• The Korean Journal of Applied Statistics
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• v.25 no.2
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• pp.351-362
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• 2012

Weather generators are statistical tools to produce synthetic sequences of daily weather variables. We propose the multisite weather generators with a spatio-temporal correlation based on hierarchical generalized linear models. We develop a computational algorithm to produce future weather variables that use three different types of green-house gases scenarios. We apply the proposed method to a daily time series of precipitation and average temperature for South Korea.

• Journal of Korea Game Society
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• v.8 no.4
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• pp.45-53
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• 2008

This paper analyzed the narrative construction of ‘World of Warcraft’ which is very popular worldwide. Narratology and ludology should be interrelated in the larger area of art, rather than existing as separate fields. Dramatic narrative constructions of game would be made when the rules of game emerge with the circumstances of play. This paper reviews how 'World of Warcraft' generates its tremendous popularity through analyzing the overall narratives of 'World of Warcraft' and sub-narratives such as quest, PvP and hunting systems. The distinctive feature of this game is its well-designed story flow which is emerged by both underlying linear original story and non-linear multi-scenario that offers rich interactivities to the game users.

• Economic and Environmental Geology
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• v.52 no.2
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• pp.199-212
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• 2019

In the machine learning techniques, the sampling strategy of the training data affects a performance of the prediction model such as generalizing ability as well as prediction accuracy. Especially, in landslide susceptibility analysis, the data sampling procedure is the essential step for setting the training data because the number of non-landslide points is much bigger than the number of landslide points. However, the previous researches did not consider the various sampling methods for the training data. That is, the previous studies selected the training data randomly. Therefore, in this study the authors proposed several different sampling methods and assessed the effect of the sampling strategies of the training data in landslide susceptibility analysis. For that, total six different scenarios were set up based on the sampling strategies of landslide points and non-landslide points. Then Random Forest technique was trained on the basis of six different scenarios and the attribute importance for each input variable was evaluated. Subsequently, the landslide susceptibility maps were produced using the input variables and their attribute importances. In the analysis results, the AUC values of the landslide susceptibility maps, obtained from six different sampling strategies, showed high prediction rates, ranges from 70 % to 80 %. It means that the Random Forest technique shows appropriate predictive performance and the attribute importance for the input variables obtained from Random Forest can be used as the weight of landslide conditioning factors in the susceptibility analysis. In addition, the analysis results obtained using specific sampling strategies for training data show higher prediction accuracy than the analysis results using the previous random sampling method.

• The Journal of the Acoustical Society of Korea
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• v.32 no.2
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• pp.116-123
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• 2013

This paper represents generation of time-varying underwater acoustic channels by performing scattering simulation with time-varying ocean surface and Kirchhoff approximation. In order to estimate the time-varying ocean surface, 1D Pierson-Moskowitz ocean power spectrum and Gaussian correlation function were used. The computed scattering coefficients are applied to the amplitudes of each impulse of BELLHOP simulation result. The scattering coefficients are then compared with measured doppler spectral density of signal components which were scattered from ocean surface and the correlation time used in the Gaussian correlation function was estimated by the comparison. Finally, bit-error-rate and channel correlation simulations were performed with the generated time-varying channel based on passive time-reversal communication scenario.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.23 no.8
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• pp.1926-1940
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• 1998

This paper is the part II of research results on the performance evaluation of fully interconnected ATM switch, and includes the performance evaluation results for bursty traffic and nonuniform distribution. The switch model is a fyully interconnected switch type proposed by ETRI and is the proper architecutre for a small-sized switch element. The proposed switch consists of two steps of buffering scheme in the switch fabric in order to effectively absorb the effect of bursty nature of ATM traffic. The switch uses bit addressing method for addressing shcmeme and thus it is easy to implement multicasting function without adding additional functional block. In order to incorporate the bursty nature of traffic in ATM networks, we use IBP(Interrupted Bernoulli Process) model as an input traffic model as well as random traffic model which has been used as a traditional traffic model. In order to design the various scenarios for simulation, we considered both uniform and nonuniform output distribution, and also implemented multicast function. In this paper, we presented the simulation results in diverse environments and evaluated the performance of the switch.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.332-332
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• 2021

도시화가 상당히 이뤄지고 기습적인 폭우의 발생이 불확실하게 나타나는 시점에서 재산 및 인명피해를 야기할 수 있는 내수침수에 대한 위험도가 증가하고 있다. 내수침수에 대한 예측을 위하여 실측강우 또는 확률강우량 시나리오를 참조하고 연구대상 지역에 대한 1차원 그리고 2차원 수리학적 해석을 실시하는 연구가 오랫동안 진행되어 왔으나, 수치해석 모형의 경우 다양한 수문-지형학적 자료 및 계측 자료를 요구하고 집약적인 계산과정을 통한 단기간 예측에 어려움이 있음이 언급되어 왔다. 본 연구에서는 위와 같은 문제점을 해결하기 위하여 단일 도시 배수분구를 대상으로 관측 강우 자료, 1, 2차원 수치해석 모형, 기계학습 및 딥러닝 기법을 적용한 실시간 홍수위험지도 예측 모형을 개발하였다. 강우자료에 대하여 실시간으로 홍수량을 예측할 수 있도록 LSTM(Long-Short Term Memory) 기법을 적용하였으며, 전국단위 강우에 대한 다양한 1차원 도시유출해석 결과를 학습시킴으로써 예측을 수행하였다. 침수심의 공간적 분포의 경우 로지스틱 회귀를 이용하여, 기준 침수심에 대한 예측을 각각 수행하였다. 홍수위험 등급의 경우 침수심, 유속 그리고 잔해인자를 고려한 홍수위험등급 공식을 적용하여 산정하였으며, 이 결과를 랜덤포레스트(Random Forest)에 학습함으로써 실시간 예측을 수행할 수 있도록 개발하였다. 침수범위 및 홍수위험등급에 대한 예측은 격자 단위로 이뤄졌으며, 검증 자료의 부족으로 침수 흔적도를 통하여 검증된 2차원 침수해석 결과와 비교함으로써 예측력을 평가하였다. 본 기법은 특정 관측강우 또는 예측강우 자료가 입력되었을 때에, 도시 유역 단위로 접근이 불가하여 통제해야 할 구간을 실시간으로 예측하여 관리할 수 있을 것으로 판단된다.

