• 천문학회보
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• 제39권1호
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• pp.75.2-75.2
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• 2014

We have developed a set of daily solar flare peak flux forecast models using the multiple linear regression (MLR), the auto regression (AR), and artificial neural network (ANN) methods. We consider input parameters as solar activity data from January 1996 to December 2013 such as sunspot area, X-ray flare peak flux, weighted total flux $T_F=1{\times}F_C+10{\times}F_M+100{\times}F_X$ of previous day, mean flare rates of a given McIntosh sunspot group (Zpc), and a Mount Wilson magnetic classification. We compute the hitting rate that is defined as the fraction of the events whose absolute differences between the observed and predicted flare fluxes in a logarithm scale are ${\leq}$ 0.5. The best three parameters related to the observed flare peak flux are as follows: weighted total flare flux of previous day (r=0.5), Mount Wilson magnetic classification (r=0.33), and McIntosh sunspot group (r=0.3). The hitting rates of flares stronger than the M5 class, which is regarded to be significant for space weather forecast, are as follows: 30% for the auto regression method and 69% for the neural network method.

• Communications for Statistical Applications and Methods
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• 제30권1호
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• pp.95-107
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• 2023

Purpose: The statistical analysis of point processes on linear networks is a recent area of research that studies processes of events happening randomly in space (or space-time) but with locations limited to reside on a linear network. For example, traffic accidents happen at random places that are limited to lying on a network of streets. This paper applies techniques developed for point processes on linear networks and the tools available in the R-package spatstat to estimate the intensity of traffic accidents in Leon County, Florida. Methods: The intensity of accidents on the linear network of streets is estimated using log-linear Poisson models which incorporate cubic basis spline (B-spline) terms which are functions of the x and y coordinates. The splines used equally-spaced knots. Ten different models are fit to the data using a variety of covariates. The models are compared with each other using an analysis of deviance for nested models. Results: We found all covariates contributed significantly to the model. AIC and BIC were used to select 9 as the number of knots. Additionally, covariates have different effects such as increasing the speed limit would decrease traffic accident intensity by 0.9794 but increasing the number of lanes would result in an increase in the intensity of traffic accidents by 1.086. Conclusion: Our analysis shows that if other conditions are held fixed, the number of accidents actually decreases on roads with higher speed limits. The software we currently use allows our models to contain only spatial covariates and does not permit the use of temporal or space-time covariates. We would like to extend our models to include such covariates which would allow us to include weather conditions or the presence of special events (football games or concerts) as covariates.

• 천문학논총
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• 제38권3호
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• pp.111-123
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• 2023

In this study, quantitative analysis is attempted on data collected from Chilgapsan Observatory Star Park in Cheongyang-gun, Chungcheongnam-do. The aim of this experimental study in which quantitative analysis of the Astronomical Science Museum in Korea is conducted is to investigate its current situation and secure basic data. As of July 31, 2023, it has had 283,931 cumulative visitors in total. It had the largest number of visitors when it opened (2009 year), after which their number reduced steadily until the pandemic (COVID-19, 2020-2022). Recently, however, the number of visitors has increased. Generally, the number of visitors is highest in August (20.8%) and least in April (4.1%). The visit rate is higher on weekends (Saturday and Sunday) than on weekdays (Monday-Friday), and groups comprise only about 5.3% of the total number of visitors. Moreover, it can be confirmed that the number of visitors increases sharply during events. Finally, it was confirmed that the visit rate was unaffected by weather conditions. Considering these results, we propose the following strategies: 1) Establish a special program that reflects "the weekend effect." 2) Prepare a plan to attract group visitors during the weekdays using "the event effect." 3) Arrange alternative programs (e.g., experiential activities) that can be conducted indoors regardless of weather conditions. We think that our findings will help establish a roadmap for the direction the Astronomical Science Museum should take and aid in preparing a strategic foundation to preemptively respond to unexpected situations (e.g., pandemics).

• 천문학회보
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• 제42권2호
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• pp.62.1-62.1
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• 2017

In space weather forecast, it is important to determine three-dimensional properties of CMEs. Using 29 limb CMEs, we examine which cone type is close to a CME three-dimensional structure. We find that most CMEs have near full ice-cream cone structure which is a symmetrical circular cone combined with a hemisphere. We develop a full ice-cream cone model based on a new methodology that the full ice-cream cone consists of many flat cones with different heights and angular widths. By applying this model to 12 SOHO/LASCO halo CMEs, we find that 3D parameters from our method are similar to those from other stereoscopic methods (i.e., a triangulation method and a Graduated Cylindrical Shell model). In addition, we derive CME mean density (${\bar{\rho}_{CME}}={\frac{M_{total}}{V_{cone}}}$) based on the full ice-cream cone structure. For several limb events, we determine CME mass by applying the Solarsoft procedure (e.g., cme_mass.pro) to SOHO/LASCO C3 images. CME volumes are estimated from the full ice-cream cone structure. For the first time, we derive average CME densities as a function of CME height for several CMEs, which are well fitted to power-law functions. We will compare densities (front and average) of geoeffective CMEs and their corresponding ICME ones.

