concord
发表于 2025-3-21 19:40:56
书目名称Data-Driven Fault Detection and Reasoning for Industrial Monitoring影响因子(影响力)<br> http://figure.impactfactor.cn/if/?ISSN=BK0263294<br><br> <br><br>书目名称Data-Driven Fault Detection and Reasoning for Industrial Monitoring影响因子(影响力)学科排名<br> http://figure.impactfactor.cn/ifr/?ISSN=BK0263294<br><br> <br><br>书目名称Data-Driven Fault Detection and Reasoning for Industrial Monitoring网络公开度<br> http://figure.impactfactor.cn/at/?ISSN=BK0263294<br><br> <br><br>书目名称Data-Driven Fault Detection and Reasoning for Industrial Monitoring网络公开度学科排名<br> http://figure.impactfactor.cn/atr/?ISSN=BK0263294<br><br> <br><br>书目名称Data-Driven Fault Detection and Reasoning for Industrial Monitoring被引频次<br> http://figure.impactfactor.cn/tc/?ISSN=BK0263294<br><br> <br><br>书目名称Data-Driven Fault Detection and Reasoning for Industrial Monitoring被引频次学科排名<br> http://figure.impactfactor.cn/tcr/?ISSN=BK0263294<br><br> <br><br>书目名称Data-Driven Fault Detection and Reasoning for Industrial Monitoring年度引用<br> http://figure.impactfactor.cn/ii/?ISSN=BK0263294<br><br> <br><br>书目名称Data-Driven Fault Detection and Reasoning for Industrial Monitoring年度引用学科排名<br> http://figure.impactfactor.cn/iir/?ISSN=BK0263294<br><br> <br><br>书目名称Data-Driven Fault Detection and Reasoning for Industrial Monitoring读者反馈<br> http://figure.impactfactor.cn/5y/?ISSN=BK0263294<br><br> <br><br>书目名称Data-Driven Fault Detection and Reasoning for Industrial Monitoring读者反馈学科排名<br> http://figure.impactfactor.cn/5yr/?ISSN=BK0263294<br><br> <br><br>
CHIP
发表于 2025-3-22 00:11:11
Multivariate Statistics Between Two-Observation Spaces,easurement spaces. The vast majority of smart manufacturing problems, such as soft measurement, control, monitoring, optimization, etc., inevitably require modeling the data relationships between the two kinds of measurement variables. This chapter’s subject is to discover the correlation between th
变形
发表于 2025-3-22 02:56:59
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冥界三河
发表于 2025-3-22 04:44:25
,Soft-Transition Sub-PCA Monitoring of Batch Processes,s, such as fermentation, polymerization, and pharmacy, is highly sensitive to the abnormal changes in operating condition. Monitoring of such processes is extremely important in order to get higher productivity. However, it is more difficult to develop an exact monitoring model of batch processes th
尽忠
发表于 2025-3-22 09:49:03
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arbovirus
发表于 2025-3-22 13:19:42
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arbovirus
发表于 2025-3-22 18:20:48
Fault Identification Based on Local Feature Correlation,as PCA, PLS, CCA, and FDA, are only suitable for solving the high-dimensional data processing with linear correlation. The kernel mapping method is the most common technique to deal with the nonlinearity, which projects the original data in the low-dimensional space to the high-dimensional space thr
尖叫
发表于 2025-3-23 01:02:44
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沉默
发表于 2025-3-23 03:08:21
Locality-Preserving Partial Least Squares Regression,e and structure-preserving properties of LPP into the PLS model. The core of LPPLS is to replace the role of PCA in PLS with LPP. When extracting the principal components of . and ., two conditions must satisfy: (1) . and . retain the most information about the local nonlinear structure of their res
钢笔尖
发表于 2025-3-23 08:34:22
Locally Linear Embedding Orthogonal Projection to Latent Structure, Monitoring process variables and their associated quality variables is essential undertaking as it can lead to potential hazards that may cause system shutdowns and thus possibly huge economic losses. Maximum correlation was extracted between quality variables and process variables by partial least