• Journal of Wellbeing Management and Applied Psychology
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• v.2 no.2
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• pp.39-45
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• 2019

The purpose of this study is to solve the problem of revision or abbreviation of questionnaires based on the previous studies suggested by many existing empirical studies. In addition, this study aims to provide the theoretical basis of the research method which has been variously approached since it presents the methodology that can directly measure the research object. For this purpose, this study proposed a more elaborate analysis method using the differences in perception of individuals who are interested in cognitive research. Specifically, the perception gap(D) can be used as an independent variable, a dependent variable, and a moderating variable. And this study suggested an effective research approach using the measurement of perception difference. The difference of perception suggested that it can be used as a measure to overcome the limitations of existing researches used it as independent variables or mediating variables that measure only one factor of expectation and performance or importance and satisfaction. In addition, it is highly likely that various analyzes on the perception differences, which are the result of measuring target factors for the same person, will be quite effective in the situation where follow-up of respondents is difficult. This study is expected to overcome various limitations reported by empirical studies such as scale utilization problem and follow-up survey difficulty. In future research, it was expected that the limitation of the factor derivation process in the research approach could be complemented by web crawling and text mining of big data analysis.

• Health Policy and Management
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• v.25 no.3
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• pp.197-206
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• 2015

Background: The purpose of this study was to propose a method for developing a measure of hospital-wide all-cause risk-standardized readmissions using administrative claims data in Korea and to discuss further considerations in the refinement and implementation of the readmission measure. Methods: By adapting the methodology of the United States Center for Medicare & Medicaid Services for creating a 30-day readmission measure, we developed a 6-step approach for generating a comparable measure using Korean datasets. Using the 2010 Korean National Health Insurance (NHI) claims data as the development dataset, hierarchical regression models were fitted to calculate a hospital-wide all-cause risk-standardized readmission measure. Six regression models were fitted to calculate the readmission rates of six clinical condition groups, respectively and a single, weighted, overall readmission rate was calculated from the readmission rates of these subgroups. Lastly, the case mix differences among hospitals were risk-adjusted using patient-level comorbidity variables. The model was validated using the 2009 NHI claims data as the validation dataset. Results: The unadjusted, hospital-wide all-cause readmission rate was 13.37%, and the adjusted risk-standardized rate was 10.90%, varying by hospital type. The highest risk-standardized readmission rate was in hospitals (11.43%), followed by general hospitals (9.40%) and tertiary hospitals (7.04%). Conclusion: The newly developed, hospital-wide all-cause readmission measure can be used in quality and performance evaluations of hospitals in Korea. Needed are further methodological refinements of the readmission measures and also strategies to implement the measure as a hospital performance indicator.

• Journal of the Korean Society of Systems Engineering
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• v.16 no.1
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• pp.58-67
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• 2020

A big data processing method to predict solar power generation using systems engineering approach is developed in this work. For developing analytical method, linear model (LM), support vector machine (SVN), and artificial neural network (ANN) technique are chosen. As evaluation indices, the cross-correlation and the mean square root of prediction error (RMSEP) are used. From multi-variable comparison test, it was found that ANN methodology provides the highest correlation and the lowest RMSEP.

• Korean Management Science Review
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• v.34 no.2
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• pp.57-66
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• 2017

The purpose of this study is to analyze the multidimensional productivity of the shipbuilding performance and to explain the role of different factors, such as man-hour, dock period, number of building block, launching process rate, automatic welding percent, and drawing fault rate which are important production-related variables in most shipbuilding companies. The shipbuilding productivity is obtained using Data Envelopment Analysis (DEA) approach. Then, a Tobit model is considered to measure the influence of different factors on the measured productivity. The results reveal that this productivity measure can substitute a representative shipbuilding productivity index (CGT/man-hour) in shipbuilding industries. Also, this multidimensional productivity analysis using DEA and Tobit reveals complex relationships between production-related variables and CGT and sale.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.5 no.2
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• pp.313-329
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• 2011

To provide a seamless network to customers, Internet service providers must promptly detect and control abnormal traffic. One approach is to shorten the traffic information measurement cycle. However, performance degradation is inevitable if traffic measurement servers merely shorten the cycle and measure all traffic. This paper presents a software architecture that can measure traffic more frequently without degrading performance by estimating the level of abnormal traffic. The algorithm in the architecture estimates the values of the interface group objects in MIB by using the IP group objects thereby reducing the number of measurements and the size of measured data. We evaluated this architecture on part of Internet service provider's IP network. When the traffic was measured 5 times more than before, the CPU usage and TPS of the proposed scheme was 7% and 41% less than that of the original scheme while the false positive rate and false negative rate were 3.2% and 2.7% respectively.

