• Journal of Korean Society on Water Environment
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• v.36 no.6
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• pp.568-580
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• 2020

Calibrating a conceptual hydrologic model necessitates selection of a calibration period that produces the most reliable prediction. This often must be chosen randomly, however, since there is no objective guidance. Observation plays the most important role in the calibration or uncertainty evaluation of hydrologic models, in which the key factors are the length of the data and the hydro-climate conditions in which they were collected. In this study, we investigated the effect of the calibration period selected on the predictive performance and uncertainty of a model. After classifying the inflows of the Hapcheon Dam from 1991 to 2019 into four hydro-climate conditions (dry, wet, normal, and mixed), a conceptual hydrologic partitioning model was calibrated using data from the same hydro-climate condition. Then, predictive performance and post-parameter statistics were analyzed during the verification period under various hydro-climate conditions. The results of the study were as follows: 1) Hydro-climate conditions during the calibration period have a significant effect on model performance and uncertainty, 2) calibration of a hydrologic model using data in dry hydro-climate conditions is most advantageous in securing model performance for arbitrary hydro-climate conditions, and 3) the dry calibration can lead to more reliable model results.

• The Korean Journal of Applied Statistics
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• v.35 no.1
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• pp.131-146
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• 2022

Logit models are commonly used to predicting and classifying categorical response variables. Most Bayesian approaches to logit models are implemented based on the Metropolis-Hastings algorithm. However, the algorithm has disadvantages of slow convergence and difficulty in ensuring adequacy for the proposal distribution. Therefore, we use auxiliary mixture sampler proposed by Frühwirth-Schnatter and Frühwirth (2007) to estimate logit models. This method introduces two sequences of auxiliary latent variables to make logit models satisfy normality and linearity. As a result, the method leads that logit model can be easily implemented by Gibbs sampling. We applied the proposed method to diabetes data from the Community Health Survey (2020) of the Korea Disease Control and Prevention Agency and compared performance with Metropolis-Hastings algorithm. In addition, we showed that the logit model using auxiliary mixture sampling has a great classification performance comparable to that of the machine learning models.

• The Korean Journal of Applied Statistics
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• v.36 no.2
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• pp.115-127
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• 2023

Multinomial probit model is a popular model for multiclass classification and choice model. Markov chain Monte Carlo (MCMC) method is widely used for estimating multinomial probit model, but its computational cost is high. However, it is well known that variational Bayesian approximation is more computationally efficient than MCMC, because it uses subsets of samples. In this study, we describe multinomial probit model with Gaussian process classification and how to employ variational Bayesian approximation on the model. This study also compares the results of variational Bayesian multinomial probit model to the results of naive Bayes, K-nearest neighbors and support vector machine for the UCI mice protein expression level data.

• The Bulletin of The Korean Astronomical Society
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• v.46 no.2
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• pp.70.2-71
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• 2021

We examine gas kinematics and star formation activities of NGC 6822, a gas-rich dwarf irregular galaxy in the Local Group at a distance of ~490 kpc. We perform profile decomposition of all the line-of-sight (LOS) HI velocity profiles of the high-resolution (42.4" × 12" spatial; 1.6 km/s spectral) HI data cube of the galaxy, taken with the Australian Telescope Compact Array (ATCA). To this end, we use a novel tool based on Bayesian Markov Chain Monte Carlo (MCMC) techniques, the so-called BAYGAUD, which allows us to decompose a velocity profile into an optimal number of Gaussian components in a quantitative manner. We group all the decomposed components into bulk-narrow, bulk-broad, and non-bulk gas components classified with respect to their velocity dispersions and the amounts of velocity offset from the global kinematics, respectively. Using the surface densities and velocity dispersions of the kinematically decomposed HI gas maps together with the rotation curve of NGC 6822, we derive Toomre-Q parameters for individual regions of the galaxy which quantify the level of local gravitational instability of the gaseous disk. We also measure the local star formation rate (SFR) of the corresponding regions in the galaxy by combining GALEX Far-ultraviolet (FUV) and WISE 22㎛ images. We then relate the gas and SFR surface densities in order to investigate the local Kennicutt-Schmidt (K-S) law of gravitationally unstable regions which are selected from the Toomre Q analysis. Of the three groups, the bulk-narrow, bulk-broad and non-bulk gas components, we find that the lower Toomre-Q values the bulk-narrow gas components have, the more consistent with the linear extension of the K-S law derived from molecular hydrogen (H2) observations.

