• Proceedings of the IEEK Conference
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• 2005.11a
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• pp.1023-1026
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• 2005

In the ubiquitous computing environment, an intelligent vehicle is defined as a sensor node with a capability of intelligence and communication in a wire and wireless network space. To make it real, a lot of problems should be addressed in the aspect of vehicle mobility, in-vehicle communication, common service platform and the connection of heterogeneous networks to provide a driver with several intelligent information services beyond the time and space. In this paper, we present an intelligent information system for managing in-vehicle sensor network and a vehicle gateway for connecting the external networks. The in-vehicle sensor network connected with several sensor nodes is used to collect sensor data and control the vehicle based on CAN protocol. Each sensor node is equipped with a reusable modular node architecture, which contains a common CAN stack, a message manager and an event handler. The vehicle gateway makes vehicle control and diagnosis from a remote host possible by connecting the in-vehicle sensor network with an external network. Specifically, it gives an access to the external mobile communication network such as CDMA. Some experiments was made to find out how long it takes to communicate between a vehicle's intelligent information system and an external server in the various environment. The results show that the average response time amounts to 776ms at fixed place, 707ms at rural area and 910ms at urban area.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.10
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• pp.1448-1453
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• 2015

In this paper, we propose an alternative method of communication to improve the reliability of in-vehicle networks by jointly using wireless communication networks. Wired Communication networks have been used in vehicles for the monitoring and the control of vehicle motion, however, the disconnection of wires or hardware fault of networks may cause a critical problem in vehicles. If the network manager detects a disconnection or faults in wired in-vehicle network like the Controller Area Network(CAN), it can redirect the communication path from the wired to the wireless communication like the Zigbee network. To show the validity and the effectiveness of the proposed in-vehicle network architecture, we implement the Electronic Stability Control(ESC) system as ECU-In-the-Loop Simulation(EILS) and verify that the control performance can be kept well even if some hardware faults like disconnection of wires occur.

• Journal of Institute of Control, Robotics and Systems
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• v.8 no.12
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• pp.1023-1030
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• 2002

Since the dynamics of autonomous underwater vehicles (AUVs) are highly nonlinear and their hydrodynamic coefficients vary with different vehicle's operating conditions, high performance control systems of AUVs are needed to have the capacities of teaming and adapting to the variations of the vehicle's dynamics. In this paper, a linearly parameterized neural network (LPNN) is used to approximate the uncertainties of the vehicle dynamics, where the basis function vector of the network is constructed according to the vehicle's physical properties. The network's reconstruction errors and the disturbances in the vehicle dynamics are assumed be bounded although the bound may be unknown. To attenuate this unknown bounded uncertainty, a certain estimation scheme for this unknown bound is introduced combined with a sliding mode scheme. The proposed controller is proven to guarantee that all signals in the closed-loop system are uniformly ultimately bounded (UUB). Numerical simulation studies are performed to illustrate the effectiveness of the proposed control scheme.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2010.10a
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• pp.66-69
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• 2010

Lately, vehicle combined vehicle and IT(Information Technology) for vehicle's safety and convenience. so, vehicles is equipped with many ECU(Electronic control unit). the ECU's transmit data about each electronic control unit with OBD(On-Board Diagnostics) Network and data about each multimedia with MOST(Media Oriented System Transport) Network. In this paper, Supplementing disadvantage of existing blackbox, Using MOST of in-vehicle multimedia network and OBD-II of in-vehicle control network, blackbox system obtain the vehicle's driving state data. so, blackbox system judge vehicle's driving state and provide vehicle's driving state information to driver. Blackbox system implement the features mentioned above. as a result, blackbox is going to be more accurate blackbox system.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.5
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• pp.414-421
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• 2007

This paper presents a engine/brake integrated VDC(Vehicle Dynamic Control) system using neural network algorithm methods for wheel slip and yaw rate control. For stable performance of vehicle, not only is the lateral motion control(wheel slip control) important but the yaw motion control of the vehicle is crucial. The proposed NNPI(Neural Network Proportional-Integral) controller operates at throttle angle to improve the performance of wheel slip. Also, the suggested NNPID controller performs at brake system to improve steering performance. The proposed controller consists of multi-hidden layer neural network structure and PID control strategy for self-learning of gain scheduling. Computer Simulation have been performed to verify the proposed neural network based control scheme of 17 dof vehicle dynamic model which is implemented in MATLAB Simulink.

• Journal of information and communication convergence engineering
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• v.8 no.5
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• pp.554-559
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• 2010

Recently, various in-vehicle networks have been developed respectively in order to accomplish their own purpose such as CAN and MOST. Various electronic devices for vehicle are controllable by the advent of networks attached to the vehicle. However, the networks also come with a variety of unique features in each network-specific communication which creates difficulty using and supporting the interoperable services among the networks. To solve this problem, each network needs a standard integration framework. In this paper, a framework is proposed and implemented. It consists of a standard protocol using XML to improve compatibility and portability. The framework makes each network interoperable by applying unique information and messages of the network in the XML standard document. The results obtained by implementation show that the framework supports the efficient communication of data between heterogeneous invehicle networks.

• Journal of the Korean Society for Precision Engineering
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• v.19 no.9
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• pp.51-58
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• 2002

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 is good results in normal conditions. But the estimation error in severe braking is discontented. In this paper, we estimate the absolute vehicle speed by using the wheel speed data from standard 50-tooth 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. Ten algorithms are verified experimentally to estimate the absolute vehicle speed and one of those is perfectly shown to estimate the vehicle speed with a 4% error during a braking maneuver.

• 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.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2000.11a
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• pp.281-286
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• 2000

This paper describes a lateral guidance system of an unmanned vehicle, using a neural network model of magneto-resistive sensor and magnetic fields. The model equation was compared with experimental sensing data. We found that the experimental result has a negligible difference from the modeling equation result. We verified that the modeling equation can be used in the unmanned vehicle simulations. As the neural network controller acquires magnetic field values(B$\_$x/, B$\_$y/, B$\_$z/) from the three-axis, the controller outputs a steering angle. The controller uses the back-propagation algorithms of neural network. The learning pattern acquisition was obtained using computer simulation, which is more exact than human driving. The simulation program was developed in order to verify the acquisition of the learning pattern, learning itself, and the adequacy of the design controller. A computer simulation of the vehicle (including vehicle dynamics and steering) was used to verify the steering performance of the vehicle controller using the neural network. Good results were obtained. Also, the real unmanned electrical vehicle using neural network controller verified good results.

• 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.