Abstract: A method includes receiving input that identifies one or more data sources and determining, based on the input, a machine learning problem type of a plurality of machine learning problem types supported by an automated model building (AMB) engine. The method also includes generating an input data set of the AMB engine based on application of one or more rules to the one or more data sources. The method further includes, based on the input data set and the machine learning problem type, initiating execution of the AMB engine to generate a neural network configured to model at least a portion of the input data set.

Abstract: A device includes communication circuitry configured to receive a message indicating an observation of an agent device. The device further includes a processor coupled to the communication circuitry and a memory. The memory stores instructions that are executable by the processor to cause the processor to perform operations. The operations include accessing a blockchain data structure. The blockchain data structure includes one or more blocks including data descriptive of observations of a plurality of agent devices, where the plurality of agent devices including the agent device. The operations also include determining, based on one or more blocks of the blockchain data structure, a behavior of the agent device. The operations also include determine whether the behavior satisfies a behavior criterion associated with the agent device.

Abstract: A method includes receiving, at a server, first sensor data from a first vehicle. The method includes receiving, at the server, second sensor data from a second vehicle. The second sensor data includes condition data indicating a road condition, engine data indicating an engine problem, booking data indicating an intended route, or a combination thereof. The method includes aggregating, at the server, a plurality of sensor readings to generate aggregated sensor data. The plurality of sensor readings include the first sensor data and the second sensor data. The method further includes transmitting a first message based on the aggregated sensor data to the first vehicle, wherein the first message causes the first vehicle to perform a first action, the first action comprising avoiding the road condition, displaying an indicator corresponding to the engine problem, displaying a booked route, or a combination thereof.

Abstract: A method includes obtaining route information at an unmanned aerial vehicle (UAV). The route information indicates a plurality of zones between an origin and a destination and a corresponding set of mobile devices for each zone. The method includes receiving first control data at the UAV from a first mobile device while in a first zone of the plurality of zones. The first mobile device is included in a first set of mobile devices corresponding to the first zone. The method further includes receiving second control data at the UAV from a second mobile device while in a second zone of the plurality of zones. The second mobile device is included in a second set of mobile devices corresponding to the second zone.

Abstract: An aspect of the present invention is to provide a system and method for predicting the remaining useful time of mechanical components such as bearings. Another aspect of the present invention is to provide a system and method for predicting the remaining useful time of bearings based on available condition monitoring data. Another aspect of the present invention is to provide a system and method for automatically deciding which columns of input information are the most significant for predicting the remaining useful life of bearings. Another aspect of the present invention is to provide a system and method for performing an analysis of both test bearings and training bearings and determining which training bearings are most similar to a given test bearing. Another aspect of the present invention is to provide a system and method for training an artificial neural network.

Abstract: A computer system includes a memory storing a data structure representing a neural network. The data structure includes a plurality of fields including values representing topology of the neural network. The computer system also includes one or more processors configured to perform neural network classification by operations including generating a vector representing at least a portion of the neural network based on the data structure. The operations also include providing the vector as input to a trained classifier to generate a classification result associated with at least the portion of the neural network, where the classification result is indicative of expected performance or reliability of the neural network. The operations also include generating an output indicative of the classification result.

Abstract: A method includes determining, by a processor of a computing device, an expected performance or reliability of a first neural network of a first plurality of neural networks. The expected performance or reliability is determined based on a vector representing at least a portion of the first neural network, where the first neural network is generated based on an automated generative technique (e.g., a genetic algorithm) and where the first plurality of neural networks corresponds to a first epoch of the automated generative technique. The method also includes responsive to the expected performance or reliability of the first neural network failing to satisfy a threshold, adjusting a parameter of the automated generative technique. The method further includes, during a second epoch of the automated generative technique, generating a second plurality of neural networks based at least in part on the adjusted parameter.

Abstract: An unmanned aerial vehicle (UAV) system comprises a hangar structure configurable to mount on a host platform. The hangar structure comprises electrical circuits comprising a charging circuit and a communications circuit. The UAV system further comprises a plurality of stackable UAVs. The plurality of stackable UAVs comprise respective batteries and control circuits. The plurality of stackable UAVs are configured to cooperate with the charging circuit to charge the batteries and to cooperate with the communications circuit to communicate with the control circuits while the plurality of stackable UAVs are in a stacked configuration within the hangar structure.

Abstract: A device includes communication circuitry configured to receive a message indicating an observation of an agent device. The device further includes a processor coupled to the communication circuitry and a memory. The memory stores instructions that are executable by the processor to cause the processor to perform operations. The operations include accessing a blockchain data structure. The blockchain data structure includes one or more blocks including data descriptive of observations of a plurality of agent devices, where the plurality of agent devices including the agent device. The operations also include determining, based on one or more blocks of the blockchain data structure, a behavior of the agent device. The operations also include determine whether the behavior satisfies a behavior criterion associated with the agent device.

Abstract: A method includes determining, by a processor of a computing device, an expected performance or reliability of a first neural network of a first plurality of neural networks. The expected performance or reliability is determined based on a vector representing at least a portion of the first neural network, where the first neural network is generated based on an automated generative technique (e.g., a genetic algorithm) and where the first plurality of neural networks corresponds to a first epoch of the automated generative technique. The method also includes responsive to the expected performance or reliability of the first neural network failing to satisfy a threshold, adjusting a parameter of the automated generative technique. The method further includes, during a second epoch of the automated generative technique, generating a second plurality of neural networks based at least in part on the adjusted parameter.

