Modelling (sub)string-length based constraints through a grammatical inference method
Proc. of the NATO Advanced Study Institute on Pattern recognition theory and applications
Learning from examples in sequences and grammatical inference
Syntactic and structural pattern recognition
An efficient algorithm for the inference of circuit-free automata
Syntactic and structural pattern recognition
Distributed recognition of patterns in time series data
Communications of the ACM
Efficient Error-Correcting Viterbi Parsing
IEEE Transactions on Pattern Analysis and Machine Intelligence
A dynamic query interface for finding patterns in time series data
CHI '02 Extended Abstracts on Human Factors in Computing Systems
Applying Grammatical Inference in Learning a Language Model for Oral Dialogue
ICGI '98 Proceedings of the 4th International Colloquium on Grammatical Inference
Fast Similarity Search in the Presence of Noise, Scaling, and Translation in Time-Series Databases
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Detail-preserving image information restoration guided by SVM based noise mapping
Digital Signal Processing
EURASIP Journal on Applied Signal Processing
Detection of power disturbances using morphological gradient wavelet
Signal Processing
A Wavelet Tour of Signal Processing, Third Edition: The Sparse Way
A Wavelet Tour of Signal Processing, Third Edition: The Sparse Way
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In this paper, we propose a framework for the automatic detection and classification of power distribution feeder disturbances based on their underlying causes. The segmentation algorithm based on either a Kalman Filter (KF) or a Wavelet Filter divides the quasi-stationary Root-Mean-Square (RMS) of the captured signal into pre-disturbance, disturbance and post-disturbance regions. The pre- and post-disturbance segments are essentially stationary while the non-stationary nature is extracted as the disturbance segment. Each region is then represented as a sequence of predefined wave patterns or primitives. A syntactically correct combination of these primitives will define the morphology of the mother RMS signal. The grammar, the production rules and the model for each class is built from a set of positive examples (I+) by using a stochastic Error-Correcting Grammar Inference (ECGI) engine. When used in combination with a k-nearest neighbour algorithm (kNN) classifier, this framework can recognise any event and learn new patterns.