42 results about "Artificial neural network" patented technology

Disclosed herein is an artificial neural network-based medical image diagnosis assistance apparatus for assisting in diagnosing a medical image based on a medical artificial neural network. A medical image diagnosis assistance apparatus according to an embodiment of the present invention 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 about a first medical image, to detect user feedback on the first analysis result input by a user, to determine whether the first analysis result and the user feedback satisfy training conditions, and to transfer the first analysis result and the user feedback satisfying the training conditions to a style learning model so that the style learning model is trained on the first analysis result and the user feedback.

A processor-implemented method performed for text-based video editing includes receiving a video input and a text prompt. The video input includes a sequence of video frames. Features of the video input are extracted to generate a latent representation of the video input. Noise is injected to the latent representation of the video input to generate a noise injected latent. The noise is conditioned on the video input. An artificial neural network (ANN) model processes the noise injected latent based on the text prompt to adapt the video input according to the text prompt.

A processor-implemented method includes obtaining a color filter array (CFA) input image, obtaining pattern information corresponding to a CFA, preprocessing the input image based on the pattern information, generating an inferred image by inputting the preprocessed input image to an artificial neural network (ANN) model, and generating an output image by selecting, for each pixel, from either one of the preprocessed input image and the inferred image, based on the pattern information.

A data processing apparatus comprises a captioning model to receive gameplay telemetry data indicative of one or more in-game properties for a session of a video game, the captioning model comprising an artificial neural network (ANN) trained to output caption data comprising one or more captions in dependence upon a learned mapping between gameplay telemetry data and caption data, one or more of the captions comprising one or more words for providing a visual description for the session of the video game, and output circuitry to output one or more of the captions.

A machine learning system is provided to enhance various aspects of machine learning models. In some aspects, a substantially photorealistic three-dimensional (3D) graphical model of an object is accessed and a set of training images of the 3D graphical mode are generated, the set of training images generated to add imperfections and degrade photorealistic quality of the training images. The set of training images are provided as training data to train an artificial neural network.

A method of wireless communication, by a user equipment (UE) includes receiving, from a base station, a jointly trained artificial neural network. The method also includes calculating a value representing at least one of (1) a gradient estimate for a weight of the jointly trained artificial neural network, or (2) the weight of the jointly trained artificial neural network. The method further includes expanding the value into a numerical system with base N into a plurality of digits. The method also includes determining a number and / or a location of the plurality of digits to transmit based on a deterministic task assignment rule received from the base station or a probabilistic task assignment rule. The method further includes transmitting the determined number and / or the determined location of the plurality of digits to the base station.

A processor-implemented method for video compression using an artificial neural network (ANN) includes receiving a video via the ANN. The ANN extracts a first set of features of a current frame of the video and a second set of features of a reference frame of the video. The ANN determines an estimate of correlation features between the first set of features of the current frame and the second set of features of the reference frame. The estimate of the correlation features are encoded and transmitted to a receiver.

A system and method are disclosed for estimating the floor area of an existing building from a 3D digital representation. Various embodiments can leverage reality capture devices, such as LIDAR laser scanners or photogrammetry techniques, to obtain 3D representations. The 3D representations may be segmented using artificial neural networks, isolating individual floors. These segmented floors may be projected into 2D and processed through another neural network to yield a precise binary representation differentiating between floor and non-floor areas. The resulting binary images allow for the computation of the total floor area in square footage for each level.

Methods, apparatus and systems that relate to hardware accelerators of artificial neural network (ANN) performance that significantly reduce the energy and area costs associated with performing vector dot-product operations in the ANN training and inference tasks. Specifically, the methods, apparatus and systems reduce the cost of bit-level flexibility stemming from aggregation logic by amortizing related costs across vector elements and reducing complexity of the cooperating narrower bitwidth units.

A data compression system (119) configured to perform data compression of input data to generate compressed input data. Performing the data compression comprises: receiving (205) input data; dividing (207) the input data into a plurality of input chunks; operating a large language model to generate (215), for each of the plurality of input chunks, a reduced summary of the input chunk; and collating (217) the plurality of reduced summaries to generate the compressed input data. Also disclosed is a generative artificial intelligence system comprising an artificial neural network (137) configured to receive the compressed input data and one or more questions, and generate, on the basis of the compressed input data, a response to each of the one or more questions. The division of the input data into a plurality of input chunks may be done according to one or more contextual boundaries of the data. Examples of contextual boundaries include paragraph, page, or section breaks, or different topics. Contextual boundaries may be identified by vectorising the data. The reduced summary may be produced by selectively discarding data from the respective input chunk.

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 digital personal assistant (DPA), (vii) 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++)), (viii) a second set of computer implementable instructions to analyze and interpret contextual data and (ix) an autonomous artificial intelligence (AI) agent is disclosed.

