Ruihan Qin | Searcharxiv

arXiv2026

An Investigation into the Applicability of Friction Velocity Estimation Methods for the Channel with A Deposit Body

This study investigates how the deposit body influences friction characteristics by altering local flow fields, which is closely related to bed shear stress. Using generalized flume experiments, the study assesses the applicability of classical uniform flow friction models in deposit body river sections, revealing frictional changes induced by flow field non-uniformity. Initially, based on \( \frac{u_*}{\sqrt{\overline{w'^2}}} = 0.85 \sim 1.15 \) under uniform flow conditions as the judgment basis, the reliability of the classical model is verified. Four models are then applied to estimate near-bed friction velocity in deposit body sections. Results show a significant alignment between the longitudinal velocity gradient and the peak friction velocity derived from the turbulent kinetic energy method (TKE). Dimensional analysis of friction indicators reveals that: (a) friction velocity is primarily influenced by turbulence intensity, with constricted and narrowed sections resembling uniform flow, while the expansion section forms a peak; (b) models incorporating flow field fluctuations (TKE, Vertical Turbulence Kinetic Energy (TKE w'), Reynolds shear stress method (RSS)) effectively capture the impact of non-uniform flow fields on friction characteristics; (c) when energy states are low or when deposit body proportions are large, the deposit body's resistance ratio increases, and peak friction velocity rises. This study provides theoretical insights into friction estimation and sediment transport in non-uniform flow fields of deposit bodies.

physics.flu-dyn↗

arXiv2024

EEG-MACS: Manifold Attention and Confidence Stratification for EEG-based Cross-Center Brain Disease Diagnosis under Unreliable Annotations

Cross-center data heterogeneity and annotation unreliability significantly challenge the intelligent diagnosis of diseases using brain signals. A notable example is the EEG-based diagnosis of neurodegenerative diseases, which features subtler abnormal neural dynamics typically observed in small-group settings. To advance this area, in this work, we introduce a transferable framework employing Manifold Attention and Confidence Stratification (MACS) to diagnose neurodegenerative disorders based on EEG signals sourced from four centers with unreliable annotations. The MACS framework's effectiveness stems from these features: 1) The Augmentor generates various EEG-represented brain variants to enrich the data space; 2) The Switcher enhances the feature space for trusted samples and reduces overfitting on incorrectly labeled samples; 3) The Encoder uses the Riemannian manifold and Euclidean metrics to capture spatiotemporal variations and dynamic synchronization in EEG; 4) The Projector, equipped with dual heads, monitors consistency across multiple brain variants and ensures diagnostic accuracy; 5) The Stratifier adaptively stratifies learned samples by confidence levels throughout the training process; 6) Forward and backpropagation in MACS are constrained by confidence stratification to stabilize the learning system amid unreliable annotations. Our subject-independent experiments, conducted on both neurocognitive and movement disorders using cross-center corpora, have demonstrated superior performance compared to existing related algorithms. This work not only improves EEG-based diagnostics for cross-center and small-setting brain diseases but also offers insights into extending MACS techniques to other data analyses, tackling data heterogeneity and annotation unreliability in multimedia and multimodal content understanding.

eess.SP↗