More isolated DC-DC converter topologies are required as a result of the growing need for different power conversion brought on by the development of new energy generation and electrified transportation. To overcome the randomness and lack of topology optimization in generating topologies based on human experience, this paper proposes a vertex prime degree-based topology self-generation method of the isolated DC-DC converters with two switches. Firstly, the two-vertex-pair transformer model is proposed, and the circuits are converted into graphs using graph theory. Secondly, the numbers and the types of components used to form the circuit are regarded as the input condition of the method. Finally, the proposed self-generation method is utilized to traverse and generate all available isolated DC-DC converter topologies with the given components. An input DC source, an output DC source, two switches, two diodes, one transformer, one inductor, and one capacitor, namely, the isolated T¹S²D²L¹C¹ DC-DC converters are examples of converter circuits. The 87 forward converters and 97 flyback converters can be generated. Taking the 87 forward converters as examples, they can be further categorized based on voltage gain into 25-boost forward converters, 24-buck forward converters, 32 buck-boost forward converters, and 6 constant voltage forward converters. Therefore, this method enables rapid and quantitative generation of new isolated DC-DC converter topologies through computer programming. It provides a topology library for engineering applications and establishes conditions for topology optimization and selection.

The modular multilevel converter based high voltage direct current (MMC-HVDC) is a dynamic power balancing system. The control system of MMC generally adopts a dual closed-loop vector control strategy based on the traditional instantaneous power model under an asymmetric grid state, which has a complex control structure and low control accuracy. This paper introduces a flexible instantaneous power model and establishes a general power equation with active power and new reactive power as control objects. In order to eliminate the twice grid-frequency ripples in both active and reactive power under asymmetric grid states, an enhanced sliding-mode MMC-HVDC direct power control strategy based on the new instantaneous power model is proposed. This approach combines the improved sliding-mode control method with the flexible instantaneous power model. Furthermore, it omits the inner-loop controller and power compensation terms while optimizing the control structure. According to simulation results, the suggested approach can more effectively take advantage of the benefits of the flexible instantaneous power model because it has superior dynamic responsiveness, control precision, and robustness under operating conditions including asymmetric grid state and parameter perturbation.

In the process of deriving topologies for power electronic converters, researchers often use topology equivalence or topology isomorphism to identify topologies with different structures but identical performance, thereby avoiding redundant studies. However, the connotations of topology equivalence and topology isomorphism differ significantly, which can easily lead to confusion. To address this issue, this paper aims to clarify the distinctions and connections between the two concepts and propose a method for accurately identifying topologies with identical performance. First, it is deduced that a necessary condition for topology isomorphism is that their adjacency matrices have the same determinant. Furthermore, it is derived that the necessary and sufficient condition for topology equivalence is that the components are identical and the fundamental loops correspond one-to-one. Subsequently, an analysis from the perspective of topology subgraphs reveals that topology isomorphism is a sufficient but not necessary condition for topology equivalence, while topology equivalence is a necessary and sufficient condition for identical performance. Based on this, the paper recommends prioritizing topology equivalence over topology isomorphism in practical applications to identify topologies with identical performance. The theoretical results are validated through case studies demonstrating their correctness and feasibility. Additionally, this paper proposes a method based on the depth-first search algorithm to automatically determine equivalent topologies, enabling the rapid and accurate identification of converter topologies with identical performance.

Up to now, massive power electronics converters have been derived to satisfy various demands in practical applications. However, most of these methods focus on discovering effective converters, ignoring the redundant switches in converters. This paper aims to delete converters with redundant switches efficiently. First, the identification principle is introduced based on the connection between legitimate switching modes and redundant switches. After that, the principle is transformed into the corresponding graph principle. Finally, graph theory and graph searching algorithms are used to develop the auto identification method based on the graph principle. The results demonstrate that all redundant switches may be effectively recognized with the use of computers when the suggested method is applied to single-inductor multi-port converters.

With the widespread application of single-inductor multiple-output (SIMO) DC-DC converters, their dynamic response has much concern for domestic and foreign researchers. To explore the theoretical limit of the optimal dynamic response for the SIMO Buck converter, this paper first establishes a corresponding mathematical model according to the working principle of the converter, and then an improved differential evolution (DE) algorithm is employed to search out the solution. In addition, the suggested approach can investigate the best dynamic response with various goals, including the best self-regulation or cross-regulation. It is also possible to know the optimal dynamic response with different constraints, such as different dynamic response times and peak inductor currents. In order to completely recognize the dynamic performance of SIMO DC-DC converters, the theoretical limit exploration of optimal dynamic response based on the heuristic DE intelligent algorithm is useful. Ideally, this will direct the controller design in order to obtain an optimized dynamic response.

