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

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.

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.

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

scholarly journals Experimental Identification of Optimized Process Parameters for FSW of AZ91C Mg Alloy Using Quadratic Regression Models

2020 ◽  
Vol 66 (12) ◽  
pp. 736-751
Author(s):  
Satheesh C ◽  
Sevvel P ◽  
Senthil Kumar R
Keyword(s):  
Mechanical Properties ◽  
Response Surface ◽  
Process Parameters ◽  
Regression Models ◽  
Mg Alloy ◽  
Regression Equations ◽  
Quadratic Regression ◽  
Input Process ◽  
Friction Stir ◽  
Pin Geometry

This experimental work aims to devise and establish quadratic regression equations, including various input criteria of a friction stir welding (FSW) technique to predict and determine the responses during the fabrication of AZ91C Mg alloy joints. The input process parameters taken into consideration include the traversing speed of the tool, the speed of rotation of the tool, its pin profile (geometry) and the axial force. A five-level, 4 four-factor composite design (of central nature) was applied, and response surface methodology (RSM) was used to formulate quadratic regression models, to develop 3D response surface charts, and to anticipate the responses for various mechanical properties. The generated quadratic mathematical model was tested and validated using the technique of analysis of variance. Validation experimental trial results outlined in the form of scatter diagrams revealed precedented coincidence with that of the generated models. The AZ91C Mg alloy joints obtained using the tool having taper cylindrical pin geometry employed at 1045 rpm, 1.5 mm/s traversing speed, under the exertion of an axial load of 4.87 kN was found to exhibit improved mechanical properties.

Optimum Composition of Aluminium Hybrid Composites for Maximum Wear Resistance

2021 ◽  
Vol 319 ◽  
pp. 18-23
Author(s):  
Kumar Prakash ◽  
Nilamber Kumar Singh
Keyword(s):  
Wear Resistance ◽  
Taguchi Method ◽  
Rotational Speed ◽  
Regression Models ◽  
Hybrid Composites ◽  
Stir Casting ◽  
Constant Load ◽  
Quadratic Regression ◽  
Pin On Disc ◽  
Wear Tests

Taguchi method is applied to optimize the compositions of Al-Mg-Ti-Cu-SiC hybrid composites for maximum wear resistance. Wear tests are performed on a pin-on-disc machine under a constant load of 3 kg on specimens of above composites prepared using Taguchi L9 orthogonal array through stir casting route. Each test is conducted for 30 minutes at disc rotational speed of 600 rpm. The contribution of each constituent is evaluated using ANOVA. Linear and quadratic regression models are also developed. The predicted results by these models are very close to the experimental results. It is found that Taguchi method is very successful in the optimization of particulate reinforcements (Mg, Ti, Cu and SiC) in the composites for maximum wear resistance.

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