It is known that correlation does not imply causality.Some relationships identified in the analysis of data are coincidental or unknown,and some are produced by real-world causality of the situation,which is problemat...It is known that correlation does not imply causality.Some relationships identified in the analysis of data are coincidental or unknown,and some are produced by real-world causality of the situation,which is problematic,since there is a need to differentiate between these two scenarios.Until recently,the proper−semantic−causality of the relationship could have been determined only by human experts from the area of expertise of the studied data.This has changed with the advance of large language models,which are often utilized as surrogates for such human experts,making the process automated and readily available to all data analysts.This motivates the main objective of this work,which is to introduce the design and implementation of a large language model-based semantic causality evaluator based on correlation analysis,together with its visual analysis model called Causal heatmap.After the implementation itself,the model is evaluated from the point of view of the quality of the visual model,from the point of view of the quality of causal evaluation based on large language models,and from the point of view of comparative analysis,while the results reached in the study highlight the usability of large language models in the task and the potential of the proposed approach in the analysis of unknown datasets.The results of the experimental evaluation demonstrate the usefulness of the Causal heatmap method,supported by the evident highlighting of interesting relationships,while suppressing irrelevant ones.展开更多
Detecting geomagnetic anomalies preceding earthquakes is a challenging yet promising area of research that has gained increasing attention in recent years.This study introduces a novel reconstruction-based modeling ap...Detecting geomagnetic anomalies preceding earthquakes is a challenging yet promising area of research that has gained increasing attention in recent years.This study introduces a novel reconstruction-based modeling approach enhanced by negative learning,employing a Bidirectional Long Short-Term Memory(BiLSTM)network explicitly trained to accurately reconstruct non-seismic geomagnetic signals while intentionally amplifying reconstruction errors for seismic signals.By penalizing the model for accurately reconstructing seismic anomalies,the negative learning approach effectively magnifies the differences between normal and anomalous data.This strategic differentiation enhances the sensitivity of the BiLSTM network,enabling improved detection of subtle geomagnetic anomalies that may serve as earthquake precursors.Experimental validation clearly demonstrated statistically significant higher reconstruction errors for seismic signals compared to non-seismic signals,confirmed through the Mann-Whitney U test with a p-value of 0.0035 for Root Mean Square Error(RMSE).These results provide compelling evidence of the enhanced anomaly detection capability achieved through negative learning.Unlike traditional classification-based methods,negative learning explicitly encourages sensitivity to subtle precursor signals embedded within complex geomagnetic data,establishing a robust basis for further development of reliable earthquake prediction methods.展开更多
Interaction between the converter and the grid may lead to harmonic oscillations.The impedance-based method is an effective way to deal with the stability issue.In this study,the impedance-based method is used to inve...Interaction between the converter and the grid may lead to harmonic oscillations.The impedance-based method is an effective way to deal with the stability issue.In this study,the impedance-based method is used to investigate the small-signal stability of a cascaded 12-pulse line-commutated converter-based high-voltage direct current(LCC-HVDC)transmission system.In the modeling part,the impedance models of the single rectifier and inverter are established respectively with consideration to the effect of frequency coupling,which has improved the accuracy of the models.Based on the models,the AC impedance models of the cascaded LCC-HVDC transmission system are established both on the rectifier and inverter side.In the stability analysis part,the stability of the system is analyzed under different working conditions.The simulation results reveal that the established impedance model can properly represent the stability of this system.The findings of this study can provide a theoretical reference for the stability design and oscillation suppression strategy of LCC-HVDC transmission systems and LCC interconnected systems.展开更多
The wide-area damping controllers(WADCs),which are essential for mitigating regional low-frequency oscillations,face cyber-physical security threats due to the vulnerability of wide-area measurement system to cyber at...The wide-area damping controllers(WADCs),which are essential for mitigating regional low-frequency oscillations,face cyber-physical security threats due to the vulnerability of wide-area measurement system to cyber attacks and wind power uncertainties.This paper introduces reachability analysis method to quantify the impact of varying-amplitude attacks and uncertain wind fluctuations on the performance of WADC.Firstly,considering wind farm integration and attack injection,a nonlinear power system model with multiple buses is constructed based on Kron reduction method to improve computational efficiency and mitigate the constraints imposed by algebraic constraints.Then,a zonotope-based polytope construction method is employed to effectively model the range of attack amplitudes and wind uncertainties.By conducting reachability analysis,the reachable set preserving the nonlinear characteristics of studied system is computed,which enables the quantification of the maximum fluctuation range of regional oscillations under the dual disturbances.Case studies are undertaken on two multi-machine power systems with wind farm integration.The obtained results emphasize the efficacy of designed method,providing valuable insights into the magnitude of the impact that attacks exert on the operational characteristics of power system under various uncertain factors.展开更多
The safe driving and operation of trains is a necessary condition for ensuring the safe operation of trains.In particular,heavy-haul trains are characterized by the difficulty in driving and operation.Considering the ...The safe driving and operation of trains is a necessary condition for ensuring the safe operation of trains.In particular,heavy-haul trains are characterized by the difficulty in driving and operation.Considering the uncertainties in train driving and operation,this paper analyzes the relationship between the safety of heavy-haul electric locomotive hauled trains and driving and operation.It studies the auxiliary intelligent driving safety operation control methods.Through K-means to identify the characteristics of drivers'driving manipulation,the hidden Markov model adaptively adjusts the train driving and operation sequence,and conducts auxiliary driving reconstruction for heavy-haul locomotive driving and operation.Based on the train running curve and the locomotive traction/braking characteristics,it smoothly controls the exertion of the traction/braking force of heavy-haul locomotives,thereby optimizing the driving safety control of heavy-haul trains in the vehicle-environment-track system.Finally,the train operation simulation and optimized driving verification are carried out by simulating some track sections.The results show that the proposed method can correct and pre-optimize driving operations,improving the smoothness of heavy-haul trains by approximately 10%.It verifies the effectiveness of the proposed train assisted driving control reconstruction method,facilitating the smooth and safe operation of heavy-haul trains.展开更多
Flexibly interconnected distribution networks(FIDN)offer improved operational efficiency and operational control flexibility of power distribution systems through DC interconnection links,and have gradually become the...Flexibly interconnected distribution networks(FIDN)offer improved operational efficiency and operational control flexibility of power distribution systems through DC interconnection links,and have gradually become the main form of distribution networks.Aiming at the impact of constant power loads and converter transmission power variations in FIDN system stability,this paper presents an impedance reshaping based stability analysis and stabilization control to enhance the stability of the interconnected system and improve the system’s dynamic load response capability.Firstly,a small-single based equivalent impedance model of FIDN system,which consists flexibly interconnected equipment,energy storage,PV units,and constant power loads,is presented,and the total output and input impedance of the DC distribution network are derived.Secondly,the impacts of constant power loads and transmission power variations on the small-signal stability of FIDN system are analyzed through Nyquist stability curves using the impedance ratio criterion.Then,an impedance reshaping-based stability enhancement strategy for the FIDN system is proposed,which can significantly improve the system stability under the operating conditions of constant power loads and transmission power variations.Finally,a MATLAB/Simulink simulation model is built and tested.The results demonstrate that the proposed impedance reshaping strategy effectively mitigates voltage dips,surges,and DC bus fluctuations,shortens transient responses under power variations,and enables rapid stability recovery with reduced voltage drop during severe AC sags.展开更多
