• Journal of Korean Society of Transportation
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• v.24 no.3 s.89
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• pp.17-27
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• 2006

Car following model is a theory that examines changes of condition and interrelationship of acceleration deceleration. headway, velocity and so on closely based on the hypothesis that the Posterior vehicle always follows the preceding vehicle. Car following mode) which is one of the research fields of microscopic traffic flow was first introduced in 1950s and was in active progress in 1960s. However, due to the limitation of data gathering the research depression was prominent for quite a while and then soon was able to tune back on track with development in global positioning system using satellite and generalization of computer use. Recently, there has been many research studies using reception materials of global Positioning system(GPS). Introducing GPS technology to traffic has made real time tracking of a vehicle position possible. Position information is sequential in terms of time and simultaneous measurement of several vehicles in continuous driving is also practicable. Above research was focused on judging whether it is feasible to overcome the following model research by adopting the GPS reception device that was restrictively proceeded due to the limitation of data gathering. For practical judgment, we measured the accuracy and confidence level of the GPS reception devices material by carrying out a practical experiment. Car following model is also being applied in simulations of traffic flow analysis, but due to the difficulty of estimating parameters the basis of the above result. it is our goal to produce an accurate calibration of car following model's parameters that is suitable in this domestic actuality.

• Proceedings of the Korea Water Resources Association Conference
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• 2002.05a
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• pp.43-50
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• 2002

Accurate quantitative forecasting of rainfall for basins with a short response time is essential to predict streamflow and flash floods. Previously, neural networks were used to develop a Quantitative Precipitation Forecasting (QPF) model that highly improved forecasting skill at specific locations in Pennsylvania, using both Numerical Weather Prediction (NWP) output and rainfall and radiosonde data. The objective of this study was to improve an existing artificial neural network model and incorporate the evolving structure and frequency of intense weather systems in the mid-Atlantic region of the United States for improved flood forecasting. Besides using radiosonde and rainfall data, the model also used the satellite-derived characteristics of storm systems such as tropical cyclones, mesoscale convective complex systems and convective cloud clusters as input. The convective classification and tracking system (CCATS) was used to identify and quantify storm properties such as life time, area, eccentricity, and track. As in standard expert prediction systems, the fundamental structure of the neural network model was learned from the hydroclimatology of the relationships between weather system, rainfall production and streamflow response in the study area. The new Quantitative Flood Forecasting (QFF) model was applied to predict streamflow peaks with lead-times of 18 and 24 hours over a five year period in 4 watersheds on the leeward side of the Appalachian mountains in the mid-Atlantic region. Threat scores consistently above .6 and close to 0.8 ∼ 0.9 were obtained fur 18 hour lead-time forecasts, and skill scores of at least 4％ and up to 6％ were attained for the 24 hour lead-time forecasts. This work demonstrates that multisensor data cast into an expert information system such as neural networks, if built upon scientific understanding of regional hydrometeorology, can lead to significant gains in the forecast skill of extreme rainfall and associated floods. In particular, this study validates our hypothesis that accurate and extended flood forecast lead-times can be attained by taking into consideration the synoptic evolution of atmospheric conditions extracted from the analysis of large-area remotely sensed imagery While physically-based numerical weather prediction and river routing models cannot accurately depict complex natural non-linear processes, and thus have difficulty in simulating extreme events such as heavy rainfall and floods, data-driven approaches should be viewed as a strong alternative in operational hydrology. This is especially more pertinent at a time when the diversity of sensors in satellites and ground-based operational weather monitoring systems provide large volumes of data on a real-time basis.

