提出新方法精准分析工业AI中的异常数据，提升系统抗干扰能力。
Robust Analysis for Resilient AI System
- 结合密度幂散度与稀疏正则，构建抗噪回归模型
- 在含异常数据下仍保持稳定性能，准确量化灾害影响
- 适用于工业AI系统验证，尤其适合高噪声场景
制造类工业互联网（MII）系统运行中产生的操作风险会引发严重数据异常，导致传统统计分析失效。本文提出一种新型鲁棒回归方法DPD-Lasso，将密度幂散度（Density Power Divergence）与Lasso正则化结合，用于分析受污染的AI韧性实验数据。我们设计了一种高效迭代算法，克服了以往计算瓶颈。该方法应用于Aerosol Jet Printing的MII测试平台，在清洁数据和含异常数据条件下均表现可靠稳定，能准确量化灾害影响。本研究确立了鲁棒回归在构建与验证工业级韧性AI系统中的关键作用。
原文摘要 · Abstract (English)
Operational hazards in Manufacturing Industrial Internet (MII) systems generate severe data outliers that cripple traditional statistical analysis. This paper proposes a novel robust regression method, DPD-Lasso, which integrates Density Power Divergence with Lasso regularization to analyze contaminated data from AI resilience experiments. We develop an efficient iterative algorithm to overcome previous computational bottlenecks. Applied to an MII testbed for Aerosol Jet Printing, DPD-Lasso provides reliable, stable performance on both clean and outlier-contaminated data, accurately quantifying hazard impacts. This work establishes robust regression as an essential tool for developing and validating resilient industrial AI systems.
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