A computer implemented method of predictive weather occurrences includes generating, by a computer processor, a training model through artificial intelligence. The training model is based on climate data processed by a variational autoencoder. A geographic location is selected for climate study. Historical weather measurements associated with the selected geographic location are retrieved from a knowledge climate database. The retrieved historical weather measurements are processed using the training model. The training model receives threshold parameters defining extremeness of weather. Extremeness is based on a weather intensity data point being farther from a norm than closer to the norm. Synthetic weather data is generated for the selected location, wherein the synthetic weather data predicts weather events satisfying the extremeness threshold parameters.

A training method of a generator network model and an electronic device for execution thereof are provided. The training method includes: extracting a first tensor matrix and a second tensor matrix, wherein the first tensor matrix and the second tensor matrix respectively represent a first picture and a second picture and individually include a plurality of first parameters and a plurality of second parameters; generating a plurality of third pictures according to a plurality of difference values between the first parameters of the first tensor matrix and the second parameters of the second tensor matrix; performing a similarity test on a plurality of original pictures and the plurality of third pictures; and adopting at least one of the third pictures whose similarity is lower than or equal to a similarity threshold as at least one new sample picture.

In an industrial control system (ICS), latent vectors are generated to represent identity or behaviors of host devices coupled to the ICS. A computing system captures communications transmitted by a host device across a network associated with the ICS. A set of values are extracted from one or more respective fields in the communication, then applied to a trained neural network. Values of a first set of fields are applied at an input layer of the trained neural network, while values of a second set of fields are applied at an output layer of the neural network. Based on the application of the neural network to the values extracted from the communication, the computing system generates a latent vector.

A system and method for providing real-time distributed micro-grid optimization using price signals to the electrical grid system by allowing bi-directional electricity usage from a distributed network of energy storage stations to form a large, distributed resource for the grid. A machine learning optimization module ingests various forms of data-from grid telemetry to traffic data to trip-to-trip data and more-in order to make informed spatiotemporal decisions about optimal pricing signals as well as strategically placing and balancing energy stores across various regions to support optimum energy usage, risk mitigation, grid fortification, and revenue generation. Energy stores are then sent updated price signals and updated parameters as to the amount of energy to hold or release.

A method for training a machine learning system using conditionally independent training data includes receiving an input dataset (p(x, y, z)). A generative adversarial network, that includes a generator and a first discriminator, uses the input dataset to generate a training data (p.sub.s (x.sub.f, y.sub.f, z.sub.f)) by generating the values (x.sub.f, y.sub.f, z.sub.f). The first discriminator determines a first loss (L.sub.1) based on (x.sub.f, y.sub.f, z.sub.f) and (x, y, z). A divergence calculator modifies the training data based on a dependence measure (γ). The divergence calculator includes a second discriminator and a third discriminator. Modifying the training data includes receiving a reference value ({tilde over (y)}), and computing, by the second discriminator, a second loss (L.sub.2) based on (x.sub.f, y.sub.f, z.sub.f) and (x.sub.f, {tilde over (y)}, z.sub.f). The third discriminator computes a third loss (L.sub.3) based on (y.sub.f, z.sub.f) and ({tilde over (y)}, z.sub.f). Further, a fourth loss (L.sub.4) is computed based on L.sub.2 and L.sub.3. The training data is output from the generator if L.sub.1 and L.sub.4 satisfy a predetermined condition.

A method includes generating first electrocardiogram (ECG) data by adding synthetic noise to naturally occurring ECG data using a first deep neural network (DNN). The method further includes providing one of: (i) the first ECG data, or (ii) second ECG data including naturally occurring noise, to a second DNN. An output is generated by the second DNN indicating whether the second DNN received the first ECG data or the second ECG data.

Provided are a patch GAN-based depth completion method and apparatus in an autonomous vehicle. The patch-GAN-based depth completion apparatus according to the present invention comprises a processor; and a memory connected to the processor, wherein the memory stores program instructions executable by the processor for performing operations in a generating unit of a generative adversarial neural network comprising a first branch and a second branch based on an encoder-decoder comprising receiving an RGB image and a sparse image through a camera and LiDAR, generating a dense first depth map by processing color information of the RGB image through the first branch, generating a dense second depth map by up-sampling the sparse image through the second branch, generating a dense final depth map by fusing the first depth map and the second depth map, and determining, by a discriminating unit of the generative adversarial neural network, whether the final depth map is fake or real by dividing the final depth map and depth measurement data into a plurality of patches.

Provided are a patch GAN-based depth completion method and apparatus in an autonomous vehicle. The patch-GAN-based depth completion apparatus according to the present invention comprises a processor; and a memory connected to the processor, wherein the memory stores program instructions executable by the processor for performing operations in a generating unit of a generative adversarial neural network comprising a first branch and a second branch based on an encoder-decoder comprising receiving an RGB image and a sparse image through a camera and LiDAR, generating a dense first depth map by processing color information of the RGB image through the first branch, generating a dense second depth map by up-sampling the sparse image through the second branch, generating a dense final depth map by fusing the first depth map and the second depth map, and determining, by a discriminating unit of the generative adversarial neural network, whether the final depth map is fake or real by dividing the final depth map and depth measurement data into a plurality of patches.

A computing system includes at least one processor, and a memory communicatively coupled to the at least one processor. The processor is configured to receive at least two successive radar images of precipitation data, generate a motion vector field using the at least two successive radar images, forecast linear prediction imagery of future precipitation using the motion vector field, and generate corrected output imagery corresponding to the forecasted linear prediction imagery of the future precipitation corrected by a first neural network. In addition, the processor is further configured to receive, by a second neural network, the linear prediction imagery, and one of observed imagery and the corrected output imagery, and distinguish, by the second neural network, between the corrected output imagery and the observed imagery to produce conditioned output imagery. The processor is also configured to display the conditioned output imagery on a display.

A method of generating audio assets, comprising the steps of: receiving an input multi-layered audio asset comprising a plurality of audio layers, generating an input multi-channel image, wherein each channel of the input multi-channel image comprises an input image representative of one of the audio layers, training a generative model on the input multi-channel image and implementing the trained generative model to generate an output multi-channel image, wherein each channel of the output multi-channel image comprises an output image representative of an output audio layer, and generating an output multi-layered audio asset based on a combination of output audio layers derived from the output images.