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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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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 of

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

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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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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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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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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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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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A hybrid-model optimization algorithm based on the Gaussian process and particle swarm optimization for Research Article

Han YAN, Chongquan ZHONG, Yuhu WU, Liyong ZHANG, Wei LU

Frontiers of Information Technology & Electronic Engineering 2023, Volume 24, Issue 11,   Pages 1557-1573 doi: 10.1631/FITEE.2200515

Abstract: s (CNNs) have been developed quickly in many real-world fields. However, CNN’s performance depends heavily on its hyperparameters, while finding suitable hyperparameters for CNNs working in application fields is challenging for three reasons: (1) the problem of encoding for different types of hyperparameters in CNNs, (2) expensive computational costs in evaluating candidate hyperparameter configuration, and (3) the problem of ensuring convergence rates and model performance during hyperparameter search. To overcome these problems and challenges, a hybrid-model optimization algorithm is proposed in this paper to search suitable hyperparameter configurations automatically based on the and (GPPSO) algorithm. First, a new encoding method is designed to efficiently deal with the CNN hyperparameter problem. Second, a hybrid-surrogate-assisted model is proposed to reduce the high cost of evaluating candidate hyperparameter configurations. Third, a novel activation function is suggested to improve the model performance and ensure the convergence rate. Intensive experiments are performed on imageclassification benchmark datasets to demonstrate the superior performance of GPPSO over state-of-the-art methods. Moreover, a case study on metal fracture diagnosis is carried out to evaluate the GPPSO algorithm performance in practical applications. Experimental results demonstrate the effectiveness and efficiency of GPPSO, achieving accuracy of 95.26% and 76.36% only through 0.04 and 1.70 GPU days on the CIFAR-10 and CIFAR-100 datasets, respectively.

Keywords： Convolutional neural network     Gaussian process     Hybrid model     Hyperparameter optimization     Mixed-variable    

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Performance monitoring of non-gaussian chemical processes with modes-switching using globality-locality

Xin Peng, Yang Tang, Wenli Du, Feng Qian

Frontiers of Chemical Science and Engineering 2017, Volume 11, Issue 3,   Pages 429-439 doi: 10.1007/s11705-017-1675-6

Abstract: based on modified structure analysis and globality and locality preserving (MSAGL) projection, for non-Gaussianlocality preserving projection to analyze the embedding geometrical manifold and extracting the non-Gaussianeffectiveness of this approach are illustrated by applying the proposed technique to the Tennessee Eastman process

Keywords： non-Gaussian processes     subspace projection     independent component analysis     locality preserving projection    

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Real time monitoring of bioreactor mAb IgG3 cell culture process dynamics via Fourier transform infraredspectroscopy: Implications for enabling cell culture process analytical technology?

Huiquan Wu, Erik Read, Maury White, Brittany Chavez, Kurt Brorson, Cyrus Agarabi, Mansoor Khan

Frontiers of Chemical Science and Engineering 2015, Volume 9, Issue 3,   Pages 386-406 doi: 10.1007/s11705-015-1533-3

Abstract: Compared to small molecule process analytical technology (PAT) applications, biotechnology product PATUnderstanding process dynamics of bioreactor cell culture process is essential to establish an appropriateprocess control strategy for biotechnology product PAT applications.Inline spectroscopic techniques for real time monitoring of bioreactor cell culture process have theFTIR spectra with the concentrations of key metabolites and protein yield of in-process samples, either

Keywords： process analytical technology (PAT)     Fourier-transform infrared (FTIR) spectroscopy     partial least squares(PLS) regression     mouse IgG3     bioreactor cell culture process     real time process monitoring    

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Real-Time Assessment and Diagnosis of Process Operating Performance

Shabnam Sedghi,Biao Huang

Engineering 2017, Volume 3, Issue 2,   Pages 214-219 doi: 10.1016/J.ENG.2017.02.004

Abstract:

Over time, the performance of processes may deviate from the initial design due to process variationsnecessary to develop systematic methods for online optimality assessment based on routine operating processmulti-modal behavior of steady-state modes is tackled utilizing the mixture probabilistic principal component regression(MPPCR) method, and dynamic principal component regression (DPCR) is used to investigate transitionsFinally, the proposed method is tested on the Tennessee Eastman (TE) benchmark simulation process in

Keywords： Optimality assessment     Probabilistic principal component regression     Multi-mode    

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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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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: To study the dynamic behavior of a process, time-resolved data are collected at different time instantsTwo 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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Title Author Date Type Operation

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

Pijush Samui, Jagan J

Journal Article

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

Nasser L. AZAD,Ahmad MOZAFFARI

Journal Article

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

Journal Article

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

Hao QIN, Shenwei ZHANG, Wenxing ZHOU

Journal Article

Estimation of flexible pavement structural capacity using machine learning techniques

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

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

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

A hybrid-model optimization algorithm based on the Gaussian process and particle swarm optimization for

Han YAN, Chongquan ZHONG, Yuhu WU, Liyong ZHANG, Wei LU

Journal Article

Performance monitoring of non-gaussian chemical processes with modes-switching using globality-locality

Xin Peng, Yang Tang, Wenli Du, Feng Qian

Journal Article

Real time monitoring of bioreactor mAb IgG3 cell culture process dynamics via Fourier transform infraredspectroscopy: Implications for enabling cell culture process analytical technology?

Huiquan Wu, Erik Read, Maury White, Brittany Chavez, Kurt Brorson, Cyrus Agarabi, Mansoor Khan

Journal Article

Real-Time Assessment and Diagnosis of Process Operating Performance

Shabnam Sedghi,Biao Huang

Journal Article

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

Siyu ZHOU, Neng ZHU

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