226 results about "Specific model" patented technology

Examples for performing wireless sensing tasks based on foundation model are described. In one example, a described method comprises: obtaining channel information (CI) data generated based on at least one wireless channel; generating a training dataset based on the CI data, wherein the training dataset comprises: a plurality of CI pairs, original CI data and a mask; training a foundation model using the training dataset based on an aggregate of a contrastive loss function and a reconstruction loss function; training a plurality of task-specific models; and performing a plurality of wireless sensing tasks based on the foundation model and the plurality of task-specific models. Each of the plurality of task-specific models is used to perform a corresponding one of the plurality of wireless sensing tasks together with the foundation model.

Systems and methods for domain-specific model-generated content item generation, evaluation, and selection can include generating a plurality of candidate model-generated content items that can then be evaluated based on one or more signals, which can then be leveraged for candidate model-generated content item selection. The plurality of candidate model-generated content items can be generated with a generative model that was tuned for domain-specific content item generation. The selected model-generated content item can be processed to generate an outline that may then be provided to a user for user interaction to generate an augmented outline. The augmented outline may then be processed to generate an updated model-generated content item.

The invention discloses an equipment state monitoring method based on multi-source information fusion, and the method comprises the steps: enabling a real-time sensor to collect the operation parameter data, vibration parameter data and environment parameter data of equipment, collecting the historical state data of the equipment with a recording label, and carrying out the preprocessing of the data; time domain features, frequency domain features and working condition features of the data are extracted according to a layering mode, and dynamic weight coefficients of all the features are set; calculating a reference threshold value by adopting a specific model; evaluating the health condition of the current equipment by adopting a depth measurement method, calculating an equipment health factor, and calculating a trend compensation item; and obtaining an equipment state monitoring dynamic threshold based on the reference threshold, the health correction item and the trend compensation item. The invention further discloses an equipment state monitoring device based on multi-source information fusion, corresponding equipment and a storage medium. According to the equipment state monitoring method based on multi-source information fusion provided by the embodiment of the invention, the accuracy, real-time performance and reliability of equipment state monitoring can be effectively improved.

The invention relates to the field of computer systems, in particular to an operation stability testing method and system oriented to a heterogeneous computing system, and the method comprises the steps: constructing a system overall operation logic model comprising a resource topological structure, a task scheduling rule and a communication link matrix; identifying a key communication path according to the communication link matrix, analyzing a resource competition hotspot in combination with a task scheduling rule, and establishing an interference model library; calling a specific model instance in the interference model library according to a test target, configuring injection parameters, and dynamically applying interference in a system operation process; key performance indexes in the system operation process are collected in real time; based on the real-time monitoring data, a multi-index weighted scoring algorithm is adopted to calculate an overall stability coefficient, a robustness grade and a self-healing capability index of the system, and a stability evaluation result is generated; and generating a visual report according to the topological structure of the operation logic model, and supporting comparison and analysis of historical versions. Unified centralized management is realized, and test efficiency and evaluation accuracy are improved.

The systems and methods disclosed herein relate to querying data using artificial intelligence models. A generalized model receives an output generation request and partitions it into segments mapped to specific domains, where each domain indicates associated databases and guidelines. The segments are routed to domain-specific models trained on domain-specific data, which generate query fragments by comparing performance metrics and system resource usage metrics. The query fragments are aggregated into an overall query that satisfies guidelines across domains. The systems and methods can include a feedback loop to adjust the domain-specific models using user interactions and performance metrics to dynamically adapt to a skill level or experience of the user.

