• 사물인터넷융복합논문지
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• 제5권2호
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• pp.33-39
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• 2019

4차 산업혁명시대로 접어들면서 많은 기술들의 발전이 이루어지고 있으며 다양한 위협요소들이 생겨나고 있다. 이러한 위협요소에 대응하기 위한 연구가 많은 분야에서 이루어지고 있다. 다양한 분야의 발전중에서도 의료기술과 지능형 자동차의 발전으로 인한 위협요소는 의료에 대한 잘못된 정보로 인한 생명에 대한 위협과 지능형 자동차를 통한 사람의 안전한 운행을 방해하여 생명을 위협하는 요소들이 가장 큰 위협요소로 대두되고 있다. 본 논문에서는 환자의 정보가 중요한 만큼 환자의 의료 기록에 대한 안전성과 신뢰성이 있는 기술을 위하여 블록체인의 기술 종류 중 프라이빗 블록체인을 사용하여 환자의 의료 기록에 대한 안전성과 효율성, 확장성을 높이는 방법과 자동차 시스템을 해킹하여 운전자의 생명을 위협하고 개인정보 및 위차파악으로 사생활 문제점에 대한 해결과 사물인터넷에서의 위변조를 방지하기 위하여 블록체인 기술을 이용한 정보보호 기법을 제안한다.

• 사물인터넷융복합논문지
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• 제7권2호
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• pp.41-45
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• 2021

본 논문은 이동 차량을 위한 동영상 콘텐츠 전송 기법에 관한 연구이다. 오늘날 우리는 출퇴근의 많은 시간을 전철, 차량 등 이동 차량에서 보내고 있다. 그리고 이동 차량에서의 무료함을 달래기 위해 YouTube, Netflix 등과 같은 동영상 서비스를 즐기는 이용자가 급증하고 있다. 동영상 콘텐츠는 텍스트 기반의 콘텐츠 보다 데이터양이 큰 특징이 있다. 이에 따라 사용자의 이동통신 데이터 사용량이 급증하고 비용이 증가하는 문제가 있다. 제안한 동영상 콘텐츠 전송 기법은 이동 차량이 무료 Wi-Fi 지역에 있을 때, 시청 중인 동영상 콘텐츠를 미리 많이 다운로드 받는다. 이러한 방법을 통해 이동 차량에서 동영상 콘텐츠를 적은 비용으로 즐길 수 있다. 제안한 기법은 이동 물체를 위한 사물인터넷(IoT)에도 활용될 수 있을 것으로 기대한다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권10호
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• pp.3955-3971
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• 2020

Due to the significant increase in the use of autonomous car technology, it is essential to integrate this technology with high-precision digital map data containing more precise and accurate roadway information, as compared to existing conventional map resources, to ensure the safety of self-driving operations. While existing map technologies may assist vehicles in identifying their locations via Global Positioning System, it is however difficult to update the environmental changes of roadways in these maps. Roadway vision algorithms can be useful for building autonomous vehicles that can avoid accidents and detect real-time location changes. We incorporate a hybrid architectural design that combines unsupervised classification of vision data with supervised joint fusion classification to achieve a better noise-resistant algorithm. We identify, via a deep learning approach, an intelligent hybrid fusion algorithm for fusing multimodal vision feature data for roadway classifications and characterize its improvement in accuracy over unsupervised identifications using image processing and supervised vision classifiers. We analyzed over 93,000 vision frame data collected from a test vehicle in real roadways. The performance indicators of the proposed hybrid fusion algorithm are successfully evaluated for the generation of roadway digital maps for autonomous vehicles, with a recall of 0.94, precision of 0.96, and accuracy of 0.92.

• 인터넷정보학회논문지
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• 제16권6호
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• pp.57-67
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• 2015

This paper proposes an Fuzzy-based Risk Reasoning Driving Strategy on VANET. Its first reasoning phase consists of a WC_risk reasoning that reasons the risk by using limited road factors such as current weather, density, accident, and construction, a DR_risk reasoning that reasons the risk by combining the driving resistance with the weight value suitable for the environment of highways and national roads, a DS_risk reasoning that judges the collision risk by using the travel direction, speed. and distance of vehicles and pedestrians, and a Total_risk reasoning that computes a final risk by using the three above-mentioned reasoning. Its second speed reduction proposal phase decides the reduction ratio according to the result of Total_risk and the reduction ratio by comparing the regulation speed of road to current vehicle's speed. Its third risk notification phase works in case current driving speed exceeds regulation speed or in case the Total_risk is higher than AV(Average Value). The Risk Notification Phase informs rear vehicles or pedestrians around of a risk according to drivers's response. If drivers use a brake according to the proposed speed reduction, the precedent vehicles transfers Risk Notification Messages to rear vehicles. If they don't use a brake, a current driving vehicle transfers a Risk Message to pedestrians. Therefore, this paper not only prevents collision accident beforehand by reasoning the risk happening to pedestrians and vehicles but also decreases the loss of various resources by reducing traffic jam.

