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.

An adaptive active disturbance rejection control strategy integrating DRL(deep reinforcement learning)with enhanced PSO(particle swarm optimization)was presented, aiming to improve the speed and thrust control performance of PMSLMs(permanent magnet synchronous linear motors).A mathematical model of the motor was established to analyze its dynamic characteristics, followed by the design of a DRLPSO control framework.This framework leveraged reward mechanisms in reinforcement learning to interact with the environment, dynamically optimized ADRC(active disturbance rejection controller)parameters to accommodate varying operating conditions and external disturbances.The modified PSO algorithm incorporated partitioned inertia weights and cyclically utilized historical global optimal data to iteratively update control policies, refining neural network weights and thereby enhancing search efficiency and optimization accuracy.Experimental results show that the proposedDRLPSO-ADRCmethod achieves significantly higher tracking precision in position and velocity, along with improved system stability and resistance to thrust disturbances, compared to conventionalPSO-ADRCalgorithms.These findings validate the effectiveness of the innovative control strategy.

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.

To achieve accurate and stable online identification of inductance parameters for PMSM(permanent magnet synchronous motor), an online inductance observation method based on virtual voltage vector excitation and current differential response was proposed, which required no additional test signal injection and was decoupled from rotor position, stator resistance, and permanent magnet flux linkage.By introducing the concept of a virtual voltage vector-oriented coordinate system, it was analytically derived and proven that the d-and q-axis inductances of a PMSM can be observed independently of the angular position in the conventional d-q synchronous reference frame.Building on this, the implementation procedure for extracting virtual voltage vectors and current differential information was discussed in detail, enabling non-intrusive inductance identification without any signal injection.The effectiveness and accuracy of the proposed method were validated by comparison with offline test procedures in IEEE standards.

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.

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%.

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.

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.

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.

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.