With the increasing integration of large-scale distributed energy resources into the grid,traditional distribution network optimization and dispatch methods struggle to address the challenges posed by both generation ...With the increasing integration of large-scale distributed energy resources into the grid,traditional distribution network optimization and dispatch methods struggle to address the challenges posed by both generation and load.Accounting for these issues,this paper proposes a multi-timescale coordinated optimization dispatch method for distribution networks.First,the probability box theory was employed to determine the uncertainty intervals of generation and load forecasts,based on which,the requirements for flexibility dispatch and capacity constraints of the grid were calculated and analyzed.Subsequently,a multi-timescale optimization framework was constructed,incorporating the generation and load forecast uncertainties.This framework included optimization models for dayahead scheduling,intra-day optimization,and real-time adjustments,aiming to meet flexibility needs across different timescales and improve the economic efficiency of the grid.Furthermore,an improved soft actor-critic algorithm was introduced to enhance the uncertainty exploration capability.Utilizing a centralized training and decentralized execution framework,a multi-agent SAC network model was developed to improve the decision-making efficiency of the agents.Finally,the effectiveness and superiority of the proposed method were validated using a modified IEEE-33 bus test system.展开更多
Based on the Monte Carlo approach and conventional error analysis theory,taking the heaviest doubly magic nucleus 208Pb as an example,we first evaluate the propagated uncertainties of universal potential parameters fo...Based on the Monte Carlo approach and conventional error analysis theory,taking the heaviest doubly magic nucleus 208Pb as an example,we first evaluate the propagated uncertainties of universal potential parameters for three typical types of single-particle energy in the phenomenological Woods–Saxon mean field.Accepting the Woods–Saxon modeling with uncorrelated model parameters,we found that the standard deviations of singleparticle energy obtained through the Monte Carlo simulation and the error propagation rules are in good agreement.It seems that the energy uncertainty of the single-particle levels regularly evoluate with certain quantum numbers to a large extent for the given parameter uncertainties.Further,the correlation properties of the single-particle levels within the domain of input parameter uncertainties are statistically analyzed,for example,with the aid of Pearson’s correlation coefficients.It was found that a positive,negative,or unrelated relationship may appear between two selected single-particle levels,which will be extremely helpful for evaluating the theoretical uncertainty related to the single-particle levels(e.g.,K isomer)in nuclear structural calculations.展开更多
In the present study,modified Ibarra,Medina and Krawinkler moment-rotation parameters are used for modeling the uncertainties in concrete moment frame structures.Correlations of model parameters in a component and bet...In the present study,modified Ibarra,Medina and Krawinkler moment-rotation parameters are used for modeling the uncertainties in concrete moment frame structures.Correlations of model parameters in a component and between two structural components were considered to analyze these uncertainties.In the first step,the structural collapse responses were obtained by producing 281 samples for the uncertainties using the Latin hypercube sampling(LHS)method,considering the probability distribution of the uncertainties and performing incremental dynamic analyses.In the second step,281 new samples were produced for the uncertainties by the central composite design(CCD)method without considering the probability distribution of the uncertainties and calculating the structural collapse responses.Then,using the response surface method(RSM)and artificial neural network(ANN)for the two simulation modes,structural collapse responses were predicted.The results indicated that the collapse responses at levels of 0 to 100%obtained from the two simulations have a high correlation coefficient of 98%.This suggests that random variables can be simulated without considering the probability distribution of uncertainties,by performing uncertainty analysis to determine structural collapse responses.展开更多
Importance measures can be used to identify the vulnerable components in an aviation system at the early design stage.However,due to lack of knowledge or less available information on the component or system,the epist...Importance measures can be used to identify the vulnerable components in an aviation system at the early design stage.However,due to lack of knowledge or less available information on the component or system,the epistemic uncertainties may be one of the challenging issues in importance evaluation.In addition,the properties of the aircraft system,which are the fundamentals of the component importance measure,including the hierarchy,dependency,randomness,and uncertainty,should be taken into consideration.To solve these problems,this paper proposes the component Uncertainty Integrated Importance Measure(component UIIM)which considers multiple epistemic uncertainties in the complex multi-state systems.The degradation process for the components is described by a Markov model,and the system reliability model is developed using the Markov hierarchal evidential network.The concept of integrated importance measure is then extended into component UIIM to evaluate the component criticality rather than the component state change criticality,from the perspective of system performance.A case study on displacement compensation hydraulic system is presented to show the effectiveness of the proposed uncertainty importance measure.The results show that the component UIIM can be an effective method for evaluating the component criticality from system performance perspective at the system early design.展开更多
In fossil energy pollution is serious and the“double carbon”goal is being promoted,as a symbol of fresh energy in the electrical system,solar and wind power have an increasing installed capacity,only conventional un...In fossil energy pollution is serious and the“double carbon”goal is being promoted,as a symbol of fresh energy in the electrical system,solar and wind power have an increasing installed capacity,only conventional units obviously can not solve the new energy as the main body of the scheduling problem.To enhance the systemscheduling ability,based on the participation of thermal power units,incorporate the high energy-carrying load of electro-melting magnesiuminto the regulation object,and consider the effects on the wind unpredictability of the power.Firstly,the operating characteristics of high energy load and wind power are analyzed,and the principle of the participation of electrofusedmagnesiumhigh energy-carrying loads in the elimination of obstructedwind power is studied.Second,a two-layer optimization model is suggested,with the objective function being the largest amount of wind power consumed and the lowest possible cost of system operation.In the upper model,the high energy-carrying load regulates the blocked wind power,and in the lower model,the second-order cone approximation algorithm is used to solve the optimizationmodelwithwind power uncertainty,so that a two-layer optimizationmodel that takes into account the regulation of the high energy-carrying load of the electrofused magnesium and the uncertainty of the wind power is established.Finally,the model is solved using Gurobi,and the results of the simulation demonstrate that the suggested model may successfully lower wind abandonment,lower system operation costs,increase the accuracy of day-ahead scheduling,and lower the final product error of the thermal electricity unit.展开更多
