Shenyang University

Abstract To address the issues of slow convergence and susceptibility to local optima prevalent in mainstream bio-inspired algorithms, this paper proposes a Enhanced Black-winged Kite Algorithm (WCTBKA) for Unmanned Aerial Vehicle (UAV) path planning and obstacle avoidance. Specifically targeting the original Black-winged Kite Algorithm’s slow convergence and susceptibility to local optima in UAV path planning and obstacle avoidance, WCTBKA incorporates three key enhancements: tent chaotic mapping, a chaotic dynamic weight factor, and a water wave dynamic evolution factor. Tent chaotic mapping replaces the standard random initialization to augment initial population diversity. The chaotic dynamic weight factor processes individuals during both the attacking and migrating phases,
thereby accelerating convergence. Concurrently, water wave dynamic evolution factor adaptively balances global exploration and local exploitation. Simulation results demonstrate that WCTBKA achieves superior comprehensive performance in convergence speed and local optima avoidance compared to current state-of-the-art methods. Quantitatively, the proposed algorithm averagely improves the optimal fitness and convergence performance by 30% and 31%, respectively.

Abstract Hybrid Quantum-Classical Convolutional Neural Networks (HQCNNs) show significant potential for image classification by embedding quantum circuits as convolutional kernels within classical CNNs, leveraging quantum computing to enhance feature extraction. While this approach provides a feasible pathway toward achieving quantum advantage under current Noisy Intermediate-Scale Quantum (NISQ) hardware constraints, existing HQCNNs face critical challenges, including limited classification accuracy and excessive quantum resource consumption when processing multi-channel data. To address these issues, this paper proposes the Quantum Multi-channel Data Uploading Convolution (QMDUC) method, which employs data re-uploading techniques to optimize quantum data encoding strategies, significantly reducing qubit requirements while maintaining complex data processing capabilities. QMDUC supports two encoding modes: Channel-Priority QMDUC (CP-QMDUC) and Data-Priority QMDUC (DP-QMDUC), enabling flexible trade-offs between qubit count and circuit depth, thereby providing adaptive solutions tailored to different quantum hardware constraints. Experimental validation on the CIFAR-10 three-channel color image dataset and Fashion-MNIST single-channel grayscale image dataset demonstrates that QMDUC significantly outperforms existing HQCNN methods in both classification accuracy and quantum resource efficiency. Specifically, under low-resource configurations, the proposed method achieves an approximate reduction in qubit requirements of 75%–95% while maintaining comparable or superior accuracy; under higher-resource configurations, it achieves approximately 10% improvement in peak accuracy with comparable qubit consumption to existing methods, providing an effective technical pathway for practical applications of quantum machine learning in computer vision.

Abstract High-precision relative attitude measurement is a critical prerequisite for autonomous recovery of micro unmanned aerial vehicles (UAVs) in GNSS-restricted and dynamic environments. However, motion blur and drastic scale changes often lead to a surge in visual observation noise, causing significant drift in state estimation. To address this, this paper proposes a holistic visual metrology system that deeply fuses enhanced perception with uncertainty suppression. First, at the perception layer, an improved YOLO11n-Landing network serves as a high-fidelity instrument, utilizing multi-scale feature enhancement and anti-blur attention mechanisms to boost detection confidence in dynamic scenes. Second, a height-constrained Kalman filter was constructed at the fusion layer, explicitly managing uncertainty propagation through a physical covariance model. The validity of the optimal fusion interval was confirmed based on parameter sensitivity analysis. Crucially, at the system level, a perception-control coupled isolation mechanism has been introduced. Unlike traditional loosely coupled architectures, this mechanism uses the visual confidence factor as an information hub, synchronously mapping it to the filter covariance, switching gain K , and boundary layer thickness. This dynamically reconstructs the system’s response bandwidth, actively isolating invalid observations, and physically blocking the propagation of measurement outliers into the control loop. Experimental results demonstrate that compared to traditional benchmark methods, this integrated system significantly converges the root mean square error to 0.156 m, reduces the error variance by approximately 74.7%, and achieves a 99.0% landing success rate in Monte Carlo simulations. This work not only provides a highly system-integrity-preserving technical solution for robust autonomous landing of micro UAVs, but its core coupling paradigm also demonstrates broad prospects for generalization to high-risk tasks such as aerial docking and close-range inspection.

This study examines the mechanical properties and microstructural evolution of Fe–Cr–Ni–Ti–Mo martensitic stainless steel, focusing on the synergistic effect of aging temperature on precipitation and reverted austenite. The nonlinear variation in mechanical properties correlates with the evolution of nanoscale precipitates and changes in the fraction and morphology of reverted austenite. As aging temperature increases, reverted austenite forms along lath martensite boundaries, transitioning from film‐like to block‐like morphology. Aging at 500°C yielded optimal mechanical properties: ultimate tensile strength = 1765 MPa, yield strength (YS) = 1691 MPa, EL = 7.7%, RA = 16.9%, and Charpy impact energy = 18.5 J. This performance is attributed to a high density of nanoscale Ni 3 (Ti, Al) intermetallic compounds, Mo‐rich precipitates, and film‐like reverted austenite within the lath martensitic matrix. The strengthening mechanism is elucidated through theoretical models relating YS to microstructural characteristics. Furthermore, the enhancement of ductility and toughness is explained based on microstructural evolution under different heat treatment conditions, providing comprehensive understanding of structure–property relationships in martensitic stainless steels.