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
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
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
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
From the perspective of state-channel interaction,standard quantum teleportation can be viewed as a communication process characterized by both input and output,functioning as a quantum depolarizing channel.To achieve...From the perspective of state-channel interaction,standard quantum teleportation can be viewed as a communication process characterized by both input and output,functioning as a quantum depolarizing channel.To achieve a precise quantification of the quantumness introduced by this channel,we examine its uncertainties,which encompass both statedependent and state-independent uncertainties.Specifically,for qudit systems,we provide general formulas for these uncertainties.We analyze the uncertainties associated with standard quantum teleportation when induced by isotropic states,Werner states,and X-states,and we elucidate the correlation between these uncertainties and the parameters of the specific mixed states.Our findings demonstrate the validity of quantifying these uncertainties.展开更多
This paper designs distributed Nash equilibrium seeking strategies for heterogeneous dynamic cyber-physical systems.In particular, we are concerned with parametric uncertainties in the control channel of the players. ...This paper designs distributed Nash equilibrium seeking strategies for heterogeneous dynamic cyber-physical systems.In particular, we are concerned with parametric uncertainties in the control channel of the players. Moreover, the weights on communication links can be compromised by time-varying uncertainties, which can result from possibly malicious attacks,faults and disturbances. To deal with the unavailability of measurement of optimization errors, an output observer is constructed,based on which adaptive laws are designed to compensate for physical uncertainties. With adaptive laws, a new distributed Nash equilibrium seeking strategy is designed by further integrating consensus protocols and gradient search algorithms.Moreover, to further accommodate compromised communication weights resulting from cyber-uncertainties, the coupling strengths of the consensus module are designed to be adaptive. As a byproduct, the coupling strengths are independent of any global information. With theoretical investigations, it is proven that the proposed strategies are resilient to these uncertainties and players' actions are convergent to the Nash equilibrium. Simulation examples are given to numerically validate the effectiveness of the proposed strategies.展开更多
Seismic fragility analysis(SFA)is known as an effective probabilistic-based approach used to evaluate seismic fragility.There are various sources of uncertainties associated with this approach.A nuclear power plant(NP...Seismic fragility analysis(SFA)is known as an effective probabilistic-based approach used to evaluate seismic fragility.There are various sources of uncertainties associated with this approach.A nuclear power plant(NPP)system is an extremely important infrastructure and contains many structural uncertainties due to construction issues or structural deterioration during service.Simulation of structural uncertainties effects is a costly and time-consuming endeavor.A novel approach to SFA for the NPP considering structural uncertainties based on the damage state is proposed and examined.The results suggest that considering the structural uncertainties is essential in assessing the fragility of the NPP structure,and the impact of structural uncertainties tends to increase with the state of damage.Subsequently,machine learning(ML)is found to be superior in high-precision damage state identification of the NPP for reducing the time of nonlinear time-history analysis(NLTHA)and could be applied in the damage state-based SFA.Also,the impact of various sources of uncertainties is investigated through sensitivity analysis.The Sobol and Shapley additive explanations(SHAP)method can be complementary to each other and able to solve the problem of quantifying seismic and structural uncertainties simultaneously and the interaction effect of each parameter.展开更多
The increasingly stringent performance requirement in integrated circuit manufacturing, characterized by smaller feature sizes and higher productivity, necessitates the wafer stage executing a extreme motion with the ...The increasingly stringent performance requirement in integrated circuit manufacturing, characterized by smaller feature sizes and higher productivity, necessitates the wafer stage executing a extreme motion with the accuracy in terms of nanometers. This demanding requirement witnesses a widespread application of iterative learning control(ILC), given the repetitive nature of wafer scanning. ILC enables substantial performance improvement by using past measurement data in combination with the system model knowledge. However, challenges arise in cases where the data is contaminated by the stochastic noise, or when the system model exhibits significant uncertainties, constraining the achievable performance. In response to this issue, an extended state observer(ESO) based adaptive ILC approach is proposed in the frequency domain.Despite being model-based, it utilizes only a rough system model and then compensates for the resulting model uncertainties using an ESO, thereby achieving high robustness against uncertainties with minimal modeling effort. Additionally, an adaptive learning law is developed to mitigate the limited performance in the presence of stochastic noise, yielding high convergence accuracy yet without compromising convergence speed. Simulation and experimental comparisons with existing model-based and data-driven inversion-based ILC validate the effectiveness as well as the superiority of the proposed method.展开更多
