• 융합보안논문지
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• 제13권4호
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• pp.93-100
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• 2013

2012년 시스코가 발간한 '시스코 글로벌 클라우드 인덱스 2011-2016'에 따르면 전 세계 데이터 센터 트래픽은 2016년까지 4배 가량 증가하고, 클라우드 트래픽은 6배 가량 증가할 것이라고 전망했다. 이처럼 급증하는 데이터 센터의 트래픽 대부분은 데이터 센터 및 클라우드 컴퓨팅 워크로드로 인해 발생된다. 국방부는 지난 2010년, '2012 정보화사업계획'의 일환으로 2014년까지 클라우드 컴퓨팅 기술이 포함된 국방통합정보관리소를 구축하기로 결정하였고, 현재 추진 중에 있다. 국방통합정보관리소(메가 센터) 구축 시 반드시 고려해야 할 요소 중 하나가 클라우드 컴퓨팅 트래픽이다. 국방 클라우드 컴퓨팅 시스템이 구축되고 난 이후 국방 클라우드 트래픽은 꾸준히 증가할 것이다. 본 논문에서는 국방 클라우드 컴퓨팅 시범체계를 모델로 CloudAnalyst 시뮬레이션 툴을 이용하여 클라우드 트래픽 증가에 따른 서비스 가용성을 분석하였다. 3개 시나리오를 구성하여 시뮬레이션 수행 결과, 현재 시점에서 2016년까지 예측되는 클라우드 트래픽 성장률만큼 클라우드 워크로드가 증가하여도 국방 클라우드 시범체계는 서비스 가용성을 충족한다는 결론을 도출하였다.

• 한국심리학회지 : 문화 및 사회문제
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• 제11권1호
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• pp.25-42
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• 2005

본 연구는 사람들이 사소한 교통법규를 어기는 행동을 한 후에 법규를 준수해야 한다는 일반적인 태도와 위반행동 사이에서 인지부조화를 경험하며, 그 부조화를 경감시키기 위해 법규 준수에 대한 자신의 태도를 부정적으로 변화시킬 가능성을 확인하였다. 이를 위해 교통법규 위반 (무단횡단, 불법주차, 신호위반)을 묘사하는 시나리오를 읽고, 그 위반행동에 대해 지지하는 글을 쓰게 하여 인지부조화를 경험하게 한 실험참가자들에게서 태도변화가 일어나는지를 관련 교통법규준수 태도를 반복 측정하는 피험자 내 설계를 통해 조사하였다. 실험결과에 따르면, 부조화 처치 전과 후의 일반적인 교통법규준수 태도를 비교한 결과, 전보다 후에 태도가 더 부정적으로 변화하였으며, 시나리오로 제시된 각 교통법규 위반상황과 관련된 태도문항들에서도 부조화를 경험하기 전보다 후의 각각의 교통법규 준수태도가 더 부정적으로 변하였다. 더 나아가 태도변화에서 인지부조화의 역할을 확인하기 위한 추가분석에서, 시나리오로 제시되지 않은 교통법규와 관련된 태도문항들(인지부조화와 관련 없는 문항들)에서는 반복측정 간에 유의미한 차이가 없었으며, 3개의 시나리오 중에 더 많은 시나리오의 위반행동에 대해 지지하는 글을 쓸수록 태도가 더 부정적으로 변화하는 패턴을 확인할 수 있었다 또한, 각 시나리오의 위반행동에 대해 지지하는 글을 작성한 집단이 그렇지 않은 집단보다 태도가 더 부정적으로 변화하였고 초기의 교통법규준수에 대해 긍정적인 태도를 가진 실험참가자가 더 많은 태도 변화를 보였다. 이러한 결과들은 결론적으로 사소한 법규위반행동이 법규에 대한 부정적 태도를 유도하고, 그 과정에서 인지부조화 기제가 중요한 역할을 한다는 사실을 지지한다. 규범 행동 간의 부조화에 대한 태도변화 가능성을 인지 대리 부조화적 설명과 함께 비교문화적 관점에서 논의하였다.

