With its growth in spacecraft control applications,the microcontroller(MCU)becomes increasingly sensitive to radiation and the risks of system failure.In a radiation environment,the MCU is vulnerable to impacts from h...With its growth in spacecraft control applications,the microcontroller(MCU)becomes increasingly sensitive to radiation and the risks of system failure.In a radiation environment,the MCU is vulnerable to impacts from high-energy particles,which can lead to single-event effect(SEE)that disrupt normal system operations.The pipeline of MCU,being the core structure of the system,is particularly susceptible to single-event upset(SEU)and potentially causes execution failures.However,existing radiation-hardening techniques offer limited effectiveness for pipelines.To enhance SEU resistance,this study focused on a 32-bit MCU core with eight pipeline stages,proposing a pipeline hardening approach that utilizes lockstep technology to improve fault tolerance.Signals from two processors were compared including register write data,register contents and pre-fetched instructions.Any discrepancies triggered error flags to indicate faults.When an error flag was raised,recovery was initiated through an interrupt.The interrupt handler then retrieved state information from the advanced peripheral bus(APB)slave module to restore the CPU's operational state and resume execution.By combining hardware-based state preservation with software-driven error recovery,the proposed solution demonstrated significant improvements in fault tolerance rates and performance compared to traditional checkpoint-based techniques.After completing the pipeline hardening design,a fault injection platform was utilized in this paper to simulate real-world error conditions on internal processor modules.The platform was developed based on the circuit's register-transfer-level(RTL)design and statistical results.The fault injection platform was performed by automatically finding all registers within the target design.The register values were forced to upset at the tens of nanoseconds scale in the RTL description of the circuit's design.After running the circuit's functional simulation,the statistics of the faults in registers were displayed on the platform,which evaluated the influence of SEU.The vulnerability of SEU in the circuit could be observed from the results of the soft error statistics.The post-hardening soft error rates were then measured and compared to pre-hardening data,providing a quantitative evaluation of the improvements.Using this method,the soft error rates of the modules in the MCU core such as PFU,DPU,and Cache AXIM are 40.07%,26.36%,and 27.29%respectively before hardening.The soft error rates of modules mentioned above are reduced to 0%,0.69%,and 1.11%after hardening.The hardened and non-hardened designs of the entire core were implemented in FPGA.The total resource utilization of the triple mode redundancy(TMR)is 111984,as indicated by the number of look-up tables(LUTs)and registers consumed in the FPGA.The total resource utilization of this work is 78034,and the ratio of resource utilization between this work and TMR is approximately 69.68%.The error recovery time for the hardened MCU processor was analyzed using the completion cycles of a bubble sort algorithm as a benchmark.In this paper,the average recovery cycle using the software checkpoint roll-back method is 36479.06,and the average recovery cycle using this work is 26922.5.The ratio of recovery cycles between this work and checkpoint roll-back is about 73.8%.Assessments through random fault injection and FPGA implementation indicate that this approach effectively reduces processor faults caused by soft errors while optimizing resource utilization and efficiency over triplemodular redundancy.展开更多
Graphene/copper-based composite heat sinks demonstrate extensive application potential in military equipment thermal management,high-power electronic packaging,new energy vehicles,and 5G communication systems,due to t...Graphene/copper-based composite heat sinks demonstrate extensive application potential in military equipment thermal management,high-power electronic packaging,new energy vehicles,and 5G communication systems,due to their outstanding properties,including high thermal conductivity,tunable thermal expansion coefficients,excellent mechanical strength,and low density.However,the industrial-scale application of these composites faces critical challenges during the fabrication of components with complex structures,such as inhomogeneous dispersion of graphene within the copper matrix and poor interfacial bonding between the two phases,which substantially undermine the overall performance of graphene/copper-based composites.To address these issues,the preparation methods for graphene/copper-based composite heat sinks were reviewed.For each method,a rigorous analysis was presented to clarify its inherent advantages and unavoidable restrictions.Furthermore,the latest research progress in addressing three core scientific challenges was synthesized,including uniform dispersion of graphene,interfacial optimization mechanisms,and molecular dynamics simulations for elucidating the structure-property relationships.Finally,the future development directions of graphene/copper-based composite heat sinks in engineering applications were prospected.展开更多
Online three-dimensional(3D)path planning in dynamic environments is a fundamental problem for achieving autonomous navigation of unmanned aerial vehicles(UAVs).However,existing methods struggle to model traversable d...Online three-dimensional(3D)path planning in dynamic environments is a fundamental problem for achieving autonomous navigation of unmanned aerial vehicles(UAVs).However,existing methods struggle to model traversable dynamic gaps,resulting in conservative and suboptimal trajectories.To address these challenges,this paper proposes a hierarchical reinforcement learning(RL)framework that integrates global path guidance,local trajectory generation,predictive safety evaluation,and neural network-based decision-making.Specifically,the global planner provides long-term navigation guidance,and the local module then utilizes an improved 3D dynamic window approach(DWA)to generate dynamically feasible candidate trajectories.To enhance safety in dense dynamic scenarios,the algorithm introduces a predictive axis-aligned bounding box(AABB)strategy to model the future occupancy of obstacles,combined with convex hull verification for efficient trajectory safety assessment.Furthermore,a double deep Q-network(DDQN)is employed with structured feature encoding,enabling the neural network to reliably select the optimal trajectory from the candidate set,thereby improving robustness and generalization.Comparative experiments conducted in a high-fidelity simulation environment show that the algorithm outperforms existing algorithms,reducing the average number of collisions to 0.2 while shortening the average task completion time by approximately 15%,and achieving a success rate of 97%.展开更多
Spaceborne antennas are essential for remote sensing,deep-space communication,and Earth observation,yet their trajectory planning is complicated by nonlinear base-manipulator coupling and antenna flexibility.To addres...Spaceborne antennas are essential for remote sensing,deep-space communication,and Earth observation,yet their trajectory planning is complicated by nonlinear base-manipulator coupling and antenna flexibility.To address these challenges,this paper proposes a multi-objective trajectory optimization framework.The system dynamics capture both nonlinear rigid-flexible coupling and antenna deformation through a reduced-order formulation.To enhance discretization efficiency,a predictive-terminal hp-adaptive pseudospectral method is employed,assigning collocation density based on task-phase characteristics:finer resolution is applied to dynamic segments requiring higher accuracy,especially near the terminal phase.This enables efficient transcription of the continuous-time problem into a Nonlinear Programming Problem(NLP).The resulting NLP is then solved using a multi-objective optimization strategy based on the nondominated sorting genetic algorithm II,which explores trade-offs among antenna pointing accuracy,energy consumption,and structural vibration.Numerical results demonstrate that the proposed method achieves a reduction of approximately 14.0% in control energy and 41.8%in peak actuation compared to a GPOPS-II baseline,while significantly enhancing vibration suppression.The resulting Pareto front reveals structured trade-offs and clustered solutions,offering robust and diverse options for precision,low-disturbance mission planning.展开更多
