• Journal of the Ergonomics Society of Korea
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• v.29 no.4
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• pp.435-444
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• 2010

To identify and evaluate the risk factors in WMSD work, a number of ergonomic workload evaluation methods have been developed. In the legal examination of WMSD risk factors, simple observational workload evaluation methods are widely used instead of using costly measurement equipments such as EMG and motion analyzer. The simple workload evaluation methods can be categorized into three groups; risk factor checklist methods, posture observation methods, and manual material handling task evaluation methods. In terms of the categories, this survey summarized several representative workload evaluation methods and compared them each other. Then some industrial application cases referring each the workload evaluation methods were surveyed. Due to the characteristics of each method, the selection and application procedure of workload evaluation method should be appropriate for the corresponding work. Therefore, some guidelines for the selection and application procedure of workload evaluation method were suggested.

• Proceedings of the Safety Management and Science Conference
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• 2004.11a
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• pp.81-85
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• 2004

Companies adopted a program called Six Sigma, in order to make fundamental changes in the way the company operated to fulfill customers' expectations. Six Sigma reduces the occurrence of defects. This approach derives the overall process of selection the right projects based on their potential to improve performance metrics and selection and training the right people to get the business results. However, in the course of Six Sigma process steps, companies are in the face of problems. This study is to solve the problems using TP(Thinking Process) of TOC(Theory of Constraints). TOC is methodology for solving key problem in system which is called Constraints. Nowadays, its application is going to be wide and its concept is being implemented. In this paper, it is showed possibility of application TOC to Six Sigma.

• Journal of the Korean Statistical Society
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• v.19 no.1
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• pp.24-44
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• 1990

The purpose of this paper is to extend the idea of Tamhane and Bechhofer (1977, 1979) concerning the normal means problem to some general class of distributions. The key idea in Tamhane and Bechhofer is the derivation of the computable lower bounds on the probability of a correct selection. To derive such lower bounds, they used the specific covariance structure of a multivariate normal distribution. It is shown that such lower bounds can be obtained for a class of stochastically increasing distributions under certain conditions, which is sufficiently general so as to include the normal means problem as a special application. As an application of the general theory to the scale parameters problem, a two-stage elimination type procedure for selecting the population associated with the smallest variance from among several normal populations is proposed. The design constants are tabulated and the relative efficiencies are computed.

• Journal of Korea Society of Digital Industry and Information Management
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• v.13 no.1
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• pp.197-211
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• 2017

In recent years, it is natural that the development and the maintenance of information systems are strongly dependent on outside service providers for economic reasons, especially in public sector. There has been an unexpected growth in the number of selection activities for outsourcing related works. At this time, selection of the contractor generally considers the proposals received based on the RFP(requested for proposal) and determines the ranking by experts committee. However, it is difficult even for expert giving a specific numeric score in weighting criteria or rating alternatives. In this context, an extended fuzzy TOPSIS method is applied for selection problem of IT proposals. A numerical illustration is also provided to demonstrate the applicability of the approach. This approach is very practical to help decision makers in assessing proposals during the selection phase under uncertainties.

• Journal of information and communication convergence engineering
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• v.12 no.1
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• pp.8-13
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• 2014

We consider the sensor selection problem in large sensor networks where the goal is to find the best set of sensors that maximizes application objectives. Since sensor selection typically involves a large number of sensors, a low complexity should be maintained for practical applications. We propose a geometry-based sensor selection algorithm that utilizes only the information of sensor locations. In particular, by observing that sensors clustered together tend to have redundant information, we theorize that the redundancy is inversely proportional to the distance between sensors and seek to minimize this redundancy by searching for a set of sensors with the maximum average distance. To further reduce the computational complexity, we perform an iterative sequential search without losing optimality. We apply the proposed algorithm to an acoustic sensor network for source localization, and demonstrate using simulations that the proposed algorithm yields significant improvements in the localization performance with respect to the randomly generated sets of sensors.

• Journal of the Korea Society of Computer and Information
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• v.23 no.9
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• pp.7-13
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• 2018

Recent advancement in data gathering technique improves the capability of information collecting, thus allowing the learning process between gathered data patterns and application sub-tasks. A pattern can be associated with multiple labels, demanding multi-label learning capability, resulting in significant attention to multi-label feature selection since it can improve multi-label learning accuracy. However, existing evolutionary multi-label feature selection methods suffer from ineffective search process. In this study, we propose a evolutionary search process for the task of multi-label feature selection problem. The proposed method creates large set of offspring or new feature subsets and then retains the most promising feature subset. Experimental results demonstrate that the proposed method can identify feature subsets giving good multi-label classification accuracy much faster than conventional methods.

