• The Transactions of the Korean Institute of Power Electronics
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• v.4 no.1
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• pp.50-56
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• 1999

This paper presents consideration on validity of instantaneous correlation power theory. The proposed power theory is defined and analyzed by time domain approach, thus it is easy to understand and instrument. The power is decomposed into active, fundamental reactive and harmonics components based on the autocorrelation and crosscorrelation signal techniques between voltage and current waveforms. On the compensation property, active power filter deal with three components only. Also, for real time control of active power filter, the power models with difficult concept are not cost effective. To verify the validity of the instantaneous correlation power theory, experimental work for voltage type DSP based active power filter is achieved. The power of thyristor controlled motor drives is decomposed into three orthogonal components by proposed power theory. From compensation results, validity of proposed theory is confirmed. feedback controller needs the information on some motor parameters. New recursive adaptation algorithms for rotor resistance and mutual inductance which can be applied to our nonlinear feedback controller are also presented in this paper. The recursive adaptation algorithms make the estimated values of rotor resistance and mutual inductance track their real values. Some simulation and experimental results show that the adaptation algorithms are robust against the variation of stator resistance and stator inductance.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.38 no.9
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• pp.999-1004
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• 2014

In the present paper, the effects of initial crack location on spatial randomness of fatigue crack growth resistance (FCGR) in friction stir welded (FSWed) AA7075-T651 plates were studied. The objective of this study is to characterize the statistical properties of FCGR for three different types of initial crack location (ICL) specimens. In this work, the FCGR coefficients were treated as a spatial random process. It was found that the FCGR coefficients for all initial crack location specimens closely followed a two parameter Weibull distribution. The shape parameter of the Weibull distribution for BM-ICL specimens showed the largest value of 7.50, and that for the WM-ICL specimens showed the smallest value of 2.61. In addition, the autocorrelation functions for all the ICL specimens followed the exponential function.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.5
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• pp.188-194
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• 2014

The feature extraction mechanism of the CASA(Computational Auditory Scene Analysis) system uses time continuity and frequency channel similarity to compose a correlogram of auditory elements. In segmentation, we compose a binary mask by using cross-correlation function, mask 1(speech) has the same periodicity and synchronization. However, when there is delay between autocorrelation signals with the same periodicity, it is determined as a speech, which is considered to be a drawback. In this paper, we proposed an algorithm to improve discrimination of channel similarity using Weighted Cross-correlation in segmentation. We conducted experiments to evaluate the speech segregation performance of the CASA system in background noise(siren, machine, white, car, crowd) environments by changing SNR 5dB and 0dB. In this paper, we compared the proposed algorithm to the conventional algorithm. The performance of the proposed algorithm has been improved as following: improvement of 2.75dB at SNR 5dB and 4.84dB at SNR 0dB for background noise environment.

• Water for future
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• v.17 no.4
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• pp.281-291
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• 1984

In our Koreans river basins there are many of monthly rainfall data, but unfortrnately streamflow data needed are rare. Analysing monthly rainfall data of Somjin river basin, the stochastic theory model for calculation of monthly streamflow series of that region is determined. The model is composed of Box & Jenkins stansfer function plus ARIMA residual models. This linear stochastic differenced time series equation models can adapt themselves to the structure and variety of rainfall, streamflow data on the assumption of the stationary covarience. The fiexibility of Box-Jenkins method consists mainly in the iterative technique of building an AIRMA model from observations and by the use of autocorrelation functions. The best models for Somjin river basin belong to the general calss: $Y_t=($\omega$o-$\omega$_1B) C_iX_t+$\varepsilon$t$ $Y_t$ monthly streamflow, $X_t$ : monthly rainfall, $C_i$ :monthly run-off, $$\omega$o-$\omega$_1$ : transfer parameter, $$\varepsilon$_t$ : residual The streamflow series resulted from the proposed model is satisfactory comparing with the exsting streamflow data of Somjin gauging station site.

• Journal of Biomedical Engineering Research
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• v.36 no.4
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• pp.103-108
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• 2015

Discrimination of Parkinson's disease (PD) from Essential tremor (ET) is often misdiagnosed in clinical practice. Since tremor is time-varying signal, and dominant and harmonic frequencies are shown in tremor only with moderate or severe symptom, there are some limitations to use frequency related features. Moreover, patients with PD or ET can suffer from both resting tremor and postural tremor. In this study, 28 patients with PD and 17 patients with ET were enrolled. Tremor was measured with accelerations on the more affected hand during resting and postural conditions. The ratio of root mean square (RMS) of resting tremor to RMS of postural tremor, the mean coefficients of autocorrelation function (ACF), and the mean of differences of two adjacent coefficients of ACF at resting and postural were calculated and compared between PD and ET. The performance showed 98% accuracy with support vector machine and leave-one-out cross validation. In addition, the method accurately differentiated the patients with tremor-dominant PD from patients with ET, with 100% accuracy. Therefore, the developed algorithm can assist clinicians in diagnosing and categorizing patients with tremor, especially, patients with mild symptom or the early stage of a disease, for proper treatment.

