• Economic and Environmental Geology
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• v.31 no.1
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• pp.53-57
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• 1998

Transient electromagnetic (TEM) method is also affected by cultural and natural electromagnetic (EM) noises, since it uses part of the broadband ($10^{-2}$ to $10^5Hz$) spectrum. Especially, predominant EM noise which affects a moving transmitter-receiver TEM system is sensor motion-induced noise. This noise is caused by the sensor motion in the earth magnetic field. The technique for reducing the sensor motion-induced EM noise presented in this paper is based on Halverson stacking. This Halverson stacking is generally used in a time-domain induced polarisation (IP) system to reject DC offset and linear drift. According to spectrum analysis of the vertical component of sensor motion-induced noise, the frequency range affected by the motion of an EM sensor is less than about 700 Hz in this study. With the decrease of the frequency, the spectral power caused by the motion of a sensor increases. For example, at the frequency of 200 Hz, the spectral power of the sensor motion-induced noise is $-90dBVrms^2$ while the spectral power of the EM noise measured with a fixed sensor on the ground is $-105dBVrms^2$, and at the frequency of 100 Hz, the spectral power of the sensor motion-induced noise is $-70dBVrms^2$ while the spectral power of the EM noise measured with a fixed sensor on the ground is $-105dBVrms^2$. With applying Halverson stacking to an artificial noise transient generated by adding a noise-free transient to sensor motion-induced noise measured without pulsing, it is shown that the filtered transient is nearly consistent with the noise-free transient within a delay time of $0.5{{\mu}sec}$. The inversion obtained from this filtered transient is in accord with the true model with an error of 5%.

• The Journal of the Acoustical Society of Korea
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• v.20 no.2
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• pp.21-26
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• 2001

An expectation-maximization (EM) based Bayesian adaptation method for the mean of noise is proposed for noise-robust speech recognition. In the algorithm, the on-line testing utterances are used for the unsupervised Bayesian adaptation and the prior distribution of the noise mean is estimated using the off-line training data. For the noisy speech modeling, the parallel model combination (PMC) method is employed. The proposed method has shown to be effective compared with the conventional PMC method for the speech recognition experiments in a car-noise condition.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.20 no.6
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• pp.509-515
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• 2009

In this paper, we analyzed and evaluated noise suppression characteristic of EM wave absorber. We fabricated several absorber samples in different ratios of Sendust and Amorphous with CPE(Chlorinated Ploy-ethylene) as binder and calculated material constants by measured S-parameter. Then, we confirmed that the noise suppression characteristic of EM wave absorbers using microstrip line. A microstrip line with EM wave absorber placed on its top was used to evaluate the conduction noise suppression. As a result, noise suppression effect show different charateristic by changing relative permeability. Therefore, EM wave absorber using Sendust show excellent characteristic. In particular the maximum power absorption over 90 % in 1.7 GHz to over 6 GHz has obtained when composition of Sendust:CPE=80:20wt.%.

• The Korean Journal of Applied Statistics
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• v.16 no.2
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• pp.305-319
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• 2003

This paper provides a non-Bayesian method based on the expanded EM algorithm for segmenting the magnetic resonance images degraded by bias field. For the images with the intensity as a pixel value, many segmentation methods often fail to segment it because of the bias field(with low frequency) as well as noise(with high frequency). Our contextual approach is appropriately designed by using normal mixture model incorporated with Markov random field for noise-corrective segmentation and by using the penalized likelihood to estimate bias field for efficient bias filed-correction.

• Proceedings of the KIEE Conference
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• 2003.07d
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• pp.2615-2617
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• 2003

The Kalman filter is a recursive Linear Estimator for the linear dynamic systems(LDS) affected by two different noises called process noise and measurement noise both of which are uncorrelated white. The Expectation Maximization(EM) algorithm is employed in this paper as a preprocessor to reinforce the effectiveness of Kalman estimator. Particularly, we focus on the relation between Kalman filter and EM algorithm in the LDS. In this paper, we propose a new algorithm to improve the performance on the parameter estimation via EM algorithm, which improves the overall process of Kalman filtering. Since Kalman filter algorithm not only needs the system parameters but also is very sensitive the initial state conditions, the initial conditions decided through EM turns out to be very effective. In experiments, the computer simulation results ate provided to demonstrate the superiority of the proposed algorithm.

• Proceedings of the IEEK Conference
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• 2005.11a
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• pp.873-876
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• 2005

A low noise amplifier(LNA) using electro-magnetic field simulator is designed in standard 0.25um CMOS process. Integrated spiral inductor is simulated using EM field solver. Then LNA is simulated with active device, capacitor and simulated inductor by EM field solver. A S11 and S21 of -15.45dB and 17.8dB at 2.3GHz as simulation results was achieved. A Noise Figure is 2.92dB. And Measurements show a S11 and S21 of -12.4dB and 17.8dB at 2.3GHz. A Noise Figure of 3.3dB was achieved.

• Geophysics and Geophysical Exploration
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• v.2 no.4
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• pp.180-183
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• 1999

A proto-type seismic measurement system with a reference was developed to improve S/N (signal-to-noise ratio) of seismic data, especially in noisy urban areas. Two pairs of correlation measurements (the one for microphone and geophone, and another for electromagnetic (EM) loop and geophone) were carried out near Kimpo Airport and at Kimje. The spectrum analyses were also performed to investigate the correlation of two pairs of time series; one for microphone and geophone, and another for EM loop and geophone. The sound waves measured with the microphone and the geophone are highly correlated. However, differences in the reponses are readily identifiable across 200 Hz; in the vicinity of 100 Hz, the spectral energy for geophone is 20 dB higher than that for microphone, and at near 500 Hz, the spectral energy for microphone is 30 dB higher than that for geophone. Overall, the spectral energy appears concentrated on the frequency window below 600 Hz for geophone. It contrasts with the observation of dominant frequency at the range of above 200 Hz for microphone. The wave forms of EM noise (due to an ACDC inverter) measured with EM loop and geophone are consistently and highly correlated each other. The power spectrum of the EM noise for EM loop shows that the spectral energies at odd harmonic frequencies of 60 Hz are higher than those at even harmonic frequencies of 60 Hz. It is compared to the power spectrum for geophone; the spectral energies at odd harmonics are nearly same as those at even harmonic frequencies.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2013.10a
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• pp.346-353
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• 2013

Industrial processes need to be monitored in real-time based on the input-output data observed during their operation. Abnormalities in an induction motor should be detected early in order to avoid costly breakdowns. To early identify induction motor faults, this paper effectively estimates spectral envelopes of each induction motor fault by utilizing a linear prediction coding (LPC) analysis technique and an expectation maximization (EM) algorithm. Moreover, this paper classifies induction motor faults into their corresponding categories by calculating Mahalanobis distance using the estimated spectral envelopes and finding the minimum distance. Experimental results shows that the proposed approach yields higher classification accuracies than the state-of-the-art approach for both noiseless and noisy environments for identifying the induction motor faults.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2010.05a
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• pp.365-366
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• 2010

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2011.10a
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• pp.416-417
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• 2011