40 results about "Discrete set" patented technology

Some embodiments provide a method for training a machine-trained (MT) network. The method propagates multiple inputs through the MT network to generate an output for each of the inputs. each of the inputs is associated with an expected output, the MT network uses multiple network parameters to process the inputs, and each network parameter of a set of the network parameters is defined during training as a probability distribution across a discrete set of possible values for the network parameter. The method calculates a value of a loss function for the MT network that includes (i) a first term that measures network error based on the expected outputs compared to the generated outputs and (ii) a second term that penalizes divergence of the probability distribution for each network parameter in the set of network parameters from a predefined probability distribution for the network parameter.

The invention discloses a police martial arts fighting action evaluation method and system based on peak shifting time pulses, belongs to the technical field of action behavior analysis, and solves the problem that tiny dynamic changes of joints cannot be captured in time when an existing method processes a complex motion sequence. The method comprises the steps of extracting key skeleton points from a target action video, converting serialized motion data into a discrete pulse signal set through a pulse function, and processing the pulse signal set based on a peak shifting time function. The multi-dimensional time sequence evaluation model carries out police martial arts fighting action recognition and training evaluation on the target action video based on the pulse feature set; according to the method, the time sequence relation of the skeleton points is analyzed by introducing the peak shifting time function, the time sequence and integrity of feature extraction are ensured based on the feature adaptive extraction algorithm of the flexible trigger mechanism of the water tank, and the accuracy and robustness of motion behavior evaluation are improved, so that a scientific and effective evaluation tool is provided for motion training and evaluation.

Methods and systems for optimizing a semiconductor measurement recipe that is robust to variations of hardware modeling parameters and geometric modeling errors are described herein. Robust measurement recipe optimization minimizes a cost function including one or more regularization terms that constrain the process space, and thus, significantly reduces the computational effort required to optimize a measurement recipe. This reduces overall process time and improves wafer throughput. In some examples, optimization is performed based on measurement data associated with multiple instances of a semiconductor structure; each instance characterized a different value of one or more geometric parameters of interest. In some examples, the search for optimized measurement recipes is limited to the discrete set of measurement system parameter values associated with the available measurement data set. In this manner, the performance of a particular measurement recipe is validated using existing measurement data.

A signal encoding and decoding protocol to convey discrete sets of information combined in a single carrier signal is disclosed. The protocol uses modulation of both amplitude and pulse width to carry multiple sets of information. A first signal is pulse width modulated to encode a first data and a second signal is pulse amplitude modulated to encode a second data. The two modulated signals are combined to generate a carrier signal encoded with both the first and second data.

An implementation may involve: receiving, by an application launcher, a message on behalf of a client device; determining a server application based on the message and metadata, wherein the server application is associated with a path, wherein the path includes a set of steps, wherein each of the set of steps is respectively associated with program logic to perform a discrete set of functions, wherein a generate result step causes the server application to determine an outcome of executing the path, and wherein a handle request step causes the server application to provide, to an external service, a representation of a request related to the outcome; providing, by the application launcher, a client application to the client device; and executing, by an orchestrator, the path, wherein a context of the execution is written in a database between execution of at least some of the set of steps.

Database query processing techniques are disclosed. In various embodiments, a query associated with a database is received. A byte code representation of the query is generated, including by decomposing the query into a discrete set of streaming operators defined over associated data frames, wherein the byte code includes code defining for each operator in the discrete set of streaming operators the processing to be performed by that operator and further embodies a data flow graph that defines a flow of data to and through the discrete set of streaming operators. The byte code is executed by a query processing engine to generate and return a query result.

