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bayesian inference-assisted reliability analysis framework for robotic motion systems in future factories

RELIABILITY ENGINEERING & SYSTEM SAFETY 2025年 258卷

作者： Shen, Shuoshuo Cheng, Jin Liu, Zhenyu Tan, Jianrong Zhang, Dequan Zhejiang Univ State Key Lab Fluid Power & Mechatron Syst Hangzhou 310058 Peoples R China Zhejiang Univ Engn Res Ctr Design Engn & Digital Twin Zhejiang P Hangzhou 310058 Peoples R China Hebei Univ Technol Sch Mech Engn State Key Lab Reliabil & Intelligence Elect Equipm Tianjin 300401 Peoples R China

Reliability assessment of robotic motion systems subject to complex dynamic properties and multi-source uncertainties in open environments registers an important yet challenging task. To tackle this task, this study proposes a new reliability analysis framework for robotic motion systems, which incorporates the moment-based method and bayesian inference-guided probabilistic model updating strategy. To start with, the fractional exponential moments calculated by the sparse grid method are adopted to quantify the uncertainty of performance indexes for robotic motion systems. Subsequently, a versatile mixture probability distribution model is established to evaluate the reliability of the performance indexes, facilitating the probability distribution modeling of various features. To capture sufficient uncertainty information of the system performance, two solution strategies for probabilistic model parameters are developed by incorporating the direct and sequential bayesian updating methods. With fractional exponential moments, the proposed probability model is calibrated to reconstruct the probability distribution and calculate the failure probability for robotic motion systems. The effectiveness of the proposed framework is validated by three numerical examples, wherein Monte Carlo simulation and other prevailing methods are performed for comparison. The case studies indicate that the proposed framework is viable to assess the performance reliability of robotic motion systems with satisfactory computational accuracy and efficiency.

关键词： Reliability analysis Stability analysis bayesian inference Probabilistic model updating Robotic systems

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bayesian inference of radial impurity transport in the pedestal of ASDEX Upgrade discharges using charge-exchange spectroscopy

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NUCLEAR FUSION 2025年第5期65卷 056010-056010页

作者： Gleiter, T. Dux, R. Sciortino, F. Odstrcil, T. Fajardo, D. Angioni, C. Buchner, J. McDermott, R. M. Hayward-Schneider, T. Harrer, G. F. Faitsch, M. Griener, M. Fischer, R. Wolfrum, E. Stroth, U. Max Planck Inst Plasma Phys Garching Germany Tech Univ Munich TUM Sch Nat Sci Phys Dept Garching Germany Proxima Fus Munich Germany Gen Atom San Diego CA USA Max Planck Inst Extraterr Phys Garching Germany Vienna Univ Technol Inst Appl Phys Vienna Austria Hampton Univ Sch Sci Dept Phys Hampton VA USA

We present a novel framework for quantifying radial impurity transport in the pedestal of ASDEX Upgrade (AUG) discharges. Our method is based on charge-exchange recombination spectroscopy measurements of line radiation from multiple impurity charge states, each along a radially distributed line-of-sight array in steady-state plasmas. Inverse inference based on the diffusive-convective transport solver Aurora combined with a synthetic diagnostic enables us to separate diffusive and convective transport contributions and to derive the impurity density and charge state distribution profiles. Robust uncertainty quantification is provided as the full probability distribution of the parameters is obtained according to bayesian statistics with the use of a nested sampling algorithm. The approach allows for a high radial resolution and data quality due to the steady-state plasma, but requires data from multiple impurity charge states. It is, therefore, particularly suitable for impurity transport studies in the region of steep edge gradients. In this paper, we present thorough tests of the method based on synthetic data. Furthermore, we show an application to AUG measurement data, inferring the pedestal neon transport in the quasi-continuous exhaust (QCE) regime without large edge-localized modes. The comparison of the transport result with neoclassical simulations shows a clear contribution of turbulent diffusion in the QCE pedestal. This supports the hypothesis of additional transport associated with the predicted high-n ballooning-unstable region and the observed quasi- coherent mode.

