• 제목/요약/키워드: Bayes theorem

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사례분석을 통한 베이즈 정리 기반 TBM 터널 붕괴 리스크 우선순위 도출 연구 (Study on Risk Priority for TBM Tunnel Collapse based on Bayes Theorem through Case Study)

  • 권기범;강민규;황병현;최항석
    • 대한토목학회논문집
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    • 제43권6호
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    • pp.785-791
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    • 2023
  • TBM 터널 프로젝트 내 불확실성으로 인한 사고를 예방하기 위해 리스크 관리는 필수적이다. 특히, 터널 막장면부터 지표면까지의 광범위한 피해를 초래할 수 있는 TBM 터널 붕괴는 더욱 신중히 관리되어야 한다. 또한, 각 TBM 터널 프로젝트의 시간과 비용의 제약으로 인해, 합리적 수준의 대응조치 계획을 수립하기 위한 리스크 우선순위를 도출할 필요가 있다. 이에 따라, 본 연구는 TBM 사고 사례조사를 통해 TBM 리스크 데이터베이스를 구축하였고, 베이즈 정리를 활용하여 지질요인의 TBM 터널 붕괴 리스크 우선순위를 도출하였다. 총 87건의 TBM 사고사례를 기반으로 3가지 사건과 5가지 지질요인을 포함한 TBM 리스크 데이터베이스가 구축되었다. 이때, 자갈층 지반, 고수압 함수대는 관련 사례 수가 적어 통계적 편향을 방지하기 위해 제외되었다. 데이터베이스에 베이즈 정리를 적용한 결과, 단층대와 연약지반은 TBM 터널 붕괴의 발생확률을 상당히 증가시키나, 그 외 3가지 지질요인(복합지반, 높은 상재압력, 팽창성 지반)은 붕괴와 낮은 상관성을 보였다. 결과적으로, 지질요인의 TBM 터널 붕괴 리스크 우선순위는 다음과 같다: 1) 단층대, 2) 연약지반, 3) 복합지반, 4) 높은 상재압력, 5) 팽창성 지반.

Forecasting of Various Air Pollutant Parameters in Bangalore Using Naïve Bayesian

  • Shivkumar M;Sudhindra K R;Pranesha T S;Chate D M;Beig G
    • International Journal of Computer Science & Network Security
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    • 제24권3호
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    • pp.196-200
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    • 2024
  • Weather forecasting is considered to be of utmost important among various important sectors such as flood management and hydro-electricity generation. Although there are various numerical methods for weather forecasting but majority of them are reported to be Mechanistic computationally demanding due to their complexities. Therefore, it is necessary to develop and build models for accurately predicting the weather conditions which are faster as well as efficient in comparison to the prevalent meteorological models. The study has been undertaken to forecast various atmospheric parameters in the city of Bangalore using Naïve Bayes algorithms. The individual parameters analyzed in the study consisted of wind speed (WS), wind direction (WD), relative humidity (RH), solar radiation (SR), black carbon (BC), radiative forcing (RF), air temperature (AT), bar pressure (BP), PM10 and PM2.5 of the Bangalore city collected from Air Quality Monitoring Station for a period of 5 years from January 2015 to May 2019. The study concluded that Naive Bayes is an easy and efficient classifier that is centered on Bayes theorem, is quite efficient in forecasting the various air pollution parameters of the city of Bangalore.

A novel nomogram of naïve Bayesian model for prevalence of cardiovascular disease

  • Kang, Eun Jin;Kim, Hyun Ji;Lee, Jea Young
    • Communications for Statistical Applications and Methods
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    • 제25권3호
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    • pp.297-306
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    • 2018
  • Cardiovascular disease (CVD) is the leading cause of death worldwide and has a high mortality rate after onset; therefore, the CVD management requires the development of treatment plans and the prediction of prevalence rates. In our study, age, income, education level, marriage status, diabetes, and obesity were identified as risk factors for CVD. Using these 6 factors, we proposed a nomogram based on a $na{\ddot{i}}ve$ Bayesian classifier model for CVD. The attributes for each factor were assigned point values between -100 and 100 by Bayes' theorem, and the negative or positive attributes for CVD were represented to the values. Additionally, the prevalence rate can be calculated even in cases with some missing attribute values. A receiver operation characteristic (ROC) curve and calibration plot verified the nomogram. Consequently, when the attribute values for these risk factors are known, the prevalence rate for CVD can be predicted using the proposed nomogram based on a $na{\ddot{i}}ve$ Bayesian classifier model.

