• Asia-Pacific Journal of Business
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• v.13 no.1
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• pp.213-230
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• 2022

Purpose - The purpose of this study is to find out any potential factors for explanatory variables when calculating the virtual successful bid rate in case of no collusion. Design/methodology/approach - An empirical analysis was conducted in this study with a regression analysis that included 725 bid samples under the public construction technical type bidding. Findings - The result of the basic analysis showed that there are several factors affecting the successful bid rate. First, collusion variable; second, government variable; third, successful bidder design score variable and the number of bidder variable among bidding features; fourth, turnkey variable based on the alternative method; fifth, civil works variable and plant works variable based on building work; sixth, asset variable and the fourth-quarter performance difference variable. However, the technical proposal method variable among bidding features was found to be statistically insignificant in column(4). Research implications or Originality - The significance of this research is that new variable such as the government variable and the fourth-quarter performance difference variable were added in the regression model, which showed statistically significant research results.

• Journal of Korean Society for Quality Management
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• v.37 no.2
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• pp.46-57
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• 2009

The traditional approach to economic design of control charts is based on the assumption that a process is monitored using a performance variable. However, various types of automatic test equipments recently introduced as a part of factory automation usually measure surrogate variables instead of performance variables that are costly to measure. In this article we propose a model for economic design of a control chart which uses a surrogate variable that is highly correlated with the performance variable. The optimum values of the design parameters are determined by maximizing the total average income per cycle time. Numerical studies are performed to compare the proposed $\bar{X}$ control charts with the traditional model using the examples in Panagos et al. (1985).

• Korean Management Science Review
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• v.23 no.2
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• pp.1-16
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• 2006

With the diffusion of the Internet, firms try to electronically collaborate with their partners in order to cut costs and gain profits. This, electronic Partnership, called 'Collaborative IT' is quite popular between large purchase enterprises and small-to-medium sized sub-contractors. This study investigates such relations. This study proposes three groups of research variables-interorganizational relationship, collaborative IT activity, and firm performance. the interorganizational relationship consists of trust, commitment, and asymmetry of commitment. Collaborative IT activity is composed of information sharing and workflow integration. The ultimate dependent variable is firm performance. It is hypothesized that the relationship factors influence the level of collaborative IT activity, while the latter in turn affects the firm performance. The relationship factors nay also directly affect the dependent variable. In addition, collaborative IT motive, as a moderating variable, may influence the causal relationship. By means of survey, ore hundred and eighty-two responses were obtained. Most sample companies are small-sized, in the manufacturing sector. The analysis of data reveals that both trust and commitment positively affects the level of collaborative IT activity, while asymmetry of commitment has negative effects. The workflow integration is significantly related with firm performance. Information sharing, however, has no signific3nt effects. Furthermore, asymmetry of commitment shows reverse relationship with firm performance. Collaborative IT motive works as a moderating variable between information sharing and firm performance. Finally, workflow integration is believed to mediate between relationship factors and firm performance.

• Journal of Information Technology Applications and Management
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• v.31 no.1
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• pp.11-25
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• 2024

The purpose of this study is to investigate the improvement of prediction performance and changes in variable importance in SEM-ANN two-stage analysis. 366 cosmetics repurchase-related survey data were analyzed and the results were presented. The results of this study are summarized as follows. First, in SEM-ANN two-stage analysis, SEM and ANN models were trained with train data and predicted with test data, respectively, and the R² was showed. As a result, the prediction performance was doubled from SEM 0.3364 to ANN 0.6836. Looking at this degree of R² improvement as the effect size f² of Cohen (1988), it corresponds to a very large effect at 110%. Second, as a result of comparing changes in normalized variable importance through SEM-ANN two-stage analysis, variables with high importance in SEM were also found to have high importance in ANN, but variables with little or no importance in SEM became important in ANN. This study is meaningful in that it increased the validity of the comparison by using the same learning and evaluation method in the SEM-ANN two-stage analysis. This study is meaningful in that it compared the degree of improvement in prediction performance and the change in variable importance through SEM-ANN two-stage analysis.

