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V2X Technology Trends for Next-Generation Mobility

  • Kim, Young-Hak
    • International Journal of Internet, Broadcasting and Communication
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    • v.12 no.1
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    • pp.7-13
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    • 2020
  • We describes V2X technology, a connectivity-based recognition technology that is attracting attention as a key technology for implementing autonomous driving technology, and autonomous communication modules that implement ADAS technology, a sensor-based recognition technology. It also explains the trends in V2X technology standardization centered on IEEE 802.11p, which is a WAVE technology standard based on Wi-Fi/DSRC. Finally, we will discuss the market growth trend of V2X communication modules in the United States, the leading V2X technology module, and the development of technology development trends of major domestic and international companies that are leading the global technology market related to V2X communication modules. V2X and ADAS technologies will be the biggest influence on automotive purchasing decisions. In recent years, V2I mandates have been promoted beyond V2V, mainly in developed countries such as the United States. The related industry needs to focus on the development of information transmission network technology that can support high frequency high efficiency(transmission rate) and sophisticated positioning accuracy beyond conventional vehicle communication.

A Thoracic Spine Segmentation Technique for Automatic Extraction of VHS and Cobb Angle from X-ray Images (X-ray 영상에서 VHS와 콥 각도 자동 추출을 위한 흉추 분할 기법)

  • Ye-Eun, Lee;Seung-Hwa, Han;Dong-Gyu, Lee;Ho-Joon, Kim
    • KIPS Transactions on Software and Data Engineering
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    • v.12 no.1
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    • pp.51-58
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    • 2023
  • In this paper, we propose an organ segmentation technique for the automatic extraction of medical diagnostic indicators from X-ray images. In order to calculate diagnostic indicators of heart disease and spinal disease such as VHS(vertebral heart scale) and Cobb angle, it is necessary to accurately segment the thoracic spine, carina, and heart in a chest X-ray image. A deep neural network model in which the high-resolution representation of the image for each layer and the structure converted into a low-resolution feature map are connected in parallel was adopted. This structure enables the relative position information in the image to be effectively reflected in the segmentation process. It is shown that learning performance can be improved by combining the OCR module, in which pixel information and object information are mutually interacted in a multi-step process, and the channel attention module, which allows each channel of the network to be reflected as different weight values. In addition, a method of augmenting learning data is presented in order to provide robust performance against changes in the position, shape, and size of the subject in the X-ray image. The effectiveness of the proposed theory was evaluated through an experiment using 145 human chest X-ray images and 118 animal X-ray images.

Classification of BcN Vulnerabilities Based on Extended X.805 (X.805를 확장한 BcN 취약성 분류 체계)

  • Yoon Jong-Lim;Song Young-Ho;Min Byoung-Joon;Lee Tai-Jin
    • The KIPS Transactions:PartC
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    • v.13C no.4 s.107
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    • pp.427-434
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    • 2006
  • Broadband Convergence Network(BcN) is a critical infrastructure to provide wired-and-wireless high-quality multimedia services by converging communication and broadcasting systems, However, there exist possible danger to spread the damage of an intrusion incident within an individual network to the whole network due to the convergence and newly generated threats according to the advent of various services roaming vertically and horizontally. In order to cope with these new threats, we need to analyze the vulnerabilities of BcN in a system architecture aspect and classify them in a systematic way and to make the results to be utilized in preparing proper countermeasures, In this paper, we propose a new classification of vulnerabilities which has been extended from the ITU-T recommendation X.805, which defines the security related architectural elements. This new classification includes system elements to be protected for each service, possible attack strategies, resulting damage and its criticalness, and effective countermeasures. The new classification method is compared with the existing methods of CVE(Common Vulnerabilities and Exposures) and CERT/CC(Computer Emergency Response Team/Coordination Center), and the result of an application to one of typical services, VoIP(Voice over IP) and the development of vulnerability database and its management software tool are presented in the paper. The consequence of the research presented in the paper is expected to contribute to the integration of security knowledge and to the identification of newly required security techniques.

A comparative Study of Wayland window system and X window system for Smart Device (스마트 디바이스를 위한 Wayland window system과 X window system에 관한 비교 연구)

  • Kim, Min-Jeong;Lee, Gwang-Lim;Choi, Jinhee
    • Proceedings of the Korea Information Processing Society Conference
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    • 2015.10a
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    • pp.15-18
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    • 2015
  • Wayland는 open source community에서 30년 가까이 사용 되어 온 X window system을 대체하기 위해 개발된 새로운 window system이다. X window system은 network transparent한 특성을 기반으로 여러 영역에서 사용되어 왔지만 단일 기기에서의 UX에 필수적은 rendering, event processing, 그리고 compositing 등의 특성에 구조적으로 최적화 되어 있지 않다는 문제가 있다. 이 논문에서는 Tizen에 적용된 case를 통해 X window system과 Wayland의 구조적인 장단점을 비교하여 실측 데이터를 통해 구조적 차이로 인한 성능 차이를 설명한다.

Development of the Neural Network Steering Controller based on Magneto-Resistive Sensor of Intelligent Autonomous Electric Vehicle (자기저항 센서를 이용한 지능형 자율주행 전기자동차의 신경회로망 조향 제어기 개발)

  • 김태곤;손석준;유영재;김의선;임영철;이주상
    • 제어로봇시스템학회:학술대회논문집
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    • 2000.10a
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    • pp.196-196
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    • 2000
  • This paper describes a lateral guidance system of an autonomous 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 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 teaming pattern, teaming itself, and the adequacy of the design controller. The performance of the controller can be verified through simulation. The real autonomous electric vehicle using neural network controller verified good results.

