• Journal of Korean Society of Transportation
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• v.20 no.7
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• pp.107-116
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• 2002

Crash Cushion is a kind of safety facilities on roadside which acts the role of absorbing impact energy when vehicles are driven out of normal route such as Gore area of freeway off ramp. Criteria for severity index considering accident occurrence possibility are needed to have strong effect on installing the facilities. However, present criteria for establishing crash cushion design do not include such processes. Therefore, the paper presents two kinds of study to develop criteria for severity index. First of all, development of accident forecasting model on freeway off ramp is presented. The module is a relationship between accidents and road environment by negative binomial distribution (NB) which is called to reflect very well quality of accidents at Gore of crash cushion installed freeway Secondly, freeway exiting behavior model is developed because the human factor is the most important one. However, many literatures have shown between road environment and accidents which are more quantitative than human factor. The study supposed advanced process steps on actual freeway and analysed correlation between variables and accidents. The criteria for severity index is presented to determine whether to install or not by benefit cost analysis for each module. The standard for severity index will help to determine whether to install the crash cushion or not and to estimate severity for freeway and off ramp.

• Journal of the Korea Society of Computer and Information
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• v.28 no.11
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• pp.29-42
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• 2023

In this paper, we propose a method to enhance the prediction accuracy of solar irradiance for three major South Korean cities: Seoul, Busan, and Incheon. Our method entails the development of five generative models-vanilla GAN, CTGAN, Copula GAN, WGANGP, and TVAE-to generate independent variables that mimic the patterns of existing training data. To mitigate the bias in model training, we derive values for the dependent variables using random forests and deep neural networks, enriching the training datasets. These datasets are integrated with existing data to form comprehensive solar irradiance prediction models. The experimentation revealed that the augmented datasets led to significantly improved model performance compared to those trained solely on the original data. Specifically, CTGAN showed outstanding results due to its sophisticated mechanism for handling the intricacies of multivariate data relationships, ensuring that the generated data are diverse and closely aligned with the real-world variability of solar irradiance. The proposed method is expected to address the issue of data scarcity by augmenting the training data with high-quality synthetic data, thereby contributing to the operation of solar power systems for sustainable development.

• Journal of the Korean earth science society
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• v.42 no.6
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• pp.632-645
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• 2021

Satellite sea surface temperature (SST) composites provide important data for numerical forecasting models and for research on global warming and climate change. In this study, six types of representative SST composite database were collected from 2007 to 2018 and the characteristics of spatial structures of SSTs were analyzed in seas around the Korean Peninsula. The SST composite data were compared with time series of in-situ measurements from ocean meteorological buoys of the Korea Meteorological Administration by analyzing the maximum value of the errors and its occurrence time at each buoy station. High differences between the SST data and in-situ measurements were detected in the western coastal stations, in particular Deokjeokdo and Chilbaldo, with a dominant annual or semi-annual cycle. In Pohang buoy, a high SST difference was observed in the summer of 2013, when cold water appeared in the surface layer due to strong upwelling. As a result of spectrum analysis of the time series SST data, daily satellite SSTs showed similar spectral energy from in-situ measurements at periods longer than one month approximately. On the other hand, the difference of spectral energy between the satellite SSTs and in-situ temperature tended to magnify as the temporal frequency increased. This suggests a possibility that satellite SST composite data may not adequately express the temporal variability of SST in the near-coastal area. The fronts from satellite SST images revealed the differences among the SST databases in terms of spatial structure and magnitude of the oceanic fronts. The spatial scale expressed by the SST composite field was investigated through spatial spectral analysis. As a result, the high-resolution SST composite images expressed the spatial structures of mesoscale ocean phenomena better than other low-resolution SST images. Therefore, in order to express the actual mesoscale ocean phenomenon in more detail, it is necessary to develop more advanced techniques for producing the SST composites.

• Journal of Intelligence and Information Systems
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• v.23 no.4
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• pp.127-146
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• 2017