This paper introduces a novel fractional-order model based on the Caputo-Fabrizio(CF)derivative for analyzing computer virus propagation in networked environments.The model partitions the computer population into four...This paper introduces a novel fractional-order model based on the Caputo-Fabrizio(CF)derivative for analyzing computer virus propagation in networked environments.The model partitions the computer population into four compartments:susceptible,latently infected,breaking-out,and antivirus-capable systems.By employing the CF derivative—which uses a nonsingular exponential kernel—the framework effectively captures memory-dependent and nonlocal characteristics intrinsic to cyber systems,aspects inadequately represented by traditional integer-order models.Under Lipschitz continuity and boundedness assumptions,the existence and uniqueness of solutions are rigorously established via fixed-point theory.We develop a tailored two-step Adams-Bashforth numerical scheme for the CF framework and prove its second-order accuracy.Extensive numerical simulations across various fractional orders reveal that memory effects significantly influence virus transmission and control dynamics;smaller fractional orders produce more pronounced memory effects,delaying both infection spread and antivirus activation.Further theoretical analysis,including Hyers-Ulam stability and sensitivity assessments,reinforces the model’s robustness and identifies key parameters governing virus dynamics.The study also extends the framework to incorporate stochastic effects through a stochastic CF formulation.These results underscore fractional-order modeling as a powerful analytical tool for developing robust and effective cybersecurity strategies.展开更多
Deep rock engineering is affected by coupled thermo-hydro-mechanical(THM)-dynamic fields,necessitating the elucidation of the dynamic mechanical behavior and failure mechanisms.This study utilized a Multi-field Couple...Deep rock engineering is affected by coupled thermo-hydro-mechanical(THM)-dynamic fields,necessitating the elucidation of the dynamic mechanical behavior and failure mechanisms.This study utilized a Multi-field Coupled Controlled Split Hopkinson Pressure Bar(MCC-SHPB)system to elucidate the cross-scale dynamic responses of rocks and the boundaries of failure modes under THM coupling.Impact tests were conducted on green sandstone under coupled conditions of temperature(25℃-80℃),confining pressure(0-15 MPa),and seepage water pressure(0-15 MPa).Scanning electron microscopy(SEM)microstructural characterization and COMSOL Multiphysics numerical simulations were conducted,and a dynamic constitutive theoretical framework and failure-prediction methodology were established.We investigated the impact toughness index(I_(t)),dynamic modulus(E_(d)),dynamic triaxial compressive strength(TCS_(d)),fragmentation degree(W),and failure modes of green sandstone under thermo-confining pressure-seepage-impact loading conditions.The key findings reveal that the(I_(t))reflects different energy regulation mechanisms across different confining pressure regimes.Thermal-microcrack interactions dominate at low pressure,and energy absorption prevails at high pressure.A triphasic dynamic modulus model captures stiffness evolution under energy-driven conditions,revealing cross-scale crack nucleation-propagation and fragment reorganization.The TCSd inflection point signifies energy dissipation shifts,causing nonlinear skeleton bearing-capacity degradation.A critical criterion based on the W was established to distinguish between the two failure modes and predict the unstable failure initiation.Numerical simulations were used to elucidate the effects of inertia-dominated crack propagation and stress wave interference,validating the critical criterion and the predictive accuracy of the theoretical model during cross-scale failure.This study provides a theoretical foundation for assessing the dynamic stability of rock masses subjected to multi-field coupling during deep resource exploitation.展开更多
Chassis-by-wire technology has gained significant attention,with the scope of chassis domain control expanding from traditional two-dimensional plane motion control to encompass three-dimensional space motion control....Chassis-by-wire technology has gained significant attention,with the scope of chassis domain control expanding from traditional two-dimensional plane motion control to encompass three-dimensional space motion control.Modern chassis-by-wire systems manage an increasing number of heterogeneous chassis execution systems,including distributed drive,all-wheel drive(AWD),brake-by-wire(BBW),steer-by-wire(SBW),rear-wheel steering(RWS),active stabilizer bar(ASB)and active suspension system(ASS),greatly enhancing the controllable degrees of freedom compared to conventional chassis configurations.To advance research in chassis domain control,it is essential to understand how these heterogeneous execution systems influence vehicle dynamics.This paper focuses on the modeling and analysis of the lateral,longitudinal,and vertical chassis control and execution systems,-as well as their impact on vehicle lateral motion.Using a vehicle simulation platform,both the vehicle dynamics model and the individual dynamics models of each execution system were developed to analyze the influence of these systems on lateral dynamics.Additionally,a hierarchical control architecture was designed to control the vehicle’s lateral stability.The effectiveness of the proposed control scheme was demonstrated and validated through hardware-in-the-loop(HIL)tests and real-world vehicle testing.展开更多
Compared to other energy sources,nuclear reactors offer several advantages as a spacecraft power source,including compact size,high power density,and long operating life.These qualities make nuclear power an ideal ene...Compared to other energy sources,nuclear reactors offer several advantages as a spacecraft power source,including compact size,high power density,and long operating life.These qualities make nuclear power an ideal energy source for future deep space exploration.A whole system model of the space nuclear reactor consisting of the reactor neutron kinetics,reactivity control,reactor heat transfer,heat exchanger,and thermoelectric converter was developed.In addition,an electrical power control system was designed based on the developed dynamic model.The GRS method was used to quantitatively calculate the uncertainty of coupling parameters of the neutronics,thermal-hydraulics,and control system for the space reactor.The Spearman correlation coefficient was applied in the sensitivity analysis of system input parameters to output parameters.The calculation results showed that the uncertainty of the output parameters caused by coupling parameters had the most considerable variation,with a relative standard deviation<2.01%.Effective delayed neutron fraction was most sensitive to electrical power.To obtain optimal control performance,the non-dominated sorting genetic algorithm method was employed to optimize the controller parameters based on the uncertainty quantification calculation.Two typical transient simulations were conducted to test the adaptive ability of the optimized controller in the uncertainty dynamic system,including 100%full power(FP)to 90%FP step load reduction transient and 5%FP/min linear variable load transient.The results showed that,considering the influence of system uncertainty,the optimized controller could improve the response speed and load following accuracy of electrical power control,in which the effectiveness and superiority have been verified.展开更多
