• Journal of Mechanical Science and Technology
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• v.18 no.4
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• pp.689-698
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• 2004

Uncertainties generated from the individual measured variables have an influence on the uncertainty of the experimental result through a data reduction equation. In this study, a performance test of a single stage axial type turbine is conducted, and total-to-total efficiencies are measured at the various off-design points In the low pressure and cold state. Based on an experimental apparatus, a data reduction equation for turbine efficiency is formulated and six measured variables are selected. Codes are written to calculate the efficiency, the uncertainty of the efficiency, and the sensitivity of the efficiency uncertainty by each of the measured quantities. The influence of each measured variable on the experimental result is figured out. Results show that the largest uncertainty magnification factor (UMF) value is obtained by the inlet total pressure among the six measured variables, and its value is always greater than one. The UMF values of the inlet total temperature, the torque, and the RPM are always one. The uncertainty percentage contribution (UPC) of the RPM shows th, lowest influence on the uncertainty of the turbine efficiency, but the UPC of the torque has the largest influence to the result among the measured variables. These results are applied to find the correct direction for meeting an uncertainty requirement of the experimental result in the planning or development Phase of experiment, and also to offer ideas for preparing a measurement system in the planning phase.

• Proceedings of the KIEE Conference
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• 2003.07c
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• pp.1782-1784
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• 2003

This paper evaluates the uncertainty to measurement of the power of measuring system used in the performance evaluation of electrical apparatus and introduces the reduction method of the uncertainty.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.14 no.6
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• pp.47-54
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• 2014

Based on the indiscernible relation of rough set, the inevitability of superposition and inconsistency of data makes the reduction of attributes very important in information system. Rough set has difficulty in the difference of attribute reduction between consistent and inconsistent information system. In this paper, we propose the new uncertainty measure and attribute reduction algorithm by Bayesian posterior probability for correlation analysis between condition and decision attributes. We compare the proposed method and the conditional information entropy to address the uncertainty of inconsistent information system. As the result, our method has more accuracy than conditional information entropy in dealing with uncertainty via mutual information of condition and decision attributes of information system.

• Structural Engineering and Mechanics
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• v.76 no.4
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• pp.529-540
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• 2020

Concurrent topology optimization of macrostructure and microstructure has attracted significant interest due to its high structural performance. However, most of the existing works are carried out under deterministic conditions, the obtained design may be vulnerable or even cause catastrophic failure when the load position exists uncertainty. Therefore, it is necessary to take load position uncertainty into consideration in structural design. This paper presents a computational method for robust concurrent topology optimization with consideration of load position uncertainty. The weighted sum of the mean and standard deviation of the structural compliance is defined as the objective function with constraints are imposed to both macro- and micro-scale structure volume fractions. The Bivariate Dimension Reduction method and Gauss-type quadrature (BDRGQ) are used to quantify and propagate load uncertainty to calculate the objective function. The effective properties of microstructure are evaluated by the numerical homogenization method. To release the computation burden, the decoupled sensitivity analysis method is proposed for microscale design variables. The bi-directional evolutionary structural optimization (BESO) method is used to obtain the black-and-white designs. Several 2D and 3D examples are presented to validate the effectiveness of the proposed robust concurrent topology optimization method.

• Journal of Institute of Control, Robotics and Systems
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• v.5 no.6
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• pp.667-675
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• 1999

In this paper, we propose a robust controller design of RTP(Rapid Thermal Processing) system using structured uncertainty approach. Using the weighted mixed sensitivity function, we solve the robust stability problem against disturbance and temperature variation, and design a $\mu$ controller using curve fitting method against structured uncertainty. Also the reduction method should be requried because of the difficulty of implementaion with the obtained high order controller. We dal with robust stability and performance of RTP system by the design of $\mu$ controller for original model and Schur balanced reduced model. Finally the simulation results are proposed to show the validity of the proposed method.

