77 results about "Hybrid approach" patented technology

A method and an apparatus for signaling high-level syntax of a hybrid INR approach. One or more syntax elements providing for reconstructing at least one part of an image or a 3D object using at least one hybrid Implicit Neural Representation network are obtained. The at least one hybrid Implicit Neural Representation network comprises at least one mapping transformation that maps input coordinates of the image or 3D object to a group of features and an INR network that takes as input the group of features and outputs values of the image or 3D object at the input coordinates. The obtained one or more syntax elements indicate parameters relating to the at least one mapping transformation and are signaled in a bitstream.

In various examples, parking area detection for autonomous and / or semi-autonomous systems and applications is described herein. Systems and methods described herein may use a hybrid method to more accurately determine geometries of the parking areas within environments. For instance, sensor data (e.g., image data, etc.) may be processed using one or more edge detection techniques to determine edge features (e.g., two-dimensional pixels, three-dimensional points, etc.) associated with an environment that includes a parking area. A predicted geometry associated with the parking area, as determined using one or more machine learning models, may then be used to filter the edge features in order to identify a portion of the edge features that represent the parking area. One or more optimization techniques may then be used to determine the final geometry associated with the parking area based at least on the filtered edge features.

There is provided a computer-implemented method for automated gap analysis that performs the steps of: generating a job competencies dataset, generating a course competencies dataset; generating a missing competencies dataset based on the job competencies dataset and the course competencies dataset, and outputting a recommended course dataset. The method identifies job competencies missing in course competencies, and recommends courses in which to include the missing competencies. The approach to competency identification comprises either a hybrid approach composed of rule-based matching and / or similarity matching, or a pre-trained large language model (LLM).

The invention discloses a wind power prediction method and system based on deep fusion of physical knowledge, a medium and a terminal, relates to the technical field of wind power generation, and mainly aims to solve the problem of low prediction result accuracy caused by low reliability of a prediction model due to a shallow fusion level in an existing hybrid method. Comprising the steps of obtaining current operation data and current environment data of a target wind power plant; determining current wind field physical data based on the current operation data and the current environment data; performing deep fusion processing on the current operation data, the current environment data and the current wind field physical data to obtain a current fusion feature vector; current wind field state parameters are calculated according to the current wind field physical data, wind power value prediction operation is carried out according to the current fusion feature vector and the current wind field state parameters, and a current predicted wind power value is obtained; and performing operation state monitoring operation and power grid dispatching operation by using the current predicted wind power value, and formulating an operation maintenance strategy.

Methods, systems, and computer-readable media are used in graph-based machine learning of analog integrated circuits (ICs). In a first aspect, functional device pairs in transistor-level circuits are detected and classified using a hybrid approach combining a subgraph isomorphism algorithm, such as VF2, with a trained relational-graph convolutional network (RGCN). The VF2 algorithm identifies candidate pairs and initial categories, while the RGCN filters false positives using link prediction scores, preserving category labels for validated pairs. In a second aspect, a relational GraphSAGE model performs multi-class link prediction on a heterogeneous graph with netlist and functional relation edge types, labeling device pairs into analog primitive categories without technology-dependent features and merging overlapping pairs into larger functional groups. In a third aspect, circuit performance is predicted using a hierarchy-aware graph neural network comprising an edge-conditioned convolution (ECC) layer and multiple Circuit graph isomorphism network layers corresponding to hierarchy levels of device groupings.

A graph database system stores and analyzes connections and relationships between entities using a database structure that represents users, messages, threads, and attachments as nodes with interconnecting edges. The system implements a hybrid approach to electronic communication classification combining self-hosted language models with computer server clustering for pattern matching and suggestion generation. Additionally, the system includes mechanisms for creating and suggesting reusable electronic communication components based on semantic analysis of communication patterns. The system architecture enables relationship analysis, digital content management, and privacy-preserving electronic communication classification.

