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An effective structural uncertainty analysis method with augmented input space 认领 引用
1
作者 Jinjin CHEN Caijun XUE 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2026年第5期330-346,共17页
For the uncertainty analysis of aeronautical structures,it is common for input parameters to exhibit sparsity due to the limitations of experimental costs.This paper proposes an uncertainty analysis approach for aeron... For the uncertainty analysis of aeronautical structures,it is common for input parameters to exhibit sparsity due to the limitations of experimental costs.This paper proposes an uncertainty analysis approach for aeronautical structures with sparse experimental data.A Gaussian Process Regression(GPR)-based surrogate modeling approach is developed,integrating an augmented input space to compensate for limited test samples.The augmented space generates supplementary training data through iterative expansion,enabling continuous refinement of the GPR model to establish accurate variable-response mappings.A nested Monte Carlo sampling strategy propagates probability box(P-box)parameters until response boundaries converge.The proposed method is validated through a numerical case and an aeronautical structural application.Subsequently,it is implemented for uncertainty analysis of breaking strength in civil aircraft fuse pins,with comparative studies conducted against two traditional engineering method.The framework effectively addresses uncertainty propagation challenges without requiring additional physical tests,offering enhanced computational efficiency for safety–critical structural assessments.Key innovations include the adaptive augmented space mechanism and convergence-driven P-box boundary determination,which collectively advance sparse-data uncertainty analysis in aerospace engineering applications. 展开更多
关键词 Aleatory and epistemic uncertainty Distributional P-boxes Gaussian process regression Uncertainty analysis Uncertainty propagation
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A Quad-Step Approach to Uncertainty-Aware Deep Learning for Skin Cancer Classification 认领 引用
2
作者 Hamzeh Asgharnezhad Pegah Tabarisaadi +2 位作者 Abbas Khosravi Roohallah Alizadehsani U.Rajendra Acharya 《Journal of Bionic Engineering》 SCIE EI CSCD 2026年第3期1849-1874,共26页
Accurate skin cancer diagnosis is vital for early treatment and improved patient outcomes.Deep learning models have shown promise in automating skin cancer classification,yet challenges remain due to data scarcity and... Accurate skin cancer diagnosis is vital for early treatment and improved patient outcomes.Deep learning models have shown promise in automating skin cancer classification,yet challenges remain due to data scarcity and limited uncertainty awareness.This study presents a comprehensive evaluation of deep learning-based skin lesion classification with transfer learning and UQ on the HAM10000 dataset.We benchmark several pre-trained feature extractors(including Contrastive Language-Image Pre-training(CLIP)variants,ResNet50,DenseNet121,VGG16,EfficientNet-V2-Large,and ConvNeXt Large)combined with traditional classifiers such as SVM,XGBoost,and logistic regression.Multiple PCA settings(64,128,256,512)are explored,with LAION CLIP ViT-H/14 and ViT-L/14 at PCA-256 achieving the strongest baseline results.In the UQ phase,Monte Carlo Dropout(MCD),Ensemble,and Ensemble Monte Carlo Dropout(EMCD)are applied and evaluated using uncertainty-aware metrics(UAcc,USen,USpe,UPre).Ensemble methods with PCA-256 provide the best balance between accuracy and reliability.Further improvements are obtained through feature fusion of top-performing extractors at PCA-256.Finally,we propose a feature-fusion-based model trained with a Predictive Entropy(PE)loss function,which outperforms all prior configurations across both standard and uncertainty-aware evaluations,advancing trustworthy deep learning-based skin cancer diagnosis. 展开更多
关键词 Deep learning Machine learning Classification Uncertainty quantification Skin cancer
An uncertainty evaluation for storm surge risk analysis based on information utilization efficiency 认领 引用
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作者 Guilin LIU Siyu DING +3 位作者 Shichun SONG Bokai YANG Pengyu ZHU Liping WANG 《Journal of Oceanology and Limnology》 SCIE CAS CSCD 2026年第2期545-559,共15页
With the development of methods for predicting extreme hydrological elements using probabilistic approaches,several commonly used methods have emerged for analyzing the risk of storm surge disasters,including the Annu... With the development of methods for predicting extreme hydrological elements using probabilistic approaches,several commonly used methods have emerged for analyzing the risk of storm surge disasters,including the Annual Maxima method,the Peak-Over-Threshold method,the Gumbel distribution,and the Weibull distribution.Meanwhile,and emphases have been placed on assessing and comparing the applicability and stability of these various methods.To evaluate the rationality of different methods,we an entropy uncertainty analysis method was introduced based on information utilization efficiency,in which the sample Stochastic uncertainty is measured by the ratio of information entropy before and after sampling,i.e.,the information extraction efficiency of the sampling method.Additionally,the cognitive uncertainty of the research method is assessed by the ratio of mutual information between the model and the sample to the information entropy of the sample,i.e.,the information extraction efficiency of the mathematical model.Furthermore,we incorporated the group probability calculation method,information entropy and mutual information theory to analyze and calculate the entropy uncertainty more accurately.By applying this analysis to the design wave height and the recurrence period projected in the sea area west Guangdong of China,we believed that the most reasonable hazard assessment method shall be based on the over-threshold method combined with the Pareto distribution.Conversely,the assessment method based on the process extreme value method is deemed insufficiently reasonable and requires further research. 展开更多
