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.

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.

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.

An ER-MKKNN(enhanced random mixed kernel K-nearest neighbors algorithm)was developed to meet the requirements of base station network traffic prediction in ultra-dense 5 G/6G environments.A hybrid kernel function was formed by combining a radial basis function kernel with a white-noise kernel, thereby overcoming the trade-off between nonlinear relationship modeling and noise suppression that plagues single-kernel methods.Dual random subsampling of both samples and features, together with a randomized hyperparameter-interval strategy, was employed to bolster generalization stability in high-dimensional, sparse settings.A dynamic weight-allocation mechanism based on inversion of out-of-bag errors was introduced to improve robustness against abrupt traffic fluctuations.Finally, a multi-level parallel architecture was implemented to deliver a scalable prediction framework for ultra-dense network topologies.Experimental evaluations show that ER-MKKNN outperformed deep-learning models in root mean square error, mean absolute percentage error and mean absolute error, respectively, establishing a new technical pathway for intelligent network operations and maintenance.

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.

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 generation mechanism and variation patterns of the ship corrosion electric field under navigation conditions, the galvanic corrosion cathode of the ship propeller was equated to a rotating disk, and an equivalent model of the corrosion electric field of the rotating disk under turbulent medium conditions was established.Combining the boundary layer theory in fluid mechanics and electrochemical corrosion related theories, the boundary layer flow state and corrosion current density on the surface of a disk under laminar and turbulent medium flow conditions were calculated, and differentiation treatment on the disk was performed.The multiple point charge superposition method was used to calculate the corrosion electric field of a rotating disk under the control of oxygen mass transfer in a flowing medium.The variation law of corrosion electric field on rotating disks at different speeds was studied and experimentally verified.The results indicate that as the rotational speed of the disk increases, the corrosion electric field gradually increases.When the flow state of the medium on the surface of the disk gradually transitions from laminar to turbulent, the corrosion electric field modulus increases significantly.

Semantic segmentation of building facades from 3D mesh data is essential for scene understanding but often relies on costly fine-grained annotations.In response to this issue, a semi-supervised learning approach was proposed, introducing a semi-supervised semantic segmentation method based on contrastive learning SS_CC(semi-supervised semantic segmentation based on contrastive learning and consistency regularization)to segment building facades in 3D mesh data.In the SS_CC method, the enhanced contrastive learning module exploited the class separability between positive and negative samples to more effectively utilize class-specific feature information.Additionally, the proposed feature-space consistency regularization loss improved the discriminative capability of the extracted building facade features by leveraging global feature representations.Experimental results show that the proposed SS_CC method outperforms some mainstream methods in F1 score and mIoU, and has relatively better segmentation performance on building walls and windows.