• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 1999.05a
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• pp.75-80
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• 1999

As current manufacturing processes require high spindle speed and precise machining, increasing accuracy by reducing volumetric errors of the machine itself, particularly thermal errors, is very important. Thermal errors can be estimated by many empirical models, for example, an FEM model, a neural network model, a linear regression model, an engineering judgment model etc. This paper discusses to make a modeling of thermal errors efficiently through backward elimination and fuzzy logic strategy. The model of a thermal error using fuzzy logic strategy overcome limitation of accuracy in the linear regression model or the engineering judgment model. And this model is compared with the engineering judgment model. It is not necessary complex process such like multi-regression analysis of the engineering judgment model. A fuzzy model does not need to know the characteristics of the plant, and the parameters of the model can be mathematically calculated. Like a regression model, this model can be applied to any machine, but it delivers greater accuracy and robustness.

• Journal of the Ergonomics Society of Korea
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• v.31 no.1
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• pp.77-83
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• 2012

Objective: The aim of this paper is to introduce a method that can measure and analyze the judgment gaps between service providers and customers. Background: It is important to understand the good service that service providers and customers are thinking. If there is judgment gap between service providers and customers, it would cause an unsatisfactory service. The judgment gap should be thoroughly investigated for a good service design. Method: Lens model is a human decision making model that was proposed by Brunswick(1952). This study indicates whether the Lens model can be applied to analyze judgment gaps between service providers and customers. As a case study, a library lending service was selected. 5 librarians and 15 customers participated in the experiment that investigates their judgments on a good service. The obtained data were analyzed by a modified lens model. Results: Cue weighting policies of consumers and service providers were similar, except that consumers gave higher weight on tangibility than service providers. Service providers and consumers had a good knowledge on the service quality, but they could not well apply the knowledge to judge it. Conclusion: The lens model may be used to analyze judgment gaps between service providers and consumers in the other service areas. The decision cues that were used in this study can be changed, depending on the characteristics of the target service. Application: The method that is proposed in this study may help to investigate and analyze both consumers' and service providers' judgments on a variety of services.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.62 no.6
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• pp.881-885
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• 2013

In this paper, we present a deterioration judgment model of urban rail power equipment using driving history, the frequency and number of failures. In addition, we have developed a deterioration judgment program based on the derived failure rate. A deterioration judgment model of power equipments on metro system was designed to establish how much environmental factors, such as thermal cycling, humidity, overvoltage and partial discharge. The deterioration rate of the transformers followed the Arrhenius log life versus reciprocal Kelvin temperature (hotspot temperature) relation. The deterioration judgment program is linked to the online condition monitoring system of urban railway system. The deterioration judgment program is based on the user interface it is possible to apply immediately to the urban rail power equipment.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.24 no.10 s.181
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• pp.2589-2596
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• 2000

As current manufacturing processes require high spindle speed and precise machining, increasing accuracy by reducing volumetric errors of the machine itself, particularly thermal errors, is very important. Thermal errors can be estimated by many empirical models, for example, an FEM model, a neural network model, a linear regression model, an engineering judgment model, etc. This paper discusses to make a modeling of thermal errors efficiently through backward elimination and fuzzy logic strategy. The model of a thermal error using fuzzy logic strategy overcomes limitation of accuracy in the linear regression model or the engineering judgment model. It shows that the fuzzy model has more better performance than linear regression model, though it has less number of thermal variables than the other. The fuzzy model does not need to have complex procedure such like multi-regression and to know the characteristics of the plant, and the parameters of the model can be mathematically calculated. Also, the fuzzy model can be applied to any machine, but it delivers greater accuracy and robustness.

• Journal of the Ergonomics Society of Korea
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• v.25 no.4
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• pp.81-90
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• 2006

Despite the government promoting women's participation in the engineering field, some statistics show that it has yet to be achieved. Potential reasons for this phenomenon include lower level of applications by women, or inherent gender gap in the professional field. Therefore, this study attempted to find impact of gender on college admission from the Lens Model perspective and Signal Detection Theory. This study consisted of three phases: identifying the necessary cues used in the admission process, analyzing existing data, and conducting two experiments to identify the effect of gender on admission decisions. Although the college application consisted of many cues, only five cues, school ranking, GPA, SAT score, resident status, and gender, were used to capture the officers' judgment policies for engineering admissions. Two experiments were conducted to investigate the impact of the gender factor in college admission. The enrollment officers first were presented with the existing data without the gender and asked to make dichotomous judgments. Secondly, the officers were asked to perform the judgment task with the gender cue present. Results showed that the gender did not play an important role in the judgments as expected. However, ideographical analyses on judgment strategies revealed that there were significant differences between the admission officers. Possible training implications are discussed.

