• International conference on construction engineering and project management
• /
• 2022.06a
• /
• pp.238-244
• /
• 2022

Civil projects are associated with many uncertainties because they involve a long duration, many resources, a large area, and many supply chains. Therefore, the price of a civil project is not simply proportional to the quantity and unit price of the item but has a variable value, including uncertainty risk. This study investigates the influence of the uncertainty factors in the pre-bid clarification document on bid price formation during the project bidding phase. To this end, civil projects from the California Department of Transportation (Caltrans) were used as research data. This study randomly selected fifty sample data from each of twelve counties from 2008-to 2020: six hundred. The authors observed that each project sample had 0 to n query cases due to uncertainty. Then, this study examined the project uncertainty cases and categorized them into the following four uncertainty factors: 'conflict' (UF1), 'impossibility' (UF2), 'lack' (UF3), and 'missing' (UF4). Under the extracting process, the cases are classified into four uncertainty factors. With the project not containing any uncertainty factors as a control group, the project containing these uncertainty factors was designated as an experimental group. After comparing the bidder's price, the experimental group's bid price was higher than the control group's. This result suggests that uncertainty factors in bid documents induce bidders to set a high bid price as a defense against uncertainty.

• Nuclear Engineering and Technology
• /
• v.54 no.11
• /
• pp.4272-4279
• /
• 2022

In this study, a cross section stochastic sampling (S.S.) capability is implemented into both the McCARD continuous energy Monte Carlo code and MIG multiple-correlated data sampling code. The ENDF/B-VII.1 covariance data based 30 group cross section sets and the SCALE6 covariance data based 44 group cross section sets are sampled by the MIG code. Through various uncertainty quantification (UQ) benchmark calculations, the McCARD/MIG results are verified to be consistent with the McCARD stand-alone sensitivity/uncertainty (S/U) results and the XSUSA S.S. results. UQ analyses for Three Mile Island Unit 1, Peach Bottom Unit 2, and Kozloduy-6 fuel pin problems are conducted to provide the uncertainties of k_eff and microscopic and macroscopic cross sections by the McCARD/MIG code system. Moreover, the SNU S/U formulations for uncertainty propagation in a MC depletion analysis are validated through a comparison with the McCARD/MIG S.S. results for the UAM Exercise I-1b burnup benchmark. It is therefore concluded that the SNU formulation based on the S/U method has the capability to accurately estimate the uncertainty propagation in a MC depletion analysis.

• Proceedings of the Korean Vacuum Society Conference
• /
• 2010.02a
• /
• pp.413-413
• /
• 2010

In semiconductor manufacturing field, all equipments have various sensors to diagnosis the situations of processes. For increasing the accuracy of diagnosis, hundreds of sensors are emplyed. As sensors provide millions of data, the process diagnosis from them are unrealistic. Besides, in some cases, the results from some data which have same conditions are different. We want to find some information, such as data and knowledge, from the data. Nowadays, fault detection and classification (FDC) has been concerned to increasing the yield. Certain faults and no-faults can be classified by various FDC tools. The uncertainty in semiconductor manufacturing, no-faulty in faulty and faulty in no-faulty, has been caused the productivity to decreased. From the uncertainty, the rough set theory is a viable approach for extraction of meaningful knowledge and making predictions. Reduction of data sets, finding hidden data patterns, and generation of decision rules contrasts other approaches such as regression analysis and neural networks. In this research, a RGB sensor was used for diagnosis plasma instead of optical emission spectroscopy (OES). RGB data has just three variables (red, green and blue), while OES data has thousands of variables. RGB data, however, is difficult to analyze by human's eyes. Same outputs in a variable show different outcomes. In other words, RGB data includes the uncertainty. In this research, by rough set theory, decision rules were generated. In decision rules, we could find the hidden data patterns from the uncertainty. RGB sensor can diagnosis the change of plasma condition as over 90% accuracy by the rough set theory. Although we only present a preliminary research result, in this paper, we will continuously develop uncertainty problem solving data mining algorithm for the application of semiconductor process diagnosis.