A method for natural language to SQL (NL2SQL) statements driven by a hybrid approach combining templates and a large language model includes the following steps: constructing a pre-built SQL template library to assist the large language model in quickly matching syntactically correct SQL template skeletons; constructing a database schema information knowledge base to assist the large language model in accurately extracting entities; constructing a large-model natural language parsing and entity extraction engine to extract entity information; constructing a large-model-based template matching engine to match corresponding target SQL templates from the template library using the large language model; and constructing a large-model-based template filling engine to fill the extracted entity information into the corresponding placeholders of the selected target SQL template, generating the final executable SQL statement. By using pre-built SQL templates to ensure syntactic correctness and utilizing the large language model to parse natural language and extract entities to ensure semantic flexibility, this method solves the syntax errors and illusion problems that may occur when generating SQL using pure LLM, significantly improving the practicality and accuracy of NL2SQL technology.

The invention provides a behavior tree dynamic construction method, system and device and a medium, and the method comprises the steps: obtaining original data from a voice sensor, an image sensor and a touch sensor, carrying out the normalization and event abstract semantic processing of the original data, and obtaining a semantic event object; analyzing a corresponding relation type according to the event object, and constructing an event semantic relation graph which is constructed by adopting a predefined rule and large model mixed mode; defining a mapping rule from an event semantic relation graph to behavior tree nodes, and converting a relation type in the semantic relation graph into a behavior tree node type, including setting sequence nodes corresponding to a causal relation and setting condition nodes corresponding to a condition relation; analyzing the event semantic relation graph into a behavior tree structure according to a mapping rule, and constructing a behavior tree; and recording an execution result in the operation process of the behavior tree, and adjusting the event confidence coefficient according to the execution result to optimize the dynamic construction of the behavior tree in the next round. According to the method, the expandable behavior tree meeting the personalized expression of the accompanying robot can be created, so that the accompanying experience of the robot is improved.

The invention discloses a power distribution network voltage control method and a related device, and the method comprises the steps: carrying out the precise sensing of a multi-scale voltage situation in a space-time and physical mixed manner, namely predicting the future node voltage and a corresponding multi-scale node voltage out-of-limit risk index, obtaining a basic control strategy through a main channel, and carrying out the prediction of a multi-scale node voltage. A corresponding robust compensation strategy is obtained by adopting an anti-interference channel, the high-dimensional problem solving efficiency is improved in combination with a Red-beak-Baijie optimization algorithm, all distributed energy sources are equivalent to a quantized virtual impedance network in dual-channel MPC optimization and power distribution network node voltage control, the dimension optimization bottleneck is broken through, and the power distribution network node voltage control efficiency is improved. Finally, a global prediction-hierarchical decision-edge execution full-chain control system can be formed, and the method is suitable for voltage stability control in a high-proportion distributed energy access scene.

The invention discloses a sound field model construction method based on a mixture of a parabolic equation theory and a finite element method, and belongs to the technical field of sound field propagation. According to the method, a sound field is divided into a far-field region and a near-field region, a parabolic equation is established in the far-field region, a PINN model is constructed, an initial sound field result calculated by the parabolic equation is corrected and optimized by using the PINN model in each step of calculation by the parabolic equation, and a final sound field result is obtained. The method comprises the following steps: performing grid division in a near-field region, establishing a wave equation in each grid unit, constructing a unit stiffness matrix and a unit mass matrix, finally assembling a global stiffness matrix and a global mass matrix, and further solving detailed information of a three-dimensional sound field; and sound field information at the junction is processed through a multi-physics field coupling boundary condition at the junction. According to the method, a proper calculation method can be selected according to the physical characteristics of different areas, and the propagation rules of sound waves in different scales and environments can be better reflected.

In one implementation, a shifted-window transformer with sorted grouping is used for point cloud compression. The points are first sorted according to a specified order and then the sorted points are grouped into windows with an equal number of points. All resulting windows are operated upon by self-attention to obtain initial attention features, followed by shifting the windows and another self-attention. We utilize the proposed transformer for lossless compression of point clouds via the octree representation and lossy compression via feature coding. Downsampling can be used to obtain features at different resolutions, which can be combined in a multi-scale solution. In addition, in a hybrid approach, the features can be divided into two parts: one for the window attention and the other for convolutions. The same sorting strategy can be used throughout the proposed architecture, or the network can switch between different sorting strategies every few attention layers.

