• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권2호
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• pp.1162-1179
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• 2017

In this paper, we propose indoor location tracking method using RSSI(Received Signal Strength Indicator) value received from BLE(Bluetooth Low Energy) beacon. Due to the influence of various external environmental factors, it is very difficult to improve the accuracy in indoor location tracking. In order to solve this problem, we propose a novel method of reducing the noise generated in the external environment by using a double Gaussian filter. In addition, the value of the RSSI signal generated in the BLE beacon is different for each device. In this study, we propose a method to allocate additional weights in order to compensate the intensity of signal generated in each device. This makes it possible to improve the accuracy of indoor location tracking using beacons. The experiment results show that the proposed method effectively decrease the RSSI deviation and increase location accuracy. In order to verify the usefulness of this study, we compared the Kalman filter algorithm which is widely used in signal processing. We further performed additional experiments for application area for indoor location service and find that the proposed scheme is useful for BLE-based indoor location service.

• 전자공학회논문지
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• 제54권3호
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• pp.108-118
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• 2017

본 논문은 수중 로봇 항법에 사용하기 위한 영상 소나 기반 SLAM (simultaneous localization and mapping) 방법을 제안하고, 성능 평가를 위해 실제 로봇에 탑재하여 실험한 내용을 소개한다. 일반적인 수중 항법은 관성 센서에서 출력되는 정보를 바탕으로 로봇의 위치 및 자세(x,y,z,${\phi}$,${\theta}$,${\psi}$)를 추정한다. 하지만, 장시간 주행할 경우 위치 오차의 누적으로 인하여 정확도가 감소하게 된다. 이에 본 논문에서는 영상 소나로부터 얻을 수 있는 외부 정보를 바탕으로 관성 항법의 위치 추정 성능을 높이고 지도 작성을 수행할 수 있는 SLAM 방법을 제안하고자 한다. 영상 소나를 위한 인공 표식물과 확률 기반 물체 인식 구조를 통해 인공 표식물의 인식 성능을 높이고, 이를 통해 얻게 된 인공 표식물의 위치 정보를 활용하여 관성 항법의 누적 오차를 줄이고자 한다. 항법 알고리즘으로는 확장형 칼만 필터(Extended Kalman Filter, EKF)를 적용하여 로봇의 위치 및 자세를 추정하고 지도를 작성한다. 제안한 방법은 선박해양플랜트연구소에서 보유 중인 수중 로봇 'yShark'에 탑재하여 대형 수조에서 실시간 검증을 수행하였다.

• Structural Engineering and Mechanics
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• 제64권4호
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• pp.449-459
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• 2017

High-rise buildings are generally sensitive to strong winds. The evaluation of wind loads for the structural design, structural health monitoring (SHM), and vibration control of high-rise buildings is of primary importance. Nevertheless, it is difficult or even infeasible to measure the wind loads on an existing building directly. In this regard, a new inverse method for evaluating wind loads on high-rise buildings is developed in this study based on a discrete-time Kalman filter. The unknown structural responses are identified in conjunction with the wind loads on the basis of limited structural response measurements. The algorithm is applicable for estimating wind loads using different types of wind-induced response. The performance of the method is comprehensively investigated based on wind tunnel testing results of two high-rise buildings with typical external shapes. The stability of the proposed algorithm is evaluated. Furthermore, the effects of crucial factors such as cross-section shapes of building, the wind-induced response type, errors of structural modal parameters, covariance matrix of noise, noise levels in the response measurements and number of vibration modes on the identification accuracy are examined through a detailed parametric study. The research outputs of the proposed study will provide valuable information to enhance our understanding of the effects of wind on high-rise buildings and improve codes of practice.

• Smart Structures and Systems
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• 제26권6호
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• pp.693-701
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• 2020

The conventional Kalman Filter (KF) provides a promising way for structural state estimation. However, the physical parameters of structural systems or models should be available for the estimation. Moreover, it is not applicable when the loadings applied to the structures are unknown. To circumvent the aforementioned limitations, a two-stage KF with unknown input approach is proposed for the simultaneous identification of structural parameters and unknown loadings. In stage 1, a modified observation equation is employed. The structural state vector is estimated by KF on the basis of structural parameters identified at the previous time-step. Then, the unknown input is identified by Least Squares Estimation (LSE). In stage 2, based on the concept of sensitivity matrix, the structural parameters are updated at the current time-step by using the estimated structural states obtained from stage 1. The effectiveness of the proposed approach is numerically validated via a five-story shearing model under random and earthquake excitations. Shaking table tests on a five-story structure are also employed to demonstrate the performance of the proposed approach. It is demonstrated from numerical and experimental results that the proposed approach can be used for the identification of parameters of structure and the external force applied to it with acceptable accuracy.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1986년도 한국자동제어학술회의논문집; 한국과학기술대학, 충남; 17-18 Oct. 1986
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• pp.244-247
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• 1986

