• Journal of the Semiconductor & Display Technology
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• v.20 no.2
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• pp.6-13
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• 2021

Cases and studies on the selection method of CTQ are relatively active, but there are few cases or studies on the selection method of CTP which is important in the device industry. In fact, many companies simply select and manage CTP from the point of contact based on their experience and intuition. The purpose of this study is to present an evaluation model and a mathematical decision model for rational and systematic CTP selection to improve the process quality of semiconductor equipment lines. In the evaluation model, AHP (Analytic Hierarchy Process) analysis technique was applied to show objective and quantitative figures, and Fuzzy decision-making model was used to solve the ambiguity and uncertainty in the decision-making process. Decision Value (DV) was presented. The subjects were 22 process factors managed in the Plating Process that the representative equipment line can do. As a result, the evaluation model proposed in this study can support more efficient and effective decision-making for process quality improvement by more objectively measuring the problem of subjective CTP selection in manufacturing sites.

• Journal of Korean Institute of Industrial Engineers
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• v.15 no.2
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• pp.45-55
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• 1989

This paper deals with development of a computer-aided process selection and sequencing technique and its software for metal cutting processes of rotational parts. The process selection procedure consists of selection for proper machining operations and machine tools suitable for the selected operations. Machining operations are selected based on machining surface features and machine tools are selected by employing a conversion table which converts machining operations into machine tools. The process sequence is determined by the proper manipulation of the precedence relation matrix. A computer program for the proposed technique is developed by using Turbo-Pascal on IBM PC/AT compatible system. The proposed technique works well to real problems.

• The Journal of Society for e-Business Studies
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• v.17 no.1
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• pp.53-74
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• 2012

This study developed a method for selection of available human resources for incomingjob allocation that considers factors affecting resource performance in the business process management (BPM) environment. For many years, resource selection has been treated as a very important issue in scheduling due to its direct influence on the speed and quality of task accomplishment. Even though traditional resource selection can work well in many situations, it might not be the best choice when dealing with human resources. Humanresource performance is easily affected by several factors such as workload, queue, working hours, inter-arrival time, and others. The resource-selection rule developed in the present study considers factors that affect human resource performance. We used a Bayesian Network (BN) to incorporate those factors into a single model, which we have called the Bayesian Selection Rule (BSR). Our simulation results show that the BSR can reduce waiting time, completion time and cycle time.

• Asia pacific journal of information systems
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• v.3 no.2
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• pp.55-73
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• 1993

The importance of IS project selection process has been recognized by many IS researchers as well as IS practitioners. The ideal selection process should provide an organization with best IS project from many competing proposals. However, researchers have found that some organizational biases exist in making the selection decisions. This means different selection mechanisms favor projects with different characteristics. The purpose of this study is to reexamine previous findings to determine if the biases still exist in rapidly changing IS environment. An exploratory case study was conducted to gain deeper understanding of the actual IS project selection process. Then scenario approach was used for the empirical study. Some conflicting findings from the previous studies are discussed.

• Communications for Statistical Applications and Methods
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• v.12 no.3
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• pp.659-672
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• 2005

This paper evaluates discretization of continuous variables to select relevant variables for supervised learning using mutual information. Three discretization methods, MDL, Histogram and 4-Intervals are considered. The process of discretization and variable subset selection is evaluated according to the classification accuracies with the 6 real data sets of UCI databases. Results show that 4-Interval discretization method based on quantiles, is robust and efficient for variable selection process. We also visually evaluate the appropriateness of the selected subset of variables.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2004.10a
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• pp.313-316
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• 2004

Model selection is the process that sets up the regularization parameter in the support vector machine or regularization network by using the external methods such as general cross validation or L-curve criterion. This paper suggests that the regularization parameter can be obtained simultaneously within the learning process of neural networks without resort to separate selection methods. In this paper, extended kernel method is introduced. The relationship between regularization parameter and the bias term in the extended kernel is established. Experimental results show the effectiveness of the new model selection method.

• International Journal of Concrete Structures and Materials
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• v.6 no.2
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• pp.87-100
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• 2012

The process of selecting a repair material is a typical one of multi-criteria decision-making (MCDM) problems. In this study Analytical Hierarch Process was applied to solve this MCDM problem. Many factors affecting a process to select an optimal repair material can be classified into quantitative and qualitative requirements and this study handled only quantitative items. Quantitative requirements in the optimal selection model for repair material were divided into two parts, namely, the required chemical performance and the required physical performance. The former is composed of alkali-resistance, chloride permeability and electrical resistivity. The latter is composed of compressive strength, tensile strength, adhesive strength, drying shrinkage, elasticity and thermal expansion. The result of the study shows that this method is the useful and rational engineering approach in the problem concerning the selection of one out of many candidate repair materials even if this study was limited to repair material only for chloride-deteriorated concrete.

• Journal of the Korean Society for Precision Engineering
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• v.9 no.3
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• pp.53-60
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• 1992

This paper deals with developing an Expert system for tool selection using knowledge base system approach, and its application. For the sake of building of knowledge base, the information from process through sensor, tool handbook and interview with expert are referrenced and managed. The system developed shows good application flexibility in providing the actual cutting process with the selection of tool(insert, holder) and cutting conditions(feed, speed, rake type, and so on), is found as a useful system for real-time machining process. The Expert system for tool selection is written in TURBO PROLOG ver. 2.0 for inference engine capability, and can be run in interactive mode for user friendliness. In order to apply the system developed in actual cutting process, more parameters should be considered and scrutinized, and the system should be further extended in modular basis.

• Journal of information and communication convergence engineering
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• v.13 no.2
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• pp.97-104
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• 2015

In this paper, we propose a data envelopment analysis (DEA) and analytic hierarchy process (AHP) integrated model to improve the selection process in the acquisition of a weapon system which is the key component to the success of the project. In particular, we applied DEA in the first stage to choose a frontier group among the candidates in the selection process of the next-generation fighter system (the 3rd FX) in Korea. Then, by using the Delphi technique, we surveyed military experts and applied AHP to determine the best choice among the candidates. The results of the study match the actual decision made by the Korean government in the weapon system acquisition. The results of the proposed DEA-AHP integrated method in the selection of the next-generation fighter systems in Korea demonstrate the usefulness of the method. In this paper, we also discuss the future implications of the proposed model.

• Journal of Korean Society for Quality Management
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• v.40 no.3
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• pp.347-358
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• 2012

Purpose: Given the mixture components - process variables experimental data, we propose the strategy to find the proper combined model. Methods: Process variables are factors in an experiment that are not mixture components but could affect the blending properties of the mixture ingredients. For example, the effectiveness of an etching solution which is measured as an etch rate is not only a function of the proportions of the three acids that are combined to form the mixture, but also depends on the temperature of the solution and the agitation rate. Efficient designs for the mixture components - process variables experiments depend on the mixture components - process variables model which is called a combined model. We often use the product model between the canonical polynomial model for the mixture and process variables model as a combined model. Results: First we choose the reasonable starting models among the class of admissible product models and practical combined models suggested by Lim(2011) based on the model selection criteria and then, search for candidate models which are subset models of the starting model by the sequential variables selection method or all possible regressions procedure. Conclusion: Good candidate models are screened by the evaluation of model selection criteria and checking the residual plots for the validity of the model assumption. The strategy to find the proper combined model is illustrated with examples in this paper.