Limits...
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.


Shape of region.
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4541826&req=5

sensors-15-15179-f011: Shape of region.

Mentions: If there is only one region in an image, the interval feature is set to be a large value. Different events also have different region shapes, for reasons that were described in Section 1. The region where a vehicle is passing is similar to the letter “V”, due to its large energy but short duration. Other events have a lower energy and a relatively long duration time, so many “V” shapes are observed in the region in the time domain and a round shape is formed in the image. The size and roundness of the event regions for walking and digging are both different due to their different energies. The roundness of the region can be used as the shape feature. The process of obtaining the shape of the region is shown in Figure 11. Firstly, the boundary of the region must be acquired. In this paper, the boundary can be found using a boundary tracking method [14] which is a simple and efficient method. The distance from the centroid to every point of the boundary is calculated using Equation (11): (11)Dik=/bik−ci/,k∈(1,⋯,k)


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

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

Shape of region.
© Copyright Policy
Related In: Results  -  Collection

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

sensors-15-15179-f011: Shape of region.
Mentions: If there is only one region in an image, the interval feature is set to be a large value. Different events also have different region shapes, for reasons that were described in Section 1. The region where a vehicle is passing is similar to the letter “V”, due to its large energy but short duration. Other events have a lower energy and a relatively long duration time, so many “V” shapes are observed in the region in the time domain and a round shape is formed in the image. The size and roundness of the event regions for walking and digging are both different due to their different energies. The roundness of the region can be used as the shape feature. The process of obtaining the shape of the region is shown in Figure 11. Firstly, the boundary of the region must be acquired. In this paper, the boundary can be found using a boundary tracking method [14] which is a simple and efficient method. The distance from the centroid to every point of the boundary is calculated using Equation (11): (11)Dik=/bik−ci/,k∈(1,⋯,k)

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.