Spectrum map construction,which is crucial in cognitive radio(CR)system,visualizes the invisible space of the electromagnetic spectrum for spectrum-resource management and allocation.Traditional reconstruction methods...Spectrum map construction,which is crucial in cognitive radio(CR)system,visualizes the invisible space of the electromagnetic spectrum for spectrum-resource management and allocation.Traditional reconstruction methods are generally for twodimensional(2D)spectrum map and driven by abundant sampling data.In this paper,we propose a data-model-knowledge-driven reconstruction scheme to construct the three-dimensional(3D)spectrum map under multi-radiation source scenarios.We firstly design a maximum and minimum path loss difference(MMPLD)clustering algorithm to detect the number of radiation sources in a 3D space.Then,we develop a joint location-power estimation method based on the heuristic population evolutionary optimization algorithm.Considering the variation of electromagnetic environment,we self-learn the path loss(PL)model based on the sampling data.Finally,the 3D spectrum is reconstructed according to the self-learned PL model and the extracted knowledge of radiation sources.Simulations show that the proposed 3D spectrum map reconstruction scheme not only has splendid adaptability to the environment,but also achieves high spectrum construction accuracy even when the sampling rate is very low.展开更多
The Electrical Power System(EPS)is one of the spacecraft’s key subsystems,and its operational status directly affects the lifespan and performance of the entire spacecraft.The corresponding fault diagnosis has always...The Electrical Power System(EPS)is one of the spacecraft’s key subsystems,and its operational status directly affects the lifespan and performance of the entire spacecraft.The corresponding fault diagnosis has always been the discussion focus to ensure spacecraft reliability.In this paper,a few-shot unsupervised fault diagnosis method based on the improved Newman community division algorithm is proposed,to approach the scarcity of fault data samples and the inconspicuous characteristics of abnormal data.Firstly,aiming to capture the overall relevance of the fault dataset,a complex network model is built by adopting the K-Dynamic time warping distance Adjacent Nodes(KDAN)method.Based on the complex network model,the Newman community divisions algorithm is improved by using the Quantum-behaved Particle Swarm Optimization(QPSO).Subsequently,in order to evaluate the feasibility of the proposed method,experimental validation was conducted using an open-source dataset.The results indicate that the average accuracy can reach 96.43% for fault data diagnosis,and an F1_score of 97.76%with only 17.65%of the dataset used for training.The proposed method can accurately classify abnormal data by identifying the community structure in the data network,significantly improve the efficiency of the community divisions algorithm and reduce its complexity,and provide a new solution for fault diagnosis in large-scale complex systems.展开更多
The Good Wife is an American TV series that focuses on women’s independence,politics,and law.The drama has been remade in China,Japan,and South Korea.This research aims to use Nida’s Functional Equivalence Theory to...The Good Wife is an American TV series that focuses on women’s independence,politics,and law.The drama has been remade in China,Japan,and South Korea.This research aims to use Nida’s Functional Equivalence Theory to analyze the methods of its English-to-Chinese subtitle translation by considering social,cultural,and historic backgrounds between China and America.After data collection and case analysis,the study found that:(1)Five major translation methods are adopted in the subtitle translation of The Good Wife.They are free translation,variation,literal translation,addition,and omission.Among them,free translation is the most frequently used,while omission is used least.(2)The subtitle translation of films and TV series is limited by time and space restrictions,social-cultural differences,and other factors.When translating,translators should try to use humorous words,euphemism,intonation,and other ways,and combine different methods such as literal translation,free translation,variation,addition,omission,and other methods to seek equivalence both in the meaning and function of subtitles under the guidance of Functional Equivalence Theory.展开更多
This paper proposes a threat assessment framework for non-cooperative satellites by analyzing their motion characteristics,developing a quantitative evaluation methodology,and demonstrating its effectiveness via repre...This paper proposes a threat assessment framework for non-cooperative satellites by analyzing their motion characteristics,developing a quantitative evaluation methodology,and demonstrating its effectiveness via representative scenarios with neural network acceleration.The framework first establishes a threat evaluation model that integrates three core parameters:capability,opportunity,and hidden values.Subsequently,this research systematically investigates the critical role of transfer windows in threat quantification and introduces a transfer window-based threat assessment approach.The proposed methodology is validated through multiple representative scenarios,with simulation results demonstrating superior performance compared to conventional methods relying solely on optimal transfer windows or minimum distance metrics,enabling more nuanced threat ranking in scenarios where traditional techniques prove inadequate.To address computational demands,a neural networkbased approximation system is implemented to achieve a 25,200×speedup(0.005 s vs.baseline 126 s per 1000-sample batch)through parallel processing,maintaining 99.3%accuracy.Finally,the study explores the framework's extensibility to diverse NCS objectives.It identifies discrepancies between intention inference models and threat evaluation paradigms,providing methodological insights for next-generation space domain awareness systems.展开更多
With the increase of semiconductor integration density,in order to cope with the increase of wafer defect complexity and types,especially the low recognition accuracy of overlapping mixed defects and unknown wafer def...With the increase of semiconductor integration density,in order to cope with the increase of wafer defect complexity and types,especially the low recognition accuracy of overlapping mixed defects and unknown wafer defects,this study proposes a lightweight model for wafer defect detection called LightWMNet.First,using a hierarchical attention Encoder-Decoder architecture,the features of wafer defect pattern(WDP)are channel recalibrated to generate high-resolution fine-grained features and low-resolution coarse-grained features.Secondly,the backbone network incorporates two novel attention modules—feedforward spatial attention(FFSa)and feedforward channel attention(FFCa)—to amplify responses in critical defect regions and suppress noise from stochastic discrete pixels.These mechanisms synergistically enhance feature discriminability without introducing significant parametric overhead.Finally,the Dice loss function and the cross entropy loss function are combined to jointly evaluate the segmentation and classification accuracy of the model.Experimental results on the public mixed wafer defect dataset MixedWM38 show that the pixel accuracy(PA),intersection over union(IoU)and Dice coefficient of the proposed network reach 98.26%,94.83%and 97.22%,respectively.Without significantly increasing the computational complexity and size of the model,compared with the existing state-of-the-art(SOTA)model,the classification accuracy of lightWMNet in single defect,three mixed defects and four mixed defects is improved by 0.5%,0.25%and 0.89%respectively.Furthermore,we used transfer learning for the first time to evaluate the model's generalisation ability for unseen defect categories.The results showed that LightWMNet still has a certain recognition ability even in untrained wafer defects.展开更多