In order to compare and optimize a series of DC-DC converters with similar topological structures and functions, a comprehensive topology evaluation method based on the analytic hierarchy process and entropy method is proposed. A hierarchical evaluation index system is created by compiling the common DC-DC converter performance indices. Analytic hierarchy process and entropy methods are used to assign weights to the indices in the index system, and the strategy focusing on analytic hierarchy process was adopted to get the combined weights. Additionally, each DC-DC converter’s evaluation value is obtained using the linear weighted average method. Finally, 11 DC-DC converters with a voltage gain of D² are selected as the example to carry out a comprehensive evaluation using the proposed method, and a feasibility analysis of the evaluation result is given. The result indicates that the selected topologies have good performance.

Single-inductor multi-port (SIMP) converters have great application potential due to their “more silicon less magnetic” feature. However, there are so many switching modes of SIMP converters that the modulation strategy design is complicated. At present, the design of the modulation strategy is mainly to manually select the switching mode sequence and carry out modal analysis, which requires professional knowledge of power electronics. This research proposes a reinforcement learning (RL) based modulation strategy design technique for SIMP converters. The proposed method uses a neural network (NN) to produce modulation strategies, takes known conditions such as port voltages and converter structure as the input of the NN and leverages a set of basic principles to provide rewards for training the NN, avoiding complex artificial design. With RL, the NN sums up experience during trial-and-error without human intervention, and finally generates the optimal modulation strategy under different operating conditions. Based on this method, modulation strategies are designed for a SIMP converter and the effectiveness of the method is verified by experiments.

The prognosis and health management (PHM) technology of lithium-ion batteries helps reduce the probability of failure and improve the service time by analyzing current reliability and formulating a comprehensive control strategy. Analysis and forecasting of battery failure are key components of battery PHM technology. This article examines the state of research on the main PHM technologies, such as reliability analysis, testing, and growth technology, failure diagnosis and early warning algorithms, failure model based on digital twin technology, and fault analysis and probability prediction. And the article introduced the general architecture for PHM technology on cloud battery management systems for electric vehicles. The advantages and disadvantages of existing technologies are summarized, and the challenges and future perspectives of PHM technology are prospected.

The cyclone global navigation satellite system (CYGNSS) provides high-quality global navigation satellite system reflectometry (GNSS-R) data that can be reliably used for the retrieval of significant wave height (SWH). Due to the high dynamic nature of CYGNSS, the received signal is easily affected by environmental factors, and the complexity of sea conditions makes it difficult for simple models to accurately retrieve SWH. To address the above issues, this article proposed an SWH retrieval model based on multivariate machine learning. According to the mechanism of wave formation and the analysis of the correlation between CYGNSS parameters and SWH, relevant parameters were selected, and three training schemes were designed, involving five parameters, nine parameters, and 17 parameters, respectively. Random forest (RF) and convolutional neural network (CNN) were used to train and verify the retrieval model, and the SWH retrieval results were compared with the reference values of the European Centre for Medium-Range Weather Forecasts (ECMWF). The best retrieval model among them was the 17-parameter CNN retrieval model, with root mean square error（RMSE）was 0.184 0 m and $ {R}^{2} $= 0.948 5. Compared with the 17-parameter CNN retrieval model, the 9-parameter CNN retrieval model reduced training time by 24% and has minimal accuracy loss. However, the 9-parameter retrieval model performed poorly in terms of generalization evaluation compared to the 17-parameter retrieval model. To improve the generalization ability of the model, wind speed was added as a parameter to the 17-parameter retrieval model, resulting in a 17 + 1-parameter generalization model. The best generalization model among them was the 17 + 1 parameter RF generalization model, with RMSE was 0.497 1 m and $ {R}^{2} $= 0.584 6. This effectively proves that the model proposed in this article has good potential in SWH retrieval.

To reduce the difficulty of extracting small object features from high-resolution remote sensing images, a dual-encoder feature fusion network model based on convolution structure and self-attention mechanism was proposed, which was suitable for semantic segmentation of high-resolution remote sensing images. Firstly, a dual-encoder structure was designed to extract global and local detail information of remote sensing images and improve the segmentation accuracy of small object features. Secondly, a feature aggregation module was used to aggregate feature information at different stages, so as to embed more global contextual information. Finally, an edge thinning loss module was used to improve the recognition ability of the model for object edge information. The $m_{F_1} $ average value of the segmentation results on the ISPRS Vaihingen and Potsdam datasets achieved 91.28% and 93.16%, respectively. Compared with the current mainstream algorithms, the segmentation accuracy of small objects like cars and the overall segmentation accuracy were improved. The proposed model solves the problem of inaccurate segmentation of small objects and edge information in the semantic segmentation of high-resolution remote sensing images to a certain extent.

