Pattern recognition approach to identify natural clusters of acoustic emission signals

Pattern Recognition Letters

Volumn 33, Issue 1, 2012, Pages 17-23

  Sause, M G R ^a   Gribov, A ^a   Unwin, A R ^a   Horn, S ^a  

^a UNIVERSITY OF AUGSBURG   (Germany)

Author keywords

Acoustic emission; Feature selection; Pattern recognition; Search strategies; Voting scheme

Indexed keywords

ACOUSTIC EMISSION SIGNAL; ARTIFICIAL DATASETS; AUTOMATED EVALUATION; CLASSIFICATION PERFORMANCE; CLUSTER SEPARATION; CLUSTER STRUCTURE; CLUSTER VALIDATION; DEGREE OF SEPARATION; K-MEANS ALGORITHM; NATURAL CLUSTERS; NUMBER OF CLUSTERS; OPTIMAL PARTITIONS; SEARCH STRATEGIES; SIGNAL FEATURES; VALIDATION METHODS; VOTING SCHEMES;

ACOUSTIC EMISSION TESTING; ACOUSTIC EMISSIONS; CARBON FIBERS; NUMERICAL METHODS; PATTERN RECOGNITION; RATING; SEPARATION;

FEATURE EXTRACTION;

EID: 80055001476     PISSN: 01678655     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patrec.2011.09.018     Document Type: Article

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