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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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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-Gaussian

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

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An adaptive data-driven method for accurate prediction of remaining useful life of rolling bearings

Yanfeng PENG, Junsheng CHENG, Yanfei LIU, Xuejun LI, Zhihua PENG

Frontiers of Mechanical Engineering 2018, Volume 13, Issue 2,   Pages 301-310 doi: 10.1007/s11465-017-0449-7

Abstract:

A novel data-driven method based on Gaussian mixture model (GMM) and distance evaluation technique

Keywords： Gaussian mixture model     distance evaluation technique     health state     remaining useful life     rolling bearing    

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

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 Gaussian, the features related to the capacity degradation of LIBs are utilized to train the neural network modelconsidered to have a similar degradation pattern, which is used to determine the initial Dual Exponential Model

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

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Simulation of abrasive flow machining process for 2D and 3D mixture models

Rupalika DASH,Kalipada MAITY

Frontiers of Mechanical Engineering 2015, Volume 10, Issue 4,   Pages 424-432 doi: 10.1007/s11465-015-0366-6

Abstract: In the current work, a 2D model was designed, and the flow analysis, force calculation, and materialAnother 3D model for a swaging die finishing using AFM was simulated at different viscosities of theTwo phases were considered for the flow analysis, and multiphase mixture model was taken into account

Keywords： abrasive flow machining (AFM)     computational fluid dynamics (CFD) modeling     mixture model    

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A saliency and Gaussian net model for retinal vessel segmentation Research Articles

Lan-yan XUE, Jia-wen LIN, Xin-rong CAO, Shao-hua ZHENG, Lun YU

Frontiers of Information Technology & Electronic Engineering 2019, Volume 20, Issue 8,   Pages 1075-1086 doi: 10.1631/FITEE.1700404

Abstract: A novel deep learning structure called the Gaussian net (GNET) model combined with a saliency model isA saliency image is used as the input of the GNET model replacing the original image.The GNET model adopts a bilaterally symmetrical structure.Experimental results indicate that the GNET model can obtain more precise features and subtle details

Keywords： Retinal vessel segmentation     Saliency model     Gaussian net (GNET)     Feature learning    

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ApproximateGaussian conjugacy: parametric recursive filtering under nonlinearity,multimodality, uncertainty, and constraint, and beyond Review

Tian-cheng LI, Jin-ya SU, Wei LIU, Juan M. CORCHADO

Frontiers of Information Technology & Electronic Engineering 2017, Volume 18, Issue 12,   Pages 1913-1939 doi: 10.1631/FITEE.1700379

Abstract: ., recursion from a Gaussian or Gaussian mixture (GM) prior to a Gaussian/GM posterior (termed ‘Gaussianmultimodality (including target maneuver), intractable uncertainties (such as unknown inputs and/or non-Gaussianattention is paid to nonlinear systems with an informative observation, multimodal systems including Gaussianmixture posterior and maneuvers, and intractable unknown inputs and constraints, to fill some gaps inIn addition, we provide some new thoughts on alternatives to the first-order Markov transition model

Keywords： Kalman filter     Gaussian filter     Time series estimation     Bayesian filtering     Nonlinear filtering     Constrainedfiltering     Gaussian mixture     Maneuver     Unknown inputs    

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Spacecraft damage infrared detection algorithm for hypervelocity impact based on double-layer multi-target segmentation Research Article

Xiao YANG, Chun YIN, Sara DADRAS, Guangyu LEI, Xutong TAN, Gen QIU,yinchun.86416@163.com,chunyin@uestc.edu.cn

Frontiers of Information Technology & Electronic Engineering 2022, Volume 23, Issue 4,   Pages 571-586 doi: 10.1631/FITEE.2000695

Abstract: To detect spacecraft damage caused by hypervelocity impact, we propose an advanced spacecraft defect extraction algorithm based on infrared imaging detection. The (GMM) is used to classify the temperature change characteristics in the sampled data of the infrared video stream and reconstruct the image to obtain the infrared reconstructed image (IRRI) reflecting the defect characteristics. The designed segmentation objective function is used to ensure the effectiveness of results for noise removal and detail preservation, while taking into account the complexity of IRRI (that is, the required trade-offs are different). A multi-objective optimization algorithm is introduced to achieve balance between detail preservation and noise removal, and a multi-objective evolutionary algorithm based on decomposition (MOEA/D) is used for optimization to ensure damage segmentation accuracy. Experimental results verify the effectiveness of the proposed algorithm.

