• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2005.05a
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• pp.840-845
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• 2005

This paper presents a robust optimal design method for a periodic structure type of MEMS resonator that is vulnerable to mode localization. The robust configuration of such a MEMS resonator to fabrication error is implemented by changing the regularity of periodic structure. For the mathematical convenience, the MEMS resonator is first modeled as a multi pendulum system. The index representing the measure of mode variation is then introduced using the perturbation method and the concept of modal assurance criterion. Finally, the optimal intentional mistuning, minimizing the expectation of the irregularity measure for each substructure, is determined for the normal distributed fabrication error and its robustness in the design of MEMS resonator to the fabrication error is demonstrated with numerical examples.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.15 no.8 s.101
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• pp.931-938
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• 2005

This paper presents a robust optimal design method for a periodic structure type of MEMS resonator that is vulnerable to mode localization. The robust configuration of such a MEMS resonator to fabrication error is implemented by changing the regularity of periodic structure For the mathematical convenience, the MEMS resonator is first modeled as a multi-pendulum system. The index representing the measure of mode variation is then introduced using the perturbation method and the concept of modal assurance criterion. Finally, the optimal intentional mistuning, minimizing the expectation of the irregularity measure for each substructure, is determined for the normal distributed fabrication error and its robustness in the design of MEMS resonator to the fabrication error is demonstrated with numerical examples.

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.12
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• pp.1182-1188
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• 2010

This paper proposes a precise localization algorithm for a quickly moving mobile robot. In order to localize a mobile robot with active beacon sensors, a relatively long time is needed, since the distance to the beacon is measured using the flight time of the ultrasonic signal. The measurement time does not cause a high error rate when the mobile robot moves slowly. However, with an increase of the mobile robot's speed, the localization error becomes too high to use for accurate mobile robot navigation. Therefore, in this research into high speed mobile robot operations, instead of using two active beacons for localization an active beacon and dual compass are utilized to localize the mobile robot. This new approach resolves the high localization error caused by the speed of the mobile robot. The performance of the precise localization algorithm was verified by comparing it to the conventional method through real-world experiments.

• The Journal of Korea Robotics Society
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• v.17 no.1
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• pp.25-31
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• 2022

Moving among multiple floors is one of the most challenging tasks for indoor autonomous robots. Most of the previous researches for indoor mapping and localization have focused on singular floor environment. In this paper, we present an algorithm that creates a multi-floor map using 3D point cloud. We implement localization within the multi-floor map using a LiDAR and an IMU. Our algorithm builds a multi-floor map by constructing a single-floor map using a LOAM-based algorithm, and stacking them through global registration that aligns the common sections in the map of each floor. The localization in the multi-floor map was performed by adding the height information to the NDT (Normal Distribution Transform)-based registration method. The mean error of the multi-floor map showed 0.29 m and 0.43 m errors in the x, and y-axis, respectively. In addition, the mean error of yaw was 1.00°, and the error rate of height was 0.063. The real-world test for localization was performed on the third floor. It showed the mean square error of 0.116 m, and the average differential time of 0.01 sec. This study will be able to help indoor autonomous robots to operate on multiple floors.

• Progress in Superconductivity
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• v.4 no.1
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• pp.19-26
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• 2002

We have earlier developed a 40-channel SQUID system. An important figure of merit of a MEG system is the localization error, within which the underlying current source can be localized. With this system, we investigated the localization error in terms of the standard deviation of the coordinates of the ECDs and the systematic error due to inadequate modeling. To do this, we made localization of single current dipoles from tangential components of auditory evoked fields. Equivalent current dipoles (ECD) at N1m peak were estimated based on a locally fitted spherical conductor model. In addition, we made skull phantom and simulation measurements to investigate the contribution of various errors to the localization error. It was found that the background noise was the main source of the errors that could explain the observed standard deviation. Further, the amount of systematic error, when modeling the head with a spherical conductor, was much less than the standard deviation due to the background noise. We also demonstrated the performance of the system by measuring the evoked fields to grammatical violation in sentence comprehension.

• Proceedings of the KSME Conference
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• 2007.05a
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• pp.1155-1160
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• 2007

This paper presents position error for the robot localization system using the ultrasonic wave. The distance between the receiver and a beacon can be computed by using the difference between times of flight. The distance information measured by ultrasonic wave has errors. The position is calculated by distances, and this error is caused by distance errors. The position error is different from receiver¡s position. And the position is also calculated by beacon location. This paper calculates worst case position error within measuring area, and finds beacons location to reduce the position error.

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.4
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• pp.368-373
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• 2010

A new indoor mobile robot localization method is presented. Robot recognizes well designed single color landmarks on the ceiling by vision system, as reference to compute its precise position. The proposed likelihood prediction based method enables the robot to estimate its position based only on the orientation of landmark.The use of single color landmarks helps to reduce the complexity of the landmark structure and makes it easily detectable. Edge based optical flow is further used to compensate for some landmark recognition error. This technique is applicable for navigation in an unlimited sized indoor space. Prediction scheme and localization algorithm are proposed, and edge based optical flow and data fusing are presented. Experimental results show that the proposed method provides accurate estimation of the robot position with a localization error within a range of 5 cm and directional error less than 4 degrees.

• Proceedings of the Korea Information Processing Society Conference
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• 2010.11a
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• pp.829-832
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• 2010

Wireless Sensor Networks have been proposed for several location-dependent applications. For such systems, the cost and limitations of the hardware on sensing nodes prevent the use of range-based localization schemes that depend on absolute point to point distance estimates. Because coarse accuracy is sufficient for most sensor network applications, solutions in range-free localization are being pursued as a cost-effective alternative to more expensive range-based approaches. In this paper, we proposed a modified DV-Hop (range-free localization) algorithm which reduces node's location error and cumulated distance error by minimizing localization error. Simulation results have verified the high estimation accuracy with our approach which outperforms the classical DV-Hop.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.10
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• pp.2047-2051
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• 2009

In this paper, we propose the localization method on WSN(Wireless Sensor Network) using fuzzy model and Kalman filter. The proposed method is as follows: First, we estimate the distance of RSSI(Receive Signal Strength Index) by using fuzzy model in order to minimize the distance error. Second, we use a triangulation measurement for estimating the localization. And then, we minimize the localization error using a Kalman filter. Finally, we show the effectiveness and feasibility of the proposed method through some experiments.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.6
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• pp.814-819
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• 2010

This paper is presented to study the error minimization of angular velocity for AGV(autonomous ground vehicle). The error minimization of angular velocity is related to localization technique which is the most important technique for autonomous vehicle. Accelerometer, yaw gyro and electronic compass have been used to measure angular velocity. And methods for error minimization of angular velocity have been actively studied through probabilistic methods and sensor fusion for AGVs. However, those sensors still occure accumulated error by mathematical error, system characters of each sensor, and computational cost are increased greatly when several sensor are used to correct accumulated error. Therefore, this paper studies about error minimization of angular velocity that just uses encoder and gyro. To experiment, we use autonomous vehicle which is made by ourselves. In experimental result, we verified that the localization error of proposed method has even less than the localization errors which we just used encoder and gyro respectively.