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Modeling of unconfined compressive strength of soil-RAP blend stabilized with Portland cement using multivariateadaptive regression spline

Ali Reza GHANIZADEH, Morteza RAHROVAN

Frontiers of Structural and Civil Engineering 2019, Volume 13, Issue 4,   Pages 787-799 doi: 10.1007/s11709-019-0516-8

Abstract: the unconfined compressive strength (UCS) of soil-RAP blend stabilized with Portland cement based on multivariateadaptive regression spline (MARS).

Keywords： full-depth reclamation     soil-reclaimed asphalt pavement blend     Portland cement     unconfined compressive strength     multivariateadaptive regression spline    

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Interactive image segmentation with a regression based ensemble learning paradigm Article

Jin ZHANG, Zhao-hui TANG, Wei-hua GUI, Qing CHEN, Jin-ping LIU

Frontiers of Information Technology & Electronic Engineering 2017, Volume 18, Issue 7,   Pages 1002-1020 doi: 10.1631/FITEE.1601401

Abstract: This paper presents a novel interactive image segmentation method via a regression-based ensemble modelFirst, two spline regressors with a complementary nature are constructed based on multivariate adaptiveregression splines (MARS) and smooth thin plate spline regression (TPSR).Next, a support vector regression (SVR) based decision fusion model is adopted to integrate the results

Keywords： Interactive image segmentation     Multivariate adaptive regression splines (MARS)     Ensemble learning     Thin-platespline regression (TPSR)     Semi-supervised learning     Support vector regression (SVR)    

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Expanding the Scope of Multivariate Regression Approaches in Cross-Omics Research Article

Xiaoxi Hu, Yue Ma, Yakun Xu, Peiyao Zhao, Jun Wang

Engineering 2021, Volume 7, Issue 12,   Pages 1725-1731 doi: 10.1016/j.eng.2020.05.028

Abstract: in the amount of high-dimensional data and thus have increased the demand for proper and efficient multivariateregression methods.Numerous traditional multivariate approaches such as principal component analysis have been used broadlyreduced rank regression and subspace assisted regression with row sparsity, which hold potential tomethod extensions provide valuable guidelines for future omics research, especially with respect to multivariate

Keywords： Multivariate regression methods     Reduced rank regression     Sparsity     Dimensionality reduction     Variable    

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Obstacle-circumventing adaptive control of a four-wheeled mobile robot subjected to motion uncertainties

Frontiers of Mechanical Engineering 2023, Volume 18, Issue 3, doi: 10.1007/s11465-023-0753-3

Abstract: To address this challenge, this paper proposes an obstacle-circumventing adaptive control (OCAC) frameworkSpecifically, a novel anti-disturbance terminal slide mode control with adaptive gains is formulated,By introducing sub-target points, a new sub-target dynamic tracking regression obstacle avoidance strategy

Keywords： four-wheeled mobile robot     obstacle-circumventing adaptive control     adaptive anti-disturbance terminalsliding mode control     sub-target dynamic tracking regression obstacle avoidance    

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Performance evaluation of circulating fluidized bed incineration of municipal solid waste by multivariate

Hua Tao, Pinjing He, Yi Zhang, Wenjie Sun

Frontiers of Environmental Science & Engineering 2017, Volume 11, Issue 6, doi: 10.1007/s11783-017-0945-3

Abstract: analyzed to assess the overall performance of CFB incineration by applying the Mahalanobis distance as a multivariate

Keywords： Municipal solid waste     Incineration     Circulating fluidized bed     Load change     Multivariate outlier detection    

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Multiple regression models for energy consumption of office buildings in different climates in China

Siyu ZHOU, Neng ZHU

Frontiers in Energy 2013, Volume 7, Issue 1,   Pages 103-110 doi: 10.1007/s11708-012-0220-z

Abstract: Then on the basis of the simulated results, the multiple regression models were developed respectivelyAccording to the analysis of regression coefficients, the appropriate building envelope design schemesAt last, the regression model evaluations consisting of the simulation evaluations and the actual caseevaluations were performed to verify the feasibility and accuracy of the regression models.It is believed that the regression models developed in this paper can be used to estimate the energy