关键词 uncertainty storm surge information entropy mutual information group probability calculation method
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Analysis method for evaluating uncertainty in the machining process of thin-walled parts 认领 引用
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作者 Xiaoyue Li Zhaoze Sun +2 位作者 Hao Qi Yue Guo Shuowei Bai 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2026年第3期136-147,共12页
The machining process of thin-walled components is full of many uncertainties,resulting in problems such as high batch inconsistency and low pass rate.In this paper,the Bayesian network uncertainty inference model of ... The machining process of thin-walled components is full of many uncertainties,resulting in problems such as high batch inconsistency and low pass rate.In this paper,the Bayesian network uncertainty inference model of machining process is constructed.The influence mechanism of input variables on machining distortion un-certainty is clarified.Initially,the uncertain variables are collected and the inference model of machining dis-tortion is constructed based on root-branch-leaf Bayesian network structure.The weight of the influence of each input variable on machining distortion uncertainty and maximum influence path are obtained.Next,an inverse Bayesian network is established,with the uncertainty inference results used as prior information to carry out inverse inference of machining distortion uncertainty.The influence possibility of the related factors of the main influence variables on the uncertainty of machining distortion is obtained.Aviation aluminum alloy T-shaped part was taken as an example,and the influence mechanism of initial residual stress,surface residual stress,cutting force and their related factors on the machining distortion uncertainty was investigated.The results indicated that the influential weights of initial residual stress,surface residual stress and cutting force on the machining distortion uncertainty was 0.33(maximum),0.23 and 0.04 respectively.The factors related to initial residual stress,surface residual stress,and cutting force had influence weights of 0.099(maximum),0.009,and 0.001 on the machining distortion uncertainty.The probabilities of the effects were 0.920,0.075 and 0.005,respectively.Finally,the paper compares the proposed model with MC-GBRT and BiLSTM-UP,showing average improvements of 46.7%and 63.1%in stability and 35.7%and 20.6%in computational speed,respectively.This paper proposes a Bayesian network-based machining distortion uncertainty inference model that effectively reveals the mapping mechanism between initial residual stress,surface residual stress,cutting force,and the machining distortion uncertainty in thin-walled components. 展开更多
关键词 Thin-walled components Machining distortion Uncertainty Bayesian network inference model Inverse Bayesian inference
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A novel dexterity optimization scheme with kinematic uncertainty handling capability for redundant space manipulators 认领 引用
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作者 Xiaohang YANG Dongxu CHEN +2 位作者 Zhiyuan ZHAO Jingdong ZHAO Hong LIU 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2026年第1期643-655,共13页
In the construction and maintenance for large space equipment,it is essential to ensure the control accuracy and improve the dexterity of the space manipulator.In this paper,a FiniteTime Convergence Kinematic Control(... In the construction and maintenance for large space equipment,it is essential to ensure the control accuracy and improve the dexterity of the space manipulator.In this paper,a FiniteTime Convergence Kinematic Control(FTCKC)added with Acceleration Level Dexterity Optimization(ALDO)scheme is proposed to solve the kinematic uncertainty and dexterity optimization problems of redundant space manipulators.Concretely,distinguishing from the asymptotic convergence property of traditional adaptive Jacobian methods,the FTCKC scheme is adopted to construct the equality constraint to address the model uncertainty problem,and its error can converge within a finite time.Subsequently,the dexterity index is reconstructed at acceleration level by a multi-level target handling method.Then,the equality constraint,optimization task,and limit constraints are reformulated as a quadratic programming problem.Moreover,a Recurrent Neural Network(RNN)is engineered for the constructed FTCKC-ALDO scheme.Finally,the superiority of the FTCKC-ALDO-RNN scheme is verified by experiments. 展开更多
关键词 Dexterity optimization Model uncertainty Recurrent Neural Network(RNN) Trajectory tracking Large space equipment
