• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2002.12a
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• pp.93-97
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• 2002

In this paper, we propose intelligent steering control system that apply LIBL(Linguistic Instruction Based Learning) method to steering system of ship and take the place of process that linguistic instruction such as officer's steering instruction is achieved via ableman. We embody ableman's suitable steering manufacturing model using fuzzy inference rule by specific method of study, and apply LIBL method to present suitable meaning element and evaluation rule to steering system of ship, embody intelligent steering gear control system that respond more efficiently on officer's linguistic instruction. We presented evaluation rule to constructed steering manufacturing model based on ableman's experience, and propose rudder angle for steering system, compass bearing arrival time, meaning element of stationary state, and correct ableman manufacturing model rule using fuzzy inference. Also, we apply LIBL method to ship control simulator and confirmed the effectiveness.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.05a
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• pp.432-433
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• 2023

오늘날 AI 기술은 다양한 분야에서 활용되며 발전해나가고 있다. 하지만 AI 모델의 복잡도가 증가하며 AI의 산출 결과의 해석이 불가능한 Black-box 성격을 지니게 되었고, 이는 실 환경에서 AI 도입의 커다란 걸림돌로 작용하고 있다. 이에 따라 AI 판단 결과에 대한 Interpretation을 제공하는AI Decision Support의 중요성이 커지는 추세이다. 본 논문에서는 Reference 기반 Rule을 통해 AI 모델의 판단 결과에 대한 해석을 제공하고 입력된 데이터에 관한 Rule 적합도를 산출하여 AI Decision Support를 제공하고자 한다. 또한, Rule 적합도 정보를 기반으로 기존의 모델보다 정확한산출 결과를 통해 수집된 데이터의 Label을 확정시킨다. 이를 토대로 AI 모델의 업데이트를 실행하여 지속적으로 AI의 성능을 개선하면서도 지속 운용이 가능한 AI 운용 프레임워크를 제안한다.

• Structural Engineering and Mechanics
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• v.62 no.4
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• pp.507-517
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• 2017

Due to the discontinuous nature of uncertainty quantification in conventional evidence theory(ET), the computational cost of reliability analysis based on ET model is very high. A novel ET model based on fuzzy distribution and the corresponding combination rule to synthesize the judgments of experts are put forward in this paper. The intersection and union of membership functions are defined as belief and plausible membership function respectively, and the Murfhy's average combination rule is adopted to combine the basic probability assignment for focal elements. Then the combined membership functions are transformed to the equivalent probability density function by a normalizing factor. Finally, a reliability analysis procedure for structures with the mixture of epistemic and aleatory uncertainties is presented, in which the equivalent normalization method is adopted to solve the upper and lower bound of reliability. The effectiveness of the procedure is demonstrated by a numerical example and an engineering example. The results also show that the reliability interval calculated by the suggested method is almost identical to that solved by conventional method. Moreover, the results indicate that the computational cost of the suggested procedure is much less than that of conventional method. The suggested ET model provides a new way to flexibly represent epistemic uncertainty, and provides an efficiency method to estimate the reliability of structures with the mixture of epistemic and aleatory uncertainties.

• Journal of Korea Society of Industrial Information Systems
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• v.15 no.3
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• pp.59-65
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• 2010

This paper proposes a modified heuristic mixed model assembly line (MMAL) balancing algorithm that provides consistent station assignments on a model by model basis as well as on a station by station. Basically, some of single model line balancing techniques are modified and incorporated to be fit into the MMAL. The proposed algorithm is based on N.T. Thomopoulos' [8] method and supplemented with several well proven single model line balancing techniques proposed in the literature until recently. Hoffman's precedence matrix [2] is used to indicate the ordering relations among tasks. Arcus' Rule IX [1] is applied to generate rapidly a fairly large number of feasible solutions. Consequently, this proposed algorithm reduces the fluctuations in operation times among the models as well as the stations and the balance delays. A numerical example shows that the proposed algorithm can provide a good feasible solution in a relatively short time and generate relatively better solutions comparing to other three existing methods.

