• 제목/요약/키워드: Vehicle network

검색결과 1,520건 처리시간 0.035초

Comparison of Topology Based-Routing Protocols in Wireless Network

  • Sharma, Vikas;Ganpati, Anita
    • Journal of Multimedia Information System
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    • 제6권2호
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    • pp.61-66
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    • 2019
  • VANET (Vehicular Ad-hoc Network) is a mobile Ad-hoc Network which deals with the moving vehicles. VANET supports Intelligent Transport Systems (ITS) which is related to different modes of transport and traffic management techniques. VANETs enabled users to be informed and make them safer. VANET uses IEEE 802.11p standard wireless access protocol for communication. An important and necessary issue of VANET is to design routing protocols. In a network, communication takes place by the use of the routing protocols. There are mainly two types of communications used such as Vehicle to Vehicle (V2V) and Vehicle to Infrastructure (V2I) in VANET. Vehicles can send and receive messages among them and also to and from infrastructure used. In this paper, AODV, DSR and DSDV are compared by analysing the results of simulation on various metrics such as average throughput, instant throughput, packet delivery ratio and residual energy. Findings indicates utilization of AODV and DSR is more applicable for these metrics as compared to DSDV. A network simulator (NS2) is used for simulation.

지능형 자동차의 분산형 시스템을 위한 FlexRay 네트워크 시스템의 구현 (Implementation of FlexRay Network System for Distributed Systems of Intelligent Vehicle)

  • 하경남;이원석;이경창;이석
    • 제어로봇시스템학회논문지
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    • 제13권10호
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    • pp.933-939
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    • 2007
  • Safety critical systems such as x-by-wire systems require in-vehicle network systems that can interconnect various sensors, actuators, and controllers. These networks need to have high data rate, deterministic operation, and fault tolerance. Recently, FlexRay protocol that is a time-triggered protocol has been introduced, and many automotive companies have been focusing on this protocol. This paper presents a design method of FlexRay network system and implementation of FlexRay-based motor control system.

Torque Ripples Minimization of DTC IPMSM Drive for the EV Propulsion System using a Neural Network

  • Singh, Bhim;Jain, Pradeep;Mittal, A.P.;Gupta, J.R.P.
    • Journal of Power Electronics
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    • 제8권1호
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    • pp.23-34
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    • 2008
  • This paper deals with a Direct Torque Control (DTC) of an Interior Permanent Magnet Synchronous Motor (IPMSM) for the Electric Vehicle (EV) propulsion system using a Neural Network (NN). The Conventional DTC with optimized switching lookup table and three level torque controller generates relatively large torque ripples in an electric vehicle motor drive. For reducing the torque ripples, a three level torque controller is hereby replaced by the five level torque controller. Furthermore, the switching lookup table of the five level torque controller based DTC is replaced with a Neural Network. These DTC schemes of an IPMSM drive are simulated using MATLAB/SIMULINK. The simulated results are compared with the conventional DTC and it is found that the ripples in the torque, as well as in the stator current, are reduced drastically.

Control validation of Peugeot 3∞8 HYbrid4 Vehicle Using a Reduced-scale Power HIL Simulation

  • Letrouve, Tony;Lhomme, Walter;Bouscayrol, Alain;Dollinger, Nicolas
    • Journal of Electrical Engineering and Technology
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    • 제8권5호
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    • pp.1227-1233
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    • 2013
  • The new engineering challenges lead to a control of a vehicle more and more complex. To tackle this issue, Hardware-In-the-Loop (HIL) simulation is used in the development of real-time embedded systems. In this paper, the control of a double parallel hybrid vehicle is validated using a reduced power HIL simulation. A graphical description is used in order to organize the emulation and control. Some experimental results of a versatile testbed are given for the Peugeot $3{\infty}8$ HYbrid4.

궤도차량의 동적 제어를 위한 퍼지-뉴런 제어 알고리즘 개발 (Development of a Neural-Fuzzy Control Algorithm for Dynamic Control of a Track Vehicle)

  • 서운학
    • 한국공작기계학회:학술대회논문집
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    • 한국공작기계학회 1999년도 추계학술대회 논문집 - 한국공작기계학회
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    • pp.142-147
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    • 1999
  • This paper presents a new approach to the dynamic control technique for track vehicle system using neural network-fuzzy control method. The proposed control scheme uses a Gaussian function as a unit function in the neural network-fuzzy, and back propagation algorithm to train the fuzzy-neural network controller in the framework of the specialized learning architecture. It is proposed a learning controller consisting of two neural network-fuzzy based on independent reasoning and a connection net with fixed weights to simply the neural networks-fuzzy. The performance of the proposed controller is shown by simulation for trajectory tracking of the speed and azimuth of a track vehicle.

