• Proceedings of the Korean Operations and Management Science Society Conference
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• 2004.05a
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• pp.487-493
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• 2004

This paper consider a six sigma project for improving the TIP life time in a spot welding process. The project follows a disciplined process of five phases: define, measure, analyze, improve, and control. A process map is used to identify process input and output variables. Nine key process input variables are selected by using C&E matrix and FMEA, and finally four vital few input variables are selected from analyze phase. The optimum process conditions of the four vital few input variables are jointly obtained by maximizing TIP life time using DOE.

• Journal of Korean Society for Quality Management
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• v.33 no.1
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• pp.88-98
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• 2005

This paper considers a six sigma project for improving the TIP life time in a spot welding process. The project follows a disciplined process of five phases: define, measure, analyze, improve, and control. A process map is used to identify process input and output variables. Nine key process input variables are selected by using C&E matrix and FMEA, and finally four vital few input variables are selected from analyze phase. The optimum process conditions of the vital few input variables are jointly obtained by maximizing TIP life time using DOE and alternative selection method.

• Journal of Korean Society for Quality Management
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• v.33 no.3
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• pp.135-148
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• 2005

This paper considers a six sigma project for reducing the project costs through project risk management. The project follows a disciplined process of five phases: define, measure, analyze, improve, and control. A risk management process map is used to identify process input and output variables. Seven key process input variables are selected by using C&E diagram and X-Y matrix and finally four vital few input variables are selected by the related statistical analysis. The optimum alternatives of the vital few input variables are obtained by the method of PUGH matrix. The process is running on control plan and we obtained substantial project cost reductions in early stage of the control phase.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.32B no.6
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• pp.821-827
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• 1995

This paper presents a process control language for constructing multiloop control system. which include advanced control algorithms. In order to make controller, this language uses function blocks that do specific operations. Then, the total control algorithm is a set of function blocks, of which each block is represented as a function code. The function code is a line of simple ASCII codes denoting function, input, output, parameters. It is possible to use variables as input/output port of any block. Compared with other language using function block concept, the proposed one enables to use advanced control algorithms undefinitely, such as fuzzy, neural network, predictive controller, etc., because vector and matrix variables as input/output can be used freely in this language. To raise flexibility, we put an intermediate level, which is C-language code, between function code and target-dependent operation code.

• Journal of Welding and Joining
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• v.26 no.2
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• pp.85-91
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• 2008

In general, the quality of a welding process tends to vary with depending on the work environment or external disturbances. Hence, in order to achieve the desirable quality of welding, we should have the optimal welding condition that is not significantly affected by these changes in the environment or external disturbances. In this study, we used a dual response surface method in consideration of both the mean output variables and the standard deviation in order to optimize the V-groove arc welding process. The input variables for GMA welding process with the dual response surface are welding voltage, welding current and welding speed. The output variables are the welding quality function using the shape factor of bead geometry. First, we performed welding experiment on the interested area according to the central composite design. From the results obtained, we derived the regression model on the mean and standard deviation between the input and output variables of the welding process and then obtained the dual response surface. Finally, using the grid search method, we obtained the input variables that minimize the object function which led to the optimal V-groove arc welding process.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.16 no.6
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• pp.125-132
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• 2017

Generally, in machining operations, the required machining performance can be obtained by properly combining several machining parameters properly. In this research, we construct a simulation model, which that predicts the relationship between the input variables and output variables in the turning operation. Input variables necessary for the turning operation include cutting speed, feed, and depth of cut. Surface roughness and electrical current consumption are used as the output variables. To construct the simulation model, an Artificial Neural Network (ANN) is employed. With theIn ANN, training is necessary to find appropriate weights, and the Ant Colony Optimization (ACO) technique is used as a training tool. EspeciallyIn particular, for the continuous domain, ACOR is adopted and athe related algorithm is developed. Finally, the effects of the algorithm on the results are identified and analyzsed.

• Journal of Digital Contents Society
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• v.6 no.4
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• pp.235-242
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• 2005

This Study examines influencing factors such as input, process, and output variables on1 student's satisfaction in cyber-education. That is to study on the effectiveness of input, process, and output variables for cyber-education and how does student's interaction moderate influencing factors and student satisfaction. The study was carried out through literature and empirical study. Questionnaire was used to varify the hypothesis based on which the input-process-output with system models were established. The result of hypothesis verification in this study is as follows : First, learning hour and grade showed a positive influence on the students' satisfaction in learning factors. Second reliant of professor, recognized teaming participate, and contents showed a positive influence on the students' satisfaction in system factors. Third, an interesting findings emerged throughout the analysis, showed that process variables were rather meaning factor than input variables.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.15 no.4
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• pp.37-42
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• 2001

This paper resents a biological reaction model of describing processing features in treating wastewater via activated sludge A proposed model is designed by combining fuzzy rules investigating several elements which have influence on variables to be supervised BOD and SS are suggested as common variables in input and output variables, and O$_2$quantity is closed as input variable. We chose triangular type membership functions for input variables and determined the grades in each membership function based upon process data According to simulation result to show the validity of proposed model, fuzzy model's outputs give almost similar data to process output under same input conditions.

• Journal of the Korean Society of Safety
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• v.38 no.1
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• pp.1-8
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• 2023

It is extremely important to design safety-guaranteed industrial processes because such process determine the ultimate outcomes of industrial activities, including worker safety. Application of artificial intelligence (AI) in industrial safety involves modeling industrial safety systems by using vast amounts of safety-related data, accident prediction, and accident prevention based on predictions. As a preliminary step toward realizing AI-based industrial safety in Korea, this study discusses neural network-based modeling of industrial safety systems. The input variables that are the most discriminatory relative to the output variables of industrial safety processes are selected using two information-theoretic measures, namely entropy and cross entropy. Normalized frequency and severity of industrial accidents are selected as the output variables. Our simulation results confirm the effectiveness of the proposed neural network model and, therefore, the feasibility of extending the model to include more input and output variables.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.15 no.2
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• pp.131-138
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• 2016

In order to improve the performance of an extended Kalman filter, a simplified indirect inference method (SIIM) fuzzy logic system (FLS) is proposed. The proposed FLS is composed of two fuzzy input variables, four fuzzy rules and one fuzzy output. Two normalized fuzzy input variables are the variance between the trace of a prior and a posterior covariance matrix, and the residual error of a Kalman algorithm. One fuzzy output variable is the weighting factor to adjust for the Kalman gain. There is no need to decide the number and the membership function of input variables, because we employ the normalized monotone increasing/decreasing function. The single parameter to be determined is the magnitude of a universe of discourse in the output variable. The structure of the proposed FLS is simple and easy to apply to various nonlinear state estimation problems. The simulation results show that the proposed FLS has strong adaptability to estimate the states of the incoming/outgoing moving objects, and outperforms the conventional extended Kalman filter algorithm by providing solutions that are more accurate.