The methods and systems are directed to automated computer analysis and machine learning. Specifically, the systems and methods for using machine learning to generate fault prediction models and applying the fault prediction models to logical circuits to optimize test point insertion determinations and optimize fault detection in the logical circuit. Disclosed are methods and systems that that generates training data from training circuits (and optionally generate training circuits), trains a learning segment (which may include an artificial neural network (ANN)) using the training data. The learning segment (once trained) generates fault prediction models to predict the quality of a TP inserted on a given circuit location and optimize TPI for a given circuit. The methods and systems described provide computational (CPU / processing) time advantages and precision over conventional methods.

Testing circuitry and methods are disclosed for use with analog neural memory in deep learning artificial neural networks. The analog neural memory comprises one or more arrays of nonvolatile memory cells. The testing circuitry and methods can be utilized during sort tests, qualification tests, and other tests to verify programming operations of one or more cells.

Various techniques are provided for providing neural networks with increased efficiency. In one example, a system includes a first artificial neural network (ANN), a second ANN, and a logic device. The first ANN is configured to receive a first plurality of data inputs associated with a data stream and process the first data inputs to generate a first inference output after a first latency. The second ANN is configured to receive a second plurality of data inputs associated with the data stream and process the second data inputs to generate a second inference output after a second latency less than the first latency. The logic device is configured to receive the second inference output before the first inference output is generated. Additional systems and methods are also provided.

A method and a system for validating a trained artificial neural network on the basis of a test data set.

An artificial neural network for processing low-resolution images to generate super-resolution images includes feed-forward connections between layers. The network includes an input layer, one or more convolution layers, wherein the input layer is connected to a first convolution layer of the convolution layers, and a output layer connected to a last convolution layer of the convolution layers. An input image is input to the input layer and to at least one of the convolution layers, an initial output of the input layer is input to at least one of the convolution layers, and a layer output of at least one of the convolution layers is input to at least one subsequent convolution layer.

The invention discloses a direct-current micro-grid energy storage system performance balance droop control method, which comprises the following steps of: firstly, evaluating the residual capacity of each energy storage unit battery on line in real time based on an ANN battery capacity evaluation model; secondly, calculating a battery performance index based on the residual capacity of the battery, and calculating a performance index deviation degree of each energy storage unit battery; determining a system working mode according to the maximum deviation degree of the battery performance indexes of all the energy storage units, and starting droop control based on performance balance when the deviation of the battery performance indexes is large; next, each energy storage unit corrects the initial droop coefficient according to the battery performance index deviation degree to obtain an optimized droop coefficient, then a reference value of the battery output voltage of each energy storage unit is obtained through droop control, and finally a PWM switching signal is generated through voltage-current double closed-loop control of an energy storage unit interface DC / DC converter to enable the output to follow the reference value. Through the method, the overall service life of the direct-current micro-grid energy storage system can be effectively prolonged.

A method for generating a module configured to determine concentration of an analyte in a sample of a body fluid is disclosed. The method includes providing a first set of measurement data derived from images of one or more test strips indicating a color transformation in response to a body fluid containing an analyte. The images can be recorded by multiple devices with differing cameras, software and / or hardware device configurations for image recording and image data processing. A neural network model can be generated in a machine learning process applying an artificial neural network and a module configured to determine concentration of an analyte in a second sample of a body fluid can be generated. Further, the present disclosure includes a system for generating the module as well as a method and a system for determining concentration of an analyte in a sample of a bodily fluid.

A method for converting an input image (Iin) having a first dynamic range (Δ1), into an output image (Iout), having a second dynamic range (Δ2) distinct from the first dynamic range, the input image being represented by an input luminance component (Yin) comprising first input pixel values, and at least one input chrominance component (Cbin, Crin) comprising second input pixel values, the output image being represented by an output luminance component (Yout) comprising first output pixel values, and at least one output chrominance component (Cbout, Crout) comprising second output pixels values, the method comprising the steps of: a) determining at least one statistical value (sin) associated with the input image, based on at least part of the first input pixel values, b) determining each first output pixel value based on a corresponding first input pixel value and said at least one statistical value, c) applying on first input nodes (115, 116, 117) of an artificial neural network (CNN1), the second input pixel values, respectively, and applying on at least one second node of the artificial neural network, the at least one statistical value, the artificial neural network being configured to provide on respective output nodes (111,112), the second output pixel values. A corresponding device (1) for converting an input image into an output image is also described.

The invention provides a system and method for training artificial neural networks for solving multiple tasks simultaneously, wherein the artificial neural network comprises at least one capsule layer. The invention also provides a system and a method for solving multiple tasks simultaneously, wherein the artificial neural network comprises at least one capsule layer. The invention further provides additional connected aspects.

A controller is configured to generate configuration parameters using an artificial neural network and to use the configuration parameters in interacting with a non-volatile memory. At least one of the configuration parameters is not a threshold voltage for reading the non-volatile memory. The controller is configured to implement the artificial neural network. The controller may have a prediction buffer configured to store multiple sets of configuration parameters generated by the artificial neural network. The controller may select one of the sets as the configuration parameters.