Keywords： regression model     energy consumption     building envelope     office building     different climates    

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Using hybrid models to predict blood pressure reactivity to unsupported back based on anthropometric characteristics

Gurmanik KAUR,Ajat Shatru ARORA,Vijender Kumar JAIN

Frontiers of Information Technology & Electronic Engineering 2015, Volume 16, Issue 6,   Pages 474-485 doi: 10.1631/FITEE.1400295

Abstract: In this work, principal component analysis (PCA) is fused with forward stepwise regression (SWR), artificialneural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), and the least squares support vector

Keywords： Blood pressure (BP)     Principal component analysis (PCA)     Forward stepwise regression     Artificial neuralnetwork (ANN)     Adaptive neuro-fuzzy inference system (ANFIS)     Least squares support vector machine (    

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Dynamic response surface methodology using Lasso regression for organic pharmaceutical synthesis

Frontiers of Chemical Science and Engineering 2022, Volume 16, Issue 2,   Pages 221-236 doi: 10.1007/s11705-021-2061-y

Abstract: Two approaches can be adopted in the estimation of the model parameters: stepwise regression, used inseveral of previous publications, and Lasso regression, which is newly incorporated in this paper forTherefore, DRSM with Lasso regression can provide faster and more accurate data-driven models for a variety

Keywords： data-driven modeling     pharmaceutical organic synthesis     Lasso regression     dynamic response surface methodology    

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Interaction behavior and load sharing pattern of piled raft using nonlinear regression and LM algorithm-based

Frontiers of Structural and Civil Engineering 2021, Volume 15, Issue 5,   Pages 1181-1198 doi: 10.1007/s11709-021-0744-6

Abstract: The obtained results are then checked statistically with nonlinear multiple regression (NMR) and artificial

Keywords： interaction     load sharing ratio     piled raft     nonlinear regression     artificial neural network    

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Multivariable regression model for Fox depth correction factor

Ravi Kant MITTAL, Sanket RAWAT, Piyush BANSAL

Frontiers of Structural and Civil Engineering 2019, Volume 13, Issue 1,   Pages 103-109 doi: 10.1007/s11709-018-0474-6

Abstract: Therefore, this paper presents a non-linear regression model for the analysis of effect of embedment

Keywords： settlement     embedment     Fox depth correction factor     regression     multivariable    

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Assessment of temporal and spatial variations in water quality using multivariate statistical methods

Xue LI,Pengjing LI,Dong WANG,Yuqiu WANG

Frontiers of Environmental Science & Engineering 2014, Volume 8, Issue 6,   Pages 895-904 doi: 10.1007/s11783-014-0736-z

Abstract: temporal and spatial variations of water quality data sets for the Xin'anjiang River through the use of multivariate

Keywords： Xin'anjiang River     multivariable statistical analysis     temporal variation     spatial variation     water quality    

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Reasonable Scale of Megacity Central Area Based on Multivariate Data and a Traffic Perspective

Lu Huapu, Bai Zhuotong,Wu Zhouhao, Fu Zhihuan

Strategic Study of CAE 2022, Volume 24, Issue 6,   Pages 146-153 doi: 10.15302/J-SSCAE-2022.06.013

Abstract:

The central area of a megacity, which features high-intensity development, large population density, and concentrated urban functions, demonstrates the most prominent urban problems. Addressing the urban malaise in megacities necessitates analyzing and demonstrating the reasonable scale of the megacity central area. This study proposes that commuting time is the core controlling factor determining the reasonable scale of the megacity central area. Combining with point of interest data of multi-type land use and the geographic information system data for street administrative division, we identified the current central urban areas of ten cities in China using big data analysis and the clustering method; their current traffic efficiencies were then evaluated based on path navigation data via web maps and mobile phone signaling data verification. Finally, demonstration results were presented through quantitative analysis. Our study shows that the current central areas of the ten megacities cannot satisfy residents' need for a proper commuting time; considering factors such as technological development and improvement in the level of governance, 13‒15 km is the upper limit of equivalent radius for the central area of a megacity.

