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Ekintzailea edo enpresa bazara eta zure negozioaren errentagarritasuna sustatu eta hobetu nahi baduzu, zure erakundeko aktibo ukiezinak behar bezala babestuz, gune honetan aurkituko duzu behar duzuna.
61
resultados
Última actualización
15/04/2026 [08:01:00]
Resultados 50 a 61 de 61
Resumen de: US20260080619A1
0000 The present disclosure relates to the geometrically accurate reconstruction of a scene based on an implicit representation provided by a neural network. A method of reconstructing an environment of at least one camera device can include capturing by the at least one camera device a plurality of images of an environment of the at least one camera device. The method can also include obtaining an implicit representation of the environment based on the plurality of images by means of a neural network and reconstructing the environment based on the implicit representation, including reconstructing at least one object of the environment having a flat surface. The implicit representation is obtained based on an objective function of the neural network comprising a regularization term obtained based on Singular Value Decomposition.
Resumen de: US20260080561A1
The present disclosure provides a control method of a broadcast monitoring system, a control apparatus of a broadcast monitoring system, a computer device, and a computer storage medium, and belongs to the field of image recognition and terminal broadcast monitoring. The control method of a broadcast monitoring system includes: obtaining a detected image; performing gaze recognition on the detected image through a pre-trained target neural network model, to obtain a recognition result of the detected image; and sending the recognition result to a terminal, so that the terminal determines a display state based on at least the recognition result.
Resumen de: US20260079456A1
A computer-implemented method includes receiving, at a neural network, input data indicating one or more tasks associated with production, wherein the neural network is integrated with cognitive architecture that includes an imaginal memory buffer, utilizing the input data indicating one or more tasks with one or more production rule sets associated with an expert decision, obtain goal data indicating the expert decision utilizing imaginal memory buffer, selecting, from the imaginal memory buffer, one or more sectors associated with goal data indicating the novice decision, goal data indicating the intermediate decision, and goal data indicating the expert decision to obtain data indicating decision-making results, and in response to meeting a convergence threshold utilizing the data indicating decision-making results, outputting a simulation associated with a recommendation indicating information associated with at least the input data indicating one or more tasks associated with production.
Resumen de: US20260080675A1
0000 A data processing method is applied to image processing. The method includes: obtaining a first image and a second image, where the first image and the second image include text; obtaining an image feature of the first image and an image feature of the second image through a first neural network; obtaining, through a second neural network, a text feature of text included in the first image and a text feature of text included in the second image; performing fusion on a first feature representation and a third feature representation to obtain a first target feature representation; performing fusion on a second feature representation and a fourth feature representation to obtain a second target feature representation; determining a loss based on a relationship between the first target feature representation and the second target feature representation; and updating the first neural network based on the loss.
Resumen de: US20260080529A1
An image inspection apparatus includes a learned neural network storage storing a neural network that previously learns weighting factors between input, intermediate and output layers, and an inferer determining failure/no-failure of a workpiece and classify the workpiece to classes based on an image of the workpiece. The inferer performs first and second inferences. In the first inference, the inferer determines failure/no-failure of the workpiece based on failure/no-failure feature quantities that are obtained by providing the workpiece image to the neural network and a failure/no-failure determination boundary. In the second inference, the inferer define a classification boundary to be used to classify an inspection workpiece to the classes in a feature quantity space of the neural network based on classification feature quantities that represent the different-type classification workpiece images, and classifies a workpiece to the classes based on classification feature quantities of an image of the workpiece and the classification boundary.
Resumen de: US20260080569A1
0000 As one aspect disclosed herein, an image processing method may be proposed. The method is executed in an electronic device comprising one or more processors and one or more memories for storing instructions to be executed by the one or more processors, and may comprise the steps of: acquiring a plurality of mixed images of a sample including a plurality of biological molecules; and generating unmixed images of at least one of the plurality of biological molecules from the plurality of mixed images by using an unmixing matrix. The value of at least one element included in the unmixing matrix may be determined on the basis of artificial neural network model training.
