• Transactions of the Korean Society for Noise and Vibration Engineering
• /
• v.19 no.4
• /
• pp.347-354
• /
• 2009

Frequency analysis is one of the most useful way to analyze response signal for the purpose of grasping the dynamic characteristics of system through Fourier transformation. Although it is very effective way for frequency analysis, it is hard to analyze out a specific sound or vibration component which is correlated with others. In this thesis, source contribution analysis tool for NI-PXI equipment is developed with LabVIEW using coherences of MISO(multiple-input single-output) model. For the purpose of examining propriety of developed tool, simulation is performed with several correlated signals that have different frequency range. After checking the OCF(ordinary coherence function) and PCF(partial coherence function) of the each signal for concerned frequency domain, an experiment is conducted on an evaporator that cause the principal noise of a refrigerator. This developed tool will be expected to build up more convenient and serviceable measurement system.

• Wind and Structures
• /
• v.24 no.1
• /
• pp.59-78
• /
• 2017

Non-stationarity and non-Gaussian property are two of the most important characteristics of wind. These two features are studied in this study based on wind speed records measured at different heights from a 325 m high meteorological tower during the synoptic wind storms. By using the time-frequency analysis tools, it is found that after removing the low frequency trend of the longitudinal wind, the retained fluctuating wind speeds remain to be asymmetrically non-Gaussian distributed. Results show that such non-Gaussianity is due to the weak-stationarity of the detrended fluctuating wind speed. The low frequency components of the fluctuating wind speeds mainly contribute to the non-zero skewness, while distribution of the high frequency component is found to have high kurtosis values. By further studying the decomposed wind speed, the mechanisms of the non-Gaussian distribution are examined from the phase, turbulence energy point of view.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.13 no.2
• /
• pp.253-260
• /
• 2009

This paper, mathematically analyzes the performance of newly proposed TR-UWB system which the frequency components of a UWB pulse were processed so that the system could be implemented with ADCs of a few MHz sampling rate, and presents the comparison with an existing frequency-domain based TR-UWB system. The comparison is mainly based on the SNR ratio which depends on the mean and the variance of the frequency components. We also shows that the simulation results to support the theoretical analysis where the comparison is made under the IEEE 802.15.3a channel model as well as AWGN channel.

• Proceedings of the KIEE Conference
• /
• 1999.07c
• /
• pp.1478-1480
• /
• 1999

The accurate analysis of power system requires detailed load model. There are two basic approaches in modeling the load characteristics. One is to directly measure the voltage and frequency sensitivity of the load P and Q at substations and feeders. The other is to build up a composite load model from each load component. Each of these methods has advantages and disadvantages. This paper presents load composition for KEPCO's power system to develop load models by the component-based load modeling.

• Proceedings of the Computational Structural Engineering Institute Conference
• /
• 2007.04a
• /
• pp.27-32
• /
• 2007

Determination of crack depth in filed using the self-calibrating surface wave transmission measurement and the cutting frequency in the transmission function (TRF) is very difficult due to variations of the measurement conditions. In this study, it is proposed to use the measured full TRF as a feature for crack depth assessment. A principal component analysis (PCA) is employed to generate a basis of the measured TRFs for various crack cases. The measured TRFs are represented by their projections onto the most significant principal components. Then artificial neural networks (NNs) using the PCA-compressed TRFs is applied to assess the crack in concrete. Experimental study is carried out for five different crack cases to investigate the effectiveness of the proposed method. Results reveal that the proposed method can be effectively used for the crack depth assessment of concrete structures.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
• /
• v.4 no.2
• /
• pp.77-82
• /
• 2011

In this paper, we investigate source separation algorithms for stereo audio mixed using amplitude panning method. This source separation algorithms can be used in various applications such as up-mixing, speech enhancement, and high quality sound source separation. The methods in this paper estimate the panning angles of individual signals using the principal component analysis being applied in time-frequency tiles of the input signal and independently extract each signal through directional filtering. Performances of the methods were evaluated through computer simulations.

• Proceedings of the Acoustical Society of Korea Conference
• /
• spring
• /
• pp.9-14
• /
• 2002

We propose a new algorithm for blind source separation (BSS), in which frequency-domain independent component analysis (FDICA) and time-domain ICA (TDICA) are combined to achieve a superior source-separation performance under reverberant conditions. Generally speaking, conventional TDICA fails to separate source signals under heavily reverberant conditions because of the low convergence in the iterative learning of the inverse of the mixing system. On the other hand, the separation performance of conventional FDICA also degrades significantly because the independence assumption of narrow-band signals collapses when the number of subbands increases. In the proposed method, the separated signals of FDICA are regarded as the input signals for TDICA, and we can remove the residual crosstalk components of FDICA by using TDICA. The experimental results obtained under the reverberant condition reveal that the separation performance of the proposed method is superior to that of conventional ICA-based BSS methods.

• The Transactions of the Korean Institute of Electrical Engineers
• /
• v.36 no.11
• /
• pp.813-818
• /
• 1987

In this paper, high resolution spectral estimation by AR modelling and principal comonent analysis is proposed. The given data can be expanded by the eigenvectors of the estimated covariance matrix. The eigenspectrum is obtained for each eigenvector using the Autoressive(AR) spectral estimation technique. The final spectrum estimate is obtained by weighting each eigenspectrum with the corresponding eigenvalue and summing them. Although the proposed method increases in computational complexity, it shows good frequency resolution especially for short data records and narrow-band data whose signal-to-noise ratio is low.

• Proceedings of the Korea Institute of Convergence Signal Processing
• /
• 2003.06a
• /
• pp.78-81
• /
• 2003

본 논문은 SVM(support vector machine)을 이용한 음성인식기에 대해 효과적인 특징 파라메터를 제안한다. SVM은 특징 공간에서 비선형 경계를 찾아 분류하는 방법으로 적은 학습 데이터에서도 좋은 분류 성능을 나타낸다고 알려져 있으며 최적의 특징 파라메터를 선택하기 위해 본 논문에서는 SVM을 이용한 음성인식기를 사용하여 PCA(principal component analysis), ICA(independent component analysis) 알고리즘을 적용하여 MFCC(met frequency cepstrum coefficient)의 특징 공간을 변화시키면서 각각의 인식 성능을 비교 검토하였다. 실험 결과 ICA에 의한 특징 파라메터가 가장 우수한 성능을 나타내었으며 특징 공간에서 각 클래스의 분포도 또한 ICA가 가장 높은 선형 분별성을 나타내었다.

• 제어로봇시스템학회:학술대회논문집
• /
• 2001.10a
• /
• pp.22.1-22
• /
• 2001

In this paper we present Multiple-Channel Active Noise Control[ANC] system by employing Independent Component Analysis[ICA] and Adaptive Network Fuzzy Inference System[ANFIS]. ICA is widely used in signal processing and communication and it use prewhiting and appropriate choice of non-linearities, ICA can separate mixed signal. ANFIS controller is trained with the hybrid learning algorithm to optimize its parameters for adaptively canceling noise. This new method which minimizes a statistical dependency of mutual information(MI) in mixed low frequency noise signal and there is no need to secondary path modeling. The proposed implementations achieve more powerful and stable noise reduction than Filtered-X LMS algorithms which is needed for LTI assumption and precise secondary error