• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.10
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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.

• Proceedings of the IEEK Conference
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• 2002.06c
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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.

• Journal of Korean Society of Transportation
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• v.12 no.3
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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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• 1997.10a
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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.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.7
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• pp.263-272
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• 2016

Modular product design has contributed to flexible product modification and development, production lead time reduction, and increasing product diversity. Modular product design aims to develop a product architecture that is composed of detachable modules. These modules are constructed by maximizing the similarity of components based on physical and functional interaction analysis among components. Accordingly, a systematic procedure for clustering the components, which is a main activity in modular product design, is proposed in this paper. The first phase in this procedure is to build a component-to-component correlation matrix by analyzing physical and functional interaction relations among the components. In the second phase, network flow modeling is applied to find clusters of components, maximizing their correlations. In the last phase, a network flow model formulated with linear programming is solved to find the clusters and to make them modular. Finally, the proposed procedure in this research and its application are illustrated with an example of modularization for a vacuum cleaner.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2001.05a
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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.

• Journal of Korea Society of Industrial Information Systems
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• v.19 no.2
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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.

• Journal of the Korean Geotechnical Society
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• v.16 no.2
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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.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.5
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• pp.395-399
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• 2001

This paper describes modular fuzzy inference systems(MFIS) with adaptive capability to extract fuzzy inference modules from observation data through the learning process. The proposed MFIS is based on the structural similarity to Tagaki-Sugeno fuzzy models and a modular neural architecture. The learning of MFIS is done by assigning new fuzzy inference modules and by updating the parameters of existing modules. The fuzzy inference modules consist of local model network and fuzzy gating network. The parameters of the MFIS are updated by the standard LMS algorithm. The performance of the MFIS is illustrated with adaptive control of a nonlinear dynamic system.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.11 no.2
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• pp.13-26
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• 2001

This paper presents the network security modeling methodology and simulation using the hierarchical and modular modeling and simulation framework. Recently, Howard and Amoroso developed the cause-effect model of the cyber attack, defense, and consequences, Cohen has been proposed the simplified network security simulation methodology using the cause-effect model, however, it is not clear that it can support more complex network security model and also the model-based cyber attack simulation. To deal with this problem, we have adopted the hierarchical and modular modeling and simulation environment so called the System Entity Structure/Model Base (SES/MB) framework which integrates the dynamic-based formalism of simulation with the symbolic formalism of AI. Several simulation tests performed on sample network system verify the soundness of our method.