Forecasting with full use of data without interpolation on logistic curve model with missing data

2021 ◽  
Author(s):  
Daisuke Satoh ◽  
Ryutaro Matsumura
Keyword(s):  
Missing Data ◽  
Logistic Curve ◽  
Use Of Data ◽  
Curve Model ◽  
Logistic Curve Model

Application of Curve Model Fitting Methods to Settlement Prediction

2014 ◽  
Vol 580-583 ◽  
pp. 651-654
Author(s):  
Ming Wu ◽  
Jia Lun Niu
Keyword(s):  
Curve Fitting ◽  
Model Fitting ◽  
Hyperbolic Model ◽  
Industrial Zone ◽  
Logistic Curve ◽  
Settlement Prediction ◽  
Curve Model ◽  
Simulation Results ◽  
Logistic Curve Model

Both Hyperbolic model and Logistic curve model have certain applicability to settlement prediction of soft sub-grade. Based on the observational settlement data of soft sub-grade in an industrial zone, the features of Hyperbolic model and Logistic curve model are studied. By using curve fitting methods with Origin software to predict the sub-grade settlement value and analyze the simulation results, compare these two models to determine which one is more reasonable. The results show that the Logistic curve model is more accurate and reasonable, it has the value of popularization and application in engineering.

Logistic curve model of cavitation erosion progress in metallic materials

Wear ◽  
2010 ◽  
Vol 268 (7-8) ◽  
pp. 855-862 ◽  
Author(s):  
Shuji Hattori ◽  
Kohei Maeda
Keyword(s):  
Cavitation Erosion ◽  
Metallic Materials ◽  
Logistic Curve ◽  
Curve Model ◽  
Logistic Curve Model

scholarly journals Statistical arrival models to estimate missed passage counts at fish weirs

2016 ◽  
Vol 73 (8) ◽  
pp. 1251-1260 ◽  
Author(s):  
Suresh Andrew Sethi ◽  
Catherine Bradley
Keyword(s):  
Missing Data ◽  
Case Studies ◽  
Statistical Approach ◽  
Pacific Salmon ◽  
Linear Interpolation ◽  
Fish Population ◽  
Data Simulation ◽  
Yukon River ◽  
Curve Model ◽  
Selection Of

Missed counts are commonplace when enumerating fish passing a weir. Typically “connect-the-dots” linear interpolation is used to impute missed passage; however, this method fails to characterize uncertainty about estimates and cannot be implemented when the tails of a run are missed. Here, we present a statistical approach to imputing missing passage at weirs that addresses these shortcomings, consisting of a parametric run curve model to describe the smoothed arrival dynamics of a fish population and a process variation model to describe the likelihood of observed data. Statistical arrival models are fit in a Bayesian framework and tested with a suite of missing data simulation trials and against a selection of Pacific salmon (Oncorhynchus spp.) case studies from the Yukon River drainage, Alaska, USA. When compared against linear interpolation, statistical arrival models produced equivalent or better expected accuracy and a narrower range of bias outcomes. Statistical arrival models also successfully imputed missing passage counts for scenarios where the tails of a run were missed.

A New S-N Curve Model of Fiber Reinforced Plastic Composite

2011 ◽  
Vol 462-463 ◽  
pp. 484-488 ◽  
Author(s):  
Peng Gang Mu ◽  
Xiao Peng Wan ◽  
Mei Ying Zhao
Keyword(s):  
Experimental Data ◽  
Fatigue Life ◽  
Fiber Reinforced ◽  
Constant Amplitude ◽  
Logistic Curve ◽  
Fiber Reinforced Plastic ◽  
Plastic Composite ◽  
Reinforced Plastic ◽  
Curve Model ◽  
Stress Ratios

Based on an amount of fatigue experimental data of fiber reinforced plastic composite, a new three-parameter S-N curve model is proposed to describe the relationships between the loads and fatigue life under constant amplitude cyclic loading. As the logistic curve behaves as sigmoidal which is the similar with previous S-N models, and from this comparability, an S-N equation with logistic’ form has been established. The model can assess the fatigue behaviors of FRP under various loading conditions, such as, tension-tension (T-T), tension-compression (T-C) or compression-compression (C-C) loading under different stress ratios of the whole region of fatigue life. Several examples are employed to illustrate that the model has ability to fit several different sets of experimental data accurately.

Comparative Evaluation Of Lactobacillus Plantarum Strains Through Microbial Growth Kinetics

2021 ◽  
Author(s):  
Georgi Kostov ◽  
Rositsa Denkova-Kostova ◽  
Vesela Shopska ◽  
Bogdan Goranov ◽  
Zapryana Denkova
Keyword(s):  
Lactic Acid ◽  
Growth Kinetics ◽  
Weibull Model ◽  
Acid Synthesis ◽  
Batch Cultivation ◽  
Probiotic Properties ◽  
Curve Model ◽  
Microbial Growth Kinetics ◽  
Logistic Curve Model ◽  
Kinetics Of

The study of the growth kinetics of lactobacilli with pronounced probiotic properties in their batch cultivation is essential. Various models based on the logistic curve model, containing parameters showing the influence of the accumulating lactic acid on the biosynthesis of the product, as well as parameters showing the sensitivity of the cells to lactic acid were used to model the growth kinetics in the present work. The rate constant of adaptation of the studied strains to the used nutrient medium and the induction period were also determined. The kinetics of lactic acid synthesis was determined according to the Weibull model.

scholarly journals PC program extending the two-stage polynomial growth curve model to allow missing data

1993 ◽  
Vol 33 (3-4) ◽  
pp. 287-296 ◽  
Author(s):  
Amy M. Furey ◽  
Thomas R. Ten Have ◽  
Charles J. Kowalski ◽  
Emet D. Schneiderman ◽  
Stephen M. Willis
Keyword(s):  
Missing Data ◽  
Growth Curve ◽  
Polynomial Growth ◽  
Growth Curve Model ◽  
Two Stage ◽  
Curve Model

AIC for Growth Curve Model with Monotone Missing Data

2021 ◽  
pp. 1-15
Author(s):  
Ayaka Yagi ◽  
Takashi Seo ◽  
Yasunori Fujikoshi
Keyword(s):  
Missing Data ◽  
Growth Curve ◽  
Growth Curve Model ◽  
Curve Model
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