IMPLEMENTATION OF DATA ASSIMILATION METHODOLOGY FOR PHYSICAL MODEL UNCERTAINTY EVALUATION USING POST-CHF EXPERIMENTAL DATA

  • Heo, Jaeseok;Lee, Seung-Wook;Kim, Kyung Doo
    • Nuclear Engineering and Technology
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    • 제46권5호
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    • pp.619-632
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    • 2014
  • The Best Estimate Plus Uncertainty (BEPU) method has been widely used to evaluate the uncertainty of a best-estimate thermal hydraulic system code against a figure of merit. This uncertainty is typically evaluated based on the physical model's uncertainties determined by expert judgment. This paper introduces the application of data assimilation methodology to determine the uncertainty bands of the physical models, e.g., the mean value and standard deviation of the parameters, based upon the statistical approach rather than expert judgment. Data assimilation suggests a mathematical methodology for the best estimate bias and the uncertainties of the physical models which optimize the system response following the calibration of model parameters and responses. The mathematical approaches include deterministic and probabilistic methods of data assimilation to solve both linear and nonlinear problems with the a posteriori distribution of parameters derived based on Bayes' theorem. The inverse problem was solved analytically to obtain the mean value and standard deviation of the parameters assuming Gaussian distributions for the parameters and responses, and a sampling method was utilized to illustrate the non-Gaussian a posteriori distributions of parameters. SPACE is used to demonstrate the data assimilation method by determining the bias and the uncertainty bands of the physical models employing Bennett's heated tube test data and Becker's post critical heat flux experimental data. Based on the results of the data assimilation process, the major sources of the modeling uncertainties were identified for further model development.

베이즈이론을 이용한 가뭄 확률 전망 기법 고도화 (Improvement in probabilistic drought prediction method using Bayes' theorem)

  • 김대호;김영오
    • 한국수자원학회:학술대회논문집
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    • 한국수자원학회 2020년도 학술발표회
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    • pp.153-153
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    • 2020
  • 우리나라에선 크고 작은 가뭄 피해가 자주 일어나고 있으며 최근엔 유래 없는 다년가뭄이 발생하면서 가뭄에 대한 경각심이 커지고 있다. 가뭄에 적절하게 대응하여 피해를 경감시키기 위해서는 신뢰도 높은 가뭄 예측이 선행되어야 한다. 이에 본 연구는 앙상블 예측과 베이즈이론(Bayes' theorem)을 수문학적 가뭄지수 중 하나인 SRI(Standardized Runoff Index)에 적용해 가뭄 확률 전망을 실시했으며 이를 EDP(Ensemble Drought Prediction)라고 칭하였다. 국내 8개 댐유역에서 EDP를 생성하고 개선하는 과정은 다음과 같이 진행된다. 우선 TANK모형을 활용한 1개월 선행 유량 예측(Ensemble Streamflow Prediction, ESP)의 결과를 SRI로 변환하여 EDP 확률분포를 생성한다. 그런 다음, EDP를 개선하기 위해 그 기초인 ESP에서 미흡한 토양수분 초기조건을 보완하고자 베이즈이론을 활용했다. APCC(APEC Climate Center)의 위성 관측 SMI(Soil Moisture Index) 자료로 SRI와의 회귀식을 구축, 이를 우도함수로 정의해 사전 EDP 분포를 업데이트한 EDP+ 확률분포를 생성했다. 그 결과, EDP와 EDP+ 모두 심도가 깊은 가뭄을 전망할수록 예측력이 기후학적 예측보다 좋지 않았다. 그럼에도 우도함수로 사용한 회귀식의 정확도가 높을수록 EDP+의 정확도도 향상되는 경향이 나타났으며, 이는 베이즈이론을 사용한다면 가뭄 확률 전망을 개선할 수 있다는 것을 의미하고 있다. 하지만, 확정 전망 정확도는 확률 전망 정확도와는 관계가 없었는데 이는 확정 전망과 확률 전망이 본질적으로 다르기 때문인 것으로 사료된다.

