Particle filtering （PF） is being applied successfully in nonlinear and/or non-Gaussian system failure prognosis. However, for failure prediction of many complex systems whose dynamic state evolution models involve t...Particle filtering （PF） is being applied successfully in nonlinear and/or non-Gaussian system failure prognosis. However, for failure prediction of many complex systems whose dynamic state evolution models involve time-varying parameters, the tradi- tional PF-based prognosis framework will probably generate serious deviations in results since it implements prediction through iterative calculation using the state models. To address the problem, this paper develops a novel integrated PF-LSSVR frame- work based on PF and least squares support vector regression （LSSVR） for nonlinear system failure prognosis. This approach employs LSSVR for long-term observation series prediction and applies PF-based dual estimation to collaboratively estimate the values of system states and parameters of the corresponding future time instances. Meantime, the propagation of prediction un- certainty is emphatically taken into account. Therefore, PF-LSSVR avoids over-dependency on system state models in prediction phase. With a two-sided failure definition, the probability distribution of system remaining useful life （RUL） is accessed and the corresponding methods of calculating performance evaluation metrics are put forward. The PF-LSSVR framework is applied to a three-vessel water tank system failure prognosis and it has much higher prediction accuracy and confidence level than traditional PF-based framework.展开更多
This paper investigates adaptive containment control for a class of fractional-order multi-agent systems(FOMASs)with time-varying parameters and disturbances.By using the bounded estimation method,the difficulty gener...This paper investigates adaptive containment control for a class of fractional-order multi-agent systems(FOMASs)with time-varying parameters and disturbances.By using the bounded estimation method,the difficulty generated by the timevarying parameters and disturbances is overcome.The command filter is introduced to solve the complexity problem inherent in adaptive backstepping control.Meanwhile,in order to eliminate the effect of filter errors,a novel distributed error compensating scheme is constructed,in which only the local information from the neighbor agents is utilized.Then,a distributed adaptive containment control scheme for FOMASs is developed based on backstepping to guarantee that the outputs of all the followers are steered to the convex hull spanned by the leaders.Based on the extension of Barbalat's lemma to fractional-order integrals,it can be proven that the containment errors and the compensating signals have asymptotic convergence.Finally,three simulation examples are given to show the feasibility and effectiveness of the proposed control method.展开更多
In this paper, a filtering method is presented to estimate time-varying parameters of a missile dual control system with tail fins and reaction jets as control variables. In this method, the long-short-term memory(LST...In this paper, a filtering method is presented to estimate time-varying parameters of a missile dual control system with tail fins and reaction jets as control variables. In this method, the long-short-term memory(LSTM) neural network is nested into the extended Kalman filter(EKF) to modify the Kalman gain such that the filtering performance is improved in the presence of large model uncertainties. To avoid the unstable network output caused by the abrupt changes of system states,an adaptive correction factor is introduced to correct the network output online. In the process of training the network, a multi-gradient descent learning mode is proposed to better fit the internal state of the system, and a rolling training is used to implement an online prediction logic. Based on the Lyapunov second method, we discuss the stability of the system, the result shows that when the training error of neural network is sufficiently small, the system is asymptotically stable. With its application to the estimation of time-varying parameters of a missile dual control system, the LSTM-EKF shows better filtering performance than the EKF and adaptive EKF(AEKF) when there exist large uncertainties in the system model.展开更多
In this paper,an adaptive dynamic event-triggered asymptotic control scheme is designed for strict-feedback systems with time-varying parameters.The congelation of variables technique is employed to address the time-v...In this paper,an adaptive dynamic event-triggered asymptotic control scheme is designed for strict-feedback systems with time-varying parameters.The congelation of variables technique is employed to address the time-varying parameters in the system.During the controller design,two parameter estimation adaptive laws are constructed.The tuning function is introduced in the design process to avoid over-parameterization.To further conserve communication resources,a dynamic eventtriggered approach is proposed,in which a non-negative variable is introduced to update the threshold parameter dynamically.The global uniform asymptotic stability of the closed-loop system is proven through the Lyapunov stability analysis.The feasibility of the scheme is demonstrated by simulation.展开更多
This paper is concerned with a class of nonlinear fractional differential equations with a disturbance parameter in the integral boundary conditions on the infinite interval.By using Guo-Krasnoselskii fixed point theo...This paper is concerned with a class of nonlinear fractional differential equations with a disturbance parameter in the integral boundary conditions on the infinite interval.By using Guo-Krasnoselskii fixed point theorem,fixed point index theory and the analytic technique,we give the bifurcation point of the parameter which divides the range of parameter for the existence of at least two,one and no positive solutions for the problem.And,by using a fixed point theorem of generalized concave operator and cone theory,we establish the maximum parameter interval for the existence of the unique positive solution for the problem and show that such a positive solution continuously depends on the parameter.In the end,some examples are given to illustrate our main results.展开更多
The two-dimensional grating serves as a critical component in plane grating interferometers for achieving high-precision multidimensional displacement measurements.The calibration of grating groove density and orthogo...The two-dimensional grating serves as a critical component in plane grating interferometers for achieving high-precision multidimensional displacement measurements.The calibration of grating groove density and orthogonality error of grating grooves not only improves the positioning accuracy of grating interferometers but also provides essential feedback for optimizing two-dimensional grating fabrication.This study proposes a method for simultaneous calibration of these parameters using orthogonal heterodyne laser interferometry.A two-dimensional grating interferometer is built with the grating to be measured,and a biaxial laser interferometer provides a displacement reference for it.The phase mapping relationship between grating interference and laser interference is established.The interference phase information obtained by any two displacements can simultaneously solve the above three parameters and obtain the grating installation error.The feasibility of the proposed method is verified by using a 1200 gr/mm two-dimensional grating.The standard deviation of the grating groove density in the X and Y directions is 0.012 gr/mm and 0.014 gr/mm,respectively.The standard deviation of the orthogonality error of grating grooves is 0.004°,and the standard deviation of the installation error is 0.002°.Compared with the atomic force microscope method,the consistency of the grating groove density in the X and Y directions is better than 0.03 gr/mm and 0.06 gr/mm,and the orthogonality error of grating grooves is better than 0.008°.The experimental results show that the proposed method can be simply and efficiently applied to the calibration of the grating line parameters of the two-dimensional grating.展开更多