• Journal of the Korean Association of Geographic Information Studies
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• v.21 no.4
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• pp.64-80
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• 2018

In Korea, citizens can only know general information about crime. Thus it is difficult to know how much they are exposed to crime. If the police can predict the crime risky area, it will be possible to cope with the crime efficiently even though insufficient police and enforcement resources. However, there is no prediction system in Korea and the related researches are very much poor. From these backgrounds, the final goal of this study is to develop an automated crime prediction system. However, for the first step, we build a big data set which consists of local real crime information and urban physical or non-physical data. Then, we developed a crime prediction model through machine learning method. Finally, we assumed several possible scenarios and calculated the probability of crime and visualized the results in a map so as to increase the people's understanding. Among the factors affecting the crime occurrence revealed in previous and case studies, data was processed in the form of a big data for machine learning: real crime information, weather information (temperature, rainfall, wind speed, humidity, sunshine, insolation, snowfall, cloud cover) and local information (average building coverage, average floor area ratio, average building height, number of buildings, average appraised land value, average area of residential building, average number of ground floor). Among the supervised machine learning algorithms, the decision tree model, the random forest model, and the SVM model, which are known to be powerful and accurate in various fields were utilized to construct crime prevention model. As a result, decision tree model with the lowest RMSE was selected as an optimal prediction model. Based on this model, several scenarios were set for theft and violence cases which are the most frequent in the case city J, and the probability of crime was estimated by $250{\times}250m$ grid. As a result, we could find that the high crime risky area is occurring in three patterns in case city J. The probability of crime was divided into three classes and visualized in map by $250{\times}250m$ grid. Finally, we could develop a crime prediction model using machine learning algorithm and visualized the crime risky areas in a map which can recalculate the model and visualize the result simultaneously as time and urban conditions change.

• Korean Journal of Environmental Biology
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• v.39 no.4
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• pp.526-535
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• 2021

Spatial sampling design plays an important role in GIS-based modeling studies because it increases modeling efficiency while reducing the cost of sampling. In the field of agricultural systems, research demand for high-resolution spatial databased modeling to predict and evaluate climate change impacts is growing rapidly. Accordingly, the need and importance of spatial sampling design are increasing. The purpose of this study was to design spatial sampling of paddy fields (11,386 grids with 1 km spatial resolution) in Korea for use in agricultural spatial modeling. A stratified random sampling design was developed and applied in 2030s, 2050s, and 2080s under two RCP scenarios of 4.5 and 8.5. Twenty-five weather and four soil characteristics were used as stratification variables. Stratification and sample allocation were optimized to ensure minimum sample size under given precision constraints for 16 target variables such as crop yield, greenhouse gas emission, and pest distribution. Precision and accuracy of the sampling were evaluated through sampling simulations based on coefficient of variation (CV) and relative bias, respectively. As a result, the paddy field could be optimized in the range of 5 to 21 strata and 46 to 69 samples. Evaluation results showed that target variables were within precision constraints (CV<0.05 except for crop yield) with low bias values (below 3%). These results can contribute to reducing sampling cost and computation time while having high predictive power. It is expected to be widely used as a representative sample grid in various agriculture spatial modeling studies.

• The Korean Journal of Applied Statistics
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• v.34 no.5
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• pp.685-696
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• 2021

The Korean Economically Active Population Survey (KEAPS) is a national survey that produces employment-related statistics. The main purpose of the survey is to find out the economic activity status (employed/ unemployed/ non-employed) of the people. KEAPS has a unique characteristics caused by the survey method. In this study, through understanding of structural non-response and utilization of past data, we would like to present an improved imputation model. The performance of the proposed model is compared with the existing model through simulation. The performance of the imputation models is evaluated based on the degree of mathing/nonmatching rates. For this, we employ the KEAPS data in November 2019. For the randomly selected ones among the total 59,996 respondents, the six explanatory variables, which are critical in determining the economic activity states, are treated as non-response. The proposed model includes industry variable and job status variable in addition to the explanatory variables used in the precedent research. This is based on the linkage and utilization of past data. The simulation results confirm that the proposed model with additional variables outperforms the existing model in the precedent research. In addition, we consider various scenarios for the number of non-responders by the economic activity status.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.1
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• pp.276-290
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• 2023

Several efforts in Korea are currently underway with the goal of commercializing autonomous vehicles. Hence, various studies are emerging on autonomous vehicles that drive safely and quickly according to operating guidelines. The current study examines the path search of an autonomous vehicle from a microscopic viewpoint and tries to prove the efficiency required by learning the lane change of an autonomous vehicle through Deep Q-Learning. A SUMO was used to achieve this purpose. The scenario was set to start with a random lane at the starting point and make a right turn through a lane change to the third lane at the destination. As a result of the study, the analysis was divided into simulation-based lane change and simulation-based lane change applied with Deep Q-Learning. The average traffic speed was improved by about 40% in the case of simulation with Deep Q-Learning applied, compared to the case without application, and the average waiting time was reduced by about 2 seconds and the average queue length by about 2.3 vehicles.