• Journal of The Korean Society of Agricultural Engineers
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• v.49 no.4
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• pp.13-22
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• 2007

The uncertainty in water quality model predictions is inevitably high due to natural stochasticity, model uncertainty, and parameter uncertainty. An integrated modeling system under uncertainty was described and demonstrated for use in watershed management and receiving-water quality prediction. A watershed model (HSPF), a receiving water quality model (WASP), and a wetland model (NPS-WET) were incorporated into an integrated modeling system (modified-BASINS) and applied to the Hwaseong Reservoir watershed. Reservoir water quality was predicted using the calibrated integrated modeling system, and the deterministic integrated modeling output was useful for estimating mean water quality given future watershed conditions and assessing the spatial distribution of pollutant loads. A Monte Carlo simulation was used to investigate the effect of various uncertainties on output prediction. Without pollution control measures in the watershed, the concentrations of total nitrogen (T-N) and total phosphorous (T-P) in the Hwaseong Reservoir, considering uncertainty, would be less than about 4.8 and 0.26 mg 4.8 and 0.26 mg $L^{-1}$, respectively, with 95% confidence. The effects of two watershed management practices, a wastewater treatment plant (WWTP) and a constructed wetland (WETLAND), were evaluated. The combined scenario (WWTP + WETLAND) was the most effective at improving reservoir water quality, bringing concentrations of T-N and T-P in the Hwaseong Reservoir to less than 3.54 and 0.15 mg ${L^{-1}$, 26.7 and 42.9% improvements, respectively, with 95% confidence. Overall, the Monte Carlo simulation in the integrated modeling system was practical for estimating uncertainty and reliable in water quality prediction. The approach described here may allow decisions to be made based on probability and level of risk, and its application is recommended.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2014.10a
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• pp.467-471
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• 2014

The natural frequencies of a mechanical system are determined by the system parameters such as masses and stiffness of the system. Since material irregularities and manufacturing tolerances always exist in most of practical engineering situations, the system parameters always have uncertainties. As the uncertainties of the parameters increase, the uncertainties of the system natural frequencies also increases. Then, the reliability of the system deteriorates. So, the uncertainty of the system natural frequencies should be analyzed accurately and considered in the design of the system. In order to analyze the uncertainty of the system natural frequencies employing most of existing uncertainty analysis methods, the probability distributions of the uncertain system parameters should be identified. In most practical situations, however, identification of the probability distributions is almost impossible because of limited time and cost. For that case, the reliability should be estimated based on finite samples of the system parameters. In this paper, sample based reliability estimation method employing extreme value theory was proposed. Using the proposed estimation method, sample based reliability design of the system natural frequencies was conducted.

• Transactions on Control, Automation and Systems Engineering
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• v.1 no.1
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• pp.32-43
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• 1999

Adaptive robust control scheme is introduced for flexible joint manipulator with nonlinearities and uncertainties. The system does not satisfy the matching condition due to insufficient actuators for each node. The control only relies on the assumption that the bound of uncertainty exists. Thus, the bounded value does not need to be known a prior. The control utilizes the update law by estimating the bound of the uncertainties. The control scheme uses the backstepping method and constructs a state transformation. Also, stability analysis is done for both transformed system and original system.

• Nuclear Engineering and Technology
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• v.53 no.3
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• pp.860-865
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• 2021

A data-based model, such as an AAKR model is widely used for monitoring the drifts of sensors in nuclear power plants. However, since a training dataset and a test dataset for a data-based model cannot be constructed with the data from all the possible states, the model uncertainty cannot be good enough to represent the uncertainty of estimations. In fact, the errors of estimation grow much bigger if the incoming data come from inexperienced states. To overcome this limitation of the model uncertainty, a new measure of uncertainty for a data-based model is developed and the predicted uncertainty is introduced. The predicted uncertainty is defined in every estimation according to the incoming data. In this paper, the AAKR model is used as a data-based model. The predicted uncertainty is similar in magnitude to the model uncertainty when the estimation is made for the incoming data from the experienced states but it goes bigger otherwise. The characteristics of the predicted model uncertainty are studied and the usefulness is demonstrated with the pressure signals measured in the flow-loop system. It is expected that the predicted uncertainty can quite reduce the false alarm by using the variable threshold instead of the fixed threshold.

