• Journal of KIISE:Software and Applications
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• v.34 no.10
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• pp.929-936
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• 2007

The novel vision-based scheme for real-time extracting traffic parameters is proposed in this paper. Detecting and tracking of vehicle is processed at local region installed by operator. Local region is divided to segmented regions by edge and frame difference, and the segmented regions are classified into vehicle, road, shadow and headlight by statistical and geometrical features. Vehicle is detected by the result of the classification. Traffic parameters such as velocity, length, occupancy and distance are estimated by tracking using template matching at local region. Because background image are not used, it is possible to utilize under various conditions such as weather, time slots and locations. It is performed well with 90.16% detection rate in various databases. If direction, angle and iris are fitted to operating conditions, we are looking forward to using as the core of traffic monitoring systems.

• Journal of Korean Society on Water Environment
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• v.28 no.3
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• pp.460-466
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• 2012

Perchlorate is used in a number of applications as an oxidizer in solid propellants, munitions and fireworks and is one of the endocrine disrupting chemicals, which interferes with iodide uptake into the thyroid gland. The purpose of this study was to investigate perchlorate occurrence and distribution with a results of analysis of 94 samples collected from military facilities in Korea from October 11 to October 23, 2011. Overall, among all of the 94 samples analyzed, perchlorate was detected in 6.4% of the total number of samples above $4{\mu}g/L$ (minimum reported limit) and the average concentration was $26.1{\mu}g/L$ and the maximum concentration was $107.7{\mu}g/L$ which was observed in surface water near manufacturing site of ammunition. By site classification, perchlorate was detected at one site in 4 manufacturing sites of ammunition and the maximum concentration was $107.7{\mu}g/L$ which was six times higher than that in guideline for perchlorate in Nakdong River and resulted from point source discharge. Perchlorate was detected at 3 sites in 78 measurements for shooting area and the maximum concentration was $12.4{\mu}g/L$ which was collected in dringking water and perchlorate in another sample was detected above MRL in shooting area was collected right away after shooting. These results showed that long term monitoring was needed considering weather conditions and shooting schedules.

• Journal of The Korean Astronomical Society
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• v.45 no.2
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• pp.31-38
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• 2012

In this study we apply Support Vector Machine (SVM) to the prediction of geo-effective halo coronal mass ejections (CMEs). The SVM, which is one of machine learning algorithms, is used for the purpose of classification and regression analysis. We use halo and partial halo CMEs from January 1996 to April 2010 in the SOHO/LASCO CME Catalog for training and prediction. And we also use their associated X-ray flare classes to identify front-side halo CMEs (stronger than B1 class), and the Dst index to determine geo-effective halo CMEs (stronger than -50 nT). The combinations of the speed and the angular width of CMEs, and their associated X-ray classes are used for input features of the SVM. We make an attempt to find the best model by using cross-validation which is processed by changing kernel functions of the SVM and their parameters. As a result we obtain statistical parameters for the best model by using the speed of CME and its associated X-ray flare class as input features of the SVM: Accuracy=0.66, PODy=0.76, PODn=0.49, FAR=0.72, Bias=1.06, CSI=0.59, TSS=0.25. The performance of the statistical parameters by applying the SVM is much better than those from the simple classifications based on constant classifiers.

• Journal of Korean Society of Forest Science
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• v.86 no.1
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• pp.25-34
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• 1997

The optimum global natural vegetation mapping(GNVM) system was selected as a series of the study to estimate potential forest area of the globe. To select the system, three types of GNVM systems which are simple system with Light Climatic Dataset(LCD), altitude-allowed system with LCD and altitude-allowed system with Heavy Climatic Dataset(HCD) were established and compared. The three GNVM systems spherically interpolate such spotty climate data as those observed at weather stations the world over onto $1^{\circ}{\times}1^{\circ}$ grid points, product vegetation type classification, and produce a potential natural vegetation(PNV) map and a PNV area. As a result of comparison with three GNVM systems, altitude-allowed LCD system represented natural vegetation distribution better than other versions. The difference between the simple system versus the one with altitude allowance indicated that the simple version tends to over-represent the warmer climate areas and under-represent cold and hostile climate areas. In the difference between altitude-allowed versions of LCD and HCD, HCD version tended to overestimate moist climate areas and to underestimate dry climate areas.

• Journal of the Korean Association of Geographic Information Studies
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• v.7 no.4
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• pp.57-64
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• 2004

In order to decrease the area damaged by forest fires and to prevent the occurrence of forest fires, the forest fire danger rating system was developed to estimate forest fire risk by means of weather, topography, and forest type. Forest fires occurrence prediction needs to improve continually. Logistic regression and spatial analysis was used in developing the forest fire occurrence probability model. The forest fire danger index in accordance to the probability of forest fire occurrence was used in the classification of forest fire occurrence risk regions.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.38 no.3
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• pp.269-279
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• 2020

Traffic estimation mainly involves surveying equipment such as automatic vehicle classification, vehicle detection system, toll collection system, and personnel surveys through CCTV (Closed Circuit TeleVision), but this requires a lot of manpower and cost. In this study, we proposed a method of estimating traffic volume using deep learning and stereo CCTV to overcome the limitation of not detecting the entire vehicle in case of single CCTV. COCO (Common Objects in Context) dataset was used to train deep learning models to detect vehicles, and each vehicle was detected in left and right CCTV images in real time. Then, the vehicle that could not be detected from each image was additionally detected by using affine transformation to improve the accuracy of traffic volume. Experiments were conducted separately for the normal road environment and the case of weather conditions with fog. In the normal road environment, vehicle detection improved by 6.75% and 5.92% in left and right images, respectively, than in a single CCTV image. In addition, in the foggy road environment, vehicle detection was improved by 10.79% and 12.88% in the left and right images, respectively.

