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Updated: May 4, 2026

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

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超德:混合集成预测-错误重建-基于多变量室内环境时间序列数据异常检测

Vandha Pradwiyasma Widartha1, Chang Soo Kim1

  • 1Department of Information System, Pukyong National University, Busan 608737, Republic of Korea.

Sensors (Basel, Switzerland)
|January 10, 2026
PubMed
概括

基于错误重建的混合综合预测异常检测 (HIPER-CHAD) 模型可靠地检测室内环境数据中的微妙异常. 这种新的方法通过将正常行为建模与预测不确定性分开来实现高准确度,优于现有方法.

关键词:
的 LSTM 标志.在VAE,它是VAE.检测异常检测异常检测混合型 混合型 混合型 混合型多变量的多变量.时间序列时间序列

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