In one implementation, a shifted-window transformer with sorted grouping is used for point cloud compression. The points are first sorted according to a specified order and then the sorted points are grouped into windows with an equal number of points. All resulting windows are operated upon by self-attention to obtain initial attention features, followed by shifting the windows and another self-attention. We utilize the proposed transformer for lossless compression of point clouds via the octree representation and lossy compression via feature coding. Downsampling can be used to obtain features at different resolutions, which can be combined in a multi-scale solution. In addition, in a hybrid approach, the features can be divided into two parts: one for the window attention and the other for convolutions. The same sorting strategy can be used throughout the proposed architecture, or the network can switch between different sorting strategies every few attention layers.

This invention introduces an advanced framework for channel estimation in IRS-enabled wireless communication systems, leveraging a novel concept of "angular domain suppression" in the angular domain By strategically segmenting large antenna arrays and classifying scattering clusters into ordinary and controlled types, the method allows efficient decomposition of the channel into Type-S, Type-L, and Type-M channels. Passive beamforming at the IRS and active beamforming at the base station enable precise manipulation of signal reflections to control support sets. The process incorporates compressive sensing, compressive sensing, MMSE, LS, or machine learning techniques and metric-driven estimation, minimizing pilot overhead and computational complexity. The framework ensures resource-efficient estimation by terminating when predefined performance metrics are achieved. Furthermore, the system optimizes channel modeling through a hybrid approach, integrating ordinary scatterers and IRS elements for improved signal accuracy. This innovation provides a robust solution for enhancing communication reliability and efficiency in modern IRS-enabled networks.

In various examples, parking area detection for autonomous and / or semi-autonomous systems and applications is described herein. The systems and methods described herein may use a hybrid approach to more accurately determine the geometry of a parking area within an environment. For example, sensor data (e.g., image data, etc.) may be processed using one or more edge detection techniques to determine edge features (e.g., two-dimensional pixels, three-dimensional points, etc.) associated with an environment including a parking area. The edge features may then be filtered using predicted geometries associated with the parking area, as determined using one or more machine learning models, to identify a portion of the edge features representative of the parking area. One or more optimization techniques may then be used to determine a final geometry associated with the parking area based at least on the filtered edge features.

Hybrid classical-quantum systems for improved vehicle routing use a combination of genetic algorithms, simulated annealing, and quantum annealing. The hybrid method allows further exploration of the solution space of a capacitated vehicle routing problem (CVRP), thus identifying improved routing results. Nodes (i.e., locations) are initially clustered, and the node clusters are processed via a genetic algorithm to determine potential solutions. The potential solutions are evolved using simulated annealing to generate evolved solutions, each having evolved node clusters. An evolved solution is selected. Each of the evolved node clusters for the selected evolved solution is separately annealed by a quantum annealer to determine the optimal route through the evolved node clusters.

A hybrid approach is employed to determine an optimal packing arrangement of cargo blocks within containers loaded onto a vehicle. Cargo block data is accessed, where the cargo blocks are to be arranged into containers for transport by the vehicle having a payload area. Each cargo block is assigned to the containers subject to constraints on the cargo and the containers. A quantum annealer is invoked to individually solve an optimization problem for subsections of the payload area, where the quantum annealer determines an optimal packing arrangement of cargo blocks within the containers for each subsection of the payload area.

This invention provides a multi-task learning resource optimization method based on the lottery hypothesis, belonging to the field of multi-task learning and neural network optimization technology. This method employs a sparse expert hybrid approach based on the lottery hypothesis to effectively prune neural network model parameters, thereby significantly reducing the computational cost and storage overhead of the neural network model. The sparse expert network structure retains high sensitivity to key task information. The introduction of a softmax-based router and mask matrix allows each task to select and activate suitable expert network components based on the features of its input data. Cross-training and iterative amplitude pruning optimize the omnipotent expert matrix, further enhancing the model's generalization ability in handling complex tasks and different data distributions. Task-specific layers independently process and optimize the features of each task, reducing mutual interference between tasks and improving the accuracy of feature extraction and the efficiency of task processing.

A hybrid method for green intelligent manufacturing (GiM) combines the carbon reduction and the energy saving into the intelligent manufacturing based Industry 4.1 cloud platform. GiM assists companies to achieve the goal of net zero transition and help them advance to Industry 4.2 as soon as possible by simultaneously taking carbon footprint and energy issues into account. GiM collects large volumes of essential data (including carbon footprint) via cyber physical agents (CPAs), and sends them to two critical services of carbon management and intelligent energy management system (iEMS) deployed on the cloud platform. The two critical services optimize the energy dispatch schedule by strictly following the requirements of energy saving, carbon reduction, and net zero. Then, the state of zero defects of intelligent manufacturing achieved in Industry 4.1 can be upgraded to net zero of GiM in Industry 4.2.

