• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2012.11a
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• pp.57-59
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• 2012

음장 합성 기법은 라우드스피커 어레이를 사용하여 보다 넓은 청취 공간을 제공하는 음향 재생 기법이다. 이를 이용한 렌더링 성능을 좌우하는 요소 중 하나로 스피커의 간격이 있다. 스피커의 간격은 앨리어싱 주파수와 직접적인 관계가 있으며 간격이 좁을수록 높은 성능을 보인다. 그런데 좁은 간격의 어레이를 사용하는 경우 라우드스피커의 저역 한계 주파수가 높아지게 마련이며 따라서 필수적으로 저주파 대역을 위한 서브 우퍼를 사용해야 한다. 이런 경우에 서브 우퍼에 의한 음상정위 오류가 있을 수 있다. 본 논문에서는 선행 효과를 이용하여 이 문제에 대한 해결방안을 제시한다.

• Proceedings of the Korean Information Science Society Conference
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• 2006.10d
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• pp.80-84
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• 2006

센서 네트워크에서 노드들의 위치 인식에는 여러 가지 기법이 이용된다. 그 중 소리를 이용한 위치인식 기법은 정확도가 높고 넓은 지역에서도 활용이 가능하다는 장점이 있다. 소리를 이용한 위치 인식 기법은 음원으로부터 노드까지의 거리 정보 수집을 통하여 위치 인식이 가능하다. 하지만 기존의 기법에서는 거리를 정확하게 측정을 하는 것에만 초점이 맞춰져 있었으며 위치인식에 필요한 정보의 부족으로 위치인식 과정을 진행시키지 못하는 경우에 있어서는 고려를 하고 있지 않다. 본 논문에서는 소리를 이용한 위치인식 과정에서 발생할 수 있는 에러 보정 기법을 제안한다.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.12A
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• pp.1177-1184
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• 2008

Non-line-of-sight (NLOS) propagation can severely weaken the accuracy of ranging and localization in wireless location systems. NLOS bias mitigation techniques have recently been proposed to relieve the NLOS effects, but positively rely on the capability to accurately distinguish between LOS and NLOS propagation scenarios. This paper proposes an energy-capture-based NLOS identification method for LDR-UWB systems, based on the analysis of the characteristics of the channel impulse response (CIR). With this proposed energy capture method, the probability of successfully identifying NLOS is much improved than the existing methods, such as the kurtosis method, the strongest path compare method, etc. This NLOS identification method can be employed in adaptive modulation scheme to decrease bit error ratio (BER) level for certain signal-to-noise ratio (SNR). The BER performance with the adaptive modulation can be significantly enhanced by selecting proper modulation method with the knowledge of channel information from the proposed NLOS identification method.

• Journal of Institute of Control, Robotics and Systems
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• v.15 no.2
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• pp.132-138
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• 2009

In this paper, control of an omni-directional mobile robot is presented. Relying on encoder measurements to define the azimuth angle yields the dead-reckoned situation which the robot fails in localization. The azimuth angle error due to dead-reckoning is compensated and corrected by the magnetic compass sensor. Noise from the magnetic compass sensor has been filtered out. Kinematics and dynamics of the omni-directional mobile robot are derived based on the global coordinates and used for simulation studies. Experimental studies are also conducted to show the correction by the magnetic compass sensor.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.52 no.8
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• pp.500-505
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• 2003

In this paper, the method to register multiple sets of skull CT images to absolute coordinate system is proposed. Contrary to correspondence paired mapping of previous techniques, four anatomical landmark points, three coplanar points and one non-coplanar point, compose three principal axes simple and unique for efficient registration by means of rigid body transformation. Throughout the numerical simulation with added random noises, the error performances in terms of different rotation and rounding-off of landmark points, and incorrect localization of anatomical landmark and target points are quantitatively analyzed to generalize the proposed technique. Experiments using real skull CT images demonstrate the feasibility for an efficient use in clinical practice.

• Proceedings of the KSPS conference
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• 2005.11a
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• pp.117-120
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• 2005

This paper suggests an algorithm that can estimate the direction of the sound source in realtime. Our intelligent service robot, WEVER, is used to implement the proposed method at the home environment. The algorithm uses the time difference and sound intensity information among the recorded sound source by four microphones. Also, to deal with noise of robot itself, the kalman filter is implemented. The proposed method takes shorter execution time than that of an existing algorithm to fit the real-time service robot. The result shows relatively small error within the range of ${\pm}$ 7 degree.

• IEMEK Journal of Embedded Systems and Applications
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• v.6 no.2
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• pp.108-116
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• 2011

Non-line-of-sight (NLOS) error is the most common and also a major source of errors in wireless location system. A novel weighting factor (NWF) method is presented in this paper, based on the RSS(Received Signal Strength) measurements, path loss model and Circular Disk of Scatterers Model (CDSM). The proposed positioning method effectively weighted the TOA distance measurements for each Base Station (BS). Simulation results show that the proposed method efficiently weighted the distance measurements and achieve higher localization accuracy than that of Linear Line of Position (LLOP) and Believable Factor Algorithm (BFA).

• Journal of Platform Technology
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• v.6 no.4
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• pp.18-25
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• 2018

This paper describes a real-time human and robot tracking method in Intelligent Space with multi-camera networks. The proposed method detects candidates for humans and robots by using the histogram of oriented gradients (HOG) feature in an image. To classify humans and robots from the candidates in real time, we apply cascaded structure to constructing a strong classifier which consists of many weak classifiers as follows: a linear support vector machine (SVM) and a radial-basis function (RBF) SVM. By using the multiple view geometry, the method estimates the 3D position of humans and robots from their 2D coordinates on image coordinate system, and tracks their positions by using stochastic approach. To test the performance of the method, humans and robots are asked to move according to given rectangular and circular paths. Experimental results show that the proposed method is able to reduce the localization error and be good for a practical application of human-centered services in the Intelligent Space.

• Journal of Multimedia Information System
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• v.8 no.1
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• pp.23-30
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• 2021

Palmprint has become a popular biometric modality; however, palmprint recognition has not been conducted in video media. Video palmprint recognition (VPR) has some advantages that are absent in image palmprint recognition. In VPR, the registration and recognition can be automatically implemented without users' manual manipulation. A good-quality image can be selected from the video frames or generated from the fusion of multiple video frames. VPR in contactless mode overcomes several problems caused by contact mode; however, contactless mode, especially mobile mode, encounters with several revere challenges. Double-line-single-point (DLSP) assisted placement technique can overcome the challenges as well as effectively reduce the localization error and computation complexity. This paper modifies DLSP technique to reduce the invalid area in the frames. In addition, the valid frames, in which users place their hands correctly, are selected according to finger gap judgement, and then some key frames, which have good quality, are selected from the valid frames as the gallery samples that are matched with the query samples for authentication decision. The VPR algorithm is conducted on the system designed and developed on mobile device.

• The Journal of Korea Robotics Society
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• v.18 no.3
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• pp.330-336
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• 2023

In this paper, a novel line-based simultaneous localization and mapping (SLAM) using a loss function of vanishing point measurements is proposed. In general, the Huber norm is used as a loss function for point and line features in feature-based SLAM. The proposed loss function of vanishing point measurements is based on the unit sphere model. Because the point and line feature measurements define the reprojection error in the image plane as a residual, linear loss functions such as the Huber norm is used. However, the typical loss functions are not suitable for vanishing point measurements with unbounded problems. To tackle this problem, we propose a loss function for vanishing point measurements. The proposed loss function is based on unit sphere model. Finally, we prove the validity of the loss function for vanishing point through experiments on a public dataset.