• The Transactions of the Korea Information Processing Society
• /
• v.4 no.1
• /
• pp.237-245
• /
• 1997

In this paper, a flow control mechanism is proposed which is based on the priority control between communication path of a node. In this scheme, demanding length of a data queue for any pre-defined, then each node in that path is forced to maintains buffer size under the limit by controlling priority level of the path. The communication path which requires higher bandwidth sets its demanding queue length smaller. By providing relationship between the priority of a path and length of its queue, the high bandwidth requesting path has a better chance to get high bandwidth by defining the smaller demanding queue size. And also, by forcing a path which has high flow rate to maintain small queue size in the path of the communication, the scheme keep the transmission delay of the path small. The size of the demanding queue of a path is regularly adjusted to meet the applications requirement, and the load status of the network during the life time of the communication. The priority control based on the demanding queue size is also provided in the intermediate nodes as well as the end nodes. By that the flow control can provide a quicker result than end to-end flow control, it provides better performance advantage especially for the high speed network.

• Journal of Korean Society of Transportation
• /
• v.18 no.4
• /
• pp.75-85
• /
• 2000

The Purpose of this study is to analyze wether the composition of training sample have a relation with the Predictive ability and the learning results of ANNs(Artificial Neural Networks) fur predicting one cycle ahead of the queue length(veh.) in a signalized intersection. In this study, ANNs\` training sample is classified into the assumption of two cases. The first is to utilize time-series(Per cycle) data of queue length which would be detected by one detector (loop or video) The second is to use time-space correlated data(such as: a upstream feed-in flow, a link travel time, a approach maximum stationary queue length, a departure volume) which would be detected by a integrative vehicle detection systems (loop detector, video detector, RFIDs) which would be installed between the upstream node(intersection) and downstream node. The major findings from this paper is In Daechi Intersection(GangNamGu, Seoul), in the case of ANNs\` training sample constructed by time-space correlated data between the upstream node(intersection) and downstream node, the pattern recognition ability of an interrupted traffic flow is better.

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.20 no.6
• /
• pp.26-36
• /
• 2021

In this study, a deep learning model for predicting the queue length was developed using the information collected from the image detector. Then, a multiple regression analysis model, a statistical technique, was derived and compared using two indices of mean absolute error(MAE) and root mean square error(RMSE). From the results of multiple regression analysis, time, day of the week, occupancy, and bus traffic were found to be statistically significant variables. Occupancy showed the most strong impact on the queue length among the variables. For the optimal deep learning model, 4 hidden layers and 6 lookback were determined, and MAE and RMSE were 6.34 and 8.99. As a result of evaluating the two models, the MAE of the multiple regression model and the deep learning model were 13.65 and 6.44, respectively, and the RMSE were 19.10 and 9.11, respectively. The deep learning model reduced the MAE by 52.8% and the RMSE by 52.3% compared to the multiple regression model.

• Journal of Communications and Networks
• /
• v.5 no.3
• /
• pp.275-281
• /
• 2003

Random Early Detection (RED) [1] is an active queue management scheme which has been deployed extensively to reduce packet loss during congestion. Although RED can improve loss rates, its performance depends severely on the tuning of its operating parameters. The idea of adaptively varying RED parameters to suit the network conditions has been investigated in [2], where the maximum packet dropping probability $max_p$ has been varied. This paper focuses on adaptively varying the queue weight $\omega_q$ in conjunction with $max_p$ to improve the performance. We propose two algorithms viz., $\omega_q$-thresh and $\omega_q$-ewma to adaptively vary $\omega_q$. The performance is measured in terms of the packet loss percentage, link utilization and stability of the instantaneous queue length. We demonstrate that varying $\omega_q$ and $max_p$ together results in an overall improvement in loss percentage and queue stability, while maintaining the same link utilization. We also show that $max_p$ has a greater influence on loss percentage and queue stability as compared to $\omega_q$, and that varying $\omega_q$ has a positive influence on link utilization.

