• Earthquakes and Structures
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• v.17 no.2
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• pp.131-141
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• 2019

Structural control systems have uncertainties in their structural parameters and control devices which by using reliability analysis, uncertainty can be modeled. In this paper, reliability of controlled structures equipped with semi-active Magneto-Rheological (MR) dampers is investigated. For this purpose, at first, the effect of the structural parameters and damper parameters on the reliability of the seismic responses are evaluated. Then, the reliability of MR damper force is considered for expected levels of performance. For sensitivity analysis of the parameters exist in Bouc- Wen model for predicting the damper force, the importance vector is utilized. The improved first-order reliability method (FORM), is used to reliability analysis. As a case study, an 11-story shear building equipped with 3 MR dampers is selected and numerically obtained experimental data of a 1000 kN MR damper is assumed to study the reliability of the MR damper performance for expected levels. The results show that the standard deviation of random variables affects structural reliability as an uncertainty factor. Thus, the effect of uncertainty existed in the structural model parameters on the reliability of the structure is more than the uncertainty in the damper parameters. Also, the reliability analysis of the MR damper performance show that to achieve the highest levels of nominal capacity of the damper, the probability of failure is greatly increased. Furthermore, by using sensitivity analysis, the Bouc-Wen model parameters which have great importance in predicting damper force can be identified.

• Proceedings of the Korea Society of Information Technology Applications Conference
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• 2002.06a
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• pp.123-135
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• 2002

Being in era of the digital economy, the influence of IT that effects on our economy is getting greater. Along with this effect, IT becomes origin of predominance of competition in every category of industry. Regarding new trends in industry, how companies mutate their IT skill become important factor. Every industry has their own color and environment of IT and uncertainty is different. For a company, It is most important to plan own strategy with strategic view on industrial environment. The idea of this study is to find the difference of Skill Requirement and Job Requirement in every category of industry by find what are the most important jobs and skills of IT in every industry and to analyze the results. The second idea is to find what kinds of skill are required in every sector of job. Base on the analyzed data, we classified the uncertainty in every industry by Duncan′s "classification of environment", and extracted some pattern within the skill and job in industry that found in our study by applying OIP model. We set skills by categorized curriculum of specialized IT education center, then with IT specialist, checked and retouched the results and surveyed with IT people in every industry on skill set and job of IT. The summery of this study is as follow : 1. Importance of IT skill is differentiated in each industry. It shows that IT skills, which requested in a field are differentiated by uncertainty of environment that comes from the character of industry. 2. Importance of IT job differs by the fields of industry. It shows as IT skills are differentiated, the importance of job that apply these skills is differentiated. 3. As the character of each job that work on is diversified, the importance of skills are diversified in each field of jobs. The Result of this study can give the idea to who designs curriculum and builds educational contentsthat would fulfill the need of fields. Also this study would be meaningful that it opens the field of study of skill requirement in Korea.

• Nuclear Engineering and Technology
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• v.42 no.6
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• pp.690-703
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• 2010

Sampling based uncertainty analysis was carried out to quantify uncertainty in predictions of best estimate code RELAP5/MOD3.2 for a thermal hydraulic test (10% hot leg break LOCA) performed in the Large Scale Test Facility (LSTF) as a part of an IAEA coordinated research project. The nodalisation of the test facility was qualified for both steady state and transient level by systematically applying the procedures led by uncertainty methodology based on accuracy extrapolation (UMAE); uncertainty analysis was carried out using the Latin hypercube sampling (LHS) method to evaluate uncertainty for ten input parameters. Sixteen output parameters were selected for uncertainty evaluation and uncertainty band between $5^{th}$ and $95^{th}$ percentile of the output parameters were evaluated. It was observed that the uncertainty band for the primary pressure during two phase blowdown is larger than that of the remaining period. Similarly, a larger uncertainty band is observed relating to accumulator injection flow during reflood phase. Importance analysis was also carried out and standard rank regression coefficients were computed to quantify the effect of each individual input parameter on output parameters. It was observed that the break discharge coefficient is the most important uncertain parameter relating to the prediction of all the primary side parameters and that the steam generator (SG) relief pressure setting is the most important parameter in predicting the SG secondary pressure.

• Journal of Korean Society for Atmospheric Environment
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• v.18 no.4
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• pp.327-334
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• 2002

The work presents sensitivity and uncertainty analysis of 2-compartment model for the evaluation of indoor radon pollution in a house. Effort on the development of such model is directed towards the prediction of the generation and transfer of radon in indoor air released from groundwater. The model is used to estimate a quantitative daily human exposure through inhalation of such radon based on exposure scenarios. However, prediction from the model has uncertainty propagated from uncertainties in model parameters. In order to assess how model predictions are affected by the uncertainties of model inputs, the study performs a quantitative uncertainty analysis in conjunction with the developed model. An importance analysis is performed to rank input parameters with respect to their contribution to model prediction based on the uncertainty analysis. The results obtained from this study would be used to the evaluation of human risk by inhalation associated with the indoor pollution by radon released from groundwater.