关键词： ASDEX Upgrade impurity transport pedestal charge-exchange spectroscopy bayesian inference nested sampling QCE regime

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bayesian inference of physics-based models of acoustically-forced laminar premixed conical flames

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COMBUSTION AND FLAME 2025年 274卷

作者： Giannotta, Alessandro Yoko, Matthew Cherubini, Stefania De Palma, Pietro Juniper, Matthew P. Politecn Bari Dept Mech Math & Management Via Re David 200 I-70125 Bari Italy Univ Cambridge Dept Engn Trumpington St Cambridge CB2 1PZ England

We perform twenty experiments on an acoustically-forced laminar premixed Bunsen flame and assimilate high-speed footage of the natural emission into a physics-based model containing seven parameters. The experimental rig is a ducted Bunsen flame supplied by a mixture of methane and ethylene. A high-speed camera captures the natural emission of the flame, from which we extract the position of the flame front. We use bayesian inference to combine this experimental data with our prior knowledge of this flame's behaviour. This prior knowledge is expressed through (i) a model of the kinematics of a flame front moving through a model of the perturbed velocity field, and (ii) a priori estimates of the parameters of the above model with quantified uncertainties. We find the most probable a posteriori model parameters using bayesian parameter inference, and quantify their uncertainties using Laplace's method combined with first-order adjoint methods. This is substantially cheaper than other common bayesian inference frameworks, such as Markov Chain Monte Carlo. This process results in a quantitatively-accurate physics-based reduced-order model of the acoustically forced Bunsen flame for injection velocities ranging from 1.75 m/s to 2.99 m/s and equivalence ratio values ranging from 1.26 to 1.47, using seven parameters. We use this model to evaluate the heat release rate between experimental snapshots, to extrapolate to different experimental conditions, and to calculate the flame transfer function and its uncertainty for all the flames. Since the proposed model relies on only seven parameters, it can be trained with little data and successfully extrapolates beyond the training dataset. Matlab code is provided so that the reader can apply it to assimilate further flame images into the model. Novelty and Significance Statement Thermoacoustic systems tend to be extremely sensitive to small parameter changes, which makes them difficult to model a priori from existing models in t

关键词： Thermoacoustics Data assimilation Laplace's method bayesian inference

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bayesian inference for the Urban Drainage Models Uncertainty Quantification Based on Heteroscedastic Residual Assumption

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WATER RESOURCES MANAGEMENT 2025年 1-19页

作者： Yu, Tingchao Wei, Shaosong Long, Zhihong Zhang, Tuqiao Chu, Shipeng Zhejiang Univ Coll Civil Engn & Architecture Key Lab Drinking Water Safety & Distribut Technol Hangzhou 310058 Peoples R China Guangzhou Water Supply Co Ltd Guangzhou 510600 Peoples R China

Model calibration and uncertainty analysis are critical steps for urban drainage models prior to their use. bayesian inference has been widely utilized for calibrating model parameters due to its ability to quantify both the uncertainty of model parameters and model predictions. Existing methods generally assume that the residuals are homoscedastic and follow a normal distribution with a constant variance. However, given the inherent uncertainties in inputs, model structure, parameters, and observations, the variance of residuals varies inconsistently across model prediction steps. To address this issue, a bayesian inference method based on the assumption of heteroscedastic residuals is developed for model calibration and uncertainty quantification. The results demonstrate that the heteroscedasticity residual-based method generates more reasonable prediction uncertainty intervals and provides more reliable prediction distributions compared to the existing homoscedasticity residual-based method.

关键词： Heteroscedasticity Urban drainage model Uncertainty DREAM-ZS bayesian inference

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bayesian inference of Phylogenetic Distances: Revisiting the Eigenvalue Approach

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BULLETIN OF MATHEMATICAL BIOLOGY 2025年第2期87卷 1-21页

作者： Penn, Matthew J. Scheidwasser, Neil Donnelly, Christl A. Duchene, David A. Bhatt, Samir Univ Oxford Dept Stat Oxford England Univ Copenhagen Sect Epidemiol Copenhagen Denmark Univ Oxford Pandem Sci Inst Oxford England Imperial Coll London MRC Ctr Global Infect Dis Anal London England

Using genetic data to infer evolutionary distances between molecular sequence pairs based on a Markov substitution model is a common procedure in phylogenetics, in particular for selecting a good starting tree to improve upon. Many evolutionary patterns can be accurately modelled using substitution models that are available in closed form, including the popular general time reversible model (GTR) for DNA data. For more complex biological phenomena, such as variations in lineage-specific evolutionary rates over time (heterotachy), other approaches such as the GTR with rate variation (GTR+Gamma\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$+\Gamma $$\end{document}) are required, but do not admit analytical solutions and do not automatically allow for likelihood calculations crucial for bayesian analysis. In this paper, we derive a hybrid approach between these two methods, incorporating Gamma(alpha,alpha)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\Gamma (\alpha ,\alpha )$$\end{document}-distributed rate variation and heterotachy into a hierarchical bayesian GTR-style framework. Our approach is differentiable and amenable to both stochastic gradient descent for optimisation and Hamiltonian Markov chain Monte Carlo for bayesian inference. We show the utility of our approach by studying hypotheses regarding the origins of the eukaryotic cell within the context of a universal tree of life and find evidence for a two-domain theory.

