• Communications for Statistical Applications and Methods
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• 제25권4호
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• pp.355-371
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• 2018

The two parameter negative exponential distribution has many practical applications in queuing theory such as the service times of agents in system, the time it takes before your next telephone call, the time until a radioactive practical decays, the distance between mutations on a DNA strand, and the extreme values of annual snowfall or rainfall; consequently, has many applications in reliability systems. This paper considers an estimation problem of stress-strength model with two parameter negative parameter exponential distribution. We introduce a maximum penalized likelihood method, Bayes estimator using Lindley approximation to estimate stress-strength model and compare the proposed estimators with regular maximum likelihood estimator for complete data. We also introduce a maximum penalized likelihood method, Bayes estimator using a Markov chain Mote Carlo technique for incomplete data. A Monte Carlo simulation study is performed to compare stress-strength model estimates. Real data is used as a practical application of the proposed model.

• 대한교통학회지
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• 제18권5호
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• pp.7-16
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• 2000

본 논문은 고속도로사고의 시간-공간적 반복성을 검증하고 이러한 현상의 원인을 규명하는 연구이다. 이를 위해 중부고속도로를 대상으로 계절성 유무검증과 계절성인자의 요인분석을 위한 계절군집별 모형을 개발, 해석하였다. 먼저 자가조직지도와 사고지표(월평균 사고율과 월평균 대물피해환산치) 분석을 통해, 국내 고속도로사고의 계절성이 존재하는 것을 확인하였으며, 집합적 계층적 군집분석기법을 사용하여 적정 계절군집수를 분석한 결과 겨울군집 봄가을군집, 여름군집의 3개 군집으로 분리되었다. 또한 해당 군집의 대표값은 겨울군집이 사상 자수와 사고차량수는 적은 반면 사고의 치명도는 매우 높은 것으로 나타났으며, 여름군집은 사상자수와 사고차량수는 많은 반면, 사고의 치명도는 다소 낮은 것으로 나타났다. 또한 계절군집별로 회귀모형식을 개발하여 계절군집별 사고특성을 검토한 결과, 계절성 유발인자(교통량, 안개, 결빙일수, 강설량, 강우량)와 계절군집의 사고가 매우 밀접한 관계를 가지고 있었으며 이들의 차이에 따라 국외 또는 지역별로 계절성의 특성이 다소 달라지는 것으로 분석되었다. 아울러, 이러한 연구결과는 사고다발지점선정기법, 사고예측 및 기술 모형 개발, 안전 관리를 위한 재원의 배분문제 등의 사고안전관리계획을 합리화하는 기초자료로 활용되리라 판단된다.

• 대기
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• 제23권2호
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• pp.143-160
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• 2013

The effect of 3DVAR (Three Dimension Variational data Assimilation) was examined by comparing observation and the simulations of CNTL (to which data assimilation was not applied) and ALL (to which data assimilation was applied using ground observation data and radar data) for the case of a heavy snowfall event (case A) of 11-12 February 2011 in the Yeongdong region. In case A, heavy snow intensively came in the Yeongdong coastal region rather than Daegwallyeong, in particular, around the Gangneung and Donghae regions with total precipitation in Bukgangneung at approximately 91 mm according to the AWS observation. It can be seen that compared to CNTL, ALL simulated larger precipitation along the Yeongdong coastline extending from Sokcho to Donghae while simulating smaller precipitation for inland areas including Daegwallyeong. On comparison of the total accumulated precipitations from simulations of CNTL and ALL, and the observed total accumulated precipitation, the positive effect of the assimilation of ground observation data and radar data could be identified in Bukgangneung and Donghae, on the other hand, the negative effect of the assimilation could be identified in the Daegwallyeong and Sokcho regions. In order to examine the average accuracy of precipitation prediction by CNTL and ALL for the entire Gangwon region including the major points mentioned earlier, the three hour accumulated precipitation from simulations of CNTL and ALL were divided into 5, 10, 15, 20, 25 and 30 mm/3hr and threat Scores were calculated by forecasting time. ALL showed relatively higher TSs than CNTL for all threshold values although there were some differences. That is, when considered generally based on the Gangwon region, the accuracy of precipitation prediction from ALL was improved somewhat compared to that from CNTL.

