• The Journal of Korean Institute of Communications and Information Sciences
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• v.12 no.3
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• pp.267-275
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• 1987

This paper concerns a study on the theory of tearing which analyzes a large scale network by partitioning it into a number of small subnetworks by cutting through some of the existing nodes and branches in the network. By considering of the relationship its voltage and current of node cutting before and after, the consititutive equations of tearing method is equvalent to renumbering the nodes of untorn network equations. Therefore the analysis of network is conveniently applied as same algorithm that is used in untorn network. Also the proposed nodal admittnace matrix is put in block diagonal form, therefore this method permit parallel processing analysis of subnetworks. 30 nodes network was tested and the effectiveness of the proposed algorithm was proved.

• International Journal of Control, Automation, and Systems
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• v.6 no.1
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• pp.24-34
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• 2008

A novel neural network model, termed the standard neural network model (SNNM), similar to the nominal model in linear robust control theory, is suggested to facilitate the synthesis of controllers for delayed (or non-delayed) nonlinear systems composed of neural networks. The model is composed of a linear dynamic system and a bounded static delayed (or non-delayed) nonlinear operator. Based on the global asymptotic stability analysis of SNNMs, Static state-feedback controller and dynamic output feedback controller are designed for the SNNMs to stabilize the closed-loop systems, respectively. The control design equations are shown to be a set of linear matrix inequalities (LMIs) which can be easily solved by various convex optimization algorithms to determine the control signals. Most neural-network-based nonlinear systems with time delays or without time delays can be transformed into the SNNMs for controller synthesis in a unified way. Two application examples are given where the SNNMs are employed to synthesize the feedback stabilizing controllers for an SISO nonlinear system modeled by the neural network, and for a chaotic neural network, respectively. Through these examples, it is demonstrated that the SNNM not only makes controller synthesis of neural-network-based systems much easier, but also provides a new approach to the synthesis of the controllers for the other type of nonlinear systems.

• Journal of the Korea Society of Computer and Information
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• v.24 no.8
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• pp.159-166
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• 2019

In this paper, we propose takes network marketing as a starting point for analysis, uses the theory of purchasing behavior and enterprise performance to analyze the network marketing strategy of artistic products, incorporates the practical problems encountered by some artistic products enterprises in Zhejiang Province in network marketing into theoretical research. The theoretical model of network marketing strategy acting on enterprise performance through the intermediary effect of purchasing behavior is constructed. This paper conducted an in-depth survey of three representative core domestic companies engaged in Internet marketing of artistic products, and analyzed the questionnaires of 357 respondents. The initial model was verified by statistical tools such as SPSS and AMOS, and three conclusions were drawn: Firstly, network marketing strategies of different dimensions have different effects on purchasing behavior: pricing strategy and product strategy have significant positive effects on impulse purchasing behavior, but channel strategy has no significant impact on impulse purchasing behavior; Channel strategy and product strategy have a significant positive impact on repeated purchasing behavior, but pricing strategy has no significant impact on repeated purchasing behavior. Second, user purchasing behavior has a significant positive impact on enterprise performance. Third, network marketing strategies of different dimensions have significant direct and positive impact on enterprise performance.

• Information Systems Review
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• v.16 no.2
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• pp.1-23
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• 2014

With side effects such as Phishing and Spam using personal information in Social Network Service, there is a growing need for studies related to harmful behaviors such as the reason for privacy violation. As such, this study assumed privacy violation to be ethical decision, making behavior and used the Theory of Planned Behavior and Motivation Theory, which are mostly used in social science to identify the factors affecting privacy violation. The results suggested that the Perceived Enjoyment and Punishment used in motivation studies affected privacy violation behaviors, and that the factors of the Theory of Planned Behavior such as Attitude toward Privacy Violation, Subjective Norms of Privacy Violation, and Perceived Behavioral Control with regard to Privacy Violation significantly influenced the Intention to Privacy Violation. On the other hand, Perceived Curiosity and Subjective Norms of Privacy Violation did not affect the Intention to Privacy Violation. Therefore, this study confirmed that the Theory of Planned Behavior was appropriate to explain the Intention to Privacy Violation, and that the variables of the Motivation Theory generally influenced the Attitude toward Privacy Violation. This study was significant since it extended the scope of theoretical privacy study from users and victims centered to inflictor and applied the Extended Theory of Planned Behavior using the variables of the Motivation Theory in the study of Intention to Privacy Violation. From the practical aspect, it provided the ground for privacy education based on the fact that the Attitude toward Privacy Violation can be curbed when education on the Privacy Concerns, Perceived Enjoyment, and Punishment with regard to privacy is strengthened. It also cited the need for the punishment of privacy violation and the practical ground to amend the terms and conditions of user license and Personal Information Protection Act to provide policy support.