• Journal of the Korean Solar Energy Society
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• v.30 no.6
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• pp.10-15
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• 2010

The installed capacity of wind turbines in KOREA are growing and enlarging by the central government's support program. Thus, the importance of power performance verification and its uncertainty analysis are recognizing rapidly. This paper described the power testing results of a 3MW wind turbine and analysed an uncertainty level of measurements. The measured power curves are very closely coincide with the calculated one and the annual power production under the given Rayleigh wind speed distribution are estimated with the 3.6~12.7% of uncertainty but, in the dominant wind speed region as 7~8m/s, the uncertainty are stably decreased to 6.3~5.3%.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.127-127
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• 2021

In order to estimate parameter uncertainty of hydrological models, the consideration of the likelihood functions which provide reliable parameters of model is necessary. In this study, the Bayesian Markov Chain Monte Carlo (MCMC) method with informal likelihood functions is used to analyze the uncertainty of parameters of the SURR model for estimating the hourly streamflow of Gunnam station of Imjin basin, Korea. Three events were used to calibrate and one event was used to validate the posterior distributions of parameters. Moreover, the performance of four informal likelihood functions (Nash-Sutcliffe efficiency, Normalized absolute error, Index of agreement, and Chiew-McMahon efficiency) on uncertainty of parameter is assessed. The indicators used to assess the uncertainty of the streamflow simulation were P-factor (percentage of observed streamflow included in the uncertainty interval) and R-factor (the average width of the uncertainty interval). The results showed that the sensitivities of parameters strongly depend on the likelihood functions and vary for different likelihood functions. The uncertainty bounds illustrated the slight differences from various likelihood functions. This study confirms the importance of the likelihood function selection in the application of Bayesian MCMC to the uncertainty assessment of the SURR model.

• Nuclear Engineering and Technology
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• v.54 no.12
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• pp.4534-4550
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• 2022

Integrated severe accident codes should be capable of simulating not only specific physical phenomena but also entire plant behaviors, and in a sufficiently fast time. However, significant uncertainty may exist owing to the numerous parametric models and interactions among the various phenomena. The primary objectives of this study are to present best-practice uncertainty and sensitivity analysis results regarding the evolutions of severe accidents (SAs) and fission product source terms and to determine the effects of mitigation measures on them, as expected during a short-term station blackout (STSBO) of a reference pressurized water reactor (optimized power reactor (OPR)1000). Three reference scenarios related to the STSBO accident are considered: one base and two mitigation scenarios, and the impacts of dedicated severe accident mitigation (SAM) actions on the results of interest are analyzed (such as flammable gas generation). The uncertainties are quantified based on a random set of Monte Carlo samples per case scenario. The relative importance values of the uncertain input parameters to the results of interest are quantitatively evaluated through a relevant sensitivity/importance analysis.

• Journal of the Korean Solar Energy Society
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• v.26 no.4
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• pp.63-71
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• 2006

The installed capacity of wind turbines in KOREA are growing and enlarging by the central government's support program. Thus, the importance of power performance verification and its uncertainty analysis are recognizing rapidly. This paper described the Power testing results of a 1.5MW wind turbine and analysed an uncertainty level of measurements. The measured power curves are very closely coincide with the calculated one and the annual power production under the given Rayleigh wind speed distribution are estimated with the $4.7{\sim}22.0%$ of uncertainty but, in the dominant wind speed region as $7{\sim}8m/s$, the uncertainty are stably decreased to $7{\sim}8%$.

• Journal of The Korean Astronomical Society
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• v.40 no.1
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• pp.29-36
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• 2007

The smoothed particle hydrodynamics (SPH) method is applied to construct the dispersion of fluctuations in quasi-hydrostatic configuration of an isothermal self-gravitating slab. The uncertainty of the implementation is evaluated, and a novel technique (acceleration error) is proposed to weaken this uncertainty. The two-fluid quasi-hydrostatic diffusion of small fluctuations is used to support the importance of the acceleration error. The results show that the uncertainty converges to a few percent by increasing of the SPH particle numbers. Considering the acceleration error weakens the uncertainty, and prohibits the serious dynamical consequences in slow dispersion of fluctuation in the quasi-hydrostatic evolution of the slab.

• International conference on construction engineering and project management
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• 2013.01a
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• pp.502-506
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• 2013

The cash flow forecasting is normally done by contractors in construction industry at early stages of the project for contractual decisions. The decision making in such situations involve uncertainty about future cash flows and assessment of working capital requirements gains more importance in projects constrained by cash. The traditional approach to assess the working capital requirements is deterministic in and neglects the uncertainty. This paper presents an alternate approach to assessment of working capital requirements for contractor based on fuzzy set theory by considering the uncertainty and ambiguity involved at payment periods. Statistical methods are used to deal with the uncertainty for working capital curves. Membership functions of the fuzzy sets are developed based on these statistical measures. Advantage of fuzzy peak working capital requirements is demonstrated using peak working capital requirements curves. Fuzzy peak working capital requirements curves are compared with deterministic curves and the results are analyzed. Fuzzy weighted average methodology is proposed for the assessment of peak working capital requirements.