Background:The impact of sleep disorders on active-duty soldiers’medical readiness is not currently quantified.Patient data generated at military treatment facilities can be accessed to create research reports and th...Background:The impact of sleep disorders on active-duty soldiers’medical readiness is not currently quantified.Patient data generated at military treatment facilities can be accessed to create research reports and thus can be used to estimate the prevalence of sleep disturbances and the role of sleep on overall health in service members.The current study aimed to quantify sleep-related health issues and their impact on health and nondeployability through the analysis of U.S.military healthcare records from fiscal year 2018(FY2018).Methods:Medical diagnosis information and deployability profiles(e-Profiles)were queried for all active-duty U.S.Army patients with a concurrent sleep disorder diagnosis receiving medical care within FY2018.Nondeployability was predicted from medical reasons for having an e-Profile(categorized as sleep,behavioral health,musculoskeletal,cardiometabolic,injury,or accident)using binomial logistic regression.Sleep e-Profiles were investigated as a moderator between other e-Profile categories and nondeployability.Results:Out of 582,031 soldiers,48.4%(n=281,738)had a sleep-related diagnosis in their healthcare records,9.7%(n=56,247)of soldiers had e-Profiles,and 1.9%(n=10,885)had a sleep e-Profile.Soldiers with sleep e-Profiles were more likely to have had a motor vehicle accident(p OR(prevalence odds ratio)=4.7,95%CI 2.63–8.39,P≤0.001)or work/duty-related injury(p OR=1.6,95%CI 1.32–1.94,P≤0.001).The likelihood of nondeployability was greater in soldiers with a sleep e-Profile and a musculoskeletal e-Profile(p OR=4.25,95%CI 3.75–4.81,P≤0.001)or work/dutyrelated injury(p OR=2.62,95%CI 1.63–4.21,P≤0.001).Conclusion:Nearly half of soldiers had a sleep disorder or sleep-related medical diagnosis in 2018,but their sleep problems are largely not profiled as limitations to medical readiness.Musculoskeletal issues and physical injury predict nondeployability,and nondeployability is more likely to occur in soldiers who have sleep e-Profiles in addition to these issues.Addressing sleep problems may prevent accidents and injuries that could render a soldier nondeployable.展开更多
Most predictive maintenance studies have emphasized accuracy but provide very little focus on Interpretability or deployment readiness.This study improves on prior methods by developing a small yet robust system that ...Most predictive maintenance studies have emphasized accuracy but provide very little focus on Interpretability or deployment readiness.This study improves on prior methods by developing a small yet robust system that can predict when turbofan engines will fail.It uses the NASA CMAPSS dataset,which has over 200,000 engine cycles from260 engines.The process begins with systematic preprocessing,which includes imputation,outlier removal,scaling,and labelling of the remaining useful life.Dimensionality is reduced using a hybrid selection method that combines variance filtering,recursive elimination,and gradient-boosted importance scores,yielding a stable set of 10 informative sensors.To mitigate class imbalance,minority cases are oversampled,and class-weighted losses are applied during training.Benchmarking is carried out with logistic regression,gradient boosting,and a recurrent design that integrates gated recurrent units with long short-term memory networks.The Long Short-Term Memory–Gated Recurrent Unit(LSTM–GRU)hybrid achieved the strongest performance with an F1 score of 0.92,precision of 0.93,recall of 0.91,ReceiverOperating Characteristic–AreaUnder the Curve(ROC-AUC)of 0.97,andminority recall of 0.75.Interpretability testing using permutation importance and Shapley values indicates that sensors 13,15,and 11 are the most important indicators of engine wear.The proposed system combines imbalance handling,feature reduction,and Interpretability into a practical design suitable for real industrial settings.展开更多
Deployable Composite Thin-Walled Structures(DCTWS)are widely used in space applications due to their ability to compactly fold and self-deploy in orbit,enabled by cutouts.Cutout design is crucial for balancing structu...Deployable Composite Thin-Walled Structures(DCTWS)are widely used in space applications due to their ability to compactly fold and self-deploy in orbit,enabled by cutouts.Cutout design is crucial for balancing structural rigidity and flexibility,ensuring material integrity during large deformations,and providing adequate load-bearing capacity and stability once deployed.Most research has focused on optimizing cutout size and shape,while topology optimization offers a broader design space.However,the anisotropic properties of woven composite laminates,complex failure criteria,and multi-performance optimization needs have limited the exploration of topology optimization in this field.This work derives the sensitivities of bending stiffness,critical buckling load,and the failure index of woven composite materials with respect to element density,and formulates both single-objective and multi-objective topology optimization models using a linear weighted aggregation approach.The developed method was integrated with the commercial finite element software ABAQUS via a Python script,allowing efficient application to cutout design in various DCTWS configurations to maximize bending stiffness and critical buckling load under material failure constraints.Optimization of a classical tubular hinge resulted in improvements of 107.7%in bending stiffness and 420.5%in critical buckling load compared to level-set topology optimization results reported in the literature,validating the effectiveness of the approach.To facilitate future research and encourage the broader adoption of topology optimization techniques in DCTWS design,the source code for this work is made publicly available via a Git Hub link:https://github.com/jinhao-ok1/Topo-for-DCTWS.git.展开更多
Aiming at the scale adaptation of automatic driving target detection algorithms in low illumination environments and the shortcomings in target occlusion processing,this paper proposes a YOLO-LKSDS automatic driving d...Aiming at the scale adaptation of automatic driving target detection algorithms in low illumination environments and the shortcomings in target occlusion processing,this paper proposes a YOLO-LKSDS automatic driving detection model.Firstly,the Contrast-Limited Adaptive Histogram Equalisation(CLAHE)image enhancement algorithm is improved to increase the image contrast and enhance the detailed features of the target;then,on the basis of the YOLOv5 model,the Kmeans++clustering algorithm is introduced to obtain a suitable anchor frame,and SPPELAN spatial pyramid pooling is improved to enhance the accuracy and robustness of the model for multi-scale target detection.Finally,an improved SEAM(Separated and Enhancement Attention Module)attention mechanism is combined with the DIOU-NMS algorithm to optimize the model’s performance when dealing with occlusion and dense scenes.Compared with the original model,the improved YOLO-LKSDS model achieves a 13.3%improvement in accuracy,a 1.7%improvement in mAP,and 240,000 fewer parameters on the BDD100K dataset.In order to validate the generalization of the improved algorithm,we selected the KITTI dataset for experimentation,which shows that YOLOv5’s accuracy improves by 21.1%,recall by 36.6%,and mAP50 by 29.5%,respectively,on the KITTI dataset.The deployment of this paper’s algorithm is verified by an edge computing platform,where the average speed of detection reaches 24.4 FPS while power consumption remains below 9 W,demonstrating high real-time capability and energy efficiency.展开更多
