A concept based few-shot learning method is disclosed. The method includes estimating a task embedding corresponding to a task to be executed from support data that is a small amount of learning data; calculating a slot probability of a concept memory necessary for a task based on the task embedding; extracting features of query data that is test data, and of the support data; comparing local features for the extracted features with slots of a concept memory to extract a concept, and generating synthesis features to have maximum similarity to the extracted features through the slots of the concept memory; and calculating a task execution result from the synthesis feature and the extracted concept by applying the slot probability as a weight.

A concept based few-shot learning method is disclosed. The method includes estimating a task embedding corresponding to a task to be executed from support data that is a small amount of learning data; calculating a slot probability of a concept memory necessary for a task based on the task embedding; extracting features of query data that is test data, and of the support data; comparing local features for the extracted features with slots of a concept memory to extract a concept, and generating synthesis features to have maximum similarity to the extracted features through the slots of the concept memory; and calculating a task execution result from the synthesis feature and the extracted concept by applying the slot probability as a weight.

A highly versatile data processing is implemented on data collected in a manufacturing process. A data processing device includes: a calculation part configured to collect a plurality of data groups associated with a predetermined step of a process, and calculate effects in the predetermined step for each of the plurality of data groups; a dividing part configured to divide a feature space such that a distribution of each of the plurality of data groups associated with the predetermined step in the feature space is classified for each of the calculated effects; and an output part configured to output specific data that specifies respective regions of the divided feature space.

A highly versatile data processing is implemented on data collected in a manufacturing process. A data processing device includes: a calculation part configured to collect a plurality of data groups associated with a predetermined step of a process, and calculate effects in the predetermined step for each of the plurality of data groups; a dividing part configured to divide a feature space such that a distribution of each of the plurality of data groups associated with the predetermined step in the feature space is classified for each of the calculated effects; and an output part configured to output specific data that specifies respective regions of the divided feature space.

A power load data prediction method and device, and a storage medium are disclosed. In an embodiment, the he method comprises: acquiring historical power load data of a one-dimensional time sequence, the historical power load data including values of corresponding time points; mapping the values of corresponding time points to a coordinate system in which a horizontal axis is a set time period, and a vertical axis is time points within the time period, and performing marking at each mapping point by using predetermined pixel values corresponding to the values to obtain a mapping image, wherein different values correspond to different pixel values; and inputting the pixel values of the mapping image to a trained data prediction model, and acquiring a power load data prediction value output by the data prediction model. The method and device and the storage medium can improve the prediction accuracy of the power load data.

A power load data prediction method and device, and a storage medium are disclosed. In an embodiment, the he method comprises: acquiring historical power load data of a one-dimensional time sequence, the historical power load data including values of corresponding time points; mapping the values of corresponding time points to a coordinate system in which a horizontal axis is a set time period, and a vertical axis is time points within the time period, and performing marking at each mapping point by using predetermined pixel values corresponding to the values to obtain a mapping image, wherein different values correspond to different pixel values; and inputting the pixel values of the mapping image to a trained data prediction model, and acquiring a power load data prediction value output by the data prediction model. The method and device and the storage medium can improve the prediction accuracy of the power load data.

Disclosed are a signal analysis method and system based on obtaining and recognizing a noise panorama distribution model, which relate to the technical field of signal analysis. The method includes the following steps: in a rich condition measurement environment, performing repeated measurements on a reference sample and a test sample to respectively obtain a plurality of measurement results; processing the measurement results of the reference sample and the test sample to respectively form training data of the reference sample and the test sample; based on the training data of the reference sample and the test sample, enabling a model to recognize a signal and noise from the measurement results, and distinguish between the reference sample and the test sample; and inputting a measurement result of a sample to be recognized to the trained artificial intelligence model, where an output result is a specific type of the sample to be recognized.

Disclosed are a signal analysis method and system based on obtaining and recognizing a noise panorama distribution model, which relate to the technical field of signal analysis. The method includes the following steps: in a rich condition measurement environment, performing repeated measurements on a reference sample and a test sample to respectively obtain a plurality of measurement results; processing the measurement results of the reference sample and the test sample to respectively form training data of the reference sample and the test sample; based on the training data of the reference sample and the test sample, enabling a model to recognize a signal and noise from the measurement results, and distinguish between the reference sample and the test sample; and inputting a measurement result of a sample to be recognized to the trained artificial intelligence model, where an output result is a specific type of the sample to be recognized.

A technology for supporting an event-based transaction using a continuous data stream relating to a vehicle includes: continuously acquiring a data stream of input data relating to the vehicle or relating to an environment of the vehicle from at least one device, the input data to be used in a transaction; calculating a consistency of the input data, errors in the input data, errors in an interpretation of the input data in relation to the transaction, or errors in relation to the data stream of the input data; and improving the input data to be used in the transaction by correcting or adding information before executing the transaction.

Apparatuses and methods in a communication system are disclosed. In a network element, an encoder module obtains as an input network data that is representative of the current condition of the communications network, the network data comprising a plurality of values indicative of the performance of network elements and performs (800) feature reduction providing at its output a set of activations. A clustering module performs (802) batch normalisation and an amplitude limitation to the output of the encoder module to obtain normalised activations. A clustering control module calculates a projection of the normalised activations and determines (804) a clustering loss. A decoder module calculates (806) a reconstruction loss. The network element backpropagates the reconstruction loss and the clustering loss through the modules.