Non-parametric system identification with Gaussian processes for underwater vehicles is explored in this research with the purpose of modelling autonomous underwater vehicle(AUV) dynamics with a low amount of data. Mu...Non-parametric system identification with Gaussian processes for underwater vehicles is explored in this research with the purpose of modelling autonomous underwater vehicle(AUV) dynamics with a low amount of data. Multi-output Gaussian processes and their aptitude for modelling the dynamic system of an underactuated AUV without losing the relationships between tied outputs are used. The simulation of a first-principle model of a Remus 100 AUV is employed to capture data for the training and validation of the multi-output Gaussian processes. The metric and required procedure to carry out multi-output Gaussian processes for AUV with 6 degrees of freedom(DoF) is also shown in this paper. Multi-output Gaussian processes compared with the popular technique of recurrent neural network show that multi-output Gaussian processes manage to surpass RNN for non-parametric dynamic system identification in underwater vehicles with highly coupled DoF with the added benefit of providing the measurement of confidence.展开更多
A novel model named Multi-scale Gaussian Processes (MGP) is proposed. Motivated by the ideas of multi-scale representations in the wavelet theory, in the new model, a Gaussian process is represented at a scale by a li...A novel model named Multi-scale Gaussian Processes (MGP) is proposed. Motivated by the ideas of multi-scale representations in the wavelet theory, in the new model, a Gaussian process is represented at a scale by a linear basis that is composed of a scale function and its different translations. Finally the distribution of the targets of the given samples can be obtained at different scales. Compared with the standard Gaussian Processes (GP) model, the MGP model can control its complexity conveniently just by adjusting the scale pa-rameter. So it can trade-off the generalization ability and the empirical risk rapidly. Experiments verify the fea-sibility of the MGP model, and exhibit that its performance is superior to the GP model if appropriate scales are chosen.展开更多
In order to meet the demand of online optimal running, a novel soft sensor modeling approach based on Gaussian processes was proposed. The approach is moderately simple to implement and use without loss of performance...In order to meet the demand of online optimal running, a novel soft sensor modeling approach based on Gaussian processes was proposed. The approach is moderately simple to implement and use without loss of performance. It is trained by optimizing the hyperparameters using the scaled conjugate gradient algorithm with the squared exponential covariance function employed. Experimental simulations show that the soft sensor modeling approach has the advantage via a real-world example in a refinery. Meanwhile, the method opens new possibilities for application of kernel methods to potential fields.展开更多
Data-driven paradigms are well-known and salient demands of future wireless communication. Empowered by big data and machine learning techniques,next-generation data-driven communication systems will be intelligent wi...Data-driven paradigms are well-known and salient demands of future wireless communication. Empowered by big data and machine learning techniques,next-generation data-driven communication systems will be intelligent with unique characteristics of expressiveness, scalability, interpretability, and uncertainty awareness, which can confidently involve diversified latent demands and personalized services in the foreseeable future. In this paper, we review a promising family of nonparametric Bayesian machine learning models,i.e., Gaussian processes(GPs), and their applications in wireless communication. Since GP models demonstrate outstanding expressive and interpretable learning ability with uncertainty, they are particularly suitable for wireless communication. Moreover, they provide a natural framework for collaborating data and empirical models(DEM). Specifically, we first envision three-level motivations of data-driven wireless communication using GP models. Then, we present the background of the GPs in terms of covariance structure and model inference. The expressiveness of the GP model using various interpretable kernels, including stationary, non-stationary, deep and multi-task kernels,is showcased. Furthermore, we review the distributed GP models with promising scalability, which is suitable for applications in wireless networks with a large number of distributed edge devices. Finally, we list representative solutions and promising techniques that adopt GP models in various wireless communication applications.展开更多
Let {X(t), t ≥ 0} be a centered stationary Gaussian process with correlation r(t)such that 1-r(t) is asymptotic to a regularly varying function. With T being a nonnegative random variable and independent of X(t), the...Let {X(t), t ≥ 0} be a centered stationary Gaussian process with correlation r(t)such that 1-r(t) is asymptotic to a regularly varying function. With T being a nonnegative random variable and independent of X(t), the exact asymptotics of P(sup_(t∈[0,T])X(t) > x) is considered, as x → ∞.展开更多
Let X={X(t),t∈R+} be a centered space anisotropic Gaussian process values in R^(d) with non-stationary increments,whose components are independent but may not be identically distributed.Under certain conditions,then ...Let X={X(t),t∈R+} be a centered space anisotropic Gaussian process values in R^(d) with non-stationary increments,whose components are independent but may not be identically distributed.Under certain conditions,then almost surely c_(1)≤ϕ-m(X([0,1]))≤c_(2),where ϕ denotes the exact Hausdorff measure associated with function ϕ(s)=s1/α_(k)+Σ_(i=1)k(1-αi/αk)log log 1/s for some 1≤k≤d,(α_(1),…,α_(d))∈(0,1]^(d).We also obtain the exact Hausdorff measure of the graph of X on[0,1].展开更多