Hepatocellular carcinoma presents with three distinct immune phenotypes,including immune-desert,immune-excluded,and immune-inflamed,indicating various treatment responses and prognostic outcomes.The clinical applicati...Hepatocellular carcinoma presents with three distinct immune phenotypes,including immune-desert,immune-excluded,and immune-inflamed,indicating various treatment responses and prognostic outcomes.The clinical application of multi-omics parameters is still restricted by the expensive and less accessible assays,although they accurately reflect immune status.A comprehensive evaluation framework based on“easy-to-obtain”multi-model clinical parameters is urgently required,incorporating clinical features to establish baseline patient profiles and disease staging;routine blood tests assessing systemic metabolic and functional status;immune cell subsets quantifying subcluster dynamics;imaging features delineating tumor morphology,spatial configuration,and perilesional anatomical relationships;immunohistochemical markers positioning qualitative and quantitative detection of tumor antigens from the cellular and molecular level.This integrated phenomic approach aims to improve prognostic stratification and clinical decision-making in hepatocellular carcinoma management conveniently and practically.展开更多
To address the zero-sample challenge in preparation parameter design for newly developed alloys,a novel machine learning strategy that integrates basic dataset construction with Bayesian optimization,was proposed.The ...To address the zero-sample challenge in preparation parameter design for newly developed alloys,a novel machine learning strategy that integrates basic dataset construction with Bayesian optimization,was proposed.The impact of basic sample dataset construction methods,optimization benchmarks and multi-objective utility functions on Bayesian optimization was investigated.It was found that the combination of orthogonal design,linear benchmark,and shifted multiplicative utility function exhibits the best optimization performance.The strategy was then applied to a new Cu-Ni-Co-Si alloy with ultra-low Co content(0.7 wt.%Co),previously designed by our research team.Rapid optimization of six preparation parameters in the two-stage deformation and aging process of the zero-sample alloy was achieved through only 23 experiments.The measured ultimate tensile strength and electrical conductivity of the new alloy were 878 MPa and 44.0%(IACS),respectively,reaching the comprehensive performance level of the Cu-Ni-Co-Si alloy(C70350 alloy)containing 1.0-2.0 wt.%Co.展开更多
Presented in this study is a novel method for estimating the depth of single underwater source in shallow water,utilizing vector sensors.The approach leverages the depth distribution of the broadband Stokes parameters...Presented in this study is a novel method for estimating the depth of single underwater source in shallow water,utilizing vector sensors.The approach leverages the depth distribution of the broadband Stokes parameters to estimate source depth accurately.Unlike traditional matched field processing(MFP)and matched mode processing(MMP),the proposed approach can estimate source depth directly from the data received by sensors without requiring complete environmental information.Firstly,the broadband Stokes parameters(BSP)are established using the normal mode theory.Then the nonstationary phase approximation is used to simplify the theoretical derivation,which is necessary when dealing with broadband integrals.Additionally,range terms of the BSP are eliminated by normalization.By analyzing the depth distribution of the normalized broadband Stokes parameters(NBSP),it is found that the NBSP exhibit extreme values at the source depth,which can be used for source depth estimation.So the proposed depth estimation method is based on searching the peaks of the NBSP.Simulations show that this method is effective in relatively simple shallow water environments.Finally,the effect of source range,frequency bandwidth,sound speed profile(SSP),water depth,and signal-to-noise ratio(SNR)are studied.The findings indicate that the proposed method can accurately estimate the source depth when the SNR is greater than-5 d B and does not need to consider model mismatch issues.Additionally,variations in environmental parameters have minimal impact on estimation accuracy.Compared to MFP,the proposed method requires a higher SNR,but demonstrates superior robustness against fluctuations in environmental parameters.展开更多
As binary geological media,soil-rock mixtures(SRMs)exhibit a distinct gradational composition,leading to their unique mechanical behaviors.To appraise the stability of SRM slopes,it is essential to determine equivalen...As binary geological media,soil-rock mixtures(SRMs)exhibit a distinct gradational composition,leading to their unique mechanical behaviors.To appraise the stability of SRM slopes,it is essential to determine equivalent parameters of SRMs,which are typically obtained through experimental and numerical methods.In contrasted to other numerical methods,the numerical manifold method(NMM)is more effective in addressing SRM problems.This is because the high-precision regular mathematical meshes in NMM can be used without aligning with the soil-rock interfaces and boundaries of SRMs.In the current research,the equivalent strength parameters of SRMs,i.e.the equivalent cohesion ce and internal friction angleϕ_(e),are determined using NMM.Initially,an NMM triaxial numerical model is established and validated based on triaxial experiments.Subsequently,the soil and rock parameters are derived through parameter inversion.Moreover,the impacts of rock content,size,shape and rock blocks'major-axis orientation on ce andϕ_(e) of SRMs are thoroughly examined using the NMM triaxial numerical model.Additionally,a fitting function is proposed to linkϕ_(e) to the rock content and size of SRMs.When other influencing factors are fixed,the above fitting model leads to the following conclusions:(1)the predictedϕ_(e) of SRMs increase with the increase of rock content;and(2)SRM samples with smaller rocks display a higher predictedϕ_(e).展开更多
After a long period of water flooding development,the oilfield has entered the middle and high water cut stage.The physical properties of reservoirs are changed by water erosion,which directly impacts reservoir develo...After a long period of water flooding development,the oilfield has entered the middle and high water cut stage.The physical properties of reservoirs are changed by water erosion,which directly impacts reservoir development.Conventional numerical reservoir simulation methodologies typically employ static assumptions for model construction,presuming invariant reservoir geological parameters throughout the development process while neglecting the reservoir’s temporal evolution characteristics.Although such simplifications reduce computational complexity,they introduce substantial descriptive inaccuracies.Therefore,this paper proposes a meshless numerical simulation method for reservoirs that considers time-varying characteristics.This method avoids the meshing in traditional numerical simulation methods.From the fluid flow perspective,the reservoir’s computational domain is discretized into a series of connection units.An influence domain with a certain radius centered on the nodes is selected,and one-dimensional connection units are established between the nodes to achieve the characterization of the flow topology structure of the reservoir.In order to reflect the dynamic evolution of the reservoir’s physical properties during the water injection development process,the time-varying characteristics are incorporated into the formula of the seepage characteristic parameters in the meshless calculation.The change relationship of the permeability under different surface fluxes is considered to update the calculated connection conductivity in real time.By combining with the seepage control equation for solution,a time-varying meshless numerical simulation method is formed.The results show that compared with the numerical simulationmethod of the connection elementmethod(CEM)that only considers static parameters,this method has higher simulation accuracy and can better simulate the real migration and distribution of oil and water in the reservoir.Thismethod improves the accuracy of reservoir numerical simulation and the development effect of oilfields,providing a scientific basis for optimizing the water injection strategy,adjusting the production plan,and extending the effective production cycle of the oilfield.展开更多
