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Dynamic response surface methodology using Lasso regression for organic pharmaceutical synthesis
《化学科学与工程前沿(英文)》 2022年 第16卷 第2期 页码 221-236 doi: 10.1007/s11705-021-2061-y
关键词: data-driven modeling pharmaceutical organic synthesis Lasso regression dynamic response surface methodology
Ali Reza GHANIZADEH, Morteza RAHROVAN
《结构与土木工程前沿(英文)》 2019年 第13卷 第4期 页码 787-799 doi: 10.1007/s11709-019-0516-8
关键词: full-depth reclamation soil-reclaimed asphalt pavement blend Portland cement unconfined compressive strength multivariate adaptive regression spline
Xuying Ma, Ian Longley, Jennifer Salmond, Jay Gao
《环境科学与工程前沿(英文)》 2020年 第14卷 第3期 doi: 10.1007/s11783-020-1221-5
关键词: LUR Air pollution modelling GIS spatial analysis GDAL/OGR Python Pollutant concentration mapping
Tanvi SINGH, Mahesh PAL, V. K. ARORA
《结构与土木工程前沿(英文)》 2019年 第13卷 第3期 页码 674-685 doi: 10.1007/s11709-018-0505-3
关键词: batter piles oblique load test neural network M5 model tree random forest regression ANOVA
Modeling the methyldiethanolamine-piperazine scrubbing system for CO
Stefania Moioli,Laura A. Pellegrini
《化学科学与工程前沿(英文)》 2016年 第10卷 第1期 页码 162-175 doi: 10.1007/s11705-016-1555-5
关键词: vapor-liquid equilibrium methyldietanolamine piperazine regression Electrolyte-NRTL
Multiple regression models for energy consumption of office buildings in different climates in China
Siyu ZHOU, Neng ZHU
《能源前沿(英文)》 2013年 第7卷 第1期 页码 103-110 doi: 10.1007/s11708-012-0220-z
关键词: regression model energy consumption building envelope office building different climates
《结构与土木工程前沿(英文)》 2021年 第15卷 第5期 页码 1181-1198 doi: 10.1007/s11709-021-0744-6
关键词: interaction load sharing ratio piled raft nonlinear regression artificial neural network
Multivariable regression model for Fox depth correction factor
Ravi Kant MITTAL, Sanket RAWAT, Piyush BANSAL
《结构与土木工程前沿(英文)》 2019年 第13卷 第1期 页码 103-109 doi: 10.1007/s11709-018-0474-6
关键词: settlement embedment Fox depth correction factor regression multivariable
《环境科学与工程前沿(英文)》 2023年 第17卷 第6期 doi: 10.1007/s11783-023-1676-2
● A novel framework integrating quantile regression with machine learning is proposed.
关键词: Driver-response Upper boundary of relationship Interpretable machine learning Quantile regression Total phosphorus Chlorophyll a
Pijush Samui, Jagan J
《结构与土木工程前沿(英文)》 2013年 第7卷 第2期 页码 133-136 doi: 10.1007/s11709-013-0202-1
关键词: unsaturated soil effective stress parameter Gaussian process regression (GPR) artificial neural network (ANN) variance
Faezehossadat KHADEMI,Mahmoud AKBARI,Sayed Mohammadmehdi JAMAL,Mehdi NIKOO
《结构与土木工程前沿(英文)》 2017年 第11卷 第1期 页码 90-99 doi: 10.1007/s11709-016-0363-9
关键词: concrete 28 days compressive strength multiple linear regression artificial neural network ANFIS sensitivity analysis (SA)
Surinder Kumar GILL, Meenu GUPTA, P. S. SATSANGI
《机械工程前沿(英文)》 2013年 第8卷 第2期 页码 187-200 doi: 10.1007/s11465-013-0262-x
Machining of plastic materials has become increasingly important in any engineering industry subsequently the prediction of cutting forces. Forces quality has greater influence on components, which are coming in contact with each other. So it becomes necessary to measure and study machined forces and its behavior. In this research work, experimental investigations are conducted to determine the effects of cutting conditions and tool geometry on the cutting forces in the turning of the unidirectional glass fiber reinforced plastics (UD-GFRP) composites. In this experimental study, carbide tool (K10) having different tool nose radius and tool rake angle is used. Experiments are conducted based on the established Taguchi’s technique L18 orthogonal array on a lathe machine. It is found that the depth of cut is the cutting parameter, which has greater influence on cutting forces. The effect of the tool nose radius and tool rake angles on the cutting forces are also considerably significant. Based on statistical analysis, multiple regression model for cutting forces is derived with satisfactory coefficient (R2). This model proved to be highly preferment for predicting cutting forces.
