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
본 논문에서는 무선 센서 네트워크(WSN)에서 효율적인 노드 수준 대상 분류 기법을 제안합니다. 음향 및 지진 정보를 활용하며, WSN에서 차량 분류 정확도를 통해 성능을 검증합니다. 리소스의 엄격한 제한으로 인해 WSN 시스템에서는 매개변수 분류자가 비모수적 분류자보다 더 선호됩니다. 파라메트릭 분류기로서 GMM(Gaussian Mixture Model) 알고리즘은 WSN에서 대상을 분류하는 데 좋은 성능을 보여줄 뿐만 아니라 센서 노드에 적합한 리소스가 거의 필요하지 않습니다. 또한, 당사의 센서 융합 방식은 CART(Classification and Regression Tree) 알고리즘으로 생성된 의사결정 트리를 사용하여 정확도를 향상시켜 더 적은 자원을 사용하여 분류율을 크게 향상시킵니다. WSN의 실제 데이터셋을 이용한 실험 결과, 제안한 방식은 94.10%의 분류율을 보이며 k-최근접 이웃과 서포트 벡터 머신보다 성능이 뛰어남을 알 수 있다.
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부
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
부
@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},}
부
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 -