In various examples, self-supervised learning may be used to pre-train an encoder network of a masked prediction model to reconstruct masked regions of an input representation of 3D detections such as LiDAR point cloud(s). Spatial and / or temporal masking may be applied to a projected representation of 3D detections (e.g., a two-dimensional (2D) projection image), and the masked prediction model (e.g., a masked auto-encoder or joint-embedding predictive architecture) may be used to reconstruct a representation of the masked regions (e.g., reflection characteristic(s) stored in corresponding pixels or cells of the projected representation, a latent representation of the reflection characteristic(s)) during iterations of self-supervised learning. As such, the pre-trained encoder network of the masked prediction model may be used as a foundation model and fine-tuned with a task-specific output head or its pre-trained weights may be used to initialize a task-specific model.

An iterative method for monitoring a computing device characterized by metric data to be monitored, including, for each iteration, of collecting metric data over a predetermined interval of time, detecting a seasonality pattern of said metric data over said predetermined interval of time, determining an interval-specific model representing the detected seasonality pattern, calculating modelled data using said determined model and the collected metric data, comparing the calculated modelled data with the collected metric data to calculate a score characterizing the difference between the calculated modelled data and the collected metric data, calculating an anomaly likelihood for each data of the collected metric data using the calculated score, detecting an anomaly on a data when probability that the value of said data is an anomaly is greater than a predetermined threshold.

A system includes a hardware processor configured to execute a machine learning (ML) model training pipeline to train an ML model using data relevant to a world of a digital persona to provide a dialogue model, generate, using the dialogue model, first conversational outputs, train the dialogue model, based on the first conversational outputs, to avoid hallucinations and / or undesirable expressions to provide a guardrailed dialogue model, generate, using the guardrailed dialogue model, second conversational outputs, train the guardrailed dialogue model, based on the second conversational outputs and persona data identifying interaction characteristics of the digital persona to provide a persona-specific model, generate, using the persona-specific model, a response to a scripted question, determine a quality score for the response, and further train the persona-specific model or validate the persona-specific model for human interaction, depending upon whether the quality score fails to satisfy or satisfies a quality criterion.

One or more systems, devices, computer program products and / or computer-implemented methods of use provided herein relate to assessing certainty of artificial intelligence models used for detection or segmentation of pathologies. Accordingly, a system can comprise a memory that can store computer executable components. The system can further comprise a processor that can execute at least one of the computer executable components. The computer executable components can comprise a synthetic data generation component that generates biologically-inspired synthetic data that approximates a task-specific data manifold of a medical image from a radiomic features perspective; an artificial intelligence component that uses an artificial intelligence model to learn relevant representations of the synthetic data for an at least one image task; and a training component that utilizes the relevant representations and the artificial intelligence model to generate a task-specific model for the at least one image analysis task.

The invention discloses a dynamic modeling tracking control method for a self-adaptive fiber placement compaction mechanism, and belongs to the technical field of specific model calculation systems, and the method comprises the following steps: S1, building a kinematic model of the self-adaptive fiber placement compaction mechanism; s2, establishing a workpiece surface model, and solving tangent point coordinates and position coordinates of the driving wheel, the driven wheel and the ground; s3, according to the force balance equation and the moment balance equation of each rod, the relation between each bearing reaction and the external force is solved; s4, establishing a dynamic model of the driving device; and S5, error analysis and optimization are carried out, and it is ensured that the main pressure is within the fluctuation range. According to the device, it can be ensured that the rollers output constant compaction force when the abrupt change molded surface structure is laid, and the forming defect caused by uneven compaction force is avoided.

Methods and systems for improving modernization of legacy software using an intelligent discovery platform are described herein. A client-based agent may generate metadata regarding the received legacy software. The code metadata may be analyzed by a code classifier module, which computes a plurality of score factors from the metrics from the legacy software metadata using a knowledge base from a modernization platform. The classified code metadata may be used by a project-specific model to derive a plurality of sub-scores based on the plurality of score factors associated with the legacy software. An analytics engine may then identify a code module from the legacy software having a greatest derived vulnerability score factor. A graphical interface including reconstructed code, corresponding modern code, and an explanation of vulnerabilities may then be generated by the analytics and reporting component for the identified code module.

