DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kim, Youngsoo | ko |
dc.contributor.author | Jeong, Sangbae | ko |
dc.contributor.author | Kim, Daeyoung | ko |
dc.date.accessioned | 2009-11-26T09:15:48Z | - |
dc.date.available | 2009-11-26T09:15:48Z | - |
dc.date.created | 2012-02-06 | - |
dc.date.created | 2012-02-06 | - |
dc.date.issued | 2008-11 | - |
dc.identifier.citation | IEICE TRANSACTIONS ON COMMUNICATIONS, v.E91B, no.11, pp.3544 - 3551 | - |
dc.identifier.issn | 0916-8516 | - |
dc.identifier.uri | http://hdl.handle.net/10203/13484 | - |
dc.description.abstract | In this paper, an efficient node-level target classification scheme in wireless sensor networks (WSNs) is proposed. It uses acoustic and seismic information. and its performance is verified by the classification accuracy of vehicles in a WSN. Because of the hard limitation in resources. parametric classifiers should be more preferable than non-parametric ones in WSN systems. As a parametric classifier, the Gaussian mixture model (GMM) algorithm not only shows good performances classify targets in WSNs. but it also requires very few resources suitable to a sensor node. In addition. our sensor fusion method uses a decision tree. generated by the classification and regression tree (CART) algorithm, to improve the accuracy. so that the algorithm drives a considerable increase of the classification rate using less resources. Experimental results using a real dataset of WSN show that the proposed scheme shows a 94.10% classification rate and outperforms the k-nearest neighbors and the support vector machine. | - |
dc.description.sponsorship | This research was supported by the MKE (Ministry of Knowledge Economy), Korea, under the ITRC (Information Technology Research Center) support program supervised by the IITA (Institute of Information Technology Advancement) (IITA-2008-C1090-801- 0015), and the Korea Science and Engineering oundation(KOSEF) grant funded by the Korean government(MOST) (No. R0A-2008-000-10038-0). | en |
dc.language | English | - |
dc.language.iso | en_US | en |
dc.publisher | IEICE-INST ELECTRONICS INFORMATION COMMUNICATIONS ENG | - |
dc.subject | TRACKING | - |
dc.title | A GMM-Based Target Classification Scheme for a Node in Wireless Sensor Networks | - |
dc.type | Article | - |
dc.identifier.wosid | 000261410400016 | - |
dc.identifier.scopusid | 2-s2.0-67651124977 | - |
dc.type.rims | ART | - |
dc.citation.volume | E91B | - |
dc.citation.issue | 11 | - |
dc.citation.beginningpage | 3544 | - |
dc.citation.endingpage | 3551 | - |
dc.citation.publicationname | IEICE TRANSACTIONS ON COMMUNICATIONS | - |
dc.identifier.doi | 10.1093/ietcom/e91-b.11.3544 | - |
dc.contributor.localauthor | Kim, Daeyoung | - |
dc.contributor.nonIdAuthor | Kim, Youngsoo | - |
dc.contributor.nonIdAuthor | Jeong, Sangbae | - |
dc.type.journalArticle | Article | - |
dc.subject.keywordAuthor | target classification | - |
dc.subject.keywordAuthor | sensor network | - |
dc.subject.keywordAuthor | Gaussian mixture model (GMM) | - |
dc.subject.keywordAuthor | classification and regression tree (CART) | - |
dc.subject.keywordAuthor | decision tree | - |
dc.subject.keywordPlus | TRACKING | - |
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