• Journal of Information Processing Systems
• /
• v.2 no.1
• /
• pp.52-57
• /
• 2006

One of the important functions of an Intelligent Transportation System (ITS) is to classify vehicle types using a vision system. We propose a method using machine-learning algorithms for this classification problem with 3-D object model fitting. It is also necessary to detect road lanes from a fixed traffic surveillance camera in preparation for model fitting. We apply a background mask and line analysis algorithm based on statistical measures to Hough Transform (HT) in order to remove noise and false positive road lanes. The results show that this method is quite efficient in terms of quality.

• Journal of Auto-vehicle Safety Association
• /
• v.11 no.3
• /
• pp.7-12
• /
• 2019

AEB (Autonomous Emergency Braking system), a system in which vehicles automatically recognize forward objects or pedestrians and actively brake when forward collisions are expected, has been mandated by NHTSA (National Highway Traffic Safety Administration) and IIHS (Insurance Institute for Highway Safety) for all vehicles sell in the United States since 2022, and AEB research is also actively underway in korea. In this study, it can be confirmed that the passenger injury is reduced according to the AEB detection distance when it is assumed that the AEB is mounted in the actual event generated from KIDAS (Korea New Car Assessment Program) data through various analysis programs.

• International Journal of High-Rise Buildings
• /
• v.2 no.4
• /
• pp.331-340
• /
• 2013

The main topic of this paper is to show a possibility of indoor air quality enhancement and the fan energy savings in underground parking facilities by applying the demand-controlled ventilation (DCV) strategy based on the real-time variation of the traffic load. The established ventilation rate is estimated by considering the passing distance, CO emission rate, idling time of a vehicle, and the floor area of the parking facility. However, they are hard to be integrated into the real-time DCV control. As a solution to this problem, the minimum ventilation rate per a single vehicle is derived in this research based on the actual ventilation data acquired from several existing underground parking facilities. And then its applicability to the DCV based on the real-time variation of the traffic load is verified by simulating the real-time carbon monoxide concentration variation. The energy saving potentials of the proposed DCV strategy is also checked by comparing it with those for the current underground parking facility ventilation systems found in the open literature.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2022.05a
• /
• pp.469-472
• /
• 2022

With the recent development of intelligent transportation systems, various technologies applying deep learning technology are being used. To crackdown on illegal vehicles and criminal vehicles driving on the road, a vehicle type classification system capable of accurately determining the type of vehicle is required. This study proposes a vehicle type classification system optimized for mobile traffic control systems using YOLO(You Only Look Once). The system uses a one-stage object detection algorithm YOLOv5 to detect vehicles into six classes: passenger cars, subcompact, compact, and midsize vans, full-size vans, trucks, motorcycles, special vehicles, and construction machinery. About 5,000 pieces of domestic vehicle image data built by the Korea Institute of Science and Technology for the development of artificial intelligence technology were used as learning data. It proposes a lane designation control system that applies a vehicle type classification algorithm capable of recognizing both front and side angles with one camera.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.26 no.5
• /
• pp.1223-1234
• /
• 2016

The Industrial control system is adopted by various industry field and national infrastructure, therefore if it received cyber attack, the serious security problems can be occured in the public sector. For this reason, security requirements of the industrial control system have been proposed, in accordance with the security guidelines of the electronic control system, and it is operated by separate from the external and the internal network. Nevertheless, cyber attack by malware (such as Stuxnet) targeting to control system have been occurred continuously, and also the real-time detection of untrusted traffic is very difficult because there are some difficulty of keeping up with quickly evolving the advent of new-variant malicious codes. In this paper, we propose the traffic analysis architecture for providing secure industrial control system based on the analyzed the security threats, the security requirements, and our proposed architecture.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.14 no.7
• /
• pp.3093-3115
• /
• 2020

Network anomaly detection system plays an essential role in detecting network anomaly and ensuring network security. Anomaly detection system based machine learning has become an increasingly popular solution. However, due to the unbalance and high-dimension characteristics of network traffic, the existing methods unable to achieve the excellent performance of high accuracy and low false alarm rate. To address this problem, a new network anomaly detection method based on data balancing and recursive feature addition is proposed. Firstly, data balancing algorithm based on improved KNN outlier detection is designed to select part respective data on each category. Combination optimization about parameters of improved KNN outlier detection is implemented by genetic algorithm. Next, recursive feature addition algorithm based on correlation analysis is proposed to select effective features, in which a cross contingency test is utilized to analyze correlation and obtain a features subset with a strong correlation. Then, random forests model is as the classification model to detection anomaly. Finally, the proposed algorithm is evaluated on benchmark datasets KDD Cup 1999 and UNSW_NB15. The result illustrates the proposed strategies enhance accuracy and recall, and decrease the false alarm rate. Compared with other algorithms, this algorithm still achieves significant effects, especially recall in the small category.

