• Smart Structures and Systems
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• 제17권3호
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• pp.445-470
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• 2016

In this study, the Bayesian probabilistic framework is investigated for modal identification and modal identifiability based on the field measurements provided in the structural health monitoring benchmark problem of an instrumented cable-stayed bridge named Ting Kau Bridge (TKB). The comprehensive structural health monitoring system on the cable-stayed TKB has been operated for more than ten years and it is recognized as one of the best test-beds with readily available field measurements. The benchmark problem of the cable-stayed bridge is established to stimulate investigations on modal identifiability and the present paper addresses this benchmark problem from the Bayesian prospective. In contrast to deterministic approaches, an appealing feature of the Bayesian approach is that not only the optimal values of the modal parameters can be obtained but also the associated estimation uncertainty can be quantified in the form of probability distribution. The uncertainty quantification provides necessary information to evaluate the reliability of parametric identification results as well as modal identifiability. Herein, the Bayesian spectral density approach is conducted for output-only modal identification and the Bayesian model class selection approach is used to evaluate the significance of different modes in modal identification. Detailed analysis on the modal identification and modal identifiability based on the measurements of the bridge will be presented. Moreover, the advantages and potentials of Bayesian probabilistic framework on structural health monitoring will be discussed.

• 한국지능시스템학회논문지
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• 제17권3호
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• pp.316-321
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• 2007

본 연구에서는 베이지안 망 구조 학습에서, 학습 데이터의 특정 부분집합이 학습된 망의 각 연결 구조(edge)의 형성에 기여하는 정도를 정량화하는 방법을 제안한다. 생물학 정보의 분석 등에 베이지안 망 학습을 이용하는 경우, 제안된 방법은 망의 각 연결 구조의 형성에 특정 군집 데이터가 기여하는 정도의 정량화가 가능하다. 제안된 방법의 유효성을 보이기 위해, 벤치마크 베이지안 망을 이용하여 제안된 방법이 망 연결 구조에 대한 데이터 군집별 기여도를 효과적으로 정량화 할 수 있음을 보인다.

• 대한공간정보학회지
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• 제18권4호
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• pp.61-69
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• 2010

비탈면 혹은 절성토지의 파괴로 사람과 재산에 심각한 피해를 입히기 때문에 미리 산사태 취약성 분석을 수행하여 개발 혹은 자연재해로부터 위험을 대비하는 것이 필요하다. 기존의 산사태 취약성 분석은 휴리스틱, 통계학적, 결정론적 혹은 확률론적 방법을 통해 이뤄졌다. 그러나, 적은 현장정보 등으로 분석의 신뢰도가 떨어지거나, 전문가의 경험과 지식을 기존 정량적인 해석모델에 반영하기 어려웠다. 본 연구는 산사태 취약성 분석에 대한 전문가 지식과 공간입력자료의 시맨틱을 추출하여 온톨로지 모델을 구축하고, 이를 베이시안 네트워크에 반영하여 확률적인 산사태 모델링을 제안하였다. 기존에 전문가 수작업으로 이뤄지던 베이시안 네트워크의 구조 생성을 온톨로지 모델의 지식추론으로 자동화하고, 현장정보뿐만 아니라 전문가 지식을 모델링에 반영하여 조건부 산사태 발생확률분포를 작성하였다. 이 결과를 GIS에 적용하여 산사태 취약성 지도를 작성하였다. 검증을 위해 충남 홍성일원의 오서산 지역에 적용한 결과 기존 산사태 발생흔적과 86.5% 일치하였다. 본 연구를 통해 일반 사용자도 전문가 도움 없이도 광역적인 산사태 취약성 분석이 가능하리라 기대된다.

• Smart Structures and Systems
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• 제29권1호
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• pp.41-51
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• 2022

Cables are critical components of cable-stayed bridges. A structural health monitoring system provides real-time cable tension recording for cable health monitoring. However, the measurement data involve multiple sources of variability, i.e., varying environmental and operational factors, which increase the complexity of cable condition monitoring. In this study, a one-class classification method is developed for cable condition assessment using Bayesian factor analysis (FA). The single-peaked vehicle-induced cable tension is assumed to be relevant to vehicle positions and weights. The Bayesian FA is adopted to establish the correlation model between cable tensions and vehicles. Vehicle weights are assumed to be latent variables and the influences of different transverse positions are quantified by coefficient parameters. The Bayesian theorem is employed to estimate the parameters and variables automatically, and the damage index is defined on the basis of the well-trained model. The proposed method is applied to one cable-stayed bridge for cable damage detection. Significant deviations of the damage indices of Cable SJS11 were observed, indicating a damaged condition in 2011. This study develops a novel method to evaluate the health condition of individual cable using the FA in the Bayesian framework. Only vehicle-induced cable tensions are used and there is no need to monitor the vehicles. The entire process, including the data pre-processing, model training and damage index calculation of one cable, takes only 35 s, which is highly efficient.

• 대한간호학회지
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• 제41권3호
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• pp.423-431
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• 2011

Purpose: The study was designed to determine the discriminating ability of a Bayesian network (BN) for predicting risk for pressure ulcers. Methods: Analysis was done using a retrospective cohort, nursing records representing 21,114 hospital days, 3,348 patients at risk for ulcers, admitted to the intensive care unit of a tertiary teaching hospital between January 2004 and January 2007. A BN model and two logistic regression (LR) versions, model-I and .II, were compared, varying the nature, number and quality of input variables. Classification competence and case coverage of the models were tested and compared using a threefold cross validation method. Results: Average incidence of ulcers was 6.12%. Of the two LR models, model-I demonstrated better indexes of statistical model fits. The BN model had a sensitivity of 81.95%, specificity of 75.63%, positive and negative predictive values of 35.62% and 96.22% respectively. The area under the receiver operating characteristic (AUROC) was 85.01% implying moderate to good overall performance, which was similar to LR model-I. However, regarding case coverage, the BN model was 100% compared to 15.88% of LR. Conclusion: Discriminating ability of the BN model was found to be acceptable and case coverage proved to be excellent for clinical use.