Thermal Protection System(TPS)with thick tiles,low thermal conductivity,and a short re-entry stage stands as a critical element within reusable aircraft,whose reliability is related to the function and changes with th...Thermal Protection System(TPS)with thick tiles,low thermal conductivity,and a short re-entry stage stands as a critical element within reusable aircraft,whose reliability is related to the function and changes with their physical properties,external conditions,and degradation.Meanwhile,due to the limitation of testing resources,epistemic uncertainties stemming from the small samples are present in TPS reliability modeling.However,current TPS reliability modeling methods face challenges in characterizing the relationships among reliability and physical properties,external conditions,degradation,and epistemic uncertainties.Therefore,under the framework of belief reliability theory,a TPS reliability model is constructed,which takes into account the physical principle,external conditions,performance degradation,and epistemic uncertainties.A reliability simulation algorithm is proposed to calculate TPS reliability.Through a case study and comparison analysis,the proposed method is validated as more effective than the existing method.Additionally,reliability sensitivity analysis is conducted to identify the sensitive factors of reliability under the condition of small samples,through which suggestions are provided for TPS functional design and improvement.展开更多
The Reliability-Based Design Optimization(RBDO)of complex engineering structures considering uncertainties has problems of being high-dimensional,highly nonlinear,and timeconsuming,which requires a significant amount ...The Reliability-Based Design Optimization(RBDO)of complex engineering structures considering uncertainties has problems of being high-dimensional,highly nonlinear,and timeconsuming,which requires a significant amount of sampling simulation computation.In this paper,a basis-adaptive Polynomial Chaos(PC)-Kriging surrogate model is proposed,in order to relieve the computational burden and enhance the predictive accuracy of a metamodel.The active learning basis-adaptive PC-Kriging model is combined with a quantile-based RBDO framework.Finally,five engineering cases have been implemented,including a benchmark RBDO problem,three high-dimensional explicit problems,and a high-dimensional implicit problem.Compared with Support Vector Regression(SVR),Kriging,and polynomial chaos expansion models,results show that the proposed basis-adaptive PC-Kriging model is more accurate and efficient for RBDO problems of complex engineering structures.展开更多
This study introduces a real-time data-driven battery management scheme designed to address uncertainties in load and generation forecasts,which are integral to an optimal energy storage control system.By expanding on...This study introduces a real-time data-driven battery management scheme designed to address uncertainties in load and generation forecasts,which are integral to an optimal energy storage control system.By expanding on an existing algorithm,this study resolves issues discovered during implementation and addresses previously overlooked concerns,resulting in significant enhancements in both performance and reliability.The refined real-time control scheme is integrated with a day-ahead optimization engine and forecast model,which is utilized for illustrative simulations to highlight its potential efficacy on a real site.Furthermore,a comprehensive comparison with the original formulation was conducted to cover all possible scenarios.This analysis validated the operational effectiveness of the scheme and provided a detailed evaluation of the improvements and expected behavior of the control system.Incorrect or improper adjustments to mitigate forecast uncertainties can result in suboptimal energy management,significant financial losses and penalties,and potential contract violations.The revised algorithm optimizes the operation of the battery system in real time and safeguards its state of health by limiting the charging/discharging cycles and enforcing adherence to contractual agreements.These advancements yield a reliable and efficient real-time correction algorithm for optimal site management,designed as an independent white box that can be integrated with any day-ahead optimization control system.展开更多
This paper discusses the design of event-triggered output-feedback controller for a class of nonlinear time-delay systems with multiple uncertainties. In sharp contrast to previous works, the considered systems posses...This paper discusses the design of event-triggered output-feedback controller for a class of nonlinear time-delay systems with multiple uncertainties. In sharp contrast to previous works, the considered systems possess two important characteristics: (i) The uncertain nonlinear terms meet the linearly unmeasurable-states dependent growth with the growth rate being an unknown function of the input and output. (ii) There exist input matching uncertainty and unknown measurement sensitivity. By introducing a single dynamic gain and employing a cleverly devised event-triggering mechanism (ETM), we design a new gain-based event-triggered output-feedback controller, which globally regulates all states of the considered systems and maintains global boundedness of the closed-loop system. Furthermore, the estimation of input matching uncertainty achieves convergence towards its actual value, and Zeno behavior does not happen. Two simulation examples including a practical one show that the proposed approach is effective.展开更多
