3118 results about "Artificial neuronal network" patented technology

A sensor data evaluation device may include an artificial neural network, configured to receive and process first testing data, and output a first testing data output, wherein the first testing data represent sensor data of a first sensor detecting a sensor input; and one or more processors, configured to receive sensor deficiency information representing a deficiency of a second sensor; generate second testing data, wherein the second testing data are based on the sensor deficiency information; implement the artificial neural network to receive and process the second testing data; and output from the output layer a second testing data output; determine a difference between the first testing data output and the second testing data output; and if the difference is outside of one or more predetermined ranges or does not satisfy one or more predetermined criteria, send a signal representing a result of the determined difference.

A computer-implemented method for performing at least one computational operation on an encrypted input by at least one processor of a server in a client-server setting, where parameters of the computational operation are private to the server and the input is private to the client is provided. The method includes receiving, by the server, a ciphertext c of a leveled homomorphic encryption (LHE) scheme as encrypted input. Randomness is homomorphically added by the server to the ciphertext c and the resulting ciphertext b is transmitted to the client. The server receives a refreshed ciphertext b′ obtained by the client in a ciphertext refresh procedure including decrypting and re-encrypting the ciphertext b. The server homomorphically removes the previously added randomness from the received refreshed ciphertext b′ to obtain a refreshed ciphertext c′. The server performs the at least one computational operation on the refreshed ciphertext c′.

Methods, systems, and techniques for metadata determination and storage. A large language model that is implemented using at least one artificial neural network receives an initial prompt that includes a query related to the metadata. The metadata is in respect of data that is part of a dataset, and the initial prompt includes context for the query. The large language model determines the metadata in response to the query using the context. Once determined, the metadata is stored in the dataset such that the metadata is associated with the data to which it relates.

An intelligent (self-learning) sensor-aware and / or context-aware subsystem comprising (i) a System-on-a-Chip (SoC), (ii) a radio transceiver, (iii) a microphone, (iv) a voice processing module, (v) a (bio-inspired) neuromorphic event camera or a hyperspectral camera, (vi) a first set of computer implemental instructions in artificial neural networks (ANN) (which may include a transformer model / diffusion model or Poisson flow generative model++ (PFGM++)), (vii) a second set of computer implementable instructions to analyze and interpret contextual data and (viii) an autonomous artificial intelligence (AI) agent is disclosed.

In a method, for training an artificial neural network (ANN) to reduce noise and / or artefacts in an electron microscopy image, a plurality of training image pairs is generated. For each pair, an undistorted synthetic specimen image and a distorted image are created by simulating additional noise and / or artefact features. The ANN is trained, in which the distorted images are used as input and the corresponding undistorted images as output. An adversarial training strategy is used in which the ANN is trained, as a generator network, in conjunction with concomitantly training a further ANN, as a discriminator network, to differentiate output produced by the generator network from synthetic images in the training set. In training, parameters of the ANN and further ANN are optimized using a generator loss function and a discriminator loss function, in which a dependency exists between said loss functions to train the networks in an adversarial manner.

A method training an artificial neural network (ANN) on a remote host computes, using a trusted process deployed in a trusted execution environment (TEE) on the remote host, a key-pair for a homomorphic encryption scheme and shares, by the trusted process, the public key (PK) of the key-pair with an untrusted process deployed on the remote host. The method splits the training procedure of the ANN between the untrusted process and the trusted process, wherein the untrusted process computes encrypted inputs to neurons of the ANN by means of the homomorphic encryption scheme, while the trusted process computes outputs of the neurons based on the respective encrypted inputs to the neurons as provided by the untrusted process.

A machine vision system may include a meta-imager including a meta-optic and a polarization-sensitive photodetector. A machine vision system may further include and at least one processor operably coupled to the polarization-sensitive photodetector, and at least one memory operably coupled to the at least one processor. A machine vision system may be configured to: receive, from the polarization-sensitive photodetector, a plurality of feature maps; input, into a trained artificial neural network, the plurality of feature maps; and process, using the trained artificial neural network, the plurality of feature maps to recognize an object.

