• Proceedings of the Computational Structural Engineering Institute Conference
• /
• 2006.04a
• /
• pp.275-282
• /
• 2006

System algorithms estimated by deterministic input may occur the error between predicted and actual output. Especially, actual system can't predict the exact outputs due to uncertainty and tolernce of input parameters. A single output to a set of inputs has a limited value without the variation. Hence, we should consider various scatters caused by the load assessment, material characteristics, stress analysis and manufacturing methods in order to perform the robust design or etimate the reliability of structure. The system design with uncertainty should perform the probabilistic structural optimization with the statistical response and the reliability. This method calculated the probability distributions of the characteristics such as stress by combining stress analysis, response surface methodology and Monte Carlo simulation and got the probabilistic sensitivity. The sensitivity of structural response with respect to in constant design variables was estimated by fracture probability. Therefore, this paper proposed the probabilistic reliability design method for fracture of uncorved freight end beam and the design criteria by fracture probability.

• Journal of the Korean Society for Precision Engineering
• /
• v.25 no.12
• /
• pp.89-99
• /
• 2008

In this paper, the position tracking control problem of the servo system with nonlinear dynamic friction is issued. The nonlinear dynamic friction contains a directly immeasurable friction state variable and the uncertainty caused by incomplete parameter modeling and its variations. In order to provide the efficient solution to these control problems, we propose the composite control scheme, which consists of the robust friction state observer, the FNN approximator and the approximation error estimator with sliding mode control. In first, the sliding mode controller and the robust friction state observer is designed to estimate the unknown internal state of the LuGre friction model. Next, the FNN estimator is adopted to approximate the unknown lumped friction uncertainty. Finally, the adaptive approximation error estimator is designed to compensate the approximation error of the FNN estimator. Some simulations and experiments on the servo system assembled with ball-screw and DC servo motor are presented. Results show the remarkable performance of the proposed control scheme. The robust friction state observer can successfully identify immeasurable friction state and the FNN estimator and adaptive approximation error estimator give the robustness to the proposed control scheme against the uncertainty of the friction parameters.

• Journal of the Korea Institute of Military Science and Technology
• /
• v.23 no.4
• /
• pp.346-354
• /
• 2020

Nowadays, it is required to apply low observable technology to weapon systems in operation or under development. Radar Cross Section(RCS) is a measure of the scattered power in an given direction when a target is illuminated by an incident wave and used as a parameter to estimate the low observable performance of weapon system. RCS of a target can be calculated by various numerical methods. However, measurement is also needed to estimate RCS of a complex target because it is difficult to estimate theoretically. To acquire reliable measurement results, an analysis of measurement uncertainty is essential. In this paper, error components and uncertainties of near-field RCS measurement system which was constructed in ASTEC(Aerospace System Test & Evaluation Center) were analyzed based on the IEEE recommended practice for radar cross-section test procedures(IEEE Std. 1502-2007) which describes the uncertainty of RCS measurement and unique error components of this near-field measurement system were also identified.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.59 no.6
• /
• pp.1159-1166
• /
• 2010

To improve prediction quality of a nonlinear prediction system, the system's capability for uncertainty of nonlinear data should be satisfactory. This paper presents a TSK fuzzy prediction system that can consider and deal with the uncertainty of nonlinear data sufficiently. In the design procedures of the proposed system, HCBKA(Hierarchical Correlationship-Based K-means clustering Algorithm) was used to generate the accurate fuzzy rule base that can control output according to input efficiently, and the first-order difference method was applied to reflect various characteristics of the nonlinear data. Also, multiple prediction systems were designed to analyze the prediction tendencies of each difference data generated by the difference method. In addition, to enhance the prediction quality of the proposed system, an error compensation method was proposed and it compensated the prediction error of the systems suitably. Finally, the prediction performance of the proposed system was verified by simulating two typical time series examples.

• IE interfaces
• /
• v.25 no.4
• /
• pp.382-392
• /
• 2012

Reactor protection system to keep nuclear safety and operational economy of plants requires high reliability. Such a high reliability of the system can be achieved through the redundant design of components. However, common cause failures of components reduce the benefits of redundant design. Thus, the common cause failure analysis, to accurately calculate the reliability of the reactor protection system, is carried out using alpha-factor model. Analysis results to 24 operating months are that 1) the system reliability satisfies the reliability goal of EPRI-URD and 2) the common cause failure contributes 90% of the system unreliability. The uncertainty analysis using alpha factor parameters of 0.05 and 0.95 quantile values shows significantly large difference in the system unreliability.

