Patents by Inventor Martin Sehr

Martin Sehr 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: 11979419
    Abstract: Examples of techniques for threat detection in an industrial process system are described herein. An aspect includes determining a plurality of subsystems of an industrial process system. Another aspect includes, for each of the plurality of subsystems, constructing and training a respective deep autoencoder (DAE) model of the subsystem based on data corresponding to the industrial process system. Another aspect includes monitoring the industrial process system using the plurality of DAE models corresponding to the plurality of subsystems. Another aspect includes, based on the plurality of DAE models, determining a cyberattack in a subsystem of the plurality of subsystems.
    Type: Grant
    Filed: April 9, 2019
    Date of Patent: May 7, 2024
    Assignee: SIEMENS AKTIENGESELLSCHAFT
    Inventors: Chengtao Wen, Mohamed El Amine Houyou, Juan L. Aparicio Ojea, Mathias Maurmaier, Martin Sehr, Tao Cui
  • Publication number: 20240109558
    Abstract: Systems and techniques are provided for adjusting the planned path of an autonomous vehicle to converge with the actual path. An example method can include determining a first pose error associated with a first pose component from one or more pose components, wherein the first pose error is based on a first difference between an actual pose of an autonomous vehicle and a planned pose of the autonomous vehicle; determining that the first pose error associated with the first pose component exceeds a pose error threshold corresponding to the first pose component; and generating a new planned pose of the autonomous vehicle that yields a reduced first pose error associated with the first pose component, wherein the reduced first pose error is based on a second difference between the actual pose of the autonomous vehicle and the new planned pose of the autonomous vehicle.
    Type: Application
    Filed: September 22, 2022
    Publication date: April 4, 2024
    Inventors: Rushil Goradia, Dogan Gidon, Martin Sehr, Pradeep Bhatta
  • Patent number: 11941451
    Abstract: A system and method are disclosed for orchestrating the execution of computing tasks. An orchestration engine can receive task requests over a network from a plurality of process engines. The process engines may correspond to respective edge or field devices that are remotely located as compared to the orchestration engine. Each task request may indicate at least one task requirement for executing a respective computing task. A plurality of computing instances that have available computing resources can be selected from a set of computing instances. A predicted runtime can be generated for each of the computing tasks. In an example, based on the predicted runtimes, task requirements, available computing resources, and associated network conditions, a schedule and allocation scheme are determined by the orchestration engine.
    Type: Grant
    Filed: August 30, 2019
    Date of Patent: March 26, 2024
    Assignee: SIEMENS AKTIENGESELLSCHAFT
    Inventors: Ines Ugalde Diaz, Martin Sehr, Juan L. Aparicio Ojea, Michael Unkelbach
  • Patent number: 11883947
    Abstract: A system controller for visual servoing includes a technology module with dedicated hardware acceleration for deep neural network that retrieves a desired configuration of a workpiece object being manipulated by a robotic device and receives visual feedback information from one or more sensors on or near the robotic device that includes a current configuration of the workpiece object. The hardware accelerator executes a machine learning model trained to process the visual feedback information and determine a configuration error based on a difference between the current configuration of the workpiece object and the desired configuration of the workpiece object. A servo control module adapts a servo control signal to the robotic device for manipulation of the workpiece object in response to the configuration error.
    Type: Grant
    Filed: September 30, 2019
    Date of Patent: January 30, 2024
    Assignee: SIEMENS AKTIENGESELLSCHAFT
    Inventors: Heiko Claussen, Martin Sehr, Eugen Solowjow, Chengtao Wen, Juan L. Aparicio Ojea
  • Publication number: 20240012400
    Abstract: A computer-implemented method for failure classification of a surface treatment process includes receiving one or more process parameters that influence one or more failure modes of the surface treatment process and receiving sensor data pertaining to measurement of one or more process states pertaining to the surface treatment process. The method includes processing the received one or more process parameters and the sensor data by a machine learning model deployed on an edge computing device controlling the surface treatment process to generate an output indicating, in real-time, a probability of process failure via the one or more failure modes. The machine learning model is trained on a supervised learning regime based on process data and failure classification labels obtained from physics simulations of the surface treatment process in combination with historical data pertaining to the surface treatment process.
