Human Adaptive Mechatronics(HAM)includes human and computer system in a closed loop.Elderly person with disabilities,normally carry out their daily routines with some assistance to move their limbs.With the short fall...Human Adaptive Mechatronics(HAM)includes human and computer system in a closed loop.Elderly person with disabilities,normally carry out their daily routines with some assistance to move their limbs.With the short fall of human care takers,mechatronics devices are used with the likes of exoskeleton and exosuits to assist them.The rehabilitation and occupational therapy equipments utilize the electromyography(EMG)signals to measure the muscle activity potential.This paper focuses on optimizing the HAM model in prediction of intended motion of upper limb with high accuracy and to increase the response time of the system.Limb characteristics extraction from EMG signal and prediction of optimal controller parameters are modeled.Time and frequency based approach of EMG signal are considered for feature extraction.The models used for estimating motion and muscle parameters from EMG signal for carrying out limb movement predictions are validated.Based on the extracted features,optimal parameters are selected by Modified Lion Optimization(MLO)for controlling the HAM system.Finally,supervised machine learning makes predictions at different points in time for individual sensing using Support Vector Neural Network(SVNN).This model is also evaluated based on optimal parameters of motion estimation and the accuracy level along with different optimization models for various upper limb movements.The proposed model of human adaptive controller predicts the limb movement by 96%accuracy.展开更多
LuGre model has been widely used in friction modeling and compensation.However,the new friction regime,named prestiction regime,cannot be accurately characterized by LuGre model in the latest research.With the extensi...LuGre model has been widely used in friction modeling and compensation.However,the new friction regime,named prestiction regime,cannot be accurately characterized by LuGre model in the latest research.With the extensive experimental observations of friction behaviors in the prestiction,some variables were abstracted to depict the rules in the prestiction regime.Based upon the knowledge of friction modeling,a novel friction model including the presliding regime,the gross sliding regime and the prestiction regime was then presented to overcome the shortcomings of the LuGre model.The reason that LuGre model cannot estimate the prestiction friction was analyzed in theory.Feasibility analysis of the proposed model in modeling the prestiction friction was also addressed.A parameter identification method for the proposed model based on multilevel coordinate search algorithm was presented.The proposed friction compensation strategy was composed of a nonlinear friction observer and a feedforward mechanism.The friction observer was designed to estimate the friction force in the presliding and the gross sliding regimes.And the friction force was estimated based on the model in the prestiction regime.The comparative trajectory tracking experiments were conducted on a simulator of inertially stabilization platforms among three control schemes:the single proportional–derivative(PD)control,the PD with LuGre model-based compensation and the PD with compensator based on the presented model.The experimental results reveal that the control scheme based on the proposed model has the best tracking performance.It reduces the peak-to-peak value(PPV)of tracking error to 0.2 mrad,which is improved almost 50%compared with the PD with LuGre model-based compensation.Compared to the single PD control,it reduces the PPV of error by 66.7%.展开更多
Healthcare mechatronics is a typical multidisciplinary field involving machinery,medicine,computer,and automation,which has been widely applied in respiratory therapy,urology robot,rehabilitation exoskeleton,artificia...Healthcare mechatronics is a typical multidisciplinary field involving machinery,medicine,computer,and automation,which has been widely applied in respiratory therapy,urology robot,rehabilitation exoskeleton,artificial heart,etc.Existing progresses has some defects in modeling,design and implementation of healthcare mechatronics.Therefore,exploring new design theories,key technologies and typical applications is an effective to promote the rapid development of this field.展开更多
Owing to the harsh conditions,wind turbine bearings are prone to faults,and the resulting fault information is easily submerged by strong noise disturbance,making conventional diagnosis challenging.Therefore,this stud...Owing to the harsh conditions,wind turbine bearings are prone to faults,and the resulting fault information is easily submerged by strong noise disturbance,making conventional diagnosis challenging.Therefore,this study presents an innovative bearing fault diagnosis approach predicated on Parameter⁃Optimized Symplectic Geometry Mode Decomposition(POSGMD)and Improved Convolutional Neural Network(ICNN).Firstly,assisted by the relative entropy⁃based adaptive selection of embedding dimension,a POSGMD is presented to adaptively decompose the collected bearing vibration signals into various Symplectic Geometry Components(SGC),which can solve the problem of manual selection of the embedding dimension in the raw Symplectic Geometry Mode Decomposition(SGMD).Meanwhile,the signal reconstruction on the decomposed SGC is conducted based on kurtosis⁃weighted principle to obtain the reconstructed signals.Subsequently,the Continuous Wavelet Transform(CWT)of the reconstructed signals is calculated to generate the corresponding time⁃frequency images as sample set.Finally,an ICNN is introduced for model training and automatic recognition of bearing faults.Two case studies are used to validate the presented methods efficacy.Comparing the presented method with traditional fault diagnosis methods,experimental results show that it can achieve greater identification accuracy and superior anti⁃noise resilience.This work provides a practical and effective solution for fault diagnosis in wind turbine bearings,contributing to the timely detection of faults and the reliable operation of wind turbines or other rotational machinery in industrial applications.展开更多
Soft robotic manipulators represent a rapidly evolving field characterized by inherent compliance,adaptability,and safe interactions within unstructured environments.Over the past decade(2015-2025),significant advance...Soft robotic manipulators represent a rapidly evolving field characterized by inherent compliance,adaptability,and safe interactions within unstructured environments.Over the past decade(2015-2025),significant advancements have trans-formed their capabilities through novel designs inspired by biological systems,advanced modeling frameworks,sophisti-cated control strategies,and integration into diverse real-world applications.Recent innovations in multifunctional mate-rials and emerging actuation technologies have markedly expanded manipulator performance,reliability,and dexterity.Concurrently,developments in modeling have progressed from simplified geometric methods toward highly accurate physics-based and hybrid data-driven approaches,substantially improving real-time prediction and controllability.Coupled with these developments,adaptive and robust control strategies-including learning-based techniques-have enabled unprec-edented autonomy and precision in challenging application domains such as Minimally Invasive Surgery(MIS),precision agriculture,deep-sea exploration,disaster recovery,and space missions.Despite these remarkable strides,key challenges remain,notably regarding scalability,long-term material durability,robust integrated sensing,and standardized evaluation procedures.This review comprehensively synthesizes recent advances,critically evaluates state-of-the-art methodologies,and systematically identifies existing gaps to provide a clear roadmap and targeted research directions,guiding future developments toward the broader adoption and optimal utilization of soft robotic manipulators.展开更多
