229 results about "Model representation" patented technology

Methods and systems for a document review process are provided. The method includes receiving an input digital model representation comprising at least one externally-accessible model endpoint for generating a digital artifact. Then, generating a document splice comprising access to multiple document subunits, with at least one document subunit written in a natural language and comprising the digital artifact; the access to each document subunit is provided through an externally-accessible document endpoint for the subunit. Then, generating a document by combining the document subunits, and generating a view associated with the document, based on an user authorization result including selective access rights to the document subunits. The view comprises access to the digital model representation, the digital artifact, each document subunit, and the document. Finally, receiving a user input and updating, via one of the externally-accessible document endpoints, the document splice based on the user input.

Methods and systems for generating digital threads that connect models and software tools for an interconnected digital engineering and certification ecosystem are provided. In one embodiment, a process for generating a software-code-defined digital thread includes receiving a first model representation of a first engineering model, a second model representation of a second engineering model, and an intent input. Determining, using a machine learning (ML) model, one or more model endpoints in the first and the second model representations. Generating, using a script-generating ML model, a platform orchestration script that accomplishes the intent input by connecting the first model representation and the second model representation, using the one or more model endpoints. Finally, storing the platform orchestration script as the software-code-defined digital thread. The digital threading of digital engineering models, with and without AI assistance, leads to exponential increases in efficiency of designing, testing, certifying, manufacturing, and operating physical products, allowing physical products to be made at digital speeds.

A method to employ a three-dimensional (3D) model is provided. The method may include: displaying, on a graphical user interface (GUI) of a display device, at least one selectable option corresponding to at least one 3D model stored in a storage, each of the at least one 3D model corresponding to a 3D data representation and a location tracking data representation; receiving a selection of an option from a user via the GUI, the selected option corresponding to a selected 3D model of a reference surface; in response to receipt of the selection of the option, switching to display a real-time camera view on the GUI; determining that the reference surface is present in the real-time camera view; and superimposing at least one of the 3D data representation or the location tracking data representation of the selected 3D model on the reference surface displayed in the real-time camera view.

The invention provides a three-dimensional visualization expression method for a multi-scale geological structure model, including: collecting geological exploration data which includes geophysical information, drilling information, outcrop survey information, seismic information and geological age data; performing interpolation simulation on the geological exploration data to obtain an exploration sample set; preprocessing the exploration sample set to obtain preprocessed data, the preprocessing process including data cleaning, format unification and noise removal; constructing a multi-scale geological structure model according to the preprocessed data based on a three-dimensional modeling technology; performing scale decomposition on the geological structure model using a multi-scale analysis method to form model representations at different scale levels; and converting the model representations into 3D visualization images using a 3D graphics processing technology. The invention can effectively improve the expression ability of geological model and user experience, and has important practical application value for geological exploration and resource assessment.

Methods and systems enabling multimodal inputs for interacting with a live digital object are provided. The system receives the live digital object which includes a digital artifact extracted from a digital model file through a model representation. The system receives a user's security level and determines the user's access permission and modification permission to access and modify the digital artifact. The system accesses a multimodal interface configured to receive a conversational input and a spatial input, outputs the digital artifact to the multimodal interface based on tire access permission, receives from the multimodal interface a conversational input and a spatial input from tire user, and generates a modified digital artifact from the digital artifact via the digital model representation, based on the modification permission and on the conversational or spatial input. The multimodal interface may include conventional interfaces (GUIs / APIs), conversational interfaces (text / voice), and spatial computing interfaces (VR / AR / MR / gestural).

Methods and systems enabling multimodal inputs for interacting with a live digital object are provided. The system receives the live digital object which includes a digital artifact extracted from a digital model file through a model representation. The system receives a user's security level and determines the user's access permission and modification permission to access and modify the digital artifact. The system accesses a multimodal interface configured to receive a conversational input and a spatial input, outputs the digital artifact to the multimodal interface based on the access permission, receives from the multimodal interface a conversational input and a spatial input from the user, and generates a modified digital artifact from the digital artifact via the digital model representation, based on the modification permission and on the conversational or spatial input. The multimodal interface may include conventional interfaces (GUIs / APIs), conversational interfaces (text / voice), and spatial computing interfaces (VR / AR / MR / gestural).

