• ETRI Journal
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• v.24 no.4
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• pp.323-327
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• 2002

In this study, we propose a novel mobile tracking method based on Multi-Criteria Decision Making (MCDM), in which uncertain parameters-the received signal strength, the distance between the mobile and the base station, the moving direction, and the previous location-are used in the decision process using the aggregation function in the fuzzy set theory. Through numerical results, we show that our proposed mobile tracking method provides a better performance than the conventional method using the received signal strength.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.15 no.26
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• pp.59-66
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• 1992

This Paper, first, tries to optimize the output specifications with uncertain characteristics. And then aims to solve the problem not only by making use of transformed multiple regression equation which can yield objective function of output characteristics but also by formulating developed multiple fuzzy goal programming using fuzzy set theory which can treat uncertainty easily, and the efficiency of these techniques, will be also demonstrated through a case study.

• Proceedings of the IEEK Conference
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• 2000.07b
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• pp.677-680
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• 2000

The technique of reinforcement learning algorithm is extended to solve the multiobjective control problem for uncertain dynamic systems. A multiobjective adaptive critic structure is proposed in order to realize a max-min method in the reinforcement learning process. Also, the proposed reinforcement learning technique is applied to a multiobjective satisfactory fuzzy logic controller design in which fuzzy logic subcontrollers are assumed to be derived from human experts. Some simulation results are given in order to show effectiveness of the proposed method.

• Journal of Information Management
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• v.21 no.1
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• pp.34-52
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• 1990

The probabilistic reasoning method using Bayesian theory is studied to use uncertain or incomplete knowledge in ways that take the uncertainty into account. This method presented for knowledge acquisition process of expert system to evaluate certainty degree of input knowledge.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.77.3-77
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• 2001

An extended robust H$\infty$ filter is proposed for a nonlinear uncertain system. We also analyze the characteristics of the proposed filter such as an H$\infty$ performance criterion using the Lyapunov function method. The analysis results show that proposed filter has a robustness against disturbances such as process and measurement noises and against parameter uncertainties. Then the in-flight alignment for a strapdown inertial navigation system is designed using the presented filter. Simulation results show that the proposed filter effectively improve the performance.

• Journal of Korean Institute of Industrial Engineers
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• v.39 no.1
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• pp.10-19
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• 2013

Dispatching rule plays an important role in a hybrid flow shop. Finding the appropriate dispatching rule becomes more challenging when there are multiple criteria, uncertain demands, and dynamic manufacturing environment. Using a single dispatching rule for the whole shop or a set of rules based on a single criterion is not sufficient. Therefore, a multi-criteria decision making technique using 'the order preference by similarity to ideal solution' (TOPSIS) and 'analytic hierarchy process' (AHP) is presented. The proposed technique is aimed to find the most suitable set of dispatching rules under different manufacturing scenarios. A simulation based case study on a PCB manufacturing process is presented to illustrate the procedure and effectiveness of the proposed methodology.

• Asia pacific journal of information systems
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• v.6 no.1
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• pp.5-20
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• 1996

Today's firms exist and run business in an uncertain and rapidly changing environment in terms of industry, market, technology, economic conditions, and culture. To be competitive, at least to survive, firms must cope with and manage uncertainty effectively. In other words, firms should be equipped with powerful weapons to capture competitive advantage over their competitors. There are several ways for a firm to capture competitive edge over its competitors such as cost leadership, quality of the products and services, manpower, higher productivity, and technology. Among these, technology, especially information technology, could be the most effective weapon for competitive advantages since it is possible to monitor competitors' movement as well as to provide appropriate information with both planning and control phases through an information system. In this paper, a competitive weapon in action, a Process Control System which is developed by and installed at a U.S petroleum company would the described.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.2
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• pp.173-178
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• 2007

This paper is to establish an appropriate model for predicting the temperature decreases in the batch transferred from the refining process to the caster in steel-making companies. Mathematical modeling of the temperature decreases between the processes is difficult, since the reaction mechanism by which the temperature changes in a molten steel batch is dynamic, uncertain and complex. Three soft computing techniques are examined using the same data, namely the multiple regression, fuzzy regression, and neural net (NN) models. To compare the accuracy of these three models, a limited number of input variables are selected from those variables significantly affecting the temperature decrease. The results show that the difference in accuracy between the three models is not statistically significant. Nonetheless, the NN model is recommended because of its adaptive ability and robustness. The method presented in this paper allows the temperature decrease to be predicted without requiring any precise metallurgical knowledge.

• Journal of Korean Institute of Industrial Engineers
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• v.23 no.1
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• pp.39-54
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• 1997

This paper presents four process models for machining processes : 1) an economical mathematical model of machining process, 2) a prediction model for surface roughness, 3) a decision model for fuzzy cutting conditions, and 4) a judgment model of machinability with automatic selection of cutting conditions. Each model was developed the economic machining, and these models were applied to theories widely studied in industrial engineering which are nonlinear programming, computer simulation, fuzzy theory, and neural networks. The results of this paper emphasize the human oriented domain of a nonlinear programming problem. From a viewpoint of the decision maker, fuzzy nonlinear programming modeling seems to be apparently more flexible, more acceptable, and more reliable for uncertain, ill-defined, and vague problem situations.

• Journal of the Korean Operations Research and Management Science Society
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• v.16 no.1
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• pp.135-146
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• 1991

This paper shows the infinite horizon model of Partially Observable Markov Decision Process with lagged information. The lagged information is uncertain delayed observation of the process under control. Even though the optimal policy of the model exists, finding the optimal policy is very time consuming. Thus, the aim of this study is to find an .eplison.-optimal stationary policy minimizing the expected discounted total cost of the model. .EPSILON.- optimal policy is found by using a modified version of the well known policy iteration algorithm. The modification focuses to the value determination routine of the algorithm. Some properties of the approximation functions for the expected discounted cost of a stationary policy are presented. The expected discounted cost of a stationary policy is approximated based on these properties. A numerical example is also shown.