(Quasi-)closed-form results for the statistical properties of unmanned aerial vehicle(UAV)airto-ground channels are derived for the first time using a novel spatial-vector-based method from a threedimensional(3-D)arbi...(Quasi-)closed-form results for the statistical properties of unmanned aerial vehicle(UAV)airto-ground channels are derived for the first time using a novel spatial-vector-based method from a threedimensional(3-D)arbitrary-elevation one-cylinder model.The derived results include a closed-form expression for the space-time correlation function and some quasi-closed-form ones for the space-Doppler power spectrum density,the level crossing rate,and the average fading duration,which are shown to be the generalizations of those previously obtained from the two-dimensional(2-D)one-ring model and the 3-D low-elevation one-cylinder model for terrestrial mobile-to-mobile channels.The close agreements between the theoretical results and the simulations as well as the measurements validate the utility of the derived channel statistics.Based on the derived expressions,the impacts of some parameters on the channel characteristics are investigated in an effective,efficient,and explicable way,which leads to a general guideline on the manual parameter estimation from the measurement description.展开更多
The S38C railway axle undergoes induction hardening,resulting in a gradient-distributed microstructure and mechanical properties.The accurate identification of gradient-distributed plastic parameters for the S38C axle...The S38C railway axle undergoes induction hardening,resulting in a gradient-distributed microstructure and mechanical properties.The accurate identification of gradient-distributed plastic parameters for the S38C axle remains a challenging task.To tackle this challenge,the present study proposes a novel approach for identifying the gradient-distributed plastic parameters for the S38C axle by integrating nano-indentation techniques with the machine learning method.Firstly,nano-indentation tests are conducted along the radial direction of the S38C axle to obtain the gradient-distributed load-displacement curves,nano-hardness,and elastic modulus.Subsequently,the dimensionless analysis is performed to obtain the representative stress,strain,and yield stress from load-displacement curves.These parameters are then incorporated into the machine learning method as physical information to identify the gradient-distributed plastic parameters of the S38C axle.The results indicate that the proposed method based on the physics-informed neural network and multi-fidelity neural network successfully identifies the gradient-distributed plastic parameters of the S38C axles and demonstrates superior prediction accuracy and generalization compared with the purely data-driven machine learning method.展开更多
Nonconvex optimisation plays a crucial role in science and industry.However,existing methods often encounter local optima or provide inferior solutions when solving nonconvex optimisation problems,lacking robustness i...Nonconvex optimisation plays a crucial role in science and industry.However,existing methods often encounter local optima or provide inferior solutions when solving nonconvex optimisation problems,lacking robustness in noise scenarios.To address these limitations,we aim to develop a robust,efficient and globally convergent solver for nonconvex optimisation.This is achieved by combining the efficient local exploitation ability of a parameter-learnt neural dynamics(PLND)model with the global search capability of the coevolutionary mechanism.We combine their characteristics to design a coevolutionary neural dynamics with learnable parameters(CNDLP)model.The gradient information is used to find the optimal solution more effectively,and neural dynamics models have robustness,which ensures that the influence of noise can be effectively suppressed in the calculation process.Theoretical analyses show the global convergence and robustness of the designed CNDLP model.Numerical experiments on 9 benchmark functions and a practical engineering design example are conducted with five existing meta-heuristic algorithms.Benchmarks cover diverse problems,from classical landscapes like benchmark Shubert to high-dimensional cases such as 30-dimensional Rosenbrock.Results confirm CNDLP's excellent performance in both solution quality and convergence speed under noise.展开更多
The shale gas development in China faces challenges such as complex reservoir conditions and high development costs.Based on the pore pressure and geostress coupling theory,this paper studies the geostress evolution l...The shale gas development in China faces challenges such as complex reservoir conditions and high development costs.Based on the pore pressure and geostress coupling theory,this paper studies the geostress evolution laws and fracture network characteristics of shale gas infill wells.A mechanism model of CN platform logging data and geomechanical parameters is established to simulate the influence of parent well’s production on the geostress in the infill well area.It is suggested that with the increase of production time,normal fault stress state and horizontal stress deflection will occur.The smaller the parent well spacing and the longer the production time,the earlier the normal fault stress state appears and the larger the range.Based on the model,the fracture network morphology and construction parameters of infill wells are optimized.parentparentparentparent The results indicate that:1:A well spacing of 500 m achieves a Pareto optimum between“full reserve coverage”and“stress barrier”;2:A parent well recovery degree of 30%corresponds to the critical point of stress reversal,where the lateral deflection rate of the infill fracture is less than 8%and the SRV loss is minimized;3:6-cluster intensive completion with twice the liquid intensity increases the fracture complexity index by 1.7 times,enhances well group EUR by 15.4%,and reduces single-well cost by 22%.This research fills the theoretical gap in the collaborative optimization of“multi-parameter,multi-objective and multi-constraint”and provide parameter optimization basis for shale gas infill well development in China and help to improve the development efficiency and economic benefits.展开更多
Accurate estimation of photovoltaic(PV)parameters is essential for optimizing solar module perfor-mance and enhancing resource efficiency in renewable energy systems.This study presents a process innovation by introdu...Accurate estimation of photovoltaic(PV)parameters is essential for optimizing solar module perfor-mance and enhancing resource efficiency in renewable energy systems.This study presents a process innovation by introducing,for the first time,the Triangulation Topology Aggregation Optimizer(TTAO)integrated with parallel computing to address PV parameter estimation challenges.The effectiveness and robustness of TTAO are rigorously evaluated using two standard benchmark datasets(KC200GT and R.T.C.France solar cells)and a real-world dataset(Poly70W solar module)under single-,double-,and triple-diode configurations.Results show that TTAO consistently achieves superior accuracy by producing the lowest RMSE values and faster convergence compared to state-of-the-art metaheuristic algorithms.In addition,the integration of parallel computing significantly enhances computational efficiency,reducing execution time by up to 85%without compromising accuracy.Validation using real-world data further demonstrates TTAO’s adaptability and practical relevance in renewable energy systems,effectively bridging the gap between theoretical modeling and real-world implementation for PV system monitoring and optimization,contributing to climate mitigation through improved solar energy performance.展开更多
