• 대한전기학회논문지:전력기술부문A
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• 제48권10호
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• pp.1239-1246
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• 1999

This paper presents a new algorithm to construct the modular network model for SSR analysis by simply applying KCL to each node and KVL to all branches connected to the node sequentially. This method has advantages that the model can be derived directly from the system data for transient stability study and turbine/generator shaft model, the resulted model in the form of augmented state matrix is very sparse, and thus efficient SSR study of a large scale system becomes possible. The proposed algorithm is verified with the IEEE First and Second Benchmark models.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2002년도 하계종합학술대회 논문집(3)
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• pp.139-142
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• 2002

The brain of the human is the best model for the artificial intelligence and is studied by many natural, medical scientists and engineers. In the engineering department, the brain model becomes a main subject in the area of development of a system that can represent and think like human. In this paper, we approach and define the function of the brain biologically and especially, make a model for the function of cerebral cortex, known as a part that performs behavior inference and decision for sensitive information from the thalamus. Therefore, we try to make a model for the transfer process of the brain. The brain takes the sensory information from sensory organ, proceeds behavior inference and decision and finally, commands behavior to the motor nerves. We use the modular neural network in this model. finally, we would like to design the intelligent system that can sense, recognize, think and decide like the brain by learning the information process in the brain with the modular neural network.

• 대한교통학회지
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• 제12권3호
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• pp.49-64
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• 1994

The development of a mathematical modular framework based on Petri Network theory to model a traffic network is the subject of this paper. Traffic intersections are the primitive elements of a transportation network and are characterized as event driven and asynchronous systems. Petri network have been utilized to model these discrete event systems; further analysis of their structure can reveal information relevant to the concurrency, parallelism, synchronization, and deadlock avoidance issuse. The Petri-net based model of a generic traffic junction is presented. These modular networks are effective in synchronizing their components and can be used for modeling purposes of an asynchronous large scale transportation system. The derived model is suitable for simulations on a multiprocessor computer since its program execution safety is secured. The software pseudocode for simulating a transportation network model on a multiprocessor system is presented.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1997년도 한국자동제어학술회의논문집; 한국전력공사 서울연수원; 17-18 Oct. 1997
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• pp.488-491
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• 1997

The PMGS(Plant Model Generating System) was developed based on modular modeling method and fluid network calculation concept. Fluid network calculation is used as a method of real-time computation of fluid network, and the module which has a topology with node and branch is defined to take advantages of modular modeling. Also, the database which have a shared memory as an instance is designed to manage simulation data in real-time. The applicability of the PMGS was examined implementing the HRSG(Heat Recovery Steam Generator) control logic on DCS.

• 한국산학기술학회논문지
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• 제17권7호
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• pp.263-272
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• 2016

모듈러 제품 설계는 유연한 제품 수정, 제품 개발 및 생산 리드타임 감소, 제품의 다양성 증대와 같은 많은 장점들이 있다. 모듈러 제품 설계의 목적은 여러 개의 모듈들로 구성된 제품의 아키텍쳐를 효율적으로 개발하는 것인데, 이들 모듈들은 컴포넌트들 간 물리적, 기능적 상호관계 분석을 토대로 컴포넌트들 간의 유사성을 최대화함으로써 만들어 질 수 있다. 본 연구에서는 모듈러 제품 설계의 핵심 작업인 모듈화를 위하여 3개 단계로 이뤄진 체계적인 절차를 제시하고자 한다. 첫 번째 단계는 컴포넌트들 간 물리적, 기능적 상호관계 분석을 통한 컴포넌트들 간 상관 관계 매트릭스를 구성하는 것이고, 두 번째 단계는 컴포넌트들 간 상관 관계를 최대화하는 컴포넌트들의 군집들을 찾아내기 위하여 네트워크 흐름으로 모델링하는 것이다. 마지막으로 세 번째 단계에서는 선형 계획 모형인 네트워크 흐름 모델을 풀어서 컴포넌트들의 군집들을 찾아내고 이들을 모듈화 하는 것이다. 본 연구에서 제시한 절차의 이해와 실제 적용을 위하여 진공 청소기 모듈화 사례에 적용해 보고 절차의 타당성을 보여준다.

