• ETRI Journal
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• v.41 no.4
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• pp.473-482
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• 2019

High availability and reliability have been considered promising requirements for the support of seamless network services such as real-time video streaming, gaming, and virtual and augmented reality. Increased availability can be achieved within a local area network with the use of the virtual router redundancy protocol that utilizes backup routers to provide a backup path in the case of a master router failure. However, the network may still lose a large number of packets during a failover owing to a late failure detections and lazy responses. To achieve an efficient failover, we propose the implementation of fast detection with virtual router redundancy protocol (FDVRRP) in which the backup router quickly detects a link failure and immediately serves as the master router. We implemented the FDVRRP using open neutralized network operating system (OpenN2OS), which is an open-source-based network operating system. Based on the failover performance test of OpenN2OS, we verified that the FDVRRP exhibits a very fast failure detection and a failover with low-overhead packets.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.9
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• pp.4287-4306
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• 2016

Network resilience provides an effective way to overcome the problem of network failure and is crucial to Internet protocol (IP) network management. As one of the main challenges in network resilience, recovering from link failure is important to maintain the constancy of packets being transmitted. However, existing failure recovery approaches do not handle the traffic engineering problem (e.g., tuning the routing-protocol parameters to optimize the rerouting traffic flow), which may cause serious congestions. Moreover, as the lack of QoS (quality of service) restrictions may lead to invalid rerouting traffic, the QoS requirements (e.g., bandwidth and delay) should also be taken into account when recovering the failed links. In this paper, we first develop a probabilistically correlated failure model that can accurately reflect the correlation between link failures, with which we can choose reliable backup paths (BPs). Then we construct a mathematical model for the failure recovery problem, which takes maximum rerouting traffic as the optimizing objective and the QoS requirements as the constraints. Moreover, we propose a heuristic algorithm for link failure recovery, which adopts the improved k shortest path algorithm to splice the single BP and supplies more protection resources for the links with higher priority. We also prove the correctness of the proposed algorithm. Moreover, the time and space complexity are also analyzed. Simulation results under NS2 show that the proposed algorithm improves the link failure recovery rate and increases the QoS satisfaction rate significantly.

• Korean Management Science Review
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• v.11 no.1
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• pp.187-196
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• 1994

Time-Space-Time(T-S-T) switching network is modeled as a graceful degrading system. Call blocking probability is defined as a measure of performance. Several performance related measures are suggested under the presence of failure. An optimization model is proposed, which determines optimal values of system parameters of the switching network.

• Proceedings of the KSR Conference
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• 2011.05a
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• pp.1414-1419
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• 2011

In this paper we study the modeling to quantitatively assess the failure rate of USN system designed for fault-tolerant architecture, aiming at applying the world's best domestic USN technology to safety-critical railways. In order to apply the USN system to the safety-critical field like a train control sector that the failures of controllers may cause severe railway accidents such as train collision and derailment, the quantitative reliability and safety evaluation recommended in IEC 62278 must be preceded. We also develop the evaluation model for overall system failure rate for the distributed network structure, which is the characteristics of USN system. Especially, we allocate reliability targets to component units, and present an availability evaluation plan through the plan on the quantitative achievement of failure rate for sensor nodes, gateways, radio-communication network and servers, along with the failure rate model of the overall system considering network operational features.

• Journal of the Korea Institute of Military Science and Technology
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• v.1 no.1
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• pp.82-91
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• 1998

This paper presents a neural networks based approach for the problem of sensor failure detection and accommodation for ship without physical redundancy in the sensors. The designed model consists of two neural networks. The first neural network is responsible for the failure detection and the second neural network is responsible for the failure identification and accommodation. On the yaw rate sensor of ship, simulation results indicates that the proposed method can be useful as failure detector and sensor estimator.