A data imputation algorithm based on a graph neural network is proposed to address the issue of missing flight ground support data. Firstly, to reduce the impact of noise in the original data on training denoising autoencoder is applied to enhance the reliability of feature extraction. Secondly, a graph representation learning framework is established to get the first embedding, using aggregation functions to aggregate the features of nodes within the sampling interval to achieve state updating. Furthermore, a long and short-term memory neural network is constructed to embed the temporal feature of flights to obtain the final state space of the hidden layer. Lastly, a loss function is suggested to iterate the deconvolution neural network, which is employed for feature restoration. The final flight ground operation data imputation result was acquired after numerous iterations, and the technique was evaluated using ground operation data from a specific airport in Southwest China from April to June 2018. The results showed that compared to other algorithms, the proposed algorithm imputation error decreased by an average of about 74% at low missing rates. At higher missing rates, the imputation proposed algorithm error decreased by an average of about 68%. When the number of iterations of the proposed algorithm is about 100 and the regularization coefficient is about 0.5, the imputation error reaches the lowest.

A high-precision dynamic temperature control system is proposed, developed, and tested with the goal of addressing the issue that the existing ground temperature environment simulation equipment finds it challenging to realize the temperature deviation test of scientific loads with extremely high temperature control accuracy in deep space exploration. The system uses a liquid nitrogen cold plate as the cold source, and it realizes the high precision static and dynamic temperature control on the 300mm×300mm contact surface by means of electric heating, heat pipe heat transfer, and solid structure heat conduction. The test results demonstrate that the temperature control system can accomplish high-precision temperature stability control with a temperature variation of less than ±0.001℃ within the contact surface temperature range of −10～45 °C. In addition, it can also realize the linear control of temperature rise and fall rate of ± 0.05 ℃/1 000 s～1 ℃/1 000 s on the load contact surface. Compared with the set temperature curve, the whole-process temperature tracking error is less than 0.003 ℃ and 0.04 ℃. The established temperature control system has the ability to conduct high-precision temperature dynamic deviation tests. It will have good application prospects in tasks such as deep space exploration payload development and testing.

By analyzing the impact of mechanical vibration on the safety of lithium ion batteries in various application scenarios, such as transportation and aerospace, the 18650 battery was used as a sample, and the.influence of vibration conditions on the internal structure, cyclic aging mechanism, electrical performance, and thermal runaway characteristics of lithium ion batteries was investigated. The results indicate that vibration can significantly induce alterations in the internal microstructure of lithium ion batteries. Prolonged exposure to vibration stress can lead to deformation of the negative electrode collector, resulting in compression on the electrode roll. Additionally, momentary vibration stress can dynamically affect the contact area between the electrode material and the diaphragm, impeding the migration of lithium ions during the embedding and de-embedding processes. Consequently, this exacerbates the fluctuation of Coulomb efficiency and energy efficiency, leading to insufficient discharge under vibration conditions. Furthermore, thermal runaway experiments demonstrate that the time required to trigger thermal runaway shortens as the duration of vibration increases. Compared with that of non-vibrating batteries, the initial explosion time of thermal runaway of batteries subjected to 600 hours of vibration shortens to 72 seconds, and the combustion-induced explosion time shortens to 114 seconds. Meanwhile, the interval between initial and combustion-induced explosions is shortened. Moreover, the instability of the impact force produced by combustion-induced explosion increases.

Accurate prediction of flight ground service is the key to achieving fine flight management and improving management efficiency of the airport collaborative decision making (A-CDM) system. Therefore, a multi-node dynamic prediction method for flight ground service based on a cascaded multi-output gradient boosting regression tree model was proposed. The cascaded framework was built to realize the prediction information transmission and result updates between different service schedules. The dynamic prediction algorithm of flight ground service was designed based on gradient boosting regression tree which could be used for multi-node prediction. By taking a typical busy airport as an object, a feature set was constructed, covering flight basic attributes and level information transmission. The results show that the proposed method can effectively realize the dynamic prediction of key node completion time in flight ground service. The initial prediction accuracy of each node within ±5 min reaches more than 80%, and the prediction performance gradually improves as the flight ground service continues. The final prediction accuracy of over 60% of nodes within ±5min exceeds 95%. It provides effective method support for improving the flight operation predictability and the collaborative decision making ability of multi-agents in airports.