• Journal of Astronomy and Space Sciences
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• 제37권1호
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• pp.43-50
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• 2020

We have developed an algorithm for tracking coronal mass ejection (CME) propagation that allows us to estimate CME speed and its arrival time at Earth. The algorithm may be used either to forecast the CME's arrival on the day of the forecast or to update the CME tracking information for the next day's forecast. In our case study, we successfully tracked CME propagation using the algorithm based on g-values of interplanetary scintillation (IPS) observation provided by the Institute for Space-Earth Environmental Research (ISEE). We were able to forecast the arrival time (Δt = 0.30 h) and speed (Δv = 20 km/s) of a CME event on October 2, 2000. From the CME-interplanetary CME (ICME) pairs provided by Cane & Richardson (2003), we selected 50 events to evaluate the algorithm's forecast capability. Average errors for arrival time and speed were 11.14 h and 310 km/s, respectively. Results demonstrated that g-values obtained continuously from any single station observation were able to be used as a proxy for CME speed. Therefore, our algorithm may give stable daily forecasts of CME position and speed during propagation in the region of 0.2-1 AU using the IPS g-values, even if IPS velocity observations are insufficient. We expect that this algorithm may be widely accepted for use in space weather forecasting in the near future.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2018년도 학술발표회
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• pp.154-154
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• 2018

To interpret the climate projections for the future as well as present, recognition of the consequences of the climate internal variability and quantification its uncertainty play a vital role. The Korean Peninsula belongs to the Far East Asian Monsoon region and its rainfall characteristics are very complex from time and space perspective. Its internal variability is expected to be large, but this variability has not been completely investigated to date especially using models of high temporal resolutions. Due to coarse spatial and temporal resolutions of General Circulation Models (GCM) projections, several studies adopted dynamic and statistical downscaling approaches to infer meterological forcing from climate change projections at local spatial scales and fine temporal resolutions. In this study, stochastic downscaling methodology was adopted to downscale daily GCM resolutions to hourly time scale using an hourly weather generator, the Advanced WEather GENerator (AWE-GEN). After extracting factors of change from the GCM realizations, these were applied to the climatic statistics inferred from historical observations to re-evaluate parameters of the weather generator. The re-parameterized generator yields hourly time series which can be considered to be representative of future climate conditions. Further, 30 ensemble members of hourly precipitation were generated for each selected station to quantify uncertainty. Spatial map was generated to visualize as separated zones formed through K-means cluster algorithm which region is more inconsistent as compared to the climatological norm or in which region the probability of occurrence of the extremes event is high. The results showed that the stations located near the coastal regions are more uncertain as compared to inland regions. Such information will be ultimately helpful for planning future adaptation and mitigation measures against extreme events.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권1호
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• pp.216-238
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• 2023

In intelligent transportation systems, traffic management is an important task. The accurate forecasting of traffic characteristics like flow, congestion, and density is still active research because of the non-linear nature and uncertainty of the spatiotemporal data. Inclement weather, such as rain and snow, and other special events such as holidays, accidents, and road closures have a significant impact on driving and the average speed of vehicles on the road, which lowers traffic capacity and causes congestion in a widespread manner. This work designs a model for multivariate short-term traffic congestion prediction using SLSTM_AE-BiLSTM. The proposed design consists of a Bidirectional Long Short Term Memory(BiLSTM) network to predict traffic flow value and a Convolutional Neural network (CNN) model for detecting the congestion status. This model uses spatial static temporal dynamic data. The stacked Long Short Term Memory Autoencoder (SLSTM AE) is used to encode the weather features into a reduced and more informative feature space. BiLSTM model is used to capture the features from the past and present traffic data simultaneously and also to identify the long-term dependencies. It uses the traffic data and encoded weather data to perform the traffic flow prediction. The CNN model is used to predict the recurring congestion status based on the predicted traffic flow value at a particular urban traffic network. In this work, a publicly available Caltrans PEMS dataset with traffic parameters is used. The proposed model generates the congestion prediction with an accuracy rate of 92.74% which is slightly better when compared with other deep learning models for congestion prediction.