Unmanned Aerial Vehicle(UAV)plays a prominent role in various fields,and autonomous navigation is a crucial component of UAV intelligence.Deep Reinforcement Learning(DRL)has expanded the research avenues for addressin...Unmanned Aerial Vehicle(UAV)plays a prominent role in various fields,and autonomous navigation is a crucial component of UAV intelligence.Deep Reinforcement Learning(DRL)has expanded the research avenues for addressing challenges in autonomous navigation.Nonetheless,challenges persist,including getting stuck in local optima,consuming excessive computations during action space exploration,and neglecting deterministic experience.This paper proposes a noise-driven enhancement strategy.In accordance with the overall learning phases,a global noise control method is designed,while a differentiated local noise control method is developed by analyzing the exploration demands of four typical situations encountered by UAV during navigation.Both methods are integrated into a dual-model for noise control to regulate action space exploration.Furthermore,noise dual experience replay buffers are designed to optimize the rational utilization of both deterministic and noisy experience.In uncertain environments,based on the Twin Delay Deep Deterministic Policy Gradient(TD3)algorithm with Long Short-Term Memory(LSTM)network and Priority Experience Replay(PER),a Noise-Driven Enhancement Priority Memory TD3(NDE-PMTD3)is developed.We established a simulation environment to compare different algorithms,and the performance of the algorithms is analyzed in various scenarios.The training results indicate that the proposed algorithm accelerates the convergence speed and enhances the convergence stability.In test experiments,the proposed algorithm successfully and efficiently performs autonomous navigation tasks in diverse environments,demonstrating superior generalization results.展开更多
The micro-riblet structures have been demonstrated effective in controlling the Total Pressure Loss(TPL)of aero-engine blades.However,due to the considerable scale gap between micro-texture and an actual aero-engine b...The micro-riblet structures have been demonstrated effective in controlling the Total Pressure Loss(TPL)of aero-engine blades.However,due to the considerable scale gap between micro-texture and an actual aero-engine blade,wind tunnel tests and numerical simulations with massive grids directly describing the global flow field are costly for aerodynamic evaluation.Furthermore,the fine micro surface structure brings unavoidable manufacturing errors,and the probability prediction contributes to gaining the confidence interval of the results.Therefore,a novel relay-based probabilistic model for multi-fidelity scenarios in the TPL prediction of a compressor cascade with micro-riblet surfaces is proposed to trade off accuracy and efficiency.Combined with the low-fidelity flow data generated by an aerodynamic solution strategy using the boundary surrogate model and the high-fidelity flow data from the experiment,the relay-based modeling has been achieved through knowledge transferring,and the confidence interval can be provided by the Gaussian Process Regression(GPR)model.The TPL of compressor cascades with micro-riblet surfaces under different surface structures at March number Ma=0.64,0.74,0.84 have been evaluated using the Relay-Based Probabilistic(RBP)model.The results illustrate that the RBP model could provide higher accuracy than the Single-Fidelity-Data-Driven(SFDD)prediction model,which show the promising potential of multi-fidelity scenarios data fusion in the aerodynamic evaluation of multi-scale configurations.展开更多
Quick and accurate determination of the optimal synchrophase angle is crucial for synchrophasing control of multi-propeller aircraft with low noise.This paper proposes a novel noise prediction and optimization strateg...Quick and accurate determination of the optimal synchrophase angle is crucial for synchrophasing control of multi-propeller aircraft with low noise.This paper proposes a novel noise prediction and optimization strategy,developing a continuous and accurate noise prediction model and obtaining its minimum by solving the Hessian matrix and Fourier-Frobenius matrix.Firstly,a novel propeller noise prediction method uses acoustic simulation pressure signals and improved propeller signatures theory to accurately estimate noise for all synchrophase angles and receiving points.Secondly,a novel optimization approach is proposed to solve the analytical solution of the minimum propeller noise:(A)A noise objective function is established,and use its first derivatives’zeros and Hessian matrix to determine the function minimum.(B)A novel Euler formula transform method is proposed to convert trigonometric polynomials into algebraic polynomials,changing the zeros of the former into those of the latter.(C)Utilize the Fourier-Frobenius matrix method to solve the zeros of algebraic polynomials.To assess the computation time and accuracy,a turboprop aircraft with two six-bladed propellers was analyzed using the computational fluid dynamics and acoustic analogy method,providing acoustic pressure signals at 20 receivers for noise prediction and optimization.The Durand-Kerner and Fourier-Frobenius matrix methods were compared.Results demonstrate that improved propeller signatures theory is more accurate,and the Hessian matrix+Fourier-Frobenius matrix method is faster and more precise than the Hessian matrix+Durand-Kerner method.展开更多
In the field of intelligent air combat,real-time and accurate recognition of within-visual-range(WVR)maneuver actions serves as the foundational cornerstone for constructing autonomous decision-making systems.However,...In the field of intelligent air combat,real-time and accurate recognition of within-visual-range(WVR)maneuver actions serves as the foundational cornerstone for constructing autonomous decision-making systems.However,existing methods face two major challenges:traditional feature engineering suffers from insufficient effective dimensionality in the feature space due to kinematic coupling,making it difficult to distinguish essential differences between maneuvers,while end-to-end deep learning models lack controllability in implicit feature learning and fail to model high-order long-range temporal dependencies.This paper proposes a trajectory feature pre-extraction method based on a Long-range Masked Autoencoder(LMAE),incorporating three key innovations:(1)Random Fragment High-ratio Masking(RFH-Mask),which enforces the model to learn long-range temporal correlations by masking 80%of trajectory data while retaining continuous fragments;(2)Kalman Filter-Guided Objective Function(KFG-OF),integrating trajectory continuity constraints to align the feature space with kinematic principles;and(3)Two-stage Decoupled Architecture,enabling efficient and controllable feature learning through unsupervised pre-training and frozen-feature transfer.Experimental results demonstrate that LMAE significantly improves the average recognition accuracy for 20-class maneuvers compared to traditional end-to-end models,while significantly accelerating convergence speed.The contributions of this work lie in:introducing high-masking-rate autoencoders into low-informationdensity trajectory analysis,proposing a feature engineering framework with enhanced controllability and efficiency,and providing a novel technical pathway for intelligent air combat decision-making systems.展开更多
This study examines the viscoelastic-plastic behavior of thermoplastic resin poly-ether-ether-ketone(PEEK)under high temperature and strain rate conditions,highlighting its potential in aerospace applications due to i...This study examines the viscoelastic-plastic behavior of thermoplastic resin poly-ether-ether-ketone(PEEK)under high temperature and strain rate conditions,highlighting its potential in aerospace applications due to its impact resistance.A dualhardening constitutive model that combines physical and phenomenological approaches is developed to simulate the mechanical behavior of PEEK.The model explicitly incorporates its marked tension-compression asymmetry in plasticity and relaxation,along with thermal softening at high strain rates,enabling accurate predictions over a wide range of temperatures and strain rates with minimal parameters.This study establishes a comprehensive workflow from experimentation to finite element(FE)simulation for thermoplastic resins.Uniaxial tensile and compression tests(23℃-180℃,0.00229s^(-1)-0,19361s^(-1))and split Hopkinson pressure bar(SHPB)tests(1094.08s^(-1)-5957.88s^(-1))are performed to capture stress-strain responses across various conditions,with small-scale specimens enhancing fracture strain measurement accuracy,and quantify the Taylor-Quinney factor of the PEEK material during the adiabatic heating process.The findings demonstrate that the proposed constitutive model effectively predicts yield points across different strain rates and temperatures,with parameters easily obtainable through simple experimental methods,enhancing its practical applications.展开更多