    Type: Application
    Filed: August 28, 2020
    Publication date: January 11, 2024
    Applicant: Siemens Corporation
    Inventors: Shashank Tamaskar, Martin Sehr, Eugen Solowjow, Wei Xi Xia, Juan L. Aparicio Ojea, Ines Ugalde Diaz
  • Publication number: 20230359864
    Abstract: An edge device can be configured to perform industrial control operations within a production environment that defines a physical location. The edge device can include a plurality of neural network layers that define a deep neural network. The edge device be configured to obtain data from one or more sensors at the physical location defined by the production environment. The edge device can be further configured to perform one or more matrix operations on the data using the plurality of neural network layers so as to generate a large scale matrix computation at the physical location defined by the production environment. In some examples, the edge device can send the large scale matrix computation to a digital twin simulation model associated with the production environment, so as to update the digital twin simulation model in real time.
    Type: Application
    Filed: August 31, 2020
    Publication date: November 9, 2023
    Applicant: Siemens Corporation
    Inventors: Martin Sehr, Eugen Solowjow, Wei Xi Xia, Shashank Tamaskar, Ines Ugalde Diaz, Heiko Claussen, Juan L. Aparicio Ojea
  • Publication number: 20230316115
    Abstract: A computer-implemented method includes operating a controllable physical device to perform a task. The method also includes miming forward simulations of the task by a physics engine based on one or more physics parameters. The physics engine communicates with a parameter data layer where each of the one or more physics parameters is modeled with a probability distribution. For each forward simulation run, a tuple of parameter values is sampled from the probability distribution of the one or more physics parameters and fed to the physics engine. The method includes obtaining an observation pertaining to the task from the physical environment and a corresponding forward simulation outcome associated with each sampled tuple of parameter values. The method then includes updating the probability distribution of the one or more physics parameters in the parameter data layer based on the observation from the physical environment and the corresponding forward simulation outcomes.
    Type: Application
    Filed: August 28, 2020
    Publication date: October 5, 2023
    Applicant: Siemens Aktiengesellschaft
    Inventors: Juan L. Aparicio Ojea, Heiko Claussen, Ines Ugalde Diaz, Martin Sehr, Eugen Solowjow, Chengtao Wen, Wei Xi Xia, Xiaowen Yu, Shashank Tamaskar
  • Publication number: 20230214665
    Abstract: Distributed neural network boosting is performed by a neural network system through operating at least one processor. A method comprises providing a boosting algorithm that distributes a model among a plurality of processing units each being a weak learner of multiple weak learners that can perform computations independent from one another yet process data concurrently. The method further comprises enabling a distributed ensemble learning which enables a programmable logic controller (PLC) to use more than one processing units of the plurality of processing units to scale an application and training the multiple weak learners using the boosting algorithm. The multiple weak learners are machine learning models that do not capture an entire data distribution and are purposefully designed to predict with a lower accuracy. The method further comprises using the multiple weak learners to vote for a final hypothesis based on a feed forward computation of neural networks.
    Type: Application
    Filed: April 17, 2020
    Publication date: July 6, 2023
    Inventors: Wei Xi Xia, Xiaowen Yu, Shashank Tamaskar, Juan L. Aparicio Ojea, Heiko Claussen, Ines Ugalde Diaz, Martin Sehr, Eugen Solowjow, Chengtao Wen
  • Patent number: 11667034
    Abstract: Computerized system and method are provided. A robotic manipulator (12) is arranged to grasp objects (20). A gripper (16) is attached to robotic manipulator (12), which includes an imaging sensor (14). During motion of robotic manipulator (12), imaging sensor (14) is arranged to capture images providing different views of objects in the environment of the robotic manipulator. A processor (18) is configured to find, based on the different views, candidate grasp locations and trajectories to perform a grasp of a respective object in the environment of the robotic manipulator. Processor (18) is configured to calculate respective values indicative of grasp quality for the candidate grasp locations, and, based on the calculated respective values indicative of grasp quality for the candidate grasp locations, processor (18) is configured to select a grasp location likely to result in a successful grasp of the respective object.
    Type: Grant
    Filed: February 12, 2020
    Date of Patent: June 6, 2023
    Assignee: Siemens Aktiengesellschaft
    Inventors: Heiko Claussen, Martin Sehr, Eugen Solowjow, Chengtao Wen, Juan L. Aparicio Ojea
  • Publication number: 20230108920
    Abstract: A system for supporting artificial intelligence inference in an edge computing device associated with a physical process or plant includes a neural network training module, a neural network testing module and a digital twin of the physical process or plant. The neural network training module is configured to train a neural network model for deployment to the edge computing device based on data including baseline training data and field data received from the edge computing device. The neural network testing module configured to validate the trained neural network model prior to deployment to the edge computing device by leveraging the digital twin of the physical process or plant.
