Patents by Inventor Michael J. Giering
Michael J. Giering has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).
-
Patent number: 11776368Abstract: A selective intrusion detection system includes a Doppler transceiver configured and adapted to receive Doppler return signals indicative of moving targets present in a surveillance space. A processor is operatively connected to the Doppler transceiver to convert Doppler return signals into spectrograms and to determine whether any given spectrogram is indicative of presence of a human or another moving target, like a domestic pet. An alarm is operatively connected to the processor, wherein the processor and alarm are configured to provide an alert in the event the processor determines any given spectrogram is indicative of a human, and to forego providing an alert in the event the processor determines any given spectrogram is indicative of another moving target only.Type: GrantFiled: April 26, 2021Date of Patent: October 3, 2023Assignee: UTC Fire & Security Americas Corporation, Inc.Inventors: Mathias Pantus, Jeroen Te Paske, Pascal Van De Mortel, Leon Mintjens, Sorin Costiner, Michael J. Giering, Robert Labarre, Mark Vogel, Vijaya Ramaraju Lakamraju
-
Patent number: 11676009Abstract: A method for designing a material for an aircraft component according to one example includes training a neural network to correlate microstructural features of an alloy with material properties of the alloy by at least providing a set of images of the alloy. Each of the images in the set of images has varied constituent compositions and at least one patch of corresponding data is embedded into the image. The method also includes determining non-linear relationships between the microstructural features and corresponding empirically determined material properties via a machine learning algorithm, receiving a set of desired material properties of the alloy for aircraft component, and determining a set of microstructural features capable of achieving the desired material properties of the alloy based on the determined non-linear relationships.Type: GrantFiled: October 4, 2019Date of Patent: June 13, 2023Assignee: Raytheon Technologies CorporationInventors: Nagendra Somanath, Ryan B. Noraas, Michael J Giering, Olusegun T Oshin
-
Patent number: 11485520Abstract: A method for designing a material for an aircraft component includes training a neural network to correlate microstructural features of an alloy with material properties of the alloy by at least providing a set of images of the alloy to the neural network. Each of the images in the set of images has varied constituent compositions. The method further includes providing the neural network with a set of determined material properties corresponding to each image, associating the microstructural features of each image with the set of empirically determined data corresponding to the image, and determining non-linear relationships between the microstructural features and corresponding empirically determined material properties via a machine learning algorithm, receiving a set of desired material properties of the alloy for aircraft component, and determining a set of microstructural features capable of achieving the desired material properties of the alloy based on the determined non-linear relationships.Type: GrantFiled: August 17, 2018Date of Patent: November 1, 2022Assignee: Raytheon Technologies CorporationInventors: Nagendra Somanath, Ryan B. Noraas, Michael J. Giering
-
Patent number: 11422546Abstract: A method includes fusing multi-modal sensor data from a plurality of sensors having different modalities. At least one region of interest is detected in the multi-modal sensor data. One or more patches of interest are detected in the multi-modal sensor data based on detecting the at least one region of interest. A model that uses a deep convolutional neural network is applied to the one or more patches of interest. Post-processing of a result of applying the model is performed to produce a post-processing result for the one or more patches of interest. A perception indication of the post-processing result is output.Type: GrantFiled: December 18, 2015Date of Patent: August 23, 2022Assignee: RAYTHEON TECHNOLOGIES CORPORATIONInventors: Michael J. Giering, Kishore K. Reddy, Vivek Venugopalan, Soumik Sarkar
-
Patent number: 11397134Abstract: A tool for monitoring a part condition includes a computerized device having a processor and a memory. The computerized device includes at least one of a camera and an image input and a network connection configured to connect the computerized device to a data network. The memory stores instructions for causing the processor to perform the steps of providing an initial micrograph of a part to a trained model, providing a data set representative of operating conditions of the part to the trained model, and outputting an expected state of the part from the trained model based at least in part on the input data set and the initial micrograph.Type: GrantFiled: December 3, 2018Date of Patent: July 26, 2022Assignee: Raytheon Technologies CorporationInventors: Nagendra Somanath, Anya B. Merli, Ryan B. Noraas, Michael J. Giering, Olusegun T. Oshin
-
Patent number: 11340602Abstract: A method includes converting time-series data from a plurality of prognostic and health monitoring (PHM) sensors into frequency domain data. One or more portions of the frequency domain data are labeled as indicative of one or more target modes to form labeled target data. A model including a deep neural network is applied to the labeled target data. A result of applying the model is classified as one or more discretized PHM training indicators associated with the one or more target modes. The one or more discretized PHM training indicators are output.Type: GrantFiled: December 18, 2015Date of Patent: May 24, 2022Assignee: RAYTHEON TECHNOLOGIES CORPORATIONInventors: Michael J. Giering, Madhusudana Shashanka, Soumik Sarkar, Vivek Venugopalan
-
Patent number: 11248989Abstract: A system for providing real time aircraft engine sensor analysis includes a computer system configured to receive an engine operation data set in real time. The computer system includes a machine learning based analysis tool and a user interface configured to display a real time analysis of the engine operation data set. The user interface includes at least one portion configured to identify a plurality of anomalies in the engine operation data set.Type: GrantFiled: June 21, 2019Date of Patent: February 15, 2022Assignee: Raytheon Technologies CorporationInventors: Nagendra Somanath, Justin R. Urban, Michael J. Giering, Quan Long, Alexandria Dorgan
