• International Journal of Control, Automation, and Systems
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• v.3 no.2
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• pp.183-194
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• 2005

In this study, we introduce an advanced architecture of genetically optimized Hybrid Fuzzy Neural Networks (gHFNN) and develop a comprehensive design methodology supporting their construction. A series of numeric experiments is included to illustrate the performance of the networks. The construction of gHFNN exploits fundamental technologies of Computational Intelligence (CI), namely fuzzy sets, neural networks, and genetic algorithms (GAs). The architecture of the gHFNNs results from a synergistic usage of the genetic optimization-driven hybrid system generated by combining Fuzzy Neural Networks (FNN) with Polynomial Neural Networks (PNN). In this tandem, a FNN supports the formation of the premise part of the rule-based structure of the gHFNN. The consequence part of the gHFNN is designed using PNNs. We distinguish between two types of the linear fuzzy inference rule-based FNN structures showing how this taxonomy depends upon the type of a fuzzy partition of input variables. As to the consequence part of the gHFNN, the development of the PNN dwells on two general optimization mechanisms: the structural optimization is realized via GAs whereas in case of the parametric optimization we proceed with a standard least square method-based learning. To evaluate the performance of the gHFNN, the models are experimented with a representative numerical example. A comparative analysis demonstrates that the proposed gHFNN come with higher accuracy as well as superb predictive capabilities when comparing with other neurofuzzy models.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.6
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• pp.2841-2860
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• 2018

Vehicular networks play an important role in current intelligent transportation networks and have gained much attention from academia and industry. Vehicular networks can be enhanced by Long Term Evolution-Vehicle (LTE-V) technology, which has been defined in a series of standards by the 3rd Generation Partnership Project (3GPP). LTE-V technology is a systematic and integrated V2X solution. To guarantee secure LTE-V communication, security and privacy issues must be addressed before the network is deployed. The present study aims to improve the security functionality of vehicular LTE networks by proposing an efficient and secure ID-based message authentication scheme for vehicular networks, named the ESMAV. We demonstrate its ability to simultaneously support both mutual authentication and privacy protection. In addition, the ESMAV exhibit better performance in terms of overhead computation, communication cost, and security functions, which includes privacy preservation and non-frameability.

• Journal of the Korean Society for Precision Engineering
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• v.16 no.12
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• pp.119-125
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• 1999

This paper gives an overview of the various approaches to designing statistical pattern recognition scheme based on Bayes discrimination rule and the artificial neural networks for rotating machine condition classification. Concerning to Bayes discrimination rule, this paper contains the linear discrimination rule applied to classification into several multivariate normal distributions with common covariance matrices, the quadratic discrimination rule under different covariance matrices. Also we discribes k-nearest neighbor method to directly estimate a posterior probability of each class. Five features are extracted in time domain vibration signals. Employing these five features, statistical pattern classifier and neural networks have been established to detect defects on rotating machine. Four different cases of rotation machine were observed. The effects of k number and neural networks structures on monitoring performance have also been investigated. For the comparison of diagnosis performance of these two method, their recognition success rates are calculated form the test data. The result of experiment which classifies the rotating machine conditions using each method presents that the neural networks shows the highest recognition rate.

• Journal of Korea Multimedia Society
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• v.18 no.11
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• pp.1358-1366
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• 2015

This paper proposes an automatic composition method using the training capability of artificial neural networks and chord progression rules that are widely used by human composers. After training a given song, the new melody is generated by the trained artificial neural networks through applying a different initial melody to the neural networks. The generated melody should be modified to fit the rhythm and chord progression rules for generating natural melody. In order to achieve this object, we devised a post-processing method such as chord candidate generation, chord progression, and melody correction. From some tests we could find that the melody after the post-processing was very improved from the melody generated by artificial neural networks. This enables our composition system to generate a melody which is similar to those generated by human composers.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.51 no.9
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• pp.467-473
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• 2002

In this paper, a fault location algorithm is suggested for line to line faults in distribution networks. Conventional fault location algorithms use the symmetrical component transformation, a very useful tool for transmission network analysis. However, its application is restricted to balanced network only. Distribution networks are, in general, operated in unbalanced manners, therefore, conventional methods cannot be applied directly, which is the reason why there are few research results on fault location in distribution networks. Especially, the line to line fault is considered as a more difficult subject. The proposed algorithm uses direct 3-phase circuit analysis, which means it can be applied not only to balanced networks but also to unbalanced networks like distribution a network. The comparisons of simulation results between one of conventional methods and the suggested method are presented to show its effectiveness and accuracy.

