ScholarWorks@Dongguk University: Lee, Yung seop

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Lee, Yung seop (이영섭)

College of Natural Science (Department of Statistics)

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Tel 02-2260-3218
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Classification:3||deep learning:3||Deep learning:3||pollen:3||ARIMA:3||Pollen allergy:2||textmining:2||Climate change:2||Pollen:2||텍스트 마이닝:2||canonical forest:2||Cluster analysis:2||기후변화:2||pollen allergy:2||unstructured format:2||children:2||rotation forest:2||climate change:2||random rotation ensemble:2||feature selection:2||mask RCNN:2||Combined sewer overflows:1||Correlation:1||Cyber physics systems:1||Data fusion:1||Decision tree:1||Dependent random probability measures:1||Dirichlet process mixture:1||Downscaling:1||EM algorithm:1||Earthquake network:1||Electromagnetic compatibility:1||Ensemble:1||Estimator measure:1||Explainable AI:1||Face:1||Finite element analysis:1||Fungal spores:1||Global efficiency:1||Global horizontal irradiance:1||Hackensack River:1||IDW method:1||IDW 방법:1||Image:1||India:1||Interpolation:1||KMeans method:1||KMeans 방법:1||Latent Dirichlet allocation:1||Linear dimension reduction:1||MAE(Mean Absolute Error):1||Machine learning:1||MannKendall test:1||Manufacturing:1||Match Fixing:1||Memory semiconductors:1||Mixture models:1||Modularity:1||Multiview learning:1||Multivariate analysis:1||Natural language processing:1||Optimization:1||Packaging:1||Partial collapse:1||Partial least squares  discriminant analysis:1||Pettitt test:1||Photogrammetry:1||Pooled standard deviation method:1||Principal component analysis:1||RMSE(Root Mean Square Error):1||Random access memory:1||Random forest:1||Regression model:1||Respiratory viral infection:1||Risk grade:1||SARIMA:1||SARIMA(Seasonal ARIMA):1||Sasang constitution:1||Seasonal ARIMA:1||Sen's slope:1||Sex characteristics:1||Shortterm solar irradiance forecasting:1||Smallworldness:1||Solar irradiance:1||Sports:1||Standardizedt bootstrap method:1||Stepwise regression:1||Strain:1||Stress:1||Substrates:1||Symptom index:1||Temporal structured data:1||Text:1||Textmining:1||The Korea Meteorological Administration:1||Threshold level:1||Time series analysis:1||Topic Modeling:1||Uncertainty:1||Unstructured format:1||Urban estuary:1||VAR:1||Variable importance:1||Visualization:1||WMO registered stations:1||Ward method.:1||Ward 방법:1||Water quality:1||Wind prediction model:1||Wire:1||Word cloud:1||Xavier initialization:1||adjusted normal ratio methods:1||air pollutant:1||allergic disease:1||anthroscopy:1||attention:1||bootstrap:1||change point:1||classification:1||cluster analysis:1||comparative analysis:1||confidence interval:1||consecutive missing value:1||convolution neural network:1||corpus linguistics:1||correlation:1||correlation integral method:1||data integration:1||deep sequencing:1||deformable convolutional network:1||deformable convolutional networks:1||density analysis:1||diagnosis:1||dissimilarity:1||document classification:1||ensemble:1||exponential distribution:1||face:1||fractal dimension:1||functional data analysis:1||greenhouse gas emission scenario:1||gridded data of numerical model:1||group feature:1||hierarchical clustering:1||initialization:1||inventory uncertainty:1||long short term memory:1||machine learning:1||manufacturing industry:1||maximumlikelihood method:1||mean restrictions:1||meta analysis:1||meteorological data:1||meteorological stations:1||microarray gene expression data:1||microarrays:1||missing value imputation:1||mold:1||multimodal learning:1||multiview learning:1||multiclass classification:1||neural network models:1||object detection:1||packaging optimization:1||permutation:1||photogrammetry:1||photovoltaic power:1||precipitation:1||procrustes align:1||quality characteristics:1||recurrent neural network:1||regional prediction:1||regression analysis:1||regression method:1||respiratory symptom:1||restricted normal mixture model:1||saturation:1||seasonal average temperature:1||semantic change:1||severe weather announcement:1||similarity:1||skin prick test:1||solar irradiance:1||statistical design of experiments (DOEs):1||supervised classification:1||target clustering:1||temperature forecasting:1||temperatures:1||termdocument matrix:1||textmining:1||the Korea Meteorological Administration:1||time lag analysis:1||time series:1||trace data:1||trend analysis:1||truncated distribution:1||unstructured data:1||unsupervised classification:1||wind farm:1||wind power forecasting:1||word cloud:1||word2vec:1||강수량:1||객체탐색:1||결측값 대체:1||계층적 군집분석:1||군산풍력단지:1||군집분석:1||군집분석 보간법:1||기상 자료:1||기상관서:1||기상관측지점:1||기온:1||딥러닝:1||메타분석:1||문서분류:1||밀도분석:1||분류:1||붓스트랩:1||비대칭형 분포:1||비정형 자료:1||상관관계:1||상관적분:1||상세화:1||세계기상기구 등록 기후관측소:1||수정된 정규화비율 방법:1||수치모델 격자자료:1||수평면전사량:1||스포츠:1||승부조작:1||시게열 모형:1||시계열 분석:1||시계열 분석(Time series analysis):1||시계열모형:1||신경망모형:1||신뢰구간:1||앙상블:1||연속적 결측:1||예측 평가 척도:1||온실가스 배출 시나리오:1||용어문서 행렬:1||인공 신경망 모형:1||인벤토리 불확도:1||일사량:1||지수분포:1||초단시간 일사량 예보:1||태양광 발전량(Solar power generation):1||토픽모델링:1||판례분석:1||풍력 예측 모형:1||풍력발전량 예측:1||프랙탈 차원:1||회귀 모형:1||회귀 방법:1||회귀분석:1||ARIMA (Autoregressive intergrated moving average):1||ARMA models:1||Allergen:1||Alternaria:1||Analyzing Precedents:1||Anthropogenic effect:1||Artificial neural network:1||Assortative coefficient:1||Asymmetric distribution:1||Autoregressive integrated moving average:1||Autoregressive species sampling models:1||Average clustering coefficient:1||Bayesian analysis:1||Beauty:1||Bootstrap:1||CH4 emission:1||Call center:1||Canonical linear discriminant analysis:1||Cladosporium:1||Climatological station:1||

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