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Updated: Jan 10, 2026

Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation
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通过由光谱集群驱动的子域适应网络,对跨域轴承的剩余有用寿命预测

Zhiqing Xu1, Christopher W K Chow1, Md Mizanur Rahman1

  • 1Sustainable Infrastructure and Resource Management (SIRM), UniSA STEM, University of South Australia, Mawson Lakes, Adelaide, SA 5095, Australia.

Sensors (Basel, Switzerland)
|November 27, 2025
PubMed
概括

本研究引入了一个新的子域适应网络,用于轴承的剩余使用寿命 (RUL) 预测. 通过动态调整子域边界和权重特征,SC-SAN模型提高了准确性,优于现有方法.

关键词:
剩余的使用寿命预测频谱聚类是指光谱聚类.适应子域的调整.分域调整对齐的方法

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