To address the issue of scarce labeled samples and operational condition variations that degrade the accuracy of fault diagnosis models in variable-condition gearbox fault diagnosis,this paper proposes a semi-supervis...To address the issue of scarce labeled samples and operational condition variations that degrade the accuracy of fault diagnosis models in variable-condition gearbox fault diagnosis,this paper proposes a semi-supervised masked contrastive learning and domain adaptation(SSMCL-DA)method for gearbox fault diagnosis under variable conditions.Initially,during the unsupervised pre-training phase,a dual signal augmentation strategy is devised,which simultaneously applies random masking in the time domain and random scaling in the frequency domain to unlabeled samples,thereby constructing more challenging positive sample pairs to guide the encoder in learning intrinsic features robust to condition variations.Subsequently,a ConvNeXt-Transformer hybrid architecture is employed,integrating the superior local detail modeling capacity of ConvNeXt with the robust global perception capability of Transformer to enhance feature extraction in complex scenarios.Thereafter,a contrastive learning model is constructed with the optimization objective of maximizing feature similarity across different masked instances of the same sample,enabling the extraction of consistent features from multiple masked perspectives and reducing reliance on labeled data.In the final supervised fine-tuning phase,a multi-scale attention mechanism is incorporated for feature rectification,and a domain adaptation module combining Local Maximum Mean Discrepancy(LMMD)with adversarial learning is proposed.This module embodies a dual mechanism:LMMD facilitates fine-grained class-conditional alignment,compelling features of identical fault classes to converge across varying conditions,while the domain discriminator utilizes adversarial training to guide the feature extractor toward learning domain-invariant features.Working in concert,they markedly diminish feature distribution discrepancies induced by changes in load,rotational speed,and other factors,thereby boosting the model’s adaptability to cross-condition scenarios.Experimental evaluations on the WT planetary gearbox dataset and the Case Western Reserve University(CWRU)bearing dataset demonstrate that the SSMCL-DA model effectively identifies multiple fault classes in gearboxes,with diagnostic performance substantially surpassing that of conventional methods.Under cross-condition scenarios,the model attains fault diagnosis accuracies of 99.21%for the WT planetary gearbox and 99.86%for the bearings,respectively.Furthermore,the model exhibits stable generalization capability in cross-device settings.展开更多
Scalable simulation leveraging real-world data plays an essential role in advancing autonomous driving,owing to its efficiency and applicability in both training and evaluating algorithms.Consequently,there has been i...Scalable simulation leveraging real-world data plays an essential role in advancing autonomous driving,owing to its efficiency and applicability in both training and evaluating algorithms.Consequently,there has been increasing attention on generating highly realistic and consistent driving videos,particularly those involving viewpoint changes guided by the control commands or trajectories of ego vehicles.However,current reconstruction approaches,such as Neural Radiance Fields and 3D Gaussian Splatting,frequently suffer from limited generalization and depend on substantial input data.Meanwhile,2D generative models,though capable of producing unknown scenes,still have room for improvement in terms of coherence and visual realism.To overcome these challenges,we introduce GenScene,a world model that synthesizes front-view driving videos conditioned on trajectories.A new temporal module is presented to improve video consistency by extracting the global context of each frame,calculating relationships of frames using these global representations,and fusing frame contexts accordingly.Moreover,we propose an innovative attention mechanism that computes relations of pixels within each frame and pixels in the corresponding window range of the initial frame.Extensive experiments show that our approach surpasses various state-of-the-art models in driving video generation,and the introduced modules contribute significantly to model performance.This work establishes a new paradigm for goal-oriented video synthesis in autonomous driving,which facilitates on-demand simulation to expedite algorithm development.展开更多
To address the issue that hybrid flow shop production struggles to handle order disturbance events,a dynamic scheduling model was constructed.The model takes minimizing the maximum makespan,delivery time deviation,and...To address the issue that hybrid flow shop production struggles to handle order disturbance events,a dynamic scheduling model was constructed.The model takes minimizing the maximum makespan,delivery time deviation,and scheme deviation degree as the optimization objectives.An adaptive dynamic scheduling strategy based on the degree of order disturbance is proposed.An improved multi-objective Grey Wolf(IMOGWO)optimization algorithm is designed by combining the“job-machine”two-layer encoding strategy,the timing-driven two-stage decoding strategy,the opposition-based learning initialization population strategy,the POX crossover strategy,the dualoperation dynamic mutation strategy,and the variable neighborhood search strategy for problem solving.A variety of test cases with different scales were designed,and ablation experiments were conducted to verify the effectiveness of the improved strategies.The results show that each improved strategy can effectively enhance the performance of the IMOGWO.Additionally,performance analysis was conducted by comparing the proposed algorithm with three mature and classical algorithms.The results demonstrate that the proposed algorithm exhibits superior performance in solving the hybrid flow-shop scheduling problem(HFSP).Case validations were conducted for different types of order disturbance scenarios.The results demonstrate that the proposed adaptive dynamic scheduling strategy and the IMOGWO algorithm can effectively address order disturbance events.They enable rapid response to order disturbance while ensuring the stability of the production system.展开更多
Hypersonic morphing vehicle(HMV)can reconfigure aerodynamic geometries in real time,adapting to diverse needs like multi-mission profiles and wide-speed-range flight,spanwise morphing and sweep angle variation are rep...Hypersonic morphing vehicle(HMV)can reconfigure aerodynamic geometries in real time,adapting to diverse needs like multi-mission profiles and wide-speed-range flight,spanwise morphing and sweep angle variation are representative large-scale wing reconfiguration modes.To meet the HMV's need for an increased lift and a lift to drag ratio during hypersonic maneuverability and cruise or reentry equilibrium glide,this paper proposes an innovative single-DOF coupled morphing-wing system.We then systematically analyze its open-loop kinematics and closed-loop connectivity constraints,and the proposed system integrates three functional modules:the preset locking/release mechanism,the coupled morphing-wing mechanism,and the integrated wing locking with active stiffness control mechanism.Experimental validation confirms stable,continuous morphing under simulated aerodynamic loads.The experimental results indicate:(i)SMA actuators exhibit response times ranging from 18 s to 160 s,providing sufficient force output for wing unlocking;(ii)The integrated wing locking with active stiffness control mechanism effectively secures wing positions while eliminating airframe clearance via SMA actuation,improving the first-order natural frequency by more than 17%;(iii)The distributed aerodynamic loading system enables precise multi-stage follow-up loading during morphing,with the coupled morphing wing maintaining stable,continuous operation under 0-3500 N normal loads and 110-140 N axial force.The proposed single-DOF coupled morphing mechanism not only simplifies and improves structural efficiency but also demonstrates superior performance in locking control,stiffness enhancement,and aerodynamic responsiveness.This establishes a foundational framework for the design of future intelligent morphing configurations and the implementation of flight control systems.展开更多
Conventional locking/release mechanisms often face challenges in aircraft wing separation processes,such as excessive impact loads and insufficient synchronization.These may cause structural damage to the airframe or ...Conventional locking/release mechanisms often face challenges in aircraft wing separation processes,such as excessive impact loads and insufficient synchronization.These may cause structural damage to the airframe or attitude instability,seriously compromising mission reliability.To address this engineering challenge,this paper proposes a multi-point low-impact locking/release mechanism based on the mobility model and energy conversion strategy.Through establishing a DOF constraint framework system,this paper systematically analyzes the energy transfer and conversion characteristics during the wing separation process,reveals the generation mechanism of impact loads,and conducts research on low-impact design based on energy conversion strategy.Building on this foundation,a single-point locking/release mechanism employing parallel trapezoidal key shaft structure was designed,which increases frictional contact time and reduces the energy release rate,thereby achieving low-impact characteristics.The mechanism's performance was validated through physical prototype development and systematic functional testing(including unlocking force,synchronization,and impact tests).Experimental results demonstrate:(1)Under 14 kN preload condition,the maximum unlocking force was only 92.54 N,showing a linear relationship with preload that satisfies the"strong-connection/weak-unlock"design requirement;(2)Wing separation was completed within 46 ms,with synchronization time difference among three separation mechanisms stably controlled within 12-14 ms,proving rapid and reliable operation;(3)The unlocking impact acceleration ranged between 26 and 73 g,below the 100 g design limit,confirming the effectiveness of the energy conversion strategy.The proposed low-impact locking/release mechanism design method based on energy conversion strategy resolves the traditional challenges of high impact and synchronization deficiencies.The synergistic optimization mechanism of"structural load reduction and performance improvement"provides a highly reliable technical solution for wing separable mechanisms while offering novel design insights for wing connection/separation systems engineering.展开更多
