• ETRI Journal
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• v.28 no.6
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• pp.761-769
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• 2006

In this paper, we address the problem of closely spaced source localization using sensor array processing. In particular, the performance efficiency (measured in terms of the root mean square error) of the unconditional maximum likelihood (UML) algorithm for estimating the direction of arrival (DOA) of near-field sources is evaluated. Four parameters are considered in this evaluation: angular separation among sources, signal-to-noise ratio (SNR), number of snapshots, and number of sources (multiple sources). Simulations are conducted to illustrate the UML performance to compute the DOA of sources in the near-field. Finally, results are also presented that compare the performance of the UML DOA estimator with the existing multiple signal classification approach. The results show the capability of the UML estimator for estimating the DOA when the angular separation is taken into account as a critical parameter. These results are consistent in both low SNR and multiple-source scenarios.

• Transactions of the Korean Society of Automotive Engineers
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• v.16 no.2
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• pp.18-28
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• 2008

In inverse acoustic problem with nearfield sources, it is important to separate multiple acoustic sources and to measure the position of each target. This paper proposes a new algorithm by applying MUSIC(Multiple Signal Classification) to the outputs of discrete wavelet transformation with sub-band selection based on the entropy threshold, Some numerical experiments show that the proposed method can estimate the more precise positions than a conventional MUSIC algorithm under moderately correlated signal and relatively low signal-to-noise ratio case.

• Journal of Advanced Navigation Technology
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• v.26 no.2
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• pp.78-84
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• 2022

In this paper, we propose a closed-form based 3D localization method in the presence of multiple signal sources. General localization methods such as TDOA, AOA, and FDOA can estimate a location when a single signal source exists. When there are multiple unknown signal sources, there is a limit in estimating the location. The proposed method calculates a cross-correlation vector of signals received by sensors having an array antenna, and estimates TDOA and AOA values from the cross-correlation values. Then, the coordinate transformation is performed using the position of the reference sensor. Then, the coordinate rotation is performed using the estimated AOA value for the transformed coordinates, and then the three-dimensional position of each emitter is estimated. The proposed method verifies its performance through computer simulation.

• Phonetics and Speech Sciences
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• v.4 no.3
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• pp.103-110
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• 2012

In this paper, a target signal detection method using multiple signal classification (MUSIC) algorithm is proposed. The MUSIC algorithm is a subspace-based direction of arrival (DOA) estimation method. Using the inverse of the eigenvalue-weighted eigen spectra, the algorithm detects the DOAs of multiple sources. To apply the algorithm in target signal detection for GSC-based beamforming, we utilize its spectral response for the DOA of the target source in noisy conditions. The performance of the proposed target signal detection method is compared with those of the normalized cross-correlation (NCC), the fixed beamforming, and the power ratio method. Experimental results show that the proposed algorithm significantly outperforms the conventional ones in receiver operating characteristics (ROC) curves.

• Smart Structures and Systems
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• v.8 no.2
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• pp.219-237
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• 2011

This paper presents novel research into the source localization of multiple impacts. Source localization technology for single impact loads in a plate structure has been used for health monitoring. Most of research on source localization has been focused only on the localization of single impacts. Overlapping of dispersive waves induced by multiple impacts and reflection of those waves from the edge of the plate make it difficult to localize the sources of multiple impacts using traditional source localization technology. The method solving the overlapping problem and the reflection problem is presented in the paper. The suggested method is based on pre-signal processing technology using band pass filter and optimal filter. Results from numerical simulation and from experimentation are presented, and these verify the capability of the proposed method.

• Journal of Electrical Engineering and Technology
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• v.11 no.2
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• pp.353-360
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• 2016

Series hybrid electric vehicles (SHEVs) having multiple power sources such as an engine- generator (EnGen), a battery, and an ultra-capacitor require a power control unit with high power density and reliable control operation. However, manufacturing using separate individual power converters has the disadvantage of low power density and requires a large number of power and signal cable wires. It is also difficult to implement the optimal power distribution and fault management algorithm because of the communication delay between the units. In order to address these concerns, this approach presents a design methodology and a power control algorithm of an integrated power converter for the SHEVs powered by multiple power sources. In this work, the design methodology of the integrated power control unit (IPCU) is firstly elaborately described, and then efficient and reliable power distribution algorithms are proposed. The design works are verified with product-level and vehicle-level performance experiments on a 10-ton SHEV.

• The Journal of the Acoustical Society of Korea
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• v.29 no.8
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• pp.509-515
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• 2010

This paper presents a new method for upmixing mono signal to stereo signal with guaranteeing high stereophonic image quality (SIQ) and large apparent source width (ASW). The proposed method consists of analysis phase and synthesis phase. In analysis phase, a mono signal is first decomposed into multiple sound sources by the use of high-rank nonnegative matrix factorization. Then the multiple sources are clustered into two groups based on tonality criterion. In synthesis phase, one group is directly fed into left and right channels while the other group is decorrelated before being fed into each channel. Subjective tests reveals that the proposed method gives listener high SIQ and large ASW with minimizing timbral distortions.

• 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.

• Journal of the Korean Society for Precision Engineering
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• v.15 no.11
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• pp.218-226
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• 1998

In this study a neural network based expert system designed to diagnose operating status of a rotating spindle system supported by ball bearings was introduced. In order to facilitate practical failure situations, five exemplary abnormal status was fabricated. Out of several possible data source locations, seven most effective spots were chosen and proven to be the most successful in predicting single and multiple abnormalities. Increased signal strength was measured around where abnormality was embedded. Signal mea-surement locations producing high prediction rate were also classified. Even though multiple abnormalities were hard to be decoupled into their individual causes, proposed diagnostic system was somewhat effective in predicting such cases under certain combination of sensor locations. Among several abnormal operating conditions, highest prediction rate can be expected when signal is spoiled by the failure or damage in outer race. Proposed diagnostic system was again proven to be the most effective system in analyzing and ranking the importance of data sources.

• Proceedings of the Korean Institute of Communication Sciences Conference
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• 1991.10a
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• pp.123-126
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• 1991

A generalization of null-spectrum for use in the estimation of directions of arrival of signal sources is considered in this paper. The upper and lower bounds of the generalized null-spectrum, the maximum and minimum null-spectra, are also derived. We observed that the maximum null-spectrum has higher resolution capability than other null-spectra including the two well-known null-spectra, the multiple signal classification null-spectrum and the Min-Norm null-spectrum.