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Comparison of hot rolled steel mechanical property prediction models using linear multiple regression, non-linear multiple regression and non-linear artificial neural networks

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IRONMAKING & STEELMAKING 2005年第5期32卷 435-442页

作者： Jones, DM Watton, J Brown, KJ Corus Strip Prod UK ECM2 Port Talbot SA13 2EZ England Univ Cardiff Wales Sch Engn Cardiff S Glam Wales

In the manufacture of rolled steel from a hot strip mill, the final mechanical properties, such as yield strength, ultimate tensile strength and elongation to fracture, are important requirements specified by the customer. The use of mathematical modelling techniques such as multiple regression analysis, or computational developments such as artificial neural networks, can result in the creation of acceptably accurate predictive models. However, the accuracy of any predictive model will depend on the quality of data used in its creation, and thus a brief statistical analysis of the mechanical property data used for model development is discussed. In the present paper a comparison of the application of linear multiple regression, non-linear multiple regression and non-linear neural networks is made for various steel families using data taken from the Corus Port Talbot hot strip mill. A statistical summary of their relative predictive errors is given, and although all three are comparable, the non-linear, black box approach of a suitably structured neural network provides overall more accurate predictive models than the use of linear or non-linear multiple regression.

关键词： individual and moving range linear multiple regression non-linear multiple regression neural networks yield strength ultimate tensile strength elongation to fracture

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Development of prediction model through linear multiple regression for the prediction of longitudinal stiffness of embroidered fabric

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FASHION AND TEXTILES 2020年第1期7卷 1-24页

作者： Dutta, Anirban Chatterjee, Biswapati Govt Coll Engn & Text Technol Serampore 12 William Carey Rd Serampore 712201 WB India

Embroidery through computer aided semi-automatic machines is one of the most widely used option for the surface ornamentation of apparel fabrics at present. Since the embroidery process includes addition of certain amount of embroidery-threads depending upon the design motif, it is quite obvious that basic physical and functional properties of fabric are subject to change. It is therefore important to develop an algorithm or empirical equation for proper prediction of the properties of the embroidered fabric, relevant to its required end-use in apparel industry. In this context, an effort has been made to determine a prediction equation through linear multiple regressions for the prediction of longitudinal stiffness of embroidered fabric in terms of flexural rigidity in warp direction of the base fabric, considering the input parameters as warp-way flexural rigidity of the base fabric, breaking load and linear density of the embroidery thread, stitch density, average stitch length and average stitch angle of the embroidery design. The final Prediction model is statistically verified taking new embroidery samples of different varieties. It is found that the model can predict with a very satisfactory level of accuracy. Also, the influences of the embroidery parameters in this context have been analyzed through the corresponding regression coefficients and the three dimensional (3D) surface curves. Stitch density has been emerged as the most influential parameter, followed by the stitch length and the stitch angle.

关键词： Prediction of Stiffness of embroidered fabric Computerized embroidery machine linear multiple regression Apparel fabric Flexural rigidity

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Development of prediction model through linear multiple regression for the prediction and analysis of the GSM of embroidered fabric

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RESEARCH JOURNAL OF TEXTILE AND APPAREL 2020年第1期24卷 53-71页

作者： Dutta, Anirban Chatterjee, Biswapati Govt Coll Engn & Text Technol Dept Text Technol Serampore India Govt Coll Engn & Text Technol Dept Text Serampore WB India

Purpose The purpose of this paper is to establish the regression equation based upon a set of samples prepared through structured design of experiment and form a prediction model for prediction of the areal density gram per square meter (GSM) of the embroidered fabrics and study the influence of basic input parameters. Design/methodology/approach Embroidery samples are prepared taking input parameters as GSM of the base fabric, linear density of the embroidery thread and stitch density of the embroidery design. Three levels of values are identified for each of the input parameters. Taguchi and Box-Behnken experiment design principles are used to prepare two sets of samples. linear multiple regression is used to determine the prediction equations based upon each of the two sets and the combined set as well. Prediction equations are statistically verified for the prediction accuracy. Also, surface curves are prepared to study the influence of embroidery parameters on the GSM. Findings It is found that all the three prediction models developed in this study can predict with a very satisfactory level of accuracy. However, the regression equation based upon the data set prepared according to Taguchi experiment design is emerged as the prediction model with highest level of prediction accuracy. Corresponding equation coefficients and several three-dimensional surface curves are used to study the influence of embroidery parameters and it is found that the stitch density is the most influential input parameter followed by stitch length and the GSM of base fabric. Originality/value This paper presents a very fundamental study to reveal the effect of embroidery parameters on the GSM, through development of regression equations. It can help future researchers in optimizations of input parameters and forming a technical guideline for the embroidery designers for selection of the design parameters for a desired GSM of embroidered fabric.

