• Journal of Astronomy and Space Sciences
• /
• v.40 no.1
• /
• pp.11-18
• /
• 2023

The ionosphere is one of the key components of the near-Earth's space environment and has a practical consequence to the human society as a nearest region of the space environment to the Earth. Therefore, it becomes essential to specify and forecast the state of the ionosphere using both the observations and numerical models. In particular, numerical modeling of the ionosphere is a prerequisite not only for better understanding of the physical processes occurring within the ionosphere but also for the specification and forecast of the space weather. There are several approaches for modeling the ionosphere, including data-based empirical modeling, physics-based theoretical modeling and data assimilation modeling. In this review, these three types of the ionospheric model are briefly introduced with recently available models. And among those approaches, fundamental aspects of the physics-based ionospheric model will be described using the basic equations governing the mid-latitude ionosphere. Then a numerical solution of the equations will be discussed with required boundary conditions.

• Journal of Information Processing Systems
• /
• v.19 no.1
• /
• pp.130-138
• /
• 2023

The traffic flow in an urban area is affected by the date, weather, and regional traffic flow. The existing methods are weak to model the dynamic road network features, which results in inadequate long-term prediction performance. To solve the problems regarding insufficient capacity for dynamic modeling of road network structures and insufficient mining of dynamic spatio-temporal features. In this study, we propose a novel traffic flow prediction framework called shared spatio-temporal attention convolution optimization network (SSTACON). The shared spatio-temporal attention convolution layer shares a spatio-temporal attention structure, that is designed to extract dynamic spatio-temporal features from historical traffic conditions. Subsequently, the graph optimization module is used to model the dynamic road network structure. The experimental evaluation conducted on two datasets shows that the proposed method outperforms state-of-the-art methods at all time intervals.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2009.05a
• /
• pp.120-124
• /
• 2009

기후변화로 인한 사회, 경제, 자원, 환경, 수자원 등에 영향분석은 세계적인 연구 트렌드로 자리 잡고 있다. 다양한 모형들이 기후변화 영향을 효과적으로 평가하기 위해서 개발되고 있으나 주로 강우-유출 모형을 통한 유출의 변화 특성을 모의하는데 대부분의 연구가 초점을 맞추고 있다. 그러나 기본적으로 사용되는 강수량자료의 정확한 추정이 기후변화 연구에서 가장 중요하다고 해도 과언이 아니다. 이러한 관점에서 GCM 기후모형으로부터 유도된 기후변화 시나리오로부터 여러 단계로 가공하여 모형의 입력 자료로 사용하기 위한 강수량 자료를 생산하게 된다. 이러한 과정을 총칭해서 Downscaling이라고 한다. 본 연구에서는 기후모형으로 얻은 정보를 유역단위의 수문시나리오로 변환하기 위한 통계학적 Downscaling의 연구이론 변천 상황을 종합적으로 검토하고 각 모형이 갖는 장단점을 분석하고자 한다. 즉, Weather Generator, Single-site Nonstationary Markov Chain, Multi-site Nonstationary Markov Chain, Multi-site Weather State Based Markov Model 등 다양한 모델의 변화 및 진보 과정을 살펴보고 실제 국내 유역에 적용하여 모형의 타당성을 평가해보고자 한다. 이를 위해 IPCC 기후변화 시나리오를 활용하였으며 일강수량자료계열의 특성치, 극치수문량 변동특성 등 기후변화에 따른 영향분석을 일부 실시하여 분석하였다.

• Journal of the Optical Society of Korea
• /
• v.19 no.5
• /
• pp.437-443
• /
• 2015

In this paper, we analyze the performance of the all-optical multihop radio over a free space optical (RoFSO) communication system with amplify-and-forward (AF) relays under varying weather conditions. The proposed channel model considers the propagation loss, attenuation and atmospheric fading modeled by the Gamma-Gamma (GG) distribution. Both the amplified spontaneous emission (ASE) noise in the all-optical relays and the background noise projected onto receiver apertures have been considered in the analysis. The lower bound analytical expressions for the end-to-end bit error rate (BER) and outage probability are derived for the multihop system employing the all-optical relays with the full channel state information (CSI). Meanwhile, the exact results for BER and outage probability are obtained via Monte Carlo simulation. Results indicate the performance of the proposed system will be improved by the multihop transmission technology. For a fixed number of relays, the BER and outage probability will be increased with the deterioration of the weather conditions.

• Solar Energy
• /
• v.17 no.3
• /
• pp.3-11
• /
• 1997

Recently the construction of atrium buildings has increased but along with it many problems in thermal environment have arised. since the exterior wall of glass, indoor temperature is greatly influenced by weather conditions and since the space volume is very large, the vertical air temperature is not uniform. So, in this study, a Vertical Temperature Distribution Model was developed to predict the vertical air temperature of an atrium and evaluate the effects of the design parameters on the air temperature distribution of an atrium. To consider the characteristics of the vertical air temperature distribution in an atrium, the Satosh Togari's Macroscopic Model was used basically for the calculation of the vertical air temperature distribution in large space and the solar radiation analysis model and natural ventilation analysis model in atrium. And to calculate the unsteady-state inside wall surface temperature(boundary condition), the finite difference method was used. For the verification of the developed temperature distribution program, numerical evaluation of air flow by the ${\kappa}-{\varepsilon}$ turbulence model and in-situ test was conducted in parallel. The results of this study, the developed temperature distribution program was seen to predict the thermal condition of the atrium very accurately.

