• The KIPS Transactions:PartC
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• v.17C no.2
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• pp.205-214
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• 2010

As network traffic is dramatically increasing due to the popularization of Internet, the need for application traffic classification becomes important for the effective use of network resources. In this paper, we present an application traffic classification method based on fixed IP-port information. A fixed IP-port is a {IP address, port number, transport protocol}triple dedicated to only one application, which is automatically collected from the behavior analysis of individual applications. We can classify the Internet traffic more accurately and quickly by simple packet header matching to the collected fixed IP-port information. Therefore, we can construct a lightweight, fast, and accurate real-time traffic classification system than other classification method. In this paper we propose a novel algorithm to extract the fixed IP-port information and the system architecture. Also we prove the feasibility and applicability of our proposed method by an acceptable experimental result.

• Journal of the Korean Society of Industry Convergence
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• v.27 no.4_2
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• pp.1001-1007
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• 2024

Recent deep learning algorithms for object detection in real-time play a crucial role in various applications such as autonomous driving, traffic monitoring, health care, and water quality monitoring. The size of small objects, in particular, significantly impacts the accuracy of detection models. However, data containing small objects can lead to underfitting issues in models. Therefore, this study developed a deep learning model capable of quickly detecting small objects to provide more accurate predictions. The RE-SOD (Residual block based Small Object Detector) developed in this research enhances the detection performance for small objects by using RGB separation preprocessing and residual blocks. The model achieved an accuracy of 1.0 in image classification and an mAP50-95 score of 0.944 in object detection. The performance of this model was validated by comparing it with real-time detection models such as YOLOv5, YOLOv7, and YOLOv8.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.9
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• pp.1402-1408
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• 2017

Recently, services for personal biometric data analysis based on real-time monitoring systems has been increasing and many of them have focused on recognition of emotions. In this paper, we propose a classification model to classify anxiety emotion using biometric data actually collected from people. We propose to deploy the support vector machine to build a classification model. In order to improve the classification accuracy, we propose two data pre-processing procedures, which are normalization and data deletion. The proposed algorithms are actually implemented based on Real-time Traffic Flow Measurement structure, which consists of data collection module, data preprocessing module, and creating classification model module. Our experiment results show that the proposed classification model can infers anxiety emotions of people with the accuracy of 65.18%. Moreover, the proposed model with the proposed pre-processing techniques shows the improved accuracy, which is 78.77%. Therefore, we can conclude that the proposed classification model based on the pre-processing process can improve the classification accuracy with lower computation complexity.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.42 no.3
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• pp.612-622
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• 2017

Long-lived flowed termed as elephant flows in data center networks have a tendency to consume a lot of bandwidth, leaving delay-sensitive short-lived flows referred to as mice flows choked behind them. This results in non-trivial delays for mice flows, eventually degrading application performance running on the network. Therefore, a datacenter network should be able to classify, detect, and visualize elephant flows as well as provide QoS guarantees in real-time. In this paper we aim to focus on: 1) a proposed framework for real-time detection and visualization of elephant flows in SDN using sFlow. This allows to examine elephant flows traversing a switch by double-clicking the switch node in the topology visualization UI; 2) an approach to guarantee QoS that is defined and administered by a SDN controller and specifications offered by OpenFlow. In the scope of this paper, we will focus on the use of rate-limiting (traffic-shaping) classification technique within an SDN network.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.10
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• pp.3771-3792
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• 2021

With the rapid development of science and technology, several high-performance networks have emerged with various new applications. Consequently, financially or socially motivated attacks on specific networks have also steadily become more complicated and sophisticated. To reduce the damage caused by such attacks, administration of network traffic flow in real-time and precise analysis of past attack traffic have become imperative. Although various traffic analysis methods have been studied recently, they continue to suffer from performance limitations and are generally too complicated to apply in existing systems. To address this problem, we propose a method to calculate the correlation between the malicious and normal flows and classify attack traffics based on the corresponding correlation values. In order to evaluate the performance of the proposed method, we conducted several experiments using examples of real malicious traffic and normal traffic. The evaluation was performed with respect to three metrics: recall, precision, and f-measure. The experimental results verified high performance of the proposed method with respect to first two metrics.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37 no.5B
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• pp.348-356
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• 2012