关键词： Apparel fabric linear multiple regression Prediction of GSM of embroidered fabric Property of embroidered fabric Stitch Taguchi and box-Behnken GSM of embroidered fabric Computerized embroidery

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linear regression Model for Substantiation of Sustainable State Policy in a Digital Economy 11

Linear Regression Model for Substantiation of Sustainable St...

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IEEE 11th International Conference on Dependable Systems, Services and Technologies (DESSERT) - IoT, Big Data and AI for a Safe & Secure World and Industry 4.0

作者： Dorofyeyev, Oleksandr Lozinska, Tamara Ponochovnyi, Yuriy Vlasenko, Tetiana Poltava State Agr Acad Dept Publ Management & Adm Poltava Ukraine Poltava State Agr Acad Dept Informat Syst & Technol Poltava Ukraine Petro Vasylenko Natl Tech Univ Agr Dept Prod Business & Management Org Kharkiv Ukraine

ISBN: (纸本)9781728199573

The article discusses the use of digital mathematical models in justifying public policy for sustainable economic development. The wider expediency uses of statistical analysis methods to substantiate public administration decisions in the digital economy is explained. The technique of forming and preparation of experimental data for the construction of a mathematical regression model is described. The hypothesis of normal distribution of the input data was tested according to the statistical criteria of Kolmogorov-Smirnov and Lilliefors and Shapiro-Wilk. Box-Cox transformation was used to normalize the data. The linear multiple regression coefficients are determined by the least squares method. The quality of the resulting model was evaluated using the Student's and Fisher's criteria. The results of the forecast of financial stability of agricultural enterprises for 2020-2022 and the factor indicators indicate the negative dynamics of the effective indicator of their financial condition. The proposed model can be used to continuously monitor the financial condition of agricultural enterprises by linking it to the State Statistics Service of Ukraine.

关键词： digital economy state policy of sustainable development agricultural enterprise financial sustainability mathematical simulation linear multiple regression normal distribution

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Assessment of the occurrence of climate change and its effects on planting date and growth duration of rainfed wheat in the western and northwestern regions of Iran

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PADDY AND WATER ENVIRONMENT 2022年第2期20卷 241-253页

作者： Mohammadi, Elham Movahedi, Saeed Mohammadi, Reza Golkari, Sabar Univ Isfahan Fac Geog Sci & Planning Esfahan Iran Agr Res Educ & Extens Org AREEO Dryland Agr Res Inst Sararood Branch Kermanshah Iran Agr Res Educ & Extens Org AREEO Dryland Agr Res Inst Maragheh Iran

In this study, the effect of climate change on planting date and growth duration of rainfed wheat in the west and northwest parts of Iran has been investigated. The occurrence of climate change in the region was first evaluated for the base period (1992-2018) using two nonparametric tests of Mann-Kendall and Sen's slope estimator. Then, the climatic parameters of maximum temperature, minimum temperature and precipitation were simulated under RCP4.5 scenario for the period 2019-2039 based on downscaled output data of the Community Climate System Model (CCSM4) using LARS WG software. The growth period was obtained using a linear multiple regression model, which was selected based on R-square and accounted for 87% of its total variation. The results predicted that the average annual temperature will increase by 2 degrees C, while the average annual precipitation will increase by 30% by the end of 2039. Planting dates were determined based on two indices combining temperature and precipitation for the base and future periods. The results showed that climate change effects at the 2039 horizon will shorten by 18 days the wheat growth period and the appropriate planting time for rainfed wheat will be reduced by 2-19 days.

关键词： Climate change linear multiple regression Plant growth period Rainfed wheat RCP4 5 scenario

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Prediction of base and subbase resilient modulus (Mr) using regression methodology

JOURNAL OF THE FACULTY OF ENGINEERING AND ARCHITECTURE OF GA...

