This paper solves the problem of model-free dual-arm space robot maneuvering after non-cooperative target capture under high control quality requirements.The explicit system model is unavailable,and the maneuvering mi...This paper solves the problem of model-free dual-arm space robot maneuvering after non-cooperative target capture under high control quality requirements.The explicit system model is unavailable,and the maneuvering mission is disturbed by the measurement noise and the target adversarial behavior.To address these problems,a model-free Combined Adaptive-length Datadriven Predictive Controller(CADPC)is proposed.It consists of a separated subsystem identification method and a combined predictive control strategy.The subsystem identification method is composed of an adaptive data length,thereby reducing sensitivity to undetermined measurement noises and disturbances.Based on the subsystem identification,the combined predictive controller is established,reducing calculating resource.The stability of the CADPC is rigorously proven using the Input-to-State Stable(ISS)theorem and the small-gain theorem.Simulations demonstrate that CADPC effectively handles the model-free space robot post operation in the presence of significant disturbances,state measurement noise,and control input errors.It achieves improved steady-state accuracy,reduced steady-state control consumption,and minimized control input chattering.展开更多
Ultrafine-grained(UFG)pure titanium was produced by equal channel angular pressing for 4 passes,followed by rotatory swaging at room temperature.The strain-controlled low-cycle fatigue tests of UFG and coarse-grained(...Ultrafine-grained(UFG)pure titanium was produced by equal channel angular pressing for 4 passes,followed by rotatory swaging at room temperature.The strain-controlled low-cycle fatigue tests of UFG and coarse-grained(CG)pure titanium were conducted by Instron electro-hydraulic servo fatigue testing machine in the strain amplitude range of 0.5%—1.1%at room temperature.Transmission electron microscope(TEM)and scanning electron microscope were used to investigate the microstructure and fracture surface of UFG pure titanium after fatigue tests.Results show that UFG pure titanium exhibits a longer low-cycle fatigue life,compared with the CG pure titanium.For example,at a total strain amplitude of 0.5%,UFG and CG pure titanium has fatigue life of 10850 and 4820 cycles,respectively.Significant cyclic softening occurs in UFG pure titanium,except in the case of a total strain amplitude of 0.5%.Hysteresis loop area is increased rapidly with the increase in strain amplitude.The fracture surface shows that the fatigue crack is initiated from the specimen surface.A series of fatigue striations and many microcracks exist in the propagation region.With the increase in strain amplitude,the predominant failure mode is transformed from ductile failure into quasi-cleavage failure.Dislocation slip is the main plastic deformation mechanism of UFG pure titanium during low-cycle fatigue deformation.展开更多
In this paper,we identify conditions on the change of rings to induce functors between the two pure derived(resp.,pure singularity)categories.Then we construct recollements of pure derived categories and pure singular...In this paper,we identify conditions on the change of rings to induce functors between the two pure derived(resp.,pure singularity)categories.Then we construct recollements of pure derived categories and pure singularity categories for a formal triangular matrix ring,respectively.As an application,we study the pure global dimension of a formal triangular matrix ring.展开更多
This study integrates multiple sources of data(transaction data,policy text,public opinion data)with visualization techniques(such as heat maps,time-series trend charts,3D building brochures)to construct an analysis f...This study integrates multiple sources of data(transaction data,policy text,public opinion data)with visualization techniques(such as heat maps,time-series trend charts,3D building brochures)to construct an analysis framework for the Chengdu real estate market.By using the Adaptive Neuro-Fuzzy Inference System(ANFIS)prediction model,spatial GIS(Geographic Information System analysis)analysis,and interactive dashboards,this study reveals market differentiation,policy impacts,and changes in demand structure,thereby providing decision support for the government,enterprises,and homebuyers.展开更多
To address the issue of instability or even imbalance in the orientation and attitude control of quadrotor unmanned aerial vehicles(QUAVs)under random disturbances,this paper proposes a distributed antidisturbance dat...To address the issue of instability or even imbalance in the orientation and attitude control of quadrotor unmanned aerial vehicles(QUAVs)under random disturbances,this paper proposes a distributed antidisturbance data-driven event-triggered fusion control method,which achieves efficient fault diagnosis while suppressing random disturbances and mitigating communication conflicts within the QUAV swarm.First,the impact of random disturbances on the UAV swarm is analyzed,and a model for orientation and attitude control of QUAVs under stochastic perturbations is established,with the disturbance gain threshold determined.Second,a fault diagnosis system based on a high-gain observer is designed,constructing a fault gain criterion by integrating orientation and attitude information from QUAVs.Subsequently,a model-free dynamic linearization-based data modeling(MFDLDM)framework is developed using model-free adaptive control,which efficiently fits the nonlinear control model of the QUAV swarm while reducing temporal constraints on control data.On this basis,this paper constructs a distributed data-driven event-triggered controller based on the staggered communication mechanism,which consists of an equivalent QUAV controller and an event-triggered controller,and is able to reduce the communication conflicts while suppressing the influence of random interference.Finally,by incorporating random disturbances into the controller,comparative experiments and physical validations are conducted on the QUAV platforms,fully demonstrating the strong adaptability and robustness of the proposed distributed event-triggered fault-tolerant control system.展开更多
Wetting deformation in earth-rockfill dams is a critical factor influencingdam safety.Although numerous mathematical models have been developed to describe this phenomenon,most of them rely on empirical formulations a...Wetting deformation in earth-rockfill dams is a critical factor influencingdam safety.Although numerous mathematical models have been developed to describe this phenomenon,most of them rely on empirical formulations and lack prior knowledge of model parameters,which is essential for Bayesian parameter inversion to enhance accuracy and reduce uncertainty.This study introduces a datadriven approach to establishing prior knowledge of earth-rockfill dams.Driving factors are utilized to determine the potential range of model parameters,and settlement changes within this range are calculated.The results are iteratively compared with actual monitoring data until the calculated range encompasses the observed data,thereby providing prior knowledge of the model parameters.The proposed method is applied to the right-bank earth-rockfilldam of Danjiangkou.Employing a Gibbs sample size of 30,000,the proposed method effectively calibrates the prior knowledge of the wetting model parameters,achieving a root mean square error(RMSE)of 5.18 mm for the settlement predictions.By comparison,the use of non-informative priors with sample sizes of 30,000 and 50,000 results in significantly larger RMSE values of 11.97 mm and 16.07 mm,respectively.Furthermore,the computational efficiencyof the proposed method is demonstrated by an inversion computation time of 902 s for 30,000 samples,which is notably shorter than the 1026 s and 1558 s required for noninformative priors with 30,000 and 50,000 samples,respectively.These findingsunderscore the superior performance of the proposed approach in terms of both prediction accuracy and computational efficiency.These results demonstrate that the proposed method not only improves the predictive accuracy but also enhances the computational efficiency,enabling optimal parameter identificationwith reduced computational effort.This approach provides a robust and efficientframework for advancing dam safety assessments.展开更多
