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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    

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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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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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operation in the presence of different random noises and uncertainty: Implementation of generalized Gaussian

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: Then, 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 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

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

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Parametric control of structural responses using an optimal passive tuned mass damper under stationary Gaussian

Min-Ho CHEY, Jae-Ung KIM

Frontiers of Structural and Civil Engineering 2012, Volume 6, Issue 3,   Pages 267-280 doi: 10.1007/s11709-012-0170-x

Abstract: parameters of a TMD, such as the optimal tuning frequency and optimal damping ratio, to stationary Gaussian

Keywords： tuned mass damper     parametric optimization     passive control     white noise     earthquake excitation    

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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 is

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

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Optimal signal design strategywith improper Gaussian signaling in the Z-interference channel Article

Dan LI, Shan WANG, Fang-lin GU

Frontiers of Information Technology & Electronic Engineering 2017, Volume 18, Issue 11,   Pages 1900-1912 doi: 10.1631/FITEE.1700030

Abstract: design strategy to achieve the Pareto boundary (boundary of the achievable rate region) with improper Gaussianthe Z-interference channel (Z-IC) under the assumption that the interference is treated as additive Gaussian

Keywords： Z-interference channel     Improper Gaussian signaling     Sum-rate     Pareto boundary     Covariance     Pseudo-covariance    

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The Improving Characteristics of the Gradual Gaussian Multidimensional Pre-filter for Optical Flow Estimation

Fu Jun,Xu Weipu

Strategic Study of CAE 2004, Volume 6, Issue 12,   Pages 56-61

Abstract:

Based on the unified estimation-theoretic framework, an effective method of using the gradual GaussianAfter pre-filtering the image sequence by the Gaussian multidimensional filter, the average PSNR of the

Keywords： optical flow computing     Gaussian multidimensional filter     PSNR     motion estimation    

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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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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 Gaussian

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

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New decentralized control technique based on substructure and LQG approaches

Ying LEI, Ying LIN,

Frontiers of Mechanical Engineering 2009, Volume 4, Issue 4,   Pages 386-392 doi: 10.1007/s11465-009-0041-x

Abstract: In this paper, a new decentralized control technique is proposed based on the linear quadratic Gaussian

Keywords： substructures     decentralized control     linear quadratic Gaussian (LQG)     Kalman filter     unknown input     least-squares    

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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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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

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

operation in the presence of different random noises and uncertainty: Implementation of generalized Gaussian

Nasser L. AZAD,Ahmad MOZAFFARI

Journal Article

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

Journal Article

Parametric control of structural responses using an optimal passive tuned mass damper under stationary Gaussian

Min-Ho CHEY, Jae-Ung KIM

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

Optimal signal design strategywith improper Gaussian signaling in the Z-interference channel

Dan LI, Shan WANG, Fang-lin GU

Journal Article

The Improving Characteristics of the Gradual Gaussian Multidimensional Pre-filter for Optical Flow Estimation

Fu Jun,Xu Weipu

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

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

New decentralized control technique based on substructure and LQG approaches

Ying LEI, Ying LIN,

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