• Proceedings of the KIEE Conference
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• 2015.07a
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• pp.1323-1324
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• 2015

최근, 다수의 이동 로봇으로 구성된 무선 네트워크 기반 군집 로봇 시스템을 제한적인 환경을 벗어나 다양하고 동적인 환경에서 운용하기 위한 연구가 진행 중이다. 특히, 다수 로봇의 위치를 측정하기 위해 실내 환경에 기반 시설 없이 로봇에 장착된 센서들에 의해 위치를 추정하는 방법이 필요하다. 이를 위해, 본 논문에서는 로봇간의 상대적인 정보를 통해 다수 이동 로봇의 거리를 추정하는 방법을 제안한다. 제안한 방법은 먼저, 다수 이동 로봇의 거리를 추정하기 위해 무선 신호를 기반으로 하는 RSSI 방법을 이용하여 다수 이동 로봇의 거리를 추정한다. 그 다음, 추정된 거리와 추측 항법(Dead Reckoning)을 융합하여 이동 중인 로봇의 거리를 추정하는 방법을 제안한다. 마지막으로, 시뮬레이션 도구를 이용하여 응용 가능성을 증명한다.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.46 no.1
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• pp.71-78
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• 2009

Sound source localization systems in service robot applications estimate the direction of a human voice. Time delay information obtained from a few separate microphones is widely used for the estimation of the sound direction. Correlation is computed in order to calculate the time delay between two signals. In addition, PHAT weighting function can be applied to significantly improve the accuracy of the estimation. However, FFT and IFFT operations in the PHAT weighting function occupy more than half of the area of the sound source localization system. Thus efficient FFT and IFFT designs are essential for the IP implementation of sound source localization system. In this paper, we propose an efficient FFT/IFFT design method based on the characteristics of human voice.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.40 no.2
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• pp.233-238
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• 2020

Most deep learning (DL) approaches for bridge damage localization based on a structural health monitoring system commonly use supervised learning-based DL models. The supervised learning-based DL model requires the response data obtained from sensors on the bridge and also the label which indicates the damaged state of the bridge. However, it is impractical to accurately obtain the label data in fields, thus, the supervised learning-based DL model has a limitation in that it is not easily applicable in practice. On the other hand, an unsupervised learning-based DL model has the merit of being able to train without label data. Considering this advantage, this study aims to propose and theoretically validate a damage localization approach for bridges using a variational autoencoder, a representative unsupervised learning-based DL network: as a result, this study indicated the feasibility of VAE for damage localization.

• Journal of Korea Multimedia Society
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• v.15 no.9
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• pp.1112-1125
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• 2012

To realize LBS (Location Based Service), typically GPS is mostly used. However, this system can be only used in out-sides. Furthermore, the use of the GPS in sensor networks is not efficient due to the low power consumption. Hence, we propose methods for the location positioning which is runnable at indoor in this paper. The proposed methods elaborate the location positioning system via applying K-NN(K-Nearest Neighbour) Algorithm with its intermediate values based on IEEE 802.15.4 technology; which is mostly used for the sensor networks. Logically the accuracy of the location positioning is proportional to the number of sampling sensor nodes' RSS according to the K-NN. By the way, numerous sampling uses a lot of sensor networks' resources. In order to reduce the number of samplings, we, instead, attempt to use the intermediate values of K-NN's signal boundaries, so that our proposed methods are able to positioning almost two times as accurate as the general ways of K-NN's result.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.38 no.7
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• pp.693-701
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• 2010

In this paper, it is proposed that an attitude and position estimation algorithm based on a stereo camera system for a helmet tracker. Stereo camera system consists of two CCD camera, a helmet, infrared LEDs and a frame grabber. Fifteen infrared LEDs are feature points which are used to determine the attitude and position of the helmet. These features are arranged in triangle pattern with different distance on the helmet. Vision-based the attitude and position algorithm consists of feature segmentation, projective reconstruction, model indexing and attitude estimation. In this paper, the attitude estimation algorithm using UQ (Unit Quaternion) is proposed. The UQ guarantee that the rotation matrix is a unitary matrix. The performance of presented algorithm is verified by simulation and experiment.