• Journal of the Korea Institute of Military Science and Technology
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• v.20 no.6
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• pp.758-766
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• 2017

In this paper, we propose a robust duality of CNN(Du-CNN) method which can classify the target and clutter in coastal environment for IR Imaging Sensor. In coastal environment, there are various clutter that have many similarities with real target due to diverse change of air temperature, water temperature, weather and season. Also, real target have various feature due to the same reason. Thus, the proposed Du-CNN method adopts human's multiple personality utilization and CNN technique to learn and classify target and clutter. This method has an advantage of the real time operation. Experimental results on sampled dataset of real infrared target and clutter demonstrate that the proposed method have better success rate to classify the target and clutter than general CNN method.

• Korea and Global Affairs
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• v.3 no.2
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• pp.131-154
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• 2019

The state behavior has a strong consequence with the national strategic culture. However, different scholars hold different views on the classification of the national strategic culture. As one of the most significant land neighbors in West China, Pakistan is China's all-weather strategic cooperative partner. Understanding Pakistan's strategic culture will not only help understand Pakistan's national policies and state behavior, but also help deepen China-Pakistan cooperation. Cutting in from the perspectives of geography, social economy, culture, history and military, the author believes that the formation mechanism of Pakistan's strategic culture is mainly affected by the following four factors: geopolitical environment, production mode and lifestyle, cultural tradition, historical experience and diplomatic relations. The analysis has found that Pakistan's strategic culture has been shaped by Islam and can be classified as an outward-oriented strategic culture, the state behavior also shows a strong Islamic identity.

• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.112-112
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• 2018

최근 이상기후로 인한 국부적인 혹은 광역적인 가뭄이 빈번하게 발생하고 있는 추세이며 발생횟수 뿐 아니라 가뭄 심도 및 지속기간이 과거보다 크게 증가하여 그에 따른 피해가 커질 것으로 예측되고 있다. 특히, 2014~2015년도의 유례없는 가뭄으로 인해 저수지 용수공급이 제한되면서 많은 농가들이 피해를 입었다. 본 연구의 목적은 전국 농업용 저수지를 대상으로 기상청 3개월 예보자료를 활용 할 수 있는 농업용 저수지 저수율 다중선형 회귀 모형을 개발하여 저수율 전망정보를 생산하는 것이다. 본 연구에서는 전국에 적용 가능한 저수율 다중선형 회귀 모형개발을 위해 5개의 기상요소(강수량, 최고기온, 최저기온, 평균기온, 평균풍속)와 관측 저수지 저수율을 활용했다. 기상자료는 2002년부터 2017년까지의 기상청 63개 지상관측소로부터 기상관측자료를 수집하였다. 본 연구에서는 저수율 전망 단계를 세 단계로 나누었다. 첫 번째 단계로 농어촌공사에서 전국 511개 용수구역을 대상으로 군집분석 및 의사결정나무 분석을 통해 제시한 65개 대표저수지를 대상으로 기상자료 및 관측 저수율 자료를 이용하여 다중선형 회귀분석을 실시하였다. 수집한 기상요소와 저수율을 독립변수로 하여 월별 회귀식을 산정한 결과 결정계수($R^2$)는 0.51~0.95로 나타났다. 두 번째 단계로 대표저수지의 회귀분석 결과를 전국의 저수지로 확대하기 위해 나이브 베이즈 분류법을 적용하여 전국 3098개의 저수지를 65의 군집으로 분류하고 각각의 군집에 해당되는 월별 회귀식을 산정하였다. 마지막으로 전국 저수지로 산정된 회귀식과 농업 가뭄 예측을 위해 기상청의 GS5(Global Seasonal Forecasting System 5) 3개월 예보자료를 수집하여 회귀식에 적용해 2017년 전국 저수지의 3개월 저수율 전망정보를 생산하였다. 본 연구의 전국 저수지 군집결과 기반의 저수율 전망기술은 2017년도 관측 저수율과 비교한 결과 유의한 상관성을 나타냈으며 이 결과는 추후 농업용 저수지의 물 공급 및 농업가뭄 전망 자료로서 이용이 가능할 것으로 판단된다.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.30 no.4
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• pp.275-281
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• 2019

A synthetic aperture radar(SAR) satellite is more effective than an optical satellite for target analysis because an SAR satellite can provide two-dimensional electromagnetic scattering distribution of a target during all-weather and day-and-night operations. To conduct target analysis while considering the earth observation interval of an SAR satellite, observing a specific area as wide as possible would be advantageous. However, wider the observation area, worse is the resolution of the associated SAR satellite image. Although conventional methods for improving the resolution of radar images can be employed for addressing this issue, few studies have been conducted for improving the resolution of SAR satellite images and analyzing the performance. Hence, in this study, the applicability of conventional methods to SAR satellite images is investigated. SAR target detection was first applied to Korea Multipurpose Satellite-5(KOMPSAT-5) SAR images provided by Korea Aerospace Research Institute for extracting target responses. Extrapolation, RELAX, and MUSIC algorithms were subsequently applied to the target responses for improving the resolution, and the corresponding performance was thereby analyzed.