• Journal of Communications and Networks
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• 제14권3호
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• pp.310-318
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• 2012

Detecting intrusion attacks accurately and rapidly in wireless networks is one of the most challenging security problems. Intrusion attacks of various types can be detected by the change in traffic flow that they induce. Wireless industrial networks based on the wireless networks for industrial automation-process automation (WIA-PA) standard use a superframe to schedule network communications. We propose an intrusion detection system for WIA-PA networks. After modeling and analyzing traffic flow data by time-sequence techniques, we propose a data traffic prediction model based on autoregressive moving average (ARMA) using the time series data. The model can quickly and precisely predict network traffic. We initialized the model with data traffic measurements taken by a 16-channel analyzer. Test results show that our scheme can effectively detect intrusion attacks, improve the overall network performance, and prolong the network lifetime.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2007년도 하계종합학술대회 논문집
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• pp.147-148
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• 2007

BICC protocol is a relay protocol adaptable to ATM and IP based core networks compared to ISUP protocol to TDM networks. Using BICC protocol, multi-rate bearer traffic such as voice and video can flow in the relay core networks. BICC protocol is standardized as WCDMA circuit switching networks in 3GPP Release 4. Thus KTF is now operating core networks using BICC protocol. In this paper, we describe the background and characteristics of BICC protocol. We also provide the status of KTF WCDMA core networks using BICC. To show the efficiency of BICC protocol an analytical simulation is given in which the results can be expected by intuitive observation.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• 제9권2호
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• pp.83-89
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• 2005

In this paper a new algorithm of learning and evolving artificial neural networks using gene expression programming (GEP) is presented. Compared with other traditional algorithms, this new algorithm has more advantages in self-learning and self-organizing, and can find optimal solutions of artificial neural networks more efficiently and elegantly. Simulation experiments show that the algorithm of evolving weights or thresholds can easily find the perfect architecture of artificial neural networks, and obviously improves previous traditional evolving methods of artificial neural networks because the GEP algorithm imitates the evolution of the natural neural system of biology according to genotype schemes of biology to crossover and mutate the genes or chromosomes to generate the next generation, and the optimal architecture of artificial neural networks with evolved weights or thresholds is finally achieved.

• Structural Engineering and Mechanics
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• 제36권2호
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• pp.165-179
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• 2010

Maximum deflection in a beam is a design criteria and occurs generally at or close to the mid-span. Neural networks have been developed for the continuous composite beams to predict the inelastic mid-span deflections (typically for 20 years, considering cracking, and time effects, i.e., creep and shrinkage, in concrete) from the elastic moments and elastic mid-span deflections (neglecting instantaneous cracking and time effects). The training and testing data for the neural networks is generated using a hybrid analytical-numerical procedure of analysis. The neural networks have been validated for four example beams and the errors are shown to be small. This methodology, of using networks enables a rapid estimation of inelastic mid-span deflections and requires a computational effort almost equal to that required for the simple elastic analysis. The neural networks can be extended for the composite building frames that would result in huge saving in computational time.

• Journal of information and communication convergence engineering
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• 제7권2호
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• pp.141-147
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• 2009

To initialize and maintain self-organizing networks such as mobile ad hoc networks, address allocation protocol is essentially required. However, centralized approaches that pervasively used in traditional networks are not recommended in this kind of networks since they cannot handle with mobility efficiently. In addition, previous distributed approaches suffer from inefficiency with control overhead caused by duplicated address detection and management of available address pool. In this paper, we propose a new dynamic address allocation scheme, which is based on adaptive partition. An available address is managed in distributed way by multiple agents and partitioned adaptively according to current network environments. Finally, simulation results reveal that a proposed scheme is superior to previous approach in term of address acquisition delay under diverse simulation scenarios.

• ETRI Journal
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• 제29권4호
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• pp.524-526
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• 2007

In this letter, we study the multicast protection problem in sparse-splitting wavelength-division multiplexing (WDM) optical network, and propose a novel multicast protection algorithm called the shared source-leaf path-based protection (SLPP) algorithm. Unlike the proposals in previous studies, the backup paths derived by SLPP can share wavelength with the primary tree in sparse-splitting WDM networks. Simulations are used to evaluate the effectiveness of the SLPP algorithm.

• Journal of Communications and Networks
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• 제13권1호
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• pp.50-55
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• 2011

Spectrum leasing for cognitive radio (CR) networks is an effective way to improve the spectrum utilization. This paper presents an opportunistic relaying based spectrum leasing for CR networks where the primary users lease their frequency band to the cognitive users. The cognitive users act as relays for the primary users to improve the channel capacity, and this improved capacity is used for the transmission of secondary users' data. We show that the cognitive users can use a significant portion of the communication resource of primary networks while maintaining a fixed target data rate for the primary users. Moreover, the primary network is also benefited by the cooperating cognitive users in terms of outage probability. Information theoretic analysis and simulation results are presented to evaluate the performances of both primary and cognitive networks.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1996년도 하계학술대회 논문집 B
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• pp.1186-1188
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• 1996

Neural networks and fuzzy systems have attracted the attention of many researehers recently. In general, neural networks are used to obtain information about systems from input/output observation and learning procedure. On the other hand, fuzzy systems use fuzzy rules to identify or control systems. In this paper we present a generalized FCMAC(Fuzzified Cerebellar Model Articulation Controller) networks, by integrating fuzzy systems with the CMAC(Cerebellar Model Articulation Controller) networks. We propose a direct adaptive controller design based on FCMAC(fuzzified CMAC) networks. Simulation results reveal that the proposed adaptive controller is practically feasible in nonlinear plant control.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제9권3호
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• pp.956-970
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• 2015

In this paper, we study the design of network coding for the generalized transmit scheme in multiple input multiple output Y channel, where K users wish to exchange specified and shared information with each other within two slots. Signal space alignment at each user and the relay is carefully constructed to ensure that the signals from the same user pair are grouped together. The cross-pair interference can be canceled during both multiple accessing channel phase and broadcasting channel phase. The proposed signal processing scheme achieves the degrees of freedom of ${\eta}(K)=K^2$ with fewer user antennas.

• 한국정밀공학회지
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• 제28권7호
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• pp.814-820
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• 2011

It takes long time in numerical simulation because structural design for tire requires the nonlinear material property. Neural networks has been widely studied to engineering design to reduce numerical computation time. The numbers of hidden layer, hidden layer neuron and training data have been considered as the structural design variables of neural networks. In application of neural networks to optimize design, there are a few studies about arrangement method of input layer neurons. To investigate the effect of input layer neuron arrangement on neural networks, the variables of tire contour design and tension in bead area were assigned to inputs and output for neural networks respectively. Design variables arrangement in input layer were determined by main effect analysis. The number of hidden layer, the number of hidden layer neuron and the number of training data and so on have been considered as the structural design variables of neural networks. In application to optimization design problem of neural networks, there are few studies about arrangement method of input layer neurons. To investigate the effect of arrangement of input neurons on neural network learning tire contour design parameters and tension in bead area were assigned to neural input and output respectively. Design variables arrangement in input layer was determined by main effect analysis.