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Mitigate the Negative TL using Adaptive Thresholding for Fault Diagnosis
Pavan Kumar Mp,
Kun Chih Jimmy Chen
電子研究所
研究成果
:
Conference contribution
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同行評審
2
引文 斯高帕斯（Scopus）
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Keyphrases
Adaptive Thresholding
100%
Amount of Training
33%
Batch Means
33%
Catastrophic Forgetting
66%
Data-centric
33%
Deep Learning
33%
Deep Learning Model
33%
Deep Model
33%
Environmental Conditions
33%
Fault Diagnosis
100%
Fourth Industrial Revolution
33%
Health Assessment
33%
Health Management
66%
Health Technology Management
33%
Inductive Transfer
33%
Industrial Systems
33%
Input-oriented
33%
Labeled Data
33%
Mechanical Equipment
33%
Mini
33%
Negative Transfer
66%
Performance Enhancement
33%
Pre-Trained Parameters
33%
Prognostics Management
100%
Regularization Method
33%
Target Domain
33%
Training Data
33%
Computer Science
Deep Learning Method
50%
Deep Learning Model
50%
Enhance Performance
50%
Fault Diagnosis
100%
Generalizability
50%
Industrial System
50%
Regularization
100%
Training Data
50%
Social Sciences
Fourth Industrial Revolution
33%
Health Assessment
33%
Health Management
100%
Mechanical Equipment
33%
Technology Management
33%
Psychology
Health Assessment
100%
Learning Model
100%