• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2011.10a
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• pp.717-722
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• 2011

In this paper, noise source localization of the Korean high speed train was conducted by using delay and sum beam-forming method of a microphone array. At first, the microphone array having irregular configuration was designed and the resolution of which was analyzed from parameters such as 3-dB bandwidth and maximum side-lobe level. After the demonstration, the microphone array was applied on the high speed train and noise localization of the high speed train driving at 300 km/h was performed successfully.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.19 no.4
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• pp.355-362
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• 2009

Sound source localization method based on the time delay of arrival(TDOA) is applied to many research fields such as a robot auditory system, teleconferencing and so on. When multi-microphones are utilized to localize the source in 3 dimensional space, the conventional localization methods based on TDOA decide the actual source position using the TDOAs from all microphone arrays and the detection measure, which represents the errors between the actual source position and the estimated ones. Performance of these methods usually depends on the number of microphones because it determines the resolution of an estimated position. In this paper, we proposed the localization method using spatially mapped GCC functions. The proposed method does not use just TDOA for localization such as previous ones but it uses spatially mapped GCC functions which is the cross correlation function mapped by an appropriate mapping function over the spatial coordinate. A number of the spatially mapped GCC functions are summed to a single function over the global coordinate and then the actual source position is determined based on the summed GCC function. Performance of the proposed method for the noise effect and estimation resolution is verified with the real environmental experiment. The mean value of estimation error of the proposed method is much smaller than the one based on the conventional ones and the percentage of correct estimation is improved by 30% when the error bound is ${\pm}20^{\circ}$.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.80.2-80
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• 2002

The problem of sound source localization is to determine the position of sound sources using the measurement of the acoustic signals received by microphones. To develop a good sound source localization system which is applicable to a mobile platform such as robots, a time delay estimator with low computational complexity and robustness to background noise or reverberations is necessary. In this paper, an explicit adaptive time delay estimation method for a sound source localization system is proposed. Proposed explicit adaptive time estimation algorithm employs direct adaptation of the delay parameter using a transform-based optimization technique, rather than...

• Journal of Biomedical Engineering Research
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• v.31 no.6
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• pp.456-463
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• 2010

In the present study, we proposed a new subspace scanning algorithm to enhance the spatial resolution of electroencephalography (EEG) and magnetoencephalography(MEG) source localization. Subspace scanning algorithms, represented by the multiple signal classification (MUSIC) algorithm and the first principal vector (FINE) algorithm, have been widely used to localize asynchronous multiple dipolar sources in human cerebral cortex. The conventional MUSIC algorithm used principal component analysis (PCA) to extract the noise vector subspace, thereby having difficulty in discriminating two or more closely-spaced cortical sources. The FINE algorithm addressed the problem by using only a part of the noise vector subspace, but there was no golden rule to determine the number of noise vectors. In the present work, we estimated a non-orthogonal signal vector set using independent component analysis (ICA) instead of using PCA and performed the source scanning process in the signal vector subspace, not in the noise vector subspace. Realistic 2D and 3D computer simulations, which compared the spatial resolutions of various algorithms under different noise levels, showed that the proposed ICA-MUSIC algorithm has the highest spatial resolution, suggesting that it can be a useful tool for practical EEG/MEG source localization.

• Geophysics and Geophysical Exploration
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• v.10 no.4
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• pp.285-292
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• 2007

Magnetotelluric data recorded in the middle part of the Korean Peninsula are contaminated by severe noises at dead-band frequencies. In this study, we estimated the location of noise source using a source localization method. Since conventional beamforming techniques were not adequate for the localization of electromagnetic sources, we used the matched field processing and a genetic algorithm. The solutions for the strong noise signals tend to be localized in a narrow area, whereas those for natural MT signals shows randomly distributed patterns. The strong noise sources are mainly located in the western part of Kyonggi-do.

