• Communications for Statistical Applications and Methods
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• v.17 no.5
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• pp.697-712
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• 2010

This paper is concerned with the statistical analysis and development of stochastic models for the demand for life insurance policy loans. For these, firstly the characteristics of the regression trend, periodicity and dependence of the monthly demand for life insurance policy loans are investigated by a statistical analysis of the monthly demand data for the years 1999 through 2008. Secondly, the causal relationships between the demand for life insurance policy loans and the economic variables including unemployment rate and inflation rate for the period are investigated. The results show that inflation rate is main factor influencing policy loan demands. The overall evidence, however, failed to establish unidirectional causality relationships between the demand series and the other variables under study. Finally, based on these, univariate time series model and transfer function model where the demand series is related to one input series are derived, respectively, for the prediction of the demand for life insurance policy loans. A statistical procedure for using the model to predict the demand for life insurance policy loans is also proposed.

• Journal of Ecology and Environment
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• v.33 no.1
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• pp.47-57
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• 2010

To understand day-to-day fluctuations in soil moisture content in Seoul, I simulated daily soil moisture content from 1908 to 2009 using long-term climatic precipitation and temperature data collected at the Surface Synoptic Meteorological Station in Seoul for the last 98 years with a hydrological simulation model, BROOK. The output data set from the BROOK model allowed me to examine day-to-day fluctuations and the severity and duration of droughts in the Seoul area. Although the soil moisture content is highly dependent on the occurrence of precipitation, the pattern of changes in daily soil moisture content was clearly quite different from that of precipitation. Generally, there were several phases in the dynamics of daily soil moisture content. The period from mid-May to late June can be categorized as the initial period of decreasing soil moisture content. With the initiation of the monsoon season in late June, soil moisture content sharply increases until mid-July. From the termination of the rainy season in mid-July, daily soil moisture content decreases again. Highly stochastic events of typhoons from late June to October bring large amount of rain to the Korean peninsula, culminating in late August, and increase the soil moisture content again from late August to early September. From early September until early October, another sharp decrease in soil moisture content was observed. The period from early October to mid-May of the next year can be categorized as a recharging period when soil moisture content shows an increasing trend. It is interesting to note that no statistically significant increase in mean annual soil moisture content in Seoul, Korea was observed over the last 98 years. By simulating daily soil moisture content, I was also able to reconstruct drought phenomena to understand the severity and duration of droughts in Seoul area. During the period from 1908 to 2009, droughts in the years 1913, 1979, 1939, and 2006 were categorized as 'severe' and those in 1988 and 1982 were categorized as 'extreme'. This information provides ecologists with further potential to interpret natural phenomenon, including tree growth and the decline of tree species in Korea.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.5
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• pp.757-764
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• 2017

As the reduction of greenhouse gases(GHGs) emission has become a global issue, the microgrid markets are growing rapidly. With the sudden changes in the market, Korean government suggested a new business model called 'Self-Sufficient Energy Islands'. Its main concern is a stand-alone microgrid composed of Distributed Energy Resources(DERs) such as Renewable Energy Sources(RESs), Energy Storage System(ESS) and Fuel Cell, in order to minimize the emission of GHGs. According to these trend, this paper is written to propose an optimal sizing method of DERs in a stand-alone microgrid by using Genetic Algorithm(GA), one of the representative stochastic methods. It is to minimize the net present cost with the variables, size of RESs and ESS. In the process for optimization, the sunless days are considered as additional constraints. Through the case study analysis, the size of DERs installed in a microgrid system has been computed using the proposed method in MATLAB. And the result of MATLAB is compared with that of HOMER(Hybrid Optimization of Multiple Energy Resources), a well-known energy modeling software.

• Journal of Korea Water Resources Association
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• v.34 no.5
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• pp.533-542
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• 2001

In order to establish drought policy, the estimation of drought period for each drought situation should be preceded. Non-linear Water Balance Model(NWBM) and palmer Drought Severity Index (PDSI) can be used for analysis of drought period. As a water balance method considering moisture transfer between land surface and atmosphere, NWBM can be used to estimate transition time between dry and wet period induced by stochastic fluctuations. PDSI is also water balance method to show drought severity comparing actual precipitation with climatically appropriate precipitation based on precipitation and potential evapotranspiration. In this study, the drought periods are estimated using NWBM and PDSI for the Han River Basin. The drought periods according to the soil moisture estimated by NWBS and the drought periods according to drought severity index estimated by PDSI show similar trend. The estimated drought period from extreme drought to wet condition for the Han River Basin is about 3years.