Ship outfitting is a key process in shipbuilding.Efficient and high-quality ship outfitting is a top priority for modern shipyards.These activities are conducted at different stations of shipyards.The outfitting plan ...Ship outfitting is a key process in shipbuilding.Efficient and high-quality ship outfitting is a top priority for modern shipyards.These activities are conducted at different stations of shipyards.The outfitting plan is one of the crucial issues in shipbuilding.In this paper,production scheduling and material ordering with endogenous uncertainty of the outfitting process are investigated.The uncertain factors in outfitting equipment production are usually decision-related,which leads to difficulties in addressing uncertainties in the outfitting production workshops before production is conducted according to plan.This uncertainty is regarded as endogenous uncertainty and can be treated as non-anticipativity constraints in the model.To address this problem,a stochastic two-stage programming model with endogenous uncertainty is established to optimize the outfitting job scheduling and raw material ordering process.A practical case of the shipyard of China Merchants Heavy Industry Co.,Ltd.is used to evaluate the performance of the proposed method.Satisfactory results are achieved at the lowest expected total cost as the complete kit rate of outfitting equipment is improved and emergency replenishment is reduced.展开更多
Robustness against measurement uncertainties is crucial for gas turbine engine diagnosis.While current research focuses mainly on measurement noise,measurement bias remains challenging.This study proposes a novel perf...Robustness against measurement uncertainties is crucial for gas turbine engine diagnosis.While current research focuses mainly on measurement noise,measurement bias remains challenging.This study proposes a novel performance-based fault detection and identification(FDI)strategy for twin-shaft turbofan gas turbine engines and addresses these uncertainties through a first-order Takagi-Sugeno-Kang fuzzy inference system.To handle ambient condition changes,we use parameter correction to preprocess the raw measurement data,which reduces the FDI’s system complexity.Additionally,the power-level angle is set as a scheduling parameter to reduce the number of rules in the TSK-based FDI system.The data for designing,training,and testing the proposed FDI strategy are generated using a component-level turbofan engine model.The antecedent and consequent parameters of the TSK-based FDI system are optimized using the particle swarm optimization algorithm and ridge regression.A robust structure combining a specialized fuzzy inference system with the TSK-based FDI system is proposed to handle measurement biases.The performance of the first-order TSK-based FDI system and robust FDI structure are evaluated through comprehensive simulation studies.Comparative studies confirm the superior accuracy of the first-order TSK-based FDI system in fault detection,isolation,and identification.The robust structure demonstrates a 2%-8%improvement in the success rate index under relatively large measurement bias conditions,thereby indicating excellent robustness.Accuracy against significant bias values and computation time are also evaluated,suggesting that the proposed robust structure has desirable online performance.This study proposes a novel FDI strategy that effectively addresses measurement uncertainties.展开更多
For uncertainty quantification of complex models with high-dimensional,nonlinear,multi-component coupling like digital twins,traditional statistical sampling methods,such as random sampling and Latin hypercube samplin...For uncertainty quantification of complex models with high-dimensional,nonlinear,multi-component coupling like digital twins,traditional statistical sampling methods,such as random sampling and Latin hypercube sampling,require a large number of samples,which entails huge computational costs.Therefore,how to construct a small-size sample space has been a hot issue of interest for researchers.To this end,this paper proposes a sequential search-based Latin hypercube sampling scheme to generate efficient and accurate samples for uncertainty quantification.First,the sampling range of the samples is formed by carving the polymorphic uncertainty based on theoretical analysis.Then,the optimal Latin hypercube design is selected using the Latin hypercube sampling method combined with the"space filling"criterion.Finally,the sample selection function is established,and the next most informative sample is optimally selected to obtain the sequential test sample.Compared with the classical sampling method,the generated samples can retain more information on the basis of sparsity.A series of numerical experiments are conducted to demonstrate the superiority of the proposed sequential search-based Latin hypercube sampling scheme,which is a way to provide reliable uncertainty quantification results with small sample sizes.展开更多
The continuously increasing renewable energy sources(RES)and demand response(DR)are becoming crucial sources of system flexibility.Consequently,decision-dependent uncertainties(DDUs),inter-changeably referred to as en...The continuously increasing renewable energy sources(RES)and demand response(DR)are becoming crucial sources of system flexibility.Consequently,decision-dependent uncertainties(DDUs),inter-changeably referred to as endogenous uncertainties,impose new characteristics on power system dis-patch.The DDUs faced by system operators originate from uncertain dispatchable resources such as RES units or DR,while reserve providers encounter DDUs from the uncertain reserve deployment.Thus,a systematic framework was established in this study to address robust dispatch problems with DDUs.The main contributions are drawn as follows.①The robust characterization of DDUs was unfolded with a dependency decomposition structure.②A generic DDU coping mechanism was manifested as the bilateral matching between uncertainty and flexibility.③The influence of DDU incorporation on the convexity/non-convexity of robust dispatch problems was analyzed.④Generic solution algorithms adaptive for DDUs were proposed.Under this framework,the inherent distinctions and correlations between DDUs and decision-independent uncertainties(DIUs)were revealed,laying a fundamental theoretical foundation for the economic and reliable operation of RES-dominated power systems.Illustrative applications in the source and demand sides are provided to show the significance of considering DDUs and demonstrate the proposed theoretical results.展开更多
Geomechanical properties of rocks vary across different measurement scales,primarily due to heterogeneity.Micro-scale geomechanical tests,including micro-scale“scratch tests”and nano-scale nanoindentation tests,are ...Geomechanical properties of rocks vary across different measurement scales,primarily due to heterogeneity.Micro-scale geomechanical tests,including micro-scale“scratch tests”and nano-scale nanoindentation tests,are attractive at different scales.Each method requires minimal sample volume,is low cost,and includes a relatively rapid measurement turnaround time.However,recent micro-scale test results–including scratch test results and nanoindentation results–exhibit tangible variance and uncertainty,suggesting a need to correlate mineral composition mapping to elastic modulus mapping to isolate the relative impact of specific minerals.Different research labs often utilize different interpretation methods,and it is clear that future micro-mechanical tests may benefit from standardized testing and interpretation procedures.The objectives of this study are to seek options for standardized testing and interpretation procedures,through two specific objectives:(1)Quantify chemical and physical controls on micro-mechanical properties and(2)Quantify the source of uncertainties associated with nanoindentation measurements.To reach these goals,we conducted mechanical tests on three different scales:triaxial compression tests,scratch tests,and nanoindentation tests.We found that mineral phase weight percentage is highly correlated with nanoindentation elastic modulus distribution.Finally,we conclude that nanoindentation testing is a mineralogy and microstructure-based method and generally yields significant uncertainty and overestimation.The uncertainty of the testing method is largely associated with not mapping pore space a priori.Lastly,the uncertainty can be reduced by combining phase mapping and modulus mapping with substantial and random data sampling.展开更多
