标题： 模糊决策在流体不稳定性中的应用：基于自编码器的重构与基于规则的异常分类 显示英文标题

标题： Fuzzy Decisions on Fluid Instabilities: Autoencoder-Based Reconstruction meets Rule-Based Anomaly Classification

摘要： 冲击波在阴影图成像中的分类由于标记数据有限和复杂的流动结构而具有挑战性。 本研究提出了一种混合框架，将无监督自编码器模型与模糊推理系统相结合，以生成和解释异常图。 在评估的方法中，结合了基于模糊规则系统的混合$\beta$-VAE 自编码器最有效地捕捉了相干冲击特征，整合了空间上下文以提高异常分类效果。 该方法实现了可解释的无监督流动中断分类，并为实验和工业流体应用中的实时、物理信息诊断奠定了基础。 显示英文摘要

摘要： Shockwave classification in shadowgraph imaging is challenging due to limited labeled data and complex flow structures. This study presents a hybrid framework that combines unsupervised autoencoder models with a fuzzy inference system to generate and interpret anomaly maps. Among the evaluated methods, the hybrid $\beta$-VAE autoencoder with a fuzzy rule-based system most effectively captured coherent shock features, integrating spatial context to enhance anomaly classification. The resulting approach enables interpretable, unsupervised classification of flow disruptions and lays the groundwork for real-time, physics-informed diagnostics in experimental and industrial fluid applications.