Load frequency control(LFC)is a critical function to balance the power consumption and generation.Thegrid frequency is a crucial indicator for maintaining balance.However,the widely used information and communication ...Load frequency control(LFC)is a critical function to balance the power consumption and generation.Thegrid frequency is a crucial indicator for maintaining balance.However,the widely used information and communication infrastructure for LFC increases the risk of being attacked by malicious actors.The dynamic load altering attack(DLAA)is a typical attack that can destabilize the power system,causing the grid frequency to deviate fromits nominal value.Therefore,in this paper,we mathematically analyze the impact of DLAA on the stability of the grid frequency and propose the network parameter regulation(NPR)to mitigate the impact.To begin with,the dynamic LFC model is constructed by highlighting the importance of the network parameter.Then,we model the DLAA and analyze its impact on LFC using the theory of second-order dynamic systems.Finally,we model the NPR and prove its effect in mitigating the DLAA.Besides,we construct a least-effort NPR considering its infrastructure cost and aim to reduce the operation cost.Finally,we carry out extensive simulations to demonstrate the impact of the DLAA and evaluate the mitigation performance of NPR.The proposed cost-benefit NPR approach can not only mitigate the impact of DLAA with 100%and also save 41.18$/MWh in terms of the operation cost.展开更多
Deep coal mining rock support structures using rock bolts face complex geological conditions such as high ground temperatures and groundwater.Rock mass deformation and failure caused by bolt failure frequently occur,m...Deep coal mining rock support structures using rock bolts face complex geological conditions such as high ground temperatures and groundwater.Rock mass deformation and failure caused by bolt failure frequently occur,making it crucial to enhance the anchoring performance of rock bolts.First,the stress state of the anchor rod under axial loading across five stages of any anchored segment is analyzed.The shear stress patterns at the anchoring interface during different stages are elucidated.A refined mechanical model of the anchoring interface incorporating surface rib parameters is established.A failure criterion for the anchoring interface under the influence of ground temperature or groundwater is derived and validated.Second,the influence of anchor rib parameters on anchoring force is abalyzed,and in-situ shear tests are conducted.Results indicate that increasing the rib angle and optimizing rib spacing can enhance anchoring force.To minimize the shear component of axial force at the anchor interface,the rib angle of the anchor bolt should not be less than 70°.When the anchor grout possesses high inherent strength,the spacing between ribs on the anchor bolt surface may be increased(to 24 mm or greater).Finally,methods for enhancing the anchoring performance of bolts in deep complex strata are proposed,providing technical references for the safe and efficient support of tunnel rock masses in similar geological conditions.展开更多
This study provides a new experimental framework to measure the static and dynamic electrical parameters for a solar panel of multiple cells.The study evaluates its static parameters,including its resultant diodes’sa...This study provides a new experimental framework to measure the static and dynamic electrical parameters for a solar panel of multiple cells.The study evaluates its static parameters,including its resultant diodes’saturation currents,diodes’ideality factors,series,and shunt resistances.Such parameters are essential to characterise the steady-state performance of a solar panel.Additionally,the dynamic parameters as the equivalent junction and diffusion capacitances are also experimentally measured.These parameters impact the performance of the panel at variable solar irradiance,temperature,and load conditions.A solar panel of 36 series-connected cells has been utilised in this research to undertake this experimental evaluation.This work addresses a gap in the recent literature regarding a full evaluation of the internal electrical parameters in a whole solar panel of multiple cells.Firstly,a dark experimental environment has been developed so that no influence from external light sources can affect the measurements being taken.The panel is then stimulated with different types of electrical stresses in various circuit configurations to measure the required static and dynamic parameters.For the solar panel under study,the series and shunt resistances per cell have been evaluated to be 18.91 mΩand 5.6 kΩ,respectively,while the junction and diffusion capacitances have shown direct and inverse relationships,respectively,with the applied voltage as expected.The outcomes of these experimental setups highlighted the importance of the developed comprehensive framework in this research to be employed to assess the quality of the solar panel and its real-time performance at variable operational conditions.展开更多
This study investigated the impacts of key parameters in CAM6's deep convection and cloud physics schemes on the simulation of summer-mean precipitation over East Asia through conducting perturbed parameter ensemb...This study investigated the impacts of key parameters in CAM6's deep convection and cloud physics schemes on the simulation of summer-mean precipitation over East Asia through conducting perturbed parameter ensemble(PPE)experiments.Utilizing the experimental platform of CAM6,a suite of 128 PPE simulations spanning 19792014 were generated through simultaneously perturbing 12 selected parameters.Using EOF analysis,this study firstly extracted the first two leading modes of the precipitation simulation biases.The authors further pinpointed the most critical parameters that have the most influential effects on the precipitation simulation biases,through conducting generalized linear model analysis.The first leading mode of precipitation simulation biases is primarily influenced by parameters from the cloud physics scheme,including the linear effects of dcs and eii,and the nonlinear effect of rhminl*dcs.These parameters influence the simulated total precipitation(PrecT)mainly by altering the large-scale precipitation(PrecL).The second leading mode is predominantly governed by the convection scheme parameter dmpdz,reflecting a competition between the changes in convective precipitation(PrecC)and PrecL in response to variations in dmpdz.An increase in dmpdz induces decreased PrecC and increased PrecL in East Asia,and both of the changes collectively shape the ultimate PrecT response to the adjusted dmpdz.Lastly,it is noteworthy that the nonlinear effect due to the interaction among parameters warrants attention when concurrently adjusting multiple parameters,and the precipitation biases from the PPE simulations resemble those identified through EOF analysis on the AMIP simulations,implying our findings may provide potential reference for other AGCMs.展开更多
基金Aeronautical Science Foundation of China (20100751010, 2010ZD11007)
文摘Particle filtering （PF） is being applied successfully in nonlinear and/or non-Gaussian system failure prognosis. However, for failure prediction of many complex systems whose dynamic state evolution models involve time-varying parameters, the tradi- tional PF-based prognosis framework will probably generate serious deviations in results since it implements prediction through iterative calculation using the state models. To address the problem, this paper develops a novel integrated PF-LSSVR frame- work based on PF and least squares support vector regression （LSSVR） for nonlinear system failure prognosis. This approach employs LSSVR for long-term observation series prediction and applies PF-based dual estimation to collaboratively estimate the values of system states and parameters of the corresponding future time instances. Meantime, the propagation of prediction un- certainty is emphatically taken into account. Therefore, PF-LSSVR avoids over-dependency on system state models in prediction phase. With a two-sided failure definition, the probability distribution of system remaining useful life （RUL） is accessed and the corresponding methods of calculating performance evaluation metrics are put forward. The PF-LSSVR framework is applied to a three-vessel water tank system failure prognosis and it has much higher prediction accuracy and confidence level than traditional PF-based framework.
