• 대기
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• 제26권3호
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• pp.373-386
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• 2016

The future variability of Wind Energy Density (WED) over the Korean Peninsula under RCP climate change scenario is projected using ensemble analysis. As for the projection of the future WED, changes between the historical period (1981~2005) and the future projection (2021~2050) are examined by analyzing annual and seasonal mean, and Coefficient of Variation (CV) of WED. The annual mean of WED in the future is expected to decrease compared to the past ones in RCP 4.5 and RCP 8.5 respectively. However, the CV is expected to increase in RCP 8.5. WEDs in spring and summer are expected to increase in both scenarios RCP 4.5 and RCP 8.5. In particular, it is predicted that the variation of CV for WED in winter is larger than other seasons. The time series of WED for three major wind farms in Korea exhibit a decrease trend over the future period (2021~2050) in Gochang for autumn, in Daegwanryeong for spring, and in Jeju for autumn. Through analyses of the relationship between changes in wind energy and pressure gradients, the fact that changes in pressure gradients would affect changes in WED is identified. Our results can be used as a background data for devising a plan to develop and operate wind farm over the Korean Peninsula.

• 응용통계연구
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• 제34권3호
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• pp.401-410
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• 2021

판별분류분석에서 널리 이용되는 k-최근접 이웃 분류 방법은 고정된 이웃의 수만을 고려하여 자료의 국소적 특징을 반영하지 못하는 한계가 있다. 이에 자료의 국소적 구조를 고려하여 이웃의 개수를 선택하는 적응 최근접이웃방법이 개발된 바 있다. 고차원 자료의 분석에 있어서는 k-최근접 이웃 분류를 사용하기 전에 랜덤 투영 기법 등을 활용하여 차원 축소를 수행하는 것이 일반적이다. 이렇게 랜덤 투영시킨 다수의 분류 결과들을 면밀히 조합하여 투표를 통해 최종 할당을 하는 기법이 최근 개발된 바 있다. 본 연구에서는 고차원 자료에서의 분석을 위해 적응 최근접이웃방법과 랜덤 투영 앙상블 기법을 조합한 새로운 판별분류 기법을 제안하였다. 제안된 방법은 기존에 개발된 방법에 비해 분류 정확성 측면에서 더 뛰어남을 모의실험 및 실제 사례 분석을 통해 확인하였다.

• 한국자기공명학회논문지
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• 제27권4호
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• pp.28-34
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• 2023

We study the linear-nonlinear quantum transport theory of Wannier-Landau transition system in the confinement of electrons by a square well confinement potential. We use the projected Liouville equation method with the ensemble density projection technique. We select the dynamic value under a linearly oscillatory external field. We derive the dynamic value formula and the memory factor functions in three electron phonon coupling systems and electron impurity coupling systems of two transition types, the intra-band transitions and inter-band transitions. We obtain results that can be applied directly to numerical analyses. For simple example of application, we analyze the absorption power and line-widths of ZnO, through the numerical calculation of the theoretical result in the Landau system.

• Communications for Statistical Applications and Methods
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• 제27권1호
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• pp.109-128
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• 2020

A climate-impact projection usually consists of several stages, and the uncertainty of the projection is known to be quite large. It is necessary to assess how much each stage contributed to the uncertainty. We call an uncertainty quantification method in which relative contribution of each stage can be evaluated as uncertainty decomposition. We propose a new Bayesian model for uncertainty decomposition in climate change impact assessments. The proposed Bayesian model can incorporate uncertainty of natural variability and utilize data in control period. We provide a simple and efficient Gibbs sampling algorithm using the auxiliary variable technique. We compare the proposed method with other existing uncertainty decomposition methods by analyzing streamflow data for Yongdam Dam basin located at Geum River in South Korea.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권8호
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• pp.2030-2052
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• 2023

