• Sleep Medicine and Psychophysiology
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• v.19 no.1
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• pp.11-17
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• 2012

Objective: Among various methods developed to quantitatively explore electroencephalograms (EEG), we focused on a wavelet method that was known to yield robust results under nonstationary conditions. The aim of this study was thus to introduce the wavelet method and demonstrate its potential use in clinical sleep studies. Method: This study involved artificial EEG specifically designed to validate the wavelet method. The method was performed to obtain time-dependent spectral power and phase angles of the signal. Synchrony of multichannel EEG was analyzed by an order parameter of the instantaneous phase. The standard methods, such as Fourier transformation and coherence, were also performed and compared with the wavelet method. The method was further validated with clinical EEG and ERP samples available as pilot studies at academic sleep centers. Result: The time-frequency plot and phase synchrony level obtained by the wavelet method clearly showed dynamic changes in the EEG waveforms artificially fabricated. When applied to clinical samples, the method successfully detected changes in spectral power across the sleep onset period and identified differences between the target and background ERP. Conclusion: Our results suggest that the wavelet method could be an alternative and/or complementary tool to the conventional Fourier method in quantifying and identifying EEG and ERP biomarkers robustly, especially when the signals were nonstationary in a short time scale (1-100 seconds).

• The Journal of the Acoustical Society of Korea
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• v.24 no.3E
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• pp.87-89
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• 2005

The acoustical sources of intake and exhaust systems in fluid machines are often characterized by the source impedance and strength using linear frequency-domain modeling. In the case of the sources which are nonlinear and time-variant, however, the source parameters were sometimes incorrectly obtained. In this paper, the source model and direct measurement technique are modified in order to evaluate the effect due to nonlinear and periodically time-varying source character as well as the linear property of the reflectivity of in-duct fluid machine source. With a priori known kinematical information of the source, the types of nonlinear time-variant terms can be presumed by a simple physical model, in which there is practically no restriction on the form of the model. The concept of source impedance can be extendable by introducing the linear frequency response function for each nonlinear or time-variant input. Extending the conventional method and adapting the reverse MISO technique, it is possible to develop a direct method that can deal with the nonlinear time-variant source parameters. The proposed direct method has a novel feature that there is no restriction on the probability or spectral natures of the excited sound pressure data. The present method is verified by the simulated measurements for simplified fluid machines. It is thought that the proposed method would be useful in predicting the insertion loss or the radiated sound level from intake or exhaust systems.

• Journal of IKEEE
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• v.3 no.1 s.4
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• pp.141-149
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• 1999

If signal time duration of MC-CDMA method which has studied for next-generation high-speed data transmission is not sufficiently large compared to delay spread of channel, the performance is degraded by generation of intersymbol interference. In this paper, this problem will solve through serial to parallel convertor and make large sufficiently time duration of signal compared to delay spread of channel and rise variable spectral efficiency through the number of serial to parallel convertor subchannel we will add to parallel frequency diversity block for improve the performance in mobile Communication. Spectral efficiency of the proposed system is counted and compared to spectral efficiency of MC-CDMA and investigated through computer simulations by using multipath Rayleigh fading channel.

• The Journal of the Acoustical Society of Korea
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• v.28 no.6
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• pp.526-533
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• 2009

This paper proposes a formant synthesis method of Haegeum sounds using cepstral envelope for spectral modeling. Spectral modeling synthesis (SMS) is a technique that models time-varying spectra as a combination of sinusoids (the "deterministic" part), and a time-varying filtered noise component (the "stochastic" part). SMS is appropriate for synthesizing sounds of string and wind instruments whose harmonics are evenly distributed over whole frequency band. Formants extracted from cepstral envelope are parameterized for synthesis of sinusoids. A resonator by Impulse Invariant Transform (IIT) is applied to synthesize sinusoids and the results are bandpass filtered to adjust magnitude. The noise is calculated by first generating the sinusoids with formant synthesis, subtracting them from the original sound, and then removing some harmonics remained. Linear interpolation is used to model noise. The synthesized sounds are made by summing sinusoids, which are shown to be similar to the original Haegeum sounds.

• Korean Journal of Crystallography
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• v.9 no.1
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• pp.53-63
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• 1998

The Cd1-xZnxS thin films were grown on the Si(100) wafers by a hot wall epitaxy method (HWE). the source and substrate temperature are 600℃ and 440℃, respectively. The crystalline structure of epilayers was investigated by double crystal X-ray diffraction (DCXD). Hall effect on the sample was measured by the van der Pauw method and the carrier density and mobility dependence of Hall characteristics on temperature was also studied. In order to explore the applicability as a photoconductive cell, we measured the sensitivity (γ), the ratio of photocurrent to darkcurrent (pc/dc), maximum allowable power dissipation (MAPD), spectral response and response time. The results indicated that the best photoconductive characteristic were observed in the Cd0.53Zn0.47S samples annealed in Cu vapor comparing with in Cd, Se, air and vacuum vapour. Then we obtained the sensitivity of 0.99, the value of pc/dc of 1.65 × 107, the MAPD of 338mW, and the rise and decay time of 9.7 ms and 9.3 ms, respectively.