The moving morphable component(MMC)topology optimization method,as a typical explicit topology optimization method,has been widely concerned.In the MMC topology optimization framework,the surrogate material model is m...The moving morphable component(MMC)topology optimization method,as a typical explicit topology optimization method,has been widely concerned.In the MMC topology optimization framework,the surrogate material model is mainly used for finite element analysis at present,and the effectiveness of the surrogate material model has been fully confirmed.However,there are some accuracy problems when dealing with boundary elements using the surrogate material model,which will affect the topology optimization results.In this study,a boundary element reconstruction(BER)model is proposed based on the surrogate material model under the MMC topology optimization framework to improve the accuracy of topology optimization.The proposed BER model can reconstruct the boundary elements by refining the local meshes and obtaining new nodes in boundary elements.Then the density of boundary elements is recalculated using the new node information,which is more accurate than the original model.Based on the new density of boundary elements,the material properties and volume information of the boundary elements are updated.Compared with other finite element analysis methods,the BER model is simple and feasible and can improve computational accuracy.Finally,the effectiveness and superiority of the proposed method are verified by comparing it with the optimization results of the original surrogate material model through several numerical examples.展开更多
Soft pneumatic structures are promising for the actuation of soft machines,and substantial advances have occurred in their innovative design and functional verification.However,most pneumatic structures lack self-sens...Soft pneumatic structures are promising for the actuation of soft machines,and substantial advances have occurred in their innovative design and functional verification.However,most pneumatic structures lack self-sensing abilities,resulting in a lack of motion state feedback and difficulty in achieving real-time closed-loop control.Herein,a soft pneumatic composite structure(SPCS)with integrated actuation and sensing abilities is developed by combining a bellows-shaped magnetic elastomer and a wire structure.The SPCS can generate an induced voltage under deformation.The SPCS mechanical and magnetoelectric characteristics are studied comprehensively.The SPCS experimental maximum contraction is 27 mm,which is close to the theoretical and numerical results.When the SPCS is actuated by a pressure of-40 kPa,it will generate a peak induced voltage of 1.01 mV.With the increase in magnetic powder content and turns of the spiral wire,the induced voltage also increases.Additionally,two SPCSs are used to develop a self-sensing actuator,which can accurately perceive the bending direction and recognize the magnitude and direction of external force.A self-sensing soft gripper is developed,which can sense the grasping status and predict the width of grasped objects.Furthermore,a smart vehicle detection system composed of two SPCSs is proposed,which can detect the number,speed,and weight of passing vehicles.Consequently,the SPCS has numerous potential applications in soft sensors and self-sensing intelligent soft machines.展开更多
This study presents a framework involving statistical modeling and machine learning to accurately predict and optimize the mechanical and damping properties of hybrid granite-epoxy(G-E)composites reinforced with cast ...This study presents a framework involving statistical modeling and machine learning to accurately predict and optimize the mechanical and damping properties of hybrid granite-epoxy(G-E)composites reinforced with cast iron(CI)filler particles.Hybrid G-E composite with added cast iron(CI)filler particles enhances stiffness,strength,and vibration damping,offering enhanced performance for vibration-sensitive engineering applications.Unlike conventional approaches,this work simultaneously employs Artificial Neural Networks(ANN)for highaccuracy property prediction and Response Surface Methodology(RSM)for in-depth analysis of factor interactions and optimization.A total of 24 experimental test data sets of varying input factors(granite weight%,epoxy weight%,and CI filler weight%)were utilized to train and test the prediction models using an ANN approach and further analyze the interaction effects using RSM.Mechanical properties,including tensile,compressive,and flexural strength,elastic modulus,density and damping properties measured under various testing conditions,were set as output parameters for prediction.This study analyzed and optimized the performance of the ANN model using Bayesian Regularization and Levenberg-Marquardt algorithms to identify the best performing number of neurons in the hidden layer for achieving the highest prediction accuracy.The proposed ANN framework achieved an exceptional average determination coefficient(R2)exceeding 99%,with Bayesian Regularization demonstrating remarkable stability in the 22-neuron range and minimal variation across all properties.RSM and ANN form a powerful framework for predicting and optimizing hybrid G-E composite properties,enabling efficient design for vibration-critical applications with reduced experimental effort and performance optimization.展开更多
To explore the distribution law of the temperature field in the motor pump and the influence of the fanshaped DC channel with spoiler in the pump housing on its heat dissipation performance.This study takes the arc-ge...To explore the distribution law of the temperature field in the motor pump and the influence of the fanshaped DC channel with spoiler in the pump housing on its heat dissipation performance.This study takes the arc-gear type hydraulicmotor pump as the research object.In COMSOL,a coupled heat transfer simulationmodel of themotor pump’s fluid-solid coupling is established,and the internal temperature field characteristics are analyzed.To improve the heat dissipation effect of the motor pump,it is proposed to arrange spoiler in the fan-shaped DC channel of the pump housing to enhance heat dissipation.Three types of spoilers,namely,wing-shaped,inclined rectangle-shaped,and wave-shaped,are designed.The simulation results show that when the motor pump operates under rated conditions,due to the poor heat dissipation environment inside the motor pump,the high-temperature areas of the motor pump are concentrated in the rotor and permanent magnet parts.After arranging the spoiler,the turbulent kinetic energy and vorticity in the fan-shaped DC channel of the pump housing are significantly enhanced.All three spoiler structures can reduce the maximum temperature of each component of the motor.According to the comprehensive performance evaluation criterion(PEC),the inclined rectangle-shaped structure has the best comprehensive heat transfer performance(PEC=1.114),while the wave-shaped structure has higher heat transfer efficiency but greater pressure loss.The wing-shaped structure has relatively limited enhancement effect on heat dissipation.This study systematically quantifies the influence of different spoiler structures on heat dissipation performance and flowresistance characteristics,providing a solution for enhancing the heat dissipation of the motor pump.展开更多
Conventional ignition methods are proving to be ineffective for low-sensitivity energetic materials,highlighting the need to investigate alternative ignition systems,such as laser-based techniques.Over the past decade...Conventional ignition methods are proving to be ineffective for low-sensitivity energetic materials,highlighting the need to investigate alternative ignition systems,such as laser-based techniques.Over the past decade,lasers have emerged as a promising solution,providing focused energy beams for controllable,efficient,and reliable ignition in the field of energetic materials.This study presents a comparative analysis of two state-of-the-art ignition approaches:direct laser ignition and laser-driven flyer ignition.Experiments were performed using a Neodymium-doped Yttrium Aluminum Garnet(Nd:YAG)laser at different energy beam levels to systematically evaluate ignition onset.In the direct laser ignition test setup,the laser beam was applied directly to the energetic tested material,while laserdriven flyer ignition utilized 40 and 100μm aluminum foils,propelled at velocities ranging from 300 to 1250 m/s.Comparative analysis with the Lawrence and Trott model substantiated the velocity data and provided insight into the ignition mechanisms.Experimental results indicate that the ignition time for the laser-driven flyer method was significantly shorter,with the pyrotechnic composition achieving complete combustion faster compared to direct laser ignition.Moreover,precise ignition thresholds were determined for both methods,providing critical parameters for optimizing ignition systems in energetic materials.This work elucidates the advantages and limitations of each technique while advancing next-generation ignition technology,enhancing the reliability and safety of propulsion systems.展开更多