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Incorporating delay propagation into decision-making for optimizing prefabricated building supply chain networks under uncertainty 认领 引用
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作者 Qiang DU Qian CHEN +3 位作者 Jing YANG Jiajie ZHOU Libiao BAI Xixi LUO 《ENGINEERING Management》 CSCD 2026年第2期363-385,共23页
Prefabricated buildings are crucial for the transformation of the construction industry,while the Prefabricated Building Supply Chain Network(PBSCN)that supports their implementation is subject to uncertainties in pro... Prefabricated buildings are crucial for the transformation of the construction industry,while the Prefabricated Building Supply Chain Network(PBSCN)that supports their implementation is subject to uncertainties in production,transportation,and installation.These uncertainties lead to schedule delays and cost increases,which significantly hinder the widespread adoption of prefabricated buildings.To address these issues,this paper develops a three-tier optimization model that integrates component factories,logistics providers,and contractors to improve resource allocation and reduce total costs.This model explicitly accounts for uncertainty-induced delay propagation across stages and incorporates its impacts into the decision-making process through work stoppage cost at the construction site.A Scenario-Based Stochastic Programming(SBSP)approach is employed to determine optimal decisions,while Monte Carlo Simulation(MCS)is utilized to generate representative scenarios.Furthermore,the proposed model is extended to incorporate a carbon trading mechanism to examine the interaction between environmental regulation and supply chain decisions.The model's effectiveness is validated through a hypothetical case adapted from a real-world project,in which the optimal solutions involved concentrating approximately 6%of orders in the baseline case and 33.0%35.5%in the largescale experiment.Results show that proactively accounting for uncertainties not only reduced costs but also strengthened coordination among entities to improve resource utilization.This paper provides practical decision support for PBSCN stakeholders,helping them mitigate risks,optimize order allocation,and improve overall supply chain performance in an uncertain environment. 展开更多
关键词 prefabricated buildings supply chain network coordination uncertainty management scenariobased stochastic programming cost optimization
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An anisotropy-dynamic ant colony optimization with probabilistic fracture uncertainty quantification for sub-seismic fault detection 认领 引用
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作者 Shi-Chang Li Yang Zhao +5 位作者 Cheng-Gang Xian Qi-Ya Qiao Fu-Yu Zhu Xing Liang Jie-Hui Zhang Lan-Lan Yan 《Petroleum Science》 SCIE EI CAS CSCD 2026年第5期2527-2544,共18页
The precise characterization of subsurface fracture systems,especially sub-seismicfractures below seismic resolution,is critical for developing complex hydrocarbon reservoirs.While antcolony optimization(Aco)introduce... The precise characterization of subsurface fracture systems,especially sub-seismicfractures below seismic resolution,is critical for developing complex hydrocarbon reservoirs.While antcolony optimization(Aco)introduced"ant tracking"for seismicfracture detection,traditional methods rely on isotropic post-stack attributes,ignoring azimuthal anisotropy—a key indicator of fracture orientation and density.The azimuth-aware anisotropic bayes ACO(Ani-Bayes ACO)integrated pre-stack anisotropy via Bayesian priors but suffered from deterministic constraints and staticheuristics,limiting its ability to model conjugate fracture systems or parameter uncertainty.To resolve these limitations,we propose the anisotropy-dynamic ACO(ADACO)algorithm.ADACO replaces deterministic constraints with probabilistic,dynamically evolving fracture parameter distributions:von Mises for orientation and lognormal for density,both parameterized by elliptical fitting credibility.During optimization,a Hidden Markov Model(HMM)globally evaluates path consistency,while elite-path feedback iteratively focuses the distributions.Thisenablesuncertainty-quantified fracture prediction,multi-settracking,and autonomous adaptation to fracture clustering.Validation in a complex shale gas reservoir showed 85%consistency with drilling data-a significant improvement over Ani-Bayes AcO(46%).ADAcO thus provides a robust tool for sub-seismic fracture characterization. 展开更多
关键词 Ant colony optimization Fracture detection Azimuthal anisotropy Hidden Markov Model(HMM) Uncertainty quantification
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State Estimation With Model Uncertainty Using Structure Variational Bayesian and Transfer Learning 认领 引用
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作者 Shuang Gao Xiaoli Luan +2 位作者 Biao Huang Shunyi Zhao Fei Liu 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2026年第3期715-727,共13页
