• Advances in Computational Design
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• v.2 no.4
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• pp.313-331
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• 2017

In this study, teaching-learning based optimization (TLBO) is improved by incorporating model of multiple teachers, adaptive teaching factor, self-motivated learning, and learning through tutorial. Modified TLBO (MTLBO) is applied for simultaneous topology, shape, and size optimization of space and planar trusses to study its effectiveness. All the benchmark problems are subjected to stress, displacement, and kinematic stability constraints while design variables are discrete and continuous. Analyses of unacceptable and singular topologies are prohibited by seeing element connectivity through Grubler's criterion and the positive definiteness. Performance of MTLBO is compared to TLBO and state-of-the-art algorithms available in literature, such as a genetic algorithm (GA), improved GA, force method and GA, ant colony optimization, adaptive multi-population differential evolution, a firefly algorithm, group search optimization (GSO), improved GSO, and intelligent garbage can decision-making model evolution algorithm. It is observed that MTLBO has performed better or found nearly the same optimum solutions.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2005.11a
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• pp.158-161
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• 2005

In this study, the defect diagnosis of the gas turbine engine was tried using Support Vector Machine(SVM). It is known that SVM can find the optimal solution mathematically through classifying two groups and searching for the Hyperplane of the arbitrary nonlinear boundary. The method for the decision of the gas turbine defect quantitatively was proposed using the Multi Layer SVM for classifying two groups and it was verified that SVM was shown quicker and more reliable diagnostic results than the existing Multi Layer Perceptron(MLP).

• Journal of Korean Society of Industrial and Systems Engineering
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• v.32 no.3
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• pp.142-158
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• 2009

The supplier selection problem is perhaps the most important component of the purchasing function. Some of the common and influential criteria in the selection of a supplier include quality, price, delivery, and service. These evaluation criteria often conflict, however, and it is frequently impossible to find a supplier that excels in all areas. In addition, some of the criteria are quantitative and some are qualitative. Thus, a methodology is needed that can capture both subjective and objective evaluation measures. The Analytic Hierarchy Process(AHP) is a decision-making method for ranking alternative courses of action when multiple criteria must be considered. This paper proposes the AHP-based approach which can structure the supplier selection process and the achievements-based procedure which can allocate order quantities for the selected suppliers In automotive part manufacturers. Also, through the practical case of 'D' automotive part manufacturing company, we shows that the proposed AHP based supplier selection approach and the achievements-based allocation procedure of order quantity can be successfully applied for supplier selection and order quantity allocation problems.

• Korean Journal of Construction Engineering and Management
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• v.7 no.1 s.29
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• pp.89-99
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• 2006

Usually feasibility analysis in a narrow sense is a economic analysis of project. Feasibility analysis focused in this study is confined to the matter of finance. Many studies have been executed in qualitative element which include decision-making process or prediction of housing market. But it is difficult to find economic analysis related to characteristics of planning. In this study, floor area ratio, selling area ratio and term of works are adopted as the Characteristics of Planning. So, the purpose of this study is to analyze the Influence of the characteristics of planning on the cost of apartment by means of multiple regression analysis and what-if method.

• Journal of Korean Academy of Nursing Administration
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• v.14 no.3
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• pp.321-331
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• 2008

Purpose: This study examined the association of job satisfaction and job stress with the self regulation of nurses. Method: This research was descriptive in its design and used a self-administered questionnaire. The study subjects were 173 nurses who worked in the three university teaching hospitals. The instruments used in the study were self-regulation scale, job satisfaction scale and job stress scale. The data were analyzed by ANOVA, t-test, Pearson Correlation Coefficient, and multiple regression. Results: The mean score of self regulation, job satisfaction and job stress were 4.58, 1.90 and 3.31, respectively. The degree of error and confidence in decision making of the study subjects was associated with the extent of job satisfaction and job stress. Conclusion: Self regulation made an influence in a statistically significant way on nurses' job satisfaction and job stress. Therefore, we need to develop strategies to enhance the self regulation of nurses to improve their job satisfaction and job stress in a positive way.

