• International Journal of Contents
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• v.5 no.1
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• pp.21-26
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• 2009

Peer to Peer (P2P) utilizes the resources of an offered service without the need for a central server or preexistent server-client, making it a desirable network environment for data exchange based on direct connection between peers. Proposed by Sun Microsystems. JXTA(Juxtapose) is a typical P2P system and distributed computing model that does not require central service resources and is flexible to deal with various network configuration changes. Meanwhile. Mobile Ad-hoc NETwork(MANET) is a typical wireless network configured with mobile nodes and without an infrastructure. where a network is established by direct connection or through other peers in the propagation area. Thus, MANET maintains the latest path information by establishing paths and changing path information for communication between peers in a highly mobile wireless network. Accordingly. this article proposes the JXTAMANET method for wireless networks to enable JXTA to be applied to MANET. NS2 is used to evaluate the performance and the proposed architecture is shown to produce better results than a conventional flooding method.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10a
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• pp.14-19
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• 1993

In this paper, we proposed an indirect learning and direct adaptive control schemes using neural networks, i.e., composite adaptive neural control, for a class of continuous nonlinear systems. With the indirect learning method, the neural network learns the nonlinear basis of the system inverse dynamics by a modified backpropagation learning rule. The basis spans the local vector space of inverse dynamics with the direct adaptation method when the indirect learning result is within a prescribed error tolerance, as such this method is closely related to the adaptive control methods. Also hash addressing technique, similar to the CMAC functional architecture, is introduced for partitioning network hidden nodes according to the system states, so global neuro control properties can be organized by the local ones. For uniform stability, the sliding mode control is introduced when the neural network has not sufficiently learned the system dynamics. With proper assumptions on the controlled system, global stability and tracking error convergence proof can be given. The performance of the proposed control scheme is demonstrated with the simulation results of a nonlinear system.

• Korean Management Science Review
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• v.26 no.3
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• pp.169-184
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• 2009

Recently, mobile telecommunication businesses contend with each other to expand their customer base by using aggressive marketing strategies. In order to determine if there strategies are effective, customer's switching behavior needs to be studied. This study identifies and analyzes direct, indirect factors that may customer switching behavior : attractiveness of alternatives, network externality, and switching cost. Results reveals that attractiveness of alternatives, network externalities have a direct impact on customer switching behavior. These two factors also have moderating effects on customer switching behavior but the switching cost does not In short, network externalities and alternatives strategically determine the success of 3.5G service. In this regard, mobile business should improve their own attractiveness of alternatives by developing specialized service in 3.5G service.

• International Journal of Control, Automation, and Systems
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• v.3 no.4
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• pp.552-563
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• 2005

In this paper, we propose a wavelet based fuzzy neural network (WFNN) based direct adaptive control scheme for the solution of the tracking problem of mobile robots. To design a controller, we present a WFNN structure that merges the advantages of the neural network, fuzzy model and wavelet transform. The basic idea of our WFNN structure is to realize the process of fuzzy reasoning of the wavelet fuzzy system by the structure of a neural network and to make the parameters of fuzzy reasoning be expressed by the connection weights of a neural network. In our control system, the control signals are directly obtained to minimize the difference between the reference track and the pose of a mobile robot via the gradient descent (GD) method. In addition, an approach that uses adaptive learning rates for training of the WFNN controller is driven via a Lyapunov stability analysis to guarantee fast convergence, that is, learning rates are adaptively determined to rapidly minimize the state errors of a mobile robot. Finally, to evaluate the performance of the proposed direct adaptive control system using the WFNN controller, we compare the control results of the WFNN controller with those of the FNN, the WNN and the WFM controllers.

• Knowledge Management Research
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• v.17 no.4
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• pp.77-104
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• 2016

The effective management of R&D employees is critical for a small or medium sized firm's sustainable growth. R&D employees have professional skills and choose expertise-oriented or management-oriented careers in the process of organizational socialization. This study synthetically verifies the direct and indirect effects of R&D employees' social network and self-esteem on their career orientation by organizational commitment based on social network theory and social recognition theory. The research model has been analyzed through structural equation modeling using survey responses from 220 R&D employees at small- and medium-sized firms in Korea. The analysis results show that internal network activities have direct and indirect impacts on organizational commitment and career orientation, but external network activities do not have significant effects on self-esteem, organizational commitment, or career orientation. There is no consensus in prior studies on whether expert orientation and management orientation are distinct concepts. In this study, these two types of orientation are verified as distinct concepts. It is also found that R&D employees' internal network activities are significant factors for a company's growth. A company should implement an educational system of roles and duties using which individuals can pursue career progression. In addition, it is necessary to provide career development programs such as job rotation, mentoring, and career counseling.