• Journal of Korea Trade
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• v.27 no.2
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• pp.96-114
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• 2023

Purpose - This paper elucidates a nexus between the occurrence of rare disaster events and the volatility of economic growth by distinguishing the likelihood of rare events from stochastic volatility. We provide new empirical facts based on a quarterly time series. In particular, we focus on the role of financial liberalization in spreading the economic crisis in developing countries. Design/methodology - We use quarterly data on consumption expenditure (real per capita consumption) from 44 countries, including advanced and developing countries, ending in the fourth quarter of 2020. We estimate the likelihood of rare event occurrences and stochastic volatility for countries using the Bayesian Markov chain Monte Carlo (MCMC) method developed by Barro and Jin (2021). We present our estimation results for the relationship between rare disaster events, stochastic volatility, and growth volatility. Findings - We find the global common disaster event, the COVID-19 pandemic, and thirteen country-specific disaster events. Consumption falls by about 7% on average in the first quarter of a disaster and by 4% in the long run. The occurrence of rare disaster events and the volatility of gross domestic product (GDP) growth are positively correlated (4.8%), whereas the rare events and GDP growth rate are negatively correlated (-12.1%). In particular, financial liberalization has played an important role in exacerbating the adverse impact of both rare disasters and financial market instability on growth volatility. Several case studies, including the case of South Korea, provide insights into the cause of major financial crises in small open developing countries, including the Asian currency crisis of 1998. Originality/value - This paper presents new empirical facts on the relationship between the occurrence of rare disaster events (or stochastic volatility) and growth volatility. Increasing data frequency allows for greater accuracy in assessing a country's specific risk. Our findings suggest that financial market and institutional stability can be vital for buffering against rare disaster shocks. It is necessary to preemptively strengthen the foundation for financial stability in developing countries and increase the quality of the information provided to markets.

• The Bulletin of The Korean Astronomical Society
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• v.45 no.1
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• pp.61.4-62
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• 2020

We present H I gas kinematics and star formation activities of NGC 6822, a dwarf galaxy located in the Local Volume at a distance of ~490 kpc. We perform profile decomposition of the line-of-sight velocity profiles of the high-resolution (~42.4" × 12") spatial; ~1.6 km/s spectral) H I data cube taken with the Australia Telescope Compact Array (ATCA). For this, we use a new tool, the so-called BAYGAUD (BAYesian GAUssian Decompositor) which is based on Bayesian Markov Chain Monte Carlo (MCMC) techniques, allowing us to decompose a line-of-sight velocity profile into an optimal number of Gaussian components in a quantitative manner. We classify the decomposed H I gas components of NGC 6822 into kinematically cold, warm or hot ones with respect to their velocity dispersion: 1) cold: < 4 km/s, 2) warm: 4 ~ 8 km/s, 3) hot: > 8 km/s. We then derive the Toomre-Q parameters of NGC 6822 using the kinematically decomposed H I gas maps. We also correlate their gas surface densities with the surface star formation rates derived using both GALEX far-ultraviolet and WISE 22 micron data to examine the impact of gas turbulence caused by stellar feedback on the Kennicutt-Schmidt (K-S) law. The kinematically cold component is likely to better follow the linear extension of the Kennicutt-Schmidt (K-S) law for molecular hydrogen (H2) at the low gas surface density regime where H I is not saturated.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.182-182
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• 2022

4차산업 기술이 상수도 관망 분야에도 활발히 도입되며 스마트워터 구축에 기술적인 기반이 마련되고 있다. 이중 디지털트윈의 경우 컴퓨터에 현실 속 사물의 쌍둥이를 만들고, 현실에서 발생할 수 있는 상황을 컴퓨터로 시뮬레이션함으로써 결과를 미리 예측하는 기술로 정의된다. 즉, 디지털트윈의 핵심기술은 시각화와 시뮬레이션 모형의 연계로 실시간 상황 표출뿐만 아니라 시뮬레이션 모형 입력값의 미래 변화를 추정하여 해당 사물의 상태를 예측하는 것이라고 할 수 있다. 상수도 관망의 경우도 디지털트윈 모형 구축 시 정교한 시뮬레이션 모형과 연계를 통해 관측 데이터의 표출과 함께 미관측 지점의 데이터를 추정 및 표출하는 것이 중요하다. 본 연구에서는 디지털트윈 모형 구축에 가장 필수적이라고 할 수 있는 상수도 관망의 해석 프로그램 매개변수 검보정 모형을 소개한다. 대표적인 상수도 관망해석 프로그램인 EPANET2.2의경우 입력값으로 주로 수요량과 관로의 조도계수를 요구하며, 본 연구에서는 수요량은 알고 있는 것으로 가정하고 관로의 조도계수만 Markov-Chain Monte Carlo (MCMC)를 사용하여 검보정한다. 해당 모형은 (1) 실시간 조도계수 추정이 가능하면, (2) 동시에 누수 탐지가 가능하고, (3) 관로의 기능적 노후를 정의하여 향후 디지털트윈 모형 구현 시 관로 노후를 표출할 수 있는 기반을 구축한다. 우선 실시간 조도계수 추정은 데이터베이스와 연동하여 진행하며, MCMC 모형을 활용한 관로 별 조도계수의 분포에 따라 정상범위 내 변동이 발생하는지 여부를 판단한다. 이때 정상범위를 벗어난 변동이 발생하는 경우 잠재적 누수가 존재하는 것으로 가정하며, 콜모고로프-스미르노프(KS) 테스트를 통해 이를 판단한다. 기능적 노후는 관로의 통수능과 연관이 있으며, 추정한 조도계수에 따른 관로의 통수능을 산정하여 결과를 표출한다. 본 연구에서 제안한 모형은 향후 상수도 관망 디지털트윈 구현에 핵심 요소기술로 활용할 수 있을 것으로 기대한다.

