• Proceedings of the Korean Society of Agricultural Engineers Conference
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• 2000.10a
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• pp.384-389
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• 2000

A artificial neural network model was developed to analyze and forecast the flow fluctuation at small streams in the Balan watershed. Backpropagation neural networks were found to perform very well in forecasting daily streamflows. In order to deal with slow convergence and an appropriate structure, two algorithms were proposed for speeding up the convergence of the backpropagation method, and the Bayesian Information Criterion(BIC) was proposed for obtaining the optimal number of hidden nodes. From simulations using daily flows at the HS#3 watershed of the Balan Watershed Project, which is 412,5 ㏊ in size and relatively steep in landscape, it was found that those algorithms perform satisfactorily.

• Journal of the Korea institute for structural maintenance and inspection
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• v.2 no.3
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• pp.223-234
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• 1998

In this study, an attempt is made to apply the concept of fuzzy-bayesian theory to the integrity assessment of structure system, and uncertainty states are represented in terms of fuzzy sets which define several linguistic variables such as "very good", "good", "average", "poor", "very poor", etc. Especially, the concept of fuzzy conditional probability aids to derive a new reliability analysis which includes the subjective assessment of engineers without introducing any additional correction factors. The fuzzy concept are also used as reliability indexes for the condition assessment based on the proposed models, the proposed fuzzy theory-based approach with the results of PEM and AFOSM are applied to retaining wall.

• Journal of the Korea Society of Computer and Information
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• v.11 no.3
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• pp.239-248
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• 2006

Current context-aware applications In ubiquitous computing environments make the assumption that the context they are dealing with is correct. However, in reality, both sensed and interpreted context informations are often uncertain or imperfect. In this paper, we propose a probability extension model to ontology-based model for rep resenting uncertain contexts and use Bayesian networks to resolve about uncertainty of context informations. The proposed model can support the development and operation of various context-aware services, which are required in the ubiquitous computing environment.

• Journal of Korea Water Resources Association
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• v.54 no.10
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• pp.769-777
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• 2021

This study investigated the change of the achievement rate of the target water quality conditioned on the occurrence of severe drought, to assess the effects of meteorological drought on the water quality management in the Nakdong River basin. Using three drought indices with difference time scales such as 30-, 60-, 90-day, i.e., SPI30, SPI60, SPI90, and three water quality indicators such as biochemical oxygen demand (BOD), total organic carbon (TOC), and total phosphorus (T-P), we first analyzed the relationship between severe drought occurrence water quality change in mid-sized watersheds, and identified the watersheds in which water quality was highly affected by severe drought. The Bayesian network models were constructed for the watersheds to probabilistically assess the relationship between severe drought and water quality management. Among 22 mid-sized watersheds in the Nakdong River basin, four watersheds, such as #2005, #2018, #2021, and #2022, had high environmental vulnerability to severe drought. In addition, severe drought affected spring and fall water quality in the watershed #2021, summer water quality in the #2005, and winter water quality in the #2022. The causal relationship between drought and water quality management is usufaul in proactive drought management.

• Journal of Korea Water Resources Association
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• v.46 no.1
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• pp.59-71
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• 2013

The lack of sufficient flood data being kept across Korea has made it difficult to assess reliable estimates of the design flood while relatively sufficient rainfall data are available. In this regard, a rainfall simulation based derivation technique of flood frequency curve has been proposed in some of studies. The main issues in deriving the flood frequency curve is to develop the rainfall simulation model that is able to effectively reproduce extreme rainfall. Also the rainfall-runoff modeling that can convey uncertainties associated with model parameters needs to be developed. This study proposes a systematic approach to fully consider rainfallrunoff related uncertainties by coupling a piecewise Kernel-Pareto based multisite daily rainfall generation model and Bayesian HEC-1 model. The proposed model was applied to generate runoff ensemble at Daechung Dam watershed, and the flood frequency curve was successfully derived. It was confirmed that the proposed model is very promising in estimating design floods given a rigorous comparison with existing approaches.