A revised two-level costate prediction algorithm is developed for the optimization of nonlinear nuclear power plant. The algorithm is proved to converge very well, and appears to require substantially small computation time and storage than previous nonlinear optimization algorithm. To cope with unknown external disturbances, we construct a closed loop control system. In order to get a smaller sampling time, this paper proposes the two-level Kalman filter.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
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• pp.387-387
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• 2000

In this paper, we present a model reference sliding mode control for the system with input/output disturbance. The proposed model reference sliding mode control makes always the error remain on the surface in finite time. Therefore the system is insensitive to external disturbance. Simulation results are included to illustrate the effectiveness of proposed scheme.

• 제어로봇시스템학회논문지
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• 제21권12호
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• pp.1167-1172
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• 2015

This paper describes the implementation of wearable AHRS for an electromagnetic motion capture system that can trace and analyze human motion on the principal nine axes of inertial sensors. The module provides a three-dimensional (3D) attitude and heading angles combining MEMS gyroscopes, accelerometers, and magnetometers based on the extended Kalman filter, and transmits the motion data to the 3D simulation via Wi-Fi to realize the unrestrained movement in open spaces. In particular, the accelerometer in AHRS is supposed to measure only the acceleration of gravity, but when a sensor moves with an external linear acceleration, the estimated linear acceleration could compensate the accelerometer data in order to improve the precision of measuring gravity direction. In addition, when an AHRS is attached in an arbitrary position of the human body, the compensation of the axis of rotation could improve the accuracy of the motion capture system.

• Journal of Advanced Marine Engineering and Technology
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• 제39권2호
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• pp.187-194
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• 2015

Accurate relative location estimation is a key requirement in indoor localization systems based on wireless sensor networks (WSNs). However, although these systems have applied not only various optimization algorithms but also fusion with sensors to achieve high accuracy in position determination, they are difficult to provide accurate relative azimuth and locations to users because of cumulative errors in inertial sensors with time and the influence of external magnetic fields. This paper based on ultra-wideband positioning system, which is relatively suitable for indoor localization compared to other wireless communications, presents an indoor localization system for estimating relative azimuth and location of location-unaware nodes, referred to as target nodes without applying any algorithms with complex variable and constraints to achieve high accuracy. In the proposed method, the target nodes comprising three mobile nodes estimate the relative distance and azimuth from two reference nodes that can be installed by users. In addition, in the process of estimating the relative localization information acquired from the reference nodes, positioning errors are minimized through a novel modified rounding estimation technique in which Kalman filter is applied without any time consumption algorithms. Experimental results show the feasibility and validity of the proposed system.

• 제어로봇시스템학회논문지
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• 제17권2호
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• pp.157-163
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• 2011

This paper proposes a hybrid image stabilizing system which uses both optical image stabilizing system based on EKF (Extended Kalman Filter) and digital image stabilization based on SURF (Speeded Up Robust Feature). Though image information is one of the most efficient data for object recognition, it is susceptible to noise which results from internal vibration as well as external factors. The blurred image obtained by the camera mounted on a robot makes it difficult for the robot to recognize its environment. The proposed system estimates shaking angle through EKF based on the information from inclinometer and gyro sensor to stabilize the image. In addition, extracting the feature points around rotation axis using SURF which is robust to change in scale or rotation enhances processing speed by removing unnecessary operations using Hessian matrix. The experimental results using the proposed hybrid system shows its effectiveness in extended frequency range.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
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• pp.80-80
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• 2000

The GPS can provide accurate position information on the earth. But GPS receiver can't give position information inside buildings. DR(Dead-Reckoning) or INS(Inertial Navigation System) gives position information continuously indoors as well as outdoors, because they do not depend on the external navigation information. But in general, the inertial sensors severely suffer from their drift errors, the error of these navigation system increases with time. GPS and DR sensors can be integrated together with Kalman filter to overcome these problems. In this paper, we developed a personal navigation system which can be carried by person, using GPS and electronic pedometer. The person's footstep is detected by an accelerometer installed in vertical direction and the direction of movement is sensed by gyroscope and magnetic compass. In this case the step size is varying with person and changing with circumstance, so determining step size is the problem. In order to calculate the step size of detected footstep, the neural network method is used. The teaming pattern of the neural network is determined by human walking pattern data provided by 3-axis accelerometer and gyroscope. We can calculate person's location with displacement and heading from this information. And this neural network method that calculates step size gives more improved position information better than fixed step size.