• Journal of The Korean Astronomical Society
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• v.56 no.2
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• pp.277-286
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• 2023

It is difficult to distinguish the pure signal produced by an orbiting planetary companion around giant stars from other possible sources, such as stellar spots, pulsations, or certain activities. Since 2003, we have obtained radial (RV) data from evolved stars using the high-resolution, fiber-fed Bohyunsan Observatory Echelle Spectrograph (BOES) at the Bohyunsan Optical Astronomy Observatory (BOAO). Here, we report the results of RV variations in the binary star HD 135438. We found two significant periods: 494.98 d with eccentricity of 0.23 and 8494.1 d with eccentricity of 0.83. Considering orbital stability, it is impossible to have two companions in such close orbits with high eccentricity. To determine the nature of the changes in the RV variability, we analyzed indicators of stellar spot and stellar chromospheric activity to find that there are no signals related to the significant period of 494.98 d. However, we calculated the upper limits of rotation period of the rotational velocity and found this to be 478-536 d. One possible interpretation is that this may be closely related to the rotational modulation of an orbital inclination at 67-90 degrees. The other signal corresponding to the period of 8494.1 d is probably associated with a stellar companion orbiting the giant star. A Markov Chain Monte Carlo (MCMC) simulation considering a single companion indicates that HD 135438 system hosts a stellar companion with 0.57^+0.017 _-0.017 M_⊙ with an orbital period of 8498 d.

• Journal of Korea Water Resources Association
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• v.53 no.2
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• pp.107-119
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• 2020

This study analyzed past drought characteristics based on the observed rainfall data and performed a long-term outlook for future extreme droughts using Representative Concentration Pathways 8.5 (RCP 8.5) climate change scenarios. Standardized Precipitation Index (SPI) used duration of 1, 3, 6, 9 and 12 months, a meteorological drought index, was applied for quantitative drought analysis. A single long-term time series was constructed by combining daily rainfall observation data and RCP scenario. The constructed data was used as SPI input factors for each different duration. For the analysis of meteorological drought observed relatively long-term since 1954 in Korea, 12 rainfall stations were selected and applied 10 general circulation models (GCM) at the same point. In order to analyze drought characteristics according to climate change, trend analysis and clustering were performed. For non-stationary frequency analysis using sampling technique, we adopted the technique DEMC that combines Bayesian-based differential evolution ("DE") and Markov chain Monte Carlo ("MCMC"). A non-stationary drought frequency analysis was used to derive Severity-Duration-Frequency (SDF) curves for the 12 locations. A quantitative outlook for future droughts was carried out by deriving SDF curves with long-term hydrologic data assuming non-stationarity, and by quantitatively identifying potential drought risks. As a result of performing cluster analysis to identify the spatial characteristics, it was analyzed that there is a high risk of drought in the future in Jeonju, Gwangju, Yeosun, Mokpo, and Chupyeongryeong except Jeju corresponding to Zone 1-2, 2, and 3-2. They could be efficiently utilized in future drought management policies.

• Journal of Life Science
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• v.21 no.3
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• pp.385-392
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• 2011

The otter (Lutra lutra) in Korea is classified as a first grade endangered species and is managed under state control. We performed a phylogenetic analysis of the otter that inhabits the Changnyeong, Jinju, and Geoje areas in Gyeongsangnamdo, Korea using mtDNA and microsatellite (MS) markers. As a result of the analysis using the 676-bp D-loop sequence of mtDNA, six haplotypes were estimated from five single nucleotide polymorphisms. The genetic distance between the Jinju and Geoje areas was greater than distances within the areas, and the distance between Jinju and Geoje was especially clear. From the phylogenetic tree estimated using the Bayesian Markov chain Monte Carlo analysis by the MrBays program, two subgroups, one containing samples from Jinju and the other containing samples from the Changnyeong and Geoje areas were clearly identified. The result of a parsimonious median-joining network analysis also showed two clear subgroups, supporting the result of the phylogenetic analysis. On the other hand, in the consensus tree estimated using the genetic distances estimated from the genotypes of 13 MS markers, there were clear two subgroups, one containing samples from the Jinju, Geoje and Changnyeong areas and the other containing samples from only the Jinju area. The samples were not identically classified into each subgroup defined by mtDNA and MS markers. It could be inferred that the differential classification of samples by the two different marker systems was because of the different characteristics of the marker systems used, that is, the mtDNA was for detecting maternal lineage and the MS markers were for estimating autosomal genetic distances. Nonetheless, the results from the two marker systems showed that there has been a progressive genetic fixation according to the habitats of the otters. Further analyses using not only newly developed MS markers that will possess more analytical power but also the whole mtDNA are needed. Expansion of the phylogenetic analysis using otter samples collected from the major habitats in Korea should be helpful in scientifically and efficiently maintaining and preserving them.