• Journal of information and communication convergence engineering
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• 제8권5호
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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.

• 한국멀티미디어학회논문지
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• 제19권12호
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• pp.1909-1918
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• 2016

This paper presents a unified framework for joint Convolutional Neural Network (CNN) based vehicle detection by leveraging multi-spectral image pairs. With the observation that under challenging environments such as night vision and limited light source, vehicle detection in a single color image can be more tractable by using additional far-infrared (FIR) image, we design joint CNN architecture for both RGB and FIR image pairs. We assume that a score map from joint CNN applied to overall image can be considered as confidence of vehicle existence. To deal with various scale ratios of vehicle candidates, multi-scale images are first generated scaling an image according to possible scale ratio of vehicles. The vehicle candidates are then detected on local maximal on each score maps. The generation of overlapped candidates is prevented with non-maximal suppression on multi-scale score maps. The experimental results show that our framework have superior performance than conventional methods with a joint framework of multi-spectral image pairs reducing false positive generated by conventional vehicle detection framework using only single color image.

• 한국정보통신학회:학술대회논문집
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• 한국해양정보통신학회 2009년도 추계학술대회
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• pp.219-222
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• 2009

최근 자동차산업과 통신 기술이 발전함에 따라 ITS(Intelligent Transportation System)의 핵심인 차량내 네트워크(In Vehicle network) 기술이 대두되고 있다. 그러나 현재 제공되고 있는 차량 내 네트워크 기술은 하나의 네트워크로 이루어 진 것이 아니라, 통신 속도와 비용 및 효율성 측면을 고려하여 필요에 따라서 다르게 구성이 되어 상용화 되어 있다. 따라서 차량 네트워크 통신 및 설계의 복잡성이 증대 되었고 이를 연동할 수 있는 프레임 워크가 요구되어진다. 따라서 본 논문에서는 차내망 이기종 네트워크간의 연동을 위한 프레임워크를 설계하였다.

• 한국정보통신학회논문지
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• 제14권4호
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• pp.901-908
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• 2010

자동차 내에는 그 용도에 따라 다양한 네트워크가 존재한다. 그러나 현재 제공되고 있는 차량내 네트워크 기술은 하나의 네트워크로 통합되어 운용되는 것이 아니라, 통신 속도와 비용 및 효율성 측면을 고려하여 필요에 따라 서 다르게 구성이 되어 상용화 되어 있다. 따라서 각 네트워크간의 정보 교환을 위한 차량 네트워크 통신 및 설계의 복잡성이 증대 되었고, 이에 따라 지능형 교통 시스템을 위한 이들 네트워크를 연동할 수 있는 프레임워크가 반드시 필요하다. 따라서 본 논문에서는 지능형 교통 시스템을 위한 이기종 차량 네트워크의 연동을 위한 프레임워크를 제안하고, 구현하였다. 제안한 프레임워크는 호환 가능한 프로토콜과 메시지변환, 송수신, 분석모듈로 구성된다. 구현결과 프레임워크는 이기종 차내 네트워크간의 정보교환이 원활함을 보여주었다.

• 한국정보기술학회 영문논문지
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• 제9권2호
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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.

• 한국멀티미디어학회논문지
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• 제21권8호
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• pp.897-908
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• 2018

In this paper, we propose a security framework for a cluster drones network using the MAVLink (Micro Air Vehicle Link) application protocol based on FANET (Flying Ad-hoc Network), which is composed of ad-hoc networks with multiple drones for IoT services such as remote sensing or disaster monitoring. Here, the drones belonging to the cluster construct a FANET network acting as WTRP (Wireless Token Ring Protocol) MAC protocol. Under this network environment, we propose an efficient algorithm applying the Lightweight Encryption Algorithm (LEA) to the CTR (Counter) operation mode of WPA2 (WiFi Protected Access 2) to encrypt the transmitted data through the MAVLink application. And we study how to apply LEA based on CBC (Cipher Block Chaining) operation mode used in WPA2 for message security tag generation. In addition, a modified Diffie-Hellman key exchange method is approached to generate a new key used for encryption and security tag generation. The proposed method and similar methods are compared and analyzed in terms of efficiency.

• 한국정보통신학회:학술대회논문집
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• 한국해양정보통신학회 2010년도 춘계학술대회
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• pp.430-433
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• 2010

차량에 부착되는 배선을 줄이고, 전장 기기의 수용성을 높이기 위한 차량 네트워크가 도입되었다. 차량 네트워크의 등장으로 차량에 부착된 전장기기들 간의 통신이 가능해졌으나, 다양한 네트워크가 탑재됨에 따라 각 네트워크별 고유의 기능만을 사용하고 네트워크간의 통신을 이용한 서비스를 지원하기 어려운 실정이다. 이에 로컬 네트워크들은 통합 프레임워크의 필요성이 요구되어진다. 따라서 본 논문에서는 XML을 이용하여 호환성과 이식성을 높일 수 있는 차량 네트워크 통합 프레임워크를 제안하고, 구현하였다. XML 표준 문서는 각기 네트워크 고유의 정보와 메시지를 적용함으로써, 통합 프레임워크에 호환가능하게 하였으며, 구현결과 이기종 네트워크간 정보교환이 원활함을 보여주었다.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 1997년도 춘계학술대회 논문집
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• pp.133-139
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• 1997

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 propored a learning controller consisting of two neural network-fuzzy based on independent resoning and a connection net with fixed weights to simply the neural network-fuzzy. The performance of the proposed controller is shown by simulation for trajectory tracking of the speed and azimuth of a track vehicle

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1998년도 춘계학술대회 학술발표 논문집
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• pp.107-111
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• 1998

This paper presents a now approach to the design of intelligent contorl system 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. Moreover, We develop a Windows 95 version dynamic simulator which can simulate a track vehicle model in 3D graphics space. 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 dynamic simulator for track vehicle is developed by Microsoft Visual C++. Graphic libraries, OpenGL, by Silicon Graphics, Inc. were utilized for 3D Graphics. The performance of the proposed controller is illustrated by simulation for trajectory tracking of track vehicle speed.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1997년도 한국자동제어학술회의논문집; 한국전력공사 서울연수원; 17-18 Oct. 1997
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• pp.131-134
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• 1997

This paper presents a new approach to the design of intelligent control system 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 on 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.