Designs of linear-quadratic regression models for facile conversion of carbohydrate into high value (5-(ethoxymethyl)furan-2-carboxaldehyde) fuel chemical

2019 ◽  
Vol 196 ◽  
pp. 410-417 ◽  
Author(s):  
Surachai Karnjanakom ◽  
Panya Maneechakr
Keyword(s):  
Regression Models ◽  
Quadratic Regression ◽  
Linear Quadratic

D-Optimal Designs for Quadratic Regression Models

2000 ◽  
pp. 189-195 ◽  
Author(s):  
E. E. M. van Berkum ◽  
B. Pauwels ◽  
P. M. Upperman
Keyword(s):  
Regression Models ◽  
Optimal Designs ◽  
Quadratic Regression

Artificial neural network and regression models for prediction of sewing thread consumption for multilayered fabric assembly at lockstitch 301 seam

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Md Vaseem Chavhan ◽  
M. Ramesh Naidu ◽  
Hayavadana Jamakhandi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Regression Model ◽  
Regression Models ◽  
Network Models ◽  
Quadratic Regression ◽  
Neural Network Models ◽  
Content Type ◽  
Sewing Thread ◽  
Artificial Neural

Purpose This paper aims to propose the artificial neural network (ANN) and regression models for the estimation of the thread consumption at multilayered seam assembly stitched with lock stitch 301. Design/methodology/approach In the present study, the generalized regression and neural network models are developed by considering the fabric types: woven, nonwoven and multilayer combination thereof, with basic sewing parameters: sewing thread linear density, stitch density, needle count and fabric assembly thickness. The network with feed-forward backpropagation is considered to build the ANN, and the training function trainlm of MATLAB software is used to adjust weight and basic values according to the optimization of Levenberg Marquardt. The performance of networks measured in terms of the mean squared error and the layer output is set according to the sigmoid transfer function. Findings The proposed ANN and regression model are able to predict the thread consumption with more accuracy for multilayered seam assembly. The predictability of thread consumption from available geometrical models, regression models and industrial empirical techniques are compared with proposed linear regression, quadratic regression and neural network models. The proposed quadratic regression model showed a good correlation with practical thread consumption value and more accuracy in prediction with an overall 4.3% error, as compared to other techniques for given multilayer substrates. Further, the developed ANN network showed good accuracy in the prediction of thread consumption. Originality/value The estimation of thread consumed while stitching is the prerequisite of the garment industry for inventory management especially with the introduction of the costly high-performance sewing thread. In practice, different types of fabrics are stitched at multilayer combinations at different locations of the stitched product. The ANN and regression models are developed for multilayered seam assembly of woven and nonwoven fabric blend composition for better prediction of thread consumption.

Designs for estimating an extremal point of quadratic regression models in a hyperball

Metrika ◽  
2003 ◽  
Vol 58 (2) ◽  
pp. 193-208 ◽  
Author(s):  
Viatcheslav B. Melas ◽  
Andrey Pepelyshev ◽  
Russell C. H. Cheng
Keyword(s):  
Regression Models ◽  
Extremal Point ◽  
Quadratic Regression

Efficacy of climate transfer functions: introduction of Eurasian populations of Larix into Alberta

1999 ◽  
Vol 29 (11) ◽  
pp. 1660-1668 ◽  
Author(s):  
Gerald E Rehfeldt ◽  
Nadja M Tchebakova ◽  
Leonard K Barnhardt
Keyword(s):  
Regression Models ◽  
Transfer Functions ◽  
Mean Annual Precipitation ◽  
Mean Annual Temperature ◽  
Quadratic Regression ◽  
Degree Days ◽  
Plant Populations ◽  
Growth And Survival ◽  
The Mean ◽  
Practical Implications

Growth and survival of eight populations of Larix sukaczewii Dylis and one of both Larix sibirica Ledeb. and Larix gmelinii (Rupr.) Rupr. were used to assess the effectiveness of climate transfer functions for predicting the 13-year performance of Eurasian provenances introduced to Alberta. Quadratic regression models showed that transfer distances for five climate variables (mean annual temperature, degree-days <0°C, mean temperature in the coldest month, ratio of the mean annual temperature to mean annual precipitation, and the summer-winter temperature range) were particularly effective in predicting height and survival. Optimal transfer distances did not differ significantly from zero, and as a result, the best growth and survival in Alberta should be obtained by matching the provenance climate to that of the planting site for the five variables. Verification of the climate transfer functions with independent data from Russian provenance tests were strongly supportive. The results demonstrate the effectiveness of climate transfer functions for describing the response of plant populations to the environment and thereby have practical implications in reforestation.

Performance Evaluation of African Breadfruit (Treculia Africana) Seed Dehuller

2018 ◽  
Vol 22 (4) ◽  
pp. 51-60
Author(s):  
Okey Francis Obi ◽  
Clement O. Akubuo
Keyword(s):  
Performance Evaluation ◽  
Regression Model ◽  
Recent Effort ◽  
Quadratic Regression ◽  
Linear Quadratic ◽  
Quadratic Regression Model ◽  
African Breadfruit ◽  
Treculia Africana ◽  
Non Linear

AbstractThis paper reports the effect of the parboiling time on dehulled kernel out-turns (DKO) of African breadfruit seeds, and the most recent effort to upgrade an existing dehuller and its performance. Two common and readily available varieties – Treculia var. africana and var. inverse were used in the study. The seeds were parboiled for 0 (control), 2, 5, 8, 11 and 14 minutes and then dehulled. The result revealed that the parboiling time had a significant effect on the DKO of the two varieties of the seed. The DKO increased from 0 to 5 min of the treatment, after which it decreased considerably up to 14 min of the parboiling time. The obtained data were used to develop a non-linear quadratic regression model to predict the DKO as a function of the parboiling time. The performance evaluation of the breadfruit seeds dehuller revealed that it was significantly influenced by the variety.

Study on Establishing Regression Model to Predict Leg Girth of Young Women

2011 ◽  
Vol 120 ◽  
pp. 528-532
Author(s):  
Yi Jie Zhang ◽  
Fei Yuan ◽  
Jie Sun ◽  
Juan Feng Jin ◽  
Feng Yuan Zou
Keyword(s):  
Regression Model ◽  
Young Women ◽  
Regression Models ◽  
Measurement Data ◽  
Multiple Linear Regression Model ◽  
Regression Method ◽  
Quadratic Regression ◽  
Compression Stockings ◽  
Best Fitting ◽  
Spss Software

Using Martin Meter from Japan to measure 110 young women body. Using SPSS software for correlation analysis of measurement data, then selecting width, thickness and weight as elements used for establishing leg girth regression models. Adopting backward regression method to choose variable further, and establishing Multiple Linear regression model, Multivariate Quadratic regression model, Multivariate Cubic regression model to predict the girth of each part of leg which related to clothing. After testing and comparing the effect of each model, choosing out the best fitting modle for each part. The validity of modles were tested by randomly selecting 20 young women’s leg data,comparing with manual measurements, gaining satisfactory results. So as to provide an important technical support for human leg two-dimensional non-contact measurement and be further applied to research on medical compression stockings.

Function-on-function quadratic regression models

2020 ◽  
Vol 142 ◽  
pp. 106814
Author(s):  
Yifan Sun ◽  
Qihua Wang
Keyword(s):  
Regression Models ◽  
Quadratic Regression
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