• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1995.10b
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• pp.249-257
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• 1995

In this paper, we demeonstrate that neural networks can be used effectively for the control of nonlinear dynamical system. To adaptively control a plant, there are two distinct approach. these are direct control and indirect control. Both direct and Indirect adaptive control are trained using static back propagation. In indirect, using the resulting identification model, which contains neural networks and linear dynamical elements as subsystems, the parameters of the controller are adjusted.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.4
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• pp.53-62
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• 2023

Effects-Based Operations (EBO) refers to a process for achieving strategic goals by focusing on effects rather than attrition-based destruction. For a successful implementation of EBO, identifying key nodes in an adversary network is crucial in the process of EBO. In this study, we suggest a network-based approach that combines network centrality and optimization to select the most influential nodes. First, we analyze the adversary's network structure to identify the node influence using degree and betweenness centrality. Degree centrality refers to the extent of direct links of a node to other nodes, and betweenness centrality refers to the extent to which a node lies between the paths connecting other nodes of a network together. Based on the centrality results, we then suggest an optimization model in which we minimize the sum of the main effects of the adversary by identifying the most influential nodes under the dynamic nature of the adversary network structure. Our results show that key node identification based on our optimization model outperforms simple centrality-based node identification in terms of decreasing the entire network value. We expect that these results can provide insight not only to military field for selecting key targets, but also to other multidisciplinary areas in identifying key nodes when they are interacting to each other in a network.

• Journal of Distribution Science
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• v.20 no.10
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• pp.51-60
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• 2022

Purpose: The aim of this research is to study 1) the effects of internal factors (technological capability and entrepreneurial orientation) that affect Thailand's hotel business network capability. 2) the effects of external factors (government policy and trust relationship) that affect Thailand's hotel business network capability. 3) the impact of network capability on the firm performance. 4) the moderating effect of absorptive capacity between network capability and firm performance. Research design, data and methodology: The test model collected data from a mail survey of 164 hotel businesses in Thailand. The correlation and multiple regression were adopted to analyze and test the proposed hypotheses. Results: Interestingly, technological capability, entrepreneurship orientation, and trust relationship have a direct impact on network capability. However, network capability still does not have a significant relationship with firm performance in all dimensions. Surprisingly, the absorptive capacity does not have a moderating effect on the relationship of network capability on firm performance of hotel businesses in Thailand. Conclusions: This research found that the hotel business should focus on analyzing the external and internal environment as it affects network building, which will guide the creation of strategies for further increasing hotel distribution channels and competitive advantage.

• JSTS:Journal of Semiconductor Technology and Science
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• v.4 no.4
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• pp.258-262
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• 2004

Storage over Ethernet (SoE) is a network storage architecture that allows direct attachment of existing ATA/ATAPI devices to Ethernet without a separate server. Unlike SAN, no server computer intervenes between the storage and the client hosts. We propose a SoE disk controller (SoEDC) amenable to low-cost, single-chip implementation that processes a simplified L3/L4 protocol and converts commands between Ethernet and ATA/ATAPI, while the rest of the complex tasks are performed by the remote hosts. Thanks to simple architecture and protocol, the SoEDC implemented on a single $4mm{\times}4mm$ chip in 0.18um CMOS technology achieves maximum throughput of 55MB/s on Gigabit Ethernet, which is comparable to that of a high-performance disk storage locally attached to a host computer.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.8
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• pp.3435-3454
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• 2016

Device-to-device (D2D) communication is a potential solution to the incessant increase in data traffic on cellular networks. The greatest problem is how to control the interference between D2D users and cellular mobile users, and between D2D users themselves. This paper proposes a solution for this issue by putting the full control privilege in cellular network using the software-defined networking (SDN) concept. A software virtual switch called Open vSwitch and several components are integrated into mobile devices for data forwarding and radio resource mapping, whereas the control functions are executed in the cellular network via a SDN controller. This allows the network to assign radio resources for D2D communication directly, thus reducing interference. This solution also brings out many benefits, including resource efficiency, energy saving, topology flexibility, etc. The advantages and disadvantages of this architecture are analyzed by both a mathematical method and a simple implementation. The result shows that implementation of this solution in the next generation of cellular networks is feasible.