Unmanned aerial vehicle （UAV） has been widely used in various fields, target tracking is one of the key technologies of UAV applications. A UAV tracking algorithm based on feature fusion and segmented attention is proposed to solve the problems of UAV appearance changes and external interference when tracking targets. To improve the expression ability of fusion features, the weights of the three features are adaptively determined after the Siamese network has extracted histogram of oriented gradients (HOG), color names (CN), and deep convolution features from template and search images. Secondly, the improved feature segmentation attention mechanism is used to enhance the attention of the effective region in the template image feature information, so as to achieve more effective target similarity matching. The resultant feature vector is then transformed to YCbCr space to lower the computation cost. The feature response graph is then obtained using the discrete cosine transform (DCT), and classification regression is used to determine the final target location. Experiments show that the algorithm can reduce the influence of appearance change and external factors on tracking performance, and improve the accuracy of target tracking.

In view of the problem of critical aircraft identification in air traffic situations, the existing research fails to fully consider the spatiotemporal effect in actual air traffic operation. Therefore, a method of critical aircraft identification based on a temporal network was proposed. Based on the convergence relationship between aircraft and its complexity, the temporal network model was constructed by the neighbor topological overlap coefficient, and the critical aircraft was determined based on the eigenvector centrality. Network attacks on critical aircraft nodes were carried out to observe the changes in sector complexity and compared with attacks based on static network indicators. The improved genetic algorithm was used to assign a new sector entry time to the aircraft node deleted by the network attack, so as to verify the selection effect of the critical aircraft. Actual data verification shows that compared with static network attacks, the proposed method can reduce the average sector complexity more efficiently when removing critical aircraft, and the improved genetic algorithm has higher convergence when solving the time allocation problem of critical aircraft entering the sector, making the sector complexity more stable in a certain period of time. The analysis of the control effect of critical aircraft shows that the temporal network method is more accurate than the static network in identifying the aircraft that has a greater influence on the sector complexity in a period of time.

To improve the angular momentum envelope analysis efficiency of typical gimbal-type momentum exchange devices such as single-gimbal control moment gyroscope (SGCMG), double-gimbal momentum wheel (DGMW), and magnetically suspended control and sensing gyroscope (MSCSG), a gimbal equivalent-based angular momentum envelope analysis method of gimbal-type momentum wheel was proposed. The similarities and differences of angular momentum exchange principles between SGCMG and DGMW based on a mechanical solid gimbal and MSCSG based on a magnetic levitation micro-gimbal. The equivalent coefficients between radial/axial angular momentum components of SGCMG and MSCSG were studied and designed. The biorthogonal solid gimbal of SGCMG was used to construct the equivalent model of the MSCSG micro-gimbal. The angular momentum expressions of ergodic methods for MSCSG and DGMW were compared, and the applicability of the gimbal equivalent method in the angular momentum analysis of DGMW was analyzed. The error of the gimbal equivalent method was quantified to prove that the absolute error and the direction error of angular momentum calculated by the gimbal equivalent method are both less than one thousandth compared with the traditional ergodic method. The parameter dimensions of the two methods were analyzed to prove the rapidity of the gimbal equivalent method. The angular momentum envelopes of MSCSG and DGMW were simulated and compared based on the traditional ergodic method and the gimbal equivalent method, respectively, which proved the effectiveness of the method. This method has a wider application in angular momentum envelope analysis of MSCSG groups.

Qualified missile damage effectiveness is an important guarantee to give full play to combat effectiveness. The damage effectiveness identification test research is especially important to determine whether the missile damage effectiveness is up to par. In view of the limitations of the existing damage effectiveness quantitative characterization index of "single-shot damage probability", it is difficult to fully describe the missile's damage effectiveness based on a single damage standard. The damage effectiveness is represented by the probability of occurrence of different damage grade results, taking into account the results of multiple damage grades created by a single missile striking the target. The missile damage effectiveness is identified from the perspective of multiple damage grade standards, which are helpful for a thorough examination of the missile damage effectiveness. In addition, in order to overcome the problem of insufficient information on small sample missile assembly tests, the Bayesian method is used to fuse multiple sources of prior information with the system contribution in the design of identification tests, and the test schemes with sufficient information utilization and controllable risk of both parties are designed. The example shows that compared with the quantitative characterization index of a single damage standard, this research describes the missile damage effectiveness more comprehensively, and compares it with the improved binomial distribution hypothesis test method, which verifies the superiority of this method.