Reduced-order models offer a cost-effective and accurate approach to analyzing high-dimensional combustion problems.These surrogate models are built in a data-driven manner by combining computational fluid dynamics si...Reduced-order models offer a cost-effective and accurate approach to analyzing high-dimensional combustion problems.These surrogate models are built in a data-driven manner by combining computational fluid dynamics simulations with Proper Orthogonal Decomposition(POD)for dimensionality reduction and Gaussian Process Regression(GPR)for nonlinear regression.However,these models can yield physically inconsistent results,such as negative mass fractions.As a linear decomposition method,POD complicates the enforcement of constraints in the reduced space,while GPR lacks inherent provisions to ensure physical consistency.To address these challenges,this study proposes a novel constrained reduced-order model framework that enforces physical consistency in predictions.Dimensionality reduction is achieved by downsampling the dataset through low-cost Singular Value Decomposition(lcSVD)using optimal sensor placement,ensuring that the retained data points preserve physical information in the reduced space.We integrate finite-support parametric distribution functions,such as truncated Gaussian and beta distribution scaled to the interval[a,b],into the GPR framework.These bounded likelihood functions explicitly model the observational noise in the bounded space and use variational inference to approximate analytically intractable posterior distributions,producing GP estimations that satisfy physical constraints by construction.We validate the proposed methods using a synthetic dataset and a benchmark case of one-dimensional laminar NH3/H2 flames.The results show that the thermo-chemical state predictions comply with physical constraints while maintaining the high accuracy of unconstrained reduced-order models.展开更多
The packaging quality of coaxial laser diodes(CLDs)plays a pivotal role in determining their optical performance and long-term reliability.As the core packaging process,high-precision laser welding requires precise co...The packaging quality of coaxial laser diodes(CLDs)plays a pivotal role in determining their optical performance and long-term reliability.As the core packaging process,high-precision laser welding requires precise control of process parameters to suppress optical power loss.However,the complex nonlinear relationship between welding parameters and optical power loss renders traditional trial-and-error methods inefficient and imprecise.To address this challenge,a physics-informed(PI)and data-driven collaboration approach for welding parameter optimization is proposed.First,thermal-fluid-solid coupling finite element method(FEM)was employed to quantify the sensitivity of welding parameters to physical characteristics,including residual stress.This analysis facilitated the identification of critical factors contributing to optical power loss.Subsequently,a Gaussian process regression(GPR)model incorporating finite element simulation prior knowledge was constructed based on the selected features.By introducing physics-informed kernel(PIK)functions,stress distribution patterns were embedded into the prediction model,achieving high-precision optical power loss prediction.Finally,a Bayesian optimization(BO)algorithm with an adaptive sampling strategy was implemented for efficient parameter space exploration.Experimental results demonstrate that the proposedmethod effectively establishes explicit physical correlations between welding parameters and optical power loss.The optimized welding parameters reduced optical power loss by 34.1%,providing theoretical guidance and technical support for reliable CLD packaging.展开更多
Global Navigation Satellite System(GNSS)imaging method(GIM)has been successfully applied to global regions to investigate vertical land motion(VLM)of the Earth's surface.GNSS images derived from conventional GIM m...Global Navigation Satellite System(GNSS)imaging method(GIM)has been successfully applied to global regions to investigate vertical land motion(VLM)of the Earth's surface.GNSS images derived from conventional GIM method may present fragmented patches and encounter problems caused by excessive smoothing of velocity peaks,leading to difficulty in short-wavelength deformation detection and improper geophysical interpretation.Therefore,we propose a novel GNSS imaging method based on Gaussian process regression with velocity uncertainty considered(GPR-VU).Gaussian processing regression is introduced to describe the spatial relationship between neighboring site pairs as a priori weights and then reweight velocities by known station uncertainties,converting the discrete velocity field to a continuous one.The GPR-VU method is applied to reconstruct VLM images in the southwestern United States and the eastern Qinghai-Xizang Plateau,China,using the GNSS position time series in vertical direction.Compared to the traditional GIM method,the root-mean-square(RMS)and overall accuracy of the confusion matrix of the GPR-VU method increase by 5.0%and 14.0%from the 1°×1°checkerboard test in the southwestern United States.Similarly,the RMS and overall accuracy increase by 33.7%and 15.8%from the 6°×6°checkerboard test in the eastern Qinghai-Xizang Plateau.These checkerboard tests validate the capability to effectively capture the spatiotemporal variations characteristics of VLM and show that this algorithm outperforms the sparsely distributed network in the Qinghai-Xizang Plateau.The images from the GPR-VU method using real data in both regions show significant subsidence around Lassen Volcanic in northern California within a 30 km radius,slight uplift in the northern Sichuan Basin,and subsidence in its central and southern sections.These results further qualitatively illustrate consistency with previous findings.The GPR-VU method outperforms in diminishing the effect by fragmented patches,excessive smoothing of velocity peaks,and detecting potential short-wavelength deformations.展开更多
