• Title/Summary/Keyword: 로지스틱

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Logistic Regressions with Sensory Evaluation Data about Hanwoo Steer Beef (한우 거세우 고기 관능평가 데이터의 로지스틱 회귀분석)

  • Lee, Hye-Jung;Kim, Jae-Hee
    • The Korean Journal of Applied Statistics
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    • v.23 no.5
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    • pp.857-870
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    • 2010
  • This study was conducted to investigate the relationship between the socio-demographic factors and the Korean consumers palatability evaluation grades with Hanwoo sensory evaluation data from 2006 to 2008 by National Institute of Animal Science. The dichotomy logistic regression model and the multinomial logistic regression model are fitted with the independent variables such as the consumer living location, age, gender occupation, monthly income, beef cut and the the palatability grade as the categorical dependent variable and tenderness, 리avor and juiciness as the continuous dependent variable. Stepwise variable selection procedure is incorporated to find the final model and odds ratios are calculated to nd the associations between categories.

Image Quality Enhancement by Using Logistic Equalization Function (로지스틱 평활화 함수에 의한 영상의 화질개선)

  • Cho, Yong-Hyun
    • Journal of the Korean Institute of Intelligent Systems
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    • v.20 no.1
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    • pp.30-35
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    • 2010
  • This paper presents a quality enhancement of images by using a histogram equalization based on the symmetric logistic function. The histogram equalization is a simple and effective spatial processing method that it enhances the quality by adjusting the brightness of image. The logistic function that is a sigmoidal nonlinear transformation function, is applied to non-linearly enhance the brightness of the image according to its intensity level frequency. We propose a flexible and symmetrical logistic function by only using the intensity with maximum frequency in an histogram and the total number of pixels. The proposed function decreases the computation load of an exponential function in the traditional logistic function. The proposed method has been applied for equalizing 5 images with a different resolution and histogram distribution. The experimental results show that the proposed method has the superior enhancement performances compared with the source images and the traditional global histogram equalization, respectively.

Image Histogram Equalization Using Flexible Logistic Transformation Function (유연한 로지스틱 변환함수를 이용한 영상의 히스토그램 평활화)

  • Cho, Yong-Hyun
    • Journal of the Korean Institute of Intelligent Systems
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    • v.19 no.6
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    • pp.787-795
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    • 2009
  • This paper presents a histogram equalization based on the logistic function for enhancing the quality of images. The histogram equalization is a simple and effective spatial processing method that it enhances the quality by adjusting the brightness of image. The logistic function that is a nonlinear transformation function is applied to adaptively enhance the brightness of the image according to its intensity level frequency. We propose a flexible and asymmetrical logistic function by only using the intensity level with maximum frequency and the maximum intensity level in an histogram, and the total number of pixels. The proposed function excludes both the computation load of an exponential function and the heuristic setting of an optimal parameter values in the traditional logistic function. The proposed method has been applied for equalizing many images with a different resolution and histogram distribution. The experimental results show that the proposed method has the superior enhancement performances and the faster equalizing speed compared with the traditional histogram equalization and the adaptively modified histogram equalization, respectively. And the proposed histogram equalization can be used in various multimedia systems in real-time.

Flood Risk Forecasting using Logistic Regression for the Han River Basin (로지스틱 회귀분석을 활용한 한강권역 홍수위험 예보기법 개발)

