91 results about "Generative modeling" patented technology

The invention relates to a dynamic data pipeline construction method based on artificial intelligence and multi-modal data processing, and belongs to the technical field of data fusion treatment and artificial intelligence. According to the method, a multi-modal deep learning model is adopted, semantic analysis is performed on business documents, user requirements and function specification instructions, and key indexes, data requirements and logic constraints are extracted; constructing a multi-modal knowledge graph, performing unified representation and feature extraction on structured and unstructured data resources, and performing embedded learning by using a graph neural network; based on a deep semantic matching model and a hybrid recommendation system, realizing accurate matching of business requirements and data resources; modeling generation of a data processing flow into a Markov decision process by adopting a reinforcement learning strategy, and automatically generating an optimized data processing workflow; the adaptability of the model to new business requirements is improved by utilizing a meta-learning algorithm; and dynamic scheduling and execution of tasks are realized through a workflow engine. According to the invention, the data processing efficiency can be improved.

A system includes a transmitter configured to transmit a radar signal towards a target object. A receiver is configured to receive a received signal in response to the transmitted radar signal. The system includes a processor configured to receive, using the receiver, a received signal, execute an iterative procedure using a desired signal prior and an interference signal prior and to determine an estimate of an interference signal component of the received signal and an estimate of the desired signal component of the received signal, wherein the interference signal prior is determined using generative modeling and each iteration of the iterative procedure executes a data consistency operation, use the estimate of the interference signal component and the received signal to determine a desired signal, and processing the desired signal to determine an attribute of the target object.

The invention discloses a low-altitude internet-of-things task optimization method based on auction and diffusion learning, and the method comprises the steps: constructing an air-ground cooperation edge calculation frame integrating an unmanned plane, an air base station and a ground base station, and facing the low-altitude application scenes with the uncertainty of task arrival, the heterogeneous calculation resources, the time-varying communication conditions and the like; on a large time scale, an auction algorithm based on a VCG mechanism is designed, and excitation compatible distribution of the unmanned aerial vehicle to a task area is realized; on a small time scale, a heterogeneous agent near-end strategy optimization algorithm (D-HAPPO) fused with a latent variable diffusion model is provided, and the strategy diversity and the environmental adaptability are improved by generating modeling. According to the invention, by introducing the condition generation process, the dynamic collaborative optimization of the task unloading decision and the route planning is realized, and the task completion rate and the energy consumption efficiency of the unmanned system in a complex scene are effectively improved.

The invention belongs to the technical field of seismic exploration, and discloses a robust full-waveform inversion method, system and device based on generative modeling, and a medium, and the method comprises the steps: obtaining seismic observation data; the method comprises the following steps: starting from random Gaussian noise, establishing a model space comprising a plurality of initial velocity models through an unconditional score model, and determining a global optimal initial velocity model of the model space by adopting a strategy search algorithm; de-noising is carried out on the global optimal initial velocity model based on back diffusion, seismological observation data are introduced as observation constraints, the velocity model is updated by calculating the mismatch gradient of forward modeling data of the current velocity model and the seismological observation data, and an implicit condition sample is obtained; performing forward diffusion processing on the implicit condition sample to obtain a speed model for a subsequent annealing time step; and when the annealing process reaches a preset condition, outputting a final speed model. According to the method, the robustness and accuracy of full-waveform inversion are improved, and the bottleneck problem of traditional full-waveform inversion is solved.

The invention discloses a welding seam automatic generation modeling method and device, computer equipment and a storage medium. The method comprises the steps that a three-dimensional part model is read, and geometric information and topological structures of parts in the three-dimensional part model are extracted; in combination with part boundary features, intersection line features and a spatial proximity relation, weld joint connection positions between the parts are recognized, and the weld joint type of each connection position is marked; constructing a three-dimensional model of the welding seam in the three-dimensional model space, and recording geometric parameters of the welding seam; according to the geometric parameters extracted from the three-dimensional model of the welding seam and the material attributes of the corresponding parts, automatically generating welding seam process information, and associating the welding seam process information with the three-dimensional model of the welding seam; and performing three-dimensional display on the welding seam in a visual interface based on the three-dimensional model of the welding seam and the welding seam process information. According to the technical scheme, integration of welding seam geometric modeling and process parameter reasoning is achieved, the welding seam modeling efficiency is remarkably improved, the manual operation amount is reduced, and the modeling integrity and parameter consistency are ensured.

