• Journal of the Korean Society for Nondestructive Testing
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• v.23 no.1
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• pp.21-29
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• 2003

It is very important to reduce the computational time in estimating the parameters of vision control algorithm for robot's position control in real time. Unfortunately, the batch estimation commonly used requires too murk computational time because it is iteration method. So, the batch estimation has difficulty for robot's position control in real time. On the other hand, the Extended Kalman Filtering(EKF) has many advantages to calculate the parameters of vision system in that it is a simple and efficient recursive procedures. Thus, this study is to develop the EKF algorithm for the robot's vision control in real time. The vision system model used in this study involves six parameters to account for the inner(orientation, focal length etc) and outer (the relative location between robot and camera) parameters of camera. Then, EKF has been first applied to estimate these parameters, and then with these estimated parameters, also to estimate the robot's joint angles used for robot's operation. finally, the practicality of vision control scheme based on the EKF has been experimentally verified by performing the robot's position control.

• Journal of the Korea Convergence Society
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• v.11 no.6
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• pp.7-14
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• 2020

A Battery Monitoring System capable of State-of-Charge(SOC) estimation using the Extended Kalman Filter(EKF) is described in this paper. In order to accurately estimate the SOC of the battery, the battery cells were modeled as the Thevenin equivalent circuit model. The Thevenin model's parameters were measured in experiments. For the Battery Monitoring System, we designed a battery monitoring device that can calculate the SOC estimation using the EKF and a monitoring server that controls multiple battery monitoring devices. We also develop a web-based dashboard for controlling and monitoring batteries. Especially the computation of the monitoring server could be reduced by calculating the battery SOC estimation at each Battery Monitoring Device.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.15 no.12
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• pp.1031-1043
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• 1990

A sequential recursive Kalman filtering algorithm, using causal image model, which is designed to operate in real time in the scanning mode is developed to enhance quality of 64Kbps videocodec images via function of suppression of various noises and optimum restoration. In order to improve its performance, adapted an averaging of pixel values between processing lines and adaptive filtering strategy based on the local spatial variance. Effecttiveness of the Kalman filtering algorithm proposed has been proved in the processed test kalman filtering algorithm proposed has been proved in the processed test images and the NMSE, LOGMSE measured, therefore, it may proposes possibility of the usage in videocodec for pre- and post- processing.

• Journal of Broadcast Engineering
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• v.20 no.4
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• pp.568-579
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• 2015

Recently, mobile devices with touch display panel are widely used. Accuracy and reaction speed of touch signal are very important in touch devices. Therefore, we need to develop an effective algorithm to reduce touch noise quickly and accurately. This paper proposes a touch noise reduction algorithm using Kalman filtering in consideration of signal motion. First, a specific pre-emphasis processing is applied to an input signal so as to maximize the effect of Kalman filtering. In other words, a pure signal in the touch signal increases but noise in the touch signal decreases. Next, motion of the signal is detected. Motion estimation is performed only if motion is detected. If we detect motion by using the only neighborhood of the signal, we can reduce about 75% of the computation in comparison with examining the entire area. Finally, Kalman filtering using the previous state of current signal is performed. Experimental results show that the proposed algorithm suppresses touch noise sufficiently without degradation of the pure signal

• Journal of the Korea Society for Simulation
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• v.19 no.4
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• pp.199-208
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• 2010

Positions of tactical objects are represented as Time, Space and Position Information(TSPI) in modeling and simulations(M&S). The format and required information record for TSPI is investigated by referring the TSPI object model of the Test and Training Enabling Architecture(TENA), which has been developed by the United States Department of Defense. The most sophisticated tactical data link, Link-16 has a Precise Participant Location and Information (PPLI) message. We study the data format for exchanging TSPI data based on the PPLI message. To evaluate and track positions of tactical objects, we consider the Kalman filter for linear systems, and the extended Kalman filter and the unscented Kalman filter for nonlinear systems. Based on motion equations of a ballistic missile, the tracking performance for the trajectory of the ballistic missile is simulated by the unscented Kalman filter.

