The issues of small UAV(unmanned aerial vehicle)target size, limited pixel coverage in images, weak texture detail information, and the difficulty in effectively extracting infrared UAV target features, which lead to low detection accuracy, were addressed by proposing a multiscale learning-based target detection algorithm.A multi-scale feature fusion structure was constructed in the neck network of the model, and a multi-scale feature learning module was introduced.Features from both deep and shallow networks were cascaded to capture target features at multiple scales, enriching the semantic and feature information of the feature map, which significantly improved the detection accuracy of small UAV targets.During training, SIoU was used in place of CIoU loss, minimizing the network model′s loss and enhancing the regression accuracy. Experimental results demonstrate that, compared to other infrared small target detection algorithms and mainstream methods, the proposed approach effectively improves the detection accuracy of UAV targets and meet the detection accuracy requirements for UAV target detection in practical applications.

The StS(state-to-state)model and MT(multi-temperature)model were used to numerically simulate and analyze the high-temperature air flow of 11 chemical species behind normal shock waves.The StS model resolved vibrational levels of neutral molecules and electronic levels of neutral atoms;the MT model distinguished the translational-rotational temperature, vibrational temperatures of neutral molecules, and the electron temperature.Simulation results for velocities ranging from 5 km/s to 11 km/s before the shock front demonstrate that immediately behind the shock wave, due to the dissociation and ionization reactions, the higher vibrational levels of molecules and the higher excited electronic levels of atoms are underpopulated relative to the Boltzmann distribution at the corresponding temperatures.Compared to the StS model, the MT model shows that the excitation of vibrational and electronic energies and the attainment of thermal equilibrium in different energy modes occur later, while chemical reactions also take place later but reach chemical equilibrium earlier.The MT model underpredicts vibrational energy loss from chemical reactions while overpredicting electronic energy loss due to electron-impact ionization.Moreover, obtained derived vibrational temperatures of molecules and electron temperature fail to accurately characterize the nonequilibrium population distributions of particle energy levels.

To meet the requirements of lightweight and low error sensitivity in the optimization design of stiffened panels, the optimization design of stiffened panels was carried out considering the twist angle error of stringers.The finite element model of post-buckling instability of stiffened panels under axial compression was established, and the sensitivity of the load-carrying capacity to the twist angle error on stringers and the distribution position of the torsional stringer was analyzed.On this basis, a sequential approximate optimization method based on surrogate model was proposed by using parallel sequential sampling strategy, and the lightweight design of stiffened panel was carried out under the influence of twist angle error of stringers.The optimized results show that, compared with the optimization design scheme without error influence, the optimization scheme considering the twist angle error of stringers has lower sensitivity to the twist angle error when the weight is reduced by more than 32%, which can effectively improve the reliability and engineering application value of the optimized structure.

The PHM index is directly affected the design of PHM and the availability of equipment.In response to the shortage of theoretical and implementable methods, a graded demonstration method was introduced, outlining a progression from comprehensive efficiency indicators to PHM comprehensive indicators and then to PHM capability indicators.The availability was selected as the comprehensive efficiency indicator, and the health evaluation rate was defined as the PHM comprehensive indicator.The relationship between availability and health evaluation rate was derived.The optimal health evaluation rate was obtained by maximizing the availability.It was deduced that the health evaluation rate was equal to the product of fault coverage and evaluation accuracy, where depend on the number of sensors and the accuracy of diagnostic/prognostic methods, respectively.This conclusion can guide PHM design.The effectiveness and practicality of this method were verified by cases.

Efficient inference deployment of large language models faces severe challenges in resource-constrained scenarios.Although current mainstream inference optimization techniques have improved model inference efficiency to some extent, they still suffer from issues like coarse-grained deployment and poor inference accuracy.Based on the discovery that different operators exhibit varying degrees of GPU affinity, an OATO(operator-aware tensor offloading)approach was proposed.OATO could extract operators′semantic knowledge and used it to design an intelligent scheduling algorithm, which further yielded a globally optimal model-deployment plan.Meanwhile, the OATO approach was integrated into the latest large model inference framework Llama.cpp to implement an operator-aware tensor offloading enhanced inference engine, referred to as OALlama.cpp.Experimental results show that compared with the state-of-the-art inference engines Llama.cpp and FlexGen, OALlama.cpp achieves the best inference performance on three large models.Notably, in the scenario where 75% of the LlaMA3-8B model weights are loaded on the GPU, the first-token generation speed of OALlama.cpp is nearly doubled compared with FlexGen and Llama.cpp.

