• KSCE Journal of Civil and Environmental Engineering Research
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• v.38 no.4
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• pp.505-516
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• 2018

In order to establish an efficient bridge maintenance strategy, the future performance of a bridge must be estimated by considering the current performance, which allows more rational way of decision-making in the prediction model with higher accuracy. However, personnel-based existing maintenance may result in enormous maintenance costs since it is difficult for a bridge administrator to estimate the bridge performance exactly at a targeting management level, thereby disrupting a rational decision making for bridge maintenance. Therefore, in this work, we developed a representative performance prediction model for each bridge element considering uncertainty using domestic bridge inspection data, and proposed a bayesian updating method that can apply the developed model to actual maintenance bridge with higher accuracy. Also, the feasibility analysis based on calculation of maintenance cost for monitoring maintenance scenario case is performed to propose advantages of the Bayesian-updating-driven preventive maintenance in terms of the cost efficiency in contrast to the conventional periodic maintenance.

• Proceedings of the Korean Information Science Society Conference
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• 2007.10c
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• pp.362-366
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• 2007

다양한 기능과 센서를 탑재한 최신 모바일 디바이스는 사용자의 위치, 전화기록, SMS, 사진, 동영상 등 사용자에 관한 다양한 정보를 지속적으로 수집할 수 있기 때문에 개인의 생활을 이해하고 다양한 서비스를 제공할 수 있는 가능성을 가지고 있다. 하지만, 모바일 장치의 성능 제약 및 환경 불확실성으로 인해 아직까지 많은 연구 과제들이 남아 있다. 본 논문에서는 이러한 모바일 환경의 문제를 극복하기 위해 베이지안 네트워크를 이용한 라이프 로그 분석 모델 및 자동 학습 방법을 제안한다. 제안하는 베이지안 네트워크 모델은 모듈화 되어서 계산량은 감소되었으며, 자동 학습 방법을 통해 지속적인 업데이트가 가능하다. 이는 제안하는 방법이 복잡한 확률 모델을 자동으로 분할하는 방법과 분할된 상태에서의 유기적인 추론 방법을 포함하고 있기에 가능하다. 실험에서는 실제 모바일 장치에서 수집된 로그 데이터를 이용하여 제안하는 방법에 의한 실험 결과를 분석하고 분할을 통한 효율성 향상을 논의 한다.

• Journal of the Korean Institute of Intelligent Systems
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• v.26 no.5
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• pp.335-342
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• 2016

When dynamics changes occurred in an existing Bayesian Network (BN), the related parameters embedding on the BN have to be updated to new parameters adapting to changed patterns. In this case, these parameters have to be updated with the consideration of the causalities in the BN. This research suggests a framework for updating parameters dynamically using Expectation Maximization (EM) algorithm and Harmony Search (HS) algorithm among several Meta-Heuristics techniques. While EM is an effective algorithm for estimating hidden parameters, it has a limitation that the generated solution converges a local optimum in usual. In order to overcome the limitation, this paper applies HS for tracking the global optimum values of Maximum Likelihood Estimators (MLE) of parameters. The proposed method suggests a learning and propagation framework of BN with dynamic changes for overcoming disadvantages of EM algorithm and converging a global optimum value of MLE of parameters.

• Journal of the Korea Concrete Institute
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• v.21 no.3
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• pp.359-366
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• 2009

A new life-cycle cost (LCC) evaluation scheme for marine reinforced concrete structures is proposed. In this method, unlike the conventional life-cycle cost evaluation performed during the design process, the life-cycle cost is updated successively whenever new information of the chloride penetration is available. This updating is performed based on the Bayesian approach. For important structures, information required for this new method can be obtained without any difficulties because it is a common element of various types of monitoring systems. Using the new method, the life-cycle maintenance cost of structures can be estimated with a good precision.