With the advancement of more electric aircraft(MEA)technology,the application of electro-hydrostatic actuators(EHAs)in aircraft actuation systems has become increasingly prevalent.This paper focuses on the modeling an...With the advancement of more electric aircraft(MEA)technology,the application of electro-hydrostatic actuators(EHAs)in aircraft actuation systems has become increasingly prevalent.This paper focuses on the modeling and mode switching analysis of EHA used in the primary flight control actuation systems of large aircraft,addressing the challenges associated with mode switching.First,we analyze the functional architecture and operational characteristics of multi-mode EHA,and sumarize the operating modes and implementation methods.Based on the EHA system architecture,we then develop a theoretical mathematical model and a simulation model.Using the simulation model,we analyze the performance of the EHA during normal operation.Finally,the performance of the EHA during mode switching under various functional switching scenarios is investigated.The results indicate that the EHA meets the performance requirements in terms of accuracy,bandwidth,and load capacity.Additionally,the hydraulic cylinder operates smoothly during the EHA mode switching,and the response time for switching between different modes is less than the specified threshold.These findings validate the system performance of multi-mode EHA,which helps to improve the reliability of EHA and the safety of aircraft flight control systems.展开更多
This research presents an advanced study on the modeling and stability analysis of electro-hydraulic control modules used in intelligent chassis systems.Firstly,a comprehensive nonlinear mathematical model of the elec...This research presents an advanced study on the modeling and stability analysis of electro-hydraulic control modules used in intelligent chassis systems.Firstly,a comprehensive nonlinear mathematical model of the electro-hydraulic power-shift system is developed,incorporating pipeline characteristics through impedance analysis and examining coupling effects between the pilot solenoid valve,main valve,and pipeline.Then,the model’s accuracy is validated through experimental testing,demonstrating high precision and minimal model errors.A comparative analysis between simulation data(both with and without pipeline characteristics)and experimental results reveals that the model considering pipeline parameters aligns more closely with experimental data,highlighting its superior accuracy.The research further explores the influence of key factors on system stability,including damping coefficient,feedback cavity orifice diameter,spring stiffness,pipeline length,and pipeline diameter.Significant findings include the critical impact of damping coefficient,orifice diameter,and pipeline length on stability,while spring stiffness has a minimal effect.These findings provide valuable insights for optimizing electro-hydraulic control modules in intelligent chassis systems,with practical implications for automotive and construction machinery applications.展开更多
A non-negative latent factor(NLF)model is able to be built efficiently via a single latent factor-dependent,non-negative and multiplicative update(SLF-NMU)algorithm for performing precise representation to high-dimens...A non-negative latent factor(NLF)model is able to be built efficiently via a single latent factor-dependent,non-negative and multiplicative update(SLF-NMU)algorithm for performing precise representation to high-dimensional and incomplete(HDI)matrix from many kinds of big-data-related applications.However,an SLF-NMU algorithm updates a latent factor relying on the current update increment only without considering past learning information,making a resultant model suffer from slow convergence.To address this issue,this study proposes a proportional integral(PI)controller-enhanced NLF(PI-NLF)model with two-fold ideas:1)Designing an increment refinement(IR)mechanism,which formulates the current and past update increments as the proportional and integral terms of a PI controller,thereby assimilating the past update information into the learning scheme smoothly with high efficiency;2)Deriving an IR-based SLF-NMU(ISN)algorithm,which updates a latent factor following the principle of an IR mechanism,thus significantly accelerating an NLF model's convergence rate.The simulation results on eight HDI matrices collected by real applications validate that a PI-NLF model outstrips several leading-edge models in both computational efficiency and accuracy when estimating missing data within an HDI matrix.The proposed PI-NLF model can be effectively applied to applications involving HDI matrix like e-commerce system,social network,and cloud service system.The code is available at https://github.com/yuanyeswu/PINLF/blob/mainIPINLF-code.zip.展开更多
Fatigue loads on wind turbines are critical factors that significantly influence operational lifespan and reliability.The passive yaw control of wind turbines often fails to capture the dynamic gradient changes of win...Fatigue loads on wind turbines are critical factors that significantly influence operational lifespan and reliability.The passive yaw control of wind turbines often fails to capture the dynamic gradient changes of wind speed and direction in the wind field,leading to an increased risk of load overload,severely affecting operational lifespan and reducing power generation efficiency.This impact is even more pronounced during the passage of a cold front.To address this issue,this paper proposes an independent variable-pitch control method that optimizes predictions by utilizing the spatiotemporal relationship between pre-observed cold front patterns and their dynamic propagation.First,a cold front and cold front propagation model suitable for engineering applications was derived.And a non-uniform inflow load model of turbine is established,which,combined with tower vibration response and rotor dynamic loads,accurately simulates the force distribution under complex inflow conditions.Subsequently,a pre-observation-based active cyclic pitch control method is presented,dynamically computing optimal pitch angle sequences by predicting wind field trends.This method eliminates the need for iterative optimization algorithms and reduces control latency to achieve proactive load management.Simulation verification shows that the proposed control strategy can effectively reduce key structural loads and increase power generation without relying on complex optimization algorithms.This method provides a practical solution for improving the economic benefits and operational reliability of wind farms under special wind conditions.展开更多
This paper introduces a small perturbation frequency domain thermal analysis model based on the nonlinear dynamics model.The model can be applied to study the high-precision temperature control of thermal systems unde...This paper introduces a small perturbation frequency domain thermal analysis model based on the nonlinear dynamics model.The model can be applied to study the high-precision temperature control of thermal systems under low-frequency complex perturbations.The frequency domain characteristics of the space gravitational wave detection satellite are analyzed,and a multi-channel perturbation structure is established.The effects of three kinds of heat flow perturbations,including external heat flow,power generation power,and waste heat of electronic equipment,on the temperature through five transfer paths are investigated.It has been discovered that the waste heat from electronic equipment inside the satellite has the most noticeable effect on the temperature power spectral density of temperature-sensitive optical loads,serving as the primary factor influencing thermal stability.For complex noise signals,the small perturbation analysis method can decompose the different frequency components or ranges,reducing the problem to linearized analysis and simplifying complex calculations.The results indicate that the temperature power spectral density decreases as signal frequency increases,with low-frequency signals exerting a greater influence on temperature stability.The small perturbation analysis method is a novel and effective method for temperature control of space thermal systems,with high accuracy and stability.展开更多
In this work,a computational modelling and analysis framework is developed to investigate the thermal buckling behavior of doubly-curved composite shells reinforced with graphene-origami(G-Ori)auxetic metamaterials.A ...In this work,a computational modelling and analysis framework is developed to investigate the thermal buckling behavior of doubly-curved composite shells reinforced with graphene-origami(G-Ori)auxetic metamaterials.A semi-analytical formulation based on the First-Order Shear Deformation Theory(FSDT)and the principle of virtual displacements is established,and closed-form solutions are derived via Navier’s method for simply supported boundary conditions.The G-Ori metamaterial reinforcements are treated as programmable constructs whose effective thermo-mechanical properties are obtained via micromechanical homogenization and incorporated into the shell model.A comprehensive parametric study examines the influence of folding geometry,dispersion arrangement,reinforcement weight fraction,curvature parameters,and elastic foundation support on the critical buckling temperature(CBT).The results reveal that,under optimal folding geometry and reinforcement alignment with principal stress trajectories,the CBT can increase by more than 150%.Furthermore,the combined effect of G-Ori reinforcement and elastic foundation substantially enhances thermal buckling resistance.These findings establish design guidelines for architected composite shells in applications such as aerospace thermal skins,morphing structures,and thermally-responsive systems,and illustrate the potential of auxetic graphene metamaterials for multifunctional,lightweight,and thermally robust structural components.展开更多