The trade-off between leaf size and leafing intensity(i.e.,the number of leaves per unit stem size)is a key axis of trait covariation across the diversity of plant foliage deployment.However,the functional significanc...The trade-off between leaf size and leafing intensity(i.e.,the number of leaves per unit stem size)is a key axis of trait covariation across the diversity of plant foliage deployment.However,the functional significance of leafing intensity and its possible combinations with leaf size in dealing with water limitation remains unclear.Using Populus euphratica as an illustrative tree species growing in hyper-arid climates,we investigated how leaf size and leafing intensity co-varied under varying water stresses.In the Ebinor lowlands and the upper reaches of the Tarim River(NW China),we sampled>1800 current-year twigs from 505 trees across 14 sites along a climatic gradient characterized by precipitation,potential evapotranspiration and vapor pressure deficit.Leafing intensity based on stem mass(LIM)decreased with climatic aridity,primarily due to greater stem mass,but not fewer leaves.This indicates a higher investment in structural support for leaf attachment under water stress.Both leaf area and mass decreased with LIM at a lower-than-proportional rate,with the decrease in leaf size being more pronounced under drier climates.This suggests that higher LIM incurs a high cost of reducing leaf size in water-limited habitats.These findings challenge the assumption that higher leafing intensity always confers an advantage ready for environmental stresses due to higher developmental flexibility offered by more axillary buds.Rather,we propose that a strategy of lower leafing intensity,with greater structural support for leaf attachment and less compromise in leaf size,can be advantageous under water limitation.展开更多
Microservices have revolutionized traditional software architecture. While monolithic designs continue to be common, particularly in legacy applications, there is a growing trend towards the modularity, independent de...Microservices have revolutionized traditional software architecture. While monolithic designs continue to be common, particularly in legacy applications, there is a growing trend towards the modularity, independent deployability, and flexibility offered by microservices, which is further enhanced by developments in cloud technology. This shift towards microservice architecture meets the modern business need for agility, facilitating rapid adaptability in a competitive landscape. Microservices offer an agile framework and, in many cases, can simplify the development process, though the implementation can vary and sometimes introduce complexities. Unlike monolithic systems, which can be cumbersome to modify, microservices enable quicker adjustments and faster deployment times, essential in today’s dynamic environment. This article delves into the essence of microservices and explores their growing prominence in the software industry.展开更多
As the mining industry continues to expand and international oil prices increase,more rigorous demands are being placed on the design of mining equipment.Given this,there is an urgent need to develop new power-driven ...As the mining industry continues to expand and international oil prices increase,more rigorous demands are being placed on the design of mining equipment.Given this,there is an urgent need to develop new power-driven mining equipment to solve the problems of high energy consumption and insufficient power coupling of current equipment.This study proposed a design of a hybrid power system for underground Load Haul Dump(LHD).The proposed design integrated Quality Function Deployment(QFD)and Theory of Inventive Problem Solving(TRIZ).It identified 7 user requirements and 10 related technical features,formulated 11 innovative design solutions,and ultimately adopting an electric drive hybrid power scheme.This scheme effectively addressesd power transmission coupling problems and improve the efficiency of loaders.A 6 m³hybrid power loader prototype has been developed,which reduces operational energy consumption and advances the electrification and green,low-carbon evolution of mining equipment.展开更多
1.Introduction Climate change mitigation pathways aimed at limiting global anthropogenic carbon dioxide(CO_(2))emissions while striving to constrain the global temperature increase to below 2℃—as outlined by the Int...1.Introduction Climate change mitigation pathways aimed at limiting global anthropogenic carbon dioxide(CO_(2))emissions while striving to constrain the global temperature increase to below 2℃—as outlined by the Intergovernmental Panel on Climate Change(IPCC)—consistently predict the widespread implementation of CO_(2)geological storage on a global scale.展开更多
The rapid evolution of Fifth-Generation(5G)networks and the strategic development of Sixth-Generation(6G)technologies have significantly advanced the implementation of air-ground integrated networks with seamless cove...The rapid evolution of Fifth-Generation(5G)networks and the strategic development of Sixth-Generation(6G)technologies have significantly advanced the implementation of air-ground integrated networks with seamless coverage.Unmanned Aerial Vehicles(UAVs),serving as high-mobility aerial platforms,are extensively utilized to enhance coverage in long-distance emergency communication scenarios.The resource-constrained communication environments in emergencies by classifying UAVs into swarm UAVs and relay UAVs as aerial communication nodes is inversitgated.A horizontal deployment strategy for swarm UAVs is formulated through K-means clustering algorithm optimization,while a vertical deployment scheme is established using convex optimization methods.The minimum-path trajectory planning for relay UAVs is optimized via the Particle Swarm Optimization(PSO)algorithm,enhancing communication reliability between UAV swarms and terrestrial base stations.A three-dimensional heterogeneous network architecture is realized by modeling spatial multi-hop relay links.Experimental results demonstrate that the proposed joint UAV relay optimization framework outperforms conventional algorithms in both coverage performance and relay capability during video stream transmission,achieving significant improvements in coverage enhancement and relay efficiency.This work provides technical foundations for constructing high-reliability air-ground cooperative systems in emergency communications.展开更多
1.Introduction The rapid expansion of satellite constellations in recent years has resulted in the generation of massive amounts of data.This surge in data,coupled with diverse application scenarios,underscores the es...1.Introduction The rapid expansion of satellite constellations in recent years has resulted in the generation of massive amounts of data.This surge in data,coupled with diverse application scenarios,underscores the escalating demand for high-performance computing over space.Computing over space entails the deployment of computational resources on platforms such as satellites to process large-scale data under constraints such as high radiation exposure,restricted power consumption,and minimized weight.展开更多
For large-scale heterogeneous multi-agent systems(MASs)with characteristics of dense-sparse mixed distribution,this paper investigates the practical finite-time deployment problem by establishing a novel crossspecies ...For large-scale heterogeneous multi-agent systems(MASs)with characteristics of dense-sparse mixed distribution,this paper investigates the practical finite-time deployment problem by establishing a novel crossspecies bionic analytical framework based on the partial differential equation-ordinary differential equation(PDE-ODE)approach.Specifically,by designing a specialized network communication protocol and employing the spatial continuum method for densely distributed agents,this paper models the tracking errors of densely distributed agents as a PDE equivalent to a human disease transmission model,and that of sparsely distributed agents as several ODEs equivalent to the predator population models.The coupling relationship between the PDE and ODE models is established through boundary conditions of the PDE,thereby forming a PDE-ODE-based tracking error model for the considered MASs.Furthermore,by integrating adaptive neural control scheme with the aforementioned biological models,a“Flexible Neural Network”endowed with adaptive and self-stabilized capabilities is constructed,which acts upon the considered MASs,enabling their practical finite-time deployment.Finally,effectiveness of the developed approach is illustrated through a numerical example.展开更多