• Journal of Communications and Networks
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• v.10 no.3
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• pp.268-286
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• 2008

In this work, we review the routing models that use game theoretical methodologies. A very common assumption in the analysis and development of networking algorithms is the full cooperation of the participating nodes. Most of the analytical tools are based on this assumption. However, the reality may differ considerably. The existence of multiple domains belonging to different authorities or even the selfishness of the nodes themselves could result in a performance that significantly deviates from the expected one. Even though it is known to be extensively used in the fields of economics and biology, game theory has attracted the interest of researchers in the field of communication networking as well. Nowadays, game theory is used for the analysis and modeling of protocols in several layers, routing included. This review aims at providing an elucidation of the terminology and principles behind game theory and the most popular and recent routing models. The examined networks are both the traditional networks where latency is of paramount importance and the emerging ad hoc and sensor networks, where energy is the main concern.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.4A
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• pp.373-378
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• 2007

In this paper, the Game Theory based power control for OFDM system is studied, which has attained intensive interest as a core artificial intelligent technology for Cognitive Radio and its efficiency is evaluated using performance metrics such as system throughput and fairness. Utility Function for joint user centric and network centric power control is defined and simulation results show that game theory based power control is far better than closed loop power control. The contribution of this paper is to formalize the game theory based power control toward the Cognitive Radio that recognizes and adapts to the radio communication environments.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2004.04a
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• pp.477-482
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• 2004

Due to the traffic congestion of the city and public transportation-oriented policies, public transportation is receiving more attention and being used increasingly However, relatively less research on the design and distribution of public transportation network and limitations in quantitative approaches have made implementation and operation practically difficult. Over- or under-supply of transportation routes caused unbalanced connectivity among areas and differences in time, expenses and metal burden of users. On the other hand, the Space Syntax theory, designed to calculate the connectivity of urban or architectural space, helps generate quantitative connectivity of whole space simply based on the spacial structure. This study modified the original Space Syntax algorithm to fit the public transportation problem and showed how it is appied to a network by creating an artificial network using the GIS.

• Journal of Information Technology Services
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• v.12 no.4
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• pp.145-164
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• 2013

With the rapidly expanding social network service, the distribution of information shows that social networks have evolved into platforms of communication and new information sharing among users. Previous studies are focused on the motivational factors of information sharing through social networking service. However, in this study, we focus on the factors that affect intention to share information in terms of both user's psychological motivation and functional characteristics of social network service. This study shows that factors such as enjoyfulness, image, identity and communication positively affect the attitude and intention of information sharing.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1727-1731
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• 2004

We propose an ART2 neural network-based fault diagnosis method to diagnose of sensor in the gas monitoring system. In the proposed method, using thermal modulation of operating temperature of sensor, the signal patterns are extracted from the voltage of load resistance. Also, fault classifier by ART2 NN (adaptive resonance theory 2 neural network) with uneven vigilance parameters is used for fault isolation. The performances of the proposed fault diagnosis method are shown by simulation results using real data obtained from the gas monitoring system.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.9
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• pp.1673-1679
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• 2010

A photovoltaic (PV) generator is significantly regarded as one important alternative of renewable energy systems recently. Fault detection and diagnosis of engineering dynamic systems is a fundamental issue to timely prevent unexpected damages in industry fields. This paper presents an intelligent monitoring approach and fault detection technique for PV generator systems by means of artificial neural network and statistical signal detection theory. We devise a multi-Fourier neural network model for representing dynamics of PV systems and apply a general likelihood ratio test (GLRT) approach for investigating our decision making algorithm in fault detection and diagnosis. We make use of a test-bed of ubiquitous sensor network (USN) based PV monitoring systems for testing our proposed fault detection methodology. Lastly, a real-time experiment is accomplished for demonstrating its reliability and practicability.