To address the challenge of achieving decentralized,scalable,and adaptive control for large-scale multiple unmanned aerial vehicle(multi-UAV)swarms in dynamic urban environments with obstacles and wind perturbations,w...To address the challenge of achieving decentralized,scalable,and adaptive control for large-scale multiple unmanned aerial vehicle(multi-UAV)swarms in dynamic urban environments with obstacles and wind perturbations,we proposed a hybrid framework integrating adaptive reinforcement learning(RL),multi-modal perception fusion,and enhanced pigeon flock optimization(PFO)with curiosity-driven exploration to enable robust autonomous and formation control.The framework leverages meta-learning to optimize RL policies for real-time adaptation,fuses sensor data for precise state estimation,and enhances PFO with learned leader-follower dynamics and exploration rewards to maintain cohesive formations and explore uncertain areas.For swarms of 10–30 UAVs,it achieves 34%faster convergence,61%reduced stability root mean square error(RMSE),88%fewer collisions and 85.6%–92.3%success rates in target detection and encirclement,outperforming standard multi-agent RL,pure PFO,and single-modality RL.Three-dimensional trajectory visualizations confirm cohesive formations,collision-free maneuvers,and efficient exploration in urban search-and-rescue scenarios.Innovations include meta-RL for rapid adaptation,multi-modal fusion for robust perception,and curiosity-driven PFO for scalable,decentralized control,advancing real-world multi-UAV swarm autonomy and coordination.展开更多
Re-entry gliding vehicles exhibit high maneuverability,making trajectory prediction a key factor in the effectiveness of defense systems.To overcome the limited fitting accuracy of existing methods and their poor adap...Re-entry gliding vehicles exhibit high maneuverability,making trajectory prediction a key factor in the effectiveness of defense systems.To overcome the limited fitting accuracy of existing methods and their poor adaptability to maneuver mode mutations,a trajectory prediction method is proposed that integrates online maneuver mode identification with dynamic modeling.Characteristic parameters are extracted from tracking data for parameterized modeling,enabling real-time identification of maneuver modes.In addition,a maneuver detection mechanism based on higher-order cumulants is introduced to detect lateral maneuver mutations and optimize the use of historical data.Simulation results show that the proposed method achieves accurate trajectory prediction during the glide phase and maintains high accuracy under maneuver mutations,significantly enhancing the prediction performance of both three-dimensional trajectories and ground tracks.展开更多
In this paper,a practical method named linear active disturbance rejection control(LADRC)with adaptive tuning is proposed for attitude control of small-scale unmanned helicopter.The proposed method accounts for both e...In this paper,a practical method named linear active disturbance rejection control(LADRC)with adaptive tuning is proposed for attitude control of small-scale unmanned helicopter.The proposed method accounts for both external disturbances and internal dynamic uncertainties,as well as parameter deviations arising from parameter uncertainty,while maintaining a relatively small number of adjustable parameters.Furthermore,it addresses the limitation that conventional active disturbance rejection control methods cannot be rigorously analyzed for stability.The total disturbance of unmanned helicopter is estimated and compensated by designed LADRC.The introduction of adaptive control realizes online parameter tuning,which eliminates parameter deviation and further improves control precision.Moreover,it also provides a novel idea to prove the stability of controller,so that it can be analyzed by Lyapunov function.Finally,the anti-disturbance performance and effectiveness of proposed method are verified by numerical simulation.展开更多
This study is dedicated to numerically investigate the dynamic behavior of a missile released from a helicopter under the influence of downwash from the rotating rotors using AGM-114 Hellfire and UH-60 as the testcase...This study is dedicated to numerically investigate the dynamic behavior of a missile released from a helicopter under the influence of downwash from the rotating rotors using AGM-114 Hellfire and UH-60 as the testcase.Simulations are conducted using unsteady Reynolds-averaged Navier-Stokes(URANS)with shear stress transport(SST)k-ωturbulence model,incorporating six-degree-of-freedom(6-DOF)motion and overset grid.Two releasing scenarios,viz.,hover and forward flight,are analyzed under varying missile launch thrust and helicopter forward flight speed.Results reveal that the rotor downwash significantly affects the stability of the missile,particularly during hovering case,where low thrust prolongs wake interaction.In forward flight,the increased airspeed can in principle reduce wake influence but introduces asymmetrical aerodynamic effects on the trajectory of the missile.The findings offer guidance for missile release and launch planning in rotorcraft operations.展开更多
With the rapid development of the aviation industry,air travel has become one of the most important modes.Improving the service quality of civil aviation airports is crucial to their competitiveness.This study intends...With the rapid development of the aviation industry,air travel has become one of the most important modes.Improving the service quality of civil aviation airports is crucial to their competitiveness.This study intends to develop a scientific and rational evaluation methodology and framework for assessing service quality in civil aviation airports,thereby providing a theoretical foundation and practical guidance for enhancing service standards in the aviation industry.First,the study constructs a CRITIC-bidirectional grey possibility clustering model,which uses the CRITIC method to determine the weights of indicators and integrates the forward grey possibility clustering model and the inverse grey possibility clustering model to determine possibility functions from two perspectives.Second,a service quality evaluation index system for civil airports is constructed from four dimensions,and the weights of each index within the system are subsequently calculated.Finally,the constructed model is applied to evaluate the service quality of nine domestic civil airports.Based on the clustering results,targeted countermeasures and suggestions are proposed.Empirical results demonstrate that,compared to the traditional grey possibility clustering model,the proposed model balances the objectivity of indicator weighting,the objectivity of possibility function construction,and the simplicity of the computational process,thereby possessing significant theoretical and practical implications.展开更多
The sulfation and decomposition process has proven effective in selectively extracting lithium from lepidolite.It is essential to clarify the thermochemical behavior and kinetic parameters of decomposition reactions.A...The sulfation and decomposition process has proven effective in selectively extracting lithium from lepidolite.It is essential to clarify the thermochemical behavior and kinetic parameters of decomposition reactions.Accordingly,comprehensive kinetic study by employing thermalgravimetric analysis at various heating rates was presented in this paper.Two main weight loss regions were observed during heating.The initial region corresponded to the dehydration of crystal water,whereas the subsequent region with overlapping peaks involved complex decomposition reactions.The overlapping peaks were separated into two individual reaction peaks and the activation energy of each peak was calculated using isoconversional kinetics methods.The activation energy of peak 1 exhibited a continual increase as the reaction conversion progressed,while that of peak 2 steadily decreased.The optimal kinetic models,identified as belonging to the random nucleation and subsequent growth category,provided valuable insights into the mechanism of the decomposition reactions.Furthermore,the adjustment factor was introduced to reconstruct the kinetic mechanism models,and the reconstructed models described the kinetic mechanism model more accurately for the decomposition reactions.This study enhanced the understanding of the thermochemical behavior and kinetic parameters of the lepidolite sulfation product decomposition reactions,further providing theoretical basis for promoting the selective extraction of lithium.展开更多