In this paper,we are concerned with the asymptotic behavior,as u→∞,of P{sup_t∈|0,T|X_u(t)>u},where X_u(t),t∈|0,T|,u>0 is a family of centered Gaussian processes with continuous trajectories.A key application...In this paper,we are concerned with the asymptotic behavior,as u→∞,of P{sup_t∈|0,T|X_u(t)>u},where X_u(t),t∈|0,T|,u>0 is a family of centered Gaussian processes with continuous trajectories.A key application of our findings concerns P{sup_t∈|0,T|(X(t)+g(t))>u},as u→∞,for X a centered Gaussian process and g some measurable trend function.Further applications include the approximation of both the ruin time and the ruin probability of the Brownian motion risk model with constant force of interest.展开更多
This paper introduces techniques in Gaussian process regression model for spatiotemporal data collected from complex systems.This study focuses on extracting local structures and then constructing surrogate models bas...This paper introduces techniques in Gaussian process regression model for spatiotemporal data collected from complex systems.This study focuses on extracting local structures and then constructing surrogate models based on Gaussian process assumptions.The proposed Dynamic Gaussian Process Regression(DGPR)consists of a sequence of local surrogate models related to each other.In DGPR,the time-based spatial clustering is carried out to divide the systems into sub-spatio-temporal parts whose interior has similar variation patterns,where the temporal information is used as the prior information for training the spatial-surrogate model.The DGPR is robust and especially suitable for the loosely coupled model structure,also allowing for parallel computation.The numerical results of the test function show the effectiveness of DGPR.Furthermore,the shock tube problem is successfully approximated under different phenomenon complexity.展开更多
In this paper,we establish some limit theorems on the increments of an l^p-valued multi- parameter Gaussian process under weaker conditions than those of Cs(?)rg(?)-Shao theorems published in Ann.Probab.(1993).
The dynamic soft sensor based on a single Gaussian process regression(GPR) model has been developed in fermentation processes.However,limitations of single regression models,for multiphase/multimode fermentation proce...The dynamic soft sensor based on a single Gaussian process regression(GPR) model has been developed in fermentation processes.However,limitations of single regression models,for multiphase/multimode fermentation processes,may result in large prediction errors and complexity of the soft sensor.Therefore,a dynamic soft sensor based on Gaussian mixture regression(GMR) was proposed to overcome the problems.Two structure parameters,the number of Gaussian components and the order of the model,are crucial to the soft sensor model.To achieve a simple and effective soft sensor,an iterative strategy was proposed to optimize the two structure parameters synchronously.For the aim of comparisons,the proposed dynamic GMR soft sensor and the existing dynamic GPR soft sensor were both investigated to estimate biomass concentration in a Penicillin simulation process and an industrial Erythromycin fermentation process.Results show that the proposed dynamic GMR soft sensor has higher prediction accuracy and is more suitable for dynamic multiphase/multimode fermentation processes.展开更多
Let {X（t）, t ≥ 0} be a standard（zero-mean, unit-variance） stationary Gaussian process with correlation function r（·） and continuous sample paths. In this paper, we consider the maxima M（T） = max{X（t）, ...Let {X（t）, t ≥ 0} be a standard（zero-mean, unit-variance） stationary Gaussian process with correlation function r（·） and continuous sample paths. In this paper, we consider the maxima M（T） = max{X（t）, t∈ [0, T ]} with random index TT, where TT /T converges to a non-degenerate distribution or to a positive random variable in probability, and show that the limit distribution of M（TT） exists under some additional conditions related to the correlation function r（·）.展开更多
Low pressure chemical vapor deposition(LPCVD) is one of the most important processes during semiconductor manufacturing.However,the spatial distribution of internal temperature and extremely few samples makes it hard ...Low pressure chemical vapor deposition(LPCVD) is one of the most important processes during semiconductor manufacturing.However,the spatial distribution of internal temperature and extremely few samples makes it hard to build a good-quality model of this batch process.Besides,due to the properties of this process,the reliability of the model must be taken into consideration when optimizing the MVs.In this work,an optimal design strategy based on the self-learning Gaussian process model(GPM) is proposed to control this kind of spatial batch process.The GPM is utilized as the internal model to predict the thicknesses of thin films on all spatial-distributed wafers using the limited data.Unlike the conventional model based design,the uncertainties of predictions provided by GPM are taken into consideration to guide the optimal design of manipulated variables so that the designing can be more prudent Besides,the GPM is also actively enhanced using as little data as possible based on the predictive uncertainties.The effectiveness of the proposed strategy is successfully demonstrated in an LPCVD process.展开更多
