• Journal of Korean Society of Industrial and Systems Engineering
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• v.30 no.2
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• pp.99-105
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• 2007

According to the automated manufacturing processes followed by the development of computer manufacturing technologies, products or quality characteristics produced on the processes have measured and recorded automatically. Much amount of data daily produced on the processes may not be efficiently analyzed by current statistical methodologies (i.e., statistical quality control and statistical process control methodologies) because of the dimensionality associated with many input and response variables. Although a number of statistical methods to handle this situation, there is room for improvement. In order to overcome this limitation, we integrated data mining and robust design approach in this research. We find efficiently the significant input variables that connected with the interesting response variables by using the data mining technique. And we find the optimum operating condition of process by using RSM and robust design approach.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2001.04a
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• pp.259-266
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• 2001

This paper presents the results of a structural analysis to determine design variables such as the inner basket array type, and thicknesses of the outer shell and the lid and bottom of a spent nuclear fuel disposal canister. The canister construction type introduced here is a solid structure with a cast iron insert and a corrosion resistant overpack, which is designed for the spent nuclear fuel disposal in a deep repository in the crystalline bedrock, entailing an evenly distributed load of hydrostatic pressure from the groundwater and large swelling pressure from the bentonite buffer. Hence, the canister must be designed to withstand these large pressure loads. Many design variables may affect the structural strength of the canister. In this study, among those variables, the array type of inner baskets and thicknesses of outer shell and lid and bottom are attempted to be determined through a linear static structural analysis. Canister types studied here are one for the pressurized water reactor (PWR) fuel and another for the Canadian deuterium and uranium reactor (CANDU) fuel.

• Asia pacific journal of information systems
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• v.4 no.1
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• pp.139-168
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• 1994

Many researchers and practitioners have paid much attention to identifying some critical factors for the successful implementation of the TPS, EDP and MIS. However, not much effort have been focused on the implementation issues of DSS which is different from TPS or MIS in terms of the scope of the system, evaluation criteria, decision types served, and level of users. Because of these differences, implemention of DSS should be planned and approached from different aspects of TPS, EDP and MIS. A conceptual model includes four characteristics as independent variables and three dependent variables such as system usage, user's satisfaction, and user's attitude. This paper is to empirically prove the usefulness of the model and to identify a model for the successful implementation of DSS in Korea. It will provide a better understanding of the variables associated with the successful DSS implementation.

• Transactions of Materials Processing
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• v.11 no.7
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• pp.581-588
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• 2002

Among the processes to produce micro lens, the process using press molding is a new technology to simplify the process, but it contains many unknown variables. The press-molding process proposed in this paper was simplified into two step process, the first step is the pressing to design the preform for glass element, the second step is the annealing to reduce the residual stress. It is important to estimate the amount of shrinkage of glass gob and the residual stress during process. It Is difficult to evaluate the process variables as mentioned above through the experiment. The influences due to process variables was evaluated by using FEM parametric analysis. The results in this paper can be applicable to produce micro lens.

• Journal of Korean Institute of Industrial Engineers
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• v.13 no.2
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• pp.105-115
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• 1987

Although there are many theoretical methods for the transformation of random variables. it is difficult to find probability density functions for the new random variables because of the complexity in mathematics. The author developed a simulation model solving the above difficulties using FBI (Fortran Based Interpreter) routines. The FBI is a kind of language Interpreter analyzing the arithmetic statement in character data forms. In this paper. the FBI routines will be explained and the structure and applications of simulation model will be also demonstrated. Polynomial curve fitting method is applied to define the probability density function which can not be defined by well-known pdf. This program can also be used for instructing mathematical statistics and identifying distribution of the simulated data.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.32 no.4
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• pp.145-154
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• 1983

This paper presents a new method for optimal real and reactive power dispatch for the economic operation of a power system. Unlike the usual approach of minimizing the transmission loss, this method minimizes the total production cost not only for the real power optimization problem, but also for the reactive power optimization. The control variables are real power generation of units for real power optimization, and reactive power optimization. The constraints are the operating limits on these control variables and the limits on the bus voltages. Methematical models are developed to represent the sensitivity relationships between dependent and control variables for both real and reactive power optimization modules, and thus eliminate the use of B-coefficients. In order to handle many functional inequality constraints, a modified version of the gradient projection method is developed for optimization procedure, and has shown a remarkable advantage in computation efficiency.

• Journal of Fisheries and Marine Sciences Education
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• v.19 no.1
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• pp.74-83
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• 2007

These days business logistics has become one of the very important factors of logistics cost reduction and customer service satisfaction. In order to control business logistics more effectively, many companies must be concerned about logistics performance.This study aims at analyzing information system characteristic and logistics performance. The information system characteristic variables are considered IS resource, IS strategic role. The logistics performance variables are considered logistic cost, customer service.For data on the 301 sampled company, a mail survey using a questionnaire was conducted in this study. The following results were obtained.First, IS characteristic variables were significant for logistics performance. Specially, efficient management of IS manpower and IS planning, present IS were significant for logistics performance. Second, there also were IS manpower and IS planning, present IS that indentifing high logistics performance group and low logistics performance group.

• Computers and Concrete
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• v.13 no.2
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• pp.173-185
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• 2014

Multivariate analysis is a statistical technique that investigates relationship between multiple predictor variables and response variable and it is a very commonly used statistical approach in cement and concrete industry. During model building stage, however, many predictor variables are included in the model and possible collinearity problems between these predictors are generally ignored. In this study, use of partial least squares (PLS) analysis for evaluating the relationships among the cement and concrete properties is investigated. This regression method is known to decrease the model complexity by reducing the number of predictor variables as well as to result in accurate and reliable predictions. The experimental studies showed that the method can be used in the multivariate problems of cement and concrete industry effectively.

• The Transactions of the Korea Information Processing Society
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• v.6 no.10
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• pp.2693-2706
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• 1999

Reengineering to object-oriented system is needed to maintain the system and satisfy requirements of structure change. Target systems which should be reengineered to object-oriented system are difficult to change because these systems have no design document or their design document is inconsistent of source code. Using design document to identifying objects for these systems is improper. There are several researches which identify objects through procedural source code analysis. In this paper, we propose automatic object identification method based on clustering of VTFG(Variable-Type-Function Graph) which represents relations among variables, types, and functions. VTFG includes relations among variables, types, and functions that may be basis of objects, and weights of these relations. By clustering related variables, types, and functions using their weights, our method overcomes limit of existing researches which identify too big objects or objects excluding many functions. The method proposed in this paper minimizes user's interaction through automatic object identification and make it easy to reenginner procedural system to object-oriented system.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2001.06a
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• pp.243-252
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• 2001

Recently, as most companies recognize the importance of the customer relationship management, they strongly believe that they must know who their customers are. The job of a customer is very important information for us to understand the customer. However, since most customers are reluctant to reveal themselves, they do not let us know their jobs, and even provide false information about their jobs. The target domain of our research is mobile telecommunication. In this research, we developed a system that identifies the customer's job by utilizing the Call Detailed Record. From the Call Detailed Record, we extracted such variables as 'Average Monthly Payment'and 'Age of the Customer'and so forth. Moreover, we generated many summary variables and derived variables such as 'Number of Calls during Work Hours in Weekday', and 'Ratio of Calls from other Mobile Telephone'. Using artificial neural networks, we developed a two-step Job Identification System. In the first step, it identifies the four job classes then in the second step, it subdivides these four job classes into seven jobs. The accuracy of identifying the seven jobs was 69.1%.