• Journal of the Society of Naval Architects of Korea
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• v.29 no.4
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• pp.234-243
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• 1992

Fatigue crack initiation life has a wide scatter and this makes the fatigue design of structural members difficult. In order to make the fatigue life distribution clear, it is required to prepare a large number of specimens and repeat the fatigue tests under the same loading condition. Such fatigue tests usually take much time and cost. In this study, a fatigue testing method using a multi-notched test specimen for the purpose of estimating the distribution function of fatigue crack initiation life by small number of fatigue tests is used. The purpose of this study is to verify the above fatigue testing method of a multi-notched specimen by using Bayesian reliability analysis, Least square method and Skewness method for the determination of unknown Weibull parameters. The multi-notched specimen is a specimen in which several tens of statistically identical notches are prepared.

• Journal of Korea Multimedia Society
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• v.11 no.3
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• pp.301-314
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• 2008

The existing event recognition is accomplished with the limited systematic foundation, and thus much longer learning time is needed for emergency scenario interpretation due to large scale of probability data. In this paper, we propose a method for nile-based event recognition of an independent object(human) which extract a feature vectors from the object and analyze the behavior pattern of each object and interpretation of emergency scenarios using a probability and object's events. The event rule of an independent object is composed of the Primary-event, Move-event, Interaction-event, and 'FALL DOWN' event and is defined through feature vectors of the object and the segmented motion orientated vector (SMOV) in which the dynamic Bayesian network is applied. The emergency scenario is analyzed using current state of an event and its post probability. In this paper, we define diversified events compared to that of pre-existing method and thus make it easy to expand by increasing independence of each events. Accordingly, semantics information, which is impossible to be gained through an.

• Geotechnical Engineering
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• v.7 no.3
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• pp.21-32
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• 1991

The purpose of this paper is to develop a reliability model for slope stability of Earth-rockfill dams which accounts for all uncertainties encountered. The uncertain factors of the design variables include the cohesion, the angle of internal friction, and the porewater Pressure in each zone. More specifically, the model errors in estimating those variables are studied in depth. To reduce the uncertainties due to model errors, updated design variables are obtained using Bayesian Theory. For stability analysis, both the two-dimesional stability analysis and the three-dimensional stability analysis where the end effects and the system reliability concept are considered are used for the reliability calculations. The deterministic safety factor by the three-dimensional analysis is lager than that by the two-dimensional anlysis. However, the probability of failure by the three-dimensional analysis is about 3.5 times larger that by the two-dimensional analysis. It is because the system reliability concept is used in the three-dimensional analysis. The sensitivity analysis shows that the probability of failure is more sensitive to the uncertainty of the cohesion than that of the angle of internal friction.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.12 no.2
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• pp.175-180
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• 2012

Currently datamining sector is interested and applied in many areas. In other words, datamining is predicting the future to discover hidden correlations and make decisions. To interpret data on various aspects can be converted to real expectation. Analyzing the results even a simple can be found big difference. The properties associated with breast cancer by about applying bayesian theory is used to predict the probability. In the past patient data, doctors may be obtaining by applying evidence-based care for patients with the results of examination and By using the the past patient data.

• Geophysics and Geophysical Exploration
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• v.2 no.2
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• pp.112-121
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• 1999

This article reviews the development of geophysical inverse theory. In a series of articles published in 1967, 1968, and 1979, G. Backus and F. Gilbert a trade-off between model resolution and estimation errors in geophysical inverse problems, and gave a criterion to compromise the reciprocal relation. Although the criterion was not clear in the physical point of view, it had been extensively used in the interpretation of geophysical date in the 1970s. This was the starting point of the fruitful development of inverse theory in geophysics. A reasonable criterion to compromise the reciprocal relation was derived to solve linear problems by D. D. jackson in 1979, introducing the concept of a priori information about unknown model parameters. This Jackson's approach was extended to solve nonlinear problems on the basis o probabilistic approach to the inverse problems formulated by A. Tarantola and B. Vallete in 1982. At the end of 1980s ABIC (Akaike Bayesian Information Criterion) was introduced for selecting a more reasonable model in geophysics. Now the date inversion is regarded as the process of extracting new information from observed data, combining in with a priori information about model parameters, and constructing a more clear image of model.