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Xiuzhu Yang

Publications and source records attributed to Xiuzhu Yang.

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Analysis of Convergence for the IPA-AC Method

The Improved Partial Area-Analytical Calculation (IPA-AC) method represents a leading meshfree discretization strategy for peridynamic models, distinguished by its rigorous geometric treatment of boundary intersections via dual corrections of integration weights and quadrature points. Despite its empirical success in suppressing boundary-induced geometric errors, a systematic theoretical characterization of its convergence behaviors under distinct scaling limits has remained elusive. This work establishes a unified convergence framework for the IPA-AC method applied to both scalar and tensor kernels. By leveraging the Lax Equivalence Theorem, we explicitly derive error estimates that reveal the method's performance across three critical limiting regimes. The theoretical analysis, substantiated by numerical validation, demonstrates that: (1) for a fixed horizon $\delta$, the method achieves robust second-order convergence $\mathcal{O}(h ^{2})$ with respect to the mesh size $h$; (2) for a fixed mesh, the discretization error scales as $\mathcal{O}(\delta^{-2})$, indicating a sensitivity to the nonlocal length scale; and (3) the method does not satisfy the Asymptotic Compatibility (AC) condition. These findings clarify that while the IPA-AC method offers superior accuracy for simulating fixed nonlocal models, it requires a sufficiently large horizon-to-mesh ratio to mitigate intrinsic discretization errors when approximating the local limit.

math.NA

Dense People Counting Using IR-UWB Radar with a Hybrid Feature Extraction Method

People counting is one of the hottest issues in sensing applications. The impulse radio ultra-wideband (IR-UWB) radar has been extensively applied to count people, providing a device-free solution without illumination and privacy concerns. However, performance of current solutions is limited in congested environments due to the superposition and obstruction of signals. In this letter, a hybrid feature extraction method based on curvelet transform and distance bin is proposed. 2-D radar matrix features are extracted in multiple scales and multiple angles by applying the curvelet transform. Furthermore, the distance bin is introduced by dividing each row of the matrix into several bins along the propagating distance to select features. The radar signal dataset in three dense scenarios is constructed, including people randomly walking in the constrained area with densities of 3 and 4 persons per square meter, and queueing with an average distance of 10 centimeters. The number of people is up to 20 in the dataset. Four classifiers including decision tree, AdaBoost, random forest and neural network are compared to validate the hybrid features, and random forest performs the highest accuracies of all above 97% in three dense scenarios. Moreover, to ensure the reliability of the hybrid features, three other features including cluster features, activity features and CNN features are compared. The experimental results reveal that the proposed hybrid feature extraction method exhibits stable performance with significantly superior effectiveness.

eess.SP