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operation in the presence of different random noises and uncertainty: Implementation of generalized Gaussianprocess regression machine

Nasser L. AZAD,Ahmad MOZAFFARI

Frontiers of Mechanical Engineering 2015, Volume 10, Issue 4,   Pages 405-412 doi: 10.1007/s11465-015-0354-x

Abstract: the modeling and analysis of automotive engines’ behavior during coldstart operations by using regressionThis encourages automotive engineers to take advantage of knowledge-based modeling tools and regressionThen, by using a Gaussian process regression machine (GPRM), a reliable model is used for the sake ofThe simulation results attest the efficacy of GPRM for the considered case study.

Keywords： automotive engine     calibration     coldstart operation     Gaussian process regression machine (GPRM)     uncertainty    

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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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Estimation of flexible pavement structural capacity using machine learning techniques

Nader KARBALLAEEZADEH, Hosein GHASEMZADEH TEHRANI, Danial MOHAMMADZADEH SHADMEHRI, Shahaboddin SHAMSHIRBAND

Frontiers of Structural and Civil Engineering 2020, Volume 14, Issue 5,   Pages 1083-1096 doi: 10.1007/s11709-020-0654-z

Abstract: In this study, three machine learning methods entitled Gaussian process regression, M5P model tree, andUsing machine learning methods instead of back-calculation improves the calculation process quality and

Keywords： transportation infrastructure     flexible pavement     structural number prediction     Gaussian process regression    

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prediction method for remaining useful life of lithium-ion batteries based on a neural network and Gaussianprocess regression

Frontiers in Energy doi: 10.1007/s11708-023-0906-4

Abstract: prediction accuracy of the RUL of LIBs, a two-phase RUL early prediction method combining neural network and Gaussianprocess regression (GPR) is proposed.

Keywords： lithium-ion batteries     RUL prediction     double exponential model     neural network     Gaussian process regression    

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of driver-response relationships: identifying factors using a novel framework integrating quantile regressionwith interpretable machine learning

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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SPT based determination of undrained shear strength: Regression models and machine learning

Walid Khalid MBARAK, Esma Nur CINICIOGLU, Ozer CINICIOGLU

Frontiers of Structural and Civil Engineering 2020, Volume 14, Issue 1,   Pages 185-198 doi: 10.1007/s11709-019-0591-x

Abstract: With this study, along with the conventional methods of simple and multiple linear regression models,three machine learning algorithms, random forest, gradient boosting and stacked models, are developedMoreover, to facilitate the use of machine learning algorithms for prediction purposes, entire source

Keywords： undrained shear strength     linear regression     random forest     gradient boosting     machine learning     standard    

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Inverse Gaussian process-based corrosion growth modeling and its application in the reliability analysis

Hao QIN, Shenwei ZHANG, Wenxing ZHOU

Frontiers of Structural and Civil Engineering 2013, Volume 7, Issue 3,   Pages 276-287 doi: 10.1007/s11709-013-0207-9

Abstract: This paper describes an inverse Gaussian process-based model to characterize the growth of metal-loss

Keywords： pipeline     metal-loss corrosion     inverse Gaussian process     measurement error     hierarchical Bayesian     Markov    

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Identification of important factors influencing nonlinear counting systems Research Article

Xinmin ZHANG, Jingbo WANG, Chihang WEI, Zhihuan SONG,xinminzhang@zju.edu.cn,wangjingbobo@zju.edu.cn,chhwei@zju.edu.cn,songzhihuan@zju.edu.cn

Frontiers of Information Technology & Electronic Engineering 2022, Volume 23, Issue 1,   Pages 123-133 doi: 10.1631/FITEE.2000324

Abstract: Identifying factors that exert more influence on system output from data is one of the most challenging tasks in science and engineering. In this work, a of the (SA-GGPR) model is proposed to identify of the . In SA-GGPR, the GGPR model with Poisson likelihood is adopted to describe the . The GGPR model with Poisson likelihood inherits the merits of nonparametric kernel learning and Poisson distribution, and can handle complex s. Nevertheless, understanding the relationships between model inputs and output in the GGPR model with Poisson likelihood is not readily accessible due to its nonparametric and kernel structure. SA-GGPR addresses this issue by providing a quantitative assessment of how different inputs affect the system output. The application results on a simulated and a real have demonstrated that the proposed SA-GGPR method outperforms several state-of-the-art methods in identification accuracy.

