• Title/Summary/Keyword: 모형적합도

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통계적 분류방법을 이용한 문화재 정보 분석

  • Kang, Min-Gu;Sung, Su-Jin;Lee, Jin-Young;Na, Jong-Hwa
    • Proceedings of the Korea Society for Industrial Systems Conference
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    • 2009.05a
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    • pp.120-125
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    • 2009
  • 본 논문에서는 통계적 분류방법을 이용하여 문화재 자료의 분석을 수행하였다. 분류방법으로는 선형판별분석, 로지스틱회귀분석, 의사결정나무분석, 신경망분석, SVM분석을 사용하였다. 각각의 분류방법에 대한 개념 및 이론에 대해 간략히 소개하고, 실제자료 분석에서는 "지역별 문화재 통계분석 및 모형개발 연구 1차(2008)"에 사용된 자료 중 익산시 자료를 근거로 매장문화재에 대한 분류방법별 적합모형을 구축하였다. 구축된 모형과 모의실험의 결과를 통해 각각의 적합모형에 대한 비교를 수행하여 모형의 성능을 비교하였다. 분석에 사용된 도구로는 최근 가장 관심을 갖는 R-project를 사용하였다.

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유비쿼터스 환경 적용을 위한 일반적 상황 모형 구축

  • Park Tae-Hwan;Choe Geun-Ho;Gwon O-Byeong
    • Proceedings of the Korea Inteligent Information System Society Conference
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    • 2006.06a
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    • pp.133-140
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    • 2006
  • 유비쿼터스 컴퓨팅 기술이 발전함에 따라 상황인지는 유비쿼터스 환경을 구성하는 중요한 요소들 중의 하나로 안정되고 있다. 그러나 아직까지 모든 유비쿼터스 컴퓨팅 환경에 적용될만한 일반적인 상황모형 (generic context model)은 제시되지 못하고 있다. 따라서 본 논문의 목적은 혼합형 다단계(hybrid multi-level) 다이어그램을 이용해 보다 적합한 일반적 상황모형을 제안하는 것이다. 이를 위해 먼저 기존의 다양한 상황 모형들을 분석하고 이를 토대로 유비쿼터스 지능 공간에서의 컴퓨팅 시스템 환경에 적합한 일반적 상황모형을 제안하였다.

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Short-term Peak Power Demand Forecasting using Model in Consideration of Weather Variable (기상 변수를 고려한 모델에 의한 단기 최대전력수요예측)

  • 고희석;이충식;최종규;지봉호
    • Journal of the Institute of Convergence Signal Processing
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    • v.2 no.3
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    • pp.73-78
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    • 2001
  • BP neural network model and multiple-regression model were composed for forecasting the special-days load. Special-days load was forecasted using that neural network model made use of pattern conversion ratio and multiple-regression made use of weekday-change ratio. This methods identified the suitable as that special-days load of short and long term was forecasted with the weekly average percentage error of 1∼2[%] in the weekly peak load forecasting model using pattern conversion ratio. But this methods were hard with special-days load forecasting of summertime. therefore it was forecasted with the multiple-regression models. This models were used to the weekday-change ratio, and the temperature-humidity and discomfort-index as explanatory variable. This methods identified the suitable as that compared forecasting result of weekday load with forecasting result of special-days load because months average percentage error was alike. And, the fit of the presented forecast models using statistical tests had been proved. Big difficult problem of peak load forecasting had been solved that because identified the fit of the methods of special-days load forecasting in the paper presented.

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An Approach for the NHPP Software Reliability Model Using Erlang Distribution (어랑 분포를 이용한 NHPP 소프트웨어 신뢰성장 모형에 관한 연구)

  • Kim Hee-Cheul;Choi Yue-Soon;Park Jong-Goo
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.10 no.1
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    • pp.7-14
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    • 2006
  • The finite failure NHPP models proposed in the literature exhibit either constant, monotonic increasing or monotonic decreasing failure occurrence rates per fault. In this paper, we propose the Erlang reliability model, which can capture the increasing nature of the failure occurrence rate per fault. Equations to estimate the parameters of the Erlang finite failure NHPP model based on failure data collected in the form of inter-failure times are developed. For the sake of proposing shape parameter of the Erlang distribution, we used to the goodness-of-fit test of distribution. Data set, where the underlying failure process could not be adequately described by the existing models, which motivated the development of the Erlang model. Analysis of the failure data set which led us to the Erlang model, using arithmetic and Laplace trend tests, goodness-of-fit test, bias tests is presented.

Test of Homogeneity for Intermittent Panel AR(1) Processes and Application (간헐적인 패널 1차 자기회귀과정들의 동질성 검정과 적용)

  • Lee, Sung Duck;Kim, Sun Woo;Jo, Na Rae
    • The Korean Journal of Applied Statistics
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    • v.27 no.7
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    • pp.1163-1170
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    • 2014
  • The concepts and structure of intermittent panel time series data are introduced. We suggest a Wald test statistic for the test of homogeneity for intermittent panel first order autoregressive model and its limit distribution is derived. We consider the fitting the model with pooling data using sample mean at the time point if homogeneity for intermittent panel AR(1) is satisfied. We performed simulations to examine the limit distribution of the homogeneity test statistic for intermittent panel AR(1). In application, we fit the intermittent panel AR(1) for panel Mumps data and investigate the test of homogeneity.

