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Recognition of a Phase-Sensitivity OTDR Sensing System Based on Morphologic Feature Extraction.

Sun Q, Feng H, Yan X, Zeng Z - Sensors (Basel) (2015)

Bottom Line: In the method proposed in this paper, the time-space domain signal is used for feature extraction instead of the time domain signal.Feature vectors are obtained from morphologic features of time-space domain signals.A scatter matrix is calculated for the feature selection.

View Article: PubMed Central - PubMed

Affiliation: Tianjin University, State Key Laboratory of Precision Measurement Technology & Instruments, 92 Weijin Road, Nankai District, Tianjin 300072, China. sunqiansohu@126.com.

ABSTRACT
This paper proposes a novel feature extraction method for intrusion event recognition within a phase-sensitive optical time-domain reflectometer (Φ-OTDR) sensing system. Feature extraction of time domain signals in these systems is time-consuming and may lead to inaccuracies due to noise disturbances. The recognition accuracy and speed of current systems cannot meet the requirements of Φ-OTDR online vibration monitoring systems. In the method proposed in this paper, the time-space domain signal is used for feature extraction instead of the time domain signal. Feature vectors are obtained from morphologic features of time-space domain signals. A scatter matrix is calculated for the feature selection. Experiments show that the feature extraction method proposed in this paper can greatly improve recognition accuracies, with a lower computation time than traditional methods, i.e., a recognition accuracy of 97.8% can be achieved with a recognition time of below 1 s, making it is very suitable for Φ-OTDR system online vibration monitoring.

No MeSH data available.


(a) Image of walking after threshold segmentation; (b) Image of digging after threshold segmentation; (c) Image of vehicle passing after threshold segmentation.
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sensors-15-15179-f008: (a) Image of walking after threshold segmentation; (b) Image of digging after threshold segmentation; (c) Image of vehicle passing after threshold segmentation.

Mentions: An artificial threshold will lead to errors, because a high threshold will contain noise and a low threshold will discard information of the characteristics in the region. The Otsu threshold is calculated by the grey level histogram [12]. The main principle of this method is to find the maximum between-class variance threshold. The Otsu threshold regards the event region and the background as two classes. Firstly an initial threshold is set, and classification between the two classes can be calculated using Equation (8). Then a threshold is found which will maximize this formula: (8)η=σB2σG2 where is the between-class variance and is the global variance, which is a constant. The best threshold will maximize : (9)σB2(tbest)=maxσB2(t) is the optimal threshold. As can be seen from Figure 8, the segmentation method is effective and satisfactory for our purposes. There are some noise points appearing in the images in Figure 8, which are one order of magnitude smaller than the event region, and are widely dispersed. Median filtering is an effective way to eliminate these noise points [13]. Also, there are some holes in the images that have the potential to seriously impact the effects of feature extraction, so an image dilation strategy was used for filling these holes.


Recognition of a Phase-Sensitivity OTDR Sensing System Based on Morphologic Feature Extraction.

Sun Q, Feng H, Yan X, Zeng Z - Sensors (Basel) (2015)

(a) Image of walking after threshold segmentation; (b) Image of digging after threshold segmentation; (c) Image of vehicle passing after threshold segmentation.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4541826&req=5

sensors-15-15179-f008: (a) Image of walking after threshold segmentation; (b) Image of digging after threshold segmentation; (c) Image of vehicle passing after threshold segmentation.
Mentions: An artificial threshold will lead to errors, because a high threshold will contain noise and a low threshold will discard information of the characteristics in the region. The Otsu threshold is calculated by the grey level histogram [12]. The main principle of this method is to find the maximum between-class variance threshold. The Otsu threshold regards the event region and the background as two classes. Firstly an initial threshold is set, and classification between the two classes can be calculated using Equation (8). Then a threshold is found which will maximize this formula: (8)η=σB2σG2 where is the between-class variance and is the global variance, which is a constant. The best threshold will maximize : (9)σB2(tbest)=maxσB2(t) is the optimal threshold. As can be seen from Figure 8, the segmentation method is effective and satisfactory for our purposes. There are some noise points appearing in the images in Figure 8, which are one order of magnitude smaller than the event region, and are widely dispersed. Median filtering is an effective way to eliminate these noise points [13]. Also, there are some holes in the images that have the potential to seriously impact the effects of feature extraction, so an image dilation strategy was used for filling these holes.

Bottom Line: In the method proposed in this paper, the time-space domain signal is used for feature extraction instead of the time domain signal.Feature vectors are obtained from morphologic features of time-space domain signals.A scatter matrix is calculated for the feature selection.

View Article: PubMed Central - PubMed

Affiliation: Tianjin University, State Key Laboratory of Precision Measurement Technology & Instruments, 92 Weijin Road, Nankai District, Tianjin 300072, China. sunqiansohu@126.com.

ABSTRACT
This paper proposes a novel feature extraction method for intrusion event recognition within a phase-sensitive optical time-domain reflectometer (Φ-OTDR) sensing system. Feature extraction of time domain signals in these systems is time-consuming and may lead to inaccuracies due to noise disturbances. The recognition accuracy and speed of current systems cannot meet the requirements of Φ-OTDR online vibration monitoring systems. In the method proposed in this paper, the time-space domain signal is used for feature extraction instead of the time domain signal. Feature vectors are obtained from morphologic features of time-space domain signals. A scatter matrix is calculated for the feature selection. Experiments show that the feature extraction method proposed in this paper can greatly improve recognition accuracies, with a lower computation time than traditional methods, i.e., a recognition accuracy of 97.8% can be achieved with a recognition time of below 1 s, making it is very suitable for Φ-OTDR system online vibration monitoring.

No MeSH data available.