arXiv subjects

Yunxing Liu

Publications and source records attributed to Yunxing Liu.

4 recordsLinked to original sources

arXiv2026

On the degradation of hot spot performance due to mid-to-high-mode hydrodynamic instabilities

In an ignited design of inertial confinement fusion, the role of mid-to-high-mode hydrodynamic instabilities in degrading hot-spot performance, beyond reducing temperature, remains unclear. To address this, we propose an isobaric criterion to assess the isobaric assumption that forms the theoretical basis of the hot spot. The most dangerous mode l = 12 is determined through a balance between perturbation growth and ablation stabilization induced by thermal conduction. Thermal conduction outperforms convection when the Peclet number is much less than 1. Therefore, for mid-to-high modes, thermal conduction makes the hot spot isobaric before the outer mass inflow restores the lost heat. Consequently, neglecting thermal conduction overestimates pressure and underestimates volume. These results enhance our understanding of mid-to-high modes in degrading hot-spot performance, and suggest that thermal conduction losses may reduce performance even if perturbations are nearly stabilized by ablation.

physics.flu-dyn↗

arXiv2026

A theoretical model for quantifying the imprinting sensitivity of direct-drive inertial confinement fusion implosions

To quantify the sensitivity of diverse implosion designs to laser imprinting, we developed an equivalent perturbation model that maps laser imprinting as the initial target surface perturbation. By incorporating imperfections in target fabrication and thermal smoothing in the plasma, the model shows a reduced implosion sensitivity to laser imprinting, extending the analysis beyond geometric irradiation. The imprinting sensitivity threshold is defined as $\frac{δh_{\text{proxy}}}{δh_{\text{tar}}(0)} = 0.1$, where $δh_{\text{proxy}}$ is the imprinting amplitude and $δh_{\text{tar}}(0)$ is the initial target perturbation amplitude. Radiation-hydrodynamics simulations confirm that when $\frac{δh_{\text{proxy}}}{δh_{\text{tar}(0)}} \leq 0.1$, variations in nonlinear onset time and adiabat remain within 12\% of that with $δh_{\text{tar}}(0)$ alone. Moreover, the imprinting sensitivity is supported by OMEGA experiments. Overall, for linear perturbations of medium-to-high modes in direct-drive, the model enhances our physical understanding of how laser and target perturbations evolve and serves as a simplified tool to optimize implosion performance.

physics.plasm-ph↗

arXiv2025

Selective Structured State Space for Multispectral-fused Small Target Detection

Target detection in high-resolution remote sensing imagery faces challenges due to the low recognition accuracy of small targets and high computational costs. The computational complexity of the Transformer architecture increases quadratically with image resolution, while Convolutional Neural Networks (CNN) architectures are forced to stack deeper convolutional layers to expand their receptive fields, leading to an explosive growth in computational demands. To address these computational constraints, we leverage Mamba's linear complexity for efficiency. However, Mamba's performance declines for small targets, primarily because small targets occupy a limited area in the image and have limited semantic information. Accurate identification of these small targets necessitates not only Mamba's global attention capabilities but also the precise capture of fine local details. To this end, we enhance Mamba by developing the Enhanced Small Target Detection (ESTD) module and the Convolutional Attention Residual Gate (CARG) module. The ESTD module bolsters local attention to capture fine-grained details, while the CARG module, built upon Mamba, emphasizes spatial and channel-wise information, collectively improving the model's ability to capture distinctive representations of small targets. Additionally, to highlight the semantic representation of small targets, we design a Mask Enhanced Pixel-level Fusion (MEPF) module for multispectral fusion, which enhances target features by effectively fusing visible and infrared multimodal information.

cs.CV↗

arXiv2021

Scale-Aware Network with Regional and Semantic Attentions for Crowd Counting under Cluttered Background

Crowd counting is an important task that shown great application value in public safety-related fields, which has attracted increasing attention in recent years. In the current research, the accuracy of counting numbers and crowd density estimation are the main concerns. Although the emergence of deep learning has greatly promoted the development of this field, crowd counting under cluttered background is still a serious challenge. In order to solve this problem, we propose a ScaleAware Crowd Counting Network (SACCN) with regional and semantic attentions. The proposed SACCN distinguishes crowd and background by applying regional and semantic self-attention mechanisms on the shallow layers and deep layers, respectively. Moreover, the asymmetric multi-scale module (AMM) is proposed to deal with the problem of scale diversity, and regional attention based dense connections and skip connections are designed to alleviate the variations on crowd scales. Extensive experimental results on multiple public benchmarks demonstrate that our proposed SACCN achieves satisfied superior performances and outperform most state-of-the-art methods. All codes and pretrained models will be released soon.

cs.CV↗