Target Classification in Sparse Sampling Acoustic Sensor Networks Using IDDC Algorithm

Cited 0 time in webofscience Cited 0 time in scopus
  • Hit : 459
  • Download : 1
The analysis of time series using data mining techniques can be effective when all targets have their own inherent patterns in a sparse sampling acoustic sensor network where no valid feature of frequency can be extracted. However, both problems of local time shifting and spatial variations should be solved to deploy the time series analysis. This paper presents time-warped similarity measure algorithms in order to solve the two problems through time series, and we propose the IDDC (Improved Derivative DTW-Cosine) algorithm to deliver the optimal result and prove the performance with some experiments. The experimental results show that the object classification accuracy rate of the proposed algorithm outperforms the other time-warped similarity measure algorithms by at least 10.23%. Since this proposed algorithm produces such a satisfactory result with sparse sampling data, it allows us to classify objects with relatively low overhead.
Publisher
Springer Verlag (Germany)
Issue Date
2007
Citation

Lecture Notes in Computer Science, Vol.4809, pp.568-578

ISBN
978-3-540-77089-3
ISSN
0302-9743
DOI
10.1007/978-3-540-77090-9_53
URI
http://hdl.handle.net/10203/13569
Appears in Collection
CS-Journal Papers(저널논문)

qr_code

  • mendeley

    citeulike


rss_1.0 rss_2.0 atom_1.0