• Journal of Korea Spatial Information System Society
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• v.10 no.3
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• pp.55-65
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• 2008

For obtaining telematics traffic information(travel time or speed in an individual link), there are many kinds of devices to collect traffic data. Since the GPS satellite signals have been released to civil society, thank to the development of GPS technology, the GPS has become a very useful instrument for collecting traffic data. GPS can reduce the cost of installation and maintenance in contrast with existing traffic detectors which must be stationed on the ground. But. there are Problems when GPS data is applied to the existing filtering techniques used for analyzing the data collected by other detectors. This paper proposes a method to provide users with correct traffic information through filtering abnormal data caused by the unusual driving in collected data based on GPS. We have developed an algorithm that can be applied to real-time GPS data and create more reliable traffic information, by building patterns of past data and filtering abnormal data through selection of filtering areas using Quartile values. in order to verify the proposed algorithm, we experimented with actual traffic data that include probe cars equipped with a built-in GPS receiver which ran through Gangnam Street in Seoul. As a result of these experiments, it is shown that link travel speed data obtained from this algorithm is more accurate than those obtained by existing systems.

• Journal of Korean Society of Transportation
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• v.24 no.7 s.93
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• pp.27-40
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• 2006

The traffic information is provided, which based on the volume of traffic, speed, occupancy collected through the currently operating Vehicle Detector System(VDS). In addition to the trend in utilization fold of traffic information is increasing gradually with the applied various fields and users. Missing data in Vehicle detector data means series of data transmitted to controller without specific property. The missing data does not have a data property, so excluded at the whole data Process Hence, increasing ratio of missing data in VDS data inflicts unreliable representation of actual traffic situation. This study presented the imputation process due out which applied the methodologies that utilized adjacent stations reference and historical data utilize about missing data. Applied imputation process methodologies to VDS data or SeoHaeAn/Kyongbu Expressway, currently operation VDS, after processes at missing data ratio of an option. Imputation process held presented to per lane-30seconds-period, and morning/afternoon/daily time scope ranges classified, and analyzed an error of imputed data preparing for actual data. The analysis results, an low error occurred relatively in the results of the imputation process way that utilized a historical data compare with adjacent stations reference methods.

• Wind and Structures
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• v.31 no.4
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• pp.321-334
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• 2020

Engineering type tropical cyclone (TC) wind field models are used to estimate TC wind hazard. Some of the models are well-calibrated using observation data, while others are not extensively compared and verified. They are all proxies to the real TC wind fields. The computational effort for their use differs. In the present study, a comparison of the predicted wind fields is presented by considering three commonly used models: the gradient wind field model, slab-resolving model, and a linear height-resolving model. These models essentially predict the horizontal wind speed at a different height. The gradient wind field model and linear height-resolving model are simple to use while the nonlinear slab-resolving model is more compute-intensive. A set of factors is estimated and recommended such that the estimated TC wind hazard by using these models becomes more consistent. The use of the models, including the developed set of factors, for estimating TC wind hazard over-water and over-land is presented by considering the historical tracks for a few sites. It is shown that the annual maximum TC wind speed can be adequately modelled by the generalized extreme value distribution.

• Journal of Korean Society of Transportation
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• v.13 no.4
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• pp.5-29
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• 1995

Advanced Traveler Inoformation Systems*ATIS) , as a subsystem of ITS influence the travel choices of dreivers by providing them with historical, real-time and predictive information to supprot travel decisions and consequently improves the speed and quality of travel. For thesuccessul accomplishment of ATIS, the time-dependent variations of traffic in a road network and travel times of vehicles during their journey must be predicted . The purpose of this study is to evaluate the past developments in the dynamic route choice models and to apply the instantaneous dynamic user optimal route choice model. recently formulated with flow propagation constraints by Ran, Boyce and LeBlanc, to the real transportation network of Seocho-Ku in Seoul. As input data for this application, the time-dependent travel rates are estimated and the link travel time function is derived. The modelis validated from three view points : the efficiency of model itself the ability to predict traffic volume and travel time on links, and the optimal traffic control.

• Proceedings of the KSR Conference
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• 2010.06a
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• pp.2242-2248
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• 2010

Wide range of methodology of reliability prediction for system exists. For railway field, MIL-HDBK-217F, which has not been revised since early in 1990, is used for reliability prediction if historical data is not available. Since this standard has been published, quality and performance of electronic products have been improved rapidly and various kinds of items have been released, however new versions of items could not be released because the prediction standard could not follow up the speed of the production. Thus, this thesis introduces Telcordia SR-332 Issue 2 and would like to compare and analyze the result from MIL-HDBK-217F together with some cases we performed.