Keywords： reasonable scale     multivariate data     central urban area     megacity     happiness    

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of driver-response relationships: identifying factors using a novel framework integrating quantile regression

Frontiers of Environmental Science & Engineering 2023, Volume 17, Issue 6, doi: 10.1007/s11783-023-1676-2

Abstract:

● A novel framework integrating quantile regression with machine learning

Keywords： Driver-response     Upper boundary of relationship     Interpretable machine learning     Quantile regression    

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Uncertainty Quantification for Multivariate Eco-Hydrological Risk in the Xiangxi River within the Three Article

Yurui Fan,Guohe Huang,Yin Zhang,Yongping Li

Engineering 2018, Volume 4, Issue 5,   Pages 617-626 doi: 10.1016/j.eng.2018.06.006

Abstract: ">This study develops a multivariateeco-hydrological risk-assessment framework based on the multivariate copula method in order to evaluateThe probabilistic features of bivariate and multivariate hydrological risk are also characterized.

Keywords： Flood risk     Copula     Multivariate flood frequency analysis     Distribution     Markov chain Monte Carlo    

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Determination of effective stress parameter of unsaturated soils: A Gaussian process regression approach

Pijush Samui, Jagan J

Frontiers of Structural and Civil Engineering 2013, Volume 7, Issue 2,   Pages 133-136 doi: 10.1007/s11709-013-0202-1

Abstract: This article examines the capability of Gaussian process regression (GPR) for prediction of effective

Keywords： unsaturated soil     effective stress parameter     Gaussian process regression (GPR)     artificial neural network    

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Title Author Date Type Operation

Modeling of unconfined compressive strength of soil-RAP blend stabilized with Portland cement using multivariateadaptive regression spline

Ali Reza GHANIZADEH, Morteza RAHROVAN

Journal Article

Interactive image segmentation with a regression based ensemble learning paradigm

Jin ZHANG, Zhao-hui TANG, Wei-hua GUI, Qing CHEN, Jin-ping LIU

Journal Article

Expanding the Scope of Multivariate Regression Approaches in Cross-Omics Research

Xiaoxi Hu, Yue Ma, Yakun Xu, Peiyao Zhao, Jun Wang

Journal Article

Obstacle-circumventing adaptive control of a four-wheeled mobile robot subjected to motion uncertainties

Journal Article

Performance evaluation of circulating fluidized bed incineration of municipal solid waste by multivariate

Hua Tao, Pinjing He, Yi Zhang, Wenjie Sun

Journal Article

Multiple regression models for energy consumption of office buildings in different climates in China

Siyu ZHOU, Neng ZHU

Journal Article

Using hybrid models to predict blood pressure reactivity to unsupported back based on anthropometric characteristics

Gurmanik KAUR,Ajat Shatru ARORA,Vijender Kumar JAIN

Journal Article

Dynamic response surface methodology using Lasso regression for organic pharmaceutical synthesis

Journal Article

Interaction behavior and load sharing pattern of piled raft using nonlinear regression and LM algorithm-based

Journal Article

Multivariable regression model for Fox depth correction factor

Ravi Kant MITTAL, Sanket RAWAT, Piyush BANSAL

Journal Article

Assessment of temporal and spatial variations in water quality using multivariate statistical methods

Xue LI,Pengjing LI,Dong WANG,Yuqiu WANG

Journal Article

Reasonable Scale of Megacity Central Area Based on Multivariate Data and a Traffic Perspective

Lu Huapu, Bai Zhuotong,Wu Zhouhao, Fu Zhihuan

Journal Article

of driver-response relationships: identifying factors using a novel framework integrating quantile regression

Journal Article

Uncertainty Quantification for Multivariate Eco-Hydrological Risk in the Xiangxi River within the Three

Yurui Fan,Guohe Huang,Yin Zhang,Yongping Li

Journal Article

Determination of effective stress parameter of unsaturated soils: A Gaussian process regression approach

Pijush Samui, Jagan J

Journal Article