The evolution of China-ASEAN relations ranks among the most significant geopolitical and economic dynamics of the 21st Century.Comprising 10 Southeast Asian nations,ASEAN has held the position of China’s largest trad...The evolution of China-ASEAN relations ranks among the most significant geopolitical and economic dynamics of the 21st Century.Comprising 10 Southeast Asian nations,ASEAN has held the position of China’s largest trading partner since 2020.This partnership is underpinned by sustained economic growth,political stability,security cooperation,and vibrant socio-cultural exchanges.Over the past two decades,the China-ASEAN relationship has emerged as a main axis in Asia’s geopolitical and economic landscape.By 2025,this partnership has entered a more intricate and strategic phase marked by deeper economic collaboration,expanded multilateral diplomacy,and mounting challenges stemming from global developments such as tari"wars and South China Sea tensions.展开更多
Robots are finding increasing application in aircraft composite structure assembly due to their flexibility and the growing demand of aircraft manufacturers for high production rates.The contact force of the composite...Robots are finding increasing application in aircraft composite structure assembly due to their flexibility and the growing demand of aircraft manufacturers for high production rates.The contact force of the composite frame in a robotic assembly of the aircraft composite fuselage panel can hardly be controlled due to the multi-surface variable contact stiffness caused by compliance and complex shape with multiple mating surfaces.The paper proposes a robotic assembly system for the aircraft composite fuselage frame with a compliant contact force control strategy using the Gaussian process surrogate model.First,a robotic assembly system is introduced,and the global coordinate system transformation model is built.Then,a compliant force control architecture is designed to generate the desired output force.Subsequently,a Gaussian process surrogate model with uncertainties is utilized to model the complicated relationship between the robot’s output force and the normal contact force acting on the mating surface of the composite frame.Furthermore,an optimal contact force control strategy is implemented to improve the contact quality.Finally,an experiment demonstrates that the proposed methodology can ensure that the contact force on each surface is within the limit of the engineering specification and uniformly distributed,improving the quality compared to the traditional assembly process.展开更多
The accuracy of landslide susceptibility prediction(LSP)mainly depends on the precision of the landslide spatial position.However,the spatial position error of landslide survey is inevitable,resulting in considerable ...The accuracy of landslide susceptibility prediction(LSP)mainly depends on the precision of the landslide spatial position.However,the spatial position error of landslide survey is inevitable,resulting in considerable uncertainties in LSP modeling.To overcome this drawback,this study explores the influence of positional errors of landslide spatial position on LSP uncertainties,and then innovatively proposes a semi-supervised machine learning model to reduce the landslide spatial position error.This paper collected 16 environmental factors and 337 landslides with accurate spatial positions taking Shangyou County of China as an example.The 30e110 m error-based multilayer perceptron(MLP)and random forest(RF)models for LSP are established by randomly offsetting the original landslide by 30,50,70,90 and 110 m.The LSP uncertainties are analyzed by the LSP accuracy and distribution characteristics.Finally,a semi-supervised model is proposed to relieve the LSP uncertainties.Results show that:(1)The LSP accuracies of error-based RF/MLP models decrease with the increase of landslide position errors,and are lower than those of original data-based models;(2)70 m error-based models can still reflect the overall distribution characteristics of landslide susceptibility indices,thus original landslides with certain position errors are acceptable for LSP;(3)Semi-supervised machine learning model can efficiently reduce the landslide position errors and thus improve the LSP accuracies.展开更多