The performance of lithium-ion batteries(LIBs)gradually declines over time,making it critical to predict the battery’s state of health(SOH)in real-time.This paper presents a model that incorporates health indicators ...The performance of lithium-ion batteries(LIBs)gradually declines over time,making it critical to predict the battery’s state of health(SOH)in real-time.This paper presents a model that incorporates health indicators and ensemble Gaussian process regression(EGPR)to predict the SOH of LIBs.Firstly,the degradation process of an LIB is analyzed through indirect health indicators(HIs)derived from voltage and temperature during discharge.Next,the parameters in the EGPR model are optimized using the gannet optimization algorithm(GOA),and the EGPR is employed to estimate the SOH of LIBs.Finally,the proposed model is tested under various experimental scenarios and compared with other machine learning models.The effectiveness of EGPR model is demonstrated using the National Aeronautics and Space Administration(NASA)LIB.The root mean square error(RMSE)is maintained within 0.20%,and the mean absolute error(MAE)is below 0.16%,illustrating the proposed approach’s excellent predictive accuracy and wide applicability.展开更多
Robots are finding increasing application in aircraft composite structure assembly due to their flexibility and the growing demand of aircraft manufacturers for high production rates.The contact force of the composite...Robots are finding increasing application in aircraft composite structure assembly due to their flexibility and the growing demand of aircraft manufacturers for high production rates.The contact force of the composite frame in a robotic assembly of the aircraft composite fuselage panel can hardly be controlled due to the multi-surface variable contact stiffness caused by compliance and complex shape with multiple mating surfaces.The paper proposes a robotic assembly system for the aircraft composite fuselage frame with a compliant contact force control strategy using the Gaussian process surrogate model.First,a robotic assembly system is introduced,and the global coordinate system transformation model is built.Then,a compliant force control architecture is designed to generate the desired output force.Subsequently,a Gaussian process surrogate model with uncertainties is utilized to model the complicated relationship between the robot’s output force and the normal contact force acting on the mating surface of the composite frame.Furthermore,an optimal contact force control strategy is implemented to improve the contact quality.Finally,an experiment demonstrates that the proposed methodology can ensure that the contact force on each surface is within the limit of the engineering specification and uniformly distributed,improving the quality compared to the traditional assembly process.展开更多
Reliable calculations of nuclear binding energies are crucial for advancing the research of nuclear physics. Machine learning provides an innovative approach to exploring complex physical problems. In this study, the ...Reliable calculations of nuclear binding energies are crucial for advancing the research of nuclear physics. Machine learning provides an innovative approach to exploring complex physical problems. In this study, the nuclear binding energies are modeled directly using a machine-learning method called the Gaussian process. First, the binding energies for 2238 nuclei with Z > 20 and N > 20 are calculated using the Gaussian process in a physically motivated feature space, yielding an average deviation of 0.046 MeV and a standard deviation of 0.066 MeV. The results show the good learning ability of the Gaussian process in the studies of binding energies. Then, the predictive power of the Gaussian process is studied by calculating the binding energies for 108 nuclei newly included in AME2020. The theoretical results are in good agreement with the experimental data, reflecting the good predictive power of the Gaussian process. Moreover, the α-decay energies for 1169 nuclei with 50 ≤ Z ≤ 110 are derived from the theoretical binding energies calculated using the Gaussian process. The average deviation and the standard deviation are, respectively, 0.047 MeV and 0.070 MeV. Noticeably, the calculated α-decay energies for the two new isotopes ^ (204 )Ac(Huang et al. Phys Lett B 834, 137484(2022)) and ^ (207) Th(Yang et al. Phys Rev C 105, L051302(2022)) agree well with the latest experimental data. These results demonstrate that the Gaussian process is reliable for the calculations of nuclear binding energies. Finally, the α-decay properties of some unknown actinide nuclei are predicted using the Gaussian process. The predicted results can be useful guides for future research on binding energies and α-decay properties.展开更多
文摘Non-parametric system identification with Gaussian processes for underwater vehicles is explored in this research with the purpose of modelling autonomous underwater vehicle(AUV) dynamics with a low amount of data. Multi-output Gaussian processes and their aptitude for modelling the dynamic system of an underactuated AUV without losing the relationships between tied outputs are used. The simulation of a first-principle model of a Remus 100 AUV is employed to capture data for the training and validation of the multi-output Gaussian processes. The metric and required procedure to carry out multi-output Gaussian processes for AUV with 6 degrees of freedom(DoF) is also shown in this paper. Multi-output Gaussian processes compared with the popular technique of recurrent neural network show that multi-output Gaussian processes manage to surpass RNN for non-parametric dynamic system identification in underwater vehicles with highly coupled DoF with the added benefit of providing the measurement of confidence.