• 한국인터넷방송통신학회논문지
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• 제24권1호
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• pp.65-74
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• 2024

최근 자율주행 분야에서는 영상 처리 기술이 중요한 역할을 하고 있다. 그 중에서도 영상 식별 기술은 자율주행 차량의 안전성과 성능에 매우 중요한 역할을 한다. 이에 따라 본 논문에서는 융복합 영상 식별 시스템을 개발하여 자율주행 차량의 안전성과 성능을 향상시키는 것을 목표로 한다. 본 연구에서는 다양한 영상 식별 기술을 활용하여 차량주변 환경의 객체를 인식하고 추적하는 시스템을 구축한다. 이를 위해 머신 러닝과 딥 러닝 알고리즘을 활용하며, 이미지처리 및 분석 기술을 통해 실시간으로 객체를 식별하고 분류한다. 또한, 본 연구에서는 영상 처리 기술과 차량 제어 시스템을 융합하여 자율주행 차량의 안전성과 성능을 높이는 것을 목표로 한다. 이를 위해, 식별된 객체의 정보를 차량 제어시스템에 전달하여 자율주행 차량이 적절하게 반응하도록 한다. 본 연구에서 개발된 융복합 영상 식별 시스템은 자율주행 차량의 안전성과 성능을 크게 향상시킬 것으로 기대된다. 이를 통해 자율주행 차량의 상용화가 더욱 가속화될 것으로 기대된다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권3호
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• pp.1533-1556
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• 2017

The concept of trust in vehicular ad hoc networks (VANETs) is usually utilized to assess the trustworthiness of the received data as well as that of the sending entities. The quality of safety applications in VANETs largely depends on the trustworthiness of exchanged data. In this paper, we propose a self-organized distributed trust computing framework (DTCF) for VANETs to compute the trustworthiness of each vehicle, in order to filter out malicious nodes and recognize fully trusted nodes. The proposed framework is solely based on the investigation of the direct experience among vehicles without using any recommendation system. A tier-based dissemination technique for data messages is used to filter out non authentic messages and corresponding events before even going farther away from the source of the event. Extensive simulations are conducted using Omnet++/Sumo in order to investigate the efficiency of our framework and the consistency of the computed trust metrics in both urban and highway environments. Despite the high dynamics in such networks, our proposed DTCF is capable of detecting more than 85% of fully trusted vehicles, and filtering out virtually all malicious entities. The resulting average delay to detect malicious vehicles and fraudulent data is showed to be less than 1 second, and the computed trust metrics are shown to be highly consistent throughout the network.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권11호
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• pp.5299-5320
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• 2019

Interest in self-driving vehicle research has been rapidly increasing, and related research has been continuously conducted. In such a fast-paced self-driving vehicle research area, the development of advanced technology for better convenience safety, and efficiency in road and transportation systems is expected. Here, we investigate research in self-driving vehicles and analyze the main technologies of driverless car software, including: technical aspects of autonomous vehicles, traffic infrastructure and its communications, research techniques with vision recognition, deep leaning algorithms, localization methods, existing problems, and future development directions. First, we introduce intelligent self-driving car and road infrastructure algorithms such as machine learning, image processing methods, and localizations. Second, we examine the intelligent technologies used in self-driving car projects, autonomous vehicles equipped with multiple sensors, and interactions with transport infrastructure. Finally, we highlight the future direction and challenges of self-driving vehicle transportation systems.

• International Journal of Internet, Broadcasting and Communication
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• 제13권2호
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• pp.103-111
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• 2021

Among transport vehicles, Special Vehicles (SVs) are seriously exposed to energy and environmental problems. In particular, elevator cars used when moving objects in high-rise buildings increase the engine's rotational speed (radian per second: RPM). At this time, when the vehicle accelerates rapidly while idling, energy consumption increases explosively along with the engine speed, and a lot of soot is generated. The purpose of this paper is to develop a bi-directional DC-DC converter for control of vehicle power and secondary battery used in an elevated ladder vehicle (EC) used in the moving industry. As a result of this paper, the performance test of the converter was conducted. The charging/discharging state of the converter was simulated using DC power supply and DC electronic load, and a performance experiment was conducted to measure the input/output power of the converter through a power meter. Through this experimental result, it was confirmed that the efficiency was more than 92% in Buck mode and Boost mode at maximum 1.2kW output.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제10권2호
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• pp.542-558
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• 2016

Vehicular Ad Hoc Networks (VANETs) are self-configuring networks where the nodes are vehicles equipped with wireless communication technologies. In such networks, limitation of signal coverage and fast topology changes impose difficulties to the proper functioning of the routing protocols. Traditional Mobile Ad Hoc Networks (MANET) routing protocols lose their performance, when communicating between vehicles, compromising information exchange. Obviously, most applications critically rely on routing protocols. Thus, in this work, we propose a methodology for investigating the performance of well-established protocols for MANETs in the VANET arena and, at the same time, we introduce a routing protocol, called Genetic Network Protocol (G-NET). It is based in part on Dynamic Source Routing Protocol (DSR) and on the use of Genetic Algorithms (GAs) for maintenance and route optimization. As G-NET update routes periodically, this work investigates its performance compared to DSR and Ad Hoc on demand Distance Vector (AODV). For more realistic simulation of vehicle movement in urban environments, an analysis was performed by using the VanetMobiSim mobility generator and the Network Simulator (NS-3). Experiments were conducted with different number of vehicles and the results show that, despite the increased routing overhead with respect to DSR, G-NET is better than AODV and provides comparable data delivery rate to the other protocols in the analyzed scenarios.

• International Journal of Internet, Broadcasting and Communication
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• 제14권2호
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• pp.47-54
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• 2022

For safe driving of autonomous vehicles, road damage detection is very important to lower the potential risk. In order to ensure safety while an autonomous vehicle is driving on the road, technology that can cope with various obstacles is required. Among them, technology that recognizes static obstacles such as poor road conditions as well as dynamic obstacles that may be encountered while driving, such as crosswalks, manholes, hollows, and speed bumps, is a priority. In this paper, we propose a method to extract similarity of images and find damaged road images using OpenCV image processing and CNN algorithm. To implement this, we trained a CNN model using 280 training datasheets and 70 test datasheets out of 350 image data. As a result of training, the object recognition processing speed and recognition speed of 100 images were tested, and the average processing speed was 45.9 ms, the average recognition speed was 66.78 ms, and the average object accuracy was 92%. In the future, it is expected that the driving safety of autonomous vehicles will be improved by using technology that detects road obstacles encountered while driving.