• Communications for Statistical Applications and Methods
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• 제11권1호
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• pp.49-58
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• 2004

In this talk we proposed an asymptotic test for dimensionality in the latent variable model for probabilistic principal component analysis with missing values at random. Proposed algorithm is a sequential likelihood ratio test for an appropriate Normal latent variable model for the principal component analysis. Modified EM-algorithm is used to find MLE for the model parameters. Results from simulations and real data sets give us promising evidences that the proposed method is useful in finding necessary number of components in the principal component analysis with missing values at random.

• 산업경영시스템학회지
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• 제23권60호
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• pp.23-36
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• 2000

A probabilistic model of burr formation in face milling of gray cast iron is proposed. During a face milling operation, an irregular pattern of the edge profile consisting of burrs and edge breakouts is observed at the end of cut. Based on the metal cutting theory, we derive a probabilistic model. The operational bayesian modeling approach is adopted to include the relevant theory in the model.

• Wind and Structures
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• 제22권1호
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• pp.17-41
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• 2016

To address the uncertainty of the flight trajectories caused by the turbulence and gustiness of the wind field over the roof and in the wake of a building, a 3-D probabilistic trajectory model of flat-type wind-borne debris is developed in this study. The core of this methodology is a 6 degree-of-freedom deterministic model, derived from the governing equations of motion of the debris, and a Monte Carlo simulation engine used to account for the uncertainty resulting from vertical and lateral gust wind velocity components. The influence of several parameters, including initial wind speed, time step, gust sampling frequency, number of Monte Carlo simulations, and the extreme gust factor, on the accuracy of the proposed model is examined. For the purpose of validation and calibration, the simulated results from the 3-D probabilistic trajectory model are compared against the available wind tunnel test data. Results show that the maximum relative error between the simulated and wind tunnel test results of the average longitudinal position is about 20%, implying that the probabilistic model provides a reliable and effective means to predict the 3-D flight of the plate-type wind-borne debris.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제7권4호
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• pp.711-725
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• 2013

A heuristic with multi-byte suffix matching plays an important role in real pattern matching algorithms. By skipping many characters at a time in the process of comparing a given pattern with the text, the pattern matching algorithm based on a heuristic with multi-byte suffix matching shows a faster average search time than algorithms based on deterministic finite automata. Based on various experimental results and simulations, the previous works show that the pattern matching algorithms with multi-byte suffix matching performs well. However, there have been limited studies on the mathematical model for analyzing the performance in a standard manner. In this paper, we propose a new probabilistic model, which evaluates the performance of a heuristic with multi-byte suffix matching in an average-case search. When the theoretical analysis results and experimental results were compared, the proposed probabilistic model was found to be sufficient for evaluating the performance of a heuristic with suffix matching in the real pattern matching algorithms.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2016년도 학술발표회
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• pp.197-197
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• 2016

Forecasting future drought events in a region plays a major role in water management and risk assessment of drought occurrences. The creeping characteristics of drought make it possible to mitigate drought's effects with accurate forecasting models. Drought forecasts are inevitably plagued by uncertainties, making it necessary to derive forecasts in a probabilistic framework. In this study, a new probabilistic scheme is proposed to forecast droughts, in which a discrete-time finite state-space hidden Markov model (HMM) is used aggregated with the Representative Concentration Pathway 8.5 (RCP) precipitation projection (HMM-RCP). The 3-month standardized precipitation index (SPI) is employed to assess the drought severity over the selected five stations in South Kore. A reversible jump Markov chain Monte Carlo algorithm is used for inference on the model parameters which includes several hidden states and the state specific parameters. We perform an RCP precipitation projection transformed SPI (RCP-SPI) weight-corrected post-processing for the HMM-based drought forecasting to derive a probabilistic forecast that considers uncertainties. Results showed that the HMM-RCP forecast mean values, as measured by forecasting skill scores, are much more accurate than those from conventional models and a climatology reference model at various lead times over the study sites. In addition, the probabilistic forecast verification technique, which includes the ranked probability skill score and the relative operating characteristic, is performed on the proposed model to check the performance. It is found that the HMM-RCP provides a probabilistic forecast with satisfactory evaluation for different drought severity categories, even with a long lead time. The overall results indicate that the proposed HMM-RCP shows a powerful skill for probabilistic drought forecasting.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권9호
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• pp.4684-4705
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• 2019

Collaborative filtering recommender systems are vulnerable to shilling attacks in which malicious users may inject biased profiles to promote or demote a particular item being recommended. To tackle this problem, many robust collaborative recommendation methods have been presented. Unfortunately, the robustness of most methods is improved at the expense of prediction accuracy. In this paper, we construct a robust Bayesian probabilistic matrix factorization model for collaborative filtering recommender systems by incorporating the detection of user anomaly rating behaviors. We first detect the anomaly rating behaviors of users by the modified K-means algorithm and target item identification method to generate an indicator matrix of attack users. Then we incorporate the indicator matrix of attack users to construct a robust Bayesian probabilistic matrix factorization model and based on which a robust collaborative recommendation algorithm is devised. The experimental results on the MovieLens and Netflix datasets show that our model can significantly improve the robustness and recommendation accuracy compared with three baseline methods.