Polynomial Chaos Expansion(PCE)has gained significant popularity among engineers across various engineering disciplines for uncertainty analysis.However,traditional PCE suffers from two major drawbacks.First,the ortho...Polynomial Chaos Expansion(PCE)has gained significant popularity among engineers across various engineering disciplines for uncertainty analysis.However,traditional PCE suffers from two major drawbacks.First,the orthogonality of polynomial basis functions holds only for independent input variables,limiting the model’s ability to propagate uncertainty in dependent variables.Second,PCE encounters the"curse of dimensionality"due to the high computational cost of training the model with numerous polynomial coefficients.In practical manufacturing,compressor blades are subject to machining precision limitations,leading to deviations from their ideal geometric shapes.These deviations require a large number of geometric parameters to describe,and exhibit significant correlations.To efficiently quantify the impact of high-dimensional dependent geometric deviations on the aerodynamic performance of compressor blades,this paper firstly introduces a novel approach called Data-driven Sparse PCE(DSPCE).The proposed method addresses the aforementioned challenges by employing a decorrelation algorithm to directly create multivariate basis functions,accommodating both independent and dependent random variables.Furthermore,the method utilizes an iterative Diffeomorphic Modulation under Observable Response Preserving Homotopy regression algorithm to solve the unknown coefficients,achieving model sparsity while maintaining fitting accuracy.Then,the study investigates the simultaneous effects of seven dependent geometric deviations on the aerodynamics of a high subsonic compressor cascade by using the DSPCE method proposed and sensitivity analysis of covariance.The joint distribution of the dependent geometric deviations is determined using Quantile-Quantile plots and normal copula functions based on finite measurement data.The results demonstrate that the correlations between geometric deviations significantly impact the variance of aerodynamic performance and the flow field.Therefore,it is crucial to consider these correlations for accurately assessing the aerodynamic uncertainty.展开更多
Current research on the dynamics and vibrations of geared rotor systems primarily focuses on deterministic models.However,uncertainties inevitably exist in the gear system,which cause uncertainties in system parameter...Current research on the dynamics and vibrations of geared rotor systems primarily focuses on deterministic models.However,uncertainties inevitably exist in the gear system,which cause uncertainties in system parameters and subsequently influence the accurate evaluation of system dynamic behavior.In this study,a dynamic model of a geared rotor system with mixed parameters and model uncertainties is proposed.Initially,the dynamic model of the geared rotor-bearing system with deterministic parameters is established using a finite element method.Subsequently,a nonparametric method is introduced to model the hybrid uncertainties in the dynamic model.Deviation coefficients and dispersion parameters are used to reflect the levels of parameter and model uncertainty.For example,the study evaluates the effects of uncertain bearing and mesh stiffness on the vibration responses of a geared rotor system.The results demonstrate that the influence of uncertainty varies among different model types.Model uncertainties have a more significant than parametric uncertainties,whereas hybrid uncertainties increase the nonlinearities and complexities of the system’s dynamic responses.These findings provide valuable insights into understanding the dynamic behavior of geared system with hybrid uncertainties.展开更多
Reliability is a crucial metric in aerospace engineering.The results of reliability assessments for components like aerospace electromagnetic relays directly impact the development and operational reliability of aeros...Reliability is a crucial metric in aerospace engineering.The results of reliability assessments for components like aerospace electromagnetic relays directly impact the development and operational reliability of aerospace engineering systems.Current methods for analyzing the reliability of aerospace electromagnetic relays have limitations,such as neglecting the combined effects of multiple uncertain factors,degradation of key component properties,and the influence of fluctuations in aerospace environments.Additionally,these methods often assume a single-type uncertainty in the manufacturing process,leading to significant deviations between the analysis results and actual measurement results.To address these issues,this study proposes an efficient timedependent reliability analysis method based on the HL-RF algorithm,considering a hybrid of probabilistic and interval uncertainty that accounts for degradation and environmental conditions.The proposed method is applied to the reliability analysis of actual aerospace electromagnetic relay products and compared with traditional methods,demonstrating significant advantages.The proposed method has been applied to the time-dependent reliability analysis of actual aerospace electromagnetic relay products under different environmental conditions.The analysis results exhibit an error margin within 5.12% compared to actual measurement results.Compared to analysis methods solely based on probabilistic uncertainty quantification or interval uncertainty quantification,this method reduces the analysis error by 52% and 67% respectively.When compared to two other state-of-the-art methods that integrate probabilistic and interval uncertainty quantification,the error reduction is 23%.These demonstrate the superiority of the proposed method and validates its effectiveness.The presented approach has the potential to be extended for reliability analysis in other aerospace electromechanical systems.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
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).
基金Project supported by the National Natural Science Foundation of China(Grant No.12201300).