• Journal of Communications and Networks
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• 제16권5호
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• pp.534-547
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• 2014

Large-scale failures resulting from natural disasters or intentional attacks are now causing serious concerns for communication network infrastructure, as the impact of large-scale network connection disruptions may cause significant costs for service providers and subscribers. In this paper, we propose a new framework for the analysis and prevention of network service disruptions in large-scale failure scenarios. We build dynamic deterministic and probabilistic models to capture the impact of regional failures as they evolve with time. A probabilistic failure model is proposed based on wave energy behaviour. Then, we develop a novel approach for preventive protection of the network in such probabilistic large-scale failure scenarios. We show that our method significantly improves uninterrupted delivery of data in the network and reduces service disruption times in large-scale regional failure scenarios.

• 한국도로학회논문집
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• 제9권4호
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• pp.75-82
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• 2007

본 연구에서는 다차로 고속도로에서 트럭주행 차로를 제한하는 전략이 교통류 변수(평균속도, 차선변경 횟수, 총교통량의 변화)에 어떤 영향을 미치는 지를 알아보았고, 또한 본 연구에서 제안된 트럭주행 차로를 제한하는 전략에 대한 대안들의 적용 타당성을 알아보았다. 교통 시뮬레이션을 위해 두 가지 형태의 가상 네트워크와 교통수요를 설정했으며, 트럭 차량의 주행을 제한하는 차로의 수에 근거하여 3가지 실행가능한 시나리오를 설정하였다. PARAMICS 시뮬레이션 모형이 주요 분석 Tool로 사용되었다. 통계분석을 통해 시나리오에 따른 교통류 변수에 대한 영향을 분석하였다. 결과적으로, 트럭주행 차로를 제한하는 전략은 다차로 고속도로에서 교통류의 흐름에 긍정적인 영향을 미치는 것으로 나타났다. 또한, 이 연구는 트럭 주행 차로를 제한하는 전략이 성공적으로 시행되기 위해 트럭 차량의 주행을 제한하는 차로의 수의 결정이 중요한 요소가 될 수 있다는 것을 보여주었다.

• ETRI Journal
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• 제44권2호
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• pp.194-207
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• 2022

In the last decade, substantial progress has been achieved in intelligent traffic control technologies to overcome consistent difficulties of traffic congestion and its adverse effect on smart cities. Edge computing is one such advanced progress facilitating real-time data transmission among vehicles and roadside units to mitigate congestion. An edge computing-based deep reinforcement learning system is demonstrated in this study that appropriately designs a multiobjective reward function for optimizing different objectives. The system seeks to overcome the challenge of evaluating actions with a simple numerical reward. The selection of reward functions has a significant impact on agents' ability to acquire the ideal behavior for managing multiple traffic signals in a large-scale road network. To ascertain effective reward functions, the agent is trained withusing the proximal policy optimization method in several deep neural network models, including the state-of-the-art transformer network. The system is verified using both hypothetical scenarios and real-world traffic maps. The comprehensive simulation outcomes demonstrate the potency of the suggested reward functions.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제9권8호
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• pp.2875-2893
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• 2015

Existing coding aware routing algorithms focused on novel routing metric design that captures the characteristics of network coding. However, in packet coding algorithm, they use opportunistic coding scheme which didn't consider the queue state of the coding node and are equivalent to the conventional store-and-forward method in light traffic load condition because they never delay packets and there are no packets in the output queue of coding node, which results in no coding opportunity. In addition, most of the existing algorithms assume that all flows participating in the network have equal rate. This is unrealistic since multi-rate environments are often appeared. To overcome above problem and expand network coding to light traffic load scenarios, we present an enhanced coding-aware routing algorithm based on queue state and local topology (OQMCAR), which consider the queue state of coding node in packet coding algorithm where the control policy is of threshold-type. OQMCAR is a unified framework to merge single rate case and multiple rate case, including the light traffic load scenarios. Simulations results show that our scheme can achieve higher throughput and lower end-to-end delay than the current mechanisms using COPE-type opportunistic coding policy in different cases.