• 한국측량학회지
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• 제32권5호
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• pp.469-479
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• 2014

네트워크 RTK 기술은 전리층 및 대류층 지연, 위성 궤도력 오차 등과 같은 거리에 종속된 오차의 보정모델링을 통해 GNSS 측위 정확도를 향상할 수 있는 기법이다. 본 연구에서는 전리층 교란의 극대화 시기인 Cycle24 기간 중, 인천지역 내 20점의 통합기준점을 대상으로 N-RTK (VRS 및 FKP) 측량을 실시하고 초기화시간, 성분별 측위정확도 및 좌표 교차를 비교 분석하였다. 연구결과, 측위정확도는 VRS가 FKP에 비해 우수하였고 두 기법 모두, 고도성분은 수평성분에 비해 2배 이상의 표준편차를 보였는데 이는 전자밀도 변동에 따른 전리층교란과 굴절지수의 변동으로 발생되는 대류층의 요동에 따른 것으로 보인다. 각 통합기준점에서 기법별 초기화는 VRS가 FKP에 비해 빠르게 수렴되었다. 이는 N-RTK를 위한 표준화된 고압축 전송형식의 활용과 국내 이동 통신 인프라에 의한 기준국 보정신호의 신호지연이 최소라는 고려 하에서 두 기법간의 기본원리의 차이, 서로 다른 보정 기준망에 따른 상이한 오차특성 및 FKP 보정값의 비선형 특성에 기인된 것으로 분석된다. 특히, 태양흑점폭발과 플레어로 인하여 우주전파환경의 변화가 발생되는 동안에 정확도의 저하, 초기화시간의 연장, 관측도중 재초기화, 심한 경우 초기화 실패 등의 현상이 발생됨을 확인할 수 있었다.

• 한국농공학회논문집
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• 제61권3호
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• pp.1-14
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• 2019

North Korea has frequently suffered from extreme agricultural crop droughts, which have led to food shortages, according to the Food and Agriculture Organization (FAO). The increasing frequency of extreme droughts, due to global warming and climate change, has increased the importance of enhancing the national capacity for drought management. Historically, a meteorological drought index based on data collected from weather stations has been widely used. But it has limitations in terms of the distribution of weather stations and the spatial pattern of drought impacts. Satellite-based data can be obtained with the same accuracy and at regular intervals, and is useful for long-term change analysis and environmental monitoring and wide area access in time and space. The Evaporative Stress Index (ESI), a satellite-based drought index using the ratio of potential and actual evaporation, is being used to detect drought response as a index of the droughts occurring rapidly over short periods of time. It is more accurate and provides faster analysis of drought conditions compared to the Standardized Precipitation Index (SPI), and the Palmer Drought Severity Index (PDSI). In this study, we analyze drought events during 2015-2017 in North Korea using the ESI satellite-based drought index to determine drought response by comparing with it with the SPI and SPEI drought indices.

• 한국소음진동공학회논문집
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• 제17권4호
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• pp.342-349
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• 2007

Following the 2002 World-Cup held in Korea, studies have been actively conducted on plans to utilize all-weather stadiums of fine figures, where large-scale spaces are available for various utilizations. In Japan, dome-type stadiums have been built and are utilizing across the whole nation not only for sports events but also variety of other large-scale events. PTFE(poly tetra fluoro ethylene) is one of the membrane material mainly used for the outer ceiling surface of membrane structures. However, there has not been enough research on the acoustical properties of PTFE membrane material which has been widely used in the multi-purpose stadiums. In this study, air permeability values and sound absorption coefficient of PTFE membrane materials were measured and evaluated in the gymnasium. From the results of measurements of sound absorption coefficient and air permeability of inner membrane materials, it was found that the sound absorption coefficient was good in the air permeability range of $5{\sim}15\;cc/cm^2/s$. Also the relation ship between air permeability and sound absorption coefficient was very high and the sound absorption coefficient was the highest in the range of $6{\sim}9\;cc/cm^2/s$. Secondly, an analysis on the measurements sound absorption characteristics of inner membrane material reveals that the overall sound absorption coefficient was stabilized(higher than 0.5 throughout the whole frequency bands) when the air space behind the membrane material was deeper than 600 mm. When PTFE sound absorptive membrane material was installed in the ceiling of gymnasium, it was confirmed that sound absorptive membrane material can reduce reverberation and increase speech intelligibility in the gymnasium.