• Proceedings of the Optical Society of Korea Conference
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• 2000.02a
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• pp.68-68
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• 2000

In this paper a very brief review of historical development of optical science and technology in China is presented. More attention has been pain on Modem Optics, which developed since 1950s. The recent development of optical science and technology in following fields are introduced. 1. Optical engineering and instrumentation (tracking theodolites, high speed cameras, satellite laser ranging systems, satellite flying attitude control, cameras for remote sensing, astronomical optical instrument) 2. Applied optics (adaptive optics, optical metrology, infrared optics, optical processing, optical holography) 3. Laser science and technology (ultrashort pulse lasers, UV-X ray lasers, high power laser facilities and laser fusion, laser isotope separation) 4. Laser and nonlinear materials (rare earth elements doped laser glasses and crystals, tunable laser crystals, borate series and organic nonlinear crystals) 5. Optoelectronic science and technology (Optical communication, optical data storage, optical computing) The current situation and developing prospect of optical and optoelectronic industry in China are presented. Furthermore it points out that the optical industry could be developed vigorously only if products development capacity is enhanced and new products industrialization is heightened. The main research and education institutions in the optics field in China, as well as the Chinese Optical Society (COS) are introduced.

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

• Proceedings of the KSRS Conference
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• 2007.10a
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• pp.353-356
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• 2007

With advances in sensor technology, current researches on the pertinent techniques are actively directed toward the way which enables the USN computing service. For many applications using sensor networks, the incoming data are by nature characterized as high-speed, continuous, real-time and infinite. Due to such uniqueness of sensor data characteristics, for some instances a finite-sized buffer may not accommodate the entire incoming data, which leads to inevitable loss of data, and requirement for fast processing makes it impossible to conduct a thorough investigation of data. In addition to the potential problem of loss of data, incoming data in its raw form may exhibit high degree of complexity which evades simple query or alerting services for capturing and extracting useful information. Furthermore, as traditional mining techniques are developed to handle fixed, static historical data, they are not useful and directly applicable for analyzing the sensor data. In this paper, (1) describe how three mining techniques (sensor data outlier analysis, sensor pattern analysis, and sensor data prediction analysis) are appropriate for the USN middleware structure, with their application to the stream data in ocean environment. (2) Another proposal is a middleware structure based on USN environment adaptive to above mining techniques. This middleware structure includes sensor nodes, sensor network common interface, sensor data processor, sensor query processor, database, sensor data mining engine, user interface and so on.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2008.03a
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• pp.16-21
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• 2008

Aero-engine, as one kind of rotating machinery with complex structure and high rotating speed, has complicated vibration faults. Therefore, condition monitoring and fault diagnosis system is very important for airplane security. In this paper, a vibration data acquisition and intelligent fault diagnosis system is introduced. First, the vibration data acquisition part is described in detail. This part consists of hardware acquisition modules and software analysis modules which can realize real-time data acquisition and analysis, off-line data analysis, trend analysis, fault simulation and graphical result display. The acquisition vibration data are prepared for the following intelligent fault diagnosis. Secondly, two advanced artificial intelligent(AI) methods, mapping-based and rule-based, are discussed. One is artificial neural network(ANN) which is an ideal tool for aero-engine fault diagnosis and has strong ability to learn complex nonlinear functions. The other is data mining, another AI method, has advantages of discovering knowledge from massive data and automatically extracting diagnostic rules. Thirdly, lots of historical data are used for training the ANN and extracting rules by data mining. Then, real-time data are input into the trained ANN for mapping-based fault diagnosis. At the same time, extracted rules are revised by expert experience and used for rule-based fault diagnosis. From the results of the experiments, the conclusion is obvious that both the two AI methods are effective on aero-engine vibration fault diagnosis, while each of them has its individual quality. The whole system can be developed in local vibration monitoring and real-time fault diagnosis for aero-engine.

• The Journal of Korean Institute of Information Technology
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• v.17 no.9
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• pp.11-17
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• 2019

In recent years, damage and loss of life and property have been occurred frequently due to swelling waves in the East Sea. Swelling waves are not easy to predict because they are caused by various factors. In this research, we build a model for predicting the swelling waves occurrence in the East Coast of Korea using machine learning technique. We collect historical data of unloading interruption in the Pohang Port, and collect air pressure, wind speed, direction, water temperature data of the offshore Pohang Port. We select important variables for prediction, and test various machine learning prediction algorithms. As a result, tide level, water temperature, and air pressure were selected, and Random Forest model produced best performance. We confirm that Random Forest model shows best performance and it produces 88.86% of accuracy