• Title, Summary, Keyword: logistic curve

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Determination of the Protecting Periods of Frost Damage at Early Age in Cold Weather Concreting (한중콘크리트의 초기 동해 방지를 위한 초기 양생기간의 산정)

  • 한천구;한민철
    • Journal of the Korea Concrete Institute
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    • v.12 no.3
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    • pp.47-55
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    • 2000
  • Protections from the frost damage at early ages are one of the serious problems to be considered in cold weather concreting. Frost damage at early ages brings about the harmful influences on the concrete structures such as surface cracks and declination of strength development. Therefore, in this paper, protecting periods of frost damage at early ages according to the standard specifications provided in KCI(Korean Concrete Institute) are suggested by appling logistic curve, which evaluates the strength development of concrete with maturity. W/B, kinds of cement and curing temperatures are selected as test parameters. According to the results, the estimation of strength development by logistic curve has a good agreement between calculated values and measured values. As W/B and compressive strength for protecting from frost damages at early ages increase, it is prolonged. It shows that the protecting periods of FAC(Fly Ash Cement) and BSC(Blast-furnace Slag Cement) concrete are longer than those of OPC(Ordinary Portland Cement) concrete. The protecting peridos from frost damage at early age by JASS are somewhat shorter than those by this paper.

Prediction model of surface subsidence for salt rock storage based on logistic function

  • Wang, Jun-Bao;Liu, Xin-Rong;Huang, Yao-Xian;Zhang, Xi-Cheng
    • Geomechanics and Engineering
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    • v.9 no.1
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    • pp.25-37
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    • 2015
  • To predict the surface subsidence of salt rock storage, a new surface subsidence basin model is proposed based on the Logistic function from the phenomenological perspective. Analysis shows that the subsidence curve on the main section of the model is S-shaped, similar to that of the actual surface subsidence basin; the control parameter of the subsidence curve shape can be changed to allow for flexible adjustment of the curve shape. By using this model in combination with the MMF time function that reflects the single point subsidence-time relationship of the surface, a new dynamic prediction model of full section surface subsidence for salt rock storage is established, and the numerical simulation calculation results are used to verify the availability of the new model. The prediction results agree well with the numerical simulation results, and the model reflects the continued development of surface subsidence basin over time, which is expected to provide some insight into the prediction and visualization research on surface subsidence of salt rock storage.

Comparison between Logistic Regression and Artificial Neural Networks as MMPI Discriminator (MMPI 분석도구로서 인공신경망 분석과 로지스틱 회귀분석의 비교)

  • Lee, Jaewon;Jeong, Bum Seok;Kim, Mi Sug;Choi, Jee Wook;Ahn, Byung Un
    • Korean Journal of Biological Psychiatry
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    • v.12 no.2
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    • pp.165-172
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    • 2005
  • Objectives:The purpose of this study is to 1) conduct a discrimination analysis of schizophrenia and bipolar affective disorder using MMPI profile through artificial neural network analysis and logistic regression analysis, 2) to make a comparison between advantages and disadvantages of the two methods, and 3) to demonstrate the usefulness of artificial neural network analysis of psychiatric data. Procedure:The MMPI profiles for 181 schizophrenia and bipolar affective disorder patients were selected. Of these profiles, 50 were randomly placed in the learning group and the remaining 131 were placed in the validation group. The artificial neural network was trained using the profiles of the learning group and the 131 profiles of the validation group were analyzed. A logistic regression analysis was then conducted in a similar manner. The results of the two analyses were compared and contrasted using sensitivity, specificity, ROC curves, and kappa index. Results:Logistic regression analysis and artificial neural network analysis both exhibited satisfactory discriminating ability at Kappa index of greater than 0.4. The comparison of the two methods revealed artificial neural network analysis is superior to logistic regression analysis in its discriminating capacity, displaying higher values of Kappa index, specificity, and AUC(Area Under the Curve) of ROC curve than those of logistic regression analysis. Conclusion:Artificial neural network analysis is a new tool whose frequency of use has been increasing for its superiority in nonlinear applications. However, it does possess insufficiencies such as difficulties in understanding the relationship between dependent and independent variables. Nevertheless, when used in conjunction with other analysis tools which supplement it, such as the logistic regression analysis, it may serve as a powerful tool for psychiatric data analysis.

