• KSII Transactions on Internet and Information Systems (TIIS)
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• 제10권12호
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• pp.5249-5267
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• 2016

Cognitive radio is an efficient technique to improve spectrum efficiency and relieve the pressure of spectrum resources. In this paper, we investigate the spectrum sensing period in cooperative relay cognitive radio networks; analyze the relationship between the available capacity and the signal-to-noise ratio of the received signal of second users, the target probability of detection and the active probability of primary users. Finally, we derive the closed form expression of the optimal spectrum sensing period in terms of maximum throughput. We simulate the probability of false alarm and available capacity of cognitive radio networks and compare optimal spectrum sensing period scheme with fixed sensing period one in these performance. Simulation results show that the optimal sensing period makes the cognitive networks achieve the higher throughput and better spectrum sensing performance than the fixed sensing period does. Cooperative relay cognitive radio networks with optimal spectrum sensing period can achieve the high capacity and steady probability of false alarm in different target probability of detection. It provides a valuable reference for choosing the optimal spectrum sensing period in cooperative relay cognitive radio networks.

• Journal of Communications and Networks
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• 제16권4호
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• pp.371-377
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• 2014

Localized topology control is attractive for obtaining reduced network graphs with desirable features such as sparser connectivity and reduced transmit powers. In this paper, we focus on studying how to prolong network lifetime in the context of localized topology control for wireless multi-hop networks. For this purpose, we propose an energy efficient localized topology control algorithm. In our algorithm, each node is required to maintain its one-hop neighborhood topology. In order to achieve long network lifetime, we introduce a new metric for characterizing the energy criticality status of each link in the network. Each node independently builds a local energy-efficient spanning tree for finding a reduced neighbor set while maximally avoiding using energy-critical links in its neighborhood for the local spanning tree construction. We present the detailed design description of our algorithm. The computational complexity of the proposed algorithm is deduced to be O(mlog n), where m and n represent the number of links and nodes in a node's one-hop neighborhood, respectively. Simulation results show that our algorithm significantly outperforms existing work in terms of network lifetime.

• Steel and Composite Structures
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• 제18권3호
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• pp.547-563
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• 2015

Deflection in a beam of a composite frame is a serviceability design criterion. This paper presents a methodology for rapid prediction of long-term mid-span deflections of beams in composite frames subjected to service load. Neural networks have been developed to predict the inelastic mid-span deflections in beams of frames (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 cracking, and time effects). These models can be used for frames with any number of bays and stories. The training, validating, and testing data sets for the neural networks are generated using a hybrid analytical-numerical procedure of analysis. Multilayered feed-forward networks have been developed using sigmoid function as an activation function and the back propagation-learning algorithm for training. The proposed neural networks are validated for an example frame of different number of spans and stories and the errors are shown to be small. Sensitivity studies are carried out using the developed neural networks. These studies show the influence of variations of input parameters on the output parameter. The neural networks can be used in every day design as they enable rapid prediction of inelastic mid-span deflections with reasonable accuracy for practical purposes and require computational effort which is a fraction of that required for the available methods.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1994년도 Proceedings of the Korea Automatic Control Conference, 9th (KACC) ; Taejeon, Korea; 17-20 Oct. 1994
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• pp.411-415
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• 1994

An adaptive learning control scheme by use of multilayer neural networks for compensating for uncertainties in nonlinear dynamic system is examined. Multilayer neural networks are introduced to map the uncertainties in nonlinear dynamics and perform nonlinear state feedback. Parameters of neural networks are adjusted by conventional back-propagation algorithms modified with the projection operation. Effectiveness of the proposed scheme for tracking control are demonstrated through computer simulations.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제10권8호
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• pp.3529-3550
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• 2016

In heterogeneous networks (HetNets), energy saving is vital for a sustainable network development. Many techniques, such as spectrum allocation, network planning, etc., are used to improve the network energy efficiency (EE). In this paper, micro BSs utilizing cell range expansion (CRE) and spectrum allocation are considered in multi-channel heterogeneous networks to improve EE. Hotspot region is assumed to be covered by micro BSs which can ensure that the hotspot capacity is greater than the average demand of hotspot users. The expressions of network energy efficiency are derived under shared, orthogonal and hybrid subchannel allocation schemes, respectively. Particle swarm optimization (PSO) algorithm is used to solve the optimal ratio of subchannel allocation in orthogonal and hybrid schemes. Based on the results of the optimal analysis, we propose three service control strategies on the basis of large-scale user behaviors, i.e., adjust micro cell rang expansion (AmCRE), adjust micro BSs density (AmBD) and adjust micro BSs transmit power (AmBTP). Both theoretical and simulation results show that using shared subchannel allocation scheme in AmBD strategies can obtain maximal EE with a very small area ratio. Using orthogonal subchannel allocation scheme in AmCRE strategies can obtain maximal EE when area ratio is larger. Using hybrid subchannel allocation scheme in AmCRE strategies can obtain maximal EE when area ratio is large enough. No matter which service control strategy is used, orthogonal spectrum scheme can obtain the maximal hotspot user rates.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권4호
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• pp.1624-1647
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• 2020