THE mechanical response and deformation mechanisms of pure nickel under nanoindentation were systematically investigated using molecular dynamics(MD)simulations,with a particular focus on the novel interplay between c...THE mechanical response and deformation mechanisms of pure nickel under nanoindentation were systematically investigated using molecular dynamics(MD)simulations,with a particular focus on the novel interplay between crystallographic orientation,grain boundary(GB)proximity,and pore characteristics(size/location).This study compares single-crystal nickel models along[100],[110],and[111]orientations with equiaxed polycrystalline models containing 0,1,and 2.5 nm pores in surface and subsurface configurations.Our results reveal that crystallographic anisotropy manifests as a 24.4%higher elastic modulus and 22.2%greater hardness in[111]-oriented single crystals compared to[100].Pore-GB synergistic effects are found to dominate the deformation behavior:2.5 nm subsurface pores reduce hardness by 25.2%through stress concentration and dislocation annihilation at GBs,whereas surface pores enable mechanical recovery via accelerated dislocation generation post-collapse.Additionally,size-dependent deformation regimes were identified,with 1 nm pores inducing negligible perturbation due to rapid atomic rearrangement,in contrast with persistent softening in 2.5 nm pores.These findings establish atomic-scale design principles for defect engineering in nickel-based aerospace components,demonstrating how crystallographic orientation,pore configuration,and GB interactions collectively govern nanoindentation behavior.展开更多
Storm-enhanced density(SED)and the tongue of ionization(TOI)are key ionospheric storm-time structures whose rapid evolution and fine-scale variability remain challenging to capture with conventional empirical high-lat...Storm-enhanced density(SED)and the tongue of ionization(TOI)are key ionospheric storm-time structures whose rapid evolution and fine-scale variability remain challenging to capture with conventional empirical high-latitude drivers.In this study,we examine the May 10–11,2024,superstorm using the Thermosphere–Ionosphere–Electrodynamics General Circulation Model(TIEGCM)with observation-constrained high-latitude forcing.Auroral precipitation parameters(energy flux and mean energy)are assimilated from a Defense Meteorological Satellite Program(DMSP)Special Sensor Ultraviolet Spectrographic Imager(SSUSI)using a multi-resolution Gaussian process(Lattice Kriging)approach,whereas high-latitude convection potentials are derived by assimilating Super Dual Auroral Radar Network(SuperDARN)observations with the Thomas and Shepherd(2018)model(TS18).For comparison,an additional simulation is performed using empirical models for both convection and auroral forcing.The results show that during the main phase of the May 10 storm,the data-driven simulation provides a more realistic depiction of the SED source region than does the empirical model run by capturing its rapid intensification more clearly and reproducing its spatial location and structural features with higher fidelity.These improvements lead to a more accurate representation of its poleward extension into the polar cap that develops into the TOI.Above the ionospheric F2 peak over the SED source region,SuperDARN-constrained potentials generate stronger and more localized E×B drifts that dominate plasma uplift and drive its transport into the polar cap,although neutral winds and downward ambipolar diffusion partially offset these effects.Below the F2 peak,neutral winds and photochemical processes play a major role in shaping the spatial extent and intensity of the SED and TOI.These results highlight the role of observation-constrained high-latitude drivers in representing ionosphere–thermosphere responses during extreme storms and suggest their relevance for improving physical interpretation and model performance.展开更多
The development of novel quantum many-body computational algorithms relies on robust benchmarking.However,generating such benchmarks is often hindered by the massive computational resources required for exact diagonal...The development of novel quantum many-body computational algorithms relies on robust benchmarking.However,generating such benchmarks is often hindered by the massive computational resources required for exact diagonalization or quantum Monte Carlo simulations,particularly at finite temperatures.In this work,we propose a new algorithm for obtaining thermal pure quantum states,which allows efficient computation of both mechanical and thermodynamic properties at finite temperatures.We implement this algorithm in our open-source C++template library,Physica.Combining the improved algorithm with state-of-the-art software engineering,our implementation achieves high performance and numerical stability.As an example,we demonstrate that for the 4×4 Hubbard model,our method runs approximately 10~3times faster than HΦ3.5.2.Moreover,the accessible temperature range is extended down toβ=32 across arbitrary doping levels.These advances significantly push forward the frontiers of benchmarking for quantum many-body systems.展开更多
The scaling-up of electrochemical CO_(2)reduction requires circumventing the CO_(2)loss as carbonates under alkaline conditions.Zero-gap MEA cell configurations with a proton exchange membrane represent an alternative...The scaling-up of electrochemical CO_(2)reduction requires circumventing the CO_(2)loss as carbonates under alkaline conditions.Zero-gap MEA cell configurations with a proton exchange membrane represent an alternative solution in a pure acidic system,but the catalyst layer in direct contact with the hydrated proton environment usually leads to H_(2)evolution dominating.Herein,we show that polydimethyldiallyl-ammonium-chloride-coated Ag(Ag@PDDA)electrode exhibits outstanding performance with a FE of 86%,a single-pass conversion of 72%,and a stability of 28 h for CO production in pure-acid MEA compared with ammonium poly(N-methyl-piperidine-co-pterphenyl)decorated Ag(Ag/QAPPT)and cetyltrimethylammonium bromide decorated Ag(Ag/CTAB).The in situ ATR-SEIRAS reveal that PDDA creates a positive charge-rich protective outer layer and an N-rich hybrid inner layer,which not only suppresses the migration of H+during the electrolysis process and blocks the direct contact between H2O and Ag catalyst,but also promotes the generation from CO_(2)to*COOH in a pure-acid system.This work highlights the importance of polyelectrolyte engineering in regulating the electrocatalytic interface and accelerates the development of proton exchange membrane CO_(2)electrolysis.展开更多
The Wufeng–Longmaxi Formation derives its name from the Upper Ordovician Wufeng Formation and the Lower Silurian Longmaxi Formation,found in sequence in the Sichuan Basin.This formation hosts rich shale gas reservoir...The Wufeng–Longmaxi Formation derives its name from the Upper Ordovician Wufeng Formation and the Lower Silurian Longmaxi Formation,found in sequence in the Sichuan Basin.This formation hosts rich shale gas reservoirs,and its shale gas enrichment patterns are examined in this study using data from 1197 shale samples collected from 14 wells.Five basic and three key parameters,eight in all,are assessed for each sample.The five basic parameters include burial depth and the contents of four mineral types—quartz,clay,carbonate,and other minerals;the three key parameters,representing shale gas enrichment,are total organic carbon(TOC)content,porosity,and gas content.The SHapley Additive exPlanations(SHAP)analysis originated in game theory is used here in an interpretable machine learning framework,to address issues of heterogeneous data structure,noisy relationships,and multi-objective optimization.An evaluation of the ranking,contribution values,and conditions of changes for these parameters offers new quantitative insights into shale gas enrichment patterns.A quantitative analysis of the relationship between data-sets identifies the primary factors controlling TOC,porosity,and gas content of shale gas reservoirs.The results show that TOC and porosity jointly influence gas content;mineral content has a significant impact on both,TOC and porosity;and the burial depth governs porosity which,in turn,affects the conditions under which shale gas is preserved.Input parameter thresholds are also determined and provide a basis for the establishment of quantitative criteria to evaluate shale gas enrichment.The predictive accuracy of the model used in this study is significantly improved by the step-wise addition of two input parameters,namely TOC and porosity,separately and together.Thus,the game theory method in big data-driven analysis uses a combination of TOC and porosity to evaluate the gas content with encouraging results—suggesting that these are the key parameters that indicate source rock and reservoir properties.展开更多