An occluded vehicle tracking method that fuses full-scale features with trajectory correction based on the Deep SORT algorithm is proposed to improve the tracking drift and identity switching (IDS) problems caused by occlusion in vehicle tracking. First, a full-scale feature extraction network is introduced to extract features of different scales of the target and to achieve adaptive dynamic fusion to enhance the apparent features of the target. Then, a trajectory correction approach is suggested to fix the tracking trajectory during the occlusion process, and the Kalman filter parameters are adjusted in order to minimize the cumulative linear errors during the occlusion process and optimize the target motion features. Finally, occluded vehicle tracking is achieved by combining the appearance features and the motion features. The feasibility of the proposed method is verified by ablation experiments and visualized analysis. The suggested approach successfully resolves the IDS issue in obscured vehicle tracking and increases the robustness of vehicle tracking, as demonstrated by experimental results on the KITTI dataset, which yield the highest overall score of 78.13% and the fewest number of IDS (192), when compared to typical existing methods.

During the parallel separation process of two stages to orbit (TSTO) vehicles, there are multiple reflections of shock waves between the first and second stage vehicles. The complex flow has a great impact on the pressure distribution, torque characteristics and flight attitude of the vehicles, and may even seriously affect the safety of the separation between stages of the vehicles. The parallel separation properties of the two stages to orbit vehicle model created by the National Numerical Wind Tunnel Project are examined using adaptation techniques for unstructured hybrid mesh and the self-developed National Numerical Wind Tunnel Project software, NNW-FlowStar. The simulation results are compared with the wind tunnel test data, and the reliability and effectiveness of NNW-FlowStar simulation of parallel separation characteristics of vehicles are confirmed. The research shows that the NNW-FlowStar can better simulate the parallel separation characteristics of the two stages to orbit the vehicle. The numerical simulation results are in good agreement with the test results. The calculated flow field structure is consistent with the wind tunnel test. Using the mesh adaptive technology can effectively improve the simulation accuracy. T In order to circle the vehicle, the two stages will separate in parallel and travel through several typical flow stages, including combination flow, gap flow, small channel flow, big channel flow, and free flow. During the whole process, the shock structures change rapidly, and there are complex flow phenomena such as shock wave interference, boundary layer interference and shock wave/boundary layer interference.

With the rapid development of the civil aviation ticket fare search system business, the internal network traffic scale of the search system cluster continues to grow. Under high-load network traffic, a cluster data synchronization scheme based on the Gossip protocol was developed. This solution is designed from the transport layer and application layer of the network protocol and uses the user datagram protocol (UDP) transmission protocol in the transport layer to reduce the number of connections and interactions between cluster nodes in order to achieve low traffic and low data consumption during network transmission. At the application layer, the Gossip propagation protocol is used to achieve eventual consistency of data and ensure the reliability of data transmission. In the cluster monitoring process, the effectiveness and dependability of data synchronization are guaranteed by the combination of the Gossip propagation protocol at the application layer and the UDP transmission protocol at the transport layer.

Based on the quasi-linear implicit aeroelastic modeling method, a coupled flexible multibody dynamics model suitable for distributed multi-rotor/tilting wing aircraft was established, and its aeroelastic coupling dynamics characteristics were studied. Based on the moderately deformed beam model and quasi-steady theory, the Pitt-Peters dynamic inflow model and Floquet theory were used to establish a calculation method for solving the dynamic characteristics of the aeroelastic coupling system of the multi-rotor/high aspect ratio flexible tilting wing. On the basis of verifying the correctness of the theoretical model, the dynamic characteristics of the distributed multi-rotor/tilting wing coupling system, the whirl flutter characteristics, and the dynamic response characteristics of aeroelastic coupling were studied. The results show that the rotor and the nacelle have the greatest influence on the torsional mode of the wing, and the coupling between the rotor and the wing will increase the overall mode shape of the rotor; increasing the number of rotors and deploying the lift blades can improve the stability of the system at low speed. However, increasing the number of rotors will reduce the torsional frequency of the wings and thus reduce the flutter speed; increasing the effective angle of attack of the rotors and the angle of attack of the wings can increase the flutter speed of the system, and increasing the rotor speed will reduce the flutter speed of the system; as the flying speed increases, the system first occurs the torsional instability of the wing and then the chordwise bending instability of the wing. The vibration response of the system has experienced vibration convergence, small-amplitude limit-cycle flutter, and large-scale multi-frequency limit-cycle flutter. The form of wing flutter is the coupling of vertical and chordwise bending and torsional motion, and its three-dimensional coupling effect is remarkable. The degree of modal coupling between the rotor and the wing is also deepening.

