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.

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.

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.

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%.

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.

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 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.

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.

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.

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.