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
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
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
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
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
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
Molten salt reactors(MSRs)are a promising candidate for Generation IV reactor technologies,and the small modular molten salt reactor(SM-MSR),which utilizes low-enriched uranium and thorium fuels,is regarded as a wise ...Molten salt reactors(MSRs)are a promising candidate for Generation IV reactor technologies,and the small modular molten salt reactor(SM-MSR),which utilizes low-enriched uranium and thorium fuels,is regarded as a wise development path to accelerate deployment time.Uncertainty and sensitivity analyses of accidents guide nuclear reactor design and safety analyses.Uncertainty analysis can ascertain the safety margin,and sensitivity analysis can reveal the correlation between accident consequences and input parameters.Loss of forced cooling(LOFC)represents an accident scenario of the SM-MSR,and the study of LOFC could offer useful information to improve physical thermohydraulic and structural designs.Therefore,this study investigates the uncertainty of LOFC consequences and the sensitivity of related parameters.The uncertainty of the LOFC consequences was analyzed using the Monte Carlo method,and multiple linear regression was employed to analyze the sensitivity of the input parameters.The uncertainty and sensitivity analyses showed that the maximum reactor outlet fuel salt temperature was 725.5℃,which is lower than the acceptable criterion,and five important parameters influencing LOFC consequences were identified.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
In the task of Facial Expression Recognition(FER),data uncertainty has been a critical factor affecting performance,typically arising from the ambiguity of facial expressions,low-quality images,and the subjectivity of...In the task of Facial Expression Recognition(FER),data uncertainty has been a critical factor affecting performance,typically arising from the ambiguity of facial expressions,low-quality images,and the subjectivity of annotators.Tracking the training history reveals that misclassified samples often exhibit high confidence and excessive uncertainty in the early stages of training.To address this issue,we propose an uncertainty-based robust sample selection strategy,which combines confidence error with RandAugment to improve image diversity,effectively reducing overfitting caused by uncertain samples during deep learning model training.To validate the effectiveness of the proposed method,extensive experiments were conducted on FER public benchmarks.The accuracy obtained were 89.08%on RAF-DB,63.12%on AffectNet,and 88.73%on FERPlus.展开更多
Response analysis of structures involving non-probabilistic uncertain parameters can be closely related to optimization.This paper provides a review on optimization-based methods for uncertainty analysis,with focusing...Response analysis of structures involving non-probabilistic uncertain parameters can be closely related to optimization.This paper provides a review on optimization-based methods for uncertainty analysis,with focusing attention on specific properties of adopted numerical optimization approaches.We collect and discuss the methods based on nonlinear programming,semidefinite programming,mixed-integer programming,mathematical programming with complementarity constraints,difference-of-convex programming,optimization methods using surrogate models and machine learning techniques,and metaheuristics.As a closely related topic,we also overview the methods for assessing structural robustness using non-probabilistic uncertainty modeling.We conclude the paper by drawing several remarks through this review.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
Interpreting is a fast-paced activity where interpreters must make quick choices when faced with uncertainty. This study looks at how professional interpreters handle linguistic uncertainty in English-Chinese sight tr...Interpreting is a fast-paced activity where interpreters must make quick choices when faced with uncertainty. This study looks at how professional interpreters handle linguistic uncertainty in English-Chinese sight translation, with a focus on the strategies they use. By analyzing transcription data alongside instructor evaluations, we found that interpreters relied most on creative interpretation and omission, while strategies like paraphrasing, simplification, transformation, addition, and generalization appeared less often. The results show a clear preference for strategies that keep communication flowing without adding unnecessary cognitive load. These findings support the Processing Economy Hypothesis, which suggests interpreters naturally seek efficient ways to process language while maintaining meaning. The study also highlights practical implications for interpreter training, emphasizing the value of flexible, economy-oriented strategies to help interpreters stay fluent under pressure.展开更多