• Korean Journal of Environment and Ecology
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• v.23 no.1
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• pp.50-55
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• 2009

To understand the status of the migrating Oriental Honey Buzzard(Pernis ptilorhynchus) in Korea, we carried out a preliminary study on the number and local movements of the Honey Buzzard from September 2005 to October 2007 at Hongdo Island, Jeonnam Province, Korea. A total of 702 Honey Buzzards in 2005, 404 individuals in 2006, and 659 individuals in 2007 were recorded during the fall migration season, but during the spring migration season only two and eight birds were observed in 2006 and 2007 respectively. The migrating Honey Buzzard showed peak numbers from 20 September to 5 October at the study site. According to observation of arrival time of538 Honey Buzzard in October 2007, a total of 2l6 birds(40.l %) were passing over Hongdo Island between 7:00 AM to 8:00 AM, rising skyward on columns of warm air. This suggests the use of thermal currents and avoidance of nocturnal movement for crossing water. They preferred moderate winds(<5 m/s in speed) from northwest, but the migrating number apparently decreased in case of strong winds (${\geqq}5 m/s$). This suggests that wind speed and direction are important environmental factors for migration of the Oriental Honey Buzzard. At present, it is unclear which breeding population(from north-eastern China or Japan) passes over Hongdo Island. Moreover, it is difficult to prove migration route and to understand migratory behaviors of the Oriental Honey Buzzard in Korea. To prove the breeding sites and routes of the migrants over the study site, we need more detailed and further studies such as satellite tracking researches.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.48 no.12
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• pp.977-985
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• 2020

KOMPSAT-3A launched in 2015 provides Middle InfraRed(MIR) images with 3.3~5.2㎛. Though the satellite provide high resolution images for estimating bright temperature of ground objects, it is different from existing satellites developed for natural science purposes. An atmospheric compensation process is essential in order to estimate the surface brightness temperature from a single channel MIR image of KOMPSAT-3A. However, even after the atmospheric compensation process, there is a brightness temperature error due to various factors. In this paper, we analyzed the cause of the brightness temperature estimation error by tracking signal flow from camera physical characteristics to image processing. Also, we study on possibility of improvement of MIR brightness temperature bias error of KOMPSAT-3A using GEOKOMPSAT-2A. After bias compensation of a real nighttime image with a large bias error, it was confirmed that the surface brightness temperature of KOMPSAT-3A and GEOKOMPSAT-2A have correlation. We expect that the GEOKOMPSAT-2A images will be helpful to improve MIR brightness temperature bias error of KOMPSAT-3A.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.5
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• pp.325-333
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• 2021

With the rapidly changing era of the fourth industrial revolution, the utilization of IT technology is increasing. In addition, the demand for security authentication is increasing as shared services or IoT technologies are being developed as new business models. Security authentication is becoming increasingly important for all intelligent devices such as self-driving cars. However, most location-based security authentication technologies are being developed mainly with technologies that utilize server proximity or satellite location tracking, which limits the scope of their physical use. Location-based security authentication technology has recently been developed as a complementary replacement technology. In this study, we introduce location-based security authentication technology using cell broadcasting technology, which has a wider range of applications and is more convenient and business-friendly than existing location-based security authentication technologies. We also introduced application cases and business models related to this. In addition to the current status of technology development, we analyzed current changes in business models being employed. Based on our analysis results, this study draws the implication that technology diversification is necessary to improve the performance of innovative technologies. It is meaningful that it has found and studied advanced technologies other than existing location authentication methods and systems.

• Journal of Korea Water Resources Association
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• v.55 no.2
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• pp.111-120
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• 2022

The concept of soil moisture memory was used as a method for quantifying the function of soil to control water flow, which evaluates the average residence time of precipitation. In order to characterize the soil moisture memory, a new measurement index called stored precipitation fraction (F_p(f)) was used by tracking the increments in soil moisture by the precipitation event. In this study, the temporal and spatial distribution of soil moisture memory was evaluated along with the slope and soil characteristics of the surface (0~5 cm) soil by using satellite- and model-based precipitation and soil moisture in the Korean peninsula, from 2019 to 2020. The spatial deviation of the soil moisture memory was large as the stored precipitation fraction in the soil decreased preferentially along the mountain range at the beginning (after 3 hours), and the deviation decreased overall after 24 hours. The stored precipitation fraction in the soil clearly decreased as the slope increased, and the effect of drainage of water in the soil according to the composition ratio of the soil particle size was also shown. In addition, average soil moisture contributed to the increase and decrease of hydraulic conductivity, and the rate of rainfall transfer to the depths affected the stored precipitation fraction. It is expected that the results of this study will greatly contribute in clarifying the relationship between soil moisture memory and surface characteristics (slope, soil characteristics) and understanding spatio-temporal variation of soil moisture.