文摘With its growth in spacecraft control applications,the microcontroller(MCU)becomes increasingly sensitive to radiation and the risks of system failure.In a radiation environment,the MCU is vulnerable to impacts from high-energy particles,which can lead to single-event effect(SEE)that disrupt normal system operations.The pipeline of MCU,being the core structure of the system,is particularly susceptible to single-event upset(SEU)and potentially causes execution failures.However,existing radiation-hardening techniques offer limited effectiveness for pipelines.To enhance SEU resistance,this study focused on a 32-bit MCU core with eight pipeline stages,proposing a pipeline hardening approach that utilizes lockstep technology to improve fault tolerance.Signals from two processors were compared including register write data,register contents and pre-fetched instructions.Any discrepancies triggered error flags to indicate faults.When an error flag was raised,recovery was initiated through an interrupt.The interrupt handler then retrieved state information from the advanced peripheral bus(APB)slave module to restore the CPU's operational state and resume execution.By combining hardware-based state preservation with software-driven error recovery,the proposed solution demonstrated significant improvements in fault tolerance rates and performance compared to traditional checkpoint-based techniques.After completing the pipeline hardening design,a fault injection platform was utilized in this paper to simulate real-world error conditions on internal processor modules.The platform was developed based on the circuit's register-transfer-level(RTL)design and statistical results.The fault injection platform was performed by automatically finding all registers within the target design.The register values were forced to upset at the tens of nanoseconds scale in the RTL description of the circuit's design.After running the circuit's functional simulation,the statistics of the faults in registers were displayed on the platform,which evaluated the influence of SEU.The vulnerability of SEU in the circuit could be observed from the results of the soft error statistics.The post-hardening soft error rates were then measured and compared to pre-hardening data,providing a quantitative evaluation of the improvements.Using this method,the soft error rates of the modules in the MCU core such as PFU,DPU,and Cache AXIM are 40.07%,26.36%,and 27.29%respectively before hardening.The soft error rates of modules mentioned above are reduced to 0%,0.69%,and 1.11%after hardening.The hardened and non-hardened designs of the entire core were implemented in FPGA.The total resource utilization of the triple mode redundancy(TMR)is 111984,as indicated by the number of look-up tables(LUTs)and registers consumed in the FPGA.The total resource utilization of this work is 78034,and the ratio of resource utilization between this work and TMR is approximately 69.68%.The error recovery time for the hardened MCU processor was analyzed using the completion cycles of a bubble sort algorithm as a benchmark.In this paper,the average recovery cycle using the software checkpoint roll-back method is 36479.06,and the average recovery cycle using this work is 26922.5.The ratio of recovery cycles between this work and checkpoint roll-back is about 73.8%.Assessments through random fault injection and FPGA implementation indicate that this approach effectively reduces processor faults caused by soft errors while optimizing resource utilization and efficiency over triplemodular redundancy.
基金Research Start-Up Fund Project of Anhui Polytechnic University(S022023017)University Research Project of Anhui Province(2023AH050937)+1 种基金Anhui Polytechnic University Research Foundation for Introducing Talents(2022YQQ003)Anhui Province Key Laboratory of Intelligent Vehicle Chassis by Wire。
文摘Graphene/copper-based composite heat sinks demonstrate extensive application potential in military equipment thermal management,high-power electronic packaging,new energy vehicles,and 5G communication systems,due to their outstanding properties,including high thermal conductivity,tunable thermal expansion coefficients,excellent mechanical strength,and low density.However,the industrial-scale application of these composites faces critical challenges during the fabrication of components with complex structures,such as inhomogeneous dispersion of graphene within the copper matrix and poor interfacial bonding between the two phases,which substantially undermine the overall performance of graphene/copper-based composites.To address these issues,the preparation methods for graphene/copper-based composite heat sinks were reviewed.For each method,a rigorous analysis was presented to clarify its inherent advantages and unavoidable restrictions.Furthermore,the latest research progress in addressing three core scientific challenges was synthesized,including uniform dispersion of graphene,interfacial optimization mechanisms,and molecular dynamics simulations for elucidating the structure-property relationships.Finally,the future development directions of graphene/copper-based composite heat sinks in engineering applications were prospected.
基金supported by the Postgraduate Research&Practice Innovation Program of Nanjing University of Aeronautics and Astronautics(NUAA)(No.xcxjh20251502)。
文摘Online three-dimensional(3D)path planning in dynamic environments is a fundamental problem for achieving autonomous navigation of unmanned aerial vehicles(UAVs).However,existing methods struggle to model traversable dynamic gaps,resulting in conservative and suboptimal trajectories.To address these challenges,this paper proposes a hierarchical reinforcement learning(RL)framework that integrates global path guidance,local trajectory generation,predictive safety evaluation,and neural network-based decision-making.Specifically,the global planner provides long-term navigation guidance,and the local module then utilizes an improved 3D dynamic window approach(DWA)to generate dynamically feasible candidate trajectories.To enhance safety in dense dynamic scenarios,the algorithm introduces a predictive axis-aligned bounding box(AABB)strategy to model the future occupancy of obstacles,combined with convex hull verification for efficient trajectory safety assessment.Furthermore,a double deep Q-network(DDQN)is employed with structured feature encoding,enabling the neural network to reliably select the optimal trajectory from the candidate set,thereby improving robustness and generalization.Comparative experiments conducted in a high-fidelity simulation environment show that the algorithm outperforms existing algorithms,reducing the average number of collisions to 0.2 while shortening the average task completion time by approximately 15%,and achieving a success rate of 97%.
基金supported by the National Natural Science Foundation of China(No.62173107).
文摘Spaceborne antennas are essential for remote sensing,deep-space communication,and Earth observation,yet their trajectory planning is complicated by nonlinear base-manipulator coupling and antenna flexibility.To address these challenges,this paper proposes a multi-objective trajectory optimization framework.The system dynamics capture both nonlinear rigid-flexible coupling and antenna deformation through a reduced-order formulation.To enhance discretization efficiency,a predictive-terminal hp-adaptive pseudospectral method is employed,assigning collocation density based on task-phase characteristics:finer resolution is applied to dynamic segments requiring higher accuracy,especially near the terminal phase.This enables efficient transcription of the continuous-time problem into a Nonlinear Programming Problem(NLP).The resulting NLP is then solved using a multi-objective optimization strategy based on the nondominated sorting genetic algorithm II,which explores trade-offs among antenna pointing accuracy,energy consumption,and structural vibration.Numerical results demonstrate that the proposed method achieves a reduction of approximately 14.0% in control energy and 41.8%in peak actuation compared to a GPOPS-II baseline,while significantly enhancing vibration suppression.The resulting Pareto front reveals structured trade-offs and clustered solutions,offering robust and diverse options for precision,low-disturbance mission planning.