The invention discloses a traffic infrastructure full life cycle carbon emission assessment method based on a knowledge graph. The method comprises the steps that knowledge is acquired through multi-source data acquisition, entities, attributes and relations related to carbon emission are defined, and knowledge graph ontology design and graph construction are carried out; establishing a carbon emission evaluation model based on an artificial neural network on a framework of the knowledge graph through data preprocessing, multi-layer perceptron network structure design and model training; and in combination with the carbon emission evaluation result and the optimization knowledge in the knowledge graph, generating a specific optimization strategy, and performing combinatorial optimization of the optimization strategy to formulate a recommendable optimization scheme for execution of an optimization decision. According to the scheme provided by the invention, carbon emission can be accurately quantified, a scientific and reasonable optimization strategy is generated, dynamic adjustment is supported, and a systematic and intelligent solution is provided for green and low-carbon development of traffic infrastructures and other engineering projects.

A computer-implemented method for generating image sequences includes receiving, at a computing device, values of a set of weights for a diffusion model. The weights are generated by training a first artificial neural network using training frames of training image data in combination with a first set of data derived from the frames of training image data. The values of the set of weights are adjusted during the training. A second artificial neural network present on the computing uses the values of the set of weights and receives a second set of data derived from frames of image data containing at least some scene information present in the training frames of training image data. The second set of data is provided to the second artificial neural network to implement the diffusion model. Images corresponding to the frames of image data are then generated by the second artificial neural network.

The invention provides a rock mass trace automatic extraction method and system based on point cloud, and belongs to the technical field of geotechnical engineering / data processing. The method comprises the following steps: acquiring regular body point cloud data and rock mass point cloud data, taking the regular body point cloud data as a source domain based on an induced transfer learning thought, training a universal trace feature point extraction model by using an artificial neural network (ANN) technology, and then transferring the trained trace feature point extraction model to a target domain, namely actual rock mass point cloud. And carrying out coarse extraction on the rock mass point cloud trace feature points, and then sequentially carrying out fine extraction, feature point connection and trace combination processing on the rock mass trace feature points to obtain a final rock mass trace. According to the method, for the problem that threshold selection of different rock mass data densities is difficult, differences of different rock mass point clouds are considered, a trace feature point extraction method combining a multi-scale surface change index and machine learning is provided, threshold setting in the point clouds is not needed, and trace extraction precision and efficiency are improved.

The invention relates to a deep learning-based historical urban style integrity evaluation method and system. The method comprises the following steps of: obtaining urban street view image data of a plurality of point locations; extracting a sky ratio and a green vision rate of an urban street scene image according to the urban street scene image data, respectively taking the sky ratio and the green vision rate as a street profile scale spatial feature and a natural element spatial feature, and calculating a signage color number and a color harmony degree of the urban street scene image as building color spatial features; calculating a traditional material proportion and material consistency of the urban street scene image as material mechanism spatial features; for the urban street view image data, obtaining an expert score obtained based on an ELO scoring algorithm; and constructing and training an evaluation model based on an artificial neural network based on the various spatial features and the small-scale expert scoring data set, and realizing historical urban style and appearance integrity evaluation by using the evaluation model.

Disclosed herein is an artificial neural network-based medical image analysis apparatus for analyzing a medical image based on a medical artificial neural network. The artificial neural network-based medical image analysis apparatus includes a computing system, and the computing system includes at least one processor. The at least one processor is configured to acquire or receive a first analysis result obtained through the inference of a first artificial neural network from a first medical image, to input the first analysis result to a second artificial neural network, to acquire a first evaluation result obtained through the inference of the second artificial neural network from the first analysis result, and to provide the first evaluation result to a user as an evaluation result for the first medical image and the first analysis result.

A set of values can be selected from a plurality of fields of a table in a database. At least one adverb or adjective can be selected for the set of values. Join paths for values in the set of values can be determined. A structured query language pattern can be determined based, at least in part, on at least one value in the set of values, the at least one adverb or adjective for the set of values, and the join paths for the set of values. The structured query language pattern can be stored to first training data configured, at least in part, for use in machine learning to train a text-to-SQL model, the text-to-SQL model comprising a first artificial neural network and configured to convert first natural language text to a first structured query language query.

