• 제어로봇시스템학회논문지
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• 제3권1호
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• pp.1-8
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• 1997

In order to reject the steady-state tracking error, it is common to introduce integral compensators in servosystems for constant reference signals. However, if the mathematical model of the plant is exact and no disturbance input exists, the integral compensation is not necessary. From this point of view, a two-degree-of-freedom(2DOF) servosystem has been proposed, in which the integral compensation is effective only when there is a modeling error or a disturbance input. The present paper considers robust stability of this 2DOF servosystem incorporating an observer to the structured and unstructured uncertainties of the controlled plant. A robust stability condition is obtained using Riccati inequality, which is written in a linear matrix inequality (LMI) and independent of the gain of the integral compensator. This result impies that if the plant uncertainty is in the allowable set defined by the LMI condition, a high-gain integral compensation can be carried preserving robust stability to accelerate the tracking response.

• 한국수자원학회논문집
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• 제55권spc1호
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• pp.1295-1303
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• 2022

안정적인 수도 공급을 위한 상수도관망의 역할이 더욱 주목받음에 따라 비정상 상황에 대한 신속한 탐지와 적절한 대처 역시 중요시되고 있다. 장치에 의존한 탐지기법 등 기존의 방법론에는 한계가 존재하므로 데이터를 이용한 모델 기반의 방법이 개발되었다. 하지만 상수도관망 내 측정 데이터는 불확실성을 가져 실제 사용량과 다르다. 따라서 본 연구에서는 기계학습 방법의 하나인 인공신경망 모델을 이용하여 상수도관망 압력값을 예측함에 있어 데이터 불확실성의 영향을 조사한다. 정규분포를 따르는 임의의 값을 고려하여 데이터에 측정치 오류를 형성하고 측정치 오류 여부 및 종류에 따라 총 9가지 데이터를 인공신경망 모델을 통해 예측해 경향성을 비교한다. 분석을 통해 데이터 불확실성이 증가할수록 모델 성능이 감소하며, 출력데이터의 측정치 오류가 모델 성능에 미치는 정도가 더 큼을 확인하였다. 특히 입력데이터와 출력데이터의 측정 오차 크기가 동일한 경우 예측 정확도는 각각 72.25%, 38.61%로 큰 차이를 보였다. 따라서 ANN 모델 예측 성능 향상을 위해서는 입력 데이터보다 출력데이터인 주절점의 측정 오류 크기를 줄이는 것이 중요하다.

• Nuclear Engineering and Technology
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• 제56권2호
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• pp.762-769
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• 2024

The TMSR-SF0 simulator is an integral effect thermal-hydraulic experimental system for the development of thorium molten salt reactor (TMSR) program in China. The simulator has two heat transport loops with liquid FLiNaK. In literature, the 95% level confidence uncertainties of the thermophysical properties of FLiNaK are recommended, and the uncertainties of density, heat capacity, thermal conductivity and viscosity are ±2%, ±10, ±10% and ±10% respectively. In order to investigate the effects of thermophysical properties uncertainties on the molten salt heat transport system, the uncertainty and sensitivity analysis of the heat transfer characteristics of the simulator system are carried out on a RELAP5 model. The uncertainties of thermophysical properties are incorporated in simulation model and the Monte Carlo sampling method is used to propagate the input uncertainties through the model. The simulation results indicate that the uncertainty propagated to core outlet temperature is about ±10 ℃ with a confidence level of 95% in a steady-state operation condition. The result should be noted in the design, operation and code validation of molten salt reactor. In addition, more experimental data is necessary for quantifying the uncertainty of thermophysical properties of molten salts.

• 한국공작기계학회논문집
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• 제15권5호
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• pp.72-78
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• 2006

The paper proposes reference model error feedback control scheme for motion control system with hard non-linear components as like saturation and dead-zone in plant input part. Additionally, the plant has the system uncertainty effected by plant model parameter deviation and disturbance. The control algorithm uses the reference model to apply additional feedback loop with the error between reference model output and actual output effected by disturbance and non-linear components. And the stability evaluation based on Popov stability and controller design method are formulated to be performed. The effectiveness of the proposed scheme is examined by simulations. The results are proven by reasonable performances following reference model responses with good disturbance rejection performance without over-tuning of controller.

• 전기전자학회논문지
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• 제22권1호
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• pp.110-120
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• 2018

본 논문에서는, 볼-빔 시스템에 입-출력 궤환 선형화 기법을 적용하여 추종 궤적 r(t)를 추종하도록 제어기를 설계하였다. 설계한 제어기로 시뮬레이션 및 실험에 적용한 결과, 실험에서 오차가 크게 발생하였다. 이러한 이유는 외란 및 입력정합조건을 만족하지 못해 발생한 것으로 판단되어 볼-빔 시스템의 기존 모델링에서 적절한 외란을 추가하여, 시뮬레이션을 통해 실험 결과와 비슷한 유효한 모델링임을 입증하였다. 그러나, 여전히 저하된 성능으로 인해 bang-bang 제어기를 추가로 적용하였다. 결과적으로, 시스템의 불확실성에 대해 강인하고 향상된 성능을 시뮬레이션 및 실험결과를 통해 검증하였다.

