• Smart Structures and Systems
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• v.26 no.2
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• pp.175-184
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• 2020

Conventional Monte Carlo simulation-based methods for seismic risk assessment of water networks often require excessive computational time costs due to the hydraulic analysis. In this study, an Artificial Neural Network-based surrogate model was proposed to efficiently evaluate the flow-based system reliability of water distribution networks. The surrogate model was constructed with appropriate training parameters through trial-and-error procedures. Furthermore, a deep neural network with hidden layers and neurons was composed for the high-dimensional network. For network training, the input of the neural network was defined as the damage states of the k-dimensional network facilities, and the output was defined as the network system performance. To generate training data, random sampling was performed between earthquake magnitudes of 5.0 and 7.5, and hydraulic analyses were conducted to evaluate network performance. For a hydraulic simulation, EPANET-based MATLAB code was developed, and a pressure-driven analysis approach was adopted to represent an unsteady-state network. To demonstrate the constructed surrogate model, the actual water distribution network of A-city, South Korea, was adopted, and the network map was reconstructed from the geographic information system data. The surrogate model was able to predict network performance within a 3% relative error at trained epicenters in drastically reduced time. In addition, the accuracy of the surrogate model was estimated to within 3% relative error (5% for network performance lower than 0.2) at different epicenters to verify the robustness of the epicenter location. Therefore, it is concluded that ANN-based surrogate model can be utilized as an alternative model for efficient seismic risk assessment to within 5% of relative error.

• Journal of KIISE:Computer Systems and Theory
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• v.31 no.12
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• pp.716-725
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• 2004

It is extremely important to minimize network access time in constructing a high-performance PC cluster system. For PC cluster systems, it is possible to reduce network access time by maintaining network cache in each cluster node. This paper presents a Network Cache Coherent NUMA (NCC-NUMA) system to utilize network cache by locating shared memory on the PCI bus, and the NCC-NUMA card which is core module of the NCC-NUMA system is developed. The NCC-NUMA card is directly plugged into the PCI slot of each node, and contains shared memory, network cache, shared memory control module and network control module. The network cache is maintained for the shared memory on the PCI bus of cluster nodes. The coherency mechanism between the network cache and the shared memory is based on the IEEE SCI standard. According to the SPLASH-2 benchmark experiments, the NCC-NUMA system showed improvements of 56% compared with an SCI-based cluster without network cache.

• KIPS Transactions on Software and Data Engineering
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• v.4 no.9
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• pp.425-430
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• 2015

The Sensor Registry System(SRS) provides sensor metadata to a user for instant use and seamless interpretation of sensor data in a heterogeneous sensor network environment. The existing sensor registry system cannot provide sensor metadata in case that the network connection is not available or is unstable. To resolve the problem, this paper proposes an extension of sensor registry system using network coverage information. The extended system sends a set of sensor metadata to the user by using network coverage open data (mobile vendors, signal strength, communication type). The extended SRS proposed in this paper supports a safer sensor metadata provision than the existing SRS, and it thus improves the quality of application services.

• Journal of Navigation and Port Research
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• v.28 no.5
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• pp.435-441
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• 2004

This paper deals with modelling and identification of a shipboard stabilized satellite antenna system using the optimal neural network structure. It is difficult for shipboard satellite antenna system to control and identification because of their approximating ability of nonlinear function So it is important to design the neural network with optimal structure for minimum error and fast response time. In this paper, a neural network structure using genetic algorithm is optimized And genetic algorithm is also used for identifying a shipboard satellite antenna system It is noticed that the optimal neural network structure actually describes the real movement of ship well. Through practical test, the optimal neural network structure is shown to be effective for modelling the shipboard satellite antenna system.

• 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 and Communication Engineering
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• v.21 no.10
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• pp.1999-2007
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• 2017

In this paper, we implement a multi-band network selection (MNS) system based on Linux operating system which determines the optimal communication link for given network conditions among the available LTE, very high frequency (VHF), and high frequency (HF). The implemented software consists of a network interface, an MNS server, and a user GUI. We perform indoor test to verify the function of the implemented MNS system using two sets of MNS system. To this end, two types of VHF communication links that follow ITU-R M.1842-1 Annex 1 and Annex 4 are emulated in software. In addition, the HF transmission (reception) port of one MNS is directly connected to the HF reception (transmission) port of another MNS. We demonstrate through indoor tests that the implemented MNS system can support seamless maritime communication service in spite of artificial disconnection or re-connection of LTE, VHFs, and HF. The implemented MNS system is applicable to various maritime communication services including e-navigation.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2012.10a
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• pp.903-905
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• 2012

Recent research on Augmented Reality is Actively expand and Augmented reality feature added to the social network system (Social Network System) has become a necessity. In this paper, GPS-based Augmented Reality System for Social Network is introduced, is proposed. This system can add recent check-in friends in facebook by automatically to synchronizing the location coordinate, and it could also adding location coordinates system is represented in a real-world environment by AR, is Marker-based AR system that was Commonly used AR system is a huge cost by handheld devices in processing and storage space, the disadvantages of the marker-based AR systems can be solved by using Location-based AR applications. Therefore, the proposed GPS-based Augmented Reality System for Social Network, automatically searches for the optimal speed for Wifi and 4G network to iOS Hand AR system was desired in future.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2008.05a
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• pp.865-868
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• 2008

Remote Sur-veillance network system for water contamination is developed using sensor network. The wireless sensor network is one of effective solutions for monitoring water contamination on wide area such s river. Existing sur-veillance system for water contamination has the disadvantage in installation cost, complexity of adding a new node replacing a defective node. The proposed system has cost effective solutions compared with the existing system.

• International Journal of Naval Architecture and Ocean Engineering
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• v.6 no.2
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• pp.484-495
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• 2014

This paper introduces the use of network theory to model and analyze disparate ship design information. This work will focus on a ship's distributed systems and their intra- and intersystem structures and interactions. The three system to be analyzed are: a passageway system, an electrical system, and a fire fighting system. These systems will be analyzed individually using common network metrics to glean information regarding their structures and attributes. The systems will also be subjected to community detection algorithms both separately and as a multiplex network to compare their similarities, differences, and interactions. Network theory will be shown to be useful in the early design stage due to its simplicity and ability to model any shipboard system.

• Journal of the Korean Society for Precision Engineering
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• v.15 no.2
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• pp.35-44
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• 1998

This paper describes multiple fault diagnosis method in complex system with hierarchical structure. Complex system is divided into subsystem, item and component. For diagnosing this hierarchical complex system, it is necessary to implement special neural network. We introduced Modular Artificial Neural Network(MANN) for this purpose. MANN consists of four level neural network, first level for symptom classification, second level for item fault diagnosis, third level for component symptom classification, forth level for component fault diagnosis. Each network is multi layer perceptron with 7 inputs, 30 hidden node and 7 outputs trained by backpropagation. UNIX IPC(Inter Process Communication) is used for implementing MANN with multitasking and message transfer between processes in SUN workstation. We tested MANN in reactor system.