When assessing seismic liquefaction potential with data-driven models,addressing the uncertainties of establishing models,interpreting cone penetration tests(CPT)data and decision threshold is crucial for avoiding bia...When assessing seismic liquefaction potential with data-driven models,addressing the uncertainties of establishing models,interpreting cone penetration tests(CPT)data and decision threshold is crucial for avoiding biased data selection,ameliorating overconfident models,and being flexible to varying practical objectives,especially when the training and testing data are not identically distributed.A workflow characterized by leveraging Bayesian methodology was proposed to address these issues.Employing a Multi-Layer Perceptron(MLP)as the foundational model,this approach was benchmarked against empirical methods and advanced algorithms for its efficacy in simplicity,accuracy,and resistance to overfitting.The analysis revealed that,while MLP models optimized via maximum a posteriori algorithm suffices for straightforward scenarios,Bayesian neural networks showed great potential for preventing overfitting.Additionally,integrating decision thresholds through various evaluative principles offers insights for challenging decisions.Two case studies demonstrate the framework's capacity for nuanced interpretation of in situ data,employing a model committee for a detailed evaluation of liquefaction potential via Monte Carlo simulations and basic statistics.Overall,the proposed step-by-step workflow for analyzing seismic liquefaction incorporates multifold testing and real-world data validation,showing improved robustness against overfitting and greater versatility in addressing practical challenges.This research contributes to the seismic liquefaction assessment field by providing a structured,adaptable methodology for accurate and reliable analysis.展开更多
BACKGROUND Currently,there is limited research examining the relationship between anxiety,depression,coping styles,and illness uncertainty in patients with cervical cancer(CC)undergoing radiotherapy.Addressing this ga...BACKGROUND Currently,there is limited research examining the relationship between anxiety,depression,coping styles,and illness uncertainty in patients with cervical cancer(CC)undergoing radiotherapy.Addressing this gap could provide valuable insights and more reliable evidence for clinical practice targeting this patient population.AIM To analyze the anxiety,depression,and coping styles of patients with CC undergoing radiotherapy and explore their correlations with illness uncertainty.METHODS A total of 200 patients with CC undergoing radiotherapy at The First Affiliated Hospital of Soochow University between June 2018 and June 2022 were enrolled.Anxiety and depression were assessed using the Hospital Anxiety and Depression Scale(HADS),comprising subscales for anxiety(HADS-A)and depression(HADS-D).Coping styles were evaluated using the Jalowiec Coping Scale(JCS-60),comprising dimensions such as confrontive,evasive,optimistic,fatalistic,emotive,palliative,supportive,and self-reliant.Illness uncertainty was measured using the Mishel Uncertainty in Illness Scale(MUIS),encompassing ambiguity,complexity,information deficit,and unpredictability.Correlations among anxiety,depression,coping styles,and illness uncertainty were analyzed.RESULTS During radiotherapy,the mean scores were 7.12±3.39 for HADS-A,6.68±3.49 for HADS-D,1.52±0.23 for JCS-60,and 93.40±7.44 for MUIS.Anxiety(HADS-A≥8)was present in 39.5%of patients,depression(HADS-D≥8)in 41.0%,and both in 14.0%.Anxiety was significantly positively correlated with ambiguity,unpredictability,and total MUIS score(P<0.05).Depression was significantly positively correlated with ambiguity,information deficit,unpredictability,and total MUIS score(P<0.05).Most patients adopted an optimistic coping style,whereas the emotive style was least utilized.Evasive,fatalistic,and emotive coping styles were significantly positively correlated with illness uncertainty,whereas the self-reliant style was significantly negatively correlated with unpredictability(P<0.05).CONCLUSION Anxiety,depression,and coping styles in patients with CC undergoing radiotherapy correlate significantly with their level of illness uncertainty.Medical staff should address patients’psychological status and coping strategies by providing targeted information to reduce negative emotions,foster adaptive coping styles,and decrease illness uncertainty.展开更多
