• International Journal of Contents
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• v.12 no.3
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• pp.12-16
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• 2016

Brain computer interfaces (BCI) usually have focused on classifying the explicitly-expressed intentions of humans. In contrast, implicit intentions should be considered to develop more intelligent systems. However, classifying implicit intention is more difficult than explicit intentions, and the difficulty severely increases for subject independent classification. In this paper, we address the subject independent classification of implicit intention based on electroencephalography (EEG) signals. Among many machine learning models, we use the support vector machine (SVM) with radial basis kernel functions to classify the EEG signals. The Fisher scores are evaluated after extracting the gamma, beta, alpha and theta band powers of the EEG signals from thirty electrodes. Since a more discriminant feature has a larger Fisher score value, the band powers of the EEG signals are presented to SVM based on the Fisher score. By training the SVM with 1-out of-9 validation, the best classification accuracy is approximately 65% with gamma and theta components.

• Phonetics and Speech Sciences
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• v.7 no.1
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• pp.11-17
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• 2015

This paper deals with the correlation between physical fatigue and speech signals. A treadmill task to increase fatigue and a set of subjective questionnaire for rating tiredness were designed. The results from the questionnaire and the collected bio-signals showed that the designed task imposes physical fatigue. The t-test for two-related-samples between the speech signals and fatigue showed that the parameters statistically significant to fatigue are fundamental frequency, first and second formant frequencies, long term average spectral slope, smoothed pitch perturbation quotient, relative average perturbation, pitch perturbation quotient, cepstral peak prominence, and harmonics to noise ratio. According to the experimental results, it is shown that mouth is opened small and voice is changed to be breathy as the physical fatigue accumulates.

• The Bulletin of The Korean Astronomical Society
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• v.37 no.1
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• pp.76.1-76.1
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• 2012

One of the main challenges for future 21-cm observations is to remove foregrounds which are several orders of magnitude more intense than the HI signal. We propose a new technique for removing foregrounds of the redshifted 21-cm observations. We consider multi-frequency interferometer observations. We assume that the 21-cm signals in different frequency channels are uncorrelated and the foreground signals change slowly as a function of frequency. When we add the visibilities of all channels, the foreground signals increase roughly by a factor of N because they are highly correlated. However, the 21-cm signals increase by a factor of sqrt{N} because the signals in different channels contribute randomly. This enables us to obtain an accurate shape of the foreground angular power spectrum. Then, we obtain the 21-cm power spectrum by subtracting the foreground power spectrum obtained this way. We describe how to obtain the average power spectrum of the 21-cm signal.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.5 no.3
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• pp.15-21
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• 1996

This study is focused on the prediction of appropriate tool life by clarifying the correlation between progressive tool wear and AE(Acoustic Emission) signals, while cutting stainless steel by end mill on the machining center. The results of this study were that RMSAE tends to increase linearly along with the increase of the cutting speed, and it was more sensitive to depth of cut than to the variation of feed rate at the same cutting conditions, and RMSAE increases around 0.21mm flank wear hereby AE-HIT also increases. AE signals depend upon tool wear and fracture from the above results. Therefore, the AE signals can be utilized in order to monitor the tool condition.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.32B no.7
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• pp.989-1002
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• 1995

In this paper, we propose a new algorithm, so called the Signal Decorrelation via Virtual Translation of Array (SDVTA) algorithm, for estimating the directions of arrival(DOA's) of narrowband coherent signals incident on a general array. An effective procedure is composed of transforming the steering matrix of the original array into that of the virtually translated sensor array and taking the average of the transformed covariance matrices in order to decorrelate the coherent signals. The advantage of this approach is in that 1) it can estimate the DOA's of m-1 coherent signals(M : the number of array sensors) since the effective aperture size is never reduced. 2) a geometry of array is unrestricted for solving the narrowband coherency problem. 3) the efficiency of signal decorrelation does not depend on the phase differences between coherent signals unlike the Coherent Signal Subspace Method (CSM). Simulation results are illustrated to demonstrate the superior performance of this new algorithm in comparison with the normal MUSIC and examine the comparative performance with the various choices of the optimal transformation matrix under coherent signal environments.