• Proceedings of the KIEE Conference
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• 2009.07a
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• pp.1743_1744
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• 2009

In this paper, we propose a double network control approach for linear systems. Generally, there are two network control system structures: the direct structure and the hierarchical structure. Here, the hierarchical structure consists of a main controller and a remote controller. The network delay of the structure only appears in the closed loop between the main controller and the remote system. However, the delay can exist between the remote controller and the actuator. Therefore, we design the double network system with delays between the main controller and the remote system, and the remote controller and the actuator. Finally, we carry out simulations on the linear system to illustrate the effectiveness of the proposed control method.

• Proceedings of the KIEE Conference
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• 2006.07d
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• pp.1949-1950
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• 2006

In this paper, a direct controller for nonlinear plants using a neural network is presented. The controller is composed of an approximate controller and a neural network auxiliary controller. The approximate controller gives the rough control and the neural network controller gives the complementary signal to further reduce the output tracking error. This method does not put too much restriction on the type of nonlinear plant to be controlled. In this method, a RBF neural network is trained and the system has a stable performance for the inputs it has been trained for. Simulation results show that it is very effective and can realize a satisfactory control of the nonlinear system.

• Journal of Internet Computing and Services
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• v.18 no.4
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• pp.7-17
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• 2017

Despite various types of online social networks having different topological and functional characteristics, the kinds of online social networks considered in social recommendations are highly restricted. The pervasiveness of social networks has brought scholarly attention to expanding the scope of social recommendations into more diverse and less explored types of online social networks. As a preliminary attempt, this study examined the information-sharing patterns of a new type of online social network - unilateral (directed) network - and assessed the feasibility of the network as a useful information source. Specifically, this study mainly focused on the presence of shared interests in unilateral networks, because the shared information is the inevitable condition for utilizing the networks as a feasible source of personalized recommendations. As the results, we discovered that user pairs with direct and distant links shared significantly more similar information than the other non-connected pairs. Individual users' social properties were also significantly correlated with the degree of their information similarity with social connections. We also found the substitutability of online social networks for the top cohorts anonymously chosen by the collaborative filtering algorithm.

• Korean Journal of Hydrosciences
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• v.10
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• pp.47-58
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• 1999

Protection of groundwater resources from contamination has been of increasing concern throughout the past decades. In practice, however, groundwater monitoring is performed based on the experience and intuition of experts or on the convenience. In dealing with groundwater contamination, we need to know what contaminants have the potential to threat the water quality and the distribution and concentration of the plumes. Monitoring of the subsurface environment through remote geophysical techniques or direct sampling from wells can provide such information. Once known, the plume can be properly menaged. Evaluation of existing methodologies for groundwater monitoring network design revealed that one should select an appropriate design method based on the purpose of the network and the avaliability of field information. Integer programming approach, one of the general purpose network design tools, and a cost-to-go function evaluation approach for special purpose network design were tested for field applicability. For the same contaminated aquifer, two approaches resulted in different well locations. The amount of information, however, was about the same.

• Proceedings of the KIEE Conference
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• 1998.07b
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• pp.401-403
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• 1998

This paper describes the neural network control method for the identification and control of chaotic nonlinear dynamical systems effectively. In our control method, the controlled system is modeled by an unknown NARMA model, and a feedforward neural network is used for identifying the chaotic system. The control signals are directly obtained by minimizing the difference between a setpoint and the output of the neural network model. Since learning algorithm guarantees that the output of the neural network model approaches that of the actual system, it is shown that the control signals obtained can also make the real system output close to the setpoint.