• Journal of the Korean Operations Research and Management Science Society
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• v.26 no.3
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• pp.65-77
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• 2001

Survivability of a network is one of the most important issues in designing present-day communication networks. the k-edge survivability of a given network is defined as the percentage of total traffic surviving the worst case failure of k edges. Although several researches calculated k-edge survivability on small networks by enumeration, prior research has considered how to calculate k-edge survivability on large networks. In this paper, we develop an efficient procedure to obtain lower and upper bounds on the k-edge survivability of a network.

• ETRI Journal
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• v.30 no.5
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• pp.696-706
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• 2008

With the increasing need for intelligent environment monitoring applications and the decreasing cost of manufacturing sensor devices, it is likely that a wide variety of sensor networks will be deployed in the near future. In this environment, the way to access heterogeneous sensor networks and the way to integrate various sensor data are very important. This paper proposes the common system for middleware of sensor networks (COSMOS), which provides integrated data processing over multiple heterogeneous sensor networks based on sensor network abstraction called the sensor network common interface. Specifically, this paper introduces the sensor network common interface which defines a standardized communication protocol and message formats used between the COSMOS and sensor networks.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2005.09a
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• pp.339-343
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• 2005

Boolean networks(BN) construction is one of the commonly used methods for building gene networks from time series microarray data. However, BN has two major drawbacks. First, it requires heavy computing times. Second, the binary transformation of the microarray data may cause a loss of information. This paper propose two methods using liner regression to construct gene regulatory networks. The first proposed method uses regression based BN variable selection method, which reduces the computing time significantly in the BN construction. The second method is the regression based network method that can flexibly incorporate the interaction of the genes using continuous gene expression data. We construct the network structure from the simulated data to compare the computing times between Boolean networks and the proposed method. The regression based network method is evaluated using a microarray data of cell cycle in Caulobacter crescentus.

• ETRI Journal
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• v.28 no.4
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• pp.517-520
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• 2006

In this letter, a new sharing mechanism, SRLG sharing, is proposed, which allows the links of the same shared risk link group (SRLG) in a primary light tree to share protections in WDM optical networks. In previous studies, how to share spare resources with SRLG constraints has not been studied in multicast optical networks. In this letter, considering SRLG sharing, we propose a novel algorithm -multicast with SRLG sharing (MSS)- to establish a protection light tree. Finally, the algorithm MSS and the algorithm multicast with no SRLG sharing (MNSS) are compared through a simulation to show that our new sharing scheme of SRLG sharing is more efficient than that of no SRLG sharing in terms of spare resource utilization and blocking probability.

• Proceedings of the IEEK Conference
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• 2002.07b
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• pp.880-883
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• 2002

We propose an effective wavelength converter method using frequency shifter for photonic node, and examine the scalability of Generalized Multiprotocol Label Switching (GMPLS) networks. The ana1ysis is examined based on signal to noise ratio (SNR) for present 2.4 and 10 Gbit/s Synchronous Digital Hierarchy (SDH) networks, and next generation 2.7 and 10.8 Gbit/s Optical Transport Networks (OTN) format The proposed 100 channels GMPLS networks using optical frequency shifters are shown to be applicable to transmission network spanning over 1206 km(24 nodes) in 2.7 Gbit/s trunk networks. Transmission over more than 310 km (6 nodes) is also possible in 2.7 Gbit/s Metroporitan Area Networks(MAN).

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.1
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• pp.68-86
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• 2015

Future wireless network aims to integrate different radio access networks (RANs) to provide a seamless access and service continuity. In this paper, a new resource denotation method is proposed in the WLAN and LTE heterogeneous networks based on a concept of spectral bandwidth mapping. This method simplifies the denotation of system resources and makes it possible to calculate system residual capacity, upon which an economic model-based network selection algorithm is designed in both under-loaded and over-loaded scenarios in the heterogeneous networks. The simulation results show that this algorithm achieves better performance than the utility function-based access selection (UFAS) method proposed in [12] in increasing system capacity and system revenue, achieving load balancing and reducing the new call blocking probability in the heterogeneous networks.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.11
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• pp.4405-4418
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• 2015

Different network provides different service. To maximize the profit, heterogeneous networks form a whole, which may either compete or cooperate with each other. In this paper, the healthcare monitor network architecture is introduced to build the competitive and cooperative mechanisms of heterogeneous networks which contain three networks, namely, cellular network, WLAN and WMAN. This paper considers the natural growth rate of the network with competitive and cooperative effects. Then, the stability of the proposed model and its equilibrium points are analyzed by the ordinary differential principle. Finally, simulation results show that the natural growth rate cannot increase the profit of the network, but effective cooperative among heterogeneous networks can increase the profit of each network, and competitive may decrease the profit of each network.

• Journal of Information Processing Systems
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• v.14 no.2
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• pp.455-468
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• 2018

The concepts of graph theory are applied to model and analyze dynamics of computer networks, biochemical networks and, semantics of social networks. The analysis of dynamics of complex networks is important in order to determine the stability and performance of networked systems. The analysis of non-stationary and nonlinear complex networks requires the applications of ordinary differential equations (ODE). However, the process of resolving input excitation to the dynamic non-stationary networks is difficult without involving external functions. This paper proposes an analytical formulation for generating solutions of nonlinear network ODE systems with functional decomposition. Furthermore, the input excitations are analytically resolved in linearized dynamic networks. The stability condition of dynamic networks is determined. The proposed analytical framework is generalized in nature and does not require any domain or range constraints.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2004.07b
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• pp.1234-1238
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• 2004

A Controlled neural networks are proposed in order to measure nonlinear environments in adaptive and in realtime. The structure of it is similar to recurrent neural networks: a delayed output as the input and a delayed error between tile output of plant and neural networks as a bias input. In addition, we compute the desired value of hidden layer by an optimal method instead of transfering desired values by backpropagation and each weights are updated by RLS(Recursive Least Square). Consequently, this neural networks are not sensitive to initial weights and a learning rate, and have a faster convergence rate than conventional neural networks. This new neural networks is Error Estimated Neural Networks. We can estimate nonlinear models in realtime by the proposed networks and control nonlinear models. To show the performance of this one, we have various experiments. And this controller call prove effectively to be control in the environments of various systems.

• Journal of the Korean Society for Nondestructive Testing
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• v.24 no.4
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• pp.362-370
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• 2004

In this work, the collapse moment due to wall-thinning defects is estimated by using fuzzy neural networks. The developed fuzzy neural networks have been applied to the numerical data obtained from the finite element analysis. Principal component analysis is used to preprocess the input signals into the fuzzy neural network to reduce the sensitivity to the input change and the fuzzy neural networks are trained by using the data set prepared for training (training data) and verified by using another data set different (independent) from the training data. Also, two fuzzy neural networks are trained for two data sets divided into the two classes of extrados and intrados defects, which is because they have different characteristics. The relative 2-sigma errors of the estimated collapse moment are 3.07% for the training data and 4.12% for the test data. It is known from this result that the fuzzy neural networks are sufficiently accurate to be used in the wall-thinning monitoring of elbows.