  • Lee, Seon Mi;Choi, Youngje;Yi, Jaeeung
    • Proceedings of the Korea Water Resources Association Conference
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    • 2021.06a
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    • pp.354-354
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    • 2021
  • 2020년은 장마기간이 49일간 지속됨에 따라 침수, 산사태 등 많은 홍수피해가 발생하였다. 특히 서울에서는 한강 본류의 수위가 급격하게 증가함에 따라 둔치 및 도로 침수 피해가 발생하였다. 이처럼 하천의 수위증가로 인한 홍수피해에 대응하기 위해 홍수통제소 및 기초지자체에서는 홍수특보를 발령한다. 이 홍수특보는 수위관측소 지점별 계획홍수량의 50 %, 70 % 이상의 홍수량이 발생할 경우 홍수주의보와 홍수경보가 발령되며, 이 기준은 각 권역별로 동일하다. 하지만 2017년 의정부시에서는 중랑천 수위증가로 인해 주변 지역에 침수피해가 발생하였지만, 이때 홍수량은 계획홍수량 대비 약 30 %에 불과하였다. 이처럼 한강권역 내 하천수위 증가로 인한 홍수피해는 계획홍수량의 50 % 이내에서 발생하기도 한다. 이에 본 연구에서는 한강권역을 대상으로 현재 2단계로 발령되는 홍수특보를 3단계로 세분화하고자 하였다. 단계별 홍수량 위험기준을 산정하기 위해 과거 홍수피해 발생 이력이 있는 한강권역 내 43개의 수위관측소 지점을 선정하였으며, 지점별 홍수기 동안의 홍수량 및 피해액 자료를 수집하였다. 각 단계별 홍수량 기준을 산정하기 위해서는 로지스틱 회귀분석 방법을 활용하여 피해발생 확률을 산정하였다. 1단계 기준은 계획홍수량 대비 홍수량 비율과 홍수피해 발생여부를 고려한 이항 로지스틱 회귀분석 모델을 구축한 후 3계 도함수에 적용하여 홍수피해 발생확률이 급격하게 증가하는 특이점을 산정하였다. 2단계와 3단계 기준은 다항 로지스틱 회귀분석 중 계층형 로지스틱 회귀분석을 활용하여 지점별 피해액 비율이 60 ~ 80 %, 80 ~ 100 % 구간에 속할 확률을 산정하고, 1단계와 동일한 방법으로 특이점을 산정하였다. 그 결과 지점별로 기존 제공되고 있는 홍수특보 기준을 과거 발생한 홍수피해를 고려하여 세분화할 수 있었으며, 이 결과는 지역별 홍수피해 저감대책에 활용될 수 있을 것으로 판단된다.

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Comparative Analysis of Predictors of Depression for Residents in a Metropolitan City using Logistic Regression and Decision Making Tree (로지스틱 회귀분석과 의사결정나무 분석을 이용한 일 대도시 주민의 우울 예측요인 비교 연구)

  • Kim, Soo-Jin;Kim, Bo-Young
    • The Journal of the Korea Contents Association
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    • v.13 no.12
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    • pp.829-839
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    • 2013
  • This study is a descriptive research study with the purpose of predicting and comparing factors of depression affecting residents in a metropolitan city by using logistic regression analysis and decision-making tree analysis. The subjects for the study were 462 residents ($20{\leq}aged{\angle}65$) in a metropolitan city. This study collected data between October 7, 2011 and October 21, 2011 and analyzed them with frequency analysis, percentage, the mean and standard deviation, ${\chi}^2$-test, t-test, logistic regression analysis, roc curve, and a decision-making tree by using SPSS 18.0 program. The common predicting variables of depression in community residents were social dysfunction, perceived physical symptom, and family support. The specialty and sensitivity of logistic regression explained 93.8% and 42.5%. The receiver operating characteristic (roc) curve was used to determine an optimal model. The AUC (area under the curve) was .84. Roc curve was found to be statistically significant (p=<.001). The specialty and sensitivity of decision-making tree analysis were 98.3% and 20.8% respectively. As for the whole classification accuracy, the logistic regression explained 82.0% and the decision making tree analysis explained 80.5%. From the results of this study, it is believed that the sensitivity, the classification accuracy, and the logistics regression analysis as shown in a higher degree may be useful materials to establish a depression prediction model for the community residents.