This paper proposes a novel approach to address parameter uncertainties for state estimation in Markovian jump linear systems by leveraging transfer learning.Assume that the source domain model is available and reliab... This paper proposes a novel approach to address parameter uncertainties for state estimation in Markovian jump linear systems by leveraging transfer learning.Assume that the source domain model is available and reliable,and the target domain model has significant model parameter uncertainties.To enhance estimation performance in the target domain,the proposed method transfers model knowledge from the source domain and adjusts it using a tuning factor before incorporating it into the target domain estimator.More specifically,this approach involves transferring the modified probability density functions of state prediction from the source domain to the target domain and determining the tuning factor via structure variational Bayesian inference using measurements in the target domain.Using numerical examples and a 1-DOF torsion system,we showcase the competitiveness of the proposed state estimator compared to the existing robust state estimation methods when dealing with parameter uncertainties.The results highlight its capability to improve estimation accuracy in practical scenarios,showcasing its potential for real-world applications. 展开更多
关键词 Markovian jump linear systems parameter uncertainty state estimation structure variational inference transfer learning
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A Coordinated Thermal Power-Energy Storage Planning Method for Addressing Renewable Energy Uncertainty 认领 引用
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作者 Cheng Yang Xiuyu Yang +2 位作者 Gangui Yan Hongda Dong Chenggang Li 《Energy Engineering》 EI 2026年第5期342-365,共24页
The integration of renewable energy introduces significant uncertainty into daily power system operation scenarios.Traditional deterministic unit commitment methods struggle to adapt to these conditions,often resultin... The integration of renewable energy introduces significant uncertainty into daily power system operation scenarios.Traditional deterministic unit commitment methods struggle to adapt to these conditions,often resulting in poor economic performance and high curtailment rates in planning outcomes.To address these challenges,this paper proposes a coordinated thermal power-energy storage planning methodology for managing renewable energy uncertainty.First,the operational effectiveness of daily unit commitment under uncertain renewable energy scenarios is analyzed,with quantitative assessment of how different commitment strategies impact supply-demand balance and economic performance.Subsequently,by conducting flexibility evaluation under multiple renewable energy output profiles in typical days,an entropy weight-based method for determining daily unit commitment is developed.This approach evaluates the performance of commitment strategies across multiple uncertain scenarios using various flexibility metrics,enabling the identification of strategies that effectively accommodate uncertainty.Furthermore,building upon the entropy weight-based unit commitment methodology,a coordinated thermal power-energy storage planning model is formulated with the objective of minimizing expected costs across all scenarios.Finally,using actual measurement data from a Northeast China power grid,multiple typical-day uncertainty scenarios are constructed,and case study analysis validates the effectiveness of the proposed methodology. 展开更多
关键词 RE uncertainty joint fire-storage planning entropy weighting method flexibility evaluation
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Uncertainty-informed large vision model for automated recognition of excavated rock chips in tunnel boring machines 认领 引用
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作者 Xuefeng Ou Wentao Zhou +1 位作者 Tongming Qu Junsheng Yang 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2026年第6期4474-4486,共13页
Real-time identification of rock chip size and shape distributions from muck images plays a critical role in intelligently optimizing cutterhead thrust and torque parameters for tunnel boring machines(TBM).However,com... Real-time identification of rock chip size and shape distributions from muck images plays a critical role in intelligently optimizing cutterhead thrust and torque parameters for tunnel boring machines(TBM).However,complex light environments in field images are difficult to recognize via traditional methods.This paper proposes a U-Net-SAM framework integrating semantic segmentation and the vision foundation model—Segment Anything Model(SAM),combined with dropout-based uncertainty analysis,achieving efficient rock chip segmentation and parameter quantification.First,a U-Net is trained to identify the rock mass centroid as an automatic SAM prompt.Next,an overlap region optimization strategy based on Intersection over Union(IoU)and a noise filtering method is employed to tackle boundary blurring and particle adhesion.Finally,a Dropout layer is added to implement the committee-based uncertainty analysis model and quantify predictive uncertainty.Results show that:(1)U-Net-SAM improves mean F1-score and PA by 9.1%and 7.8%over U-Net;(2)A strong correlation between prediction standard deviation(SD)and error rate validates the proposed uncertainty quantification strategy.This framework provides reliable rock chip perception for intelligent TBM tunneling,with potential applications in other engineering scenarios. 展开更多
关键词 Tunnel boring machine Rock chip recognition Semantic segmentation Vision foundation model Uncertainty analysis U-Net-SAM
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Correction:Uncertainty and sensibility analysis of loss-of-forced-cooling accidents for 150-MWt molten salt reactors 认领 引用