The key challenge in the preparation of perovskite solar cells is to enhance the reproducibility of PSC manufacturing,particularly by better controlling multiple high-dimensional process parameters.This study proposes...The key challenge in the preparation of perovskite solar cells is to enhance the reproducibility of PSC manufacturing,particularly by better controlling multiple high-dimensional process parameters.This study proposes a machine learning(ML)approach to efficiently predict and analyze perovskite film fabrication processes.By evaluating five classic ML algorithms on 130 experimental data sets from blade-coating parameters,the Random Forest(RF)model was identified as the most effective,enabling rapid prediction of over 100,000 parameter sets in just 10 min-equivalent to 3 years of manual experimentation.The RF model demonstrated strong predictive accuracy,with an R^(2) close to 0.8.This approach led to the identification of optimal process parameter combinations,significantly improving the reproducibility of PSCs and reducing performance variance by approximately threefold,thereby advancing the development of scalable manufacturing processes.展开更多
Mitigating vortex-induced vibrations(VIV)in flexible risers represents a critical concern in offshore oil and gas production,considering its potential impact on operational safety and efficiency.The accurate predictio...Mitigating vortex-induced vibrations(VIV)in flexible risers represents a critical concern in offshore oil and gas production,considering its potential impact on operational safety and efficiency.The accurate prediction of displacement and position of VIV in flexible risers remains challenging under actual marine conditions.This study presents a data-driven model for riser displacement prediction that corresponds to field conditions.Experimental data analysis reveals that the XGBoost algorithm predicts the maximum displacement and position with superior accuracy compared with Support vector regression(SVR),considering both computational efficiency and precision.Platform displacement in the Y-direction demonstrates a significant positive correlation with both axial depth and maximum displacement magnitude.The fourth point displacement exhibits the highest contribution to model prediction outcomes,showing a positive influence on maximum displacement while negatively affecting the axial depth of maximum displacement.Platform displacement in the X-and Y-directions exhibits competitive effects on both the riser’s maximum displacement and its axial depth.Through the implementation of XGBoost algorithm and SHapley Additive exPlanation(SHAP)analysis,the model effectively estimates the riser’s maximum displacement and its precise location.This data-driven approach achieves predictions using minimal,readily available data points,enhancing its practical field applications and demonstrating clear relevance to academic and professional communities.展开更多
The constitutive models of shape memory alloys(SMAs)play an important role in facilitating the widespread application of such types of alloys in various engineering fields.However,to accurately describe the deformatio...The constitutive models of shape memory alloys(SMAs)play an important role in facilitating the widespread application of such types of alloys in various engineering fields.However,to accurately describe the deformation behaviors of SMAs,the concepts in classical plasticity are employed in the existing constitutive models,and a series of complex mathematical equations are involved.Such complexity brings inconvenience for the construction,implementation,and application of the constitutive models.To overcome these shortcomings,a data-driven constitutive model of SMAs is developed in this work based on the artificial neural network(ANN).In the proposed model,the components of the strain tensor in principal space,ambient temperature,and the maximum equivalent strain in the deformation history from the initial state to the current loading state are chosen as the input features,and the components of the stress tensor in principal space are set as the output.The proposed ANN-based constitutive model is implemented into the finite element program ABAQUS by deriving its consistent tangent modulus and writing a user-defined material subroutine.The stress-strain responses of SMA material under various loading paths and at different ambient temperatures are used to train the ANN model,which is generated from the existing constitutive model(numerical experiments).To validate the capability of the proposed model,the predicted stress-strain responses of SMA material,and the global and local responses of two typical SMA structures are compared with the corresponding numerical experiments.This work demonstrates a good potential to obtain the constitutive model of SMAs by pure data and avoid the need for vast stores of knowledge for the construction of constitutive models.展开更多
Based on the educational evaluation reform,this study explores the construction of an evidence-based value-added evaluation system based on data-driven,aiming to solve the limitations of traditional evaluation methods...Based on the educational evaluation reform,this study explores the construction of an evidence-based value-added evaluation system based on data-driven,aiming to solve the limitations of traditional evaluation methods.The research adopts the method of combining theoretical analysis and practical application,and designs the evidence-based value-added evaluation framework,which includes the core elements of a multi-source heterogeneous data acquisition and processing system,a value-added evaluation agent based on a large model,and an evaluation implementation and application mechanism.Through empirical research verification,the evaluation system has remarkable effects in improving learning participation,promoting ability development,and supporting teaching decision-making,and provides a theoretical reference and practical path for educational evaluation reform in the new era.The research shows that the evidence-based value-added evaluation system based on data-driven can reflect students’actual progress more fairly and objectively by accurately measuring the difference in starting point and development range of students,and provide strong support for the realization of high-quality education development.展开更多
The features of additive manufacturing(AM)have made commercially pure titanium(CP-Ti)an attractive candidate material for biomedical implants.However,achieving high strength and ductility is challenging because of the...The features of additive manufacturing(AM)have made commercially pure titanium(CP-Ti)an attractive candidate material for biomedical implants.However,achieving high strength and ductility is challenging because of the columnar structures and fine martensite formation.This study investigated the effect of carbon nanotubes(CNTs)addition on the microstructure and mechanical properties of grade 1 CP-Ti(Gr-1)during the laser powder bed fusion(L-PBF)process.A minute amount of 0.2%mass fraction(wt%)CNTs addition resulted in a high yield strength of approximately 700 MPa and exceptional ductility of 25.7%.Therein,a portion of the CNTs dissolved in the matrix as solute atoms,contributing to solution strengthening,while others were transformed into Ti C_(x)through an in situ reaction with the Ti matrix.Furthermore,the addition of CNTs resulted in the formation of a larger fraction of equiaxed grains and increased the activity of basal and prismatic slip systems.Hence,Gr-1 with CNTs exhibited significantly increased ductility while maintaining a high strength comparable to that of Gr-1 without CNTs.The insights gained from this study provide a novel approach for designing strong and ductile Ti alloys for AM.展开更多
