• 전자공학회논문지
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• 제54권4호
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• pp.91-98
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• 2017

전기 임피던스 단층촬영 기법은 도메인의 표면에 부착된 전극들을 통해 주입된 전류와 측정된 전압 데이터를 기반으로, 미지의 도전율 분포를 복원하는 비파괴 기술이다. 이 논문에서는 전기 임피던스 단층촬영법에서 일반적 Tikhonov 조정을 갖는 역문제를 풀고 도전율 분포를 복원하기 위해 절단된 특이값 분해 기반의 역문제 해법을 제안하였다. 역문제 계산시간을 줄이기 위해 일반 조정행렬을 역행렬 항목에서 분리시키고 절단된 특이값 분해 방법을 적용하였다. 제안한 방법의 성능을 검증하기 위해 모의실험과 팬텀실험을 수행하고 복원결과를 비교하였다.

• 미생물학회지
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• 제39권2호
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• pp.67-75
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• 2003

비교유전체학의 주요 주제 중 유전자서열을 분류하고 단백질기능을 예측하는 연구가 있으며, 이를 위해 단백질 구조, 공통서열 및 바인딩 위치 예측등의 방법과 함께, 전유전체 서열에서 구해지는 유사도 그래프를 분석해 상동유전자를 검색하는 계산학적인 접근방법이 있다. 유사도그래프를 사용한 방법은 서열에 대한 기존 지식에 의존하지 않는 장점이 있지만 유사도 하한값과 같은 주관적인 임계값이 필요한 단점이 있다. 본 논문에서는 반복적으로 그래프를 분해하는 이전의 방법을 일반화시켜, 유사도 그래프에 기반한 유전자 서열군집분석 방법론과 객관적이고 안정적인 파라미터 임계값 계산 방법을 제안한다. 제시된 방법으로 알려진 미생물 유전체 서 열을 분석하여 이전의 방법인 BAG 알고리즘 결과와 비교했다.

• 전자공학회논문지
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• 제50권1호
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• pp.246-253
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• 2013

본 논문에서는 유동의 안정된 흐름 제어를 위한 유동제어에 대해 다룬다. 전산유체역학 해석을 통해 제공된 대용량의 유동 데이터를 POD 방법을 통하여 축약하고, 제어측면에서 시간 및 주파수 영역에서의 분석에 근거하여 적절한 수준의 저차 모델링한다. 한편, 유동장 표면에 부착된 압력센서로부터 공간상의 유동상태 추정을 위해 신경망 구조를 갖는 유동추정기를 구성하고, 되먹임 유동제어기를 설계함으로써 유동제어루프를 구성한다.

• 대한기계학회논문집A
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• 제24권5호
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• pp.1215-1230
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• 2000

Parallel Approaches using Cray T3E which is NIPP (Massively Parallel Processors) machine are presented for the efficient computation of the finite element analysis of 3-D shape rolling processes. D omain decomposition method coupled with parallel linear equation solver is used. Domain decomposition is applied for obtaining element tangent stifffiess matrices and residual vectors. Direct and iterative parallel algorithms are used for solving the linear equations. Direct algorithm is_parallel version of direct banded matrix solver. For iterative algorithms, the well-known preconditioned conjugate gradient solver with Jacobi preconditioner is also employed. Moreover a new effective iterative scheme with block inverse matrix preconditioner, which is named by present authors, is presented and its results are compared with the one using Jacobi preconditioner. PVM and MPI are used for message passing and synchronization between processors. The performance and efficiency of each algorithm is discussed and comparisons are made among different algorithms.

• Wind and Structures
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• 제7권2호
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• pp.107-130
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• 2004

This paper presents a time-domain approach for analyzing nonlinear random vibrations of long-span suspended cables under transversal wind. A consistent continuous model of the cable, fully accounting for geometrical nonlinearities inherent in cable behavior, is adopted. The effects of spatial correlation are properly included by modeling wind velocity fluctuation as a random function of time and of a single spatial variable ranging over cable span, namely as a one-variate bi-dimensional (1V-2D) random field. Within the context of a Galerkin's discretization of the equations governing cable motion, a very efficient Monte Carlo-based technique for second-order analysis of the response is proposed. This procedure starts by generating sample functions of the generalized aerodynamic loads by using the spectral decomposition of the cross-power spectral density function of wind turbulence field. Relying on the physical meaning of both the spectral properties of wind velocity fluctuation and the mode shapes of the vibrating cable, the computational efficiency is greatly enhanced by applying a truncation procedure according to which just the first few significant loading and structural modal contributions are retained.

• 한국물환경학회지
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• 제23권4호
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• pp.551-560
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• 2007

The present study analyzed noise reduction and long/short-term components for discharge, TOC concentration, and TOC load data in order to understand the data characteristics better. For the purpose, wavelet transform which can reduce noise from raw data and has flexible resolution in time and frequency domain was applied and the theory of nonlinear dynamics was also used to determine the last decomposition level for wavelet transform. Wavelet function of 'db10' and the 7th level for the last decomposition of wavelet transform were applied for the all data in the present study. Also the results revealed that the energy ratios of approximation components with 187-hour periodicity decomposed from 7th level of wavelet transform were 94.71% (discharge), 99.00% (TOC concentration), and 93.84% (TOC load), respectively. In addition, the energy ratios of detail components showed the range between 1.00% and 6.17%, which were extremely small comparing to the energy ratios of approximation components, therefore, the first and second detail components might be considered as noise components included in the raw data.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권2호
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• pp.412-434
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• 2023