• The Journal of the Korea Contents Association
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• v.10 no.3
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• pp.53-63
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• 2010

Recently, various monitoring systems are implemented actively by using wireless sensor networks(WSN). When implementing WSN-based monitoring system, there are three important issues to consider. At First, we need to consider a sensor node failure detection method to support the ongoing monitoring. Secondly, because sensor nodes use limited battery power, we need an efficient data filtering method to reduce energy consumption. At Last, a reducing processing overhead method is necessary. The existing Kalman filtering scheme has good performance on data filtering, but it causes too much processing overhead to estimate sensed data. To solve these problems, we, in this paper, propose a new data filtering scheme based on data statical analysis. First, the proposed scheme periodically aggregates node survival massages to support a node failure detection. Secondly, to reduce energy consumption, it sends the sample data with a node survival massage and do data filtering based on those messages. Finally, it analyzes the sample data to estimate filtering range in a server. As a result, each sensor node can use only simple compare operation for filtering data. In addition, we show from our performance analysis that the proposed scheme outperforms the Kalman filtering scheme in terms of the number of sending messages.

• Bulletin of the Korean Space Science Society
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• 1994.09a
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• pp.11-11
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• 1994

• Proceedings of the Korea Information Processing Society Conference
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• 2005.11a
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• pp.639-642
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• 2005

융합과 필터링(Fusion and Filtering: F/F) 기법은 신호처리, 제어 등 많은 공학분야에서 사용되며 현재 파티클 필터(Particle Filter: PF)가 최근 가장 각광받고 있다. 그러나 비선형 시스템과 모델링 하기 어려운 에러조건 때문에 기존의 파티클 필터조차 제대로 다루지 못하는 공학환경이 존재한다. 이에 파티클 필터뿐만 아니라 칼만 계열(Kalman varieties)의 필터 방법들을 통합할 수 있는 Constraint Manifold(CM) 기반 융합과 필터링 방법이 제안되었다. 본 논문에서는 CM 기반 필터링을 효과적으로 수행할 수 있도록 제한 조건 표현에 대한 방법론을 제시하면 시뮬레이션을 통해 기존 파티클 필터와의 성능 비교를 수행하였다.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.26 no.3
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• pp.263-273
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• 2008

The precision of sensors' position is particularly important in the application of road extraction or digital map generation. In general, the various ranging solution systems such as GPS, Total Station, and Laser Ranger have been employed for the position of the sensor. Basically, the ranging solution system has problems that the signal may be blocked or degraded by various environmental circumstances and has low temporal resolution. To overcome those limitations a IMU/range integrated system could be introduced. In this paper, after pointing out the limitation of extended Kalman filter which has been used for workhorse in navigation and geodetic community, the two sampling based nonlinear filters which are sigma point Kalman filter using nonlinear transformation and carefully chosen sigma points and particle filter using the non-gaussian assumption are implemented and compared with extended Kalman filter in a simulation test. For the ranging solution system, the GPS and Total station was selected and the three levels of IMUs(IMU400C, HG1700, LN100) are chosen for the simulation. For all ranging solution system and IMUs the sampling based nonlinear filter yield improved position result and it is more noticeable that the superiority of nonlinear filter in low temporal resolution such as 5 sec. Therefore, it is recommended to apply non-linear filter to determine the sensor's position with low degree position sensors.

• Journal of the Institute of Convergence Signal Processing
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• v.14 no.4
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• pp.243-248
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• 2013

In wireless sensor networks, consensus algorithms for dynamic systems may flexibly usable for their data fusion of a sensor network. In this paper, a distributed data fusion filter is implemented using an average consensus based on distributed sensor data, which is composed of some sensor nodes and a sink node to track the mean values of n sensors' data. The consensus filter resolve the problem of data fusion by a distribution Kalman filtering scheme. We showed that the consensus filter has an optimal convergence to decrease of noise propagation and fast tracking ability for input signals. In order to verify for the results of consensus filtering, we showed the output signals of sensor nodes and their filtering results, and then showed the result of the combined signal and the consensus filtering using zeegbee communication.