Analysis of a bipartite (two-mode) network is a significant research area to understand the formation of social communities, economic systems, drug side effect topology, etc. in complex information systems. Most of the previous works talk about a projection-based model or latent feature model, which predicts the link based on singular similarity. The projection-based models suffer from the loss of structural information in the projected network and the latent feature is hardly present. This work proposes a novel method for link prediction in the bipartite network based on an ensemble of composite similarities, overcoming the issues of model-based and latent feature models. The proposed method analyzes the structure, neighborhood nodes as well as latent attributes between the nodes to predict the link in the network. To illustrate the proposed method, experiments are performed with five real-world data sets and compared with various state-of-art link prediction methods and it is inferred that this method outperforms with ~3% to ~9% higher using area under the precision-recall curve (AUC-PR) measure. This work holds great significance in the study of biological networks, e-commerce networks, complex web-based systems, networks of drug binding, enzyme protein, and other related networks in understanding the formation of such complex networks. Further, this study helps in link prediction and its usability for different purposes ranging from building intelligent systems to providing services in big data and web-based systems.

• 한국우주과학회:학술대회논문집(한국우주과학회보)
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• 한국우주과학회 1995년도 한국우주과학회보 제4권1호
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• pp.21-21
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• 1995

The structures of 17GHz microwave burst for bipolar sunspots have investigated. which included the effects of the projected shapes of radio sources as they traverse across the solar disk using a magnetic loop employing a model of solenoid coils. An ensemble of high-energy electrons confined in the loop be assumed. The projected brightnesls distributions of gyrosynchrotron emission in x- and o-modes are computed and converted into total intensity and circular polarization difference at 17GHz for various heliocentric distances using numerical integration of the transfer equation along the line of sight. The results of computations at 17GHz for optical thin case will be presented. and the effects of the orientation of the loop will be discussed in detail, as well as the effect of size, position, Structure, and polarization of the emission. Also the results of the various physical P8lrameters such as the strength of magnetic field. high and low energy cut-off of accelerated electrons. spectral index and density of electrons will be preslmted. After comparing the results of model calculation with observations. we found that the observations can be well explained in terms of a loop model and its projection effect.effect.

• 대기
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• 제24권3호
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• pp.403-417
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• 2014

Future changes in seasonal mean temperature and precipitation over East Asia under anthropogenic global warming are investigated by comparing the historical run for 1979~2005 and the Representative Concentration Pathway (RCP) 4.5 run for 2006~2100 with 20 coupled models which participated in the phase five of Coupled Model Inter-comparison Project (CMIP5). Although an increase in future temperature over the East Asian monsoon region has been commonly accepted, the prediction of future precipitation under global warming still has considerable uncertainties with a large inter-model spread. Thus, we select best five models, based on the evaluation of models' performance in present climate for boreal summer and winter seasons, to reduce uncertainties in future projection. Overall, the CMIP5 models better simulate climatological temperature and precipitation over East Asia than the phase 3 of CMIP and the five best models' multi-model ensemble (B5MME) has better performance than all 20 models' multi-model ensemble (MME). Under anthropogenic global warming, significant increases are expected in both temperature and land-ocean thermal contrast over the entire East Asia region during both seasons for near and long term future. The contrast of future precipitation in winter between land and ocean will decrease over East Asia whereas that in summer particularly over the Korean Peninsula, associated with the Changma, will increase. Taking into account model validation and uncertainty estimation, this study has made an effort on providing a more reliable range of future change for temperature and precipitation particularly over the Korean Peninsula than previous studies.

• 한국환경복원기술학회지
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• 제19권6호
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• pp.19-30
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• 2016