• Proceedings of the Korean Information Science Society Conference
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• 2006.06b
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• pp.127-129
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• 2006

모바일 디바이스에서 얻을 수 있는 로그에는 다양한 개인정보가 풍부하게 포함되어 있으면서도 제약이 많아 활용이 어렵다. 그 동안은 모바일 장치의 용량, 파워의 제약과 정보 분석의 어려움으로 로그 정보를 무시해온 것이 일반적이었다. 본 논문에서는 모바일 디바이스의 다양한 로그 정보를 분석하여 사용자에게 의미 있는 상황(특이성)을 탐지해낼 수 있는 정보 분석 방법을 제안한다. 불확실한 상황에서의 정확성 향상을 위해 규칙/패턴 분석에 의한 특이성 추론뿐만 아니라 베이지안 네트워크를 활용한 확률적인 접근 방법을 활용한다. 이때, 복잡하지 않고 연산이 효율적으로 이루어질 수 있도록 BN을 모듈화하고 모듈화된 BN의 상호보완적인 확률 추론을 위한 BN 처리 과정을 제안한다. 그리고, 특이성 추출 모듈을 주기적으로 업데이트함으로써 성능을 향상시키기 위한 학습알고리즘을 소개한다.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.48 no.7
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• pp.537-546
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• 2020

The reliability used as a performance indicator during the development of space launch vehicle should be validated by the launch success probability, and the launch data need to be fed back for reliability management. In this paper, the launch data of space launch vehicles around the world were investigated and statistically analyzed for the success probabilities according to the launch vehicle models and maturity. The Bayesian estimation of launch success probability was reviewed and analyzed by comparing the estimated success probabilities using several prior distributions and the statistical success probability. We presented the method of generating prior distribution function and considerations for Bayesian estimation.

• The Journal of the Korea Contents Association
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• v.21 no.11
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• pp.77-86
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• 2021

A method of constructing a war simulation based on Bayesian Inference was proposed as a method of constructing heterogeneous historical war data obtained with a time difference into a single model. A method of applying a linear regression model can be considered as a method of predicting future battles by analyzing historical war results. However it is not appropriate for two heterogeneous types of historical data that reflect changes in the battlefield environment due to different times to be suitable as a single linear regression model and violation of the model's assumptions. To resolve these problems a Bayesian inference method was proposed to obtain a post-distribution by assuming the data from the previous era as a non-informative prior distribution and to infer the final posterior distribution by using it as a prior distribution to analyze the data obtained from the next era. Another advantage of the Bayesian inference method is that the results sampled by the Markov Chain Monte Carlo method can be used to infer posterior distribution or posterior predictive distribution reflecting uncertainty. In this way, it has the advantage of not only being able to utilize a variety of information rather than analyzing it with a classical linear regression model, but also continuing to update the model by reflecting additional data obtained in the future.

• Transactions of the Korean Society of Mechanical Engineers
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• v.16 no.4
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• pp.675-683
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• 1992

Mapping and navigation system based on certainty grids for an autonomous mobile robt operating in unknown and unstructured environment is described. The system uses sonar range data to build a map of robot's surroundings. The range data from sonar sensor are integrated into a probability map that is composed of two dimensional grids which contain the probabilities of being occupied by the objects in the environment. A Bayesian model is used to estimate the uncertainty of the sensor information and to update the existing probability map with new range data. The resulting two dimensional map is used for path planning and navigation. In this paper, the Bayesian updating model which was successfully simulated in our earlier work is implemented on a mobile robot and is shown to be valid in the real world through experiment. This paper also proposes a technique for reducing for reducing specular reflection problem of sonar system which seriousely deteriorates the map quality, and a new path planning method based on weighted distance, which enables the robot to efficiently navigate in an unknown area.

• 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.

• Food Science and Preservation
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• v.30 no.3
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• pp.459-471
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• 2023

The Bayesian network (BN) model was applied to analyze the characteristic variables that affect compliance with safety inspections of farmed eel during the production stage, using the data from 30,063 cases of eel aquafarm safety inspection in the Integrated Food Safety Information Network (IFSIN) from 2012 to 2021. The dataset for establishing the BN model included 77 non-conforming cases. Relevant HACCP data, geographic information about the aquafarms, and environmental data were collected and mapped to the IFSIN data to derive explanatory variables for nonconformity. Aquafarm HACCP certification, detection history of harmful substances during the last 5 y, history of nonconformity during the last 5 y, and the suitability of the aquatic environment as determined by the levels of total coliform bacteria and total organic carbon were selected as the explanatory variables. The highest achievable eel aquafarm noncompliance rate by manipulating the derived explanatory variables was 24.5%, which was 94 times higher than the overall farmed eel noncompliance rate reported in IFSIN between 2017 and 2021. The established BN model was validated using the IFSIN eel aquafarm inspection results conducted between January and August 2022. The noncompliance rate in the validation set was 0.22% (15 nonconformances out of 6,785 cases). The precision of BN model prediction was 0.1579, which was 71.4 times higher than the non-compliance rate of the validation set.