The invention relates to a method for operating an at least assisted parking function (3) for a vehicle (1), comprising: providing sensor information (10) describing a parking space (6); and determining (S2) a parking trajectory (8) from a current location of the vehicle (1) to a parking location (9) in the parking space (6) by applying a trajectory planning algorithm (11) to the provided sensor information (10). The method may include at least assisted operation (S4) of the vehicle (1) to move it along the determined parking trajectory (8). The trajectory planning algorithm (11) comprises an artificial neural network trained by reinforcement learning using a curriculum (23).

Disclosed herein are a method, an apparatus, and a storage medium for image encoding / decoding. An intra-prediction mode for the target block is derived, and intra-prediction for the target block that uses the derived intra-prediction mode is performed. The intra-prediction mode for the target block is derived using an artificial neural network, and an MPM list for the target block is derived using information about the target block, pieces of information about blocks adjacent to the target block, and the artificial neural network. The artificial neural network outputs one or more available intra-prediction modes. Further, the artificial neural network outputs match probabilities for one or more candidate intra-prediction modes, and each of the match probabilities for the candidate intra-prediction modes indicates a probability that the corresponding candidate intra-prediction mode matches the intra-prediction mode for the target block.

The present invention discloses a radar scattering field simulation method for a three-dimensional moving target group combined with a neural network, and belongs to the field of electromagnetic simulation technology. The method of the present invention converts the electromagnetic response of a three-dimensional moving target group into the response of the target when it is distributed at different positions, and extracts the three-dimensional numerical Green's function of the randomly distributed target group in the solution domain by changing the spatial distribution of the target group; the three-dimensional numerical Green's function is split into scattering terms and coupling terms, and the artificial neural network is trained separately; when training the artificial neural network, according to the characteristics of the moving target group, its position distribution and scattering terms / coupling terms are input into the artificial neural network as the main features; the scattering terms and coupling terms output by the artificial neural network model are combined into numerical Green's function terms, which can be applied to integral equations to achieve fast and accurate solution of the scattering field of the three-dimensional moving target group.

The present invention is related to a method intended for manufacturing a composite structure, preferably for an aircraft. The method comprises training a set of artificial neural networks (3.1, 3.2, 3.3) to obtain a composite structure which fulfills certain specification requirements (material properties, shear loads, compression loads, buckling constraints). Said neural networks are trained with data obtained from analytical equations and from a finite element model. The invention is also related to a computer program product and a computer-readable storage medium which perform some steps of such a method. Finally, the invention further provides an aircraft (12) which comprises composite structures (13, 14, 15) manufactured by means of said method.

The invention discloses a saline-alkali soil improvement control analysis method and system based on digital twinning, and belongs to the technical field of saline-alkali soil improvement control analysis, and the method comprises the steps: firstly obtaining and preprocessing the terrain, soil permeability and precipitation data of saline-alkali soil, predicting a retention water accumulation region through employing a support vector machine algorithm, and estimating the amount of retained water by using a Kriging interpolation method. Then, the dynamic change process of the retained water is simulated through an artificial neural network algorithm, the information is integrated into a digital twinborn model, and visualization of spatial distribution and dynamic change of the retained water is achieved. And finally, on the basis of retained water information in the digital twin model, optimizing the layout and depth of the drainage ditch, selecting proper drainage materials and modes, and forming a complete drainage design scheme. According to the method, accurate prediction, dynamic simulation and visual analysis of the retention water of the saline-alkali soil are realized through a method of combining data driving and an intelligent algorithm, and a scientific basis and decision support are provided for improvement of the saline-alkali soil and agricultural production.

Systems, methods, and instrumentalities are disclosed for video encoding and / or video decoding using artificial neural networks (e.g., convolutional neural networks or recurrent neural networks), attention, and / or attention with spatial attributes. For example, an apparatus may be configured to perform one or more of the following: obtaining a context block, a current block, and a latent vector associated with the context block; performing at least one convolution on the context block, the reference block, and the latent vector; generating motion flow data associated with the current block based on the at least one convolution; or generating a bitstream the comprises an indication of the motion flow data. The motion flow data may be quantized. The generated bitstream may comprise an indication of the quantized motion flow data.