关键词： bayesian inference GTR Genetic distance Ukaryotes

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bayesian inference of Phenotypic Plasticity of Cancer Cells Based on Dynamic Model for Temporal Cell Proportion Data

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BIOMETRICAL JOURNAL 2025年第3期67卷 e70055页

作者： Chen, Shuli Wang, Yuman Zhou, Da Hu, Jie Sun Yat Sen Univ Sch Math Guangzhou Guangdong Peoples R China Xiamen Univ Sch Math Sci Xiamen Fujian Peoples R China

Mounting evidence underscores the prevalent hierarchical organization of cancer tissues. At the foundation of this hierarchy reside cancer stem cells, a subset of cells endowed with the pivotal role of engendering the entire cancer tissue through cell differentiation. In recent times, substantial attention has been directed toward the phenomenon of cancer cell plasticity, where the dynamic interconversion between cancer stem cells and nonstem cancer cells has garnered significant interest. Since the task of detecting cancer cell plasticity from empirical data remains a formidable challenge, we propose a bayesian statistical framework designed to infer phenotypic plasticity within cancer cells, utilizing temporal data on cancer stem cell proportions. Our approach is grounded in a stochastic model, adept at capturing the dynamic behaviors of cells. Leveraging bayesian analysis, we scrutinize the moment equation governing cancer stem cell proportions, derived from the Kolmogorov forward equation of our stochastic model. Our methodology introduces an improved Euler method for parameter estimation within nonlinear ordinary differential equation models, also extending insights to compositional data. Extensive simulations robustly validate the efficacy of our proposed method. To further corroborate our findings, we apply our approach to analyze published data from SW620 colon cancer cell lines. Our results harmonize with in situ experiments, thereby reinforcing the utility of our method in discerning and quantifying phenotypic plasticity within cancer cells.

关键词： bayesian inference cell proportion dynamical model phenotypic plasticity

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bayesian inference for Laplace distribution based on complete and censored samples with illustrations

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JOURNAL OF APPLIED STATISTICS 2025年第4期52卷 914-935页

作者： Sun, Wanyue Zhu, Xiaojun Zhang, Zhehao Balakrishnan, N. Xian Jiaotong Liverpool Univ Dept Financial & Actuarial Math Suzhou Peoples R China McMaster Univ Dept Math & Stat Hamilton ON Canada

In this paper, bayesian estimates are derived for the location and scale parameters of the Laplace distribution based on complete, Type-I, and Type-II censored samples under different prior settings. Subsequently, bayesian point and interval estimates, as well as the associated statistical inference, are discussed in detail. The developed methods are then applied to two real data sets for illustrative purposes. Moreover, a detailed Monte Carlo simulation study is carried out for evaluating the performance of the inferential methods developed here. Finally, we provide a brief discussion of the established results to demonstrate their practical utility and present some associated problems of further interest. Overall, this study fills an existing gap in the development of bayesian inferential techniques for the parameters of the two-parameter Laplace distribution, making this research innovative and offering more investigative implications. It showcases the potential for broader methodological applications of bayesian inference for complex real-world data sets, especially in scenarios involving different forms of censoring. This research provides a critical tool for statistical analysis in different fields such as engineering and finance, where the Laplace distribution is frequently adopted as a fundamental model.

关键词： Laplace distribution bayesian inference Monte Carlo simulation Type-I censored samples Type-II censored samples

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bayesian inference by visuomotor neurons in the prefrontal cortex

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PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA 2025年第13期122卷 e2420815122页

作者： Langlois, Thomas A. Charlton, Julie A. Goris, Robbe L. T. MIT Dept Brain & Cognit Sci Cambridge MA 02139 USA Princeton Univ Princeton Neurosci Inst Princeton NJ 08540 USA Univ Texas Austin Ctr Perceptual Syst Austin TX 78712 USA