• Convergence Security Journal
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• v.8 no.1
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• pp.91-100
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• 2008

The previous works in sensor networks security have focused on the aspect of confidentiality, authentication and integrity based on cryptographic primitives. There has been no prior work to assess the survivability in systematic way. Accordingly, this paper presents a survivability model of wireless sensor networks using software rejuvenation for dual adaptive cluster head. The survivability model has state transition to reflect status of real wireless sensor networks. In this paper, we only focus on a survivability model which is capable of describing cluster head compromise in the networks and able to switch over the redundant cluster head in order to increase the survivability of that cluster. Second, this paper presents how to enhance the survivability of sensor networks using software rejuvenation methodology for dual cluster head in wireless sensor network. We model and analyze each cluster as a stochastic process based on Semi Markov Process (SMP) and Discrete Time Markov Chain (DTMC). The proof of example scenarios and numerical analysis shows the feasibility of our approach.

• Journal of Communications and Networks
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• v.16 no.4
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• pp.378-384
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• 2014

Replication routing can greatly improve the data delivery performance by enabling multiple replicas of the same packet to be transmitted towards its destination simultaneously. It has been studied extensively recently and is now a widely accepted routing paradigm in delay tolerant networks (DTNs). However, in this field, the issue of how to maximize the utilization efficiency of limited replication quota in a resource-saving manner and therefore making replication routing to be more efficient in networks with limited resources has not received enough attention. In this paper, we propose a DTN routing protocol with back-pressure based replica distribution. Our protocol models the replica distribution problem from a resource allocation perspective and it utilizes the idea of back-pressure algorithm, which can be used for providing efficient network resource allocation for replication quota assignment among encountered nodes. Simulation results demonstrate that the proposed protocol significantly outperforms existing replication routing protocols in terms of packet delay and delivery ratio.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.51 no.8
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• pp.409-416
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• 2002

This paper presents a fault location algorithm using direct 3-phase circuit analysis for distribution power networks. The unbalanced feature of distribution networks due to single phase loads or asymmetric operation prohibits us from using the conventional symmetrical component transformation. Even though the symmetrical component transformation provides us with a very easy tool in three phase network analysis, it is limited to balanced systems in utilizing its strong point, which is not suitable for distribution networks. In this paper, a fault location algorithm using direct 3-phase circuit analysis is developed. The algorithm is derived and it Is shown that the proposed method if we use matrix inverse lemma, is not more difficult then the conventional methods using symmetrical component transformation. Since the symmetrical component transformation is not used in the suggested method, unbalanced networks also can be handled with the same difficulty as balanced networks. The case study results show the correctness and effectiveness of the proposed algorithm.

• Journal of Communications and Networks
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• v.18 no.2
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• pp.227-237
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• 2016

Data collection is a key function for wireless sensor networks. There has been numerous data collection scheduling algorithms, but they fail to consider the deep and complex relationship among network lifetime, sink aggregated information and sample cycle for wireless sensor networks. This paper gives the upper bound on the sample period under the given network topology. An optimal schedule algorithm focusing on aggregated information named OSFAI is proposed. In the schedule algorithm, the nodes in hotspots would hold on transmission and accumulate their data before sending them to sink at once. This could realize the dual goals of improving the network lifetime and increasing the amount of information aggregated to sink. We formulate the optimization problem as to achieve trade-off among sample cycle, sink aggregated information and network lifetime by controlling the sample cycle. The results of simulation on the random generated wireless sensor networks show that when choosing the optimized sample cycle, the sink aggregated information quantity can be increased by 30.5%, and the network lifetime can be increased by 27.78%.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2010.05a
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• pp.84-86
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• 2010

Time information and time synchronization are fundamental building blocks in wireless sensor networks since many sensor network applications need time information for object tracking, consistent state updates, duplicate detection and temporal order delivery. Various time synchronization protocols have been proposed for sensor networks because of the characteristics of sensor networks which have limited computing power and resources. However, none of these protocols have been designed with time representation scheme in mind. Global time format such as UTC TOD (Universal Time Coordinated, Time Of Day) is very useful in sensor network applications. In this paper we propose time keeping and synchronization method for global time presentation in wireless sensor networks.