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.

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.

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.

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 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 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.

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.

Particle transport simulations using stochastic methods face significant challenges on conventional von Neumann architectures, particularly due to random branching events and irregular memory access patterns.These limitations stem from the fundamental mismatch between probabilistic algorithms and deterministic computing paradigms.To bridge the gap between architecture and algorithms, a probabilistically tunable true random number generator was developed based on spintronic and ferroelectric devices.The physical randomness of spintronic devices was leveraged to provide a physical random source for the architecture, and the throughput of random bits was enhanced through optimized control logic and writing mechanisms.Next, programmable synapses were designed based on the memristive properties of ferroelectric devices, enabling nonvolatile continuous weight storage with tunable probabilities.The experimental results indicate that the proposed approach achieves performance improvements ranging from 171 to 1028 times compared to a general-purpose CPU when solving a sample transport problem.Furthermore, compared to existing spin-transfer torque magnetic tunnel junction based true random number generators, the developed method not only enables tunable probability random sampling but also achieves a throughput of 303 Mbit/s when generating uniformly distributed random sequences.

In view of the contradiction between the need of the electromagnetic sled for real-time accurate position and speed information and the limitation or high cost of traditional position and speed measurement methods, a new measurement system based on vernier caliper structure was proposed and designed.The principle of high precision positioning and the corresponding position analysis method was expounded, and the position prediction algorithm and Kalman filter algorithm were designed to improve the accuracy and real-time performance.The hardware circuit and software program were designed to realize the function, and a synchronous belt guide rail experimental platform was built to verify the designed system.The test results show that the system can achieve millimeter-level positioning accuracy, and performs well in terms of real-time capability, accuracy and engineering application.The positioning and speed measurement system was applied to the electromagnetic levitation propulsion platform.

Modular multilevel converters exhibit significant capacitor voltage ripple under low-speed, high-torque operating conditions. Existing high-frequency injection suppression schemes increase device current stress and losses while introducing overmodulation risk, and their parameter optimization lacks full operational-condition adaptability.To resolve this issue, a high-frequency injection parameter adaptive optimization strategy considering multiple constraints was proposed.Based on system characteristics and a steady-state model, a variable-step gradient descent algorithm was employed offline to generate a minimum injection-amplitude base parameter reference table that satisfies both capacitor voltage ripple and modulation wave constraints.Subsequently, an online adaptive correction mechanism was designed.Injection parameters were dynamically adjusted in real-time according to acquired capacitor voltage ripple and modulation information, compensating for model deviations and operational variations, forming a coordinated architecture of offline global optimization and online local refinement.Simulation and experimental results show that the proposed strategy maintains the capacitor voltage ripple suppression effect while significantly reducing high-frequency circulating currents, demonstrating dynamic tracking capability for the optimal objective.