• 천문학회지
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• 제35권2호
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• pp.67-73
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• 2002

We have made a topological study of cosmic microwave background (CMB) polarization maps by simulating the AMiBA experiment results. A ACDM CMB sky is adopted to make mock interferometric observations designed for the AMiBA experiment. CMB polarization fields are reconstructed from the AMiBA mock visibility data using the maximum entropy method. We have also considered effects of Galactic foregrounds on the CMB polarization fields. The genus statistic is calculated from the simulated Q and U polarization maps, where Q and U are Stokes parameters. Our study shows that the Galactic foreground emission, even at low Galactic latitude, is expected to have small effects on the CMB polarization field. Increasing survey area and integration time is essential to detect non-Gaussian signals of cosmological origin through genus measurement.

• 한국음향학회지
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• 제6권3호
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• pp.31-42
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• 1987

본 논문은 초음파의 감쇠정보를 이용하여 조직을 정량화하기 위한 연구이다. 비선형 매질에서의 제반특성 (비선형감쇠, Non-Gaussian특성, 산란등) 때문에 생기는 측정오차를 줄일 수 있는 스펙트럴 모멘트법을 제안하였다. 본 연구에서는 비선형 감쇠 및 산란특성을 고려하여 R. G신호의 Z. C. D(Zero Crossing Density)를 측정하는 방법에 이론적인 기초를 두고, 산란매질에서 수신된 반사파의 P. S. D(Power Spectral Density) 와 스펙트럴 모멘트를 이용하여 주파수의존 감쇠계수를 구하였다. 또한 이론의 유효성을 확인하기 위한 시제시스템을 개발하였다.

• 대한음성학회지:말소리
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• 제66호
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• pp.105-116
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• 2008

In this research, we propose a speaker identification system using a kernel method which is expected to model the non-linearity of speech features well. We have been using principal component analysis (PCA) successfully, and extended to kernel PCA, which is used for many pattern recognition tasks such as face recognition. However, we cannot use kernel PCA for speaker identification directly because the storage required for the kernel matrix grows quadratically, and the computational cost grows linearly (computing eigenvector of $l{\times}l$ matrix) with the number of training vectors I. Therefore, we use greedy kernel PCA which can approximate kernel PCA with small representation error. In the experiments, we compare the accuracy of the greedy kernel PCA with the baseline Gaussian mixture models using MFCCs and PCA. As the results with limited enrollment data show, the greedy kernel PCA outperforms conventional methods.

• 한국멀티미디어학회논문지
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• 제14권12호
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• pp.1544-1548
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• 2011

A process of choosing a subset of original features, so called feature selection, is considered as a crucial preprocessing step to image processing applications. There are already large pools of techniques developed for machine learning and data mining fields. In this paper, basically two methods, non-feature selection and feature selection, are investigated to compare their predictive effectiveness of classification. Color co-occurrence feature is used for defining image features. Standard Sequential Forward Selection algorithm are used for feature selection to identify relevant features and redundancy among relevant features. Four color spaces, RGB, YCbCr, HSV, and Gaussian space are considered for computing color co-occurrence features. Gray-level image feature is also considered for the performance comparison reasons. The experimental results are presented.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1987년도 전기.전자공학 학술대회 논문집(II)
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• pp.854-858
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• 1987

This paper considers impulsive noise which produce burst error in high speed(approx.160Kbps) data transmission like ISDN(Integrated Servise Digital Network) using PSTN(Public Switching Telephone Network). To begin with, we obtains the transfer function of subscriber line to calculate the variation of bandwidth when the gain of receiver is fixed and channel capacity of non-gaussian channel in upper-and lower bound, and evaluates the transmission capability. In this paper compares channel capacity bounds which obtains when probability density function of impulsive noise is Laplacian distribution function with impulsive noise generated by waveform synthesier.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2006년 학술대회 논문집 정보 및 제어부문
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• pp.266-268
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• 2006

In this paper, we design a Linear Quadratic Gaussian(LQG) controller for active suspensions. We can improve the inherent suspension problem, trade-off between the ride quality and the suspension travel by selecting appropriate weights in the LQ-objective function. Using an optimization-algorithm, Evolution Strategy(ES), we find the proper control gains for selected frequencies, which have major effects on the vibrations of the vehicle's state variables. The frequencies and proper control gains are used for the neural network data. During a vehicle running, the trained on-line neural network is activated and provides the proper gains for non-trained frequencies.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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• pp.1530-1533
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• 2005

In this paper, we design a Linear Quadratic Gaussian controller for the active suspension. We can improve the inherent suspension problem, trade-off between the ride quality and the suspension travel by selecting appropriate weights in the LQ-objective function. Because any definite rules for selecting weights do not exist, we use an optimization-algorithm, Evolution Strategy (ES) to find the proper control gains for selected frequencies, which have major effects on the vibrations of the vehicle's state variables. The frequencies and proper control gains are used for the neural network data. During a vehicle running, the trained on-line neural network is activated and provides the proper gains for non-trained frequencies. For the full-state feedback control, Kalman filter observes the full states and Fourier transform is used to detect the frequency of the road.

• 천문학논총
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• 제22권3호
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• pp.75-81
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• 2007

In this study we present basic principles and features of RVI2CELL, a precise RV (radial velocity) estimation program to process stellar spectra obtained through iodine cell. RVI2CELL is very robust and fast program. The instrument profile can be modeled as a sum of Gaussian functions or a non-parametric arbitrary shape. The RV accuracy estimated by observation of a RV standard star Tau Ceti indicates about 9 m/s.

• 한국해양공학회:학술대회논문집
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• 한국해양공학회 2000년도 춘계학술대회 논문집
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• pp.40-45
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• 2000

Presented are the estimation of the probability distribution of fatigue crack growth life and reliability assessment of structures by simulating material resistance to fatigue crack growth along a crack path. The material resistance is treated as a Weibull stochastic process. A non-Gaussian stochastic fields simulation method proposed by Shimozuka, et al is applied with the statistical data obtained experimentally. Test results are obtained for $\Delta$K constant amplitude load in tension with stress ratio of R=0.2 and three specimen thicknesses of 6, 12 and 18mm. This simulation method is useful to estimate the probability distribution of fatigue crack growth life and the smallest life.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 1999년도 추계종합학술대회 논문집
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• pp.368-371
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• 1999

In this paper, we propose a neural network processor architecture with on-chip learning and with reconfigurability according to the data dependencies of the algorithm applied. For the neural network model applied, the proposed architecture can be configured into either SIMD or SRA(Systolic Ring Array) without my changing of on-chip configuration so as to obtain a high throughput. However, changing of system configuration can be controlled by user program. To process activation function, which needs amount of cycles to get its value, we design it by using PWL(Piece-Wise Linear) function approximation method. This unit has only single latency and the processing ability of non-linear function such as sigmoid gaussian function etc. And we verified the processing mechanism with EBP(Error Back-Propagation) model.