-
Publication number: 20210319678Abstract: A selective intrusion detection system includes a Doppler transceiver configured and adapted to receive Doppler return signals indicative of moving targets present in a surveillance space. A processor is operatively connected to the Doppler transceiver to convert Doppler return signals into spectrograms and to determine whether any given spectrogram is indicative of presence of a human or another moving target, like a domestic pet. An alarm is operatively connected to the processor, wherein the processor and alarm are configured to provide an alert in the event the processor determines any given spectrogram is indicative of a human, and to forego providing an alert in the event the processor determines any given spectrogram is indicative of another moving target only.Type: ApplicationFiled: April 26, 2021Publication date: October 14, 2021Applicant: CARRIER Fire & Security Americas Corporation, Inc.Inventors: Mathias Pantus, Jeroen Te Paske, Pascal Van De Mortel, Leon Mintjens, Sorin Costiner, Michael J. Giering, Robert Labarre, Mark Vogel, Vijaya Ramaraju Lakamraju
-
Patent number: 11062207Abstract: Data indicative of a plurality of observations of an environment are received at a control system. Machine learning using deep reinforcement learning is applied to determine an action based on the observations. The deep reinforcement learning applies a convolutional neural network or a deep auto encoder to the observations and applies a training set to locate one or more regions having a higher reward. The action is applied to the environment. A reward token indicative of alignment between the action and a desired result is received. A policy parameter of the control system is updated based on the reward token. The updated policy parameter is applied to determine a subsequent action responsive to a subsequent observation.Type: GrantFiled: October 30, 2017Date of Patent: July 13, 2021Assignee: RAYTHEON TECHNOLOGIES CORPORATIONInventors: Michael J. Giering, Kishore K. Reddy, Vivek Venugopalan, Amit Surana, Soumalya Sarkar
-
Publication number: 20210208068Abstract: An example SPR detection system includes a first prism having a first surface adjacent to a first metal layer exposed to a sample gas, and a second prism having a second surface adjacent to a second metal layer exposed to a reference gas. At least one light source is configured to provide respective beams to the first and second surfaces, where each of the beams causes SPR of a respective one of the metal layers. At least one photodetector is configured to measure a reflection property of reflections of the respective beams from the metal layers during the SPR. A controller is configured to determine whether a target gas is present in the sample gas based on a known composition of the reference gas and at least one of an electrical property of the first and second metal layers during the SPR and the reflection property of the metal layers.Type: ApplicationFiled: May 10, 2019Publication date: July 8, 2021Inventors: David L. Lincoln, Michael J. Birnkrant, Jose-Rodrigo Castillo-Garza, Marcin Piech, Catherine Thibaud, Michael J. Giering, Kishore K. Reddy, Vivek Venugopalan
-
Publication number: 20210103805Abstract: A method for designing a material for an aircraft component according to one example includes training a neural network to correlate microstructural features of an alloy with material properties of the alloy by at least providing a set of images of the alloy. Each of the images in the set of images has varied constituent compositions and at least one patch of corresponding data is embedded into the image. The method also includes determining non-linear relationships between the microstructural features and corresponding empirically determined material properties via a machine learning algorithm, receiving a set of desired material properties of the alloy for aircraft component, and determining a set of microstructural features capable of achieving the desired material properties of the alloy based on the determined non-linear relationships.Type: ApplicationFiled: October 4, 2019Publication date: April 8, 2021Inventors: Nagendra Somanath, Ryan B. Noraas, Michael J Giering, Olusegun T Oshin
-
Publication number: 20200400531Abstract: A system for providing real time aircraft engine sensor analysis includes a computer system configured to receive an engine operation data set in real time. The computer system includes a machine learning based analysis tool and a user interface configured to display a real time analysis of the engine operation data set. The user interface includes at least one portion configured to identify a plurality of anomalies in the engine operation data set.Type: ApplicationFiled: June 21, 2019Publication date: December 24, 2020Inventors: Nagendra Somanath, Justin R. Urban, Michael J. Giering, Quan Long, Alexandria Dorgan
-
Patent number: 10860879Abstract: A method includes detecting at least one region of interest in a frame of image data. One or more patches of interest are detected in the frame of image data based on detecting the at least one region of interest. A model including a deep convolutional neural network is applied to the one or more patches of interest. Post-processing of a result of applying the model is performed to produce a post-processing result for the one or more patches of interest. A visual indication of a classification of defects in a structure is output based on the result of the post-processing.Type: GrantFiled: May 16, 2016Date of Patent: December 8, 2020Assignee: RAYTHEON TECHNOLOGIES CORPORATIONInventors: Michael J. Giering, Kishore K. Reddy, Vivek Venugopalan
-
Patent number: 10776659Abstract: A method of compressing data in the context of a decision-making task includes receiving raw data, analyzing the raw data to determine content of the raw data, and adjusting one or more one data compression parameters in a compression algorithm. The adjustment of the one or more compression parameters is based on the content of the raw data and a received decision-making task to produce a modified compression algorithm. The raw data is thereafter compressed using the modified compression algorithm and output as compressed data.Type: GrantFiled: December 15, 2016Date of Patent: September 15, 2020Assignee: Goodrich CorporationInventors: Edgar A. Bernal, Kishore K. Reddy, Michael J. Giering