Objective:Sepsis exhibits remarkable heterogeneity in disease progression trajectories,and accurate identificationof distinct trajectory-based phenotypes is critical for implementing personalized therapeutic strategie...Objective:Sepsis exhibits remarkable heterogeneity in disease progression trajectories,and accurate identificationof distinct trajectory-based phenotypes is critical for implementing personalized therapeutic strategies and prognostic assessment.However,trajectory clustering analysis of time-series clinical data poses substantial methodological challenges for researchers.This study provides a comprehensive tutorial framework demonstrating six trajectory modeling approaches integrated with proteomic analysis to guide researchers in identifying sepsis subtypes after laparoscopic surgery.Methods:This study employs simulated longitudinal data from 300 septic patients after laparoscopic surgery to demonstrate six trajectory modeling methods(group-based trajectory modeling,latent growth mixture modeling,latent transition analysis,time-varying effect modeling,K-means for longitudinal data,agglomerative hierarchical clustering)for identifying associations between predefinedsequential organ failure assessment trajectories and 25 proteomic biomarkers.Clustering performance was evaluated via multiple metrics,and a biomarker discovery pipeline integrating principal component analysis,random forests,feature selection,and receiver operating characteristic analysis was developed.Results:The six methods demonstrated varying performance in identifying trajectory structures,with each approach exhibiting distinct analytical characteristics.The performance metrics revealed differences across methods,which may inform context-specificmethod selection and interpretation strategies.Conclusion:This study illustrates practical implementations of trajectory modeling approaches under controlled conditions,facilitating informed method selection for clinical researchers.The inclusion of complete R code and integrated proteomics workflows offers a reproducible analytical framework connecting temporal pattern recognition to biomarker discovery.Beyond sepsis,this pipeline-oriented approach may be adapted to diverse clinical scenarios requiring longitudinal disease characterization and precision medicine applications.The comparative analysis reveals that each method has distinct strengths,providing a practical guide for clinical researchers in selecting appropriate methods based on their specificstudy goals and data characteristics.展开更多
This article presents an adaptive intelligent control strategy applied to a lumped-parameter evaporator model,i.e.,a simplified dynamic representation treating the evaporator as a single thermal node with uniform temp...This article presents an adaptive intelligent control strategy applied to a lumped-parameter evaporator model,i.e.,a simplified dynamic representation treating the evaporator as a single thermal node with uniform temperature distribution,suitable for control design due to its balance between physical fidelity and computational simplicity.The controller uses a wavelet-based neural proportional,integral,derivative(PID)controller with IIR filtering(infinite impulse response).The dynamic model captures the essential heat and mass transfer phenomena through a nonlinear energy balance,where the cooling capacity“Qevap”is expressed as a non-linear function of the compressor frequency and the temperature difference,specifically,Q_(evap)=k_(1)u(T_(in)−T_(e))with u as compressor frequency,Te evaporator temperature,and Tin inlet fluid temperature.The operating conditions of the system,in general terms,focus on the following variables,the overall thermal capacity is 1000 J/K,typical for small-capacity heat exchangers,The mass flow is 0.05 kg/s,typical for secondary liquid cooling circuits,the overall loss coefficient of 50 W/K that corresponds to small evaporators with partial insulation,the temperatures(inlet)of 10℃and the temperature of environment of 25℃,thermal load of 200 W that corresponds to a small-scaled air conditioning applications.To handle system nonlinearities and improve control performance,aMorlet wavelet-based neural network(Wavenet)is used to dynamically adjust the PID gains online.An IIR filter is incorporated to smooth the adaptive gains,improving stability and reducing oscillations.In contrast to prior wavelet-or neural-adaptive PID controllers in HVAC applications,which typically adjust gains without explicit filtering or not tailored to evaporator dynamics,this work introduces the first PID–Wavenet scheme augmented with an IIR-based stabilization layer,specifically designed to address the combined challenges of nonlinear evaporator behavior,gain oscillation,and real-time implementability.The proposed controller(PID-Wavenet+IIR)is implemented and validated inMATLAB/Simulink,demonstrating superior performance compared to a conventional PID tuned using Simulink’s auto-tuning function.Key results include a reduction in settling time from 13.3 to 8.2 s,a reduction in overshoot from 3.5%to 0.8%,a reduction in steady-state error from 0.12℃ to 0.02℃and a 13%reduction in energy overall consumption.The controller also exhibits greater robustness and adaptability under varying thermal loads.This explicit integration of wavelet-driven adaptation with IIR-filtered gain shaping constitutes the main methodological contribution and novelty of the work.These findings validate the effectiveness of the wavelet-based adaptive approach for advanced thermal management in refrigeration and HVAC systems,with potential applications in controlling variable-speed compressors,liquid chillers,and compact cooling units.展开更多
This study investigates the uncertain dynamic characterization of hybrid composite plates by employing advanced machine-assisted finite element methodologies.Hybrid composites,widely used in aerospace,automotive,and s...This study investigates the uncertain dynamic characterization of hybrid composite plates by employing advanced machine-assisted finite element methodologies.Hybrid composites,widely used in aerospace,automotive,and structural applications,often face variability in material properties,geometric configurations,and manufacturing processes,leading to uncertainty in their dynamic response.To address this,three surrogate-based machine learning approaches like radial basis function(RBF),multivariate adaptive regression splines(MARS),and polynomial neural networks(PNN)are integrated with a finite element framework to efficiently capture the stochastic behavior of these plates.The research focuses on predicting the first three natural frequencies under material uncertainties,which are critical to ensuring structural reliability.Monte Carlo simulation(MCS)is used as a benchmark for generating probabilistic datasets,including mean values,standard deviations,and probability density functions.The surrogate models are then trained and validated against these datasets,enabling accurate representation of uncertainty with substantially fewer samples compared to conventionalMCS.Among the methods studied,the RBFmodel demonstrates superior performance,closely approximating MCS results with a reduced sample size,thereby achieving significant computational savings.The proposed framework not only reduces computational time and costs but also maintains high predictive accuracy,making it well-suited for complex engineering systems.Beyond free vibration analysis,the methodology can be extended to more sophisticated scenarios,such as forced vibration,damping effects,and nonlinear structural responses.Overall,this work presents a computationally efficient and robust approach for surrogate-based uncertainty quantification,advancing the analysis and design of hybrid composite structures under uncertainty.展开更多
This article proposes a Gaussian process(GP) based model predictive control(MPC) method to solve the tracking control of wheeled mobile robot( WMR) with uncertain model parameters.Firstly,a Gaussian process velocity p...This article proposes a Gaussian process(GP) based model predictive control(MPC) method to solve the tracking control of wheeled mobile robot( WMR) with uncertain model parameters.Firstly,a Gaussian process velocity prediction model is proposed to compensate for the unknown dynamic model,as the kinematic model cannot accurately characterize the motion characteristics of the robot.Then,by introducing the Lorentz function,the improved iterative linear quadratic regulator(iLQR) method is used to solve the nonlinear MPC(NMPC) controller with constraints.In addition,in order to reduce computational burden,a closed gradient calculation method is introduced to improve algorithm efficiency.Finally,the feasibility and effectiveness of this method are verified through simulation and experiment.展开更多
基金supported by University Grant Agency of Matej Bel University in Banská Bystrica project number UGA-14-PDS-2025.