• Nuclear Engineering and Technology
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• v.33 no.5
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• pp.483-497
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• 2001

In this work, a neuro-fuzzy inference system combined with the wavelet denoising, PCA (principal component analysis) and SPRT (sequential probability ratio test) methods is developed to detect the relevant sensor failure using other sensor signals. The wavelet denoising technique is applied to remove noise components in input signals into the neuro-fuzzy system The PCA is used to reduce the dimension of an input space without losing a significant amount of information. The PCA makes easy the selection of the input signals into the neuro-fuzzy system. Also, a lower dimensional input space usually reduces the time necessary to train a neuro-fuzzy system. The parameters of the neuro-fuzzy inference system which estimates the relevant sensor signal are optimized by a genetic algorithm and a least-squares algorithm. The residuals between the estimated signals and the measured signals are used to detect whether the sensors are failed or not. The SPRT is used in this failure detection algorithm. The proposed sensor-monitoring algorithm was verified through applications to the pressurizer water level and the hot-leg flowrate sensors in pressurized water reactors.

• Journal of electromagnetic engineering and science
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• v.7 no.4
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• pp.201-206
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• 2007

When the M-ary signal experiences the Rayleigh fading, the diversity schemes can reduce the effects of fading since the probability that all the signals components will fade simultaneously are reduced considerably. The symbol error probabilities for various M-ary signals, such as MDPSK, MPSK and MQAM, are mathematically derived for the SC-2(Selection Combining 2) demodulation system, whereby the two signals with the two largest amplitudes are coherently combined among the L branches. On the other hand, maximum ratio combining(MRC) requires the individual signals from each path to be time-aligned, cophased, optimally weighted by their own fading amplitude, and then summed. The propagation model used in this paper is the frequency-nonselective slow Rayleigh fading channel corrupted by the Additive White Gaussian Noise(AWGN). The numerical results presented in this paper are expected to provide information for the design of radio system using M-ary modulation method for above mentioned channel environment.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 1999.11a
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• pp.127-131
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• 1999

In this paper, using a wide-band AE sensor with the frequency range from 100[kHz], the frequency spectra of AE signals generated from the corona discharges of the needle-plane electrode was analyzed to determine the proper ultrasonic sensor. We also examined the relationship between the magnitude of corona discharge and the magnitude of AE signals in peak-to-peak value under the application of 60[Hz] AC high-voltage in oil. From these results, the main frequency spectra of AE signals emitted from the corona discharges of the needle-plane gap were found to be 130[kHz] by the fast fourier transform. The magnitude of AE signals was proportional to the magnitude of corona discharge and discharge current pulse with increasing the applied voltages. Also the detection of corona discharge point location by AE signals was found to be possible by using two sensors.

• Journal of Mechanical Science and Technology
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• v.18 no.10
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• pp.1738-1746
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• 2004

When there are low signal to noise relationships or low coherences between measured pressure and a reference sensor, a pressure field measured and estimated by NAH (Nearfield Acoustic Holography) becomes noisy on the hologram and source planes. This paper proposes a method to obtain the high coherent de-noised pressure signals from low coherent noisy ones by combining a wavelet algorithm with NAH. The proposed method obtains the de-noised field from acoustic fields on a noise source plane reconstructed through backward propagation of NAH. Thus this method does not need high coherent pressure signals on the hologram surface while the conventional nearfield acoustic holography requires high-coherent signals. The proposed method was verified by numerical simulation using noisy signals, composed of original signals and imposed noises distributed on the hologram surface.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.16 no.5
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• pp.445-448
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• 1991

The two-sample locally optimum rank detection scheme is obtained which uses rank and sign statistics for detection of random signals in additive noise. It is shown that the detector is similar in structure to the locally optimum detector for random signals and to the one-sample locally optimum rank detector for random signals. It is also shown that the detector is a generalization of the two-sample locally optimum rank detector for known signals. In addition , the problem of two-sample locally optimum rank detection of random signals in multiple input case is considered briefly.