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62
results.
Updated on
22/04/2026 [07:21:00]
Results 25 to 50 of 62
Absstract of: US20260100030A1
Provided is an electronic apparatus including memory configured to store at least one instruction, and a processor configured to execute the at least one instruction to obtain a first image including an object, input the first image to a first neural network model that is configured to be trained by using a plurality of second images in relation to a plurality of predefined types, obtain first probability information including a first probability of the object corresponding to a first type among the plurality of types and a second probability of the object corresponding to a second type among the plurality of types, obtain second probability information, through a second neural network model, indicating a type of the object included in the first image, by using a plurality of third images corresponding to the first type and a plurality of fourth images corresponding to the second type based on a difference between the first probability and the second probability being less than a first threshold value and based on a first input, and identify the type of the object based on the second probability information.
Absstract of: US20260100267A1
0000 Deep learning methods and systems for detecting biomarkers within volumetric biomedical imaging dataset using such deep learning methods and systems are provided. Embodiments predict the clinically useful biomarkers in optical coherent tomography images, ultrasound images, magnetic resonance imaging images, and computed tomography images using deep neural networks.
Absstract of: EP4722968A2
Methods and systems that provide data privacy for implementing a neural network-based inference are described. A method includes injecting stochasticity into the data to produce perturbed data, wherein the injected stochasticity satisfies an ε-differential privacy criterion and transmitting the perturbed data to a neural network or to a partition of the neural network for inference.
Absstract of: WO2026070140A1
The present disclosure relates to vehicle control based on a neural network. In particular, the present disclosure relates to determining confidence in the output of a neural network by comparing a firing pattern observed during operation of a vehicle with a reference firing pattern obtained by observing firing of neurons during a training phase.
Absstract of: WO2026065613A1
An electronic nose instrument operable in both scheduled and on-demand modes and a method for online real-time detection and analysis of multi-component odors. A hardware unit of the electronic nose instrument mainly comprises: a gas-sensitive sensor array module (I), a headspace sampling module (II), a pressurization cylinder (III), a computer control and analysis module (IV), a backup power supply (V), and a clean air cylinder (VI). A main housing integrates the first four components. Within a cycle time T=180-600 s, the pressurization cylinder (III) significantly increases a gas-sensitive response by means of short-term pressure multiplication. The gas-sensitive sensor array obtains a 50-dimensional sensing sample for single detection. A large odor dataset X comprises online detection data from the electronic nose instrument, and offline detection data from olfactometry and chromatography etc. The detection data is decomposed into multiple single-concentration sub-tasks. A machine learning cascade model is formed by multiple learning groups consisting of single neurons, and shallow and deep neural networks. The electronic nose instrument can flexibly achieve online real-time identification of odor pollutants and multi-component concentration estimation and prediction.
Absstract of: WO2026064957A1
Disclosed is a feature fusion classification method for multiple types of packaging bag, relating to the technical field of classification of multiple types of packaging bag. On the basis of a random forest concept, the present invention provides a feature fusion classification algorithm for multiple types of packaging bag. By means of three different classification methods: a support vector machine, template matching, and a neural network, denoising processing is performed on images of various packaging bags transmitted from a camera using a median filter, and on the basis of a homomorphic filtering algorithm, enhancement processing is performed on the denoised images. The images are classified by separately using a support vector machine model, a template matching algorithm, and a neural network model, and a majority rule-based voting mechanism is implemented for prediction results of the three methods, to obtain a final result. The voting mechanism-based feature fusion classification algorithm for multiple types of packaging bag of the present invention provides high accuracy, reduces error generation, and obtains more accurate results.
Absstract of: WO2026069497A1
This information processing device performs predetermined processing using a neural network model and comprises an inference unit that performs the predetermined processing using the model. The model includes a positional encoding unit that calculates relative positional information of each token in a token string using a wavelet function, and an attention mechanism that calculates a latent representation of the token string using the positional information.
Absstract of: WO2026070418A1
In this information processing method using a neural network for a structure represented as a set of nodes arranged in space, a computer executes processing including: receiving input of a state of each node; calculating, on the basis of states between the nodes, a frame representing a coordinate axis for each node; and extracting, using the frame, information having predetermined symmetry of the structure from each node.
Absstract of: US20260093717A1
Certain aspects of the disclosure provide for a difference analysis method. In certain aspects, a difference analysis method may include embedding a set of source documents into a knowledge graph, wherein each source document is embedded in the knowledge graph as a set of segments and a set of associations connecting two or more segments. A difference may be determined between a first segment in the set of segments of a first source document and a second segment in the set of segments of a second source document. In response to determining the difference between the first segment in the set of segments of the first source document and the second segment in the set of segments of the second source document, determining a significance of the difference on the second source document based on one or more associations of the set of associations connected to the second segment.