• Proceedings of the IEEK Conference
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• summer
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• pp.382-386
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• 2004

Recently, with the machine learning trend, most of the machine translation systems on over the world use two syntax tree sets of two relevant languages to learn syntactic tree transfer rules. However, for the English-Vietnamese language pair, this approach is impossible because until now we have not had a Vietnamese syntactic tree set which is correspondent to English one. Building of a very large correspondent Vietnamese syntactic tree set (thousands of trees) requires so much work and take the investment of specialists in linguistics. To take advantage from our available English-Vietnamese Corpus (EVC) which was tagged in word alignment, we choose the SITG (Stochastic Inversion Transduction Grammar) model to construct English- Vietnamese syntactic tree sets automatically. This model is used to parse two languages at the same time and then carry out the syntactic tree transfer. This English-Vietnamese bilingual syntactic tree set is the basic training data to carry out transferring automatically from English syntactic trees to Vietnamese ones by machine learning models. We tested the syntax analysis by comparing over 10,000 sentences in the amount of 500,000 sentences of our English-Vietnamese bilingual corpus and first stage got encouraging result $(analyzed\;about\;80\%)[5].$ We have made use the TBL algorithm (Transformation Based Learning) to carry out automatic transformations from English syntactic trees to Vietnamese ones based on that parallel syntactic tree transfer set[6].

• Proceedings of the Korea Water Resources Association Conference
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• 2012.05a
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• pp.116-116
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• 2012

하천유역 내의 인자를 이용하여 댐의 하천유량(stream flow)을 예측하는 일은 수문특성의 연구와 자연재해에 대한 대비 및 수공구조물과 방재시설의 설계 시 중요한 역할을 한다. 이러한 연구는 과거부터 활발히 이루어졌으며, 아직도 보다 높은 정확도의 결과를 얻기 위해 많은 연구들이 이루어지고 있다. 특히 기존의 유역 내 자료를 통해 비선형적 모델인 인공신경망(artificial neural network)을 이용한 하천유량을 예측하는 연구 역시 활발히 이루어지고 있다. 본 연구의 목적은 여러 유역인자들 중 하천유량에 가장 영향을 미치는 변수를 추출하고 보다 정확한 예측모델을 구축하는 것이다. 기존의 입력자료 선정기법중의 하나인 상호정보량(mutual information)과 수문기상자료의 비선형 동역학적 성분을 추출하는 웨이블렛 변환(wavelet transform)을 사용하여 인공신경망에 적용시켰다. 인공신경망을 적용하는 경우, 수문자료에 있어서 변수의 선택과 자료의 상태가 강우예측의 결과에 큰 영향을 미친다. 이러한 변수의 선택에 있어서 상호정보량을 바탕으로 한 인공신경망 입력변수 선택기법이 많이 사용되고 있다. 일반적으로 시계열자료는 경향성(trend), 주기성(periodicity) 및 추계학적 성분(stochastic component)의 선형조합으로 가정될 수 있으며, 특히 경향성과 주기성은 시계열 모형을 위해 제거되어야 할 결정론적 성분으로 취급한다. 즉. 수문 기상자료에 포함되어 있는 경향성과 주기성과 같은 비선형 동역학적 잡음(nonlinear dynamical noise)을 제거하고 입력자료의 카오스적 거동을 보이는 성분을 분리하기 위해 웨이블렛 변환을 사용하였다. 대상유역은 한강 유역에 포함되어 있는 충주댐으로 선택하였다. 유역 내 다양한 인자들과 하천유량사이의 상호정보량을 구해 영향력이 가장 큰 변수를 추출하고, 그 자료를 웨이블렛 변환을 적용하여 인공신경망의 입력자료로 사용하였다. 본 논문에서는 위와 같은 과정을 이용해 추정한 하천유량 결과와 기존의 방법인 상호정보량을 이용해 인공신경망을 적용한 결과를 실제자료와 비교하였다.

