Acoustic–Seismic Mixed Feature Extraction Based on Wavelet Transform for Vehicle Classification in Wireless Sensor Networks

Zhang, Heng; Pan, Zhongming; Zhang, Wenna

doi:10.3390/s18061862

Cited by 13 publications

(6 citation statements)

References 29 publications

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“…The SensIT experiment employed AAV vehicles and DW vehicles, while the introduced experiment in Figure 7 utilized personnel and small vehicles as the target to be classified, using part of the selected acoustic signals from four target types and extracting the feature data using the WCER feature extraction algorithm [ 22 ]. The target category label corresponding to the AAV vehicle target feature data was set to be {1 0 0 0}; the target category label corresponding to the DW vehicle sample feature data was set to be {0 1 0 0}; the target category label corresponding to the personnel target feature data was set to be {0 0 1 0}; and the target category label corresponding to the small vehicle target feature data was set to be {0 0 0 1}.…”

Section: Performance Of Multi-dbn Weighted Voting Algorithmmentioning

confidence: 99%

A Neural Network-Based Weighted Voting Algorithm for Multi-Target Classification in WSN

Zhang,

Zhou

2023

Sensors

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One of the most important applications in the wireless sensor networks (WSN) is to classify mobile targets in the monitoring area. In this paper, a neural network(NN)-based weighted voting classification algorithm is proposed on the basis of the NN-based classifier and combined with the idea of voting strategy, which is implemented on the nodes of the WSN monitoring system by means of the “upper training, lower transplantation” approach. The performance of the algorithm is verified by using real-world experimental data, and the results show that the proposed method has a higher accuracy in classifying the target signal features, achieving an average classification accuracy of about 85% when utilizing a deep neural network (DNN) and deep belief network (DBN) as the base classifier. The experiment reveals that the NN-based weighted voting algorithm enhances the target classification accuracy by approximately 5% in comparison to the single NN-based classifier, but the memory and computation time required for the algorithm to run are also increased at the same time. Compared to the FFNN classifier, which exhibited the highest classification accuracy among the four selected methods, the algorithm achieves an improvement of approximately 8.8% in classification accuracy. However, it incurs greater overhead time to run.

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Section: Performance Of Multi-dbn Weighted Voting Algorithmmentioning

confidence: 99%

A Neural Network-Based Weighted Voting Algorithm for Multi-Target Classification in WSN

Zhang,

Zhou

2023

Sensors

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“…A nearly orthogonal design of the biorthogonal filter bank was utilized as the orthogonal filter bank to decompose the signal sequence. The wavelet decomposition process of the signal sequence is described in detail in our previous work [22]. …”

Section: Comparison Of Two-class Classifiersmentioning

confidence: 99%

“…After introducing the feature extraction method, wavelet coefficient energy rate (WCER) method [22], and the dataset utilized for validating the proposed method, Section 2 compares the performances of commonly-used two-class classifiers. Section 3 compares the two available precursor algorithms of multi-class classification algorithms.…”

Section: Introductionmentioning

confidence: 99%

Cross-Voting SVM Method for Multiple Vehicle Classification in Wireless Sensor Networks

Zhang

Pan

2018

Sensors

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A novel multi-class classification method named the voting-cross support vector machine (SVM) method was proposed in this study, for classifying vehicle targets in wireless sensor networks. The advantages and disadvantages of available methods were summarized, after a comparative analysis of commonly used multi-objective classification algorithms. To improve the classification accuracy of multi-class classification and ensure the low complexity of the algorithm for engineering implementation on wireless sensor network (WSN) nodes, a framework was proposed for cross-matching and voting on the category to which the vehicle belongs after combining the advantages of the directed acyclic graph SVM (DAGSVM) method and binary-tree SVM method. The SVM classifier was selected as the basis two-class classifier in the framework, after comparing the classification performance of several commonly used methods. We utilized datasets acquired from a real-world experiment to validate the proposed method. The calculated results demonstrated that the cross-voting SVM method could effectively increase the classification accuracy for the classification of multiple vehicle targets, with a limited increase in the algorithm complexity. The application of the cross-voting SVM method effectively improved the target classification accuracy (by approximately 7%), compared with the DAGSVM method and the binary-tree SVM method, whereas time consumption decreased by approximately 70% compared to the DAGSVM method.

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“…This method provides ideological reference for the feature extraction of different geological structures. Later, Zhang et al [10] proposed a feature extraction method based on the energy ratio of target signal wavelet coefficients, which takes the energy ratio as the feature vector and uses the SVM to classify it. Chang et al [11] combined multi-domain energy distribution with radial neural network to achieve feature extraction and classification and identification of seismic signals, while ensuring strong robustness, the classification accuracy is improved.…”

Section: Introductionmentioning

confidence: 99%

“…Later, Zhang et al. [10] proposed a feature extraction method based on the energy ratio of target signal wavelet coefficients, which takes the energy ratio as the feature vector and uses the SVM to classify it. Chang et al.…”

Section: Introductionmentioning

confidence: 99%

Guided wave signal‐based sensing and classification for small geological structure

Sun

Song

Zhou

et al. 2023

IET Signal Processing

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Sensing, Computing and Communication Integration (SC2) is widely believed as a new enabling technology. A non‐negative tensor sparse factorisation (NTSF) algorithm based on tensor analysis is proposed for sensing and classification of Small Geological Structure in coal mines. Utilising this method, advanced detection of geological anomalies hidden in coal seams was achieved. The morphological properties of geological anomalies in coal seams and the propagation characteristics of guided waves were first thoroughly studied. A three‐dimensional (3D) medium geometry model was developed for a complicated coal seam with Goaf, collapse column, scouring zone, and tiny fault based on COMSOL Multiphysics. On this model, the third‐order tensors data was constructed. Then, the TUCKER‐based NTSF algorithm was employed for feature extraction and classification. To achieve multi‐dimensional feature, the two‐dimensional data in the form of a matrix is collected, and a multiplicative update method is introduced to update the algorithm iteratively. Finally, the Support Vector Machine (SVM) multi‐classifier with Gaussian radial basis kernel function is selected for classification of Small Geological Structure. The experimental results show that the classification accuracy based on the NTSF and SVM is as high as 97.33%, which demonstrates that the proposed algorithm is suitable for Sensing and Classification of Small Geological Structure in coal mines.

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Acoustic–Seismic Mixed Feature Extraction Based on Wavelet Transform for Vehicle Classification in Wireless Sensor Networks

Cited by 13 publications

References 29 publications

A Neural Network-Based Weighted Voting Algorithm for Multi-Target Classification in WSN

A Neural Network-Based Weighted Voting Algorithm for Multi-Target Classification in WSN

Cross-Voting SVM Method for Multiple Vehicle Classification in Wireless Sensor Networks

Guided wave signal‐based sensing and classification for small geological structure

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