Objective Traditional Chinese medicine(TCM)incorporates traditional diagnostic methods and several major treatment modalities including Chinese herbal medicine,Chinese patent medicine,and non-pharmacological methods s...Objective Traditional Chinese medicine(TCM)incorporates traditional diagnostic methods and several major treatment modalities including Chinese herbal medicine,Chinese patent medicine,and non-pharmacological methods such as acupuncture and tuina.Even though TCM is used daily by more than 70,000 healthcare facilities and over 700,000 clinical practitioners in China,there is a poor understanding of the extent to which TCM diagnostic methods are used,how different treatment modalities are deployed in general,and what major factors may affect the integration of TCM and Western medicine.This study aimed to fill this void in the literature.Methods In the 2021 National Healthcare Improvement Evaluation Survey,we included three questions gauging the perception and practices of TCM amongst physicians working in TCM-related facilities,investigating the frequency of their deployment of TCM diagnostic methods,and predominant TCM treatment methods.Our empirical analysis included descriptive statistics,intergroup chi-square analysis,and binary logistic regression to examine the association between different types of facilities and individual characteristics and TCM utilization patterns.Results A total of 7618 clinical physicians comprised our study sample.Among them,84.27%have integrated TCM and Western medicine in their clinical practice,and 80.77%of TCM practitioners used the 4 diagnostic methods as a tool in their clinical practice.Chinese herbal medicine was the most widely utilized modality by Chinese TCM physicians(used by 88.49%of respondents),compared with the Chinese patent medicine and non-pharmacological TCM methods,which were used by 73.14%,and 69.39%,respectively.Herbal tea as an out-of-pocket health-maintenance intervention is also a notable practice,recommended by 29.43%of physicians.Significant variations exist across certain institutions,departments,and individual practitioners.Conclusion Given that most of the surveyed physicians integrated TCM with Western medicine in their clinical practices,the practice of“pure TCM”appears to be obsolete in China’s tertiary healthcare institutions.Notably,remarkable variation exists in the use of different TCM modalities across institutions and among individuals,which might be related to and thus limited by the practitioners’experience.Future research focusing on the efficacy and safety of TCM interventions for specific diseases,the development of standardized clinical guidelines,and the enhancement of TCM education and training are called for to optimize TCM-Western medicine integration.展开更多
The wireless signals emitted by base stations serve as a vital link connecting people in today’s society and have been occupying an increasingly important role in real life.The development of the Internet of Things(I...The wireless signals emitted by base stations serve as a vital link connecting people in today’s society and have been occupying an increasingly important role in real life.The development of the Internet of Things(IoT)relies on the support of base stations,which provide a solid foundation for achieving a more intelligent way of living.In a specific area,achieving higher signal coverage with fewer base stations has become an urgent problem.Therefore,this article focuses on the effective coverage area of base station signals and proposes a novel Evolutionary Particle Swarm Optimization(EPSO)algorithm based on collective prediction,referred to herein as ECPPSO.Introducing a new strategy called neighbor-based evolution prediction(NEP)addresses the issue of premature convergence often encountered by PSO.ECPPSO also employs a strengthening evolution(SE)strategy to enhance the algorithm’s global search capability and efficiency,ensuring enhanced robustness and a faster convergence speed when solving complex optimization problems.To better adapt to the actual communication needs of base stations,this article conducts simulation experiments by changing the number of base stations.The experimental results demonstrate thatunder the conditionof 50 ormore base stations,ECPPSOconsistently achieves the best coverage rate exceeding 95%,peaking at 99.4400%when the number of base stations reaches 80.These results validate the optimization capability of the ECPPSO algorithm,proving its feasibility and effectiveness.Further ablative experiments and comparisons with other algorithms highlight the advantages of ECPPSO.展开更多
The parachute deployment conditions during the terminal entry phase in Mars landing missions exhibit critical impact on landing precision.In this article,aiming at the requirements of safe parachute deployment and acc...The parachute deployment conditions during the terminal entry phase in Mars landing missions exhibit critical impact on landing precision.In this article,aiming at the requirements of safe parachute deployment and accurate landing,a multidimensional parachute deployment box for determining deployment condition during Mars landing was proposed.First,an extremerange optimization model was established,synthesizing the dynamics and constraints of both parachute descent and powered descent phases.Then,on the basis of the two-dimensional altitude-velocity deployment box,a multi-dimensional parachute deployment box characterized by altitude,velocity,flight-path angle,and extreme range was constructed through the integration of extreme range information.Furthermore,an evaluation index for landing precision was formulated and a deployment control logic was proposed for minimizing landing deviation.Finally,the proposed deployment box was simulated in a Mars landing mission.The results demonstrate that the proposed box effectively satisfies safe deployment and landing precision demands,eliminating the range-to-go error at the terminal of the entry phase.展开更多
The quantity of space debris on Earth orbit has escalated tremendously in recent years, presenting a significant hazard to human space operations. It is urgent to develop effective measures to capture and remove vario...The quantity of space debris on Earth orbit has escalated tremendously in recent years, presenting a significant hazard to human space operations. It is urgent to develop effective measures to capture and remove various space debris. For this purpose, this paper presents a tendon-actuated flexible deployable manipulator. The flexible manipulator consists of several deployable units connected by Cardan joints and actuated by tendons. Compared with the present technologies for capturing space debris such as rigid robotic arm or flying net, this flexible manipulator is deployable, reusable, lightweight and applicable to the capture of large space debris. In order to investigate its deployment dynamics, an accurate dynamic model of the flexible manipulator is established based on the natural coordinate formulation (NCF) and the absolute nodal coordinate formulation (ANCF). Subsequently, numerical simulations are carried out to study the effects of system parameters and the base satellite on its deployment dynamics. Finally, ground experiments for both deployment and bending of the flexible manipulator are conducted to verify its effectiveness and feasibility.展开更多