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JOURNAL OF THE FACULTY OF ENGINEERING AND ARCHITECTURE OF GAZI UNIVERSITY 2020年第1期35卷 507-517页

作者： Yilmaz, Altan Mehmet Akif Ersoy Univ Fac Engn & Architecture Dept Civil Engn TR-15030 Burdur Turkey

Resilient modulus is an important design parameter for highway pavement structures because it represents the structural strength of pavement layers. The resilient modulus depends on the factors such as applied stress, loading time, water content, dry density and gradation. This paper demonstrates the applicability of regression methodology for estimating the (M-r) Resilient modulus of pavement layers using the results of dynamic triaxial tests. Aggregate samples were collected from several different quarries of central Anatolia. All of the aggregate sources were igneous rock (basalt, and trachy-basalt). Initial work content of triaxial tests to obtain the resilient modulus of cylindrical aggregate samples. Than new mathematical model proposed by using the results of dynamic triaxial tests. In the regression model;aggregate physical properties, aggregate mixture properties and loading factors (a total of 8 variables) which are used as input parameters and the resilient modulus of the aggregate mixture obtained as output. In order to compare the effectiveness of the new method, coefficients for the Uzan constitutive model were also determined for laboratory testing and were compared with the approach described in this paper. Performance parameters of R-2 :0.98 and Standard error: 10.51 was obtained from model prediction. These results are quite sufficient, and the regression model assumed the resilient response to be like a function by using the stated material parameters. So, this approach makes it possible to estimate the resilient modulus of the different aggregates samples in real-time.

关键词： Granular materials highway pavements resilient modulus linear multiple regression

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Improving the Estimation of Above Ground Biomass Using Dual Polarimetric PALSAR and ETM plus Data in the Hyrcanian Mountain Forest (Iran)

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REMOTE SENSING 2014年第5期6卷 3693-3715页

作者： Attarchi, Sara Gloaguen, Richard TU Bergakad Freiberg TUBAF Inst Geol Remote Sensing Grp D-09599 Freiberg Germany Helmholtz Inst Freiberg Resource Technol Remote Sensing Grp D-09599 Freiberg Germany

The objective of this study is to develop models based on both optical and L-band Synthetic Aperture Radar (SAR) data for above ground dry biomass (hereafter AGB) estimation in mountain forests. We chose the site of the Loveh forest, a part of the Hyrcanian forest for which previous attempts to estimate AGB have proven difficult. Uncorrected ETM+ data allow a relatively poor AGB estimation, because topography can hinder AGB estimation in mountain terrain. Therefore, we focused on the use of atmospherically and topographically corrected multispectral Landsat ETM+ and Advanced Land-Observing Satellite/Phased Array L-band Synthetic Aperture Radar (ALOS/PALSAR) to estimate forest AGB. We then evaluated 11 different multiple linear regression models using different combinations of corrected spectral and PolSAR bands and their derived features. The use of corrected ETM+ spectral bands and GLCM textures improves AGB estimation significantly (adjusted R-2 = 0.59;RMSE = 31.5 Mg/ha). Adding SAR backscattering coefficients as well as PolSAR features and textures increase substantially the accuracy of AGB estimation (adjusted R-2 = 0.76;RMSE = 25.04 Mg/ha). Our results confirm that topographically and atmospherically corrected data are indispensable for the estimation of mountain forest's physical properties. We also demonstrate that only the joint use of PolSAR and multispectral data allows a good estimation of AGB in those regions.

关键词： Landsat7/ETM+ ALOS/PALSAR L-band above ground biomass (AGB) DBH linear multiple regression topographic effects Hyrcanian mountainous forest Iran

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Statistical approach in alginate membrane formulation for cell encapsulation in a GMP-based cell factory

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ACTA BIOMATERIALIA 2008年第4期4卷 943-949页

作者： Villani, S. Marazzi, M. Bucco, M. Faustini, M. Klinger, M. Gaetani, P. Crovato, F. Vigo, D. Caviggioli, F. Torre, M. L. Univ Milan Dipartimento Sci & Tecnol Vet Sicurezza Alimentar I-20133 Milan Italy Univ Pavia Dipartimento Sci Sanit Applicate & Psicocomportam I-27100 Pavia Italy Osped Niguarda Ca Granda Ctr Riferimento Reg Colture Cellulari I-20133 Milan Italy Univ Pavia Dipartimento Chim Farmaceut I-27100 Pavia Italy IRCCS Ist Clin Humanitas Rozzano Italy

A cell encapsulation technology in alginate has been developed with the aim of obtaining cell controlled release or three-dimensional cultures. The aim of this work is to verify the predictability of alginate capsules for large-scale production by Good Manufacturing Practice (GMP) standardized procedures in a cell factory. A cell-free capsule model was performed following the GMP guidelines: an opaque agent suspension in a bivalent cation solution (Ca2+, Ba2+, Sr2+) was dropped in a sodium alginate solution, obtaining capsules presenting a liquid core surrounded by a gel alginate membrane. The concentration of the ion, and the treatment with protamine, can considerably vary the characteristics of the capsules (weight, whole diameter, core diameter, gel capsule thickness, capsule strength). It is therefore possible to optimize the performance of the capsules, relating the molecular structure and size of the polymeric membrane to the desired functional properties. Technological resources are available for large-scale cell encapsulation intended for advanced therapies (gene therapy, somatic cell therapy and tissue engineering) in a cell factory, following GMP guidelines. (c) 2008 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.