A method and an apparatus for signaling high-level syntax of a hybrid INR approach. One or more syntax elements providing for reconstructing at least one part of an image or a 3D object using at least one hybrid Implicit Neural Representation network are obtained. The at least one hybrid Implicit Neural Representation network comprises at least one mapping transformation that maps input coordinates of the image or 3D object to a group of features and an INR network that takes as input the group of features and outputs values of the image or 3D object at the input coordinates. The obtained one or more syntax elements indicate parameters relating to the at least one mapping transformation and are signaled in a bitstream.

This invention discloses an AI-based modeling method for assessing the health status of integrated power supply boxes, comprising the following steps: S1, constructing a multivariate time series; S2, modeling the multivariate time series trajectory using a neural control differential equation model to obtain the latent space trajectory; S3, performing dynamic modal decomposition on the latent space trajectory to construct a modal health space; S4, generating an enhanced training sample set using a manifold interpolation hybrid method in the modal health space; S5, inputting the hybrid modal vectors from the enhanced training sample set into a health classification module; S6, inputting the latent space trajectory into a temporal semantic playback module to generate the final predicted state trajectory; S7, comparing the final predicted state trajectory with the actual historical state trajectory, and generating an anomaly marker and performing a state rollback operation when the semantic difference exceeds a set tolerance threshold. This invention integrates neural differential modeling, interpolation enhancement, and temporal semantic playback to achieve accurate assessment of the health status of integrated power supply boxes.

The invention discloses a motor bearing fault recognition method based on vibration signal analysis. According to the method, recognition of a motor bearing of an orbital wing type wind power generation device is achieved through a hybrid method of multi-modal feature extraction and a convolutional neural network. The method comprises the following steps: firstly, decomposing a complex vibration signal by adopting an empirical Fourier decomposition (EFD) method to obtain multi-scale signal components of a plurality of frequency bands, thereby effectively revealing information of an internal structure and different frequency levels in the signal; then, multi-modal feature extraction is carried out on each component in a time domain, a frequency domain and a time-frequency domain, a time-frequency signal obtained through S transformation can further capture transient features of a bearing fault, background noise and working condition disturbance are effectively inhibited, and key state change information is extracted from a non-stationary signal; and finally, the structure of the convolutional neural network is improved, multi-scale and multi-modal feature information is fused, an efficient and robust classification model is constructed, and rapid and accurate recognition of different types of bearing faults is realized.

This invention relates to a method for constructing a disaster-bearing entity based on a structure-function-behavior model, comprising the following steps: S1 entity structural semantic modeling; S2 entity functional semantic modeling; S3 behavioral semantic modeling; S4 constructing a semantic fusion module and building urban natural disaster risk factor data and knowledge data, and using the risk factor data and knowledge data to fuse the semantics formed in the models built in steps S1-S3 to realize the construction of the disaster-bearing entity. The method also includes a data and mechanism-driven twin model update method, specifically including: updating the twin model using a change detection model, and update management, wherein the change detection model includes the detection of structural and semantic changes. The method also includes a disaster risk prediction method, which uses a hybrid approach consisting of decision trees and node data formed by urban natural disaster risk factor data and knowledge data. This achieves data analysis and prediction of urban entity disaster situations with visualization of the sum of structure-function-behavior factors.

The implementation leverages a hybrid approach using both smaller and larger LLMs to generate and refine content in response to user queries / requests for content generation. In various implementations, a smaller LLM is used to handle user queries for content generation, generating initial content in response to those queries. Text hints can be generated using both the user queries and the initial content, and these hints can be configured to further include requests for focused editing. A larger LLM can be used to handle such text hints to generate focused editing of the initiated content, refining the initial content and resulting in revised content (with improved accuracy) in response to user queries for content generation.

