• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권7호
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• pp.3465-3479
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• 2017

Information-Centric Vehicular Ad Hoc Network (IC-VANET) is a promising network architecture for the future intelligent transport system. Video streaming applications over IC-VANET not only enrich infotainment services, but also provide the drivers and pedestrians real-time visual information to make proper decisions. However, due to the characteristics of wireless link and frequent change of the network topology, the packet loss seriously affects the quality of video streaming applications. In this paper, we propose a REceiver-Driven loss reCOvery Mechanism (REDCOM) to enhance video dissemination over IC-VANET. A Markov chain based estimation model is introduced to capture the real-time network condition. Based on the estimation result, the proposed REDCOM recovers the lost packets by requesting additional forward error correction packets. The REDCOM follows the receiver-driven model of IC-VANET and does not require the infrastructure support to efficiently overcome packet losses. Experimental results demonstrate that the proposed REDCOM improves video quality under various network conditions.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권3호
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• pp.1213-1237
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• 2019

Vehicular Ad-hoc Network (VANET) facilities envision future Intelligent Transporting Systems (ITSs) by providing inter-vehicle communication for metrics such as road surveillance, traffic information, and road condition. In recent years, vehicle manufacturers, researchers and academicians have devoted significant attention to vehicular communication technology because of its highly dynamic connectivity and self-organized, decentralized networking characteristics. However, due to VANET's high mobility, dynamic network topology and low communication coverage, dissemination of large data packets (e.g. multimedia content) is challenging. Clustering enhances network performance by maintaining communication link stability, sharing network resources and efficiently using bandwidth among nodes. This paper proposes a mobility-based, multi-hop clustering algorithm, (MBCA) for multimedia content broadcasting over an IEEE 802.11p-LTE-enabled hybrid VANET architecture. The OMNeT++ network simulator and a SUMO traffic generator are used to simulate a network scenario. The simulation results indicate that the proposed clustering algorithm over a hybrid VANET architecture improves the overall network stability and performance, resulting in an overall 20% increased cluster head duration, 20% increased cluster member duration, lower cluster overhead, 15% improved data packet delivery ratio and lower network delay from the referenced schemes [46], [47] and [50] during multimedia content dissemination over VANET.

• 한국산학기술학회논문지
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• 제12권3호
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• pp.1336-1340
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• 2011

서로 다른 응용을 위한 중복된 유비쿼터스 네트워크는 결국 어느 지점에서는 중복된 채널이 만들어 지며, 채널 혼선으로 인하여 전체 네트워크의 불안정뿐만 아니라, 보안 문제 그리고 기기 오작동 등의 심각한 문제를 야기 시킬 수 있다. 따라서 본 논문에서는 같은 지역내 다른 목적의 중복된 유비쿼터스용 ZigBee 네트워크간의 혼선 문제를 근본적으로 회피하기 위한 지능형 채널 할당 기법을 제안하고, 이러한 제안기법을 응용하여 무선 임베디드 시스템을 개발하였다.

• 한국산학기술학회논문지
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• 제10권11호
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• pp.3193-3199
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• 2009

본 실시간 지능형 홈 네트워크 제어시스템은 여러 모바일 기기를 이용하여 통신이 가능한 곳이면 언제 어디서든지 실시간으로 시스템 제어와 모니터링이 가능한 시스템이다. 본 논문은 실시간 지능형 홈 네트워크 제어시스템을 구현하기 위하여 무선 홈 네트워크 기술 중 ZigBee 기술을 이용하여 각종 USN 센서를 제어하는 서버 모듈 구현과 모바일 기기에 탑재되어 사용자에 의해 구동되는 클라이언트 모듈을 GUI환경으로 구성하여 설계 및 구현하였다.

• 한국지능시스템학회논문지
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• 제17권7호
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• pp.887-892
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• 2007

본 논문에서는 지능형 서비스 로봇의 네트워크에서 제어의 실시간이 보장되면서 많은 데이터를 처리할 수 있는 개방형 표준 이더넷 호환성을 확보한 산업용 이더넷 프로토콜인 EtherCAT을 기반으로 하여 네트워크의 물리 계층을 100BaseFx인 광케이블 인터페이스 모듈을 설계하고 구현하여 센서 및 모터제어 시스템에 적용하고, 테스트를 통해 지능형 서비스 로봇 내부 네트워크로서의 적합성을 제시하고자 한다.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2001년도 춘계학술대회 학술발표 논문집
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• pp.101-104
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• 2001

This paper deals with the speed-up of interval regression neural network. We propose an improved method of adjusting the parameter alpha used in the interval regression neural network to improve the learning speed and regression performance. Finally, we provide numerical examples to evaluate the performance of the proposed method.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2000년도 하계학술대회 논문집 D
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• pp.2446-2451
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• 2000

This paper presents the conceptual model of open & distributed intelligent control network for BAS. The characteristics and definition of this network also is proposed from theoretical study of LonWorks and a comparison between LonWorks and conventional network.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1997년도 추계학술대회 논문집 학회본부
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• pp.675-678
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• 1997

In this research we continue the study of nuclear power plant steam generator's intelligent modeling. This model represents the input-output behavior and is a preliminary stage for intelligent control. Among many intelligent models available, we study neural network models that have been proven as universal function approximators. We select multilayer perceptrons, circular backpropagation networks, piecewise linearly trained networks and recurrent neural networks as the candidates for the steam generator's intelligent models. We take the input-output pairs from steam generator's reference model and train the neural network models. We validate trained neural network models as intelligent models of steam generator.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2004년도 추계학술대회 학술발표 논문집 제14권 제2호
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• pp.303-308
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• 2004

Fuzzy logic, neural network, fuzzy-neural network play an important as the key technology of linguistic modeling for intelligent control and decision making in complex systems. The fuzzy-neural network (FNN) learning represents one of the most effective algorithms to build such linguistic models. This paper proposes learning approach of fuzzy-neural network by immune algorithm. The proposed learning model is presented in an immune based fuzzy-neural network (FNN) form which can handle linguistic knowledge by immune algorithm. The learning algorithm of an immune based FNN is composed of two phases. The first phase used to find the initial membership functions of the fuzzy neural network model. In the second phase, a new immune algorithm based optimization is proposed for tuning of membership functions and structure of the proposed model.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권2호
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• pp.514-537
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• 2020

Wireless sensor network (WSN) is a distributed network composed of many sensory nodes. It is precisely due to the clustering unevenness and cluster head election randomness that the energy consumption of WSN is excessive. Therefore, a many-objective optimization WSN energy balance model is proposed for the first time in the clustering stage of LEACH protocol. The four objective is considered that the cluster distance, the sink node distance, the overall energy consumption of the network and the network energy consumption balance to select the cluster head, which to better balance the energy consumption of the WSN network and extend the network lifetime. A many-objective optimization algorithm to optimize the model (LEACH-ABF) is designed, which combines adaptive balanced function strategy with penalty-based boundary selection intersection strategy to optimize the clustering method of LEACH. The experimental results show that LEACH-ABF can balance network energy consumption effectively and extend the network lifetime when compared with other algorithms.