• Journal of Institute of Control, Robotics and Systems
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• v.13 no.7
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• pp.683-687
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• 2007

In last decades, several linearization methods for the AOA measurements have been proposed, for example, Gauss-Newton method and Closed-Form solution. Gauss-Newton method can achieve high accuracy, but the convergence of the iterative process is not always ensured if the initial guess is not accurate enough. Closed-Form solution provides a non-iterative solution and it is less computational. It does not suffer from convergence problem, but estimation error is somewhat larger. This paper proposes a Self-Tuning Weighted Least Square AOA algorithm that is a modified version of the conventional Closed-Form solution. In order to estimate the error covariance matrix as a weight, a two-step estimation technique is used. Simulation results show that the proposed method has smaller positioning error compared to the existing methods.

• Structural Monitoring and Maintenance
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• v.9 no.2
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• pp.157-177
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• 2022

In this paper, an innovative finite element updating method is presented based on the variation wavelet transform coefficients of Auto/cross-correlations function (WTCF). The Quasi-linear sensitivity of the wavelet coefficients of the WTCF concerning the structural parameters is evaluated based on incomplete measured structural responses. The proposed algorithm is used to estimate the structural parameters of truss and plate models. By the solution of the sensitivity equation through the least-squares method, the finite element model of the structure is updated for estimation of the location and severity of structural damages simultaneously. Several damage scenarios have been considered for the studied structure. The parameter estimation results prove the high accuracy of the method considering measurement and mass modeling errors.

• Journal of Institute of Control, Robotics and Systems
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• v.20 no.8
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• pp.868-874
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• 2014

This paper proposes a novel mapping algorithm in Omni-directional Vision SLAM based on an obstacle's feature extraction using Lucas-Kanade Optical Flow motion detection and images obtained through fish-eye lenses mounted on robots. Omni-directional image sensors have distortion problems because they use a fish-eye lens or mirror, but it is possible in real time image processing for mobile robots because it measured all information around the robot at one time. In previous Omni-Directional Vision SLAM research, feature points in corrected fisheye images were used but the proposed algorithm corrected only the feature point of the obstacle. We obtained faster processing than previous systems through this process. The core of the proposed algorithm may be summarized as follows: First, we capture instantaneous $360^{\circ}$ panoramic images around a robot through fish-eye lenses which are mounted in the bottom direction. Second, we remove the feature points of the floor surface using a histogram filter, and label the candidates of the obstacle extracted. Third, we estimate the location of obstacles based on motion vectors using LKOF. Finally, it estimates the robot position using an Extended Kalman Filter based on the obstacle position obtained by LKOF and creates a map. We will confirm the reliability of the mapping algorithm using motion estimation based on fisheye images through the comparison between maps obtained using the proposed algorithm and real maps.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37 no.7B
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• pp.586-594
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• 2012

The drastic growth of mobile communication and spreading of smart phone make the significant attention on Location Based Service. The one of most important things for vitalization of LBS is the accurate estimating position for mobile object. Focusing on AP's probabilistic position estimation, we develop an AP distribution map and new pattern matching algorithm for position estimation. The developed approaches can strengthen the advantages of Radio fingerprint based Wi-Fi Positioning System, especiall on the algorithms and data handling. Compared on the existing approaches of fingerprint pattern matching algorithm, we achieve the comparable higher performance on both of average error of estimation and deviation of errors. Furthermore all fingerprint data have been harvested from the actual measurement of radio fingerprint of Seoul, Kangnam area. This can approve the practical usefulness of proposed methodology.

• Journal of Positioning, Navigation, and Timing
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• v.10 no.3
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• pp.169-177
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• 2021

In this paper, we propose a novel global navigation satellite system (GNSS) spoofing detection technique through an array antenna-based direction of arrival (DoA) estimation of satellite and spoofer. Specifically, we consider a sophisticated GNSS spoofing attack scenario where the spoofer can accurately mimic the multiple pseudo-random number (PRN) signals since the spoofer has its own GNSS receiver and knows the location of the target receiver in advance. The target GNSS receiver precisely estimates the DoA of all PRN signals using compressed sensing-based orthogonal matching pursuit (OMP) even with a small number of samples, and it performs spoofing detection from the DoA estimation results of all PRN signals. In addition, considering the initial situation of a sophisticated spoofing attack scenario, we designed the algorithm to have high spoofing detection performance regardless of the relative spoofing signal power. Therefore, we do not consider the assumption in which the power of the spoofing signal is about 3 dB greater than that of the authentic signal. Then, we introduce design parameters to get high true detection probability and low false alarm probability in tandem by considering the condition for the presence of signal sources and the proximity of the DoA between authentic signals. Through computer simulations, we compare the DoA estimation performance between the conventional signal direction estimation method and the OMP algorithm in few samples. Finally, we show in the sophisticated spoofing attack scenario that the proposed spoofing detection technique using OMP-based estimated DoA of all PRN signals outperforms the conventional spoofing detection scheme in terms of true detection and false alarm probability.