• International Journal of Naval Architecture and Ocean Engineering
• /
• v.13 no.1
• /
• pp.292-305
• /
• 2021

The present study concerns uncertainty assessment of powering prediction from towing tank model tests, suggested by the International Towing Tank Conference (ITTC). The systematic uncertainty of towing tank tests was estimated by allowance of test setup and measurement accuracy of ITTC. The random uncertainty was varied from 0 to 8% of the measurement. Randomly generated inputs of test conditions and measurement data sets under systematic and random uncertainty are used to statistically analyze resistance and propulsive performance parameters at the full scale. The error propagation through an extrapolation procedure is investigated in terms of the sensitivity and coefficient of determination. By the uncertainty assessment, it is found that the uncertainty of resultant powering prediction was smaller than the test uncertainty.

• Proceedings of the Korean Operations and Management Science Society Conference
• /
• 1990.04a
• /
• pp.272-288
• /
• 1990

The uncertainty quantification process in probabilistic Risk Assessment usually involves a specification of the uncertainty in the input data and the propagation of this uncertainty to the final risk results. The distributional sensitivity analysis is to study the impact of the various assumptions made during the quantification of input parameter uncertainties on the final output uncertainty. The uncertainty importance of input parameters, in this case, should reflect the degree of changes in the whole output distribution and not just in a point estimate value. A measure of the uncertainty importance is proposed in the present paper. The measure is called the distributional sensitivity measure(DSM) and explicitly derived from the definition of the Kullback's discrimination information. The DSM is applied to three typical discrimination information. The DSM is applied to three typical cases of input distributional changes: 1) Uncertainty is completely eliminated, 2) Uncertainty range is increased by a factor of 10, and 3) Type of distribution is changed. For all three cases of application, the DSM-based importance ranking agrees very well with the observed changes of output distribution while other statistical parameters are shown to be insensitive.

• Structural Engineering and Mechanics
• /
• v.29 no.5
• /
• pp.469-488
• /
• 2008

Finite element methods have often been used for structural analyses of various mechanical problems. When finite element analyses are utilized to resolve mechanical systems, numerical uncertainties in the initial data such as structural parameters and loading conditions may result in uncertainties in the structural responses. Therefore the initial data have to be as accurate as possible in order to obtain reliable structural analysis results. The typical finite element method may not properly represent discrete systems when using uncertain data, since all input data of material properties and applied loads are defined by nominal values. An interval finite element analysis, which uses the interval arithmetic as introduced by Moore (1966) is proposed as a non-stochastic method in this study and serves a new numerical tool for evaluating the uncertainties of the initial data in structural analyses. According to this method, the element stiffness matrix includes interval terms of the lower and upper bounds of the structural parameters, and interval change functions are devised. Numerical uncertainties in the initial data are described as a tolerance error and tree graphs of uncertain data are constructed by numerical uncertainty combinations of each parameter. The structural responses calculated by all uncertainty cases can be easily estimated so that structural safety can be included in the design. Numerical applications of truss and frame structures demonstrate the efficiency of the present method with respect to numerical analyses of structural uncertainties.

• Journal of Korean Society of Transportation
• /
• v.28 no.3
• /
• pp.119-130
• /
• 2010

Reconstruction analysis of traffic accident is done by analyzing diverse data such as the road, accident traces and damage on the automobile. Most data can be a variable in the process of analysis, and measurement error of the data occurs from the investigator, tool and the given environment. Therefore, accident analysis always has some risks of measurement uncertainty. This research quantify the uncertainty in traffic accident analysis by conducting repetitive measurement experiments for variables with high probability of uncertainly such as length (i.e. geometric structure of the road, tire marks) and coefficient of friction. This paper also suggests an analysis result for the uncertainly of photographic observation of automobile crush measurement. These statistical distributions can help determine appropriate ranges for the input data in order to estimate the accident reconstruction uncertainty.

