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130
resultados
Última actualización
04/10/2026 [07:08:00]
Resultados 125 a 130 de 130
Resumen de: WO2026188138A1
One embodiment of the present invention sets forth a technique for managing a design of a system. The technique includes determining, via a user interface, a set of data associated with a set of artifacts included in the design of the system and a system diagram included in the set of artifacts based on the set of data. The technique also includes generating, via execution of one or more machine learning models, a first portion of a system implementation included in the set of artifacts based on the system diagram. The technique further includes receiving, via the user interface, a first set of user inputs specifying one or more changes to the system diagram and generating, via execution of the one or more machine learning models, a second portion of the system implementation based on the one or more changes to the system diagram.
Resumen de: EP4804085A1
A computer-implemented method, wherein the method determining a contribution of, optionally pairwise or higher-order, combinations of features between at least two application feature sets to an application output of a trained machine learning model, each combination of features comprising at least one feature of a first application feature set and at least one feature of a second application feature set.
Resumen de: US20250148482A1
0000 A computing system for detecting patterns in data is provided. The computing system includes a model engine configured to receive an initial dataset, and segment the initial dataset into a plurality of subsets. The model engine is further configured to assign a weight to each subset based at least in part on an age of the subset, train a machine learning model on each subset separately in accordance with the assigned weighting for that subset. The model engine is further configured to receive a candidate dataset, analyze the candidate dataset using the trained machine learning model, and assign a score to the candidate dataset based on the analysis. The computing system further includes a rules engine configured to receive the candidate dataset and the corresponding score from the model engine, and generate and output, based at least in part on the score, a decision regarding the candidate dataset.
Resumen de: GB2642421A
Method for training a neuro-symbolic machine learning model, comprising: for each image depicting at least two objects of a training dataset: inputting the image into a neural module 102, 200, 202 to obtain bounding boxes and features therein (digit); inputting each bounding box and object feature into a symbolic module (106, Fig.1; rest of Fig.2) to obtain a plurality of possible labels i.e. partial labels 212 and possible relationships 210 as a new partially-labelled training dataset; and training the neuro-symbolic model (neural module and the symbolic module) by calculating a loss from a ground truth label for the image. The symbolic module may use a set of logical rules to constrain the labels and explanations (R1-R5, Fig.7). The trained neuro-symbolic model may generate a scene graph, perform action recognition, perform visual question answering (Fig.4) or control an autonomous or semi-autonomous electronic device. The electronic device may be a moveable robot or a wearable augmented reality device.
Nº publicación: EP4804364A1 09/09/2026
Solicitante:
HITACHI ENERGY LTD [CH]
Hitachi Energy Ltd
Resumen de: EP4804364A1
Rotor angle instability is a key criterion of dynamic stability in power networks. State-of-the-art machine-learning approaches are difficult to scale and have limited inputs with which to make predictions as to rotor angle instability. Accordingly, disclosed embodiments utilize a machine-learning model that is applied to bus voltage angles, which are local quantities available at every bus in the power network, to derive a prediction of the risk of rotor angle stability in the power network. These predictions may be biased in order to avoid false negatives. The machine-learning model may be a message-passing neural network. The resulting predictor is capable of quickly and reliably flagging potential rotor instability within a power network.
BOPI
Sede Electrónica