• The Journal of Information Systems
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• v.28 no.2
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• pp.25-52
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• 2019

Purpose Despite the recent increase in the use of selection tools using artificial intelligence (AI), far less is known about the effectiveness of them in recruitment and selection research. Design/methodology/approach This paper tests the impact of AI-based initial screening and interview on intentions to apply. We also examine the moderating role of individual difference (i.e., reliability on technology) in the relationship. Findings Using policy-capturing with undergraduate students at a large university in South Korea, this study showed that AI-based interview has a negative effect on intentions to apply, where AI-based initial screening has no effect. These results suggest that applicants may have a negative feeling of AI-based interview, but they may not AI-based initial screening. In other words, AI-based interview can reduce application rates, but AI-based screening not. Results also indicated that the relationship between AI-based initial screening and intentions to apply is moderated by the level of applicant's reliability on technology. Specifically, respondents with high levels of reliability are more likely than those with low levels of reliability to apply for firms using AI-based initial screening. However, the moderating role of reliability was not significant in the relationship between the AI interview and the applying intention. Employing uncertainty reduction theory, this study indicated that the relationship between AI-based selection tools and intentions to apply is dynamic, suggesting that organizations should carefully manage their AI-based selection techniques throughout the recruitment and selection process.

• Information Systems Review
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• v.16 no.1
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• pp.73-88
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• 2014

According to the widespread use of smartphone, various and abundant mobile applications have been used and lots of new companies have developed mobile applications. To revitalize mobile application market, a lot of research and experiments have been performed in application development and delivery industries. The purpose of this research is to figure out the usage pattern of smartphone application and purchasing criteria of mobile application. We categorized smartphone application users with respect to application selection criteria and examined the characteristics of the categorized users. As a result, the factors of purchasing criteria of the users with respect to smartphone application selection criteria and application type were categorized into 3 groups: aggressive usage group, middle usage group, and passive usage group. The result of this study indicates that the relationship between purchasing criteria and usage pattern in mobile application market was actualized by measuring the purchasing factors of user. Therefore this fact suggests that it is very important to measure the accurate purchasing factors of its user for setting up the marketing strategy of mobile application market.

• The KIPS Transactions:PartA
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• v.13A no.6 s.103
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• pp.533-540
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• 2006

This paper describes the design and implementation of a grid system META (Metacomputing Environment using Test-run of Application) which facilitates the execution of a CFD (Computational Fluid Dynamics) analysis program on distributed environment. The grid system META allows the CFD program developers can access the computing resources distributed over the network just like one computer system. The research issues involved in the grid computing include fault-tolerance, computing resource selection, and user-interface design. In this paper, we exploits an automatic resource selection scheme for executing the parallel SPMD (Single Program Multiple Data) application written in MPI (Message Passing Interface). The proposed resource selection scheme is informed from the network latency time and the elapsed time of the kernel loop attained from test-run. The network latency time highly influences the executional performance when a parallel program is distributed and executed over several systems. The elapsed time of the kernel loop can be used as an estimator of the whole execution time of the CFD Program due to a common characteristic of CFD programs. The kernel loop consumes over 90% of the whole execution time of a CFD program.

• Journal of the Korean Operations Research and Management Science Society
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• v.41 no.3
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• pp.1-21
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• 2016

The Markowitz portfolio selection model uses estimators to deduce input parameters. However, the estimation errors of input parameters negatively influence the performance of portfolios. Therefore, this model cannot be reliably applied to real-world investments. To overcome this problem, we suggest an algorithm that can exclude stocks with large estimation error from the portfolio by applying a tracking signal to the Markowitz portfolio selection model. By calculating the tracking signal of each stock, we can monitor whether unexpected departures occur on the outcomes of the forecasts on rate of returns. Thereafter, unreliable stocks are removed. By using this approach, portfolios can comprise relatively reliable stocks that have comparatively small estimation errors. To evaluate the performance of the proposed approach, a 10-year investment experiment was conducted using historical stock returns data from 6 different stock markets around the world. Performance was assessed and compared by the Markowitz portfolio selection model with additional constraints and other benchmarks such as minimum variance portfolio and the index of each stock market. Results showed that a portfolio using the proposed approach exhibited a better Sharpe ratio and rate of return than other benchmarks.