• Disaster Prevention Review
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• v.15 no.4
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• pp.136-137
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• 2013

• Nuclear Engineering and Technology
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• v.53 no.10
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• pp.3275-3285
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• 2021

A Nuclear Power Plant (NPP) is a complex dynamic system-of-systems with highly nonlinear behaviors. In order to control the plant operation under both normal and abnormal conditions, the different systems in NPPs (e.g., the reactor core components, primary and secondary coolant systems) are usually monitored continuously, resulting in very large amounts of data. This situation makes it possible to integrate relevant qualitative and quantitative knowledge with artificial intelligence techniques to provide faster and more accurate behavior predictions, leading to more rapid decisions, based on actual NPP operation data. Data-driven models (DDM) rely on artificial intelligence to learn autonomously based on patterns in data, and they represent alternatives to physics-based models that typically require significant computational resources and might not fully represent the actual operation conditions of an NPP. In this study, a feed-forward backpropagation artificial neural network (ANN) model was trained to simulate the interaction between the reactor core and the primary and secondary coolant systems in a pressurized water reactor. The transients used for model training included perturbations in reactivity, steam valve coefficient, reactor core inlet temperature, and steam generator inlet temperature. Uncertainties of the plant physical parameters and operating conditions were also incorporated in these transients. Eight training functions were adopted during the training stage to develop the most efficient network. The developed ANN model predictions were subsequently tested successfully considering different new transients. Overall, through prompt prediction of NPP behavior under different transients, the study aims at demonstrating the potential of artificial intelligence to empower rapid emergency response planning and risk mitigation strategies.

• Journal of Navigation and Port Research
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• v.32 no.1
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• pp.65-72
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• 2008

This study is aimed to contribute in establishing new port policy by research and analysis in design of global port logistics network in the East Asia region. In order to build such port logistics network, 21 ports located in East Asia among the world's 50th largest ports were selected in this study. Furthermore, the amount of container cargo and the ports of call were analyzed to categorize the subject ports in East Asia. Finally, this study tries to find economic network between the base port in East Asia, EU or North America and feeder ports in terms of logistics cost. As a result, Singapore, Hong Kong, Shanghai, and Busan ports were found as the representative ports in the East Asia that may connect North America and Europe with the minimum logistics expenses. Therefore, to maintain the stable cargo volume in ports, Korea should promote not only the overseas terminal operation which links to the Singapore, Hong Kong, and Shanghai ports, but also establish the global port logistic network connecting the Busan port.

• Journal of Digital Contents Society
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• v.19 no.4
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• pp.693-700
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• 2018

Virtual router redundancy protocol (VRRP) was designed as a solution to support fast fail-over in case of network failure. There exists virtual router which acts as default gateway in LAN, and the virtual router is dynamically elected between master and backup router. Through this protocol, end-hosts can be provided seamless network service. However, it needs expensive license fees and maintenance costs to adopt current commercial network operating systems. Furthermore, they are commonly enterprise proprietary software and inherently closed source. In order to tackle these problem, Open N2OS which is open source based open network software platform was developed. It has no dependency on hardware equipment, and provides high availability, scalability, various networking functions. In this paper, we handle VRRP operation and mechanism with related command line interface (CLI).

• Convergence Security Journal
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• v.16 no.2
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• pp.19-23
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• 2016

The design, management, and operation of network infrastructure have evolved during the last few years, leveraging on innovative technologies and architectures. With such a huge trend, due to the flexibility and significant economic potential of these technologies, software defined networking (SDN) and network functions virtualization (NFV) are emerging as the most critical key enablers. SDN/NFV enhancing the infrastructure agility, thus network operators and service providers are able to program their own network functions (e.g., gateways, routers, load balancers) on vendor independent hardware substrate. They facilitating the design, delivery and operation of network services in a dynamic and scalable manner. In NFV, the management and orchestration (MANO) orchestrates other specific managers such as the virtual infrastructure manager (VIM) and the VNF Manager (VNFM). In this paper, we examine the contents of these NFV MANO systematically and proposes a security system in a virtualized environment.

• The Journal of the Korea Contents Association
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• v.9 no.8
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• pp.41-48
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• 2009

As enactment and enforcement of capital markets integration law, investment banks are going to be appeared in our financial market and be able to provide payment services. To provide these kinds of services, investment banks need to be participated in the financial network. As the financial network enormously affect the economy, the operation of the network will require a variety of risk managements. In this paper we define operational risk management criteria for the financial network such as security, in-time response, economical efficiency and stability to be required for the healthy economy and propose the configuration of the financial network system based on virtual private networks for investment banks to provide payment services. Finally we analyze that the proposed VPN configuration for financial networks has high security and in-time response with the cost and operation effective.

• The Transactions of the Korea Information Processing Society
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• v.3 no.3
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• pp.449-459
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• 1996

The main goal of this paper is to introduce a novel definition for fuzzy mathematical morphology and a neural network implementation. The generalized- mean operator plays the key role for the definition. Such definition is well suited for neural network implementation. The first stage of the shared-weight neural network has adequate architecture to perform morphological operation. The shared- weight network performs classification based on the features extracted with the fuzzy morphological operation defined in this paper. Therefore, the parameters for the fuzzy definition can be optimized using neural network learning paradigm. Learning rules for the structuring elements, degree of membership, and weighting factors are precisely described. In application to handwritten digit recognition problem, the fuzzy morphological shared-weight neural network produced the results which are comparable to the state-of art for this problem.

• Journal of the Korea Society of Computer and Information
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• v.14 no.10
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• pp.23-29
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• 2009

This paper presents the development of embedded network educational system. This is an educational equipment which enables user to have training over Network Configuration and Embedded network programming practice on Internet environment. The network education system is developed on embedded environment. based on using ethernet interface. On the development environment. PAX255 VLSI chip is used for the processor, the ADSv1.2 for debugging, uC/OS276 for RTOS. The system software was developed using C language. The ping program provided an educational environment for the student to compile and load it to run after doing practice of demonstration behavior. Afterwards programming procedure starts the step-by-step training just like the demonstration function. In other words, programming method how to design the procedure of ARP operation and ICMP operation is explained.

• Korean Journal of Hydrosciences
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• v.6
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• pp.107-118
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• 1995

The water distribution system problem consists of finding a minimum cost system design subject to hydraulic and operation constraints. The design of new branchin network in a paddy irrigation system is presented here. The program based on the linear programming formulation is aimed at finding the optimal economical combination of two main factors : the capital cost of pipe network and the energy cost. Two loading conditions and booster pumps for design of pipe network are considered to obtain the least cost design.

• Proceedings of the KAIS Fall Conference
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• 2007.05a
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• pp.150-153
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• 2007

The intent of this paper is to describe a neural network structure called multi dynamic neural network(MDNN), and examine how it can be used in developing a learning scheme for computing robot inverse kinematic transformations. The architecture and learning algorithm of the proposed dynamic neural network structure, the MDNN, are described. Computer simulations are demonstrate the effectiveness of the proposed learning using the MDNN.