关键词: unidirectional glass fiber reinforced plastics (UD-GFRP) composites machining cutting forces (tangential feed and radial force) ANOVA regression modeling carbide tool (K10)
SPT based determination of undrained shear strength: Regression models and machine learning
Walid Khalid MBARAK, Esma Nur CINICIOGLU, Ozer CINICIOGLU
《结构与土木工程前沿(英文)》 2020年 第14卷 第1期 页码 185-198 doi: 10.1007/s11709-019-0591-x
关键词: undrained shear strength linear regression random forest gradient boosting machine learning standard penetration test
Presentation of regression analysis, GP and GMDH models to predict the pedestrian density in various
Iraj BARGEGOL; Seyed Mohsen HOSSEINIAN; Vahid NAJAFI MOGHADDAM GILANI; Mohammad NIKOOKAR; Alireza OROUEI
《结构与土木工程前沿(英文)》 2022年 第16卷 第2期 页码 250-265 doi: 10.1007/s11709-021-0785-x
关键词: pedestrian density regression analysis GP model GMDH model
Jun Yang, Jingyun Wang, Pengwei Qiao, Yuanming Zheng, Junxing Yang, Tongbin Chen, Mei Lei, Xiaoming Wan, Xiaoyong Zhou
《环境科学与工程前沿(英文)》 2020年 第14卷 第3期 doi: 10.1007/s11783-019-1216-2
关键词: Soil Heavy metal Influencing factor Categorical regression Identification method
标题 作者 时间 类型 操作
Dynamic response surface methodology using Lasso regression for organic pharmaceutical synthesis
期刊论文
Modeling of unconfined compressive strength of soil-RAP blend stabilized with Portland cement using multivariateadaptive regression spline
Ali Reza GHANIZADEH, Morteza RAHROVAN
期刊论文
PyLUR: Efficient software for land use regression modeling the spatial distribution of air pollutants
Xuying Ma, Ian Longley, Jennifer Salmond, Jay Gao
期刊论文
Modeling oblique load carrying capacity of batter pile groups using neural network, random forest regression
Tanvi SINGH, Mahesh PAL, V. K. ARORA
期刊论文
Modeling the methyldiethanolamine-piperazine scrubbing system for CO
Stefania Moioli,Laura A. Pellegrini
期刊论文
Multiple regression models for energy consumption of office buildings in different climates in China
Siyu ZHOU, Neng ZHU
期刊论文
Interaction behavior and load sharing pattern of piled raft using nonlinear regression and LM algorithm-based
期刊论文
Multivariable regression model for Fox depth correction factor
Ravi Kant MITTAL, Sanket RAWAT, Piyush BANSAL
期刊论文
of driver-response relationships: identifying factors using a novel framework integrating quantile regression
期刊论文
Determination of effective stress parameter of unsaturated soils: A Gaussian process regression approach
Pijush Samui, Jagan J
期刊论文
Multiple linear regression, artificial neural network, and fuzzy logic prediction of 28 days compressive
Faezehossadat KHADEMI,Mahmoud AKBARI,Sayed Mohammadmehdi JAMAL,Mehdi NIKOO
期刊论文
Prediction of cutting forces in machining of unidirectional glass fiber reinforced plastics composite
Surinder Kumar GILL, Meenu GUPTA, P. S. SATSANGI
期刊论文
SPT based determination of undrained shear strength: Regression models and machine learning
Walid Khalid MBARAK, Esma Nur CINICIOGLU, Ozer CINICIOGLU
期刊论文
Presentation of regression analysis, GP and GMDH models to predict the pedestrian density in various
Iraj BARGEGOL; Seyed Mohsen HOSSEINIAN; Vahid NAJAFI MOGHADDAM GILANI; Mohammad NIKOOKAR; Alireza OROUEI
期刊论文