Under sustained strong stochastic impact loads,floating-supported friction plates are susceptible to the formation of fatigue cracks that propagate along the rim.The nonlinearity and randomness introduced by the crack...Under sustained strong stochastic impact loads,floating-supported friction plates are susceptible to the formation of fatigue cracks that propagate along the rim.The nonlinearity and randomness introduced by the cracked teeth participating in the impacts significantly influence the service life and reliability of the transmission system.In this paper,an improved stiffness excitation modeling method is developed for friction plate teeth with rim cracks.It overcomes the limitations of traditional approaches that fail to accurately assess the narrow-band,large-diameter friction plate teeth with rim cracks due to constraints imposed by boundary conditions.Then,an original dynamic impact model for the floating-supported friction plate and inner hub system is proposed,incorporating the effects of bending-torsional-axial-tilting coupled motions on tooth mesh excitations and dynamic responses.This model addresses the limitations of conventional models that only consider bending-torsion coupling,thereby providing a more comprehensive representation of the system's multi-dimensional dynamic behavior.The effects of the crack propagation depth and the number of cracked teeth on the stochastic impact characteristics and vibration responses of the system are investigated.Furthermore,finite element simulations and experimental tests are conducted to validate the cracked tooth stiffness excitations and dynamic impact responses,respectively.The proposed model is anticipated to provide both a theoretical foundation and practical guidance for fault diagnosis and reliability assessment of clutch friction plates.展开更多
Images taken in dim environments frequently exhibit issues like insufficient brightness,noise,color shifts,and loss of detail.These problems pose significant challenges to dark image enhancement tasks.Current approach...Images taken in dim environments frequently exhibit issues like insufficient brightness,noise,color shifts,and loss of detail.These problems pose significant challenges to dark image enhancement tasks.Current approaches,while effective in global illumination modeling,often struggle to simultaneously suppress noise and preserve structural details,especially under heterogeneous lighting.Furthermore,misalignment between luminance and color channels introduces additional challenges to accurate enhancement.In response to the aforementioned difficulties,we introduce a single-stage framework,M2ATNet,using the multi-scale multi-attention and Transformer architecture.First,to address the problems of texture blurring and residual noise,we design a multi-scale multi-attention denoising module(MMAD),which is applied separately to the luminance and color channels to enhance the structural and texture modeling capabilities.Secondly,to solve the non-alignment problem of the luminance and color channels,we introduce the multi-channel feature fusion Transformer(CFFT)module,which effectively recovers the dark details and corrects the color shifts through cross-channel alignment and deep feature interaction.To guide the model to learn more stably and efficiently,we also fuse multiple types of loss functions to form a hybrid loss term.We extensively evaluate the proposed method on various standard datasets,including LOL-v1,LOL-v2,DICM,LIME,and NPE.Evaluation in terms of numerical metrics and visual quality demonstrate that M2ATNet consistently outperforms existing advanced approaches.Ablation studies further confirm the critical roles played by the MMAD and CFFT modules to detail preservation and visual fidelity under challenging illumination-deficient environments.展开更多
The effect of plasma and charged particle interaction with spacecraft in a low Earth orbit(LEO)environment leads to ion focusing and the formation of an ion void in the downstream region as a result of charging.Simula...The effect of plasma and charged particle interaction with spacecraft in a low Earth orbit(LEO)environment leads to ion focusing and the formation of an ion void in the downstream region as a result of charging.Simulations and investigations using a fixed potential imposed on the spacecraft showed the nonsignificance of geophysical parameter changes to ion focusing.Variation of the temperature ratio(T_(r))contributed only to local ion focusing and manifested as two-ion streamers dispersed at the upper and lower edges of the spacecraft-the outermost layers of the satellite structure at the top and bottom,respectively.A simulation involving changing the ambient plasma density(N_(p))also showed the formation of local ion focusing,in which ions were more concentrated as the density increased.Furthermore,auroral electron density(N_(ae))variation had no clear impact on ion focusing,as indicated by static two-ion structures in the wake field.However,variation of the object potential(ϕ)strongly affected ion focusing formation,leading to distortion of the initial ion void region behind the spacecraft.The formation of ion focusing in this study was subject to the electric field produced by the object potential and the ambipolar electric field resulting from plasma expansion in the downstream region.展开更多
The continuous improvement of solar thermal technologies is essential to meet the growing demand for sustainable heat generation and to support global decarbonization efforts.This study presents the design,implementat...The continuous improvement of solar thermal technologies is essential to meet the growing demand for sustainable heat generation and to support global decarbonization efforts.This study presents the design,implementation,and validation of a real-time monitoring framework based on the Internet ofThings(IoT)and cloud computing to enhance the thermal performance of evacuated tube solar water heaters(ETSWHs).A commercial system and a custom-built prototype were instrumented with Industry 4.0 technologies,including platinum resistance temperature detectors(PT100),solar irradiance and wind speed sensors,a programmable logic controller(PLC),a SCADAinterface,and a cloud-connected IoT gateway.Data were processed locally and transmitted to cloud storage for continuous analysis and visualization via amobile application.Experimental results demonstrated the prototype’s superior thermal energy storage capacity−47.4 vs.36.2 MJ for the commercial system,representing a 31%—achieved through the novel integration of Industry 4.0 architecture with an optimized collector design.This improvement is attributed to optimized geometric design parameters,including a reduced tilt angle,increased inter-tube spacing,and the incorporation of an aluminum reflective surface.These modifications collectively enhanced solar heat absorption and reduced optical losses.The framework effectively identified thermal stratification,monitored environmental effects on heat transfer,and enabled real-time system diagnostics.By integrating automation,IoT,and cloud computing,the proposed architecture establishes a scalable and replicable model for the intelligent management of solar thermal systems,facilitating predictive maintenance and future integration with artificial intelligence for performance forecasting.This work provides a practical,data-driven approach to digitizing and optimizing heat transfer systems,promoting more efficient and sustainable solar thermal energy applications.展开更多
Humanoid robots hold significant promise for social interaction and emotional companionship.However,their effectiveness hinges on the ability to convey nuanced and authentic emotions.Here,we presented a universal huma...Humanoid robots hold significant promise for social interaction and emotional companionship.However,their effectiveness hinges on the ability to convey nuanced and authentic emotions.Here,we presented a universal humanoid robot head with a facial kinematics model.Using a reinforcement learning framework guided by symmetry assessment,emotion decoupling,and MLLM authenticity evaluation,our system autonomously learns to generate adaptive facial expressions through dynamic landmark adjustments.By transferring the simulation training results to real-world environments,the robot can perform natural and expressive expressions.Another novel feature is the independent regulation of emotion intensity and expression magnitude across emotional categories,which enhances the ability to achieve culturally adaptive and socially resonant robotic expressions significantly.This research advances adaptive humanoid interaction,offering an easier and more efficient pathway toward culturally resonant and psychologically plausible robotic expressions.展开更多
基金This work was supported by the Deanship of Scientific Research,King Khalid University,Kingdom of Saudi Arabia under research Grant Number(R.G.P.2/100/41).
