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over coldstart operation in the presence of different random noises and uncertainty: Implementation of generalizedGaussian process 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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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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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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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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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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Isomorphism analysis on generalized modules oriented to the distributed parameterized intelligent product

Shasha ZENG, Weiping PENG, Tiaoyu LEI

Frontiers of Mechanical Engineering 2020, Volume 15, Issue 1,   Pages 12-23 doi: 10.1007/s11465-019-0555-9

Abstract: product platform (DPIPP) contains many agents of a product minimum approximate autonomous subsystem (generalizedIn this study, a new method of isomorphism analysis on generalized modules oriented to DPIPP is proposedFirst, on the basis of the bill of material partition and generalized module mining, the parameters ofSecond, similarity calculation of generalized modules is realized by improving the clustering using representativesGeneralized modules with a similar structure are combined to complete the isomorphism analysis.

Keywords： distributed parameterized intelligent product platform     generalized module     isomorphism analysis     product    

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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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Novel method of constructing generalized Hoberman sphere mechanisms based on deployment axes

Xuemin SUN, Yan-An YAO, Ruiming LI

Frontiers of Mechanical Engineering 2020, Volume 15, Issue 1,   Pages 89-99 doi: 10.1007/s11465-019-0567-5

Abstract: This study proposes a method of constructing type II generalized angulated elements (GAEs II) HobermanThird, a flowchart of constructing the generalized Hoberman sphere mechanism on the basis of deploymentFinally, four generalized Hoberman sphere mechanisms based on a fully enclosed regular hexahedron, arithmetic

Keywords： deployable mechanism     type II generalized angulated elements     Hoberman sphere mechanism     deployment axes    

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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: This integration is further combined with a generalized algorithm to realize multi-.

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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Multiscale RBF-based central high resolution schemes for simulation of generalized thermoelasticity problems

Hassan YOUSEFI, Alireza TAGHAVI KANI, Iradj MAHMOUDZADEH KANI

Frontiers of Structural and Civil Engineering 2019, Volume 13, Issue 2,   Pages 429-455 doi: 10.1007/s11709-018-0483-5

Abstract: This proposed method is used for simulation of generalized coupled thermoelasticity problems including

Keywords： central high resolution schemes     RBFs     higher order accuracy     generalized thermoelasticity     multiresolution-based    

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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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Title Author Date Type Operation

over coldstart operation in the presence of different random noises and uncertainty: Implementation of generalizedGaussian process 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

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

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

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

Isomorphism analysis on generalized modules oriented to the distributed parameterized intelligent product

Shasha ZENG, Weiping PENG, Tiaoyu LEI

Journal Article

Simulation and analysis of grinding wheel based on Gaussian mixture model

Yulun CHI, Haolin LI

Journal Article

Novel method of constructing generalized Hoberman sphere mechanisms based on deployment axes

Xuemin SUN, Yan-An YAO, Ruiming 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

Multiscale RBF-based central high resolution schemes for simulation of generalized thermoelasticity problems

Hassan YOUSEFI, Alireza TAGHAVI KANI, Iradj MAHMOUDZADEH KANI

Journal Article

Real-Time Assessment and Diagnosis of Process Operating Performance

Shabnam Sedghi,Biao Huang

Journal Article