Progressing beyond the stowage and deployment of reflectors and designing for multiple deployed states result in reflector shape reconfiguration,thus allowing for new functions including radiation pattern reconfigurat...Progressing beyond the stowage and deployment of reflectors and designing for multiple deployed states result in reflector shape reconfiguration,thus allowing for new functions including radiation pattern reconfiguration,and is valuable for space applications such as satellite-based radar and communications.This paper introduces a concept for achieving the deployment and shape reconfiguration of a paraboloid reflector using a 7R-8R(revolute joint)truss network.By realizing reconfigurability mechanically,complex electronic systems such as phased arrays can be avoided,and adopting a single-degree-of-freedom(DOF)design further reduces the number of required actuators.The proposed reflector is axisymmetric and can be doubly curved.It comprises a flexible mesh surface supported by a rigid truss network constructed from 7R and 8R linkages.Approximation of multiple target surfaces is achieved by synthesizing the truss network dimensions using a multiobjective optimization approach.The non-dominated sorting genetic algorithm is used in conjunction with analytical dimension parameterization and forward kinematics computation to determine the optimal dimensions for the truss network.In the resulting designs,the reflector follows a single-DOF trajectory,on which it unfolds from a compact stowed bundle toward a deployed state approximating a doubly curved target surface,then onwards to additional deployed states approximating different target surfaces.Design studies are conducted to demonstrate the reflector’s ability to approximate different target surfaces and continuously transform between such surfaces.This study proposes a new method for reconfiguring reflector shape mechanically,thus uniquely reconfiguring the shape of a doubly curved surface and achieving both deployment and shape reconfiguration under a unified single-DOF motion.展开更多
Machine learning(ML)has been increasingly adopted to solve engineering problems with performance gauged by accuracy,efficiency,and security.Notably,blockchain technology(BT)has been added to ML when security is a part...Machine learning(ML)has been increasingly adopted to solve engineering problems with performance gauged by accuracy,efficiency,and security.Notably,blockchain technology(BT)has been added to ML when security is a particular concern.Nevertheless,there is a research gap that prevailing solutions focus primarily on data security using blockchain but ignore computational security,making the traditional ML process vulnerable to off-chain risks.Therefore,the research objective is to develop a novel ML on blockchain(MLOB)framework to ensure both the data and computational process security.The central tenet is to place them both on the blockchain,execute them as blockchain smart contracts,and protect the execution records on-chain.The framework is established by developing a prototype and further calibrated using a case study of industrial inspection.It is shown that the MLOB framework,compared with existing ML and BT isolated solutions,is superior in terms of security(successfully defending against corruption on six designed attack scenario),maintaining accuracy(0.01%difference with baseline),albeit with a slightly compromised efficiency(0.231 second latency increased).The key finding is MLOB can significantly enhances the computational security of engineering computing without increasing computing power demands.This finding can alleviate concerns regarding the computational resource requirements of ML-BT integration.With proper adaption,the MLOB framework can inform various novel solutions to achieve computational security in broader engineering challenges.展开更多
Mesh reflector antennas are the mainstream of large space-borne antennas,and the stretching of the truss achieves their deployment.Currently,the truss is commonly designed to be a single degree of freedom(DOF)deployab...Mesh reflector antennas are the mainstream of large space-borne antennas,and the stretching of the truss achieves their deployment.Currently,the truss is commonly designed to be a single degree of freedom(DOF)deployable mechanism with synchronization constraints.However,each deployable unit’s drive distribution and resistance load are uneven,and the forced synchronization constraints lead to the flexible deformation of rods and difficulties in the deployment scheme design.This paper introduces an asynchronous deployment scheme with a multi-DOF closed-chain deployable truss.The DOF of the truss is calculated,and the kinematic and dynamic models are established,considering the truss’s and cable net’s real-time coupling.An integrated solving algorithm for implicit differential-algebraic equations is proposed to solve the dynamic models.A prototype of a six-unit antenna was fabricated,and the experiment was carried out.The dynamic performances in synchronous and asynchronous deployment schemes are analyzed,and the results show that the cable resistance and truss kinetic energy impact under the asynchronous deployment scheme are minor,and the antenna is more straightforward to deploy.The work provides a new asynchronous deployment scheme and a universal antenna modeling method for dynamic design and performance improvement.展开更多
The research on optimization methods for constellation launch deployment strategies focused on the consideration of mission interval time constraints at the launch site.Firstly,a dynamic modeling of the constellation ...The research on optimization methods for constellation launch deployment strategies focused on the consideration of mission interval time constraints at the launch site.Firstly,a dynamic modeling of the constellation deployment process was established,and the relationship between the deployment window and the phase difference of the orbit insertion point,as well as the cost of phase adjustment after orbit insertion,was derived.Then,the combination of the constellation deployment position sequence was treated as a parameter,together with the sequence of satellite deployment intervals,as optimization variables,simplifying a highdimensional search problem within a wide range of dates to a finite-dimensional integer programming problem.An improved genetic algorithm with local search on deployment dates was introduced to optimize the launch deployment strategy.With the new description of the optimization variables,the total number of elements in the solution space was reduced by N orders of magnitude.Numerical simulation confirms that the proposed optimization method accelerates the convergence speed from hours to minutes.展开更多
The aerial deployment method enables Unmanned Aerial Vehicles(UAVs)to be directly positioned at the required altitude for their mission.This method typically employs folding technology to improve loading efficiency,wi...The aerial deployment method enables Unmanned Aerial Vehicles(UAVs)to be directly positioned at the required altitude for their mission.This method typically employs folding technology to improve loading efficiency,with applications such as the gravity-only aerial deployment of high-aspect-ratio solar-powered UAVs,and aerial takeoff of fixed-wing drones in Mars research.However,the significant morphological changes during deployment are accompanied by strong nonlinear dynamic aerodynamic forces,which result in multiple degrees of freedom and an unstable character.This hinders the description and analysis of unknown dynamic behaviors,further leading to difficulties in the design of deployment strategies and flight control.To address this issue,this paper proposes an analysis method for dynamic behaviors during aerial deployment based on the Variational Autoencoder(VAE).Focusing on the gravity-only deployment problem of highaspect-ratio foldable-wing UAVs,the method encodes the multi-degree-of-freedom unstable motion signals into a low-dimensional feature space through a data-driven approach.By clustering in the feature space,this paper identifies and studies several dynamic behaviors during aerial deployment.The research presented in this paper offers a new method and perspective for feature extraction and analysis of complex and difficult-to-describe extreme flight dynamics,guiding the research on aerial deployment drones design and control strategies.展开更多