文摘A novel model named Multi-scale Gaussian Processes (MGP) is proposed. Motivated by the ideas of multi-scale representations in the wavelet theory, in the new model, a Gaussian process is represented at a scale by a linear basis that is composed of a scale function and its different translations. Finally the distribution of the targets of the given samples can be obtained at different scales. Compared with the standard Gaussian Processes (GP) model, the MGP model can control its complexity conveniently just by adjusting the scale pa-rameter. So it can trade-off the generalization ability and the empirical risk rapidly. Experiments verify the fea-sibility of the MGP model, and exhibit that its performance is superior to the GP model if appropriate scales are chosen.
文摘In order to meet the demand of online optimal running, a novel soft sensor modeling approach based on Gaussian processes was proposed. The approach is moderately simple to implement and use without loss of performance. It is trained by optimizing the hyperparameters using the scaled conjugate gradient algorithm with the squared exponential covariance function employed. Experimental simulations show that the soft sensor modeling approach has the advantage via a real-world example in a refinery. Meanwhile, the method opens new possibilities for application of kernel methods to potential fields.
基金supported in part by the National Key R&D Program of China with grant No. 2018YFB1800800by the Basic Research Project No. HZQB-KCZYZ-2021067 of Hetao Shenzhen-HK S&T Cooperation Zone+3 种基金by Natural Science Foundation of China (NSFC) with grants No. 92067202 and No. 62106212by Shenzhen Outstanding Talents Training Fund 202002by Guangdong Research Projects No. 2017ZT07X152 and No. 2019CX01X104by China Postdoctoral Science Foundation with grant No. 2020M671899。
文摘Data-driven paradigms are well-known and salient demands of future wireless communication. Empowered by big data and machine learning techniques,next-generation data-driven communication systems will be intelligent with unique characteristics of expressiveness, scalability, interpretability, and uncertainty awareness, which can confidently involve diversified latent demands and personalized services in the foreseeable future. In this paper, we review a promising family of nonparametric Bayesian machine learning models,i.e., Gaussian processes(GPs), and their applications in wireless communication. Since GP models demonstrate outstanding expressive and interpretable learning ability with uncertainty, they are particularly suitable for wireless communication. Moreover, they provide a natural framework for collaborating data and empirical models(DEM). Specifically, we first envision three-level motivations of data-driven wireless communication using GP models. Then, we present the background of the GPs in terms of covariance structure and model inference. The expressiveness of the GP model using various interpretable kernels, including stationary, non-stationary, deep and multi-task kernels,is showcased. Furthermore, we review the distributed GP models with promising scalability, which is suitable for applications in wireless networks with a large number of distributed edge devices. Finally, we list representative solutions and promising techniques that adopt GP models in various wireless communication applications.
基金Supported by the Scientific Research Fund of Sichuan Provincial Education Department(12ZB082)the Scientific research cultivation project of Sichuan University of Science&Engineering(2013PY07)+1 种基金the Scientific Research Fund of Shanghai University of Finance and Economics(2017110080)the Opening Project of Sichuan Province University Key Laboratory of Bridge Non-destruction Detecting and Engineering Computing(2018QZJ01)
文摘Let {X(t), t ≥ 0} be a centered stationary Gaussian process with correlation r(t)such that 1-r(t) is asymptotic to a regularly varying function. With T being a nonnegative random variable and independent of X(t), the exact asymptotics of P(sup_(t∈[0,T])X(t) > x) is considered, as x → ∞.
基金partially supported by National Natural Science Foundation of China(12371150,12071003)Natural Science Foundation of Anhui Polytechnic University(2022AH050955,2022YQQ025,Xjky2022163)Zhejiang Province Philosophy and Social Science Planning Routine Subject(24NDJC131YB).
