• The Korean Journal of Applied Statistics
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• v.36 no.1
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• pp.85-100
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• 2023

The stochastic volatility (SV) model is one of the main methods of modeling time-varying volatility. In particular, SV model is actively used in estimation and prediction of financial market volatility and option pricing. This paper attempts to model the time-varying volatility of the bitcoin market price using SV model. Hidden Markov model (HMM) is combined with the SV model to capture characteristics of regime switching of the market. The HMM is useful for recognizing patterns of time series to divide the regime of market volatility. This study estimated the volatility of bitcoin by using data from Upbit, a cryptocurrency trading site, and analyzed it by dividing the volatility regime of the market to improve the performance of the SV model. The MCMC technique is used to estimate the parameters of the SV model, and the performance of the model is verified through evaluation criteria such as MAPE and MSE.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.288-288
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• 2021

수문 모델링을 이용하여 미계측 유역의 유출을 예측하고 나아가 수문 현상을 이해하기 위해서는 기존과는 다른 새로운 모형 보정 전략과 평가 방법이 필요하다. 위성 관측자료의 가용성 증가는 미계측 유역에서 수문 모형의 예측 성능을 확보할 기회를 제공한다. 유역 내 증발산 과정은 물 순환 과정을 설명하는 주요한 부분 중 하나이다. 또한 식생에 대한 정보는 증발산 과정과 밀접한 연관을 가지기 때문에 간접적으로 유역의 증발산 과정을 이해할 수 있는 중요한 정보이다. 본 연구는 미계측 유역의 하천유량을 예측하기 위해 위성 관측 기반의 식생 정보만을 이용하여 보정된 생태 수문 모형의 잠재력을 조사한다. 이러한 보정 방법은 관측된 하천유량 자료가 있어야 하지 않기에 미계측 유역의 하천유량 예측에 특히 유용할 것이다. 모델링 실험은 관측 하천유량 자료가 존재하는 5개의 댐 유역(남강댐, 안동댐, 합천댐, 임하댐)에 대해 수행되었다. 본 연구에서는 식생동역학이 결합 된 집체형 수문 모델을 이용하였으며, MODIS 잎면적지수(Leaf Area Index, LAI) 자료를 이용하여 모형을 보정하였다. 보정된 모형으로부터 생산된 일 유량 결과는 관측 유량 자료와 비교된다. 또한, 전통적인 관측 유량 기반의 모형 보정 방법과 비교된다. 그 결과 LAI 시계열을 이용한 모형의 보정으로 획득한 유량의 적합도는 남강댐, 안동댐, 합천댐 유역에서 KGE가 임계치 이상으로 나타나 만족스러운 결과를 보여주지만, 임하댐 유역은 KGE가 임계치 이하로 계산되었다. 그러나 해당 유역에 대해 관측 유량을 기반으로 모형 보정 결과 또한 좋지 않은 적합도를 보여주기에 이는 LAI 자료 기반 접근법의 문제가 아닌 입력정보 또는 모형 자체에 포함된 오차로 인해 해당 유역의 특성을 반영하기에 어려운 것으로 판단된다. 이러한 결과는 증발산 과정에 주요한 식생 정보의 제약만으로도 비교적 만족스럽게 유역의 수문 순환을 재현할 수 있다는 가능성을 보여준다.

• Journal of the Korea Society of Computer and Information
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• v.29 no.7
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• pp.99-107
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• 2024

In an environment where computer-related technologies are rapidly changing, cyber threats continue to emerge as they are advanced and diversified along with new technologies. Therefore, in this study, we would like to collect security-related news articles, conduct LDA topic modeling, and examine trends. To that end, news articles from January 2020 to August 2023 were collected and major topics were derived through LDA analysis. After that, the flow by topic was grasped and the main origin was analyzed. The analysis results show that ransomware attacks in 2021 and hacking of virtual asset exchanges in 2023 are major issues in the recent security sector. This allows you to check trends in security issues and see what research should be focused on in the future. It is also expected to be able to recognize the latest threats and support appropriate response strategies, contributing to the development of effective security measures.