Services using artificial intelligence have begun to emerge in daily life. Artificial intelligence is applied to products in consumer electronics and communications such as artificial intelligence refrigerators and speakers. In the financial sector, using Kensho's artificial intelligence technology, the process of the stock trading system in Goldman Sachs was improved. For example, two stock traders could handle the work of 600 stock traders and the analytical work for 15 people for 4weeks could be processed in 5 minutes. Especially, big data analysis through machine learning among artificial intelligence fields is actively applied throughout the financial industry. The stock market analysis and investment modeling through machine learning theory are also actively studied. The limits of linearity problem existing in financial time series studies are overcome by using machine learning theory such as artificial intelligence prediction model. The study of quantitative financial data based on the past stock market-related numerical data is widely performed using artificial intelligence to forecast future movements of stock price or indices. Various other studies have been conducted to predict the future direction of the market or the stock price of companies by learning based on a large amount of text data such as various news and comments related to the stock market. Investing on commodity asset, one of alternative assets, is usually used for enhancing the stability and safety of traditional stock and bond asset portfolio. There are relatively few researches on the investment model about commodity asset than mainstream assets like equity and bond. Recently machine learning techniques are widely applied on financial world, especially on stock and bond investment model and it makes better trading model on this field and makes the change on the whole financial area. In this study we made investment model using Support Vector Machine among the machine learning models. There are some researches on commodity asset focusing on the price prediction of the specific commodity but it is hard to find the researches about investment model of commodity as asset allocation using machine learning model. We propose a method of forecasting four major commodity indices, portfolio made of commodity futures, and individual commodity futures, using SVM model. The four major commodity indices are Goldman Sachs Commodity Index(GSCI), Dow Jones UBS Commodity Index(DJUI), Thomson Reuters/Core Commodity CRB Index(TRCI), and Rogers International Commodity Index(RI). We selected each two individual futures among three sectors as energy, agriculture, and metals that are actively traded on CME market and have enough liquidity. They are Crude Oil, Natural Gas, Corn, Wheat, Gold and Silver Futures. We made the equally weighted portfolio with six commodity futures for comparing with other commodity indices. We set the 19 macroeconomic indicators including stock market indices, exports & imports trade data, labor market data, and composite leading indicators as the input data of the model because commodity asset is very closely related with the macroeconomic activities. They are 14 US economic indicators, two Chinese economic indicators and two Korean economic indicators. Data period is from January 1990 to May 2017. We set the former 195 monthly data as training data and the latter 125 monthly data as test data. In this study, we verified that the performance of the equally weighted commodity futures portfolio rebalanced by the SVM model is better than that of other commodity indices. The prediction accuracy of the model for the commodity indices does not exceed 50% regardless of the SVM kernel function. On the other hand, the prediction accuracy of equally weighted commodity futures portfolio is 53%. The prediction accuracy of the individual commodity futures model is better than that of commodity indices model especially in agriculture and metal sectors. The individual commodity futures portfolio excluding the energy sector has outperformed the three sectors covered by individual commodity futures portfolio. In order to verify the validity of the model, it is judged that the analysis results should be similar despite variations in data period. So we also examined the odd numbered year data as training data and the even numbered year data as test data and we confirmed that the analysis results are similar. As a result, when we allocate commodity assets to traditional portfolio composed of stock, bond, and cash, we can get more effective investment performance not by investing commodity indices but by investing commodity futures. Especially we can get better performance by rebalanced commodity futures portfolio designed by SVM model.

• Journal of the Korean earth science society
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• v.34 no.3
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• pp.209-223
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• 2013

The purpose of this study was to estimate the contributed concentration of each emission source to $CH_4$ by verifying the simulated concentration of $CH_4$ in the Korean peninsula, and then to compare the $CH_4$ emission used to the $CH_4$ simulation with that of a box model. We simulated the Weather Research Forecasting-Community Multiscale Air Quality (WRF-CMAQ) model to estimate the mean concentration of $CH_4$ during the period of April 1 to 22 August 2010 in the Korean peninsula. The $CH_4$ emissions within the model were adopted by the anthropogenic emission inventory of both the EDGAR of the global emissions and the GHG-CAPSS of the green house gases in Korea, and by the global biogenic emission inventory of the MEGAN. These $CH_4$ emission data were validated by comparing the $CH_4$ modeling data with the concentration data measured at two different location, Ulnungdo and Anmyeondo in Korea. The contributed concentration of $CH_4$ estimated from the domestic emission sources in verification of the $CH_4$ modeling at Ulnungdo was represented in about 20%, which originated from $CH_4$ sources such as stock farm products (8%), energy contribution and industrial processes (6%), wastes (5%), and biogenesis and landuse (1%) in the Korean peninsula. In addition, one that transported from China was about 9%, and the background concentration of $CH_4$ was shown in about 70%. Furthermore, the $CH_4$ emission estimated from a box model was similar to that of the WRF-CMAQ model.

