• The Plant Pathology Journal
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• v.17 no.6
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• pp.370.4-370
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• 2001

• Proceedings of the Korean Fiber Society Conference
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• 1991.06b
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• pp.76-76
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• 1991

• Proceedings of the KIEE Conference
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• 1988.11a
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• pp.107-110
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• 1988

The quadratic cost function J(x) and the normalized residuals $r_N$ are conventionally used for identifying the presence and location of bed measurements in power system state estimation. These are "post estimation" tests and therefore require the complete re-estimation of system states whenever bed data is identified. This paper presents a pre-filter for reducing errors of the raw measurements using an adaptive filter. Each measurement is processed by the adaptive filter before being used in the state estimation. The performance of the adaptive filter is tested and the results are shown in this paper.

• Journal of Institute of Control, Robotics and Systems
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• v.19 no.7
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• pp.640-645
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• 2013

This paper presents a step length estimation algorithm for Pedestrian Dead Reckoning using linear calibrated ZUPT (zero velocity update) with a foot mounted IMU. The IMU consists of 3 axis accelerometer, gyro and magnetometer. Attitude of IMU is estimated using an inertial navigation algorithm. To increase accuracy of step length estimation algorithm, we propose a stance detection algorithm and an enhanced ZUPT. The enhanced ZUPT calculates firefighter's step length considering velocity error caused by sensor bias during one step. This algorithm also works efficiently at various motions, such as crawling, sideways and stair stepping. Through experiments, the step length estimation performance of the proposed algorithm is verified.

• Journal of Institute of Control, Robotics and Systems
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• v.19 no.4
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• pp.334-340
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• 2013

Using a relationship between step length and accelerometer output, step length can be estimated. In this paper, we propose a new step length estimation algorithm, which can be used both for the plane and the slope by compensating the slope angle. The slope angle is estimated using a barometer and the slope angle is compensated by observing how the slope affects the step length estimation. The proposed algorithm is verified using five adult man walking data, where the average length error is about 3% regardless of the slope.

• Journal of Institute of Control, Robotics and Systems
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• v.15 no.12
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• pp.1223-1231
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• 2009

We derive a systematic and iterative calibration algorithm, and position and pose estimation algorithm for the mobile robots in formation system based on the vision system. In addition, we develop a coordinate matching algorithm which calculates matched sequence of order in both extracted image coordinates and object coordinates for non interactive calibration and pose estimation. Based on the results of calibration, we also develop a camera simulator to confirm the results of calibration and compare the results of simulations with those of experiments in position and pose estimation.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.5
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• pp.407-412
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• 2015

This paper presents the design of a lab-built powered knee prosthesis based on a BLDC motor, a sensored impedance control using a force sensor, and a sensorless impedance control through torque estimation. Firstly, we describe the structure of the lab-built powered knee prosthesis and its limitations. Secondly, we decompose the gait cycle into five stages and apply the position-based impedance control for the powered knee prosthesis. Thirdly, we perform an experiment for the torque estimation and the sensorless impedance control of the prosthesis. The experimental results show that we can use the torque estimation to control the low impedance during the swing phase, although the estimated torque data has a delay compared with the measured torque by a load cell.

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.9
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• pp.904-908
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• 2008

Every autonomous system like a robot needs a power source known as a battery. And proper management of the battery is very important for proper operation. To know State of Charge(SOC) of a battery is the very core of proper battery management. In this paper, the SOC estimation problem is tackled based on the well known Extended Kalman Filter(EKF). Combined the existing battery model is used and then EKF is employed to estimate the SOC. SOC table is constructed by extensive experiment under various conditions and used as a true SOC. To verify the estimation result, extensive experiment is performed with various loads. The comparison result shows the battery estimation problem can be well solved with the technique proposed in this paper. The result of this paper can be used to develop related autonomous system.

• Proceedings of the KIEE Conference
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• 2001.07a
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• pp.178-180
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• 2001

State estimation in power system is to estimate state variable value which minimizes the error from the real state measured by the gauge and connection state of the circuit breaker. In the past, it was difficult to determine measure function considering the correlation of the measured values. In this paper, an extended state estimation is proposed to process easily various kinds of estimation variable. The proposed algorithm is developed by expanding state variable concept based on many measured values and treating correlation between estimation variable and state variable, it is considered that the state variable satisfy some limitations named "Equality Limitation conditions".

• Journal of Institute of Control, Robotics and Systems
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• v.22 no.6
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• pp.470-476
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• 2016

This paper proposes a practical algorithm for the reduction of measurement errors due to drift in a micro-electromechanical system (MEMS) gyros that are used for a mobile robot. Any drift in a MEMS gyro will cause an unbounded growth of errors in the estimation of heading, which makes it nearly useless in applications that require high accuracy over a long operating time. In proposed method, maneuvers of a cleaning robot are observed through encoders' measurement process and a decision to correct bias drift will be made if necessary. The method used in this paper is called the "heading estimation filter". To evaluate the accuracy of the proposed method, a comparison was made between the estimation of the heading of the cleaning robot and one from a motion capture system.