• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.2
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• pp.153-159
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• 2019

Job creation is the most important issue in the labor market these days, and the quality of jobs is also very important in order to resolve the mismatches that are taking place in the labor market. Kim Young-min (2014) developed the "2012 Quality of Employment Index" with twenty indicators in seven categories, including employment opportunities, to objectively assess the local labor market. This method presents the concept of the aggregate indicator, 'Quality of Work Index', and has the advantage of being easy to produce. However, it is difficult to statistically verify the adequacy of the constitutive indicators and, based on this, make them a single aggregate index through statistical techniques. Therefore, we developed an alternative '2012 Descent Work Index' and a confidence interval using Principal Component Analysis(PCA) and Unobserved Component Model(UCM) presented by Gi-Choon Kang & Myung-jig Kim (2014) and also calculated an alternative '2017 Descent Work Index' using the first half of 2017 local area labour force survey and compared its changes by region. The results of the empirical analysis show that the rank correlation coefficient between two methods of aggregating indicators, simple weight used in Young-min Kim's research, PCA method and UCM used in this study, were found to be statistically significant under 5% significance level. This implies that all methods are found to be useful. However, the PCA and UCM which determine scientific and objective weights based on data are preferred to Young-min Kim's approach. Since it provides us not only the level of aggregate indicator but also its confidence intervals, it is possible to compare ranking with the consideration of statistical significance. Therefore, it is expected that the method of constructing an aggregating indicator using UCM will be widely used in many areas in the future.

• Structural Engineering and Mechanics
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• v.35 no.2
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• pp.127-140
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• 2010

This paper focuses on a number of criteria that enable controlling the influence of geometric simplification on the quality of finite element (FE) computations. To perform the mechanical simulation of a component, the corresponding geometric model typically needs to be simplified in accordance with hypotheses adopted regarding the component's mechanical behaviour. The method presented herein serves to compute an a posteriori indicator for the purpose of estimating the significance of each feature removal. This method can be used as part of an adaptive process of geometric simplification. If a shape detail removed during the shape simplification process proves to be influential on mechanical behaviour, the particular detail can then be reinserted into the simplified model, thus making it possible to readapt the initial simulation model. The fields of application for such a method are: static problems involving linear elastic behaviour, and linear thermal problems with stationary conduction.

• Smart Structures and Systems
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• v.6 no.9
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• pp.991-1005
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• 2010

The key to detecting damage to civil engineering structures is to find an effective damage indicator. The damage indicator should promptly reveal the location of the damage and accurately identify the state of the structure. We propose to use the distance measures of low-order AR models as a novel damage indicator. The AR model has been applied to parameterize dynamical responses, typically the acceleration response. The premise of this approach is that the distance between the models, fitting the dynamical responses from damaged and undamaged structures, may be correlated with the information about the damage, including its location and severity. Distance measures have been widely used in speech recognition. However, they have rarely been applied to civil engineering structures. This research attempts to improve on the distance measures that have been studied so far. The effect of varying the data length, number of parameters, and other factors was carefully studied.

• Structural Engineering and Mechanics
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• v.91 no.4
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• pp.369-382
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• 2024

The earthquake loss assessment framework of ductile reinforced concrete (or RC) frame using component-performance -based methodology was studied in this paper. The elasto-plastic rotation angle was used as the damage indicator of structural component, and the damage-to-loss model was proposed on the basis of the deformation indicator of structural component. Dynamic instability during incremental dynamic analysis was taken as collapse criterion, and column failure was taken as criterion that structure has to be demolished. Expected earthquake losses of low-rise, mid-rise and high-rise RC frames were discussed. The expected earthquake loss encompassed collapse loss, demolition loss and repair loss. Furthermore, component groups of RC frame were divided into structural components, nonstructural components and rugged components. The results indicate that ductile RC frame is more likely to be demolished than collapse, especially in low-rise and mid-rise RC frames. Furthermore, the less collapse margin ratio the structure has, the more demolition probability the structure will suffer under rare earthquake. The demolition share of total earthquake loss might be more prominent than repair share and collapse share in ductile RC frame.

• International Journal of Computer Science & Network Security
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• v.22 no.10
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• pp.91-96
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• 2022

Article materials reflect the results of scientific research and generalization of experience concerning quality measurement of student's educational activities in the context of innovative development of the educational process, which is ensured by introducing educational innovations. The main point of monitoring of higher education students' activities and also phenomenon of education quality, particularly its results, are determined in the research. Guided by the scientific theory and personal experience of scientific and pedagogical activities, the attempt to single out the key components, important indicators and to introduce component indicator model of quality of higher education students' activities on the qualimetry base has been performed. Methodical solutions concerning the application of the developed model to determine the dynamics of pedagogical students' educational achievements by particular educational components in the process of innovative development of educational process are proposed. The advanced studies that relate to the development of methods for monitoring the quality of pedagogical higher education students' activities on the basis of systemic, competence and qualimetry approaches taking into account the levels of education and chosen specialties have been decided.

