Abstract: Various embodiments disclose a glow plug for a solid oxide fuel cell system. The glow plug includes a housing having a first end portion and a second end portion. The glow plug includes a heating element longitudinally disposed in the housing, extending from the second end portion of the housing towards the first end portion and extending outwardly from the housing for igniting fuel. Further, the glow plug includes a pair of coiled wires electrically connected to the heating element. Further, the glow plug includes a potting compound disposed within the second end portion of the housing for securing electrical coupling of the pair of coiled wires with the heating element. Furthermore, the glow plug includes a sealing element configured to form an air-tight connection between the housing and the heating element. The sealing element is positioned on top of the potting compound.

Abstract: A flow assurance digital avatar is provided that combines the simulation of fluid flow through a network of oilfield facilities including reservoirs, wells and pipelines, detection and visualization of possible flow-related issues and risks in the network of oilfield facilities, user evaluation of possible optimizations (what-if scenarios) in the operation of the network of oilfield facilities for fixes and workovers with respect to flow-related issues and risks, and user evaluation and management of possible tasks or actions for the fixes and workovers for the flow-related issues and risks. Other aspects are described and claimed.

Abstract: The present disclosure is related to a cyber-security system that includes a Supervisory Control and Data Acquisition (SCADA) network monitor configured to receive a data set from a power system network, an event manager, and a mitigation system, where the SCADA network monitor includes an anomaly detector.

Abstract: Methods and systems for image registration using an intelligent artificial agent are disclosed. In an intelligent artificial agent based registration method, a current state observation of an artificial agent is determined based on the medical images to be registered and current transformation parameters. Action-values are calculated for a plurality of actions available to the artificial agent based on the current state observation using a machine learning based model, such as a trained deep neural network (DNN). The actions correspond to predetermined adjustments of the transformation parameters. An action having a highest action-value is selected from the plurality of actions and the transformation parameters are adjusted by the predetermined adjustment corresponding to the selected action. The determining, calculating, and selecting steps are repeated for a plurality of iterations, and the medical images are registered using final transformation parameters resulting from the plurality of iterations.

Abstract: Embodiments of the present disclosure relate to generating control signals for energy sources. Some embodiments include a method, the method including generating a first control signal at least partially responsive to a request signal and at least partially responsive to a first capability of a first energy source to change a first energy output. The first control signal may be indicative of a first portion of a requested amount of energy to be provided by the first energy source. The method may also include generating a second control signal at least partially responsive to the request signal and at least partially responsive to a second capability of a second energy source to change a second energy output. The second control signal may be indicative of a second portion of the requested amount of energy to be provided by the second energy source. Related devices, systems and methods are also disclosed.

Abstract: Embodiments of the present disclosure relate to parallel mask rule checking on evolving mask shapes in optical proximity correction (OPC) flows for integrated circuit designs. Systems and methods are disclosed that perform mask (manufacturing) rule checks (MRC) in parallel, sharing information to maintain symmetry when violations are corrected. In an embodiment the shared information is also used to minimize changes to the geometric area of proposed mask shapes resulting from the OPC. In contrast to conventional systems, MRC is performed for multiple edges in parallel, sharing information between the different edges to encourage symmetry. In an embodiment, all edges may be adjusted in parallel to reduce mask-edge traversal bias.

Abstract: Machine learning is used to train a network to estimate a three-dimensional (3D) body surface and body regions of a patient from surface images of the patient. The estimated 3D body surface of the patient is used to determine an isocenter of the patient. The estimated body regions are used to generate heatmaps representing visible body region boundaries and unseen body region boundaries of the patient. The estimation of 3D body surfaces, the determined patient isocenter, and the estimated body region boundaries may assist in planning a medical scan, including automatic patient positioning.

Abstract: Methods and systems for image registration using an intelligent artificial agent are disclosed. In an intelligent artificial agent based registration method, a current state observation of an artificial agent is determined based on the medical images to be registered and current transformation parameters. Action-values are calculated for a plurality of actions available to the artificial agent based on the current state observation using a machine learning based model, such as a trained deep neural network (DNN). The actions correspond to predetermined adjustments of the transformation parameters. An action having a highest action-value is selected from the plurality of actions and the transformation parameters are adjusted by the predetermined adjustment corresponding to the selected action. The determining, calculating, and selecting steps are repeated for a plurality of iterations, and the medical images are registered using final transformation parameters resulting from the plurality of iterations.

Abstract: For patient weight estimation in a medical imaging system, a patient model, such as a mesh, is fit to a depth image. One or more feature values are extracted from the fit patient model, reducing the noise and clutter in the values. The weight estimation is regressed from the extracted features.

Abstract: A method for calibrating a water injection network model includes obtaining measurements at a wellhead for a plurality of injection wells included in an injection pipeline network and determining an injection rate reconciliation for the plurality of injection wells. This method further includes dividing the injection pipeline network into a plurality of main-loops and subdividing the plurality of main-loops into a plurality of sub-loops. A friction factor multiplier is determined to match a simulated pressure drop in each of the plurality of sub-loops to a measured pressure drop at the wellhead and the water injection network model is calibrated by using the friction factor multiplier.