• Nuclear Engineering and Technology
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• v.33 no.2
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• pp.121-131
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• 2001

An investigation of the sensitivity depletion laws for rhodium SPNDs was performed to reduce the uncertainty of the sensitivity depletion laws used in Combustion Engineering (CE) reactors and to develop calculational tools that provide the sensitivity depletion laws to interpret the signal of the newly designed rhodium SPND into the local neutron flux. The calculational tools developed in this work are computer programs for a time-dependent neutron flux distribution in the rhodium emitter during depletion and for a time-dependent beta escape probability that a beta particle generated in the emitter escapes into the collector. These programs provide the sensitivity depletion laws and show the reduction of the uncertainty by about 1 % compared to that of the method employed by CE in interpreting the signal into the local neutron flux. A reduction in the uncertainty by 1 % in interpreting the signal into the local neutron flux reduces the uncertainty tv about 1 % in interpreting the signal into the local power and lengthens the lifetime of the rhodium SPND by about 10% or more.

• Journal of Environmental Health Sciences
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• v.41 no.6
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• pp.425-437
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• 2015

Objectives: Risk assessment is a tool for predicting and reducing uncertainty related to the effects of future activities. Probability approaches are the main elements in risk assessment, but confusion about the interpretation and use of assessment factors often undermines the message of the analyses. The aim of this study is to provide a guideline for systematic reduction plans regarding uncertainty in risk assessment. Methods: Articles and reports were collected online using the key words "uncertainty analysis" on risk assessment. Uncertainty analysis was conducted based on reports focusing on procedures for analysis methods by the World Health Organization (WHO) and U.S. Environmental Protection Agency (USEPA). In addition, case studies were performed in order to verify suggested methods qualitatively and quantitatively with exposure data, including measured data on toluene and styrene in residential spaces and multi-use facilities. Results: Based on an analysis of the data on uncertainty, three major factors including scenario, model, and parameters were identified as the main sources of uncertainty, and tiered approaches were determined. In the case study, the risk of toluene and styrene was evaluated and the most influential factors were also determined. Five reduction plans were presented: providing standard guidelines, using reliable exposure factors, possessing quality controls for analysis and scientific expertise, and introducing a peer review system. Conclusion: In this study, we established a method for reducing uncertainty by taking into account the major factors. Also, we showed a method for uncertainty analysis with tiered approaches. However, uncertainties are difficult to define because they are generated by many factors. Therefore, further studies are needed for the development of technical guidelines based on the representative scenario, model, and parameters developed in this study.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2009.06a
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• pp.245-246
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• 2009

• 제어로봇시스템학회:학술대회논문집
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• 1995.10a
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• pp.348-351
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• 1995

This note proposes a robust LQR method for systems with structured real parameter uncertainty based on Riccati equation approach. Emphasis is on the reduction of design conservatism in the sense of quadratic performance by utilizing the uncertainty structure. The class of uncertainty treated includes all the form of additive real parameter uncertainty, which has the multiple rank structure. To handle the structure of uncertainty, the scaling matrix with block diagonal structure is introduced. By changing the scaling matrix, all the possible set of uncertainty structures can be represented. Modified algebraic Riccati equation (MARE) is newly proposed to obtain a robust feedback control law, which makes the quadratic cost finite for an arbitrary scaling matrix. The remaining design freedom, that is, the scaling matrix is used for minimizing the upper bound of the quadratic cost for all possible set of uncertainties within the given bounds. A design example is shown to demonstrate the simplicity and the effectiveness of proposed method.

• Korean Journal of Adult Nursing
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• v.24 no.6
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• pp.597-606
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• 2012

Purpose: This study was to describe the uncertainty, depression, physical symptom, and family support among patients undergoing dialysis. Further, the factors that impact uncertainty were also examined. Methods: A convenience sample of 145 patients who received dialysis was selected. A descriptive correlation study was conducted. Data were collected using structured questionnaires and the collected data were analyzed using descriptive statistics and multiple regression analysis. Results: The patient who received more than five years of dialysis reported higher levels on inconsistency of uncertainty than patient with less than five years. These latter patients' reported uncertainty was positively correlated with depression, whereas, patients family support was correlated with uncertainty. The group's uncertainty with less than five years of dialysis explained about 13% of the variance. In contrast, variables of education level, family support, and monthly income were predictors of uncertainty and explained 33% of the variation. Conclusion: These results can provide for nursing intervention to facilitate reduction of uncertainty. To provide dialysis period-sensitive nursing intervention for uncertainty among dialysis patient, depression should be considered below five years. While factors such as education level, family support, and monthly income should be taken into account over five years.