The aim of this paper is to prove another variation on the Heisenberg uncertainty principle,we generalize the quantitative uncertainty relations in n different(time-frequency)domains and we will give an algorithm for ...The aim of this paper is to prove another variation on the Heisenberg uncertainty principle,we generalize the quantitative uncertainty relations in n different(time-frequency)domains and we will give an algorithm for the signal recovery related to the canonical Fourier-Bessel transform.展开更多
Foraminifera are shell-bearing microorganisms that are commonly found in marine deposits on the seabed.They are important indicators in many analyses,are used in climate change research,monitoring marine environments,...Foraminifera are shell-bearing microorganisms that are commonly found in marine deposits on the seabed.They are important indicators in many analyses,are used in climate change research,monitoring marine environments,evolutionary studies,and are also frequently used in the oil and gas industry.Although some research has focused on automating the classification of foraminifera images,few have addressed the uncertainty in these classifications.Although foraminifera classification is not a safety-critical task,estimating uncertainty is crucial to avoid misclassifications that could overlook rare and ecologically significant species that are informative indicators of the environment in which they lived.Uncertainty estimation in deep learning has gained significant attention and many methods have been developed.However,evaluating the performance of these methods in practical settings remains a challenge.To create a benchmark for uncertainty estimation in the classification of foraminifera,we administered a multiple choice questionnaire containing classification tasks to four senior geologists.By analyzing their responses,we generated human-derived uncertainty estimates for a test set of 260 images of foraminifera and sediment grains.These uncertainty estimates served as a baseline for comparison when training neural networks in classification.We then trained multiple deep neural networks using a range of uncertainty quantification methods to classify and state the uncertainty about the classifications.The results of the deep learning uncertainty quantification methods were then analyzed and compared with the human benchmark,to see how the methods performed individually and how the methods aligned with humans.Our results show that human-level performance can be achieved with deep learning and that test-time data augmentation and ensembling can help improve both uncertainty estimation and classification performance.Our results also show that human uncertainty estimates are helpful indicators for detecting classification errors and that deep learning-based uncertainty estimates can improve calibration and classification accuracy.展开更多
As the core of spatial planning in China,delineation of the production-living-ecological space(PLES)refers to dividing the overall land use into three functional spaces.Spatial units are optimally configured as the mo...As the core of spatial planning in China,delineation of the production-living-ecological space(PLES)refers to dividing the overall land use into three functional spaces.Spatial units are optimally configured as the most suitable functional type,while beset by various uncertainties.Weight uncertainties,being affected by subjective preferences,are highly arbitrary and seriously affect PLES.Taking Xuzhou as the study area,this paper studies the perturbation mechanism and response measure of weight uncertainties on PLES.First,weight samples are obtained through quasi-random sampling to serve as sources of uncertainties for input into the optimized delineation of PLES.Next,the Monte Carlo simulation is applied to simulate the spatial probability distribution of PLES.The global sensitivity analysis method is then adopted to identify the main sources that cause uncertainties in the delineation of PLES.Subsequently,the flexible space(FS)of PLES at a certain level of significance is formulated by comparing the distribution probabilities of spatial units for different functional spaces,acting as a countermeasure for the perturbation.The results show that weight uncertainties bring disturbances to the PLES by affecting the multi-criteria evaluation(MCE)of PLES delineation.The PLES is affected by the weight uncertainties of the factors alone or through interactions with other weights.FS is the spatial response measure of PLES when uncertainties occurred at a certain level of significance.The study introduces the perspective of uncertainty for PLES,which contributes toward improving the scientificity and reliability of PLES.展开更多
The practical predictability of hail precipitation rates is significantly influenced by initial meteorological perturbations,stemming from various uncertainty sources.This study thoroughly assessed the predictability ...The practical predictability of hail precipitation rates is significantly influenced by initial meteorological perturbations,stemming from various uncertainty sources.This study thoroughly assessed the predictability of hail precipitation rates in both climatologically and flow-dependent perturbed ensembles(CEns and FEns).These ensembles incorporated initial meteorological uncertainties derived separately from two operational ensembles.Leveraging the Weather Research and Forecasting model,we conducted cloud-resolving simulations of an idealized hailstorm.The practical predictability of hail responded comparably to both climatological and flow-dependent uncertainties,which was revealed across the entire ensemble of 50 members.However,a notable difference emerged when comparing the peak hail precipitation rates among the top 10 and bottom 10 members.From a thermodynamic perspective,the primary source of uncertainty in hail precipitation lay in the significant variations in temperature stratification,particularly at-20℃and-40℃.On the microphysical front,perturbations within CEns generated greater uncertainty in the process of rainwater collection by hail,contributing significantly to the microphysical growth mechanisms of hail.Furthermore,the findings reveal a stronger dependency of hail precipitation uncertainty on thermodynamic perturbations compared to kinematic perturbations.These insights enhance the comprehension of the practical predictability of hail and contribute significantly to the understanding of ensemble forecasting for hail events.展开更多
Generative adversarial network(GAN)models are widely used in mechanical designs.The aim in the airfoil shape design is to obtain shapes that exhibits the required aerodynamic performance,and conditional GAN is used fo...Generative adversarial network(GAN)models are widely used in mechanical designs.The aim in the airfoil shape design is to obtain shapes that exhibits the required aerodynamic performance,and conditional GAN is used for that aim.However,the output of GAN contains uncertainties.Additionally,the uncertainties of labels have not been quantified.This paper proposes an uncertainty quantification method to estimate the uncertainty of labels using Monte Carlo dropout.In addition,an uncertainty reduction method is proposed based on imbalanced training.The proposed method was evaluated for the airfoil generation task.The results indicated that the uncertainty was appropriately quantified and successfully reduced.展开更多