Pure Mg boasting a relatively small corrosion rate is a potential biodegradable metal material for implants.However,its degradation behavior in the complex physiological environment is still a lack of understanding.In...Pure Mg boasting a relatively small corrosion rate is a potential biodegradable metal material for implants.However,its degradation behavior in the complex physiological environment is still a lack of understanding.In this work,we investigated the effect of corrosion product film layers on the degradation behavior of pure Mg in physiological environments.Pure Mg shows a faster corrosion rate in simulated body fluid(SBF)compared to NaCl solution.Hydrogen evolution experiments indicate that the degradation rate of pure Mg in SBF decreases rapidly within the first 12 h but stabilizes afterward.The rapid deposition of low-solubility calcium phosphate on the pure Mg in SBF provides protection to the substrate,resulting in a gradual decrease in the degradation rates.Consequently,the corrosion product film of pure Mg formed in SBF exhibits a layered structure,with the upper layer consisting of dense Ca_(3)(PO_(4))_(2)/Mg_(3)(PO_(4))_(2) and the lower layer consisting of Mg(OH)_(2)/MgO.Electrochemical impedance spectroscopy(EIS)shows that the resistance of the corrosion product film increases over time,indicating gradual strengthening of the corrosion resistance.The 4-week degradation results in the femoral marrow cavity of mice are consistent with the result in SBF in vitro.展开更多
We propose an integrated method of data-driven and mechanism models for well logging formation evaluation,explicitly focusing on predicting reservoir parameters,such as porosity and water saturation.Accurately interpr...We propose an integrated method of data-driven and mechanism models for well logging formation evaluation,explicitly focusing on predicting reservoir parameters,such as porosity and water saturation.Accurately interpreting these parameters is crucial for effectively exploring and developing oil and gas.However,with the increasing complexity of geological conditions in this industry,there is a growing demand for improved accuracy in reservoir parameter prediction,leading to higher costs associated with manual interpretation.The conventional logging interpretation methods rely on empirical relationships between logging data and reservoir parameters,which suffer from low interpretation efficiency,intense subjectivity,and suitability for ideal conditions.The application of artificial intelligence in the interpretation of logging data provides a new solution to the problems existing in traditional methods.It is expected to improve the accuracy and efficiency of the interpretation.If large and high-quality datasets exist,data-driven models can reveal relationships of arbitrary complexity.Nevertheless,constructing sufficiently large logging datasets with reliable labels remains challenging,making it difficult to apply data-driven models effectively in logging data interpretation.Furthermore,data-driven models often act as“black boxes”without explaining their predictions or ensuring compliance with primary physical constraints.This paper proposes a machine learning method with strong physical constraints by integrating mechanism and data-driven models.Prior knowledge of logging data interpretation is embedded into machine learning regarding network structure,loss function,and optimization algorithm.We employ the Physically Informed Auto-Encoder(PIAE)to predict porosity and water saturation,which can be trained without labeled reservoir parameters using self-supervised learning techniques.This approach effectively achieves automated interpretation and facilitates generalization across diverse datasets.展开更多
In order to study the characteristics of pure fly ash-based geopolymer concrete(PFGC)conveniently,we used a machine learning method that can quantify the perception of characteristics to predict its compressive streng...In order to study the characteristics of pure fly ash-based geopolymer concrete(PFGC)conveniently,we used a machine learning method that can quantify the perception of characteristics to predict its compressive strength.In this study,505 groups of data were collected,and a new database of compressive strength of PFGC was constructed.In order to establish an accurate prediction model of compressive strength,five different types of machine learning networks were used for comparative analysis.The five machine learning models all showed good compressive strength prediction performance on PFGC.Among them,R2,MSE,RMSE and MAE of decision tree model(DT)are 0.99,1.58,1.25,and 0.25,respectively.While R2,MSE,RMSE and MAE of random forest model(RF)are 0.97,5.17,2.27 and 1.38,respectively.The two models have high prediction accuracy and outstanding generalization ability.In order to enhance the interpretability of model decision-making,we used importance ranking to obtain the perception of machine learning model to 13 variables.These 13 variables include chemical composition of fly ash(SiO_(2)/Al_(2)O_(3),Si/Al),the ratio of alkaline liquid to the binder,curing temperature,curing durations inside oven,fly ash dosage,fine aggregate dosage,coarse aggregate dosage,extra water dosage and sodium hydroxide dosage.Curing temperature,specimen ages and curing durations inside oven have the greatest influence on the prediction results,indicating that curing conditions have more prominent influence on the compressive strength of PFGC than ordinary Portland cement concrete.The importance of curing conditions of PFGC even exceeds that of the concrete mix proportion,due to the low reactivity of pure fly ash.展开更多
Dear Editor,This letter investigates the fuzzy prescribed-time control(PTC)problem for a class of uncertain pure feedback nonlinear systems.Firstly,a novel prescribed-time stability lemma is introduced,which plays a c...Dear Editor,This letter investigates the fuzzy prescribed-time control(PTC)problem for a class of uncertain pure feedback nonlinear systems.Firstly,a novel prescribed-time stability lemma is introduced,which plays a critical role in stability analysis.Unlike existing PTC algorithms,where the nonlinear functions are typically known or satisfy a linear growth condition,our approach does not require such assumptions.To address these unknown factors,fuzzy logic systems(FLSs)are employed.Based on the new prescribed-time stability lemma,it is proven that the controller and all system states converge to the origin within the prescribed time and remain there.Finally,the effectiveness of the proposed algorithm is validated through a simulation example.展开更多
基金supported by the National Natural Science Foundation of China(No.12372045)the National Key Research and the Development Program of China(Nos.2023YFC2205900,2023YFC2205901)。
文摘This paper solves the problem of model-free dual-arm space robot maneuvering after non-cooperative target capture under high control quality requirements.The explicit system model is unavailable,and the maneuvering mission is disturbed by the measurement noise and the target adversarial behavior.To address these problems,a model-free Combined Adaptive-length Datadriven Predictive Controller(CADPC)is proposed.It consists of a separated subsystem identification method and a combined predictive control strategy.The subsystem identification method is composed of an adaptive data length,thereby reducing sensitivity to undetermined measurement noises and disturbances.Based on the subsystem identification,the combined predictive controller is established,reducing calculating resource.The stability of the CADPC is rigorously proven using the Input-to-State Stable(ISS)theorem and the small-gain theorem.Simulations demonstrate that CADPC effectively handles the model-free space robot post operation in the presence of significant disturbances,state measurement noise,and control input errors.It achieves improved steady-state accuracy,reduced steady-state control consumption,and minimized control input chattering.