• 한국항해학회지
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• 제18권4호
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• pp.33-47
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• 1994

On developing port system, the performance tests of system in relation to ship maneuver generally consists of the three parts: the channel transit, the manoeuvring in a turning basin and the docking/undocking. The quantifications of risk of an accident has priviously been difficult due to the low occurrence of accidents relative to the number of transits. Additionally, accident statistics could not be related port system because of the large number of factors contributing to the accident. such as human error, equipment failure, visibility, light, traffic. etc. In case of the channel transit, "Relative Risk Factor(RRF)" or "Relative Risk Factor for Meeting Traffic" was proposed as the as the measures derived to quantify the relative risk of accident by M.W.Smith. This factor measure the tracking performance, the turning performance and the passing performance at meeting traffic. On the other hand, the safety of berthing maneuver is not measured with a few evaluating factors as controlled due to complex controllabilites such as steering, engine, side thrusters or tugs. This work, therefore, aims to propose the evaluating measure by the Analytic Hierarchy Process(AHP). Six experimental scenarios were establised under the various environmental conditions as independent variables. In every simulation, the difficulty of maneuver was scored by captain and compared with AHP scores. The results show almost same and from which the weights of eight evaluating factors could be fixed. Additionally, the limit value of relative factor in berthing safety to six scenarios could be estimated to 0.11.e estimated to 0.11.

• 자동차안전학회지
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• 제14권2호
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• pp.45-50
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• 2022

The vehicle motion in urban environment is determined by surrounding traffic flow, which cause understanding the flow to be a factor that dominantly affects the motion planning of the vehicle. The traffic flow in this urban environment is accessed using various urban infrastructure information. This paper represents a color recognition algorithm for traffic lights to perceive traffic condition which is a main information among various urban infrastructure information. Deep learning based vision open source realizes positions of traffic lights around the host vehicle. The data are processed to input data based on whether it exists on the route of ego vehicle. The colors of traffic lights are estimated through pixel values from the camera image. The proposed algorithm is validated in intersection situations with traffic lights on the test track. The results show that the proposed algorithm guarantees precise recognition on traffic lights associated with the ego vehicle path in urban intersection scenarios.

• International Journal of Computer Science & Network Security
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• 제23권4호
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• pp.48-54
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• 2023

Nowadays, a blind man finds it very difficult to cross the roads. They should be very vigilant with every step they take. To resolve this problem, Convolutional Neural Networks(CNN) is a best method to analyse the data and automate the model without intervention of human being. In this work, a traffic signal recognition system is designed using CNN for the visually impaired. To provide a safe walking environment, a voice message is given according to light state and timer state at that instance. The developed model consists of two phases, in the first phase the CNN model is trained to classify different images captured from traffic signals. Common Objects in Context (COCO) labelled dataset is used, which includes images of different classes like traffic lights, bicycles, cars etc. The traffic light object will be detected using this labelled dataset with help of object detection model. The CNN model detects the color of the traffic light and timer displayed on the traffic image. In the second phase, from the detected color of the light and timer value a text message is generated and sent to the text-to-speech conversion model to make voice guidance for the blind person. The developed traffic light recognition model recognizes traffic light color and countdown timer displayed on the signal for safe signal crossing. The countdown timer displayed on the signal was not considered in existing models which is very useful. The proposed model has given accurate results in different scenarios when compared to other models.

• 한국항공운항학회지
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• 제22권2호
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• pp.1-8
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• 2014

This paper introduces a new framework of predicting the arrival time of an aircraft by incorporating the probabilistic information of what type of trajectory pattern will be applied by human air traffic controllers. The proposed method is based on identifying the major patterns of vectored trajectories and finding the statistical relationship of those patterns with various traffic complexity factors. The proposed method is applied to the traffic scenarios in real operations to demonstrate its performances.