• The KIPS Transactions:PartC
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• v.15C no.2
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• pp.93-102
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• 2008

The network weather map and bandwidth time-series graph are popularly used to understand the current and past traffic condition of NSP, ISP, and enterprise networks. These systems collect traffic performance data from a SNMP agent running on the network devices such as routers and switches, store the gathered information into a DB, and display the network performance status in the form of a time-series graph or a network weather map using Web user interface. Most of current enterprise networks are constructed in the form of a hierarchical tree-like structure with multi-Gbps Ethernet links, which is quietly different from the national or world-wide backbone network structure. This paper focuses on the network weather map for current enterprise network. We start with the considering points in developing a network weather map system suitable for enterprise network. Based on these considerings, this paper proposes the best way of using SNMP in constructing a network weather map system. To prove our idea, we designed and developed a network weather map system for our campus network, which is also described in detail.

• MALSORI
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• no.50
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• pp.51-69
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• 2004

In this paper, we present a one-pass semi-dynamic network decoding framework that inherits both advantages of fast decoding speed from static network decoders and memory efficiency from dynamic network decoders. Our method is based on the novel language model network representation that is essentially of finite state machine (FSM). The static network derived from the language model network [1][2] is partitioned into smaller subnetworks which are static by nature or self-structured. The whole network is dynamically managed so that those subnetworks required for decoding are cached in memory. The network is near-minimized by applying the tail-sharing algorithm. Our decoder is evaluated on the 25k-word Korean broadcast news transcription task. In case of the search network itself, the network is reduced by 73.4% from the tail-sharing algorithm. Compared with the equivalent static network decoder, the semi-dynamic network decoder has increased at most 6% in decoding time while it can be flexibly adapted to the various memory configurations, giving the minimal usage of 37.6% of the complete network size.

• Proceedings of the KIEE Conference
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• 2004.11c
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• pp.255-257
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• 2004

The study for ubiquitous computing infra is proceeding actively, it make possible to use service and access network anywhere, anytime because of wire/wireless communication technology and progress of hardware. Domestically, study for the network mobility support technology which is the key technology for future ubiquitous computing realization have progressed, but that is insufficient. Especially, there is no study for independent mobility support study about railway wireless network. So, this study propose network mobility management technology for mobile network infra in railway and proper network model in train.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.631-634
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• 2004

In this paper, the Bayesian recurrent neural network (BRNN) is proposed to predict time series data. Among the various traditional prediction methodologies, a neural network method is considered to be more effective in case of non-linear and non-stationary time series data. A neural network predictor requests proper learning strategy to adjust the network weights, and one need to prepare for non-linear and non-stationary evolution of network weights. The Bayesian neural network in this paper estimates not the single set of weights but the probability distributions of weights. In other words, we sets the weight vector as a state vector of state space method, and estimates its probability distributions in accordance with the Bayesian inference. This approach makes it possible to obtain more exact estimation of the weights. Moreover, in the aspect of network architecture, it is known that the recurrent feedback structure is superior to the feedforward structure for the problem of time series prediction. Therefore, the recurrent network with Bayesian inference, what we call BRNN, is expected to show higher performance than the normal neural network. To verify the performance of the proposed method, the time series data are numerically generated and a neural network predictor is applied on it. As a result, BRNN is proved to show better prediction result than common feedforward Bayesian neural network.

• Proceedings of the KIEE Conference
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• 2007.04a
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• pp.409-412
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• 2007

Demand for the technology for access to device control network in industry and for access to building automation system via internet is on the increase. In such technology integration of a device control network with a data network such as internet and organizing wide-ranging DCS(distributed control system) is needed, and it can be realized in the framework of VDN(virtual device network). Specifications for device control network and data network are quite different because of the differences in application. So a router that translates the communication protocol between device control network and data network, and efficiently transmits information to destination is needed for implementation of the VDN(virtual device network). This paper proposes the concept of NCS(networked control system) based on VDN(virtual device network) and suggests the routing algorithm that uses embedded system.