文摘Let X={X(t),t∈R+} be a centered space anisotropic Gaussian process values in R^(d) with non-stationary increments,whose components are independent but may not be identically distributed.Under certain conditions,then almost surely c_(1)≤ϕ-m(X([0,1]))≤c_(2),where ϕ denotes the exact Hausdorff measure associated with function ϕ(s)=s1/α_(k)+Σ_(i=1)k(1-αi/αk)log log 1/s for some 1≤k≤d,(α_(1),…,α_(d))∈(0,1]^(d).We also obtain the exact Hausdorff measure of the graph of X on[0,1].
基金support of the HECO2-Axe-1 project,funded by the Walloon Region under the REPowerEU initiative,Bel-giumfinancial support from the ERC proof of concept grant INVENT(grant agreement No:101123406).
文摘Reduced-order models offer a cost-effective and accurate approach to analyzing high-dimensional combustion problems.These surrogate models are built in a data-driven manner by combining computational fluid dynamics simulations with Proper Orthogonal Decomposition(POD)for dimensionality reduction and Gaussian Process Regression(GPR)for nonlinear regression.However,these models can yield physically inconsistent results,such as negative mass fractions.As a linear decomposition method,POD complicates the enforcement of constraints in the reduced space,while GPR lacks inherent provisions to ensure physical consistency.To address these challenges,this study proposes a novel constrained reduced-order model framework that enforces physical consistency in predictions.Dimensionality reduction is achieved by downsampling the dataset through low-cost Singular Value Decomposition(lcSVD)using optimal sensor placement,ensuring that the retained data points preserve physical information in the reduced space.We integrate finite-support parametric distribution functions,such as truncated Gaussian and beta distribution scaled to the interval[a,b],into the GPR framework.These bounded likelihood functions explicitly model the observational noise in the bounded space and use variational inference to approximate analytically intractable posterior distributions,producing GP estimations that satisfy physical constraints by construction.We validate the proposed methods using a synthetic dataset and a benchmark case of one-dimensional laminar NH3/H2 flames.The results show that the thermo-chemical state predictions comply with physical constraints while maintaining the high accuracy of unconstrained reduced-order models.
基金funded by the National Key R&D Program of China,Grant No.2024YFF0504904.
文摘The packaging quality of coaxial laser diodes(CLDs)plays a pivotal role in determining their optical performance and long-term reliability.As the core packaging process,high-precision laser welding requires precise control of process parameters to suppress optical power loss.However,the complex nonlinear relationship between welding parameters and optical power loss renders traditional trial-and-error methods inefficient and imprecise.To address this challenge,a physics-informed(PI)and data-driven collaboration approach for welding parameter optimization is proposed.First,thermal-fluid-solid coupling finite element method(FEM)was employed to quantify the sensitivity of welding parameters to physical characteristics,including residual stress.This analysis facilitated the identification of critical factors contributing to optical power loss.Subsequently,a Gaussian process regression(GPR)model incorporating finite element simulation prior knowledge was constructed based on the selected features.By introducing physics-informed kernel(PIK)functions,stress distribution patterns were embedded into the prediction model,achieving high-precision optical power loss prediction.Finally,a Bayesian optimization(BO)algorithm with an adaptive sampling strategy was implemented for efficient parameter space exploration.Experimental results demonstrate that the proposedmethod effectively establishes explicit physical correlations between welding parameters and optical power loss.The optimized welding parameters reduced optical power loss by 34.1%,providing theoretical guidance and technical support for reliable CLD packaging.
基金supported by the National Natural Science Foundation of China(Grant No.42274035)the Major Science and Technology Program for Hubei Province(No.2022AAA002)the Hunan Provincial Land Surveying and Mapping Project(HNGTCH-2023-05)。
文摘Global Navigation Satellite System(GNSS)imaging method(GIM)has been successfully applied to global regions to investigate vertical land motion(VLM)of the Earth's surface.GNSS images derived from conventional GIM method may present fragmented patches and encounter problems caused by excessive smoothing of velocity peaks,leading to difficulty in short-wavelength deformation detection and improper geophysical interpretation.Therefore,we propose a novel GNSS imaging method based on Gaussian process regression with velocity uncertainty considered(GPR-VU).Gaussian processing regression is introduced to describe the spatial relationship between neighboring site pairs as a priori weights and then reweight velocities by known station uncertainties,converting the discrete velocity field to a continuous one.The GPR-VU method is applied to reconstruct VLM images in the southwestern United States and the eastern Qinghai-Xizang Plateau,China,using the GNSS position time series in vertical direction.Compared to the traditional GIM method,the root-mean-square(RMS)and overall accuracy of the confusion matrix of the GPR-VU method increase by 5.0%and 14.0%from the 1°×1°checkerboard test in the southwestern United States.Similarly,the RMS and overall accuracy increase by 33.7%and 15.8%from the 6°×6°checkerboard test in the eastern Qinghai-Xizang Plateau.These checkerboard tests validate the capability to effectively capture the spatiotemporal variations characteristics of VLM and show that this algorithm outperforms the sparsely distributed network in the Qinghai-Xizang Plateau.The images from the GPR-VU method using real data in both regions show significant subsidence around Lassen Volcanic in northern California within a 30 km radius,slight uplift in the northern Sichuan Basin,and subsidence in its central and southern sections.These results further qualitatively illustrate consistency with previous findings.The GPR-VU method outperforms in diminishing the effect by fragmented patches,excessive smoothing of velocity peaks,and detecting potential short-wavelength deformations.