• 한국정보통신학회:학술대회논문집
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• 한국해양정보통신학회 2001년도 춘계종합학술대회
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• pp.369-373
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• 2001

In this paper, we proposes the steps adopted to construct the neural network model for GMAW welds. Conventional, automated process generally involves sophisticated sensing and control techniques applied to various processing parameters. Welding parameters are influenced by numerous factors, such as welding current, arc voltage, torch travel speed, electrode condition and shielding gas type and flow rate etc. In traditional work, the structural mathematical models have been used to represent this relationship. Contrary to the traditional model method, neural network models are based on non-parametric modeling techniques. For the welding process modeling, the non-linearity at well as the coupled input characteristics makes it apparent that the neural network is probably the most suitable candidate for this task. Finally, a suitable proposal to improve the construction of the model has also been presented in the paper.

• 한국산업정보학회논문지
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• 제19권2호
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• pp.51-58
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• 2014

As WSNs(Wireless Sensor Networks) are being deployed widely in diverse application areas, their management and maintenance become more important. Recent sensor node software takes modular software architectures in pursuit of flexible software management and energy efficient reprogramming. To realize an flexible and efficient modular architecture particularly on resource constrained mote-class sensor nodes that are implemented with MCUs(Micro-Controller Units) of a single address space. an appropriate module linking model is essential to resolve and bind the inter-module global symbols. This paper identifies a design space of module linking model and respectively their implementation frameworks. We then establish a taxonomy for module linking models by exploring the design space of module linking models. Finally, we suggest an implementation framework respectively for each module linking model in the taxonomy. We expect that this work lays the foundations for systematic innovation toward more flexible and efficient modular software architectures for WSNs.

• 한국지반공학회논문집
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• 제16권2호
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• pp.71-77
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• 2000

In this paper, consolidation settlements with time at vertical drain sites were predicted by artificial neural networks. Laboratory test results and field measurements of two vertical drain sites were used for training and testing neural networks. Predicted consolidation settlements by trained artificial neural networks were compared with measured settlements by field instrumentation. To improve the prediction accuracy, modular artificial neural networks were studied. From the results of applying artificial neural networks to the same situation, it was shown that modular artificial neural network model was more accurate for the prediction of the consolidation settlements than the general model.

• 한국지능시스템학회논문지
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• 제11권5호
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• pp.395-399
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• 2001

이 논문은 학습을 통해 관측 데이터로부터 퍼지 추론 모듈을 생성할 수 있는 적응 능력을 갖는 모듈화 퍼지추론 시스템을 제안한다. 제안한 시스템은 TS 퍼지모델과 모듈화 신경회로망의 구조적 유사성을 기초로 한다. 학습과정은 새로운 퍼지추론 모듈의 생성과 모듈 파라미터의 갱신으로 구성된다. 퍼지추론 모듈은 국부모델망과 퍼지 게이팅망으로 구성된다. 제안한 시스템의 파라미터들은 표준 LMS 알고리즘을 이용하여 최적화된다. 제안한 시스템의 성능은 비선형 동적 시스템 적응제어에의 응용을 통해서 입증된다.

• 정보보호학회논문지
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• 제11권2호
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• pp.13-26
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• 2001

본 논문은 계층 구조적이고 모듈화 된 모델링 및 시뮬레이션 프레임워크를 이용한 네트워크 보안 모델링과 시뮬레이션 기법의 연구를 주목적으로 한다. 최근, Howard와 Amroso는 사이버 공격, 방어 및 결과에 대한 원인-결과 모델을 개발하였다. 또한, Cohen은 원인-결과 모델을 이용하여 단순한 네트워크 보안 시뮬레이션 방법론을 제안한 바 있으나, 복잡한 네트워크 보안과 모델과 모델 기반의 사이버 공격에 대한 시뮬레이션은 불가능한 실정이다. 따라서, 본 논문에서 는 인공지능의 기호적 형식론과 시뮬레이션의 동역학적 형식론을 체계적이고 통합한 System Entity Structure/Model Base(SES/MB)을 통하여 계층 구조적이고 모듈화 된 네트워크 보안 모델링 및 시뮬레이션 방법론을 제안하고 사이버 공격 시나리오를 이용한 사례연구를 통하여 타당성을 검증하였다.