文摘Human Adaptive Mechatronics(HAM)includes human and computer system in a closed loop.Elderly person with disabilities,normally carry out their daily routines with some assistance to move their limbs.With the short fall of human care takers,mechatronics devices are used with the likes of exoskeleton and exosuits to assist them.The rehabilitation and occupational therapy equipments utilize the electromyography(EMG)signals to measure the muscle activity potential.This paper focuses on optimizing the HAM model in prediction of intended motion of upper limb with high accuracy and to increase the response time of the system.Limb characteristics extraction from EMG signal and prediction of optimal controller parameters are modeled.Time and frequency based approach of EMG signal are considered for feature extraction.The models used for estimating motion and muscle parameters from EMG signal for carrying out limb movement predictions are validated.Based on the extracted features,optimal parameters are selected by Modified Lion Optimization(MLO)for controlling the HAM system.Finally,supervised machine learning makes predictions at different points in time for individual sensing using Support Vector Neural Network(SVNN).This model is also evaluated based on optimal parameters of motion estimation and the accuracy level along with different optimization models for various upper limb movements.The proposed model of human adaptive controller predicts the limb movement by 96%accuracy.
基金Projects(51135009,51105371) supported by the National Natural Science Foundation of China
文摘LuGre model has been widely used in friction modeling and compensation.However,the new friction regime,named prestiction regime,cannot be accurately characterized by LuGre model in the latest research.With the extensive experimental observations of friction behaviors in the prestiction,some variables were abstracted to depict the rules in the prestiction regime.Based upon the knowledge of friction modeling,a novel friction model including the presliding regime,the gross sliding regime and the prestiction regime was then presented to overcome the shortcomings of the LuGre model.The reason that LuGre model cannot estimate the prestiction friction was analyzed in theory.Feasibility analysis of the proposed model in modeling the prestiction friction was also addressed.A parameter identification method for the proposed model based on multilevel coordinate search algorithm was presented.The proposed friction compensation strategy was composed of a nonlinear friction observer and a feedforward mechanism.The friction observer was designed to estimate the friction force in the presliding and the gross sliding regimes.And the friction force was estimated based on the model in the prestiction regime.The comparative trajectory tracking experiments were conducted on a simulator of inertially stabilization platforms among three control schemes:the single proportional–derivative(PD)control,the PD with LuGre model-based compensation and the PD with compensator based on the presented model.The experimental results reveal that the control scheme based on the proposed model has the best tracking performance.It reduces the peak-to-peak value(PPV)of tracking error to 0.2 mrad,which is improved almost 50%compared with the PD with LuGre model-based compensation.Compared to the single PD control,it reduces the PPV of error by 66.7%.
文摘Healthcare mechatronics is a typical multidisciplinary field involving machinery,medicine,computer,and automation,which has been widely applied in respiratory therapy,urology robot,rehabilitation exoskeleton,artificial heart,etc.Existing progresses has some defects in modeling,design and implementation of healthcare mechatronics.Therefore,exploring new design theories,key technologies and typical applications is an effective to promote the rapid development of this field.
基金Jiangsu Association for Science and Technology Youth Talent Support Project(Grant No.JSTJ-2024-031)National Natural Science Foundation of China(Grant No.52005265)Natural Science Fund for Colleges and Universities in Jiangsu Province(Grant No.20KJB460002)。
文摘Owing to the harsh conditions,wind turbine bearings are prone to faults,and the resulting fault information is easily submerged by strong noise disturbance,making conventional diagnosis challenging.Therefore,this study presents an innovative bearing fault diagnosis approach predicated on Parameter⁃Optimized Symplectic Geometry Mode Decomposition(POSGMD)and Improved Convolutional Neural Network(ICNN).Firstly,assisted by the relative entropy⁃based adaptive selection of embedding dimension,a POSGMD is presented to adaptively decompose the collected bearing vibration signals into various Symplectic Geometry Components(SGC),which can solve the problem of manual selection of the embedding dimension in the raw Symplectic Geometry Mode Decomposition(SGMD).Meanwhile,the signal reconstruction on the decomposed SGC is conducted based on kurtosis⁃weighted principle to obtain the reconstructed signals.Subsequently,the Continuous Wavelet Transform(CWT)of the reconstructed signals is calculated to generate the corresponding time⁃frequency images as sample set.Finally,an ICNN is introduced for model training and automatic recognition of bearing faults.Two case studies are used to validate the presented methods efficacy.Comparing the presented method with traditional fault diagnosis methods,experimental results show that it can achieve greater identification accuracy and superior anti⁃noise resilience.This work provides a practical and effective solution for fault diagnosis in wind turbine bearings,contributing to the timely detection of faults and the reliable operation of wind turbines or other rotational machinery in industrial applications.
基金Open access funding provided by The Science,Technology&Innovation Funding Authority(STDF)in cooperation with The Egyptian Knowledge Bank(EKB).
文摘Soft robotic manipulators represent a rapidly evolving field characterized by inherent compliance,adaptability,and safe interactions within unstructured environments.Over the past decade(2015-2025),significant advancements have trans-formed their capabilities through novel designs inspired by biological systems,advanced modeling frameworks,sophisti-cated control strategies,and integration into diverse real-world applications.Recent innovations in multifunctional mate-rials and emerging actuation technologies have markedly expanded manipulator performance,reliability,and dexterity.Concurrently,developments in modeling have progressed from simplified geometric methods toward highly accurate physics-based and hybrid data-driven approaches,substantially improving real-time prediction and controllability.Coupled with these developments,adaptive and robust control strategies-including learning-based techniques-have enabled unprec-edented autonomy and precision in challenging application domains such as Minimally Invasive Surgery(MIS),precision agriculture,deep-sea exploration,disaster recovery,and space missions.Despite these remarkable strides,key challenges remain,notably regarding scalability,long-term material durability,robust integrated sensing,and standardized evaluation procedures.This review comprehensively synthesizes recent advances,critically evaluates state-of-the-art methodologies,and systematically identifies existing gaps to provide a clear roadmap and targeted research directions,guiding future developments toward the broader adoption and optimal utilization of soft robotic manipulators.
基金supported by the National Natural Science Foundation of China Funded Project(Project Name:Research on Robust Adaptive Allocation Mechanism of Human Machine Co-Driving System Based on NMS Features,Project Approval Number:52172381).
