Abstract:An anti-off-target control method for small video satellite target tracking based on visual field zoning was proposed to address the problem of high initial relative angular velocity between small video satellite and observation targets, which can easily deviate from the camera field of view and cause off target. This method divided the rectangular imaging field of the satellite camera into inner and outer parts according to the inscribed circle. The inner and outer parts of the inscribed circle were designed with tracking controllers based on potential function and Euler rotation method respectively. By using the Barbarat‘s lemma, the asymptotic stability of control laws in two parts was proved, and it was also proved that the potential function controller can ensure that target would not leave the inscribed circle of visual field after entering it. Through controller comparison and simulation, the results show that the Euler rotation method has a stronger ability to suppress target deviation from the field of view compared with PD control. Compared with the full Euler rotation method, the field of view partition control combining the Euler rotation method and potential function method can effectively achieve anti-off-target control for faster maneuvering targets, thereby achieving continuous tracking and observation.

Abstract:Continuous-wave laser Doppler velocimeters (LDV) are limited in low-altitude velocity measurement due to factors such as the low power of continuous-wave lasers and optical system diffraction. By introducing the concept of "virtual distance" to expand the feuillet? model, the time-domain echo signal model for pulsed LDV has been established. Simulation results indicate that pulsed LDV can perform velocity measurement through the accumulation of hard target echo signals. Pulsed LDV can also utilize longer laser pulse widths for detection without restricted by spatial resolution. this paper verifies the feasibility of high-precision velocity measurement using long pulses with pulsed LDV, laying a theoretical foundation for future experimental validation of the pulsed LDV. Pulsed LDV is capable of detecting echo signals scattered from targets at 5 km and beyond, significantly extending the working distance range of LDV. This makes LDV applicable in integrated navigation of low-altitude aircrafts and planetary surface landing navigation for spacecrafts.

Abstract:Traffic flow prediction plays a crucial role in alleviating traffic congestion. Many research methods did not fully explore dynamic hidden correlations in traffic data. To address this challenge, an encoder-decoder-based traffic prediction model was proposed by studying the dynamic spatio-temporal variation characteristics. In the model, both encoder and decoder mainly consisted of multi-head spatio-temporal attention mechanism modules, and a connection attention mechanism was added in between to analyze the spatio-temporal correlations of the road network. The model also used a dynamic embedding module consisting of a combination of both spatio-temporal embedding coding and adaptive graph convolution to analyze the dynamic and static information of nodes. Experiments on two real datasets demonstrated that the spatio-temporal model outperformed the baseline model for long- and short-term traffic prediction. Thus, the spatio-temporal encoder-decoder model can effectively handle complex spatio-temporal sequences and improve the traffic flow prediction accuracy.

Abstract:In the existing remote sensing image generation online map methods, the most common method is based on the fully supervised learning model or unsupervised learning model. To address the resource consumption issue of obtaining precise paired samples in existing fully supervised learning, while also considering the quality of network map generation, a novel semi-supervised online map generation model based on generative adversarial networks was proposed, which aimed to realize the direct generation of intelligent remote sensing images into network maps by using only a few precisely matched data and a large amount of unpaired data. In addition, a semi-supervised learning strategy based on transformation consistency regularization and sample enhanced consistency was designed, which overcomed the inconsistency problem caused by imprecise paired data and derives better generalization performance of the model. Adequate comparison experiments were conducted on different map datasets. The generated online maps outperform the competing methods on the quantitative metrics and visual quality, which validate the effectiveness and speed of semi supervised network map generation methods.

Abstract:Since the thrust characteristics directly affect the launch speed, altitude and flight range of the water rocket, in order to improve the thrust performance of the water rocket, a flexible and deformable self-pressurized elastic air pressure cabin scheme was designed based on the existing fixed volume pressure chamber, and its performance was evaluated. With Bernoulli"s theorem and deformation coordination relation, a coupled model of internal pressure, nozzle velocity and thrust of water rocket was established. Moreover, the influence of different initial states (water volume ratio and inflation air pressure) on water rocket thrust was also studied with the numerical calculation method, and the thrust difference was compared and analyzed further between the fixed air pressure cabin and the elastic air pressure cabin under the same initial conditions. The research results show that the improved flexible and deformable self-pressurized elastic air pressure cabin could effectively increase the water jet velocity during the launch, and the thrust generated by the water rocket in the same initial state increases significantly by 46.95%. Important reference for improving the flight performance of water rockets and the optimal design of new flexible deformation water rocket scheme is provided.