Vehicular network is one of the most important subjects for researchers in recent years. Anycast routing protocols have many applications in vehicular ad hoc networks. The aim of an anycast protocol is sending packets to at least one of the receivers among candidate receivers. Studies done on anycast protocols over vehicular networks, however, have capability of implementation on some applications; they are partial, and application specific. No need to say that the lack of a comprehensive study, having a strong analytical background, is felt. Mathematical modeling in vehicular networks is difficult because the topology of these networks is dynamic. In this paper, it has been demonstrated that vehicular networks can be modeled based on time-expanded networks. The focus of this article is on geographical anycast. Three different scenarios were proposed including sending geographic anycast packet to exactly-one-destination, to at-least-one-destination, and to K-anycast destination, which can cover important applications of geographical anycast routing protocols. As the proposed model is of MILP type, a decentralized heuristic algorithm was presented. The evaluation process of this study includes the production of numerical results by Branch and Bound algorithm in general algebraic modeling system (GAMS) software and simulation of the proposed protocol in OMNET++ simulator. The comprehension of the result of proposed protocol and model shows that the applicability of this proposed protocol and its reactive conformity with the presented models based on presented metrics.

• 전기학회논문지
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• 제67권8호
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• pp.1071-1079
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• 2018

In this study, the design methodology as well as network architecture of Support Vector Machine based Polynomial Neural Network, which is a kind of the dynamically generated neural networks, is introduced. The Support Vector Machine based polynomial neural networks is given as a novel network architecture redesigned with the aid of polynomial neural networks and Support Vector Machine. The generic polynomial neural networks, whose nodes are made of polynomials, are dynamically generated in each layer-wise. The individual nodes of the support vector machine based polynomial neural networks is constructed as a support vector machine, and the nodes as well as layers of the support vector machine based polynomial neural networks are dynamically generated as like the generation process of the generic polynomial neural networks. Support vector machine is well known as a sort of robust pattern classifiers. In addition, in order to enhance the structural flexibility as well as the classification performance of the proposed classifier, multi-objective particle swarm optimization is used. In other words, the optimization algorithm leads to sequentially successive generation of each layer of support vector based polynomial neural networks. The bench mark data sets are used to demonstrate the pattern classification performance of the proposed classifiers through the comparison of the generalization ability of the proposed classifier with some already studied classifiers.

• 한국경영과학회:학술대회논문집
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• 대한산업공학회/한국경영과학회 1993년도 춘계공동학술대회 발표논문 및 초록집; 계명대학교, 대구; 30 Apr.-1 May 1993
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• pp.457-458
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• 1993

• 한국경영과학회:학술대회논문집
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• 대한산업공학회/한국경영과학회 1993년도 춘계공동학술대회 발표논문 및 초록집; 계명대학교, 대구; 30 Apr.-1 May 1993
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• pp.455-456
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• 1993

• 한국수자원학회논문집
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• 제34권4호
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• pp.303-316
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• 2001

본 연구에서는 중소하천수계에서 수문학적 예측을 위하여 Hybrid Neural Networks의 일종인 반경기초함수(RBF) 신경망모형이 적용되었다. RBF 신경망모형은 4종류의 매개변수로 구성되어 있으며, 지율 및 지도훈련과정으로 이루어져있다. 반경기초함수로서 가우스핵함수(GKF)가 이용되었으며, GKF의 매개변수인 중심과 폭은 K-Means 군집알고리즘에 의해 최적화 된다. 그리고 RBF 신경망모형의 매개변수인 중심, 폭, 연결강도와 편차벡터는 훈련을 통하여 최적 매개변수의 값이 결정되며, 이 매개변수들을 이용하여 모형의 검증과정이 이루어진다. RBF 신경망모형은 한국의 IHP 대표유역중 하나인 위천유역에 적용하였으며, 모형의 훈련과 검증을 위하여 10개의 강우사상을 선택하였다. 또한 RBF 신경망모형과 비교검토하기 위하여 엘만 신경망(ENN)모형을 이용하였으며, ENN 모형은 일단게 할선역전파(OSSBP) 및 탄성역전파(RBP)알고리즘으로 이루어져 있다. 모형의 훈련과 검증과정을 통하여 RBF 신경망모형이 ENN 모형보다 양호한 결과를 나타내는 것으로 분석되었다. RBF 신경망모형은 훈련시키는데 시간이 적게 들고, 이론적 배경이 부족한 수문학자들도 쉽게 사용할 수 있는 신경망모형이다.