文摘From the perspective of state-channel interaction,standard quantum teleportation can be viewed as a communication process characterized by both input and output,functioning as a quantum depolarizing channel.To achieve a precise quantification of the quantumness introduced by this channel,we examine its uncertainties,which encompass both statedependent and state-independent uncertainties.Specifically,for qudit systems,we provide general formulas for these uncertainties.We analyze the uncertainties associated with standard quantum teleportation when induced by isotropic states,Werner states,and X-states,and we elucidate the correlation between these uncertainties and the parameters of the specific mixed states.Our findings demonstrate the validity of quantifying these uncertainties.
基金supported by the National Key R&D Program of China(2022ZD0119604)the National Natural Science Foundation of China(NSFC)(62173181,62222308,62221004)the Natural Science Foundation of Jiangsu Province(BK20220139)
文摘This paper designs distributed Nash equilibrium seeking strategies for heterogeneous dynamic cyber-physical systems.In particular, we are concerned with parametric uncertainties in the control channel of the players. Moreover, the weights on communication links can be compromised by time-varying uncertainties, which can result from possibly malicious attacks,faults and disturbances. To deal with the unavailability of measurement of optimization errors, an output observer is constructed,based on which adaptive laws are designed to compensate for physical uncertainties. With adaptive laws, a new distributed Nash equilibrium seeking strategy is designed by further integrating consensus protocols and gradient search algorithms.Moreover, to further accommodate compromised communication weights resulting from cyber-uncertainties, the coupling strengths of the consensus module are designed to be adaptive. As a byproduct, the coupling strengths are independent of any global information. With theoretical investigations, it is proven that the proposed strategies are resilient to these uncertainties and players' actions are convergent to the Nash equilibrium. Simulation examples are given to numerically validate the effectiveness of the proposed strategies.
基金National Natural Science Foundation of China under Grant Nos.52208191 and 51908397Shanxi Province Science Foundation for Youths under Grant No.201901D211025China Postdoctoral Science Foundation under Grant No.2020M670695。
文摘Seismic fragility analysis(SFA)is known as an effective probabilistic-based approach used to evaluate seismic fragility.There are various sources of uncertainties associated with this approach.A nuclear power plant(NPP)system is an extremely important infrastructure and contains many structural uncertainties due to construction issues or structural deterioration during service.Simulation of structural uncertainties effects is a costly and time-consuming endeavor.A novel approach to SFA for the NPP considering structural uncertainties based on the damage state is proposed and examined.The results suggest that considering the structural uncertainties is essential in assessing the fragility of the NPP structure,and the impact of structural uncertainties tends to increase with the state of damage.Subsequently,machine learning(ML)is found to be superior in high-precision damage state identification of the NPP for reducing the time of nonlinear time-history analysis(NLTHA)and could be applied in the damage state-based SFA.Also,the impact of various sources of uncertainties is investigated through sensitivity analysis.The Sobol and Shapley additive explanations(SHAP)method can be complementary to each other and able to solve the problem of quantifying seismic and structural uncertainties simultaneously and the interaction effect of each parameter.
基金supported by National Natural Science Foundation of China(52375530,52075132)Natural Science Foundation of Heilongjiang Province(YQ2022E025)+4 种基金State Key Laboratory of Precision Electronic Manufacturing Technology and Equipment(Guangdong University of Technology)(JMDZ202312)Fundamental Research Funds for the Central Universities(HIT.OCEF.2024034)China Postdoctoral Science Foundation(2019M651278,2020T130155)Heilongjiang Province Postdoctoral Science Foundation(LBH-Z19066)Space Drive and Manipulation Mechanism Laboratory of BICE and National Key Laboratory of Space Intelligent Control,No BICE-SDMM-2024-01
文摘The increasingly stringent performance requirement in integrated circuit manufacturing, characterized by smaller feature sizes and higher productivity, necessitates the wafer stage executing a extreme motion with the accuracy in terms of nanometers. This demanding requirement witnesses a widespread application of iterative learning control(ILC), given the repetitive nature of wafer scanning. ILC enables substantial performance improvement by using past measurement data in combination with the system model knowledge. However, challenges arise in cases where the data is contaminated by the stochastic noise, or when the system model exhibits significant uncertainties, constraining the achievable performance. In response to this issue, an extended state observer(ESO) based adaptive ILC approach is proposed in the frequency domain.Despite being model-based, it utilizes only a rough system model and then compensates for the resulting model uncertainties using an ESO, thereby achieving high robustness against uncertainties with minimal modeling effort. Additionally, an adaptive learning law is developed to mitigate the limited performance in the presence of stochastic noise, yielding high convergence accuracy yet without compromising convergence speed. Simulation and experimental comparisons with existing model-based and data-driven inversion-based ILC validate the effectiveness as well as the superiority of the proposed method.