• 한국환경과학회지
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• 제15권4호
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• pp.319-324
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• 2006

Release rate is one of the important items for the environmental impact assessment caused by radioactive materials in case of an accidental release from the nuclear facilities. In this study, the uncertainty of the estimated release rate is evaluated using Monte Carlo method. Gaussian plume model and linear programming are used for estimating the release rate of a source material. Tracer experiment is performed at the Yeoung-Kwang nuclear site to understand the dispersion characteristics. The optimized release rate was 1.56 times rather than the released source as a result of the linear programming to minimize the sum of square errors between the observed concentrations of the experiment and the calculated ones using Gaussian plume model. In the mean time, 95% confidence interval of the estimated release rate was from 1.41 to 2.53 times compared with the released rate as a result of the Monte Carlo simulation considering input variations of the Gaussian plume model. We confirm that this kind of the uncertainty evaluation for the source rate can support decision making appropriately in case of the radiological emergencies.

• 한국안전학회지
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• 제31권1호
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• pp.25-32
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• 2016

A sensitivity analysis of FDS(Fire Dynamics Simulator) results for the input uncertainty of heat release rate (Q) which might be the most influencing parameter to fire behaviors was performed. The calculated results were compared with experimental data obtained by the OECD/NEA PRISME project. The sensitivity of FDS results with the change in Q was also compared with the empirical correlations suggested in previous literature. As a result, the change in the specified Q led to the different dependence of major quantities such as temperature and species concentrations for the over- and under-ventilated fire conditions, respectively. It was also found that the sensitivity of major quantities to uncertain value of Q showed the significant difference in results obtained using the previous empirical correlations.

• Structural Engineering and Mechanics
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• 제56권2호
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• pp.257-273
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• 2015

Numerical value of correlation between effective parameters in the strength of a structure is as important as its stochastic properties in determining the safety of the structure. In this article investigation is made about the variation of coefficient of correlation between effective parameters in corrosion initiation time of reinforcement and the time of concrete cover cracking in reinforced concrete (RC) structures. Presence of many parameters and also error in measurement of these parameters results in uncertainty in determination of corrosion initiation and the time to crack initiation. In this paper, assuming diffusion process as chloride ingress mechanism in RC structures and considering random properties of effective parameters in this model, correlation between input parameters and predicted time to corrosion is calculated using the Monte Carlo (MC) random sampling. Results show the linear correlation between corrosion initiation time and effective input parameters increases with increasing uncertainty in the input parameters. Diffusion coefficient, concrete cover, surface chloride concentration and threshold chloride concentration have the highest correlation coefficient respectively. Also the uncertainty in the concrete cover has the greatest impact on the coefficient of correlation of corrosion initiation time and the time of crack initiation due to the corrosion phenomenon.

• Geomechanics and Engineering
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• 제30권3호
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• pp.259-272
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• 2022

Rockburst is a dynamic, multivariate, and non-linear phenomenon that occurs in underground mining and civil engineering structures. Predicting rockburst is challenging since conventional models are not standardized. Hence, machine learning techniques would improve the prediction accuracies. This study describes decision based uncertainty models to predict rockburst in underground engineering structures using gradient boosting algorithms (GBM). The model input variables were uniaxial compressive strength (UCS), uniaxial tensile strength (UTS), maximum tangential stress (MTS), excavation depth (D), stress ratio (SR), and brittleness coefficient (BC). Several models were trained using different combinations of the input variables and a 3-fold cross-validation resampling procedure. The hyperparameters comprising learning rate, number of boosting iterations, tree depth, and number of minimum observations were tuned to attain the optimum models. The performance of the models was tested using classification accuracy, Cohen's kappa coefficient (k), sensitivity and specificity. The best-performing model showed a classification accuracy, k, sensitivity and specificity values of 98%, 93%, 1.00 and 0.957 respectively by optimizing model ROC metrics. The most and least influential input variables were MTS and BC, respectively. The partial dependence plots revealed the relationship between the changes in the input variables and model predictions. The findings reveal that GBM can be used to anticipate rockburst and guide decisions about support requirements before mining development.

• Nuclear Engineering and Technology
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• 제19권4호
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• pp.249-265
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• 1987

고준위 방사성폐기물 처분장에 대한 확률론적 위험도 평가를 위해 지금까지 많은 방법들이 제안되어 왔다. 이 계는 많은 불확실성을 갖는 입력 변수들을 갖고 있어서 이 입력변수들에 대해 계산된 위험도 역시 많은 불착실성을 갖는다. 본 논문에서는 이러한 점들을 조직적으로 분석하기 위하여 여러가지 불확실도 및 민감도 분석 방법들이 개발되었고 고준위 폐기물 처분장의 위험도 평가에 적용되었다. 본 논문을 통해 개발된 통계 패키지 SPUSA는 통계적 열여유도 분석, 방사선원 불확실도 분석등 등의 분야에도 사용될 수 있다.