• Journal of Broadcast Engineering
• /
• v.14 no.4
• /
• pp.475-487
• /
• 2009

We propose a visible light road-to-vehicle communication system at intersection as one of ITS technique. In this system, the communication between vehicle and a LED traffic light is approached using LED traffic light as a transmitter, and on-vehicle high-speed camera as a receiver. The LEDs in the transmitter are emitted in 500Hz and those emitting LEDs are captured by a high-speed camera for making communication. Here, the luminance value of each LED in the transmitter should be found for consecutive frames to achieve effective communication. For this purpose, first the transmitter should be identified, then it should be tracked for consecutive frames while the vehicle is moving, by processing the images from the high-speed camera. In our previous work, the transmitter was identified by getting the subtraction of two consecutive frames. In this paper, we mainly introduce an algorithm to track the identified transmitter in consecutive frames. Experimental results using appropriate images showed the effectiveness of the proposal.

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.11 no.5
• /
• pp.78-88
• /
• 2012

AVIs on the road are installed for (1) security (2) and for traffic, and they are various managed by (1) police department, (2) local government, (3) national highway management, (4) Korean highway corporation. But although the collected data of the plate number, the travel time, the picture of the car are same, they are used in purposes of its installation because the managements are different and the data are difficult to be connected with each other. For this reason, this study is to appraise the application for creating traffic information by integrating these data, and to suggest the introduction of spatial detection system which integrated security-traffic AVI DB for the purpose of reliability improvement of center's velocity. The estimating sections of link travel information seems to be expanded, and the error rate between the center's velocity and the experimental value will be reduced if integrated DB of traffic and security AVIs is used for creating traffic information. Also, the crime prevention and arrest rate is expected to rise in the future.

• Journal of Korean Society of Transportation
• /
• v.32 no.5
• /
• pp.522-530
• /
• 2014

Spatio-temporal congestion evolution pattern can be reproduced using the VDS(Vehicle Detection System) historic speed dataset in the TMC(Traffic Management Center)s. Such dataset provides a pool of spatio-temporally experienced traffic conditions. Traffic flow pattern is known as spatio-temporally recurred, and even non-recurrent congestion caused by incidents has patterns according to the incident conditions. These imply that the information should be useful for traffic prediction and traffic management. Traffic flow predictions are generally performed using black-box approaches such as neural network, genetic algorithm, and etc. Black-box approaches are not designed to provide an explanation of their modeling and reasoning process and not to estimate the benefits and the risks of the implementation of such a solution. TMCs are reluctant to employ the black-box approaches even though there are numerous valuable articles. This research proposes a more readily understandable and intuitively appealing data-driven approach and developes an algorithm for identifying congestion patterns for recurrent and non-recurrent congestion management and information provision.

• Journal of Electrical Engineering and Technology
• /
• v.12 no.5
• /
• pp.2051-2066
• /
• 2017

This paper deals with reconfigurable secured mobile systems where the reconfigurability has the potential of providing a required adaptability to change the system requirements. The reconfiguration scenario is presented as a run-time automatic operation which allows security mechanisms and the addition-removal-update of software tasks. In particular, there is a definite requirement for filtering and intrusion detection mechanisms that will use fewer resources and also that will improve the security on the secured mobile devices. Filtering methods are used to control incoming traffic and messages, whereas, detection methods are used to detect malware events. Nevertheless, when different reconfiguration scenarios are applied at run-time, new security threats will be emerged against those systems which need to support multiple security objectives: Confidentiality, integrity and availability. We propose in this paper a new approach that efficiently detects threats after reconfigurable scenarios and which is based on filtering and intrusion detection methods. The paper's contribution is applied to Android where the evaluation results demonstrate the effectiveness of the proposed middleware in order to detect the malicious events on reconfigurable secured mobile systems and the feasibility of running and executing such a system with the proposed solutions.