• 한국정보과학회:학술대회논문집
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• 한국정보과학회 2006년도 한국컴퓨터종합학술대회 논문집 Vol.33 No.1 (D)
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• pp.43-45
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• 2006

Ubiquitous 핵심에 위치한 RFID(Radio Frequency IDentification)는 관련 H/W 및 S/W 기술의 발전에 따라 RFID 데이터를 수집하고 분석하여 이를 필요로 하는 응용 서비스에게 제공하거나 연동할 수 있는 RFID 데이터 처리 시스템이 요구된다. 본 논문은 EPCglobal의 표준을 따른 EPC-IS(Electronic Product Code Information Service)를 위한 성능 향상 방안을 제안한다. 제안을 바탕으로 구현한 EPC-IS는 Rule 기반의 Connection Point를 지원함으로써 Legacy Enterprise 시스템과 원활히 연동할 수 있을 뿐만 아니라 Policy 기반의 Backup Scheduling을 제공하여 RFID 시스템의 저장소 관리를 효율적으로 할 수 있는 RFID 데이터처리 시스템이다.

• International Journal of Control, Automation, and Systems
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• 제2권1호
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• pp.118-124
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• 2004

This paper proposes an approach to deriving a fuzzy classifier based on evolutionary supervised clustering, which identifies the optimal clusters necessary to classify classes. The clusters are formed by multi-dimensional weighted Euclidean distance, which allows clusters of varying shapes and sizes. A cluster induces a Gaussian fuzzy antecedent set with unique variance in each dimension, which reflects the tightness of the cluster. The fuzzy classifier is com-posed of as many classification rules as classes. The clusters identified for each class constitute fuzzy sets, which are joined by an "and" connective in the antecedent part of the corresponding rule. The approach is evaluated using six data sets. The comparative results with different classifiers are given.are given.

• 전자공학회논문지B
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• 제33B권11호
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• pp.1-10
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• 1996

In this paper, we suggest a new identification method based on the takagi-sugeno fuzzy model which prepresents an envrionmental stiffness and propose a method to decide PD gains of the PD controller. It is difficult to perform a compliance task due to characteristics of robot itself and uncertain work envronment. Therefore, in this paper, we identify the fuzzy rule by dividing the relationship of input-output data into several piecewise-linear equations using the hough transform which is the one this fuzzy model, we propose a method to design the pD gain. We show the validity of this method by the experiment of tracking the surface of the paper box as an example of variable environment using robot manipulator and force sensing system. As a performance index, we use the settling time, and perform an analysis between conventional PD contorllers and this controller.

• 한국경영과학회:학술대회논문집
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• 한국경영과학회 2007년도 추계학술대회 및 정기총회
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• pp.183-189
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• 2007

In this paper, we developed the fuzzy group method data handling-type (GMDH) Model and applied it to demand forecasting of educational expenses. At present, GMDH family of modeling algorithms discovers the structure of empirical models and it gives only the way to get the most accurate identification and demand forecasts in case of noised and short input sampling. In distinction to fuzzy system, the results are explicit mathematical models, obtained in a relative short time. In this paper, an adaptive learning network is proposed as a kind of fuzzy GMDH. The proposed method can be reinterpreted as a multi-stage fuzzy decision rule which is called as the fuzzy GMDH. The fuzzy GMDH-type networks have several advantages compared with conventional multi-layered GMDH models. Therefore, many types of nonlinear systems can be automatically modeled by using the fuzzy GMDH. A computer program is developed and successful applications are shown in the field of demand forecasting problem of educational expenses with the number of factors considered.

• Architectural research
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• 제16권2호
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• pp.57-65
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• 2014

A study on the static analysis of Timoshenko beams is presented. A beam element is developed by using isogeometric approach based on Timoshenko beam theory which allows the transverse shear deformation. The identification of transverse shear locking is conducted by three refinement schemes such as h-, p- and k-refinement and compared to other reference solutions. From numerical examples, the present beam element does not produce any shear locking in very thin beam situations even with full Gauss integration rule. Finally, the benchmark tests described in this study is provided as future reference solutions for Timoshenko beam problems based on isogeometric approach.

• 한국경영과학회지
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• 제18권3호
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• pp.79-100
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• 1993

This paper proposes an idea of reverse modeling that analyzes LP models and then converts them into an object-oriented model repository. The process of reverse modeling consists of (1) identifying and analyzing source models by meta processor, (2) model decomposition and generalization to scan the models and divide them into model components, and (3) deriving model selection rules from the components by rule generator. Through the process, we can extract reusable model components and build a model base with model selectioon rules. Examples with models created by SML and MODLER modeling languages are given to illustrate the methods. The model base management capabilities provided by reverse modeling can increase the reusabioity of current modeling tools.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2006년 학술대회 논문집 정보 및 제어부문
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• pp.366-368
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• 2006

Electronic warfare Support(ES) system measures pulse characteristics for received RF signals that received from all directions. ES system discriminates the pulse trains that have a rule, correlationship, continuance from collected data and analyze the characteristics of the data, and identify the emitters by comparison with emitter identification data(EID). Because pulse density is very high and various signal source exists at modem signal environments, high-speed and accurate signal analysis is needed for realtime countermeasure to emitters. Grouping alleviates the load of signal analysis process and supports reliable analysis. In this paper, we suggest an adaptive clustering scheme regarding signal patterns.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1997년도 추계학술대회 학술발표 논문집
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• pp.252-256
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• 1997

The success of a fuzzy neural network(FNN) control system solving any given problem critically depends on the architecture of the network. Various attempts have been made in optimizing its structure using genetic algorithm automated designs. This paper presents a new approach to structurally optimized designs of FNN models. A messy genetic algorithm is used to obtain structurally optimized FNN models. Structural optimization is regarded important before neural networks based learning is switched into. We have applied the method to the problem of a numerical approximation

• 한국정보처리학회논문지
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• 제2권2호
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• pp.209-214
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• 1995

일반적으로 퍼지 시스템 모델링에 있어서, 퍼지 규칙을 인식하고 퍼지 추론의 소속함수를 조정하기란 매우 어렵다.본 논문에서는 인공신경망을 이용함으로써,자동으로 퍼지 규칙을 인식하고 동시에 퍼지 추론의 소속함수를 조정할수 있는 방법을 제시하였다. 본 모델은 역전파를 기본으로 한 알고리즘으로 학습하며,이 방법의 타당성을 로보트 매니퓰레이터를 통해 검증한다.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 1997년도 춘계학술대회 논문집
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• pp.208.2-212
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• 1997

In a model based friction comensation for a frictional system,the performance of the system is inflenceed by the selection of the friction model. Especially, when a real plant have dynamic friction characteritics, the compensation of friction with a static friction model may deteriorate the perfomance. For the system we constlucted an adaptiv parameter estimation and friction compensation with a newly introduced dynamic friction model proposed by Canudas et.[1]. The model depicts varios frictional phenomena,such as Stibeck effect,frictional memory, Stick-slip motion. Parmeter identification algorithm are followed conventional RLSM adaptive rule. The stability for the closed system was proved by the Lyapunov stability. The result say that if a real system have dynamic friction property,the friction compensation with the dynamic friction model will improve the perfomance moreover static friction model based compensation may lead to the system unstable.