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作者 Kai Wang Chao-Qun Wang +2 位作者 Qun Yang Zhao-Zhong He Na-Xiu Wang 《Nuclear Science and Techniques》 SCIE EI CAS CSCD 2026年第5期394-394,共1页
Correction to:Nuclear Science and Techniques(2025)36:111 http://gffzzd3cc09b8251d45dfswqkuwcxbn9656wf0.ffgz.tsg.suse.edu.cn/10.1007/s41365-025-01681-9.In the sentence beginning‘The weights of the parameters used for the…’in this article,the text‘RCSs’should have ... Correction to:Nuclear Science and Techniques(2025)36:111 http://gffzzd3cc09b8251d45dfswqkuwcxbn9656wf0.ffgz.tsg.suse.edu.cn/10.1007/s41365-025-01681-9.In the sentence beginning‘The weights of the parameters used for the…’in this article,the text‘RCSs’should have read‘SCRs’.In Table 7 of this article,the column header ρ_fuel was incorrect and should have read CPv_fuel.For completeness and transparency,the old incorrect version and the corrected version of Table 7 are displayed below. 展开更多
关键词 uncertainty analysis molten salt reactors loss forced cooling accidents nuclear science techniques weights parameters sensitivity analysis
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Uncertainty quantification for the ascent phase of launch vehicles using Bayesian inference 认领 引用
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作者 CHAO Tao LI Xiaonan +2 位作者 SHANG Xiaobing MA Ping YANG Ming 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2026年第2期485-503,共19页
The launch process of a multi-stage launch vehicle is significantly influenced by uncertain parameters,including air density,aerodynamic parameters,and engine thrust,which often exhibit deviation.Predicting the trajec... The launch process of a multi-stage launch vehicle is significantly influenced by uncertain parameters,including air density,aerodynamic parameters,and engine thrust,which often exhibit deviation.Predicting the trajectory range of the launch vehicle under the influence of uncertainty is essential before launch,and uncertainty quantification serves as a crucial method to address this challenge.In traditional uncertainty quantification for launch vehicles,unknown parameters are often assigned specific distributions based on prior knowledge.However,prior knowledge is sometimes subjective,and unknown parameters are often assigned conservative ranges to meet safety margins.In addition,the flight data of the past launch is precious,especially in quantifying the uncertainty of reusable or same-type launch vehicles.This paper utilizes flight data to estimate parameters base on Bayesian methods and integrates the estimation results with prior knowledge,which can more objectively set the distribution of uncertain parameters.Reasonable distribution has a positive impact on uncertainty quantification,which can avoid control strategies that are not robust enough or overly redundant.Therefore,the uncertainty quantification for launch vehicles is discussed under different information sources.In addition,the algorithm is accelerated based on Gaussian process regression and polynomial chaos expansions. 展开更多
关键词 launch vehicle uncertainty quantification Bayesian inference launch experience Gaussian process regression polynomial chaos expansions
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Hybridndiff-UQ:Uncertainty quantification for hybrid neural differentiable modeling 认领 引用
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作者 Deepak Akhare Tengfei Luo Jian-Xun Wang 《Theoretical & Applied Mechanics Letters》 EI CAS CSCD 2026年第2期1-24,共24页
The hybrid neural differentiable models mark a significant advancement in the field of scientific machine learning.These models,integrating numerical representations of known physics into deep neural networks,offer en... The hybrid neural differentiable models mark a significant advancement in the field of scientific machine learning.These models,integrating numerical representations of known physics into deep neural networks,offer enhanced predictive capabilities and show great potential for data-driven modeling of complex physical systems.However,a critical and yet unaddressed challenge lies in the quantification of inherent uncertainties stemming from multiple sources.Addressing this gap,we introduce a novel method,uncertainty quantification for hybrid neural differentiable modeling,for effective and efficient uncertainty propagation and estimation in hybrid neural differentiable models,leveraging the strengths of deep ensemble Bayesian learning and nonlinear transformations.Specifically,our approach effectively discerns and quantifies both aleatoric uncertainties,arising from data noise,and epistemic uncertainties,resulting from model-form discrepancies and data sparsity.This is achieved within a Bayesian model averaging framework,where aleatoric uncertainties are modeled through hybrid neural models.The unscented transformation plays a pivotal role in enabling the flow of these uncertainties through the nonlinear functions within the hybrid model.In contrast,epistemic uncertainties are estimated using an ensemble of stochastic gradient descent trajectories.This approach offers a practical approximation to the posterior distribution of both the network parameters and the physical parameters.Notably,our framework is designed for simplicity in implementation and high scalability,making it suitable for parallel computing environments.The merits of the proposed method have been demonstrated through problems governed by both ordinary and partial differentiable equations. 展开更多