Absstract of: WO2026069149A1
It is described a method for processing an image using a vision graph neural network, said vision graph neural network comprising a window-based grapher module (7) including a first fully connected layer with batch normalization (9), a windows partitioning module (10), a dynamic graph convolution module (11), a windows reverse module (12), a second fully connected layer with batch normalization (13) and a skip connection (15), wherein said window-based grapher module (7) is configured to: - process a feature vector (X) of said image (2) through said first fully connected layer with batch normalization (9) to obtain a normalized feature vector; - partition said normalized feature vector into a plurality of non-overlapping windows using said windows partitioning module (10); - for each window, construct a graph where nodes represent patches of said image (2) within the respective window and edges represent relationships between said nodes, and apply a graph convolutional operation to each graph to update node features within each window using said dynamic graph convolution module (11); - reshape the updated node features from each window back into the format of said normalized feature vector using said windows reverse module (12); - process the reshaped feature vector through said second fully connected layer with batch normalization (13); combine said feature vector (X) directly with an output of said second fully connected layer with batch normalization (13) using said skip c
Absstract of: US20260093769A1
0000 A method for solving constrained combinatorial optimization task includes receiving data associated with a constrained combinatorial optimization task and optimization variables, the data including a set of constraints defined over subsets of the optimization variables. A hypergraph is constructed based on the set of constraints and optimization variables. Each node and hyperedge of the hypergraph corresponds to an optimization variable and a constraint respectively. A hypergraph neural network is initialized based on the hypergraph and trained using unsupervised learning to output a continuous assignment for each optimization variable. The training includes updating a plurality of learnable input embeddings associated with the nodes and weight parameters of the network. The continuous assignment is then mapped to a discrete assignment selected from a set of discrete values to yield a solution to the constrained combinatorial optimization task.
Absstract of: WO2026071683A1
According to an embodiment of the present disclosure, disclosed is a method for predicting the price of cryptocurrency on the basis of an artificial neural network. The method may comprise the steps of: acquiring monitoring reference information from a user terminal; generating a chart image according to the monitoring reference information; generating a pattern prediction result corresponding to the chart image on the basis of an artificial neural network-based pattern prediction model; and transmitting, to the user terminal, notification information generated on the basis of the pattern prediction result.
Absstract of: US20260094429A1
Techniques related to poly-scale kernel-wise convolutional neural network layers are discussed. A poly-scale kernel-wise convolutional neural network layer is applied to an input volume to generate an output volume and include filters each having a number of filter kernels with the same sample rate and differing dilation rates optionally in a repeating pattern of dilation rate groups within each of filters with the pattern of dilation rate groups offset between the filters the poly-scale kernel-wise convolutional neural network layer.
Absstract of: US20260094399A1
0000 Apparatuses, systems, and techniques to generate bounding box information. In at least one embodiment, for example, bounding box information is generated based, at least in part, on a plurality of candidate bounding box information.
Absstract of: WO2026066156A1
Disclosed in the present invention is a method for constructing a GAN-based defect detection model for pole pieces of a blade battery. The method comprises the following steps: collecting several images of defective target pole pieces; pre-processing the images to obtain pre-processed images, and extracting valid defective regions from the images; acquiring contour information for the valid regions, and accurately classifying the contour information on the basis of characteristic parameters; performing data augmentation on classified data, so as to obtain an augmented dataset; and in the dataset, using defect types and position information as labels to train a neural network, and using a neural network model as a defect detection model for pole pieces of a blade battery. In the present invention, a dataset that has undergone data augmentation is inputted into a network as a training set, such that the problem of a severe shortage of training samples caused by numerous types of battery pole piece defects and low occurrence probabilities of individual samples is solved, and the detection accuracy of a defect detection model for pole pieces of a battery is greatly improved, thereby enabling the rapid and accurate detection of various defects and position information of pole pieces of a blade battery.
Absstract of: EP4718326A2
0001 The technical solution involves a board card including a storage component, an interface apparatus, a control component, and an artificial intelligence chip. The artificial intelligence chip is connected to the storage component, the control component, and the interface apparatus, respectively; the storage component is used to store data; the interface apparatus is used to implement data transfer between the artificial intelligence chip and an external device; and the control component is used to monitor a state of the artificial intelligence chip. The board card is used to perform an artificial intelligence operation.
Absstract of: EP4718327A1
0001 An electronic device for executing a neural network model including a non-linear operation and an operation method thereof are provided. The operation method of the electronic device includes obtaining data to be inferred and obtaining an inference result of the data output from the neural network model as the data is input to the neural network model including a plurality of nodes, wherein, in an inference process, a first weight applied when a value of a first node among the plurality of nodes is transmitted to a second node may be updated based on a value of a first reference node, which is any one of the plurality of nodes.