• 대기
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• 제29권4호
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• pp.451-461
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• 2019

Medium-range forecast is highly dependent on ensemble forecast data. However, operational weather forecasters have not enough time to digest all of detailed features revealed in ensemble forecast data. To utilize the ensemble data effectively in medium-range forecasting, representative weather patterns in East Asia in this study are defined. The k-means clustering analysis is applied for the objectivity of weather patterns. Input data used daily Mean Sea Level Pressure (MSLP) anomaly of the ECMWF ReAnalysis-Interim (ERA-Interim) during 1981~2010 (30 years) provided by the European Centre for Medium-Range Weather Forecasts (ECMWF). Using the Explained Variance (EV), the optimal study area is defined by 20~60°N, 100~150°E. The number of clusters defined by Explained Cluster Variance (ECV) is thirty (k = 30). 30 representative weather patterns with their frequencies are summarized. Weather pattern #1 occurred all seasons, but it was about 56% in summer (June~September). The relatively rare occurrence of weather pattern (#30) occurred mainly in winter. Additionally, we investigate the relationship between weather patterns and extreme weather events such as heat wave, cold wave, and heavy rainfall as well as snowfall. The weather patterns associated with heavy rainfall exceeding 110 mm day^-1 were #1, #4, and #9 with days (%) of more than 10%. Heavy snowfall events exceeding 24 cm day^-1 mainly occurred in weather pattern #28 (4%) and #29 (6%). High and low temperature events (> 34℃ and < -14℃) were associated with weather pattern #1~4 (14~18%) and #28~29 (27~29%), respectively. These results suggest that the classification of various weather patterns will be used as a reference for grouping all ensemble forecast data, which will be useful for the scenario-based medium-range ensemble forecast in the future.

• 대한원격탐사학회지
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• 제33권4호
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• pp.401-410
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• 2017

Estimation of snow depth using optical image is conducted by using correlation with Snow Cover Fraction (SCF). Various algorithms have been proposed for the estimation of snow cover fraction based on Normalized Difference Snow Index (NDSI). In this study we tested linear, quadratic, and exponential equations for the generation of snow cover fraction maps using data from the Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua satellite in order to evaluate their applicability to the complex terrain of South Korea and to search for improvements to the estimation of snow depth on this landscape. The results were validated by comparison with in-situ snowfall data from weather stations, with Root Mean Square Error (RMSE) calculated as 3.43, 2.37, and 3.99 cm for the linear, quadratic, and exponential approaches, respectively. Although quadratic results showed the best RMSE, this was due to the limitations of the data used in the study; there are few number of in-situ data recorded on the station at the time of image acquisition and even the data is mostly recorded on low snowfall. So, we conclude that linear-based algorithms are better suited for use in South Korea. However, in the case of using the linear equation, the SCF with a negative value can be calculated, so it should be corrected. Since the coefficients of the equation are not optimized for this area, further regression analysis is needed. In addition, if more variables such as Normalized Difference Vegetation Index (NDVI), land cover, etc. are considered, it could be possible that estimation of national-scale snow depth with higher accuracy.

