• The Transactions of the Korean Institute of Power Electronics
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• v.5 no.2
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• pp.131-139
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• 2000

일반적으로 시스템 인식과 제어에 이용하는 다층 신경회로망은 기존의 역전파 알고리즘을 이용한다. 그러나 결선강도에 대한 오차의 기울기를 구하는 방법이기 때문에 국부적 최소점에 빠지기 쉽고, 수렴속도가 매우 늦으며 초기 결선강도 값들이나 학습계수에 민감하게 반응한다. 이와 같은 단점을 개선하기 위하여 확장된 칼만 필터링 기법을 역전파 알고리즘에 결합하였으나 계산상의 복잡성 때문에 망의 크기가 증가하면 실제 적용할 수 없다. 최근 신경회로망을 선형과 비선형 구간으로 구분하고 칼만 필터링 기법을 도입하여 수렴속도를 빠르게 하고 초기 결선강도 값에 크게 영향을 받지 않도록 개선하였으나, 여전히 은닉층의 선형 오차값을 역전파 알고리즘에 의해서 계산하기 때문에 학습계수에 민감하다는 단점이 있다. 본 논문에서는 위에서 언급한 기존의 신경회로망 알고리즘의 문제점을 개선하기 위하여 은닉층의 목표값을 최적기법에 의하여 직접계산하고 각각의 결선강도 값은 반복최소 자승법으로 온라인 학습하는 알고리즘을 제안하고 이들 신경회로망 알고리즘과 비교하고자 한다. 여러 가지 시뮬레이션과 실험을 통하여 제안된 방법이 초기 결선강도에 크게 영향을 받지 않으며, 기존의 학습계수 선정에 따른 문제점을 해결함으로써 신경회로망 모델에 기초한 실시간 제어기 설계에 응용할 수 있도록 하였다. 또한, 유도전동기의 속도추정과 제어에 적용하여 좋은 결과를 보였다.

• Journal of Power Electronics
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• v.16 no.2
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• pp.643-653
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• 2016

The conventional methods used to evaluate battery state-of-charge (SOC) cannot accommodate the chemistry nonlinearities, measurement inaccuracies and parameter perturbations involved in estimation systems. In this paper, an impedance-based equivalent circuit model has been constructed with respect to a LiFePO₄ battery by approximating the electrochemical impedance spectrum (EIS) with RC circuits. The efficiencies of approximating the EIS with RC networks in different series-parallel forms are first discussed. Additionally, the typical hysteresis characteristic is modeled through an empirical approach. Subsequently, a methodology incorporating an H-infinity observer designated for open-circuit voltage (OCV) observation and a hysteresis model developed for OCV-SOC mapping is proposed. Thereafter, evaluation experiments under FUDS and UDDS test cycles are undertaken with varying temperatures and different current-sense bias. Experimental comparisons, in comparison with the EKF based method, indicate that the proposed SOC estimator is more effective and robust. Moreover, test results on a group of Li-ion batteries, from different manufacturers and of different chemistries, show that the proposed method has high generalization capability for all the three types of Li-ion batteries.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.37 no.12
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• pp.1225-1231
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• 2009

One reaction wheel cluster of satellite usually has four reaction wheels. Each wheel is not arranged parallel to the attitude axis of satellite. Therefore, if one reaction wheel is broken, it is very hard to isolate the fault except using the sensors of wheel itself. In this paper, the isolator of satellite reaction wheel cluster is designed. Using a dual filter, FDP(Fault Detection Parameter) is made to detect fault, and using a multi-hypothesis extended Kalman filter, fault isolation of wheel cluster is done. We verify the improvement of isolation performance of wheel cluster by simulation with 4-reaction wheel cluster.

• Advances in aircraft and spacecraft science
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• v.7 no.6
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• pp.495-516
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• 2020

Nonlinear state estimation is a desirable and required technique for many situations in engineering (e.g., aircraft/spacecraft tracking, space situational awareness, collision warning, radar tracking, etc.). Due to high standards on performance in these applications, in the last few decades, there was an increasing demand for methods that are able to provide more accurate results. However, because of the mathematical complexity introduced by the nonlinearities of the models, the nonlinear state estimation uses techniques that, in practice, are not so well-established which, leads to sub-optimal results. It is important to take into account that each method will have advantages and limitations when facing specific environments. The main objective of this paper is to provide a comprehensive overview and interpretation of the most well-known methods for nonlinear state estimation with Gaussian priors. In particular, the Kalman filtering methods: EKF (Extended Kalman Filter), UKF (Unscented Kalman Filter), CKF (Cubature Kalman Filter) and EnKF (Ensemble Kalman Filter) with an aerospace perspective.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.32 no.3
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• pp.271-279
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• 2014

As an alternative way to overcome the weakness of the global navigation satellite system (GNSS) in hostile situation, a gravity gradient referenced navigation (GGRN) has been developed. This paper analyzed the performance of GGRN with respect to the initial errors, DB resolution as well as update rates. On the basis of simulations, it was found that the performance of GGRN is getting worse when initial errors exist but the navigation results are rapidly converged. Also, GGRN generates better results when DB resolution is higher and update rates are shorter than 20 seconds. However, it is difficult to deduce the optimal parameters for the navigation because some trajectories show better performance in case low-resolution DB is applied or long update rate is supposed. Therefore, further analysis to derive specific update conditions to improve the performance has been performed. Those update conditions would not be generalized for all cases although maximum improvement rate is over 200% in certain case. In the future, some more developments and tests on the combination of various geophysical data and/or algorithms are necessary to construct more stable and reliable navigation system.

