• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.7
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• pp.907-912
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• 2007

The present level of ubiquitous computing technology have developed to the point where Home-server provides services that user require directly for user in the intelligent space. But it will need intelligent system to provides more active services for user in the near future. In this paper, we define the environment information about situation that user is in as Context, and collect the Context that stereotype as 4W1H form for construct the system that can decision service will be provide from information about a situation that user is in, without user's involvement. Additionally we collect information about user's emotional state, use these informations as nodes of Bayesian network for probabilistic reasoning. From that, we materialize Context Awareness system about it that what kind of situation user is in. And, we propose the Context-based Service reasoning model using Bayesian Network from the result of Context Awareness.

• Journal of KIISE:Software and Applications
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• v.31 no.12
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• pp.1569-1580
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• 2004

One commonly used approach to deal with uncertainty is Bayesian network which represents joint probability distributions of domain. There are some attempts to team the structure of Bayesian networks automatically and recently many researchers design structures of Bayesian network using evolutionary algorithm. However, most of them use the only one fittest solution in the last generation. Because it is difficult to combine all the important factors into a single evaluation function, the best solution is often biased and less adaptive. In this paper, we present a method of generating diverse Bayesian network structures through fitness sharing and combining them by Bayesian method for adaptive inference. In order to evaluate performance, we conduct experiments on learning Bayesian networks with artificially generated data from ASIA and ALARM networks. According to the experiments with diverse conditions, the proposed method provides with better robustness and adaptation for handling uncertainty.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.39 no.1
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• pp.135-146
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• 2019

In this paper, the preventive maintenance strategy in operation stage of a bridge using Bayesian inference is proposed. The proposed technique can be used to predict the variation in the performance (or condition) of the bridge with higher accuracy, considering the uncertainty of monitoring. The applicability of the proposed method to the existing bridges is verified and analyzed that have an advantage in terms of maintenance cost efficiency compared to the conventional periodic maintenance system, which establishes maintenance after damage. It is expected that the proposed preventive maintenance method can be used to overcome the limitation of the conventional periodic maintenance method and to make practical bridge maintenance decision.

• Journal of Navigation and Port Research
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• v.47 no.1
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• pp.1-9
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• 2023

Many studies have analyzed marine accidents, and since marine accident information is updated every year, it is necessary to periodically analyze and identify the causes. The purpose of this study was to prevent accidents by identifying and analyzing the causes of marine accidents using previous and new data. In marine accident data, 1,921 decisions by the Korea Maritime Safety Tribunal on marine accidents on fishing ships over 16 years were collected in consideration of the specificity of fishing ships, and 1,917 cases of accident notification text history by the Ministry of Maritime Affairs and Fisheries over 3 years were collected. The decision data and text data were classified according to variables and quantified. Prior probability was calculated using a Bayesian network using the quantified data, and fishing ship marine accidents were predicted using backward propagation. Among the two collected datasets, the decision data did not provide the types of fishing ships and fishing areas, and because not all fishing ship accidents were included in the decision data, the text data were selected. The probability of a fishing ship marine accident in which engine damage would occur in the West Sea was 0.0000031%, as calculated by backward propagation. The expected effect of this study is that it is possible to analyze marine accidents suitable for the characteristics of actual fishing ships using new accident notification text data to analyze fishing ship marine accidents. In the future, we plan to conduct research on the causal relationship between variables that affect fishing ship marine accidents.

• School Mathematics
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• v.15 no.3
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• pp.603-617
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• 2013

The major purpose of this article is to examine what kind of gap exists between mathematically gifted students' probability knowledge and the reality actually applying that knowledge and then analyze the cause of the gap. To attain the goal, 23 elementary mathematically gifted students at the highest level from G region were provided with problem situations internalizing a probability and expectation, and the problems are in series in which conditions change one by one. The study task is in a gaming situation where there can be the most reasonable answer mathematically, but the choice may differ by how much they consider a certain condition. To collect data, the students' individual worksheets are collected, and all the class procedures are recorded with a camcorder, and the researcher writes a class observation report. The biggest reason why the students do not make a decision solely based on their own mathematical knowledge is because of 'impracticality', one of the properties of probability, that in reality, all things are not realized according to the mathematical calculation and are impossible to be anticipated and also their own psychological disposition to 'avoid loss' about their entry fee paid. In order to provide desirable probability education, we should not be limited to having learners master probability knowledge included in the textbook by solving the problems based on algorithmic knowledge but provide them with plenty of experience to apply probabilistic inference with which they should make their own choice in diverse situations having context.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.25 no.8A
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• pp.1153-1159
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• 2000

This paper applies a seasonal ARIMA model to the timely forecasting in a line utilization and its confidence interval on the base of the past data of the lido utilization that QoS of the network is greatly influenced by and proposes the prediction algorithm of threshold violation in line utilization using the seasonal ARIMA model. We can predict the time of threshold violation in line utilization and provide the confidence based on probability. Also, we have evaluated the validity of the proposed model and estimated the value of a proper threshold and a detection probability, it thus appears that we have maximized the performance of this algorithm.