Devices, systems, methods, and media are disclosed for domain adaptation of a trained machine learning model using hybrid learning. A hybrid approach to domain adaptation is disclosed that combines aspects of discrepancy-based, adversarial, and reconstruction-based approaches to achieve an end-to-end trained model for performing a prediction task (such as semantic segmentation) on a sparsely labeled dataset in a target domain, by leveraging a richly-labeled dataset in the source domain. Some embodiments may also provide a trained domain translation model for generating synthetic data samples in a first domain based on input data samples from a second domain.

The application discloses a multi-layer protection recovery and resource allocation method of an optical network, and relates to the technical field of network communication.The application comprehensively considers multi-service characteristics and network resource dynamic scheduling, and proposes a hybrid method of multi-routing selection and network service slicing, so that protection based on a WDM optical network in a complex network environment is realized.Meanwhile, a work domain and a protection domain are arranged in a bandwidth allocation period, and a flexible resource allocation scheme is adopted, so that network resource utilization can be improved, and the work route can be protected.

A method for managing integrity of a tubular comprises obtaining fluid transportation system data, wherein the tubular is a component within a fluid transportation system. The method comprises determining, via a mechanistic model, a mechanistic corrosion rate of the tubular based on the fluid transportation system data. The method comprises determining, via a learning machine, a residual corrosion rate of the tubular based on the fluid transportation system data. The method comprises determining, via a hybrid model, a final corrosion rate of the tubular based on the mechanistic corrosion rate and the residual corrosion rate.

The application discloses a dynamic programming-game hybrid method for solving satellite cooperative task scheduling, introduces a sequence constraint interval dynamic programming and an elite asynchronous updating mechanism on the basis of a traditional game theory cooperative method; a complex single-satellite best response problem is converted into a path optimization problem with side swing maneuver and illumination constraints through the sequence constraint dynamic programming, so that a task sequence with the maximum marginal contribution is obtained to improve the quality of local decision, and the algorithm has the ability to process complex space-time constraints; through the elite node strategy updating mechanism, system shock caused by multi-agent cooperative conflict is greatly reduced to improve the algorithm convergence speed, and the distributed network is more effectively guided to converge to a high-quality Nash equilibrium state; compared with a traditional greedy strategy or a heuristic algorithm, the method has better global optimization performance, the algorithm has faster convergence speed and stronger stability, and the cooperative scheme planned by the method has higher system total income and fewer resource conflicts.

A system and method for enhancing pilot situational awareness stores to an onboard navigational database hybrid approach procedure sets, e.g., approaches to a landing or other waypoint wherein a first portion of the approach is performed or executed according to a first approach procedure set (e.g., to a final approach fix (FAF) or other waypoint), and a second portion of the approach (e.g., once the FAF / waypoint is sequenced) is performed / executed according to a different approach procedure set, e.g., an RNP AR-to-LPV hybrid approach. Flight displays provide textual approach indicators, e.g., when the hybrid approach is loaded and arming of the first segment is upcoming, or when the first and second segments are respectively armed and activated (e.g., when the necessary conditions are satisfied). Hybrid approach indicators may alternatively or additionally be provided via flight mode annunciators (FMA) of the flight display.

This application discloses a wind power prediction method, system, medium, and terminal that deeply integrates physical knowledge, relating to the field of wind power generation technology. Its main purpose is to improve the problem of low reliability of prediction models and consequently reduced accuracy of prediction results in existing hybrid methods due to shallow fusion levels. The method includes: acquiring current operating data and current environmental data of the target wind farm; determining current wind farm physical data based on the current operating data and current environmental data; performing deep fusion processing on the current operating data, current environmental data, and current wind farm physical data to obtain a current fused feature vector; calculating current wind farm state parameters based on the current wind farm physical data; performing wind power prediction based on the current fused feature vector and current wind farm state parameters to obtain the current predicted wind power value; and using the current predicted wind power value for operational status monitoring, grid dispatching, and the formulation of operation and maintenance strategies.

Rendering an avatar for a user in a communication session includes obtaining enrollment data associated with the user. For each of one or more frames of the communication session, a set of expression latents is obtained for the user. A first texture for a first portion of the face of the user is generated based on the enrollment data and expression latents. A combined albedo map is generated based on one or more identity textures from the enrollment data and the first texture. A target texture is generated based on the one or more identity textures and the combined albedo map for a particular frame of the one or more frames.