• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.12
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• pp.2046-2052
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• 2016

Ramp metering is one of the most efficient and widely used control methods for an intelligent transportation management system on a freeway. Its objective is to control and upgrade freeway traffic by regulating the number of vehicles entering the freeway entrance ramp, in such a way that not only the alleviation of the congestion but also the smoothing of the traffic flow around the desired density level can be achieved for the maintenance of the maximum mainline throughput. When the cycle of the signal detection is larger than that of the system process, the density tracking problem needs to be considered in the form of the discrete-time system. Therefore, a discrete-time sliding mode control method is proposed for the ramp metering problem in the presence of both input constraint in the on-ramp and exogenous disturbance in the off-ramp considering the random behavior of the driver. Simulations were performed using a validated second-order macroscopic traffic flow model in Matlab environment and the simulation results indicate that proposed control method can achieve better performance than previously well-known ALINEA strategy in the sense that mainstream flow throughput is maximized and congestion is alleviated even in the presence of input constraint and exogenous disturbance.

• International Journal of Highway Engineering
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• v.16 no.4
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• pp.127-134
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• 2014

PURPOSES : To operate more efficient traffic management system, it is utmost important to detect the change in congestion level on a freeway segment rapidly and reliably. This study aims to develop classification method of congestion change type. METHODS: This research proposes two classification methods to capture the change of the congestion level on freeway segments using the dedicated short range communication (DSRC) data and the vehicle detection system (VDS) data. For developing the classification methods, the decision tree models were employed in which the independent variable is the change in congestion level and the covariates are the DSRC and VDS data collected from the freeway segments in Korea. RESULTS : The comparison results show that the decision tree model with DSRC data are better than the decision tree model with VDS data. Specifically, the decision tree model using DSRC data with better fits show approximately 95% accuracies. CONCLUSIONS : It is expected that the congestion change type classified using the decision tree models could play an important role in future freeway traffic management strategy.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.10
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• pp.5143-5158
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• 2018

Improving the intrusion detection system (IDS) is a pressing need for cyber security world. With the growth of computer networks, there are constantly daily new attacks. Machine Learning (ML) is one of the most important fields which have great contribution to address the intrusion detection issues. One of these issues relates to the imbalance of the diverse classes of network traffic. Accuracy paradox is a result of training ML algorithm with imbalanced classes. Most of the previous efforts concern improving the overall accuracy of these models which is truly important. However, even they improved the total accuracy of the system; it fell in the accuracy paradox. The seriousness of the threat caused by the minor classes and the pitfalls of the previous efforts to address this issue is the motive for this work. In this paper, we consolidated stratified sampling, cost function and weighted Support Vector Machine (WSVM) method to address the accuracy paradox of ID problem. This model achieved good results of total accuracy and superior results in the small classes like the User-To-Remote and Remote-To-Local attacks using the improved version of the benchmark dataset KDDCup99 which is called NSL-KDD.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.25 no.4
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• pp.807-816
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• 2015

During the last decade most of coordinated security breaches are performed by the means of botnets, which is a large overlay network of compromised computers being controlled by remote botmaster. Due to high volumes of traffic to be analyzed, the challenge is posed by managing tradeoff between system scalability and accuracy. We propose a novel Hadoop-based P2P botnet detection method solving the problem of scalability and having high accuracy. Moreover, our approach is characterized not to require labeled data and applicable to encrypted traffic as well.

• Journal of The Korean Association of Information Education
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• v.2 no.2
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• pp.269-277
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• 1998

In most of the distributed deadlock detection algorithms using messages called probes, only a portion of the generated messages are effectively used, and hence the wasted probes cause heavy communication traffic. In this paper, a distributed deadlock detection algorithm is proposed which can efficiently detect deadlocks making use of those residue probes. Our algorithm is complete in the sense that they detect not only those deadlocks in which the initiator is involved as most other algorithms do, but all the other deadlocks that are present anywhere in a connected wait-for-graph. To detect all the deadlocks, the algorithms known to be most efficient require O(ne) messages, where e and n are the number of edges and nodes in the graph, respectively. The single execution of the presented algorithm can accomplish the same task with O(e) messages.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.9 no.1
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• pp.9-18
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• 2010