The technical scheme of the invention discloses an epilepsy diagnosis system based on electroencephalogram data channel selection and a KAN convolutional spiking neural network, which is characterized by comprising a data processing module, a channel feature extraction module, a self-adaptive channel selection module and a feature classification module. According to the method, an effective solution is provided by introducing KAN convolution, channel selection and a spiking neural network. The introduced attention mechanism allows the model to dynamically pay attention to the most relevant EEG channel and signal characteristics, so that personalized channel selection is realized. Through the mechanism, the model can distribute different weights for different individuals, only key signals most related to epileptic seizure are selected, the calculation burden of redundant channels is avoided, and meanwhile the classification precision is improved. Finally, the artificial neural network is converted into the pulse neural network, an event-driven calculation mode is adopted, energy consumption is effectively reduced, and the method is particularly suitable for being applied to portable equipment.

The invention provides a composite material performance prediction and process optimization method based on a neural network, and the method comprises the steps: firstly collecting multi-source data in the preparation and test process of a composite material, carrying out the preprocessing of the data, screening key feature variables as input variables, constructing a feedforward artificial neural network model, and predicting and outputting the performance indexes of the composite material. And training the model, performing iterative optimization on model parameters, and optimizing composite material process parameters by using the optimized model based on a reverse optimization strategy of a genetic algorithm to obtain an optimal process parameter combination. The invention provides a scientific, efficient and reliable tool for design and optimization of composite materials, and particularly has wide application prospects in high-requirement industries such as aerospace and the like.

The present disclosure relates to a method for generating a composite image, executed by one or more processors. The method for generating a composite image includes receiving a foreground image, receiving a background image, generating information on a position and size within the background image from the foreground image and the background image using a first artificial neural network, and generating a composite image based on the foreground image, the background image, and the information on the position and size within the background image.

The invention discloses a new energy boost transformer abnormity early warning system and method, and the system comprises a data collection module which employs a sensor technology, a data collection card carries out the collection of the data of a new energy boost transformer, and carries out the early warning of the new energy boost transformer. The sensors comprise a temperature sensor, a voltage sensor, a current sensor, a vibration sensor and an oil level sensor. Feature vectors are selected by adopting a wavelet transform algorithm, a boost variation anomaly positioning model is established by adopting an improved artificial neural network to determine an anomaly position, parameters of an anomaly detection model are determined by adopting a genetic algorithm, and an anomaly type is judged. The MFE-TS model is established to optimize the objective function so as to improve the accuracy of new energy boost variation abnormity identification, the man-machine interaction module is adopted to display the abnormal position and the abnormal type on the user interface, and the new energy boost variation abnormity early warning capability is improved.

Devices, methods, and systems related to ultrasound imaging or modulating of the nervous system are described. The devices may comprise, for example, one or more ultrasound transducers, wherein an ultrasound transducer from the one or more ultrasound transducers can comprise an implantable ultrasound transducer, wherein the implantable ultrasound transducer can comprise a sonolucent window. The methods and systems may also comprise, for example, a method of imaging and / or modulating the nervous system of a subject using the one or more ultrasound transducers. The method of imaging and / or modulating the nervous system of the subject can be based on a closed-loop operation, wherein an iteration of the imaging and / or the modulating of the nervous system is based on a prior iteration. The closed-loop operation can comprise ultrasound imaging and ultrasound-based modulating or electrophysiological modulating. The methods can further comprise methods of analyzing ultrasound data, such as via an artificial neural network.

A data-centric system for analyzing agricultural crops, consisting of: a data acquisition module configured to capture images of agricultural fields using cameras, unmanned aerial vehicles (UAVs) or sensors, with the sensors collecting data on soil moisture, temperature, light, humidity and pH; a data preprocessing module configured to: resize the acquired images to a standardized dimension suitable for input to a deep learning model; apply noise reduction using a Gaussian filter; improve image contrast through histogram equalization; and perform image magnification through rotation, reflection, and scaling transformations; a feature engineering module configured to extract the following features: color features, which include color histograms, mean, and standard deviation of color channels; texture features using Gray-Level Co-occurrence Matrix (GLCM) properties, which include contrast, dissimilarity, homogeneity, energy, angular moment (ASM), and correlation; shape features, which include contour area, perimeter, aspect ratio, and roundness; and other features, which include the green pixel ratio and edge density; a classification module configured to: implement deep learning-based classification models selected from the group consisting of Support Vector Machine (SVM), Artificial Neural Network (ANN), Convolutional Neural Network (CNN), ResNet18, Random Forest (RF), SegNet, VGGNet, Naive Bayes (NBG), Decision Tree (DT), K-Nearest Neighbors (KNN), and DeepLab; detecting and classifying weed species in the images of agricultural fields; and diagnosing plant diseases based on the features extracted from the images of agricultural fields; an output module comprises a user interface configured to display the classification and recognition results; and a recommendation module configured to suggest treatment solutions for diagnosed plant diseases through the output module's user interface.