In this paper,we investigate covert communications under multi-antenna detection,and explore the impacts of the warden’s channel state information(CSI)availability and the noise uncertainty on system covert capabilit...In this paper,we investigate covert communications under multi-antenna detection,and explore the impacts of the warden’s channel state information(CSI)availability and the noise uncertainty on system covert capability.The detection performance at warden is analyzed in two cases under the perfect and statistical CSI at warden,respectively.In particular,for the former one,the warden utilizes the likelihood ratio(LR)detector,while for the latter one,the generalized likelihood ratio(GLR)detector is adopted.We first consider the scenario where the blocklength is finite,and demonstrate that the covert rate under both cases asymptotically goes to zero as the blocklength goes to infinity.Subsequently,we take the noise uncertainty at the warden into account which leads to positive covert rate,and characterize the covert rate for infinite blocklength.Specially,we derive the optimal transmit power for the legitimate transmitter that maximizes the covert rate.Besides,the rate gap under two cases,with different CSI availability at the warden,can be presented in closed form.Finally,numerical results validate the effectiveness of our theoretical analysis and also demonstrate the impacts of the factors studied on the system covertness.展开更多
In this paper,a pair of dynamic high-gain observer and output feedback controller is proposed for nonlinear systems with multiple unknown time delays.By constructing Lyapunov-Krasovskii functionals,it shows that globa...In this paper,a pair of dynamic high-gain observer and output feedback controller is proposed for nonlinear systems with multiple unknown time delays.By constructing Lyapunov-Krasovskii functionals,it shows that global state asymptotic regulation can be ensured by introducing a single dynamic gain;furthermore,global asymptotic stabilization can be achieved by choosing a sufficiently large static scaling gain when the upper bounds of all system parameters are known.Especially,the output coefficient is allowed to be non-differentiable with unknown upper bound.This paper proposes a generalized Lyapunov matrix inequality based dynamic-gain scaling method,which significantly simplifies the design computational complexity by comparing with the classic backstepping method.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(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.
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.
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supported by the Youth Innovation Promotion Association(YIPA)(No.E329290101)of the Chinese Academy of Sciences。
文摘Molten salt reactors(MSRs)are a promising candidate for Generation IV reactor technologies,and the small modular molten salt reactor(SM-MSR),which utilizes low-enriched uranium and thorium fuels,is regarded as a wise development path to accelerate deployment time.Uncertainty and sensitivity analyses of accidents guide nuclear reactor design and safety analyses.Uncertainty analysis can ascertain the safety margin,and sensitivity analysis can reveal the correlation between accident consequences and input parameters.Loss of forced cooling(LOFC)represents an accident scenario of the SM-MSR,and the study of LOFC could offer useful information to improve physical thermohydraulic and structural designs.Therefore,this study investigates the uncertainty of LOFC consequences and the sensitivity of related parameters.The uncertainty of the LOFC consequences was analyzed using the Monte Carlo method,and multiple linear regression was employed to analyze the sensitivity of the input parameters.The uncertainty and sensitivity analyses showed that the maximum reactor outlet fuel salt temperature was 725.5℃,which is lower than the acceptable criterion,and five important parameters influencing LOFC consequences were identified.
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.
文摘In the task of Facial Expression Recognition(FER),data uncertainty has been a critical factor affecting performance,typically arising from the ambiguity of facial expressions,low-quality images,and the subjectivity of annotators.Tracking the training history reveals that misclassified samples often exhibit high confidence and excessive uncertainty in the early stages of training.To address this issue,we propose an uncertainty-based robust sample selection strategy,which combines confidence error with RandAugment to improve image diversity,effectively reducing overfitting caused by uncertain samples during deep learning model training.To validate the effectiveness of the proposed method,extensive experiments were conducted on FER public benchmarks.The accuracy obtained were 89.08%on RAF-DB,63.12%on AffectNet,and 88.73%on FERPlus.