• The Journal of Information Systems
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• v.14 no.1
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• pp.19-40
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• 2005

The history of computer virus comes along with that of computer. Computer virus han surfaced as a serious problem in information age. The advent of open network and widespread use of Internet made the problem even more urgent. As a method of defense for computer virus most companies use anti-virus software. Selecting appropriate anti-virus software involves various criteria and thus it is a multiple-attribute decision making problem. The purpose of this study is to prioritize anti-virus software evaluation factors. To do that, first of all, important evaluation factors are selected based on previous research on anti-virus software as well as general software evaluation models. Then, a questionnaire survey was conducted on end-users, system administrators and anti-virus software developers. The survey result was analyzed with ExpertChoice 2000 which is based on Analytic hierarchy Process technique. This study found that there are clear differences among three survey groups regarding the relative importance of overall evaluation factors. End-user group ranked "cost" first, but it was the least important factor to developer group. Developers pointed out "operational support" ad the most important factor. There were also obvious differences in the relative importance of detail evaluation items. Both end-users and system administrators shared 7 common items among top 10 most important items. Moreover, neither of the two groups ranked any of the items in the "operational support" factor in top 10, whereas all 4 items in the factor were included in top 10 by developer group.

• The KIPS Transactions:PartC
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• v.12C no.6 s.102
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• pp.793-798
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• 2005

We focus on the RP (Rendezvous Point) system model in the multicast network based on BcN (Broadband Convergence Network) integrating broadcasting, telecommunication and Internet with one. Based on the condition multiple queues with different service and single server, when the arrivals tome in group with the site of the group geometrically distributed, we define the relationship between incoming arrival rate and corresponding buffer size. We also investigate the Profit according to both Service Provider and Network Operator Then we make a decision whether a new service request is accepted or not based on given interning rate range.

• Journal of Information Technology Services
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• v.15 no.1
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• pp.197-210
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• 2016

With the advent of text analytics, VOC (Voice of Customer) data become an important resource which provides the managers and marketing practitioners with consumer's veiled opinion and requirements. In other words, making relevant use of VOC data potentially improves the customer responsiveness and satisfaction, each of which eventually improves business performance. However, unstructured data set such as customers' complaints in VOC data have seldom used in marketing practices such as predicting service time as an index of service quality. Because the VOC data which contains unstructured data is too complicated form. Also that needs convert unstructured data from structure data which difficult process. Hence, this study aims to propose a prediction model to improve the estimation accuracy of the level of customer satisfaction by combining unstructured from textmining with structured data features in VOC. Also the relationship between the unstructured, structured data and service processing time through the regression analysis. Text mining techniques, sentiment analysis, keyword extraction, classification algorithms, decision tree and multiple regression are considered and compared. For the experiment, we used actual VOC data in a company.

• The Journal of Asian Finance, Economics and Business
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• v.5 no.4
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• pp.21-34
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• 2018

The study tests the Fama and French three-factor model by using the newly created Islamic equity style indices. Based on a dataset from May 2006 to April 2011, the three-factor model is tested based on returns of Islamic unit trust funds using the Generalized Method of Moments (GMM) methodology. The sample period is also divided between periods before and after the Global Financial Crisis in August 2008 to test for robustness, and the Bai and Perron (2003) multiple structural break test was used to determine the structural break in the series. The analysis shows that the Fama and French model is valid for Islamic unit trust funds before and after the collapse of Lehman Brothers. The result further indicates the reversal of size effect. As for trading strategies, value funds outperform growth funds by annualized 3.13 percent for the full period. During pre-crisis period, value funds perform better than growth funds while in post-crisis, size factor yields better return than other strategies. As policy suggestion, fund managers need to be aware of the reversal of size effect, and they need to ensure a more transparent stock selection process so that investors can make an informed decision in their asset allocation.

• Journal of the Korean Society for Precision Engineering
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• v.9 no.4
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• pp.22-35
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• 1992

In highly automated machining centers, intelligent sensor fddeback systems are indispensable on order to monitor their operations, to ensure efficient metal removal, and to initate remedial action in the event of accident. In this study, an on-line tool wear detection system for thrning operations is developed, and experimentally evaluated. The system employs multiple sensors and the signals from these sensors are processed using a multichannel autoegressive (AR) series model. The resulting output from the signal processing block is then fed to a previously tranied artificial neural network (multiayered perceptron) to make a final decision on the state of the cutting tool. To learn the necessary input/output mapping for tool wear detection, the weithts and thresholds of the network are adjusted according to the back propagation (BP) method during off-line training. The results of experimental evaluation show that the system works well over a wide range of cutting conditions, and the ability of the system to detect tool wear is improved due to the generalization, fault-tolearant and self-ofganizing properties of the neural network.