The invention discloses a SysMLv2.0 model library multiplexing method based on definition driving. The method comprises the following steps: S1, constructing a model definition library containing structures, behaviors, interfaces, constraints and semantic attributes; s2, analyzing a modeling demand, identifying a to-be-multiplexed module, and determining a definition unit identifier; s3, calling the target definition unit and inserting the target definition unit into the current model in a reference mode; s4, performing context adaptation to generate a binding relationship; s5, setting a version identification mechanism, and establishing a version mapping index between the use unit and the definition unit; s6, registering a reference relationship and an adaptation rule, and generating a modeling metadata document; s7, automatically updating the content of the use unit according to the metadata document in the modeling process; and S8, executing consistency verification after use unit integration is completed, and ensuring correct interface connection, conflict-free constraint logic and complete version binding. According to the method, a definition-driven model system is constructed, and cross-context semantic multiplexing and version consistency modeling management are realized.

The invention relates to the technical field of digital core modeling and artificial intelligence modeling, in particular to a weathered sandstone microscopic image generation method based on a conditional generative adversarial network, which comprises the following steps: acquiring and analyzing sandstone samples with different weathering degrees to construct a weathered sandstone multi-scale database; performing weathering geological knowledge extraction on the weathered sandstone multi-scale database to construct a sandstone feature tag set; and training the conditional generative adversarial network by using the sandstone feature tag set to obtain a weathered sandstone microscopic image generation model, and generating a weathered sandstone microstructure image with physical consistency by using the weathered sandstone microscopic image generation model. Therefore, the problems that in existing weathered sandstone physical and mechanical property modeling, the depth incidence relation between the microstructure and the macro-mechanical property is difficult to reveal, and a traditional modeling method is difficult to effectively fuse the advantages of a weathering geological mechanism and modeling generation, so that a reconstruction result lacks physical consistency and weathering response rationality are solved.

Relevance scores may be determined based on text included in a document. The text may be divided into a text portions, with the relevance scores being determined based on a comparison of a text portion of the plurality of text portions with a criterion specified in natural language. A subset of the plurality of text portions may be selected based on the plurality of relevance scores, with each of the subset of the plurality of text portions having a relevance score surpassing a threshold. A criteria evaluation prompt may be sent to a remote text generation modeling system via a communication interface. The criteria evaluation prompts may include an instruction to evaluate one or more of the subset of text portions against the criterion.

Systems and methods for generating customizable models for predicting user behavior are provided. Such a method includes: obtaining first structured event data representative of first events performed by one or more users; training a generative language model using the first structured event data; obtaining second structured event data representative of one or more second events performed by a user; and predicting, at least in part by applying the second structured event data as an input to the trained generative language model, third structured event data representative of behavior associated with the user according to one or more customizable analytic outputs.

The invention discloses a bridge substructure modeling scheme generation method and device, equipment and a medium, and relates to the technical field of bridge engineering, and the method comprises the steps: obtaining bridge superstructure design input parameters, constructing substructure design task information, and generating design parameters through big language model reasoning; and a modeling script is automatically generated, and structural response calculation is carried out. Different modeling strategies are judged and executed according to the calculation result, finally, a target modeling scheme meeting the requirement is intelligently generated, design parameter generation and automatic modeling of the bridge substructure in a non-standard bridge type scene are achieved, and the design efficiency and accuracy are improved.