基金Natural Science Foundation of Shaanxi Province (2023-JC-YB-312)。
文摘Ultrafine-grained(UFG)pure titanium was produced by equal channel angular pressing for 4 passes,followed by rotatory swaging at room temperature.The strain-controlled low-cycle fatigue tests of UFG and coarse-grained(CG)pure titanium were conducted by Instron electro-hydraulic servo fatigue testing machine in the strain amplitude range of 0.5%—1.1%at room temperature.Transmission electron microscope(TEM)and scanning electron microscope were used to investigate the microstructure and fracture surface of UFG pure titanium after fatigue tests.Results show that UFG pure titanium exhibits a longer low-cycle fatigue life,compared with the CG pure titanium.For example,at a total strain amplitude of 0.5%,UFG and CG pure titanium has fatigue life of 10850 and 4820 cycles,respectively.Significant cyclic softening occurs in UFG pure titanium,except in the case of a total strain amplitude of 0.5%.Hysteresis loop area is increased rapidly with the increase in strain amplitude.The fracture surface shows that the fatigue crack is initiated from the specimen surface.A series of fatigue striations and many microcracks exist in the propagation region.With the increase in strain amplitude,the predominant failure mode is transformed from ductile failure into quasi-cleavage failure.Dislocation slip is the main plastic deformation mechanism of UFG pure titanium during low-cycle fatigue deformation.
基金Supported by Scientific and Technological Innovation Programs of Higher Education Institutions in Shanxi(Grant No.2025L092)the National Natural Science Foundation of China(Grant No.12071120).
文摘In this paper,we identify conditions on the change of rings to induce functors between the two pure derived(resp.,pure singularity)categories.Then we construct recollements of pure derived categories and pure singularity categories for a formal triangular matrix ring,respectively.As an application,we study the pure global dimension of a formal triangular matrix ring.
基金Chengdu City Philosophy and Social Sciences Research Center“artificial intelligence+urban communication”theory and Application Research Center Project“Chengdu real estate vertical market public opinion data visualization research”(Project No.RZCC2025017).
文摘This study integrates multiple sources of data(transaction data,policy text,public opinion data)with visualization techniques(such as heat maps,time-series trend charts,3D building brochures)to construct an analysis framework for the Chengdu real estate market.By using the Adaptive Neuro-Fuzzy Inference System(ANFIS)prediction model,spatial GIS(Geographic Information System analysis)analysis,and interactive dashboards,this study reveals market differentiation,policy impacts,and changes in demand structure,thereby providing decision support for the government,enterprises,and homebuyers.
基金supported in part by the National Natural Science Foundation of China,Grant/Award Number:62003267the Key Research and Development Program of Shaanxi Province,Grant/Award Number:2023-GHZD-33Open Project of the State Key Laboratory of Intelligent Game,Grant/Award Number:ZBKF-23-05。
文摘To address the issue of instability or even imbalance in the orientation and attitude control of quadrotor unmanned aerial vehicles(QUAVs)under random disturbances,this paper proposes a distributed antidisturbance data-driven event-triggered fusion control method,which achieves efficient fault diagnosis while suppressing random disturbances and mitigating communication conflicts within the QUAV swarm.First,the impact of random disturbances on the UAV swarm is analyzed,and a model for orientation and attitude control of QUAVs under stochastic perturbations is established,with the disturbance gain threshold determined.Second,a fault diagnosis system based on a high-gain observer is designed,constructing a fault gain criterion by integrating orientation and attitude information from QUAVs.Subsequently,a model-free dynamic linearization-based data modeling(MFDLDM)framework is developed using model-free adaptive control,which efficiently fits the nonlinear control model of the QUAV swarm while reducing temporal constraints on control data.On this basis,this paper constructs a distributed data-driven event-triggered controller based on the staggered communication mechanism,which consists of an equivalent QUAV controller and an event-triggered controller,and is able to reduce the communication conflicts while suppressing the influence of random interference.Finally,by incorporating random disturbances into the controller,comparative experiments and physical validations are conducted on the QUAV platforms,fully demonstrating the strong adaptability and robustness of the proposed distributed event-triggered fault-tolerant control system.
基金supported by the National Key R&D Program of China(Grant No.2023YFC3209504)Natural Science Foundation of Wuhan(Grant No.2024040801020271)the Fundamental Research Funds for Central Public Welfare Research Institutes(Grant No.CKSF2025718/YT).
文摘Wetting deformation in earth-rockfill dams is a critical factor influencingdam safety.Although numerous mathematical models have been developed to describe this phenomenon,most of them rely on empirical formulations and lack prior knowledge of model parameters,which is essential for Bayesian parameter inversion to enhance accuracy and reduce uncertainty.This study introduces a datadriven approach to establishing prior knowledge of earth-rockfill dams.Driving factors are utilized to determine the potential range of model parameters,and settlement changes within this range are calculated.The results are iteratively compared with actual monitoring data until the calculated range encompasses the observed data,thereby providing prior knowledge of the model parameters.The proposed method is applied to the right-bank earth-rockfilldam of Danjiangkou.Employing a Gibbs sample size of 30,000,the proposed method effectively calibrates the prior knowledge of the wetting model parameters,achieving a root mean square error(RMSE)of 5.18 mm for the settlement predictions.By comparison,the use of non-informative priors with sample sizes of 30,000 and 50,000 results in significantly larger RMSE values of 11.97 mm and 16.07 mm,respectively.Furthermore,the computational efficiencyof the proposed method is demonstrated by an inversion computation time of 902 s for 30,000 samples,which is notably shorter than the 1026 s and 1558 s required for noninformative priors with 30,000 and 50,000 samples,respectively.These findingsunderscore the superior performance of the proposed approach in terms of both prediction accuracy and computational efficiency.These results demonstrate that the proposed method not only improves the predictive accuracy but also enhances the computational efficiency,enabling optimal parameter identificationwith reduced computational effort.This approach provides a robust and efficientframework for advancing dam safety assessments.
基金The National Natural Science Foundation of China(Grant No.12462006)Beijing Institute of Structure and Environment Engineering Joint Innovation Fund(No.BQJJ202414).