关键词 Differentiable programming Scientific machine learning Grey box modeling Uncertainty quantification Scalable bayesian learning
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Impact of climate policy uncertainty on urban carbon emissions:Empirical evidence from China 认领 引用
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作者 Changfei Nie Jiaxin Peng Yuan Feng 《Chinese Journal of Population,Resources and Environment》 CSCD 2026年第2期165-175,共11页
The effect of climate policy on carbon emissions has been widely analyzed;however,the literature on climate policy uncertainty(CPU)is limited.We used panel data from 281 Chinese cities from 2006 to 2022 to construct a... The effect of climate policy on carbon emissions has been widely analyzed;however,the literature on climate policy uncertainty(CPU)is limited.We used panel data from 281 Chinese cities from 2006 to 2022 to construct a fixed effects model that examines the causal relationship between CPU and carbon emissions(CE).The results reveal that CPU significantly increases urban CE and the potential mechanisms lie in two aspects:increasing urban energy consumption on the demand side and inhibiting urban green innovation on the supply side.The moderating effect indicates that the emissions trading scheme pilot policy mitigates the carbon increasing effect.Additionally,the heterogeneity analysis reveals that CPU strongly affects CE in southeastern coastal cities,cities with lower administrative levels,large cities,and non-resource-based cities.Our findings provide novel insights for policymakers in designing flexible climate policies to address climate challenges and promote sustainable development. 展开更多
关键词 Climate policy uncertainty Carbon emissions Climatic risk Energy consumption Green innovation
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A Review of Optimization and Solution Methods for New Power Systems with Uncertainty 认领 引用 被引量：1
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作者 Zemin Liang Songyu Gao Qi Yao 《Energy Engineering》 EI 2026年第4期19-46,共28页
For mixed-integer programming(MIP)problems in new power systems with uncertainties,existing studies tend to address uncertainty modeling or MIP solution methods in isolation.They overlook core bottlenecks arising from... For mixed-integer programming(MIP)problems in new power systems with uncertainties,existing studies tend to address uncertainty modeling or MIP solution methods in isolation.They overlook core bottlenecks arising from their coupling,such as variable dimension explosion,disrupted constraint separability,and conflicts in solution logic.To address this gap,this paper focuses on the coupling effects between the two and systematically conducts three aspects of work:first,the paper summarizes the uncertainty optimization methods suitable for addressing uncertainty-related issues in power systems,along with their respective advantages and disadvantages.It also clarifies the specific forms and operational mechanisms through which these uncertainty optimization methods are integrated into MIP models.Meanwhile,based on the application scenarios of new power systems,the paper delineates the applicable boundaries of different optimization methods;second,the paper organizes three categories of solution methods,which are exact solution methods,decomposition-based methods,and meta-heuristic algorithms.It focuses on analyzing the improvement paths of various solution methods for resolving coupling bottlenecks,as well as their applicability in different types of power system optimization problems;finally,providing a summary and presenting an outlook on future directions:artificial intelligence-enabled optimization,development of dedicated solvers for extreme scenarios,and dynamic modeling of multi-source uncertainties.This study aims to help researchers in the field of new power systems quickly grasp uncertainty optimization methods and core solution methods,bridge existing research gaps,and promote the development of this field. 展开更多
关键词 Uncertainty new power system renewable energy optimal scheduling
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Generation of fission yield covariance matrices and its application in uncertainty analysis of decay heat 认领 引用
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作者 Wen-Di Chen Tao Ye +3 位作者 Hai-Rui Guo Jia-Hao Chen Bo Yang Yang-Jun Ying 《Nuclear Science and Techniques》 SCIE EI CAS CSCD 2026年第6期232-246,共15页
The uncertainties and covariance matrices of the fission yield are important in the uncertainty analysis of the decay heat.At present,there are no covariance matrices of fission yield given in the evaluated nuclear da... The uncertainties and covariance matrices of the fission yield are important in the uncertainty analysis of the decay heat.At present,there are no covariance matrices of fission yield given in the evaluated nuclear data library,although they provide uncertainties with good estimates.In this study,the generalized least squares(GLS)updating approach was adopted to evaluate the fission yield covariances with constraints from the basic physical conservation equation and chain yield data,using the nuclear data files from ENDF/B-Ⅷ.0,JENDL-5,and JEFF-3.3.Based on the original and updated data,summation calculations were performed for the fission pulse decay heat of thermal neutron-induced fission of235U.The uncertainties of the decay heat were obtained using the generalized perturbation theory,including the uncertainties propagated from the fission yield,decay energy,decay constant,and branching ratio.The original uncorrelated yield data contributed to a~4%uncertainty at all times,and dominated the decay heat uncertainty at cooling times longer than 100 s.With the generated covariance matrices,the uncertainty of the calculated decay heat was significantly reduced,and the decay energy data generally made a major contribution.The relative uncertainties at cooling time 0.1 s were~10%for ENDF/B-Ⅷ.0,JEFF-3.3,and~5%for JENDL-5,and those at cooling time 105 s were approximately 1%for the three libraries.The influence of the GLS updating procedure on the contributions of important fission products to the decay heat and their sensitivity coefficients is also discussed. 展开更多