• ETRI Journal
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• v.15 no.2
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• pp.35-51
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• 1993

This paper presents function approximation based on nonparametric estimation. As an estimation model of function approximation, a three layered network composed of input, hidden and output layers is considered. The input and output layers have linear activation units while the hidden layer has nonlinear activation units or kernel functions which have the characteristics of bounds and locality. Using this type of network, a many-to-one function is synthesized over the domain of the input space by a number of kernel functions. In this network, we have to estimate the necessary number of kernel functions as well as the parameters associated with kernel functions. For this purpose, a new method of parameter estimation in which linear learning rule is applied between hidden and output layers while nonlinear (piecewise-linear) learning rule is applied between input and hidden layers, is considered. The linear learning rule updates the output weights between hidden and output layers based on the Linear Minimization of Mean Square Error (LMMSE) sense in the space of kernel functions while the nonlinear learning rule updates the parameters of kernel functions based on the gradient of the actual output of network with respect to the parameters (especially, the shape) of kernel functions. This approach of parameter adaptation provides near optimal values of the parameters associated with kernel functions in the sense of minimizing mean square error. As a result, the suggested nonparametric estimation provides an efficient way of function approximation from the view point of the number of kernel functions as well as learning speed.

• Journal of the Korean Institute of Intelligent Systems
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• v.8 no.6
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• pp.106-118
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• 1998

The study concerns a design procedure of rule-based systems. The proposed rule-based fuzzy modeling implements system structure and parameter identification in the efficient from of "IF..., THEN..." statements, and exploits the theory of system optimization and fuzzy implication rules. The method for rule-based fuzzy modeling concerns the from of the conclusion part of the the rules that can be constant. Both triangular and Gaussian-like membership function are studied. The optimization hinges on an autotuning algorithm that covers as a modified constrained optimization method known as a complex method. The study introduces a weighted performance index (objective function) that helps achieve a sound balance between the quality of results produced for the training and testing set. This methodology sheds light on the role and impact of different parameters of the model on its performance. The study is illustrated with the aid of two representative numerical examples.

• Journal of Korean Society for Quality Management
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• v.31 no.3
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• pp.114-125
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• 2003

In this paper, we deal with the problem of modeling of agent-based robot manufacturing cell. Its role is becoming increasingly important in automated manufacturing systems. For Object Oriented & Queueing Petri Nets (OO&QPNs), an extended formalism for the combined quantitative and qualitative analysis of different systems is used for structure and performance analysis of mobile robotic cell. In the case study, the OO&QPN model of a mobile robotic cell is represented and analyzed, considering multi-class parts, non-preemptive priority and alternative routing. Finally, the comparison of performance values between Shortest Process Time (SPT) rule and First Come First Serve (FCFS) rule is suggested. In general, SPT rule is most suitable for parts that have shorter processing time than others.

• Proceedings of the KOSOMBE Conference
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• v.1992 no.05
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• pp.155-160
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• 1992

Liver function test is one of the most common tests for diagnosis and follow-up of patients and for heal th screening. Automatic interpretation and suggestions on the diagnostic possibilities contribute to shorten the interpretation time of the test results and help to provide qualified health care. Fuzzy logic has been recently introduced and being spread for these purposes. The present study aims at model Ins the foray rule-based laboratory diagnosis system. The fuzzy rule-based laboratory diagnosis system was applied to the diagnosis regarding liver function test. The system was evaluated by comparing with the stepwise multivariate discriminant function analysis, which showed similar results, and the overall accuracy of the fuzzy diagnosis system was about 80%.

• Asia pacific journal of information systems
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• v.14 no.1
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• pp.79-100
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• 2004

As a part of ebXML(Electronic Business using eXtensible Markup Language) framework, BPSS(Business Process Specification Schema) has been provided to support the direct specification of the set of elements required to configure a runtime system in order to execute a set of ebXML business transactions. The BPS,' is available in two stand-alone representations, a UML version and an XML version. Due to the limitations of UML notations and XML syntax, however, current ebXML BPSS specification fails to specify formal semantic constraints completely. In this study, we propose a constraint classification scheme for the BPSS specification and describe how to formally represent those semantic constraints using OCL(Object Constraint Language). As a way to validate p Business Process Specification(BPS) with the formal semantic constraints, we suggest a rule-based approach to represent the formal constraints and demonstrate its detailed mechanism for applying the rule-based constraints to the BPS with a prototype implementation.

• International Journal of Internet, Broadcasting and Communication
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• v.10 no.3
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• pp.88-97
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• 2018

Chronic diseases management can be effectively achieved through early detection, continuous treatment, observation, and self-management, rather than a radar approach where patients are treated only when they visit a medical facility. However, previous studies have not been able to provide integrated chronic disease management services by considering generalized services such as hypertension and diabetes management, and difficult to expand and link to other services using only specific sensors or services. This paper proposes clinical rule flow model based on medical data analysis to provide personalized care for chronic disease management. Also, we implemented that as Rule-based Smart Healthcare System (RSHS). The proposed system executes chronic diseases management rules, manages events and delivers individualized knowledge information by user's request. The proposed system can be expanded into a variety of applications such as diet and exercise service in the future.