基金National Key Scientific Instrument and Equipment Development Project under Grant No.61827801the open research fund of State Key Laboratory of Integrated Services Networks,No.ISN22-11+1 种基金Natural Science Foundation of Jiangsu Province,No.BK20211182open research fund of National Mobile Communications Research Laboratory,Southeast University,No.2022D04。
文摘Spectrum map construction,which is crucial in cognitive radio(CR)system,visualizes the invisible space of the electromagnetic spectrum for spectrum-resource management and allocation.Traditional reconstruction methods are generally for twodimensional(2D)spectrum map and driven by abundant sampling data.In this paper,we propose a data-model-knowledge-driven reconstruction scheme to construct the three-dimensional(3D)spectrum map under multi-radiation source scenarios.We firstly design a maximum and minimum path loss difference(MMPLD)clustering algorithm to detect the number of radiation sources in a 3D space.Then,we develop a joint location-power estimation method based on the heuristic population evolutionary optimization algorithm.Considering the variation of electromagnetic environment,we self-learn the path loss(PL)model based on the sampling data.Finally,the 3D spectrum is reconstructed according to the self-learned PL model and the extracted knowledge of radiation sources.Simulations show that the proposed 3D spectrum map reconstruction scheme not only has splendid adaptability to the environment,but also achieves high spectrum construction accuracy even when the sampling rate is very low.
基金supported in part by the Natural Science Foundation of Shanghai,China(No.23ZR1432400)the Shanghai Pilot Program for Basic Research-Chinese Academy of Science(No.JCYJ-SHFY-2022-015).
文摘The Electrical Power System(EPS)is one of the spacecraft’s key subsystems,and its operational status directly affects the lifespan and performance of the entire spacecraft.The corresponding fault diagnosis has always been the discussion focus to ensure spacecraft reliability.In this paper,a few-shot unsupervised fault diagnosis method based on the improved Newman community division algorithm is proposed,to approach the scarcity of fault data samples and the inconspicuous characteristics of abnormal data.Firstly,aiming to capture the overall relevance of the fault dataset,a complex network model is built by adopting the K-Dynamic time warping distance Adjacent Nodes(KDAN)method.Based on the complex network model,the Newman community divisions algorithm is improved by using the Quantum-behaved Particle Swarm Optimization(QPSO).Subsequently,in order to evaluate the feasibility of the proposed method,experimental validation was conducted using an open-source dataset.The results indicate that the average accuracy can reach 96.43% for fault data diagnosis,and an F1_score of 97.76%with only 17.65%of the dataset used for training.The proposed method can accurately classify abnormal data by identifying the community structure in the data network,significantly improve the efficiency of the community divisions algorithm and reduce its complexity,and provide a new solution for fault diagnosis in large-scale complex systems.
文摘The Good Wife is an American TV series that focuses on women’s independence,politics,and law.The drama has been remade in China,Japan,and South Korea.This research aims to use Nida’s Functional Equivalence Theory to analyze the methods of its English-to-Chinese subtitle translation by considering social,cultural,and historic backgrounds between China and America.After data collection and case analysis,the study found that:(1)Five major translation methods are adopted in the subtitle translation of The Good Wife.They are free translation,variation,literal translation,addition,and omission.Among them,free translation is the most frequently used,while omission is used least.(2)The subtitle translation of films and TV series is limited by time and space restrictions,social-cultural differences,and other factors.When translating,translators should try to use humorous words,euphemism,intonation,and other ways,and combine different methods such as literal translation,free translation,variation,addition,omission,and other methods to seek equivalence both in the meaning and function of subtitles under the guidance of Functional Equivalence Theory.
基金supported by the National Key R&D Programof China:Gravitational Wave Detection Project(Grant Nos.2021YFC2026,2021YFC2202601,2021YFC2202603)the Na-tional Natural Science Foundation of China(Grant Nos.12172288and 12472046)。
文摘This paper proposes a threat assessment framework for non-cooperative satellites by analyzing their motion characteristics,developing a quantitative evaluation methodology,and demonstrating its effectiveness via representative scenarios with neural network acceleration.The framework first establishes a threat evaluation model that integrates three core parameters:capability,opportunity,and hidden values.Subsequently,this research systematically investigates the critical role of transfer windows in threat quantification and introduces a transfer window-based threat assessment approach.The proposed methodology is validated through multiple representative scenarios,with simulation results demonstrating superior performance compared to conventional methods relying solely on optimal transfer windows or minimum distance metrics,enabling more nuanced threat ranking in scenarios where traditional techniques prove inadequate.To address computational demands,a neural networkbased approximation system is implemented to achieve a 25,200×speedup(0.005 s vs.baseline 126 s per 1000-sample batch)through parallel processing,maintaining 99.3%accuracy.Finally,the study explores the framework's extensibility to diverse NCS objectives.It identifies discrepancies between intention inference models and threat evaluation paradigms,providing methodological insights for next-generation space domain awareness systems.
基金supported by the National Natural Science Foundation of China under Grant 61573183.
文摘With the increase of semiconductor integration density,in order to cope with the increase of wafer defect complexity and types,especially the low recognition accuracy of overlapping mixed defects and unknown wafer defects,this study proposes a lightweight model for wafer defect detection called LightWMNet.First,using a hierarchical attention Encoder-Decoder architecture,the features of wafer defect pattern(WDP)are channel recalibrated to generate high-resolution fine-grained features and low-resolution coarse-grained features.Secondly,the backbone network incorporates two novel attention modules—feedforward spatial attention(FFSa)and feedforward channel attention(FFCa)—to amplify responses in critical defect regions and suppress noise from stochastic discrete pixels.These mechanisms synergistically enhance feature discriminability without introducing significant parametric overhead.Finally,the Dice loss function and the cross entropy loss function are combined to jointly evaluate the segmentation and classification accuracy of the model.Experimental results on the public mixed wafer defect dataset MixedWM38 show that the pixel accuracy(PA),intersection over union(IoU)and Dice coefficient of the proposed network reach 98.26%,94.83%and 97.22%,respectively.Without significantly increasing the computational complexity and size of the model,compared with the existing state-of-the-art(SOTA)model,the classification accuracy of lightWMNet in single defect,three mixed defects and four mixed defects is improved by 0.5%,0.25%and 0.89%respectively.Furthermore,we used transfer learning for the first time to evaluate the model's generalisation ability for unseen defect categories.The results showed that LightWMNet still has a certain recognition ability even in untrained wafer defects.
