• 한국정밀공학회지
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• 제18권11호
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• pp.107-115
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• 2001

Most of rotating machineries supported by contact bearing accompany lowering efficiency, vibration and wear. Moreover, because of vibration, which is occurred in rotating shaft, they have the limits of driving speed and precision. The rotor system has parametric variations or external disturbances such as mass unbalance variations in long operation. Therefore, it is necessary to research about magnetic bearing, which is able to support the shaft without mechanical contact and to control rotor vibration without being affected by external disturbances or parametric changes. Magnetic bearing system in the paper is composed of position sensor, digital controller, actuating amplifier and electromagnet. This paper applied the robust control method using quantitative feedback theory (QFT) to control the magnetic bearing. It also proposed design skill of optimal controller, in case the system has structured uncertainty, unstructured uncertainty and disturbance. Reduction of vibration is verified at critical rotating speed even external disturbance exists. Unbalance response, a serious problem in rotating machinery, is improved by magnetic bearing using QFT algorithm.

• 한국건설관리학회:학술대회논문집
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• 한국건설관리학회 2001년도 학술대회지
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• pp.257-260
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• 2001

현재 많은 연구가 진행중인 버퍼(Buffer)는 공정상의 충격완화를 위한 수단으로 쓰이고 있다. 충격은 불확실성(uncertainty)에 기인하여 발생되는 변이(variation)에 의해 발생되는데, 기존의 버퍼는 변이를 그대로 받아들이고, 다만 충격을 줄이기 위한 수단으로 사용되었다. 본 연구에서는 제한된 자원과 정보에서 나타나는 여러 변이들을 파악하고 해결하는 수단으로써, 버퍼의 세분화를 제시한다. 스크리닝 버퍼(screening buffer), 당김버퍼(pulling buffer), 쉴딩버퍼(shielding buffer), 작업버퍼(working buffer)로 세분화된 과정을 통하여, 불필요한 작업의 절감과 프로세스의 간결화, 과다한 정보의 차단으로 인한 의사결정과정의 신속함, 작업의 신뢰성(reliability)을 확보하고자 한다.

• 한국전산구조공학회:학술대회논문집
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• 한국전산구조공학회 2008년도 정기 학술대회
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• pp.422-427
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• 2008

This study is to illustrate the usefulness of Kriging Dimension Reduction Method(KDRM), which is to construct probability distribution of response function in the presence of the physical uncertainty of input variables. DRM has recently received increased attention due to its sensitivity-free nature and efficiency that considerable accuracy is obtained with only a few number of analyses. However, the DRM has a number of drawbacks such as instability and inaccuracy for functions with increased nonlinearity. As a remedy, Kriging interpolation technique is incorporated which is known as more accurate for nonlinear functions. The KDRM is applied and compared with MCS methods in a compression coil spring design problem. The effectiveness and accuracy of this method is verified.

• 대한기계학회논문집A
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• 제33권9호
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• pp.870-877
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• 2009

This study provides how the Dimension Reduction (DR) method as an efficient technique for reliability analysis can acquire its increased efficiency when it is applied to highly nonlinear problems. In the highly nonlinear engineering systems, 4N+1 (N: number of random variables) sampling is generally recognized to be appropriate. However, there exists uncertainty concerning the standard for judgment of non-linearity of the system as well as possibility of diverse degrees of non-linearity according to each of the random variables. In this regard, this study judged the linearity individually on each random variable after 2N+1 sampling. If high non-linearity appeared, 2 additional sampling was administered on each random variable to apply the DR method. The applications of the proposed sampling to the examples produced the constant results with increased efficiency.

• Environmental Analysis Health and Toxicology
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• 제18권1호
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• pp.1-13
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• 2003

To estimate the annual WTP for risk reduction of environmental problems such as outdoor and indoor air pollution, and drinking water contamination, a questionnaire survey was conducted by dichotomous contingent valuation method in Seoul. Several covariate models based on Turnbull, Weibull and Spike models were developed and applied to WTP estimation with uncertainty analysis. WTP estimates for risk reduction of air pollution were 13,000 won, 12,000 won, and 10,000 won per month in low-bounded Turnbull, Weibull and Spike models, respectively. The estimates for indoor air pollution were 17,000 won,20,000 won and 21,000 won and these for drinking water contamination were 10,000 won, 13,000 won and 14,000 won in each model, respectively. Goodness of fit for Weibull model was better than those for other models. WTP estimates for indoor air pollution were higher than those for other pollution problems.

