The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Dalam kertas ini, skim pengelasan sasaran peringkat nod yang cekap dalam rangkaian sensor wayarles (WSN) dicadangkan. Ia menggunakan maklumat akustik dan seismik, dan prestasinya disahkan oleh ketepatan klasifikasi kenderaan dalam WSN. Oleh kerana pengehadan yang sukar dalam sumber, pengelas parametrik harus lebih disukai daripada yang bukan parametrik dalam sistem WSN. Sebagai pengelas parametrik, algoritma model campuran Gaussian (GMM) bukan sahaja menunjukkan prestasi yang baik untuk mengelaskan sasaran dalam WSN, tetapi ia juga memerlukan sangat sedikit sumber yang sesuai dengan nod sensor. Di samping itu, kaedah gabungan sensor kami menggunakan pepohon keputusan, yang dijana oleh algoritma pepohon klasifikasi dan regresi (CART), untuk meningkatkan ketepatan, supaya algoritma memacu peningkatan kadar pengelasan yang agak menggunakan sumber yang kurang. Keputusan eksperimen menggunakan set data sebenar WSN menunjukkan bahawa skema yang dicadangkan menunjukkan kadar pengelasan 94.10% dan mengatasi jiran k-terhampir dan mesin vektor sokongan.
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Salinan
Youngsoo KIM, Sangbae JEONG, Daeyoung KIM, "A GMM-Based Target Classification Scheme for a Node in Wireless Sensor Networks" in IEICE TRANSACTIONS on Communications,
vol. E91-B, no. 11, pp. 3544-3551, November 2008, doi: 10.1093/ietcom/e91-b.11.3544.
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 to 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.
URL: https://global.ieice.org/en_transactions/communications/10.1093/ietcom/e91-b.11.3544/_p
Salinan
@ARTICLE{e91-b_11_3544,
author={Youngsoo KIM, Sangbae JEONG, Daeyoung KIM, },
journal={IEICE TRANSACTIONS on Communications},
title={A GMM-Based Target Classification Scheme for a Node in Wireless Sensor Networks},
year={2008},
volume={E91-B},
number={11},
pages={3544-3551},
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 to 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.},
keywords={},
doi={10.1093/ietcom/e91-b.11.3544},
ISSN={1745-1345},
month={November},}
Salinan
TY - JOUR
TI - A GMM-Based Target Classification Scheme for a Node in Wireless Sensor Networks
T2 - IEICE TRANSACTIONS on Communications
SP - 3544
EP - 3551
AU - Youngsoo KIM
AU - Sangbae JEONG
AU - Daeyoung KIM
PY - 2008
DO - 10.1093/ietcom/e91-b.11.3544
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E91-B
IS - 11
JA - IEICE TRANSACTIONS on Communications
Y1 - November 2008
AB - 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 to 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.
ER -