Abstract: A computer system generates an outline of a roof of a structure based on a set of lateral images depicting the structure. For each image in the set of lateral images, one or more rooflines corresponding to the roof of the structure are determined. The computer system determines how the rooflines connect to one another. Based on the determination, the rooflines are connected to generate an outline of the roof.

Abstract: A computer system maintains structure data indicating geometrical constraints for each structure category of a plurality of structure categories. The computer system generates a virtual 3D representation of a structure based on a set of images depicting the structure. For each image in the set of images, one or more landmarks are identified. Based on the landmarks, a candidate structure category is selected. The virtual 3D representation is generated based on the geometrical constraints of the candidate structure category and the landmarks identified in the set of images.

Abstract: A method for managing soft errors associated with one or more safety programmable logic controllers (PLCs) is provided. The method includes receiving an expected soft error rate for type(s) of input/output (I/O) modules over time, receiving respective soft error data that was aggregated by the respective safety PLCs based on soft errors detected by I/O modules coupled to the respective safety PLCs. Actual soft error rates are determined per I/O module type based on the received soft error data, and soft error rates are predicted for the safety PLC(s) per I/O module type. The actual and/or predicted soft error rates are compared to the expected soft error rate per I/O module type. The method further includes taking one or more actions in response to a threshold deviation between the actual and/or predicted soft error rates relative to the expected soft error rate for the corresponding I/O module type.

Abstract: A computer system generates an outline of a roof of a structure based on a set of lateral images depicting the structure. For each image in the set of lateral images, one or more rooflines corresponding to the roof of the structure are determined. The computer system determines how the rooflines connect to one another. Based on the determination, the rooflines are connected to generate an outline of the roof.

Abstract: A computer system generates an outline of a roof of a structure based on a set of lateral images depicting the structure. For each image in the set of lateral images, one or more rooflines corresponding to the roof of the structure are determined. The computer system determines how the rooflines connect to one another. Based on the determination, the rooflines are connected to generate an outline of the roof.

Abstract: A computer system generates an outline of a roof of a structure based on a set of lateral images depicting the structure. For each image in the set of lateral images, one or more rooflines corresponding to the roof of the structure are determined. The computer system determines how the rooflines connect to one another. Based on the determination, the rooflines are connected to generate an outline of the roof.

Abstract: A computer system maintains structure data indicating geometrical constraints for each structure category of a plurality of structure categories. The computer system generates a virtual 3D representation of a structure based on a set of images depicting the structure. For each image in the set of images, one or more landmarks are identified. Based on the landmarks, a candidate structure category is selected. The virtual 3D representation is generated based on the geometrical constraints of the candidate structure category and the landmarks identified in the set of images.

Abstract: A computer system maintains structure data indicating geometrical constraints for each structure category of a plurality of structure categories. The computer system generates a virtual 3D representation of a structure based on a set of images depicting the structure. For each image in the set of images, one or more landmarks are identified. Based on the landmarks, a candidate structure category is selected. The virtual 3D representation is generated based on the geometrical constraints of the candidate structure category and the landmarks identified in the set of images.

Abstract: A method for managing soft errors associated with one or more safety programmable logic controllers (PLCs) is provided. The method includes receiving an expected soft error rate for type(s) of input/output (I/O) modules over time, receiving respective soft error data that was aggregated by the respective safety PLCs based on soft errors detected by I/O modules coupled to the respective safety PLCs. Actual soft error rates are determined per I/O module type based on the received soft error data, and soft error rates are predicted for the safety PLC(s) per I/O module type. The actual and/or predicted soft error rates are compared to the expected soft error rate per I/O module type. The method further includes taking one or more actions in response to a threshold deviation between the actual and/or predicted soft error rates relative to the expected soft error rate for the corresponding I/O module type.

Abstract: A computer system maintains structure data indicating geometrical constraints for each structure category of a plurality of structure categories. The computer system generates a virtual 3D representation of a structure based on a set of images depicting the structure. For each image in the set of images, one or more landmarks are identified. Based on the landmarks, a candidate structure category is selected. The virtual 3D representation is generated based on the geometrical constraints of the candidate structure category and the landmarks identified in the set of images.

Abstract: A computer system maintains structure data indicating geometrical constraints for each structure category of a plurality of structure categories. The computer system generates a virtual 3D representation of a structure based on a set of images depicting the structure. For each image in the set of images, one or more landmarks are identified. Based on the landmarks, a candidate structure category is selected. The virtual 3D representation is generated based on the geometrical constraints of the candidate structure category and the landmarks identified in the set of images.

