MATERIAL DETECTION SYSTEMS

• US 20170227673A1
• Filed: 02/08/2016
• Published: 08/10/2017
• Est. Priority Date: 02/08/2016
• Status: Abandoned Application

• Current Assignee: Goodrich Corporation (Raytheon Technologies Corporation d/b/a RTX)
• Original Assignee: Goodrich Corporation (Raytheon Technologies Corporation d/b/a RTX)
• Inventors: Giering, Michael J., Venugopalan, Vivek, Reddy, Kishore K.
• Application Number:
  US15/017,740
• Publication Number:
  US 20170227673A1
• Time in Patent Office: Days
• CPC Class Codes: G01N 21/314 with comparison of measurements at specific and non-specific wavelengths dual wavelength spectrometry G01J3/427
• CPC Class Codes: G01N 2201/12 Circuits of general importance; Signal processing

Abstract

A method for detecting one or more predetermined materials, includes receiving sensor data from an optical sensor system, wherein the sensor data indicates a plurality of wavelengths, processing, in real time, the sensor data using a recurrent neural network to correlate the sensor data with one or more predetermined materials, detecting the presence of the one or more predetermined materials based on the correlated sensor data, and outputting a correlation signal indicating whether the one or more predetermined materials have been detected. The method can further include receiving feedback from an operator indicating whether the correlation signal is accurate, and modifying a correlation model of the recurrent neural network based on the feedback to enhance correlating the sensor data to the one or more predetermined materials.

Claims

1. A system, comprising:

an optical sensing system configured to sense a plurality of wavelengths and output sensor data;

a material detection system operatively connected to the sensing system to receive the sensor data, wherein the material detection system includes a recurrent neural network and is configured to;

receive sensor data from an optical sensor system, wherein the sensor data indicates a plurality of wavelengths;

process, in real time, the sensor data using a recurrent neural network to correlate the sensor data with one or more predetermined materials;

detect the presence of the one or more predetermined materials based on the correlated sensor data; and

output a correlation signal indicating whether the one or more predetermined materials have been detected.

2. The system of claim 1, wherein the recurrent neural network is further configured to:

receive feedback from an operator indicating whether the correlation signal is accurate; and

modify a correlation model of the recurrent neural network based on the feedback to enhance correlating the sensor data to the one or more predetermined materials.

3. The system of claim 1, wherein the predetermined material includes one or more explosives or precursors thereof.

4. The system of claim 1, wherein the feedback is a reward indicating a high correlation to the one or more predetermined materials.

5. A method for detecting one or more predetermined materials, comprising:

receiving sensor data from an optical sensor system, wherein the sensor data indicates a plurality of wavelengths;

processing, in real time, the sensor data using a recurrent neural network to correlate the sensor data with the one or more predetermined materials;

detecting the presence of the one or more predetermined materials based on the correlated sensor data; and

outputting a correlation signal indicating whether the one or more predetermined materials have been detected.

6. The method of claim 5, further comprising:

receiving feedback from an operator indicating whether the correlation signal is accurate; and

modifying a correlation model of the recurrent neural network based on the feedback to enhance correlating the sensor data to the one or more predetermined materials.

7. The method of claim 5, wherein processing includes correlating a series of wavelengths with the existence of the one or more predetermined materials.

8. The method of claim 7, wherein detecting the presence of one or more materials includes detecting the presence of one or more explosives or precursors thereof.

9. A computer readable medium, comprising computer executable instructions configured to be executed by a processor, the instructions comprising:

receiving sensor data from an optical sensor system, wherein the sensor data indicates a plurality of wavelengths;

processing, in real time, the sensor data using a recurrent neural network to correlate the sensor data with one or more predetermined materials;

detecting the presence of the one or more predetermined materials based on the correlated sensor data; and

outputting a correlation signal indicating whether the one or more predetermined materials have been detected.