• 산업경영시스템학회지
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• 제44권1호
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• pp.37-44
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• 2021

The information products dramatically reduce the production costs of vertically differentiated products. Information products are also more likely to be affected by network externalities. Thus the proliferation of digital products is increasing the interests in network externality and vertical product differentiation. In step with this trend, the impact of network externalities on price competition in vertically differentiated markets has been continuously studied. Existing studies related to this topic have assumed that network externalities increase consumers' willingness to pay per unit quality. The results show that higher quality products are affected more by network externality. However, network externality is essentially a concept affected by the size of the consumer, not a concept associated with quality. In this work, unlike previous studies, we present a new market model that reflects the essential definition of network externality. Based on the proposed market model, we derive both simultaneous and sequential Nash equilibria and analyze them numerically. The main results obtained from the analysis can be summarized as follows. First, network externalities primarily increase the demand for low-quality products and have a secondary impact on the demand for high-quality products. Second, the larger the quality difference between products, the more profitable they are. It also has been shown that sequential pricing methods are more advantageous in terms of revenue than simultaneous pricing method.

• 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.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2000년도 추계학술대회 학술발표 논문집
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• pp.115-118
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• 2000

In order to approximate a nonlinear function, modular wavelet networks combining wavelet theory and modular concept based on single layer neural network have been proposed as an alternative to conventional wavelet neural networks and kind of modular network. Modular wavelet networks provide better approximating performance than conventional one. In this paper, we propose an effective method to construct an optimal modualr wavelet network using genetic algorithm. This is verified through experimental results.

• Transactions on Control, Automation and Systems Engineering
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• 제2권3호
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• pp.196-200
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• 2000

A neural controller for process control is proposed that combines a conventional multi-loop PID controller with a neural network. The concept of target signal based on feedback error is used fur on-line learning of the neural network. This controller is applied to distillation column control to illustrate its effectiveness. The result shows that the proposed neural controller can cope well with disturbance, strong interactions, time delays without any prior knowledge of the process.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1992년도 한국자동제어학술회의논문집(국제학술편); KOEX, Seoul; 19-21 Oct. 1992
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• pp.112-117
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• 1992

The Real-time Distributed Control Systems(RDCS) consist of several distributed control processes which share a network medium to exchange their data. Performance of feedback control loops in the RDCS is subject to the network-induced delays from sensor to controller and from controller to actuator. The network-induced delays are directly dependent upon the data sampling times of the control components which share a network medium. In this study, a scheduling algorithm of determining data sampling times is developed using the window concept, where the sampling data from the control components dynamically share a limited number of windows.

• Transactions on Control, Automation and Systems Engineering
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• 제2권2호
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• pp.136-139
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• 2000

A neural controller for process control is proposed that combines a conventional multi-loop PID controller with a neural network. The concept of target signal based on feedback error is used for on-line learning of the neural network. This controller is applied to distillation column control to illustrate its effectiveness. The result shows that the proposed neural controller can cope well with disturbance, strong interactions, time delays without any prior knowledge of the process.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2001년도 하계종합학술대회 논문집(1)
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• pp.321-324
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• 2001

The multiple home PC, and home computing environment has undergone a significant evolution. And Internet explosion needs an integrated networking infrastructure within the home. Home networking is a concept that applies to the networking and application technologies to increase the comfort and ensure sharing and management of home resources as well as the provision of new, enhanced services. This paper proposes a Unification Protocol Architecture for home network. This Unification Protocol Architecture expect to a reference model in home network and to a foundation of the IIA (Internet Information Appliance) industry.

• 기술사
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• 제42권6호
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• pp.36-41
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• 2009

Smart Grid is grafting IT(information technology) techniques on existing electric power network, supplier and the consumer to do real-time exchange of information lead to both direction and energy efficiency optimization, it is a next potential electric power network method. Because of applying various distributed electric power sources, the electric power network system will follow in size and it is dispersive and it will operate independently, and it become the intelligent electric power network, which in consumer demand reacts at real-time, because of using various sensors. In this article explain concept, features, and contemporary background of Smart Grid, and describe improve reliability of the electric power quality.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1993년도 Fifth International Fuzzy Systems Association World Congress 93
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• pp.977.1-1000
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• 1993

This paper presents a fuzzy neural network, called the fuzzy counterpropagation network, that structures its inputs and generates its outputs in a manner based on counterpropagation networks. The fuzzy counterpropagation network is developed by incorporating the concept of fuzzy clustering into the hidden layer responses. Three learning algorithms are introduced for use with the proposed network. Simulations demonstrate that fuzzy counterpropagation networks with the proposed learning algorithms work well on approximating bipolar and continuous functions.

• 한국원자력학회:학술대회논문집
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• 한국원자력학회 1998년도 춘계학술발표회논문집(1)
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• pp.273-278
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• 1998

Belief network(or Bayesian network) based on Bayes' rule in probabilistic theory can be applied to the reasoning of diagnostic systems. This paper describes the basic theory of concept and feasibility of using the network for diagnosis of nuclear power plants. An example shows that the probabilities of root causes of a failure are calculated from the measured or believed evidences.