• Proceedings of the Safety Management and Science Conference
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• 2008.11a
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• pp.189-199
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• 2008

It is good to use DEA method as it can measure the efficiency without depending on a specific function like cost function. The method also finds out the most efficient group among the sample groups and gives us a specific number. For example, it shows what kind of factor of inefficient group gives how much input and produces how much output. Originally DEA, which was developed by Charnes, Cooper and Rhodes, allows us not only to measure the relative efficiency of Decision Making Units(DMUs) of non-profit organizations whose success cannot be measured by a single bottom-line figure such as profit but also to integrate several variables, which have different measuring scale, into a single model. Therefore we can use physical scales and financial scales simultaneously in the same model without any transformation process. In this study, price and measurable performance indexes of vehicles are used as input and outputs respectively. The purpose of this study is to propose an effective approach for evaluating the relative efficiency of vehicles and to determine the vehicles have high performance efficiency compared to product cost.

• Journal of the Earthquake Engineering Society of Korea
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• v.11 no.4
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• pp.13-23
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• 2007

This paper focuses on providing a practical approach for decision making in Performance-Based Design (PBD). Satisfactory performance is defined by several performance objectives that place limits on direct (monetary) loss and on a tolerable probability of collapse. No specific limits are placed on conventional engineering parameters such as forces or deformations, although it is assumed that sound capacity design principles are followed in the design process. The proposed design procedure incorporates different performance objectives up front, before the structural system is created, and assists engineers in making informed decisions on the choice of an effective structural system and its stiffness (period), base shear strength, and other important global structural parameters. The tools needed to implement this design process are (1) hazard curves for a specific ground motion intensity measure, (2) mean loss curves for structural and nonstructural subsystems, (3) structural response curves that relate, for different structural systems, a ground motion intensity measure to the engineering demand parameter (e.g., interstory drift or floor acceleration) on which the subsystem loss depends, and (4) collapse fragility curves. Since the proposed procedure facilitates decision making in the conceptual design process, it is referred to as a Design Decision Support System, DDSS. Implementation of the DDSS is illustrated in an example to demonstrate its practicality.

• Journal of information and communication convergence engineering
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• v.5 no.3
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• pp.233-238
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• 2007

An approach to identify Chinese event types is proposed in this paper which combines a good feature selection policy and a Maximum Entropy (ME) model. The approach not only effectively alleviates the problem that classifier performs poorly on the small and difficult types, but improve overall performance. Experiments on the ACE2005 corpus show that performance is satisfying with the 83.5% macro - average F measure. The main characters and ideas of the approach are: (1) Optimal feature set is built for each type according to local feature selection, which fully ensures the performance of each type. (2) Positive and negative features are explicitly discriminated and combined by using one - sided metrics, which makes use of both features' advantages. (3) Wrapper methods are used to search new features and evaluate the various feature subsets to obtain the optimal feature subset.

• Journal of the Korean Society of Safety
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• v.28 no.1
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• pp.74-80
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• 2013

This study describes structural reliability analysis of actively-controlled structure for which random vibration analysis is incorporated into the first-order reliability method (FORM) framework. The existing approaches perform the reliability analysis based on the RMS response, whereas the proposed study uses the peak response for the reliability analysis. Therefore, the proposed approach provides us a meaningful performance measure of the active control system, i.e., realistic failure probability. In addition, it can deal with the uncertainties in the system parameters as well as the excitations in single-loop reliability analysis, whereas the conventional random vibration analysis requires double-loop reliability analysis; one is for the system parameters and the other is for stochastic excitations. The effectiveness of the proposed approach is demonstrated through a numerical example where the proposed approach shows fast and accurate reliability (or inversely failure probability) assessment results of the dynamical active control system against random seismic excitations in the presence of parametric uncertainties of the dynamical structural system.

• Proceedings of the KSPS conference
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• 2005.11a
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• pp.103-106
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• 2005

In this paper, we propose a language modeling approach to improve the performance of a large vocabulary continuous speech recognition system. The proposed approach is based on the active learning framework that helps to select a text corpus from a plenty amount of text data required for language modeling. The perplexity is used as a measure for the corpus selection in the active learning. From the recognition experiments on the task of continuous Korean speech, the speech recognition system employing the language model by the proposed language modeling approach reduces the word error rate by about 6.6 % with less computational complexity than that using a language model constructed with randomly selected texts.