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 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 receiving an input data set, each entry including multiple features. The method includes receiving a user input identifying a target feature of the multiple features and a target value of the target feature. The method includes determining, one or more correlated features of the multiple features. The method includes providing the input data set to multiple neural networks (including multiple VAEs) to train the multiple neural networks. The method includes generating a simulated data set based on the input data set, each entry including at least the target feature and the one or more correlated features. Values of the one or more correlated features are randomized or pseudorandomized and the target feature is fixed at the target value. The method includes providing the simulated data set to the multiple neural networks to generate output data and displaying a GUI based on the output data.

Abstract: During training mode, first input data is provided to a first neural network to generate first output data indicating that the first input data is classified in a first cluster. The first input data includes at least one of a continuous feature or a categorical feature. Second input data is generated and provided to at least one second neural network to generate second output data. The at least one second neural network corresponds to a variational autoencoder. An aggregate loss corresponding to the second output data is determined, including at least one of evaluating a first loss function for the continuous feature or evaluating a second loss function for the categorical feature. Based on the aggregate loss, at least one parameter of at least one neural network is adjusted. During use mode, the neural networks are used to determine cluster identifications and anomaly likelihoods for received data samples.

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 method includes receiving one or more n-gram vectors for a file as input to a file classifier, where the one or more n-gram vectors indicate occurrences of groups of entropy indicators in a sequence of entropy indicators representing the file. The method also includes generating, based on the one or more n-gram vectors, output including classification data associated with the file, the classification data indicating whether the file includes malware.

Abstract: A method includes receiving, at a server, a first file attribute from a computing device, the first file attribute associated with a file. The method also includes determining, based on the first file attribute, that a classification for the file is unavailable. The method further includes determining the classification for the file based on a trained file classification model accessible to the server and sending the classification to the computing device. The method includes sending at least the classification to a base prediction cache associated with a second server.

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 system and method for generating a heuristic is provided. A heuristic is capable of identifying data patterns. The method includes: extracting a data set from multiple input sources; creating a set of unique elements used across the data set; organizing the data set into a geometric structure; grouping portions of the data in the geometric structure into a plurality sub geometric structures; determining base attributes for each sub geometric structure using the set of unique elements; identifying trends in the base attributes among the sub geometric structures; and outputting the heuristic as a combination of the base attributes and the trends.

Abstract: A system and method for creating a core cognitive fingerprint. A core cognitive fingerprint can be used to capture the evolution of a system state and potentially respond with a predetermined action if the fingerprint falls within a threshold. The method includes: identifying a set of time frames within which data is extracted; providing a plurality of pattern recognizers; processing the extracted data through the plurality of pattern recognizers to generate an initial set of elements; extracting identified relationships amongst the initial set of elements; modifying the initial set of elements to include the identified relationships to create an intermediate set of elements; comparing the intermediate set of elements against assigned values to weigh each element in the intermediate set of elements to create a final set of elements; and using the final set of elements as a cognitive fingerprint.

Abstract: A system and method for creating a core cognitive fingerprint are provided. A core cognitive fingerprint can be used to capture and summarize the evolution of a system state and potentially respond with a predetermined action if the fingerprint falls within a defined threshold.

Abstract: A system and method for generating a heuristic is provided. A heuristic is capable of identifying data patterns. The method includes: extracting a data set from multiple input sources; creating a set of unique elements used across the data set; organizing the data set into a geometric structure; grouping portions of the data in the geometric structure into a plurality sub geometric structures; determining base attributes for each sub geometric structure using the set of unique elements; identifying trends in the base attributes among the sub geometric structures; and outputting the heuristic as a combination of the base attributes and the trends.

Abstract: A system and method for creating a core cognitive fingerprint are provided. A core cognitive fingerprint can be used to capture and summarize the evolution of a system state and potentially respond with a predetermined action if the fingerprint falls within a defined threshold.