基金National Key R&D Program of China(2018YFA0702200)National Natural Science Foundation of China(61627809,62173080)Liaoning Revitalization Talents Program(XLYC1801005)。
文摘This paper investigates adaptive containment control for a class of fractional-order multi-agent systems(FOMASs)with time-varying parameters and disturbances.By using the bounded estimation method,the difficulty generated by the timevarying parameters and disturbances is overcome.The command filter is introduced to solve the complexity problem inherent in adaptive backstepping control.Meanwhile,in order to eliminate the effect of filter errors,a novel distributed error compensating scheme is constructed,in which only the local information from the neighbor agents is utilized.Then,a distributed adaptive containment control scheme for FOMASs is developed based on backstepping to guarantee that the outputs of all the followers are steered to the convex hull spanned by the leaders.Based on the extension of Barbalat's lemma to fractional-order integrals,it can be proven that the containment errors and the compensating signals have asymptotic convergence.Finally,three simulation examples are given to show the feasibility and effectiveness of the proposed control method.
文摘In this paper, a filtering method is presented to estimate time-varying parameters of a missile dual control system with tail fins and reaction jets as control variables. In this method, the long-short-term memory(LSTM) neural network is nested into the extended Kalman filter(EKF) to modify the Kalman gain such that the filtering performance is improved in the presence of large model uncertainties. To avoid the unstable network output caused by the abrupt changes of system states,an adaptive correction factor is introduced to correct the network output online. In the process of training the network, a multi-gradient descent learning mode is proposed to better fit the internal state of the system, and a rolling training is used to implement an online prediction logic. Based on the Lyapunov second method, we discuss the stability of the system, the result shows that when the training error of neural network is sufficiently small, the system is asymptotically stable. With its application to the estimation of time-varying parameters of a missile dual control system, the LSTM-EKF shows better filtering performance than the EKF and adaptive EKF(AEKF) when there exist large uncertainties in the system model.
基金supported by the Shandong Provincial Natural Science Foundation(Nos.ZR2023QF039 and ZR2024MF068)the Qingdao Natural Science Foundation(No.23-2-1-123-zyyd-jch)+2 种基金the Taishan Scholars Project of Shandong Province of China(No.tstp20230624)the Science and Technology Plan for Youth Innovation of Universities in Shandong Province(No.2022KJ301)the National Natural Science Foundation of China(No.62373205).
文摘In this paper,an adaptive dynamic event-triggered asymptotic control scheme is designed for strict-feedback systems with time-varying parameters.The congelation of variables technique is employed to address the time-varying parameters in the system.During the controller design,two parameter estimation adaptive laws are constructed.The tuning function is introduced in the design process to avoid over-parameterization.To further conserve communication resources,a dynamic eventtriggered approach is proposed,in which a non-negative variable is introduced to update the threshold parameter dynamically.The global uniform asymptotic stability of the closed-loop system is proven through the Lyapunov stability analysis.The feasibility of the scheme is demonstrated by simulation.
基金Supported by the National Natural Science Foundation of China(11361047)Fundamental Research Program of Shanxi Province(20210302124529)。
文摘This paper is concerned with a class of nonlinear fractional differential equations with a disturbance parameter in the integral boundary conditions on the infinite interval.By using Guo-Krasnoselskii fixed point theorem,fixed point index theory and the analytic technique,we give the bifurcation point of the parameter which divides the range of parameter for the existence of at least two,one and no positive solutions for the problem.And,by using a fixed point theorem of generalized concave operator and cone theory,we establish the maximum parameter interval for the existence of the unique positive solution for the problem and show that such a positive solution continuously depends on the parameter.In the end,some examples are given to illustrate our main results.
文摘The two-dimensional grating serves as a critical component in plane grating interferometers for achieving high-precision multidimensional displacement measurements.The calibration of grating groove density and orthogonality error of grating grooves not only improves the positioning accuracy of grating interferometers but also provides essential feedback for optimizing two-dimensional grating fabrication.This study proposes a method for simultaneous calibration of these parameters using orthogonal heterodyne laser interferometry.A two-dimensional grating interferometer is built with the grating to be measured,and a biaxial laser interferometer provides a displacement reference for it.The phase mapping relationship between grating interference and laser interference is established.The interference phase information obtained by any two displacements can simultaneously solve the above three parameters and obtain the grating installation error.The feasibility of the proposed method is verified by using a 1200 gr/mm two-dimensional grating.The standard deviation of the grating groove density in the X and Y directions is 0.012 gr/mm and 0.014 gr/mm,respectively.The standard deviation of the orthogonality error of grating grooves is 0.004°,and the standard deviation of the installation error is 0.002°.Compared with the atomic force microscope method,the consistency of the grating groove density in the X and Y directions is better than 0.03 gr/mm and 0.06 gr/mm,and the orthogonality error of grating grooves is better than 0.008°.The experimental results show that the proposed method can be simply and efficiently applied to the calibration of the grating line parameters of the two-dimensional grating.