A database (POH) provides semi-structured data about a first deployed system and other deployed systems, wherein each deployed system is an industrial automation system at a different location, and wherein each deployed system consists of modules. A data ingestion component (DIC) uses an ontology (ONT) to integrate the semi-structured data into a single knowledge graph (KG), by representing a structural development of each deployed system over time, by creating an event node (EN) for each structure-changing event in the deployed system and attaching a subgraph to the event node representing a context of the structure-changing event. A generative deep-learning component (DL) has been trained with the knowledge graph to perform knowledge graph completion. It receives as input a new subgraph with a new event node and generates a recommended action for a structural change of one of the deployed systems, by completing the new subgraph with at least one action node (AN) identifying the recommended action and at least one module node (MN) identifying a first module that is added, replaced, or removed by the recommended action in the first deployed system. This modelling strategy for the knowledge-graph breaks down the continuous flow of changes at a customer site into a discrete set of subgraphs for which well-established similarity metrics exist, allowing for an automated recommending of changes to the deployed system. This automation of a previously time-consuming, costintensive, and largely manual process yields a number of benefits.

Methods and systems for one or more computers, in which a method includes obtaining encoding sequences of an input data item, in which each encoding sequence includes a respective encoding vector at each position of multiple positions. The method includes generating a combined encoding sequence by, at each position, combining the respective encoding vectors at the position in the multiple of encoding sequences. The method includes processing the combined encoding sequence using a deduplicator neural network to generate a deduplicated encoding sequence that includes a respective deduplicated encoding vector for each of the positions and applying a tokenizer to the deduplicated encoding sequence to identify, for each deduplicated encoding vector, a discrete representation of the deduplicated encoding vector generated from respective codebook vectors from each of a set of one or more codebooks, in which each codebook is a respective discrete set of codebook vectors.

The present application discloses a method, device, equipment and medium for brain-computer interaction intention recognition based on AI glasses. The method includes: obtaining intention recognition data segments of a target user for constructing a discrete set of duration and a discrete set of frequency, obtaining a key time-frequency feature point position sequence and an overall feature point position sequence; obtaining the number of occurrences and confidence scores of the key time-frequency feature point position sequence in the overall feature point position sequence, and obtaining a local anomaly evaluation score corresponding to the key time-frequency feature point position sequence according to the number of occurrences and the confidence score; obtaining multiple data blocks, and constructing a frequency conversion set according to the multiple data blocks; obtaining data fluctuation information and a graph complexity score corresponding to the data block; determining a decoding threshold adjustment factor according to the intention complexity score and the anomaly evaluation score, standardizing the intention recognition data segments, and obtaining normalized decoded data to obtain the user intention instruction corresponding to the target user, so as to realize the interactive control function of the AI ​​glasses.

Certain aspects provide a method for creating a discrete set of variants of a customizable wearable object, comprising: receiving a baseline 3D design file for a baseline 3D customizable wearable object; obtaining a fitting population comprising a set of 3D anatomy models representing potential wearers of the customizable wearable object; generating a plurality of modified 3D customizable wearable objects based on fitting the baseline 3D customizable wearable object to each 3D anatomy model of the set of 3D anatomy models; selecting one or more of the plurality of modified 3D customizable wearable objects as the discrete set of variants of the customizable wearable object; and generating a set of 3D design files corresponding to the discrete set of variants of the customizable wearable object.

Imitation learning, or artificial intelligence-based learning from demonstration, aims to acquire an agent policy by observing and mimicking the behavior demonstrated in expert demonstrations. Imitation learning can be used to generate reliable and robust learned policies in a variety of tasks involving sequential decision-making, such as autonomous driving and robotics tasks. However, existing methods that use next-token-prediction (NTP) models, where the policy reduces to a classifier over a discrete set of trajectory tokens, suffer from covariate shift due to their open-loop training a closed-loop execution. The present disclosure provides closed-loop fine tuning of autonomous agent policies in a manner that can mitigate covariate shift.

Methods and apparatus for compressing and decompressing weight values associated with a neural network. The input weight values are divided into a plurality of groups, wherein each group is processed using a scaling factor. For each set of scaled input weight values, a codebook is identified from a plurality of codebooks, where each codebook represents a discrete set of centroid values. The input weight values within each group are encoded using centroid values from the identified codebook, thereby producing encoded weight values including a codebook index and a centroid index. During decompression, the encoded weight values are processed using corresponding codebooks and scaling factors to reconstruct output weight values. A codebook is generated by identifying similar distributions of scaled input weight values across different groups and clustering these values to determine centroid values. The processing element performs a decompression operation to reconstruct the weight values for neural network operations.

