• Architectural research
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• 제13권3호
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• pp.49-56
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• 2011

Competitive bidding in construction is concerned with contractors making strategic decisions in respect of determination of bid price if contractors opt to bid. This study presents a strategy model for deciding optimum tender price with reflecting appropriate profit in competitive bidding using Bayesian regression analysis (BRA). The purpose of the developed model is to help contractors to secure suitable profitability by predicting the actual profit based on key variables. They may affect construction cost at bidding phase, ultimately which help contractors to secure high quality output. The model was tested empirically by application to a bidding dataset collected from a large South Korea contractor. BRA allows contractors to estimate more accurate actual profit by reflecting not only objective information but also subjective experiences and judgments. Consequently, the model can contribute to improvement of decision-making process for setting an optimum tender price.

• 한국신뢰성학회:학술대회논문집
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• 한국신뢰성학회 2002년도 정기학술대회
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• pp.273-273
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• 2002

In this paper, we investigate a software release policy with software reliability growth factor during the warranty period by assuming that the software reliability growth is assumed to occur after the testing phase with probability p and the software reliability growth is not assumed to occur after the testing phase with probability 1-p. The optimal release policy to minimize the expected total software cost is discussed. Numerical examples are shown to illustrate the results of the optimal policy. And we consider a Bayesian decision theoretic approach to determine an optimal software release policy. This approach enables us to update our uncertainty when determining optimal software release time, When the failure time is Weibull distribution with uncertain parameters, a bayesian approach is established. Finally, numerical examples are presented for illustrative propose.

• 상하수도학회지
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• 제23권2호
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• pp.241-249
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• 2009

It is well known that the trend of water demand in large-size water supply systems has been suddenly changed, and many expansions of water supply facilities become unnecessary. To be cost-effective, thus, politicians as well as many professionals lay stress on the adaptive management of water supply facilities. Failure in adapting to the new trend of demand is sure to be the most critical reason of unnecessary expansions. Hence, we try to develop the model and modeling procedure that do not depend on the old data of demand, and provide engineers with the fast learning process. To forecast water demand of Seoul, the Bayesian parameter estimation was applied, which is a representative method for statistical pattern recognition. It results that we can get a useful time-series model after observing water demand during 6 years, although trend of water demand were suddenly changed.

• 대한간호학회지
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• 제45권3호
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• pp.429-438
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• 2015

Purpose: This study was conducted to develop key performance indicators (KPIs) for home care nursing (HCN) based on a balanced scorecard, and to construct a performance prediction model of strategic objectives using the Bayesian Belief Network (BBN). Methods: This methodological study included four steps: establishment of KPIs, performance prediction modeling, development of a performance prediction model using BBN, and simulation of a suggested nursing management strategy. An HCN expert group and a staff group participated. The content validity index was analyzed using STATA 13.0, and BBN was analyzed using HUGIN 8.0. Results: We generated a list of KPIs composed of 4 perspectives, 10 strategic objectives, and 31 KPIs. In the validity test of the performance prediction model, the factor with the greatest variance for increasing profit was maximum cost reduction of HCN services. The factor with the smallest variance for increasing profit was a minimum image improvement for HCN. During sensitivity analysis, the probability of the expert group did not affect the sensitivity. Furthermore, simulation of a 10% image improvement predicted the most effective way to increase profit. Conclusion: KPIs of HCN can estimate financial and non-financial performance. The performance prediction model for HCN will be useful to improve performance.

• 한국안전학회지
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• 제35권1호
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• pp.79-86
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• 2020

Failure data that has systematically managed for a long time has high reliability to an estimated volume. But since much cost and effort are needed to secure reliability data, data from overseas country is used in quantitative risk analysis in many workplaces. Reliability of the data that can be collected in workplaces can be dropped because of insufficient sample or lack of observation time. Therefore, estimated data is difficult to use as it is and environment and characteristic of the workplace cannot be reflected by using data from overseas country. So this study used Bayesian method that can be used reflecting both reliability data from overseas country and workplace failure data that has less samples. As a setting toward difficult situation that securing sufficient failure data cannot be achieved, we composed workplace failure data equivalent to mass observation time 20%(t=17000), 40%(t=24000), 60%(t=31000), 80%(t=38000) and IEEE data by using Bayesian method.

• 한국항해항만학회지
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• 제41권5호
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• pp.287-296
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• 2017

The Formal Safety Assessment (FSA) is a structured and systematic methodology developed by the IMO, aimed at assessing the risk of vessels and recommending the method to control intolerable risks, thereby enhancing maritime safety, including protection of life, health, the marine environment and property, by using risk analysis and cost-benefit assessment. While the FSA has mostly been applied to merchant vessels, it has rarely been applied to a DP vessel, which is one of the special purpose vessels in the offshore industry. Furthermore, most of the FSA has been conducted so far by using the Fault Tree Analysis tool, even though there are many other risk analysis tools. This study carried out the FSA for safe operation of DP vessels by using the Bayesian network, under which conditional probability was examined. This study determined the frequency and severity of DP LOP incidents reported to the IMCA from 2001 to 2010, and obtained the Risk Index by applying the Bayesian network. Then, the Risk Control Options (RCOs) were identified through an expert brainstorming and DP vessel simulations. This study recommends duplication of PRS, regardless of the DP class and PRS type and DP system specific training. Finally, this study verified that the Bayesian network and DP simulator can also serve as an effective tool for FSA implementation.

