• Convergence Security Journal
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• v.13 no.4
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• pp.93-100
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• 2013

In 2012, According to 'Cisco Global Cloud Index 2011-2016', the Cisco company forecasted that global data center traffic will nearly quadruple and cloud traffic will nearly sextuple by 2016. Such a rapid growing of traffic is caused by traffic inside the data center and cloud computing workloads. In 2010, the Ministry of National Defense decided to build a Mega Center including the cloud computing technology by 2014, as part of the '2012 Information Service Plan', which is now underway. One of the factors to consider is cloud computing traffic to build a Mega Center. Since the K-defense cloud computing system is built, K-defense cloud computing traffic will increase steadily. This paper, analyzed the availability of K-defense cloud computing service with the K-defense cloud computing traffic increasing using K-Defense cloud computing test system and CloudAnalyst simulation tool. Created 3 scenarios and Simulated with these scenarios, the results are derived that the availability of K-defense cloud computing test system is fulfilled, even cloud workloads are increased as muh as forecasted cloud traffic growth from now until 2016.

• Korean Journal of Culture and Social Issue
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• v.11 no.1
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• pp.25-42
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• 2005

The present research examined the proposition that once people violate traffic regulations, they would experience cognitive dissonance and subsequently engage in changing their attitudes toward the law negatively in order to reduce the dissonance. In an experiment, participants were presented with three scenarios in which a person violated traffic laws, and they were asked to imagine themselves as the person of the scenarios and write statements supporting the unlawful behaviors. Participants' attitudes toward the general traffic law and the regulations related to the violations were measured 8 weeks before and right after the experimental treatment. The results, as expected, showed that their attitudes toward the general traffic law and the specific regulations in the scenarios changed negatively after writing the statements. In each secnarios, the participants who chose to wrote statements supporting the unlawful behaviors showed great attitude changes that those who did not write the statements. Furthermore, attitudes toward the regulations that were not directly related to the scenarios did not change significantly, and participants who were expected to experience stronger dissonance arousal (e.g.., supported more unlawful behaviors or had have more positive attitudes toward the law before the experiment) showed greater attitude changes. These results support the effects of trivial unlawful behaviors on attitudes toward the law and strongly suggest the role of cognitive dissonance underlying the effects.

• Journal of Communications and Networks
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• v.16 no.5
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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.

• International Journal of Highway Engineering
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• v.9 no.4
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• pp.75-82
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• 2007

This study was performed to investigate impacts of truck lane restrictions on multilane highways on traffic flow variables such as average speed, the frequency of lane changes, and change in traffic volume and also to verify whether or not different lane restriction scenarios were proper. Two types of hypothetical highway networks and OD demands were developed for traffic simulation models in order to conduct the experimental study. Three types of scenarios were also developed according to the number of restricted lanes for trucks. The PARAMICS microscopic traffic simulation software package was used as the primary analytical tool. Statistical analysis was conducted with simulation outputs. Results showed that truck lane restrictions may lead to positive impacts on traffic flow on multilane highways. In addition, this study demonstrated that the number of restricted lanes can be very an important factor to lead successful implementation of truck lane restrictions.

• ETRI Journal
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• v.44 no.2
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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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• v.9 no.8
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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.

• Journal of the Korean Institute of Navigation
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• v.18 no.4
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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.

• Journal of Auto-vehicle Safety Association
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• v.14 no.2
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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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• v.23 no.4
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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.

• IEMEK Journal of Embedded Systems and Applications
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• v.19 no.5
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• pp.235-242
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• 2024

In the context of urban autonomous driving, where various types of traffic lights are encountered, traffic light recognition technology is of paramount importance. We have designed a high-performance traffic light recognition module tailored to scenarios encountered in domestic urban driving and devised a dataset construction process. In this paper, we focus on minimizing the camera's dependency to enhance traffic light recognition performance. The camera is used solely to distinguish the color information of traffic lights, while accurate location information of the traffic lights is obtained through localization and a map. Based on the information from these components, camera RoIs (Region of Interest) are extracted and transmitted to the embedded board. The transmitted images are then sent back to the main system for autonomous driving control. The processing time for one traffic light RoI averages 43.2 ms. We achieve processing times of average 93.4 ms through batch inference to meet real-time requirements. Additionally, we design a data construction process for collecting, refining, and storing traffic light datasets, including semi-annotation-based corrections.