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.

Abstract: A correspondence between frames of a set of medical image data is determined where the set of medical image data includes at least one frame acquired without contrast medium and at least one frame acquired with contrast medium. First data representing a first image frame acquired without contrast medium is received. Second data representing a second image frame acquired with contrast medium is received. A position of a feature of a medical device in the second image frame is determined at least partly on the basis of a position of the feature determined from the first image frame.

Abstract: Systems and methods for expert-assisted classification are described herein. An example method for evaluating an expert-assisted classifier can include providing a cascade classifier including a plurality of classifier stages; and providing a simulated expert stage between at least two of the classifier stages. The simulated expert stage can be configured to validate or contradict an output of one of the at least two classifier stages. The method can also include classifying each of a plurality of records into one of a plurality of categories using the cascade classifier combined with the simulated expert stage; and determining whether the simulated expert stage improves performance of the cascade classifier.

Abstract: A method of deriving one or more medical scene model characteristics for use by one or more software applications is disclosed. The method includes receiving one or more sensor data streams. Each sensor data stream of the one or more sensor data steams includes position information relating to a medical scene. A medical scene model including a three-dimensional representation of a state of the medical scene is dynamically updated based on the one or more sensor data streams. Based on the medical scene model, the one or more medical scene model characteristics are derived.

Abstract: A cloud security system and method designed to protect users' data in case of accidental leaks in a cloud computing environment. Secured hashing of the names of folders stored on the cloud data storage are generated and persisted using multiple iterations of cryptographic hash functions along with a concatenated random number for each of the folder names, thereby providing protection against vulnerability of the folder names. The proposed system is a dual-layer framework consisting of a control layer and a data layer. The control layer is responsible for cryptographic hashing and persistence of the folder name, hashed name, salt, and iterations in a database. The control layer communicates with the data layer and provides the hashed folder names to persist the user data cloud storage.

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: 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: A method and system for determining fractional flow reserve (FFR) for a coronary artery stenosis of a patient is disclosed. In one embodiment, medical image data of the patient including the stenosis is received, a set of features for the stenosis is extracted from the medical image data of the patient, and an FFR value for the stenosis is determined based on the extracted set of features using a trained machine-learning based mapping. In another embodiment, a medical image of the patient including the stenosis of interest is received, image patches corresponding to the stenosis of interest and a coronary tree of the patient are detected, an FFR value for the stenosis of interest is determined using a trained deep neural network regressor applied directly to the detected image patches.

Abstract: For anomaly detection based on topogram predication from surface data, a sensor captures the outside surface of a patient. A generative adversarial network (GAN) generates a topogram representing an interior anatomy based on the outside surface of the patient. An X-ray image of the patient is acquired and compared to the generated topogram. By quantifying the difference between the real X-ray image and the predicted one, anatomical anomalies may be detected.

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.