Abstract: Systems, apparatus, articles of manufacture, and methods are disclosed to determine a time to perform an agricultural operation, the method comprising: obtaining agricultural operation data, the agricultural operation data representing farmer decisions to perform agricultural operations; filtering the agricultural operation data to remove data associated with crop growing cycles outside an expected crop growing cycle duration based on a respective type of the crop, the filtering to create filtered agricultural operation data; accessing geospatial data, the geospatial data including at least climate data and satellite imaging data; combining the filtered agricultural operation data with the geospatial data based on a geographic and time similarity to create a training data set representing historic farmer decisions and corresponding agricultural and geospatial conditions; and training a machine learning model to generate a proposed time to perform an agricultural operation based on existing agricultural and ge

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 virtual plant operator system for use in an industrial plant. A data aggregator monitors operating data within the industrial plant and sends the operating data as a current state of the industrial plant to an operator assistant. The operator assistant includes a digital twin of the industrial plant and an artificial intelligence engine. The digital twin of the industrial plant receives the current state and simulates plant operations based on the current state. The artificial intelligence engine has at least one machine-learned model. The machine-learned model processes the simulated plant operations based on the current state to determine a recommendation output. The recommendation output includes stabilizing actions to the plant operations in the industrial plant and/or a predicted degree of shutdown responsive to each of the stabilizing actions.

Abstract: A machine-learned method and system for use in operating an industrial plant. A digital twin of the industrial plant is configured to simulate plant operations based on operating variables from a data store. A machine-learned model comprises a stabilizing agent and a disrupting agent. The stabilizing agent modifies the operating variables within the digital twin to perform a stabilizing action for limiting a degree of shutdown and the disrupting agent modifies the operating variables within the digital twin to perform a disruptive action for increasing the degree of shutdown. A composite action reward is configured to reward the machine-learned model for reducing the degree of shutdown from an initial state of the digital twin to a post-action-state of the digital twin.

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 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: 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: Network of safety PLCs employs multi-PLC verification of a programming application before allowing the application to reprogram any PLC on the safety network. Each PLC on the safety network is equipped with authentication capability that detects attempts to reprogram the PLC and issues an authentication challenge requiring the programming application to process a proof-of-work. The authentication challenge is also sent to other PLCs on the safety network along with the response from the programming application for verification purposes. The other PLCs process the authentication challenge and check the response from the programming application for acceptability. If a majority of the PLCs on the safety network determines the response from the programming application is correct, then the programming application is verified and may proceed with the reprogramming.

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.