Disclosed is an approach for learning probability distributions as differentiable quantum circuits (DQC) that enable efficient quantum generative modelling (QGM) and synthetic data generation. A method includes training of a differentiable quantum circuits (DCQ) based model, where data is encoded in a latent space with a phase feature map, followed by a variational quantum circuit. The trained model is then mapped to the bit basis using a fixed unitary transformation, coinciding with a quantum Fourier transform circuit in the simplest case. This allows fast sampling from parametrized distributions using a single-shot readout. Simplified latent space training provides models that are automatically differentiable. Samples from propagated stochastic differential equations (SDEs) can be accessed by solving a stationary Fokker-Planck equation and time-dependent Kolmogorov backward equation on a quantum computer. A route to multidimensional generative modelling is opened with qubit registers explicitly correlated via a (fixed) entangling layer.

The invention discloses a multi-agent system for generating a trestle design model under the drive of a large model, and relates to the technical field of BIM and AI crossing. The working process of the system comprises the steps that after a user inputs an instruction, the system automatically verifies parameters such as span combination and bridge floor total length, and the number of steel pipe piles is deduced through bridge pier rank calculation; the architect agent adopts a hierarchical layout algorithm and completes component coordinate derivation in combination with a default table; a programmer agent generates modeling codes according to a priority rule, and the problems of parameter missing, family library mismatching and the like are solved through an error self-correction strategy. The system integrates a long-term / short-term memory mechanism, realizes design parameter tracing and modeling process closed-loop optimization, and remarkably improves the design efficiency and modeling automation level of a complex trestle structure.

Text generation prompts may be determined based on an input document and a text generation prompt template. The text generation prompts may include text from the input document and questions related to the text. The text generation prompts may be sent to a remote text generation modeling system, which may respond with text generation prompt response messages including novel text portions generated by a text generation model. The text generation prompt response messages may be parsed to generate answers corresponding with the questions.

Systems and methods described relate to the synthesis of content using generative models. In at least one embodiment, a score-based generative model can use a stochastic differential equation with critically-damped Langevin diffusion to learn to synthesize content. During a forward diffusion process, noise can be introduced into a set of auxiliary (e.g., “velocity”) values for an input image to learn a score function. This score function can be used with the stochastic differential equation during a reverse diffusion denoising process to remove noise from the image to generate a reconstructed version of the input image. A score matching objective for the critically-damped Langevin diffusion process can require only the conditional distribution learned from the velocity data. A stochastic differential equation based integrator can then allow for efficient sampling from these critically-damped Langevin diffusion models.

The invention belongs to the field of reinforcement learning of artificial intelligence and the field of natural language processing, and discloses a reinforcement learning-based large language model jailbreak test method, which comprises the following steps of: generating and modeling a jailbreak suffix into a sequence decision task, autonomously generating a candidate suffix through a test model, and querying a target model; and evaluating the returned text by the continuous composite reward. The continuous composite reward comprises three parts, namely (1) token-level rejection probability, (2) semantic unsafe probability of an external security model and (3) multi-anchor semantic alignment, so that continuous, dense and stable training feedback is realized. A test model is updated by adopting a Group Relative Policy Optimization (Group Relative Policy Optimization) method, so that the success rate of prison break is remarkably improved, and the query cost is reduced. The method has the advantages of being stable in training, high in cross-model generalization ability, excellent in performance on strict and safe alignment models and the like.

A distributed generative modeling scheme employs a jointly-trained neural network path (118) implementing a generative model (112) at a UE (110) instead of wholly at a remote application server (106). An encoder neural network (120) at a base station (108) between the application server and the UE operates to, in effect, encode input prompt information (116) from the application server for efficient transmission to the UE. At the UE, a decoder neural network (122) is employed to, in effect, decode the encoded prompt information to recover a representation (126) of the original prompt information. The decoded prompt information is provided as an input to the generative model at the UE for processing, along with, in some instances, one or more additional inputs such as local sensor data (144), to generate output content (128) that is then processed by one or more UX components (114) associated with the UE.