Keywords： Hypervelocity impact damage     Defect detection     Gaussian mixture model     Image segmentation    

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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: evaluating candidate hyperparameter configuration, and (3) the problem of ensuring convergence rates and modelTo overcome these problems and challenges, a hybrid-model optimization algorithm is proposed in thisSecond, a hybrid-surrogate-assisted model is proposed to reduce the high cost of evaluating candidateThird, a novel activation function is suggested to improve the model performance and ensure the convergence

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

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A new constrained maximum margin approach to discriminative learning of Bayesian classifiers None

Ke GUO, Xia-bi LIU, Lun-hao GUO, Zong-jie LI, Zeng-min GENG

Frontiers of Information Technology & Electronic Engineering 2018, Volume 19, Issue 5,   Pages 639-650 doi: 10.1631/FITEE.1700007

Abstract: We applied the proposed CMM approach to learn Bayesian classifiers based on Gaussian mixture models,

Keywords： Discriminative learning     Statistical modeling     Bayesian pattern classifiers     Gaussian mixture models     UCI    

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A study on quality evaluation for bituminous mixture using X-ray CT

Satoshi TANIGUCHI, Keiichiro OGAWA, Jun OTANI, Itaru NISHIZAKI

Frontiers of Structural and Civil Engineering 2013, Volume 7, Issue 2,   Pages 89-101 doi: 10.1007/s11709-013-0197-7

Abstract: The objective of this paper is to propose a new quality evaluation method for asphalt concrete mixtureIn the mixture examination, histograms of CT-value and four segmentation images made from CT images expressedthe material and mixture characterization such as particle size and the difference in bitumen contentand mixture type visibly and the bitumen content varies with the threshold values.

Keywords： asphalt concrete mixture     aggregate     bitumen     bitumen content     quality evaluation     X-ray CT    

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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: hybrid-driven approach for tracking multiple highly maneuvering targets, leveraging the advantages of both and model-basedThe time-varying constant velocity model is integrated into the (GP) of to improve the performancesignificant performance improvements in comparison with widely used algorithms such as the interactive multi-model

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

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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 effectiveThe results show that the developed GPR is reliable model for prediction of of unsaturated soil.

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

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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-lossThe model parameters are evaluated using the Bayesian methodology by combining the inspection data obtainedThe results indicate that the model in general can predict the growth of corrosion defects reasonablyParametric analyses associated with the growth model as well as reliability assessment of the pipelinebased on the growth model are also included in the example.

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

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Fatigue of asphalt binder, mastic and mixture at low temperature

Dong WANG, Linbing WANG, Guoqing ZHOU

Frontiers of Structural and Civil Engineering 2012, Volume 6, Issue 2,   Pages 166-175 doi: 10.1007/s11709-012-0157-7

Abstract: experiment method is developed to evaluate the performances of asphalt binder, mastic and fine aggregates mixtureThe micro-structure analysis of mastic and mixture indicates that the fatigue resistance is closely relatedwith the air void content of specimen. 3D digital specimens are developed to model the fatigue of theasphalt binder, mastic and mixture specimens based on the finite element method (FEM).Fatigue damage of asphalt concrete is simplified by a damage model.

Keywords： fatigue     asphalt mixture     asphalt binder     mastic     finite element method (FEM)     X-ray tomography    

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Title Author Date Type Operation

Simulation and analysis of grinding wheel based on Gaussian mixture model

Yulun CHI, Haolin LI

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

An adaptive data-driven method for accurate prediction of remaining useful life of rolling bearings

Yanfeng PENG, Junsheng CHENG, Yanfei LIU, Xuejun LI, Zhihua PENG

Journal Article

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

Journal Article

Simulation of abrasive flow machining process for 2D and 3D mixture models

Rupalika DASH,Kalipada MAITY

Journal Article

A saliency and Gaussian net model for retinal vessel segmentation

Lan-yan XUE, Jia-wen LIN, Xin-rong CAO, Shao-hua ZHENG, Lun YU

Journal Article

ApproximateGaussian conjugacy: parametric recursive filtering under nonlinearity,multimodality, uncertainty, and constraint, and beyond

Tian-cheng LI, Jin-ya SU, Wei LIU, Juan M. CORCHADO

Journal Article

Spacecraft damage infrared detection algorithm for hypervelocity impact based on double-layer multi-target segmentation

Xiao YANG, Chun YIN, Sara DADRAS, Guangyu LEI, Xutong TAN, Gen QIU,yinchun.86416@163.com,chunyin@uestc.edu.cn

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

A new constrained maximum margin approach to discriminative learning of Bayesian classifiers

Ke GUO, Xia-bi LIU, Lun-hao GUO, Zong-jie LI, Zeng-min GENG

Journal Article

A study on quality evaluation for bituminous mixture using X-ray CT

Satoshi TANIGUCHI, Keiichiro OGAWA, Jun OTANI, Itaru NISHIZAKI

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

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

Pijush Samui, Jagan J

Journal Article

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

Hao QIN, Shenwei ZHANG, Wenxing ZHOU

Journal Article

Fatigue of asphalt binder, mastic and mixture at low temperature

Dong WANG, Linbing WANG, Guoqing ZHOU

Journal Article