18400700. SYSTEMS AND METHODS FOR UTILIZING MACHINE LEARNING TO MINIMIZE A POTENTIAL OF DAMAGE TO FIBER OPTIC CABLES simplified abstract (NEC Corporation)
Contents
- 1 SYSTEMS AND METHODS FOR UTILIZING MACHINE LEARNING TO MINIMIZE A POTENTIAL OF DAMAGE TO FIBER OPTIC CABLES
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 SYSTEMS AND METHODS FOR UTILIZING MACHINE LEARNING TO MINIMIZE A POTENTIAL OF DAMAGE TO FIBER OPTIC CABLES - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Original Abstract Submitted
SYSTEMS AND METHODS FOR UTILIZING MACHINE LEARNING TO MINIMIZE A POTENTIAL OF DAMAGE TO FIBER OPTIC CABLES
Organization Name
Inventor(s)
Tiejun J. Xia of Richardson TX (US)
Glenn A. Wellbrock of Wichita KS (US)
Ming-Fang Huang of Princeton NJ (US)
Ting Wang of West Windsor NJ (US)
SYSTEMS AND METHODS FOR UTILIZING MACHINE LEARNING TO MINIMIZE A POTENTIAL OF DAMAGE TO FIBER OPTIC CABLES - A simplified explanation of the abstract
This abstract first appeared for US patent application 18400700 titled 'SYSTEMS AND METHODS FOR UTILIZING MACHINE LEARNING TO MINIMIZE A POTENTIAL OF DAMAGE TO FIBER OPTIC CABLES
Simplified Explanation
The patent application describes a device that receives sensing data from a fiber sensor device associated with a fiber optic cable, which detects activities that could potentially damage the cable. The device processes this data using a machine learning model to determine the threat level to the fiber optic cable based on historical information.
- The device receives sensing data from a fiber sensor device associated with a fiber optic cable.
- The sensing data includes information such as amplitudes, frequencies, patterns, times, and locations of vibration signals along the cable.
- A machine learning model is used to analyze the sensing data and determine the threat level to the fiber optic cable.
- The machine learning model is trained based on historical information regarding detected vibrations, sources of vibrations, and threat levels to the cable.
Potential Applications
This technology could be applied in various industries where fiber optic cables are used, such as telecommunications, internet service providers, and infrastructure monitoring.
Problems Solved
This technology helps in early detection of activities that could potentially damage fiber optic cables, allowing for timely intervention to prevent costly repairs and service disruptions.
Benefits
The device provides real-time monitoring of fiber optic cables, enhancing the overall reliability and security of the network infrastructure. It also enables proactive maintenance and reduces the risk of cable damage.
Potential Commercial Applications
"Enhancing Fiber Optic Cable Security and Reliability with Machine Learning Technology"
Possible Prior Art
One possible prior art could be the use of traditional sensors to monitor vibrations along fiber optic cables, but the application of machine learning for threat level determination is a novel approach.
Unanswered Questions
How does the device differentiate between normal vibrations and those that pose a threat to the fiber optic cable?
The device likely uses the historical information and patterns of vibrations to distinguish between normal activities and potential threats.
What actions can the device perform based on the threat level identified?
The device may trigger alerts, initiate maintenance procedures, or even shut down sections of the fiber optic cable network to prevent damage.
Original Abstract Submitted
A device may receive, from a fiber sensor device, sensing data associated with a fiber optic cable, the sensing data being produced by an activity that poses a threat of damage to the fiber optic cable, and the sensing data identifying: amplitudes of vibration signals, frequencies of the vibration signals, patterns of the vibration signals, times associated with the vibration signals, and locations along the fiber optic cable associated with the vibration signals. The device may process, with a machine learning model, the sensing data to determine a threat level of the activity to the fiber optic cable, the machine learning model having been trained based on historical information regarding detected vibrations, historical information regarding sources of the detected vibrations, and historical information regarding threat levels to the fiber optic cable. The device may perform one or more actions based on the threat level to the fiber optic cable.
