• ETRI Journal
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• 제42권6호
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• pp.815-826
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• 2020

We propose a two-dimensional (2D) scattering-center-extraction (SCE) method using sparse recovery based on the compressive-sensing theory, even with data missing from the received radar cross-section (RCS) dataset. First, using the proposed method, we generate a 2D grid via adaptive discretization that has a considerably smaller size than a fully sampled fine grid. Subsequently, the coarse estimation of 2D scattering centers is performed using both the method of iteratively reweighted least square and a general peak-finding algorithm. Finally, the fine estimation of 2D scattering centers is performed using the orthogonal matching pursuit (OMP) procedure from an adaptively sampled Fourier dictionary. The measured RCS data, as well as simulation data using the point-scatterer model, are used to evaluate the 2D SCE accuracy of the proposed method. The results indicate that the proposed method can achieve higher SCE accuracy for an incomplete RCS dataset with missing data than that achieved by the conventional OMP, basis pursuit, smoothed L0, and existing discrete spectral estimation techniques.

• Food Science and Biotechnology
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• 제18권5호
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• pp.1199-1203
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• 2009

Melamine is a chemical intermediate to manufacture amino resins and plastics, which cannot be used as food additive since it can cause kidney stones. A qualitative determination method of melamine residue in powdered and liquid milk was developed using Fourier transform infrared (FTIR) spectroscopic technique. The calibration sets consisted of 21 standard melamine solutions, in which 1% trichloroacetic acid aqueous solution and acetonitrile (3:1, v/v) were used as solvent. The model was validated using 10 standard melamine solutions which were unused to build up the calibration set. Infrared (IR) absorbance peaks specific to almost all chemical groups in melamine molecule were shown in the spectral range between 1,100 and 1,800/cm. Combined partial least squares (PLS)-$2^{nd}$ derivative calibration model coupled with mean centering (MC) mathematical enhancement showed the highest correlation coefficients ($R^2$>0.99). In brief, the FTIR technique can be used for quantitative analysis of melamine in milk samples.

• Journal of Electrical Engineering and Technology
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• 제8권2호
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• pp.288-294
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• 2013

The diagnosis of motor failures using an on-line method has been the aim of many researchers and studies. Several spectral analysis techniques have been developed and are used to facilitate on-line diagnosis methods in industry. This paper discusses the first application of a motor flux spectral analysis to the identification of broken rotor bar (BRB) faults in induction motors using a multiple signal classification (MUSIC) technique as an on-line diagnosis method. The proposed method measures the leakage flux in the radial direction using a radial flux sensor which is designed as a search coil and is installed between stator slots. The MUSIC technique, which requires fewer number of data samples and has a higher detection accuracy than the traditional fast Fourier transform (FFT) method, then calculates the motor load condition and extracts any abnormal signals related to motor failures in order to identify BRB faults. Experimental results clearly demonstrate that the proposed method is a promising candidate for an on-line diagnosis method to detect motor failures.

• 수면정신생리
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• 제19권1호
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• pp.11-17
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• 2012

목 적 : 본 연구는 뇌파를 정량분석하는 새로운 방법의 하나인 wavelet 방법을 소개하고 아울러 그것이 임상 수면뇌파 분석에 유용하다는 것을 검증하기 위해 시도되었다. 방 법 : Wavelet 방법을 검증하기 위해 수학적으로 만들어진 인공뇌파들과, 입면주기 임상 뇌파 샘플 하나와 GoNoGo 프레임으로 측정된 ERP 샘플 하나가 사용되었다. Wavelet방법론으로 계산된 time-frequency 파워 스펙트럼과 위상 동조화 정도가 Fourier 및 moving windows 방법으로 계산된 스펙트럼과 coherence 결과들과 비교 분석되었다. 결 과 : Wavelet 방법은 인공뇌파에 인위적으로 포함된 파형의 특징들을 성공적으로 분해해내었다. 임상뇌파 샘플로 한 검증에서도 그 유효성이 확인되었는데, 입면주기 전후로 보이는 스펙트럼의 변화를 유의미하게 확인할 수 있었으며, 표적(target) 및 배경(background) ERP 파형의 특징을 시간-주파수 도표(time-frequency plot)으로 잘 표현하였다. 결 론 : 이러한 결과를 미루어볼 때, wavelet 방법은 임상 뇌파를 정량 분석함에 있어서, Fourier 방법을 효과적으로 대체 혹은 보완함을 알 수 있었다. 특히, 뇌파가 수초에서 수백초의 짧은 시간단위에서 급격한 변화를 보이는 입면주기뇌파와 ERP 분석에 wavelet 방법의 적합성이 크다고 볼 수 있다.