文摘Response analysis of structures involving non-probabilistic uncertain parameters can be closely related to optimization.This paper provides a review on optimization-based methods for uncertainty analysis,with focusing attention on specific properties of adopted numerical optimization approaches.We collect and discuss the methods based on nonlinear programming,semidefinite programming,mixed-integer programming,mathematical programming with complementarity constraints,difference-of-convex programming,optimization methods using surrogate models and machine learning techniques,and metaheuristics.As a closely related topic,we also overview the methods for assessing structural robustness using non-probabilistic uncertainty modeling.We conclude the paper by drawing several remarks through this review.
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this paper was supported by Humanities and Social Science Youth Foundation of Ministry of Education,China[23YJC740018]。
文摘Interpreting is a fast-paced activity where interpreters must make quick choices when faced with uncertainty. This study looks at how professional interpreters handle linguistic uncertainty in English-Chinese sight translation, with a focus on the strategies they use. By analyzing transcription data alongside instructor evaluations, we found that interpreters relied most on creative interpretation and omission, while strategies like paraphrasing, simplification, transformation, addition, and generalization appeared less often. The results show a clear preference for strategies that keep communication flowing without adding unnecessary cognitive load. These findings support the Processing Economy Hypothesis, which suggests interpreters naturally seek efficient ways to process language while maintaining meaning. The study also highlights practical implications for interpreter training, emphasizing the value of flexible, economy-oriented strategies to help interpreters stay fluent under pressure.
文摘When assessing seismic liquefaction potential with data-driven models,addressing the uncertainties of establishing models,interpreting cone penetration tests(CPT)data and decision threshold is crucial for avoiding biased data selection,ameliorating overconfident models,and being flexible to varying practical objectives,especially when the training and testing data are not identically distributed.A workflow characterized by leveraging Bayesian methodology was proposed to address these issues.Employing a Multi-Layer Perceptron(MLP)as the foundational model,this approach was benchmarked against empirical methods and advanced algorithms for its efficacy in simplicity,accuracy,and resistance to overfitting.The analysis revealed that,while MLP models optimized via maximum a posteriori algorithm suffices for straightforward scenarios,Bayesian neural networks showed great potential for preventing overfitting.Additionally,integrating decision thresholds through various evaluative principles offers insights for challenging decisions.Two case studies demonstrate the framework's capacity for nuanced interpretation of in situ data,employing a model committee for a detailed evaluation of liquefaction potential via Monte Carlo simulations and basic statistics.Overall,the proposed step-by-step workflow for analyzing seismic liquefaction incorporates multifold testing and real-world data validation,showing improved robustness against overfitting and greater versatility in addressing practical challenges.This research contributes to the seismic liquefaction assessment field by providing a structured,adaptable methodology for accurate and reliable analysis.
基金Supported by National Natural Science Foundation of China,No.81602792The Natural Science Foundation of the Jiangsu Higher Education Institutions of China,No.23KJB310023+5 种基金Jiangsu Provincial Medical Key Discipline,No.ZDXK202235The Maternal and Child Health Research Project of Jiangsu Province,No.F202210The Project of State Key Laboratory of Radiation Medicine and Protection,Soochow University,No.GZK1202101Suzhou Science and Technology Development Plan Project,No.KJXW2020008BOXI Natural Science Cultivation Foundation of China of The First Affiliated Hospital of Soochow University,No.BXQN202107Clinical Diagnosis and Treatment Technology Innovation Project Youth Characteristic Technology Project of The First Affiliated Hospital of Soochow University,No.2100201.
