• Communications for Statistical Applications and Methods
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• v.24 no.1
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• pp.67-80
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• 2017

Over the last few decades, ensemble forecasts based on global climate models have become an important part of climate forecast due to the ability to reduce uncertainty in prediction. Moreover in ensemble forecast, assessing the prediction uncertainty is as important as estimating the optimal weights, and this is achieved through a probabilistic forecast which is based on the predictive distribution of future climate. The Bayesian model averaging has received much attention as a tool of probabilistic forecasting due to its simplicity and superior prediction. In this paper, we propose a new Bayesian model averaging method for probabilistic ensemble forecasting. The proposed method combines a deterministic ensemble forecast based on a multivariate regression approach with Bayesian model averaging. We demonstrate that the proposed method is better in prediction than the standard Bayesian model averaging approach by analyzing monthly average precipitations and temperatures for ten cities in Korea.

• Proceedings of the Korean Geotechical Society Conference
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• 2005.03a
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• pp.185-190
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• 2005

In this study, a probabilistic prediction model for debris flow occurrence was developed using a logistic regression analysis. The model can be applicable to metamorphic rocks and granite area. In order to develop the prediction model, detailed field survey and laboratory soil tests were conducted both in the northern and the southern Gyeonggi province and in Sangju, Gyeongbuk province, Korea. The six landslide triggering factors were selected by a logistic regression analysis as well as several basic statistical analyses. The six factors consist of two topographic factors and four geological and geotechnical factors. The model assigns a weight value to each selected factor. The verification results reveal that the model has 86.5% of prediction accuracy. Therefore, it is possible to predict landslide occurrence in a probabilistic and quantitative manner.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.39 no.2
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• pp.166-175
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• 2002

In this paper. we predict tracking performance of the probabilistic strongest neighbor filter (PSNF). The PSNF is known to be consistent and superior to the probabilistic data association filter (PDAF) in both performance and computation. The PSNF takes into account the probability that the measurement with the strongest intensity in the neighborhood of the predicted target measurement location is not target-originated. The tracking performance of the PSNF is quantified in terms of its estimation error covariance matrix. The estimation error covariance matrix is approximately evaluated by using the hybrid conditional average approach (HYCA). We performed numerical experiments to show the validity of our performance prediction.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.5
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• pp.684-690
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• 2007

We describe our method to predict the direction of conditional probabilistic dependencies between clusters of random variables. Selected variables called 'gateway variables' are used to predict the conditional probabilistic dependency relations between clusters. The direction of conditional probabilistic dependencies between clusters are predicted by finding directed acyclic graph (DAG)-shaped dependency structure between the gateway variables. We show that our method shows meaningful prediction results in determining directions of conditional probabilistic dependencies between clusters.

• The Journal of Engineering Geology
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• v.14 no.2
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• pp.211-222
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• 2004

In this study, a probabilistic prediction model for debris flow occurrence was developed using a logistic regression analysis. The model can be applicable to metamorphic rocks and granite area. order to develop the prediction model, detailed field survey and laboratory soil tests were conducted both in the northern and the southern Gyeonggi province and in Sangju, Gyeongbuk province, Korea. The seven landslide triggering factors were selected by a logistic regression analysis as well as several basic statistical analyses. The seven factors consist of two topographic factors and five geological and geotechnical factors. The model assigns a weight value to each selected factor. The verification results reveal that the model has 90.74% of prediction accuracy. Therefore, it is possible to predict landslide occurrence in a probabilistic and quantitative manner.

• Journal of the Korea Institute of Military Science and Technology
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• v.16 no.5
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• pp.631-638
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• 2013

The probabilistic fatigue life analysis is one of the common methods to account the uncertainty of parameters on the structural failure. Frequently, the Bayesian approach has been demonstrated as a proper method to show the uncertainty of parameters. In this work, the application of probabilistic fatigue life prediction method for the aircraft structure was studied. This effort was conducted by using the PoF(Probability of Failure) based on Bayesian approach. Furthermore, numerical example was carried out to confirm the validation of the suggested approach. In conclusion, it was shown that the Bayesian approach can calculate the probabilistic fatigue lives and the quantitative value of PoF effectively for the aircraft structural component. Moreover the calculated probabilistic fatigue lives can be utilized to determine the optimized inspection period of aircraft structures.

• Atmosphere
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• v.28 no.4
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• pp.509-520
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• 2018

The performance of the newly designed Pusan National University Coupled General Circulation Model (PNU CGCM) Ensemble Forecast System which produce 40 ensemble members for 12-month lead prediction is evaluated and analyzed in terms of boreal winter temperature over South Korea (S. Korea). The influence of ensemble size on prediction skill is examined with 40 ensemble members and the result shows that spreads of predictability are larger when the size of ensemble member is smaller. Moreover, it is suggested that more than 20 ensemble members are required for better prediction of statistically significant inter-annual variability of wintertime temperature over S. Korea. As for the ensemble average (ENS), it shows superior forecast skill compared to each ensemble member and has significant temporal correlation with Automated Surface Observing System (ASOS) temperature at 99% confidence level. In addition to forecast skill for inter-annual variability of wintertime temperature over S. Korea, winter climatology around East Asia and synoptic characteristics of warm (above normal) and cold (below normal) winters are reasonably captured by PNU CGCM. For the categorical forecast with $3{\times}3$ contingency table, the deterministic forecast generally shows better performance than probabilistic forecast except for warm winter (hit rate of probabilistic forecast: 71%). It is also found that, in case of concentrated distribution of 40 ensemble members to one category out of the three, the probabilistic forecast tends to have relatively high predictability. Meanwhile, in the case when the ensemble members distribute evenly throughout the categories, the predictability becomes lower in the probabilistic forecast.

• Proceedings of the Earthquake Engineering Society of Korea Conference
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• 2003.09a
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• pp.311-316
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• 2003

The compressive strength of concrete is a criterion to produce concrete. However, the tests on the compressive strength are complicated and time-consuming. More importantly, it is too late to make improvement even if the test result does not satisfy the required strength, since the test is usually performed at the 28th day after the placement of Concrete at the Construction site. Therefore, strength prediction before the placement of concrete is highly desirable. This study presents the probabilistic technique for predicting the compressive strength of concrete on the basis of concrete mix proportions. The estimation of the strength is based on the probabilistic neural network, and show that the present methods are very efficient and reasonable in predicting the compressive strength of concrete probabilistically.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2003.10a
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• pp.7-16
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• 2003

In this paper, we propose a probabilistic framework to predict the interaction probability of proteins. The notion of domain combination and domain combination pair is newly introduced and the prediction model in the framework takes domain combination pair as a basic unit of protein interactions to overcome the limitations of the conventional domain pair based prediction systems. The framework largely consists of prediction preparation and service stages. In the prediction preparation stage, two appearance pro-bability matrices, which hold information on appearance frequencies of domain combination pairs in the interacting and non-interacting sets of protein pairs, are constructed. Based on the appearance probability matrix, a probability equation is devised. The equation maps a protein pair to a real number in the range of 0 to 1. Two distributions of interacting and non-interacting set of protein pairs are obtained using the equation. In the prediction service stage, the interaction probability of a protein pair is predicted using the distributions and the equation. The validity of the prediction model is evaluated fur the interacting set of protein pairs in Yeast organism and artificially generated non-interacting set of protein pairs. When 80% of the set of interacting protein pairs in DIP database are used as foaming set of interacting protein pairs, very high sensitivity(86%) and specificity(56%) are achieved within our framework.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.23 no.60
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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.