Techniques are described for automatically generating visual model representations of buildings based at least in part on captured external imagery of the buildings, and using the generated building visual models to generate and present new images and optionally in additional manners, such as to improve navigation of a building and / or its surroundings. The described techniques may include acquiring building data from a plurality of exterior acquisition locations at multiple heights and view angles of an exterior of a building (e.g., using a flying drone and / or other flying device that captures the data), generating visual model representation(s) of the building (e.g., a 3D Gaussian Splat model), and using the generated visual model representation(s) to generate and present a new image with a view of the building exterior from a particular pose along with associated user-manipulatable controls.

The invention discloses a neural network image-text analysis and cross-framework code generation method and system, and the method comprises the steps: analyzing a neural network architecture picture through a visual large model, and extracting a layer type, a connection relation and a topological structure feature; performing semantic understanding on text description by combining a large language model, and extracting layer parameters and configuration information in a standardized manner; utilizing a multi-modal alignment mechanism to fuse vision and text features, and generating unified model representation; and finally, directly generating an executable code supporting a mainstream framework based on a large language model and grammar check. According to the method, the limitation of traditional manual coding is broken through, end-to-end generation from a complex framework to a multi-framework code is achieved, the problems of cross-framework adaptation and semantic understanding are solved, the development efficiency of deep learning is improved, and the method is suitable for scientific research verification and industrial deployment scenes.

This application discloses a model training method, a power prediction method, and a device. A model includes at least a first model and a second model. The method includes: obtaining a dataset including historical power data and historical meteorological data in preset duration; determining a weight of a loss function of each model based on the dataset; constructing a target loss function, where the first model and the second model may be used to represent input data, and data represented by the first model is not completely the same as data represented by the second model; and finally training the models based on the target loss function. More complete information is obtained from different modal data and different time scales based on a currently available data source, to optimize the learning model and achieve higher prediction precision.

The invention relates to a large language model dynamic routing method and device based on context learning model characterization and a readable storage medium. Query is embedded and mapped to a language model input space by using a projection model, semantic alignment is realized, a representative evaluation set covering multi-dimensional capability is automatically screened from a benchmark question bank, and the representative evaluation set is used for evaluating the multi-dimensional capability. Performance characteristics of the model on an evaluation set are efficiently obtained at a time, and high-quality context model capability representation is formed; then real-time query embedding and context model capability representation are combined, a lightweight routing language model is used for supervised learning, so that the fine-grained model distinguishing capability is achieved, an increment embedding updating mechanism is designed, and when a new model is accessed or an old model is upgraded, cold start can be rapidly completed only through a very small number of fixed questions, so that the efficiency is improved. The calculation and maintenance cost is greatly reduced, and the accuracy, real-time performance and flexible expansibility of model routing are effectively improved.

Systems and methods for generating an editable design using an auto-regressive pre-trained large language model (LLM) are disclosed. The method includes: receiving a prompt to generate the editable design; sequentially generating a set of tokens of model representation data for the editable design, each token in the set of tokens defining an attribute of the editable design; for each token in the set of tokens, determining whether the token is a predicted special token associated with a design asset or a non-special token; upon determining that the token is a non-special token, providing the non-special token as an input to the LLM to generate a next token in the set of tokens; upon determining that the token is a predicted special token: replacing the predicted special token with a replacement special token associated with a design asset stored in a design asset library; and providing the replacement special token as the input to the LLM to generate the next token in the set of tokens.

The invention relates to a three-dimensional grid model processing method, computer equipment, a readable storage medium and a program product. The method comprises the steps of performing voxelization on a three-dimensional grid model of a target object to obtain a plurality of voxels corresponding to the target object in a preset reference space; determining the visibility value of the voxel according to the projection information of the voxel under the preset camera visual angle; determining a position relation label of the voxel relative to a reference point according to a communication relation between the voxel and the reference point in a preset reference space; screening out a target voxel from the voxels according to the visibility values and the position relation labels of the voxels; and determining model characterization parameters of the three-dimensional grid model according to the spatial information of the target voxel in the three-dimensional grid model. By adopting the method, the generation precision of the three-dimensional model can be improved.

