Bernard Marie et al., 2019 - Google Patents
Pattern recognition algorithm and software design of an optical fiber vibration signal based on Φ-optical time-domain reflectometryBernard Marie et al., 2019
View PDF- Document ID
- 9909069217464989085
- Author
- Bernard Marie T
- Han D
- An B
- Publication year
- Publication venue
- Applied optics
External Links
Snippet
A phase-sensitive optical time-domain reflectometry with a detection distance of 10 km is used in this paper to recognize and to detect in real time events along the perimeter security monitoring system. To optimize the signal processing in the software system and achieve the …
- 238000003909 pattern recognition 0 title abstract description 26
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
- G06F17/30861—Retrieval from the Internet, e.g. browsers
- G06F17/30864—Retrieval from the Internet, e.g. browsers by querying, e.g. search engines or meta-search engines, crawling techniques, push systems
- G06F17/30867—Retrieval from the Internet, e.g. browsers by querying, e.g. search engines or meta-search engines, crawling techniques, push systems with filtering and personalisation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
- G06F17/3061—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F17/30634—Querying
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computer systems utilising knowledge based models
- G06N5/02—Knowledge representation
- G06N5/022—Knowledge engineering, knowledge acquisition
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
-
- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B13/00—Burglar, theft or intruder alarms
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Marie et al. | Pattern recognition algorithm and software design of an optical fiber vibration signal based on Φ-optical time-domain reflectometry | |
Wang et al. | Practical multi-class event classification approach for distributed vibration sensing using deep dual path network | |
Cao et al. | Back propagation neutral network based signal acquisition for Brillouin distributed optical fiber sensors | |
Zhu et al. | Vibration pattern recognition and classification in OTDR based distributed optical-fiber vibration sensing system | |
CN103617684B (en) | Interference-type optical fiber circumference vibrating intruding recognizer | |
Peng et al. | Identifications and classifications of human locomotion using Rayleigh-enhanced distributed fiber acoustic sensors with deep neural networks | |
Tabi Fouda et al. | Research and Software Design of an Φ‐OTDR‐Based Optical Fiber Vibration Recognition Algorithm | |
Martins et al. | Early detection of pipeline integrity threats using a smart fiber optic surveillance system: the PIT-STOP project | |
Adeel et al. | Nuisance alarm reduction: using a correlation based algorithm above differential signals in direct detected phase-OTDR systems | |
CN105023379A (en) | Signal identification method of fiber perimeter early-warning system of airport | |
Mi et al. | Intrusion behavior classification method applied in a perimeter security monitoring system | |
Li et al. | Vibration monitoring for the West-East Gas Pipeline Project of China by phase optical time domain reflectometry (phase-OTDR) | |
Zhirnov et al. | Fiber-Optic Telecommunication Network Wells Monitoring by Phase-Sensitive Optical Time-Domain Reflectometer with Disturbance Recognition | |
Liu et al. | Intrusion identification using GMM-HMM for perimeter monitoring based on ultra-weak FBG arrays | |
Yu et al. | Fast information acquisition using spectra subtraction for Brillouin distributed fiber sensors | |
Shi et al. | Multi-signal feature fusion method with an attention mechanism for the Φ-OTDR event recognition system | |
Tian et al. | Temporal convolution network with a dual attention mechanism for φ-OTDR event classification | |
Cheng et al. | Dual-model hybrid pattern recognition method based on a fiber optic line-based sensor with a large amount of data | |
Huang et al. | Pattern recognition using self-reference feature extraction for φ-OTDR | |
Tomasov et al. | Enhancing fiber security using a simple state of polarization analyzer and machine learning | |
Wu et al. | Intelligent target recognition for distributed acoustic sensors by using both manual and deep features | |
Lu et al. | Signal recognition method based on Mel frequency cepstral coefficients and fast dynamic time warping for optical fiber perimeter defense systems | |
Huang et al. | High-efficient disturbance event recognition method of ϕ-OTDR utilizing region-segmentation differential phase signals | |
Peng et al. | Perimeter monitoring of urban buried pipeline subject to third-party intrusion based on fiber optic sensing and convolutional neural network | |
Shao et al. | Research on substation intrusion event identification method based on MTF and CNN |