As one of the quality indexes of the wind tunnel flow field, the dynamic pressure field of the wind tunnel directly affects the accuracy of experiment results, and the dynamic pressure field needs to be checked regularly. Dynamic pressure field verification equipment includes a five-hole probe, pipe rack, and guide rail (longitudinal beam and cross beam). The influence of verification equipment needs to be deducted when the dynamic pressure field is analyzed. Based on the national general computational fluid dynamics (CFD) software NNW-FlowStar for numerical wind tunnel engineering, a class of low-speed pressure outlet boundary conditions for simulating boundary layer flow was established. The residual convergence and calculation accuracy of the boundary conditions were verified by the low-speed flat plate turbulent boundary layer. Based on unstructured hybrid mesh and FlowStar, the numerical simulation of the FL-12 low-speed wind tunnel test section without any equipment, the test section only with the guide rail of the pipe rack, and the test section with both the pipe rack and the guide rail was carried out. The degree of influence and affected areas of the equipment were qualitatively displayed through a spatial dynamic pressure cloud diagram. The degree of influence of the verification equipment on the dynamic pressure of the test sections was given quantitatively through the spatial dynamic pressure distribution curve. In addition, the state with and without the guide rail of the pipe rack at the top of the test sections was numerically simulated, and the degree of influence of the guide rail of the pipe rack on the dynamic pressure of the test sections was obtained. The guide rail of the pipe rack had a great influence on the adjacent four probes. By decomposing the degree of influence of the verification equipment on the dynamic pressure and comparing the dynamic pressure correction curves of numerical and experimental results, the results show that the degree of influence of the guide rail of the pipe rack on the dynamic pressure of the central probe is six times that of the pipe rack. This verifies the rationality of the experimental dynamic pressure correction scheme ignoring the degree of influence of the pipe rack, and optimization suggestions to improve the accuracy of dynamic pressure verification are proposed.

Thermal insulation structure is the key factor to ensure high-speed aircraft work safely and complete various tasks. The multilayer thermal insulation structure has attracted much attention because of its light weight and excellent thermal insulation effect. The performance of the multilayer thermal insulation structure is not only related to the physical size but also closely related to the material optimization and the temperature influence. In this paper, the transient nonlinear heat transfer model of the multilayer thermal insulation structure was established, and the numerical solution strategy of the heat transfer equation was proposed. Then, by considering the material optimization, introducing the temperature variation characteristic of the thermal conductivity of the material, and taking the geometric dimensions and equivalent mechanical properties of the multilayer thermal insulation structures as the optimization constraints, an integrated mechanical-thermal optimization model of the multilayer thermal insulation structure was established. The Monte-Carlo method and the particle swarm method were utilized to obtain the solution. The simulation results show that the proposed optimization model and method can reduce the structural mass by 27.4%. The temperature effect analysis shows that the temperature variation effect has an important influence on the optimization results. If the temperature effect on the material properties is not considered, the optimal design scheme may not meet the temperature design constraints, which further leads to structural failure. Finally, the reliability and rationality of the optimization scheme are verified by the finite element model. The optimization design method of the multilayer thermal insulation structure proposed in the paper provides technical support and lays a foundation for the overall design of high-speed aircraft.

In view of the object capture and control problem of a dual-arm space robot, a new spherical cage-based scheme for dual arms to capture moving objects was designed. Firstly, in order to avoid pushing away from the object when captured by the manipulator, a structure with a spherical cage of dual arms was designed to constrain the moving object geometrically and prevent the object escape. Secondly, the collision detection model and the accurate model of the cage contact and collision model were proposed according to the contact and collision conditions between the cage and the object. The friction between the spherical cage and the object was used to reduce the speed of the object and provide a safe capturing environment for the manipulator. Furthermore, a visual servo method was adopted to design the real-time expected trajectory of dual arms to capture moving objects, and a dual-arm coordination tracking control law based on inverse dynamics was developed. Finally, the closed-loop control system simulation experiments for capturing a moving cubic object by using the free-floating dual-arm space robot based on the spherical cage were carried out. The simulation results demonstrate the capturing effectiveness of the proposed control method based on a spherical cage.

The stable control of tank pressure is crucial in the pressurization system of spacecraft tanks. In this paper, an adjustable Venturi tube based on a special conical pintle was designed, which could maintain a good linear correspondence between the pintle displacement and the flow rate, thereby improving the regulation accuracy. A closed automatic pressurization experiment based on the principle of negative feedback, as well as a system control and data acquisition based on programmable logic controllers were developed. The automatic pressurization experiment was conducted successfully. The results show that a high-precision, continuous, and stable pressure regulation over a wide flow range is accomplished by the device, offering some references for the design and improvement of the flow regulation device in the pressurization system.

