• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.38 no.5
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• pp.407-414
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• 2020

SVM (Support vector machine) analysis was performed after applying a deep learning technique based on an Inception-based model (GoogLeNet). The accuracy of automatic image classification was analyzed using an SVM with multiple virtual grid sizes. Six classes were selected from a standard land cover map. Cars were added as a separate item to increase the classification accuracy of roads. The virtual grid size was 2-5 m for natural areas, 5-10 m for traffic areas, and 10-15 m for building areas, based on the size of items and the resolution of input images. The results demonstrate that automatic classification accuracy can be increased by adopting an integrated approach that utilizes weighted virtual grid sizes for different classes.

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

• Proceedings of the Korea Concrete Institute Conference
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• 2004.11a
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• pp.663-666
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• 2004

An analysis of the annual frequency of collapse(AF) is performed for each bridge pier exposed to ship collision. AF is computed for each bridge component and vessel classification. The summation of AFs computed over all of the vessel classification intervals for a specific component should equal the annual frequency of collapse of the component. The designer should use judgment in developing a distribution of the vessel frequency data based on discrete groupings or categories of vessel size by DWT. In the present study the effect of vessel classification on the annual frequency of collapse in the ship collision risk assessment is investigated by illustrative numerical examples based on the vessel frequency data of the domestic harbor. The DWT interval for larger vessels has more effect on the ship collision risk. Therefore the expert judgement in determining the larger DWT interval is required because the design impact lateral resistances of bridge components depend on the ship collision risk.

• Journal of the Korean Society for Railway
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• v.19 no.5
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• pp.672-684
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• 2016

UIC Codes 714R & 715R recommend the use of line classifications and their usage in maintenance work by employing notional traffic loads. However, the classification has not been applied to local lines and, therefore, a new line classification system based on UIC 714R has been proposed in this study. For this, various classification models of UIC, Germany, and UK have been studied first and equivalent traffic loads based on Korail's report, as well as on train timetables, have been derived. The results of the classifications have been compared with those of major European countries and it has been shown that the proposed classification is equivalent to the average value in the European cases. The line classification can be fully utilized during the decision making process of maintenance work and will also be used to model the Reliability Centered Maintenance (RCM) in the future.

• Proceedings of the KSRS Conference
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• v.1
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• pp.260-262
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• 2006

One of future remote sensing techniques for transportation application is vehicle detection from the space, which could be the basis of measuring traffic volume and recognizing traffic condition in the future. This paper introduces an approach to vehicle detection using image object segmentation approach. The object-oriented image processing is particularly beneficial to high-resolution image classification of urban area, which suffers from noisy components in general. The project site was Dae-Jeon metropolitan area and a set of true color aerial images at 10cm resolution was used for the test. Authors investigated a variety of parameters such as scale, color, and shape and produced a customized solution for vehicle detection, which is based on a knowledge-based hierarchical model in the environment of eCognition. The highest tumbling block of the vehicle detection in the given data sets was to discriminate vehicles in dark color from new black asphalt pavement. Except for the cases, the overall accuracy was over 90%.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.16 no.10 s.115
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• pp.1005-1013
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• 2006

A survey and auditory experiment on multiple residential noises such as floor impact, airborne, bathroom, drainage and traffic noises were conducted to develop a combined rating system and to establish criteria for multiple residential noises. Subjective reactions such as annoyance, activity disturbance, sleep disturbance, and satisfaction to overall noise environment and each residential noise were recorded. The effect of individual noise perception on the evaluation of the overall noise environment was also investigated. The survey results showed that satisfaction for floor impact noise most greatly affects the overall satisfaction for overall noise environment and annoyance most greatly affects the satisfaction for individual noise sources. Auditory experiments were undertaken to determine the percent satisfaction for individual noise levels. Result of auditory experiment showed that the noise level corresponding to 40 % satisfaction is 49 dB $(L_{i,Fmax,AW})$ for floor impact and is about 40 dB(A) for airborne, drainage and traffic noise. From the results of the survey and the auditory experiments, an equation for predicting the overall satisfaction for multiple noises was developed and a classification of multiple residential noises was proposed.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38B no.8
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• pp.615-622
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• 2013

Fast and accurate signature extraction is essential to improve the performance of the payload signature-based traffic analysis methods. However the slow manual process in extracting signatures make difficult to deal with the rapidly changing application in current Internet environment. Therefore, in this paper we propose a system automatically generating signatures from ground-truth traffic data. In addition, we improve the efficiency of signature extraction by recognizing the application protocol using a protocol filters and generating signatures automatically according to the application-specific protocol contents. In order to verify the validity of the system proposed in this paper, we compared the signatures automatically generated from our system with the signatures manually created for a few popular applications.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.38 no.5
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• pp.42-51
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• 2001

Vehicle detection is the basic of traffic monitoring. Video based systems have several apparent advantages compared with other kinds of systems. However, In video based systems, shadows make troubles for vehicle detection, especially active shadows resulted from moving vehicles. In this paper, a new method that combines background subtraction and edge detection is proposed for vehicle detection and shadow rejection. The method is effective and the correct rate of vehicle detection is higher than 98% in experiments, during which the passive shadows resulted from roadside buildings grew considerably. Based on the proposed vehicle detection method, vehicle tracking, counting, classification and speed estimation are achieved so that traffic parameters concerning traffic flow is obtained to describe the load of each lane.

• Journal of the Korea Society of Computer and Information
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• v.12 no.1 s.45
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• pp.127-135
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• 2007

As the Internet is used widely, criminal offense that use computer is increasing, and an information security technology to remove this crime is becoming competitive power of the country. In this paper, we suggest network-based intrusion detection system that use fuzzy expert system. This system can decide quick intrusion decision from attack pattern applying fuzzy rule through the packet classification method that is done similarity of protocol and fixed time interval. Proposed system uses fuzzy logic to detect attack from network traffic, and gets analysis result that is automated through fuzzy reasoning. In present network environment that must handle mass traffic, this system can reduce time and expense of security

• Journal of Korean Society of Transportation
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• v.19 no.6
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• pp.161-170
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• 2001

Vehicle detection is the basic of traffic monitoring. Video based systems have several apparent advantages compared with other kinds of systems. However, In video based systems, shadows make troubles for vehicle detection. especially active shadows resulted from moving vehicles. In this paper a new method that combines background subtraction and edge detection is proposed for vehicle detection and shadow rejection. The method is effective and the correct rate of vehicle detection is higher than 98(%) in experiments, during which the passive shadows resulted from roadside buildings grew considerably. Based on the proposed vehicle detection method, vehicle tracking, counting, classification and speed estimation are achieved so that traffic information concerning traffic flow is obtained to describe the load of each lane.