关键词： alginate cell encapsulation GMP cell factory advanced therapies linear multiple regression

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Chemical Availability of Cd, Pb and Zn in Anthropogenically Polluted Soil: Assessing the Geochemical Reactivity and Oral Bioaccessibility

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Pedosphere 2017年第3期27卷 616-629页

作者： Christophe WATERLOT Francis DOUAY Aurélie PELFRENE Yncréa Hauts-de-France Laboratoire Génie Civil et géo Environnement (LGCgE)

The most recent in vitro tests used to determine metal bioaccessiblility are generally time-consuming and expensive. This study aimed at determining potential relationships between the concentrations of metals extracted using single-extraction methods and the concentrations of bioaccessible metals assessed by a harmonised in vitro test, the Unified BARGE Method （UBM）. A total number of 27 soil samples were collected from kitchen gardens and lawns with various physicochemical parameters and contamination levels. Significant relationships were obtained between Cd, Pb and Zn extracted in gastric and gastrointestinal phases and using single extractions. The best relationhips were established using acetic and citric acids for Cd, whereas for Pb, citric acid and ethylenedi- aminetetraacetic acid （EDTA） were identified as the best extractants. These relationships were improved by means of a linear multiple regression with a downward stepwise procedure involving agronomic parameters （soil cation exchange capacity and assimilated P）. This method highlighted the fact that the cation exchange capacity and P contents in soils were the two main parameters that controlled the human bioaccessibility of Cd, Pb and Zn in the gastric phase. Besides, the metal concentrations extracted with the acetic and citric acids correlated well with the metal concentrations in the gastric and gastrointestinal phases, suggesting that the bioaceessible metals were mainly in a soluble form, weakly bound to the organic matter and associated with the carbonates and the Fe and Mn oxides/hydroxides in soils.

关键词： gastric phase gastrointestinal phase linear multiple regression single extractant urban soil

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Settlement prediction of the rock-socketed piles through a new technique based on gene expression programming

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NEURAL COMPUTING & APPLICATIONS 2018年第11期29卷 1115-1125页

作者： Armaghani, Danial Jahed Faradonbeh, Roohollah Shirani Rezaei, Hossein Rashid, Ahmad Safuan A. Amnieh, Hassan Bakhshandeh Amirkabir Univ Technol Dept Civil & Environm Engn Hafez Ave Tehran 15914 Iran Tarbiat Modares Univ Dept Min Fac Engn Tehran 14115143 Iran Lorestan Univ Dept Civil Engn Fac Engn Lorestan Iran UTM Dept Geotech & Transportat Fac Civil Engn Skudai 81310 Johor Malaysia Univ Tehran Sch Min Coll Engn Tehran 111554563 Iran

The settlement design of bored piles socketed into rock has received considerable attention. Although many design methods of pile settlement are recommended in the literature, proposing new/practical technique(s) with higher performance prediction is of advantage. A new model based on gene expression programming (GEP) is presented in this paper for predicting the settlement of the rock-socketed pile. To do this, 96 piles socketed in different types of rock (mostly granite) as part of the Klang Valley Mass Rapid Transit project, Malaysia, were studied. In order to propose a predictive model with higher performance prediction, a series of GEP analyses were conducted using the most important factors on pile settlement, i.e. ratio of length in soil layer to length in rock layer, ratio of total length to diameter, uniaxial compressive strength, standard penetration test and ultimate bearing capacity. For comparison purpose, using the same dataset, linear multiple regression (LMR) technique was also performed. After developing the equations, their prediction performances were checked through several performance indices. The results demonstrated the feasibility of GEP-based predictive model of settlement. Coefficients of determination (CoD) values of 0.872 and 0.861 for training and testing datasets of GEP equation, respectively, show superiority of this model in predicting pile settlement while these values were obtained as 0.835 and 0.751 for the LMR model. Moreover, root mean square error (RMSE) values of (1.293 and 1.656 for training and testing) and (1.737 and 1.767 for training and testing) were achieved for the developed GEP and LMR models, respectively.

关键词： Rock-socketed pile Settlement Gene expression programming linear multiple regression

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