Abstract: Various embodiments disclose a glow plug for a solid oxide fuel cell system. The glow plug includes a housing having a first end portion and a second end portion. The glow plug includes a heating element longitudinally disposed in the housing, extending from the second end portion of the housing towards the first end portion and extending outwardly from the housing for igniting fuel. Further, the glow plug includes a pair of coiled wires electrically connected to the heating element. Further, the glow plug includes a potting compound disposed within the second end portion of the housing for securing electrical coupling of the pair of coiled wires with the heating element. Furthermore, the glow plug includes a sealing element configured to form an air-tight connection between the housing and the heating element. The sealing element is positioned on top of the potting compound.

Abstract: Various embodiments disclose a glow plug for a solid oxide fuel cell system. The glow plug includes a housing having a first end portion and a second end portion. The glow plug includes a heating element longitudinally disposed in the housing, extending from the second end portion of the housing towards the first end portion and extending outwardly from the housing for igniting fuel. Further, the glow plug includes a pair of coiled wires electrically connected to the heating element. Further, the glow plug includes a potting compound disposed within the second end portion of the housing for securing electrical coupling of the pair of coiled wires with the heating element. Furthermore, the glow plug includes a sealing element configured to form an air-tight connection between the housing and the heating element. The sealing element is positioned on top of the potting compound.

Abstract: Methods and systems for image registration using an intelligent artificial agent are disclosed. In an intelligent artificial agent based registration method, a current state observation of an artificial agent is determined based on the medical images to be registered and current transformation parameters. Action-values are calculated for a plurality of actions available to the artificial agent based on the current state observation using a machine learning based model, such as a trained deep neural network (DNN). The actions correspond to predetermined adjustments of the transformation parameters. An action having a highest action-value is selected from the plurality of actions and the transformation parameters are adjusted by the predetermined adjustment corresponding to the selected action. The determining, calculating, and selecting steps are repeated for a plurality of iterations, and the medical images are registered using final transformation parameters resulting from the plurality of iterations.

Abstract: For training for and performance of patient modeling from surface data in a medical system, a progressive multi-task model is used. Different tasks for scanning are provided, such as landmark estimation and patient pose estimation. One or more features learned for one task are used as fixed or constant features in the other task. This progressive approach based on shared features increases efficiency while avoiding reductions in accuracy for any given task.

Abstract: Tracking of health and resilience of physical equipment and related systems are disclosed. A system includes physical equipment and one or more processors. The physical equipment includes one or more assets. The one or more processors are configured to determine a resilience metric for the physical equipment. The resilience metric includes a real power component and a reactive power component based, at least in part, on an aggregation of real components and reactive components of adaptive capacities of the one or more assets. A cyber-physical system includes physical equipment, network equipment configured to enable the physical equipment to communicate over one or more networks, a physical anomaly detection system (ADS) configured to detect anomalies in operation of the physical equipment and provide a physical component of a cyber-physical metric, and a cyber ADS configured to detect anomalies in network communications over the one or more networks.

Abstract: Methods and systems for image registration using an intelligent artificial agent are disclosed. In an intelligent artificial agent based registration method, a current state observation of an artificial agent is determined based on the medical images to be registered and current transformation parameters. Action-values are calculated for a plurality of actions available to the artificial agent based on the current state observation using a machine learning based model, such as a trained deep neural network (DNN). The actions correspond to predetermined adjustments of the transformation parameters. An action having a highest action-value is selected from the plurality of actions and the transformation parameters are adjusted by the predetermined adjustment corresponding to the selected action. The determining, calculating, and selecting steps are repeated for a plurality of iterations, and the medical images are registered using final transformation parameters resulting from the plurality of iterations.

Abstract: A method for controlling a scanner comprises: sensing an outer surface of a body of a subject to collect body surface data, using machine learning to predict a surface of an internal organ of the subject based on the body surface data, and controlling the scanner based on the predicted surface of the internal organ.

Abstract: Synthetic CT is estimated for planning or other purposes from surface data (e.g., depth camera information). The estimation uses parameterization, such as landmark and/or segmentation information, in addition to the surface data. In training and/or application, the parameterization may be used to correct the predicted CT volume. The CT volume may be predicted as a sub-part of the patient, such as estimating the CT volume for scanning one system, organ, or type of tissue separately from other system, organ, or type of tissue.

Abstract: A trained recurrent neural network having a set of control policies learned from application of a template dataset and one or more corresponding template deep network architectures may generate a deep network architecture for performing a task on an application dataset. The template deep network architectures may have an established level or performance in executing the task. A deep network based on the deep network architecture may trained to perform the task on the application dataset. The control policies of the recurrent neural network may be updated based on the performance of the trained deep network.

Abstract: For topogram predication from surface data, a sensor captures the outside surface of a patient. A generative adversarial network (GAN) generates the topogram representing an interior organ based on the outside surface of the patient. To further adapt to specific patients, internal landmarks are used in the topogram prediction. The topogram generated by one generator of the GAN may be altered based on landmarks generated by another generator.