Abstract:To address the aerodynamic load reduction requirement when the launch vehicle flying in high wind zone during the ascending phase, an intelligent attitude control method with adaptive learning rate is proposed. Taking a certain type of launch vehicle as the research object, the dynamic model in the pitch plane is established. A deep reinforcement learning framework suitable for flight control of the launch vehicle during the ascending phase is developed based on soft Actor-Critic (SAC), and a reward function that comprehensively considers control accuracy, control system stability, and load reduction effectiveness is designed. On this basis, an adaptive iteration of learning rate is implemented based on a step-size learning rate scheduler to quickly improve the convergence of the controller, and an early stopping mechanism which can automatically end the training process is designed to enhance training efficiency. Simulations show that the proposed method can effectively improve load reduction performance of the launch vehicle while ensuring attitude tracking accuracy and control system stability. Additionally, it has strong adaptability and generalization ability to external uncertain disturbances.

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．

Abstract:Existing research on sequential ad recommendations mainly focuses on user preferences for ads, insufficiently considering positive relationships between ads. Starting from the associations between ads, incorporates both ad networks and user networks into consideration, a multi-round social advertising influence maximization model based on triggering model was constructed. An ad edge based greedy strategy based on multi-round reverse influence sampling was proposed to enhance platform revenue, with theoretical proofs of its strict lower bound guarantee. Experiments showed that compared to existing optimal methods, this method increased the average ad propagation influence revenue by 35%, significantly enhancing ad recommendation effectiveness, providing a new solution for ad sequence recommendations.

Abstract:For the time-varying trajectory tracking control problem of the relative motion configuration of spacecraft formation, a distributed coordinated control law was designed based on the Port-Hamiltonian(PH) model and generalized canonical transformation, using IDA-PBC algorithm.Through the PH modeling and generalized canonical transformation of the linear dynamics of spacecraft formation, the trajectory tracking error PH system is obtained. Under the assumption that the topological structure is connected and fixed, a distributed coordinated control law for formation trajectory tracking considering relative errors between neighboring spacecraft is derived based on IDA-PBC method using the PH model of relative motion errors. Numerical simulation verifies the effectiveness of the control law. The results show that the PH method can complete the trajectory tracking control of spacecraft formation, which provides a new effective method for the distributed coordinated control of spacecraft formation .

Abstract:In order to improve the prediction accuracy of the displacement and deformation of YANFGHE reservoir, the displacement and deformation of non-linear and non-stationary reservoir was predicted by changing the decomposition method of VMD(variational mode decomposition) and integrating VMD and LSTM (long short-term memory). A MVMDLSTM (mixed variational mode decomposition long short-term memory) model prediction method was proposed. The reliability of the new method was analyzed by comparing different single prediction models with the combined model and different data sets. The experimental results show that the MVMDLSTM model can effectively attenuate the bias of the single prediction model and the empirical mode decomposition combination model estimation, and the prediction accuracy of the MVMDLSTM model is better, which provides an effective data decision-making for the stabilization and monitoring of the prediction and warning of the reservoir's slow sliding and creeping and other small deformations.

Abstract:Aiming at the reliability-based design optimization problem of complex structural systems, an efficient optimization method based on subset simulation and Markov chain simulation in augmented space was proposed. Considering the reliability-based design optimization problem in which the design parameters were distributed parameters of basic random variables, the target failure probability was transformed into a posterior density function of the design parameters in the augmented space, obtained a set of initial failure samples in the whole design domain through subset simulation, and then adopted the efficient Markov chain simulation to generate more failure samples in the gradually smaller design domain under the sequential approximate optimization framework. The target posterior density function was estimated and updated, and the decoupling approach was used to solve the transformed optimization problem to finally obtain the optimum. Compared with the existing methods, the proposed method requires only one reliability analysis and can avoid local optimal solution, resulting in the global optimal solution. Examples were given to illustrate the applicability of the proposed method in engineering and its superiority in the accuracy and efficiency of analysis and calculation.

Abstract:Based on the differential impact of the spatial distribution of impact points on target damage effectiveness, the probability distribution model of missile hitting different important areas of target was constructed to realize expansion of the traditional hit accuracy concept. Aimed at the reality of actual missile hitting targets with complex process, high cost and low frequency, Bayesian method was used to fuse multi-source information, and missile hit accuracy was estimated based on the idea of region division, distribution determination, prior fusion and posterior solution. The Dirichlet distribution was selected as the prior distribution of hit accuracy parameters, the Dempster-Shafer (D-S) evidence theory was used to fuse the prior information and the posterior distribution of accuracy parameters was solved by Markov chain-Monte Carlo (MCMC) method. Example results show that this method can describe the probability of missile hitting different important areas of the target in detail, and scientifically integrate multiple types of prior information about hit accuracy, which provides theoretical references for missile hit accuracy estimation methods and test schemes optimization.

Abstract:Aiming at the problems that traditional effectiveness evaluation methods can not reflect the evolution, emergence and adaptability of the anti-missile equipment system, a data-driven effectiveness evaluation method of anti-missile equipment system was proposed. Based on the analysis of the characteristics of anti-missile equipment system and the shortage of traditional effectiveness evaluation method. the Bayes optimization algorithm was used to optimize the convolutional neural network hyperparameters, and the efficiency evaluation model of Bayes convolutional neural network was constructed. The flow and steps of Bayes-CNN (Bayesian convolutional neural network) system effectiveness evaluation algorithm were studied, and a set of completed efficiency evaluation algorithm was formed. Designed and validated the simulation experiment, input a lot of test data to Bayes-CNN model for training and learning, so as to obtain the simulation prediction of the effectiveness of anti-missile equipment system. The experimental results show that the error between the actual and expected output is very small, and the non-linear fitting effect is great so that it had a high degree of feasibility and reliability.