Logs and sawnwood play an important and fundamental role in the development of China's timber industry and are also China's major imports.This study explores the impact of economic policy uncertainty(EPU)on Ch...Logs and sawnwood play an important and fundamental role in the development of China's timber industry and are also China's major imports.This study explores the impact of economic policy uncertainty(EPU)on China's log and sawnwood trade by empirically analyzing the panel data of China's major trading partner countries with these two types of forest products from 2001 to 2022.The results show that the economic policy uncertainty of trading partner countries has a significant promotion effect on China's log and sawnwood trade,while China's economic policy has a significant negative effect on China's log and sawnwood trade.In terms of products,the impact of economic policy uncertainty in trading partner countries on China's sawnwood exports is significantly positive,while the impact on log exports is negative and insignificant.The per capita income of trading partner countries has a positive and significant impact on the trade of logs and sawnwood,while China's per capita income has a negative and significant impact on the trade of logs and sawnwood.The impact of real exchange rate on trade in sawnwood and total trade in logs and sawnwood is significantly positive,while the impact on trade in logs is positive but not significant.The per capita forest area ratio has a negative and significant effect on China's log imports,sawnwood imports and total imports of both logs and sawnwood.There are differences in the extent to which economic policy uncertainty affects China's trade in logs and sawnwood with developed and developing trading partners,with the overall effect on China's trade with developed trading partners being smaller than that with developing trading partners.展开更多
Selecting the optimal model helps decision-makers to reduce the uncertainty in the slope calculation process.The uncertainty quantification process using root-mean-square error(RMSE)has limitations.It can obscure loca...Selecting the optimal model helps decision-makers to reduce the uncertainty in the slope calculation process.The uncertainty quantification process using root-mean-square error(RMSE)has limitations.It can obscure local uncertainty features and neglect the statistical characteristics of uncertainty,which may hinder decision-makers'understanding and model selection.展开更多
BACKGROUND Glaucoma,a condition frequently linked to severe depression,anxiety,and sleep disturbances,affects treatment adherence while potentially compromising effectiveness.AIM To explore illness uncertainty(IU),anx...BACKGROUND Glaucoma,a condition frequently linked to severe depression,anxiety,and sleep disturbances,affects treatment adherence while potentially compromising effectiveness.AIM To explore illness uncertainty(IU),anxiety,and depressive symptoms in primary glaucoma and to discuss underlying triggers.METHODS We recruited 120 primary glaucoma cases between January 2022 and November 2023.The Mishel Uncertainty in Illness Scale(MUIS)and the Hospital Anxiety and Depression Scale(HADS)[include HADS-anxiety subscale(HADS-A)and HADS-depression subscale(HADS-D)]subscales,were used to assess IU and emotional distress(anxiety/depression),respectively.The MUIS-HADS subscale interrelationships were determined by Pearson correlation.IU-associated determinants were identified using univariate and binary logistic regression analyses.RESULTS The cohort showed a mean MUIS score of 79.73±8.97,corresponding to a moderately high IU level.The HADS-A and HADS-D scores averaged 6.57±3.89 and 7.08±5.05 points,respectively,with 15.00%of participants showing anxiety symptoms and 24.17%exhibiting depressive signs.Significant positive connections were observed between MUIS and both HADS-A(r=0.359,P<0.001)and HADSD(r=0.426,P<0.001).Univariate analysis revealed that disease duration,insomnia,monthly household income per capita,and the presence of comorbid chronic conditions were significantly associated with anxiety or depression.Multivariate analysis identified insomnia as a risk factor and higher monthly household income as a protective factor.CONCLUSION Patients with primary glaucoma experience moderate IU levels,generally low anxiety,and mild depression.Specifically,the anxiety and depression risks were 15.00%and 24.17%,respectively.A significant positive correlation existed between IU and anxiety/depression in these patients.Additionally,insomnia or lower monthly household income elevated anxiety/depression risks,enabling reliable anxiety/depression risk categorization among patients.展开更多
This study investigates the relationships between agricultural spot markets and external uncertainties through multifractal detrending moving-average cross-correlation analysis(MF-X-DMA).The dataset contains the Grain...This study investigates the relationships between agricultural spot markets and external uncertainties through multifractal detrending moving-average cross-correlation analysis(MF-X-DMA).The dataset contains the Grains&Oilseeds Index(GOI)and its five subindices for wheat,maize,soyabeans,rice,and barley.Moreover,we use three uncertainty proxies,namely,economic policy uncertainty(EPU),geopolitical risk(GPR),and Volatility Index(VIX).We observe multifractal cross-correlations between agricultural markets and uncertainties.Furthermore,statistical tests reveal that maize has intrinsic joint multifractality with all the uncertainty proxies,highly sensitive to external shocks.Additionally,intrinsic multifractality among GOI-GPR,wheat-GPR,and soyabeans-VIX is illustrated.However,other series have apparent multifractal crosscorrelations with high probabilities.Moreover,our analysis suggests that among the three types of external uncertainties,GPR has the strongest association with grain prices,excluding maize and soyabeans.展开更多
Accurate estimation of rockfall trajectories is essential for mitigation of rockfall hazards.Nowadays,Doppler radar technologies can measure rockfall trajectories with centimeter resolution.Calibrating a numerical mod...Accurate estimation of rockfall trajectories is essential for mitigation of rockfall hazards.Nowadays,Doppler radar technologies can measure rockfall trajectories with centimeter resolution.Calibrating a numerical model to fit these measured trajectories,i.e.back analysis,often involves manual trial-anderror processes and subjective goodness-of-fit criteria.Here,we propose a framework that uses the chi-square statistic to quantify the misfit between modeled and measured rockfall trajectories.The framework can also quantify the uncertainty bounds on the best-fit model parameters.The approach is validated using field data from an Australian copper mine under two scenarios.(1)We perform an unconstrained back-analysis where the initial position and velocity of the rock,in addition to the coefficients of restitution(COR),are free variables.This scenario yields a normal COR Rn?0.866±0.109 and tangential COR R_(t)=0.29±0.151 with 68%confidence.(2)We perform a constrained back-analysis using predetermined initial position and velocity of the rock,which further constrains Rn to 0.8±0.014 and Rt to 0.39±0.065.Both scenarios show a higher uncertainty in Rt than in Rn.We also demonstrate the adaptability of the back-analysis framework to two-dimensional(2D)rockfall modeling using the same data.To the best of our knowledge,this is the first quantitative goodness-of-fit metric for trajectorybased rockfall back analysis that supports the estimation of inherent uncertainty.The simplicity of the metric lends itself to robust model optimization of rockfall back-analysis and can be adapted to other model assumptions(e.g.rigid-body mechanics)and metrics(e.g.velocity or energy).展开更多
基金funded by Jilin Province Science and Technology Development Plan Project,grant number 20220203163SF.