Keywords： Important factors     Nonlinear counting system     Generalized Gaussian process regression     Sensitivity analysis     Steel casting-rolling process    

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Evaluation and prediction of slope stability using machine learning approaches

Frontiers of Structural and Civil Engineering 2021, Volume 15, Issue 4,   Pages 821-833 doi: 10.1007/s11709-021-0742-8

Abstract: In this paper, the machine learning (ML) model is built for slope stability evaluation and meets thestudied and compared hoping to make the best use of the large variety of existing statistical and ML regression>, MAE, and MSE of these methods, SVM, GBR, and Bagging are considered to be the best regressionThe performance and reliability of the nonlinear regression method are slightly better than that of thelinear regression method.

Keywords： slope stability     factor of safety     regression     machine learning     repeated cross-validation    

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Development of machine learning multi-city model for municipal solid waste generation prediction

Frontiers of Environmental Science & Engineering 2022, Volume 16, Issue 9, doi: 10.1007/s11783-022-1551-6

Abstract:

● A database of municipal solid waste (MSW) generation in China was established.

Keywords： Municipal solid waste     Machine learning     Multi-cities     Gradient boost regression tree    

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Structural performance assessment of GFRP elastic gridshells by machine learning interpretability methods

Soheila KOOKALANI; Bin CHENG; Jose Luis Chavez TORRES

Frontiers of Structural and Civil Engineering 2022, Volume 16, Issue 10,   Pages 1249-1266 doi: 10.1007/s11709-022-0858-5

Abstract: Machine learning (ML) approaches are implemented in this study, to predict maximum stress and displacementSeveral ML algorithms, including linear regression (LR), ridge regression (RR), support vector regressionAdaBoost), extreme gradient boosting (XGBoost), category boosting (CatBoost), and light gradient boosting machine

Keywords： machine learning     gridshell structure     regression     sensitivity analysis     interpretability methods    

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Simulation and analysis of grinding wheel based on Gaussian mixture model

Yulun CHI, Haolin LI

Frontiers of Mechanical Engineering 2012, Volume 7, Issue 4,   Pages 427-432 doi: 10.1007/s11465-012-0350-3

Abstract: The Gaussian mixture model (GMM) is used to transform the measured non-Gaussian field to Gaussian fields

Keywords： grinding wheel     3D topographies measurement     Gaussian mixture model     simulation    

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Hybrid-driven Gaussian process online learning for highly maneuvering multi-target tracking Research Article

Qiang GUO, Long TENG, Tianxiang YIN, Yunfei GUO, Xinliang WU, Wenming SONG

Frontiers of Information Technology & Electronic Engineering 2023, Volume 24, Issue 11,   Pages 1647-1656 doi: 10.1631/FITEE.2300348

Abstract: The performance of existing maneuvering methods for highly maneuvering targets in cluttered environments is unsatisfactory. This paper proposes a hybrid-driven approach for tracking multiple highly maneuvering targets, leveraging the advantages of both and model-based algorithms. The time-varying constant velocity model is integrated into the (GP) of to improve the performance of GP prediction. This integration is further combined with a generalized algorithm to realize multi-. Through the simulations, it has been demonstrated that the hybrid-driven approach exhibits significant performance improvements in comparison with widely used algorithms such as the interactive multi-model method and the GP motion tracker.