The invention provides a map detection method and system based on gradient feature extraction and multi-stage fusion, relates to the field of deep learning and image processing, and aims at solving the problems that in the prior art, defects exist when the problems of a fine-grained structure, boundary blur, category imbalance and the like of complex map data are solved. According to the method, gradient information and edge details are extracted from a map through a pure gradient extraction module; a progressive attention fusion module and a double-layer attention mechanism are used for enhancing feature representation by emphasizing key boundary information; through a cross-scale fusion encoder module, multi-stage enhancement and fusion are performed on high-level semantic information and high-resolution edge detail features, so that the performance of the model is improved, and the capabilities of representing semantic information and capturing edge details of the model are remarkably enhanced. The problems in the prior art are solved, the model prediction capability and accuracy are improved, and various challenges in map detection tasks can be coped with.

Various embodiments classify one or more portions of an image based on deriving an “intrinsic” modality. Such intrinsic modality acts as a substitute to a “text” modality in a multi-modal network. A text modality in image processing is typically a natural language text that describes one or more portions of an image. However, explicit natural language text may not be available across one or more domains for training a multi-modal network. Accordingly, various embodiments described herein generate an intrinsic modality, which is also a description of one or more portions of an image, except that such description is not an explicit natural language description, but rather a machine learning model representation. Some embodiments additionally leverage a visual modality obtained from a vision-only model or branch, which may learn domain characteristics that are not present in the multi-modal network. Some embodiments additionally fuse or integrate the intrinsic modality with the visual modality for better generalization.

Combining augmented reality (AR), virtual reality (VR), and photogrammetry to facilitate interactions with the physical world is disclosed. Images may be collected through various means (e.g., images taken by drones, ground-based photography, satellite imaging, etc.) and used to construct a three-dimensional (3D) model representation of the real world. This virtual environment can then be experienced by users in either a computing system (e.g., a desktop computer, laptop computer, smart phone, tablet, etc.) or a VR interface (e.g., a headset). The users can create various annotations, such as voice recordings, images, text, etc., and place these annotations in the virtual 3D model of the real world. These annotations are linked to the physical world via a coordinate system. AR “explorers” and VR users can then interact with one another via geographically aligned AR and VR models.

Described herein are platforms, systems, media, and methods for a machine learning-driven iterative drug discovery process. Some aspects comprise: receiving a first input data set comprising first binding interaction information between a target molecule and a library of compounds; processing the first input data set using a machine learning module to generate a model representation of binding interactions, wherein the representation is configured to predict binding between the target molecule and an input compound; determining an updated library of compounds using the model representation of binding interactions, wherein the updated library of compounds comprises one or more new compounds predicted to bind the target molecule; receiving a second input data set comprising second binding interaction information between the target molecule and the updated library of compounds; and processing the second input data set using the machine learning module to update the model representation of binding interactions, wherein the predictive accuracy of the updated model representation is improved.

The invention discloses a federal positioning method based on adaptive aggregation and feature alignment, and belongs to the field of indoor positioning. When federated learning is applied to indoor positioning of the Internet of Things, the problem that data of all terminal devices are not independently distributed is solved, and the influence of the non-independent distributed data on the performance of a global model is reduced by utilizing a self-adaptive federated aggregation coefficient and a feature alignment constraint; according to the algorithm, the aggregation weight of each local model is quantified by comparing the performance difference of a global model before and after local training, and particularly, a more accurate aggregation coefficient is provided for federal aggregation through a variable coefficient of a loss value; considering that differences in client data distribution may cause local model representations to have respective preferences, feature alignment constraints are used to avoid overfitting of local models to improve performance of a global model.

The invention belongs to the technical field of federated learning, and more specifically relates to a non-data-driven quantum federated learning method based on single communication. The method comprises the following steps: a plurality of clients independently train classical models by depending on respective local data, only perform single communication after training is completed, and upload model representation information to a server; after receiving the model representation information uploaded by each client, the server uniformly fuses the information to construct an integrated teacher model; the server synthesizes a pseudo sample from the random noise by using a pseudo sample generator under the guidance of the integrated teacher model; and migrating the knowledge of the integrated teacher model to the quantum student model through soft label distillation by using the generated pseudo sample, and finally obtaining a unified quantum student model at a server side. The technical problems that in existing quantum federation learning, communication overhead is large, privacy protection and efficiency are difficult to consider, and quantum hardware resources are seriously limited are solved.