文摘It is known that correlation does not imply causality.Some relationships identified in the analysis of data are coincidental or unknown,and some are produced by real-world causality of the situation,which is problematic,since there is a need to differentiate between these two scenarios.Until recently,the proper−semantic−causality of the relationship could have been determined only by human experts from the area of expertise of the studied data.This has changed with the advance of large language models,which are often utilized as surrogates for such human experts,making the process automated and readily available to all data analysts.This motivates the main objective of this work,which is to introduce the design and implementation of a large language model-based semantic causality evaluator based on correlation analysis,together with its visual analysis model called Causal heatmap.After the implementation itself,the model is evaluated from the point of view of the quality of the visual model,from the point of view of the quality of causal evaluation based on large language models,and from the point of view of comparative analysis,while the results reached in the study highlight the usability of large language models in the task and the potential of the proposed approach in the analysis of unknown datasets.The results of the experimental evaluation demonstrate the usefulness of the Causal heatmap method,supported by the evident highlighting of interesting relationships,while suppressing irrelevant ones.
基金funded by the Ministry of Higher Education through Universiti Putra Malaysia(UPM)under Grant FRGS/1/2023/STG07/UPM/02/4.
文摘Detecting geomagnetic anomalies preceding earthquakes is a challenging yet promising area of research that has gained increasing attention in recent years.This study introduces a novel reconstruction-based modeling approach enhanced by negative learning,employing a Bidirectional Long Short-Term Memory(BiLSTM)network explicitly trained to accurately reconstruct non-seismic geomagnetic signals while intentionally amplifying reconstruction errors for seismic signals.By penalizing the model for accurately reconstructing seismic anomalies,the negative learning approach effectively magnifies the differences between normal and anomalous data.This strategic differentiation enhances the sensitivity of the BiLSTM network,enabling improved detection of subtle geomagnetic anomalies that may serve as earthquake precursors.Experimental validation clearly demonstrated statistically significant higher reconstruction errors for seismic signals compared to non-seismic signals,confirmed through the Mann-Whitney U test with a p-value of 0.0035 for Root Mean Square Error(RMSE).These results provide compelling evidence of the enhanced anomaly detection capability achieved through negative learning.Unlike traditional classification-based methods,negative learning explicitly encourages sensitivity to subtle precursor signals embedded within complex geomagnetic data,establishing a robust basis for further development of reliable earthquake prediction methods.
基金supported in part by the National Natural Science Foundation of China under 52125704 and 51937001.
文摘Interaction between the converter and the grid may lead to harmonic oscillations.The impedance-based method is an effective way to deal with the stability issue.In this study,the impedance-based method is used to investigate the small-signal stability of a cascaded 12-pulse line-commutated converter-based high-voltage direct current(LCC-HVDC)transmission system.In the modeling part,the impedance models of the single rectifier and inverter are established respectively with consideration to the effect of frequency coupling,which has improved the accuracy of the models.Based on the models,the AC impedance models of the cascaded LCC-HVDC transmission system are established both on the rectifier and inverter side.In the stability analysis part,the stability of the system is analyzed under different working conditions.The simulation results reveal that the established impedance model can properly represent the stability of this system.The findings of this study can provide a theoretical reference for the stability design and oscillation suppression strategy of LCC-HVDC transmission systems and LCC interconnected systems.
基金supported in part by the Young Elite Scientists Sponsorship Program by the Chinese Society for Electrical Engineering under Grant CSEE-YESS-2022019in part by the Guangzhou Basic and Applied Basic Research Foundation under Grand 2024A04J3672in part by the National Natural Science Foundation of China under Grant 52207106.
文摘The wide-area damping controllers(WADCs),which are essential for mitigating regional low-frequency oscillations,face cyber-physical security threats due to the vulnerability of wide-area measurement system to cyber attacks and wind power uncertainties.This paper introduces reachability analysis method to quantify the impact of varying-amplitude attacks and uncertain wind fluctuations on the performance of WADC.Firstly,considering wind farm integration and attack injection,a nonlinear power system model with multiple buses is constructed based on Kron reduction method to improve computational efficiency and mitigate the constraints imposed by algebraic constraints.Then,a zonotope-based polytope construction method is employed to effectively model the range of attack amplitudes and wind uncertainties.By conducting reachability analysis,the reachable set preserving the nonlinear characteristics of studied system is computed,which enables the quantification of the maximum fluctuation range of regional oscillations under the dual disturbances.Case studies are undertaken on two multi-machine power systems with wind farm integration.The obtained results emphasize the efficacy of designed method,providing valuable insights into the magnitude of the impact that attacks exert on the operational characteristics of power system under various uncertain factors.
基金Project(U2034211)supported by the National Natural Science Foundation of ChinaProject(20232ACE01013)supported by the Major Scientific and Technological Research and Development Special Project of Jiangxi Province,China。
文摘The safe driving and operation of trains is a necessary condition for ensuring the safe operation of trains.In particular,heavy-haul trains are characterized by the difficulty in driving and operation.Considering the uncertainties in train driving and operation,this paper analyzes the relationship between the safety of heavy-haul electric locomotive hauled trains and driving and operation.It studies the auxiliary intelligent driving safety operation control methods.Through K-means to identify the characteristics of drivers'driving manipulation,the hidden Markov model adaptively adjusts the train driving and operation sequence,and conducts auxiliary driving reconstruction for heavy-haul locomotive driving and operation.Based on the train running curve and the locomotive traction/braking characteristics,it smoothly controls the exertion of the traction/braking force of heavy-haul locomotives,thereby optimizing the driving safety control of heavy-haul trains in the vehicle-environment-track system.Finally,the train operation simulation and optimized driving verification are carried out by simulating some track sections.The results show that the proposed method can correct and pre-optimize driving operations,improving the smoothness of heavy-haul trains by approximately 10%.It verifies the effectiveness of the proposed train assisted driving control reconstruction method,facilitating the smooth and safe operation of heavy-haul trains.
基金supported by the key technology project of China Southern Power Grid Corporation(GZKJXM20220041)partly by theNational Key Research andDevelopment Plan(2022YFE0205300).
文摘Flexibly interconnected distribution networks(FIDN)offer improved operational efficiency and operational control flexibility of power distribution systems through DC interconnection links,and have gradually become the main form of distribution networks.Aiming at the impact of constant power loads and converter transmission power variations in FIDN system stability,this paper presents an impedance reshaping based stability analysis and stabilization control to enhance the stability of the interconnected system and improve the system’s dynamic load response capability.Firstly,a small-single based equivalent impedance model of FIDN system,which consists flexibly interconnected equipment,energy storage,PV units,and constant power loads,is presented,and the total output and input impedance of the DC distribution network are derived.Secondly,the impacts of constant power loads and transmission power variations on the small-signal stability of FIDN system are analyzed through Nyquist stability curves using the impedance ratio criterion.Then,an impedance reshaping-based stability enhancement strategy for the FIDN system is proposed,which can significantly improve the system stability under the operating conditions of constant power loads and transmission power variations.Finally,a MATLAB/Simulink simulation model is built and tested.The results demonstrate that the proposed impedance reshaping strategy effectively mitigates voltage dips,surges,and DC bus fluctuations,shortens transient responses under power variations,and enables rapid stability recovery with reduced voltage drop during severe AC sags.