Imitation learning, or AI-based demonstration-based learning, aims to derive a policy for an agent by observing and imitating the behavior shown in expert demonstrations. Imitation learning can be used to generate reliable and robust learned policies for a variety of tasks requiring sequential decision-making, such as autonomous driving and robotics. However, existing methods that use next-token prediction (NTP) models, where the policy is reduced to a classifier over a discrete set of trajectory tokens, suffer from covariate shifting due to their open-loop training and closed-loop execution.The present disclosure provides a fine-tuning of the policies of autonomous closed-loop agents in a manner that can mitigate covariate shifting.

This disclosure relates to methods, computer-readable media, devices and systems for detecting objects in a discrete set of data points forming a 3D point cloud (or map) representing a scan of an environment and / or for improving the 3D point cloud generation using a simultaneous localization and mapping (SLAM) algorithm. In some implementations, parameter cues, such as dimensional cues of objects in the environment and / or trajectory cues of the trajectory of the scanner that may be provided by language input of a user during the data acquisition process may be utilized to realize improvements in object detection and / or mapping of the environment by the SLAM algorithm.

Database query processing techniques are disclosed. In various embodiments, a query associated with a database is received. A byte code representation of the query is generated, including by decomposing the query into a discrete set of streaming operators defined over associated data frames, wherein the byte code includes code defining for each operator in the discrete set of streaming operators the processing to be performed by that operator and further embodies a data flow graph that defines a flow of data to and through the discrete set of streaming operators. The byte code is executed by a query processing engine to generate and return a query result.

This invention belongs to the field of electric vehicle charging load prediction technology, and discloses an electric vehicle charging load prediction method based on GCN-Rollout, including the following steps: real-time acquisition of multi-source data including road network topology data, traffic flow status data, and individual electric vehicle status data; based on graph convolutional network (GCN), extracting charging load impact features reflecting spatial dependencies from the multi-source data, and preprocessing them to obtain the current state vector representing the traffic flow and charging status at the road network nodes at the current moment; defining the charging demand ratio as a decision variable, and generating a discrete set of decision variables based on the current state vector; performing simulation evaluation for each candidate decision variable in the decision variable set to construct the corresponding long-term cost evaluation function; effectively solving the problem in the prior art where the prediction model has insufficient ability to fuse multi-source dynamic information, resulting in poor prediction accuracy and disconnection from scheduling decisions.

An implementation may involve: receiving, by an application launcher, a message on behalf of a client device; determining a server application based on the message and metadata, wherein the server application is associated with a path, wherein the path includes a set of steps, wherein each of the set of steps is respectively associated with program logic to perform a discrete set of functions, wherein a generate result step causes the server application to determine an outcome of executing the path, and wherein a handle request step causes the server application to provide, to an external service, a representation of a request related to the outcome; providing, by the application launcher, a client application to the client device; and executing, by an orchestrator, the path, wherein a context of the execution is written in a database between execution of at least some of the set of steps.

A system, method, and processor architecture for an arbitrary precision nomographic analytical calculation application specific integrated circuit (APNACASIC) is provided. The APNACASIC uses a set of stacked dynamic electrically erasable programmable read-only nomographic grid layers to calculate (output) an arbitrarily-precise evaluation of an arbitrary mathematical function at an arbitrarily closely-separated discrete set of domain values (input).

This invention discloses a method for assessing the multi-energy flexibility of industrial loads considering uncertainties. The method includes: constructing a model of the energy consumption and output relationship of a production process within an industrial load considering output adjustment constraints; using graph theory to associate the production process and product storage warehouses to construct storage and output requirement constraints; constructing a discrete set of states for the industrial load and performing a feasible interval search to construct a multi-energy feasible interval; searching the feasible interval to obtain the multi-energy flexibility of the industrial load, thus achieving the assessment of multi-energy flexibility. This invention assesses the multi-energy flexible adjustment capability of industrial loads considering production uncertainties. It can be used to adjust the demand of industrial loads for various energy forms at the operational level, and can be applied to multiple fields such as electricity and multi-energy demand response. This assists energy systems in utilizing the adjustment capability of industrial loads, enhancing system flexibility, and supporting the flexible operation and optimized scheduling of energy systems.