Development of Accident Prediction Models for Freeway Interchange Ramps (고속도로 인터체인지 연결로에서의 교통사고 예측모형 개발)

  • Park, Hyo-Sin;Son, Bong-Su;Kim, Hyeong-Jin
    • Journal of Korean Society of Transportation
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    • v.25 no.3
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    • pp.123-135
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    • 2007
  • The objective of this study is to analyze the relationship between traffic accidents occurring at trumpet interchange ramps according to accident type as well as the relevant factors that led to the traffic accidents, such as geometric design elements and traffic volumes. In the process of analysis of the distribution of traffic accidents, negative binomial distribution was selected as the most appropriate model. Negative binomial regression models were developed for total trumpet interchange ramps, direct ramps, loop ramps and semi-direct ramps based on the negative binomial distribution. Based upon several statistical diagnostics of the difference between observed accidents and predicted accidents with four previously developed models, the fit proved to be reasonable. Understanding of statistically significant variables in the developed model will enable designers to increase efficiency in terms of road operations and the development of traffic accident prevention policies in accordance with road design features.

A Study on Domestic Drama Rating Prediction (국내 드라마 시청률 예측 및 영향요인 분석)

  • Kang, Suyeon;Jeon, Heejeong;Kim, Jihye;Song, Jongwoo
    • The Korean Journal of Applied Statistics
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    • v.28 no.5
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    • pp.933-949
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    • 2015
  • Audience rating competition in the domestic drama market has increased recently due to the introduction of commercial broadcasting and diversification of channels. There is now a need for thorough studies and analysis on audience rating. Especially, a drama rating is an important measure to estimate advertisement costs for producers and advertisers. In this paper, we study the drama rating prediction models using various data mining techniques such as linear regression, LASSO regression, random forest, and gradient boosting. The analysis results show that initial drama ratings are affected by structural elements such as broadcasting station and broadcasting time. Average drama ratings are also influenced by earlier public opinion such as the number of internet searches about the drama.

A Review for Non-linear Models Describing Temperature-dependent Development of Insect Populations: Characteristics and Developmental Process of Models (비선형 곤충 온도발육모형의 특성과 발전과정에 대한 고찰)

  • Kim, Dong-Soon;Ahn, Jeong Joon;Lee, Joon-Ho
    • Korean journal of applied entomology
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    • v.56 no.1
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    • pp.1-18
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    • 2017
  • Temperature-dependent development model is an essential component for forecasting models of insect pests as well as for insect population models. This study reviewed the nonlinear models which explain the relationship between temperature and development rate of insects. In the present study, the types of models were classified largely into empirical and biophysical model, and the groups were subdivided into subgroups according to the similarity of mathematical equations or the connection with original idea. Empirical models that apply analytical functions describing the suitable shape of development curve were subdivided into multiple subgroups as Stinner-based types, Logan-based types, performance models and Beta distribution types. Biophysical models based on enzyme kinetic reaction were grouped as monophyletic group leading to Eyring-model, SM-model, SS-mode, and SSI-model. Finally, we described the historical development and characteristics of non-linear development models and discussed the availability of models.

Future Weather Generation with Spatio-Temporal Correlation for the Four Major River Basins in South Korea (시공간 상관성을 고려한 일기산출기 모형을 이용한 4대강 유역별 미래 일기 변수 산출)

  • Lee, Dong-Hwan;Lee, Jae-Yong;Oh, Hee-Seok;Lee, Young-Jo
    • The Korean Journal of Applied Statistics
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    • v.25 no.2
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    • pp.351-362
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    • 2012
  • Weather generators are statistical tools to produce synthetic sequences of daily weather variables. We propose the multisite weather generators with a spatio-temporal correlation based on hierarchical generalized linear models. We develop a computational algorithm to produce future weather variables that use three different types of green-house gases scenarios. We apply the proposed method to a daily time series of precipitation and average temperature for South Korea.

자료포락분석을 활용한 신약개발 분야 국가연구개발사업의 효율성 분석

  • Eom, Ik-Cheon;Baek, Cheol-U;Hong, Se-Ho
    • Proceedings of the Korea Technology Innovation Society Conference
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    • 2015.11a
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    • pp.430-443
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    • 2015
  • 신약개발은 국민의 건강권과 직결되며 국가 차원의 제약산업 육성을 위해 매우 중요하다. 그동안 신약개발 분야와 관련하여 다양한 연구가 수행되었지만, 신약개발 분야의 국가연구개발사업에 대한 효율성 분석은 매우 부족한 실정이다. 특히 R&D 분야는 연구개발시차, 일출효과(spill-over effect) 등으로 인해 투입과 산출의 일정한 방향성과 등비율적인 증감을 가정하는 CCR 모형이 적합하지 않다. 또한 가변규모수익을 가정하는 BCC 모형도 여유분(slack)으로 인해 의사결정단위간의 명확한 우선순위 도출이 어려운 한계점이 있다. 최근에는 R&D 분야의 특수성을 고려해서 자료포락분석 모형 중 RAM(Range Adjusted Measure) 모형을 활용한 분석방법이 제시되고 있다. RAM 모형은 가변규모수익 가정 하에 투입지향이나 산출지향처럼 방향성에 대한 사전적인 가정 없이 비효율성을 최대한 제거하는 방향으로 효율성을 측정하므로 R&D 분야의 효율성 분석에 적합하다. 특히 RAM 모형은 매우 강건한 단조성을 인해 의사결정단위 간 명확한 순위 구분도 용이하다. 따라서 본 연구에서는 자료포락분석 모형 중 RAM 모형과 토빗 회귀분석이 결합된 2단계 접근법을 활용해서 범부처전기신약개발사업을 중심으로 신약개발 분야의 국가연구개발사업들에 대한 효율성을 분석하고 주요 시사점들을 논의하였다.

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