• Environmental and Resource Economics Review
• /
• v.27 no.1
• /
• pp.29-66
• /
• 2018

This study constructs an energy-extended KLEM DB of 35 Korean industries, and estimates the elasticities of substitution under various nesting structures of production technologies. Unlike most existing studies that employed only three inputs, K, L, and E, we applied a dual approach where non-energy intermediate input M is also incorporated as a production input. Our dual approach which extended that of van der Werf (2008)'s 3-input model successfully identified and estimated the multi-nested production functions. We provide the estimates of the elasticities of substitution among 4 different energy sources as well. Our estimation results would be used for energy-environment model building for Korea.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2018.05a
• /
• pp.434-434
• /
• 2018

에코톱은 가장 작고 균일하며 도면의 단위로 사용 가능한 토지, 일반적인 구성요소의 상태, 잠재자연식생, 잠재생태계 기능을 최소한의 단위로 균일하게 분류가 가능한 요소로서 천이단계 또는 토지이용이 서로 다른 패치들로 이루어진 무생물과 생물이 결합된 생태공간으로서 일반적으로 세가지 특성을 포함한다. (1) 가장 작은 동질성 가진 지도로 분류 가능한 단위, (2) 일반적인 기질조건, 잠재적 자연식생 및 잠재적 생태계 기능에 대한 동질성, 그리고 (3) 서로 다른 연속적인 토지 이용 단계에서의 패치로 구성 된다. 현재 네덜란드, 스페인을 포함한 유럽국가에서는 에코톱분류를 통한 하천을 관리하는 방안을 제시하고 있으며 이에 대한 많은 연구가 진행되고 있다. 본 연구에서는 만경강 중류 소양천 합류점의 터지네 구간을 대상으로 하천공간의 복원 이후 연중유황에 따른 에코톱의 변화를 예측하고 이에 따른 개선효과를 정량적으로 분석하는 것이 목적이다. 제방 후퇴, 제방후퇴/구하도 복원, 제방 후퇴/습지 조성 세가지 복원 시나리오를 현재지형과 비교하여 연중 유황별 흐름조건에 따라 에코톱을 도식화 하였으며, 이에 따른 에코톱 다양성 지수를 도출하여 비교분석하였다. 복원 대상지의 복원 시나리오 및 흐름조건에 따른 에코톱의 변화를 분석한 결과 '제방 후퇴/구하도 복원' 일 때 자연요소가 현재지형보다 가장 크게 증가되었으며 3가지 복원 유형 간 자연요소를 비교한 결과 '제방 후퇴/구하도 복원' 일 때 수역과 일년생 초본이 가장 많은 면적을 차지하였으며, '제방 후퇴/습지 조성' 일 때 습지와 다년생 초본이 가장 많은 면적을 차지하였다. 복원 유형 별 연중 유황 조건에 따른 에코톱 다양성 지수분석결과 제방후퇴/습지 조성시 에코톱 다양성 개선효과가 가장 큰 것으로 나타났다.

• Journal of Korea Water Resources Association
• /
• v.54 no.9
• /
• pp.705-718
• /
• 2021

Climate change has a significant impact on vegetation growth and terrestrial ecosystems. In this study, the possibility of ecological drought was investigated using satellite remote sensing data. First, the Vegetation Health Index was estimated from the Normalized Difference Vegetation Index and Land Surface Temperature provided by MODIS. Then, a joint probability model was constructed to estimate the possibility of vegetation-related drought in various precipitation/evaporation scenarios in forest areas around 60 major ASOS sites of the Meteorological Administration located throughout Korea. The results of this study show the risk pattern of drought related to forest vegetation under conditions of low atmospheric moisture supply or high atmospheric moisture demand. It also identifies the sensitivity of drought risks associated with forest vegetation under various meterological drought conditions. These findings provide insights for decision makers to assess drought risk and develop drought mitigation strategies related to forest vegetation in a warming era.

• The Korean Journal of Applied Statistics
• /
• v.35 no.3
• /
• pp.385-393
• /
• 2022

In this paper, we consider univariate time series models that are well known in the field of forecasting and we study on forecasting performance for their simple combinations. The univariate time series models include exponential smoothing methods and ARIMA (autoregressive integrated moving average) models, their extended models, and non-seasonal and seasonal random walk models, which is frequently used as benchmark models for forecasting. The median and mean are simply used for the combination method, and the data set used for performance evaluation is M3-competition data composed of 3,003 various time series data. As results of evaluating the performance by sMAPE (symmetric mean absolute percentage error) and MASE (mean absolute scaled error), we assure that the simple combinations of the univariate models perform very well in the M3-competition dataset.