• Journal of Korean Society of Water and Wastewater
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• v.23 no.3
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• pp.315-320
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• 2009

This study found that introduction of environmental systems and increased environmental investment have a major impact on K company's sustainable development. The analysis indicated that the environmental investment variable has a close relationship with production quantity, sales, and the amount of sludge treatment variables in K company. However, all other variables including the cost of disinfection chemicals, and the expenditure of sludge treatment have no relationship with the environmental investment variable. Additionally, short-term results show that the company's environmental expenditure variable is conversely related with the environmental performance variable.

• KIEE International Transactions on Power Engineering
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• v.4A no.2
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• pp.51-57
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• 2004

This paper describes the dynamic performance of a variable speed wind turbine system responding to a wide variety of wind variations. Modeling of the wind generation using power electronics interface is proposed for dynamic simulation analysis. Component models and equations are addressed and their incorporations into a transient analysis program, PSCAD/EMTDC are provided. A wind model of four components is described, which enables observing dynamic behaviors of the wind turbine resulting from wind variations. Controllable power inverter strategies are intended for capturing the maximum power under variable speed operation and maintaining reactive power generation at a pre-determined level for constant power factor control or voltage regulation control. The components and control schemes are modeled by user-defined functions. Simulation case studies provide variable speed wind generator dynamic performance for changes in wind speed

• Communications for Statistical Applications and Methods
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• v.29 no.6
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• pp.629-640
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• 2022

Variable selection is one of the most crucial tasks in supervised learning, such as regression and classification. The best subset selection is straightforward and optimal but not practically applicable unless the number of predictors is small. In this article, we propose directly solving the best subset selection via the genetic algorithm (GA), a popular stochastic optimization algorithm based on the principle of Darwinian evolution. To further improve the variable selection performance, we propose to run multiple GA to solve the best subset selection and then synthesize the results, which we call ensemble GA (EGA). The EGA significantly improves variable selection performance. In addition, the proposed method is essentially the best subset selection and hence applicable to a variety of models with different selection criteria. We compare the proposed EGA to existing variable selection methods under various models, including linear regression, Poisson regression, and Cox regression for survival data. Both simulation and real data analysis demonstrate the promising performance of the proposed method.

• 제어로봇시스템학회:학술대회논문집
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• 1986.10a
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• pp.321-325
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• 1986

A new control scheme for the state space model of the robot system using the theory of optimal multi-variable structure is presented in this paper. It is proposed to optimize multi-dimensional variable structure systems for obtaining the required stabilizing signal by minimizing a performance index with respect to the state vector in the sliding mode. It is concluded the proposed variable structure controller yields better system dynamic performance than that obtained by using the only linear optimal controller inthat responses for a step disturbance have a shorter setting time, no matter what overshoot values and rising time.

• Journal of the Korea Convergence Society
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• v.7 no.1
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• pp.197-203
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• 2016

This study is to constructs the model by the relationship between the most important ones of TPM's step activities which are derived from the result of AHP analysis and the management performance. And it is to analyze whether step activities have impact on corporate performance through the mediating variable from a convergence point. Also it is to choose four step activities - irrational field and loss improvement, preparation and system establishment for the basic conditions of 5S, initial cleaning, loss analysis of facility efficiency - as independent variables, and to analyze the effect of TPM activity level as mediating variable on management performance as dependent variable. According to the analysis, four step activities don't have direct effect on corporate performance but TPM activity level as mediating variable has a full-mediated effect on corporate performance. That is, TPM activity level has been shown to play a role as mediating variable. Therefore, higher recognition of confidence in facility, awareness of self-development and definitive management of individual work shows TPM activity can contribute to corporate performance successfully.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.11C
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• pp.907-910
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• 2010

This paper presents the design of block lengths for irregular low-density parity-check (LDPC) codes based on the maximum variable degree $d_{{\upsilon},max}$. To design a block length, the performance degradation of belief-propagation (BP) decoding performance from upper bounds on the maximum likelihood (ML) decoding performance is used as an important factor. Since for large block lengths, the performance of irregular LDPC codes is very close to the Shannon limit, we focus on moderate block lengths ($5{\times}10^2\;{\leq}\;N\;{\leq}\;4{\times}10^3$). Given degree distributions, the purpose of our paper is to find proper block lengths based on the maximum variable degree $d_{{\upsilon},max}$. We also present some simulation results which show how a block length can be optimized.