文摘BACKGROUND Currently,there is limited research examining the relationship between anxiety,depression,coping styles,and illness uncertainty in patients with cervical cancer(CC)undergoing radiotherapy.Addressing this gap could provide valuable insights and more reliable evidence for clinical practice targeting this patient population.AIM To analyze the anxiety,depression,and coping styles of patients with CC undergoing radiotherapy and explore their correlations with illness uncertainty.METHODS A total of 200 patients with CC undergoing radiotherapy at The First Affiliated Hospital of Soochow University between June 2018 and June 2022 were enrolled.Anxiety and depression were assessed using the Hospital Anxiety and Depression Scale(HADS),comprising subscales for anxiety(HADS-A)and depression(HADS-D).Coping styles were evaluated using the Jalowiec Coping Scale(JCS-60),comprising dimensions such as confrontive,evasive,optimistic,fatalistic,emotive,palliative,supportive,and self-reliant.Illness uncertainty was measured using the Mishel Uncertainty in Illness Scale(MUIS),encompassing ambiguity,complexity,information deficit,and unpredictability.Correlations among anxiety,depression,coping styles,and illness uncertainty were analyzed.RESULTS During radiotherapy,the mean scores were 7.12±3.39 for HADS-A,6.68±3.49 for HADS-D,1.52±0.23 for JCS-60,and 93.40±7.44 for MUIS.Anxiety(HADS-A≥8)was present in 39.5%of patients,depression(HADS-D≥8)in 41.0%,and both in 14.0%.Anxiety was significantly positively correlated with ambiguity,unpredictability,and total MUIS score(P<0.05).Depression was significantly positively correlated with ambiguity,information deficit,unpredictability,and total MUIS score(P<0.05).Most patients adopted an optimistic coping style,whereas the emotive style was least utilized.Evasive,fatalistic,and emotive coping styles were significantly positively correlated with illness uncertainty,whereas the self-reliant style was significantly negatively correlated with unpredictability(P<0.05).CONCLUSION Anxiety,depression,and coping styles in patients with CC undergoing radiotherapy correlate significantly with their level of illness uncertainty.Medical staff should address patients’psychological status and coping strategies by providing targeted information to reduce negative emotions,foster adaptive coping styles,and decrease illness uncertainty.
基金supported in part by the National Natural Science Foundation of China under Grants 62301117,62001094,and U19B2014in part by the National Key Laboratory of Wireless Communications Foundation under Grant 2023KP01602in part by the Natural Science Foundation of Xinjiang Uygur Autonomous Region under Grant 2022D01B184 and 2022D01A297.
文摘In this paper,we investigate covert communications under multi-antenna detection,and explore the impacts of the warden’s channel state information(CSI)availability and the noise uncertainty on system covert capability.The detection performance at warden is analyzed in two cases under the perfect and statistical CSI at warden,respectively.In particular,for the former one,the warden utilizes the likelihood ratio(LR)detector,while for the latter one,the generalized likelihood ratio(GLR)detector is adopted.We first consider the scenario where the blocklength is finite,and demonstrate that the covert rate under both cases asymptotically goes to zero as the blocklength goes to infinity.Subsequently,we take the noise uncertainty at the warden into account which leads to positive covert rate,and characterize the covert rate for infinite blocklength.Specially,we derive the optimal transmit power for the legitimate transmitter that maximizes the covert rate.Besides,the rate gap under two cases,with different CSI availability at the warden,can be presented in closed form.Finally,numerical results validate the effectiveness of our theoretical analysis and also demonstrate the impacts of the factors studied on the system covertness.
基金supported by the Zhejiang Provincial Natural Science Foundation(LY24F030011,LY23F030005)the National Natural Science Foundation of China(62373131).
文摘In this paper,a pair of dynamic high-gain observer and output feedback controller is proposed for nonlinear systems with multiple unknown time delays.By constructing Lyapunov-Krasovskii functionals,it shows that global state asymptotic regulation can be ensured by introducing a single dynamic gain;furthermore,global asymptotic stabilization can be achieved by choosing a sufficiently large static scaling gain when the upper bounds of all system parameters are known.Especially,the output coefficient is allowed to be non-differentiable with unknown upper bound.This paper proposes a generalized Lyapunov matrix inequality based dynamic-gain scaling method,which significantly simplifies the design computational complexity by comparing with the classic backstepping method.