• Transactions of the Korean Society of Mechanical Engineers
• /
• v.19 no.3
• /
• pp.697-704
• /
• 1995

The dynamic characteristics of a system can be critically influenced by system uncertainty, so the dynamic system must be analyzed stochastically in consideration of system uncertainty. This study presents the stochastic model of a nonlinear dynamic system with uncertain parameters under nonstationary stochastic inputs. And this stochastic system is analyzed by a new stochastic process closure method and moment equation method. The first moment equation is numerically evaluated by Runge-Kutta method and the second moment equation is numerically evaluated by stochastic process closure method, 4th cumulant neglect closure method and Runge-Kutta method. But the first and the second moment equations are coupled each other, so this equations are approximately evaluated by a iterative method. Finally the accuracy of the present method is verified by Monte Carlo simulation.

• International conference on construction engineering and project management
• /
• 2009.05a
• /
• pp.1081-1087
• /
• 2009

An automatic construction system in Korea is now at the stage of the full automation like in Japan, and an actual pilot project is going to be built in 2009. However, in developing a new construction system that has never been implemented before, there is a need to assess the performance and to consider the uncertainty of the system. The program evaluation and review technique (PERT) allows dealing with this uncertainty. Thus, this paper implements an analysis of the process of steel fabrication and makes suggestions for time-related problems arising from the analysis. The time required for steel erection by the automatic system was compared with that in the traditional method. In the result, finding out another construction process and improving robot performance were proposed to resolve the problems. The results will contribute to promoting the development of an efficient system for the new automatic construction system.

• Journal of the Korean Society for Precision Engineering
• /
• v.12 no.4
• /
• pp.127-134
• /
• 1995

The dynamic characteristics of a system can be critically influenced by system uncertainty, so the dynamic system must be analyzed stochastically in consideration of system uncertainty. This study presents a generalized stochastic model of dynamic system subjected to bot external and parametric nonstationary stochastic input. And this stochastic system is analyzed by a new stochastic process closure method and moment equation method. The first moment equation is numerically evaluated by Runge-Kutta method. But the second moment equation is founded to constitute an infinite coupled set of differential equations, so this equations are numerically evaluated by cumulant neglect closure method and Runge-Kutta method. Finally the accuracy of the present method is verified by Monte Carlo simulation.

• 제어로봇시스템학회:학술대회논문집
• /
• 2005.06a
• /
• pp.1264-1267
• /
• 2005

Fuzzy logic control has been widely applied for handling the system which has uncertainty or high robust system. Since the dynamic behaviors of the systems contain complexity and uncertainty in its parameters, several fuzzy logic controllers have been implemented to control room temperature in the field of air conditioning system. In this paper, the fuzzy logic control has been developed to control both in door temperature and humidity in the air conditioning systems. The manipulating variables are speed of compressor, heater and supply air flow rate. The microcomputer was used to interface with in system. The experimental results show the superior of multivaiable fuzzy logic control to keep room temperature and humidity in air conditioning system for the best comfortable.

• Nuclear Engineering and Technology
• /
• v.55 no.9
• /
• pp.3140-3149
• /
• 2023

In theory, the sharp dose falloff at the distal end of a proton beam allows for high conformal dose to the target. However, conformity has not been fully achieved in practice, primarily due to beam range uncertainty, which is approximately 4% and varies slightly across institutions. To address this issue, we developed a new range verification system prototype: a multi-slit prompt-gamma camera (MSPGC). This system features high prompt-gamma detection sensitivity, an advanced range estimation algorithm, and a precise camera positioning system. We evaluated the range measurement precision of the prototype for single spot beams with varying energies, proton quantities, and positions, as well as for spot-scanning proton beams in a simulated SSPT treatment using a phantom. Our results demonstrated high accuracy (<0.4 mm) in range measurement for the tested beam energies and positions. Measurement precision increased significantly with the number of protons, achieving 1% precision with 5 × 10⁸ protons. For spot-scanning proton beams, the prototype ensured more than 5 × 10⁸ protons per spot with a 7 mm or larger spot aggregation, achieving 1% range measurement precision. Based on these findings, we anticipate that the clinical application of the new prototype will reduce range uncertainty (currently approximately 4%) to 1% or less.