• 정보저장시스템학회:학술대회논문집
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• 2005.10a
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• pp.44-46
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• 2005

Data storage related with writing and retrieving requires high storage capacity, fast transfer rate and less access time. Today any data storage system cannot satisfy these conditions, however holographic data storage system can perform faster data transfer rate because it is a page oriented memory system using volume hologram in writing and retrieving data. System can be constructed without mechanical actuating part therefore fast data transfer rate and high storage capacity about $1Tb/cm^3$ can be realized. In this paper, to reduce errors of binary data stored in holographic data storage system, a new method for bit error reduction is suggested. We proposal Algorithm use The Extended Kalman filter. The Kalman filter reduce measurement noise. Therefore, By using this error reduction method following results are obtained; the effect of measurement nois of Pixel is decreased and the intensity profile of data page becomes uniform therefore the better data storage system can be constructed.

• The Journal of Korea Robotics Society
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• v.13 no.2
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• pp.79-85
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• 2018

This study focuses on autonomous exploration based on map expansion for an underwater robot equipped with acoustic sonars. Map expansion is applicable to large-area mapping, but it may affect localization accuracy. Thus, as the key contribution of this paper, we propose a method for underwater autonomous exploration wherein the robot determines the trade-off between map expansion ratio and position accuracy, selects which of the two has higher priority, and then moves to a mission step. An occupancy grid map is synthesized by utilizing the measurements of an acoustic range sonar that determines the probability of occupancy. This information is then used to determine a path to the frontier, which becomes the new search point. During area searching and map building, the robot revisits artificial landmarks to improve its position accuracy as based on imaging sonar-based recognition and EKF-SLAM if the position accuracy is above the predetermined threshold. Additionally, real-time experiments were conducted by using an underwater robot, yShark, to validate the proposed method, and the analysis of the results is discussed herein.

• ETRI Journal
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• v.34 no.3
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• pp.379-387
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• 2012

In this paper, an error compensation technique for a dead reckoning (DR) system using a magnetic compass module is proposed. The magnetic compass-based azimuth may include a bias that varies with location due to the surrounding magnetic sources. In this paper, the DR system is integrated with a Global Positioning System (GPS) receiver using a finite impulse response (FIR) filter to reduce errors. This filter can estimate the varying bias more effectively than the conventional Kalman filter, which has an infinite impulse response structure. Moreover, the conventional receding horizon Kalman FIR (RHKF) filter is modified for application in nonlinear systems and to compensate the drawbacks of the RHKF filter. The modified RHKF filter is a novel RHKF filter scheme for nonlinear dynamics. The inverse covariance form of the linearized Kalman filter is combined with a receding horizon FIR strategy. This filter is then combined with an extended Kalman filter to enhance the convergence characteristics of the FIR filter. Also, the receding interval is extended to reduce the computational burden. The performance of the proposed DR/GPS integrated system using the modified RHKF filter is evaluated through simulation.

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.6
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• pp.585-593
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• 2006

In this paper, we propose a vision-based simultaneous localization and map-building (SLAM) algorithm. SLAM problem asks the location of mobile robot in the unknown environments. Therefore, this problem is one of the most important processes of mobile robots in the outdoor operation. To solve this problem, Extended Kalman filter (EKF) is widely used. However, this filter requires computational power (${\sim}O(N)$, N is the dimension of state vector). To reduce the computational complexity, we applied compressed extended Kalman filter (CEKF) to stereo image sequence. Moreover, because the mobile robots operate in the outdoor environments, we should estimate full d.o.f.s of mobile robot. To evaluate proposed SLAM algorithm, we performed the outdoor experiments. The experiment was performed by using new wheeled type mobile robot, Robhaz-6W. The performance results of CEKF SLAM are presented.

• International Journal of Control, Automation, and Systems
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• v.5 no.3
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• pp.251-268
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• 2007

A Gaussian sum filter (GSF) is proposed in this paper on simultaneous localization and mapping (SLAM) for mobile robot navigation. In particular, the SLAM problem is tackled here for cases when only bearing measurements are available. Within the stochastic mapping framework using an extended Kalman filter (EKF), a Gaussian probability density function (pdf) is assumed to describe the range-and-bearing sensor noise. In the case of a bearing-only sensor, a sum of weighted Gaussians is used to represent the non-Gaussian robot-landmark range uncertainty, resulting in a bank of EKFs for estimation of the robot and landmark locations. In our approach, the Gaussian parameters are designed on the basis of minimizing the representation error. The computational complexity of the GSF is reduced by applying the sequential probability ratio test (SPRT) to remove under-performing EKFs. Extensive experimental results are included to demonstrate the effectiveness and efficiency of the proposed techniques.