基金supported and funded by the Deanship of Scientific Research at Imam Mohammad Ibn Saud Islamic University(IMSIU)(grant number IMSIU-DDRSP2601).
文摘This paper introduces a novel fractional-order model based on the Caputo-Fabrizio(CF)derivative for analyzing computer virus propagation in networked environments.The model partitions the computer population into four compartments:susceptible,latently infected,breaking-out,and antivirus-capable systems.By employing the CF derivative—which uses a nonsingular exponential kernel—the framework effectively captures memory-dependent and nonlocal characteristics intrinsic to cyber systems,aspects inadequately represented by traditional integer-order models.Under Lipschitz continuity and boundedness assumptions,the existence and uniqueness of solutions are rigorously established via fixed-point theory.We develop a tailored two-step Adams-Bashforth numerical scheme for the CF framework and prove its second-order accuracy.Extensive numerical simulations across various fractional orders reveal that memory effects significantly influence virus transmission and control dynamics;smaller fractional orders produce more pronounced memory effects,delaying both infection spread and antivirus activation.Further theoretical analysis,including Hyers-Ulam stability and sensitivity assessments,reinforces the model’s robustness and identifies key parameters governing virus dynamics.The study also extends the framework to incorporate stochastic effects through a stochastic CF formulation.These results underscore fractional-order modeling as a powerful analytical tool for developing robust and effective cybersecurity strategies.
基金supported by the National Natural Science Foundation of China(Grant Nos.12272411 and 42007259).
文摘Deep rock engineering is affected by coupled thermo-hydro-mechanical(THM)-dynamic fields,necessitating the elucidation of the dynamic mechanical behavior and failure mechanisms.This study utilized a Multi-field Coupled Controlled Split Hopkinson Pressure Bar(MCC-SHPB)system to elucidate the cross-scale dynamic responses of rocks and the boundaries of failure modes under THM coupling.Impact tests were conducted on green sandstone under coupled conditions of temperature(25℃-80℃),confining pressure(0-15 MPa),and seepage water pressure(0-15 MPa).Scanning electron microscopy(SEM)microstructural characterization and COMSOL Multiphysics numerical simulations were conducted,and a dynamic constitutive theoretical framework and failure-prediction methodology were established.We investigated the impact toughness index(I_(t)),dynamic modulus(E_(d)),dynamic triaxial compressive strength(TCS_(d)),fragmentation degree(W),and failure modes of green sandstone under thermo-confining pressure-seepage-impact loading conditions.The key findings reveal that the(I_(t))reflects different energy regulation mechanisms across different confining pressure regimes.Thermal-microcrack interactions dominate at low pressure,and energy absorption prevails at high pressure.A triphasic dynamic modulus model captures stiffness evolution under energy-driven conditions,revealing cross-scale crack nucleation-propagation and fragment reorganization.The TCSd inflection point signifies energy dissipation shifts,causing nonlinear skeleton bearing-capacity degradation.A critical criterion based on the W was established to distinguish between the two failure modes and predict the unstable failure initiation.Numerical simulations were used to elucidate the effects of inertia-dominated crack propagation and stress wave interference,validating the critical criterion and the predictive accuracy of the theoretical model during cross-scale failure.This study provides a theoretical foundation for assessing the dynamic stability of rock masses subjected to multi-field coupling during deep resource exploitation.
基金Supported by National Nature Science Foundation of China(Grant Nos.52325212,52372394)National Key Research and Development Program of China(Grant Nos.2022YFE0117100,2021YFB2501201)+1 种基金Industry-University-Research Innovation Fund for Chinese Universities(Grand No.2024HT010)Fundamental Research Funds for the Central Universities.
文摘Chassis-by-wire technology has gained significant attention,with the scope of chassis domain control expanding from traditional two-dimensional plane motion control to encompass three-dimensional space motion control.Modern chassis-by-wire systems manage an increasing number of heterogeneous chassis execution systems,including distributed drive,all-wheel drive(AWD),brake-by-wire(BBW),steer-by-wire(SBW),rear-wheel steering(RWS),active stabilizer bar(ASB)and active suspension system(ASS),greatly enhancing the controllable degrees of freedom compared to conventional chassis configurations.To advance research in chassis domain control,it is essential to understand how these heterogeneous execution systems influence vehicle dynamics.This paper focuses on the modeling and analysis of the lateral,longitudinal,and vertical chassis control and execution systems,-as well as their impact on vehicle lateral motion.Using a vehicle simulation platform,both the vehicle dynamics model and the individual dynamics models of each execution system were developed to analyze the influence of these systems on lateral dynamics.Additionally,a hierarchical control architecture was designed to control the vehicle’s lateral stability.The effectiveness of the proposed control scheme was demonstrated and validated through hardware-in-the-loop(HIL)tests and real-world vehicle testing.
基金supported by the National Natural Science Foundation of China(12305185)Natural Science Foundation of Hunan Province,China(No.2023JJ50122)+1 种基金International Cooperative Research Project of the Ministry of Education,China(No.HZKY20220355)Scientific Research Foundation of the Education Department of Hunan Province,China(No.22A0307).
文摘Compared to other energy sources,nuclear reactors offer several advantages as a spacecraft power source,including compact size,high power density,and long operating life.These qualities make nuclear power an ideal energy source for future deep space exploration.A whole system model of the space nuclear reactor consisting of the reactor neutron kinetics,reactivity control,reactor heat transfer,heat exchanger,and thermoelectric converter was developed.In addition,an electrical power control system was designed based on the developed dynamic model.The GRS method was used to quantitatively calculate the uncertainty of coupling parameters of the neutronics,thermal-hydraulics,and control system for the space reactor.The Spearman correlation coefficient was applied in the sensitivity analysis of system input parameters to output parameters.The calculation results showed that the uncertainty of the output parameters caused by coupling parameters had the most considerable variation,with a relative standard deviation<2.01%.Effective delayed neutron fraction was most sensitive to electrical power.To obtain optimal control performance,the non-dominated sorting genetic algorithm method was employed to optimize the controller parameters based on the uncertainty quantification calculation.Two typical transient simulations were conducted to test the adaptive ability of the optimized controller in the uncertainty dynamic system,including 100%full power(FP)to 90%FP step load reduction transient and 5%FP/min linear variable load transient.The results showed that,considering the influence of system uncertainty,the optimized controller could improve the response speed and load following accuracy of electrical power control,in which the effectiveness and superiority have been verified.
基金supported by the Chinese Civil Aircraft Project(No.MJ-2017-S49).