文摘Hepatocellular carcinoma presents with three distinct immune phenotypes,including immune-desert,immune-excluded,and immune-inflamed,indicating various treatment responses and prognostic outcomes.The clinical application of multi-omics parameters is still restricted by the expensive and less accessible assays,although they accurately reflect immune status.A comprehensive evaluation framework based on“easy-to-obtain”multi-model clinical parameters is urgently required,incorporating clinical features to establish baseline patient profiles and disease staging;routine blood tests assessing systemic metabolic and functional status;immune cell subsets quantifying subcluster dynamics;imaging features delineating tumor morphology,spatial configuration,and perilesional anatomical relationships;immunohistochemical markers positioning qualitative and quantitative detection of tumor antigens from the cellular and molecular level.This integrated phenomic approach aims to improve prognostic stratification and clinical decision-making in hepatocellular carcinoma management conveniently and practically.
基金supported by the National Natural Science Foundation of China(Nos.52404387,52090041,52374379,52425409)Xiaomi Young Scholars Program China,the National Postdoctoral Program for Innovative Talents,China(No.BX20230042)China Postdoctoral Science Foundation(No.2024M750174)。
文摘To address the zero-sample challenge in preparation parameter design for newly developed alloys,a novel machine learning strategy that integrates basic dataset construction with Bayesian optimization,was proposed.The impact of basic sample dataset construction methods,optimization benchmarks and multi-objective utility functions on Bayesian optimization was investigated.It was found that the combination of orthogonal design,linear benchmark,and shifted multiplicative utility function exhibits the best optimization performance.The strategy was then applied to a new Cu-Ni-Co-Si alloy with ultra-low Co content(0.7 wt.%Co),previously designed by our research team.Rapid optimization of six preparation parameters in the two-stage deformation and aging process of the zero-sample alloy was achieved through only 23 experiments.The measured ultimate tensile strength and electrical conductivity of the new alloy were 878 MPa and 44.0%(IACS),respectively,reaching the comprehensive performance level of the Cu-Ni-Co-Si alloy(C70350 alloy)containing 1.0-2.0 wt.%Co.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.12274348 and 12004335)the National Key Research and Development Program of China(Grant No.2024YFC2813800)。
文摘Presented in this study is a novel method for estimating the depth of single underwater source in shallow water,utilizing vector sensors.The approach leverages the depth distribution of the broadband Stokes parameters to estimate source depth accurately.Unlike traditional matched field processing(MFP)and matched mode processing(MMP),the proposed approach can estimate source depth directly from the data received by sensors without requiring complete environmental information.Firstly,the broadband Stokes parameters(BSP)are established using the normal mode theory.Then the nonstationary phase approximation is used to simplify the theoretical derivation,which is necessary when dealing with broadband integrals.Additionally,range terms of the BSP are eliminated by normalization.By analyzing the depth distribution of the normalized broadband Stokes parameters(NBSP),it is found that the NBSP exhibit extreme values at the source depth,which can be used for source depth estimation.So the proposed depth estimation method is based on searching the peaks of the NBSP.Simulations show that this method is effective in relatively simple shallow water environments.Finally,the effect of source range,frequency bandwidth,sound speed profile(SSP),water depth,and signal-to-noise ratio(SNR)are studied.The findings indicate that the proposed method can accurately estimate the source depth when the SNR is greater than-5 d B and does not need to consider model mismatch issues.Additionally,variations in environmental parameters have minimal impact on estimation accuracy.Compared to MFP,the proposed method requires a higher SNR,but demonstrates superior robustness against fluctuations in environmental parameters.
基金supported by the National Natural Science Foundation of China(Grant Nos.12272393 and 52130905).
文摘As binary geological media,soil-rock mixtures(SRMs)exhibit a distinct gradational composition,leading to their unique mechanical behaviors.To appraise the stability of SRM slopes,it is essential to determine equivalent parameters of SRMs,which are typically obtained through experimental and numerical methods.In contrasted to other numerical methods,the numerical manifold method(NMM)is more effective in addressing SRM problems.This is because the high-precision regular mathematical meshes in NMM can be used without aligning with the soil-rock interfaces and boundaries of SRMs.In the current research,the equivalent strength parameters of SRMs,i.e.the equivalent cohesion ce and internal friction angleϕ_(e),are determined using NMM.Initially,an NMM triaxial numerical model is established and validated based on triaxial experiments.Subsequently,the soil and rock parameters are derived through parameter inversion.Moreover,the impacts of rock content,size,shape and rock blocks'major-axis orientation on ce andϕ_(e) of SRMs are thoroughly examined using the NMM triaxial numerical model.Additionally,a fitting function is proposed to linkϕ_(e) to the rock content and size of SRMs.When other influencing factors are fixed,the above fitting model leads to the following conclusions:(1)the predictedϕ_(e) of SRMs increase with the increase of rock content;and(2)SRM samples with smaller rocks display a higher predictedϕ_(e).
基金funded by the 14th Five-Year Plan Major Science and Technology Project of CNOOC project number KJGG2021-0506.
文摘After a long period of water flooding development,the oilfield has entered the middle and high water cut stage.The physical properties of reservoirs are changed by water erosion,which directly impacts reservoir development.Conventional numerical reservoir simulation methodologies typically employ static assumptions for model construction,presuming invariant reservoir geological parameters throughout the development process while neglecting the reservoir’s temporal evolution characteristics.Although such simplifications reduce computational complexity,they introduce substantial descriptive inaccuracies.Therefore,this paper proposes a meshless numerical simulation method for reservoirs that considers time-varying characteristics.This method avoids the meshing in traditional numerical simulation methods.From the fluid flow perspective,the reservoir’s computational domain is discretized into a series of connection units.An influence domain with a certain radius centered on the nodes is selected,and one-dimensional connection units are established between the nodes to achieve the characterization of the flow topology structure of the reservoir.In order to reflect the dynamic evolution of the reservoir’s physical properties during the water injection development process,the time-varying characteristics are incorporated into the formula of the seepage characteristic parameters in the meshless calculation.The change relationship of the permeability under different surface fluxes is considered to update the calculated connection conductivity in real time.By combining with the seepage control equation for solution,a time-varying meshless numerical simulation method is formed.The results show that compared with the numerical simulationmethod of the connection elementmethod(CEM)that only considers static parameters,this method has higher simulation accuracy and can better simulate the real migration and distribution of oil and water in the reservoir.Thismethod improves the accuracy of reservoir numerical simulation and the development effect of oilfields,providing a scientific basis for optimizing the water injection strategy,adjusting the production plan,and extending the effective production cycle of the oilfield.
