Selected work

Representative Papers

Theoretical Analysis of DomiRank Centrality: Automorphism, Entropy, and Graph Transformations

Sep 09, 2026

This study addresses the unclear theoretical mechanisms of the DomiRank algorithm under graph symmetry, information entropy, and topological transformations by integrating dynamical systems, graph theory, spectral analysis, and information-theoretic frameworks. By establishing a connection between node importance dynamics and graph automorphisms, we prove that regular graphs maximize entropy, reveal the monotonically decreasing effect of the competition parameter on entropy, and derive eigenvalue conditions on the degree vector for distributional invariance. Empirical evaluations on real-world networks confirm that DomiRank serves as a tunable complement to principal eigenvector centrality, exhibiting distinct advantages under strong competition regimes. These findings provide a rigorous theoretical foundation for identifying critical nodes in complex networks.

1 citationsRead paper

Link prediction in complex networks via fusing node centrality and local similarity indices

Sep 08, 2026

论文提出通过融合节点中心性和局部相似性指数来解决复杂网络中链接预测的问题，特别是在稀疏网络中的预测能力受限问题。

0 citationsRead paper

Commercial Vehicle Braking Optimization: A Robust SIFT-Trajectory Approach

Dec 21, 2025

To address the “zero-speed false braking” issue in commercial vehicle Automatic Emergency Braking (AEB) systems caused by CAN signal inaccuracies under low-speed conditions, this paper proposes a high-precision vehicle motion state recognition method leveraging blind-zone camera video streams. The approach introduces three key innovations: (1) a five-frame sliding-window trajectory displacement statistic; (2) a dual-threshold state decision matrix; and (3) an OBD-II–driven dynamic Region-of-Interest (ROI) configuration mechanism—collectively mitigating environmental interference and false detection of moving objects. Motion estimation employs CLAHE-enhanced image preprocessing, SIFT feature extraction, and KNN-RANSAC matching, achieving real-time processing at 14.2 ms per frame on Jetson AGX Xavier for 704×576-resolution video. Experimental results show F1-scores of 99.96% for stationary and 97.78% for moving state classification. Field deployment reduces false braking incidents by 89%, achieves 100% emergency braking success rate, and maintains system fault rate below 5%.

0 citationsRead paper

DiffFit: Disentangled Garment Warping and Texture Refinement for Virtual Try-On

Jun 29, 2025

Virtual try-on (VTON) faces challenges including clothing detail loss, inaccurate human-clothing alignment, inefficient inference, and poor generalization across poses and styles. This paper proposes DiffFit, a two-stage latent diffusion framework that— for the first time—decouples geometry-aware clothing deformation from texture fidelity optimization. In Stage I, a fine-grained deformation network achieves pose-adaptive geometric alignment; in Stage II, a cross-modal conditional diffusion model synthesizes high-fidelity appearance by jointly conditioning on the original garment texture, target person image, and deformed intermediate result. This progressive design significantly improves generation stability, detail preservation, and cross-pose generalization. Evaluated on large-scale benchmarks, DiffFit surpasses state-of-the-art methods in FID, LPIPS, and user studies, achieving simultaneous optimization of inference efficiency and visual quality.

0 citationsRead paper

YOLO-FDA: Integrating Hierarchical Attention and Detail Enhancement for Surface Defect Detection

Jun 26, 2025

Industrial surface defect detection faces challenges including diverse defect morphologies, large scale variations, strong texture interference, and difficulty in fine-grained recognition. To address these, we propose an enhanced YOLO-based multi-scale defect detection framework. Our method introduces a Detail-Directed Fusion Module (DDFM) and directional asymmetric convolution to improve sensitivity to minute defects; designs attention-weighted concatenation and cross-layer attention fusion to strengthen contextual modeling; and integrates a BiFPN architecture with hierarchical attention to optimize synergistic aggregation of low-level details and high-level semantics. Extensive experiments on multiple benchmark datasets demonstrate that our approach significantly outperforms state-of-the-art methods in mAP, small-object recall, and cross-scale robustness—achieving a favorable balance between detection accuracy and generalization capability.