-
Patent number: 10733721Abstract: A material characterization system includes an imaging unit, a material characterization controller, and an imaging unit controller. The electronic imaging unit generates a test image of a specimen composed of a material. The electronic material characterization controller determines values of a plurality of parameters and maps the parameters to corresponding ground truth labeled outputs. The mapped parameters are applied to at least one test image to predict a presence of at least one target attribute of the specimen in response to applying the learned parameters. The test image is convert to a selected output image format so as to generate a synthetic image including the predicted at least one attribute. The electronic imaging unit controller performs a material characterization analysis that characterizes the material of the specimen based on the predicted at least one attribute included in the synthetic image.Type: GrantFiled: August 23, 2019Date of Patent: August 4, 2020Assignee: RAYTHEON TECHNOLOGIES CORPORATIONInventors: Michael J. Giering, Ryan B. Noraas, Kishore K. Reddy, Edgar A. Bernal
-
Publication number: 20200173885Abstract: A tool for monitoring a part condition includes a computerized device having a processor and a memory. The computerized device includes at least one of a camera and an image input and a network connection configured to connect the computerized device to a data network. The memory stores instructions for causing the processor to perform the steps of providing an initial micrograph of a part to a trained model, providing a data set representative of operating conditions of the part to the trained model, and outputting an expected state of the part from the trained model based at least in part on the input data set and the initial micrograph.Type: ApplicationFiled: December 3, 2018Publication date: June 4, 2020Inventors: Nagendra Somanath, Anya B. Merli, Ryan B. Noraas, Michael J. Giering, Olusegun T. Oshin
-
Publication number: 20200055614Abstract: A method for designing a material for an aircraft component includes training a neural network to correlate microstructural features of an alloy with material properties of the alloy by at least providing a set of images of the alloy to the neural network. Each of the images in the set of images has varied constituent compositions. The method further includes providing the neural network with a set of determined material properties corresponding to each image, associating the microstructural features of each image with the set of empirically determined data corresponding to the image, and determining non-linear relationships between the microstructural features and corresponding empirically determined material properties via a machine learning algorithm, receiving a set of desired material properties of the alloy for aircraft component, and determining a set of microstructural features capable of achieving the desired material properties of the alloy based on the determined non-linear relationships.Type: ApplicationFiled: August 17, 2018Publication date: February 20, 2020Inventors: Nagendra Somanath, Ryan B. Noraas, Michael J. Giering
-
Publication number: 20190378267Abstract: A material characterization system includes an imaging unit, a material characterization controller, and an imaging unit controller. The electronic imaging unit generates a test image of a specimen composed of a material. The electronic material characterization controller determines values of a plurality of parameters and maps the parameters to corresponding ground truth labeled outputs. The mapped parameters are applied to at least one test image to predict a presence of at least one target attribute of the specimen in response to applying the learned parameters. The test image is convert to a selected output image format so as to generate a synthetic image including the predicted at least one attribute. The electronic imaging unit controller performs a material characterization analysis that characterizes the material of the specimen based on the predicted at least one attribute included in the synthetic image.Type: ApplicationFiled: August 23, 2019Publication date: December 12, 2019Inventors: Michael J. Giering, Ryan B. Noraas, Kishore K. Reddy, Edgar A. Bernal
-
Patent number: 10452951Abstract: An imaging method includes obtaining an image with a first field of view and first effective resolution and the analyzing the image with a visual attention algorithm to one identify one or more areas of interest in the first field of view. A subsequent image is then obtained for each area of interest with a second field of view and a second effective resolution, the second field of view being smaller than the first field of view and the second effective resolution being greater than the first effective resolution.Type: GrantFiled: December 15, 2016Date of Patent: October 22, 2019Assignee: Goodrich CorporationInventors: Edgar A. Bernal, Kishore K. Reddy, Michael J. Giering
-
Patent number: 10430937Abstract: A material characterization system includes an imaging unit, a material characterization controller, and an imaging unit controller. The electronic imaging unit generates a test image of a specimen composed of a material. The electronic material characterization controller determines values of a plurality of parameters and maps the parameters to corresponding ground truth labeled outputs. The mapped parameters are applied to at least one test image to predict a presence of at least one target attribute of the specimen in response to applying the learned parameters. The test image is convert to a selected output image format so as to generate a synthetic image including the predicted at least one attribute. The electronic imaging unit controller performs a material characterization analysis that characterizes the material of the specimen based on the predicted at least one attribute included in the synthetic image.Type: GrantFiled: September 25, 2017Date of Patent: October 1, 2019Assignee: UNITED TECHNOLOGIES CORPORATIONInventors: Michael J. Giering, Ryan B. Noraas, Kishore K. Reddy, Edgar A. Bernal