• Journal of Internet Computing and Services
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• v.8 no.6
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• pp.169-180
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• 2007

When an image is captured, motion blurs are caused by relative motion between the camera and the scene, In the case of the camera is moving, the extents of the motion blur are spatially variant according to distances from the camera to the objects. Although the camera is fixed, the extents of the motion blur are spatially variant according to various speeds of the moving objects. Unexpected blur effect very often degrades the quality of the image and it needs to be restored, To restore the spatially variant blurred image, each of the point spread function (PSF) should be identified, In this paper, we propose a new method for the identification of blur extent locally from the image in which the spatially variant motion blur is caused. Experiment shows that the proposed method identifies blur extent well.

• Journal of the Optical Society of Korea
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• v.16 no.2
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• pp.174-180
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• 2012

In this study, the feasibility of spectral domain optical Doppler tomography for measuring blood flow characteristics in a micro-tube was demonstrated through several experiments. The use of an SD-ODT system in blood flow measurement can provide high resolution images (5 microns resolution). We prepared three capillary tubes to reveal the effect of different concentrations of hematocrit ratio (HR). One tube serves as the control. The two other tubes contained different concentrations of HR (5%, 25%). Three different capillary tube inlet flow velocities were tested in the present study. The Reynolds number (Re) which is based on the capillary tube inner diameter ranges from Re=6 to 48. We calculated a Doppler shift of the power spectrum of the temporal interference fringes with Kasai autocorrelation function to achieve the velocity profile of the flow. As a result, SD-ODT systems could not detect the cell depletion layer in the present study due to the limitation of spatial resolution. Nevertheless, these systems were proven to be capable of observing the RBCs of blood.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39A no.11
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• pp.637-644
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• 2014

This paper investigates the detection performance of fault location using STDR(sequence time domain reflectometry) and SSTDR(spread spectrum time domain reflectometry) with various length and types of sequences, and then, proposes an improved detection technique by eliminating the injected signal in SSTDR. The detection error rates are compared and analyzed in power line channel model with various fault locations, fault types, and spreading sequences such as m-sequence, binary Barker sequence, and 4-phase Frank sequence. It is shown that the proposed technique is able to improve the detection performance obviously when the reflected signal is weak or the fault location is extremely close.

• Journal of the Korean Society for Nondestructive Testing
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• v.32 no.5
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• pp.484-493
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• 2012

Since the 1980s, there have been many research activities devoted to quantitatively characterizing and imaging human tissues based on sound speed, attenuation coefficient, density, nonlinear B/A parameter, etc., but those efforts have not yet reached the stage of commercialization. However, a new imaging technology termed elastography, which was proposed in the early 1980s, has recently been implemented in commercial clinical ultrasound scanners, and is now being used to diagnose prostates, breasts, thyroids, livers, blood vessels, etc., more quantitatively as a complementary adjunct modality to the conventional B-mode imaging. The purpose of this article is to introduce and review various elastographic algorithms for use in quasistatic or static compression type elasticity imaging modes. Most of the algorithms are based on the crosscorrelation or autocorrelation function methods, and the fundamental difference is that the time shift is estimated by changing the lag variable in the former, while it is directly obtained from the phase shift at a fixed lag in the latter.

• Journal of the Korea Society of Computer and Information
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• v.19 no.11
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• pp.17-24
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• 2014

This paper proposes a fault detection method for low-speed rolling element bearings of an induction motor using acoustic emission signals and histogram modeling. The proposed method performs envelop modeling of the histogram of normalized fault signals. It then extracts and selects significant features of each fault using partial autocorrelation coefficients and distance evaluation technique, respectively. Finally, using the extracted features as inputs, the support vector regression (SVR) classifies bearing's inner, outer, and roller faults. To obtain optimal classification performance, we evaluate the proposed method with varying an adjustable parameter of the Gaussian radial basis function of SVR from 0.01 to 1.0 and the number of features from 2 to 150. Experimental results show that the proposed fault identification method using 0.64-0.65 of the adjustable parameter and 75 features achieves 91% in classification performance and outperforms conventional fault diagnosis methods as well.