• 한국컴퓨터정보학회논문지
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• 제14권7호
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• pp.1-8
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• 2009

본 연구에서는 소프트웨어 제품을 개발하여 테스팅을 거친 후 사용자에게 인도하는 시기를 결정하는 방출문제에 대하여 연구하였다. 따라서 최적 소프트웨어 방출 정책은 소프트웨어 요구 신뢰도를 만족시키고 소프트웨어 개발 및 유지 총비용을 최소화 시키는 정책을 수용해야 한다. 본 논문에서는 로그포아송 실행시간모형에 대하여 베이지안 모수 추정법(마코브체인 몬테칼로(MCMC) 기법 중에 하나인 깁스 샘플링과 메트로폴리스 알고리즘을 이용한 근사기법)이 사용되었다. 본 논문의 수치적인 예에서는 Musa의 T1 자료를 적용하여 최우수추정법과 베이지안 모수 추정과의 관계를 빅교하고 또한 최적 방출시기를 추정하였다.

• Asian Pacific Journal of Cancer Prevention
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• 제15권14호
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• pp.5571-5575
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• 2014

Background: The ability to predict the survival time of breast cancer patients is important because of the potential high morbidity and mortality associated with the disease. To develop a predictive inference for determining the survival of breast cancer patients, we applied a novel Bayesian method. In this paper, we propose the development of a databased statistical probability model and application of the Bayesian method to predict future survival times for White Hispanic female breast cancer patients, diagnosed in the US during 1973-2009. Materials and Methods: A stratified random sample of White Hispanic female patient survival data was selected from the Surveillance Epidemiology and End Results (SEER) database to derive statistical probability models. Four were considered to identify the best-fit model. We used three standard model-building criteria, which included Akaike Information Criteria (AIC), Bayesian Information Criteria (BIC), and Deviance Information Criteria (DIC) to measure the goodness of fit. Furthermore, the Bayesian method was used to derive future survival inferences for survival times. Results: The highest number of White Hispanic female breast cancer patients in this sample was from New Mexico and the lowest from Hawaii. The mean (SD) age at diagnosis (years) was 58.2 (14.2). The mean (SD) of survival time (months) for White Hispanic females was 72.7 (32.2). We found that the exponentiated Weibull model best fit the survival times compared to other widely known statistical probability models. The predictive inference for future survival times is presented using the Bayesian method. Conclusions: The findings are significant for treatment planning and health-care cost allocation. They should also contribute to further research on breast cancer survival issues.

• 로봇학회논문지
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• 제4권3호
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• pp.169-176
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• 2009

Low-cost sensors have been widely used for mobile robot navigation in recent years. However, navigation performance based on low-cost sensors is not good enough to be practically used. Among many navigation techniques, building of an accurate map is a fundamental task for service robots, and mapping with low-cost IR sensors was investigated in this research. The robot's orientation uncertainty was considered for mapping by modifying the Bayesian update formula. Then, the data association scheme was investigated to improve the quality of a built map when the robot's pose uncertainty was large. Six low-cost IR sensors mounted on the robot could not give rich data enough to align the range data by the scan matching method, so a new sample-based method was proposed for data association. The real experiments indicated that the mapping method proposed in this research was able to generate a useful map for navigation.

• 한국정보과학회논문지:컴퓨팅의 실제 및 레터
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• 제15권1호
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• pp.72-76
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• 2009

최근 유비쿼터스 컴퓨팅에 대한 관심이 높아지면서 유비쿼터스 환경에서의 서비스를 위한 인간과 컴퓨터의 상호 작용, 특히 인간의 행동을 인식하는 연구가 활발히 진행되고 있다. 기존의 영상기반 연구와는 달리 모바일 환경에 적합하도록 가속도 센서, 생리신호 센서 등 다양한 센서들을 활용하여 사용자의 행동을 인식하는 기법이 연구되고 있다. 본 논문에서는 멀티모달 센서들을 통합하고 동적 베이지안 네트워크를 계층적으로 구성하여 사용자의 행동을 인식하는 방법을 제안한다. 연산량이 비교적 적은 베이지안 네트워크로 전반적인 사용자 행동을 추론하고 획득된 각 행동의 확률순으로 동적 베이지안 네트워크를 구성한다. 동적 베이지안 네트워크는 OVR(One-Versus-Rest) 전략으로 학습되며, 확률순으로 행동이 검증되어 임계치를 넘는 경우 선택된 행동보다 낮은 확률의 행동에 대한 동적 베이지안 네트워크를 검증하지 않아 추론 연산량을 줄인다. 본 논문에서는 가속도 센서와 생리적 신호 센서를 기반으로 총 8가지의 행동을 인식하는 문제에 제안하는 방법을 적용하여 평균적으로 97.4%의 분류 정확률을 얻었다.