The invention discloses a causal decoupling method and device based on multi-scale noise and adversarial supervision, and the method comprises the steps: carrying out the simulation of the causal relationship of variables in a causal graph, so as to generate observation image data, and constructing a training set and a test set according to the observation image data, the corresponding causal label information and the causal graph; constructing a causal decoupling model, and performing adversarial supervision training under multi-scale noise by using the training set; and obtaining anti-fact intervention data by using the test set and the trained causal graph matrix and utilizing the trained observation data coding module and observation data decoding module. According to the method, an auto-encoder and causal acyclic constraints are fully combined, the discrimination module is trained under multi-scale noise, and high-quality adversarial supervision is performed, so that the model representation learning ability is improved, the representation understanding of the model on data with causal relationships is enhanced, the accuracy of implicit causal network prediction is improved, and the prediction efficiency is improved. And the causal decoupling accuracy is improved.

The invention discloses a flue gas desulfurization system prediction control method and system based on a self-organizing fuzzy neural network, and relates to the field of artificial intelligence driven complex dynamic system model representation and state control, and the method comprises the steps: proposing a control method framework based on the dynamic characteristics of a wet flue gas desulfurization system variable evolution process; constructing a prediction model based on the control method framework; and completing predictive control of the flue gas desulfurization system by using the predictive model. The invention provides a prediction model and a control framework based on a self-organizing fuzzy neural network aiming at the problems of strong nonlinearity of SO2 and pH states, non-stability in operation, difficulty in control and the like in a wet flue gas desulfurization system. The structure of the network can be dynamically adjusted, the network adapts to operation changes, and accurate modeling and prediction of SO2 and pH are achieved. And on the basis of prediction information, an optimal control law is dynamically solved in a rolling optimization mode, real-time tracking and accurate control of SO2 and pH states are achieved, and the control effect of the flue gas desulfurization process is remarkably improved.

A method may obtain a failure propagation model, wherein the failure propagation model comprises: a model representation of a plurality of hardware components, a set of hardware failure probabilities; and a logic that maps signals to the hardware components. A method may receive an alert signal from the aircraft. A method may map the alert signal to the plurality of hardware components. A method may perform a root-cause diagnosis of the alert signal that has been mapped to the plurality of hardware components comprising: determining via the failure propagation model, one or more combinations of hardware failures associated with the alert signal; and determining a probability of occurrence associated with the combinations of hardware failures. A method may display a report that includes combinations of hardware failures associated with the alert signal and the probability of occurrence associated with the combinations of hardware failures.

The invention provides a performance optimization method and system for a closed cooling tower, and relates to the technical field of data processing.The method comprises the steps that input parameters describing the closed cooling tower are obtained; determining a cumulative distribution function of each input parameter; discretizing each input parameter to form a discretized simulation sample; performing computational fluid mechanics simulation on the closed cooling tower by utilizing the simulation samples, and determining system response of each simulation sample; performing principal component analysis on system response data through singular value decomposition; establishing an incidence relation between the input variable and the score matrix through Kriging interpolation, and forming a complete preliminary prediction model between the input variable and the system response; through a high-dimensional model representation technology, the high-order interaction effect of the preliminary prediction model is optimized, and a performance prediction model of the closed cooling tower is obtained; in order to improve the cooling efficiency and reduce the construction cost, the design parameters of the closed cooling tower are optimized through a butterfly optimization algorithm.

The invention discloses a visual language action model-oriented target injection type fine tuning method, which comprises the following steps of: firstly, constructing any existing visual language action model, introducing a condition image generation model, and generating a target image with consistent semantics and vision according to an initial observation image and a task target instruction; secondly, target image features are injected into observation input through zero-initialization convolution, parameters are gradually increased from zero, it is ensured that interference noise is not introduced in the initial stage of fine adjustment to destroy a pre-training strategy, and in the training process, along with gradual optimization of the parameters, target image feature information is gradually fused into model representation, and the target image feature information is obtained; therefore, the understanding ability and the execution performance of the task target are improved. According to the method, through a lightweight target image injection mechanism and an efficient fine adjustment process, the performance of the model on various reference tasks can be remarkably improved in few training rounds, and the problem that an existing visual language action model cannot systematically introduce target image guidance is effectively solved.

