@inbook{ab826b3860044a69bca9ceb55961e393,
title = "A long memory process based parametric modeling and recognition of PD signal",
abstract = "We address the problem of recognition and retrieval of relatively weak industrial signal such as Partial Discharges (PD) buried in excessive noise. The major bottleneck being the recognition and suppression of stochastic pulsive interference (PI) which has similar time-frequency characteristics as PD pulse. Therefore conventional frequency based DSP techniques are not useful in retrieving PD pulses. We employ statistical signal modeling based on combination of long-memory process and probabilistic principal component analysis (PPCA). An parametric analysis of the signal is exercised for extracting the features of desired pules. We incorporate a wavelet based bootstrap method for obtaining the noise training vectors from observed data. The procedure adopted in this work is completely different from the research work reported in the literature, which is generally based on deserved signal frequency and noise frequency.",
author = "Shetty, {Pradeep Kumar}",
year = "2004",
doi = "10.1007/978-3-540-30499-9_121",
language = "English",
isbn = "3540239316",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "787--793",
editor = "Pal, {Nikhil R.} and Srimanta Pal and Nikola Kasabov and Mudi, {Rajani K.} and Parui, {Swapan K.}",
booktitle = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
address = "Germany",
}