• Proceedings of the KSR Conference
• /
• 2007.11a
• /
• pp.1158-1164
• /
• 2007

Recently, the demand on the practical application of life-cycle cost effectiveness for design and rehabilitation of civil infrastructure is rapidly growing unprecedentedly in civil engineering practice. Accordingly, it is expected that the life-cycle cost in the 21st century will become a new paradigm for all engineering decision problems in practice. However, in spite of impressive progress in the researches on the LCC, so far, most researches in Koreahave only focused on roadway bridges, which are not applicable to railway bridges. Thus, this paper presents the formulation models and methods for uncertainty-based LCCA for railroad bridges consideringboth objective statistical data available in the agency database of railroad bridges management and subjective data obtained form interviews with experts of the railway agency, which are used to anew uncertainty-based expected maintenance/repair costs including lifetime indirect costs. For reliable assessment of the life-cycle maintenance/repair costs, statistical analysis considering maintenance history data and survey data including the subjective judgments of railway experts on maintenance/management of railroad bridges, are performed to categorize critical maintenance items and associated expected costs and uncertainty-based deterioration models are developed. Finally, the formulation for simulation-based LCC analysis of railway bridges with uncertainty-based deterioration models are applied to the design-decision problem, which is to select an optimal bridge type having minimum Life-Cycle cost among various railway bridges types such as steel plate girder bridge, and prestressed concrete girder bridge in the basic design phase.

• Environmental Analysis Health and Toxicology
• /
• v.30
• /
• pp.12.1-12.7
• /
• 2015

Objectives In South Korea, health insurance data are used as material for the health insurance of national whole subject. In general, health insurance data could be useful for estimating prevalence or incidence rate that is representative of the actual value in a population. The purpose of this study was to apply the concept of episode of care (EoC) in the utilization of health insurance data in the field of environmental epidemiology and to propose an improved methodology through an uncertainty assessment of disease course and outcome. Methods In this study, we introduced the concept of EoC as a methodology to utilize health insurance data in the field of environmental epidemiology. The characterization analysis of the course and outcome of applying the EoC concept to health insurance data was performed through an uncertainty assessment. Results The EoC concept in this study was applied to heat stroke (International Classification of Disease, 10th revision, code T67). In the comparison of results between before and after applying the EoC concept, we observed a reduction in the deviation of daily claims after applying the EoC concept. After that, we categorized context, model, and input uncertainty and characterized these uncertainties in three dimensions by using uncertainty typology. Conclusions This study is the first to show the process of constructing episode data for environmental epidemiological studies by using health insurance data. Our results will help in obtaining representative results for the processing of health insurance data in environmental epidemiological research. Furthermore, these results could be used in the processing of health insurance data in the future.

• Journal of Korean Academy of Nursing
• /
• v.31 no.2
• /
• pp.244-256
• /
• 2001

The purpose of this study was to investigate the effects of coping mechanisms on uncertainty and depression. The subjects were 71 cancer patients selected from Junbook National University Hospital, and the data collection period was from June 21 to October 19 of 2000. Uncertainty was measured by using Mishel's Uncertainty Scale, problem- focused coping, and emotional-focused coping. The data was collected by a questionnaire developed by Lee (1984), and then depression measured by using Beck's depression scale. program by Pearson Correlation Coefficients, and Path analysis. The results were as follows : 1. The mean uncertainty score was 59.17, the mean problem-focused coping score was 48.78, the mean emotional-focused coping score was 42.52. 2. The mean depression score was 15.77. 3. Uncertainty in illness was significantly related to depression (p=0.003) and emotional-focused coping (p=0.028), but uncertainty was not associated with coping mechanisms. 4. When analyzed multiple regression between uncertainty, problem-focused coping, emotional- focused coping, and depression, more specifically emotional-focused coping showed a stronger association with depression than problem-focused coping. 5. Depression was highly correlated with economic status (p=0.015), educational background (p=0.005), duration of disease (p=0.045). 6. Problem-focused coping and emotional-focused coping appeared to function as moderators instead mediators on the relation between uncertainty and depression. In addition, as a whole, uncertainty showed a significant moderating effect on depression, while problem-focused coping did on depression. Finally, limitation of present findings were discussed and implications for future studies are suggested.