基金the Collaborative Innovation Project of Shanghai,China for the financial support。
文摘Unmanned Aerial Vehicle(UAV)plays a prominent role in various fields,and autonomous navigation is a crucial component of UAV intelligence.Deep Reinforcement Learning(DRL)has expanded the research avenues for addressing challenges in autonomous navigation.Nonetheless,challenges persist,including getting stuck in local optima,consuming excessive computations during action space exploration,and neglecting deterministic experience.This paper proposes a noise-driven enhancement strategy.In accordance with the overall learning phases,a global noise control method is designed,while a differentiated local noise control method is developed by analyzing the exploration demands of four typical situations encountered by UAV during navigation.Both methods are integrated into a dual-model for noise control to regulate action space exploration.Furthermore,noise dual experience replay buffers are designed to optimize the rational utilization of both deterministic and noisy experience.In uncertain environments,based on the Twin Delay Deep Deterministic Policy Gradient(TD3)algorithm with Long Short-Term Memory(LSTM)network and Priority Experience Replay(PER),a Noise-Driven Enhancement Priority Memory TD3(NDE-PMTD3)is developed.We established a simulation environment to compare different algorithms,and the performance of the algorithms is analyzed in various scenarios.The training results indicate that the proposed algorithm accelerates the convergence speed and enhances the convergence stability.In test experiments,the proposed algorithm successfully and efficiently performs autonomous navigation tasks in diverse environments,demonstrating superior generalization results.
基金supported by the National Natural Science Foundation of China(No.12301672)the Shanghai Science and Technology Innovation Action Plan(Yangfan Special Project),China(No.23YF1401300)。
文摘The micro-riblet structures have been demonstrated effective in controlling the Total Pressure Loss(TPL)of aero-engine blades.However,due to the considerable scale gap between micro-texture and an actual aero-engine blade,wind tunnel tests and numerical simulations with massive grids directly describing the global flow field are costly for aerodynamic evaluation.Furthermore,the fine micro surface structure brings unavoidable manufacturing errors,and the probability prediction contributes to gaining the confidence interval of the results.Therefore,a novel relay-based probabilistic model for multi-fidelity scenarios in the TPL prediction of a compressor cascade with micro-riblet surfaces is proposed to trade off accuracy and efficiency.Combined with the low-fidelity flow data generated by an aerodynamic solution strategy using the boundary surrogate model and the high-fidelity flow data from the experiment,the relay-based modeling has been achieved through knowledge transferring,and the confidence interval can be provided by the Gaussian Process Regression(GPR)model.The TPL of compressor cascades with micro-riblet surfaces under different surface structures at March number Ma=0.64,0.74,0.84 have been evaluated using the Relay-Based Probabilistic(RBP)model.The results illustrate that the RBP model could provide higher accuracy than the Single-Fidelity-Data-Driven(SFDD)prediction model,which show the promising potential of multi-fidelity scenarios data fusion in the aerodynamic evaluation of multi-scale configurations.
基金supported by the National Natural Science Foundation of China(Nos.51576097,51976089)the Funding for Outstanding Doctoral Dissertation in Nanjing University of Aeronautics and Astronautics,China(No.BCXJ24-05)the Aeronautical Science Foundation of China(No.2023L060052001).
文摘Quick and accurate determination of the optimal synchrophase angle is crucial for synchrophasing control of multi-propeller aircraft with low noise.This paper proposes a novel noise prediction and optimization strategy,developing a continuous and accurate noise prediction model and obtaining its minimum by solving the Hessian matrix and Fourier-Frobenius matrix.Firstly,a novel propeller noise prediction method uses acoustic simulation pressure signals and improved propeller signatures theory to accurately estimate noise for all synchrophase angles and receiving points.Secondly,a novel optimization approach is proposed to solve the analytical solution of the minimum propeller noise:(A)A noise objective function is established,and use its first derivatives’zeros and Hessian matrix to determine the function minimum.(B)A novel Euler formula transform method is proposed to convert trigonometric polynomials into algebraic polynomials,changing the zeros of the former into those of the latter.(C)Utilize the Fourier-Frobenius matrix method to solve the zeros of algebraic polynomials.To assess the computation time and accuracy,a turboprop aircraft with two six-bladed propellers was analyzed using the computational fluid dynamics and acoustic analogy method,providing acoustic pressure signals at 20 receivers for noise prediction and optimization.The Durand-Kerner and Fourier-Frobenius matrix methods were compared.Results demonstrate that improved propeller signatures theory is more accurate,and the Hessian matrix+Fourier-Frobenius matrix method is faster and more precise than the Hessian matrix+Durand-Kerner method.
文摘In the field of intelligent air combat,real-time and accurate recognition of within-visual-range(WVR)maneuver actions serves as the foundational cornerstone for constructing autonomous decision-making systems.However,existing methods face two major challenges:traditional feature engineering suffers from insufficient effective dimensionality in the feature space due to kinematic coupling,making it difficult to distinguish essential differences between maneuvers,while end-to-end deep learning models lack controllability in implicit feature learning and fail to model high-order long-range temporal dependencies.This paper proposes a trajectory feature pre-extraction method based on a Long-range Masked Autoencoder(LMAE),incorporating three key innovations:(1)Random Fragment High-ratio Masking(RFH-Mask),which enforces the model to learn long-range temporal correlations by masking 80%of trajectory data while retaining continuous fragments;(2)Kalman Filter-Guided Objective Function(KFG-OF),integrating trajectory continuity constraints to align the feature space with kinematic principles;and(3)Two-stage Decoupled Architecture,enabling efficient and controllable feature learning through unsupervised pre-training and frozen-feature transfer.Experimental results demonstrate that LMAE significantly improves the average recognition accuracy for 20-class maneuvers compared to traditional end-to-end models,while significantly accelerating convergence speed.The contributions of this work lie in:introducing high-masking-rate autoencoders into low-informationdensity trajectory analysis,proposing a feature engineering framework with enhanced controllability and efficiency,and providing a novel technical pathway for intelligent air combat decision-making systems.
文摘This study examines the viscoelastic-plastic behavior of thermoplastic resin poly-ether-ether-ketone(PEEK)under high temperature and strain rate conditions,highlighting its potential in aerospace applications due to its impact resistance.A dualhardening constitutive model that combines physical and phenomenological approaches is developed to simulate the mechanical behavior of PEEK.The model explicitly incorporates its marked tension-compression asymmetry in plasticity and relaxation,along with thermal softening at high strain rates,enabling accurate predictions over a wide range of temperatures and strain rates with minimal parameters.This study establishes a comprehensive workflow from experimentation to finite element(FE)simulation for thermoplastic resins.Uniaxial tensile and compression tests(23℃-180℃,0.00229s^(-1)-0,19361s^(-1))and split Hopkinson pressure bar(SHPB)tests(1094.08s^(-1)-5957.88s^(-1))are performed to capture stress-strain responses across various conditions,with small-scale specimens enhancing fracture strain measurement accuracy,and quantify the Taylor-Quinney factor of the PEEK material during the adiabatic heating process.The findings demonstrate that the proposed constitutive model effectively predicts yield points across different strain rates and temperatures,with parameters easily obtainable through simple experimental methods,enhancing its practical applications.