基金The Department of Defense Military Operational Medicine Research Program(MOMRP)supported this study。
文摘Background:The impact of sleep disorders on active-duty soldiers’medical readiness is not currently quantified.Patient data generated at military treatment facilities can be accessed to create research reports and thus can be used to estimate the prevalence of sleep disturbances and the role of sleep on overall health in service members.The current study aimed to quantify sleep-related health issues and their impact on health and nondeployability through the analysis of U.S.military healthcare records from fiscal year 2018(FY2018).Methods:Medical diagnosis information and deployability profiles(e-Profiles)were queried for all active-duty U.S.Army patients with a concurrent sleep disorder diagnosis receiving medical care within FY2018.Nondeployability was predicted from medical reasons for having an e-Profile(categorized as sleep,behavioral health,musculoskeletal,cardiometabolic,injury,or accident)using binomial logistic regression.Sleep e-Profiles were investigated as a moderator between other e-Profile categories and nondeployability.Results:Out of 582,031 soldiers,48.4%(n=281,738)had a sleep-related diagnosis in their healthcare records,9.7%(n=56,247)of soldiers had e-Profiles,and 1.9%(n=10,885)had a sleep e-Profile.Soldiers with sleep e-Profiles were more likely to have had a motor vehicle accident(p OR(prevalence odds ratio)=4.7,95%CI 2.63–8.39,P≤0.001)or work/duty-related injury(p OR=1.6,95%CI 1.32–1.94,P≤0.001).The likelihood of nondeployability was greater in soldiers with a sleep e-Profile and a musculoskeletal e-Profile(p OR=4.25,95%CI 3.75–4.81,P≤0.001)or work/dutyrelated injury(p OR=2.62,95%CI 1.63–4.21,P≤0.001).Conclusion:Nearly half of soldiers had a sleep disorder or sleep-related medical diagnosis in 2018,but their sleep problems are largely not profiled as limitations to medical readiness.Musculoskeletal issues and physical injury predict nondeployability,and nondeployability is more likely to occur in soldiers who have sleep e-Profiles in addition to these issues.Addressing sleep problems may prevent accidents and injuries that could render a soldier nondeployable.
基金supported by the Deanship of Scientific Research,Vice Presidency for Graduate Studies and Scientific Research,King Faisal University,Saudi Arabia Grant No.KFU253765.
文摘Most predictive maintenance studies have emphasized accuracy but provide very little focus on Interpretability or deployment readiness.This study improves on prior methods by developing a small yet robust system that can predict when turbofan engines will fail.It uses the NASA CMAPSS dataset,which has over 200,000 engine cycles from260 engines.The process begins with systematic preprocessing,which includes imputation,outlier removal,scaling,and labelling of the remaining useful life.Dimensionality is reduced using a hybrid selection method that combines variance filtering,recursive elimination,and gradient-boosted importance scores,yielding a stable set of 10 informative sensors.To mitigate class imbalance,minority cases are oversampled,and class-weighted losses are applied during training.Benchmarking is carried out with logistic regression,gradient boosting,and a recurrent design that integrates gated recurrent units with long short-term memory networks.The Long Short-Term Memory–Gated Recurrent Unit(LSTM–GRU)hybrid achieved the strongest performance with an F1 score of 0.92,precision of 0.93,recall of 0.91,ReceiverOperating Characteristic–AreaUnder the Curve(ROC-AUC)of 0.97,andminority recall of 0.75.Interpretability testing using permutation importance and Shapley values indicates that sensors 13,15,and 11 are the most important indicators of engine wear.The proposed system combines imbalance handling,feature reduction,and Interpretability into a practical design suitable for real industrial settings.
基金supported by the National Natural Science Foundation of China(No.12202295)the International(Regional)Cooperation and Exchange Projects of the National Natural Science Foundation of China(No.W2421002)+2 种基金the Sichuan Science and Technology Program(No.2025ZNSFSC0845)Zhejiang Provincial Natural Science Foundation of China(No.ZCLZ24A0201)the Fundamental Research Funds for the Provincial Universities of Zhejiang(No.GK249909299001-004)。
文摘Deployable Composite Thin-Walled Structures(DCTWS)are widely used in space applications due to their ability to compactly fold and self-deploy in orbit,enabled by cutouts.Cutout design is crucial for balancing structural rigidity and flexibility,ensuring material integrity during large deformations,and providing adequate load-bearing capacity and stability once deployed.Most research has focused on optimizing cutout size and shape,while topology optimization offers a broader design space.However,the anisotropic properties of woven composite laminates,complex failure criteria,and multi-performance optimization needs have limited the exploration of topology optimization in this field.This work derives the sensitivities of bending stiffness,critical buckling load,and the failure index of woven composite materials with respect to element density,and formulates both single-objective and multi-objective topology optimization models using a linear weighted aggregation approach.The developed method was integrated with the commercial finite element software ABAQUS via a Python script,allowing efficient application to cutout design in various DCTWS configurations to maximize bending stiffness and critical buckling load under material failure constraints.Optimization of a classical tubular hinge resulted in improvements of 107.7%in bending stiffness and 420.5%in critical buckling load compared to level-set topology optimization results reported in the literature,validating the effectiveness of the approach.To facilitate future research and encourage the broader adoption of topology optimization techniques in DCTWS design,the source code for this work is made publicly available via a Git Hub link:https://github.com/jinhao-ok1/Topo-for-DCTWS.git.
基金supported by the Key R&D Program of Shaanxi Province(No.2025CYYBXM-078).
文摘Aiming at the scale adaptation of automatic driving target detection algorithms in low illumination environments and the shortcomings in target occlusion processing,this paper proposes a YOLO-LKSDS automatic driving detection model.Firstly,the Contrast-Limited Adaptive Histogram Equalisation(CLAHE)image enhancement algorithm is improved to increase the image contrast and enhance the detailed features of the target;then,on the basis of the YOLOv5 model,the Kmeans++clustering algorithm is introduced to obtain a suitable anchor frame,and SPPELAN spatial pyramid pooling is improved to enhance the accuracy and robustness of the model for multi-scale target detection.Finally,an improved SEAM(Separated and Enhancement Attention Module)attention mechanism is combined with the DIOU-NMS algorithm to optimize the model’s performance when dealing with occlusion and dense scenes.Compared with the original model,the improved YOLO-LKSDS model achieves a 13.3%improvement in accuracy,a 1.7%improvement in mAP,and 240,000 fewer parameters on the BDD100K dataset.In order to validate the generalization of the improved algorithm,we selected the KITTI dataset for experimentation,which shows that YOLOv5’s accuracy improves by 21.1%,recall by 36.6%,and mAP50 by 29.5%,respectively,on the KITTI dataset.The deployment of this paper’s algorithm is verified by an edge computing platform,where the average speed of detection reaches 24.4 FPS while power consumption remains below 9 W,demonstrating high real-time capability and energy efficiency.
