Abstract: Technologies are shown for function level permissions control for smart contract execution to implement permissions policy on a blockchain. Permissions control rules control function calls at a system level utilizing function boundary detection instrumentation in a kernel that executes smart contracts. The detection instrumentation generates a call stack that represents a chain of function calls in the kernel for a smart contract. The permissions control rules are applied to the call stack to implement permissions control policy. Permissions control rules can use dynamic state data in the function call chain. If the dynamic state data observed in function call chains does not meet the requirements defined in the permissions control rules, then the function call can be blocked from executing or completing execution. The permissions control rules can be generated for a variety of different entities, such as a domain, user or resource.

Abstract: An inspection system includes one or more imaging devices and one or more processors. The imaging devices generate a first set of images of a work piece at a first position relative to the work piece and a second set of images of the work piece at a second position relative to the work piece. At least some of the images in the first and second sets are acquired using different light settings. The processors analyze the first set of images to generate a first prediction image associated with the first position, and analyze the second set of images to generate a second prediction image associated with the second position. The first and second prediction images include respective candidate regions. The processors merge the first and second prediction images to detect at least one predicted defect in the work piece depicted in at least one of the candidate regions.

Abstract: Technologies are shown for storing component state data for a resource on a blockchain involving generating a resource data block that corresponds to a resource that includes links that correspond to components of the resource, generating a first component state data block for a component of the resource on a blockchain that includes first state data for the first component, and setting the link for the component to reference the first component state data block. Subsequently, a second component state data block can be generated for the component with second state data and the second component state data block linked to the first component state data block.

Abstract: Provided are systems and methods for monitoring an asset via an autonomous model-driven inspection. In an example, the method may include storing an inspection plan including a virtually created three-dimensional (3D) model of a travel path with respect to a virtual asset that is created in virtual space, converting the virtually created 3D model of the travel path about the virtual asset into a physical travel path about a physical asset corresponding to the virtual asset, autonomously controlling vertical and lateral movement of the unmanned robot in three dimensions with respect to the physical asset based on the physical travel path and capturing data at one or more regions of interest, and capturing data at one or more regions of interest, and storing information concerning the captured data about the asset.

Abstract: A system includes one or more processors configured to detect perimeter edges of one or more rotor blades of a turbine assembly as depicted in a series of image frames using boundary analysis performed on the image frames. The one or more processors are configured to identify a set of the image frames as key frames based on positional offsets between the perimeter edges that are detected in the image frames and a reference blade pose such that the key frames are able to be inspected for objects-of-interest without inspecting the image frames that are not the key frames.

Abstract: Disclosed is technology for storing original work data on a derivative work data blockchain along with code for verifying that derivative work data is derivative of the original work data. The technology involves receiving derivative work data from a submitting entity along with proof data showing that the derivative work is derivative of the original work. If the derivative work data is verified as derivative, then the derivative work data is appended to the derivative work data blockchain.

Abstract: A method for generating a three-dimensional model of an asset includes receiving input parameters corresponding to constraints of a mission plan for operating an unmanned vehicle around an asset, generating the mission plan based on the input parameters including information of a representative asset type, wherein the mission plan includes waypoints identifying locations and orientations of one or more image sensors of the unmanned vehicle, generating a flight path for the unmanned vehicle connecting the waypoints that satisfy one or more predefined criteria, monitoring a vehicle state of the unmanned vehicle during execution of the flight path from one waypoint to the next waypoint, determining, at each waypoint, a local geometry of the asset sensed by the one or more image sensors, changing the mission plan on-the-fly based on the local geometry, and capturing images of the asset along waypoints of the changed mission plan.

Abstract: The present approach relates to an automatic and efficient motion plan for a drone to collect and save a qualified dataset that may be used to improve reconstruction of 3D models using the acquired data. The present architecture provides an automatic image processing context, eliminating low quality images and providing improved image data for point cloud generation and texture mapping.

Abstract: A method includes determining object class probabilities of pixels in a first input image by examining the first input image in a forward propagation direction through layers of artificial neurons of an artificial neural network. The object class probabilities indicate likelihoods that the pixels represent different types of objects in the first input image.

Abstract: A method and system, the method including receiving semantic descriptions of features of an asset extracted from a first set of images; receiving a model of the asset, the model constructed based on a second set of a plurality images of the asset; receiving, based on an optical flow-based motion estimation, an indication of a motion for the features in the first set of images; determining a set of candidate regions of interest for the asset; determining a region of interest in the first set of images; iteratively determining a matching of features in the set of candidate regions of interest and the determined region of interest in the first set of images to generate a record of matches in features between two images in the first set of images; and displaying a visualization of the matches in features between two images in the first set of images.

