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 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.

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.

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.

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.

AI chips face on-chip memory limits in deep learning.Current optimization methods focus on static computation graphs, leaving room to improve memory efficiency for dynamic graphs.To overcome this limitation, a memory optimization framework for control-flow computation graphs was developed.The framework realized operator-level memory reuse within subgraphs and further achieved recursive reuse across subgraphs by exploiting control-flow characteristics.In addition, a ping-pong buffering strategy for weight data was introduced to mitigate the memory wall between on-chip and off-chip memory, thereby allowing overlapping of memory access and computation operations within subgraphs.Validation on the domestic LUNA AI chip has demonstrated that the proposed framework improves on-chip memory utilization by 5.9% compared with existing methods.Moreover, the strategy effectively alleviates the memory wall problem by reducing data transfer time between on-chip and off-chip memory, resulting in execution efficiency improvements of up to 29%.

Power semiconductor modules are the core energy conversion units in power converters.By optimizing their design, the power density can be significantly enhanced.However, current design methods lack systematic summaries.To address this, a systematic summary across four levels(material, chip, packaging and drive)was presented.This included utilizing wide bandgap materials, enhancing chip structure, adopting advanced packaging and improving gate drive design.The underlying principles behind these methods for increasing power density were summarized, and classified and compared the existing research on improving the power density of converters based on power semiconductor module design.The primary challenges in current research were combed, and the future development trend was forecasted.

For the common stator winding short circuit and rotor eccentricity faults in surface-mounted permanent magnet synchronous motors, a flexible printed circuit board with small footprint and capable of accommodating a large number of windings was used to fabricate the detection coil, which was then arranged in the stator slots to capture magnetic field information.For the stator winding short circuit fault, a winding short circuit detection method using dual orthogonal phase-locked loop to extract fault characteristic values was proposed.This method can effectively distinguish the short circuit resistance, short circuit winding number, and fault location, and was not affected by the motor′s speed fluctuations.For the rotor eccentricity fault, a differential bridge structure of the detection coil based on high-frequency injection was proposed for eccentricity detection, and ultimately, a 2% eccentricity detection can be achieved.For the composite fault, a fault discrimination scheme based on convolutional neural networks was introduced, and the performance of different learning methods was compared.The experimental results show that under the composite fault condition, a 98% correct rate of winding short circuit assessment is achieved, and the eccentricity detection error using AlexNet with a training data proportion of 60% is only 5%.

Combining software defined networking and SR(segment routing)can optimize network performance, but in large-scale dynamic networks, excessive link utilization at key nodes can lead to a surge in queue delays.To address this, a SROD-LC(segment routing optimization algorithm based on deep reinforcement learning and load centrality theory)was proposed.By quantifying the importance of network nodes using load centrality theory, key nodes are identified and their link load states are monitored;utilizing a multi-agent reinforcement learning framework, distributed deep reinforcement learning agents are deployed at key nodes, coordinating routing decisions through a shared reward mechanism to achieve proactive optimization of link loads.At the same time, leveraging the flexibility of SR, segment identifier lists are dynamically adjusted to quickly reroute partial traffic, reducing local link utilization and avoiding potential congestion.Simulation experiments based on real network topologies show that when the proportion of SR key nodes is in the range of 0.3~0.5, the SROD-LC algorithm exhibits significant optimization effects, reducing the network′s maximum link utilization by 21%~35% compared to baseline algorithms.

To improve the design performance of long-range guided rockets, a multidisciplinary parametric model of long-range guided rockets was first established to achieve high-precision performance simulation of guided rockets.A sequence approximation optimization method based on an improved augmented radial basis function was proposed, which enhanced the generalization ability of the augmented radial basis function model through anisotropic techniques.Recursive evolution experimental design and fast cross-validation were used to improve the efficiency of approximation modeling, and an imprecise search strategy was applied for sequence sampling.The effectiveness of the proposed optimization method was verified through numerical examples.A sequence approximate optimization design of the long-range guided rocket was carried out, and the maximum range increase by 16.7% compared to before optimization while satisfying design constraints.