• Proceedings of the Korean Society of Precision Engineering Conference
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• 1995.04b
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• pp.457-462
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• 1995

This paper describes diagnosis strategy of the Flexible Multiple Fault Diagnosis Module for forecasting faults in system and deciding current machine state form sensor information. Most studydeal with diagnosis control stategy about single fault in a system, this studies deal with multiple fault diagnosis. This strategy is consist of diagnosis control module such as backward tracking expert system shell, various neural network, numerical model to predict machine state and communication module for information exchange and cooperate between each model. This models are used to describe structure, function and behavior of subsystem, complex component and total system. Hierarchical structure is very efficient to represent structural, functional and behavioral knowledge. FT(Fault Tree). ST(Symptom Tree), FCD(Fault Consequence Diagrapy), SGM(State Graph Model) and FFM(Functional Flow Model) are used to represent hierachical structure. In this study, IA(Intelligent Agent) concept is introduced to match FT component and event symbol in diagnosed system and to transfer message between each event process. Proposed diagnosis control module is made of IPC(Inter Process Communication) method under UNIX operating system.

• Proceedings of the SAREK Conference
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• 2005.11a
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• pp.288-293
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• 2005

The pressure distribution and heat transfer coefficient were measured at room temperature in the high suspension density CFB heat exchanger with multiple vertical tubes and the effective density of CFB was determined. The theoretical model for predicting heat transfer coefficient was developed in this study. The model predictions were compared with the measured heat transfer coefficient to show relatively good agreement between them.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.14 no.3
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• pp.171-180
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• 2014

In this paper, a multiple sliding surface (MSS) controller for a twin rotor multi-input-multioutput system (TRMS) with mismatched model uncertainties is proposed. The nonlinear terms in the model are regarded as model uncertainties, which do not satisfy the standard matching condition, and an MSS control technique is adopted to overcome them. In order to control the position of the TRMS, the system dynamics are pseudo-decomposed into horizontal and vertical subsystems, and two MSSs are separately designed for each subsystem. The stability of the TRMS with the proposed controller is guaranteed by the Lyapunov stability theory. Some simulation results are given to verify the proposed scheme, and the real time performances of the TRMS with the MSS controller show the effectiveness of the proposed controller.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.52 no.1
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• pp.16-21
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• 2003

The accuracy in maneuvering target tracking using multiple models is resulted in by the suitability of each target motion model to be used. The interacting multiple model (IMM) method and the adaptive IMM (AIMM) method require the predefined sub-models and the predetermined acceleration intervals, respectively, in consideration of the properties of maneuvers in order to construct multiple models. In this paper, to solve these problems, a genetic algorithm(GA) based-IMM method using fuzzy logic is proposed. In the proposed method, the acceleration input is regarded as an additive noise and a sub-model is represented as a set of fuzzy rules to calculate the time-varying variances of the process noises of a new piecewise constant white acceleration model. The proposed method is compared with the AIMM algorithm in simulation.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.19 no.3
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• pp.263-268
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• 2007

The pressure drop and heat transfer coefficient were measured at room temperature in a CFB heat exchanger with multiple vertical tubes. The circulation rate of solid particles was also measured. The theoretical model for predicting heat transfer coefficient using the solid flowrate was developed in this study. The model predictions were compared with the measured heat transfer coefficient to show relatively good agreement.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.2
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• pp.234-239
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• 2006

In this paper, we propose an interacting multiple model (IMM) method using intelligent tracking filter with fuzzy gain to reduce tracking errors for maneuvering targets. In the proposed filter, the unknown acceleration input for each sub-model is determined by mismatches between the modelled target dynamics and the actual target dynamics. After a acceleration input is detected, the state estimates for each sub-filter are modified. To modify the accurate estimation, we propose the fuzzy gain based on the relation between the filter residual and its variation. To optimize each fuzzy system, we utilize the genetic algorithm (GA). The tracking performance of the proposed method is compared with those of the adaptive interacting multiple model(AIMM) method and input estimation (IE) method through computer simulations.

• International conference on construction engineering and project management
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• 2007.03a
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• pp.653-661
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• 2007

Overseas construction projects tend to be more complex than domestic projects, being exposed to more external risks, such as politics, economy, society, and culture, as well as more internal risks from the project itself. It is crucial to have an early understanding of the project condition, in order to be well prepared in various phases of the project. This study compares a structural equation model and multiple regression analysis, in their capacity to predict cost performance of international construction projects. The structural equation model shows a more accurate prediction of cost performance than does regression analysis, due to its intrinsic capability of considering various cost factors in a systematic way.

• Proceedings of the Korean Society for Quality Management Conference
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• 1998.11a
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• pp.217-224
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• 1998

A cost model, controlling multiple dependent subprocesses with minimum cost, is derived by renewal theory approach. The optimal multiple cause-selecting control chart and individual Y control chart are thus constructed to monitor the specific product quality and overall product quality contributed by the multiple dependent subprocesses. They may be used to maintain the process with minimum cost and effectively distinguish which component of the subprocesses is out of control. The optimal design parameters of the proposed control charts can be determined by minimizing the cost model using simple grid search method, An example is given to illustrate the application of the optimal multiple cause-selecting control chart and individual Y control chart.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.4
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• pp.311-314
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• 2001

Recently, research on Intelligent Information Process has actively been under way JD various areas and gradually extended to be adaptive to uncertain and complex dynamic environments. This paper presents a Multiple Valued Logic Automata(MVL-Automata) Model, utilizing properties of difference in a Multiple Valued Logic function. That is, MVL-Automata is able to be autonomously adapted to dynamic changing since an input stling is mapped to the value of a Multiple Valued Logic function and the property of difference in a Multiple Valued Logic function is applied to state transition. Therefore, Multiple Valued Logic Automata can be widely applied to the modeling dynamically of changing environments.

• Journal of the Korean Operations Research and Management Science Society
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• v.13 no.2
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• pp.9-17
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• 1988

This vehicle scheduling problem with multiple depots and multiple vehicle types (VMM) is to determine the optimal vehicle routes to minimize the total travel costs. The object of this paper is to develope an exact algorithm for the VMM. In this paper the VMM is transformed into a mathematical model of the vehicle problem with multiple depots. Then an efficient branch and bound algorithm is developed to obtain an exact solution for this model. In order to enhance the efficiency, this algorithm emphasizes the follows; First, a heuristic algorithm is developed to get a good initial upper bound. Second, an primal-dual approach is used to solve subproblems which are called the quasi-assignment problem, formed by branching strategy is presented to reduce the number of the candidate subproblems.