基金supported by the National Natural Science Foundation of China(32460329)the Bintuan Science&Technology Program(2024AB075)to L.H.+1 种基金the National Natural Science Foundation of China(32360279)an open program from the Key Laboratory of Protection and Utilization of Biological Resources in the Tarim Basin(BRZD2004).
文摘The trade-off between leaf size and leafing intensity(i.e.,the number of leaves per unit stem size)is a key axis of trait covariation across the diversity of plant foliage deployment.However,the functional significance of leafing intensity and its possible combinations with leaf size in dealing with water limitation remains unclear.Using Populus euphratica as an illustrative tree species growing in hyper-arid climates,we investigated how leaf size and leafing intensity co-varied under varying water stresses.In the Ebinor lowlands and the upper reaches of the Tarim River(NW China),we sampled>1800 current-year twigs from 505 trees across 14 sites along a climatic gradient characterized by precipitation,potential evapotranspiration and vapor pressure deficit.Leafing intensity based on stem mass(LIM)decreased with climatic aridity,primarily due to greater stem mass,but not fewer leaves.This indicates a higher investment in structural support for leaf attachment under water stress.Both leaf area and mass decreased with LIM at a lower-than-proportional rate,with the decrease in leaf size being more pronounced under drier climates.This suggests that higher LIM incurs a high cost of reducing leaf size in water-limited habitats.These findings challenge the assumption that higher leafing intensity always confers an advantage ready for environmental stresses due to higher developmental flexibility offered by more axillary buds.Rather,we propose that a strategy of lower leafing intensity,with greater structural support for leaf attachment and less compromise in leaf size,can be advantageous under water limitation.
文摘Microservices have revolutionized traditional software architecture. While monolithic designs continue to be common, particularly in legacy applications, there is a growing trend towards the modularity, independent deployability, and flexibility offered by microservices, which is further enhanced by developments in cloud technology. This shift towards microservice architecture meets the modern business need for agility, facilitating rapid adaptability in a competitive landscape. Microservices offer an agile framework and, in many cases, can simplify the development process, though the implementation can vary and sometimes introduce complexities. Unlike monolithic systems, which can be cumbersome to modify, microservices enable quicker adjustments and faster deployment times, essential in today’s dynamic environment. This article delves into the essence of microservices and explores their growing prominence in the software industry.
文摘As the mining industry continues to expand and international oil prices increase,more rigorous demands are being placed on the design of mining equipment.Given this,there is an urgent need to develop new power-driven mining equipment to solve the problems of high energy consumption and insufficient power coupling of current equipment.This study proposed a design of a hybrid power system for underground Load Haul Dump(LHD).The proposed design integrated Quality Function Deployment(QFD)and Theory of Inventive Problem Solving(TRIZ).It identified 7 user requirements and 10 related technical features,formulated 11 innovative design solutions,and ultimately adopting an electric drive hybrid power scheme.This scheme effectively addressesd power transmission coupling problems and improve the efficiency of loaders.A 6 m³hybrid power loader prototype has been developed,which reduces operational energy consumption and advances the electrification and green,low-carbon evolution of mining equipment.
基金supported by the National Key Research and Development Program of China(2022YFE0206700)。
文摘1.Introduction Climate change mitigation pathways aimed at limiting global anthropogenic carbon dioxide(CO_(2))emissions while striving to constrain the global temperature increase to below 2℃—as outlined by the Intergovernmental Panel on Climate Change(IPCC)—consistently predict the widespread implementation of CO_(2)geological storage on a global scale.
文摘The rapid evolution of Fifth-Generation(5G)networks and the strategic development of Sixth-Generation(6G)technologies have significantly advanced the implementation of air-ground integrated networks with seamless coverage.Unmanned Aerial Vehicles(UAVs),serving as high-mobility aerial platforms,are extensively utilized to enhance coverage in long-distance emergency communication scenarios.The resource-constrained communication environments in emergencies by classifying UAVs into swarm UAVs and relay UAVs as aerial communication nodes is inversitgated.A horizontal deployment strategy for swarm UAVs is formulated through K-means clustering algorithm optimization,while a vertical deployment scheme is established using convex optimization methods.The minimum-path trajectory planning for relay UAVs is optimized via the Particle Swarm Optimization(PSO)algorithm,enhancing communication reliability between UAV swarms and terrestrial base stations.A three-dimensional heterogeneous network architecture is realized by modeling spatial multi-hop relay links.Experimental results demonstrate that the proposed joint UAV relay optimization framework outperforms conventional algorithms in both coverage performance and relay capability during video stream transmission,achieving significant improvements in coverage enhancement and relay efficiency.This work provides technical foundations for constructing high-reliability air-ground cooperative systems in emergency communications.
基金supported in part by the National Natural Science Foundation of China(62025404)in part by the National Key Research and Development Program of China(2022YFB3902802)+1 种基金in part by the Beijing Natural Science Foundation(L241013)in part by the Strategic Priority Research Program of the Chinese Academy of Sciences(XDA000000).
文摘1.Introduction The rapid expansion of satellite constellations in recent years has resulted in the generation of massive amounts of data.This surge in data,coupled with diverse application scenarios,underscores the escalating demand for high-performance computing over space.Computing over space entails the deployment of computational resources on platforms such as satellites to process large-scale data under constraints such as high radiation exposure,restricted power consumption,and minimized weight.
基金The National Key R&D Program of China(2021ZD0201300)the National Natural Science Foundation of China(624B2058,U1913602 and 61936004)+1 种基金the Innovation Group Project of the National Natural Science Foundation of China(61821003)the 111 Project on Computational Intelligence and Intelligent Control(B18024).