• 한국광학회지
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• 제23권6호
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• pp.246-250
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• 2012

We derive analytical expressions for the output spectral density and the noise power $P_{\beta}$ in noise loading analysis using Volterra kernels to characterize fiber nonlinearities. The bandwidth of the input noise source has little effect on $P_{\beta}$, but the power of the input noise source and the dispersion parameter value of the fiber have a significant effect on $P_{\beta}$. The Volterra method predicts ${\Delta}P_{\beta}[dB]$ = 30 dB/decade, which agrees very accurately over a wide range of fiber parameters compared with the numerical results by the split-step Fourier method. Therefore the Volterra method could be useful to predict the performance of a dense WDM system when we plan to upgrade fiber or increase signal power.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권2호
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• pp.495-513
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• 2020

The correlation identification of the subsurface is a novel electrical prospecting method which could suppress stochastic noise. This method is increasingly being utilized by geophysicists. It achieves the frequency response of the underground media through division of the cross spectrum of the input & output signal and the auto spectrum of the input signal. This is subject to the spectral leakage when the cross spectrum and the auto spectrum are computed from cross correlation and autocorrelation function by Discrete Fourier Transformation (DFT, "To obtain an accurate frequency response of the earth system, we propose an improved correlation identification method which uses all phase Fast Fourier Transform (APFFT) to acquire the cross spectrum and the auto spectrum. Simulation and engineering application results show that compared to existing correlation identification algorithm the new approach demonstrates more precise frequency response, especially the phase response of the system under identification.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 1999년도 Proceedings of International Symposium on Remote Sensing
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• pp.429-432
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• 1999

Vegetation cover classification is examined based on a time series NOAA/AVHRR data. Time series data analysis methods including Fourier transform, Auto-Regressive (AR) model and temporal signature similarity matching are developed to extract phenological features of vegetation from a time series NDVI data from NOAA/AVHRR and to classify vegetation types. In the Fourier transform method, typical three spectral components expressing the phenological features of vegetation are selected for classification, and also in the AR model method AR coefficients are selected. In the temporal signature similarity matching method a new index evaluating the similarity of temporal pattern of the NDVI is introduced for classification.

• Journal of Information Processing Systems
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• 제20권3호
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• pp.399-408
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• 2024

The moisture content of crude oil notably affects various aspects of oil production, storage, transportation, and exploration. However, accurately measuring this moisture content is challenging because of numerous influencing factors, leading to a lack of precision in existing detection methods. This inadequacy hinders the progress of China's petroleum industry. To overcome these challenges, this paper proposes a conductivity-based method for measuring crude oil moisture content. By employing an embedded system, we designed a sensor comprising five electrodes. Additionally, we developed signal excitation and signal processing circuits. Moreover, a software program was designed to analyze and compute the output signal using fast Fourier transform operations. This facilitated the identification of flow patterns, computation of relevant flow rates, and establishment of correlation rates based on frequency spectral characteristics. Based on experimental results, we established a functional relationship between measurement parameters and crude oil moisture content. This study enhanced the precision of moisture content measurement, thereby addressing existing limitations and fostering the advancement of China's petroleum industry.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• 제4권2호
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• pp.143-152
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• 2000

As muscular contraction is sustained, the Fourier spectrum of the myoelectric signal is shifted toward the lower frequency. This spectral density is associated with muscle fatigue. This paper describes a quantitative measurement method that performs the measurement of localized muscle fatigue by tracking changes of median frequency based on stationary wavelet transform. Applying to the human masseter muscle, the proposed method offers the much information for muscle fatigue, comparing with the conventional FFT-based method for muscle fatigue measurement.

• 천문학회지
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• 제38권4호
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• pp.445-462
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• 2005

In a companion paper, we have presented so-called Spatio-Spectral Maximum Entropy Method (SSMEM) particularly designed for Fourier-Transform imaging over a wide spectral range. The SSMEM allows simultaneous acquisition of both spectral and spatial information and we consider it most suitable for imaging spectroscopy of solar microwave emission. In this paper, we run the SSMEM for a realistic model of solar microwave radiation and a model array resembling the Owens Valley Solar Array in order to identify and resolve possible issues in the application of the SSMEM to solar microwave imaging spectroscopy. We mainly concern ourselves with issues as to how the frequency dependent noise in the data and frequency-dependent variations of source size and background flux will affect the result of imaging spectroscopy under the SSMEM. We also test the capability of the SSMEM against other conventional techniques, CLEAN and MEM.