    Type: Application
    Filed: March 30, 2020
    Publication date: April 6, 2023
    Inventors: Ines Ugalde Diaz, Heiko Claussen, Juan L. Aparicio Ojea, Martin Sehr, Eugen Solowjow, Chengtao Wen, Wei Xi Xia, Xiaowen Yu, Shashank Tamaskar
  • Publication number: 20230050387
    Abstract: According to an aspect of the present disclosure, a computer-implemented includes creating a plurality of basic skill functions for a controllable physical device of an autonomous system. Each basic skill function includes a functional description for using the controllable physical device to interact with a physical environment to perform a defined objective. The method further includes selecting one or more basic skill functions to configure the controllable physical device to perform a defined task. The method also includes determining a decorator skill function specifying at least one constraint. The decorator skill function is configured to impose, at run-time, the at least one constraint, on the one or more basic skill functions. The method further includes generating executable code by applying the decorator skill function to the one or more basic skill functions, and actuating the controllable physical device using the executable code.
    Type: Application
    Filed: February 11, 2020
    Publication date: February 16, 2023
    Inventors: Juan L. Aparicio Ojea, Heiko Claussen, Ines Ugalde Diaz, Martin Sehr, Eugen Solowjow, Chengtao Wen, Wei Xi Xia, Xiaowen Yu, Shashank Tamaskar
  • Publication number: 20220410391
    Abstract: In current applications of autonomous machines in industrial settings, the environment, in particular the devices and systems with which the machine interacts, is known such that the autonomous machine can operate in the particular environment successfully. Thus, current approaches to automating tasks within varying environments, for instance complex environments having uncertainties, lack capabilities and efficiencies. In an example aspect, a method for operating an autonomous machine within a physical environment includes detecting an object within the physical environment. The autonomous machine can determine and perform a principle of operation associated with a detected subcomponent of the object, so as to complete a task that requires that the autonomous machine interacts with the object. In some cases, the autonomous machine has not previously encountered the object.
    Type: Application
    Filed: November 22, 2019
    Publication date: December 29, 2022
    Inventors: Juan L. Aparicio Ojea, Heiko Claussen, Ines Ugalde Diaz, Martin Sehr, Eugen Solowjow, Chengtao Wen, Wei Xi Xia, Xiaowen Yu, Shashank Tamaskar
  • Publication number: 20220347853
    Abstract: A system controller for visual servoing includes a technology module with dedicated hardware acceleration for deep neural network that retrieves a desired configuration of a workpiece object being manipulated by a robotic device and receives visual feedback information from one or more sensors on or near the robotic device that includes a current configuration of the workpiece object. The hardware accelerator executes a machine learning model trained to process the visual feedback information and determine a configuration error based on a difference between the current configuration of the workpiece object and the desired configuration of the workpiece object. A servo control module adapts a servo control signal to the robotic device for manipulation of the workpiece object in response to the configuration error.
    Type: Application
    Filed: September 30, 2019
    Publication date: November 3, 2022
    Inventors: Heiko Claussen, Martin Sehr, Eugen Solowjow, Chengtao Wen, Juan L. Aparicio Ojea
  • Publication number: 20220291652
    Abstract: Examples of techniques for aspect-oriented programming based programmable logic controller (PLC) simulation are provided. An aspect including one of a hardware configuration aspect, an execution semantics aspect, and a communication architecture aspect, may be determined to be applied to a general model of an industrial system, the general model including a PLC model and a system model. The determined aspect may be applied to the general model. The industrial system may be simulated using the general model and the applied aspect.
    Type: Application
    Filed: August 23, 2019
    Publication date: September 15, 2022
    Inventors: Martin Sehr, Juan L. Aparicio Ojea, Mehrdad Niknami, Edward Lee, Martin Witte, Jörg Neidig
  • Publication number: 20220201026
    Abstract: Examples of techniques for threat detection in an industrial process system are described herein. An aspect includes determining a plurality of subsystems of an industrial process system. Another aspect includes, for each of the plurality of subsystems, constructing and training a respective deep autoencoder (DAE) model of the subsystem based on data corresponding to the industrial process system. Another aspect includes monitoring the industrial process system using the plurality of DAE models corresponding to the plurality of subsystems. Another aspect includes, based on the plurality of DAE models, determining a cyberattack in a subsystem of the plurality of subsystems.