로지스틱 회귀를 통한 경마의 입상확률모형

  • 유선경;박흥선
    • The Korean Journal of Applied Statistics
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    • v.13 no.1
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    • pp.35-43
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    • 2000
  • 본 연구에서는 우리 나라 경마의 실제자료를 이용하여 연승식 경마의 입상확률에 미치는 여러 가지 요인을 조사하였고, 이를 토대로 입상확률모형을 유도하여 보았다. 외국의 경우, 경마에 대한 통계적 접근이 다각적으로 시행되었지만, 기존의 선행방법이 배당금에 의한 입상확률에 근거를 하고 있는 반면, 본 연구에서는 경마장에서 쉽게 구할 수 있는 정보를 중심으로, 로지스틱 회귀를 이용한 방법을 시도해 보았다.

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Exploring interaction using 3-D residual plots in logistic regression model (3차원 잔차산점도를 이용한 로지스틱회귀모형에서 교호작용의 탐색)

  • Kahng, Myung-Wook
    • Journal of the Korean Data and Information Science Society
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    • v.25 no.1
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    • pp.177-185
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    • 2014
  • Under bivariate normal distribution assumptions, the interaction and quadratic terms are needed in the logistic regression model with two predictors. However, depending on the correlation coefficient and the variances of two conditional distributions, the interaction and quadratic terms may not be necessary. Although the need for these terms can be determined by comparing the two scatter plots, it is not as useful for interaction terms. We explore the structure and usefulness of the 3-D residual plot as a tool for dealing with interaction in logistic regression models. If predictors have an interaction effect, a 3-D residual plot can show the effect. This is illustrated by simulated and real data.

The Comparative Study for Truncated Software Reliability Growth Model based on Log-Logistic Distribution (로그-로지스틱 분포에 근거한 소프트웨어 고장 시간 절단 모형에 관한 비교연구)

  • Kim, Hee-Cheul;Shin, Hyun-Cheul
    • Convergence Security Journal
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    • v.11 no.4
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    • pp.85-91
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    • 2011
  • Due to the large-scale application software syslmls, software reliability, software development has animportantrole. In this paper, software truncated software reliability growth model was proposed based on log-logistic distribution. According to fixed time, the intensity function, the mean value function, the reliability was estimated and the parameter estimation used to maximum likelihood. In the empirical analysis, Poisson execution time model of the existiog model in this area and the log-logistic model were compared Because log-logistic model is more efficient in tems of reliability, in this area, the log-logistic model as an alternative 1D the existiog model also were able to confim that you can use.

Log-density Ratio with Two Predictors in a Logistic Regression Model (로지스틱 회귀모형에서 이변량 정규분포에 근거한 로그-밀도비)

  • Kahng, Myung Wook;Yoon, Jae Eun
    • The Korean Journal of Applied Statistics
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    • v.26 no.1
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    • pp.141-149
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    • 2013
  • We present methods for studying the log-density ratio that enables the selection of the predictors and the form to be included in the logistic regression model. Under bivariate normal distributional assumptions, we investigate the form of the log-density ratio as a function of two predictors. If two covariance matrices are equal, then the crossproduct and quadratic terms are not needed. If the variables are uncorrelated, we do not need the crossproduct terms, but we still need the linear and quadratic terms. We also explore other conditions in which the crossproduct and quadratic terms are not needed in the logistic regression model.

Comparison of Bias Correction Methods for the Rare Event Logistic Regression (희귀 사건 로지스틱 회귀분석을 위한 편의 수정 방법 비교 연구)

  • Kim, Hyungwoo;Ko, Taeseok;Park, No-Wook;Lee, Woojoo
    • The Korean Journal of Applied Statistics
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    • v.27 no.2
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    • pp.277-290
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    • 2014
  • We analyzed binary landslide data from the Boeun area with logistic regression. Since the number of landslide occurrences is only 9 out of 5000 observations, this can be regarded as a rare event data. The main issue of logistic regression with the rare event data is a serious bias problem in regression coefficient estimates. Two bias correction methods were proposed before and we quantitatively compared them via simulation. Firth (1993)'s approach outperformed and provided the most stable results for analyzing the rare-event binary data.