0 citationsRead paper

Recent publications

Latest Papers

Theoretical Analysis of DomiRank Centrality: Automorphism, Entropy, and Graph Transformations

Sep 09, 2026

This study addresses the unclear theoretical mechanisms of the DomiRank algorithm under graph symmetry, information entropy, and topological transformations by integrating dynamical systems, graph theory, spectral analysis, and information-theoretic frameworks. By establishing a connection between node importance dynamics and graph automorphisms, we prove that regular graphs maximize entropy, reveal the monotonically decreasing effect of the competition parameter on entropy, and derive eigenvalue conditions on the degree vector for distributional invariance. Empirical evaluations on real-world networks confirm that DomiRank serves as a tunable complement to principal eigenvector centrality, exhibiting distinct advantages under strong competition regimes. These findings provide a rigorous theoretical foundation for identifying critical nodes in complex networks.

1 citationsRead paper

Link prediction in complex networks via fusing node centrality and local similarity indices

Sep 08, 2026

论文提出通过融合节点中心性和局部相似性指数来解决复杂网络中链接预测的问题，特别是在稀疏网络中的预测能力受限问题。

0 citationsRead paper

Commercial Vehicle Braking Optimization: A Robust SIFT-Trajectory Approach

Dec 21, 2025

To address the “zero-speed false braking” issue in commercial vehicle Automatic Emergency Braking (AEB) systems caused by CAN signal inaccuracies under low-speed conditions, this paper proposes a high-precision vehicle motion state recognition method leveraging blind-zone camera video streams. The approach introduces three key innovations: (1) a five-frame sliding-window trajectory displacement statistic; (2) a dual-threshold state decision matrix; and (3) an OBD-II–driven dynamic Region-of-Interest (ROI) configuration mechanism—collectively mitigating environmental interference and false detection of moving objects. Motion estimation employs CLAHE-enhanced image preprocessing, SIFT feature extraction, and KNN-RANSAC matching, achieving real-time processing at 14.2 ms per frame on Jetson AGX Xavier for 704×576-resolution video. Experimental results show F1-scores of 99.96% for stationary and 97.78% for moving state classification. Field deployment reduces false braking incidents by 89%, achieves 100% emergency braking success rate, and maintains system fault rate below 5%.

0 citationsRead paper

DiffFit: Disentangled Garment Warping and Texture Refinement for Virtual Try-On

Jun 29, 2025

Virtual try-on (VTON) faces challenges including clothing detail loss, inaccurate human-clothing alignment, inefficient inference, and poor generalization across poses and styles. This paper proposes DiffFit, a two-stage latent diffusion framework that— for the first time—decouples geometry-aware clothing deformation from texture fidelity optimization. In Stage I, a fine-grained deformation network achieves pose-adaptive geometric alignment; in Stage II, a cross-modal conditional diffusion model synthesizes high-fidelity appearance by jointly conditioning on the original garment texture, target person image, and deformed intermediate result. This progressive design significantly improves generation stability, detail preservation, and cross-pose generalization. Evaluated on large-scale benchmarks, DiffFit surpasses state-of-the-art methods in FID, LPIPS, and user studies, achieving simultaneous optimization of inference efficiency and visual quality.

0 citationsRead paper

YOLO-FDA: Integrating Hierarchical Attention and Detail Enhancement for Surface Defect Detection

Jun 26, 2025

Industrial surface defect detection faces challenges including diverse defect morphologies, large scale variations, strong texture interference, and difficulty in fine-grained recognition. To address these, we propose an enhanced YOLO-based multi-scale defect detection framework. Our method introduces a Detail-Directed Fusion Module (DDFM) and directional asymmetric convolution to improve sensitivity to minute defects; designs attention-weighted concatenation and cross-layer attention fusion to strengthen contextual modeling; and integrates a BiFPN architecture with hierarchical attention to optimize synergistic aggregation of low-level details and high-level semantics. Extensive experiments on multiple benchmark datasets demonstrate that our approach significantly outperforms state-of-the-art methods in mAP, small-object recall, and cross-scale robustness—achieving a favorable balance between detection accuracy and generalization capability.

0 citationsRead paper