• Title/Summary/Keyword: 예측 인자

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Prediction and Analysis of PM2.5 Concentration in Seoul Using Ensemble-based Model (앙상블 기반 모델을 이용한 서울시 PM2.5 농도 예측 및 분석)

  • Ryu, Minji;Son, Sanghun;Kim, Jinsoo
    • Korean Journal of Remote Sensing
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    • v.38 no.6_1
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    • pp.1191-1205
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    • 2022
  • Particulate matter(PM) among air pollutants with complex and widespread causes is classified according to particle size. Among them, PM2.5 is very small in size and can cause diseases in the human respiratory tract or cardiovascular system if inhaled by humans. In order to prepare for these risks, state-centered management and preventable monitoring and forecasting are important. This study tried to predict PM2.5 in Seoul, where high concentrations of fine dust occur frequently, using two ensemble models, random forest (RF) and extreme gradient boosting (XGB) using 15 local data assimilation and prediction system (LDAPS) weather-related factors, aerosol optical depth (AOD) and 4 chemical factors as independent variables. Performance evaluation and factor importance evaluation of the two models used for prediction were performed, and seasonal model analysis was also performed. As a result of prediction accuracy, RF showed high prediction accuracy of R2 = 0.85 and XGB R2 = 0.91, and it was confirmed that XGB was a more suitable model for PM2.5 prediction than RF. As a result of the seasonal model analysis, it can be said that the prediction performance was good compared to the observed values with high concentrations in spring. In this study, PM2.5 of Seoul was predicted using various factors, and an ensemble-based PM2.5 prediction model showing good performance was constructed.

Statistical Analysis for Risk Factors and Prediction of Hypertension based on Health Behavior Information (건강행위정보기반 고혈압 위험인자 및 예측을 위한 통계분석)

  • Heo, Byeong Mun;Kim, Sang Yeob;Ryu, Keun Ho
    • Journal of Digital Contents Society
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    • v.19 no.4
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    • pp.685-692
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    • 2018
  • The purpose of this study is to develop a prediction model of hypertension in middle-aged adults using Statistical analysis. Statistical analysis and prediction models were developed using the National Health and Nutrition Survey (2013-2016).Binary logistic regression analysis showed statistically significant risk factors for hypertension, and a predictive model was developed using logistic regression and the Naive Bayes algorithm using Wrapper approach technique. In the statistical analysis, WHtR(p<0.0001, OR = 2.0242) in men and AGE (p<0.0001, OR = 3.9185) in women were the most related factors to hypertension. In the performance evaluation of the prediction model, the logistic regression model showed the best predictive power in men (AUC = 0.782) and women (AUC = 0.858). Our findings provide important information for developing large-scale screening tools for hypertension and can be used as the basis for hypertension research.

Forecasting of Pine-Mushroom Yield Using the Conditional Autoregressive Model (조건부 자기회귀모형을 이용한 송이버섯 생산량 예측)

  • 이진희;신기일
    • The Korean Journal of Applied Statistics
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    • v.13 no.2
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    • pp.307-320
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    • 2000
  • It has been studied to find relationships between pine-mushroom yield and climatic factors. Recently, Hyun-Park, Key-I! shin and Hyun-Joong Kim(1998) investigated relationships between pine-mushroom yield and climatic factors by autoregression model. In this paper, to improve the forecast we suggest the conditional autoregression model using probability of existing pine-mushroom production.

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Optimization of Generalized Regression Neural Network Using Statistical Processing (통계적 처리를 이용한 일반화된 회귀 신경망의 분류성능의 최적화)

  • Kim, Geun-Ho;Kim, Byun-Whan
    • Proceedings of the KIEE Conference
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    • 2002.07d
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    • pp.2749-2751
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    • 2002
  • 일반화된 회귀 신경망 (GRNN)을 이용하여 플라즈마을 분류하는 새로운 알고리즘을 보고한다. 데이터분포를 통계적인 평균치와 표준편차를 이용하여 특징지었으며, 바이어스 인자을 이용하여 9 종류의 데이터을 발생하였다. 각 데이터에 대하여 GRNN의 학습인자를 최적화하였으며, 모델성능은 예측과 분류 정확도로 나누어 바이어스와 학습인자의 함수로 분석하였다. 바이어스는 모델성능에 상당한 영향을 주었으며, 학습인자와의 상호작용을 통하여 완전 분류를 이루었다.

