A Syntactic Approach and VLSI Architectures for Seismic Signal Classification.
Abstract
Syntactic pattern recognition has been applied to seismic classification in this study. Its performance is better than many existing statistical approaches. VLSI architectures for syntactic seismic recognition are also proposed which take advantage of parallel processing and pipelining so that a constant time complexity is attainable when processing large amount of data. Application of syntactic pattern recognition to damage assessment is also proposed and demonstrated on a set of experimental data. Seismic waveforms are represented by strings of primitives, i.e., sentences, in this study. String-to-string similarity measures based on both distance and likelihood concepts are discussed along with the symmetric property and the hierarchy. A fixed-length segmentation is used in the experiment. Encouraging results comparable to those of the best statistical approaches are obtained with only two very simple features, namely zero-crossing count and log energy. Primitives are automatically selected using a hierarchical clustering procedure and two decision criteria. Nearest-neighbor decision rule and finite-state error-correcting parsers are used for classification. For error-correcting parsing, finite-state grammars are first inferred from the training samples. These two approaches have same performance in the experiment, whereas the nearest-neighbor rule is faster in speed.
Document Details
- Document Type
- Technical Report
- Publication Date
- Jan 01, 1983
- Accession Number
- ADA124398
Entities
People
- Hsi-ho Liu
- King Sun Fu
Organizations
- Purdue University