关键词 Fission yield Covariance matrix Decay heat Uncertainty analysis
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Neural predictive control for high-spinning flight vehicles:A Fokker–Planck uncertainty control framework 认领 引用
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作者 Xinrui LUO Kai SHEN +2 位作者 Zhihong DENG Jiatong WU Zhihao JIANG 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2026年第6期554-577,共24页
To address the challenges posed by nonlinear coupled dynamics in the guidance and control of High-spinning Flight Vehicles(HFV),this study proposes a neural predictive control framework.By employing a closed-loop opti... To address the challenges posed by nonlinear coupled dynamics in the guidance and control of High-spinning Flight Vehicles(HFV),this study proposes a neural predictive control framework.By employing a closed-loop optimization mechanism based on a deviation-to-control command paradigm,the proposed approach effectively mitigates mapping inaccuracy and information loss inherent in traditional multi-stage conversion processes.Furthermore,a Bayesian Neural Network(BNN)-based probabilistic modeling approach is introduced to enable uncertainty-aware multimodal predictions of both impact point distributions and control correction effectiveness.A spatiotemporal entropy measurement framework is established by incorporating altitude sensitivity factor and time decay factor to construct a dynamic uncertainty representation model,which is further refined through a covariance fusion mechanism driven by the Fokker-Planck equation to optimize control decisions.This system effectively resolves core issues such as difficulties in control response limitation,sensitivity to time-varying control gains,and the coupling between control moment and range.Experimental results indicate that the proposed framework reduces the Circular Error Probable(CEP)from 75.12 m to 0.42 m,thereby providing a robust theoretical paradigm for the precision guidance of HFV. 展开更多
关键词 Bayesian networks Control systems High-spinning flight vehicle Microspoiler Uncertainty analysis
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Bayesian deep learning framework for updating landslide susceptibility assessment based on epistemic uncertainty with InSAR augmented samples 认领 引用
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作者 Chongzhi Chen Baiyu Dong +4 位作者 Jingya Lin Zhangquan Shen Luming Fang Yuhui Weng Ke Wang 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2026年第6期4216-4232,共17页
Data-driven modeling has emerged as an effective tool to achieve high-performance landslide susceptibility assessment(LSA).However,traditional data-driven models fail to account for the epistemic uncertainty arising f... Data-driven modeling has emerged as an effective tool to achieve high-performance landslide susceptibility assessment(LSA).However,traditional data-driven models fail to account for the epistemic uncertainty arising from random data sampling,leading to incomplete assessments.Moreover,LSA is frequently treated as a static process,despite the evolving conditional factors and their relationships with landslide occurrence.This study introduced a novel Bayesian deep learning framework that updates LSA based on epistemic uncertainty combined with InSAR techniques to achieve region-wide,completely cognizant,time-varying LSA across Zhejiang Province,China.By dividing the data into past,update,and future periods for initial model building,updating,and testing,respectively,we employed Bayesian geographically weighted convolutional neural networks(BGWCNN)to capture neighborhood effects and spatial heterogeneity while quantifying epistemic uncertainty.Recognizing the epistemic deficiencies of the initial BGWCNN,we augmented samples during the updating period in high-epistemic-entropy regions using enhanced small baseline subset InSAR.By combining augmented samples with updated landslide inventory,BGWCNN was updated(U-BGWCNN)through sequential Bayesian inference.The results indicated that BGWCNN outperformed random forest by increasing the area under the receiver operating characteristic curve(AUC)by 3.2%while ensuring the best stability.U-BGWCNN reduces 90.9%of high epistemic entropy regions and obtains the highest overall performance with an AUC of 78.8%,representing a 2.5%improvement over BGWCNN.Our results also highlight the importance of slope and NDVI for LSA in Zhejiang Province.The proposed framework ensures that LSA remains current and effective across all regions,enhancing landslide risk management and mitigation efforts. 展开更多
关键词 Landslide susceptibility updating Bayesian deep learning Epistemic uncertainty InSAR