The application traffic analysis is getting more and more challenging due to the huge amount of traffic from high-speed network link and variety of applications running on wired and wireless Internet devices. Multi-level combination of various analysis methods is desired to achieve high completeness and accuracy of analysis results for a real-time analysis system, while requires much of processing burden on the contrary. This paper proposes a novel architecture for a real-time traffic analysis system which improves the processing performance on multi-core CPU environment. The main contribution of the proposed architecture is an efficient parallel processing mechanism with multiple threads of various analysis methods. The feasibility of the proposed architecture was proved by implementing and deploying it on our campus network.

• Journal of Korean Society of Transportation
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• v.26 no.2
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• pp.157-166
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• 2008

This study proposed a model to estimate traffic accident likelihood using real-time traffic data obtained from freeway traffic surveillance systems. Traffic variables representing spatio-temporal variations of traffic conditions were utilized as independent variables in the proposed models. Binary logistics regression modelings were conducted to correlate traffic variables and accident data that were collected from the Seohaean freeway during recent three years, from 2004 to 2006. To apply more reliable traffic variables, outlier filtering and data imputation were also performed. The outcomes of the model that are actually probabilistic measures of accident occurrence would be effectively utilized not only in designing warning information systems but also in evaluating the effectiveness of various traffic operations strategies in terms of traffic safety.

• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.2956-2958
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• 2000

This paper describes the design of a real time traffic data acquisition system using loop detector and piezzo sensor. Loop detector is the cheapest method to measure the speed and piezzo is used to detect the vehicle axle information. A ISA slot based I/O board is designed for data acquisition and PC process the raw traffic data and transfer the data to the host system. Simulation kit is designed with toy car kits. simulated loop detector and piezzo sensor. The data acquisition system collects up to 10 lane highway traffic data such as vehicle count. speed. length axle count. distance between the axles. The data is processed to generate traffic count, vehicle classification, which are to be used for ITS. The system architecture and simulation data is included and the system will be tested for field operation.

• Journal of KIISE:Software and Applications
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• v.34 no.11
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• pp.991-1000
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• 2007

A new robust approach to detect vehicles in a single frame of traffic scenes is presented. The method is based on the multi-level shadow classification, which has been shown to have the capability of extracting correct shadow shapes regardless of the operating conditions. The rationale of this classification is supported by the fact that shadow regions underneath vehicles usually exhibit darker gray level regardless of the vehicle brightness and illuminating conditions. Classified shadows provide string clues on the presence of vehicles. Unlike other schemes, neither background nor temporal information is utilized; thereby the performance is robust to the abrupt change of weather and the traffic congestion. By a simple evidential reasoning, the shadow evidences are combined with bright evidences to locate correct position of vehicles. Experimental results show the missing rate ranges form 0.9% to 7.2%, while the false alarm rate is below 4% for six traffic scenes sets under different operating conditions. The processing speed for more than 70 frames per second could be obtained for nominal image size, which makes the real-time implementation of measuring the traffic parameters possible.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.7 no.6
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• pp.1160-1166
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• 2006

The information of vehicle is very important for maintaining traffic order under the present complex traffic environments. This paper proposes a new vehicle plate recognition mechanism that is essential to know the information of vehicle. The proposed method uses SVM which is excellent object classification compare to other methods. Two-class SVM is used to find the location of vehicle plate and multi-class SVM is used to recognize the characters in the plate. As a real-time processing system using multi-step image processing and recognition process this method recognizes several different vehicle plates. Through the experimental results of real environmental image and recognition using the proposed method, the performance is proven.