基金supported in part by the National Key Research and Development Program of China(2021YFB2900501)in part by the Shaanxi Science and Technology Innovation Team(2023-CX-TD-03)+3 种基金in part by the Science and Technology Program of Shaanxi Province(2021GXLH-Z-038)in part by the Natural Science Foundation of Hunan Province(2023JJ40607 and 2023JJ50045)in part by the Scientific Research Foundation of Hunan Provincial Education Department(23B0713 and 24B0603)in part by the National Natural Science Foundation of China(62401371,62101275,and 62372070).
文摘(Quasi-)closed-form results for the statistical properties of unmanned aerial vehicle(UAV)airto-ground channels are derived for the first time using a novel spatial-vector-based method from a threedimensional(3-D)arbitrary-elevation one-cylinder model.The derived results include a closed-form expression for the space-time correlation function and some quasi-closed-form ones for the space-Doppler power spectrum density,the level crossing rate,and the average fading duration,which are shown to be the generalizations of those previously obtained from the two-dimensional(2-D)one-ring model and the 3-D low-elevation one-cylinder model for terrestrial mobile-to-mobile channels.The close agreements between the theoretical results and the simulations as well as the measurements validate the utility of the derived channel statistics.Based on the derived expressions,the impacts of some parameters on the channel characteristics are investigated in an effective,efficient,and explicable way,which leads to a general guideline on the manual parameter estimation from the measurement description.
基金supported by the National Key Research and Development Plan(Grant No.2022YFB3401901)the National Natural Science Foundation of China(Grant Nos.12192210,12192214,12072295,and 12222209)+1 种基金Independent Project of State Key Laboratory of Rail Transit Vehicle System(Grant No.2023TPL-T03)Fundamental Research Funds for the Central Universities(Grant No.2682023CG004).
文摘The S38C railway axle undergoes induction hardening,resulting in a gradient-distributed microstructure and mechanical properties.The accurate identification of gradient-distributed plastic parameters for the S38C axle remains a challenging task.To tackle this challenge,the present study proposes a novel approach for identifying the gradient-distributed plastic parameters for the S38C axle by integrating nano-indentation techniques with the machine learning method.Firstly,nano-indentation tests are conducted along the radial direction of the S38C axle to obtain the gradient-distributed load-displacement curves,nano-hardness,and elastic modulus.Subsequently,the dimensionless analysis is performed to obtain the representative stress,strain,and yield stress from load-displacement curves.These parameters are then incorporated into the machine learning method as physical information to identify the gradient-distributed plastic parameters of the S38C axle.The results indicate that the proposed method based on the physics-informed neural network and multi-fidelity neural network successfully identifies the gradient-distributed plastic parameters of the S38C axles and demonstrates superior prediction accuracy and generalization compared with the purely data-driven machine learning method.
基金supported by the Leading Talent of the Qinghai Province Kunlun Talents Programme・High-Level Innovative and Entrepreneurial Talents(QHKLYC-GDCXCY-2024-359).
文摘Nonconvex optimisation plays a crucial role in science and industry.However,existing methods often encounter local optima or provide inferior solutions when solving nonconvex optimisation problems,lacking robustness in noise scenarios.To address these limitations,we aim to develop a robust,efficient and globally convergent solver for nonconvex optimisation.This is achieved by combining the efficient local exploitation ability of a parameter-learnt neural dynamics(PLND)model with the global search capability of the coevolutionary mechanism.We combine their characteristics to design a coevolutionary neural dynamics with learnable parameters(CNDLP)model.The gradient information is used to find the optimal solution more effectively,and neural dynamics models have robustness,which ensures that the influence of noise can be effectively suppressed in the calculation process.Theoretical analyses show the global convergence and robustness of the designed CNDLP model.Numerical experiments on 9 benchmark functions and a practical engineering design example are conducted with five existing meta-heuristic algorithms.Benchmarks cover diverse problems,from classical landscapes like benchmark Shubert to high-dimensional cases such as 30-dimensional Rosenbrock.Results confirm CNDLP's excellent performance in both solution quality and convergence speed under noise.
文摘The shale gas development in China faces challenges such as complex reservoir conditions and high development costs.Based on the pore pressure and geostress coupling theory,this paper studies the geostress evolution laws and fracture network characteristics of shale gas infill wells.A mechanism model of CN platform logging data and geomechanical parameters is established to simulate the influence of parent well’s production on the geostress in the infill well area.It is suggested that with the increase of production time,normal fault stress state and horizontal stress deflection will occur.The smaller the parent well spacing and the longer the production time,the earlier the normal fault stress state appears and the larger the range.Based on the model,the fracture network morphology and construction parameters of infill wells are optimized.parentparentparentparent The results indicate that:1:A well spacing of 500 m achieves a Pareto optimum between“full reserve coverage”and“stress barrier”;2:A parent well recovery degree of 30%corresponds to the critical point of stress reversal,where the lateral deflection rate of the infill fracture is less than 8%and the SRV loss is minimized;3:6-cluster intensive completion with twice the liquid intensity increases the fracture complexity index by 1.7 times,enhances well group EUR by 15.4%,and reduces single-well cost by 22%.This research fills the theoretical gap in the collaborative optimization of“multi-parameter,multi-objective and multi-constraint”and provide parameter optimization basis for shale gas infill well development in China and help to improve the development efficiency and economic benefits.
基金funded by the Malaysian Ministry of Higher Education through the Fundamental Research Grant Scheme(FRGS/1/2024/ICT02/UCSI/02/1).