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Mathematical Description of Seedling Emergence of Rice and Echinochloa species as Influenced by Soil burial depth

  • Kim Do-Soon;Kwon Yong-Woong;Lee Byun-Woo
    • KOREAN JOURNAL OF CROP SCIENCE
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    • v.51 no.4
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    • pp.362-368
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    • 2006
  • A pot experiment was conducted to investigate the effects of soil burial depth on seedling emergences of rice (Oryza sativa) and Echinochloa spp. and to model such effects for mathematical prediction of seedling emergences. When the Gompertz curve was fitted at each soil depth, the parameter C decreased in a logistic form with increasing soil depth, while the parameter M increased in an exponential form and the parameter B appeared to be constant. The Gompertz curve was combined by incorporating the logistic model for the parameter C, the exponential model for the parameter M, and the constant for the parameter B. This combined model well described seedling emergence of rice and Echinochloa species as influenced by soil burial depth and predicted seedling emergence at a given time after sowing and a soil burial depth. Thus, the combined model can be used to simulate seedling emergence of crop sown in different soil depths and weeds present in various soil depths.

Receiver Operating Characteristic (ROC) Curves Using Neural Network in Classification

  • Lee, Jea-Young;Lee, Yong-Won
    • Journal of the Korean Data and Information Science Society
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    • v.15 no.4
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    • pp.911-920
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    • 2004
  • We try receiver operating characteristic(ROC) curves by neural networks of logistic function. The models are shown to arise from model classification for normal (diseased) and abnormal (nondiseased) groups in medical research. A few goodness-of-fit test statistics using normality curves are discussed and the performances using neural networks of logistic function are conducted.

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Nomogram comparison conducted by logistic regression and naïve Bayesian classifier using type 2 diabetes mellitus (T2D) (제 2형 당뇨병을 이용한 로지스틱과 베이지안 노모그램 구축 및 비교)

  • Park, Jae-Cheol;Kim, Min-Ho;Lee, Jea-Young
    • The Korean Journal of Applied Statistics
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    • v.31 no.5
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    • pp.573-585
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    • 2018
  • In this study, we fit the logistic regression model and naïve Bayesian classifier model using 11 risk factors to predict the incidence rate probability for type 2 diabetes mellitus. We then introduce how to construct a nomogram that can help people visually understand it. We use data from the 2013-2015 Korean National Health and Nutrition Examination Survey (KNHANES). We take 3 interactions in the logistic regression model to improve the quality of the analysis and facilitate the application of the left-aligned method to the Bayesian nomogram. Finally, we compare the two nomograms and examine their utility. Then we verify the nomogram using the ROC curve.

Comparison of nomogram construction methods using chronic obstructive pulmonary disease (만성 폐쇄성 폐질환을 이용한 노모그램 구축과 비교)

  • Seo, Ju-Hyun;Lee, Jea-Young
    • The Korean Journal of Applied Statistics
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    • v.31 no.3
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    • pp.329-342
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    • 2018
  • Nomogram is a statistical tool that visualizes the risk factors of the disease and then helps to understand the untrained people. This study used risk factors of chronic obstructive pulmonary disease (COPD) and compared with logistic regression model and naïve Bayesian classifier model. Data were analyzed using the Korean National Health and Nutrition Examination Survey 6th (2013-2015). First, we used 6 risk factors about COPD. We constructed nomogram using logistic regression model and naïve Bayesian classifier model. We also compared the nomograms constructed using the two methods to find out which method is more appropriate. The receiver operating characteristic curve and the calibration plot were used to verify each nomograms.

A Study of Effect on the Smoking Status using Multilevel Logistic Model (다수준 로지스틱 모형을 이용한 흡연 여부에 미치는 영향 분석)

  • Lee, Ji Hye;Heo, Tae-Young
    • The Korean Journal of Applied Statistics
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    • v.27 no.1
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    • pp.89-102
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    • 2014
  • In this study, we analyze the effect on the smoking status in the Seoul Metropolitan area using a multilevel logistic model with Community Health Survey data from the Korea Centers for Disease Control and Prevention. Intraclass correlation coefficient (ICC), profiling analysis and two types of predicted value were used to determine the appropriate multilevel analysis level. Sensitivity, specificity, percentage of correctly classified observations (PCC) and ROC curve evaluated model performance. We showed the applicability for multilevel analysis allowed for the possibility that different factors contribute to within group and between group variability using survey data.

A Study on Transportation Systems of Container Cargo in Busan Port (부산항 컨테이너 화물수송체계에 관한 연구)

  • 오석기;오윤표;윤칠용
    • Proceedings of the Korean Institute of Navigation and Port Research Conference
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    • pp.7-14
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    • 2000
  • The purpose of this study is to improvement strategies for transportation systems of container cargo in Busan port. Therefore, it was forecasted the future container cargo demand using logistic curve formula. In 2011, container cargo demand was forecasted 8.791 million TEU(T/S including 12.559 million TEU). In order to improvement transportation systems of container cargo, this study presented following; $\circled1$ port facilities expansion, $\circled2$ diversity of container transport modes. $\circled3$ make up ICD and exclusive container roads, $\circled4$ the second Seoul-Busan Expressway.

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