Resumen de: AU2025223879A1
Abstract 5 Computer-implemented method and system for assessing a non-destructive ultrasonic test on a plastic pipe weld, including the following steps: • receiving an ultrasound scan file by way of a server, 10 • a computing unit analyzing the ultrasound scan file based on predefined criteria, wherein the computing unit comprises a neural network, • the computing unit assessing the ultrasound scan file based on the predefined criteria. Figure 1 5 Abstract Computer-implemented method and system for assessing a non-destructive ultrasonic test on a plastic pipe weld, including the following steps: 10 receiving an ultrasound scan file by way of a server, a computing unit analyzing the ultrasound scan file based on predefined criteria, wherein the computing unit comprises a neural network, the computing unit assessing the ultrasound scan file based on the predefined criteria. Figure 1 ug b s t r a c t u g 100% CR USSD 100% VISUAL ? Fig. 1 ug u g % % ?
Resumen de: US20260080696A1
A method for analyzing pathological images based on a magnification-aligned transformer (MAT) is provided, in which a pathological image dataset is identified and segmented to obtain pathological image patches; the pathological image patches is screened to obtain a patch set; an MAT classification network model including a self-supervised magnification alignment module and a global-local Transformer classification module is constructed; the MAT classification network model is trained for self-supervised magnification alignment using the patch set in the self-supervised magnification alignment module; the MAT classification network model is further trained using a convolutional neural network (CNN)-transformer; and a pathological image classification prediction result is obtained using the trained MAT classification network model. A system for implementing such method is also provided.
Resumen de: WO2026059148A1
The present invention relates to a method, a system, and a computer-readable recording medium for determination of an arthritis grade using a plurality of artificial neural models, wherein a first model corresponding to a CNN-based artificial neural network model and a third model corresponding to a transformer-based artificial neural network model are trained through training data corresponding to X-ray images labeled in a first manner of labeling with a low arthritis grade, a second model corresponding to a CNN-based artificial neural network model and a fourth model corresponding to a transformer-based artificial neural network model are trained through training data corresponding to X-ray images labeled in a second manner of labeling with a high arthritis grade, and the arthritis grade for an X-ray image is determined using the first model, the second model, the third model, and the fourth model to diagnose the arthritis grade while implementing a process in which medical staff performs overall/local determination and optimistic/pessimistic determination of the X-ray image at an actual medical site.
Resumen de: US20260080249A1
0000 A multi-hardware energy-consumption-oriented channel pruning method and a related product. The method includes: ranking importance of a filter in a to-be-pruned convolutional neural network (CNN) model by using a feature distribution discrepancy (FDD) evaluation model based on a feature distribution of an original network model, and deleting a filter with a lowest importance ranking to generate a candidate first pruning model; determining an energy consumption of the candidate first pruning model by using an energy consumption estimation model based on actual measured data; performing trade-off processing on importance of a filter in the candidate first pruning model and the energy consumption of the candidate first pruning model by using a multi-objective evolutionary solving model, and obtaining a pruning scheme corresponding to each hardware device; and pruning the to-be-pruned CNN model by using the pruning scheme, and obtaining a second pruning model corresponding to each hardware device.
Nº publicación: EP4711869A1 18/03/2026
Solicitante:
DN SOLUTIONS CO LTD [KR]
DN Solutions Co., Ltd
Resumen de: EP4711869A1
The present invention relates to a multi-task real-time inference scheduling system and real-time inference scheduling method of a machine tool, wherein a central control unit is connected to each of one or more individual control units through a network, receives a use context of each machine tool through each individual control unit, generates a multi-task learning model through a neural network, infers multiple tasks required to be performed by the individual control unit of each machine tool through machine learning by using real-time use contexts collected during operation of the machine tool by a use scenario, and schedules the multiple tasks of the machine tool through machine learning.
BOPI
Sede Electrónica