Improved placement of memory and functional modules, ‘tiles’, within a tiled processor architecture are disclosed for linear algebra calculations involving vectors and matrices comprising large amounts of data. The improved placement places the data in close proximity to the functional modules performing calculations using the data. These modules enable these calculations to be performed more quickly while using less energy. These modules, in particular, improve the efficiency of the training and application of deep learning and artificial neural network systems. This Abstract and the independent Claims are concise signifiers of embodiments of the claimed inventions. The Abstract does not limit the scope of the claimed inventions.

The invention relates to a factory equipment operation and maintenance decision-making system based on multi-modal data fusion and hybrid reasoning, which comprises a multi-modal data fusion framework and a hybrid reasoning model, and is characterized in that the multi-modal data fusion framework is used for integrating multi-modal data and dynamically weighting fusion features through a cross-modal attention mechanism; the hybrid reasoning model comprises a rule reasoning module, a probabilistic reasoning module and an AI enhancement module, the rule reasoning module generates a generative rule based on an expert knowledge base, the probabilistic reasoning module processes uncertainty data by adopting a dynamic Bayesian modeling equipment state transition probability, and the AI enhancement module introduces an artificial neural network to optimize rule reasoning parameters; and different factory affair scenes are adapted in combination with transfer learning. The method has the advantages that the method is suitable for power stations, power distribution rooms, fans and other scenes, and fault diagnosis, service life prediction and dynamic maintenance strategy generation are combined.

Disclosed is a multi-output regression prediction method for small samples in a laser processing process, which relates to the technical field of machine learning, and uses an adaptive dynamic genetic algorithm (ADGA) and an adaptive moment estimation algorithm (AM) to jointly optimize an artificial neural network (ANN), the algorithms being based on enhanced small samples. Data enhancement is achieved by adding noise to output data in the laser processing process, the ADGA is used to search for optimal parameters of the ANN on a global scale, and the AM is used to optimize the gradients of the optimal parameters, thereby accelerating the convergence speed of the model and enhancing the external prediction stability of the model. When the sample size in laser processing is insufficient, stable and accurate small-sample multi-output prediction can be carried out, thereby improving processing efficiency and reducing production costs in laser processing.

The embodiment of the present invention discloses a method for predicting corneal reshaping parameters based on an artificial neural network, and relates to the technical field of orthokeratology lenses. The method comprises: obtaining an initial sample data set, deleting target samples that do not contain necessary parameters, and obtaining a basic sample data set; preprocessing the basic sample data set to obtain a final sample data set; training the initial artificial neural network model to obtain a preliminary model; using the preliminary model to forward propagate the input layer data of the basic sample data set to obtain a calculation result and compare it with the result of the basic sample to obtain a final sample data set, and using the final sample data set to train the preliminary model to obtain a prediction model; inputting actual eye detection data into the prediction model to obtain corresponding corneal reshaping parameters. The present invention splits the data to be predicted into multiple dimensions, constructs different prediction models according to each data to be predicted, and then performs distributed training and uses them in combination.

Images of a hand are obtained by a camera. A pose of the hand relative to the camera may vary due to rotation, translation, articulation of joints in the hand, and so forth. Avatars comprising texture maps from images of actual hands and three-dimensional models that describe the shape of those hands are manipulated into different poses and articulations to produce synthetic images. Given that the mapping of points on an avatar to the synthetic image is known, highly accurate annotation data is produced that relates particular points on the avatar to the synthetic image. An artificial neural network (ANN) is trained using the synthetic images and corresponding annotation data. The trained ANN processes a first image of a hand to produce a second image of the hand that appears to be in a standardized or canonical pose. The second image may then be processed to identify the user.