• Korean Journal of Remote Sensing
• /
• v.38 no.5_3
• /
• pp.887-899
• /
• 2022

Drought caused by meteorological factors negatively affects vegetation in terrestrial ecosystems. In this study, the state in which meteorological drought affects vegetation was defined as the ecological drought of vegetation, and the ecological drought condition index of vegetation (EDCI-veg) was proposed to quantitatively monitor the degree of impact. EDCI-veg is derived from a copula-based bi-variate joint probability model between vegetation and meteorological drought information, and can be expressed numerically how affected the current vegetation condition was by the drought when the drought occurred. Comparing past meteorological drought events with their corresponding vegetation condition, the proposed index was examined, and it was confirmed that EDCI-veg could properly monitor the ecological drought of vegetation. In addition, it was possible to spatially identify ecological drought conditions by creating a high-resolution drought map using remote sensing data.

• Journal of Convergence for Information Technology
• /
• v.12 no.3
• /
• pp.116-125
• /
• 2022

The purpose of this study is to analyze whether investor anxiety caused by COVID-19 affects cryptocurrency prices in the COVID-19 pandemic, and to experiment with cryptocurrency price prediction based on a deep learning model. Investor anxiety is calculated by combining Naver's Corona search index and Corona confirmed information, analyzing Granger causality with cryptocurrency prices, and predicting cryptocurrency prices using deep learning models. The experimental results are as follows. First, CCI indicators showed significant Granger causality in the returns of Bitcoin, Ethereum, and Lightcoin. Second, LSTM with CCI as an input variable showed high predictive performance. Third, Bitcoin's price prediction performance was the highest in comparison between cryptocurrencies. This study is of academic significance in that it is the first attempt to analyze the relationship between Naver's Corona search information and cryptocurrency prices in the Corona phase. In future studies, extended studies into various deep learning models are needed to increase price prediction accuracy.

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

• Korea Trade Review
• /
• v.48 no.2
• /
• pp.27-43
• /
• 2023

The maritime industry is playing an increasingly vital part in global economic expansion. Specifically, the Baltic Dry Index is highly correlated with global commodity prices. Hence, the importance of BDI prediction research increases. But, since the global situation has become more volatile, it has become methodologically more difficult to predict the BDI accurately. This paper proposes an integrated machine-learning strategy for accurately forecasting BDI trends. This study combines the benefits of a convolutional neural network (CNN) and long short-term memory neural network (LSTM) for research on prediction. We collected daily BDI data for over 27 years for model fitting. The research findings indicate that CNN successfully extracts BDI data features. On this basis, LSTM predicts BDI accurately. Model R2 attains 94.7 percent. Our research offers a novel, machine-learning-integrated approach to the field of shipping economic indicators research. In addition, this study provides a foundation for risk management decision-making in the fields of shipping institutions and financial investment.

• Journal of the Korea Society for Simulation
• /
• v.33 no.1
• /
• pp.19-26
• /
• 2024

The phase-type, PH, distribution is defined as the time to absorption into a terminal state in a continuous-time Markov chain. As the PH distribution includes family of exponential distributions, it has been widely used in stochastic models. Since the PH distribution is represented and generated by an initial probability vector and a generator matrix which is called the Markovian representation, we need to find a vector and a matrix that are consistent with given set of moments if we want simulate a PH distribution. In this paper, we propose an approach to simulate a PH distribution based on distribution function which can be obtained directly from moments. For the simulation of PH distribution of order 2, closed-form formula and streamlined procedures are given based on the Jordan decomposition and the minimal Laplace transform which is computationally more efficient than the moment matching methods for the Markovian representation. Our approach can be used more effectively than the Markovian representation in generating higher order PH distribution in queueing network simulation.

• Journal of Advanced Navigation Technology
• /
• v.15 no.3
• /
• pp.392-404
• /
• 2011

In this paper, we address the issue of joint detection schemes for uplink cellular system when base station cooperation is possible for multi-user detection in multi-cell scenario. The ZF, ML, MMSE and SIC detection are analyzed and evaluated as a conventional scheme. ML attains the optimal performance but the complexity increases exponentially, ZF/MMSE have simple structure but have poor detection performance and SIC has better performance but it has large complexity and potential of the error propagation. However, they need the increased decoder complexity as the number of iteration is increased. We propose a new joint ML and ZF/MMSE detection scheme, which combines the partial ML decoding and ZF/MMSE detection, in order to decrease the decoder complexity. Simulation results show that the proposed scheme attains same or a little bit better BER performance and expect reduced decoder complexity, specially in the case of large number of Base Station are cooperated each other.