基金supported by the steady supports scientific research of Key Laboratory of Defense Science and Technology,China(No.WDZC20220105)the National Natural Science Foundation of China(Nos.51775020,62073009,U20B2002)the Science Challenge Project,China(No.TZ2018007)。
文摘Thermal Protection System(TPS)with thick tiles,low thermal conductivity,and a short re-entry stage stands as a critical element within reusable aircraft,whose reliability is related to the function and changes with their physical properties,external conditions,and degradation.Meanwhile,due to the limitation of testing resources,epistemic uncertainties stemming from the small samples are present in TPS reliability modeling.However,current TPS reliability modeling methods face challenges in characterizing the relationships among reliability and physical properties,external conditions,degradation,and epistemic uncertainties.Therefore,under the framework of belief reliability theory,a TPS reliability model is constructed,which takes into account the physical principle,external conditions,performance degradation,and epistemic uncertainties.A reliability simulation algorithm is proposed to calculate TPS reliability.Through a case study and comparison analysis,the proposed method is validated as more effective than the existing method.Additionally,reliability sensitivity analysis is conducted to identify the sensitive factors of reliability under the condition of small samples,through which suggestions are provided for TPS functional design and improvement.
基金supported by the National Key R&D Program of China(No.2021YFB1715000)the National Natural Science Foundation of China(No.52375073)。
文摘The Reliability-Based Design Optimization(RBDO)of complex engineering structures considering uncertainties has problems of being high-dimensional,highly nonlinear,and timeconsuming,which requires a significant amount of sampling simulation computation.In this paper,a basis-adaptive Polynomial Chaos(PC)-Kriging surrogate model is proposed,in order to relieve the computational burden and enhance the predictive accuracy of a metamodel.The active learning basis-adaptive PC-Kriging model is combined with a quantile-based RBDO framework.Finally,five engineering cases have been implemented,including a benchmark RBDO problem,three high-dimensional explicit problems,and a high-dimensional implicit problem.Compared with Support Vector Regression(SVR),Kriging,and polynomial chaos expansion models,results show that the proposed basis-adaptive PC-Kriging model is more accurate and efficient for RBDO problems of complex engineering structures.
基金supported by the Israeli Ministry of Infrastructure,Energy and Water Resources.
文摘This study introduces a real-time data-driven battery management scheme designed to address uncertainties in load and generation forecasts,which are integral to an optimal energy storage control system.By expanding on an existing algorithm,this study resolves issues discovered during implementation and addresses previously overlooked concerns,resulting in significant enhancements in both performance and reliability.The refined real-time control scheme is integrated with a day-ahead optimization engine and forecast model,which is utilized for illustrative simulations to highlight its potential efficacy on a real site.Furthermore,a comprehensive comparison with the original formulation was conducted to cover all possible scenarios.This analysis validated the operational effectiveness of the scheme and provided a detailed evaluation of the improvements and expected behavior of the control system.Incorrect or improper adjustments to mitigate forecast uncertainties can result in suboptimal energy management,significant financial losses and penalties,and potential contract violations.The revised algorithm optimizes the operation of the battery system in real time and safeguards its state of health by limiting the charging/discharging cycles and enforcing adherence to contractual agreements.These advancements yield a reliable and efficient real-time correction algorithm for optimal site management,designed as an independent white box that can be integrated with any day-ahead optimization control system.
基金supported by the fund of Beijing Municipal Commission of Education(KM202210017001 and 22019821001)the Natural Science Foundation of Henan Province(222300420253).