文摘For large-scale heterogeneous multi-agent systems(MASs)with characteristics of dense-sparse mixed distribution,this paper investigates the practical finite-time deployment problem by establishing a novel crossspecies bionic analytical framework based on the partial differential equation-ordinary differential equation(PDE-ODE)approach.Specifically,by designing a specialized network communication protocol and employing the spatial continuum method for densely distributed agents,this paper models the tracking errors of densely distributed agents as a PDE equivalent to a human disease transmission model,and that of sparsely distributed agents as several ODEs equivalent to the predator population models.The coupling relationship between the PDE and ODE models is established through boundary conditions of the PDE,thereby forming a PDE-ODE-based tracking error model for the considered MASs.Furthermore,by integrating adaptive neural control scheme with the aforementioned biological models,a“Flexible Neural Network”endowed with adaptive and self-stabilized capabilities is constructed,which acts upon the considered MASs,enabling their practical finite-time deployment.Finally,effectiveness of the developed approach is illustrated through a numerical example.
基金The Chinese Academy of Medical Sciences Innovation Fund for Medical Sciences——A Strategic Study on Healthy China Development and Health System Reform(2021-I2M-1-046).
文摘Objective Traditional Chinese medicine(TCM)incorporates traditional diagnostic methods and several major treatment modalities including Chinese herbal medicine,Chinese patent medicine,and non-pharmacological methods such as acupuncture and tuina.Even though TCM is used daily by more than 70,000 healthcare facilities and over 700,000 clinical practitioners in China,there is a poor understanding of the extent to which TCM diagnostic methods are used,how different treatment modalities are deployed in general,and what major factors may affect the integration of TCM and Western medicine.This study aimed to fill this void in the literature.Methods In the 2021 National Healthcare Improvement Evaluation Survey,we included three questions gauging the perception and practices of TCM amongst physicians working in TCM-related facilities,investigating the frequency of their deployment of TCM diagnostic methods,and predominant TCM treatment methods.Our empirical analysis included descriptive statistics,intergroup chi-square analysis,and binary logistic regression to examine the association between different types of facilities and individual characteristics and TCM utilization patterns.Results A total of 7618 clinical physicians comprised our study sample.Among them,84.27%have integrated TCM and Western medicine in their clinical practice,and 80.77%of TCM practitioners used the 4 diagnostic methods as a tool in their clinical practice.Chinese herbal medicine was the most widely utilized modality by Chinese TCM physicians(used by 88.49%of respondents),compared with the Chinese patent medicine and non-pharmacological TCM methods,which were used by 73.14%,and 69.39%,respectively.Herbal tea as an out-of-pocket health-maintenance intervention is also a notable practice,recommended by 29.43%of physicians.Significant variations exist across certain institutions,departments,and individual practitioners.Conclusion Given that most of the surveyed physicians integrated TCM with Western medicine in their clinical practices,the practice of“pure TCM”appears to be obsolete in China’s tertiary healthcare institutions.Notably,remarkable variation exists in the use of different TCM modalities across institutions and among individuals,which might be related to and thus limited by the practitioners’experience.Future research focusing on the efficacy and safety of TCM interventions for specific diseases,the development of standardized clinical guidelines,and the enhancement of TCM education and training are called for to optimize TCM-Western medicine integration.
基金supported by the National Natural Science Foundation of China(Nos.62272418,62102058)Basic Public Welfare Research Program of Zhejiang Province(No.LGG18E050011)the Major Open Project of Key Laboratory for Advanced Design and Intelligent Computing of the Ministry of Education under Grant ADIC2023ZD001,National Undergraduate Training Program on Innovation and Entrepreneurship(No.202410345054).
文摘The wireless signals emitted by base stations serve as a vital link connecting people in today’s society and have been occupying an increasingly important role in real life.The development of the Internet of Things(IoT)relies on the support of base stations,which provide a solid foundation for achieving a more intelligent way of living.In a specific area,achieving higher signal coverage with fewer base stations has become an urgent problem.Therefore,this article focuses on the effective coverage area of base station signals and proposes a novel Evolutionary Particle Swarm Optimization(EPSO)algorithm based on collective prediction,referred to herein as ECPPSO.Introducing a new strategy called neighbor-based evolution prediction(NEP)addresses the issue of premature convergence often encountered by PSO.ECPPSO also employs a strengthening evolution(SE)strategy to enhance the algorithm’s global search capability and efficiency,ensuring enhanced robustness and a faster convergence speed when solving complex optimization problems.To better adapt to the actual communication needs of base stations,this article conducts simulation experiments by changing the number of base stations.The experimental results demonstrate thatunder the conditionof 50 ormore base stations,ECPPSOconsistently achieves the best coverage rate exceeding 95%,peaking at 99.4400%when the number of base stations reaches 80.These results validate the optimization capability of the ECPPSO algorithm,proving its feasibility and effectiveness.Further ablative experiments and comparisons with other algorithms highlight the advantages of ECPPSO.
基金Supported by the National Natural Science Foundation of China(62073034)。
文摘The parachute deployment conditions during the terminal entry phase in Mars landing missions exhibit critical impact on landing precision.In this article,aiming at the requirements of safe parachute deployment and accurate landing,a multidimensional parachute deployment box for determining deployment condition during Mars landing was proposed.First,an extremerange optimization model was established,synthesizing the dynamics and constraints of both parachute descent and powered descent phases.Then,on the basis of the two-dimensional altitude-velocity deployment box,a multi-dimensional parachute deployment box characterized by altitude,velocity,flight-path angle,and extreme range was constructed through the integration of extreme range information.Furthermore,an evaluation index for landing precision was formulated and a deployment control logic was proposed for minimizing landing deviation.Finally,the proposed deployment box was simulated in a Mars landing mission.The results demonstrate that the proposed box effectively satisfies safe deployment and landing precision demands,eliminating the range-to-go error at the terminal of the entry phase.
基金the National Natural Science Foundation of China(Nos.11832005,12372042,12232011)Young Elite Scientists Sponsorship Program by CAST(No.2022QNRC001)+1 种基金the Fundamental Research Funds for the Central Universities(No.NS2023002)State Key Laboratory of Mechanics and Control for Aerospace Structures(Nanjing University of Aeronautics and Astronautics)(No.MCAS-S-0223K04).
文摘The quantity of space debris on Earth orbit has escalated tremendously in recent years, presenting a significant hazard to human space operations. It is urgent to develop effective measures to capture and remove various space debris. For this purpose, this paper presents a tendon-actuated flexible deployable manipulator. The flexible manipulator consists of several deployable units connected by Cardan joints and actuated by tendons. Compared with the present technologies for capturing space debris such as rigid robotic arm or flying net, this flexible manipulator is deployable, reusable, lightweight and applicable to the capture of large space debris. In order to investigate its deployment dynamics, an accurate dynamic model of the flexible manipulator is established based on the natural coordinate formulation (NCF) and the absolute nodal coordinate formulation (ANCF). Subsequently, numerical simulations are carried out to study the effects of system parameters and the base satellite on its deployment dynamics. Finally, ground experiments for both deployment and bending of the flexible manipulator are conducted to verify its effectiveness and feasibility.
