• Journal of the Korea Society of Computer and Information
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• v.26 no.1
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• pp.45-55
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• 2021

This paper has proposed an algorithm that detecting for dense small vehicle in large image efficiently. It is consisted of two Ensemble Deep-Learning Network algorithms based on Coarse to Fine method. The system can detect vehicle exactly on selected sub image. In the Coarse step, it can make Voting Space using the result of various Deep-Learning Network individually. To select sub-region, it makes Voting Map by to combine each Voting Space. In the Fine step, the sub-region selected in the Coarse step is transferred to final Deep-Learning Network. The sub-region can be defined by using dynamic windows. In this paper, pre-defined mapping table has used to define dynamic windows for perspective road image. Identity judgment of vehicle moving on each sub-region is determined by closest center point of bottom of the detected vehicle's box information. And it is tracked by vehicle's box information on the continuous images. The proposed algorithm has evaluated for performance of detection and cost in real time using day and night images captured by CCTV on the road.

• Journal of the Korean Institute of Telematics and Electronics T
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• v.35T no.3
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• pp.46-52
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• 1998

In this paper, after inputting the computer with the attached number plate on the vehicle, using it, the color decomposition method and back propagation neural network proposed the extractable method of the vehicle number plate at high speed. This method separated R, G, B signal form input moving vehicle image to computer through video camera, then after transform this R, G, B signal into input image data of the computer by using color depth of vehicle number plate and store up binary value in the memory frame buffer. After adapting character's recognition algorithm, also improving this, by adapting back propagation neural network makes the vehicle number plate recognition system. Also minimalizing the similar color's confusion, adapting horizontal and vertical extracting algorithm by using the vehicle's rectangular architecture shows the extract and character's recognition of the vehicle number plate at high speed.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2009.10a
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• pp.98-101
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• 2009

Recently, there is use of the vehicle network for vehicle diagnostic method and Increased use of the vehicle protocol such as (CAN(Controller Area Network), MOST, LIN, FlexRay), Distributed control and data about the vehicle are being sought methods for real-time observation and monitoring and trend tends to have gone into this. In this case of automotive diagnostic module in today, there is Primarily to use DLC(Data Link Connector)Connector called self-check terminal. Generally, vehicle Diagnoses to use DLC Connector such as OBD2(On Board Diagnostics) Through Diagnostic Module(scanner). But there limit diagnostic as engine and powertrain part, and not consider user's perspective In this paper, By designing Vehicle diagnostic monitoring system using Ethernet Port, transmit and Receives CAN protocol vehicle data, and implement Easily monitoring system that provide and Diagnoses to provide vehicle's state and information to use PC.

• Transactions of the Korean Society of Automotive Engineers
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• v.15 no.5
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• pp.48-55
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• 2007

A bushing component of a vehicle suspension system is tested to capture the nonlinear behavior of rubber bushing element using the MTS 3-axes rubber test machine. The results of the tests are used to model the artificial neural network bushing model. The performances from the neural network model usually are dependent on the structure of the neural network. In this paper, maximum error, peak error, root mean square error, and error-to-signal ratio are employed to evaluate the performances of the neural network bushing model. A simple simulation is carried out to show the usefulness of the developed procedure.

• 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.

• Transactions of the Korean Society of Automotive Engineers
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• v.10 no.2
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• pp.132-140
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• 2002

A momentum backpropagation neural network is prepared to carry out real-time dynamics simulations of a passenger car. A full-car model of fifteen degrees of freedom was constructed for vehicle dynamics analysis. Human body dynamics analysis was performed for a male driver(50 percentile Korean adult) restrained by a three point seatbelt system. The trained data using the neural network were obtained using a dynamic solver, ADAMS . The neural network were formed based on the dynamics of the simulator. The optimized hidden layer was obtained by selecting the optimal number of hidden layers. The driving scenario including bump passing and lane changing has been used for the estimation of the proposed neural network. A comparison between the trained data and neural network outputs is found to be satisfactory to show the applicability of the suggested approach.

• Proceedings of the KSR Conference
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• 2008.06a
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• pp.1936-1945
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• 2008

In Korea, TCN has applied to the Korean High-speed Train (HSR350X) through G7 High-speed Train development project. TCN is the most suitable international standard communication network for distributed control systems that is adopted for high-speed of vehicle, safety and flexibility. TCN is the network exclusively for the high-speed train and electrical trains. This TCN satisfies the network standards. The network standards are real time communication, fault tolerance design, integrated data system, resistance of environment, automated recognition for modification of vehicle formation and maintenance. The purpose of this research is applying the development of WTB analyzer which is part of communication network system TCN, to check the communication of high-speed trains and electrical trains.

• Journal of Advanced Information Technology and Convergence
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• v.9 no.2
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• pp.55-63
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• 2019

Vehicle communication networks have some deficient network resources to support a vast multimedia service including safety driving information, video, news and some broadcast relayed from the playgrounds such as professional baseball games for autonomous vehicles. This paper deals with the framework for providing seamless multimedia service including safety driving information using multicast in cooperated-connected vehicle communication network (CVCN). It adopts smart-switch (SS) and smart intelligent multicast agent(SIMA) to support the seamless multimedia service. The SS manages and switches multimedia streams through SIMA in CVCN network. The SIMA to operate as an access point, is composed of multicast supporting part and control part of mobile devices/autonomous vehicles in CVCN network. Therefore this proposed technique using SS and SIMA within CVCN network is a new framework for multimedia service that can disperse the load of server.

• Journal of Mechanical Science and Technology
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• v.15 no.4
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• pp.448-458
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• 2001

The primary goal of virtual prototyping is to eliminate the need for fabricating physical prototypes, and to reduce cost and time for developing new products. A virtual prototyping seeks to create a virtual environment where the development of a new model can be flexible as well as rapid, and experiments can be carried out effectively concerning kinematics, dynamics, and control aspects of the model. This paper addresses the virtual environment used for virtual prototyping of a passenger vehicle. It has been developed using the dVISE environment that provides such useful features as actions, events, sounds, and light features. A vehicle model including features, and behaviors is constructed by employing an object-oriented paradigm and contains detailed information about a real-size vehicle. The human model is also implemented not only for visual and reach evaluations of the developed vehicle model, but also for behavioral visualization during a crash test. For the real time driving simulation, a neural network model is incorporated into the virtual environment. The cases of passing bumps with a vehicle are discussed in order to demonstrate the applicability of a set of developed models.

• Journal of the Korean Society for Precision Engineering
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• v.16 no.7
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• pp.109-118
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• 1999

A graphic software is one of the most important components of the vehicle simulator. To increase a visual reality of the simulator, the graphic software should require several technologies such as three-dimensional graphics, graphic modeling of the vehicle and the environment, drivers biomechanical models, and real-time data processing. This study presents a real time graphic driving simulator of a construction vehicle. The graphic simulator contains the three models of the construction vehicle, the human, and the environment, and employes a neural network approach to decrease an on-line dynamic computation. An excavator model is represented using an object-oriented paradigm and contains the detailed information about a real-size vehicle. The human model is introduced for objective visual evaluations of the developed excavator model. Since the environment model plays an important role in a real-time simulator, a block-based approach is implemented and a text format is utilized for easier construction of environment. The simulation results are illustrated in order to demonstrate the applicability of developed models and the neural network approach.