• Journal of the Korean Society for Precision Engineering
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• v.31 no.4
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• pp.327-334
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• 2014

This paper presents a method of fault detection and fault localization from acoustic noise measurements. In order to detect the presence of noise sources wavelet transform is applied to acoustic signal. In addition, a cross correlation based method is proposed to calculate the exact location of the noise allowing the user to quickly diagnose and resolve the source of the noise. The fault detection system is implemented using two microphones and a computer system. Experimental results show that the system can detect faults due to artifacts accidentally inserted during the manufacturing process and estimate the location of the fault with approximately 1 cm precision.

• Proceedings of the KSR Conference
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• 2007.05a
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• pp.1590-1597
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• 2007

This paper deals with the noise source localization of the Korean High Speed Train (KTX) at the speed of 300 km/h. Using Microfonarray system and beamforming technology typical pass-by noise sources and their frequency characteristics are investigated. It is primarily aimed at investigating the location and characteristics of the high speed train emission. The results from the microphone array tests are also analyzed in relation to the remarks from analytic studies and experimental investigations on the high speed train that have been done with the intention of understanding the interior noise mechanism. The acoustical image shows the low frequency noise sources mainly at the position of the under part of the train at high speeds and the related source mechanism are discussed.

• Journal of information and communication convergence engineering
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• v.5 no.1
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• pp.1-6
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• 2007

In the process of the acquisition, storage, transmission of signals, noises are generated by various causes and the degradation phenomenon by noises tends to generate serious errors for the signal with information. So, in order to analyze and remove these noises, studies on numerous mathematical methods such as the Fourier transform have been implemented. And recently there have been many ongoing wavelet-based denoising algorithms representing excellent characteristics in time-frequency localization and multiresolution analysis, but the method to remove additive white Gaussian noise (AWGN) and the impulse noise simultaneously was not given. So, to reconstruct the corrupted signal by noises, in this paper a novel wavelet-based denoising algorithm was proposed and using signal-to-noise ratio (SNR) this method was compared to conventional methods.

• The Journal of the Acoustical Society of Korea
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• v.20 no.4E
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• pp.52-60
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• 2001

This paper describes real time implementation of subband sound localization system on a floating-point DSP TI TMS320C31. The system determines two dimensional location of an active speaker in a closed room environment with real noise presents. The system consists of an two microphone array connected to TI DSP hosted by PC. The implemented sound localization algorithm is Subband CPSP which is an improved version of traditional CPSP (Cross-Power Spectrum Phase) method. The algorithm first split the input speech signal into arbitrary number of subband using subband filter banks and calculate the CPSP in each subband. It then averages out the CPSP results on each subband and compute a source location estimate. The proposed algorithm has an advantage over CPSP such that it minimize the overall estimation error in source location by limiting the specific band dominant noise to that subband. As a result, it makes possible to set up a robust real time sound localization system. For real time simulation, the input speech is captured using two microphone and digitized by the DSP at sampling rate 8192 hz, 16 bit/sample. The source location is then estimated at once per second to satisfy real-time computational constraints. The performance of the proposed system is confirmed by several real time simulation of the speech at a distance of 1m, 2m, 3m with various speech source locations and it shows over 5% accuracy improvement for the source location estimation.

• Progress in Superconductivity
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• v.4 no.1
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• pp.19-26
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• 2002

We have earlier developed a 40-channel SQUID system. An important figure of merit of a MEG system is the localization error, within which the underlying current source can be localized. With this system, we investigated the localization error in terms of the standard deviation of the coordinates of the ECDs and the systematic error due to inadequate modeling. To do this, we made localization of single current dipoles from tangential components of auditory evoked fields. Equivalent current dipoles (ECD) at N1m peak were estimated based on a locally fitted spherical conductor model. In addition, we made skull phantom and simulation measurements to investigate the contribution of various errors to the localization error. It was found that the background noise was the main source of the errors that could explain the observed standard deviation. Further, the amount of systematic error, when modeling the head with a spherical conductor, was much less than the standard deviation due to the background noise. We also demonstrated the performance of the system by measuring the evoked fields to grammatical violation in sentence comprehension.