• Smart Structures and Systems
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• 제5권1호
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• pp.95-117
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• 2009

In this paper, the time series based damage detection algorithms developed by Nair, et al. (2006) and Nair and Kiremidjian (2007) are applied to the benchmark experimental data from the National Center for Research on Earthquake Engineering (NCREE) in Taipei, Taiwan. Both acceleration and strain data are analyzed. The data are modeled as autoregressive (AR) processes, and damage sensitive features (DSF) and feature vectors are defined in terms of the first three AR coefficients. In the first algorithm developed by Nair, et al. (2006), hypothesis tests using the t-statistic are applied to evaluate the damaged state. A damage measure (DM) is defined to measure the damage extent. The results show that the DSF's from the acceleration data can detect damage while the DSF from the strain data can be used to localize the damage. The DM can be used for damage quantification. In the second algorithm developed by Nair and Kiremidjian (2007) a Gaussian Mixture Model (GMM) is used to model the feature vector, and the Mahalanobis distance is defined to measure damage extent. Additional distance measures are defined and applied in this paper to quantify damage. The results show that damage measures can be used to detect, quantify, and localize the damage for the high intensity and the bidirectional loading cases.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2016년도 추계학술대회
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• pp.956-958
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• 2016

현재, 영상처리는 다양한 분야에서 활용되고 있으며, 영상 데이터는 전송, 처리, 저장하는 과정에서 발생하는 잡음이 발생한다. 이러한 영상에 첨가된 잡음을 제거하기 위한 연구가 활발히 진행되있다. 영상에 첨가되는 잡음은 발생원인과 형태에 따라 다양한 종류가 있으며, AWGN(additive white Gaussian noise)이 대표적이다. 본 논문에서는 영상에 첨가된 AWGN을 완화하기 위해, 에지 검출을 이용하여 국부 마스크의 화소의 방향에 따라 가중치를 적용하여 처리하는 알고리즘을 제안하였다.

• 전기전자학회논문지
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• 제20권1호
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• pp.94-102
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• 2016

본 논문에서는 기계학습 알고리즘을 이용하여 다중물리(Multi-physics) 시뮬레이션의 반복 횟수를 획기적으로 줄일 수 있는 다중물리해석 예측 방법을 제안한다. 기존의 다중물리해석 시뮬레이션의 경우 소요되는 시간과 노력을 줄이기 위해 시뮬레이션 자체에 대한 방법과 환경 개선에 초점이 맞추어져 있으나 본 논문에서는 다중물리 시뮬레이션 결과를 기계학습 알고리즘으로 학습하여 추가적인 시뮬레이션을 수행하지 않고 학습된 기계학습 알고리즘을 사용하여 수십분에서 수시간에 걸리는 다중 물리 해석과 유사한 결과를 수초 내에 예측할 수 있음을 보였다. 기계학습 알고리즘 간의 성능을 비교하여 다중물리해석에 적합한 기계학습 알고리즘을 확인하였으며 가장 우수한 성능을 보인 가우시안 프로세스 회귀(Gaussian Process Regression)의 경우 100개 이하의 학습 샘플만으로도 우수한 예측 결과를 얻어낼 수 있음을 확인하였다. 제안하는 방식을 통해 시뮬레이션을 하고자 하는 모델의 형상이나 재질이 변경될 경우 기존의 시뮬레이션 결과로 학습된 알고리즘이 있다면 시뮬레이션을 반복 수행하기 전에 알고리즘을 이용하여 결과를 예측할 수 있어 시뮬레이션의 반복 횟수를 줄일 수 있을 것으로 기대한다.

• Smart Structures and Systems
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• 제24권2호
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• pp.243-256
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• 2019

Determination of the most meaningful structural modes and gaining insight into how these modes evolve are important issues for long-term structural health monitoring of the long-span bridges. To address this issue, modal parameters identified throughout the life of the bridge need to be compared and linked with each other, which is the process of mode tracking. The modal frequencies for a long-span bridge are typically closely-spaced, sensitive to the environment (e.g., temperature, wind, traffic, etc.), which makes the automated tracking of modal parameters a difficult process, often requiring human intervention. Machine learning methods are well-suited for uncovering complex underlying relationships between processes and thus have the potential to realize accurate and automated modal tracking. In this study, Gaussian mixture model (GMM), a popular unsupervised machine learning method, is employed to automatically determine and update baseline modal properties from the identified unlabeled modal parameters. On this foundation, a new mode tracking method is proposed for automated mode tracking for long-span bridges. Firstly, a numerical example for a three-degree-of-freedom system is employed to validate the feasibility of using GMM to automatically determine the baseline modal properties. Subsequently, the field monitoring data of a long-span bridge are utilized to illustrate the practical usage of GMM for automated determination of the baseline list. Finally, the continuously monitoring bridge acceleration data during strong typhoon events are employed to validate the reliability of proposed method in tracking the changing modal parameters. Results show that the proposed method can automatically track the modal parameters in disastrous scenarios and provide valuable references for condition assessment of the bridge structure.