Perceptual judgments of the environment emerge from the concerted activity of neural populations in decision-making areas downstream of the sensory cortex. When the sensory input is ambiguous, perceptual judgments can be biased by prior expectations shaped by environmental regularities. These effects are examples of bayesian inference, a reasoning method in which prior knowledge is leveraged to optimize uncertain decisions. However, it is not known how decision-making circuits combine sensory signals and prior expectations to form a perceptual decision. Here, we study neural population activity in the prefrontal cortex of macaque monkeys trained to report perceptual judgments of ambiguous visual stimuli under two different stimulus distributions. We isolate the component of the neural population response that represents the formation of the perceptual decision (the decision variable, DV), and find that its dynamical evolution reflects the integration of sensory signals and prior expectations. Prior expectations impact the DV's trajectory both before and during stimulus presentation such that DV trajectories with a smaller dynamic range result in more biased and less sensitive perceptual decisions. We show that these results resemble a specific variant of bayesian inference known as approximate hierarchical inference. Our findings expand our understanding of the mechanisms by which prefrontal circuits can execute inference.

关键词： perceptual decision-making prefrontal cortex bayesian inference

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Deep learning coupled bayesian inference method for measuring the elastoplastic properties of SS400 steel welds by nanoindentation experiment

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MEASUREMENT 2025年 242卷

作者： Wang, Mingzhi Zhang, Guitao Hou, Bingyu Wang, Weidong Xidian Univ Sch Mechano Elect Engn Xian 710071 Peoples R China CityU Xidian Joint Lab Micro Nano Mfg Shenzhen 518057 Peoples R China

Nanoindentation experiment has shown broad application prospects due to its ability to measure the mechanical properties of various materials at multiple scales. In this paper, a deep learning coupled bayesian inverse approach is proposed for measuring the elastoplastic parameters of SS400 steel welds by nanoindentation experiment. The nanoindentation experiments were performed on the SS400 steel welds, including base metal (BM), weld zone (WZ), and heat affected zone (HAZ), and the experiment load-displacement (P-h) curves were obtained. The hyper-parameters tunable artificial neural network (ANN) was established to correlate elastoplastic parameters with indentation P-h curves. Based on bayesian inference theory, the posterior density function for estimating the unknown material parameters was established. Transitional Markov chain Monte Carlo was used for sampling from the posterior density function, and the elastoplastic properties in different regions of SS400 steel welds were identified. The advantage of the established measuring method is that the hyper-parameters optimized ANN model can provide the very accurate forward relationship between material properties and indentation P-h curves. Besides, the inverse bayesian framework can quantify the potential uncertainty of the identified elastoplastic parameters. The measured elastoplastic properties of the base metal of SS400 steel show good agreement with tensile experiment data, of which the maximum measuring error is less than 12%. The measured elastoplastic properties in WZ and HAZ are also proved to be effective. The uncertainty of the identified elastoplastic parameters of SS400 steel welds can be quantified by posterior marginal distribution, using Mean and Variance values. The results proved that the proposed inverse measuring method is reliable and effective.

关键词： Welding structure Property measurement Nanoindentation Deep learning bayesian inference

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Temperature-Dependent Thermal Conductivity Identification by bayesian inference

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JOURNAL OF THERMOPHYSICS AND HEAT TRANSFER 2025年第2期39卷 337-346页

作者： Ma, Han Chen, Yuluo Chen, Qiang Li, Yanbin Fei, Qingguo Southeast Univ Jiangsu Engn Res Ctr Aerosp Machinery Sch Mech Engn Key Lab Struct & Thermal Protect High Speed Aircra Nanjing 211189 Jiangsu Peoples R China

The identification of temperature-dependent thermal conductivity in aerogel material, which is commonly used as insulation in thermal protection structures of high-speed aircraft, faces the challenge of selecting the appropriate model in engineering practice. Considering the uncertainties in the selection process of an appropriate functional model, a novel bayesian probability method computational framework based on response data is established to improve the accuracy of thermal conductivity identification. Three implementation steps are presented: 1) the database of candidate models is established;2) the reconstructed signals can be calculated by a heat transfer analysis model;and 3) the posterior probability of each candidate model is estimated to obtain the optimal thermal conductivity model and determine the characteristic coefficients. Numerical simulations of a theoretical one-dimensional heat transfer model and a curved thermal protection structure are performed to verify the proposed method. Then, a heating experimental investigation of the curved thermal protection structure is conducted to identify the temperature-dependent thermal conductivity of aerogel material. The results indicate that the temperature-varying thermal conductivity can be accurately identified by the proposed method, which can be applied to the heat transfer analysis and design of aerogel materials in high-speed aircraft.

关键词： Thermal Conductivity Coefficients Unsteady State Heat Transfer Thermal Protection System bayesian inference Thermal Conductivity Identification

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