Database query processing techniques are disclosed. In various embodiments, a query associated with a database is received. A byte code representation of the query is generated, including by decomposing the query into a discrete set of streaming operators defined over associated data frames, wherein the byte code includes code defining for each operator in the discrete set of streaming operators the processing to be performed by that operator and further embodies a data flow graph that defines a flow of data to and through the discrete set of streaming operators. The byte code is executed by a query processing engine to generate and return a query result.

An homomorphic encryption technique enables one to carry on operations on encrypted messages without decrypting them, these encrypted messages being associated to unencrypted messages that belong to a discrete set consisting of pi distinct elements, with pi being an odd number, the elements of the discrete set being such that twice the difference between any two of these elements is not an integer number, said discrete set being for instance the discrete torus Tpi={−(pi−1) / 2, −(pi−1) / 2+1, . . . , (pi−1) / 2−1, (pi−1) / 2} / pi. In particular, a specific bootstrapping procedure form such encrypted messages is disclosed. The disclosed technology concerns also an homomorphic encryption technique for carrying on operations on encrypted messages associated to unencrypted messages that belong Zp, p being equal to the product of r integer numbers pi, i=1 . . . r that are pairwise coprime. The disclosed technology concerns also associated electronic devices and systems implementing such techniques.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating an output sequence from an input sequence. One of the methods includes receiving the input sequence; processing the input sequence using a latent prediction model configured to autoregressively predict a sequence of discrete latent variables that is shorter than the output sequence and that encodes the output sequence, wherein each discrete latent variable in the sequence is selected from a discrete set of latent variables; and processing the input sequence and the predicted sequence of discrete latent variables using a parallel decoder model configured to generate the outputs in the output sequence in parallel from the input sequence and the predicted sequence of discrete latent variables.

This application discloses a method and related products for weld seam extraction and robot trajectory generation based on digital model prior guidance, relating to the field of automated welding technology. The method includes: acquiring downsampled point clouds and undownsampled point clouds, where the downsampled point clouds are obtained by downsampling a coarsely registered point cloud, and the undownsampled point clouds are the coarsely registered point clouds without downsampling; finely registering the downsampled point clouds with the digital model point cloud to obtain a fine registration matrix; projecting the undownsampled point clouds onto the spatial coordinate system of the digital model point cloud using this matrix to obtain a projected point cloud; spatially clipping the projected point cloud using a predefined region of interest (ROI) for the weld seam in the digital model point cloud to obtain a weld seam region point cloud; performing local geometric feature analysis on the weld seam region point cloud, and extracting the weld seam trajectory based on a discrete set of weld seam feature points, and generating a smooth robot welding trajectory. This application can achieve high-precision, low-computing-cost weld seam trajectory generation.

This invention relates to the field of intelligent operation and maintenance technology for new energy power generation and power electronic equipment, specifically a fault diagnosis system for photovoltaic inverter operation anomalies. The system includes: an edge data acquisition and dynamic reconstruction unit that maps discrete time-series data into high-dimensional phase space trajectory data; a topological cross-section feature extraction unit that generates a discrete set of two-dimensional intersection coordinates; a topological fingerprint generation unit that generates a topological fingerprint matrix; a cloud-based topological inference unit that generates diagnostic status results; and an anomaly tracing closed-loop control unit that monitors the diagnostic status results in real time and generates a backtracking lock command when the diagnostic status result is abnormal or suspected of being faulty, triggering the edge controller to stop overwriting the circular buffer and lock the corresponding high-frequency original waveform data. This invention enables resource-constrained microcontrollers to complete feature extraction within microseconds, ensuring the real-time performance of edge-side processing.

