Patents by Inventor Juan L. Aparicio Ojea

Juan L. Aparicio Ojea 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
  • 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: 11914350
    Abstract: For manufacturing process control, closed-loop control is provided (18) based on a constrained reinforcement learned network (32). The reinforcement is constrained (22) to account for the manufacturing application. The constraints may be for an amount of change, limits, or other factors reflecting capabilities of the controlled device and/or safety.
    Type: Grant
    Filed: August 9, 2018
    Date of Patent: February 27, 2024
    Assignee: SIEMENS AKTIENGESELLSCHAFT
    Inventors: Max Fahrenkopf, Chengtao Wen, Juan L Aparicio Ojea
  • 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: 20230330858
    Abstract: In an example aspect, a first object (e.g., an electronic component) is inserted by a robot into a second object (e.g., a PCB). An autonomous system can capture a first image of the first object within a physical environment. The first object can define a mounting interface configured to insert into the second object. Based on the first image, a robot can grasp the first object within the physical environment. While the robot grasps the first object, the system can capture a second image of the first object. The second image can include the mounting interface of the first object. Based on the second image of the first object, the system can determine a grasp offset associated with the first object. The grasp offset can indicate movement associated with the robot grasping the first object within the physical environment. The system can also capture an image of the second object. Based on the grasp offset and the image of the second object, the robot can insert the first object into the second object.
    Type: Application
    Filed: September 9, 2021
    Publication date: October 19, 2023
    Applicant: Siemens Corporation
    Inventors: Eugen Solowjow, Juan L. Aparicio Ojea, Avinash Kumar, Matthias Loskyll, Gerrit Schoettler
  • 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: 20230158679
    Abstract: Autonomous operations, such as robotic grasping and manipulation, in unknown or dynamic environments present various technical challenges. For example, three-dimensional (3D) reconstruction of a given object often focuses on the geometry of the object without considering how the 3D model of the object is used in solving or performing a robot operation task. As described herein, in accordance with various embodiments, models are generated of objects and/or physical environments based on tasks that autonomous machines perform with the objects or within the physical environments. Thus, in some cases, a given object or environment may be modeled differently depending on the task that is performed using the model. Further, portions of an object or environment may be modeled with varying resolutions depending on the task associated with the model.
    Type: Application
    Filed: April 6, 2020
    Publication date: May 25, 2023
    Inventors: Chengtao Wen, Heiko Claussen, Xiaowen Yu, Eugen Solowjow, Richard Gary McDaniel, Swen Elpelt, 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
  • Patent number: 11550288
    Abstract: Over the past several decades, rapid advances in semiconductors, automation, and control systems have resulted in the adoption of programmable logic controllers (PLCs) in an immense variety of environments. Machine learning techniques help train replacement PLCs when a legacy PLC must be replaced, e.g., due to aging or failure. The techniques facilitate the efficient adoption and correct operation of replacement PLCs in the industrial environment.
    Type: Grant
    Filed: July 30, 2018
    Date of Patent: January 10, 2023
    Assignee: Siemens Aktiengesellschaft
    Inventors: Heiko Claussen, Volkmar Sterzing, Juan L. Aparicio Ojea
  • 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: 20220391565
    Abstract: A method for automatically generating a bill of process in a manufacturing system comprising: receiving design information representative of a product to be produced; iteratively performing simulations of the manufacturing system; identifying manufacturing actions based on the simulations; optimizing the identified manufacturing actions to efficiently produce the product to be produced; generating, by the manufacturing system, a bill of process for producing the product. Simulations may be performed using a digital twin of the product being produced and a digital twin of the environment. System actions are optimized using a reinforcement learning technique to automatically produce a bill of process based on the design information of the product and task specifications.
    Type: Application
    Filed: May 25, 2022
    Publication date: December 8, 2022
    Inventors: Chengtao Wen, Juan L. Aparicio Ojea, Ines Ugalde Diaz, Gokul Narayanan Sathya Narayanan, Eugen Solowjow, Wei Xi Xia, Yash Shahapurkar, Shashank Tamaskar, Heiko Claussen
  • 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: 20220331955
    Abstract: Robotics control system (10) and method for training said robotics control system are provided. Disclosed embodiments make a gracefully blended utilization of Reinforcement Learning (RL) with conventional control by way of a dynamically adaptive interaction between respective control signals (20, 24) generated by a conventional feedback controller (18) and an RL controller (22). Additionally, disclosed embodiments make use of an iterative approach for training a control policy by effective use of virtual sensor and actuator data (60) interleaved with real-world sensor and actuator data (54). This is effective to reducing a training sample size to fulfill a blended control policy for the conventional feedback controller and the reinforcement learning controller. Disclosed embodiments may be used in a variety of industrial automation applications.
    Type: Application
    Filed: September 30, 2019
    Publication date: October 20, 2022
    Inventors: Eugen Solowjow, Juan L. Aparicio Ojea, Avinash Kumar, Matthias Loskyll
  • Publication number: 20220297295
    Abstract: A computer-implemented method for designing execution of a process by a robotic cell includes obtaining a process goal and one or more process constraints. The method includes accessing a library of constructs and a library of skills. Each construct includes a digital representation of a component of the robotic cell or a geometric transformation of the robotic cell. Each skill includes a functional description for using a robot of the robotic cell to interact with a physical environment to perform a skill objective. The method uses a simulation engine to simulate a multiplicity of designs, wherein each design is characterized by a combination of constructs and skills to achieve the process goal, and determine a set of feasible designs that meet the one or more process constraints. The method includes outputting recommended designs from the set of feasible designs.
    Type: Application
    Filed: February 8, 2022
    Publication date: September 22, 2022
    Inventors: Juan L. Aparicio Ojea, Heiko Claussen, Ines Ugalde Diaz, Yash Shahapurkar, Eugen Solowjow, Chengtao Wen, Wei Xi Xia, Gokul Narayanan Sathya Narayanan, Shashank Tamaskar
  • 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