• Journal of Korean Society of Transportation
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• v.25 no.6
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• pp.99-109
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• 2007

There have been few studies on the impacts of independent recurrent and non-recurrent congestion on freeway networks. The main reason is due partly to the lack of traffic data collected during those periods of recurrent and non-recurrent congestion and partly to the difficulty of using the simulation tools effectively. This study has suggested a methodology to analyze the independent impacts of the recurrent and non-recurrent congestion on target freeway segments. The proposed methodology is based on an elaborately calibrated simulation analysis, using real traffic data obtained during the recurrent and non-recurrent congestion periods. This paper has also summarized the evaluation results from the field tests of two ITS technologies, which were developed to provide drivers with real-time traffic information under traffic congestion. As a result, their accuracy may not be guaranteed during the transition periods such as the non-recurrent congestion. In summary, this study has been focused on the importance of non-recurrent congestion compared to recurrent congestion, and the proposed methodology is expected to provide a basic foundation for prioritizing limited government investments for improving freeway network performance degraded by recurrent or non-recurrent congestion.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.49 no.3
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• pp.62-73
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• 2012

IEEE 802.15.4 standard which has low-speed, low-power, low-cost can be efficiently used in wireless sensor network environment. Among various topologies used in IEEE 802.15.4 standard, a cluster-tree topology which has many nodes in it, transmit delay, energy consumption and data loss due to traffic concentration around the sink node. In this paper, we propose the MRS-DCA algorithm that minimizes conflicts between packets for efficient data transmission, and dynamically allocates the active period for efficient use of limited energy. The MRS-DCA algorithm allocates RP(Reservation Period) to the active period of IEEE 802.15.4 and guarantees reliable data transmission by allocating RP and CAP dynamically which is based on prediction using EWMA. The comparison result shows that the MRS-DCA algorithm reduces power consumption by reducing active period, and increasing transmission rate by avoiding collision.

• International Journal of Highway Engineering
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• v.9 no.1 s.31
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• pp.17-27
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• 2007

Since early 1990s, several developed countries have applied the Electronic Toll Collection System (ETCS) to toll roads in order to solve traffic congestion and delay problems at toll plazas. For the successful operation of the ETCS, it is important to correctly forecast the ETCS using rate. In this study, it was conceived to develop a sophisticated demand forecasting model of the ETCS for toll roads in Changwon City The Binary Logit and neural network models were tested for the model considering 11 explaining variables. The best results in prediction accuracy and goodness-of-fit were obtained on the neural network model. However, because of the difficulty in predicting the 11 variables and its fitness in wide range, the Binary Logit model which considers three policy variables only is recommended as the model to forecast the ETCS using rate.

• Journal of Internet Computing and Services
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• v.7 no.1
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• pp.131-141
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• 2006

Recently, many researchers have been studied several high performance data transmission techniques such as TCP buffer Tuning, XCP and Parallel Sockets. The Parallel Sockets is an application level library for parallel data transfer, while TCP tuning, XCP and DRS are developed on kernel level. However, parallel socket is not analyzed in detail yet and need more enhancements, In this paper, we verify performance of parallel transfer technique through several experiments and analyze character of traffic interference among socket connections. In order to enhance parallel transfer management mechanism, we predict the number of socket connections to obtain SLA of the network resource and at the same time, affected network bandwidth of existing connections is measured mathematically due to the interference of other parallel transmission. Our analytical scheme predicts very well network bandwidth for applications using the parallel socket only with 8% error.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.42 no.1
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• pp.193-204
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• 2017

The security of Internet systems critically depends on the capability to keep anti-virus (AV) software up-to-date and maintain high detection accuracy against new malware. However, malware variants evolve so quickly they cannot be detected by conventional signature-based detection. In this paper, we proposed a malware classification method based on sequence patterns generated from the network flow of malware samples. We evaluated our method with 766 malware samples and obtained a classification accuracy of approximately 40.4%. In this study, malicious codes were classified only by network behavior of malicious codes, excluding codes and other characteristics. Therefore, this study is expected to be further developed in the future. Also, we can predict the attack groups and additional attacks can be prevented.

• Journal of the Korea Society of Computer and Information
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• v.26 no.3
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• pp.1-7
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• 2021

In this paper, optimized media data retrieval and transmission based on the Hybrid CDN/P2P architecture and selective storage through user's prediction of requestability enable seamless data transfer to users and reduction of unnecessary traffic. We also propose a new media management method to minimize the possibility of transmission delay and packet loss so that media can be utilized in real time. To this end, we construct each media into logical segments, continuously compute weights for each segment, and determine whether to store segment data based on the calculated weights. We also designate scattered computing nodes on the network as local groups by distance and ensure that storage space is efficiently shared and utilized within those groups. Experiments conducted to verify the efficiency of the proposed technique have shown that the proposed method yields a relatively good performance evaluation compared to the existing methods, which can enable both initial latency reduction and seamless transmission.