The invention discloses an adversarial attack sample generation method based on machine learning, particularly relates to the field of simulated adversarial, is used for solving the problem of decision boundary mismatching caused by target model dynamic defense in adversarial sample generation, and realizes dynamic adaptation between adversarial sample generation and a target model defense strategy through a multi-link collaborative mechanism. Probe sample generation covers multi-dimensional disturbance characteristics, data support is provided for decision boundary curvature modeling, and the latter replaces model updating through deformation quantization and normal vector field driving to ensure that the gradient direction is orthogonal to the decision boundary; the orthogonal gradient projection generates an adversarial sample by using an updated parameter and a normal vector field, optimizes disturbance through orthogonality evaluation, and matches a sensitive area; through closed-loop feedback iteration, successful samples are returned to a probe data set, a curvature field and model parameters are adjusted, and a closed-loop data stream for sample generation, modeling, updating and optimization is formed; the problem of decision boundary mismatching is solved, and the migration success rate of the black box attack in the dynamic defense environment is improved.

The present disclosure provides a trajectory prediction method, device, electronic device and storage medium. In the trajectory prediction method provided by the embodiment of the present disclosure, after obtaining environmental features through the sensor data of the vehicle and the existing map data, the environmental features are first converted into an instance tag set, which includes not only the vehicle tag but also the tags of the agents representing other dynamic entities in the current scene, and then the future trajectory data of the vehicle is generated through the instance tag set and the vehicle trajectory latent space learned by the generative model. The present disclosure realizes end-to-end processing from sensor data to trajectory generation, reduces the data conversion overhead between modules, improves computing efficiency, and adopts generative modeling to improve accuracy, real-time, security and reliability.

One embodiment of the present invention sets forth a technique for generating data. The technique includes sampling from a first distribution associated with the score-based generative model to generate a first set of values. The technique also includes performing one or more denoising operations via the score-based generative model to convert the first set of values into a first set of latent variable values associated with a latent space. The technique further includes converting the first set of latent variable values into a generative output.

The present disclosure provides a system for generating gameplay content by a generative modeling system. The system can generate gameplay content via one or more machine-learning models trained using game and player data. The system can add content generated by the one or more machine-learning models to the game and player data and retrain the models using the generated content. The system can also localize generated content based on player locations and language preferences.

The system generates a three-dimensional model with layered depth images based on an input two-dimensional image. For training, layered depth images are derived from existing three-dimensional models. The system trains a machine learning model to predict multiple layered depth images from an input image of an object. The system compares the generated, multiple layered depth images to the derived layered depth images for the object to update the machine learning model during training. At inference time, the system receives an input image for an object. The system applies the machine learning model to the input image to output predicted layered depth images. The system generates a three-dimensional model from the predicted layered depth images.

A method, computer program product, and computer system for generative modelling of molecular structures for chemical applications. The method includes providing labelled training data for training a generative model over a defined feature space, where the labelled training data includes representations of molecular structures and property values for each molecular structure, and the generative model outputs generated candidate molecular structures with target properties. The method includes receiving evaluations of generated candidate molecular structure outputs from the generative model with the evaluations providing feature representations of candidates with evaluation labels. The method generates or updates decision boundary rules based on the evaluations and applies the decision boundary rules to update the labelled training data.

The invention discloses a BIM modeling method and device based on a multi-modal modeling code generation model, electronic equipment and a storage medium, and belongs to the field of digital modeling, and the method comprises the steps: obtaining a modeling demand text and a to-be-modeled image; inputting the modeling demand text and the to-be-modeled image into a multi-modal modeling code generation model, so that the multi-modal modeling code generates a BIM modeling code according to the modeling demand text and the to-be-modeled image; according to a preset regular expression, the BIM modeling code is corrected, and a corrected BIM modeling code is generated; and calling BIM modeling software to enable the BIM modeling software to generate a BIM three-dimensional model according to the corrected BIM modeling code. Therefore, by implementing the method and the device, the problem of code and demand deviation caused by single-modal input adopted by BIM modeling in the prior art can be solved.