• The Journal of the Acoustical Society of Korea
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• v.28 no.5
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• pp.461-470
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• 2009

We investigate and analyze the problems encountered in frame-based estimation of TDOA (Time Difference of Arrival) using CPSP function. Spectral leakage occurring in framing of a speech signal by a rectangular window could make estimation of CPSP spectrum inaccurate. Framing with other windows to reduce the spectral leakage distorts the signal due to the asynchronous weighting around the frame specifically both ends of the frame. These problems degrade the performance of the CPSP-based TDOA estimation. In this paper, we propose a method to alleviate those problems by pre-emphasis of the speech signal. It reduces the influence of the spectral leakage by reducing dynamic range of the spectrum of a speech signal with pre-emphasis. To validate the proposed method of pre-emphasis, we carry out TDOA estimation experiments in various noise and reverberation conditions, Experimental results have shown that the framing of pre-emphasized microphone output by a rectangular window achieves higher success rate of TDOA estimation than any other framing methods.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.30 no.1
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• pp.16-30
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• 1997

Application of digital filter to the wave analysis is studied using the observed data by wave gauge. Sea wave data obtained from wave gauge always include long wave frequency components. In order to estimate the sea wave parameters, we must re-analyzed wave data by using a digital filter and the concept of mean sea level correction method. By the wave by wave analysis and spectral methods, sea wave parameters on the basis of wave data obtained by the conventional method and digital filter are compared. The best-fitted design filter determined by the necessary conditions of frequency responses, can be obtained by calculating various transfer functions. Thus, to get the best the digital filter design, both Butterworth filter and Savitzky-Golay filter of digital filter are used in the frequency and time domain, respectively. Three cases of observation wave data are calculated by applying digital filter. The components of different frequency bands in the surf zone are coexisted in three cases. The wave data for wind wave components is computed using the digital filter the surf zone and off-surf zone, and based on the filtered data, wave parameters are calculated by the spectral analysis and wave by wave analysis methods, respectively. As a results, when sea wave data observed by wave gauge are analyzed, the Savitzky-Golay method is recommended which can well appear cut-off frequency by experimental choosing filter length in the time domain. The better mean sea level correction method is the Butterworth filter in the frequency domain.

• Journal of The Korean Society of Grassland and Forage Science
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• v.34 no.4
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• pp.277-282
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• 2014

Nutritive value analysis of feed is very important for the growth of livestock, and ensures the efficiency of feeds as well as economic status. However, general laboratory analyses require considerable time and high cost. Near-infrared reflectance spectroscopy (NIRS) is a spectroscopic technique used to analyze the nutritive values of seeds. It is very effective and less costly than the conventional method. The sample used in this study was a corn kernel and the partial least square regression method was used for evaluating nutrient composition, digestibility, and energy value based on the calibration equation. The evaluation methods employed were the coefficient of determination ($R^2$) and the root mean squared error of prediction (RMSEP). The results showed the moisture content ($R^2_{val}=0.97$, RMSEP=0.109), crude protein content ($R^2_{val}=0.94$, RMSEP=0.212), neutral detergent fiber content ($R^2_{val}=0.96$, RMSEP=0.763), acid detergent fiber content ($R^2_{val}=0.96$, RMSEP=0.142), gross energy ($R^2_{val}=0.82$, RMSEP=23.249), in vitro dry matter digestibility ($R^2_{val}=0.68$, RMSEP=1.69), and metabolizable energy (approximately $R^2_{val}$ >0.80). This study confirmed that the nutritive components of corn kernels can be predicted using near-infrared reflectance spectroscopy.

• Korean Journal of Optics and Photonics
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• v.26 no.1
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• pp.30-37
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• 2015

The design and implementation of a radiative temperature measurement system for a flash light are carried out. Since a massive amount of energy is emitted within a very short time, it is impossible to measure the temperature of a flash with a conventional method. It is also irrelevant to measure one with an optical noncontact method. In this paper, a radiative temperature measurement system using the ratio of spectral radiances over mid- and long-wavelength infrared (IR) is designed and implemented. The implemented system utilizes optical bandpass filters to divide the wavelengths within the mid- and long-wavelength IR ranges, and pyroelectric IR detectors to measure the incident optical power of each wavelength-divided channel. It is shown that the measured radiative temperature of a flash is in the range of 1393 to 1455 K. This temperature-measurement system can be utilized to obtain information about the spectral radiance of a flash as a light source, which is of crucial importance to approaching the modeling and simulation of the various effects of a flash.

• IEMEK Journal of Embedded Systems and Applications
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• v.15 no.3
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• pp.119-127
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• 2020

The detection and recognition of abnormal driving becomes crucial for achieving safety in Intelligent Transportation Systems (ITS). This paper presents a feature extraction method based on spectral data to train a neural network model for driving behavior recognition. The proposed method uses a two stage signal processing approach to derive time-saving and efficient feature vectors. For the first stage, the feature vector set is obtained by calculating variances from each frequency bin containing the power spectrum data. The feature set is further reduced in the second stage where an intersection method is used to select more significant features that are finally applied for training a neural network model. A stream of live signals are fed to the trained model which recognizes the abnormal driving behaviors. The driving behaviors considered in this study are weaving, sudden braking and normal driving. The effectiveness of the proposed method is demonstrated by comparing with existing methods, which are Particle Swarm Optimization (PSO) and Convolution Neural Network (CNN). The experiments show that the proposed approach achieves satisfactory results with less computational complexity.