文摘This paper discusses the design of event-triggered output-feedback controller for a class of nonlinear time-delay systems with multiple uncertainties. In sharp contrast to previous works, the considered systems possess two important characteristics: (i) The uncertain nonlinear terms meet the linearly unmeasurable-states dependent growth with the growth rate being an unknown function of the input and output. (ii) There exist input matching uncertainty and unknown measurement sensitivity. By introducing a single dynamic gain and employing a cleverly devised event-triggering mechanism (ETM), we design a new gain-based event-triggered output-feedback controller, which globally regulates all states of the considered systems and maintains global boundedness of the closed-loop system. Furthermore, the estimation of input matching uncertainty achieves convergence towards its actual value, and Zeno behavior does not happen. Two simulation examples including a practical one show that the proposed approach is effective.
文摘The evolution of China-ASEAN relations ranks among the most significant geopolitical and economic dynamics of the 21st Century.Comprising 10 Southeast Asian nations,ASEAN has held the position of China’s largest trading partner since 2020.This partnership is underpinned by sustained economic growth,political stability,security cooperation,and vibrant socio-cultural exchanges.Over the past two decades,the China-ASEAN relationship has emerged as a main axis in Asia’s geopolitical and economic landscape.By 2025,this partnership has entered a more intricate and strategic phase marked by deeper economic collaboration,expanded multilateral diplomacy,and mounting challenges stemming from global developments such as tari"wars and South China Sea tensions.
基金This study was supported by the Aeronautical Manufacturing Technology Institute,COMAC.
文摘Robots are finding increasing application in aircraft composite structure assembly due to their flexibility and the growing demand of aircraft manufacturers for high production rates.The contact force of the composite frame in a robotic assembly of the aircraft composite fuselage panel can hardly be controlled due to the multi-surface variable contact stiffness caused by compliance and complex shape with multiple mating surfaces.The paper proposes a robotic assembly system for the aircraft composite fuselage frame with a compliant contact force control strategy using the Gaussian process surrogate model.First,a robotic assembly system is introduced,and the global coordinate system transformation model is built.Then,a compliant force control architecture is designed to generate the desired output force.Subsequently,a Gaussian process surrogate model with uncertainties is utilized to model the complicated relationship between the robot’s output force and the normal contact force acting on the mating surface of the composite frame.Furthermore,an optimal contact force control strategy is implemented to improve the contact quality.Finally,an experiment demonstrates that the proposed methodology can ensure that the contact force on each surface is within the limit of the engineering specification and uniformly distributed,improving the quality compared to the traditional assembly process.
基金the National Natural Science Foundation of China(Grant Nos.42377164 and 52079062)the Interdisciplinary Innovation Fund of Natural Science,Nanchang University(Grant No.9167-28220007-YB2107).
文摘The accuracy of landslide susceptibility prediction(LSP)mainly depends on the precision of the landslide spatial position.However,the spatial position error of landslide survey is inevitable,resulting in considerable uncertainties in LSP modeling.To overcome this drawback,this study explores the influence of positional errors of landslide spatial position on LSP uncertainties,and then innovatively proposes a semi-supervised machine learning model to reduce the landslide spatial position error.This paper collected 16 environmental factors and 337 landslides with accurate spatial positions taking Shangyou County of China as an example.The 30e110 m error-based multilayer perceptron(MLP)and random forest(RF)models for LSP are established by randomly offsetting the original landslide by 30,50,70,90 and 110 m.The LSP uncertainties are analyzed by the LSP accuracy and distribution characteristics.Finally,a semi-supervised model is proposed to relieve the LSP uncertainties.Results show that:(1)The LSP accuracies of error-based RF/MLP models decrease with the increase of landslide position errors,and are lower than those of original data-based models;(2)70 m error-based models can still reflect the overall distribution characteristics of landslide susceptibility indices,thus original landslides with certain position errors are acceptable for LSP;(3)Semi-supervised machine learning model can efficiently reduce the landslide position errors and thus improve the LSP accuracies.
基金the National Science and Technology Major Project of China(No.J2019-I-0011)the Innovation Foundation for Doctor Dissertation of Northwestern Polytechnical University,China(No.CX2023057)for supporting the research work.