文摘With the increasing integration of large-scale distributed energy resources into the grid,traditional distribution network optimization and dispatch methods struggle to address the challenges posed by both generation and load.Accounting for these issues,this paper proposes a multi-timescale coordinated optimization dispatch method for distribution networks.First,the probability box theory was employed to determine the uncertainty intervals of generation and load forecasts,based on which,the requirements for flexibility dispatch and capacity constraints of the grid were calculated and analyzed.Subsequently,a multi-timescale optimization framework was constructed,incorporating the generation and load forecast uncertainties.This framework included optimization models for dayahead scheduling,intra-day optimization,and real-time adjustments,aiming to meet flexibility needs across different timescales and improve the economic efficiency of the grid.Furthermore,an improved soft actor-critic algorithm was introduced to enhance the uncertainty exploration capability.Utilizing a centralized training and decentralized execution framework,a multi-agent SAC network model was developed to improve the decision-making efficiency of the agents.Finally,the effectiveness and superiority of the proposed method were validated using a modified IEEE-33 bus test system.
基金the National Natural Science Foundation of China(No.11975209)the Physics Research and Development Program of Zhengzhou University(No.32410017)the Project of Youth Backbone Teachers of Colleges and Universities of Henan Province(No.2017GGJS008)。
文摘Based on the Monte Carlo approach and conventional error analysis theory,taking the heaviest doubly magic nucleus 208Pb as an example,we first evaluate the propagated uncertainties of universal potential parameters for three typical types of single-particle energy in the phenomenological Woods–Saxon mean field.Accepting the Woods–Saxon modeling with uncorrelated model parameters,we found that the standard deviations of singleparticle energy obtained through the Monte Carlo simulation and the error propagation rules are in good agreement.It seems that the energy uncertainty of the single-particle levels regularly evoluate with certain quantum numbers to a large extent for the given parameter uncertainties.Further,the correlation properties of the single-particle levels within the domain of input parameter uncertainties are statistically analyzed,for example,with the aid of Pearson’s correlation coefficients.It was found that a positive,negative,or unrelated relationship may appear between two selected single-particle levels,which will be extremely helpful for evaluating the theoretical uncertainty related to the single-particle levels(e.g.,K isomer)in nuclear structural calculations.
文摘In the present study,modified Ibarra,Medina and Krawinkler moment-rotation parameters are used for modeling the uncertainties in concrete moment frame structures.Correlations of model parameters in a component and between two structural components were considered to analyze these uncertainties.In the first step,the structural collapse responses were obtained by producing 281 samples for the uncertainties using the Latin hypercube sampling(LHS)method,considering the probability distribution of the uncertainties and performing incremental dynamic analyses.In the second step,281 new samples were produced for the uncertainties by the central composite design(CCD)method without considering the probability distribution of the uncertainties and calculating the structural collapse responses.Then,using the response surface method(RSM)and artificial neural network(ANN)for the two simulation modes,structural collapse responses were predicted.The results indicated that the collapse responses at levels of 0 to 100%obtained from the two simulations have a high correlation coefficient of 98%.This suggests that random variables can be simulated without considering the probability distribution of uncertainties,by performing uncertainty analysis to determine structural collapse responses.
基金the National Natural Science Foundation of China(Nos.52375036,U2233212,52272409,62303030)Beijing Municipal Natural Science Foundation-Fengtai Rail Transit Frontier Research Joint Foundation,China(No.L221008)+1 种基金the fellowship of China Postdoctoral Science Foundation(No.2022M710305)the program of China Scholarship Council(Nos.202106020106,202306020133).
文摘Importance measures can be used to identify the vulnerable components in an aviation system at the early design stage.However,due to lack of knowledge or less available information on the component or system,the epistemic uncertainties may be one of the challenging issues in importance evaluation.In addition,the properties of the aircraft system,which are the fundamentals of the component importance measure,including the hierarchy,dependency,randomness,and uncertainty,should be taken into consideration.To solve these problems,this paper proposes the component Uncertainty Integrated Importance Measure(component UIIM)which considers multiple epistemic uncertainties in the complex multi-state systems.The degradation process for the components is described by a Markov model,and the system reliability model is developed using the Markov hierarchal evidential network.The concept of integrated importance measure is then extended into component UIIM to evaluate the component criticality rather than the component state change criticality,from the perspective of system performance.A case study on displacement compensation hydraulic system is presented to show the effectiveness of the proposed uncertainty importance measure.The results show that the component UIIM can be an effective method for evaluating the component criticality from system performance perspective at the system early design.
基金funded by the National Key R&D Program of China,Grant Number 2019YFB1505400.
文摘In fossil energy pollution is serious and the“double carbon”goal is being promoted,as a symbol of fresh energy in the electrical system,solar and wind power have an increasing installed capacity,only conventional units obviously can not solve the new energy as the main body of the scheduling problem.To enhance the systemscheduling ability,based on the participation of thermal power units,incorporate the high energy-carrying load of electro-melting magnesiuminto the regulation object,and consider the effects on the wind unpredictability of the power.Firstly,the operating characteristics of high energy load and wind power are analyzed,and the principle of the participation of electrofusedmagnesiumhigh energy-carrying loads in the elimination of obstructedwind power is studied.Second,a two-layer optimization model is suggested,with the objective function being the largest amount of wind power consumed and the lowest possible cost of system operation.In the upper model,the high energy-carrying load regulates the blocked wind power,and in the lower model,the second-order cone approximation algorithm is used to solve the optimizationmodelwithwind power uncertainty,so that a two-layer optimizationmodel that takes into account the regulation of the high energy-carrying load of the electrofused magnesium and the uncertainty of the wind power is established.Finally,the model is solved using Gurobi,and the results of the simulation demonstrate that the suggested model may successfully lower wind abandonment,lower system operation costs,increase the accuracy of day-ahead scheduling,and lower the final product error of the thermal electricity unit.