• International Journal of Advanced Culture Technology
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• v.5 no.2
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• pp.19-25
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• 2017

The research is to identify social networks of problems that have an influence on the quality of ageing people's lives by using social network analysis, based on the premise that there are differences in networks of ageing problems in urban and rural areas. From analyzing network of ageing people's problems using NodeXL, vertices in the networks of both urban and rural areas are well-connected. For urban areas, financial poverty is the core problem related to the quality of life. It has direct connections with illness and health, family responsibility, housing, role loss in community, and employment, which have positive or negative interactions with the quality of older people's lives. For rural areas, on the other hand, role loss in community is the major problem. It has direct connections with the elderly abuse, financial poverty, leisure activity, divorce, isolation and loneliness from society, education, and suicide. As a result, the research shows that the problems of ageing people have strong linkages and interactive effects with a structure of network, and the networks are different depending on types of places for living.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.453-456
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• 1996

In this paper, the direct adaptive control using neural networks is presented for the control of chaotic nonlinear systems. The direct adaptive control method has an advantage that the additional system identification procedure is not necessary. In order to evaluate the performance of our controller design method, two direct adaptive control methods are applied to a Duffing's equation and a Lorenz equation which are continuous-time chaotic systems. Our simulation results show the effectiveness of the controllers.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.44 no.6
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• pp.1-10
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• 2007

This paper presents a direct nonlinear multivariable self-tuning PID controller using neural network which adapts to the changing parameters of the nonlinear multivariable system with noises and time delays. The nonlinear multivariable system is divided linear part and nonlinear part. The linear controller are used the self-tuning PID controller that can combine the simple structure of a PID controllers with the characteristics of a self-tuning controller, which can adapt to changes in the environment. The linear controller parameters are obtained by the recursive least square. And the nonlinear controller parameters are achieved the through the Back-propagation neural network. In order to demonstrate the effectiveness of the proposed algorithm, the computer simulation results are presented to adapt the nonlinear multivariable system with noises and time delays and with changed system parameter after a constant time. The proposed PID type nonlinear multivariable self-tuning method using neural network is effective compared with the conventional direct multivariable adaptive controller using neural network.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.35C no.6
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• pp.99-108
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• 1998

In this paper, we proposed a multi-layer neural network for direct control method of nonlinear system. The proposed control method uses neural network as the controller to learn inverse model of plant. The neural network used consists of two parts; one part is for identification of linear part, and the other is for identification of nonlinear part of inverse system. The neural network has to be learned the liner part with RLS algorithm and the nonlinear part with error of plant. From the simulation and experiment of tracking control to use one link manipulator as plant, we proved usefulness of the proposed control method to comparing to conventional direct neural network control method. By comparing the two methods, from simulation and experiment, we were convinced that the proposed control method is more simple and accuracy than the conventional method. Moreover, number of weight and bias to be controller parameter are small, and it has smaller steady state error than conventional method.

• Journal of the Economic Geographical Society of Korea
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• v.26 no.3
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• pp.256-273
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• 2023

This study analyzed the structural changes in the global foreign direct investment (FDI) network using stock data from the International Monetary Fund's Coordinated Direct Investment Survey (CDIS) for 2009~2021. The results showed that the COVID-19 pandemic had a negative impact on the FDI links between countries and the activities of reciprocal relationships. The United States, the Netherlands, and the United Kingdom consistently play central roles in the global FDI network. The degree centrality of China has changed significantly over time in confronting the volatile situation of the world economy. Cross-tabulation analysis revealed a significant association between FDI clusters and geographic regions. Within each cluster, the linkage structure of FDI partners of closely connected individual countries has exhibited differential characteristics as the global economic environment changes.