• Environmental and Resource Economics Review
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• v.7 no.2
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• pp.87-109
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• 1998

1989년 미국 환경청(Environmental Protection Agency : EPA)은 유독물질통제법(Toxic Substances Control Act : TSCA)에 의해 석면을 함유하고 있는 일부 제품의 제조, 수업, 가공 및 판매를 세 단계로 나누어 금지하였다. 본 논문은 석면규제를 금지여부결정과 금지우선순위결정으로 구분하여 각 결정에 내재된 EPA의 의사결정요인들을 추론한다. 특히 본 논문은 Magat et al.(1986), Asch and Seneca(1989), Cropper et al.(1992)이 EPA의 의사결정요인들로 제시한 내부신호(internal signals, 비용과 편익의 추정치)와 외부신호(external signals, 외부집단의 참여)를 석면규제에 적용하여 이들의 역할을 고찰한다. 한편 본 논문은 규제에 의해 영향을 받는 이익집단들이 EPA의 의견수렴기간(comment period) 동안에 제출한 의견서(written comments)가 외부신호를 나타낸다고 가정한다. Probit 모형으로 추정된 금지여부결정에 있어 EPA는 TSCA의 규정을 준수하여 비용과 편익을 균형하였고, 기업과 환경보호단체의 참여도 EPA의 의사 결정에 영향을 미쳤다. 즉, 어떤 제품의 금지에 소요되는 비용이 많으면 그 제품이 금지될 확률이 작았고, 그 제품의 금지를 반대하는 기업의 의견서가 많으면 그 제품이 금지될 확률이 작았다. 그러나 외부신호가 포함된 모형에서 내부 신호의 통계적 유의성이 낮아지는 문제가 나타났다. 한편 추정결과는 금지로 인해 감소된 암 한 건에 대한 EPA의 암묵적인(implicit) 평가가 5,000만 달러가 넘는다는 것을 보여 준다. Ordered Probit 모형으로 추정된 금지우선순위결정에 있어, 편익의 단위당 비용이 작을수록, 그리고 그 제품의 금지를 찬성하는 환경보호단체의 의견서가 많을수록 그 제품은 보다 이른 단계에서 금지되었다. 이 경우 외부신호의 계수의 통계적 유의성은 높은 반면 내부신호의 계수의 통계적 유의성은 상대적으로 낮았다.

• The Korean Journal of Applied Statistics
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• v.26 no.5
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• pp.737-746
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• 2013

The Bayesian method can be applied successfully to the estimation of the censored regression model introduced by Tobin (1958). The Bayes estimates show improvements over the maximum likelihood estimate; however, the performance of the Bayesian interval estimation is questionable. In Bayesian paradigm, the prior distribution usually reflects personal beliefs about the parameters. Such subjective priors will typically yield interval estimators with poor frequentist properties; however, an objective noninformative often yields a Bayesian procedure with good frequentist properties. We examine the performance of frequentist properties of noninformative priors for the Tobit regression model.

• The Korean Journal of Applied Statistics
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• v.28 no.3
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• pp.485-494
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• 2015

Canonical correspondence analysis is an ordination method used to visualize the relationships among sites, species and environmental variables. However, projection results are fluctuations if the samples slightly change and consistent interpretation on ecological similarity among species tends to be difficult. We use the bootstrap methods for canonical correspondence analysis to solve this problem. The bootstrap method results show that the variations of coordinate points are inversely proportional to the number of observations and coverage rates with bootstrap confidence interval approximates to nominal probabilities.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.9C
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• pp.887-898
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• 2005

In this paper, we introduce a probabilistic model based on Bayesian networks BNs) for recognizing human fatigue. First of all, we measured face feature information such as eyelid movement, gaze, head movement, and facial expression by IR illumination. But, an individual face feature information does not provide enough information to determine human fatigue. Therefore in this paper, a Bayesian network model was constructed to fuse as many as possible fatigue cause parameters and face feature information for probabilistic inferring human fatigue. The MSBNX simulation result ending a 0.95 BN fatigue index threshold. As a result of the experiment, when comparisons are inferred BN fatigue index and the TOVA response time, there is a mutual correlation and from this information we can conclude that this method is very effective at recognizing a human fatigue.