• Journal of the Korean Society for Precision Engineering
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• v.19 no.1
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• pp.119-126
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• 2002

In this paper, we present a new approach based on an elitist genetic algorithm for the task of aligning the position of a robot gripper using CCD cameras. The vision-based control scheme for the task of aligning the gripper with the desired position is implemented by image information. The relationship between the camera space location and the robot joint coordinates is estimated using a camera-space parameter modal that generalizes known manipulator kinematics to accommodate unknown relative camera position and orientation. To find the joint angles of a robot manipulator for reaching the target position in the image space, we apply an elitist genetic algorithm instead of a nonlinear least square error method. Since GA employs parallel search, it has good performance in solving optimization problems. In order to improve convergence speed, the real coding method and geometry constraint conditions are used. Experiments are carried out to exhibit the effectiveness of vision-based control using an elitist genetic algorithm with a real coding method.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.4 no.3
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• pp.595-602
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• 2000

This paper is aimed at developing an location tracking system of mobile station based on currently available mobile communication network or mobile Phone and PCS(Personal Communication System). When the location tracking of mobile stations is in services, Emergency-119, all of crime investigation, effective urban traffic management and the safety protection of Alzheimer＇s patients can be available. In order to track the location of the mobile and base station, assumption in this paper is to use the statistic characteristics of LOS when modeling the standard noise in case that radio path is LNOS environment. The standard variation of the standard noise is $\pm150$. First, location is estimated by the positioning algorithms of TOA and TDOA and compared each other. Second, after canceling the standard noise by Kalman filter, location is estimated by the above two positioning algorithms. Finally, the location by the Kalman filter and two positioning algorithms is estimated by smoothing method. As a result, 2 dimensional average location error is imvoved by 51.2m in TOA and 34.8m in TDOA when Kalman filer and two positioning algorithms are used, compared with the two positioning algorithm used. And there is 3 more meter improvement after smoothing than Kalman filer and two positioning algorithms used.

• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.4
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• pp.377-385
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• 2015

This paper proposes a novel forward vehicle tracking algorithm based on the WMIL(Weighted Multiple Instance Learning) equipped with a particle filter. In the proposed algorithm Haar-like features are used to train a vehicle object detector to be tracked and the location of the object are obtained from the recognition result. In order to combine both the WMIL to construct the vehicle detector and the particle filter, the proposed algorithm updates the object location by executing the propagation, observation, estimation, and selection processes involved in particle filter instead of finding the credence map in the search area for every frame. The proposed algorithm inevitably increases the computation time because of the particle filter, but the tracking accuracy was highly improved compared to Ababoost, MIL(Multiple Instance Learning) and MIL-based ones so that the position error was 4.5 pixels in average for the videos of national high-way, express high-way, tunnel and urban paved road scene.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.15 no.1
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• pp.127-136
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• 2002

This papers presents a practical damage detection algorithm based on damage index method that accurately assesses both the location and severity of the localized detriment in a bridge structure using only limited mode shapes. In the algorithm, the ratio of the modal vector sensitivity of an undamaged structure to that of a damaged structure is used as an indicator of damage. However, a difficulty arises when the damaged element is located at a node of mode where the amplitude of medal vector is close to zero, leading the singularity of the ratio (i.e., division-by-zero). This singularity problem is overcome by introducing a parameter denoted a sensitivity filter, a function of mode shape of the structure, in modal vector sensitivity. Using this concept, an improvement can be considerably achieved in the estimation of both degree of severity and location of damage. To verify the proposed algorithm, its numerical implementations are conducted for a simply supported beam and a 2-span continuous beam.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.1
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• pp.45-49
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• 2010

RTLS (Real Time Locating Systems) are used to track and identify the location of objects in real time. RTLS generally consist of tags, readers and engine etc. and applied in various environments. One problem encountered in RTLS is constraints for setting up readers according to environments. In these challenging environments, it is necessary to develop techniques that can use mobile readers. As mobile reader increase, whole reader increase. And it is necessary to develop technique that can select useful readers. Selected readers including mobile readers take effect performance of RTLS. This paper introduces the technique for selecting good readers using the convex hull algorithm. The result show the good performance which based on tag's location error and reduce the networking overhead.