• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2010년도 춘계학술대회 논문집
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• pp.156-157
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• 2010

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2011년도 추계학술대회 논문집
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• pp.208-209
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• 2011

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2013년도 춘계학술대회 논문집
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• pp.110-111
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• 2013

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2013년도 추계학술대회 논문집
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• pp.833-835
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• 2013

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2013년도 추계학술대회 논문집
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• pp.836-837
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• 2013

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2003년도 추계학술대회논문집
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• pp.324-329
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• 2003

An air-conditioner has various noise sources such as cooling fan noise, pumping noise, flow noise and impact noise. Among these, impact noise is the most unpleasant source. This is because the noise is produced in indoor unit of air-conditioner. To control the noise source effectively, first we must identify the noise sources. When we identify impact noise source, the measurement have to be carried out simultaneously. So we use beamforming method that requires less measurement points than intensity method and acoustic holography. The objective of this paper is to estimate the location of impact source. This objective can be achieved by using minimum variance cepstrum that is able to detect impulse embedded in noise. In this study, modified beamforming method based on cepstrum domain is proposed. Then this method applied to air-conditioner noise sources which produce impact noise.

• 로봇학회논문지
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• 제9권4호
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• pp.232-241
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• 2014

This paper proposes an underwater localization algorithm using probabilistic object recognition. It is organized as follows; 1) recognizing artificial objects using imaging sonar, and 2) localizing the recognized objects and the vehicle using EKF(Extended Kalman Filter) based SLAM. For this purpose, we develop artificial landmarks to be recognized even under the unstable sonar images induced by noise. Moreover, a probabilistic recognition framework is proposed. In this way, the distance and bearing of the recognized artificial landmarks are acquired to perform the localization of the underwater vehicle. Using the recognized objects, EKF-based SLAM is carried out and results in a path of the underwater vehicle and the location of landmarks. The proposed localization algorithm is verified by experiments in a basin.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2001년도 합동 추계학술대회 논문집 정보 및 제어부문
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• pp.111-114
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• 2001

In this paper, we supposed teller following algorithm that using sound localization for developing dialog type humanoid robot. A sound localization is studied for develop the techniques of an efficient 3-D sound system based on the psychoacoustics of spatial hearing with multimedia or virtual reality. When a robot talk with human, it is necessary that robot follow human for improved human interface and adaptive noise canceling. We apply this algorithm to robot system.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2006년도 하계종합학술대회
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• pp.247-248
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• 2006

In this paper, we propose a new sound localization algorithm for an active perception system. In an active perception system, an acquired sound is mixed with the sound of motors. So a sound localization algorithm for an active perception system requires a robustness for the noise and a computational efficiency. The proposed localization algorithm can achieve robustness and efficiency to use only sub-band channels that are contained harmonic structure of the target speech.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제14권1호
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• pp.41-48
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• 2014

This paper analyzes the Monte Carlo localization (MCL) method, which estimates the pose of an indoor mobile robot. A mobile robot must know where it is to navigate in an indoor environment. The MCL technique is one of the most influential and popular techniques for estimation of robot position and orientation using a particle filter. For the analysis, we perform experiments in an indoor environment with a differential drive robot and ultrasonic range sensor system. The analysis uses MATLAB for implementation of the MCL and investigates the effects of the control parameters on the MCL performance. The control parameters are the uncertainty of the motion model of the mobile robot and the noise level of the measurement model of the range sensor.