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Feature Voting for Object Localization via Density Ratio Estimation

  • Wang, Liantao;Deng, Dong;Chen, Chunlei
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • 제13권12호
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    • pp.6009-6027
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    • 2019
  • Support vector machine (SVM) classifiers have been widely used for object detection. These methods usually locate the object by finding the region with maximal score in an image. With bag-of-features representation, the SVM score of an image region can be written as the sum of its inside feature-weights. As a result, the searching process can be executed efficiently by using strategies such as branch-and-bound. However, the feature-weight derived by optimizing region classification cannot really reveal the category knowledge of a feature-point, which could cause bad localization. In this paper, we represent a region in an image by a collection of local feature-points and determine the object by the region with the maximum posterior probability of belonging to the object class. Based on the Bayes' theorem and Naive-Bayes assumptions, the posterior probability is reformulated as the sum of feature-scores. The feature-score is manifested in the form of the logarithm of a probability ratio. Instead of estimating the numerator and denominator probabilities separately, we readily employ the density ratio estimation techniques directly, and overcome the above limitation. Experiments on a car dataset and PASCAL VOC 2007 dataset validated the effectiveness of our method compared to the baselines. In addition, the performance can be further improved by taking advantage of the recently developed deep convolutional neural network features.

Bayes정리를 이용한 신뢰도 자료 평가용 전산코드 개발 및 응용 (A Computer Code Development for Updating Reliability Data Using Bayes' Theorem and Its Application)

  • Won-Guk Hwang;Kun Joong Yoo
    • Nuclear Engineering and Technology
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    • 제15권1호
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    • pp.41-49
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    • 1983
  • 특정 원자력발전소 안전성 계통의 신뢰도 분석을 위한 자료평가의 목적으로 전산코드를 개발하였으며 그 유용성을 입증하였다. 가압 경수로 보조급수 계통 신뢰도 분석을 위하여 개발된 전산코드를 이용하여 관련자료를 평가하였다. 이를 위하여 부품고장률의 선분포는 미국의 원자력안전성 연구보고서, 특정 발전소의 운전경험은 기 발간된 인허가자 사상보고서에서 얻었다. 분석결과 후분포는 대수정규분포 곡선에 잘 점철되며 분포의 오차인자들은 현저히 감소하는 것으로 나타났다.

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Likelihood Estimation Using Continuous-Time Markov Channels for Cognitive Radio Networks in Wireless LAN

  • Oo, Thant Zin;Thar, Kyi;Hong, Choong-Seon
    • 한국정보과학회:학술대회논문집
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    • 한국정보과학회 2012년도 한국컴퓨터종합학술대회논문집 Vol.39 No.1(D)
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    • pp.262-264
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    • 2012
  • Dynamic spectrum access and cognitive radio is a viable solution to solve congestion in ISM band. The dynamic environment of multi-channel wireless LAN is modeled by using continuous time Markov process. Bayes theorem is applied to infer channel access decisions dynamically to ensure current data transmission is switched to only likely candidate channels.

ACCURACY CURVES: AN ALTERNATIVE GRAPHICAL REPRESENTATION OF PROBABILITY DATA

  • Detrano Robert
    • 대한예방의학회:학술대회논문집
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    • 대한예방의학회 1994년도 교수 연수회(역학)
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    • pp.150-153
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    • 1994
  • Receiver operating characteristic (ROC) curves have been frequently used to compare probability models applied to medical problems. Though the curves are a measure of the discriminatory power of a model. they do not reflect the model's accuracy. A supplementary accuracy curve is derived which will be coincident with the ROC curve if the model is reliable. will be above the ROC curve if the model's probabilities are too high or below if they are too low. A clinical example of this new graphical presentation is given.

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ON-LINE ESTIMATION PROCEDURES OF DIGITAL FILTER TYPE FOR REVERBERATION CHARACTERISTICS IN CLOSED ACOUSTIC SYSTEMS BASED ON NOISY OBSERVATION

  • Hiromitsu, Seijiro;Ohta, Mitsuo
    • 한국음향학회:학술대회논문집
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    • 한국음향학회 1994년도 FIFTH WESTERN PACIFIC REGIONAL ACOUSTICS CONFERENCE SEOUL KOREA
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    • pp.680-685
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    • 1994
  • The acoustic phenomena in the actual sound systems involve a variety of compound problems. In this paper, the well-known Bayes' theorem is first employed and expanded into orthonormal and non-orthonomal series forms matched to the digital processing of lower and higher order statistical informations and the noisy observations. Proposed on-line algorithms of digital filter type are applied to the actual state estimation for a reverberation characteristics in a room under contamination of background noises.

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