文摘To address the issue of scarce labeled samples and operational condition variations that degrade the accuracy of fault diagnosis models in variable-condition gearbox fault diagnosis,this paper proposes a semi-supervised masked contrastive learning and domain adaptation(SSMCL-DA)method for gearbox fault diagnosis under variable conditions.Initially,during the unsupervised pre-training phase,a dual signal augmentation strategy is devised,which simultaneously applies random masking in the time domain and random scaling in the frequency domain to unlabeled samples,thereby constructing more challenging positive sample pairs to guide the encoder in learning intrinsic features robust to condition variations.Subsequently,a ConvNeXt-Transformer hybrid architecture is employed,integrating the superior local detail modeling capacity of ConvNeXt with the robust global perception capability of Transformer to enhance feature extraction in complex scenarios.Thereafter,a contrastive learning model is constructed with the optimization objective of maximizing feature similarity across different masked instances of the same sample,enabling the extraction of consistent features from multiple masked perspectives and reducing reliance on labeled data.In the final supervised fine-tuning phase,a multi-scale attention mechanism is incorporated for feature rectification,and a domain adaptation module combining Local Maximum Mean Discrepancy(LMMD)with adversarial learning is proposed.This module embodies a dual mechanism:LMMD facilitates fine-grained class-conditional alignment,compelling features of identical fault classes to converge across varying conditions,while the domain discriminator utilizes adversarial training to guide the feature extractor toward learning domain-invariant features.Working in concert,they markedly diminish feature distribution discrepancies induced by changes in load,rotational speed,and other factors,thereby boosting the model’s adaptability to cross-condition scenarios.Experimental evaluations on the WT planetary gearbox dataset and the Case Western Reserve University(CWRU)bearing dataset demonstrate that the SSMCL-DA model effectively identifies multiple fault classes in gearboxes,with diagnostic performance substantially surpassing that of conventional methods.Under cross-condition scenarios,the model attains fault diagnosis accuracies of 99.21%for the WT planetary gearbox and 99.86%for the bearings,respectively.Furthermore,the model exhibits stable generalization capability in cross-device settings.
基金supported by the Cultivation Program for Major Scientific Research Projects of Harbin Institute of Technology(ZDXMPY20180109).
文摘Scalable simulation leveraging real-world data plays an essential role in advancing autonomous driving,owing to its efficiency and applicability in both training and evaluating algorithms.Consequently,there has been increasing attention on generating highly realistic and consistent driving videos,particularly those involving viewpoint changes guided by the control commands or trajectories of ego vehicles.However,current reconstruction approaches,such as Neural Radiance Fields and 3D Gaussian Splatting,frequently suffer from limited generalization and depend on substantial input data.Meanwhile,2D generative models,though capable of producing unknown scenes,still have room for improvement in terms of coherence and visual realism.To overcome these challenges,we introduce GenScene,a world model that synthesizes front-view driving videos conditioned on trajectories.A new temporal module is presented to improve video consistency by extracting the global context of each frame,calculating relationships of frames using these global representations,and fusing frame contexts accordingly.Moreover,we propose an innovative attention mechanism that computes relations of pixels within each frame and pixels in the corresponding window range of the initial frame.Extensive experiments show that our approach surpasses various state-of-the-art models in driving video generation,and the introduced modules contribute significantly to model performance.This work establishes a new paradigm for goal-oriented video synthesis in autonomous driving,which facilitates on-demand simulation to expedite algorithm development.
基金funded by National Key Research and Development Program Projects of China under Grant No.2020YFB1713500.
文摘To address the issue that hybrid flow shop production struggles to handle order disturbance events,a dynamic scheduling model was constructed.The model takes minimizing the maximum makespan,delivery time deviation,and scheme deviation degree as the optimization objectives.An adaptive dynamic scheduling strategy based on the degree of order disturbance is proposed.An improved multi-objective Grey Wolf(IMOGWO)optimization algorithm is designed by combining the“job-machine”two-layer encoding strategy,the timing-driven two-stage decoding strategy,the opposition-based learning initialization population strategy,the POX crossover strategy,the dualoperation dynamic mutation strategy,and the variable neighborhood search strategy for problem solving.A variety of test cases with different scales were designed,and ablation experiments were conducted to verify the effectiveness of the improved strategies.The results show that each improved strategy can effectively enhance the performance of the IMOGWO.Additionally,performance analysis was conducted by comparing the proposed algorithm with three mature and classical algorithms.The results demonstrate that the proposed algorithm exhibits superior performance in solving the hybrid flow-shop scheduling problem(HFSP).Case validations were conducted for different types of order disturbance scenarios.The results demonstrate that the proposed adaptive dynamic scheduling strategy and the IMOGWO algorithm can effectively address order disturbance events.They enable rapid response to order disturbance while ensuring the stability of the production system.
基金supported by the National Natural Science Foundation of China(Grant No.52405257)the China Postdoctoral Science Foundation(Grant No.2024M764201).
文摘Hypersonic morphing vehicle(HMV)can reconfigure aerodynamic geometries in real time,adapting to diverse needs like multi-mission profiles and wide-speed-range flight,spanwise morphing and sweep angle variation are representative large-scale wing reconfiguration modes.To meet the HMV's need for an increased lift and a lift to drag ratio during hypersonic maneuverability and cruise or reentry equilibrium glide,this paper proposes an innovative single-DOF coupled morphing-wing system.We then systematically analyze its open-loop kinematics and closed-loop connectivity constraints,and the proposed system integrates three functional modules:the preset locking/release mechanism,the coupled morphing-wing mechanism,and the integrated wing locking with active stiffness control mechanism.Experimental validation confirms stable,continuous morphing under simulated aerodynamic loads.The experimental results indicate:(i)SMA actuators exhibit response times ranging from 18 s to 160 s,providing sufficient force output for wing unlocking;(ii)The integrated wing locking with active stiffness control mechanism effectively secures wing positions while eliminating airframe clearance via SMA actuation,improving the first-order natural frequency by more than 17%;(iii)The distributed aerodynamic loading system enables precise multi-stage follow-up loading during morphing,with the coupled morphing wing maintaining stable,continuous operation under 0-3500 N normal loads and 110-140 N axial force.The proposed single-DOF coupled morphing mechanism not only simplifies and improves structural efficiency but also demonstrates superior performance in locking control,stiffness enhancement,and aerodynamic responsiveness.This establishes a foundational framework for the design of future intelligent morphing configurations and the implementation of flight control systems.
文摘Conventional locking/release mechanisms often face challenges in aircraft wing separation processes,such as excessive impact loads and insufficient synchronization.These may cause structural damage to the airframe or attitude instability,seriously compromising mission reliability.To address this engineering challenge,this paper proposes a multi-point low-impact locking/release mechanism based on the mobility model and energy conversion strategy.Through establishing a DOF constraint framework system,this paper systematically analyzes the energy transfer and conversion characteristics during the wing separation process,reveals the generation mechanism of impact loads,and conducts research on low-impact design based on energy conversion strategy.Building on this foundation,a single-point locking/release mechanism employing parallel trapezoidal key shaft structure was designed,which increases frictional contact time and reduces the energy release rate,thereby achieving low-impact characteristics.The mechanism's performance was validated through physical prototype development and systematic functional testing(including unlocking force,synchronization,and impact tests).Experimental results demonstrate:(1)Under 14 kN preload condition,the maximum unlocking force was only 92.54 N,showing a linear relationship with preload that satisfies the"strong-connection/weak-unlock"design requirement;(2)Wing separation was completed within 46 ms,with synchronization time difference among three separation mechanisms stably controlled within 12-14 ms,proving rapid and reliable operation;(3)The unlocking impact acceleration ranged between 26 and 73 g,below the 100 g design limit,confirming the effectiveness of the energy conversion strategy.The proposed low-impact locking/release mechanism design method based on energy conversion strategy resolves the traditional challenges of high impact and synchronization deficiencies.The synergistic optimization mechanism of"structural load reduction and performance improvement"provides a highly reliable technical solution for wing separable mechanisms while offering novel design insights for wing connection/separation systems engineering.
