• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.05a
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• pp.863-865
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• 2015

A method for estimating position is AOA, TOA, TDOA, Wi-Fi, Beacon etc. A method for estimating the location in indoor environment is used mainly Wi-Fi, Beacon. The reason is that AOA, TOA and TDOA are unfit to estimate position in indoor environment. To address this problem, this paper presents a AOA algorithm based on AP having a four directional antenna. The algorithm uses only the angle received from the four antennas. This can draw linear equations for signal. And calculate the intersections of the lines. Intersections means the position of user.

• Proceedings of the KIEE Conference
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• 2004.10a
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• pp.9-11
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• 2004

This paper presents a position control system for a linear induction motor(LIM) with cage-type secondary using direct thrust control(DTC). The position controller, that combines the merits of integral-proportional(IP) speed control, is designed for the LIM. The actual position of the LIM is defected by the linear scale the resolution of 100. Thrust correction coefficient due to the end effect of the LIM is utilized in estimating actual thrust. As a result, responses of the position, speed, thrust, and flux are shown.

• The Journal of Korea Robotics Society
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• v.15 no.1
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• pp.70-76
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• 2020

Multi-floor navigation of a mobile robot requires a technology that allows the robot to safely get on and off the elevator. Therefore, in this study, we propose a method of recognizing the elevator from the current position of the robot and estimating the location of the elevator locally so that the robot can safely get on the elevator regardless of the accumulated position error during autonomous navigation. The proposed method uses a deep learning-based image classifier to identify the elevator from the image information obtained from the RGB-D sensor and extract the boundary points between the elevator and the surrounding wall from the point cloud. This enables the robot to estimate the reliable position in real time and boarding direction for general elevators. Various experiments exhibit the effectiveness and accuracy of the proposed method.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.49 no.6
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• pp.307-315
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• 2000

For the autonomy of a mobile robot it is first needed to know its position and orientation. Various methods of estimating the position of a robot have been developed. However, it is still difficult to localize the robot without any initial position or orientation. In this paper we present the method how to make the colored map and how to calculate the position and direction of a robot using the angle data of an omnidirectional image. The wall of the map is rendered with the corresponding color images and the color histograms of images and the coordinates of feature points are stored in the map. Then a mobile robot gets the color omnidirectional image at arbitrary position and orientation, segments it and recognizes objects by multiple color indexing. Using the information of recognized objects robot can have enough feature points and localize itself.

• Proceedings of the KIEE Conference
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• 2001.04a
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• pp.317-319
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• 2001

A Switched Reluctance Generator attracts much attention in the generator because of high efficiency, simplicity, and ruggedness. However, they require rotor position information to operate. In many systems, the rotor position sensor is expensive, limited and undesirable. This paper describes a new approach to estimating the rotor position of a SRG from the measured terminal voltage and current for rotor position sensorless control. The proposed method Is based on the instantaneous inductance of the SRG. The proposed technique is very simple and it is able to apply to high speed operation under the stable condition because of its simplicity. The initial rotor position estimation algorithm is efficient and reliable. The proposed method is verified by computer simulation.

• Proceedings of the IEEK Conference
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• 2006.06a
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• pp.1005-1006
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• 2006

This paper proposes a method of position estimating through compensating the autonomous mobile robot's noise. Proposed method is that estimated position error by modified Kalman filter method using neural network. We use a neural network for measurement noise covariance and system noise covariance. In order to verify the effectiveness of the proposed method, we performed experiments for position estimation. The results show that convergence and position error is reduced than the Kalman filter method.

• Journal of KIISE:Databases
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• v.34 no.4
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• pp.343-356
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• 2007

Due to advances in position monitoring technologies such as global positioning systems and sensor networks, recent position information of moving objects has the form of streaming data which are updated continuously and rapidly. In this paper we propose an efficient trajectory maintenance method that stores the streaming position data of moving objects in the limited size of storage space and estimates past positions based on the stored data. For this, we first propose a new concept of incremental extraction of position information. The incremental extraction means that, whenever a new position is added into the system, we incrementally re-compute the new version of past position data maintained in the system using the current version of past position data and the newly added position. Next, based on the incremental extraction, we present an overall framework that stores position information and estimates past positions in the stream environment. We then propose two polynomial-based methods, line-based and curve-based methods, as the method of estimating the past positions on the framework. We also propose three incremental extraction methods: equi-width, slope-based, and recent-emphasis extraction methods. Experimental results show that the proposed incremental extraction provides the relatively high accuracy (error rate is less than 3%) even though we maintain only a little portion (only 0.1%) of past position information. In particular, the curve-based incremental extraction provides very low error rate of 1.5% even storing 0.1% of total position data. These results indicate that our incremental extraction methods provide an efficient framework for storing the position information of moving objects and estimating the past positions in the stream environment.

• Proceedings of the KIEE Conference
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• 1992.07b
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• pp.1235-1237
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• 1992

In this paper, a new PWM method and estimating means of rotor position angles for BLDC motor drive are presented. The rotor position angles is predicted by calculated rotor flux from the stator voltage and current signals. The current control PWM using fuzzy logic is also suggested. Performance of the proposed controller is observed through a simulation.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2014.10a
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• pp.718-722
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• 2014

A method for estimating the cylindrical shape of a sound radiator is presented. It assumes that sound field can be measured by a linear array. A sound field, due to the radiator vibrating with uniform velocity, can be determined by its shape, size, and orientations. Measured data also can be varying from the array's position. To predict the shape of radiators from these measured data, mathematical relation between geometric parameter and measured information is needed. Assume that a radiator is cylinder, the magnitude and phase of measured pressure is related with the length and diameter of radiator, respectively. In this paper, the method for estimating length and shape of a finite cylinder by using its radiated pressure is proposed and verified through experiment.

• The Transactions of the Korean Institute of Power Electronics
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• v.22 no.3
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• pp.216-222
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• 2017

The rotor position of a PM synchronous motor is commonly estimated from the mathematical model for the sensorless control without rotor position sensors. For the magnet flux-based rotor position estimator in the stationary reference frame, the magnet flux estimator for estimating rotor position and speed includes the integrator. The integrator in the magnet flux estimator may accumulate the offset of the current sensors and the voltage drift. This continuous accumulation of the offset may cause the drift and overflow in the integrator, such that the estimated rotor position and speed may fail to track the real rotor position and speed. In this paper, the magnet flux estimator without integrator is proposed to avoid overflow in the integrator. The proposed rotor position and speed estimator based on magnet flux estimator are verified through simulation and experiment.