• Journal of the Korea Institute of Military Science and Technology
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• v.16 no.5
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• pp.666-674
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• 2013

This paper studied about the design and implementation of tactical communication network simulator in order to obtain tactical communication network parameter, such as link capacity and routing plan, and a number of exceptional cases that may occur during actual deployment by conducting simulation of a large-scale tactical communication networks. This tactical communication network simulator provides equipment models and link models of commercial OPNET simulator for tactical communication network. In addition, 6 types of simulation scenario writings convenience functions and traffic generation models that may occur in situations of tactical communication network environment were implemented in order to enhance user friendliness. By taking advantages of SITL(System-In-The-Loop) function of OPNET, the tactical communication network simulator allows users to perform interoperability test between M&S models and actual equipment in operating simulation of tactical communication network, which is run on software. In order to confirm the functions and performance of the simulator, small-scale of tactical communication network was configured to make sure interoperability between SITL-based equipment and a large-scale tactical communication network was simulated and checked how to cope with traffic generated for each network node. As the results, we were able to confirm that the simulator is operated properly.

• Journal of Internet Computing and Services
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• v.10 no.1
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• pp.19-31
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• 2009

Network management involves configuring and operating various network elements in a suitable manner. Generally, a network management system can perform basic functionalities such as configuration management, performance management, and fault management. Due to the open structure of the Internet, the volume of network traffic and the network equipment used have increased in size and complexity. Therefore, it is expensive and time consuming to develop a network management program for heterogeneous network equipment in an SNMP.based network. In order to facilitate the management of network environments and the control of heterogeneous devices in an efficient manner, we propose an Active Network Management System (ANMS) comprising an automatic generator that uses triggers to generate a network management program. The concept of triggers can be represented through event condition action rules performed in response to a change in the status of a network environment. The proposed ANMS comprises basic components for real time network management and also includes an automatic generator (AG). When the ANMS is monitoring network elements that are newly added or changed, a trigger rule is activated and these components are then able to collaborate and automatically generate a new network management program by using the information provided along with the SNMP libraries. Our method is useful for expanding the network structure and replacing network equipment. Through experiments, we have proved that our ANMS is useful when new network objects are added or changed in the network environment to expand the network structure. Further, we have verified that our ANMS system reduces the time and cost required to develop a network management program as compared to the manual method used in existing network management systems.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.694-697
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• 2003

In this paper, we propose modular network to solve difficult and complex problems that are seldom solved with Multi-Layer Neural Network(MLNN). The structure of Modular Neural Network(MNN) in researched by Jacobs and jordan is selected in this paper. Modular network consists of several Expert Networks(EN) and a Gating Network(CN) which is composed of single-layer neural network(SLNN) or multi-layer neural network. We propose modular network structure using Recurrent Neural Network(RNN), since the state of the whole network at a particular time depends on aggregate of previous states as well as on the current input. Finally, we show excellence of the proposed network compared with modular network.

• Journal of Korean Institute of Industrial Engineers
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• v.9 no.2
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• pp.47-53
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• 1983

In this paper, a sensitivity analysis is proceeded to improve the network of manufacturing process by converting the qualitative network into GERT Network and by finding equivalent probability, MFG's of variables and sensitivity equation in GERT Network. Sensitivity analysis of GERT Network is important in evaluating, reviewing and improving system. System improvement in GERT Network is achieved by increasing the equivalent probability and by decreasing the equivalent time.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.28 no.4
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• pp.1009-1019
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• 2018

Bitcoin is the first application utilizing the blockchain, but it has limitations in terms of scalability. The concept of Lightning Network was recently introduced to address the scalability problem of Bitcoin. In this paper, we found that the real-world Bitcoin Lightning Network shows the scale-free property. Therefore, the Bitcoin Lightning Network can be vulnerable to the intentional attacks targeting some specific nodes in the network while it is still robust to the random node failures. We experimentally analyze the robustness of the Bitcoin's Lightning Network via the simulation of network attack model. Our simulation results demonstrate that the real-world Lightning Network is vulnerable to target attacks that destroy a few nodes with high degree.