• Korean Journal of Agricultural and Forest Meteorology
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• v.17 no.4
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• pp.384-398
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• 2015

In this paper, the high-resolution Weather Research and Forecasting/Noah-MultiParameterization (WRF/Noah-MP) modeling system is configured for the Cheongmicheon Farmland site in Korea (CFK), and its performance in land and atmospheric simulation is evaluated using the observed data at CFK during the 2014 special observation period (21 August-10 September). In order to explore the usefulness of turning on Noah-MP dynamic vegetation in midterm simulations of surface and atmospheric variables, two numerical experiments are conducted without dynamic vegetation and with dynamic vegetation (referred to as CTL and DVG experiments, respectively). The main results are as following. 1) CTL showed a tendency of overestimating daytime net shortwave radiation, thereby surface heat fluxes and Bowen ratio. The CTL experiment showed reasonable magnitudes and timing of air temperature at 2 m and 10 m; especially the small error in simulating minimum air temperature showed high potential for predicting frost and leaf wetness duration. The CTL experiment overestimated 10-m wind and precipitation, but the beginning and ending time of precipitation were well captured. 2) When the dynamic vegetation was turned on, the WRF/Noah-MP system showed more realistic values of leaf area index (LAI), net shortwave radiation, surface heat fluxes, Bowen ratio, air temperature, wind and precipitation. The DVG experiment, where LAI is a prognostic variable, produced larger LAI than CTL, and the larger LAI showed better agreement with the observed. The simulated Bowen ratio got closer to the observed ratio, indicating reasonable surface energy partition. The DVG experiment showed patterns similar to CTL, with differences for maximum air temperature. Both experiments showed faster rising of 10-m air temperature during the morning growth hours, presumably due to the rapid growth of daytime mixed layers in the Yonsei University (YSU) boundary layer scheme. The DVG experiment decreased errors in simulating 10-m wind and precipitation. 3) As horizontal resolution increases, the models did not show practical improvement in simulation performance for surface fluxes, air temperature, wind and precipitation, and required three-dimensional observation for more agricultural land spots as well as consistency in model topography and land cover data.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.35 no.5
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• pp.1135-1146
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• 2015

This thesis aims to improve the safety of bicycle traffic for activating the use of bicycle, main means of non-powered and non-carbon transportation in order to cope with worldwide crisis such as climate change and energy depletion and to implement sustainable traffic system. In this regard, I analyzed the problem of bicycle roads currently installed and operated, and developed the bicycle accident forecasting model. Following are the processes for this. First, this study presented the current status of bicycle road in Korea as well as accident data, collect the data on bicycle traffic accidents generated throughout the country for recent 3 years (2009~2011) and analyzed the features of bicycle traffic accidents based on the data. Second, this study selected the variable affecting the number of bicycle accidents through accident feature analysis of bicycle accidents at Jeollanam-do, and developed accident forecast model using the multiple regression analysis of 'SPSS Statistics 21'. At this time, the number of accidents due to extension per road types (crossing, crosswalk, other single road) was used. To verify the accident forecast model deduced, this study used the data on bicycle accident generated in Gwangju, 2011, and compared the prediction value with actual number of accidents. As a result, it was found out that reliability of accident forecast model was secured through reconciling with actual number of cases except certain data. Third, this study carried out field survey on the bicycle road as well as questionnaire on satisfaction of bicycle road and use of bicycle for analysis of bicycle road problems, and presented safety improvement measures for the problems deduced as well as bicycle activation plans. This study is considered to serve as the fundamental data for planning and reorganizing of bicycle road in the future, and expected to improve safety of bicycle users and to promote activation of bicycle use as the means of transportation.

• Korean Journal of Agricultural and Forest Meteorology
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• v.11 no.4
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• pp.192-205
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• 2009

The unexpected wind over the Mt. Hwawang on 9 February 2009 was deadly when many spectators were watching a traditional event to burn dried grasses and the fire went out of control due to the wind. We analyzed the fatal wind based on wind flow simulations over a digitized complex terrain of the mountain with a localized heating area using a three dimensional computational fluid dynamics model, CFD_NIMR_SNU (Computational Fluid Dynamics_National Institute of Meteorological Research_Seoul National University). Three levels of fire intensity were simulated: no fire, $300^{\circ}C$ and $600^{\circ}C$ of surface temperature at the site on fire. The surface heat accelerated vertical wind speed by as much as $0.7\;m\;s^{-1}$ (for $300^{\circ}C$) and $1.1\;m\;s^{-1}$ (for $600^{\circ}C$) at the center of the fire. Turbulent kinetic energy was increased by the heat itself and by the increased mechanical force, which in turn was generated by the thermal convection. The heating together with the complex terrain and strong boundary wind induced the unexpected high wind conditions with turbulence at the mountain. The CFD_NIMR_SNU model provided valuable analysis data to understand the consequences of the fatal mountain fire. It is suggested that the place of fire was calm at the time of the fire setting due to the elevated terrain of the windward side. The suppression of wind was easily reversed when there was fire, which caused updraft of hot air by the fire and the strong boundary wind. The strong boundary wind in conjunction with the fire event caused the strong turbulence, resulting in many fire casualties. The model can be utilized in turbulence forecasting over a small area due to surface fire in conjunction with a mesoscale weather model to help fire prevention at the field.