The invention provides a self-adaptive data quality rule threshold dynamic generation system, belongs to the technical field of data management in communication operator data, and aims to solve the problem that a threshold for configuring an audit object table in a communication operator data quality module depends on artificial experience and is difficult to adapt to service changes in time. A three-layer architecture system of an auditing feature analysis layer, a rule generation modeling layer and a dynamic optimization layer is innovatively constructed. According to the method, historical data of an auditing object table are analyzed, and an lsolation Forest isolation forest algorithm is adopted, so that a dynamic quality threshold adaptive to business scene changes is automatically generated. According to the scheme, the auditing rule accuracy can be effectively improved, the manual intervention frequency of rule maintenance is reduced, and automatic operation of quality management and control of a data intermediate station is effectively supported.

The application discloses a bridge substructure modeling scheme generation method and device, equipment and medium, relates to the field of bridge engineering technology, and comprises the following steps: obtaining bridge superstructure design input parameters, constructing substructure design task information, generating design parameters by using a large language model reasoning, and then automatically generating a modeling script and performing structure response calculation. According to the calculation result, different modeling strategies are judged and executed, and finally a target modeling scheme meeting the requirements is intelligently generated, the design parameter generation and automatic modeling of the bridge substructure in a non-standard bridge type scene are realized, and the design efficiency and accuracy are improved.

Systems and methods are provided for generating a customizable model for predicting user behavior. The method comprises: obtaining first structured event data representing a first event performed by one or more users; training a generative language model using the first structured event data; obtaining second structured event data representing one or more second events performed by the user; and predicting third structured event data representing a behavior associated with the user at least in part by applying the second structured event data as input to the trained generative language model according to the one or more customizable analysis outputs.

A system is provided.SOLUTION: A system comprising: means for receiving a generation request; means for obtaining parameters related to a theme, a difficulty level, and a play style based on the generation request; generation model means for automatically generating a world or a dungeon from the obtained parameters; and means for storing and responding to data of the generated world or dungeon.SELECTED DRAWING: Figure 1

A system is provided.SOLUTION: A system comprising: means for receiving a creative idea input by a user; terminal means for transmitting the user's idea to a server; means for analyzing the received idea data; generative model means for automatically generating digital art based on the analyzed data; means for transmitting the generated digital art to the terminal; and means for displaying the transmitted digital art to the user.SELECTED DRAWING: Figure 1

The present invention discloses a system fault solution generation modeling method, generation method, device and storage medium. The modeling method includes obtaining processed fault information and its solution; performing data cleaning on first error information and constructing a sample data set based on the cleaned first error information; using the sample data set to train, verify and test an initial entity extraction model to obtain a target entity extraction model; using the target entity extraction model to extract entities from the cleaned first error information to obtain a first key entity; constructing a corpus based on the first context information, the first key entity and its solution, and constructing a knowledge graph and a fine-tuning data set based on the corpus; and fine-tuning the pre-trained model using the fine-tuning data set to obtain a solution generation model. The present invention can achieve rapid location, accurate analysis and efficient solution matching for complex fault problems, thereby improving fault handling efficiency.

A generative adversarial network (GAN) (21, 24) or any other generative modeling technique is used to learn (12) how to generate (68) an optimal robot system given performance, operation, safety, or any other specification. For example, the specification can be modeled (65) relative to an anatomical structure to confirm satisfaction of anatomical structure-based constraints or another task-specific constraint. A machine learning system (e.g., a neural network) is trained (12) to translate a given specification into a robot configuration. The network can translate a task-specific specification into one or more configurations of robot modules in the robot system. A user can enter (67) a change to the performance so that the network estimates (62) an appropriate configuration. The configuration can be translated (64) into an estimated performance by another machine learning system (e.g., a neural network), thereby allowing the operation to be modeled (65) relative to an anatomical structure (such as a medical imaging-based anatomical structure). Configurations that satisfy the constraints from the modeling (65) can be assembled (69) and used.