• International Journal of Highway Engineering
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• v.13 no.3
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• pp.167-176
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• 2011

This study's purpose is to analyse factors of determination about detouring for makinga standard model in regard of unfavorableness and uncertainty when unspecified individual recipients make a decision at the time of course detour. In order to achieve this, we surveyed SP investigation whether making a detour or not for drivers as a target who take a high way and National highway. Based on this result, we analysed detour determination factors of drivers, establishing a combination model of Decision Tree and Neural Network model. The result demonstrates the effected factors on drivers' detour determination are in ordering of the recognition of alternative routevs, reliable and frequency of using traffic information, frequency of transition routes and age. Moreover, from the outcome in comparison with an existing model and prediction through undistributed data, the rate of combination model 8.7% illustrates the most predictable way in contrast with logit model 12.8%, and Individual Model of Decision Tree 13.8% which are existed. This reveals that the analysis of drivers' detour determination factors is valid to apply. Hence, overall study considers as a practical foundation to make effective detour strategies for increasing the utility of route networking and dispersion in the volume of traffic from now on.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.37 no.6
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• pp.1033-1041
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• 2017

Path travel time based on departure time (PTTDP) is key information in advanced traveler information systems (ATIS). Despite the necessity, forecasting PTTDP is still one of challenges which should be successfully conquered in the forecasting area of intelligent transportation systems (ITS). To address this problem effectively, a methodology to dynamically predict PTTDP between motorway interchanges is proposed in this paper. The method was developed based on the relationships between traffic demands at motorway tollgates and PTTDPs between TGs in the motorway network. Two different data were used as the input of the model: traffic demand data and path travel time data are collected by toll collection system (TCS) and dedicated short range communication (DSRC), respectively. The proposed model was developed based on k-nearest neighbor, one of data mining techniques, in order for the real applications of motorway information systems. In a feasible test with real-world data, the proposed method performed effectively by means of prediction reliability and computational running time to the level of real application of current ATIS.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.19 no.3
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• pp.211-217
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• 2001

Chun-chon city is the area that is estimated to be damaged by breaking of Dam by a flood among several natural disaster. If so, what is the way that minimize the damage\ulcorner There are many ones but it may be best that we take shelter from it before the breaking of Dam. Then when must we do\ulcorner By what instrument can we minimize the damage of people. And how do we compute the time\ulcorner In this study, using buffering, overlap and network, GIS ability based on ARC/INFO. I chose six routesto take shelter outside of Chun-chon city, calculated the traffic volume of each ones, and estimated the time for decentralization of risks.

• Proceedings of the Korea Information Processing Society Conference
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• 2015.10a
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• pp.983-986
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• 2015

물리적, 논리적 공간에서 다양한 오브젝트들이 상호작용할 수 있게 되고, 오브젝트에 탑재되는 소프트웨어가 고도화 됨에 따라 엔지니어가 관리 가능한 수준의 시스템 제어가 힘들어지고 있다. 이런 복잡한 시스템의 자율적인 관리를 위해 다양한 상황에 대응 가능한 자가적응성이 요구된다. 자가적응형 소프트웨어는 대상 시스템의 목표나 QoS를 만족할 수 있도록 런타임에 스스로를 변화 시킬 수 있는 능력을 가진 소프트웨어이다. 이러한 소프트웨어는 고도화된 시스템의 관리에 있어서 엔지니어의 부담을 경감시킬수 있다. 본 논문에서 제안하는 선행적 자가적응형 시스템은 도로망과 같은 주기적 특성을 가진 시스템에서 시스템이 직면하는 상황을 사전에 예측하여 미리 대응할 수 있는 시스템이다. 이는 기존에 반응적으로 대응했던 시스템들이 적용한 정책의 효과를 보기까지 낭비되는 시간을 고려하여 해당 지연시간동안에 시스템의 목표나 QoS가 하락하는 상황을 미연에 방지할 수 있다. 본 시스템의 적용분야로 지능형교통체계를 사용하였으며, 도로망 전체에서 정체 발생빈도와 평균 이동속도 그리고 단위길이당 운행시간을 평가항목으로 사용하고, 대상 도로망 전체적인 최적화를 목표로 한다.