基金supported by Swiss National Science Foundation (Grant No. 200021166274)the National Science Centre (Poland) (Grant No. 2015/17/B/ST1/01102) (2016–2019)
文摘In this paper,we are concerned with the asymptotic behavior,as u→∞,of P{sup_t∈|0,T|X_u(t)>u},where X_u(t),t∈|0,T|,u>0 is a family of centered Gaussian processes with continuous trajectories.A key application of our findings concerns P{sup_t∈|0,T|(X(t)+g(t))>u},as u→∞,for X a centered Gaussian process and g some measurable trend function.Further applications include the approximation of both the ruin time and the ruin probability of the Brownian motion risk model with constant force of interest.
基金co-supported by the National Natural Science Foundation of China(No.12101608)the NSAF(No.U2230208)the Hunan Provincial Innovation Foundation for Postgraduate,China(No.CX20220034).
文摘This paper introduces techniques in Gaussian process regression model for spatiotemporal data collected from complex systems.This study focuses on extracting local structures and then constructing surrogate models based on Gaussian process assumptions.The proposed Dynamic Gaussian Process Regression(DGPR)consists of a sequence of local surrogate models related to each other.In DGPR,the time-based spatial clustering is carried out to divide the systems into sub-spatio-temporal parts whose interior has similar variation patterns,where the temporal information is used as the prior information for training the spatial-surrogate model.The DGPR is robust and especially suitable for the loosely coupled model structure,also allowing for parallel computation.The numerical results of the test function show the effectiveness of DGPR.Furthermore,the shock tube problem is successfully approximated under different phenomenon complexity.
基金supported by NSFC(10131040)supported by SRFDP(2002335090)+1 种基金supported by KRF(2001-042-D00008)supported by KRF(2001-042-D00008)
文摘In this paper,we establish some limit theorems on the increments of an l^p-valued multi- parameter Gaussian process under weaker conditions than those of Cs(?)rg(?)-Shao theorems published in Ann.Probab.(1993).
基金Supported by the Natural Science Foundation of Jiangsu Province of China(BK20130531)the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD[2011]6)Jiangsu Government Scholarship
文摘The dynamic soft sensor based on a single Gaussian process regression(GPR) model has been developed in fermentation processes.However,limitations of single regression models,for multiphase/multimode fermentation processes,may result in large prediction errors and complexity of the soft sensor.Therefore,a dynamic soft sensor based on Gaussian mixture regression(GMR) was proposed to overcome the problems.Two structure parameters,the number of Gaussian components and the order of the model,are crucial to the soft sensor model.To achieve a simple and effective soft sensor,an iterative strategy was proposed to optimize the two structure parameters synchronously.For the aim of comparisons,the proposed dynamic GMR soft sensor and the existing dynamic GPR soft sensor were both investigated to estimate biomass concentration in a Penicillin simulation process and an industrial Erythromycin fermentation process.Results show that the proposed dynamic GMR soft sensor has higher prediction accuracy and is more suitable for dynamic multiphase/multimode fermentation processes.
基金Supported by National Science Foundation of China(Grant No.11326175)Research Start-up Foundation of Jiaxing University(Grant No.70512021)
文摘Let {X（t）, t ≥ 0} be a standard（zero-mean, unit-variance） stationary Gaussian process with correlation function r（·） and continuous sample paths. In this paper, we consider the maxima M（T） = max{X（t）, t∈ [0, T ]} with random index TT, where TT /T converges to a non-degenerate distribution or to a positive random variable in probability, and show that the limit distribution of M（TT） exists under some additional conditions related to the correlation function r（·）.