• Journal of Environmental Health Sciences
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• v.42 no.5
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• pp.302-313
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• 2016

Objectives: This study was conducted to assess environmental health status on a local scale using environmental health-related indicators. It demonstrated the possibility of using a structural equation model, a methodological approach to provide synthesized information. Methods: Eighteen indicators were selected from official statistical data published by local governments. Each environmental health-related indicator was classified according to the PSR (pressure-state-response) model. Aggregation methods were performed using principal component analysis and fuzzy sets. Results: The five principal components were classified through principal component analysis (PCA) and obtained eigenvalues >1.0 from the initial 18 indicators. The aggregated index was obtained by condensing the original information into two broad and simple categories through fuzzy sets. Conclusion: This could be useful in that the aggregation procedure may provide a basis for establishing environmental health policies and a decision-making process. However, the availability and quality of indicators, assessment of aggregation method bias, choice of weighted scores for indicators, and other factors should be examined in future studies.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.1
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• pp.220-227
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• 2014

This paper introduces and applies the World Bank's methodology for constructing composite index or aggregating indicators. After recalculating the world competitiveness index of IMD using Unobserved Component Model(UCM) we compare it with the existing index and try to find some implications. We also try to construct the composite index for measuring the performance of local finance. We employ the Principal Component Analysis(PCA) for validating the appropriateness of selected indicators used in making the composite index. We found that the UCM and PCA are very useful and will be used widely in various evaluations such as regional development, local finance, local competitiveness and public enterprise, etc.

• Journal of Distribution Science
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• v.14 no.3
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• pp.63-71
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• 2016

Purpose - A variety of indicators are used for the diagnosis of economic situation. However, most indicators explain the past economic situation because of the time difference between the measurement and announcement. This study aims to argue for the resurrection of an idea: electricity demand can be used as an indicator of economic activity. In addition, this study made an endeavor to develop a new Real Business Index(RBI) which could quickly represent the real economic condition based on the sales statistics of industrial and public electricity. Research design, data, and methodology - In this study monthly sales of industrial and public electricity from 2000 to 2015 was investigated to analyze the relationship between the economic condition and the amount of electricity consumption and to develop a new Real Business Index. To formulate the Index, this study followed next three steps. First, we decided the explanatory variables, period, and collected data. Second, after calculating the monthly changes for each variable, standardization and estimating the weighted value were conducted. Third, the computation of RBI finalized the development of empirical model. The principal component analysis was used to evaluate the weighted contribution ratio among 3 sectors and 17 data. Hodrick-Prescott filter analysis was used to verify the robustness of out model. Results - The empirical results are as follows. First, compatibility of the predictability between the new RBI and the existing monthly cycle of coincident composite index was extremely high. Second, two indexes had a high correlation of 0.7156. In addition, Hodrick-Prescott filter analysis demonstrated that two indexed also had accompany relationship. Third, when the changes of two indexes were compared, they were found that the times when the highest and the lowest point happened were similar, which suggested that it is possible to use the new RBI index as a complementing indicator in a sense that the RBI can explain the economic condition almost in real time. Conclusion - A new economic index which can explain the economic condition needs to be developed well and rapidly in a sense that it is useful to determine accurately the current economic condition to establish economic policy and corporate strategy. The salse of electricity has a close relationship with economic conditions because electricity is utilized as a main resource of industrial production. Furthermore, the result of the sales of electricity can be gathered almost in real time. This study applied the econometrics model to the statistics of the sales of industrial and public electricity. In conclusion, the new RBI index was highly related with the existing monthly economic indexes. In addition, the comparison between the RBI index and other indexes demonstrated that the direction of the economic change and the times when the highest and lowest points had happened were almost the same. Therefore, this RBI index can become the supplementary indicator of the official indicators published by Korean Bank or the statistics Korea.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.11 no.2
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• pp.157-165
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• 2016

To provide the baseline for pricing, this paper proposes pricing-by-rating (PBR) as pricing model at micro-foundations level that can work as the baseline for all pricing models as well as an assessment criterion of business model in all circumstances. It sets up firstly WTP (willingness to pay/purchase) model from explicit needs and develops PBR based on the ordinal scale of the difference between the WTP and the WTS (willingness to supply/sell) by comparing individually the corresponding element/component of a firm's actual marketing mix 4P with that of the best SPEC (solution, price indicator by WTP, encouragement, channel) as an ideal 4P a customer expects and also by comparing the interaction between the 4P and the best SPEC as a whole collectively. And through illustrations it shows its applicability to evaluating business model in practice and finally asserts that PBR works as the baseline for pricing policy and as a criterion of business model evaluation in any circumstances.

• The Transactions of the Korean Institute of Power Electronics
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• v.25 no.5
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• pp.376-384
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• 2020

An energy storage system is composed of lithium-ion batteries in modern applications. Batteries are regarded as storage devices for renewable and residual energy. The failure of batteries can cause the performance reduction and explosion of battery systems. High maintenance cost is essential when dealing with the problem of battery safety. Therefore an accurate health diagnosis is required to ensure the high reliability of battery systems. A battery pack is a combination of single cells in series and parallel connections. A battery pack has to consider various factors to assess battery health. Battery health involves conventional factors and additional factors, such as cell-to-cell imbalance. For large applications, state-of-health (SOH) can be inaccurate because of the lack of factors that indicate the state of the battery pack. In this study, six characterization factors are proposed for improving the SOH estimation of battery packs. The six proposed characterization factors can be regarded as health indicators (HIs). The six HIs are applied to the principal component analysis (PCA) algorithm. To reflect information regarding capacity, voltage, and temperature, the PCA algorithm extracts new degradation factors by using the six HIs. The new degradation factors are applied to a multiple regression model. Results show the advancement and improvement of SOH estimation.