文摘With the advancement of more electric aircraft(MEA)technology,the application of electro-hydrostatic actuators(EHAs)in aircraft actuation systems has become increasingly prevalent.This paper focuses on the modeling and mode switching analysis of EHA used in the primary flight control actuation systems of large aircraft,addressing the challenges associated with mode switching.First,we analyze the functional architecture and operational characteristics of multi-mode EHA,and sumarize the operating modes and implementation methods.Based on the EHA system architecture,we then develop a theoretical mathematical model and a simulation model.Using the simulation model,we analyze the performance of the EHA during normal operation.Finally,the performance of the EHA during mode switching under various functional switching scenarios is investigated.The results indicate that the EHA meets the performance requirements in terms of accuracy,bandwidth,and load capacity.Additionally,the hydraulic cylinder operates smoothly during the EHA mode switching,and the response time for switching between different modes is less than the specified threshold.These findings validate the system performance of multi-mode EHA,which helps to improve the reliability of EHA and the safety of aircraft flight control systems.
基金Supported by the Basic Product Innovation Plan for Vehicle Power Scientific Research Project(Grant No.JCCPCX201704).
文摘This research presents an advanced study on the modeling and stability analysis of electro-hydraulic control modules used in intelligent chassis systems.Firstly,a comprehensive nonlinear mathematical model of the electro-hydraulic power-shift system is developed,incorporating pipeline characteristics through impedance analysis and examining coupling effects between the pilot solenoid valve,main valve,and pipeline.Then,the model’s accuracy is validated through experimental testing,demonstrating high precision and minimal model errors.A comparative analysis between simulation data(both with and without pipeline characteristics)and experimental results reveals that the model considering pipeline parameters aligns more closely with experimental data,highlighting its superior accuracy.The research further explores the influence of key factors on system stability,including damping coefficient,feedback cavity orifice diameter,spring stiffness,pipeline length,and pipeline diameter.Significant findings include the critical impact of damping coefficient,orifice diameter,and pipeline length on stability,while spring stiffness has a minimal effect.These findings provide valuable insights for optimizing electro-hydraulic control modules in intelligent chassis systems,with practical implications for automotive and construction machinery applications.
基金supported in part by the National Natural Science Foundation of China(62372385,62272078)the Chongqing Natural Science Foundation(CSTB2023NSCQ-LZX0069).
文摘A non-negative latent factor(NLF)model is able to be built efficiently via a single latent factor-dependent,non-negative and multiplicative update(SLF-NMU)algorithm for performing precise representation to high-dimensional and incomplete(HDI)matrix from many kinds of big-data-related applications.However,an SLF-NMU algorithm updates a latent factor relying on the current update increment only without considering past learning information,making a resultant model suffer from slow convergence.To address this issue,this study proposes a proportional integral(PI)controller-enhanced NLF(PI-NLF)model with two-fold ideas:1)Designing an increment refinement(IR)mechanism,which formulates the current and past update increments as the proportional and integral terms of a PI controller,thereby assimilating the past update information into the learning scheme smoothly with high efficiency;2)Deriving an IR-based SLF-NMU(ISN)algorithm,which updates a latent factor following the principle of an IR mechanism,thus significantly accelerating an NLF model's convergence rate.The simulation results on eight HDI matrices collected by real applications validate that a PI-NLF model outstrips several leading-edge models in both computational efficiency and accuracy when estimating missing data within an HDI matrix.The proposed PI-NLF model can be effectively applied to applications involving HDI matrix like e-commerce system,social network,and cloud service system.The code is available at https://github.com/yuanyeswu/PINLF/blob/mainIPINLF-code.zip.
基金supported by the National Key Research and Development Program of China,grant number 2023YFB4203200。
文摘Fatigue loads on wind turbines are critical factors that significantly influence operational lifespan and reliability.The passive yaw control of wind turbines often fails to capture the dynamic gradient changes of wind speed and direction in the wind field,leading to an increased risk of load overload,severely affecting operational lifespan and reducing power generation efficiency.This impact is even more pronounced during the passage of a cold front.To address this issue,this paper proposes an independent variable-pitch control method that optimizes predictions by utilizing the spatiotemporal relationship between pre-observed cold front patterns and their dynamic propagation.First,a cold front and cold front propagation model suitable for engineering applications was derived.And a non-uniform inflow load model of turbine is established,which,combined with tower vibration response and rotor dynamic loads,accurately simulates the force distribution under complex inflow conditions.Subsequently,a pre-observation-based active cyclic pitch control method is presented,dynamically computing optimal pitch angle sequences by predicting wind field trends.This method eliminates the need for iterative optimization algorithms and reduces control latency to achieve proactive load management.Simulation verification shows that the proposed control strategy can effectively reduce key structural loads and increase power generation without relying on complex optimization algorithms.This method provides a practical solution for improving the economic benefits and operational reliability of wind farms under special wind conditions.
基金supported by the National Key Research and Development Program of China(No.2022YFC2204400)。
文摘This paper introduces a small perturbation frequency domain thermal analysis model based on the nonlinear dynamics model.The model can be applied to study the high-precision temperature control of thermal systems under low-frequency complex perturbations.The frequency domain characteristics of the space gravitational wave detection satellite are analyzed,and a multi-channel perturbation structure is established.The effects of three kinds of heat flow perturbations,including external heat flow,power generation power,and waste heat of electronic equipment,on the temperature through five transfer paths are investigated.It has been discovered that the waste heat from electronic equipment inside the satellite has the most noticeable effect on the temperature power spectral density of temperature-sensitive optical loads,serving as the primary factor influencing thermal stability.For complex noise signals,the small perturbation analysis method can decompose the different frequency components or ranges,reducing the problem to linearized analysis and simplifying complex calculations.The results indicate that the temperature power spectral density decreases as signal frequency increases,with low-frequency signals exerting a greater influence on temperature stability.The small perturbation analysis method is a novel and effective method for temperature control of space thermal systems,with high accuracy and stability.
文摘In this work,a computational modelling and analysis framework is developed to investigate the thermal buckling behavior of doubly-curved composite shells reinforced with graphene-origami(G-Ori)auxetic metamaterials.A semi-analytical formulation based on the First-Order Shear Deformation Theory(FSDT)and the principle of virtual displacements is established,and closed-form solutions are derived via Navier’s method for simply supported boundary conditions.The G-Ori metamaterial reinforcements are treated as programmable constructs whose effective thermo-mechanical properties are obtained via micromechanical homogenization and incorporated into the shell model.A comprehensive parametric study examines the influence of folding geometry,dispersion arrangement,reinforcement weight fraction,curvature parameters,and elastic foundation support on the critical buckling temperature(CBT).The results reveal that,under optimal folding geometry and reinforcement alignment with principal stress trajectories,the CBT can increase by more than 150%.Furthermore,the combined effect of G-Ori reinforcement and elastic foundation substantially enhances thermal buckling resistance.These findings establish design guidelines for architected composite shells in applications such as aerospace thermal skins,morphing structures,and thermally-responsive systems,and illustrate the potential of auxetic graphene metamaterials for multifunctional,lightweight,and thermally robust structural components.