基金Supported by the National High Technology Research and Development Program of China(2014AA041803)the National Natural Science Foundation of China(61320106009)
文摘Low pressure chemical vapor deposition(LPCVD) is one of the most important processes during semiconductor manufacturing.However,the spatial distribution of internal temperature and extremely few samples makes it hard to build a good-quality model of this batch process.Besides,due to the properties of this process,the reliability of the model must be taken into consideration when optimizing the MVs.In this work,an optimal design strategy based on the self-learning Gaussian process model(GPM) is proposed to control this kind of spatial batch process.The GPM is utilized as the internal model to predict the thicknesses of thin films on all spatial-distributed wafers using the limited data.Unlike the conventional model based design,the uncertainties of predictions provided by GPM are taken into consideration to guide the optimal design of manipulated variables so that the designing can be more prudent Besides,the GPM is also actively enhanced using as little data as possible based on the predictive uncertainties.The effectiveness of the proposed strategy is successfully demonstrated in an LPCVD process.
基金supported by Fundamental Research Program of Shanxi Province(No.202203021211088)Shanxi Provincial Natural Science Foundation(No.202204021301049).
文摘The performance of lithium-ion batteries(LIBs)gradually declines over time,making it critical to predict the battery’s state of health(SOH)in real-time.This paper presents a model that incorporates health indicators and ensemble Gaussian process regression(EGPR)to predict the SOH of LIBs.Firstly,the degradation process of an LIB is analyzed through indirect health indicators(HIs)derived from voltage and temperature during discharge.Next,the parameters in the EGPR model are optimized using the gannet optimization algorithm(GOA),and the EGPR is employed to estimate the SOH of LIBs.Finally,the proposed model is tested under various experimental scenarios and compared with other machine learning models.The effectiveness of EGPR model is demonstrated using the National Aeronautics and Space Administration(NASA)LIB.The root mean square error(RMSE)is maintained within 0.20%,and the mean absolute error(MAE)is below 0.16%,illustrating the proposed approach’s excellent predictive accuracy and wide applicability.
基金This study was supported by the Aeronautical Manufacturing Technology Institute,COMAC.
文摘Robots are finding increasing application in aircraft composite structure assembly due to their flexibility and the growing demand of aircraft manufacturers for high production rates.The contact force of the composite frame in a robotic assembly of the aircraft composite fuselage panel can hardly be controlled due to the multi-surface variable contact stiffness caused by compliance and complex shape with multiple mating surfaces.The paper proposes a robotic assembly system for the aircraft composite fuselage frame with a compliant contact force control strategy using the Gaussian process surrogate model.First,a robotic assembly system is introduced,and the global coordinate system transformation model is built.Then,a compliant force control architecture is designed to generate the desired output force.Subsequently,a Gaussian process surrogate model with uncertainties is utilized to model the complicated relationship between the robot’s output force and the normal contact force acting on the mating surface of the composite frame.Furthermore,an optimal contact force control strategy is implemented to improve the contact quality.Finally,an experiment demonstrates that the proposed methodology can ensure that the contact force on each surface is within the limit of the engineering specification and uniformly distributed,improving the quality compared to the traditional assembly process.
基金the National Key R&D Program of China(No.2023YFA1606503)the National Natural Science Foundation of China(Nos.12035011,11975167,11947211,11905103,11881240623,and 11961141003).
文摘Reliable calculations of nuclear binding energies are crucial for advancing the research of nuclear physics. Machine learning provides an innovative approach to exploring complex physical problems. In this study, the nuclear binding energies are modeled directly using a machine-learning method called the Gaussian process. First, the binding energies for 2238 nuclei with Z > 20 and N > 20 are calculated using the Gaussian process in a physically motivated feature space, yielding an average deviation of 0.046 MeV and a standard deviation of 0.066 MeV. The results show the good learning ability of the Gaussian process in the studies of binding energies. Then, the predictive power of the Gaussian process is studied by calculating the binding energies for 108 nuclei newly included in AME2020. The theoretical results are in good agreement with the experimental data, reflecting the good predictive power of the Gaussian process. Moreover, the α-decay energies for 1169 nuclei with 50 ≤ Z ≤ 110 are derived from the theoretical binding energies calculated using the Gaussian process. The average deviation and the standard deviation are, respectively, 0.047 MeV and 0.070 MeV. Noticeably, the calculated α-decay energies for the two new isotopes ^ (204 )Ac(Huang et al. Phys Lett B 834, 137484(2022)) and ^ (207) Th(Yang et al. Phys Rev C 105, L051302(2022)) agree well with the latest experimental data. These results demonstrate that the Gaussian process is reliable for the calculations of nuclear binding energies. Finally, the α-decay properties of some unknown actinide nuclei are predicted using the Gaussian process. The predicted results can be useful guides for future research on binding energies and α-decay properties.
