• Journal of the Institute of Electronics Engineers of Korea SP
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• v.48 no.4
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• pp.153-159
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• 2011

The MUSIC (MUltiple SIgnal Classification) method estimates the directions of arrival (DOAs) of the signals impinging on a sensor array based on the fact that the noise subspace is orthogonal to the signal subspace. In the conventional MUSIC, an estimate of the basis for the noise subspace is obtained by eigendecomposing the sample matrix, which is computationally expensive. In this paper, we present a simple DOA estimation method which finds an estimate of the signal subspace basis directly from the columns of the sample matrix from which the noise power components are removed. DOA estimates are obtained by searching for minimum points of a cost function which is defined using the estimated signal subspace basis. The minimum points are efficiently found through the Brent method which employs parabolic interpolation. Simulation shows that the simple estimation method virtually has the same performance as the complex conventional method based on the eigendecomposition.

• Journal of the Korean Data and Information Science Society
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• v.24 no.6
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• pp.1275-1283
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• 2013

This paper considers the multivariate integrated process control procedure for detecting special causes in a multivariate IMA(1, 1) process. When the multivariate control chart signals, the special cause will be detected and eliminated from the process. However, when the elimination of the special cause costs high or is not practically possible, an alternative action is to readjust the process with approximately modified adjustment scheme. In this paper, we estimate parameters in the readjustment procedure after having a true signal in the multivariate integrated process control.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.18 no.2 s.117
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• pp.177-182
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• 2007

This paper presents the InGaP/GaAs HBT differential LC VCO with low phase noise performance for adaptive feedback interference cancellation system(AF-lCS). The VCO is verified with enhanced tank structure including filtering technique. The output tuning range for proposed VCO using asymmetric inductor and symmetric capacitors withlow pass filtering technique is 207 MHz. The output powers are -6.68 including balun and cable loss. The phase noise of this VCO at 10 kHz, 100 kHz and 1 MHz are -102.02 dBc/Hz, -112.04 dBc/Hz and -130.40 dBc/Hz. The VCO is designed within total size of $0.9{\times}0.9mm^2$.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2012.05a
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• pp.555-561
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• 2012

The Hilbert Huang Transform (HHT) technigue with Empirical Mode Decomposition (EMD) is one of the time-frequency domain analysis methods and it has several advantages such that analyzing non-stationary and nonlinear signal is possible. However, there are shortcomings in detecting near-range of frequencies and added noise signals. In this paper, to analyze characteristics of each method, HHT and Short-Time Fourier Transform (STFT) effective in dealing with stationary signals are compared. And with thermoacoustic instabilities signals from a Rijke tube test, HHT and the improved HHT with Ensemble Empirical Mode Decomposition (EEMD) are compared. The results show that the improved HHT is more appropriate than the original HHT due to the relative insensitivity to noise. Therefore it will result in more accurate analysis.

• Geophysics and Geophysical Exploration
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• v.27 no.3
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• pp.171-180
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• 2024

The processing of seismic data involves analyzing earthquake wave data to understand the internal structure and characteristics of the Earth, which requires high computational power. Recently, machine learning (ML) techniques have been introduced to address these challenges and have been utilized in various tasks such as noise reduction and velocity model construction. However, most studies have focused on specific seismic data processing tasks, limiting the full utilization of similar features and structures inherent in the datasets. In this study, we compared the efficacy of using receiver-wise time-series data ("receiver array") and synchronized receiver signals ("time array") from shotgathers for pretraining a Bidirectional Encoder Representations from Transformers (BERT) model. To this end, shotgather data generated from a synthetic model containing faults was used to perform noise reduction, velocity prediction, and fault detection tasks. In the task of random noise reduction, both the receiver and time arrays showed good performance. However, for tasks requiring the identification of spatial distributions, such as velocity estimation and fault detection, the results from the time array were superior.