• Journal of Korea Trade
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• v.25 no.5
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• pp.93-109
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• 2021

Purpose - The logistics industry is often featured by its location relevance and industrial concentration. Industrial concentration is conducive to the effective transmission of information by reducing transaction costs and improving transaction efficiency, thus promoting the development of trade. The main purpose of this paper is to measure the spatial total factor productivity and location quotient of the logistics industry in Korea, and to study the impact of the logistics industry efficiency and agglomeration effect on import and export trade in Korea. Design/methodology - First, used the spatial stochastic frontier method to measure the spatial total factor productivity of the logistics industry in Korea, this serves as the efficiency index of the logistics industry in various regions of Korea. Second, calculated the location quotient (LQ) of the logistics industry to measure the industry's concentration degree. Third, employed a spatial econometric model to analyze the impacts of factors such as the efficiency and concentration levels of the logistics industry on import and export trade in Korea. Findings - This study's main findings can be summarized as follows: this study found that the overall efficiency of the logistics industry in Korea needs to be improved, even though it showed an upward trend in all regions of the country; Moreover, the agglomeration level of Korea's logistics industry needs to be improved; Finally, the positive spatial correlation and industrial agglomeration effect of Korea's logistics industry had a positive impact on the country's import and export trade. Originality/value - This study is innovative in terms of research perspective and methods. Most of the previous studies have measured the development level of the logistics industry using the logistics performance index (LPI), Fewer studies have assessed through the spatial total factor productivity and location quotient of the logistics industry in Korea to measure the efficiency index of the logistics industry in various regions of Korea and concentration degree, as well as there was almost no study on the impact of logistics industry efficiency and agglomeration effect on import and export trade in Korea. This study addresses this limitation by analyzing the impacts of the efficiency and agglomeration effect of the logistics industry on import and export trade in Korea.

• The Journal of Society for e-Business Studies
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• v.24 no.1
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• pp.91-103
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• 2019

An accurate prediction on near-future cash flows plays an important role for a company to attenuate the shortage risk of cash flow by preparing a plan for future investment in advance. Unfortunately, there exists a high level of uncertainty in the types of transactions that occur in the form of receivables in inter-company transactions, unlike other types of transactions, thereby making the prediction of cash flows difficult. In this study, we analyze the trend of cash flow related to account receivables that may arise between firms, by using a stochastic approach. In particular, we utilize Variable Order Markov (VOM) model to predict how future cash flows will change based on cash flow history. As a result of this study, we show that the average accuracy of the VOM model increases about 12.5% or more compared with that of other existing techniques.

• Journal of Ecology and Environment
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• v.44 no.4
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• pp.241-255
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• 2020

Background: The stem exclusion stage is a stage of forest development that is important for understanding the subsequent understory reinitiation stage and maturation stage during which horizontal heterogeneity is formed. Over the past 11 years (2009-2019), we observed a deciduous broad-leaved forest in the Albongbunji Basin in Ulleungdo, South Korea in its stem exclusion stage, where Fagus engleriana (Engler's beech) is the dominant species, thereby analyzing the changes in the structure (density and size distributions), function (biomass and species richness), and demographics. Results: The mean stem density data presented a bell-shaped curve with initially increasing, peaking, and subsequently decreasing trends in stem density over time, and the mean biomass data showed a sigmoidal pattern indicating that the rate of biomass accumulation slowed over time. Changes in the density and biomass of Fagus engleriana showed a similar trend to the changes in density and biomass at the community level, which is indicative of the strong influence of this species on the changing patterns of forest structure and function. Around 2015, a shift between recruitment and mortality rates was observed. Deterministic processes were the predominant cause of tree mortality in our study; however, soil deposition that began in 2017 in some of the quadrats resulted in an increase in the contribution of stochastic processes (15% in 2019) to tree mortality. The development of horizontal heterogeneity was observed in forest gaps. Conclusions: Our observations showed a dramatic shift between the recruitment and mortality rates in the stem exclusion stage, and that disturbance increases the uncertainty in forest development increases. The minor changes in species composition are likely linked to regional species pool and the limited role of the life-history strategy of species such as shade tolerance and habitat affinity. Our midterm records of ecological succession exhibited detailed demographic dynamics and contributed to the improvement of an ecological perspective in the stem exclusion stage.

• The Korean Journal of Applied Statistics
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• v.34 no.3
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• pp.477-489
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• 2021

COVID-19 has spread seriously around the world in 2020 and it is still significantly affecting our whole daily life. Currently, the whole world is still undergoing the pandemic and South Korea is no exception to it. During the pandemic, South Korea had several events that prevented or accelerated its spread. To establish the prevention policies for infectious diseases, it is very important to evaluate the intervention effect of such events. The susceptible-infected-removed (SIR) model is often used to describe the dynamic behavior of the spread of infectious diseases through ordinary differential equations. However, the SIR model is a deterministic model without considering the uncertainty of observed data. To consider the uncertainty in the SIR model, the Bayesian approach can be employed, and this approach allows us to evaluate the intervention effects by time-varying functions of the infection rate in the SIR model. In this study, we describe the time trend of the spread of COVID-19 in South Korea and investigate the intervention effects for the events using the stochastic SIR model based on the Bayesian approach.