• Current Photovoltaic Research
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• 제4권4호
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• pp.159-163
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• 2016

Piled snow upon PV module interferes with Photoelectric Effect process through photovoltaic directly. As a result of this phenomenon, its generation efficiencies keep decreasing or are stuck at zero power generating status. In addition, PV facilities have been installed on those places such as water surface, roof-top, and other isolated places, dealing with conditions of "Securing high REC weighted value", "Difficulty of securing land" and so forth. Through this study, we are able to actualize the function of heating over PV modules when it snows. We adopted laminating method through heating film and modules, guaranteeing warranty more than for 25 years. Also we are trying remote control systemically, not by hardware control, to run parallel with automatic driving and monitoring system which enable to control operation time, insolation, amount of snowfall automatically. We applied analysis of actual proof to both snow removal PV system and general PV power system, and these led to bear power consumption analysis while snow-removing, and its comparison after finishing the task as "One stone, two birds." In the long run, we could carry out economic analysis against snow removal system, and this helps to verify the most maximized control method for snow removal conditons on a basis of weather information. this study shall let prevent people from negligent accidents, and improve power generation problems as mentioned from the top. Ultimately, we expect to apply this system to heavy snowfall regions in winter season in spite of its limited system installaion in Korean territory, initially.

• 한국환경과학회지
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• 제19권10호
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• pp.1187-1201
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• 2010

The formation and development conditions of the cloud streets over the yellow sea by the Cold Surge of Siberian Anticyclone Expansion which produce the heavy snowfall events over the southwestern coast, Honam District of the Korean peninsula, has been investigated through analyses of the three dimensional snow cloud structures by using the CAPPI, RHI, VAD and VVP data of X-band Radar at Muan Weather Observatory and S-band Radar at Jindo Weather Station. The data to be used are obtained from January 04, 2003, when heavy snow storm hits on Gwangju and Honam District. The PPI Radar images show that the cloud bands distribute in perpendicular to the expansion direction of the high pressure and that the radius of cloud cells is about 5~8 km with 20~30 dBz and distance between each cell is about 10 km. And but the vertical Radar images show that the cloud street is a small scale convective type cloud within height of about 3 km where a stable layer exists. From the VVP images, the time period of the high pressure expansion, the moving direction and development stages of the system are delineated. Finally, the vertical distribution of wind direction is fairly constants, while the wind speed sheer increases with altitude to 3 km.

• 대한원격탐사학회지
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• 제29권3호
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• pp.315-324
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• 2013

Spatial information of snow cover and depth distribution is a key component for snowmelt runoff modeling. Wide snow cover areas can be extracted from NOAA AVHRR or Terra MODIS satellite images. In this study eight sets of annual snow cover data (1997-2006) in two mountainous watersheds (A: Chungju-Dam and B: Soyanggang-Dam) were extracted using NOAA AVHRR images. The distribution of snow depth within the Snow Cover Area (SCA) was generated using snowfall data from ground meteorological observation stations. Snow depletion characteristics for the two watersheds were analyzed snow distribution time series data. The decreased pattern of SCA can be expressed as a logarithmic function; the determination coefficients were 0.62 and 0.68 for the A and B watersheds, respectively. The SCA decreased over 70% within 10 days from the time of maximum SCA.

• 산업기술연구
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• 제25권B호
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• pp.11-18
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• 2005

When it is raining or snowing, floating debris flows into a lake and raises its turbidity. High level of turbidity in the lake often causes eutrophication, which pollutes the water. In order to collect and present some essential information for effective management plan for water quality, we carried out our research at the watershed of Mandae-cheon located at the upstream of Soyang Lake. We examined water quality at the time of rainfall or snowfall to analyze the changes in the amount of suspend soils(SS) in the lake.

• 국제학술발표논문집
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• The 6th International Conference on Construction Engineering and Project Management
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• pp.618-619
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• 2015

The costs due to the maintenance and its method of national road have increased every year, and the maintenance costs have become higher due to the maintenance method without considering the characteristics of each region. This study is a preliminary study regarding the determination of maintenance method according to the characteristics of each region by applying variables (e.g. serviced time, traffic volume, average temperature, maximum temperature, minimum temperature, rainfall, and snowfall) to decide the maintenance and its method through the correlation analysis of road maintenance and repair period for each regional agency using the maintenance history data of national road management agency.