This study discusses the high-order diffusion method in the wavelet domain. It aims to improve the edge protection capability of the high-order diffusion method using wavelet coefficients that can reflect image information. During the first step of the proposed diffusion method, the wavelet packet decomposition is a more refined decomposition method that can extract the texture and structure information of the image at different resolution levels. The high-frequency wavelet coefficients are then used to construct the edge detection function. Subsequently, because accurate wavelet coefficients can more accurately reflect the edges and details of the image information, by introducing the idea of state weight, a scheme for recovering wavelet coefficients is proposed. Finally, the edge detection function is constructed by the module of the wavelet coefficients to guide high-order diffusion, the denoised image is obtained. The experimental results showed that the method presented in this study improves the denoising ability of the high-order diffusion model, and the edge protection index (SSIM) outperforms the main methods, including the block matching and 3D collaborative filtering (BM3D) and the deep learning-based image processing methods. For images with rich textural details, the present method improves the clarity of the obtained images and the completeness of the edges, demonstrating its advantages in denoising and edge protection.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2003년도 ISIS 2003
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• pp.539-542
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• 2003

A motor is the workhorse of our industry. The issues of preventive and condition-based maintenance, online monitoring, system fault detection, diagnosis, and prognosis are of increasing importance. Different internal motor faults (e.g., inter-turn short circuits, broken bearings, broken rotor bars) along with external motor faults (e.g., phase failure, mechanical overload, blocked rotor) are expected to happen sooner or later. This paper introduces the fault detection technique of induction motors based upon the stator current. The fault motors have rotor bar broken or rotor unbalance defect, respectively. The stator currents are measured by the current meters and stored by the time domain. The time domain is not suitable to represent the current signals, so the frequency domain is applied to display the signals. The Fourier Transformer is used for the conversion of the signal. After the conversion of the signals, the features of the signals have to be extracted by the signal processing methods like a wavelet analysis, a spectrum analysis, etc. The discovered features are entered to the pattern classification model such as a neural network model, a polynomial neural network, a fuzzy inference model, etc. This paper describes the fault detection results that use wavelet decomposition. The wavelet analysis is very useful method for the time and frequency domain each. Also it is powerful method to detect the features in the signals.

• 지능정보연구
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• 제5권1호
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• pp.103-123
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• 1999

Input filtering as a preprocessing method is so much crucial to get good performance in time series forecasting. There are a few preprocessing methods (i.e. ARMA outputs as time domain filters, and Fourier transform or wavelet transform as time-frequency domain filters) for handling time series. Specially, the time-frequency domain filters describe the fractal structure of financial markets better than the time domain filters due to theoretically additional frequency information. Therefore, we, first of all, try to describe and analyze specially some issues on the effectiveness of different filtering methods from viewpoint of the performance of a neural network based forecasting. And then we discuss about neural network model architecture issues, for example, what type of neural network learning architecture is selected for our time series forecasting, and what input size should be applied to a model. In this study an input selection problem is limited to a size selection of the lagged input variables. To solve this problem, we simulate on analyzing and comparing a few neural networks having different model architecture and also use an embedding dimension measure as chaotic time series analysis or nonlinear dynamic analysis to reduce the dimensionality (i.e. the size of time delayed input variables) of the models. Throughout our study, experiments for integration methods of joint time-frequency analysis and neural network techniques are applied to a case study of daily Korean won / U. S dollar exchange returns and finally we suggest an integration framework for future research from our experimental results.

• 멤브레인
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• 제12권2호
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• pp.97-106
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• 2002

Polysulfone (PSf/NMP/alcohol 용액과 chlorinated poly(vinyl chloride) (CPVC)/THF/Alcohol 용액에 대한 광산란 패턴을 SALS (Small angle light scattering)와 FE-SEM (field emission scanning electron microscope)을 이용하여 조사하였다. PSf 용액에서는 시간에 따라 q값의 최대 산란 강도를 보이는 광산란 거동을 나타내어 스피노달 (SD) 상분리 거동을 나타내는 반면, CPVC 용액에서는 q값이 증가함에 따라 광산란 강도가 줄어드는 핵성장 (NG) 거동을 나타냈다. 각 고분자 용액에서 상분리 중반과 후반부에서 비용매 첨가제로 사용된 알코올의 탄소수가 증가할수록 농도분극의 증가율은 줄어들었다. 또한, SD에서의 초반부의 시간에 따른 산란 강도는 비용매 첨가제의 종류에 무관하게 Cahn의 건형 이론에 잘 부합되었다. 또한, SALS 장치로 얻어진 기공크기와 전자현미경으로 얻어진 영역 크기는 상호간에 비교되었다. 20PSf/70NMP/10n-butano1 (w/w%) 용액에 대한 산란 패턴은 초기 상분리 거동에서부터 후기 거동까지 매우 선명하게 관측되었고, 초반, 중반, 그리고 후반부에 대한 SD에 대한 이론적 결과와 잘 일치하였다. 최고의 산란강도를 나타낸 각도의 크기는 n-butanol>n-propanol>methanol>no alcohol 순으로 관찰되었으며, 이 순서로 최종 형성된 막 단면의 기공 크기가 감소되는 것으로 조사되었다.