The present technology discloses the development of a trained learning model for forecasting system using artificial neural networks (ANNs) along with genetic algorithms (GA) to predict the compressive and flexural strength of engineered bamboo products. The model processes mechanical attributes including binder properties, fiber content, and curing duration that is to be compares the results with the database of the system. Due to the significant cons of manual testing in engineered bamboo products, including being time-consuming, resource-intensive, and limited scalability, the utility model provides an efficient and cost-effective alternative for determining material strength. Furthermore, the optimized testing parameters generated by the model can be stored and applied in subsequent testing of engineered bamboo products.

An example method comprises receiving a number of inputs to a system employing an artificial neural network (ANN), wherein the ANN comprises a number of ANN partitions each having respective weight matrix data and bias data corresponding thereto stored in a memory. The method includes: determining an ANN partition to which the number of inputs correspond, reading, from the memory the weight matrix data and bias data corresponding to the determined ANN partition, and a first cryptographic code corresponding to the determined ANN partition; generating, using the weight matrix data and bias data read from the memory, a second cryptographic code corresponding to the determined ANN partition; determining whether the first cryptographic code and the second cryptographic code match; and responsive to determining a mismatch between the first cryptographic code and the second cryptographic code, issuing an indication of the mismatch to a controller of the system.

An artificial neural network memory system includes at least one processor configured to generate a data access request corresponding to an artificial neural network operation; and at least one artificial neural network memory controller configured to sequentially record the data access request to generate an artificial neural network data locality pattern of the artificial neural network operation and generate an advance data access request which predicts a next data access request of the data access request generated by the at least one processor based on the artificial neural network data locality pattern.

A processor-implemented method includes obtaining, with a backbone artificial neural network, an original feature map of point cloud data. The method also includes deforming the point cloud data, with a deformation artificial neural network, into a number of deformed point cloud objects based on the original feature map of point cloud data. The method further includes combining the deformed point cloud objects into a mixed point cloud. The method still further includes extracting, with the backbone artificial neural network, a mixed feature map from the mixed point cloud. The method includes extracting a number of deformed feature maps from the deformed point cloud objects. The method still further includes computing, with a contrastive module, a loss for the backbone artificial neural network and for the deformation artificial neural network based on the mixed feature map and the deformed feature maps.

Disclosed are a method and apparatus for generating a texture map, which may include: receiving input data related to a target fabric from a user; classifying the input data into mapping data for generating texture maps corresponding to information of the target fabric; generating at least one of a normal map or a diffuse map by inputting the mapping data into an artificial neural network (ANN) model; and generating a composite texture map corresponding to the target fabric based on the normal map or the diffuse map.

The invention relates to a method for predicting anthocyanin content and residual shelf life, which comprises the following steps of: 1, performing post-harvest treatment on grape samples, collecting physiological and biochemical index data, and constructing a data set; 2, based on the physiological and biochemical index data collected in the step 1, determining the remaining shelf life of the grapes; step 3, taking the physiological and biochemical index data and post-acquisition processing conditions in the step 1 as input, taking the anthocyanin content as output, constructing an anthocyanin content prediction model based on XGBoost, and obtaining a prediction result; and 4, by taking the physiological and biochemical index data and post-acquisition processing conditions obtained in the step 1 as input and the residual shelf life obtained in the step 2 as output, constructing a residual shelf life prediction model based on ANN, and obtaining a prediction result. According to the method, the problem of prediction deviation caused by data limitation of a traditional method is solved, noise can be reduced, and high-precision decision support is provided for quality control.

An image processing system and a method therefor are disclosed. The image processing system may comprise: a data hiding device which includes an encoder that hides a secret message in a first image in a first video so as to generate a second image, and which transmits a second video including the second image; an image output device for receiving and playing back the second video; a hidden data restoration device, which includes a decoder for restoring the secret message, uses at least one camera to capture the image output device by which the second video is being played back, restores, by means of the decoder, the secret message from the captured image, and provides a user experience on the basis of the restored secret message; and a server, which generates the encoder and the decoder by applying an artificial neural network deep learning model, provides the encoder to the data hiding device, and provides the decoder to the hidden data restoration device.

A water consumer controller system wherein the water consumer includes a water receptable, and takes the form of a urinal, sink, toilet bowl 809, cistern, shower or bath, the system include a fluid in

This invention discloses an online method for judging the operational health status of the central valve under throttling heating boundary conditions. By extracting information from characteristic parameters under throttling conditions, this invention comprehensively integrates and utilizes multi-source information, sequentially extracting multi-dimensional fault state information across categories for individual characteristic parameters, ensuring the accuracy and completeness of fault state information extraction. Furthermore, this invention achieves the identification and prediction of fault state-sensitive physical quantities through dimensionality reduction decomposition of the array matrix. Combining artificial neural network sample training with targeted focusing on expert databases, this invention can achieve high-precision evaluation of the operational health status of the central valve and real-time online judgment of its operational health status under throttling heating boundary conditions, while significantly reducing the amount of sample data used. This is of great significance for promoting the safe and reliable operation of heating systems under partial load conditions.