基金supported by the National Natural Science Foundation of China(No.62350048)。
文摘To address the challenge of achieving decentralized,scalable,and adaptive control for large-scale multiple unmanned aerial vehicle(multi-UAV)swarms in dynamic urban environments with obstacles and wind perturbations,we proposed a hybrid framework integrating adaptive reinforcement learning(RL),multi-modal perception fusion,and enhanced pigeon flock optimization(PFO)with curiosity-driven exploration to enable robust autonomous and formation control.The framework leverages meta-learning to optimize RL policies for real-time adaptation,fuses sensor data for precise state estimation,and enhances PFO with learned leader-follower dynamics and exploration rewards to maintain cohesive formations and explore uncertain areas.For swarms of 10–30 UAVs,it achieves 34%faster convergence,61%reduced stability root mean square error(RMSE),88%fewer collisions and 85.6%–92.3%success rates in target detection and encirclement,outperforming standard multi-agent RL,pure PFO,and single-modality RL.Three-dimensional trajectory visualizations confirm cohesive formations,collision-free maneuvers,and efficient exploration in urban search-and-rescue scenarios.Innovations include meta-RL for rapid adaptation,multi-modal fusion for robust perception,and curiosity-driven PFO for scalable,decentralized control,advancing real-world multi-UAV swarm autonomy and coordination.
基金supported by the National Natural Science Foundation of China(12302056)the Postdoctoral Fellowship Program of China Postdoctoral Science Foundation(GZC20233445)。
文摘Re-entry gliding vehicles exhibit high maneuverability,making trajectory prediction a key factor in the effectiveness of defense systems.To overcome the limited fitting accuracy of existing methods and their poor adaptability to maneuver mode mutations,a trajectory prediction method is proposed that integrates online maneuver mode identification with dynamic modeling.Characteristic parameters are extracted from tracking data for parameterized modeling,enabling real-time identification of maneuver modes.In addition,a maneuver detection mechanism based on higher-order cumulants is introduced to detect lateral maneuver mutations and optimize the use of historical data.Simulation results show that the proposed method achieves accurate trajectory prediction during the glide phase and maintains high accuracy under maneuver mutations,significantly enhancing the prediction performance of both three-dimensional trajectories and ground tracks.
基金supported by the Aeronautical Science Foundation of China(Nos.20220058052002,20240007052001)。
文摘In this paper,a practical method named linear active disturbance rejection control(LADRC)with adaptive tuning is proposed for attitude control of small-scale unmanned helicopter.The proposed method accounts for both external disturbances and internal dynamic uncertainties,as well as parameter deviations arising from parameter uncertainty,while maintaining a relatively small number of adjustable parameters.Furthermore,it addresses the limitation that conventional active disturbance rejection control methods cannot be rigorously analyzed for stability.The total disturbance of unmanned helicopter is estimated and compensated by designed LADRC.The introduction of adaptive control realizes online parameter tuning,which eliminates parameter deviation and further improves control precision.Moreover,it also provides a novel idea to prove the stability of controller,so that it can be analyzed by Lyapunov function.Finally,the anti-disturbance performance and effectiveness of proposed method are verified by numerical simulation.
基金supported by the National Natural Science Foundation of China(No.11602200)。
文摘This study is dedicated to numerically investigate the dynamic behavior of a missile released from a helicopter under the influence of downwash from the rotating rotors using AGM-114 Hellfire and UH-60 as the testcase.Simulations are conducted using unsteady Reynolds-averaged Navier-Stokes(URANS)with shear stress transport(SST)k-ωturbulence model,incorporating six-degree-of-freedom(6-DOF)motion and overset grid.Two releasing scenarios,viz.,hover and forward flight,are analyzed under varying missile launch thrust and helicopter forward flight speed.Results reveal that the rotor downwash significantly affects the stability of the missile,particularly during hovering case,where low thrust prolongs wake interaction.In forward flight,the increased airspeed can in principle reduce wake influence but introduces asymmetrical aerodynamic effects on the trajectory of the missile.The findings offer guidance for missile release and launch planning in rotorcraft operations.
基金support supplied by the National Natural Science Foundation of China(Nos.72571136,72271120)the Ministry of Education of the People’s Republic of China Humanities and Social Science project(No.24YJA630087)。
文摘With the rapid development of the aviation industry,air travel has become one of the most important modes.Improving the service quality of civil aviation airports is crucial to their competitiveness.This study intends to develop a scientific and rational evaluation methodology and framework for assessing service quality in civil aviation airports,thereby providing a theoretical foundation and practical guidance for enhancing service standards in the aviation industry.First,the study constructs a CRITIC-bidirectional grey possibility clustering model,which uses the CRITIC method to determine the weights of indicators and integrates the forward grey possibility clustering model and the inverse grey possibility clustering model to determine possibility functions from two perspectives.Second,a service quality evaluation index system for civil airports is constructed from four dimensions,and the weights of each index within the system are subsequently calculated.Finally,the constructed model is applied to evaluate the service quality of nine domestic civil airports.Based on the clustering results,targeted countermeasures and suggestions are proposed.Empirical results demonstrate that,compared to the traditional grey possibility clustering model,the proposed model balances the objectivity of indicator weighting,the objectivity of possibility function construction,and the simplicity of the computational process,thereby possessing significant theoretical and practical implications.
基金financially supported by the National Natural Science Foundation of China(Nos.52034002 and U2202254)the Fundamental Research Funds for the Central Universities,China(No.FRF-TT-19-001)。
文摘The sulfation and decomposition process has proven effective in selectively extracting lithium from lepidolite.It is essential to clarify the thermochemical behavior and kinetic parameters of decomposition reactions.Accordingly,comprehensive kinetic study by employing thermalgravimetric analysis at various heating rates was presented in this paper.Two main weight loss regions were observed during heating.The initial region corresponded to the dehydration of crystal water,whereas the subsequent region with overlapping peaks involved complex decomposition reactions.The overlapping peaks were separated into two individual reaction peaks and the activation energy of each peak was calculated using isoconversional kinetics methods.The activation energy of peak 1 exhibited a continual increase as the reaction conversion progressed,while that of peak 2 steadily decreased.The optimal kinetic models,identified as belonging to the random nucleation and subsequent growth category,provided valuable insights into the mechanism of the decomposition reactions.Furthermore,the adjustment factor was introduced to reconstruct the kinetic mechanism models,and the reconstructed models described the kinetic mechanism model more accurately for the decomposition reactions.This study enhanced the understanding of the thermochemical behavior and kinetic parameters of the lepidolite sulfation product decomposition reactions,further providing theoretical basis for promoting the selective extraction of lithium.