基金supported by the Science and Technology Research Project of Henan Province(242102241055)the Industry-University-Research Collaborative Innovation Base on Automobile Lightweight of“Science and Technology Innovation in Central Plains”(2024KCZY315)the Opening Fund of State Key Laboratory of Structural Analysis,Optimization and CAE Software for Industrial Equipment(GZ2024A03-ZZU).
文摘The moving morphable component(MMC)topology optimization method,as a typical explicit topology optimization method,has been widely concerned.In the MMC topology optimization framework,the surrogate material model is mainly used for finite element analysis at present,and the effectiveness of the surrogate material model has been fully confirmed.However,there are some accuracy problems when dealing with boundary elements using the surrogate material model,which will affect the topology optimization results.In this study,a boundary element reconstruction(BER)model is proposed based on the surrogate material model under the MMC topology optimization framework to improve the accuracy of topology optimization.The proposed BER model can reconstruct the boundary elements by refining the local meshes and obtaining new nodes in boundary elements.Then the density of boundary elements is recalculated using the new node information,which is more accurate than the original model.Based on the new density of boundary elements,the material properties and volume information of the boundary elements are updated.Compared with other finite element analysis methods,the BER model is simple and feasible and can improve computational accuracy.Finally,the effectiveness and superiority of the proposed method are verified by comparing it with the optimization results of the original surrogate material model through several numerical examples.
基金supported by the National Natural Science Foundation of China(Grant No.52405267)the Jiangxi Provincial Natural Science Foundation(Grant Nos.20242BAB25257,20232BAB214050)+1 种基金the China Postdoctoral Science Foundation(Grant No.2024M760877)the Natural Science Foundation of Hunan Province(Grant No.2025JJ60369)。
文摘Soft pneumatic structures are promising for the actuation of soft machines,and substantial advances have occurred in their innovative design and functional verification.However,most pneumatic structures lack self-sensing abilities,resulting in a lack of motion state feedback and difficulty in achieving real-time closed-loop control.Herein,a soft pneumatic composite structure(SPCS)with integrated actuation and sensing abilities is developed by combining a bellows-shaped magnetic elastomer and a wire structure.The SPCS can generate an induced voltage under deformation.The SPCS mechanical and magnetoelectric characteristics are studied comprehensively.The SPCS experimental maximum contraction is 27 mm,which is close to the theoretical and numerical results.When the SPCS is actuated by a pressure of-40 kPa,it will generate a peak induced voltage of 1.01 mV.With the increase in magnetic powder content and turns of the spiral wire,the induced voltage also increases.Additionally,two SPCSs are used to develop a self-sensing actuator,which can accurately perceive the bending direction and recognize the magnitude and direction of external force.A self-sensing soft gripper is developed,which can sense the grasping status and predict the width of grasped objects.Furthermore,a smart vehicle detection system composed of two SPCSs is proposed,which can detect the number,speed,and weight of passing vehicles.Consequently,the SPCS has numerous potential applications in soft sensors and self-sensing intelligent soft machines.
文摘This study presents a framework involving statistical modeling and machine learning to accurately predict and optimize the mechanical and damping properties of hybrid granite-epoxy(G-E)composites reinforced with cast iron(CI)filler particles.Hybrid G-E composite with added cast iron(CI)filler particles enhances stiffness,strength,and vibration damping,offering enhanced performance for vibration-sensitive engineering applications.Unlike conventional approaches,this work simultaneously employs Artificial Neural Networks(ANN)for highaccuracy property prediction and Response Surface Methodology(RSM)for in-depth analysis of factor interactions and optimization.A total of 24 experimental test data sets of varying input factors(granite weight%,epoxy weight%,and CI filler weight%)were utilized to train and test the prediction models using an ANN approach and further analyze the interaction effects using RSM.Mechanical properties,including tensile,compressive,and flexural strength,elastic modulus,density and damping properties measured under various testing conditions,were set as output parameters for prediction.This study analyzed and optimized the performance of the ANN model using Bayesian Regularization and Levenberg-Marquardt algorithms to identify the best performing number of neurons in the hidden layer for achieving the highest prediction accuracy.The proposed ANN framework achieved an exceptional average determination coefficient(R2)exceeding 99%,with Bayesian Regularization demonstrating remarkable stability in the 22-neuron range and minimal variation across all properties.RSM and ANN form a powerful framework for predicting and optimizing hybrid G-E composite properties,enabling efficient design for vibration-critical applications with reduced experimental effort and performance optimization.
基金supported by the Henan Provincial Key Research and Development Special Project(251111220200)Natural Science Foundation of Henan Province Project(252300420446).
文摘To explore the distribution law of the temperature field in the motor pump and the influence of the fanshaped DC channel with spoiler in the pump housing on its heat dissipation performance.This study takes the arc-gear type hydraulicmotor pump as the research object.In COMSOL,a coupled heat transfer simulationmodel of themotor pump’s fluid-solid coupling is established,and the internal temperature field characteristics are analyzed.To improve the heat dissipation effect of the motor pump,it is proposed to arrange spoiler in the fan-shaped DC channel of the pump housing to enhance heat dissipation.Three types of spoilers,namely,wing-shaped,inclined rectangle-shaped,and wave-shaped,are designed.The simulation results show that when the motor pump operates under rated conditions,due to the poor heat dissipation environment inside the motor pump,the high-temperature areas of the motor pump are concentrated in the rotor and permanent magnet parts.After arranging the spoiler,the turbulent kinetic energy and vorticity in the fan-shaped DC channel of the pump housing are significantly enhanced.All three spoiler structures can reduce the maximum temperature of each component of the motor.According to the comprehensive performance evaluation criterion(PEC),the inclined rectangle-shaped structure has the best comprehensive heat transfer performance(PEC=1.114),while the wave-shaped structure has higher heat transfer efficiency but greater pressure loss.The wing-shaped structure has relatively limited enhancement effect on heat dissipation.This study systematically quantifies the influence of different spoiler structures on heat dissipation performance and flowresistance characteristics,providing a solution for enhancing the heat dissipation of the motor pump.
文摘Conventional ignition methods are proving to be ineffective for low-sensitivity energetic materials,highlighting the need to investigate alternative ignition systems,such as laser-based techniques.Over the past decade,lasers have emerged as a promising solution,providing focused energy beams for controllable,efficient,and reliable ignition in the field of energetic materials.This study presents a comparative analysis of two state-of-the-art ignition approaches:direct laser ignition and laser-driven flyer ignition.Experiments were performed using a Neodymium-doped Yttrium Aluminum Garnet(Nd:YAG)laser at different energy beam levels to systematically evaluate ignition onset.In the direct laser ignition test setup,the laser beam was applied directly to the energetic tested material,while laserdriven flyer ignition utilized 40 and 100μm aluminum foils,propelled at velocities ranging from 300 to 1250 m/s.Comparative analysis with the Lawrence and Trott model substantiated the velocity data and provided insight into the ignition mechanisms.Experimental results indicate that the ignition time for the laser-driven flyer method was significantly shorter,with the pyrotechnic composition achieving complete combustion faster compared to direct laser ignition.Moreover,precise ignition thresholds were determined for both methods,providing critical parameters for optimizing ignition systems in energetic materials.This work elucidates the advantages and limitations of each technique while advancing next-generation ignition technology,enhancing the reliability and safety of propulsion systems.