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Isotopic characterization(δ18O,δD and d-excess)and modelling of stream flow partitioning along an altitudinal gradient in the Satluj River basin(SRB)Himalayan-alluvial plain using end member mixing analysis(EMMA)and associated uncertainty 认领 引用
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作者 Akhtar Jahan Nachiketa Rai +2 位作者 M.U.Khan Tanveer Dar Sudhir Kumar 《Acta Geochimica》 EI CAS CSCD 2026年第4期1055-1084,共30页
Stable isotopic(δ18O,δD and d-excess)signatures were employed to gain valuable insights regarding key hydrological processes and modelling of stream flow runoff partitioning of Satluj River Basin(SRB)in the weste... Stable isotopic(δ18O,δD and d-excess)signatures were employed to gain valuable insights regarding key hydrological processes and modelling of stream flow runoff partitioning of Satluj River Basin(SRB)in the western Himalayan region and plains of Punjab,where surface water is extensively used for hydropower,agricultural and domestic supply.The SRB has not yet been unexplored.Spatially extensive and comprehensive stable isotope data are meagre,which constricts the thoughtful comprehension of hydrological processes,sources of moisture,the isotope-elevation relationship and provenance quantification in this transboundary river across the mountain-plain basin.The results showed a significant difference inδ18O,δD and d-excess among the main stream,tributaries and groundwater during the observed period,mainly regulated by snow-glacier melt input,evaporation and recycled moisture contribution.The deviation of the water line in each hydrological compartment from the Global Meteoric Water Line,as well as its similarity to the local,regional and local water lines,suggests that these compartments have undergone significant evaporation during the recharge process.This similarity in moisture dynamics implies a potential link between the local and regional hydrological systems.The isotopic signature integrated with back trajectories reflects the combined role of moisture sources via both westerlies and southwest monsoons followed by extensive local recycling.The isotopic altitude lapse rate(IALR)of the various hydrological compartments mimics the IALR results from the Himalayas and their surrounding catchments.Moreover,the isotopic composition(δ18O)of the main stream showed a closer fit to a second-order polynomial relationship betweenδ18O and elevation compared to other models.Furthermore,we employ end member mixing analysis(EMMA)principles to identify the possible end members and calculate contributing fractions for respective end members.Modelling of stream flow partitioning using a dual isotopic tracer(δ18OEC andδ18O-d mixing model)based EMMA revealed the three possible end members,namely glacier melt,snow melt and groundwater,contributing 29.5%,31.7%and 38.7%,respectively,via aδ18O-EC mixing model,while 29.6%,31.3%and 39.0%,respectively,via aδ18O-d mixing model.The mathematically propagated uncertainty in the respective computed mixing fractions of EMMA is approximately28%,41%and 25%with theδ18O-EC mixing model and19%,54%and 35%with theδ18O-d mixing model,respectively,reflecting realistic uncertainties.An attempt is made to acknowledge,explore and hypothesize the realistic reason for the larger documented uncertainty.The study highlights the significant contribution of groundwater originating from the mountain-plain nexus to demarcate the river isotopic variability and underlines the need to concentrate on this important hydrological reservoir in the near future to learn about groundwater hysteresis and flow dynamics in this nexus mountain-plain watershed. 展开更多
关键词 Stable isotopes Satluj River Basin Himalayas End member mixing analysis Uncertainty
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Probabilistic framework for uncertainty quantification in the seismic response of buildings 认领 引用
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作者 Moussa Leblouba Samer Barakat Raghad Awad 《Earthquake Engineering and Engineering Vibration》 SCIE EI CSCD 2026年第2期393-410,共18页
This paper introduces a probabilistic framework for enhancing the seismic design of structures by incorporating uncertainty quantification(UQ)in response analysis.Traditional design codes,often deterministic,can lead ... This paper introduces a probabilistic framework for enhancing the seismic design of structures by incorporating uncertainty quantification(UQ)in response analysis.Traditional design codes,often deterministic,can lead to either overly conservative or unreliable designs.The proposed method integrates uncertainties in vibration periods and damping ratios as random variables,using elastic response spectra and the ASCE 7-16 design response spectrum for a more accurate seismic risk assessment.The framework effectively identifies discrepancies between measured and predicted vibration periods and damping ratios through numerical examples and case studies,highlighting the risk of non-conservative designs with nominal values.It emphasizes the need to account for biases in vibration period approximations as per ASCE 7 to prevent under-conservative designs.This approach allows engineers and researchers to estimate building responses more realistically,which is crucial for appropriate seismic design and performance evaluation. 展开更多
关键词 uncertainty quantification seismic design response spectra probabilistic approach
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