基金Supported by National Natural Science Foundation of China(Grant Nos.52320105005,52035008)the New Cornerstone Science Foundation through the Xplorer Prize(Grant No.XPLORER-2020-1035).
文摘Progressing beyond the stowage and deployment of reflectors and designing for multiple deployed states result in reflector shape reconfiguration,thus allowing for new functions including radiation pattern reconfiguration,and is valuable for space applications such as satellite-based radar and communications.This paper introduces a concept for achieving the deployment and shape reconfiguration of a paraboloid reflector using a 7R-8R(revolute joint)truss network.By realizing reconfigurability mechanically,complex electronic systems such as phased arrays can be avoided,and adopting a single-degree-of-freedom(DOF)design further reduces the number of required actuators.The proposed reflector is axisymmetric and can be doubly curved.It comprises a flexible mesh surface supported by a rigid truss network constructed from 7R and 8R linkages.Approximation of multiple target surfaces is achieved by synthesizing the truss network dimensions using a multiobjective optimization approach.The non-dominated sorting genetic algorithm is used in conjunction with analytical dimension parameterization and forward kinematics computation to determine the optimal dimensions for the truss network.In the resulting designs,the reflector follows a single-DOF trajectory,on which it unfolds from a compact stowed bundle toward a deployed state approximating a doubly curved target surface,then onwards to additional deployed states approximating different target surfaces.Design studies are conducted to demonstrate the reflector’s ability to approximate different target surfaces and continuously transform between such surfaces.This study proposes a new method for reconfiguring reflector shape mechanically,thus uniquely reconfiguring the shape of a doubly curved surface and achieving both deployment and shape reconfiguration under a unified single-DOF motion.
文摘Machine learning(ML)has been increasingly adopted to solve engineering problems with performance gauged by accuracy,efficiency,and security.Notably,blockchain technology(BT)has been added to ML when security is a particular concern.Nevertheless,there is a research gap that prevailing solutions focus primarily on data security using blockchain but ignore computational security,making the traditional ML process vulnerable to off-chain risks.Therefore,the research objective is to develop a novel ML on blockchain(MLOB)framework to ensure both the data and computational process security.The central tenet is to place them both on the blockchain,execute them as blockchain smart contracts,and protect the execution records on-chain.The framework is established by developing a prototype and further calibrated using a case study of industrial inspection.It is shown that the MLOB framework,compared with existing ML and BT isolated solutions,is superior in terms of security(successfully defending against corruption on six designed attack scenario),maintaining accuracy(0.01%difference with baseline),albeit with a slightly compromised efficiency(0.231 second latency increased).The key finding is MLOB can significantly enhances the computational security of engineering computing without increasing computing power demands.This finding can alleviate concerns regarding the computational resource requirements of ML-BT integration.With proper adaption,the MLOB framework can inform various novel solutions to achieve computational security in broader engineering challenges.
基金supported by the National Key R&D Program of China(Grant No.2023YFB3407103)the National Natural Science Foundation of China(Grant Nos.52175242 and 52175027)Young Elite Scientists Sponsorship Program by China Association for Science and Technology(Grant No.2022QNRC001).
文摘Mesh reflector antennas are the mainstream of large space-borne antennas,and the stretching of the truss achieves their deployment.Currently,the truss is commonly designed to be a single degree of freedom(DOF)deployable mechanism with synchronization constraints.However,each deployable unit’s drive distribution and resistance load are uneven,and the forced synchronization constraints lead to the flexible deformation of rods and difficulties in the deployment scheme design.This paper introduces an asynchronous deployment scheme with a multi-DOF closed-chain deployable truss.The DOF of the truss is calculated,and the kinematic and dynamic models are established,considering the truss’s and cable net’s real-time coupling.An integrated solving algorithm for implicit differential-algebraic equations is proposed to solve the dynamic models.A prototype of a six-unit antenna was fabricated,and the experiment was carried out.The dynamic performances in synchronous and asynchronous deployment schemes are analyzed,and the results show that the cable resistance and truss kinetic energy impact under the asynchronous deployment scheme are minor,and the antenna is more straightforward to deploy.The work provides a new asynchronous deployment scheme and a universal antenna modeling method for dynamic design and performance improvement.
文摘The research on optimization methods for constellation launch deployment strategies focused on the consideration of mission interval time constraints at the launch site.Firstly,a dynamic modeling of the constellation deployment process was established,and the relationship between the deployment window and the phase difference of the orbit insertion point,as well as the cost of phase adjustment after orbit insertion,was derived.Then,the combination of the constellation deployment position sequence was treated as a parameter,together with the sequence of satellite deployment intervals,as optimization variables,simplifying a highdimensional search problem within a wide range of dates to a finite-dimensional integer programming problem.An improved genetic algorithm with local search on deployment dates was introduced to optimize the launch deployment strategy.With the new description of the optimization variables,the total number of elements in the solution space was reduced by N orders of magnitude.Numerical simulation confirms that the proposed optimization method accelerates the convergence speed from hours to minutes.
基金co-supported by the Natural Science Basic Research Program of Shaanxi,China(No.2023-JC-QN-0043)the ND Basic Research Funds,China(No.G2022WD).
文摘The aerial deployment method enables Unmanned Aerial Vehicles(UAVs)to be directly positioned at the required altitude for their mission.This method typically employs folding technology to improve loading efficiency,with applications such as the gravity-only aerial deployment of high-aspect-ratio solar-powered UAVs,and aerial takeoff of fixed-wing drones in Mars research.However,the significant morphological changes during deployment are accompanied by strong nonlinear dynamic aerodynamic forces,which result in multiple degrees of freedom and an unstable character.This hinders the description and analysis of unknown dynamic behaviors,further leading to difficulties in the design of deployment strategies and flight control.To address this issue,this paper proposes an analysis method for dynamic behaviors during aerial deployment based on the Variational Autoencoder(VAE).Focusing on the gravity-only deployment problem of highaspect-ratio foldable-wing UAVs,the method encodes the multi-degree-of-freedom unstable motion signals into a low-dimensional feature space through a data-driven approach.By clustering in the feature space,this paper identifies and studies several dynamic behaviors during aerial deployment.The research presented in this paper offers a new method and perspective for feature extraction and analysis of complex and difficult-to-describe extreme flight dynamics,guiding the research on aerial deployment drones design and control strategies.