Keywords： Target tracking     Gaussian process     Data-driven     Online learning     Model-driven     Probabilistic data association    

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Hybrid method integrating machine learning and particle swarm optimization for smart chemical process

Haoqin Fang, Jianzhao Zhou, Zhenyu Wang, Ziqi Qiu, Yihua Sun, Yue Lin, Ke Chen, Xiantai Zhou, Ming Pan

Frontiers of Chemical Science and Engineering 2022, Volume 16, Issue 2,   Pages 274-287 doi: 10.1007/s11705-021-2043-0

Abstract: Modeling and optimization is crucial to smart chemical process operations.However, a large number of nonlinearities must be considered in a typical chemical process accordingThus, this paper presents an efficient hybrid framework of integrating machine learning and particleSecondly, four well-known machine learning methods, namely, K-nearest neighbors, decision tree, supportvector machine, and artificial neural network, were compared and used to obtain the prediction models

Keywords： smart chemical process operations     data generation     hybrid method     machine learning     particle swarm optimization    

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Machine learning enabled prediction and process optimization of VFA production from riboflavin-mediated

Frontiers of Environmental Science & Engineering 2023, Volume 17, Issue 11, doi: 10.1007/s11783-023-1735-8

Abstract:

● Data-driven approach was used to simulate VFA production from WAS fermentation.

Keywords： Machine learning     Volatile fatty acids     Riboflavin     Waste activated sludge     eXtreme Gradient Boosting    

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Title Author Date Type Operation

operation in the presence of different random noises and uncertainty: Implementation of generalized Gaussianprocess regression machine

Nasser L. AZAD,Ahmad MOZAFFARI

Journal Article

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

Pijush Samui, Jagan J

Journal Article

Estimation of flexible pavement structural capacity using machine learning techniques

Nader KARBALLAEEZADEH, Hosein GHASEMZADEH TEHRANI, Danial MOHAMMADZADEH SHADMEHRI, Shahaboddin SHAMSHIRBAND

Journal Article

prediction method for remaining useful life of lithium-ion batteries based on a neural network and Gaussianprocess regression

Journal Article

of driver-response relationships: identifying factors using a novel framework integrating quantile regressionwith interpretable machine learning

Journal Article

SPT based determination of undrained shear strength: Regression models and machine learning

Walid Khalid MBARAK, Esma Nur CINICIOGLU, Ozer CINICIOGLU

Journal Article

Inverse Gaussian process-based corrosion growth modeling and its application in the reliability analysis

Hao QIN, Shenwei ZHANG, Wenxing ZHOU

Journal Article

Identification of important factors influencing nonlinear counting systems

Xinmin ZHANG, Jingbo WANG, Chihang WEI, Zhihuan SONG,xinminzhang@zju.edu.cn,wangjingbobo@zju.edu.cn,chhwei@zju.edu.cn,songzhihuan@zju.edu.cn

Journal Article

Evaluation and prediction of slope stability using machine learning approaches

Journal Article

Development of machine learning multi-city model for municipal solid waste generation prediction

Journal Article

Structural performance assessment of GFRP elastic gridshells by machine learning interpretability methods

Soheila KOOKALANI; Bin CHENG; Jose Luis Chavez TORRES

Journal Article

Simulation and analysis of grinding wheel based on Gaussian mixture model

Yulun CHI, Haolin LI

Journal Article

Hybrid-driven Gaussian process online learning for highly maneuvering multi-target tracking

Qiang GUO, Long TENG, Tianxiang YIN, Yunfei GUO, Xinliang WU, Wenming SONG

Journal Article

Hybrid method integrating machine learning and particle swarm optimization for smart chemical process

Haoqin Fang, Jianzhao Zhou, Zhenyu Wang, Ziqi Qiu, Yihua Sun, Yue Lin, Ke Chen, Xiantai Zhou, Ming Pan

Journal Article

Machine learning enabled prediction and process optimization of VFA production from riboflavin-mediated

Journal Article