• The Korean Journal of Applied Statistics
• /
• v.35 no.4
• /
• pp.543-552
• /
• 2022

When frost occurs, crops are directly damaged. When crops come into contact with low temperatures, tissues freeze, which hardens and destroys the cell membranes or chloroplasts, or dry cells to death. In July 2020, a sudden sub-zero weather and frost hit the Minas Gerais state of Brazil, the world's largest coffee producer, damaging about 30% of local coffee trees. As a result, coffee prices have risen significantly due to the damage, and farmers with severe damage can produce coffee only after three years for crops to recover, which is expected to cause long-term damage. In this paper, we tried to predict frost using frost generation data and weather observation data provided by the Korea Meteorological Administration to prevent severe frost. A model was constructed by reflecting weather factors such as wind speed, temperature, humidity, precipitation, and cloudiness. Using XGB(eXtreme Gradient Boosting), SVM(Support Vector Machine), Random Forest, and MLP(Multi Layer perceptron) models, various hyper parameters were applied as training data to select the best model for each model. Finally, the results were evaluated as accuracy(acc) and CSI(Critical Success Index) in test data. XGB was the best model compared to other models with 90.4% ac and 64.4% CSI, followed by SVM with 89.7% ac and 61.2% CSI. Random Forest and MLP showed similar performance with about 89% ac and about 60% CSI.

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.15 no.4
• /
• pp.68-80
• /
• 2016

This study proposes the traffic prediction and optimal traffic control system based on cell transmission model and genetic algorithm in cloud environment. The proposed prediction and control system consists of four parts. 1) Data preprocessing module detects and imputes the corrupted data and missing data points. 2) Data-driven traffic prediction module predicts the future traffic state using Multi-level K-Nearest Neighbor (MK-NN) Algorithm with stored historical data in SQL database. 3) Online traffic simulation module simulates the future traffic state in various situations including accident, road work, and extreme weather condition with predicted traffic data by MK-NN. 4) Optimal road control module produces the control strategy for large road network with cell transmission model and genetic algorithm. The results show that proposed system can effectively reduce the Vehicle Hours Traveled upto 60%.

• Journal of Korean Society of Coastal and Ocean Engineers
• /
• v.27 no.5
• /
• pp.358-363
• /
• 2015

As a preparation process for successful establishment of demonstration offshore wind farm, analyses have been made for working time at the construction site where working time is defined as the time available for marine operation to take place under given weather conditions. Data used are hourly wave and wind data from met mast, HeMOSU-1, and 3 hour numerical model data from Korea Meteorological Administration (KMA). Seasonal results show the minimum working time during winter and moderate during autumn and spring. The most working time was seen during summer on average. Monthly analyses show the most working time in May, June, and August which was higher than the working time in July and September. Working time reaches at steady state and no significant change was seen above wave height of 1.5 m and wind speed of 8 m/s.

• Journal of the Korean Society of Hazard Mitigation
• /
• v.8 no.3
• /
• pp.23-27
• /
• 2008

The new conjunctive surface-subsurface flow model at a large scale was developed by using a 1-D Diffusion Wave (DW) model for surface flow interacting with the 3-D Volume Averaged Soil-moisture Transport (VAST) model for subsurface flow for the comprehensive terrestrial water and energy predictions in Land Surface Models (LSMs). A selection of numerical implementation schemes is employed for each flow component. The 3-D VAST model is implemented using a time splitting scheme applying an explicit method for lateral flow after a fully implicit method for vertical flow. The 1-D DW model is then solved by MacCormack finite difference scheme. This new conjunctive flow model is substituted for the existing 1-D hydrologic scheme in Common Land Model (CLM), one of the state-of-the-art LSMs. The new conjunctive flow model coupled to CLM is tested for a study domain around the Ohio Valley. The simulation results show that the interaction between surface flow and subsurface flow associated with the flow routing scheme matches the runoff prediction with the observations more closely in the new coupled CLM simulations. This improved terrestrial hydrologic module will be coupled to the Climate extension of the next-generation Weather Research and Forecasting (CWRF) model for advanced regional, continental, and global hydroclimatological studies and the prevention of disasters caused by climate changes.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• v.1 no.1
• /
• pp.56-61
• /
• 2001

Activated sludge processes are broadly used in the biological wastewater treatment processes. The activated sludge processes are complex systems because of the many factors such as the variation of influent flowrate and ingredients, the complexity of biological reactions, and the various operation conditions. The main motivation o this research is to develop an intelligent control strategy for activated sludge process (ASP). ASP is a complex and nonlinear dynamic system owing to the characteristic of wastewater, the change in influent flowrate, weather conditions, and so on. The mathematical model of ASP also includes the uncertainty which is a ignored or unconsidered factor from process designers. The ASP model based on Matlabⓡ/Simulinkⓡ is developed in this paper. And the model performance is examined by IWA (International Water Association) and COST (European Cooperation in the filed of Scientific and Technical Research) data. The model tests derive steady-state results of 14 days. In this paper, fuzzy logic control approach is applied to handle DO concentrations. The fuzzy logic controller includes two inputs and one output to adjust air flowrate. The objective function for the optimization, in the implemented evolutionary strategy, is formed with focusing on improving the effluent quality and reducing the operating cost.