• Journal of the Korea Institute of Building Construction
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• v.11 no.1
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• pp.91-99
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• 2011

Bayesian network is a form of probabilistic graphical model. It incorporates human reasoning to deal with sparse data availability and to determine the probabilities of uncertain cases. In this research, bayesian network is adopted to model the problem of construction project cost. General information, time, cost, and material, the four main factors dominating the characteristic of construction costs, are incorporated into the model. This research presents verify a model that were conducted to illustrate the functionality and application of a decision support system for predicting the costs. The Markov Chain Monte Carlo (MCMC) method is applied to estimate parameter distributions. Furthermore, it is shown that not all the parameters are normally distributed. In addition, cost estimates based on the Gibbs output is performed. It can enhance the decision the decision-making process.

• Korean Journal of Head & Neck Oncology
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• v.31 no.1
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• pp.1-4
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• 2015

Fine needle aspiration cytology(FNAC) holds a main role in assessing thyroid nodules. But nonnegligible rate of thyroid cytology is reported as uncertain, indeterminate or inadequate for diagnosis. Recently, the microhistologic evaluation by core needle biopsy(CNB) under ultrasound sonographical guidance has been reported to show high accuracy for the diagnose of thyroid nodules. Aim of this review was to furnish the state of the art of this topic by summarizing previous published data about indication, diagnostic performance, and complication of CNB in thyroid lesions compared with FNAC

• 한국공간정보시스템학회:학술대회논문집
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• 2007.06a
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• pp.65-70
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• 2007

유비쿼터스 센서 네트워크 관련 기술이 급속히 발전하고 센서가 여러 분야에서 활용됨에 따라 센싱되는 데이타를 효율적으로 관리하기 위한 연구가 활발히 진행되고 있다. 일반적으로 유비쿼터스 센서 네트워크에서 센싱되는 데이타는 계통적 오차와 측정 방법의 부적합 등으로 인한 불확실성을 가지며, 또한 센싱하는 환경 및 제약으로 인해 데이터가 유사하거나 반복되는 제한성을 가진다. 그러나 기존의 연구들은 센싱되는 데이타의 이러한 특성을 고려하지 못함으로 인해 검색이 비효율적이다. 따라서, 본 논문에서는 불확실한 데이타를 고려한 기존의 인덱스를 확장하여 유비쿼터스 센서 네트워크에서 센싱되는 데이타 중 제한성을 가지는 데이타를 그룹화함으로써 효율적인 검색을 지원하는 그룹화 기법을 제시한다. 본 논문은 센싱된 데이타를 그룹화하는 기법으로써 처음에 그룹으로 설정된 영역을 이용해 그룹화하는 최초 그룹화 기법, 한 그룹 내에 최대한 많은 데이타를 그룹화하는 최적 그룹화 기법, 센싱된 데이타를 최대한 근접하게 그룹화하는 최근접 그룹화 기법을 제시한다. 마지막으로, 성능 평가를 통해 본 논문에서 제시한 그룹화 기법을 이용한 인덱스에 대한 검색 성능의 우수성을 입증한다.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2001.01a
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• pp.156-163
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• 2001

Business forecasting is vital to the success of business. There has been an increasing demand for building business forecasting software system to assist human being to do forecasting. However, the uncertain and complex nature makes is a challenging work to analyze, design and implement software solutions for business forecasting. Traditional forecasting systems in which their models are trained based on small collection of historical data could not meet such challenges at the information explosion over the Internet. This paper presents an agent oriented business forecasting approach for building intelligent business forecasting software systems with high reusability. Although agents have been applied successfully to many application domains. little work has been reported to use the emerging agent oriented technology of this paper is that it explores how agent can be used to help human to manage various business forecasting processes in the whole business forecasting life cycle.

• Journal of the Korean Operations Research and Management Science Society
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• v.13 no.2
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• pp.163-163
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• 1988

This research shows that fuzzy set theory can be useful in modeling and solving flow shop scheduling problems with uncertain processing times and illustrates a method for solving job sequencing problem which the opinions of experts disagree in each processing time. In this study, FCDS (Fuzzified Campbell-Dudek-Smith) algorithm and FNEH (Fuzzified Nawaz-Enscope-Ham) algorithm are proposed to improve the fuzzified Branch & Bound algorithm that requires long run-time and computational complexities to find the optimal sequence. These proposed algorithms are also designed to treat opinions of experts. In this paper, Fuzzy processing times are expressed as triangular fuzzy numbers and comparison method use Lee-Li method and ranking method based on the dominance property. On the basis of the proposed method, an example is presented.

• Journal of Electrical Engineering and Technology
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• v.3 no.1
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• pp.14-19
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• 2008

The paper presents an intelligent time series model to predict uncertain electricity market price in the deregulated industry environment. Since the price of electricity in a deregulated market is very volatile, it is difficult to estimate an accurate market price using historically observed data. The parameter of an intelligent time series model is obtained based on the simultaneous perturbation stochastic approximation (SPSA). The SPSA is flexible to use in high dimensional systems. Since prediction models have their modeling error, an error compensator is developed as compensation. The SPSA based intelligent model is applied to predict the electricity market price in the Pennsylvania-New Jersey-Maryland (PJM) electricity market.

• International Journal of Aeronautical and Space Sciences
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• v.4 no.2
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• pp.61-68
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• 2003

The space systems are being operated in a uncertain space environment and are desired to have autonomous capability for long periods of time without frequent telecommunications with the ground station. At the same time, requirements for new set of satellite system set of projects/systems calling for "autonomous" operations for long unattended periods of time are emerging. Since, by the nature of space systems, it is desired to perform its mission flawlessly and also it is of extreme importance to have fault-tolerant sensors and actuators for the purpose of validating science measurement data for the mission success. This studies focused on the identification/demonstration of critical technology innovations that will be applied to the Validation Control System.

• Bulletin of the Korean Mathematical Society
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• v.51 no.1
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• pp.287-301
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• 2014

In this paper, we prove a necessary optimality theorem for a nonsmooth optimization problem in the face of data uncertainty, which is called a robust optimization problem. Recently, the robust optimization problems have been intensively studied by many authors. Moreover, we give examples showing that the convexity of the uncertain sets and the concavity of the constraint functions are essential in the optimality theorem. We present an example illustrating that our main assumptions in the optimality theorem can be weakened.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.824-827
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• 1996

Most autonomous mobile robots view things only in front of them. As a result, they may collide against objects moving from the side or behind. To overcome the problem we have built an Active Omni-directional Range Sensor that can obtain omnidirectional depth data by a laser conic plane and a conic mirror. In the navigation of the mobile robot, the proposed sensor system makes a laser conic plane by rotating the laser point source at high speed and achieves two dimensional depth map, in real time, once an image capture. The experimental results show that the proposed sensor system provides the best potential for navigation of the mobile robot in uncertain environment.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2014.05a
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• pp.8-9
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• 2014

At the moment, construction industry has negative images such as faulty construction, 3D industry and corruption/bribe and they are being projected onto normal people and those engaged in the industry through various news media. Not only does this worsen the lack of skilled manpower by affecting high school students who will be responsible for the construction industry in the future but also makes the future of the industry uncertain by promoting the avoidance of industrial jobs. Therefore, this study suggested basic data for improving the images of construction industry by investigating interest and images of workers in the industry that high school students think as promising construction manpower, and extracting the present negative images through evaluation of associated images.