• 한국지반공학회논문집
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• 제25권11호
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• pp.87-103
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• 2009

지반정수의 신뢰성 높은 확률분포형을 결정하기 위해서는 분석자료에 대한 이상치 및 무작위성 검정, 적용한 확률 분포형의 매개변수 추정 및 매개변수 적합성 검정, 마지막으로 확률분포형의 적합성 검정의 과정이 필요하며, 위의 순서로 지반정수의 확률분포형을 산정할 것을 제안하였다. 본 연구에서는 제안한 절차에 따라 분석대상 지반으로 선정된 인천 송도지역 지반정수들의 확률분포형을 추정하였으며, 추가로 지반정수들의 변동성을 나타내는 변동계수를 산정하였다. 이와 같이 신뢰성 높은 지반정수들의 확률분포형과 변동계수는 확률론적 설계방법에 사용될 수 있을 뿐만 아니라 결정론적 설계에 사용될 지반정수의 합리적인 결정에 사용될 수 있는 중요한 자료로 판단된다.

• 정보과학회 논문지
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• 제44권3호
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• pp.239-245
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• 2017

확률문법은 자연어처리에서 사용되며, 확률문법에 대한 구문분석의 결과인 파스는 문법의 확률을 그대로 보존해야 한다. 대표적인 구문분석방법인 LL 파싱과 LR 파싱의 확률파싱 가능성을 살펴볼 때 LL 파싱은 문법의 확률정보를 그대로 유지하는 반면에 LR 파싱은 그렇지 않다. 확률문법과 확률파싱오토마톤과의 관계에 관한 기존 연구로 확률보존조건을 충족하는 오토마톤의 특성에 관한 연구는 진행된 바 있다. 그렇지만, 현재로서는 확률보존조건을 충족하는 오토마톤 생성모델에 관해서는 알려진 바가 없다. 본 논문에서는 단일상태파싱오토마타에 기반한 확률파싱오토마타 모델을 제안한다. 제안 모델로부터 생성되는 오토마톤은 확률보존조건을 보장하기에 별도의 확률파싱 가능 여부를 테스팅하는 단계가 불필요하고, 별도의 확률 함수를 정의하지 않아도 된다. 또한 매개인자를 적절하게 선택하여 효율적인 오토마톤의 생성이 가능하다.

• Computers and Concrete
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• 제29권1호
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• pp.1-14
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• 2022

Several aspects influence corrosive processes in reinforced concrete (RC) structures such as environmental conditions, structural geometry and mechanical properties. Since these aspects present large randomnesses, probabilistic models allow a more accurate description of the corrosive phenomena. Besides, the definition of limit states in the reliability assessment requires a proper mechanical model. In this context, this study proposes a straightforward methodology for the mechanical-probabilistic modelling of RC structures subjected to reinforcements' corrosion. An improved damage approach is proposed to define the limit states for the probabilistic modelling, considering three main degradation phenomena: concrete cracking, rebar yielding and rebar corrosion caused either by chloride or carbonation mechanisms. The stochastic analysis is evaluated by the Monte Carlo simulation method due to the computational efficiency of the Lumped Damage Model for Corrosion (LDMC). The proposed mechanical-probabilistic methodology is implemented in a computational framework and applied to the analysis of a simply supported RC beam and a 2D RC frame. Curves illustrate the probability of failure evolution over a service life of 50 years. Moreover, the proposed model allows drawing the probability of failure map and then identifying the critical failure path for progressive collapse analysis. Collapse path changes caused by the corrosion phenomena are observed.

• 한국신뢰성학회지:신뢰성응용연구
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• 제18권3호
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• pp.271-279
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• 2018

Purpose: Probabilistic safety analysis was performed to enhance the safety and reliability of nuclear power plants because traditional deterministic approach has limitations in predicting the risk of failure by crack growth. The study introduces a probabilistic approach to establish a basis for probabilistic safety assessment of passive components. Methods: For probabilistic modeling of fatigue crack growth rate (FCGR), various FCGR tests were performed either under constant load amplitude or constant ${\Delta}K$ conditions by using heat treated X-750 at low temperature with adequate cathodic polarization. Bayesian inference was employed to update uncertainties of the FCGR model using additional information obtained from constant ${\Delta}K$ tests. Results: Four steps of Bayesian parameter updating were performed using constant ${\Delta}K$ test results. The standard deviation of the final posterior distribution was decreased by a factor of 10 comparing with that of the prior distribution. Conclusion: The method for developing a probabilistic crack growth model has been designed and demonstrated, in the paper. Alloy X-750 has been used for corrosion fatigue crack growth experiments and modeling. The uncertainties of parameters in the FCGR model were successfully reduced using the Bayesian inference whenever the updating was performed.