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Adaptive learning for event modeling and characterization
Shuangshuang Dai,
Atam P. Dhawan
Electrical and Computer Engineering
Office of the Provost
Research output
:
Contribution to journal
›
Article
›
peer-review
2
Scopus citations
Overview
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Dive into the research topics of 'Adaptive learning for event modeling and characterization'. Together they form a unique fingerprint.
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Engineering
Application Monitoring
100%
Collected Data
100%
Continuous Operation
100%
Experimental Result
100%
Fuzzy Membership Function
100%
Health Monitoring
100%
Measurement Data
100%
Propulsion System
100%
Tasks
100%
Keyphrases
Adaptive Clustering
16%
Adaptive Learning
100%
Challenging Tasks
16%
Classification System
16%
Clustering Approach
16%
Computationally Efficient
16%
Continuous Operation
16%
Detection System
16%
Diagnostic Evaluation
33%
Diagnostic Safety
16%
Engine Operation
16%
Event Characterization
100%
Event Modeling
100%
Events of Interest
100%
Frequency Information
16%
Fuzzy Membership Function
16%
Health Diagnostics
16%
Health Monitoring & Evaluation
16%
K-means
16%
Measure Data
16%
Monitoring Applications
16%
Online Monitoring
16%
Propulsion System
16%
Safety Monitoring
16%
Specific Events
33%
Specific Pattern
16%
Wavelet Transform
16%
Computer Science
Adaptive Learning
100%
clustering approach
50%
Collected Data
50%
Combining Method
50%
Continuous Operation
50%
Engine Operation
50%
Experimental Result
50%
Frequency Information
50%
Fuzzy Membership Function
50%
k-means Clustering
50%
Measurement Data
50%
Monitoring Application
50%
Propulsion System
50%
Wavelet Transforms
50%