Absstract of: EP1000000A1
The invention relates to an apparatus (1) for manufacturing green bricks from clay for the brick manufacturing industry, comprising a circulating conveyor (3) carrying mould containers combined to mould container parts (4), a reservoir (5) for clay arranged above the mould containers, means for carrying clay out of the reservoir (5) into the mould containers, means (9) for pressing and trimming clay in the mould containers, means (11) for supplying and placing take-off plates for the green bricks (13) and means for discharging green bricks released from the mould containers, characterized in that the apparatus further comprises means (22) for moving the mould container parts (4) filled with green bricks such that a protruding edge is formed on at least one side of the green bricks.
Absstract of: US20260085661A1
0000 A method, system, and device for wind speed prediction and layout optimization in wind power generation are provided. The method includes: obtaining a basic wind resource dataset of a target region; constructing a physics-informed neural network model based on the basic wind resource dataset; obtaining wind speeds data at a specific location in a velocity field based on the physics-informed neural networks and constructing a training dataset; training the physics-informed neural network model based on the training dataset; reconstructing a wind speed distribution within the velocity field and predicting wind speeds for a next time period with a wind farm using the trained physics-informed neural network model; and optimizing a layout of a wind turbine cluster based on a reconstructed wind speed distribution within the velocity field. The present application reconstructs a two-dimensional velocity field of the wind farm by training the PINN and enables accurate ultra-short-term wind speed prediction.
Absstract of: US20260087344A1
0000 A method using a convolutional neural network to auto-determine a first floor height (FFH) and a FFH elevation (FFE) of a building. The FFH, and FFE of the building are determined with respect to the terrain or surface of the parcel of land on which the building is located. In turn, by knowing the FFH and/or FFE of the building on the parcel, it is possible to use that information while performing a flood risk assessment to a property without requiring a personal inspection of the parcel by a human.
Absstract of: US20260087822A1
The present invention relates to a method for monitoring a harbor performed by a computing device, the method for monitoring the harbor according to an aspect of the present invention comprising: obtaining a harbor image having a first view attribute; generating a segmentation image having the first view attribute and corresponding to the harbor image by performing an image segmentation using an artificial neural network trained to output information, from an input image, related to an object included in the input image; generating a transformed segmentation image having a second view attribute from the segmentation image having the first view attribute based on a first view transformation information used to transform an image having the first view attribute into an image having the second view attribute different from the first view attribute; and calculating berthing guide information of the ship based on the transformed segmentation image.
Absstract of: US20260088021A1
Apparatuses, systems, and techniques to facilitate understanding of media content using neural networks to adjust playback speed and volume based on environmental and other factors. In at least one embodiment, playback of media content is slowed down or sped up if audio associated with said media content is difficult to understand based on background noise, accent, difficulty of material, as well as other factors that decrease understandability of media content.
Absstract of: US20260087787A1
A method of condensing a training dataset and an image processing device are provided. The method of includes generating a cluster set by clustering a training dataset; generating an initial condensed high-resolution (HR) dataset by selecting, for each cluster included in the cluster set, one or more images from among a respective cluster in the training dataset; obtaining a first loss of a first neural network model based on the training dataset and obtaining a second loss of a second neural network model based on the initial condensed HR dataset. The method further includes generating a condensed HR dataset by updating, based on the first loss and the second loss, pixels in each of the one or more images included in each cluster of the initial condensed HR dataset; and executing an operation instruction to transmit the condensed HR dataset to an image processing device.
Absstract of: US20260089329A1
A computer-implemented method for lossy image or video compression, transmission and decoding, the method including the steps of (i) receiving an input image at a first computer system; (ii) encoding the input image using a first trained neural network, using the first computer system, to produce a latent representation; (iii) quantizing the latent representation using the first computer system to produce a quantized latent; (iv) entropy encoding the quantized latent into a bitstream, using the first computer system; (v) transmitting the bitstream to a second computer system; (vi) the second computer system entropy decoding the bitstream to produce the quantized latent; (vii) the second computer system using a second trained neural network to produce an output image from the quantized latent, wherein the output image is an approximation of the input image.
Nº publicación: WO2026061185A1 26/03/2026
Applicant:
THREE GORGES HI TECH INFORMATION TECH CO LTD [CN]
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Absstract of: WO2026061185A1
The present application discloses a model training method and apparatus, a construction safety evaluation method and apparatus, and a device. The model training method comprises: acquiring sample data; on the basis of association information between training samples and true value labels, determining label missing ratios of the training samples in different dimensions and first weight values of the training samples in different dimensions; determining second weight values of the training samples on the basis of the label missing ratios; and inputting the sample data, the first weight values, and the second weight values into a preset neural network model for training until a loss value of a target loss function of the preset neural network model meets a model convergence condition, so as to obtain a target prediction model. In this way, by improving a weighted loss function, when sample data having a partially missing label is kept, a loss value of a missing label of a sample is calculated with reference to a weight, thereby solving the problem of reduced prediction accuracy caused by missing samples, improving the accuracy of a model, and simultaneously and accurately predicting prediction values of multiple dimensions.
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