• Journal of Environmental Impact Assessment
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• v.31 no.2
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• pp.83-94
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• 2022

In this study, DNN-based models were learned using air quality determination data for 2017, 2019, and 2020 provided by the National Measurement Network (Air Korea), and this models evaluated using data from 2016 and 2018. Based on Pearson correlation coefficient 0.2, four items (SO₂, CO, NO₂, PM₁₀) were initially modeled as independent variables. In order to improve the accuracy of prediction, monthly independent modeling was carried out. The error was calculated by RMSE (Root Mean Square Error) method, and the initial model of RMSE was 5.78, which was about 46% betterthan the national moving average modelresult (10.77). In addition, the performance improvement of the independent monthly model was observed in months other than November compared to the initial model. Therefore, this study confirms that DNN modeling was effective in predicting PM_2.5 concentrations based on air pollutants concentrations, and that the learning performance of the model could be improved by selecting additional independent variables.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 2017.04a
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• pp.171-171
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• 2017

전산유체역학(CFD: Computational Fluid Dynamics)를 이용한 스마트팜 환경 내부의 정밀 제어 연구가 진행 중이다. 시계열 데이터의 난해한 동적 해석을 극복하기위해, 비선형 모델링 기법의 일종인 인공신경망을 이용하는 방안을 고려하였다. 선행 연구를 통하여 환경 데이터의 비선형 모델링을 위한 Tensorflow활용 방법이 하드웨어 가속 기능을 바탕으로 월등한 성능을 보임을 확인하였다. 그럼에도 오프라인 일괄(Offline batch)처리 방식의 한계가 있는 인공신경망 모델링 기법과 현장 보급이 불가능한 고성능 하드웨어 연산 장치에 대한 대안 마련이 필요하다고 판단되었다. CFD 해석을 위한 Solver로 SU2(http://su2.stanford.edu)를 이용하였다. 운영 체제 및 컴파일러는 1) Mac OS X Sierra 10.12.2 Apple LLVM version 8.0.0 (clang-800.0.38), 2) Windows 10 x64: Intel C++ Compiler version 16.0, update 2, 3) Linux (Ubuntu 16.04 x64): g++ 5.4.0, 4) Clustered Linux (Ubuntu 16.04 x32): MPICC 3.3.a2를 선정하였다. 4번째 개발환경인 병렬 시스템의 경우 하드웨어 가속는 OpenCL(https://www.khronos.org/opencl/) 엔진을 이용하고 저전력 ARM 프로세서의 일종인 옥타코어 Samsung Exynos5422 칩을 장착한 ODROID-XU4(Hardkernel, AnYang, Korea) SBC(Single Board Computer)를 32식 병렬 구성하였다. 분산 컴퓨팅을 위한 환경은 Gbit 로컬 네트워크 기반 NFS(Network File System)과 MPICH(http://www.mpich.org/)로 구성하였다. 공간 분해능을 계측 주기보다 작게 분할할 경우 발생하는 미지의 바운더리 정보를 정의하기 위하여 3차원 Kriging Spatial Interpolation Method를 실험적으로 적용하였다. 한편 병렬 시스템 구성이 불가능한 1,2,3번 환경의 경우 내부적으로 이미 존재하는 멀티코어를 활용하고자 OpenMP(http://www.openmp.org/) 라이브러리를 활용하였다. 64비트 병렬 8코어로 동작하는 1,2,3번 운영환경의 경우 32비트 병렬 128코어로 동작하는 환경에 비하여 근소하게 2배 내외로 연산 속도가 빨랐다. 실시간 CFD 수행을 위한 분산 컴퓨팅 기술이 프로세서의 속도 및 운영체제의 정보 분배 능력에 따라 결정된다고 판단할 수 있었다. 이를 검증하기 위하여 4번 개발환경에서 운영체제를 64비트로 개선하여 5번째 환경을 구성하여 검증하였다. 상반되는 결과로 64비트 72코어로 동작하는 분산 컴퓨팅 환경에서 단일 프로세서 기반 멀티 코어(1,2,3번) 환경보다 보다 2.5배 내외 연산속도 향상이 있었다. ARM 프로세서용 64비트 운영체제의 완성도가 낮은 시점에서 추후 성공적인 실시간 CFD 모델링을 위한 지속적인 검토가 필요하다.

• The Korean Journal of Applied Statistics
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• v.20 no.3
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• pp.541-550
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• 2007

One of main aspects of time series analysis is to forecast future values of series based on values up to a given time. The prediction interval for future values is usually obtained under the normality assumption. When the assumption is seriously violated, a transformation of data may permit the valid use of the normal theory. We investigate the prediction problem for future values in the original scale when transformations are applied in ARMA models. In this paper, we introduce the methodology based on Yeo-Johnson transformation to solve the problem of skewed data whose modelling is relatively difficult in the analysis of time series. Simulation studies show that the coverage probabilities of proposed intervals are closer to the nominal level than those of usual intervals.