• Proceedings of the Korean Information Science Society Conference
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• 2012.06b
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• pp.54-56
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• 2012

본 논문에서는 위치인식 센서기반 무인이송차량(AGV)의 이동 제어 문제를 다루고자 한다. AGV의 진행 경로를 지시하는 랜드마크를 부착할 때 사각지역(dead zone) 및 중첩 지역(overlap zone)이 존재할 경우 위치 추정 오차가 허용 범위를 크게 벗어나게 되며, 이로 인해 AGV가 오동작하는 현상이 발생한다. 이를 해결하기 위해 본 논문에서는 단일 랜드마크 대신 이웃한 2개의 랜드마크 인식을 통해 위치 추정 오차를 보정하는 방안을 제안하였다. 또한, 회전 구간에서 AGV가 방향 전환 직후 지정된 경로에 허용오차 범위 이내로 진입하도록 안쪽 바퀴와 바깥쪽 바퀴의 가속도 제어 알고리즘을 제안하였다. 본 논문에서 개발된 시스템은 화장장 시신 운구용 AGV에 적용하였다. 화장장은 기존 산업 현장에 비해 이동 공간이 협소할 뿐만 아니라 그 특성상 정밀 제어가 필요한 환경이다. 본 논문에서 제안한 방식은 모의 차량에 적용하여 그 타당성을 검증하였으며, 실제 국내 화장장에 AGV 시스템을 적용한 결과 허용오차 범위 이내에서 정상 동작함을 확인하였다.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.4
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• pp.65-71
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• 2020

In order to accurately track the trajectory of the smartphone indoors and outdoors, the WiFi Fingerprint method and the Pedestrian Dead Reckoning method are fused. The former can estimate the absolute position, but an error occurs randomly from the actual position, and the latter continuously estimates the position, but there are accumulated errors as it moves. In this paper, the model and Kalman Filter equation to fuse the estimated position data of the two methods were established, and optimal system parameters were derived. According to covariance value of the system noise and measurement noise the estimation accuracy is analyzed. Using the measured data and simulation, it was confirmed that the improved performance was obtained by complementing the two methods.

• Journal of the Korean Society of Marine Environment & Safety
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• v.20 no.2
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• pp.202-209
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• 2014

In this paper, a developed device for detecting target's location and avoiding collision is proposed. Velocity and acceleration model of target are derived to estimate target's information, i.e. position, velocity and acceleration considering process and measurement noise. Kalman filtering method applied to the estimation process and its results was confirmed by simulation. The distance measurements system using laser sensor for moving target system is also developed to confirm the effectiveness of the proposed scheme. Experiments to get information of moving target with velocity and acceleration model was executed. The data with filtering and without filtering was compared by experiments. Discontinuous measured data was changed to smooth and continuous data by Kalman filtering. It is confirmed that desired data was obtained by applying proposed scheme. UI for measuring and monitoring the target data is developed and visual and auditory alarm function is attached on the system Finally, position estimation system of moving target with good performance is achieved by low price equipments.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.5
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• pp.995-1002
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• 2022

Global Navigation Satellite System(GNSS) is a convenient system that acquires position and time information of a receiver if only satellite signals can be received anywhere in the world. However navigation signals include errors and a position error occurs according to the reception state of the signal. Also, a position error is affected by the geometric arrangement of the satellites. Therefore a receiver position performance varies by the number and status of visible satellites The condition of satellite signals is not good when the satellite rises or sets and the position change of receiver occurs when the signal is blocked by an obstacle such as a building in the urban area. In this paper, we proposed methods to improve the GNSS performance by using pseudorange correction method estimating the correction amount and the visible satellites selection method. By applying the proposed methods to an environment in which the number of visible satellites changes variously, the performance enhancement was verified.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.4
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• pp.781-790
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• 2008

In order to improve on the divergence by noise convariance in the Kalman filter position estimation, we propose a method of position estimating through compensating the autonomous mobile robot's noise. Proposed method is the modified Kalman filter using neural network. It is prevented the divergence by the estimation of measurement noise covariance and system noise covariance. In order to verify the effectiveness of the proposed method, we performed simulations and experiments for position estimation. The results show that convergence and position error is reduced than the Kalman filter method.