Abstract:In order to study the self-excited vibration of rocket in the flight test, a two-degree-of-freedom linear system based on the coupling of elastic force and aerodynamic force was established, and the displacement feedback between pitch and yaw degree-of-freedom maked the system obtain energy from the outside and cause the self-excited vibration. By analyzing the stability of the motion equation of the system, the criterion of losing asymptotic stability of equilibrium point and generating self-excited vibration was obtained. The time history curves of pitch and yaw angular displacement were obtained by solving ordinary differential equations with Runge-Kutta method. The comparison between the simulation results and the measured data shows that the vibration divergence phenomenon during the flight of rocket is the mode-coupling self-excited vibration caused by displacement feedback. In addition, the analysis results show that the beat or constant frequency vibration usually occurs before and after the self-excited vibration.

Abstract:Addressing the issue of anomaly detection on missing multivariate time series data in real IoT（Internet of things） environments, a novel method on multivariate time series anomaly detection algorithm intergrated with graph embedding of missing information was proposed. Using a joint learning framework of pre-interpolation and anomaly detection task fusion, a GNN（graph neural network） pre-interpolation module based on time series Gaussian kernel function was designed to realize the joint optimization of pre-interpolation and anomaly detection task. A graph structure learning method for embedding missing information in time series data was proposed, using graph attention mechanism to fuse missing information masking matrix and spatiotemporal feature vectors, effectively modeling the potential connections of missing data distribution in multivariate time series. The performance of the algorithm was verified on real IoT sensor datasets. Experimental results prove that the proposed method significantly outperform the mainstream two-stage methods on the task of missing multivariate time series anomaly detection. The comparative experiment of the pre-interpolation module fully prove the effectiveness of the GNN pre-interpolation layer based on the Gaussian kernel function.

Abstract:This paper uses LS-DYNA finite element analysis software and is based on JPC water penetration tests to investigate the effect of liner parameters (wall thickness, material), charge aspect ratio on the movement characteristics of JPC in water. It has been shown that the head diameter of the JPC becomes larger and produces mass erosion after entering the water, and there are two phases in the effective length of the JPC in the water, including the growth phase and the decrease phase, and the velocity of the JPC decays exponentially. Increasing the thickness of the liner and the charge aspect ratio will improve the erosion resistance and the velocity storage capacity of the JPC. The optimum range of values for the thickness of liner is in the ranges of 0.036D_k to 0.055 D_k. After a charge aspect ratio higher than 1.25, the charge structure has less effect on the movement characteristics of the JPC in water. The density of the material has an important influence on the velocity decay pattern of the JPC during water penetration, The ability of a JPC with a high density to store velocity in water, with the velocity decay pattern converging if the densities are similar. The higher density of the JPC has the greater the ability to store velocity in water, and if the densities are similar the velocity decay pattern tends to be the uniform. The copper, tantalum and tungsten liners are suitable for use in the underwater shaped charge warhead.

Abstract:FG-CNTRC demonstrate significant engineering value in advanced equipment manufacturing due to their exceptional mechanical properties and designable characteristics. The critical problem of nano-reinforcement scale effects on mechanical response mechanisms was addressed through integration of nonlocal theory with the Eshelby-Mori-Tanaka method, resulting in the development of a nano-to-macro multiscale constitutive model. Based on mathematical characterization of spatially gradient-distributed CNTs, thermo-mechanical coupling effects from environmental temperature and visco-Pasternak foundations were incorporated. Vibration governing equations for nanocomposite structures were established through Kirchhoff plate theory and energy variational principles, with characteristic frequencies of simply-supported plates subsequently solved. The influence mechanisms of CNTs" characteristic parameters and thermo-mechanical coupling effects on the natural frequency of structural systems was analyzed. The results demonstrate that the constitutive model effectively characterizes the stiffness-weakening effect induced by CNTs" scale effects. This effect simultaneously suppresses the stiffness enhancement from matrix elastic parameters while significantly increasing sensitivity to temperature variations. Moreover, the critical volume fraction for structural reciprocating vibration shows positive correlation with matrix damping parameters.

Abstract:In the few-shot recognition scenario of space targets observed at low frequency, the drastic changes in the image representation of space targets in different poses pose challenges to the extraction of discriminative features and the correlation of features between images. To address these issues, the few-shot space target recognition method based on adaptive cross fusion of local features was proposed. Based on the existing few-shot learning framework, the feature cross fusion module based on self-attention and cross-attention is used to adaptively learn the correlation between local features, improve the discriminant and robustness of feature in different poses, effectively explore the similarity between the support set and the query set, and improve the accuracy of feature association with representation differences. Meanwhile, the sample label weight based on neighborhood density is employed into the loss function to solve the learning bias problem of the network model caused by unbalanced space target datasets. Through the verification on different datasets, the proposed method is proved to achieve higher recognition accuracy．