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A Study on Improvement of Gravity model Decay Function of Transporting Demand Forecasting Considering Space Syntax (Space Syntax를 이용한 교통수요예측의 중력모형 저항함수의 개선방안)

  • Jang, Jin-Young
    • Journal of the Korea Academia-Industrial cooperation Society
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    • v.20 no.3
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    • pp.617-631
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    • 2019
  • In the four-step demand model, a gravity mode is used most commonly at the trip distribution stage. The purpose of this study was to develop a new friction factor that can express the accessibility property as a single friction factor to compensate for the variable limits of the gravity model parameters (travel time, travel cost). To derive a new friction factor, a new friction factor was derived using the space syntax that can quantify the characteristics of the urban space structure, deriving the link-unit integration degree and then using the travel time and travel distance relationship. Calibration of the derived friction factor resulted in a similar level to that of the existing friction factor. As a result of verifying the various indicators, the explanatory power was found to be excellent in the short - and long - distance range. Therefore, it is possible to derive and apply the new friction factor using the integration index, which can complement the accessibility beyond the limit of the existing shortest distance, and it is believed to be more advantageous in future utilization.

Modeling of plasma etch process using genetic algorithm optimization of neural network initial weights (유전자 알고리즘-응용 역전파 신경망 웨이트 최적화 기법을 이용한 플라즈마 식각 공정 모델링)

  • Bae, Jung-Gi;Kim, Byung-Whan
    • Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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    • 2004.11a
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    • pp.272-275
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    • 2004
  • 플라즈마 식각공정은 소자제조를 위한 미세 패턴닝 제작에 이용되고 있다. 공정 메커니즘의 정성적 해석, 최적화, 그리고 제어를 위해서는 컴퓨터 예측모델의 개발이 요구된다. 역전파 신경망 (backpropagation neural network-BPNN) 모델을 개발하는 데에는 다수의 학습인자가 관여하고 있으며, 가장 그 최적화가 어려운 학습인자는 초기웨이트이다. 모델개발시, 초기웨이트는 random 값으로 설정이 되며, 이로 인해 초기웨이트의 최적화가 어렵다. 본 연구에서는 유전자 알고리즘 (genetic algorithm-GA)을 이용하여 BPNN의 초기웨이트를 최적화하였으며, 이를 식각공정 모델링에 적용하여 평가하였다. 실리카 식각공정 데이터는 $2^3$ 인자 실험계획법을 이용하여 수집하였으며, GA에 관여하는 두 확률인자의 영향을 42 인자 실험계획법을 이용하여 최적화 하였다. 종래의 모델에 비해, 최적화된 모델은 실리카 식각률, Al 식각률, Al 선택비, 그리고 프로파일 응답에 대해서 각 기 24%, 13%,, 16%, 그리고 17%의 향상률을 보였다. 이는 제안된 최적화 기법이 플라즈마 모델의 예측성능을 증진하는데 효과적으로 응용될 수 있음을 의미한다.

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Predictors of Emergency Medical Transports Use Based on 2009 Korea Health Panel (응급환자 이송 서비스의 이용 특성과 예측 인자: 한국의료패널 2009년 데이터를 중심으로)

  • Kang, Kyunghee
    • Fire Science and Engineering
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    • v.28 no.3
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    • pp.80-86
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    • 2014
  • Based on 2009 Korea Health Panel, this study investigated socio-economic and clinical characteristics associated with emergency medical transport use, and analyzed a simple predictive model of emergency medical transport use. Analysis results were summarized as follows: First, emergency medical transports such as 119 ambulance were more used than private cars, taxis, or walk-in. Second, between a user group and a non-user group of emergency medical transports, there were statistically significant differences in age, the level of education, family composition, house type, household income, the relationship with the head of household, insurance types, the presence of handicap, the presence of chronic disease, reasons to emergency medical service use, and treatment after emergency medical service completed. Third, age, household income, the presence of handicap, reasons to emergency medical service use, and treatment after emergency medical service completed were statistically significant predictors associated with emergency medical transports use. To improve emergency medical service system, the characteristics and predictors associated with emergency medical transports are more concerned.

Correlations between Objective and Sensory Texture Measurement of Acorn Mook (객관적.주관적 검사방법에 의한 도토리묵의 텍스쳐 특성 연구)

  • 김영아;이혜수
    • Korean journal of food and cookery science
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    • v.3 no.2
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    • pp.68-74
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    • 1987
  • Objective and subjective methods were performed together for TPA analysis of acorn mook, and their correlations were analyzed. As the result of sensory evaluation, hardness and fracturability were most important factors for prediction of preference. Meanwhile, compression test with Instron Universal Testing Machine revealed that P1(maximum peak in first bite) was very effective factor representing the cheracteristics of first bite, and that P2 the latter peak in first bite) was valuable for prediction of characteristics of second bite.

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