The projection of climate-related range shift is critical information for conservation planning of Korean fir (Abies koreana E. H. Wilson). We first modeled the distribution of Korean fir under current climate condition using five single-model species distribution models (SDMs) and the pre-evaluation weighted ensemble method and then predicted the distributions under future climate conditions projected with HadGEM2-AO under four $CO_2$ emission scenarios, the Representative Concentration Pathways (RCP) 2.6, 4.5, 6.0 and 8.5. We also investigated the predictive uncertainty stemming from five individual algorithms and four $CO_2$ emission scenarios for better interpretation of SDM projections. Five individual algorithms were Generalized linear model (GLM), Generalized additive model (GAM), Multivariate adaptive regression splines (MARS), Generalized boosted model (GBM) and Random forest (RF). The results showed high variations of model performances among individual SDMs and the wide range of diverging predictions of future distributions of Korean fir in response to RCPs. The ensemble model presented the highest predictive accuracy (TSS = 0.97, AUC = 0.99) and predicted that the climate habitat suitability of Korean fir would increase under climate changes. Accordingly, the fir distribution could expand under future climate conditions. Increasing precipitation may account for increases in the distribution of Korean fir. Increasing precipitation compensates the negative effects of increasing temperature. However, the future distribution of Korean fir is also affected by other ecological processes, such as interactions with co-existing species, adaptation and dispersal limitation, and other environmental factors, such as extreme weather events and land-use changes. Therefore, we need further ecological research and to develop mechanistic and process-based distribution models for improving the predictive accuracy.

• 대기
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• 제27권2호
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• pp.133-150
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• 2017

This study evaluates CMIP5 model performance on rainy season evolution in the East Asian summer monsoon. Historical (1986~2005) simulation is analyzed using ensemble mean of CMIP5 19 models. Simulated rainfall amount is underestimated than the observed and onset and termination of rainy season are earlier in the simulation. Compared with evolution timing, duration of the rainy season is uncertain with large model spread. This area-averaged analysis results mix relative differences among the models. All model show similarity in the underestimated rainfall, but there are quite large difference in dynamic and thermodynamic processes. The model difference is shown in horizontal distribution analysis. BEST and WORST group is selected based on skill score. BEST shows better performance in northward movement of the rain band, summer monsoon domain. Especially, meridional gradient of equivalent potential temperature and low-level circulation for evolving frontal system is quite well captured in BEST. According to RCP8.5, CMIP5 projects earlier onset, delayed termination and longer duration of the rainy season with increasing rainfall amount at the end of 21st century. BEST and WORST shows similar projection for the rainy season evolution timing, meanwhile there are large discrepancy in thermodynamic structure. BEST and WORST in future projection are different in moisture flux, vertical structure of equivalent potential temperature and the subsequent unstable changes in the conditional instability.

• 전자공학회논문지CI
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• 제40권1호
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• pp.19-29
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• 2003

본 논문은 인위적으로 생성된 가상 학습 데이터와 융합 분류기를 이용한 얼굴인식 알고리즘을 제안한다. 특징공간에서의 최근접 특징 선택 방법과 연결주의 모델에 기반한 서로 다른 형태의 분류기를 융합하여 통합효과를 얻도록 하였다. 두 분류기는 모두 학습 데이터의 공간적인 분포에 따라 생성된 가상 학습데이터를 이용하여 학습되고 이용된다. 첫째로, 특징 공간에서의 각 정보(Angular Infnrmation) 를 이용하는 최근접특징각(the Nearest Feature Angle : NFA)을 이용하여 저장된 학습데이터와 가장 근접한 것을 찾고, 둘째로, 질의(Query) 얼굴 특징 정보를 정면얼굴 영상의 특징정보로 투영하여 얻은 정보에 기반한 분류기의 결과를 이용한다. 정면영상 특징정보로의 투영은 다층 신경망을 이용하여 정면 회상망(Frontal Recall Network)을 구현하였고, 이것을 여러 개 묶어 앙상블 네트웍으로 구성한 Ensemble 회상망(Ensemble Recall Network)을 사용하여 일반화 성능을 향상시켰다. 끝으로, 각 분류기의 결과에 따라 융합 분류기가 최종 결과를 선택하도록 하였다. 제안된 알고리즘을 6 종류의 서고 다른 학습/시험데이터 군에 적용하여 평균 96.33%의 인식률을 얻었다. 이것은 특징라인에 기반한 방법(the Nearest Feature Line) 평균 에러율의 61.2% 이며, 단일 분류기를 사용한 경우 보다 안정된 견과를 얻고 있다.