文摘THE mechanical response and deformation mechanisms of pure nickel under nanoindentation were systematically investigated using molecular dynamics(MD)simulations,with a particular focus on the novel interplay between crystallographic orientation,grain boundary(GB)proximity,and pore characteristics(size/location).This study compares single-crystal nickel models along[100],[110],and[111]orientations with equiaxed polycrystalline models containing 0,1,and 2.5 nm pores in surface and subsurface configurations.Our results reveal that crystallographic anisotropy manifests as a 24.4%higher elastic modulus and 22.2%greater hardness in[111]-oriented single crystals compared to[100].Pore-GB synergistic effects are found to dominate the deformation behavior:2.5 nm subsurface pores reduce hardness by 25.2%through stress concentration and dislocation annihilation at GBs,whereas surface pores enable mechanical recovery via accelerated dislocation generation post-collapse.Additionally,size-dependent deformation regimes were identified,with 1 nm pores inducing negligible perturbation due to rapid atomic rearrangement,in contrast with persistent softening in 2.5 nm pores.These findings establish atomic-scale design principles for defect engineering in nickel-based aerospace components,demonstrating how crystallographic orientation,pore configuration,and GB interactions collectively govern nanoindentation behavior.
基金The Shandong Provincial Natural Science Foundation(Grant No.ZR2022JQ18)supported this worksupported by the National Natural Science Foundation of China(NNFSC)Youth Program(Grant No.42304168)+1 种基金supported by the National Key R&D Program of China(Grant No.2022YFF0504400)the NNSFC(Grant Nos.42188101 and 42174210)。
文摘Storm-enhanced density(SED)and the tongue of ionization(TOI)are key ionospheric storm-time structures whose rapid evolution and fine-scale variability remain challenging to capture with conventional empirical high-latitude drivers.In this study,we examine the May 10–11,2024,superstorm using the Thermosphere–Ionosphere–Electrodynamics General Circulation Model(TIEGCM)with observation-constrained high-latitude forcing.Auroral precipitation parameters(energy flux and mean energy)are assimilated from a Defense Meteorological Satellite Program(DMSP)Special Sensor Ultraviolet Spectrographic Imager(SSUSI)using a multi-resolution Gaussian process(Lattice Kriging)approach,whereas high-latitude convection potentials are derived by assimilating Super Dual Auroral Radar Network(SuperDARN)observations with the Thomas and Shepherd(2018)model(TS18).For comparison,an additional simulation is performed using empirical models for both convection and auroral forcing.The results show that during the main phase of the May 10 storm,the data-driven simulation provides a more realistic depiction of the SED source region than does the empirical model run by capturing its rapid intensification more clearly and reproducing its spatial location and structural features with higher fidelity.These improvements lead to a more accurate representation of its poleward extension into the polar cap that develops into the TOI.Above the ionospheric F2 peak over the SED source region,SuperDARN-constrained potentials generate stronger and more localized E×B drifts that dominate plasma uplift and drive its transport into the polar cap,although neutral winds and downward ambipolar diffusion partially offset these effects.Below the F2 peak,neutral winds and photochemical processes play a major role in shaping the spatial extent and intensity of the SED and TOI.These results highlight the role of observation-constrained high-latitude drivers in representing ionosphere–thermosphere responses during extreme storms and suggest their relevance for improving physical interpretation and model performance.
基金Fu-Zhou Chen for helpful discussions.The work is partly supported by the National Key Research and Development Program of China(Grant No.2022YFA1402704)the National Natural Science Foundation of China(Grant No.12247101)。
文摘The development of novel quantum many-body computational algorithms relies on robust benchmarking.However,generating such benchmarks is often hindered by the massive computational resources required for exact diagonalization or quantum Monte Carlo simulations,particularly at finite temperatures.In this work,we propose a new algorithm for obtaining thermal pure quantum states,which allows efficient computation of both mechanical and thermodynamic properties at finite temperatures.We implement this algorithm in our open-source C++template library,Physica.Combining the improved algorithm with state-of-the-art software engineering,our implementation achieves high performance and numerical stability.As an example,we demonstrate that for the 4×4 Hubbard model,our method runs approximately 10~3times faster than HΦ3.5.2.Moreover,the accessible temperature range is extended down toβ=32 across arbitrary doping levels.These advances significantly push forward the frontiers of benchmarking for quantum many-body systems.
基金financial support of the National Natural Science Foundation of China(NSFC)(52394202,52021004,52301232,and 52476056)the Natural Science Foundation of Chongqing Province(2024NSCQ-MSX1109).
文摘The scaling-up of electrochemical CO_(2)reduction requires circumventing the CO_(2)loss as carbonates under alkaline conditions.Zero-gap MEA cell configurations with a proton exchange membrane represent an alternative solution in a pure acidic system,but the catalyst layer in direct contact with the hydrated proton environment usually leads to H_(2)evolution dominating.Herein,we show that polydimethyldiallyl-ammonium-chloride-coated Ag(Ag@PDDA)electrode exhibits outstanding performance with a FE of 86%,a single-pass conversion of 72%,and a stability of 28 h for CO production in pure-acid MEA compared with ammonium poly(N-methyl-piperidine-co-pterphenyl)decorated Ag(Ag/QAPPT)and cetyltrimethylammonium bromide decorated Ag(Ag/CTAB).The in situ ATR-SEIRAS reveal that PDDA creates a positive charge-rich protective outer layer and an N-rich hybrid inner layer,which not only suppresses the migration of H+during the electrolysis process and blocks the direct contact between H2O and Ag catalyst,but also promotes the generation from CO_(2)to*COOH in a pure-acid system.This work highlights the importance of polyelectrolyte engineering in regulating the electrocatalytic interface and accelerates the development of proton exchange membrane CO_(2)electrolysis.
基金funded by the Technical Development(Entrusted)Project of Science and Department of SINOPEC(Grant No.P23240-4)the National Natural Science Foundation of China(Grant Nos.42172165,42272143 and 2025ZD1403901-05)。
文摘The Wufeng–Longmaxi Formation derives its name from the Upper Ordovician Wufeng Formation and the Lower Silurian Longmaxi Formation,found in sequence in the Sichuan Basin.This formation hosts rich shale gas reservoirs,and its shale gas enrichment patterns are examined in this study using data from 1197 shale samples collected from 14 wells.Five basic and three key parameters,eight in all,are assessed for each sample.The five basic parameters include burial depth and the contents of four mineral types—quartz,clay,carbonate,and other minerals;the three key parameters,representing shale gas enrichment,are total organic carbon(TOC)content,porosity,and gas content.The SHapley Additive exPlanations(SHAP)analysis originated in game theory is used here in an interpretable machine learning framework,to address issues of heterogeneous data structure,noisy relationships,and multi-objective optimization.An evaluation of the ranking,contribution values,and conditions of changes for these parameters offers new quantitative insights into shale gas enrichment patterns.A quantitative analysis of the relationship between data-sets identifies the primary factors controlling TOC,porosity,and gas content of shale gas reservoirs.The results show that TOC and porosity jointly influence gas content;mineral content has a significant impact on both,TOC and porosity;and the burial depth governs porosity which,in turn,affects the conditions under which shale gas is preserved.Input parameter thresholds are also determined and provide a basis for the establishment of quantitative criteria to evaluate shale gas enrichment.The predictive accuracy of the model used in this study is significantly improved by the step-wise addition of two input parameters,namely TOC and porosity,separately and together.Thus,the game theory method in big data-driven analysis uses a combination of TOC and porosity to evaluate the gas content with encouraging results—suggesting that these are the key parameters that indicate source rock and reservoir properties.