Abstract: A computer system includes a memory storing a data structure representing a neural network. The data structure includes a plurality of fields including values representing topology of the neural network. The computer system also includes one or more processors configured to perform neural network classification by operations including generating a vector representing at least a portion of the neural network based on the data structure. The operations also include providing the vector as input to a trained classifier to generate a classification result associated with at least the portion of the neural network, where the classification result is indicative of expected performance or reliability of the neural network. The operations also include generating an output indicative of the classification result.

Abstract: An unmanned aerial vehicle (UAV) system comprises a hangar structure configurable to mount on a host platform. The hangar structure comprises electrical circuits comprising a charging circuit and a communications circuit. The UAV system further comprises a plurality of stackable UAVs. The plurality of stackable UAVs comprise respective batteries and control circuits. The plurality of stackable UAVs are configured to cooperate with the charging circuit to charge the batteries and to cooperate with the communications circuit to communicate with the control circuits while the plurality of stackable UAVs are in a stacked configuration within the hangar structure.

Abstract: A distributed sensor module system comprises a plurality of sensor modules configured to be aerially deployable from a deployment device, the deployment device including an unmanned aerial vehicle (UAV) or an aeronautically deployable unitized container, the plurality of sensor modules configured to communicate with each other. A first sensor module comprises a first sensor configured to obtain first sensor information from a first environment proximate to the first sensor, a processor coupled to the first sensor, the processor configured to process the first sensor information to obtain locally processed first sensor information, and a communication transceiver coupled to the processor, the communication transceiver configured to communicate the locally processed first sensor information to a second sensor module, the first sensor module and the second sensor module configured to be aerially deployable.

Abstract: A distributed sensor module system comprises a plurality of sensor modules configured to be aerially deployable from a deployment device, the deployment device including an unmanned aerial vehicle (UAV) or an aeronautically deployable unitized container, the plurality of sensor modules configured to communicate with each other. A first sensor module comprises a first sensor configured to obtain first sensor information from a first environment proximate to the first sensor, a processor coupled to the first sensor, the processor configured to process the first sensor information to obtain locally processed first sensor information, and a communication transceiver coupled to the processor, the communication transceiver configured to communicate the locally processed first sensor information to a second sensor module, the first sensor module and the second sensor module configured to be aerially deployable.

Abstract: A method includes receiving input that identifies one or more data sources and determining, based on the input, a machine learning problem type of a plurality of machine learning problem types supported by an automated model building (AMB) engine. The method also includes generating an input data set of the AMB engine based on application of one or more rules to the one or more data sources. The method further includes, based on the input data set and the machine learning problem type, initiating execution of the AMB engine to generate a neural network configured to model at least a portion of the input data set.

Abstract: A method includes determining a trainable model to provide to a trainer, the trainable model determined based on modification of one or more models of a plurality of models. The plurality of models is generated based on a genetic algorithm and corresponds to a first epoch of the genetic algorithm. Each of the plurality of models includes data representative of a neural network. The method also includes providing the trainable model to the trainer. The method further includes adding a trained model, output by the trainer based on the trainable model, as input to a second epoch of the genetic algorithm, the second epoch subsequent to the first epoch.

Abstract: A submersible vessel comprises a hull. The hull contains a plurality of subsystems. The subsystems comprise a sensor subsystem configured to sense potential target information regarding a potential target, a database subsystem configured to store target characterization information, a processing subsystem coupled to the sensing subsystem and to the database subsystem, and an ordnance subsystem. The processing subsystem is configured to process the potential target information according to the target characterization information to confirm the potential target as being a confirmed target. The ordnance subsystem comprises an ordnance magazine configured to store ordnance. The ordnance is deployable against the confirmed target, wherein the confirmed target is an aircraft.

Abstract: An autonomous vessel comprises a hull. The hull contains a plurality of subsystems. The subsystems comprise a sensor subsystem configured to sense potential target information regarding a potential target, a database subsystem configured to store target characterization information, a processing subsystem coupled to the sensing subsystem and to the database subsystem, and an ordnance subsystem. The processing subsystem is configured to process the potential target information according to the target characterization information to confirm the potential target as being a confirmed target. The ordnance subsystem comprises an ordnance magazine configured to store ordnance, the ordnance deliverable by an unmanned combat aerial vehicle (UCAV). The ordnance is deployable against the confirmed target.

Abstract: Systems and methods are provided for distributing security information. The systems and methods include a network having a plurality of nodes for storing a plurality of linearly integrated data records in a distributed file system, each linearly integrated data record including security information, a client installed on each node, each client configured to obtain the security information from at least one other node in the network, and a module contained within each client for delivering the obtained security information to an endpoint security application of the corresponding node.

Abstract: A method and system comprises a plurality of electronically controlled distributed devices and a supervisory node. The supervisory node comprises a communications interface, a processor, and a display. The supervisory node is configured to communicate with the plurality of electronically controlled distributed devices via the communications interface.