• Korean Journal of Agricultural and Forest Meteorology
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• v.10 no.2
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• pp.35-46
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• 2008

Soil moisture is one of the important components in hydrological processes and also controls the subsurface flow mechanism at a hillslope scale. In this study, time series of soil moisture were measured at a hillslope located in Gwangneung National Arboretum, Korea using a multiplex Time Domain Reflectometry(TDR) system measuring soil moisture with bi-hour interval. The Box-Jenkins transfer function and noise model was used to estimate spatial distributions of soil moisture histories between May and September, 2007. Rainfall was used as an input parameter and soil moisture at 10 cm depth was used as an output parameter in the model. The modeling process consisted of a series of procedures(e.g., data pretreatment, model identification, parameter estimation, and diagnostic checking of selected models), and the relationship between soil moisture and rainfall was assessed. The results indicated that the patterns of soil moisture at different locations and slopes along the hillslope were similar with those of rainfall during the measurment period. However, the spatial distribution of soil moisture was not associated with the slope of the monitored location. This implies that the variability of the soil moisture was determined more by rainfall than by the slope of the site. Due to the influence of vegetation activity on soil moisture flow in spring, the soil moisture prediction in spring showed higher variability and complexity than that in early autumn did. This indicates that vegetation activity is an important factor explaining the patterns of soil moisture for an upland forested hillslope.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.32 no.5
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• pp.322-330
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• 2020

Modeling for outliers in time series was carried out using the MSL and extreme high, low tide levels (EHL, HLL) data set in the Busan and Mokpo stations. The time-series model is seasonal ARIMA model including the components of the AO (additive outliers) and LS (level shift). The optimal model was selected based on the AIC value and the model parameters were estimated using the 'tso' function (in 'tsoutliers' package of R). The main results by the model application, i.e.. outliers and level shift detections, are as follows. (1) The two AO are detected in the Busan monthly EHL data and the AO magnitudes were estimated to 65.5 cm (by typhoon MAEMI) and 29.5 cm (by typhoon SANBA), respectively. (2) The one level shift in 1983 is detected in Mokpo monthly MSL data, and the LS magnitude was estimated to 21.2 cm by the Youngsan River tidal estuary barrier construction. On the other hand, the RMS errors are computed about 1.95 cm (MSL), 5.11 cm (EHL), and 6.50 cm (ELL) in Busan station, and about 2.10 cm (MSL), 11.80 cm (EHL), and 9.14 cm (ELL) in Mokpo station, respectively.

• Journal of Digital Convergence
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• v.19 no.2
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• pp.1-12
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• 2021

With the recent 4th Industrial Revolution, the growth and value of big data are continuously increasing, and the government is also actively making efforts to open and utilize public data. However, the situation still does not reach the level of demand for public data use by citizens, At this point, it is necessary to identify research trends in the public data field and seek directions for development. In this study, in order to understand the research trends related to public data, the analysis was performed using topic modeling, which is mainly used in text mining techniques. To this end, we collected papers containing keywords of 'Public data' among domestic and foreign research papers (1,437 domestically, 9,607 overseas) and performed topic modeling based on the LDA algorithm, and compared domestic and foreign public data research trends. After analysis, policy implications were presented. Looking at the time series by topic, research in the fields of 'personal information protection', 'public data management', and 'urban environment' has increased in Korea. Overseas, it was confirmed that research in the fields of 'urban policy', 'cell biology', 'deep learning', and 'cloud·security' is active.

• The Journal of Engineering Geology
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• v.24 no.2
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• pp.217-225
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• 2014

Water curtain cultivation (WCC) systems in Korea have depleted water resources in shallow aquifers through massive pumping of groundwater. The goal of this study is to simulate the groundwater variations observed from massive groundwater pumping at a site in Cheongweon. MODFLOW was used to simulate three-dimensional regional groundwater flow, and the SWAT (Soil and Water Assessment Tool) watershed hydrologic model was employed to introduce temporal changes in groundwater recharge into the MODFLOW model input. Additionally, the estimation method for groundwater discharge in WCC areas (Moon et al., 2012) was incorporated into a groundwater pumping schedule as a MODFLOW input. We compared simulated data and field measurements to determine the degree to which winter season groundwater drawdown is effectively modeled. A simulation time of 107 days was selected to match the observed groundwater drawdown from November, 2012 to March, 2013. We obtained good agreement between the simulated drawdown and observed groundwater levels. Thus, the estimation method using daily minimum temperatures, may be applicable to other cultivation areas and can serve as a guideline in simulating the regional flow of riverside groundwater aquifers.