In order to evaluate in-orbit operation characteristics of BeiDou-3 Navigation Satellite System (BDS-3) spaceborne atomic clocks, the broadcast clock offset parameters and precise clock offset data released by the International GNSS Service (IGS) Analysis Center were used to analyze the satellite’s broadcast clock offsets, time-varying characteristics of broadcast clock speed, frequency accuracy, and frequency drift rate. The results indicate that the average error of clock offset parameters of the BDS-3 satellite is about 0.41 m, which is the key factor affecting the precision of the satellite space signal. Furthermore, BDS-3 broadcast clock speed parameters fluctuate frequently, so the spaceborne clocks’ high frequency stability has not been brought into full play. The frequency accuracy of BDS-3 spaceborne clocks is poor, but that of the hydrogen clock of the geostationary orbit (GEO) satellite is high. The frequency drift rate of the BDS-3 spaceborne rubidium clock is larger, resulting in too many frequency modulation times and affecting system availability. BDS-3 hydrogen clock has a lower frequency drift rate and higher long-term stability, which indicates advantages in on-board autonomous time-keeping.

In response to the significant impact of speckle noise on the detection accuracy of synthetic aperture radar (SAR) image changes, the high network model complexity of existing capsule network-based image change detection methods, and the loss of a large amount of original image information in training samples, this paper proposed a self-supervised image change detection method based on the light capsule network (SLCapsNet). The logarithmic ratio operator difference graph was generated, and the “pseudo label” of training samples with high confidence was obtained through the maximum inter-class variance method and fuzzy C-means clustering method, which laid the foundation for self-supervised learning. The paper constructed a three-channel training sample based on the two temporal SAR images and difference graph of logarithmic ratio operators to maximize the preservation of sample information. Lightweight capsule network was designed to extract training sample features through single scale convolution, and a single scale capsule network was used to mine spatial relationships between features. Comparative experiments and ablation experiments were set up, and tests were conducted on five real SAR datasets. The experimental results show that the advantage of the proposed method is to improve the operational efficiency of the method while reducing model complexity, obtain stronger robust features, suppress the adverse impact of speckle noise on change detection performance, and improve change detection performance.

In order to improve the prediction accuracy of ultra-short-term wind power, a combined prediction model based on variational modal decomposition (VMD), hierarchical principal component analysis (HPCA), and gated recurrent unit (GRU) neural network optimized by COOT algorithm was proposed. Firstly, the submode number of VMD was determined by the energy difference method so that the original power sequence with strong nonlinearity was decomposed into a set of relatively stationary submodes. Secondly, the correlation degree value between high-dimensional meteorological features and power sequence was calculated by gray relation analysis, and the ranking and stratification were carried out. The first principal component of feature variables in each layer was extracted by principal component analysis (PCA) to realize the dimensionality reduction of high-dimensional meteorological features. Finally, the COOT algorithm was introduced to optimize the hyperparameters of the GRU prediction model, accelerate the model convergence speed, and improve the model prediction accuracy. Simulation analysis was carried out on the measured data of a wind farm in Guizhou Province, and the results show that compared with the prediction results of the traditional GRU model, the root mean square error, mean absolute error, and mean absolute percentage error of the proposed method are reduced by 67.41%, 72.25%, and 45.69%, respectively, and the prediction accuracy of the proposed method is higher than that of the other four combined prediction models, which effectively improves the prediction accuracy of ultra-short-term wind power.

The leaf spring rubber bearings are important load-bearing and vibration-absorbing components in heavy trucks and often suffer from short fatigue life and poor durability under cyclic loading conditions. This work took the leaf spring rubber bearing as the research object, calculated its vertical static stiffness, and compared it with the experimental stiffness. The reason why the experimental stiffness curve is folded was explained through bearing forces. Based on the critical plane method and fatigue crack propagation theory, the dynamic heat build-up and fatigue life of the leaf spring rubber bearing under a vertical compressive load of 260 kN were predicted by using a thermo-mechanical coupling analytical approach. The results show that the error between the simulated maximum temperature and the experimental temperature is around 5.0%, and the error between the predicted fatigue life and the mean value of the bench test is 7.5%. Based on the tensile fatigue test data of rubber specimens, a two-parameter Weibull distribution model for crack precursor size is fitted, and the initial size range of microcracks is inferred to be between 29.8 μm and 62.8 μm, with a median of 38.5 μm. The fatigue simulation life of the leaf spring rubber bearing calculated based on the distribution data of the crack precursor size ranges from 18 000 to 39 000 cycles, quantitatively explaining the fluctuation of the bench fatigue test life. This work can provide guidance for the optimal design of the fatigue life of vibration-absorbing rubber products.

