• The Journal of the Convergence on Culture Technology
• /
• v.6 no.3
• /
• pp.463-468
• /
• 2020

Artificial intelligence researches are being applied and developed in various fields. In this paper, we build a neural network by using the method of implementing artificial intelligence in the form of spiking natural networks (SNN), the next-generation of artificial intelligence research, and analyze how the number of neurons in that neural networks affect the performance of the neural networks. We also analyze how the performance of neural networks changes while increasing the amount of neural network learning. The findings will help optimize SNN-based neural networks used in each field.

• ETRI Journal
• /
• v.30 no.5
• /
• pp.627-633
• /
• 2008

The technologies for wireless communication, sensing, and computation are each progressing at faster and faster rates. Notably, they are also being combined for an amazingly large multiplicative effect. It can be envisioned that the world will eventually be covered by networks of networks of smart sensors and actuators. This fact will give rise to revolutionary applications. However, to make this vision a reality, many research challenges must be overcome. This paper describes a representative set of new applications and identifies several key research challenges.

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

• 제어로봇시스템학회:학술대회논문집
• /
• 2002.10a
• /
• pp.94.5-94
• /
• 2002

The current study of complex dynamical networks is pervading all kinds of sciences today, ranging from physical to biological, even to social sciences. its impact on modern engineering and technology is prominent and will be far-reaching. Typical complex dynamical networks include the World Wide Web, the Internet, various wireless communication networks, meta-bolic networks, biological neural networks, social connection networks, scientific cooperation and citation networks, and so on. Research on fundamental properties and dynamical features of such complex networks have become overwhelm ing. This talk will provide a brief overview of some basic concepts about com plex dynamical netwo...

• ETRI Journal
• /
• v.32 no.2
• /
• pp.292-301
• /
• 2010

Increasing convergence among heterogeneous radio networks is expected to be a key feature of future ubiquitous services. The convergence of radio networks in combination with dynamic spectrum allocation (DSA) could be a beneficial means to solve the growing demand for radio spectrum. DSA might enhance the spectrum utilization of involved radio networks to comply with user requirements for high-quality multimedia services. This paper proposes a simple spectrum allocation algorithm and presents an analytical model of dynamic spectrum resource allocation between two networks using a 4-D Markov chain. We argue that there may exist a break-even point for choosing whether or not to adopt DSA in a system. We point out certain circumstances where DSA is not a viable alternative. We also discuss the performance of DSA against the degree of resource sharing using the proposed analytical model and simulations. The presented analytical model is not restricted to DSA, and can be applied to a general resource sharing study.

• Journal of information and communication convergence engineering
• /
• v.7 no.2
• /
• pp.141-147
• /
• 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.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2012.11a
• /
• pp.765-768
• /
• 2012

사회관계망에서 영향력 전파 문제는 네트워크에 가장 영향력을 끼칠 수 있는 노드들을 찾아 전체 네트워크에 영향력을 최대화 하는 것을 목적으로 한다. 본 논문에서는 Delay-Tolerant Networks에서 각 노드의 영향력을 측정하여 가장 영향력 있는 노드 집합을 선택하는 문제를 다룬다. 노드 간 연결성이 항시 보장되지 않는 Delay-Tolerant Networks 환경에서는 전체 네트워크 정보를 정확히 알 수 없기 때문에 노드의 영향력을 정확히 측정하는 것은 매우 어렵다. 본 논문에서는 Delay-Tolerant Networks 환경에서 분산 방식으로 각자 노드가 k-Clique 구조로 커뮤니티를 구성하여 국지적 정보 (Local Information)만을 활용하여 자신의 영향력을 추정하는 방법을 제시하고 실험을 통해 제안 기법으로 산출한 노드들의 영향력이 전체 네트워크 관점에서 산출한 노드들의 영향력에 근접함을 실험을 통해 증명한다.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.7 no.10
• /
• pp.2395-2410
• /
• 2013

Many telecommunication operators around the world have multiple networks. The networks run by each operator are always of different generations, such as 2G and 3G or even 4G systems. Each system has unique characters and specified requirements for optimal operation. It brings about resource allocation problem among these networks for the operator, because the budget of each operator is limited. However, the evaluation of resource allocation among various networks under each operator is missing for long, not to mention resource allocation optimization. The operators are dying for an algorithm to end their blind resource allocation, and the Resource Allocation Optimization Algorithm for Multi-network Operator (RAOAMO) proposed in this paper is what the operators want. RAOAMO evaluates and optimizes resource allocation in the view of overall cost for each operator. It outputs a resource distribution target and corresponding optimization suggestion. Evaluation results show that RAOAMO helps operator save overall cost in various cases.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2009.05a
• /
• pp.1207-1210
• /
• 2009

The goal of this research is to apply the neural networks models for the disaggregation of the pan evaporation (PE) data, Republic of Korea. The neural networks models consist of generalized regression neural networks model (GRNNM) and multilayer perceptron neural networks model (MLP-NNM), respectively. The disaggregation means that the yearly PE data divides into the monthly PE data. And, for the performances of the neural networks models, they are composed of training and test performances, respectively. The training and test performances consist of the only historic data, respectively. From this research, we evaluate the impact of GRNNM and MLP-NNM for the disaggregation of the nonlinear time series data. We should, furthermore, construct the credible data of the monthly PE data from the disaggregation of the yearly PE data, and can suggest the methodology for the irrigation and drainage networks system.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2009.05a
• /
• pp.1215-1218
• /
• 2009

The goal of this research is to apply the neural networks models for the disaggregation of the pan evaporation (PE) data, Republic of Korea. The neural networks models consist of generalized regression neural networks model (GRNNM) and multilayer perceptron neural networks model (MLP-NNM), respectively. The disaggregation means that the yearly PE data divides into the monthly PE data. And, for the performances of the neural networks models, they are composed of training and test performances, respectively. The training data consist of the mixed data The mixed data involves the historic data and the generated data using PARMA (1,1). And, the testing data consist of the only historic data, respectively. From this research, we evaluate the impact of GRNNM and MLP-NNM for the disaggregation of the nonlinear time series data. We should, furthermore, construct the credible data of the monthly PE data from the disaggregation of the yearly PE data, and can suggest the methodology for the irrigation and drainage networks system.