• Journal of the Ergonomics Society of Korea
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• v.25 no.3
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• pp.49-60
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• 2006

Technological encroachment provides human operators with flood of information that must be analyzed to understand the environment and make judgments that lead to strategic actions. Further, the environment is not static and therefore uncertain, changing its aspect dynamically. Complexity accompanied with its dynamics imposes substantial difficulty to human operators' task. Criticality of having situational understanding becomes more important than ever. Situationalunderstanding requires the human operators possessing tacit knowledge in order for them to make the sense out of the situation while interacting with information from many heterogeneous sources, the notion of sensemaking. Sensemaking refers to the process of developing mental framework to assemble pieces of information representing different aspects of the environment that can be used to develop one's own actionable knowledge to implement their judgments in the uncertain environment. Therefore, judgment process and performance is a key component of sensemaking process. Among many judgment and decision making models, the lens model with its extension can be utilized to partially describe the judgmental aspect of sensemaking. One of the lens model parameters, unmodeled knowledge, can be a corresponding quantitative measure for the tacit knowledge that plays an important role in sensemaking. In this paper, a comprehensive literature for sensemaking is provided to formally define the notion of sensemaking in the military domain. Also, it is proposed that there is a crucial link between the sensemaking and human judgment process and performance from the lens model perspective. Potential implications for experimental framework are also proposed.

• Journal of the Korean Society for Precision Engineering
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• v.16 no.3 s.96
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• pp.215-221
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• 1999

The object of the thermal error compensation system in machine tools is improving the accuracy of a machine tool through real time error compensation. The accuracy of the machine tool totally depends on the accuracy of thermal error model. A thermal error model can be obtained by appropriate combination of temperature variables. The proposed method for optimal variable selection in the thermal error model is based on correlation grouping and successive regression analysis. Collinearity matter is improved with the correlation grouping and the judgment function which minimizes residual mean square is used. The linear model is more robust against measurement noises than an engineering judgement model that includes the higher order terms of variables. The proposed method is more effective for the applications in real time error compensation because of the reduction in computational time, sufficient model accuracy, and the robustness.

• Earthquakes and Structures
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• v.10 no.5
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• pp.1089-1109
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• 2016

Modified two-surface model (M2SM) is one of the steel elasto-plastic hysteretic constitutive models that consider both analysis accuracy and efficiency. However, when M2SM is used for complex strain history, sometimes the results are irrational due to the limitation of stress-strain path judgment. In this paper, the defect of M2SM was re-modified by improving the judgment of stress-strain paths. The accuracy and applicability of the improved method were verified on both material and structural level. Based on this improvement, the nonlinear time-history analysis was carried out for a deck-through steel arch bridge with a 200 m-long span under the ground motions of Chi-Chi earthquake and Niigata earthquake. In the analysis, we compared the results obtained by hysteretic constitutive models of improved two-surface model (I2SM) presented in this paper, M2SM and the bilinear kinematic hardening model (BKHM). Results show that, although the analysis precision of displacement response of different steel hysteretic models differs little from each other, the stress-strain responses of the structure are affected by steel hysteretic models apparently. The difference between the stress-strain responses obtained by I2SM and M2SM cannot be neglected. In significantly damaged areas, BKHM gives smaller stress result and obviously different strain response compared with I2SM and M2SM, and tends to overestimate the effect of hysteretic energy dissipation. Moreover, at some position with severe damage, BKHM may underestimate the size of seismic damaged areas. Different steel hysteretic models also have influences on structural damage evaluation results based on deformation behavior and low cycle fatigue, and may lead to completely different judgment of failure, especially in severely damaged areas.

• International conference on construction engineering and project management
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• 2015.10a
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• pp.174-178
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• 2015

This paper presents a new method for forecasting construction project cost and time at completion or at any intermediate time horizon of the project duration. The method is designed to overcome identified limitations of current applications of earned value method in forecasting project cost and time. The proposed method usesfuzzy set theory to model uncertainties associated with project performance and it integrates the earned value technique and the contractors' judgement. The fuzzy set theory is applied as an alternative approach to deterministic and probabilistic methods. Using fuzzy set theory allows contractors to: (1) perform risk analysis for different scenarios of project performance indices, and (2) perform different scenarios expressing vagueness and imprecision of forecasted project cost and time using a set of measures and indices. Unlike the current applications of Earned Value Method(EVM), The proposed method has a numberof interesting features: (1) integrating contractors' judgement in forecasting project performance; (2) enabling contractors to evaluate the risk associated with cost overrun in much simpler method comparing with that of simulation, and (3) accounting for uncertainties involved in the forecasting project cost.

• Nuclear Engineering and Technology
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• v.46 no.5
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• pp.619-632
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• 2014

The Best Estimate Plus Uncertainty (BEPU) method has been widely used to evaluate the uncertainty of a best-estimate thermal hydraulic system code against a figure of merit. This uncertainty is typically evaluated based on the physical model's uncertainties determined by expert judgment. This paper introduces the application of data assimilation methodology to determine the uncertainty bands of the physical models, e.g., the mean value and standard deviation of the parameters, based upon the statistical approach rather than expert judgment. Data assimilation suggests a mathematical methodology for the best estimate bias and the uncertainties of the physical models which optimize the system response following the calibration of model parameters and responses. The mathematical approaches include deterministic and probabilistic methods of data assimilation to solve both linear and nonlinear problems with the a posteriori distribution of parameters derived based on Bayes' theorem. The inverse problem was solved analytically to obtain the mean value and standard deviation of the parameters assuming Gaussian distributions for the parameters and responses, and a sampling method was utilized to illustrate the non-Gaussian a posteriori distributions of parameters. SPACE is used to demonstrate the data assimilation method by determining the bias and the uncertainty bands of the physical models employing Bennett's heated tube test data and Becker's post critical heat flux experimental data. Based on the results of the data assimilation process, the major sources of the modeling uncertainties were identified for further model development.