基金Key Research and Development Program of Hubei Province,China(Grant No.2022BAA096)Zhejiang Provincial Natural Science Foundation of China(This material is based upon work funded by Zhejiang Provincial Natural Science Foundation of China under Grant No.LR25A020002)support of the Center for Materials Analysis and Characterization,Material Characterization Lab,and Nanofabrication Lab at Hubei University。
文摘The key challenge in the preparation of perovskite solar cells is to enhance the reproducibility of PSC manufacturing,particularly by better controlling multiple high-dimensional process parameters.This study proposes a machine learning(ML)approach to efficiently predict and analyze perovskite film fabrication processes.By evaluating five classic ML algorithms on 130 experimental data sets from blade-coating parameters,the Random Forest(RF)model was identified as the most effective,enabling rapid prediction of over 100,000 parameter sets in just 10 min-equivalent to 3 years of manual experimentation.The RF model demonstrated strong predictive accuracy,with an R^(2) close to 0.8.This approach led to the identification of optimal process parameter combinations,significantly improving the reproducibility of PSCs and reducing performance variance by approximately threefold,thereby advancing the development of scalable manufacturing processes.
基金The research work was financially supported by the National Natural Science Foundation of China(Grant Nos.51979238 and 52301338)the Sichuan Science and Technology Program(Grant Nos.2023NSFSC1953 and 2023ZYD0140).
文摘Mitigating vortex-induced vibrations(VIV)in flexible risers represents a critical concern in offshore oil and gas production,considering its potential impact on operational safety and efficiency.The accurate prediction of displacement and position of VIV in flexible risers remains challenging under actual marine conditions.This study presents a data-driven model for riser displacement prediction that corresponds to field conditions.Experimental data analysis reveals that the XGBoost algorithm predicts the maximum displacement and position with superior accuracy compared with Support vector regression(SVR),considering both computational efficiency and precision.Platform displacement in the Y-direction demonstrates a significant positive correlation with both axial depth and maximum displacement magnitude.The fourth point displacement exhibits the highest contribution to model prediction outcomes,showing a positive influence on maximum displacement while negatively affecting the axial depth of maximum displacement.Platform displacement in the X-and Y-directions exhibits competitive effects on both the riser’s maximum displacement and its axial depth.Through the implementation of XGBoost algorithm and SHapley Additive exPlanation(SHAP)analysis,the model effectively estimates the riser’s maximum displacement and its precise location.This data-driven approach achieves predictions using minimal,readily available data points,enhancing its practical field applications and demonstrating clear relevance to academic and professional communities.
基金supported by the National Natural Science Foundation of China(NSFC)(Grant No.12322203).
文摘The constitutive models of shape memory alloys(SMAs)play an important role in facilitating the widespread application of such types of alloys in various engineering fields.However,to accurately describe the deformation behaviors of SMAs,the concepts in classical plasticity are employed in the existing constitutive models,and a series of complex mathematical equations are involved.Such complexity brings inconvenience for the construction,implementation,and application of the constitutive models.To overcome these shortcomings,a data-driven constitutive model of SMAs is developed in this work based on the artificial neural network(ANN).In the proposed model,the components of the strain tensor in principal space,ambient temperature,and the maximum equivalent strain in the deformation history from the initial state to the current loading state are chosen as the input features,and the components of the stress tensor in principal space are set as the output.The proposed ANN-based constitutive model is implemented into the finite element program ABAQUS by deriving its consistent tangent modulus and writing a user-defined material subroutine.The stress-strain responses of SMA material under various loading paths and at different ambient temperatures are used to train the ANN model,which is generated from the existing constitutive model(numerical experiments).To validate the capability of the proposed model,the predicted stress-strain responses of SMA material,and the global and local responses of two typical SMA structures are compared with the corresponding numerical experiments.This work demonstrates a good potential to obtain the constitutive model of SMAs by pure data and avoid the need for vast stores of knowledge for the construction of constitutive models.
基金This paper is the research result of“Research on Innovation of Evidence-Based Teaching Paradigm in Vocational Education under the Background of New Quality Productivity”(2024JXQ176)the Shandong Province Artificial Intelligence Education Research Project(SDDJ202501035),which explores the application of artificial intelligence big models in student value-added evaluation from an evidence-based perspective。
文摘Based on the educational evaluation reform,this study explores the construction of an evidence-based value-added evaluation system based on data-driven,aiming to solve the limitations of traditional evaluation methods.The research adopts the method of combining theoretical analysis and practical application,and designs the evidence-based value-added evaluation framework,which includes the core elements of a multi-source heterogeneous data acquisition and processing system,a value-added evaluation agent based on a large model,and an evaluation implementation and application mechanism.Through empirical research verification,the evaluation system has remarkable effects in improving learning participation,promoting ability development,and supporting teaching decision-making,and provides a theoretical reference and practical path for educational evaluation reform in the new era.The research shows that the evidence-based value-added evaluation system based on data-driven can reflect students’actual progress more fairly and objectively by accurately measuring the difference in starting point and development range of students,and provide strong support for the realization of high-quality education development.
基金financially supported by the National Key Research and Development Project of the Ministry of Science and Technology of China(No.2022YFB4601000)the Fundamental Research Funds for the Central Universities(No.2042023kf0103)the Ministry of Trade,Industry and Energy,Korea(No.20013095)。
文摘The features of additive manufacturing(AM)have made commercially pure titanium(CP-Ti)an attractive candidate material for biomedical implants.However,achieving high strength and ductility is challenging because of the columnar structures and fine martensite formation.This study investigated the effect of carbon nanotubes(CNTs)addition on the microstructure and mechanical properties of grade 1 CP-Ti(Gr-1)during the laser powder bed fusion(L-PBF)process.A minute amount of 0.2%mass fraction(wt%)CNTs addition resulted in a high yield strength of approximately 700 MPa and exceptional ductility of 25.7%.Therein,a portion of the CNTs dissolved in the matrix as solute atoms,contributing to solution strengthening,while others were transformed into Ti C_(x)through an in situ reaction with the Ti matrix.Furthermore,the addition of CNTs resulted in the formation of a larger fraction of equiaxed grains and increased the activity of basal and prismatic slip systems.Hence,Gr-1 with CNTs exhibited significantly increased ductility while maintaining a high strength comparable to that of Gr-1 without CNTs.The insights gained from this study provide a novel approach for designing strong and ductile Ti alloys for AM.