文摘Polynomial Chaos Expansion(PCE)has gained significant popularity among engineers across various engineering disciplines for uncertainty analysis.However,traditional PCE suffers from two major drawbacks.First,the orthogonality of polynomial basis functions holds only for independent input variables,limiting the model’s ability to propagate uncertainty in dependent variables.Second,PCE encounters the"curse of dimensionality"due to the high computational cost of training the model with numerous polynomial coefficients.In practical manufacturing,compressor blades are subject to machining precision limitations,leading to deviations from their ideal geometric shapes.These deviations require a large number of geometric parameters to describe,and exhibit significant correlations.To efficiently quantify the impact of high-dimensional dependent geometric deviations on the aerodynamic performance of compressor blades,this paper firstly introduces a novel approach called Data-driven Sparse PCE(DSPCE).The proposed method addresses the aforementioned challenges by employing a decorrelation algorithm to directly create multivariate basis functions,accommodating both independent and dependent random variables.Furthermore,the method utilizes an iterative Diffeomorphic Modulation under Observable Response Preserving Homotopy regression algorithm to solve the unknown coefficients,achieving model sparsity while maintaining fitting accuracy.Then,the study investigates the simultaneous effects of seven dependent geometric deviations on the aerodynamics of a high subsonic compressor cascade by using the DSPCE method proposed and sensitivity analysis of covariance.The joint distribution of the dependent geometric deviations is determined using Quantile-Quantile plots and normal copula functions based on finite measurement data.The results demonstrate that the correlations between geometric deviations significantly impact the variance of aerodynamic performance and the flow field.Therefore,it is crucial to consider these correlations for accurately assessing the aerodynamic uncertainty.
基金Supported by National Natural Science Foundation of China(Grant Nos.12072106,52005156)National Key Research and Development Program of China(Grant No.2020YFB2008101)Foundation of Henan Key Laboratory of Superhard Abrasives and Grinding Equipment,Henan University of Technology of China(Grant No.JDKFJJ2022002).
文摘Current research on the dynamics and vibrations of geared rotor systems primarily focuses on deterministic models.However,uncertainties inevitably exist in the gear system,which cause uncertainties in system parameters and subsequently influence the accurate evaluation of system dynamic behavior.In this study,a dynamic model of a geared rotor system with mixed parameters and model uncertainties is proposed.Initially,the dynamic model of the geared rotor-bearing system with deterministic parameters is established using a finite element method.Subsequently,a nonparametric method is introduced to model the hybrid uncertainties in the dynamic model.Deviation coefficients and dispersion parameters are used to reflect the levels of parameter and model uncertainty.For example,the study evaluates the effects of uncertain bearing and mesh stiffness on the vibration responses of a geared rotor system.The results demonstrate that the influence of uncertainty varies among different model types.Model uncertainties have a more significant than parametric uncertainties,whereas hybrid uncertainties increase the nonlinearities and complexities of the system’s dynamic responses.These findings provide valuable insights into understanding the dynamic behavior of geared system with hybrid uncertainties.
基金supported by the National Natural Science Foundation of China(No.52277141).
文摘Reliability is a crucial metric in aerospace engineering.The results of reliability assessments for components like aerospace electromagnetic relays directly impact the development and operational reliability of aerospace engineering systems.Current methods for analyzing the reliability of aerospace electromagnetic relays have limitations,such as neglecting the combined effects of multiple uncertain factors,degradation of key component properties,and the influence of fluctuations in aerospace environments.Additionally,these methods often assume a single-type uncertainty in the manufacturing process,leading to significant deviations between the analysis results and actual measurement results.To address these issues,this study proposes an efficient timedependent reliability analysis method based on the HL-RF algorithm,considering a hybrid of probabilistic and interval uncertainty that accounts for degradation and environmental conditions.The proposed method is applied to the reliability analysis of actual aerospace electromagnetic relay products and compared with traditional methods,demonstrating significant advantages.The proposed method has been applied to the time-dependent reliability analysis of actual aerospace electromagnetic relay products under different environmental conditions.The analysis results exhibit an error margin within 5.12% compared to actual measurement results.Compared to analysis methods solely based on probabilistic uncertainty quantification or interval uncertainty quantification,this method reduces the analysis error by 52% and 67% respectively.When compared to two other state-of-the-art methods that integrate probabilistic and interval uncertainty quantification,the error reduction is 23%.These demonstrate the superiority of the proposed method and validates its effectiveness.The presented approach has the potential to be extended for reliability analysis in other aerospace electromechanical systems.
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.
