A computer-implemented method controls input of at least a portion of a first training data set into a first machine learning algorithm. The first training data set includes data quantifying damage to a first compressor and data quantifying a first operating parameter of the first compressor. The first machine learning algorithm is executed, and data quantifying the first operating parameter is received as an output of the first machine learning algorithm. The first machine learning algorithm is trained using the received data output from the first machine learning algorithm and data quantifying the first operating parameter of the first compressor. The trained first machine learning algorithm is configured to enable determination of operability of a second compressor of a gas turbine engine.

Disclosed is a laundry scheduling apparatus. The apparatus includes a communication unit, an output unit, and a processor configured to pair with at least one washing machine via the communication unit, obtain laundry preference parameters of a user generated by learning based on at least one of a deep learning algorithm or a machine learning algorithm, using at least one of a laundry log of the user or laundry satisfaction information of the user as input data, generate laundry scheduling information by using washing machine information about the paired at least one washing machine, the laundry preference parameters, and laundry item information obtained via at least one of a user input unit, an interface unit, or a sensor, and cause the output unit to output the laundry scheduling information.

A vector-valued regularized kernel function approximation (VVRKFA) based fault diagnosis method for an analog circuit comprises the following steps: (1) obtaining fault response voltage signals of an analog circuit; (2) performing wavelet packet transform on the collected signals, and calculating wavelet packet coefficient energy values as feature parameters; (3) optimizing regularization parameters and kernel parameters of VVRKFA by using a quantum particle swarm optimization algorithm and training a fault diagnosis model; and (4) identifying a circuit fault by using the trained diagnosis model. In the invention, the classification performance of the VVRKFA method is superior to other classification algorithms, and optimization of parameters by the quantum particle swarm optimization (QPSO) algorithm is also superior to the traditional method of obtaining parameters. The fault diagnosis method provided by the invention can efficiently diagnose the component faults of the circuit, including soft faults and hard faults.

An example apparatus includes a matrix processor in circuit with a probability generator to determine a first matrix representative of element-wise multiplication between a constraint matrix and a first transpose matrix of the estimated demographic impression distribution, the constraint matrix based on the reference demographic impression distribution and determine a second matrix by multiplying the first matrix with a second transpose matrix of the constraint matrix. The apparatus further includes an error determiner in circuit with the matrix processor, the error determiner to determine an error indicator value based on the second matrix, the error indicator value indicative of an error associated with the estimated demographic impression distribution, and a probability generator to generate, in response to the error indicator value satisfying a threshold, an accuracy-improved demographic impression distribution.

This invention relates to a parameter or response assist filter that ensures that the predictions of a post-validation calibrated physics system simulator will remain within boundaries of a predetermined model validation domain. Embodiments utilize one or more filters to ensure calibrated model parameters {acute over (P)} and/or calibrated responses {tilde over (ϕ)} cause physics simulator model predictions to remain within the boundaries of the model validation domain MVD for a target application. The filters can be constructed prior to use or automatically inferred, or otherwise determined, from available measurements and other renditions of the physics system simulator during operation.

A vector-valued regularized kernel function approximation (VVRKFA) based fault diagnosis method for an analog circuit comprises the following steps: (1) obtaining fault response voltage signals of an analog circuit; (2) performing wavelet packet transform on the collected signals, and calculating wavelet packet coefficient energy values as feature parameters; (3) optimizing regularization parameters and kernel parameters of VVRKFA by using a quantum particle swarm optimization algorithm and training a fault diagnosis model; and (4) identifying a circuit fault by using the trained diagnosis model. In the invention, the classification performance of the VVRKFA method is superior to other classification algorithms, and optimization of parameters by the quantum particle swarm optimization (QPSO) algorithm is also superior to the traditional method of obtaining parameters. The fault diagnosis method provided by the invention can efficiently diagnose the component faults of the circuit, including soft faults and hard faults.

The present invention relates to a method of hashing a perceptual vector model based on a multi-scale curvature. According to a first aspect, there is provided a method of hashing a perceptual vector model based on a multi-scale curvature including: generating a multi-dimensional feature coefficient matrix, and obtaining a multi-dimensional intermediate hash coefficient matrix; and obtaining a final binary hash matrix, and enabling the multi-dimensional binary hash matrix to be hierarchically authenticated. In addition, according to a second aspect, there is provided a method of hashing a perceptual vector model based on a multi-scale curvature including: generating a hash by using a hash function; and authenticating a vector model. In addition, an error detection probability for an object attack can be lower by about 2×10.sup.−5˜2.8×10.sup.−2, and a uniqueness probability is raised by about 0.014. In addition, an entropy can be raised by about 0.875˜2.149.

This disclosure is directed to an apparatus for intelligent matching of disparate input data received from disparate input data systems in a complex computing network for establishing targeted communication to a computing device associated with the intelligently matched disparate input data.

This disclosure is directed to an apparatus for intelligent matching of disparate input data received from disparate input data systems in a complex computing network for establishing targeted communication to a computing device associated with the intelligently matched disparate input data.

Techniques and a system for quantum computing device modeling and design are provided. In one example, a system includes a modeling component and a simulation component. The modeling component models a quantum device element of a quantum computing device as an electromagnetic circuit element to generate electromagnetic circuit data for the quantum computing device. The simulation component simulates the quantum computing device using the electromagnetic circuit data to generate response function data indicative of a response function for the quantum computing device. Additionally or alternatively, a Hamiltonian is constructed based on the response function.