The invention provides a text data enhancement method for improving vector retrieval performance, and belongs to the field of computer data analysis. The method comprises the following steps: firstly, compressing long text data by using a prompt template of a large language model, and decomposing the long text data into a plurality of short texts; in the training process, the short texts replace the original long texts to be used as training data, so that the video memory size occupied by a single piece of information is saved; in order to cope with the problem that the representation capability is reduced possibly due to the fact that the text length is shortened, a guide vector is constructed by combining a plurality of short texts from the same long text, and the guide vector is used as auxiliary information to guide the encoding process of a single short text. In this way, the adverse effect of text shortening on the model representation ability can be effectively reduced, and therefore the training effect and generalization ability of the model are improved on the premise that shorter single information is used.

The invention provides a method for performing human-object interaction detection on an image based on visual language model representation. The method comprises the following steps: extracting visual features of a to-be-detected image by using a CLIP visual encoder, and extracting text features of the to-be-detected image by using a CLIP text encoder; performing representation learning on the visual features by using a representation learning module to obtain human-object interaction HOI features; and recognizing a bounding box and an interaction type of a person and an object related to interaction in the to-be-detected image according to the text features and the HOI features through an interaction detection module. According to the method, the generalization ability of a new interaction category is enhanced by utilizing priori knowledge of the pre-trained visual language model, knowledge migration from the pre-trained model is realized by exploring representation learning of the pre-trained image and the text, and the ability of the model in processing complex visual contents related to human-object interaction is enhanced. According to the method, knowledge migration from the pre-trained model is realized, and the capability of the model in processing complex visual contents related to human-object interaction is enhanced.

A computing device includes at least one processor, one or more non-transitory computer-readable storage media, a system for fine-tuning a large language model under low-bit weight-activation quantization. The computing device further comprises a graphics processing unit (GPU), a neural processing unit (NPU), or a tensor processing unit (TPU). The hardware interface module of the system is configured to load a low-bit model representation from the memory module and transmit the model representation to the GPU, NPU, or TPU for inference execution.

Methods and systems for a document review process are provided. The method includes receiving an input digital model representation comprising at least one externally-accessible model endpoint for generating a digital artifact. Then, generating a document splice comprising access to multiple document subunits, with at least one document subunit written in a natural language and comprising the digital artifact; the access to each document subunit is provided through an externally-accessible document endpoint for the subunit. Then, generating a document by combining the document subunits, and generating a view associated with the document, based on an user authorization result including selective access rights to the document subunits. The view comprises access to the digital model representation, the digital artifact, each document subunit, and the document. Finally, receiving a user input and updating, via one of the externally-accessible document endpoints, the document splice based on the user input.

Techniques and solutions are provided for defining and executing a process controller representation of a business process model. The business process model has a plurality of objects arranged in a hierarchy. Objects at multiple levels of the hierarchy are used to generate one or more of a service, a strategy, or a method of a process controller representation. A method of the process controller representation calls another process controller service or another process controller strategy. The business process model can be associated with multiple process controller representations, which can be associated with multiple, different computing systems.

In various examples, a method includes computing a current keyframe, the current keyframe being representative of an area around an autonomous vehicle at a current time based on map data. The method includes transforming a preceding keyframe to a coordinate frame of the autonomous vehicle at a first time prior to completing computation of the current keyframe to generate a first world model frame. The method includes transforming the preceding keyframe to the coordinate frame of the autonomous vehicle at a second time after the first time and prior to completing computation of the current keyframe to generate a second world model frame.

The invention belongs to the field of animal and plant breeding prediction, and discloses a rapid and accurate genome prediction method based on a heritability model, which comprises the following steps: calculating the frequency and linkage imbalance score of a marked secondary allele; refining the SNP heritability model to obtain an optimal SNP heritability model; selecting an optimal layering number; acquiring layer genotype data formed by genotype data of all individuals of the to-be-predicted data set in corresponding layers, calculating a diagonal weight matrix of each layer of SNP estimated heritability assignment to the corresponding layer based on the optimal SNP heritability model, and calculating a genetic relationship matrix between individuals of the layer genotype data corresponding to each layer; and fitting the multi-random hybrid model to obtain the genome estimation breeding value of each phenotype of all individuals in the data set to be tested. The method is based on a linear model framework, the calculation complexity is low, the key steps can be subjected to parallel processing, and the calculation speed is high; according to the method, the character specific SNP heritability model is constructed, and the model representation of a complex character genetic structure is optimized, so that the prediction performance is improved.