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Service Management Architecture for MPLS VPN Service Provisioning with High-speed Access Network

  • Park, Chan-Kyu;Hong, Daniel W.;Yun, Dong-Sik
    • 한국IT서비스학회:학술대회논문집
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    • 2006.11a
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    • pp.366-371
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    • 2006
  • To compensate the loss of leased-line subscribers and the excessive increase of residential xDSL (Digital Subscriber Line) ones of KT (Korea Telecom), the paper proposes the service model by which it can reinstate the subscription ratio status through employing next generation OSS (Operations Support System) and highquality MPLS (Multiprotocol Label Switching) VPN (Virtual Private Network). It also describes diverse modules comprising NeOSS (New OSS) of KT, followed by detailed accounts regarding the service delivery process of KT VPN. Shortly visited are the primary constituents as well as configuration parameters of MPLS VPN. Finally the network topology along with a feasible service model case is presented.

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A Virtual Grid-Based Routing Algorithm in Ad Hoc Networks (애드혹 네트워크에서의 가상 그리드 기반 라우팅 알고리즘)

  • Lee, Jong-Min;Kim, Seong-Woo
    • Journal of the Korea Society for Simulation
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    • v.16 no.2
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    • pp.17-26
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    • 2007
  • In this paper, we propose a basic virtual grid-based routing algorithm in order to devise an efficient routing method in ad hoc networks using the location information of nodes, energy level, etc. A packet is forwarded to the X-axis direction at first based on the location information of a destination node, and then it is forwarded to the Y-axis direction as its location becomes close to the destination from the viewpoint of the X-axis. Due to the selection of next hop nodes to deliver a packet from a certain node to a destination node, we can regard the whole network as a virtual grid network. The proposed routing algorithm determines routing paths using the local information such as the location information of a destination and its neighbor nodes. Thus, the routing path setup is achieved locally, by which we can expect reduction in network traffics and routing delays to a destination. To evaluate the performance of the proposed routing algorithm, we used the network simulator ns2 and compared its network throughput with that of an existing routing algorithm.

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A Study on TMN Test System Architecture (TMN 시험 시스템 구조)

  • 최영한;김장경;진병문;이준원
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.2 no.3
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    • pp.409-416
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    • 1998
  • TMN(Telecommunication Management Network) provides underlying structure to transfer, store and processing management information which are necessary for Telecommunication network and service management. The application of TMN is widely applicable to Analogue telecommunication network Public and private network switching system, Transmission system, Telecommunication related software and logical telecommunication network resource management and others. This paper consider TMN as one System Under Test(SW), and propose a new TMN test architecture, besides existing simulation based test method, which is able to test, on the basis of standardized testing procedure and testing method, directly with test system. There are several fields in protocol testing and one of them is protocol conformance testing which is defined in ISO/IEC 9646 series and it has twin document as ITU X.290 series. To apply this standardized procedure on TMN testing it is prerequisite to devise a new TMN testing architecture and set up testing procedures according to the new architecture.

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A Development of the Social Network Model for the Maternal Role of First-time Mother (초산모의 모성역할을 위한 사회적 네트워크 모형 개발)

  • Jong, In-Sun;Chung, Yeon-Kang
    • Women's Health Nursing
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    • v.9 no.1
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    • pp.50-60
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    • 2003
  • Purpose : The purpose of this study was to evaluate the factors which are related to the maternal role performance of first-time mother to improve the health of infant. Specifically a basic hypothetical model was developed based on the previous study about a model of social networks. Method : The survey was done from January to February in 2001. Total 257 mothers who have four to twelve month old first-time baby was interviewed in five community health center around country(Seoul, Choung-ju, Asan, Cheon-an, Jeju). Finally 247 data was analyzed. Data analysis was done with LISREL 8.20 program for covariance structural analysis. Results : Compared to the hypothetical model, the revised model has become parsimonious and had a better fit to the data ($X^2=167.55$ (p값=.00), $x^2/df=1.48$, GFI=0.97, AGFI=0.95, RMR=0.049, NFI=0.98, NNFI=0.99, CN=222.53). All predictive variables of the maternal role of first-time mother explained 30% of total variance in model. Social network structural characteristics and social network interactional characteristics had significant effect on the emotional support and the information support. And social network interactional characteristics had significant effect on the service support, material support and social companionship support. The service support and social companion ship support had significant effect on the maternal role strain. The emotional support and the social companion ship support had significant effect on the maternal role of first-time mother. Conclusion : As the conclusion of this study, there is in need of the developing the programmes focussed on the social network for the first-time mother.

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Robust Extraction of Lean Tissue Contour From Beef Cut Surface Image

  • Heon Hwang;Lee, Y.K.;Y.r. Chen
    • Proceedings of the Korean Society for Agricultural Machinery Conference
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    • 1996.06c
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    • pp.780-791
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    • 1996
  • A hybrid image processing system which automatically distinguished lean tissues in the image of a complex beef cut surface and generated the lean tissue contour has been developed. Because of the in homegeneous distribution and fuzzy pattern of fat and lean tissue on the beef cut, conventional image segmentation and contour generation algorithm suffer from a heavy computing requirement, algorithm complexity and poor robustness. The proposed system utilizes an artificial neural network enhance the robustness of processing. The system is composed of pre-network , network and post-network processing stages. At the pre-network stage, gray level images of beef cuts were segmented and resized to be adequate to the network input. Features such as fat and bone were enhanced and the enhanced input image was converted tot he grid pattern image, whose grid was formed as 4 X4 pixel size. at the network stage, the normalized gray value of each grid image was taken as the network input. Th pre-trained network generated the grid image output of the isolated lean tissue. A training scheme of the network and the separating performance were presented and analyzed. The developed hybrid system showed the feasibility of the human like robust object segmentation and contour generation for the complex , fuzzy and irregular image.

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