Devices, methods, and programs for monitoring electrical devices. A method for monitoring an electrical device may include obtaining data representing a sum of electrical signals of electrical devices; processing the data with a Factorial Hidden Markov Model (FHMM) to produce an estimate of an electrical signal of a first of the electrical devices; and outputting the estimate of the electrical signal of the first electrical device. The FHMM may have a factor corresponding to the first electrical device. The factor may have three or more states.

A method and apparatus for predicting based on multi-source heterogeneous data. The method comprises: acquiring, with regard to an event of a set type, at least two types of historical data that can reflect an event result; establishing a joint likelihood model of attribute data of the event of the set type and the historical data; determining an optimal estimation of the attribute data according to a maximum posterior principle; and determining, based on a probability distribution associated with the attribute data in the joint likelihood model, a parameter in the probability distribution as a prediction result of a predicted event of the set type. Some embodiments use a hierarchical model to introduce data of different sources into different data layers, unify heterogeneous data in a joint likelihood model to perform analysis, and obtain a more accurate, instant and stable prediction result through effective fusion.

Systems, computer program products, and methods are described herein for dynamically determining performance benchmarking parameters based on reinforcement learning. The present invention is configured to implement the first distributed impact simulation model on an application; initiate a reinforcement learning algorithm on the application, wherein initiating further comprises receiving a performance assessment output for the one or more application parameters; initiate an optimization policy generation engine on the performance assessment output associated with the application parameters to generate an optimization to encode the performance assessment output into rewards and costs; initiate an implementation of the optimization policy on the application to maximize an aggregated reward calculated from the second portion of the first set of actions; automatically generate a second distributed impact simulation model using the second set of actions to be implemented on the application parameters; and implement the second distributed impact simulation model on the application.

Systems, computer program products, and methods are described herein for dynamically determining performance benchmarking parameters based on reinforcement learning. The present invention is configured to implement the first distributed impact simulation model on an application; initiate a reinforcement learning algorithm on the application, wherein initiating further comprises receiving a performance assessment output for the one or more application parameters; initiate an optimization policy generation engine on the performance assessment output associated with the application parameters to generate an optimization to encode the performance assessment output into rewards and costs; initiate an implementation of the optimization policy on the application to maximize an aggregated reward calculated from the second portion of the first set of actions; automatically generate a second distributed impact simulation model using the second set of actions to be implemented on the application parameters; and implement the second distributed impact simulation model on the application.

An example apparatus includes at least one memory, instructions in the apparatus, and processor circuitry to execute the instructions to generate a reference demographic impression distribution based on first impressions of logged impressions at a first server corresponding to first client devices, access an inaccurate demographic impression distribution based on second impressions corresponding to second client devices, determine an estimated demographic impression distribution based on an inaccurate demographic impression distribution, the estimated demographic impression distribution representative of the second impressions distributed across the different demographic segments, determine a matrix based on a reference demographic impression distribution and Lagrange multiplier values, determine an error indicator value based on the matrix, generate, in response to the error indicator value satisfying a threshold, an accuracy-improved demographic impression distribution, and store, in the at least one memory, the accuracy-improved demographic impression distribution.

Methods for predicting network access probability of data files accessible over a computer network are provided. In one aspect, a method includes generating a primary data vector for a media file based on a stored data representation of the file, and providing the data vector for the file to an algorithm that uses past interaction information for at least one other media file from a collection of media files having a degree of similarity with the media file above a threshold similarity value. The method also includes receiving, as an output of the algorithm, a marketability score for the media file, the score indicative of a likelihood that a user will download the media file. Systems and machine-readable media are also provided.

Methods for predicting network access probability of data files accessible over a computer network are provided. In one aspect, a method includes generating a primary data vector for a media file based on a stored data representation of the file, and providing the data vector for the file to an algorithm that uses past interaction information for at least one other media file from a collection of media files having a degree of similarity with the media file above a threshold similarity value. The method also includes receiving, as an output of the algorithm, a marketability score for the media file, the score indicative of a likelihood that a user will download the media file. Systems and machine-readable media are also provided.

A method includes receiving a plurality of drilling parameters from a drilling operation, wherein the plurality of drilling parameters. The drilling parameters include a cuttings bed height and a friction factor between a drill string and a wellbore. The method further includes applying the plurality of drilling parameters to a friction model. The friction model utilizes a function of the cuttings bed height to determine a comprehensive friction factor. The comprehensive friction factor is applied to the plurality of drilling parameters to determine a required torque or hook load of the drill string. The method further includes providing an indication of a stuck pipe event.

A method includes receiving a plurality of drilling parameters from a drilling operation, wherein the plurality of drilling parameters. The drilling parameters include a cuttings bed height and a friction factor between a drill string and a wellbore. The method further includes applying the plurality of drilling parameters to a friction model. The friction model utilizes a function of the cuttings bed height to determine a comprehensive friction factor. The comprehensive friction factor is applied to the plurality of drilling parameters to determine a required torque or hook load of the drill string. The method further includes providing an indication of a stuck pipe event.

Aspects of the present disclosure provide systems, methods, and computer-readable storage media that support ontology driven processes to create digital twins that extend the capabilities of knowledge graphs. A dataset including an ontology and domain data corresponding to a domain associated with the ontology is obtained. A knowledge graph is constructed based on the ontology and the domain data is incorporated into the knowledge graph. The knowledge graph is exploited to derive random variables of a probabilistic graph model. The random variables may be associated with probability distributions, which may include unknown parameters. A learning process is executed to learn the unknown parameters and obtain a joint distribution of the probabilistic graph model, which may enable querying of the probabilistic graph model in a probabilistic and deterministic manner.