Abstract: A system for and a method of generating an embedding space from a training data set for a machine learning algorithm. The method includes receiving, through an input of a computer system, an input operating data point by one or more layers of a machine learning algorithm implemented on the computer system, generating an embedding space using a training data set, the embedding space including a plurality of inducing data points, each inducing data point corresponding to a cluster of training data points, comparing the input operating data point against the plurality of inducing data points, determining whether a sufficient set of supporting historical data points exists in the plurality of training data points within a fixed distance from the input operating data point, and generating an alert based on the determining to enable a user or an electronic controller to take appropriate action, and outputting the alert.

Abstract: According to some embodiments, a system and method are provided to model a sparse data asset. The system comprises a processor and a non-transitory computer-readable medium comprising instructions that when executed by the processor perform a method to model a sparse data asset. Relevant data and operational data associated with the newly operational are received. A transfer model based on the relevant data and the received operational data. An input into the transfer model is received and a predication based on data associated with the received operational data and the relevant data is output.

Abstract: A method, system, and non-transitory computer-readable medium, the method including determining automatically, by a processor, whether behavior for a model representing a plurality of entities and relationships therebetween deviates from a reference behavior for the model; determining, in response to the determination that the model does deviate from the reference behavior, at least one basis for the deviation; automatically forecasting an estimate of a remaining useful life for the model; and modifying the model to compensate for the deviation by at least one of modifying the model to accommodate the deviation and updating the model based on at least one new requirement.

Abstract: A method, system, and non-transitory computer-readable medium, the method including determining automatically, by a processor, whether behavior for a model representing a plurality of entities and relationships therebetween deviates from a reference behavior for the model; determining, in response to the determination that the model does deviate from the reference behavior, at least one basis for the deviation; automatically forecasting an estimate of a remaining useful life for the model; and modifying the model to compensate for the deviation by at least one of modifying the model to accommodate the deviation and updating the model based on at least one new requirement.

Abstract: A relative depth of points captured by at least two recording sources are determined. A first sequence of image frames acquired from a first source and a second sequence of image frames acquired from a second source are received by a data processing system. The data processing system identifies a plurality of points-of-interest, each point-of-interest being present in both the first sequence and the second sequence. The points-of-interest are clustered into common depth planes at least by comparing motion across the sequences of different points-of-interest. Results of the clustering are stored in a processor-accessible memory system.

Abstract: A relative depth of points captured by at least two recording sources are determined. A first sequence of image frames acquired from a first source and a second sequence of image frames acquired from a second source are received by a data processing system. The data processing system identifies a plurality of points-of-interest, each point-of-interest being present in both the first sequence and the second sequence. The points-of-interest are clustered into common depth planes at least by comparing motion across the sequences of different points-of-interest. Results of the clustering are stored in a processor-accessible memory system.