• Steel and Composite Structures
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• v.46 no.6
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• pp.731-744
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• 2023

Vehicle load information is an important role in operating and ensuring the structural health of cable-stayed bridges. In this regard, an efficient and economic method is proposed for vehicle load detection based on the observed cable tension and vehicle position using a graph neural network (GNN). Datasets are first generated using the practical advanced analysis program (PAAP), a robust program for modeling and considering both geometric and material nonlinearities of bridge structures subjected to vehicle load with low computational costs. With the superiority of GNN, the proposed model is demonstrated to precisely capture complex nonlinear correlations between the input features and vehicle load in the output. Four popular machine learning methods including artificial neural network (ANN), decision tree (DT), random forest (RF), and support vector machines (SVM) are refereed in a comparison. A case study of a cable-stayed bridge with the typical truck is considered to evaluate the model's performance. The results demonstrate that the GNN-based model provides high accuracy and efficiency in prediction with satisfactory correlation coefficients, efficient determination values, and very small errors; and is a novel approach for vehicle load detection with the input data of the existing monitoring system.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.2
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• pp.262-266
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• 2010

Recognizing a license plate of a vehicle has widely been issued. In this thesis, firstly, mean shift algorithm is used to filter and segment a color vehicle image in order to get candidate regions. These candidate regions are then analyzed and classified in order to decide whether a candidate region contains a license plate. We then present an approach to recognize a vehicle's license plate using the Fuzzy ARTMAP neural network, a relatively new architecture of the neural network family. We show that the proposed system is well to recognize the license plate and shows some compute simulations.

• IEMEK Journal of Embedded Systems and Applications
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• v.8 no.2
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• pp.69-78
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• 2013

As vehicles become more intelligent for safety and convenience of drivers, in-vehicle networking systems such as controller are network (CAN) have been widely used due to increasing number of electronic control unit (ECU). Recently, FlexRay was developed to replace CAN protocol in chassis networking systems, to remedy the shortage of transmission capacity and unsatisfactory real-time transmission delay of conventional CAN. However, it is difficult for vehicle network designers to calculate platform configuration registers (PCR) and determine a base cycle or slot length of FlexRay. To assist vehicle network designers for designing FlexRay cluster, this paper presents automatic field bus exchange format (FIBEX) generation algorithm from CANdb information, which is de-facto standard database format for CAN. To design this program, structures of FIBEX, CANdb and relationship among PCR variables are analyzed.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.5
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• pp.125-132
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• 2017

Vehicular safety service reduces traffic accidents and traffic congestion by informing drivers in advance of threats that may occur while driving using vehicle-to-vehicle (V2V) communications in a wireless environment. For vehicle safety services, every vehicle must broadcasts a Basic Safety Message(BSM) periodically. In congested traffic areas, however, network congestion can easily happen, reduce the message delivery ratio, increase end-to-end delay and destabilize vehicular safety service system. In this paper, to solve the network congestion problem in vehicle safety communications, we approximate the relationship between channel busy ratio and the number of vehicles and use it to estimate the total network congestion. We propose a new context-aware transmit power control algorithm which controls the transmission power based on total network congestion. The performance of the proposed algorithm is evaluated using Qualnet, a network simulator. As a result, the estimation of total network congestion is accurately approximated except in specific scenarios, and the packet error rate in vehicle safety communication is reduced through transmit power control.

• Journal of The Institute of Information and Telecommunication Facilities Engineering
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• v.8 no.3
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• pp.122-131
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• 2009

Ubiquitous-Transportation sensor network is able to realize a vehicle ad-hoc network. Since there are some problems in an existing ITS system, the new technology and traffic information strategies are requirements in this advanced system, u-TSN. The purposes of this paper is to introduce the components on u-TSN system, establish new traffic strategies for this system, and then evaluate these strategies by making a comparative study of ITS and using micro traffic simulator, AIMSUN. The strategy evaluated by AIMSUN is position-based multicast strategy which provides traffic information to vehicles using V2I (vehicle to Infrastructure) communication. This paper focuses on the providing real-time route guidance information when congestion is occurred by the incidents. This study estimates total travel time on each route by API modules. Result from simulation experiments suggests that position-based multicast strategy can achieve more optimal network performance and increased driver satisfaction since the total accumulated travel times of both the major road and the total system on position-based multicast strategy are less than those on VMS.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 1997.10a
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• pp.70-75
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• 1997

This paper proposes a new approach to the design of fuzzy-neuro control for track vehicle system using fuzzy logic based on neural network. 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 of independent reasoning and a connection net with fixed weights to simply the neural networks-fuzzy. The performance of the proposed controller is illustrated by simulation for trajectory tracking of track vehicle speed.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.61 no.6
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• pp.859-864
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• 2012

The computer network plays an important role in modern digital controllers within a safety system of a nuclear power plant. For the reliable and realtime data communication between controllers, this paper proposes a modified high-reliable MVB(multi-function vehicle bus) as a main control network for a safety system of a nuclear power plant. The proposed network supports the state-based communication in order to ensure the deterministic communication latency, and very fast network recovery when the bus master fails compare to the standard MVB. This paper also shows the implementation results using a FPGA-based testbed.

• 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.

• Korean System Dynamics Review
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• v.8 no.2
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• pp.5-23
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• 2007

According to the system dynamics model of this study, if there is a significant network effect on vehicle operating costs, it is difficult to achieve the shift to AFV even in the long term without a policy intervention because the car market is locked in to the current structure. Network effect can be caused by an increasing return to scale in fuel supply sector as well as in maintenance service sector. It is also related to the fact that the reliability and awareness of consumers on new products increases with the growth of the market share of the new products. There are several possible policy options to break the 'locked in' structure of car market, such as subsidy on vehicle price (capital cost), subsidy on fuel (operating cost) and niche management policy. Combined policy options would be more effective than relying on a single policy option to increase the market share of AFV.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2000.11a
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• pp.687-690
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• 2000

Lately, many studies have been progressed for the protection human's lives and property as holding in check accidents happened by human's carelessness or mistakes. One part of these is the development of an autonomouse vehicle. General control method of vision-based autonomous vehicle system is to determine the navigation direction by analyzing lane images from a camera, and to navigate using proper control algorithm. In this paper, characteristic points are abstracted from lane images using lane recognition algorithm with sobel operator. And then the vehicle is controlled using two proposed auto-steering algorithms. Two steering control algorithms are introduced in this paper. First method is to use the geometric relation of a camera. After transforming from an image coordinate to a vehicle coordinate, a steering angle is calculated using Ackermann angle. Second one is using a neural network algorithm. It doesn't need to use the geometric relation of a camera and is easy to apply a steering algorithm. In addition, It is a nearest algorithm for the driving style of human driver. Proposed controller is a multilayer neural network using Levenberg-Marquardt backpropagation learning algorithm which was estimated much better than other methods, i.e. Conjugate Gradient or Gradient Decent ones.