Our country has so large mountain area that the tunnel construction is inevitable and the need of incident detection that provides safe management of tunnels is increasing. In this paper, we suggest a tunnel incident detection system using computer vision techniques, which can detect the incidents in a tunnel and provides the information to the tunnel administrative office in order to help safe tunnel operation. The suggested system enhances the processing speed by using simple processing algorithm such as image subtraction, and ensures the accuracy of the system by focused on the incident detection itself rather than its classification. The system is also cost effective because the video data from 4 cameras can be simultaneously analyzed in a single PC-based system. Our system can be easily extended because the PC-based analyzer can be increased according to the number of cameras in a tunnel. Also our web-based structure is useful to connect the other remotely located tunnel incident systems to obtain interoperability between tunnels. Through the experiments the system has successfully detected the incidents in real time including dropped luggage, stoped car, traffic congestion, man walker or bicycle, smoke or fire, reverse driving, etc.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.10a
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• pp.717-719
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• 2015

The research and development of the unmanned vehicle is being carried out actively in domestic and foreign countries. The research is being carried out to provide various services so that the weakness of system such as conventional 2D-based navigation systems can be supplemented and the driving can be safer. This paper suggests the method that enables real-time video processing in more efficient way by realizing the location detection and signal recognition technique of traffic signals in video. In order to overcome the limit of conventional methods that have a difficulty in analyzing the signal as it is sensitive to brightness change, the proposed method realizes the program that grasps the depth data in front of the vehicle using video processing, analyzes the signal by detecting traffic signal and estimates color components of traffic signal in front and the distance between traffic signal and the vehicle.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.13 no.3
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• pp.66-77
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• 2014

UTIS(Urban Traffic Information System) directly collects link travel time in urban area by using probe vehicles. Therefore it can estimate more accurate link travel speed compared to other traffic detection systems. However, UTIS includes some missing data caused by the lack of probe vehicles and RSEs on road network, system failures, and other factors. In this study, we suggest a new model, based on k-NN algorithm, for imputing missing data to provide more accurate travel time information. New imputation model is an adaptive k-NN which can flexibly adjust the number of nearest neighbors(NN) depending on the distribution of candidate objects. The evaluation result indicates that the new model successfully imputed missing speed data and significantly reduced the imputation error as compared with other models(ARIMA and etc). We have a plan to use the new imputation model improving traffic information service by applying UTIS Central Traffic Information Center.

• IEMEK Journal of Embedded Systems and Applications
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• v.7 no.6
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• pp.339-343
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• 2012

Security and monitoring system has many applications and commonly used for detection, warning, alarm, etc. As the networking technology advances, user requirements are getting higher. An intelligent and cooperative surveillance system is proposed to meet current user demands and improve the performance. This paper focuses on the implementation issue for the embedded intelligent surveillance system. To cover wide area cooperative function is implemented and connected by wireless sensor network technology. Also to improve the performance lots of sensors are employed into the surveillance system to reduce the error but improve the detection probability. The proposed surveillance system is composed of vision sensor (camera), mic array sensor, PIR sensor, etc. Between the sensors, data is transferred by IEEE 802.11s or Zigbee protocol. We deployed a private network for the sensors and multiple gateways for better data throughput. The developed system is targeted to the traffic accident detection and alarm. However, its application can be easily changed to others by just changing software algorithm in a DSP chip.

• Journal of Platform Technology
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• v.11 no.6
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• pp.106-115
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• 2023

In this paper, we propose a sensor data sharing system for the safe and smooth operation of autonomous vehicles on two-way single lanes in traffic-isolated areas and implement the core module, the situational awareness technology. Two-way single lanes pose challenges for autonomous vehicles, particularly when encountering parked vehicles or oncoming traffic, leading to reversing issues. We introduce a system using infrastructure cameras to detect vehicles' approach, enter, and leave on twoway single lanes in real-time, transmitting this information to autonomous vehicles via V2N communication, thereby expanding the sensing range of the autonomous vehicles. The core part of the proposed system is the situational awareness of the two-way single lane using infrastructure cameras. In this paper, we implement this using object detection and tracking technology. Finally, we validate the implemented situational awareness technology using data collected from actual two-way single lanes.