• Journal of the Korean Data and Information Science Society
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• v.14 no.4
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• pp.1091-1102
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• 2003

The Weibull variate is commonly used as a lifetime distribution in reliability applications. Estimation of parameters is revisited in the two-parameter Weibull distribution. The method of product spacings, the method of quantile estimates and the method of least squares are applied to this distribution. A comparative study between a simple minded estimate, the maximum likelihood estimate, the product spacings estimate, the quantile estimate, the least squares estimate, and the adjusted least squares estimate is presented.

• Journal of applied mathematics & informatics
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• v.39 no.5_6
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• pp.805-811
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• 2021

Currently, batteries use commonly as energy sources for mobile electric devices. Due to the high density of energy, the energy storage state of a battery is very important information. To know the battery's energy storage state, it is necessary to find out the open state voltage of the battery. The open state voltage calculates with a mathematical model, but the computation of the real time state is complicated and requires many calculations. Therefore, the state observer designs to estimate in real time the battery open-circuit voltage as disturbance including model error. Using the estimated open voltage and applying it to the state estimation algorithm, we can estimate the charge. In this study, we first estimate the open-circuit voltage and design an estimation algorithm for estimating the state of battery charge. This includes errors in the system model and has a robust characteristic to noise. It is possible to increase the precision of the charge state estimation.

• Journal of Advanced Marine Engineering and Technology
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• v.36 no.7
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• pp.919-926
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• 2012

In order to control a LTI(Linear Time Invariant) system subjected to system noise and measurement noise, first of all, it is necessary to estimate the state of system with reliability. Kalman filtering technique has been widely used to estimate the state of the stochastic LTI system with stationary noise characteristics because of its estimation ability versus algorithm simplicity. However, it often fails to estimate the state of the LTI system of which system parameter uncertainty exists partly and/or input uncertainty exists. In this paper, a new estimation technique based on Kalman filter is suggested for stochastic LTI system under parameter uncertainty and/or input uncertainty. A fuzzy estimation algorithm against uncertainties is introduced so as to compensate the state estimate filtered by Kalman filter. In order to verify the state estimation performance of the suggested technique, several simulations are accomplished.

• The Transactions of the Korean Institute of Power Electronics
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• v.25 no.4
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• pp.303-310
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• 2020

Estimating the accurate internal state of lithium ion batteries to increase their safety and efficiency is crucial. Various algorithms are used to estimate the internal state of a lithium ion battery, such as the extended Kalman filter and sliding mode observer. A state-space model is essential in using algorithms to estimate the internal state of a battery. Two principal methods are used to express the state-space model, namely, continuous time and discrete time. In this work, the extended Kalman filter is employed to estimate the internal state of a battery. Moreover, this work presents and analyzes the estimation performance of algorithms consisting of a continuous time state-space model and a discrete time state-space model through static and dynamic profiles.

• Journal of rehabilitation welfare engineering & assistive technology
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• v.7 no.2
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• pp.1-5
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• 2013

In this paper, we proposed how to estimate the walking state in the knee disarticulation prosthesis's knee angle control. In control of the knee disarticulation prosthesis, we can estimate walking state that measurement of knee angle using absolute encoder and measurement of load on the soles using strain gage. We suggested a method of estimating the current walking states which can be divided into four cases and showed the effectiveness of the method via a series of experiments.

• Journal of Electrical Engineering and Technology
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• v.7 no.6
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• pp.891-897
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• 2012

In this paper, the observer based nonlinear state feedback controller has been developed to control the pressures of the oxygen and the hydrogen in the PEM(Proton Exchange Membrane) fuel cell system. Nonlinear model of the PEM fuel cell system was introduced to study the design problems of the state observer and model based controller. A cascade observer using the filtering technique was used to estimate the pressure derivatives of the cathode and the anode in the system. In order to estimate the pressures of the cathode and the anode, the sliding mode observer was designed by using these pressure derivatives. To estimate the oxygen pressure and the hydrogen pressure in the system, the nonlinear state observer was designed by using the cathode pressure estimates and the anode it. These results will be very useful to design the state feedback controller. The validity of the proposed observers and the controller has been investigated by using the Lyapunov's stability analysis strategy.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10a
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• pp.328-333
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• 1996

In this paper, we propose a new method to estimate robot position without landmark. At first, it is studied to estimate robot state using Markov decision rule. And, a matching method is discussed for estimating current position more accurately under the estimated current state. At second, we combine or fuse the matching method with the POMDP method in order to estimate the position under a dynamically changing environment. Finally we will show that our method can estimate the position precisely and robustly of which error are not cumulated through simulation results.

• IEMEK Journal of Embedded Systems and Applications
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• v.3 no.2
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• pp.102-108
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• 2008

This paper describes a mapping and localization based on wave algorithm[11] and Kalman filter for effective SLAM. Each robot in a multi robot system has its own task such as building a map for its local position. By combining their data into a shared map, the robot scans actively seek to verify their relative locations. For simultaneous localization the algorithm which is well known as Kalman Filter (KF) is used. For modelling the robot position we wish to know three parameters (x, y coordinates and its orientation) which can be combined into a vector called a state variable vector. The Kalman Filter is a smart way to integrate measurement data into an estimate by recognizing that measurements are noisy and that sometimes they should ignored or have only a small effect on the state estimate. In addition to an estimate of the state variable vector, the algorithm provides an estimate of the state variable vector uncertainty i.e. how confident the estimate is, given the value for the amount of error in it.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38A no.4
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• pp.318-324
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• 2013

For blind channel equalization, the reliability of signal estimate determines the convergence speed and steady-state performance of the equalizer. Therefore the nonlinear estimator and reference signal being used in signal estimate should be chosen appropriately. In this paper, to increase the reliability of the signal estimate, two errors were obtained by applying coarse signal points and dense signal points respectively to signal estimate, the relative reliabilities of two errors were calculated, then the equalizer was adapted deferentially utilizing the reliabilities. At this point, by applying two errors alternately, two modes of operation were smoothly combined. Through computer simulations the proposed method was confirmed to achieve fast transient state convergence and low steady-state error compared to traditional methods.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2003.11a
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• pp.11-14
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• 2003

The dynamics of unobservable short rate are frequently estimated directly by using a proxy. We estimate the biases resulting from this practice ("proxy problem"). To solve this problem, State-Space models have been proposed by many researchers. State-Space models have been used to estimate the unobservable variables from the observable variables in econometrics. However, applications of State-Space models often result in a misleading interpretation of the underlying processes especially when the absorbability of the State-Space model and the assumption of noise processes in the state vector are not properly considered. In this study, we propose the exact State-Space model that properly considers the faults of previous researchers to solve the proxy problem.