基金funding from the China National Key Research and Development Program(No.2023YFC3603104)the National Natural Science Foundation of China(Nos.82472243 and 82272180)+6 种基金the Fundamental Research Funds for the Central Universities(No.226-2025-00024)the Huadong Medicine Joint Funds of the Zhejiang Provincial Natural Science Foundation of China(No.LHDMD24H150001)the Key Research&Development Project of Zhejiang Province(No.2024C03240)a collaborative scientific project co-established by the Science and Technology Department of the National Administration of Traditional Chinese Medicine and the Zhejiang Provincial Administration of Traditional Chinese Medicine(No.GZY-ZJ-KJ-24082)he General Health Science and Technology Program of Zhejiang Province(No.2024KY1099)the Project of Zhejiang University Longquan Innovation Center(No.ZJDXLQCXZCJBGS2024016)Wu Jieping Medical Foundation Special Research Grant(No.320.6750.2024-23-07).
文摘Objective:Sepsis exhibits remarkable heterogeneity in disease progression trajectories,and accurate identificationof distinct trajectory-based phenotypes is critical for implementing personalized therapeutic strategies and prognostic assessment.However,trajectory clustering analysis of time-series clinical data poses substantial methodological challenges for researchers.This study provides a comprehensive tutorial framework demonstrating six trajectory modeling approaches integrated with proteomic analysis to guide researchers in identifying sepsis subtypes after laparoscopic surgery.Methods:This study employs simulated longitudinal data from 300 septic patients after laparoscopic surgery to demonstrate six trajectory modeling methods(group-based trajectory modeling,latent growth mixture modeling,latent transition analysis,time-varying effect modeling,K-means for longitudinal data,agglomerative hierarchical clustering)for identifying associations between predefinedsequential organ failure assessment trajectories and 25 proteomic biomarkers.Clustering performance was evaluated via multiple metrics,and a biomarker discovery pipeline integrating principal component analysis,random forests,feature selection,and receiver operating characteristic analysis was developed.Results:The six methods demonstrated varying performance in identifying trajectory structures,with each approach exhibiting distinct analytical characteristics.The performance metrics revealed differences across methods,which may inform context-specificmethod selection and interpretation strategies.Conclusion:This study illustrates practical implementations of trajectory modeling approaches under controlled conditions,facilitating informed method selection for clinical researchers.The inclusion of complete R code and integrated proteomics workflows offers a reproducible analytical framework connecting temporal pattern recognition to biomarker discovery.Beyond sepsis,this pipeline-oriented approach may be adapted to diverse clinical scenarios requiring longitudinal disease characterization and precision medicine applications.The comparative analysis reveals that each method has distinct strengths,providing a practical guide for clinical researchers in selecting appropriate methods based on their specificstudy goals and data characteristics.
文摘This article presents an adaptive intelligent control strategy applied to a lumped-parameter evaporator model,i.e.,a simplified dynamic representation treating the evaporator as a single thermal node with uniform temperature distribution,suitable for control design due to its balance between physical fidelity and computational simplicity.The controller uses a wavelet-based neural proportional,integral,derivative(PID)controller with IIR filtering(infinite impulse response).The dynamic model captures the essential heat and mass transfer phenomena through a nonlinear energy balance,where the cooling capacity“Qevap”is expressed as a non-linear function of the compressor frequency and the temperature difference,specifically,Q_(evap)=k_(1)u(T_(in)−T_(e))with u as compressor frequency,Te evaporator temperature,and Tin inlet fluid temperature.The operating conditions of the system,in general terms,focus on the following variables,the overall thermal capacity is 1000 J/K,typical for small-capacity heat exchangers,The mass flow is 0.05 kg/s,typical for secondary liquid cooling circuits,the overall loss coefficient of 50 W/K that corresponds to small evaporators with partial insulation,the temperatures(inlet)of 10℃and the temperature of environment of 25℃,thermal load of 200 W that corresponds to a small-scaled air conditioning applications.To handle system nonlinearities and improve control performance,aMorlet wavelet-based neural network(Wavenet)is used to dynamically adjust the PID gains online.An IIR filter is incorporated to smooth the adaptive gains,improving stability and reducing oscillations.In contrast to prior wavelet-or neural-adaptive PID controllers in HVAC applications,which typically adjust gains without explicit filtering or not tailored to evaporator dynamics,this work introduces the first PID–Wavenet scheme augmented with an IIR-based stabilization layer,specifically designed to address the combined challenges of nonlinear evaporator behavior,gain oscillation,and real-time implementability.The proposed controller(PID-Wavenet+IIR)is implemented and validated inMATLAB/Simulink,demonstrating superior performance compared to a conventional PID tuned using Simulink’s auto-tuning function.Key results include a reduction in settling time from 13.3 to 8.2 s,a reduction in overshoot from 3.5%to 0.8%,a reduction in steady-state error from 0.12℃ to 0.02℃and a 13%reduction in energy overall consumption.The controller also exhibits greater robustness and adaptability under varying thermal loads.This explicit integration of wavelet-driven adaptation with IIR-filtered gain shaping constitutes the main methodological contribution and novelty of the work.These findings validate the effectiveness of the wavelet-based adaptive approach for advanced thermal management in refrigeration and HVAC systems,with potential applications in controlling variable-speed compressors,liquid chillers,and compact cooling units.
文摘This study investigates the uncertain dynamic characterization of hybrid composite plates by employing advanced machine-assisted finite element methodologies.Hybrid composites,widely used in aerospace,automotive,and structural applications,often face variability in material properties,geometric configurations,and manufacturing processes,leading to uncertainty in their dynamic response.To address this,three surrogate-based machine learning approaches like radial basis function(RBF),multivariate adaptive regression splines(MARS),and polynomial neural networks(PNN)are integrated with a finite element framework to efficiently capture the stochastic behavior of these plates.The research focuses on predicting the first three natural frequencies under material uncertainties,which are critical to ensuring structural reliability.Monte Carlo simulation(MCS)is used as a benchmark for generating probabilistic datasets,including mean values,standard deviations,and probability density functions.The surrogate models are then trained and validated against these datasets,enabling accurate representation of uncertainty with substantially fewer samples compared to conventionalMCS.Among the methods studied,the RBFmodel demonstrates superior performance,closely approximating MCS results with a reduced sample size,thereby achieving significant computational savings.The proposed framework not only reduces computational time and costs but also maintains high predictive accuracy,making it well-suited for complex engineering systems.Beyond free vibration analysis,the methodology can be extended to more sophisticated scenarios,such as forced vibration,damping effects,and nonlinear structural responses.Overall,this work presents a computationally efficient and robust approach for surrogate-based uncertainty quantification,advancing the analysis and design of hybrid composite structures under uncertainty.
基金Supported by the Zhejiang Provincial Natural Science Foundation of China (No.LR23F030002)。
文摘This article proposes a Gaussian process(GP) based model predictive control(MPC) method to solve the tracking control of wheeled mobile robot( WMR) with uncertain model parameters.Firstly,a Gaussian process velocity prediction model is proposed to compensate for the unknown dynamic model,as the kinematic model cannot accurately characterize the motion characteristics of the robot.Then,by introducing the Lorentz function,the improved iterative linear quadratic regulator(iLQR) method is used to solve the nonlinear MPC(NMPC) controller with constraints.In addition,in order to reduce computational burden,a closed gradient calculation method is introduced to improve algorithm efficiency.Finally,the feasibility and effectiveness of this method are verified through simulation and experiment.
