Abstract: A stereoscopic vision system uses at least two cameras having different parameters to image a scene and create stereoscopic views. The different parameters of the two cameras can be intrinsic or extrinsic, including, for example, the distortion profile of the lens in the cameras, the field of view of the lens, the orientation of the cameras, the positions of the cameras, the color spectrum of the cameras, the frame rate of the cameras, the exposure time of the cameras, the gain of the cameras, the aperture size of the lenses, or the like. An image processing apparatus is then used to process the images from the at least two different cameras to provide optimal stereoscopic vision to a display.

Abstract: Drainage devices have a self-clearing capability for reducing obstructions and a controllable flow restriction capability for controlling drainage flow, and microactuators for providing such capabilities. Such a microactuator includes a frame and an appendage anchored to the frame such that the frame supports the appendage, the frame at least partially surrounds the appendage, and the appendage is disposed in an opening or window defined by the frame. The appendage includes a platform and at least one beam that anchors the platform to the frame to enable the appendage to deflect out of a plane defined by the frame. The platform may include a ferromagnetic material that enables the appendage to deflect in response to an applied magnetic field.

Abstract: Some embodiments provide a program that retrieves a set of notifications describing failures that occurred on a set of monitored devices. The program further determines a set of topics based on the set of notifications. The program also determines failure modes associated with the set of topic from a plurality of failure modes defined for the set of monitored devices. The program further determines failure modes associated with the set of notifications based on the set of topics and the failure modes associated with the set of topics. The program also receives a particular notification that includes a particular set of words describing a failure that occurred on a particular monitored device in the set of monitored devices. The program further determines a failure mode associated with the particular notification based on the particular set of words and the determined failure modes associated with the set of notifications.

Abstract: The present application relates to an automotive cargo or a tray area cover assembly. The cover assembly includes a cover for covering a cargo or a tray area of an automobile. One or more rib supports are provided for moving over the cargo or the tray area and supporting the cover.

Abstract: Provided is a system and method for training and validating models in a machine learning pipeline for failure mode analytics. The machine learning pipeline may include an unsupervised training phase, a validation phase and a supervised training and scoring phase. In one example, the method may include receiving an identification of a machine learning model, executing a machine learning pipeline comprising a plurality of services which train the machine learning model via at least one of an unsupervised learning process and a supervised learning process, the machine learning pipeline being controlled by an orchestration module that triggers ordered execution of the services, and storing the trained machine learning model output from the machine learning pipeline in a database associated with the machine learning pipeline.

Abstract: Provided is a system and method for training and validating models in a machine learning pipeline for failure mode analytics. The machine learning pipeline may include an unsupervised training phase, a validation phase and a supervised training and scoring phase. In one example, the method may include receiving a request to create a machine learning model for failure mode detection associated with an asset, retrieving historical notification data of the asset, generating an unsupervised machine learning model via unsupervised learning on the historical notification data, wherein the unsupervised learning comprises identifying failure topics from text included in the historical notification data and mapping the identified failure topics to a plurality of predefined failure modes for the asset, and storing the generated unsupervised machine learning model via a storage device.

Abstract: Provided is a system and method for training and validating models in a machine learning pipeline for failure mode analytics. The machine learning pipeline may include an unsupervised training phase, a validation phase and a supervised training and scoring phase. In one example, the method may include receiving an identification of a machine learning model, executing a machine learning pipeline comprising a plurality of services which train the machine learning model via at least one of an unsupervised learning process and a supervised learning process, the machine learning pipeline being controlled by an orchestration module that triggers ordered execution of the services, and storing the trained machine learning model output from the machine learning pipeline in a database associated with the machine learning pipeline.

Abstract: Provided is a system and method for training and validating models in a machine learning pipeline for failure mode analytics. The machine learning pipeline may include an unsupervised training phase, a validation phase and a supervised training and scoring phase. In one example, the method may include receiving a request to create a machine learning model for failure mode detection associated with an asset, retrieving historical notification data of the asset, generating an unsupervised machine learning model via unsupervised learning on the historical notification data, wherein the unsupervised learning comprises identifying failure topics from text included in the historical notification data and mapping the identified failure topics to a plurality of predefined failure modes for the asset, and storing the generated unsupervised machine learning model via a storage device.

Abstract: A system and method for generating content for an encrypted package is provided. A package may be received that includes one or more anti-tamper hash portions and encrypted data, where the encrypted data includes one or more procedural content generation instructions. A portion of the encrypted data including the one or more procedural content generation instructions may be decrypted and a data based on the execution of the one or more procedural content generation instructions and a corpus of data may be generated. The generated data may be encrypted and anti-tamper hashes may be generated based on the encrypted generated data. The generated anti-tamper hashes may be compared to the one or more anti-tamper hashes in the anti-tamper hash portion of the received package.

Abstract: A reduced-latency method for updating an application includes sourcing first data representing to a first portion of an application update from a local storage location, sourcing second data representing a remaining second portion of the application update from a remote storage location, and overwriting a currently-stored version of the application with the first data and the second data.

Abstract: Provided is a system and method for predicting leading indicators for predicting occurrence of an event at a target asset. Rather than rely on traditional manufacturer-defined leading indicators for an asset, the examples herein predict leading indicators for a target asset based on actual operating conditions at the target asset. Accordingly, unanticipated operating conditions can be considered. In one example, the method may include receiving operating data of a target resource, the operating data being associated with previous occurrences of an event at the target resource, predicting one or more leading indicators of the event at the target resource based on the received operating data, each leading indicator comprising a variable and a threshold value for the variable, and outputting information about the one or more predicted leading indicators of the target resource for display via a user interface.