• Journal of Korean Society of Transportation
• /
• v.19 no.4
• /
• pp.109-121
• /
• 2001

본 연구는 공간적 정보를 수집할 수 있는 영상검지기를 이용하여 주어진 대기행렬길이를 기반으로 하는 최적주기 알고리즘을 개발함으로써 교통신호 제어에 대한 새로운 신호계획을 제공한다. 본 연구에서는 교통수요의 공간적인 정보를 획득하는 방안으로서 영상검지기 기반의 대기행렬길이를 사용한다. 전략적 측면에서 다양한 교통상태를 적용하였으며, 주요 결과는 아래와 같다. 1. 영상검지기 기반의 대기행렬길이 계산방안을 제안한다. 이 방법은 한 링크의 상류부와 하류부에 2대의 영상검지기를 설치하여 대기행렬길이를 산출하는 방안이다. 2. 신호제어 변수인 주기 계산모형이 개발된다. 이 방법 역시 영상검지기를 기반으로 하는 대기행렬길이를 사용한다.

• The KIPS Transactions:PartC
• /
• v.13C no.5 s.108
• /
• pp.559-566
• /
• 2006

Virus throttling technique, one of many early worm detection techniques, detects the Internet worm propagation by limiting the connect requests within a certain ratio. The typical virus throttling detects worm occurrence by monitoring the length of delay queue with the fixed period of rate limiter. In this paper, we propose an algorithm that controls the period of rate limiter autonomically by utilizing the weighted average delay queue length and suggest various period determination policies that use the weighted average delay queue length as an input parameter. Through deep experiments, it is verified that the proposed technique is able to lessen inconvenience of users by reducing the connection delay time with haying just little effect on worm detection time.

• IE interfaces
• /
• v.25 no.3
• /
• pp.283-289
• /
• 2012

M/G/1 queue with server vacations period depending on the previous vacation and customer's arrival is considered. Most existing studies on M/G/1 queue with server vacations assume that server vacations are independent of customers' arrival. However, some vacations are terminated by some class of customers' arrival in certain queueing systems. In this paper, therefore, we investigate M/G/1 queue with server vacations where each vacation period has different distribution and vacation length is influenced by customers' arrival. Laplace-Stieltjes transform of the waiting time distribution and the distribution of number of customers waiting for each class of customers are respectively derived. As performance measures, mean waiting time and average number of customers waiting for each class of customers are also derived.

• The KIPS Transactions:PartC
• /
• v.11C no.1
• /
• pp.89-98
• /
• 2004

AQM (Active Queue Management) techniques such as RED (Random Early Detection) which be proposed to solve the congestion of internet perform congestion control effectively for TCP data. However, in the situation where TCP and UDP share the bottleneck link, they can not solve the problems of the unfairness and long queueing delay. In this paper, we proposed an simple queue management algorithm, called PSRED (Protocol Sensitive RED), that improves fairness and decreases queueing delay. PSRED algorithm improves fairness and decreases average queue length by distinguishes each type of flow in using protocol field of packets and applies different drop functions to them respectively.

• Journal of Internet Computing and Services
• /
• v.2 no.2
• /
• pp.65-70
• /
• 2001

In this paper, a Modified RED algorithm for congestion avoidance in IP networks is presented. The RED detects incipient congestion by computing the average queue size. By notifying only a randomly selected fraction of connection, it causes to the global synchronization or fairness problem, And also, the network characteristics need to be known in order to find th optimum average queue length. When the average queue size exceeds a minimum threshold, a modified RED algorithm drops packets based on the state of each connection. Performance is improved because of keeping the average queue size low while allowing occasional bursts of packets in the queue, we compare performance of modified RED with RED and Drop Tail in terms of goodput, network utilization and fairness.

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.17 no.4
• /
• pp.54-62
• /
• 2018

As a part of realization of artificial intelligence signal(AI Signal), this study proposed an actuated signal algorithm based on vehicle queue length that estimates in real time by deep learning. In order to implement the algorithm, we built an API(COM Interface) to control the micro traffic simulator Vissim in the tensorflow that implements the deep learning model. In Vissim, when the link travel time and the traffic volume collected by signal cycle are transferred to the tensorflow, the vehicle queue length is estimated by the deep learning model. The signal time is calculated based on the vehicle queue length, and the simulation is performed by adjusting the signaling inside Vissim. The algorithm developed in this study is analyzed that the vehicle delay is reduced by about 5% compared to the current TOD mode. It is applied to only one intersection in the network and its effect is limited. Future study is proposed to expand the space such as corridor control or network control using this algorithm.