文摘Accurate estimation of photovoltaic(PV)parameters is essential for optimizing solar module perfor-mance and enhancing resource efficiency in renewable energy systems.This study presents a process innovation by introducing,for the first time,the Triangulation Topology Aggregation Optimizer(TTAO)integrated with parallel computing to address PV parameter estimation challenges.The effectiveness and robustness of TTAO are rigorously evaluated using two standard benchmark datasets(KC200GT and R.T.C.France solar cells)and a real-world dataset(Poly70W solar module)under single-,double-,and triple-diode configurations.Results show that TTAO consistently achieves superior accuracy by producing the lowest RMSE values and faster convergence compared to state-of-the-art metaheuristic algorithms.In addition,the integration of parallel computing significantly enhances computational efficiency,reducing execution time by up to 85%without compromising accuracy.Validation using real-world data further demonstrates TTAO’s adaptability and practical relevance in renewable energy systems,effectively bridging the gap between theoretical modeling and real-world implementation for PV system monitoring and optimization,contributing to climate mitigation through improved solar energy performance.
基金supported by the project Major Scientific and Technological Special Project of Guizhou Province([2024]014).
文摘Load frequency control(LFC)is a critical function to balance the power consumption and generation.Thegrid frequency is a crucial indicator for maintaining balance.However,the widely used information and communication infrastructure for LFC increases the risk of being attacked by malicious actors.The dynamic load altering attack(DLAA)is a typical attack that can destabilize the power system,causing the grid frequency to deviate fromits nominal value.Therefore,in this paper,we mathematically analyze the impact of DLAA on the stability of the grid frequency and propose the network parameter regulation(NPR)to mitigate the impact.To begin with,the dynamic LFC model is constructed by highlighting the importance of the network parameter.Then,we model the DLAA and analyze its impact on LFC using the theory of second-order dynamic systems.Finally,we model the NPR and prove its effect in mitigating the DLAA.Besides,we construct a least-effort NPR considering its infrastructure cost and aim to reduce the operation cost.Finally,we carry out extensive simulations to demonstrate the impact of the DLAA and evaluate the mitigation performance of NPR.The proposed cost-benefit NPR approach can not only mitigate the impact of DLAA with 100%and also save 41.18$/MWh in terms of the operation cost.
基金The Natural Science Research Project of Anhui Educational Committee(No.2022AH050814)Open Fund of State Key Laboratory of Nuclear Resources and Environment(East China Universityof Technology)(No.2022NRE07)+1 种基金the National Natural Science Foundation of China(No.51964002,52174104)Open Fund of Engineering Research Center of Underground Mine Construction of Ministry of Education(No.JYBGCZX2022105).
文摘Deep coal mining rock support structures using rock bolts face complex geological conditions such as high ground temperatures and groundwater.Rock mass deformation and failure caused by bolt failure frequently occur,making it crucial to enhance the anchoring performance of rock bolts.First,the stress state of the anchor rod under axial loading across five stages of any anchored segment is analyzed.The shear stress patterns at the anchoring interface during different stages are elucidated.A refined mechanical model of the anchoring interface incorporating surface rib parameters is established.A failure criterion for the anchoring interface under the influence of ground temperature or groundwater is derived and validated.Second,the influence of anchor rib parameters on anchoring force is abalyzed,and in-situ shear tests are conducted.Results indicate that increasing the rib angle and optimizing rib spacing can enhance anchoring force.To minimize the shear component of axial force at the anchor interface,the rib angle of the anchor bolt should not be less than 70°.When the anchor grout possesses high inherent strength,the spacing between ribs on the anchor bolt surface may be increased(to 24 mm or greater).Finally,methods for enhancing the anchoring performance of bolts in deep complex strata are proposed,providing technical references for the safe and efficient support of tunnel rock masses in similar geological conditions.
文摘This study provides a new experimental framework to measure the static and dynamic electrical parameters for a solar panel of multiple cells.The study evaluates its static parameters,including its resultant diodes’saturation currents,diodes’ideality factors,series,and shunt resistances.Such parameters are essential to characterise the steady-state performance of a solar panel.Additionally,the dynamic parameters as the equivalent junction and diffusion capacitances are also experimentally measured.These parameters impact the performance of the panel at variable solar irradiance,temperature,and load conditions.A solar panel of 36 series-connected cells has been utilised in this research to undertake this experimental evaluation.This work addresses a gap in the recent literature regarding a full evaluation of the internal electrical parameters in a whole solar panel of multiple cells.Firstly,a dark experimental environment has been developed so that no influence from external light sources can affect the measurements being taken.The panel is then stimulated with different types of electrical stresses in various circuit configurations to measure the required static and dynamic parameters.For the solar panel under study,the series and shunt resistances per cell have been evaluated to be 18.91 mΩand 5.6 kΩ,respectively,while the junction and diffusion capacitances have shown direct and inverse relationships,respectively,with the applied voltage as expected.The outcomes of these experimental setups highlighted the importance of the developed comprehensive framework in this research to be employed to assess the quality of the solar panel and its real-time performance at variable operational conditions.
基金jointly supported by the National Key Research and Development Program of China [grant number 2022YFF0802004]the Excellent Youth Natural Science Foundation of Jiangsu Province [grant number BK20230061]the Joint Open Project of KLME&CIC-FEMD[grant number KLME202501]。
文摘This study investigated the impacts of key parameters in CAM6's deep convection and cloud physics schemes on the simulation of summer-mean precipitation over East Asia through conducting perturbed parameter ensemble(PPE)experiments.Utilizing the experimental platform of CAM6,a suite of 128 PPE simulations spanning 19792014 were generated through simultaneously perturbing 12 selected parameters.Using EOF analysis,this study firstly extracted the first two leading modes of the precipitation simulation biases.The authors further pinpointed the most critical parameters that have the most influential effects on the precipitation simulation biases,through conducting generalized linear model analysis.The first leading mode of precipitation simulation biases is primarily influenced by parameters from the cloud physics scheme,including the linear effects of dcs and eii,and the nonlinear effect of rhminl*dcs.These parameters influence the simulated total precipitation(PrecT)mainly by altering the large-scale precipitation(PrecL).The second leading mode is predominantly governed by the convection scheme parameter dmpdz,reflecting a competition between the changes in convective precipitation(PrecC)and PrecL in response to variations in dmpdz.An increase in dmpdz induces decreased PrecC and increased PrecL in East Asia,and both of the changes collectively shape the ultimate PrecT response to the adjusted dmpdz.Lastly,it is noteworthy that the nonlinear effect due to the interaction among parameters warrants attention when concurrently adjusting multiple parameters,and the precipitation biases from the PPE simulations resemble those identified through EOF analysis on the AMIP simulations,implying our findings may provide potential reference for other AGCMs.
