• The Journal of Korea Robotics Society
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• v.3 no.3
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• pp.255-261
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• 2008

This paper describes a network framework that support network based humanoid. The framework utilizes middleware such as CORBA (ACE/TAO) that provides PnP capability for network based humanoid. The network framework transfers data gathered from a network based humanoid to a processing group that is distributed on a network. The data types are video stream, audio stream and control data. Also, the network framework transfers service data produced by the processing group to the network based humanoid. By using this network framework, the network based humanoid can provide high quality of intelligent services to user.

• ETRI Journal
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• v.25 no.6
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• pp.445-463
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• 2003

XML-based network management, which applies XML technologies to network management, has been proposed as an alternative to existing network management. The use of XML in network management offers many advantages. However, most existing network devices are already embedded with simple network management protocol (SNMP) agents and managed by SNMP managers. For integrated network management, we present the architectures of an XML-based manager, an XML-based agent, and an XML/SNMP gateway for legacy SNMP agents. We describe our experience of developing an XML-based network management system (XNMS), XML-based agent, and an XML/SNMP gateway. We also verify the effectiveness of our XML-based agent and XML/SNMP gateway through performance tests. Our experience with developing XNMS and XML-based agents can be used as a guideline for development of XML-based management systems that fully take advantage of the strengths of XML technologies.

• International Journal of Automotive Technology
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• v.7 no.6
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• pp.687-695
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• 2006

This paper presents a network-based traction control system(TCS), where several electric control units(ECUs) are connected by a controller area network(CAN) communication system. The control system consists of four ECUs: the electric throttle controller, the transmission controller, the engine controller and the traction controller. In order to validate the traction control algorithm of the network-based TCS and evaluate its performance, a Hardware-In-the-Loop Simulation(HILS) environment was developed. Herein we propose a new concept of the HILS environment called the network-based HILS(Net-HILS) for the development and validation of network-based control systems which include smart sensors or actuators. In this study, we report that we have designed a network-based TCS, validated its algorithm and evaluated its performance using Net-HILS.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.11
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• pp.4246-4267
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• 2020

As the network goes deep into all aspects of people's lives, the number and the complexity of network traffic is increasing, and traffic classification becomes more and more important. How to classify them effectively is an important prerequisite for network management and planning, and ensuring network security. With the continuous development of deep learning, more and more traffic classification begins to use it as the main method, which achieves better results than traditional classification methods. In this paper, we provide a comprehensive review of network traffic classification based on deep learning. Firstly, we introduce the research background and progress of network traffic classification. Then, we summarize and compare traffic classification based on deep learning such as stack autoencoder, one-dimensional convolution neural network, two-dimensional convolution neural network, three-dimensional convolution neural network, long short-term memory network and Deep Belief Networks. In addition, we compare traffic classification based on deep learning with other methods such as based on port number, deep packets detection and machine learning. Finally, the future research directions of network traffic classification based on deep learning are prospected.

• 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 Korea Multimedia Society
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• v.24 no.5
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• pp.676-683
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• 2021

Wi-Fi Round-trip timing (RTT) based location estimation technology estimates the distance between the user and the AP based on the transmission and reception time of the signal. This is because reception instability and signal distortion are greater than that of a Received Signal Strength Indicator (RSSI) based fingerprint in an indoor NLOS environment, resulting in a large position error due to multipath fading. To solve this problem, in this paper, we propose an end-to-end based WiFi Trilateration Net (WTN) that combines neural network-based RTT correction and trilateral positioning network, respectively. The proposed WTN is composed of an RNN-based correction network to improve the RTT distance accuracy and a neural network-based trilateral positioning network for real-time positioning implemented in an end-to-end structure. The proposed network improves learning efficiency by changing the trilateral positioning algorithm, which cannot be learned through differentiation due to mathematical operations, to a neural network. In addition, in order to increase the stability of the TOA based RTT, a correction network is applied in the scanning step to collect reliable distance estimation values from each RTT AP.

• 한국정보통신설비학회:학술대회논문집
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• 2007.08a
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• pp.273-278
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• 2007

In the next generation wireless mobile network, various methods are studied to offer interworking and mobility between various radio networks. To offer these harmoniously, network adaptation methods based on IP is generalized, and specifications of host-based mobility method with Mobile IPv4 and Mobile IPv6 to offer IP's mobility are defined in IETF specially. However, it is insufficient to satisfy quality of service that should be offered in wireless mobile network environment. Alternatively studies about Network-Based Mobility of Proxy Mobile IPv4, Proxy Mobile IPv6 etc. are preceded. This paper presents optimum plan that can offer mobility in the next generation radio transfer communication network by comparing and analyzing IP mobility methods divided by Host-based Mobility and Network-based Mobility.

• Proceedings of the IEEK Conference
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• 1999.06a
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• pp.158-161
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• 1999

This Paper proposes the architecture of WDM(wavelength division multiplexed) based large scale photonic packet switch network, which is composed of the FC(frequency converter) and OM (output module). The features of the proposed WDM based photonic packet switch network are 2-stage switch network, and WDM based internal optical link that is connected between FC and OM. This paper evaluates the internal call blocking characteristics of the photonic packet switch network. In results, we confirmed that the proposed WDM based photonic packet switch network has the potentiality in the practical implementation.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2005.10a
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• pp.189-206
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• 2005

Much of work on network externality assumed network effects are dependent on the network size. Therefore, very little consideration is given to the view that marginal benefits from joining the network may not increase with the network size if consumer benefits come from the direct interaction with neighbors, namely local network. In this study, we used the agent-based simulation method to reexamine the effectiveness of the traditional network market strategy under the presence of the local network where two incompatible technologies compete. We found that the strategy of growing an initial customer base is not effective under the presence of the local network. Our study also showed that targeting customers based on their technology Preference is not as effective as targeting customers within the same local network. As a result, the focus of a network market strategy should be directed to taking advantage of the customer network.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.11
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• pp.4016-4027
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• 2014

Understanding network latency is important for providing consistent and acceptable levels of services in network-based applications. However, due to the difficulty of estimating applications' network demands and the difficulty of network latency modeling the management of network resources has often been ignored. We expect that, since network latency repeats cycles of congested states, a systematic classification method for network status would be helpful to simplify issues in network resource managements. This paper presents a simple empirical method to classify network status with a real operational network. By observing oscillating behavior of end-to-end latency we determine networks' status in run time. Five typical network statuses are defined based on a long-term stability and a short-term burstiness. By investigating prediction accuracies of several simple numerical models we show the effectiveness of the network status classification. Experimental results show that around 80% reduction in prediction errors depending on network status.