Abstract: A re-configurable bank of DC-DC converters has many channels, each with a DC-DC converter and a controller that senses the channel's output voltage and current to adjust a duty cycle of switch signals to the DC-DC converter. A serial bus connects to all controllers and writes digital voltage and current control targets into each controller. The controller has Digital-to-Analog Converters (DACs) that convert the targets to analog voltages that are compared to sensed output voltage and current. The comparison results are compared to a sawtooth wave to generate pulses of the switch signals that have a duty cycle adjusted for the target comparisons. In combined mode, a primary channel's controller generates switch signals for secondary channels having outputs shorted to the primary channel. Secondary channels have a mux to select switch signals from the primary controller during combined mode, and from the secondary controller during separated mode.

Abstract: Aspects of the present disclosure disclose provide systems and methods for updating, or patching, encrypted image files located at a remote location. More specifically, a content update package that includes encrypted information is received and decrypted. Based on the content update package, a first portion of data in an encrypted image file is located, where the first portion data is to be decrypted and updated based on data in the content update package. The updated data may then be encrypted, verified, and stored. When the updating, or patching, process is complete, the file version located at the remote location is the same as the latest file version. In addition, the updating, or patching, process may be split between multiple operating systems.

Abstract: A re-configurable bank of DC-DC converters has many channels, each with a DC-DC converter and a controller that senses the channel's output voltage and current to adjust a duty cycle of switch signals to the DC-DC converter. A serial bus connects to all controllers and writes digital voltage and current control targets into each controller. The controller has Digital-to-Analog Converters (DACs) that convert the targets to analog voltages that are compared to sensed output voltage and current. The comparison results are compared to a sawtooth wave to generate pulses of the switch signals that have a duty cycle adjusted for the target comparisons. In combined mode, a primary channel's controller generates switch signals for secondary channels having outputs shorted to the primary channel. Secondary channels have a mux to select switch signals from the primary controller during combined mode, and from the secondary controller during separated mode.

Abstract: Techniques for implementing and using failure curve analytics in an equipment maintenance system are disclosed. A method comprises: accessing a failure curve model for an equipment model, the failure curve model being configured to estimate lifetime failure data for the equipment model for different failure modes corresponding to different specific manners in which the equipment model is capable of failing, the lifetime failure data indicating a probability of the equipment model failing in the specific manner of the failure mode; generating first analytical data for a first failure mode of the plurality of failure modes using the failure curve model based on the first failure mode, the first analytical data indicating at least a portion of the lifetime failure data for the equipment model corresponding to the first failure mode; and causing a visualization of the first analytical data to be displayed on a computing device.

Abstract: Aspects of the present disclosure disclose provide systems and methods for updating, or patching, encrypted image files located at a remote location. More specifically, a content update package that includes encrypted information is received and decrypted. Based on the content update package, a first portion of data in an encrypted image file is located, where the first portion data is to be decrypted and updated based on data in the content update package. The updated data may then be encrypted, verified, and stored. When the updating, or patching, process is complete, the file version located at the remote location is the same as the latest file version. In addition, the updating, or patching, process may be split between multiple operating systems.

Abstract: Aspects of the present disclosure disclose provide systems and methods for updating, or patching, encrypted image files located at a remote location. More specifically, a content update package that includes encrypted information is received and decrypted. Based on the content update package, a first portion of data in an encrypted image file is located, where the first portion data is to be decrypted and updated based on data in the content update package. The updated data may then be encrypted, verified, and stored. When the updating, or patching, process is complete, the file version located at the remote location is the same as the latest file version. In addition, the updating, or patching, process may be split between multiple operating systems.

Abstract: A system and method for generating content for an encrypted package is provided. A package may be received that includes one or more anti-tamper hash portions and encrypted data, where the encrypted data includes one or more procedural content generation instructions. A portion of the encrypted data including the one or more procedural content generation instructions may be decrypted and a data based on the execution of the one or more procedural content generation instructions and a corpus of data may be generated. The generated data may be encrypted and anti-tamper hashes may be generated based on the encrypted generated data. The generated anti-tamper hashes may be compared to the one or more anti-tamper hashes in the anti-tamper hash portion of the received package.

Abstract: Aspects of the present disclosure disclose provide systems and methods for updating, or patching, encrypted image files located at a remote location. More specifically, a content update package that includes encrypted information is received and decrypted. Based on the content update package, a first portion of data in an encrypted image file is located, where the first portion data is to be decrypted and updated based on data in the content update package. The updated data may then be encrypted, verified, and stored. When the updating, or patching, process is complete, the file version located at the remote location is the same as the latest file version. In addition, the updating, or patching, process may be split between multiple operating systems.

Abstract: Provided is a system and method for training and validating models in a machine learning pipeline for failure mode analytics. The machine learning pipeline may include an unsupervised training phase, a validation phase and a supervised training and scoring phase. In one example, the method may include receiving an identification of a machine learning model, executing a machine learning pipeline comprising a plurality of services which train the machine learning model via at least one of an unsupervised learning process and a supervised learning process, the machine learning pipeline being controlled by an orchestration module that triggers ordered execution of the services, and storing the trained machine learning model output from the machine learning pipeline in a database associated with the machine learning pipeline.