基金supported by the National Natural Science Foundation of China(Grant Nos.52505101,52475087,52475089,52365010)the Early-Career Young Scientists and Technologists Project of Jiangxi Province(Grant No.20252BEJ730175)。
文摘Under sustained strong stochastic impact loads,floating-supported friction plates are susceptible to the formation of fatigue cracks that propagate along the rim.The nonlinearity and randomness introduced by the cracked teeth participating in the impacts significantly influence the service life and reliability of the transmission system.In this paper,an improved stiffness excitation modeling method is developed for friction plate teeth with rim cracks.It overcomes the limitations of traditional approaches that fail to accurately assess the narrow-band,large-diameter friction plate teeth with rim cracks due to constraints imposed by boundary conditions.Then,an original dynamic impact model for the floating-supported friction plate and inner hub system is proposed,incorporating the effects of bending-torsional-axial-tilting coupled motions on tooth mesh excitations and dynamic responses.This model addresses the limitations of conventional models that only consider bending-torsion coupling,thereby providing a more comprehensive representation of the system's multi-dimensional dynamic behavior.The effects of the crack propagation depth and the number of cracked teeth on the stochastic impact characteristics and vibration responses of the system are investigated.Furthermore,finite element simulations and experimental tests are conducted to validate the cracked tooth stiffness excitations and dynamic impact responses,respectively.The proposed model is anticipated to provide both a theoretical foundation and practical guidance for fault diagnosis and reliability assessment of clutch friction plates.
基金funded by the National Natural Science Foundation of China,grant numbers 52374156 and 62476005。
文摘Images taken in dim environments frequently exhibit issues like insufficient brightness,noise,color shifts,and loss of detail.These problems pose significant challenges to dark image enhancement tasks.Current approaches,while effective in global illumination modeling,often struggle to simultaneously suppress noise and preserve structural details,especially under heterogeneous lighting.Furthermore,misalignment between luminance and color channels introduces additional challenges to accurate enhancement.In response to the aforementioned difficulties,we introduce a single-stage framework,M2ATNet,using the multi-scale multi-attention and Transformer architecture.First,to address the problems of texture blurring and residual noise,we design a multi-scale multi-attention denoising module(MMAD),which is applied separately to the luminance and color channels to enhance the structural and texture modeling capabilities.Secondly,to solve the non-alignment problem of the luminance and color channels,we introduce the multi-channel feature fusion Transformer(CFFT)module,which effectively recovers the dark details and corrects the color shifts through cross-channel alignment and deep feature interaction.To guide the model to learn more stably and efficiently,we also fuse multiple types of loss functions to form a hybrid loss term.We extensively evaluate the proposed method on various standard datasets,including LOL-v1,LOL-v2,DICM,LIME,and NPE.Evaluation in terms of numerical metrics and visual quality demonstrate that M2ATNet consistently outperforms existing advanced approaches.Ablation studies further confirm the critical roles played by the MMAD and CFFT modules to detail preservation and visual fidelity under challenging illumination-deficient environments.
基金Kobe Universitythe National Research and Innovation Agency (BRIN)
文摘The effect of plasma and charged particle interaction with spacecraft in a low Earth orbit(LEO)environment leads to ion focusing and the formation of an ion void in the downstream region as a result of charging.Simulations and investigations using a fixed potential imposed on the spacecraft showed the nonsignificance of geophysical parameter changes to ion focusing.Variation of the temperature ratio(T_(r))contributed only to local ion focusing and manifested as two-ion streamers dispersed at the upper and lower edges of the spacecraft-the outermost layers of the satellite structure at the top and bottom,respectively.A simulation involving changing the ambient plasma density(N_(p))also showed the formation of local ion focusing,in which ions were more concentrated as the density increased.Furthermore,auroral electron density(N_(ae))variation had no clear impact on ion focusing,as indicated by static two-ion structures in the wake field.However,variation of the object potential(ϕ)strongly affected ion focusing formation,leading to distortion of the initial ion void region behind the spacecraft.The formation of ion focusing in this study was subject to the electric field produced by the object potential and the ambipolar electric field resulting from plasma expansion in the downstream region.
基金funded by the National Council of Science,Technology,and Technological Innovation(CONCYTEC)the National Program of Scientific Research and Advanced Studies(PROCIENCIA)under the E041-2022-“Applied Research Projects”competition.Contract number:PE501078609-2022-PROCIENCIA.
文摘The continuous improvement of solar thermal technologies is essential to meet the growing demand for sustainable heat generation and to support global decarbonization efforts.This study presents the design,implementation,and validation of a real-time monitoring framework based on the Internet ofThings(IoT)and cloud computing to enhance the thermal performance of evacuated tube solar water heaters(ETSWHs).A commercial system and a custom-built prototype were instrumented with Industry 4.0 technologies,including platinum resistance temperature detectors(PT100),solar irradiance and wind speed sensors,a programmable logic controller(PLC),a SCADAinterface,and a cloud-connected IoT gateway.Data were processed locally and transmitted to cloud storage for continuous analysis and visualization via amobile application.Experimental results demonstrated the prototype’s superior thermal energy storage capacity−47.4 vs.36.2 MJ for the commercial system,representing a 31%—achieved through the novel integration of Industry 4.0 architecture with an optimized collector design.This improvement is attributed to optimized geometric design parameters,including a reduced tilt angle,increased inter-tube spacing,and the incorporation of an aluminum reflective surface.These modifications collectively enhanced solar heat absorption and reduced optical losses.The framework effectively identified thermal stratification,monitored environmental effects on heat transfer,and enabled real-time system diagnostics.By integrating automation,IoT,and cloud computing,the proposed architecture establishes a scalable and replicable model for the intelligent management of solar thermal systems,facilitating predictive maintenance and future integration with artificial intelligence for performance forecasting.This work provides a practical,data-driven approach to digitizing and optimizing heat transfer systems,promoting more efficient and sustainable solar thermal energy applications.
基金supported by the National Natural Science Foundation of China(Grant No.52405041)the Major Program of the Zhejiang Provincial Natural Science Foundation of China(Grant No.LD25E050001)the Key R&D Program of Zhejiang Province(Grant No.2025C01186)。
文摘Humanoid robots hold significant promise for social interaction and emotional companionship.However,their effectiveness hinges on the ability to convey nuanced and authentic emotions.Here,we presented a universal humanoid robot head with a facial kinematics model.Using a reinforcement learning framework guided by symmetry assessment,emotion decoupling,and MLLM authenticity evaluation,our system autonomously learns to generate adaptive facial expressions through dynamic landmark adjustments.By transferring the simulation training results to real-world environments,the robot can perform natural and expressive expressions.Another novel feature is the independent regulation of emotion intensity and expression magnitude across emotional categories,which enhances the ability to achieve culturally adaptive and socially resonant robotic expressions significantly.This research advances adaptive humanoid interaction,offering an easier and more efficient pathway toward culturally resonant and psychologically plausible robotic expressions.