To investigate the process fluctuation influence on SRAM（static random-access memory）single event upset in sub-20 nm FinFET（fin field-effect transistor）process，a high precision three dimensional technology computer-aid design model based on commercial process fluctuations was established，then simulated to find the FinFET SRAM single event upset threshold under different process corners.The simulation results show that the FinFET SRAM upset threshold has less variation induced by process corner fluctuation.Meanwhile，the sensitive positions of SRAM are on the N-complementary metal oxide semiconductor.Then，to understand the the impact of specific process parameter fluctuations on the single event upset threshold，the process fluctuation factor impact on single event upset was discussed，including fin width，fin height，the oxide thickness and the work function fluctuation.The simulation results show that the first two factors did not affect the upset threshold，while the latter two factors caused slight fluctuations in the upset threshold.Significant reduction in the impact of process fluctuations on FinFET SRAM single event upset threshold is firstly found，which is of great significance for the development of highly consistent radiation hardened aerospace integrated circuits.

For high-order continuous linear multi-agent systems, a minimum-energy formation design method with sampled-data communication was proposed.Based on local neighboring information of multi-agent systems at sampling time, a time-varying formation cooperative control protocol with global control energy consumption being considered was presented.By the state-space decomposition method, the time-varying formation problem of multi-agent systems was transformed into the stability problem of decomposed non-consensus subsystems.A formation feasible condition was constructed, and sufficient conditions for the design of time-varying formation under minimum-energy constraints were obtained by the generalized eigenvalue approach, which ensured that the time-varying formation of multi-agent systems could be realized under minimum-energy constraints.Theoretical results were verified by a numerical simulation, and simulation results show that the formation control method with minimum-energy constraints can effectively reduce the global control energy consumption of time-varying formation of multi-agent systems with sampled-data communication.

In monocular vision-guided high-precision inter-platform pose measurement, existing methods require an accurate 3D model of the target platform and are unable to eliminate the impact of 3D model errors on pose measurement.To address this issue, iterative optimization was performed on the 3D model of the target platform and pose, and a new monocular vision measurement method was proposed.Specifically, the target platform′s 3D model was modeled using a set of sparse 3D keypoints.By leveraging multi-view geometric constraint information in sequential images, the sparse 3D keypoint set of the target and 6D pose were treated as parameters to be solved.An objective function was established to minimize object-space residuals, and through solving this optimization problem, iterative optimization of the sparse 3D keypoint set and pose was achieved.Additionally, a sliding window combined with a keyframe selection strategy was adopted to realize real-time and online high-precision monocular vision measurement.Experimental results demonstrate that, through iterative optimization of the sparse 3D keypoint set and pose, the proposed method achieves real-time, online high-precision monocular pose measurement under the condition of an inaccurate 3D model of target platform, while simultaneously improving the accuracy of the target′s 3D model.

For the new network communication challenges of efficient data interaction between components in open interactive environments, a novel C2N(computing and control network)was proposed.Aiming at the extreme requirements for efficiency, real-time performance, flexibility, and security, C2N adopts intelligent and simplified designs in protocol architecture, planning, application, and security design, providing high-performance and highly flexible basic network support for strong real-time collaborative fusion among heterogeneous resources.Based on a detailed investigation of relevant research work, key technologies of C2N were discussed, such as data link layer enhancement, remote direct memory access for sensor-controllers, and service-oriented sensing and control middleware.It also introduced the key technology research and test evaluation carried out by the network chip and system team of the National University of Defense Technology, and prospected future challenges and research directions to help China gain leading advantages in high-end equipment systems and innovative ecosystems.

Thermally-induced oscillatory rarefied gas flow inside a two-dimensional rectangular cavity was investigated.The effects of the Knudsen numbers and the oscillation frequency of lid temperature on the flow parameters were analyzed.The Shakhov model equation was solved numerically based on the mesoscopic approach in the near-wall region, and the macroscopic approach was adopted in the bulk flow region to reduce the computational cost.To close the numerical iteration procedure, the velocity distribution functions, served as the pseudo boundary between macroscopic and mesoscopic methods, were reconstructed using the high-order Hermite polynomials.Numerical simulations demonstrate that the temperature profile at the central vertical of the cavity predicted by the hybrid method is in good agreement with results from the mesoscopic method, with maximum error 0.23%.Besides, the computational memory cost can be saved up to about 69.91%.The hybrid approach is able to capture the nonlinear phenomenon in the thermally-induced oscillatory rarefied gas flow under high Kn numbers, where the horizontal velocity no longer obeys the law of periodic oscillating cosine function, and the rise time of the horizontal velocity is much longer than the fall time.The thickness of the viscous penetration layer and the disturbed region increases as the Kn number increases, and decreases as the St number increases.