• 한국수자원학회논문집
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• 제42권10호
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• pp.891-896
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• 2009

준설 시 발생하는 부유물의 이송 확산 과정을 해석하기 위하여 퍼프모형을 개발하였다. 본 연구에서 개발된 퍼프 모형은 추적방법에 따라 전방추적모형과 후방추적모형으로 나눌 수 있으며, 이 두 가지 추적방법은 계산효율과 수치 오차에 있어서 상이한 특성을 나타내었다. 경계처리에 있어서 입자추적모형과 상이한 방법을 사용하는 퍼프모형은 폐경계에서는 입자추적모형과 동일한 결과를 나타내지만 개경계에서는 다른 결과를 나타내었다. 또한 오염원이 임의의 공간적 분포를 갖는 경우, 퍼프모형은 입자추적모형보다는 적은 수의 퍼프를 사용할 수 있지만 이에 따른 경계면에서의 수치오차를 발생하였다. 흐름이 일정한 경우와 전단흐름의 경우에 대하여 이송 확산 수치모의를 수행하였으며, 이를 각각의 경우의 해석해 결과와 비교 분석하였다. 후방추적 퍼프모형은 전방추적 퍼프모형에 비하여 사용된 퍼프수와 관계없이 작은 오차를 발생하였으며, 전체적으로 퍼프모형이 입자모형보다는 훨씬 적은 수의 계산을 통해서도 작은 오차를 나타낼 수 있다는 것을 알 수 있었다. 그러나 Gaussian 분포를 갖는 퍼프모형은 전단흐름에서의 긴 유선형 농도분포를 모의할 수 없었고, 이에 관한 오차는 전단계수가 증가함에 따라 비선형적으로 증가하였다. 이와 같이 퍼프모형은 다양한 수환경에 적용할 경우, 뛰어난 효율성에 비해 정확도가 다소 감소하는 경향이 있지만, 입자추적모형과의 연계 모의 등을 통해 준설지점 인근의 근역에서의 오염원 해석에 사용될 수 있다.

• 말소리와 음성과학
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• 제2권2호
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• pp.63-68
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• 2010

A new version of sequential probability ratio test (SPRT) which has been investigated in utterance-length control is proposed to obtain uniform response results in speaker verification (SV). Although SPRTs can obtain fast responses in SV tests, differences in the performance may occur depending on the compositions of consonants and vowels in the sentences used. In this paper, a fast sequential probability ratio test (FSPRT) method that shows consistent performances at all times regardless of the compositions of vocalized sentences for SV will be proposed. In generating frames, the FSPRT will first conduct SV test processes with only generated frames without any overlapping and if the results do not satisfy discrimination criteria, the FSPRT will sequentially use frames applied with overlapping. With the progress of processes as such, the test will not be affected by the compositions of sentences for SV and thus fast response outcomes and even consistent performances can be obtained. Experimental results show that the FSPRT has better performance to the SPRT method while requiring less complexity with equal error rates (EER).

• 전기학회논문지
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• 제57권6호
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• pp.1116-1122
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• 2008

Since conventional methods for measuring stride length(distance) are many weaknesses, optical methods have been developed to measure stride length(distance) of human pedestrians. IR(Infrared) elements and Power LED(Light Emitting Diode) with two types of lens were used to correlate detected light intensity with stride length(distance). The suggested methods in this study are simple, convenient, and cost effective. The results can be used to analyze walking patterns of normal and disabled men, and to monitor the recovering processes of the disabled patients.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1995년도 하계학술대회 논문집 C
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• pp.1262-1264
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• 1995

The question-"How fast a electron tunnels a potential barrier?" looks like simple, but is controversy for more than 40 years. Because "tunneling" involves complicated internal processes and its definition is ambiguous. Recent experiments showed that the phase time is the best model of tunneling time among other times-for example, dwell time, Larmor clock time etc. In this paper, we simulated the tunneling time for Gaussian wave packet by program InterQuanta and compared with the phase time. In particular we focused on the effect of wave packet spreading in momentum space(or real space) which is not expressed by the phase time formula.

• 천문학회지
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• 제27권1호
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• pp.61-76
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• 1994

We investigate the dynamical evolution of globular clusters under the diffusion, the Galactic tide, and the presence of halo black holes. We compare the results with our previous work which considers the diffusion processes and the Galactic tide. We find the followings: (1) The black holes contribute the expansion of the outer part of the cluster. (2) There is no evidence for dependence on the orbital phase of the cluster as in our previous work. (3) The models of linear and Gaussian velocity distribution for the halo black holes do not show any significant differences in all cases. (4) The perturbation of black holes reduces the number of stars in lower energy regions. (5) There is a significant number of stars with retrograde orbits beyond the cutoff radius especially in the case of diffusion and the perturbation of black holes.

• 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.