• Journal of Multimedia Information System
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• v.6 no.2
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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.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.10
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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.

• Journal of Power Electronics
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• v.8 no.1
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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.

• Journal of Electrical Engineering and Technology
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• v.8 no.5
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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.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 1999.10a
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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.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.4
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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.

• Proceedings of the KSME Conference
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• 2004.04a
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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.

• Journal of Institute of Control, Robotics and Systems
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• v.18 no.8
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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.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2010.05a
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• pp.799-800
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• 2010

• Journal of Navigation and Port Research
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• v.28 no.3
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• pp.227-232
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• 2004

Vehicle dynamics control systems are complex and non-linear, so they have difficulties in developing a controller for the anti-lock braking systems and the auto-traction systems. Currently the fuzzy-logic technique to estimate the absolute vehicle speed supplies good results in normal conditions. But the estimation error in severe braking is discontented In this paper, we estimate the absolute vehicle speed of UCT(Unmanned Container Transporter) by using the wheel speed data from standard anti-lock braking system wheel speed sensors. Radial symmetric basis function of the neural network model is proposed to implement and estimate the absolute vehicle speed, and principal component analysis on input data is used 10 algorithms are verified experimentally to estimate the absolute vehicle speed and one of them is perfectly shown to estimate the vehicle speed within 4％ error during a braking maneuver.