厦门工学院

The increasing complexity of power quality disturbances (PQDs) calls for lightweight yet accurate classification methods suitable for edge deployment. This paper proposes Ghost-ResDSCNet, a multi-scale deep network that first converts voltage signals into time–frequency spectrograms via short-time Fourier transform. A residual depthwise separable convolution backbone performs efficient feature extraction, Ghost modules reduce computational cost by cheap feature generation, and a spatial pyramid pooling module captures multi-scale contextual information. On a 21-class synthetic PQD dataset, Ghost-ResDSCNet achieves an average accuracy of 99.34% on clean signals, along with 99.26%, 99.11%, and 95.80% under 40 dB, 30 dB, and 20 dB SNR respectively, with only 0.12M parameters, outperforming MobileNetV2 and EfficientNet. On a real-world five-class dataset, it attains 100.00% accuracy on clean and 40dB signals, and 99.96% and 94.02% under 30dB and 20dB SNR. Grad-CAM++ visualization confirms that the model focuses on physically interpretable time–frequency regions. The results demonstrate an effective trade-off among accuracy, robustness, and efficiency for real-time PQD monitoring in edge computing.

To address the challenges of reduced navigation accuracy and insufficient steering stability in automated guided vehicles (AGVs) caused by complex obstacle layouts and dynamic environmental changes, this paper proposes a path planning-oriented data fusion differential steering visual neuron PID control method for AGVs. By constructing a multi-sensor system integrating vision, proximity sensors, and a gyroscope, comprehensive environmental and state data of the AGV are collected, providing a multi-source information foundation for subsequent control. A visual neuron dynamic model is employed to fuse and transform multi-source data, generating bounded smooth fusion signals to enhance the accuracy and robustness of state perception. A global path planning model is established with the objectives of minimizing path length and maximizing smoothness, and solved using a particle swarm optimization algorithm, ensuring that the resulting path satisfies motion constraints while balancing efficiency and stability. Finally, a PID controller, driven by the visual neuron output and path planning results, generates differential steering control variables to achieve real-time and precise AGV steering control. Experimental results demonstrate that the proposed method effectively performs global path planning for AGVs and controls trajectory deviation within ± 15 mm. The average control success rate reaches 96.35

In this paper, we investigate several properties of a harmonic mapping f=𝒫[F] on the unit disk with boundary function F∈L^p(T) and p∈[1, ∞ ] . The coefficients of f are initially estimated, and these estimates are directly employed in analysis of the Landau theorem. Subsequently, the Schwarz lemma for the harmonic mapping f is established by applying certain properties of Gauss hypergeometric functions. This work generalizes the classical Schwarz lemma to bounded harmonic mappings. As an application, two new versions of the Schwarz-Pick lemmas for the harmonic mapping f are also discussed.

A parameter-free method, namely the generalization of the Gauss-Seidel (GGS) method, is developed to solve generalized absolute value equations (GAVE). Some results in the recent work of Edalatpour et al. [A generalization of the Gauss-Seidel iteration method for solving absolute value equations. Appl Math Comput. 2017;293:156-167] are extended. For solving linear complementarity problem, the GGS method can be seen as a special case of the accelerated modulus-based matrix splitting iteration method [Zheng and Yin. Accelerated modulus-based matrix splitting iteration methods for linear complementarity problem. Numer Algorithms. 2013;64:245-262], which is characterized by solving a GAVE subproblem in each iteration. Moreover, new convergence results of the GGS method are given. Numerical results demonstrate the effectiveness and efficiency of the GGS method.

Dual quaternions are pivotal for modeling rigid-body motions in robotics, aerospace, and computer graphics, with solving generalized Sylvester dual quaternion matrix equations central to kinematic analysis and trajectory planning. Existing methods often have slow convergence or limited applicability to coupled forms, restricting use. This paper addresses a class of such equations. We establish a necessary and sufficient condition for solution existence via real representation of quaternion matrices, vectorization, and Kronecker products. Two novel algorithms are proposed: dual quaternion generalized conjugate direction and conjugate gradient least squares algorithms, both converging in finite iterations under no rounding errors for theoretical efficiency. Numerical simulations demonstrate the algorithms outperform existing methods in convergence speed and apply to practical engineering like robotic kinematics. Furthermore, experiments on simultaneous restoration of two hand tremor-induced mixed-direction blurred color images validate their effectiveness in handling complex real-world degradation, expanding their application scope to image processing and highlighting strong practical potential.