• Proceedings of the Korea Society of Information Technology Applications Conference
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• 2005.11a
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• pp.73-76
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• 2005

The advanced information and communication technology gives vehicles another role of the third digital space, merging a physical space with a virtual space in a ubiquitous society. In the ubiquitous environment, the vehicle becomes a sensor node, which has a computing and communication capability in the digital space of wired and wireless network. An intelligent vehicle information system with a remote control and diagnosis is one of the future vehicle systems that we can expect in the ubiquitous environment. However, for the intelligent vehicle system, many issues such as vehicle mobility, in-vehicle communication, service platform and network convergence should be resolved. In this paper, an in-vehicle gateway is presented for an intelligent vehicle information system to make an access to heterogeneous networks. It gives an access to the server systems on the internet via CDMA-based hierarchical module architecture. 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 fixec place, 707ms at rural area and 910ms at urban area.

• Journal of Advanced Navigation Technology
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• v.15 no.1
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• pp.157-161
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• 2011

This paper explains an error process for multimedia collaboration works with session management running on vehicle network environment. This system consists of an FDA and FRA. FDA is an agent that detects an error by hooking techniques for multimedia system based on vehicle network environment with session management. FRA is a system that is suitable for recovering software error for multimedia system with session management based on vehicle network environment. This paper describes only FRA. When multiple local sessions is opened, each local session manager sends information of participant to global session manager and take current information about session of processing in network.

• Journal of Korea Society of Industrial Information Systems
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• v.19 no.3
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• pp.9-15
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• 2014

In the ubiquitous computing environment, a ubiquitous vehicle will be a communication node in the vehicular network as well as the means of ground transportation. It will make humans and vehicles seamlessly and remotely connected. Especially, one of the prominent services in the ubiquitous vehicle is the vehicle remote operation. However, mutual-collaboration with the in-vehicle communication network, the vehicle-to-vehicle communication network and the vehicle-to-roadside communication network is required to provide vehicle remote operation services. In this paper, an Internet-based human-vehicle interfaces and a network architecture is presented to provide remote vehicle control and diagnosis services. The performance of the proposed system is evaluated through a design and implementation in terms of the round trip time taken to get a vehicle remote operation service.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.3
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• pp.602-612
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• 2015

Current IEEE 802.11p which is suggested for vehicle to vehicle communication supports one hop communication. Thus, it has a limitation to carry out efficient data dissemination. In this thesis, we suggest LTE-D2D based vehicle network to provide efficient data dissemination in the vehicle environment. In this network architecture, we use name based message with IP packet options and we put the intermediate vehicle node called 'super vehicle node' and each normal vehicle node in the cell requests data to the super vehicle node. The super vehicle node responses data to the normal vehicle node. Performance analysis is based mathematical modeling. We compare LTE cellular network to LTE-D2D based vehicle network about throughput according to packet delivery time.

• 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.

• 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.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2009.10a
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• pp.773-776
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• 2009

Last of the car industry can be grouped in one-vehicle electronic equipment, the development of the network, and accordingly the communication between each of the network is important. The network currently being used in vehicles include CAN, LIN, MOST, FlexRay, etc. are used. Network of several different kinds of applications using the federation said they were also germ, which causes the driver some more convenient environment, the desire to drive a vehicle that is increasing. If vehicle for other network environments with one integrated environment to make it a gateway for research done actively, the more applications are expected to be developed. In this paper, using gateway between CAN bus used for Body Train-side control of the vehicle network and MOST provided for infotainment systems. In vehicle automatic safety control system will be designed by One of CAN Nodes car speed information sending to MOST Navigation while don't received GPS information in the tunnel.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 2000.11b
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• pp.124-131
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• 2000

In this study, the NEURAL NETWORK (hereinafter referred to as NN) was applied to control of the nonlinear factors for turning movement of the crawler vehicle and experiment was carried out using a small model of crawler vehicle in order to inspect an application of NN. Furthermore, CHAOS NEURAL NETWORK (hereinafter referred to as CNN) was also applied to this control so as to compare with conventional NN. CNN is especially effective for plane in many variables with local minimum which conventional NN is apt to fall into, and it is relatively useful to nonlinear factors. Experiment of turning on the slope of crawler vehicle was performed in order to estimate an adaptability of nonlinear problems by NN and CNN. The inclination angles of the road surface which the vehicles travel on, were respectively 4deg, 8deg, 12deg. These field conditions were selected by the object for changing nonlinear magnitude in turning phenomenon of vehicle. Learning of NN and CNN was carried out by referring to positioning data obtained from measurement at every 15deg in turning. After learning, the sampling data at every 15deg were interpolated based on the constructed learning system of NN and CNN. Learning and simulation programs of NN and CNN were made by C language ("Association of research for algorithm of calculating machine (1992)"). As a result, conventional NN and CNN were available for interpolation of sampling data. Moreover, when nonlinear intensity is not so large under the field condition of small slope, interpolation performance of CNN was a little not so better than NN. However, when nonlinear intensity is large under the field condition of large slope, interpolation performance of CNN was relatively better than NN.

• International Journal of Computer Science & Network Security
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• v.23 no.11
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• pp.67-72
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• 2023

In the past decade, Autonomous Vehicle Systems (AVS) have advanced at an exponential rate, particularly due to improvements in artificial intelligence, which have had a significant impact on social as well as road safety and the future of transportation systems. The fusion of light detection and ranging (LiDAR) and camera data in real-time is known to be a crucial process in many applications, such as in autonomous driving, industrial automation and robotics. Especially in the case of autonomous vehicles, the efficient fusion of data from these two types of sensors is important to enabling the depth of objects as well as the classification of objects at short and long distances. This paper presents classification of objects using CNN based vision and Light Detection and Ranging (LIDAR) fusion in autonomous vehicles in the environment. This method is based on convolutional neural network (CNN) and image up sampling theory. By creating a point cloud of LIDAR data up sampling and converting into pixel-level depth information, depth information is connected with Red Green Blue data and fed into a deep CNN. The proposed method can obtain informative feature representation for object classification in autonomous vehicle environment using the integrated vision and LIDAR data. This method is adopted to guarantee both object classification accuracy and minimal loss. Experimental results show the effectiveness and efficiency of presented approach for objects classification.