基金supported in part by the High-tech ship scientific research project of the Ministry of Industry and Information Technology of the People’s Republic of China,and the National Nature Science Foundation of China(Grant No.71671113)the Science and Technology Department of Shaanxi Province(No.2020GY-219)the Ministry of Education Collaborative Project of Production,Learning and Research(No.201901024016).
文摘Ship outfitting is a key process in shipbuilding.Efficient and high-quality ship outfitting is a top priority for modern shipyards.These activities are conducted at different stations of shipyards.The outfitting plan is one of the crucial issues in shipbuilding.In this paper,production scheduling and material ordering with endogenous uncertainty of the outfitting process are investigated.The uncertain factors in outfitting equipment production are usually decision-related,which leads to difficulties in addressing uncertainties in the outfitting production workshops before production is conducted according to plan.This uncertainty is regarded as endogenous uncertainty and can be treated as non-anticipativity constraints in the model.To address this problem,a stochastic two-stage programming model with endogenous uncertainty is established to optimize the outfitting job scheduling and raw material ordering process.A practical case of the shipyard of China Merchants Heavy Industry Co.,Ltd.is used to evaluate the performance of the proposed method.Satisfactory results are achieved at the lowest expected total cost as the complete kit rate of outfitting equipment is improved and emergency replenishment is reduced.
文摘Robustness against measurement uncertainties is crucial for gas turbine engine diagnosis.While current research focuses mainly on measurement noise,measurement bias remains challenging.This study proposes a novel performance-based fault detection and identification(FDI)strategy for twin-shaft turbofan gas turbine engines and addresses these uncertainties through a first-order Takagi-Sugeno-Kang fuzzy inference system.To handle ambient condition changes,we use parameter correction to preprocess the raw measurement data,which reduces the FDI’s system complexity.Additionally,the power-level angle is set as a scheduling parameter to reduce the number of rules in the TSK-based FDI system.The data for designing,training,and testing the proposed FDI strategy are generated using a component-level turbofan engine model.The antecedent and consequent parameters of the TSK-based FDI system are optimized using the particle swarm optimization algorithm and ridge regression.A robust structure combining a specialized fuzzy inference system with the TSK-based FDI system is proposed to handle measurement biases.The performance of the first-order TSK-based FDI system and robust FDI structure are evaluated through comprehensive simulation studies.Comparative studies confirm the superior accuracy of the first-order TSK-based FDI system in fault detection,isolation,and identification.The robust structure demonstrates a 2%-8%improvement in the success rate index under relatively large measurement bias conditions,thereby indicating excellent robustness.Accuracy against significant bias values and computation time are also evaluated,suggesting that the proposed robust structure has desirable online performance.This study proposes a novel FDI strategy that effectively addresses measurement uncertainties.
基金co-supported by the National Natural Science Foundation of China(Nos.51875014,U2233212 and 51875015)the Natural Science Foundation of Beijing Municipality,China(No.L221008)+1 种基金Science,Technology Innovation 2025 Major Project of Ningbo of China(No.2022Z005)the Tianmushan Laboratory Project,China(No.TK2023-B-001)。
文摘For uncertainty quantification of complex models with high-dimensional,nonlinear,multi-component coupling like digital twins,traditional statistical sampling methods,such as random sampling and Latin hypercube sampling,require a large number of samples,which entails huge computational costs.Therefore,how to construct a small-size sample space has been a hot issue of interest for researchers.To this end,this paper proposes a sequential search-based Latin hypercube sampling scheme to generate efficient and accurate samples for uncertainty quantification.First,the sampling range of the samples is formed by carving the polymorphic uncertainty based on theoretical analysis.Then,the optimal Latin hypercube design is selected using the Latin hypercube sampling method combined with the"space filling"criterion.Finally,the sample selection function is established,and the next most informative sample is optimally selected to obtain the sequential test sample.Compared with the classical sampling method,the generated samples can retain more information on the basis of sparsity.A series of numerical experiments are conducted to demonstrate the superiority of the proposed sequential search-based Latin hypercube sampling scheme,which is a way to provide reliable uncertainty quantification results with small sample sizes.
基金supported by the Joint Research Fund in Smart Grid(U1966601)under cooperative agreement between the National Natural Science Foundation of China(NSFC)and State Grid Corporation of China.
文摘The continuously increasing renewable energy sources(RES)and demand response(DR)are becoming crucial sources of system flexibility.Consequently,decision-dependent uncertainties(DDUs),inter-changeably referred to as endogenous uncertainties,impose new characteristics on power system dis-patch.The DDUs faced by system operators originate from uncertain dispatchable resources such as RES units or DR,while reserve providers encounter DDUs from the uncertain reserve deployment.Thus,a systematic framework was established in this study to address robust dispatch problems with DDUs.The main contributions are drawn as follows.①The robust characterization of DDUs was unfolded with a dependency decomposition structure.②A generic DDU coping mechanism was manifested as the bilateral matching between uncertainty and flexibility.③The influence of DDU incorporation on the convexity/non-convexity of robust dispatch problems was analyzed.④Generic solution algorithms adaptive for DDUs were proposed.Under this framework,the inherent distinctions and correlations between DDUs and decision-independent uncertainties(DIUs)were revealed,laying a fundamental theoretical foundation for the economic and reliable operation of RES-dominated power systems.Illustrative applications in the source and demand sides are provided to show the significance of considering DDUs and demonstrate the proposed theoretical results.
基金support of this project through the Southwest Regional Partnership on Carbon Sequestration(Grant No.DE-FC26-05NT42591)Improving Production in the Emerging Paradox Oil Play(Grant No.DE-FE0031775).
