• The Transactions of the Korea Information Processing Society
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• v.6 no.3
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• pp.710-721
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• 1999

In this paper, we have designed and implemented a parameter extraction system for analyzing Internet using SNMP. The extraction system has two modules; one is collection request module, and the other is analysis request module. The collection request module generates a polling script, which is used to collect management information from the managed system periodically. With this collected data, analysis request module extracts analysis parameters. These parameters are traffic flow analysis, interface traffic analysis, packet traffic analysis, and management traffic analysis parameter. For management activity, we have introduced two-step-analysis-view. One is Summary-View, which is used find out malfunction of a system among the entire managed systems. The Other is Specific-View. With this view we can analyze the specific system with all our analysis parameters. To show available data as indicators for line capacity planning, network redesigning decision making of performance upgrade for a network device and things like that.

• Journal of KIISE:Computer Systems and Theory
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• v.30 no.11
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• pp.610-620
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• 2003

Various shadows are one of main factors that cause errors in vision based vehicle detection. In this paper, two simple methods, land mark based method and BS & Edge method, are proposed for vehicle detection and shadow rejection. In the experiments, the accuracy of vehicle detection is higher than 96%, during which the shadows arisen from roadside buildings grew considerably. Based on these two methods, vehicle counting, tracking, classification, and speed estimation are achieved so that real-time traffic parameters concerning traffic flow can be extracted to describe the load of each lane.

• 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 Institute of Electronics Engineers of Korea CI
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• v.37 no.1
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• pp.11-20
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• 2000

This paper presents the method of extracting the contour and shape parameters for moving object tracking in traffic scenes. The contour is extracted by applying difference image method in reduction image and the features are extracted from original image to grow the accuracy of tracking. We used features such as circle distribution, center moment, and maximum and minimum ratio. Data association problem is solved by these features. Kalman filters are used for moving object tracking on real time. The simulation results indicate that the proposed algorithm appears to generate feature vectors good enough for multiple vehicle tracking.

• Journal of the Korea Society of Computer and Information
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• v.28 no.10
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• pp.1-8
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• 2023

If there is a defect in the wheel bearing, which is a major part of the car, it can cause problems such as traffic accidents. In order to solve this problem, big data is collected and monitoring is conducted to provide early information on the presence or absence of wheel bearing failure and type of failure through predictive diagnosis and management technology. System development is needed. In this paper, to implement such an intelligent wheel hub bearing maintenance system, we develop an embedded system equipped with sensors for monitoring reliability and soundness and algorithms for predictive diagnosis. The algorithm used acquires vibration signals from acceleration sensors installed in wheel bearings and can predict and diagnose failures through big data technology through signal processing techniques, fault frequency analysis, and health characteristic parameter definition. The implemented algorithm applies a stable signal extraction algorithm that can minimize vibration frequency components and maximize vibration components occurring in wheel bearings. In noise removal using a filter, an artificial intelligence-based soundness extraction algorithm is applied, and FFT is applied. The fault frequency was analyzed and the fault was diagnosed by extracting fault characteristic factors. The performance target of this system was over 12,800 ODR, and the target was met through test results.

• International Journal of Highway Engineering
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• v.20 no.1
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• pp.69-76
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• 2018

PURPOSES : This study determined the optimal usage rate of RAP (reclaimed asphalt pavement) using cold central-plant recycling (CCPR) on a road-surface layer. In addition, a mixture-aggregate gradation design and a curing method based on the proposed rate for the surface-layer mix design were proposed. METHODS : First, current research trends were investigated by analyzing the optimum moisture content, mix design, and quality standards for surface layers in Korea and abroad. To analyze the aggregate characteristics of the RAP, its aggregate-size characteristics were analyzed through the combustion asphalt content test and the aggregate sieve analysis test. Moreover, aggregate-segregation experiments were performed to examine the possibility of RAP aggregate segregation from field compaction and vehicle traffic. After confirming the RAP quality standards, coarse aggregate and fine aggregate, aggregate-gradation design and quality tests were conducted for mixtures with 40% and 50% RAP usage. The optimum moisture content of the surface-layer mixture containing RAP was tested, as was the evapotranspiration effect on the surface-layer mixture of the optimum moisture content. RESULTS : After analyzing the RAP recycled aggregate size and extraction aggregate size, 13-8mm aggregate was found to be mostly 8mm aggregate after combustion. After using surface-chipping and mixing methods to examine the possibility of RAP aggregate segregation, it was found that the mixing method contributed very little for 3.32%, and because the surface-chipping method applied compaction energy directly as the maximum assumption the separation ratio was 15.46%. However, the composite aggregate gradation did not change. Using a 40% RAP aggregate rate on the surface-layer mixture for cold central-plant recycling satisfied the Abroad quality standard. The optimum moisture content of the surface-layer mixture was found to be 7.9% using the modified Marshall compaction test. It was found that the mixture was over 90% cured after curing at $60^{\circ}C$ for two days. CONCLUSIONS : To use the cold central-plant recycling mixture on a road-surface layer, a mixture-aggregate gradation design was proposed as the RAP recycled aggregate size without considering aggregate segregation, and the RAP optimal usage rate was 40%. In addition, the modified Marshall compaction test was used to determine the optimum moisture content as a mix-design parameter, and the curing method was adapted using the method recommended by Asphalt Recycling & Reclaiming Association (ARRA).