• Journal of the Korean Institute of Telematics and Electronics A
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• v.32A no.8
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• pp.1-10
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• 1995

We have analyzed the reliability of failure models in circuit-switched networks. These models are grid topology circuit-switched networks, and each node transmits a packet to a destination node using a Flooding routing method. We have assumed that the failure of each link and node is independent. We have considered two method to analyze reliability in these models : The Karnaugh Map method and joint probability method. In this two method, we have analyzed the reliability in a small grid topology circuit switched network by a joint probability method, and comared analytic results with simulated ones. For a large grid enormous. So, we have evaluated the reliability of the network by computer simulation techniques. As results, we have found that the analytic results are very close to simulated ones in a small grid topology circuit switched network. And, we have found that network reliability decreases exponentially, according to increment of link or node failure, and network reliability is almost linearly decreased according to increment of the number of links, by which call has passed. Finally, we have found an interesting result that nodes in a center of the network are superior to the other nodes from the reliability point of view.

• Annual Conference of KIPS
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• 2016.04a
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• pp.124-126
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• 2016

Successful deployment of the Software Defined Network (SDN) depends on its ability to cope up with network failures. There are various types of failures that may occur in an SDN. The most common are switch and link failures. It is necessary to recover the network from failures for a continuous service availability. But for the real-time services fast recovery from the failure is required to minimize the service disruption time. In the proposed work, we focused on minimizing the recovery time after the failure is detected. Once the failure is detected, the controller involvement is needed to dynamically reroute the failure disrupted flows from the failed component to an alternate path. The aim of the proposed scheme is to provide a traffic management scheme which can react to the dynamic network events by rapidly modifying the forwarding behavior of the switches for faster in-band network adaptability. The proposed scheme (1) Considers the shared data and control path delay (2) Optimally utilize the network resources (3) Eliminates the need of constant monitoring overhead at the controller which results into faster detouring and ultimately rapid recovery.

• Advanced Composite Materials
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• v.18 no.3
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• pp.233-249
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• 2009

Safe installation and operation of high-pressure composite cylinders for hydrogen storage are of primary concern. It is unavoidable for the cylinders to experience temperature variation and significant thermal input during service. The maximum failure pressure that the cylinder can sustain is affected due to the dependence of composite material properties on temperature and complexity of cylinder design. Most of the analysis reported for high-pressure composite cylinders is based on simplifying assumptions and does not account for complexities like thermo-mechanical behavior and temperature dependent material properties. In the present work, a comprehensive finite element simulation tool for the design of hydrogen storage cylinder system is developed. The structural response of the cylinder is analyzed using laminated shell theory accounting for transverse shear deformation and geometric nonlinearity. A composite failure model is used to evaluate the failure pressure under various thermo-mechanical loadings. A back-propagation neural network (NNk) model is developed to predict the maximum failure pressure using the analysis results. The failure pressures predicted from NNk model are compared with those from test cases. The developed NNk model is capable of predicting the failure pressure for any given loading condition.

• Journal of Communications and Networks
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• v.9 no.4
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• pp.394-401
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• 2007

High speed all optical network is a viable option to satisfy the exponential growth of internet usage in the recent years. Optical networks offer very high bit rates and, by employing technologies like internet protocol over wavelength division multiplexing(IP-over-WDM), these high bit rates can be effectively utilized. However, failure of a network component, carrying such high speed data traffic can result in enormous loss of data in a few seconds and persistence of a failure can severely degrade the performance of the entire network. Designing IP-over-WDM networks, which can withstand failures, has been subject of considerable interest in the research community recently. Most of the research is focused on the failure of optical links in the network. This paper addresses the problem of designing IP-over-WDM networks that do not suffer service degradation in case of a single link failure. The paper proposes an approach based on the framework provided by a recent paper by M. Kurant and P. Thiran. The proposed approach can be used to design large survivable IP-over-WDM networks.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.18 no.9
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• pp.52-57
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• 2019

The key technology of the fourth industrial revolution is artificial intelligence and machine learning. In this study, FMEA was performed on fuel pumps used as key items in most systems to identify major failure components, and artificial neural networks were built using big data. The main failure mode of the fuel pump identified by the test was coil damage due to overheating. Based on the artificial neural network built, machine learning was conducted to predict the failure and the mean error rate was 4.9% when the number of hidden nodes in the artificial neural network was three and the temperature increased to $140^{\circ}C$ rapidly.