文摘Geomechanical properties of rocks vary across different measurement scales,primarily due to heterogeneity.Micro-scale geomechanical tests,including micro-scale“scratch tests”and nano-scale nanoindentation tests,are attractive at different scales.Each method requires minimal sample volume,is low cost,and includes a relatively rapid measurement turnaround time.However,recent micro-scale test results–including scratch test results and nanoindentation results–exhibit tangible variance and uncertainty,suggesting a need to correlate mineral composition mapping to elastic modulus mapping to isolate the relative impact of specific minerals.Different research labs often utilize different interpretation methods,and it is clear that future micro-mechanical tests may benefit from standardized testing and interpretation procedures.The objectives of this study are to seek options for standardized testing and interpretation procedures,through two specific objectives:(1)Quantify chemical and physical controls on micro-mechanical properties and(2)Quantify the source of uncertainties associated with nanoindentation measurements.To reach these goals,we conducted mechanical tests on three different scales:triaxial compression tests,scratch tests,and nanoindentation tests.We found that mineral phase weight percentage is highly correlated with nanoindentation elastic modulus distribution.Finally,we conclude that nanoindentation testing is a mineralogy and microstructure-based method and generally yields significant uncertainty and overestimation.The uncertainty of the testing method is largely associated with not mapping pore space a priori.Lastly,the uncertainty can be reduced by combining phase mapping and modulus mapping with substantial and random data sampling.
文摘The aim of this paper is to prove another variation on the Heisenberg uncertainty principle,we generalize the quantitative uncertainty relations in n different(time-frequency)domains and we will give an algorithm for the signal recovery related to the canonical Fourier-Bessel transform.
基金funded by the Norwegian Research Council(IKTPLUSS-IKT og digital innovasjon,project no.332901).
文摘Foraminifera are shell-bearing microorganisms that are commonly found in marine deposits on the seabed.They are important indicators in many analyses,are used in climate change research,monitoring marine environments,evolutionary studies,and are also frequently used in the oil and gas industry.Although some research has focused on automating the classification of foraminifera images,few have addressed the uncertainty in these classifications.Although foraminifera classification is not a safety-critical task,estimating uncertainty is crucial to avoid misclassifications that could overlook rare and ecologically significant species that are informative indicators of the environment in which they lived.Uncertainty estimation in deep learning has gained significant attention and many methods have been developed.However,evaluating the performance of these methods in practical settings remains a challenge.To create a benchmark for uncertainty estimation in the classification of foraminifera,we administered a multiple choice questionnaire containing classification tasks to four senior geologists.By analyzing their responses,we generated human-derived uncertainty estimates for a test set of 260 images of foraminifera and sediment grains.These uncertainty estimates served as a baseline for comparison when training neural networks in classification.We then trained multiple deep neural networks using a range of uncertainty quantification methods to classify and state the uncertainty about the classifications.The results of the deep learning uncertainty quantification methods were then analyzed and compared with the human benchmark,to see how the methods performed individually and how the methods aligned with humans.Our results show that human-level performance can be achieved with deep learning and that test-time data augmentation and ensembling can help improve both uncertainty estimation and classification performance.Our results also show that human uncertainty estimates are helpful indicators for detecting classification errors and that deep learning-based uncertainty estimates can improve calibration and classification accuracy.
基金Under the auspices of National Natural Science Foundation of China(No.42171248,42371273)。
文摘As the core of spatial planning in China,delineation of the production-living-ecological space(PLES)refers to dividing the overall land use into three functional spaces.Spatial units are optimally configured as the most suitable functional type,while beset by various uncertainties.Weight uncertainties,being affected by subjective preferences,are highly arbitrary and seriously affect PLES.Taking Xuzhou as the study area,this paper studies the perturbation mechanism and response measure of weight uncertainties on PLES.First,weight samples are obtained through quasi-random sampling to serve as sources of uncertainties for input into the optimized delineation of PLES.Next,the Monte Carlo simulation is applied to simulate the spatial probability distribution of PLES.The global sensitivity analysis method is then adopted to identify the main sources that cause uncertainties in the delineation of PLES.Subsequently,the flexible space(FS)of PLES at a certain level of significance is formulated by comparing the distribution probabilities of spatial units for different functional spaces,acting as a countermeasure for the perturbation.The results show that weight uncertainties bring disturbances to the PLES by affecting the multi-criteria evaluation(MCE)of PLES delineation.The PLES is affected by the weight uncertainties of the factors alone or through interactions with other weights.FS is the spatial response measure of PLES when uncertainties occurred at a certain level of significance.The study introduces the perspective of uncertainty for PLES,which contributes toward improving the scientificity and reliability of PLES.
基金supported by the National Natural Science Foundation of China(Grant Nos.42005005 and 42030607)the Science and Technology Department of Shaanxi Province(Grant No.2024JC-YBQN-0248)+2 种基金the Education Department of Shaanxi Province(Grant No.23JK0686)a Xi'an Science and Technology Project(Grant No.22GXFW0131)the Young Talent fund of the University Association for Science and Technology in Shaanxi(Grant No.20210706)。
文摘The practical predictability of hail precipitation rates is significantly influenced by initial meteorological perturbations,stemming from various uncertainty sources.This study thoroughly assessed the predictability of hail precipitation rates in both climatologically and flow-dependent perturbed ensembles(CEns and FEns).These ensembles incorporated initial meteorological uncertainties derived separately from two operational ensembles.Leveraging the Weather Research and Forecasting model,we conducted cloud-resolving simulations of an idealized hailstorm.The practical predictability of hail responded comparably to both climatological and flow-dependent uncertainties,which was revealed across the entire ensemble of 50 members.However,a notable difference emerged when comparing the peak hail precipitation rates among the top 10 and bottom 10 members.From a thermodynamic perspective,the primary source of uncertainty in hail precipitation lay in the significant variations in temperature stratification,particularly at-20℃and-40℃.On the microphysical front,perturbations within CEns generated greater uncertainty in the process of rainwater collection by hail,contributing significantly to the microphysical growth mechanisms of hail.Furthermore,the findings reveal a stronger dependency of hail precipitation uncertainty on thermodynamic perturbations compared to kinematic perturbations.These insights enhance the comprehension of the practical predictability of hail and contribute significantly to the understanding of ensemble forecasting for hail events.
基金supported by the Japan Society for the Promotion of Science and Grants-in-Aid for Scientific Research(Grant Nos.JP21K14064 and JP23K13239).
文摘Generative adversarial network(GAN)models are widely used in mechanical designs.The aim in the airfoil shape design is to obtain shapes that exhibits the required aerodynamic performance,and conditional GAN is used for that aim.However,the output of GAN contains uncertainties.Additionally,the uncertainties of labels have not been quantified.This paper proposes an uncertainty quantification method to estimate the uncertainty of labels using Monte Carlo dropout.In addition,an uncertainty reduction method is proposed based on imbalanced training.The proposed method was evaluated for the airfoil generation task.The results indicated that the uncertainty was appropriately quantified and successfully reduced.