Abstract: A system includes one or more processors configured to analyze obtained image data representing a rotor blade to detect a candidate feature on the rotor blade and determine changes in the size or position of the candidate feature over time. The one or more processors are configured to identify the candidate feature on the rotor blade as a defect feature responsive to the changes in the candidate feature being the same or similar to a predicted progression of the defect feature over time. The predicted progression of the defect feature is determined according to an action-guidance function generated by an artificial neural network via a machine learning algorithm. Responsive to identifying the candidate feature on the rotor blade as the defect feature, the one or more processors are configured to automatically schedule maintenance for the rotor blade, alert an operator, or stop movement of the rotor blade.

Abstract: A robotic system includes one or more optical sensors configured to separately obtain two dimensional (2D) image data and three dimensional (3D) image data of a brake lever of a vehicle, a manipulator arm configured to grasp the brake lever of the vehicle, and a controller configured to compare the 2D image data with the 3D image data to identify one or more of a location or a pose of the brake lever of the vehicle. The controller is configured to control the manipulator arm to move toward, grasp, and actuate the brake lever of the vehicle based on the one or more of the location or the pose of the brake lever.

Abstract: A system and a method for detecting anomalous activities in a distributed and decentralised network is provided. Anonymous users transacting in the network are identified and one or more transactional attributes are retrieved to define characteristics of users and associated transactional behaviour with other users. Further, user-level statistics are evaluated based on transactional attributes. Datatype representative of transactional behavior of users with other users is generated using user-level statistics of identified users. Users with similar transactional behavior are classified based on generated transactional attributes. One or more anomaly detection techniques are implemented for identifying optimum classification of users into data clusters based on the change detected in the classification of users in data clusters. Anomalous users are identified from the optimum classification for efficiently and effectively detecting anomalous activities in the network.

Abstract: A system includes a borescope and at least one processor. The borescope includes a camera configured to acquire an acquisition series of frames of at least one target component. The at least one processor is operably coupled to the camera, and is configured to acquire the acquisition series of frames from the camera; determine a blurriness metric value for each of the frames; select frames that satisfy a threshold for the blurriness metric value to form an inspection series of frames; and perform an inspection analysis for the at least one target component using the inspection series of frames.

Abstract: A system includes one or more processors configured to detect perimeter edges of one or more rotor blades of a turbine assembly as depicted in a series of image frames using boundary analysis performed on the image frames. The one or more processors are configured to identify a set of the image frames as key frames based on positional offsets between the perimeter edges that are detected in the image frames and a reference blade pose such that the key frames are able to be inspected for objects-of-interest without inspecting the image frames that are not the key frames.

Abstract: A method for generating a three-dimensional model of an asset includes receiving input parameters corresponding to constraints of a mission plan for operating an unmanned vehicle around an asset, generating the mission plan based on the input parameters including information of a representative asset type, wherein the mission plan includes waypoints identifying locations and orientations of one or more image sensors of the unmanned vehicle, generating a flight path for the unmanned vehicle connecting the waypoints that satisfy one or more predefined criteria, monitoring a vehicle state of the unmanned vehicle during execution of the flight path from one waypoint to the next waypoint, determining, at each waypoint, a local geometry of the asset sensed by the one or more image sensors, changing the mission plan on-the-fly based on the local geometry, and capturing images of the asset along waypoints of the changed mission plan.

Abstract: An asset inspection system includes a robot and a server. The robot collects inspection data corresponding to an asset. The server, includes a user interface, a processor, and a memory. The memory includes instructions that, when executed by the processor, cause the processor to receive the inspection data from the robot, display the inspection data via the user-interface, receive feedback on the inspection data via the user interface, generate a human-assisted inspection based on the received feedback, analyze the inspection data via a trained model, generate an automated inspection based on the analysis by the trained model, combine the automated inspection and the human-assisted inspection to generate an inspection report, and transmit the inspection report for review.

Abstract: The present approach relates to streaming data derived from inspection data acquired using one or more robots performing inspections of an asset or assets. Such inspections may be fully or partially automated, such as being controlled by one or more computer-based routines, and may be planned or dynamically altered in response to inputs or requirements associated with an end-user of the inspection data, such as a subscriber to the data in a publication/subscription distribution scheme. Thus, an inspection may be planned or altered based on the data needs or subscription levels of the user or customers.

Abstract: A robotic system includes a controller configured to obtain image data from one or more optical sensors and to determine one or more of a location and/or pose of a vehicle component based on the image data. The controller also is configured to determine a model of an external environment of the robotic system based on the image data and to determine tasks to be performed by components of the robotic system to perform maintenance on the vehicle component. The controller also is configured to assign the tasks to the components of the robotic system and to communicate control signals to the components of the robotic system to autonomously control the robotic system to perform the maintenance on the vehicle component.

Abstract: A generative adversarial network (GAN) system includes a generator sub-network configured to examine one or more images of actual damage to equipment. The generator sub-network also is configured to create one or more images of potential damage based on the one or more images of actual damage that were examined. The GAN system also includes a discriminator sub-network configured to examine the one or more images of potential damage to determine whether the one or more images of potential damage represent progression of the actual damage to the equipment.