    Type: Application
    Filed: April 9, 2019
    Publication date: June 23, 2022
    Inventors: Chengtao Wen, Mohamed El Amine Houyou, Juan L. Aparicio Ojea, Mathias Maurmaier, Martin Sehr, Tao Cui
  • Publication number: 20220067526
    Abstract: A computer-implemented method for training a neural network on a hardware accelerator of an edge device includes dividing a trained neural network into a domain independent portion and a domain dependent portion. The domain independent portion of the neural network is deployed onto a dedicated neural network processing unit of the hardware accelerator of the edge device, and the domain dependent portion of the neural network is deployed onto one or more additional processors of the hardware accelerator of the edge device. The domain dependent portion on the additional processors of the hardware accelerator is retrained using data collected at the edge device.
    Type: Application
    Filed: January 14, 2019
    Publication date: March 3, 2022
    Inventors: Heiko Claussen, Martin Sehr, Eugen Solowjow, Chengtao Wen, Juan L. Aparicio Ojea
  • Publication number: 20220035670
    Abstract: A system and method are disclosed for orchestrating the execution of computing tasks. An orchestration engine can receive task requests over a network from a plurality of process engines. The process engines may correspond to respective edge or field devices that are remotely located as compared to the orchestration engine. Each task request may indicate at least one task requirement for executing a respective computing task. A plurality of computing instances that have available computing resources can be selected from a set of computing instances. A predicted runtime can be generated for each of the computing tasks. In an example, based on the predicted runtimes, task requirements, available computing resources, and associated network conditions, a schedule and allocation scheme are determined by the orchestration engine.
    Type: Application
    Filed: August 30, 2019
    Publication date: February 3, 2022
    Inventors: Ines Ugalde Diaz, Martin Sehr, Juan L. Aparicio Ojea, Michael Unkelbach
  • Publication number: 20200320871
    Abstract: A traffic management system includes a traffic sensor network with a plurality of sensors providing sensor data, a traffic management unit with a data collection module, a sensor quality analytics module, and a decision support module, and one or more processors configured via executable instructions to collect the sensor data of the traffic sensor network via the data collection module, determine relative weights of the plurality of sensors based on operating states of the plurality of sensors and the sensor data via the sensor quality analytics module, and output traffic signal controlling information and sensor maintenance information based on the relative weights of the plurality of sensors via the decision support module. Further, traffic controllers and a method for managing traffic in combination with the traffic management system are described.
    Type: Application
    Filed: April 4, 2019
    Publication date: October 8, 2020
    Inventors: Glenn Massarano, Martin Sehr
  • Publication number: 20200262064
    Abstract: Computerized system and method are provided. A robotic manipulator (12) is arranged to grasp objects (20). A gripper (16) is attached to robotic manipulator (12), which includes an imaging sensor (14). During motion of robotic manipulator (12), imaging sensor (14) is arranged to capture images providing different views of objects in the environment of the robotic manipulator. A processor (18) is configured to find, based on the different views, candidate grasp locations and trajectories to perform a grasp of a respective object in the environment of the robotic manipulator. Processor (18) is configured to calculate respective values indicative of grasp quality for the candidate grasp locations, and, based on the calculated respective values indicative of grasp quality for the candidate grasp locations, processor (18) is configured to select a grasp location likely to result in a successful grasp of the respective object.
    Type: Application
    Filed: February 12, 2020
    Publication date: August 20, 2020
    Inventors: Heiko Claussen, Martin Sehr, Eugen Solowjow, Chengtao Wen, Juan L. Aparicio Ojea
  • Publication number: 20200254609
    Abstract: A system includes a robot device that comprises a non-transitory computer readable medium and a robot controller. The non-transitory computer readable medium stores one or more machine-specific modules comprising base neural network layers. The robot controller receives a task-specific module comprising information corresponding to one or more task-specific neural network layers enabling performance of a task. The robot controller collects one or more values from an operating environment, and uses the values as input to a neural network comprising the base neural network layers and the task-specific neural network layers to generate an output value. The robot controller may then perform the task based on the output value.
    Type: Application
    Filed: February 12, 2020
    Publication date: August 13, 2020
    Inventors: Heiko Claussen, Juan L. Aparicio Ojea, Martin Sehr, Eugen Solowjow, Chengtao Wen