• The Journal of the Acoustical Society of Korea
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• v.21 no.3
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• pp.330-338
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• 2002

Spontaneous speech is ungrammatical as well as serious phonological variations, which make recognition extremely difficult, compared with read speech. In this paper, for conversational speech recognition, we analyze the transcriptions of the real conversational speech, and then classify the characteristics of conversational speech in the speech recognition aspect. Reflecting these features, we obtain the baseline system for conversational speech recognition. The classification consists of long duration of silence, disfluencies and phonological variations; each of them is classified with similar features. To deal with these characteristics, first, we update silence model and append a filled pause model, a garbage model; second, we append multiple phonetic transcriptions to lexicon for most frequent phonological variations. In our experiments, our baseline morpheme error rate (WER) is 31.65%; we obtain MER reductions such as 2.08% for silence and garbage model, 0.73% for filled pause model, and 0.73% for phonological variations. Finally, we obtain 27.92% MER for conversational speech recognition, which will be used as a baseline for further study.

• Journal of Korea Water Resources Association
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• v.45 no.12
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• pp.1321-1330
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• 2012

In this study, the daily inflow at the basin of Chungju dam is predicted using wavelet-artificial neural network for nonlinear model. Time series generally consists of a linear combination of trend, periodicity and stochastic component. However, when framing time series model through these data, trend and periodicity component have to be removed. Wavelet transform which is denoising technique is applied to remove nonlinear dynamic noise such as trend and periodicity included in hydrometeorological data and simple noise that arises in the measurement process. The wavelet-artificial neural network (WANN) using data applied wavelet transform as input variable and the artificial neural network (ANN) using only raw data are compared. As a results, coefficient of determination and the slope through linear regression show that WANN is higher than ANN by 0.031 and 0.0115 respectively. And RMSE and RRMSE of WANN are smaller than those of ANN by 37.388 and 0.099 respectively. Therefore, WANN model applied in this study shows more accurate results than ANN and application of denoising technique through wavelet transforms is expected that more accurate predictions than the use of raw data with noise.

• The Journal of the Acoustical Society of Korea
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• v.25 no.7
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• pp.346-355
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• 2006

Acoustic data from a shallow water experiment in the East Sea of Korea (MAPLE IV) is Processed to investigate the Performance of matched-field geo-acoustic inversion and source localization. The receiver array consists of two legs as in an L-shape. one vertical and the other horizontal lying on the seabed. Narrowband multi-tone CW source was towed along a slightly inclined bathymetry track. The matched-field geo-acoustic inversion includes comparisons between three processing techniques. all based on the Bartlett processor as; (1) the coherent processing of the data from the full array, (2) the incoherent Product of each output from both the horizontal and vertical arrays, and (3) the cross correlation between the horizontal and vertical arrays. as well as processing each array leg separately. To verify the inversion results. matched-field source localization for low level source signal components were performed using the same Processors used at the inversion stage.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.43 no.5 s.347
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• pp.176-181
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• 2006

We have built the close range Dicke type radiometer with 35GHz of frequency, which consists of two stage low noise amplifier and diode detector to calibrate temperatures of materials. We have present thermal calibration methods using millimeter-wave radiometer. Output voltages linearly increase with temperatures between 299K and 309K. We are able to measure lower temperature using the liquid nitrogen although results are somewhat unstable.

• The Journal of the Korea institute of electronic communication sciences
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• v.9 no.2
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• pp.173-180
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• 2014

The development process of a L-Band microwave radiometer for remote sensing of soil-moisture are described in this paper. Achieving the development aim of the measurement accuracy within 2% for soil moisture content of 0~50%, the requirements and specifications of the microwave radiometer and its receiver are drawn. The receiver with high gain, high sensitivity is designed and implemented to satisfy these requirements and specifications. The receiver has the bandwidth of 40 MHz, the system gain of 50 dB and the sensitivity of average value 0.19 K, maximum value 0.313K at 1390 MHz.