文摘Logs and sawnwood play an important and fundamental role in the development of China's timber industry and are also China's major imports.This study explores the impact of economic policy uncertainty(EPU)on China's log and sawnwood trade by empirically analyzing the panel data of China's major trading partner countries with these two types of forest products from 2001 to 2022.The results show that the economic policy uncertainty of trading partner countries has a significant promotion effect on China's log and sawnwood trade,while China's economic policy has a significant negative effect on China's log and sawnwood trade.In terms of products,the impact of economic policy uncertainty in trading partner countries on China's sawnwood exports is significantly positive,while the impact on log exports is negative and insignificant.The per capita income of trading partner countries has a positive and significant impact on the trade of logs and sawnwood,while China's per capita income has a negative and significant impact on the trade of logs and sawnwood.The impact of real exchange rate on trade in sawnwood and total trade in logs and sawnwood is significantly positive,while the impact on trade in logs is positive but not significant.The per capita forest area ratio has a negative and significant effect on China's log imports,sawnwood imports and total imports of both logs and sawnwood.There are differences in the extent to which economic policy uncertainty affects China's trade in logs and sawnwood with developed and developing trading partners,with the overall effect on China's trade with developed trading partners being smaller than that with developing trading partners.
基金supported by the National Key Research and Development Program of China:Core Technologies and Software Systems of Geospatial Intelligence(Project No.:2021YFB3900900).
文摘Selecting the optimal model helps decision-makers to reduce the uncertainty in the slope calculation process.The uncertainty quantification process using root-mean-square error(RMSE)has limitations.It can obscure local uncertainty features and neglect the statistical characteristics of uncertainty,which may hinder decision-makers'understanding and model selection.
文摘BACKGROUND Glaucoma,a condition frequently linked to severe depression,anxiety,and sleep disturbances,affects treatment adherence while potentially compromising effectiveness.AIM To explore illness uncertainty(IU),anxiety,and depressive symptoms in primary glaucoma and to discuss underlying triggers.METHODS We recruited 120 primary glaucoma cases between January 2022 and November 2023.The Mishel Uncertainty in Illness Scale(MUIS)and the Hospital Anxiety and Depression Scale(HADS)[include HADS-anxiety subscale(HADS-A)and HADS-depression subscale(HADS-D)]subscales,were used to assess IU and emotional distress(anxiety/depression),respectively.The MUIS-HADS subscale interrelationships were determined by Pearson correlation.IU-associated determinants were identified using univariate and binary logistic regression analyses.RESULTS The cohort showed a mean MUIS score of 79.73±8.97,corresponding to a moderately high IU level.The HADS-A and HADS-D scores averaged 6.57±3.89 and 7.08±5.05 points,respectively,with 15.00%of participants showing anxiety symptoms and 24.17%exhibiting depressive signs.Significant positive connections were observed between MUIS and both HADS-A(r=0.359,P<0.001)and HADSD(r=0.426,P<0.001).Univariate analysis revealed that disease duration,insomnia,monthly household income per capita,and the presence of comorbid chronic conditions were significantly associated with anxiety or depression.Multivariate analysis identified insomnia as a risk factor and higher monthly household income as a protective factor.CONCLUSION Patients with primary glaucoma experience moderate IU levels,generally low anxiety,and mild depression.Specifically,the anxiety and depression risks were 15.00%and 24.17%,respectively.A significant positive correlation existed between IU and anxiety/depression in these patients.Additionally,insomnia or lower monthly household income elevated anxiety/depression risks,enabling reliable anxiety/depression risk categorization among patients.
基金supported by the National Social Science Fund Major Projects(22&ZD160).
文摘This study investigates the relationships between agricultural spot markets and external uncertainties through multifractal detrending moving-average cross-correlation analysis(MF-X-DMA).The dataset contains the Grains&Oilseeds Index(GOI)and its five subindices for wheat,maize,soyabeans,rice,and barley.Moreover,we use three uncertainty proxies,namely,economic policy uncertainty(EPU),geopolitical risk(GPR),and Volatility Index(VIX).We observe multifractal cross-correlations between agricultural markets and uncertainties.Furthermore,statistical tests reveal that maize has intrinsic joint multifractality with all the uncertainty proxies,highly sensitive to external shocks.Additionally,intrinsic multifractality among GOI-GPR,wheat-GPR,and soyabeans-VIX is illustrated.However,other series have apparent multifractal crosscorrelations with high probabilities.Moreover,our analysis suggests that among the three types of external uncertainties,GPR has the strongest association with grain prices,excluding maize and soyabeans.
基金funding from NSERC Alliance Grant ALLRP 576858e22 in partnership with Rocscience Inc.
文摘Accurate estimation of rockfall trajectories is essential for mitigation of rockfall hazards.Nowadays,Doppler radar technologies can measure rockfall trajectories with centimeter resolution.Calibrating a numerical model to fit these measured trajectories,i.e.back analysis,often involves manual trial-anderror processes and subjective goodness-of-fit criteria.Here,we propose a framework that uses the chi-square statistic to quantify the misfit between modeled and measured rockfall trajectories.The framework can also quantify the uncertainty bounds on the best-fit model parameters.The approach is validated using field data from an Australian copper mine under two scenarios.(1)We perform an unconstrained back-analysis where the initial position and velocity of the rock,in addition to the coefficients of restitution(COR),are free variables.This scenario yields a normal COR Rn?0.866±0.109 and tangential COR R_(t)=0.29±0.151 with 68%confidence.(2)We perform a constrained back-analysis using predetermined initial position and velocity of the rock,which further constrains Rn to 0.8±0.014 and Rt to 0.39±0.065.Both scenarios show a higher uncertainty in Rt than in Rn.We also demonstrate the adaptability of the back-analysis framework to two-dimensional(2D)rockfall modeling using the same data.To the best of our knowledge,this is the first quantitative goodness-of-fit metric for trajectorybased rockfall back analysis that supports the estimation of inherent uncertainty.The simplicity of the metric lends itself to robust model optimization of rockfall back-analysis and can be adapted to other model assumptions(e.g.rigid-body mechanics)and metrics(e.g.velocity or energy).