Abstract: Systems and methods are disclosed for guiding image capture of a subject by determining a location of the subject and presenting on a display graphical guides representative of perspective views of the subject to be captured. Images of the subject may then be captured and additional graphical guides are presented to the user for display for additional images to be captured. Images may be captured in a predetermined sequence of graphical guides or responsive to a user input or camera information. Captured images may be uploaded to a system for additional processing.

Abstract: A method is provided for automatically or semi-automatically analyzing process hazards and validating protection mechanisms for an industrial process. The method can involve establishing communication between a simulation tool and a process hazard analysis tool. The simulation tool simulates operation of the process according to a process model. The method can further involve creating, using the process hazard analysis tool, conditions for hazards in the process based on information learned about the industrial process from the simulation tool; for each of the hazards, simulating the hazards using the simulation tool and attempting to prevent the hazards using the process hazard analysis tool by introducing protective mechanism(s) to the process; and evaluating effectiveness of the introduced protective mechanisms for each of the hazards and creating safety requirements for the process based on the evaluated effectiveness.

Abstract: A process for receiving, from a computing device, a series of captured building images. The process continues by processing, in real-time, each building image in the series of captured building images to determine if each building image meets a minimum criterion, wherein the minimum criteria includes applicability to be used in constructing a specific digital multi-dimensional building model. The process continues by aggregating each image meeting the minimum criteria, determining when a base set of building images has been aggregated, wherein the base set of building images includes a threshold number images to model at least a partial multi-dimensional building model representing the series of captured building images, determining one or more facades present in the partial multi-dimensional building model, determining preliminary dimensions for one or more architectural features of the one or more facades and returning, incrementally (in real-time), the preliminary dimensions to the computing device.

Abstract: A computer system maintains structure data indicating geometrical constraints for each structure category of a plurality of structure categories. The computer system generates a virtual 3D representation of a structure based on a set of images depicting the structure. For each image in the set of images, one or more landmarks are identified. Based on the landmarks, a candidate structure category is selected. The virtual 3D representation is generated based on the geometrical constraints of the candidate structure category and the landmarks identified in the set of images.

Abstract: Systems and methods are disclosed for guiding image capture of a subject by determining a location of the subject and presenting on a display graphical guides representative of perspective views of the subject to be captured. Images of the subject may then be captured and additional graphical guides are presented to the user for display for additional images to be captured. Images may be captured in a predetermined sequence of graphical guides or responsive to a user input or camera information. Captured images may be uploaded to a system for additional processing.

Abstract: A computer system trains a machine learning model to estimate a real-world measurement of a feature of a structure. The machine learning model is trained using a plurality of digital image sets, wherein each image set depicts a particular structure, and a plurality of measurements, wherein each measurement is a measurement of a feature of a particular structure. After the machine learning model is trained, it is used to estimate a measurement of a feature of a particular structure depicted in a particular image set.

Abstract: A process for creating, responsive to a desired analysis, estimate information from received imagery by determining whether the imagery meets a minimum criterion for creating a multi-dimensional model that can address the desired analysis and building an aggregate set of images meeting the minimum criterion from which a multi-dimensional model is then built and an estimate responsive to the desired analysis is derived. The multi-dimensional model, and the responsive estimate, may be refined as additional images are provided to the aggregate set. Desired analyses may include dimensions such as square footage of a roof for building objects.

Abstract: Systems and methods for measuring rotational frequency in rotating machines use variable sliding windows of measurement. The systems and methods count and store the number of internal clock cycles between the start of a measurement interval and each pulse signal from a pulse generator. Rotational frequency is determined by taking a difference between the count for a most recent pulse signal and the count for some previous pulse signal within the measurement interval. The number of pulse signals that have occurred between the most-recent pulse signal and the previous pulse signal represents a window of measurement. This window of measurement, or the size thereof, may then be used along with the count difference to determine the rotational frequency. The window of measurement may then be slid to the counts for next most recent pulse signal and the next previous pulse signal to obtain a new count difference, and so on.

Abstract: A computer system trains a machine learning model to estimate a real-world measurement of a feature of a structure. The machine learning model is trained using a plurality of digital image sets, wherein each image set depicts a particular structure, and a plurality of measurements, wherein each measurement is a measurement of a feature of a particular structure. After the machine learning model is trained, it is used to estimate a measurement of a feature of a particular structure depicted in a particular image set.