At present, micro air vehicles (MAVs) are being popularized in both military and civilian fields and are playing an increasingly important role. The flapping wing organism shows its advantages of high aerodynamic efficiency, quick action, and stable hover during flight. In this paper, by studying flapping wing insects, a new type of flapping wing aircraft was proposed, and a mechanical structure model and control system with variable rotating orbit radius were designed. When the rotating radius changed, the chip controlled the rotation of the variable rail motor and then changed the position of the displacement slider from the rotation center, which was finally represented by the change in the flapping amplitude of the flapping wing through the transmission mechanism. According to the designed mechanical structure, the coordinate system was established, and the expression was derived. At the same time, XFlow was used to carry out fluid simulation tests. Through the benchmark experiment, the fluid simulation test of the variable amplitude of the two wings, and the fluid simulation test of the amplitude of the variable single wing, the corresponding conclusions were obtained, and the rationality and feasibility of the variable-amplitude flapping wing aircraft model were verified. It provided data support for follow-up research and prototype production.

To realize continuous leapfrog upgrades of the hypersonic vehicle from single-point optimal fixed configuration to full envelope optimal of morphing configuration, a quasi-wave rider profile and composite deformation scheme morphing wingspan and sweep are designed. On this basis, to reduce the computational burdens of reentry trajectory planning, the adaptive trust-region-based penalty sequence convex programming method is proposed. To increase the approximate accuracy, the path restrictions are communicated using the logarithmic convexification technique. A virtual control is introduced to replace the dynamic equation constraints. Using the penalty function method, modify the second-order cone constraint and incorporate it into the objective function to direct the iterative results in order to approximate the feasible domain. An adaptive trust region updating strategy is designed to accelerate the convergence of the sequence convex optimization algorithm. As demonstrated by the simulation results, the hypersonic morphing vehicle's range extension is 16.63% when compared to the fixed configuration, and the ATP-SCP computing time is 89.24% less than when compared to the HP pseudospectral method.

In order to overcome the difficulties of ambiguous initial circumstances, intricate process constraints, and strict terminal restrictions in the guidance of reusable launch vehicles, this work suggests an online trajectory optimization technique that combines convex optimization and fourth-order polynomial guidance. The method utilizes a fourth-order polynomial guidance approach considering terminal attitude constraints and fuel optimality as the objective to compute the initial trajectory and terminal landing time. By considering different initial conditions, different trajectories and times are obtained to ensure a successful solution of convex optimization under varying circumstances. Additionally, by applying the initial trajectory to a convex optimization algorithm with the same fuel optimality objective, guidance commands are generated to satisfy process constraints and terminal constraints, enabling a soft landing. Simulation analysis demonstrates that the proposed method exhibits better adaptability and higher reliability to different deviations compared to constant convex optimization algorithms. Furthermore, by doing away with the need to optimize terminal landing time, the suggested approach increases solution efficiency and lowers fuel consumption.

In order to achieve rapid response and vibration suppression of high-precision magnetic levitation turntable and ensure high-precision decoupling control of Lorentz force magnetic levitation omnidirectional stable platform, the working principle of radial Lorentz force magnetic bearings (RLFMBs) was first elucidated. Based on the equivalent magnetic circuit method, a dynamics model of a single RLFMB was established, and a four-degree-of-freedom translational motion dynamics model of the rotor was constructed. A decoupling controller based on the internal model structure was designed, and the strong robustness of the internal model controller was verified by the Bode diagram of the complementary sensitivity function. The simulation results show that in terms of tracking performance, the response time of deflection and translation is reduced by 59.3% and 28.2%, respectively, compared with the proportion-integral-differential (PID) control method. In terms of anti-interference performance, the residual interference of deflection and translation is reduced by 38.8% and 86.2% compared to the PID control method. This method can be applied to high-precision, high-stability, and high dynamic pointing control of the platform’s load system.

To balance the efficiency and cost of offline database construction, as well as the efficiency and accuracy of online matching and localization in WiFi fingerprint localization, this paper proposed a dual-dimensional partition localization (DDPL) algorithm based on wireless access point (AP) virtual location perception. The algorithm used geometric characteristics and propagation models to perceive AP virtual locations based on actual measurement data. In the offline phase, the spatial weight was replaced by the signal distance weight that conformed to the signal characteristics for the fingerprint database interpolation expansion, which effectively reduced the cost of database construction. At the same time, the dual-dimensional division of the fingerprint database was simplified based on the signal coverage characteristics, and the high-value localization information in each region was retained. In the online phase, the differential evaluation criteria of propagation similarity and distance sum were introduced, and the adjacent reference points (RPs) were searched efficiently and finely. In the measured scene, the proposed fingerprint localization algorithm could take into account both quality and efficiency, and the interpolation effect was improved by more than 17.4% compared with other schemes. The accuracy of the localization algorithm was at least 7.1% higher than that of similar algorithms, which had a certain application value in indoor scenes.