• Journal of Aerospace System Engineering
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• v.12 no.3
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• pp.53-57
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• 2018

The Monte Carlo simulation (MCS) method and the Guide to the Expression of Uncertainty in Measurement (GUM) are the most widely used approaches for uncertainty estimation. In this paper, MCS and GUM were used to estimate the confidence of MOI measurement equipment developed in-house. According to the results, the GUM estimated uncertainty was slightly underestimated compared to the MCS method. This difference is due to the approximation used by GUM. MOI uncertainties estimated by both methods were less than 1% of the estimate, which shows the high measurement reliability of the developed MOI measurement system.

• Proceedings of the KIEE Conference
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• 2009.07a
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• pp.1355_1357
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• 2009

This paper describes the standard system for calibrating pulse generator of partial discharge(PD) and its uncertainty. The system is consisted of digital pulse generator, digital recorder and evaluation software. The uncertainty requirement of calibrator charge is less than $\pm$ (0.1pC + 0.03q) and that of pulse duration is less than $\pm$ 10 %. The system can generate various kind of calibration pulses such as single pulse, double pulses, oscillation pulse, long-duration pulse, random pulses and evaluate their uncertainty.

• Journal of Electrical Engineering and Technology
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• v.3 no.1
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• pp.20-28
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• 2008

This paper proposes a method for solving distribution system planning problems taking into account demand uncertainty and geographical information. The proposed method can automatically select appropriate location and size of a substation, routing of feeders, and appropriate sizes of conductors while satisfying constraints, e.g. voltage drop and thermal limit. The demand uncertainty representing load growth is modeled by fuzzy numbers. Feeder routing is determined with consideration of existing infrastructure, e.g. streets and canals. The method integrates planner's experience and process optimization to achieve an appropriate practical solution. The proposed method has been tested with an actual distribution system, from which the results indicate that it can provide satisfactory plans.

• Journal of Institute of Control, Robotics and Systems
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• v.11 no.11
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• pp.920-929
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• 2005

This paper presents controller design method for nonlinear radar gimbal system with parameter uncertainty and time delay. In order to consider nonlinearity of gimbal bearing frictional torque, we firstly represent fuzzy model for the nonlinear gimbal system, which is achieved by fuzzy combination of linear models through nonlinear fuzzy membership functions. And secondly we propose a delay dependent fuzzy $H_\infty$ controller design method for the delayed fuzzy model with parameter uncertainty and design radar gimbal controller. The designed controller stabilize gimbal system and guarantee $H_\infty$ performance. A computer simulation is given to illustrate stabilized control performances of the designed controller.

• Journal of ILASS-Korea
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• v.6 no.4
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• pp.31-38
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• 2001

The Holographic Particle Velocimetry system can be a promising optical tool for the measurements of three dimensional particle velocities. In this study, the holographic panicle velocimetry system was used to measure the sizes and velocities of droplets formed by a commercial full cone spray nozzle. Uncertainty analysis was performed to identify the sources of all relevant errors and to evaluate their magnitude. The droplet velocities ranged from 10.3 to 13.3 m/s with average uncertainty of ${\pm}1.6m/s$, which is ${\pm}14%$ of the mean droplet velocity. Compared with relatively small uncertainties of velocity components in the normal direction to the optical axis, the uncertainty of the optical axis component is ${\pm}3.6m/s$. This is due to the long depth of field of droplet images in the optical axis, which is inherent feature of holographic system using forward-scattering object wave of particles.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.10
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• pp.1287-1292
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• 1999

In large-scale systems, we frequently encounter the time-delay and the uncertainty, and these should be considered in the design of controller because these are the source of the degradation of the system performance and instability of system. In this paper, we consider the robust stability of the linear large scale systems with the uncertainties and the time-delays. The considered uncertainties are both structured uncertainty and the unstructured uncertainty. Also, the considered time-delays are time-varying having finite time derivative limits. Based on the Lyapunov theorem and the linear matrix inequality(LMI) technique, we present two sufficient conditions that guarantee the robust stability of the system. The conditions are expressed as the LMI forms which can be easily checked their feasibility by using the well-known LMI control toolbox. Finally, we show by two examples that our results are less conservative than the previous results.