基金supported by the National Natural Science Foundation of China(52127801)Postdoctoral Fellowship Program of CPSF under Grant Number GZC20231545,China Postdoctoral Science Foundation(2024T170557 and 2023M742224)+1 种基金Shanghai Post-doctoral Excellence Program(No.2023440)City University of Hong Kong Donation Grants(DON-RMG No.9229021 and 9220061).
文摘Pure Mg boasting a relatively small corrosion rate is a potential biodegradable metal material for implants.However,its degradation behavior in the complex physiological environment is still a lack of understanding.In this work,we investigated the effect of corrosion product film layers on the degradation behavior of pure Mg in physiological environments.Pure Mg shows a faster corrosion rate in simulated body fluid(SBF)compared to NaCl solution.Hydrogen evolution experiments indicate that the degradation rate of pure Mg in SBF decreases rapidly within the first 12 h but stabilizes afterward.The rapid deposition of low-solubility calcium phosphate on the pure Mg in SBF provides protection to the substrate,resulting in a gradual decrease in the degradation rates.Consequently,the corrosion product film of pure Mg formed in SBF exhibits a layered structure,with the upper layer consisting of dense Ca_(3)(PO_(4))_(2)/Mg_(3)(PO_(4))_(2) and the lower layer consisting of Mg(OH)_(2)/MgO.Electrochemical impedance spectroscopy(EIS)shows that the resistance of the corrosion product film increases over time,indicating gradual strengthening of the corrosion resistance.The 4-week degradation results in the femoral marrow cavity of mice are consistent with the result in SBF in vitro.
基金supported by National Key Research and Development Program (2019YFA0708301)National Natural Science Foundation of China (51974337)+2 种基金the Strategic Cooperation Projects of CNPC and CUPB (ZLZX2020-03)Science and Technology Innovation Fund of CNPC (2021DQ02-0403)Open Fund of Petroleum Exploration and Development Research Institute of CNPC (2022-KFKT-09)
文摘We propose an integrated method of data-driven and mechanism models for well logging formation evaluation,explicitly focusing on predicting reservoir parameters,such as porosity and water saturation.Accurately interpreting these parameters is crucial for effectively exploring and developing oil and gas.However,with the increasing complexity of geological conditions in this industry,there is a growing demand for improved accuracy in reservoir parameter prediction,leading to higher costs associated with manual interpretation.The conventional logging interpretation methods rely on empirical relationships between logging data and reservoir parameters,which suffer from low interpretation efficiency,intense subjectivity,and suitability for ideal conditions.The application of artificial intelligence in the interpretation of logging data provides a new solution to the problems existing in traditional methods.It is expected to improve the accuracy and efficiency of the interpretation.If large and high-quality datasets exist,data-driven models can reveal relationships of arbitrary complexity.Nevertheless,constructing sufficiently large logging datasets with reliable labels remains challenging,making it difficult to apply data-driven models effectively in logging data interpretation.Furthermore,data-driven models often act as“black boxes”without explaining their predictions or ensuring compliance with primary physical constraints.This paper proposes a machine learning method with strong physical constraints by integrating mechanism and data-driven models.Prior knowledge of logging data interpretation is embedded into machine learning regarding network structure,loss function,and optimization algorithm.We employ the Physically Informed Auto-Encoder(PIAE)to predict porosity and water saturation,which can be trained without labeled reservoir parameters using self-supervised learning techniques.This approach effectively achieves automated interpretation and facilitates generalization across diverse datasets.
基金Funded by the Natural Science Foundation of China(No.52109168)。
文摘In order to study the characteristics of pure fly ash-based geopolymer concrete(PFGC)conveniently,we used a machine learning method that can quantify the perception of characteristics to predict its compressive strength.In this study,505 groups of data were collected,and a new database of compressive strength of PFGC was constructed.In order to establish an accurate prediction model of compressive strength,five different types of machine learning networks were used for comparative analysis.The five machine learning models all showed good compressive strength prediction performance on PFGC.Among them,R2,MSE,RMSE and MAE of decision tree model(DT)are 0.99,1.58,1.25,and 0.25,respectively.While R2,MSE,RMSE and MAE of random forest model(RF)are 0.97,5.17,2.27 and 1.38,respectively.The two models have high prediction accuracy and outstanding generalization ability.In order to enhance the interpretability of model decision-making,we used importance ranking to obtain the perception of machine learning model to 13 variables.These 13 variables include chemical composition of fly ash(SiO_(2)/Al_(2)O_(3),Si/Al),the ratio of alkaline liquid to the binder,curing temperature,curing durations inside oven,fly ash dosage,fine aggregate dosage,coarse aggregate dosage,extra water dosage and sodium hydroxide dosage.Curing temperature,specimen ages and curing durations inside oven have the greatest influence on the prediction results,indicating that curing conditions have more prominent influence on the compressive strength of PFGC than ordinary Portland cement concrete.The importance of curing conditions of PFGC even exceeds that of the concrete mix proportion,due to the low reactivity of pure fly ash.
基金supported by the National Natural Science Foundation of China(U20A20187,U22A2050)the Science Fund of Hebei Province(F2024203134,F2023203100)+3 种基金the Science and Technology Development Grant of Hebei Province(20311803D)Hebei Innovation Capability Improvement Plan project(22567619H)Basic Research Project of Shijiazhuang(241791007A)the China Scholarship Council(CSC 202308130190).
文摘Dear Editor,This letter investigates the fuzzy prescribed-time control(PTC)problem for a class of uncertain pure feedback nonlinear systems.Firstly,a novel prescribed-time stability lemma is introduced,which plays a critical role in stability analysis.Unlike existing PTC algorithms,where the nonlinear functions are typically known or satisfy a linear growth condition,our approach does not require such assumptions.To address these unknown factors,fuzzy logic systems(FLSs)are employed.Based on the new prescribed-time stability lemma,it is proven that the controller and all system states converge to the origin within the prescribed time and remain there.Finally,the effectiveness of the proposed algorithm is validated through a simulation example.