• 한국전산구조공학회:학술대회논문집
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• 한국전산구조공학회 2007년도 정기 학술대회 논문집
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• pp.135-140
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• 2007

In this paper, an Improved Dimension Reduction(IDR) method is proposed for uncertainty quantification that employes Kriging interpolation technic. It has been acknowledged that the DR method is accurate and efficient for assessing statistical moments and reliability due to the sensitivity free feature. However, the DR method has a number of drawbacks such as instability and inaccuracy for problems with increased nonlineality. In this paper, improved DR is implanted by three steps. First, the Kriging interpolation method is used to accurately approximate the responses. Second, 2N+1 and 4N+1 ADOEs are proposed to maintain high accuracy of the method for UQ analysis. Third, numerical integration scheme is used with accurate but free response values at any set of integration points of the surrogated model.

• Nuclear Engineering and Technology
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• 제16권2호
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• pp.97-105
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• 1984

고장수목 정점사상에 대한 이용불능도의 불화실성을 분석하기 위한 방법 및 전산코드를 개발하였으며 그 유용성을 검증하였다. 이 방법은 몬테카를로 방법과 모멘트 방법을 고장수목 축소 기법과 함께 조합하여 개발하였고 WASH-1400에 있는 고장수목과 신뢰도 자료를 이용하여 본 연구에서 개발된 코드의 효율성을 검증하였다. 몬테카를로 방법과의 비교결과 이 방법을 이응하면 계산시간을 상당히 줄일 수 있으며 충분히 정확한 결과를 얻을 수 있음을 입증하였다.

• 한국생산제조학회지
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• 제6권1호
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• pp.34-44
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• 1997

This study presents the recursive constraint bounding(RCB) method to reduce the cycle time in internal cylindrical plunge grinding process. This method can cope with process noise as well as modeling bias. The main features of RCB method are its utilization of measurements at the end of each cycle and its use of monotonicity analysis for determining the extremes of bias and noise. This method is investigated in simulation and evaluated by experiment in internal cylindrical plunge grinding operation. The results from simulation and experiment show that it is effective in reducing cycle time in spite of modeling uncertainty in the forms of process noise and modeling bias.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2009년도 학술발표회 초록집
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• pp.1647-1651
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• 2009

The uncertainty or imprecision associated with vague parameters and weighting sets, reduces the ability to decide what alternative is better for a particular location. To efficiently reduce the effect of imprecision frequently arising in available information, fuzzy theory has been used to improve consideration of imprecision in a Multi-Criteria Decision Analysis (MCDA) problem. Fuzzy logic offers a way to represent and handle imprecision present in continuous real world applications. A GIS implementing fuzzy set theory, (referred to in this paper as the "Spatial Fuzzy Approach") enables decision makers to express imprecise concepts associated with geographic data and provides decision makers the ability to have even more definition and discrimination in terms of the best alternatives for a particular spatial location. This study is focused on addressing questions pertaining to the methodology of floodplain analysis using GIS and Spatial Fuzzy MCDA to evaluate flood damage reduction alternatives. The issues will be examined in a case study of the Suyoung River Basin in Pusan, Korea.

• International Journal of Contents
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• 제10권1호
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• pp.18-22
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• 2014

Hidden nodes have a key role in the information processing of feed-forward neural networks in which inputs are processed through a series of weighted sums and nonlinear activation functions. In order to understand the role of hidden nodes, we must analyze the effect of the nonlinear activation functions on the weighted sums to hidden nodes. In this paper, we focus on the effect of nonlinear functions in a viewpoint of information theory. Under the assumption that the nonlinear activation function can be approximated piece-wise linearly, we prove that the entropy of weighted sums to hidden nodes decreases after piece-wise linear functions. Therefore, we argue that the nonlinear activation function decreases the uncertainty among hidden nodes. Furthermore, the more the hidden nodes are saturated, the more the entropy of hidden nodes decreases. Based on this result, we can say that, after successful training of feed-forward neural networks, hidden nodes tend not to be in linear regions but to be in saturated regions of activation function with the effect of uncertainty reduction.