Systems and methods for signal processing are described. Embodiments receive a digital signal comprising original signal values corresponding to a discrete set of original sample locations, generate modulation parameters based on the digital signal using a modulator network, wherein each of a plurality of modulator layers of the modulator network outputs a set of the modulation parameters, and generate a predicted signal value of the digital signal at an additional location using a synthesizer network, wherein each of a plurality of synthesizer layers of the synthesizer network operates based on the set of the modulation parameters from a corresponding modulator layer of the modulator network.

Methods and apparatus for compressing and decompressing weight values associated with neural networks. Input weight values are divided into groups, with each group being processed using a scale factor. For each group of scaled input weight values, a codebook is identified from multiple codebooks, where each codebook represents a discrete set of centroid values. Input weight values within each group are encoded using centroid values from the identified codebook, resulting in encoded weight values comprising codebook indices and centroid indices. During decompression, the encoded weight values are processed to reconstruct output weight values using the corresponding codebooks and scale factors. The codebooks are generated by identifying similar distributions of scaled input weight values across different groups and clustering these values to determine centroid values. A processing element performs the decompression operations to reconstruct the weight values for use in neural network operations.

Database query processing techniques are disclosed. In various embodiments, a query associated with a database is received. A byte code representation of the query is generated, including by decomposing the query into a discrete set of streaming operators defined over associated data frames, wherein the byte code includes code defining for each operator in the discrete set of streaming operators the processing to be performed by that operator and further embodies a data flow graph that defines a flow of data to and through the discrete set of streaming operators. The byte code is executed by a query processing engine to generate and return a query result.

A combination deterministic and stochastic waveform generator having a waveform generating algorithm for a waveform s(t)=exp{j2π[g∫0tfDET(τ)dτ+h∫0tfSTO(τ; x)dτ]}, wherein fDET(t) is a deterministic frequency function of time; wherein fSTO(t; x) is a stochastic frequency function of time; wherein 0≤g≤1 and 0≤h≤1 are relative weighting parameters; wherein fSTO(t; x) is parameterized on vector x comprised of a discrete set of random / pseudo-random values to yield a waveform; and wherein, for h and g values in between 0 and 1, the waveform is a combination deterministic and stochastic waveform and is partially Doppler tolerant and resistant to electronic attack. The combination deterministic and stochastic waveform is unique from pulse to pulse, is independent of velocity, can be generated by a standard waveform generator, and can be implemented with a standard modern radar system.

A signal encoding and decoding protocol to convey discrete sets of information combined in a single carrier signal is disclosed. The protocol uses modulation of both amplitude and pulse width to carry multiple sets of information. A first signal is pulse width modulated to encode a first data and a second signal is pulse amplitude modulated to encode a second data. The two modulated signals are combined to generate a carrier signal encoded with both the first and second data.

The invention relates to an optimized binary convolution unit (ESBCU) based on a channel expansion and contraction technology, and belongs to the technical field of binary neural network optimization. The optimized binary convolution unit aims to solve the problem that an existing basic binary convolution unit (BBCU) is insufficient in information expression ability due to the fact that an output value is limited to a limited discrete set. According to the core scheme, through channel expansion, contraction and copy operation, firstly, the number of input channels is expanded by t times to increase a discrete set output by binary convolution, then the number of convolution kernel output channels is reduced to 1 / t of the original number to maintain the calculation complexity unchanged, and finally, through processing of a channel copy splicing layer and a batch normalization (BN) layer, the convolution kernel output channel number is reduced to 1 / t of the original number to maintain the calculation complexity unchanged. And recovering the final output channels to the number compatible with the next layer. According to the method, on the premise of not increasing the calculation complexity, the output dynamic range of the binary convolution is effectively expanded, and the feature representation capability of the binary convolution is enhanced. Experiments show that the unit can effectively improve model performance (such as a PSNR index) in low-level visual tasks such as image super-resolution and the like, and can be flexibly integrated into various deep neural networks as a plug-and-play standard module.