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BL-CAST:Beacon-Less Broadcast Protocol for Vehicular Ad Hoc Networks

  • Khan, Ajmal;Cho, You-Ze
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • 제8권4호
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    • pp.1223-1236
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    • 2014
  • With the extension of wireless technology, vehicular ad hoc networks provide important services for the dissemination of general data and emergency warnings. However, since, the vehicle topology frequently changes from a dense to a sparse network depending on the speed of the moving vehicles and the time of day, vehicular ad hoc networks require a protocol that can facilitate the efficient and reliable dissemination of emergency messages in a highly mobile environment under dense or intermittent vehicular connectivity. Therefore, this paper proposes a new vehicular broadcast protocol, called BL-CAST, that can operate effectively in both dense and sparse network scenarios. As a low overhead multi-hop broadcast protocol, BL-CAST does not rely on the periodic exchange of beacons for updating location information. Instead, the location information of a vehicle is included in a broadcast message to identify the last rebroadcasting vehicle in an intermittently connected network. Simulation results show that BL-CAST outperforms the DV-CAST protocol in terms of the end-to-end delay, message delivery ratio and network overhead.

차량동역학해석을 위한 실험적 부싱모델 개발 (Empirical Bushing Model For Vehicle Dynamic Analysis)

  • 손정현;강태호;백운경;박동운;유완석
    • 대한기계학회:학술대회논문집
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    • 대한기계학회 2004년도 춘계학술대회
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    • pp.864-869
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    • 2004
  • In this paper, a blackbox approach is carried out to model the nonlinear dynamic bushing model. One-axis durability test is performed to describe the mechanical behavior of typical vehicle elastomeric components. The results of the tests are used to develop an empirical bushing model with an artificial neural network. The back propagation algorithm is used to obtain the weighting factor of the neural network. Since the output for a dynamic system depends on the histories of inputs and outputs, Narendra's algorithm of 'NARMAX' form is employed in the neural network bushing module. A numerical example is carried out to verify the developed bushing model.

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다수의 무인운송플랫폼 운용을 위한 센서 네트워크 시스템 (Sensor Network System to Operate Multiple Autonomous Transport Platform)

  • 남춘성;김수현;이석한;신동렬
    • 제어로봇시스템학회논문지
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    • 제18권8호
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    • pp.706-712
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    • 2012
  • This paper presents a sensor network and operation for multiple autonomous navigation platform and transport service. Multiple platform navigate with inside sensors and outside sensors while acquiring and process some useful information. Each platform communicates each other by navigational information through central main server. Efficient sensor network systems are considered for the scenario which some passengers call the service and the vehicle accomplish its transport service by transporting each caller to the destination by autonomous manners. In the scenario, all vehicles perform a role of sensor system to the central server and the server handles each information and integrate with faster procedure in the wireless 3G network.

무인 컨테이너 운송차량의 절대속도 추정을 위한 뉴럴 네크워크 모델 적용 (Absolute Vehicle Speed Estimation of Unmanned Container Transporter using Neural Network Model)

  • 하희권;오경흡
    • 한국항해항만학회지
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    • 제28권3호
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    • pp.227-232
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    • 2004
  • 차량동역학제어시스템은 복잡하고 비선형이므로 잠금방지 제동시스템 및 자동주행시스템 개발에 어려움이 있다. 차량절대속도를 추정하기 위해 퍼지 로직 기법이 최근 적용되어 정상적인 조건에서 만족할 만한 결과를 얻고 있다. 그러나 급격한 제동시 추정오차가 크게 발생되었다. 본 논문에서는 휠 속도 센서를 이용하여 무인 컨테이너 운송차량의 절대속도를 추정하기 위해, 뉴럴 네트워크 모델의 방사대칭 기저함수와 주성분 분석법을 적용하여 10개의 추정 알고리즘중 오차를 4% 이내로 추정할 수 있는 알고리즘을 제시하였다.