR smoother is linear with respect to inputs and outputs on the recent finite horizon and estimates the delayed state so that the variance of the estimation error is minimized with the unbiased constraint. Since the proposed smoother is derived with system inputs, it can be adapted to feedback control system. Additionally, the proposed smoother can give more general solution than the optimal FIR filter, because it reduced to the optimal FIR filter by setting the fixed-lag size as zero. A numerical example is presented to illustrate the performance of the proposed smoother by comparing with an optimal FIR filter and a conventional fixed-lag Kalman smoother.

• Communications for Statistical Applications and Methods
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• 제28권6호
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• pp.643-653
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• 2021

Few studies are found in literature on estimation of population quantiles using the method of ranked set sampling (RSS). The optimal RSS strategy is to select observations with at most two fixed rank order statistics from different ranked sets. In this paper, a near optimal unbalanced RSS model for estimating p^th(0 < p < 1) population quantile is proposed. Main advantage of this model is to use each rank order statistics and is distributionfree. The asymptotic relative efficiency (ARE) for balanced RSS, unbalanced optimal and proposed near-optimal methods are computed for different values of p. We also compared these AREs with respect to simple random sampling. The results show that proposed unbalanced RSS performs uniformly better than balanced RSS for all set sizes and is very close to the optimal RSS for large set sizes. For the practical utility, the near optimal unbalanced RSS is recommended for estimating the quantiles.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1996년도 한국자동제어학술회의논문집(국내학술편); 포항공과대학교, 포항; 24-26 Oct. 1996
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• pp.32-35
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• 1996

In this paper, it is shown that the dominant errors of baro-altimeters can be characterized by bias and scale factor errors. Also an optimal filter for estimating both bias and scale factor is derived based on the concept of model transition. The optimal filter is, however, not realizable because the model transition hypotheses increase exponentially. Therefore a realizable suboptimal filter using the interacting multiple model(IMM) technique is proposed. Computer simulation results show that the estimation errors of the proposed filter are smaller than those of the conventional least squares algorithm with a forgetting factor when both the bias and the scale factor are varying.

• Journal of Electrical Engineering and Technology
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• 제12권1호
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• pp.378-384
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• 2017

In this paper, an efficient short-time Fourier analysis method for the quasi-periodic signals is proposed via an optimal fixed-lag finite impulse response (FIR) smoother approach using a receding horizon scheme. In order to deal with time-varying Fourier coefficients (FCs) of quasi-periodic signals, a state space model including FCs as state variables is augmented with the variants of FCs. Through an optimal fixed-lag FIR smoother, FCs and their increments are estimated simultaneously and combined to produce final estimates. A lag size of the optimal fixed-lag FIR smoother is chosen to minimize the estimation error. Since the proposed estimation scheme carries out the correction process with the estimated variants of FCs, it is highly probable that the smaller estimation error is achieved compared with existing approaches not making use of such a process. It is shown through numerical simulation that the proposed scheme has better tracking ability for estimating time-varying FCs compared with existing ones.

• 한국소음진동공학회논문집
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• 제24권5호
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• pp.381-389
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• 2014

This study is concerned with the estimation of vibration-field of a cylindrical structure by modal expansion method(MEM). MEM is a technique that identifies modal participation factors using some of vibration signals and natural modes of the structure: The selection of sensor locations has a big influence on predicted vibration results. Therefore, this paper deals with four optimal sensor placement( OSP) methods, EFI, EFI-DPR, EVP, AutoMAC, for the estimation of vibration field. It also finds optimal sensor locations of the cylindrical structure by each OSP method and then performs MEMs. Predicted vibration results compared with reference ones obtained by forced response analysis. The standard deviations of errors between reference and predicted results were also calculated. It is utilized to select the most suitable OSP method for estimation of vibration field of the cylindrical structure.

• 대한교통학회지
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• 제23권3호
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• pp.109-123
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• 2005

본 논문에서는 지방부 간선도로에서 제공되는 단속류 구간통행시간 정보의 체계적인 수집 분석을 위해 현재 일부 국도에서 운영중인 차량번호판 매칭방식 AVI(자동차량인식장치 : Automated Vehicle Indentification)로부터 수집된 자료의 적합성분석 및 단속류 구간통행시간 자료의 적정집락간격(optimal aggregation interval)산정을 위한 통계적 결정방법을 연구하였다. 연구결과 차량번호판 매칭방식 AVI 수집자료는 통과위주의 대표차로상에서만 수집되기 때문에 차로간의 속도차가 크게 나타나는 단속류 구간에서는 전차로에 대한 통행시간 수집자료와 교통특성에 차이가 있으므로 현재의 차량번호판 매칭방식 ANI 표본수집 자료를 통해 산출된 구간통행기간을 구간의 대표값으로 적용하는 데에는 문제가 있어 이에 대한 추가적인 검토가 필요하다는 결론을 얻었다. 그리고, 단속류 구간의 통행시간 정보제공을 위한 수집자료의 적정집락간격 결정방법으로 점추정과 구간추정방법을 적용하여 모형을 개발하고, 이 모형을 적용한 결과 점추정모형이 구간추정모형보다 집락간격결정에 민감하고 보다 정확한 적정집락간격 선정이 가능한 것으로 밝혀졌으며, 단속류 구간의 적정집락간격은 5분으로 산정되어 현재 적용되고 있는 단속류 구간 5분 집락간격은 적정한 것으로 판단된다.