• Structural Monitoring and Maintenance
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• 제3권4호
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• pp.349-375
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• 2016

The tension of an arch bridge hanger is estimated using a number of experimentally identified modal frequencies. The hanger is connected through metallic plates to the bridge deck and arch. Two different categories of model classes are considered to simulate the vibrations of the hanger: an analytical model based on the Euler-Bernoulli beam theory, and a high-fidelity finite element (FE) model. A Bayesian parameter estimation and model selection method is used to discriminate between models, select the best model, and estimate the hanger tension and its uncertainty. It is demonstrated that the end plate connections and boundary conditions of the hanger due to the flexibility of the deck/arch significantly affect the estimate of the axial load and its uncertainty. A fixed-end high fidelity FE model of the hanger underestimates the hanger tension by more than 20 compared to a baseline FE model with flexible supports. Simplified beam models can give fairly accurate results, close to the ones obtained from the high fidelity FE model with flexible support conditions, provided that the concept of equivalent length is introduced and/or end rotational springs are included to simulate the flexibility of the hanger ends. The effect of the number of experimentally identified modal frequencies on the estimates of the hanger tension and its uncertainty is investigated.

• 경영과학
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• 제32권2호
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• pp.69-78
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• 2015

This paper considers a Bayesian Poisson model for multivariate count data using multiplicative rates. More specifically we compose the parameter for overall arrival rates by the product of two parameters, a common effect and an individual effect. The common effect is composed of autoregressive evolution of the parameter, which allows for analysis on seasonal effects on all multivariate time series. In addition, analysis on individual effects allows the researcher to differentiate the time series by whatevercharacterization of their choice. This type of model allows the researcher to specifically analyze two different forms of effects separately and produce a more robust result. We illustrate a simple MCMC generation combined with a Gibbs sampler step in estimating the posterior joint distribution of all parameters in the model. On the whole, the model presented in this study is an intuitive model which may handle complicated problems, and we highlight the properties and possible applications of the model with an example, analyzing real time series data involving customer arrivals to a large retail store.

• Structural Engineering and Mechanics
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• 제87권1호
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• pp.1-18
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• 2023

The paper presents a Bayesian Finite element (FE) model updating methodology by utilizing modal data. The dynamic condensation technique is adopted in this work to reduce the full system model to a smaller model version such that the degrees of freedom (DOFs) in the reduced model correspond to the observed DOFs, which facilitates the model updating procedure without any mode-matching. The present work considers both the MPV and the covariance matrix of the modal parameters as the modal data. Besides, the modal data identified from multiple setups is considered for the model updating procedure, keeping in view of the realistic scenario of inability of limited number of sensors to measure the response of all the interested DOFs of a large structure. A relationship is established between the modal data and structural parameters based on the eigensystem equation through the introduction of additional uncertain parameters in the form of modal frequencies and partial mode shapes. A novel sampling strategy known as the Metropolis-within-Gibbs (MWG) sampler is proposed to sample from the posterior Probability Density Function (PDF). The effectiveness of the proposed approach is demonstrated by considering both simulated and experimental examples.

• 응용통계연구
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• 제19권3호
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• pp.421-430
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• 2006

표본조사에서는 일반적으로 지형학적 범위가 넓거나 흔히 우리가 알고 있는 지형적 범위 즉시 또는 도 단위로 표본설계가 이루어진다. 그러므로 지형학적 범위가 작은 소지역은 충분한 표본의 확보가 불가능하며 따라서 정확한 소지역 통계를 얻는 것은 매우 어렵다. 이러한 문제로 정확한 소지역 통계를 얻기 위한 연구가 활발히 진행되고 있다. 최근 신기일과 이상은(2003)은 공간통계 모형을 이용한 소지역 추정을 연구하였다. 본 논문은 신기일과 이상은(2003)의 공간자기회귀(Spatial Autoregressive: SAR) 모형을 확장한 모형인 베이지안 자기 포아송 모형 (Bayesian Auto-Poisson Model: BAPM)을 이용한 소지역 추정에 관하여 연구하였다. 분석에 사용된 자료는 호주의 1998년 장애인 통계 (Survey of Disability, Aging and Cares:SDAC)이 며 MSE, MB 그러고 회귀 분석을 이용한 편의 분석기법이 비교에 사용되었다.

• Journal of the Korean Data and Information Science Society
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• 제22권5호
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• pp.911-919
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• 2011

최근 의학에서는 정신 질환과 관련된 위험 인자를 찾는 것이 중요해지고 있다. 인자들을 찾아서 인자들의 특성과 관련성을 파악하면 병을 사전에 예방 할 수 있다. 또한 이 연구는 의학 발전에 많은 도움을 줄 수 있다. 정신 질환에 대한 위험요인은 주로 로지스틱 회귀모형을 사용하여 찾아 왔다. 하지만 이 논문에서는 데이터마이닝 기법 중 CART, C5.0, 로지스틱, 신경망, 베이지안 네트워크 방법을 이용한다. 정신장애 질병인 섬망자료를 적용하여, 최적의 모형인 베이지안 네트워크 방법을 선택하였다. 이 베이지안 네트워크 기법을 위험 요소를 찾는데 사용하고, 이 위험인자 간의 관계를 방사형 그래프를 통해서 규명하였다.