• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.478-481
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• 1996

Tracking a target of versatile maneuver recently demands a stable adaptation of tracker, and the multiple model techniques are being developed because of its ability to produce useful information of target maneuver. This paper presents the way to apply the multiple model method in a moving-target and moving-platform scenario, and the estimation and prediction results better than those of single Kalman filter.

• 제어로봇시스템학회:학술대회논문집
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• 1988.10a
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• pp.219-224
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• 1988

A decentralised computational procedure is proposed for the optimal feedback gain matrix of large-scale discrete-time systems with time-delays. The constant feedback gain matrix is computed from the optimal state and input trajectries obtained hierarchically by the interaction prediction method. All the calculation in this approach are done off-line. The resulting gains are optimal for all the initial conditions. The interaction prediction method is applied to time-delay large-scale systems with general structures by extending the dimensions of coupling matices. A numerical exampie illustrates the algorithm.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.7
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• pp.898-902
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• 1999

In this paper, we proposed an algorithm for estimating parameters of nonlinear continuous-discrete state-space system. This algorithm uses the conventional extended Kalman filter(EKF) for estimating state variables, and modifies the recursive prediction error method for parameter estimation of the nonlinear system. Simulation results for both linear and nonlinear measurements under the environment of process and measurement noises show a convincing performance of the proposed algorithm.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.18 no.34
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• pp.107-114
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• 1995

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.

• Journal of Power Electronics
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• v.12 no.1
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• pp.1-9
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• 2012

This study investigates an application of the Hamming network-dual extended Kalman filter (DEKF) based on pattern recognition for high accuracy state-of-charge (SOC)/capacity estimation and state-of-health (SOH) prediction at various temperatures. The averaged nine discharging/charging voltage-temperature (DCVT) patterns for ten fresh Li-Ion cells at experimental temperatures are measured as representative patterns, together with cell model parameters. Through statistical analysis, the Hamming network is applied to identify the representative pattern that matches most closely with the pattern of an arbitrary cell measured at any temperature. Based on temperature-checking process, model parameters for a representative DCVT pattern can then be applied to estimate SOC/capacity and to predict SOH of an arbitrary cell using the DEKF. This avoids the need for repeated parameter measuremet.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.1521-1521
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• 2001

The objective of the research being presented was to construct and calibrate a spectrometer for the analysis of single kernels of corn. In light of the difficulties associated with capturing the spatial variability in composition of corn kernels by single-beam spectrometry, a hyperspectral imaging spectrometer was constructed with the intention that it would be used to analyze single kernels of corn for the prediction of moisture and oil content. The spectrometer operated in the range of 750- 1090 nanometers. After evaluating four methods of standardizing the output from the spectrometer, calibrations were made to predict whole-kernel moisture and oil content from the hyperspectral image data. A genetic algorithm was employed to reduce the number of wavelengths imaged and to optimize the calibrations. The final standard errors of prediction during cross-validation (SEPCV) were 1.22% and 1.25% for moisture and oil content, respectively. It was determined, by analysis of variance, that the accuracy and precision of single-kernel corn analysis by hyperspectral imaging is superior to the single kernel reference chemistry method (as tested).

• Transactions of Materials Processing
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• v.25 no.1
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• pp.56-60
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• 2016

Precise prediction and control of the strip profile is crucial for automatic process set-up and operation of a hot strip mill. In the current study, we present the effect of post-deformation on the steady-state strip profile. The process was simulated by a 3-D elastic-plastic finite element (FE) analysis. Comparisons are made between the strip profile measured at the roll exit and the steady-state strip profile. The results raised an issue with regard to the importance of taking into account the effect of post-deformation.

• Journal of Power Electronics
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• v.11 no.1
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• pp.45-50
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• 2011

This paper proposes a method for comparing and evaluating anti-windup proportional-integral (PI) control strategies. The so-called PI plane is used and its coordinate is composed of the error and the integral state. In addition, an anti-windup PI controller with integral state prediction is proposed. The anti-windup scheme can be easily analyzed and evaluated on the PI plane in detail. Representative anti-windup methods are experimentally applied to the speed control of a vector-controlled induction motor driven by a pulse width modulated (PWM) voltage-source inverter (VSI). The experimental results compare the anti-windup PI controllers. It is empathized that the initial value of the integral state at the beginning of the linear range dominates the control performance in terms of overshoot and settling time.

• Korean Journal of Remote Sensing
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• v.7 no.1
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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.

• Journal of Electrical Engineering and Technology
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• v.12 no.3
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• pp.1053-1063
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• 2017

Power prediction is critical to improve power efficiency in Smart Grids. Markov chain provides a useful tool for power prediction. With careful investigation of practical power datasets, we find an interesting phenomenon that the stochastic property of practical power datasets does not follow the Markov features. This mismatch affects the prediction accuracy if directly using Markov prediction methods. In this paper, we innovatively propose a spatial transform based data processing to alleviate this inconsistency. Furthermore, we propose an enhanced power prediction method, named by Spatial Mapping Markov-Difference (SMMD), to guarantee the prediction accuracy. In particular, SMMD adopts a second prediction adjustment based on the differential data to reduce the stochastic error. Experimental results validate that the proposed SMMD achieves an improvement in terms of the prediction accuracy with respect to state-of-the-art solutions.