Methods and systems for improving modernization of legacy software using an intelligent discovery platform are described herein. A client-based agent may generate metadata regarding the received legacy software. The code metadata may be analyzed by a code classifier module, which computes a plurality of score factors from the metrics from the legacy software metadata using a knowledge base from a modernization platform. The classified code metadata may be used by a project-specific model to derive a plurality of sub-scores based on the plurality of score factors associated with the legacy software. An analytics engine may then identify a code module from the legacy software having a greatest derived vulnerability score factor. A graphical interface including reconstructed code, corresponding modern code, and an explanation of vulnerabilities may then be generated by the analytics and reporting component for the identified code module.

Disclosed is a computer-implemented method and system for training a subject-specific machine learning model to infer inherent subject features from recorded or live video data. The system preprocesses the visual and audio channels, converting audio to text, and employs multiple pre-trained extraction models to generate feature embeddings. Ground truth data is obtained to guide training, where weights are assigned to produce and combine predicted feature values. Model performance is optimized by minimizing error. The trained feature extraction models are deployed on an edge device, while the subject-specific model resides in the cloud. A lightweight edge model, derived via knowledge distillation and model compression, supports local inferencing with reduced reliance on cloud resources. Synchronization ensures iterative updates for sustained accuracy.

Embodiments described herein provide for audio processing operations that evaluate characteristics of audio signals that are independent of the speaker's voice. A neural network architecture trains and applies discriminatory neural networks tasked with modeling and classifying speaker-independent characteristics. The task-specific models generate or extract feature vectors from input audio data based on the trained embedding extraction models. The embeddings from the task-specific models are concatenated to form a deep-phoneprint vector for the input audio signal. The DP vector is a low dimensional representation of the each of the speaker-independent characteristics of the audio signal and applied in various downstream operations.

Devices, systems, and process for generating fixture specific models for use in adjusting pricing of real-time betting lines are described. A system may include a front-end system including an event-activity-fixture (EAF) server that adjusts betting lines based on results of simulations generated by an EAF simulation server (EAFSS). The EAFSS generates the simulations using fixture specific models that have been generated based upon adaptations of generic models, where the adaptations occurring using historic and real-time EAF data and fixture specific modeling data. The fixture specific models adapted from one or more generic models are leveled and stored in a database for use by the EAFSS on a real-time basis as the EAF occurs. A server for adapting the generic models instantiate one or more computer engines including a model adaptation engine, a generic modeling engine, an EAF data engine, and an FSM leveling engine.

The invention relates to the technical field of biomedical analysis instruments, and discloses a DNA quantitative fluorometer calibration system based on artificial intelligence optimization, which comprises a data acquisition module for acquiring a dynamic fluorescence characteristic spectrum for representing the whole process of fluorescence reaction; and an intelligent calibration and diagnosis core model in the data processing unit analyzes the spectrogram by using a long short-term memory network and an attention mechanism so as to generate a multi-dimensional diagnosis result containing predicted concentration, confidence score and interference early warning. According to the method, the dynamic fluorescence characteristic spectrum of the standard substance is used as a scene anchor point, the preset global basic model is finely adjusted, and the special session model is generated, so that the real-time self-adaptive calibration of the change of the instrument and the reagent is realized. According to the method, the defects that a traditional method depends on an end point value, is easily interfered and is static in calibration are overcome, and the accuracy, reliability and long-term stability of a DNA quantitative result are remarkably improved.

The invention discloses an intelligent inspection and fault diagnosis method for power equipment and related equipment, and the method comprises the steps: carrying out the preprocessing of multi-source data, and obtaining an adaptive diagnosis model and a trend prediction model through the matching of a diagnosis-prediction model according to the specific model, working condition and data type of the current equipment through the matching of a diagnosis model and a prediction model; performing anomaly diagnosis on the multi-source data by using the matched diagnosis model to obtain abnormal data; and finally, deep fusion with multi-dimensional information such as the historical state, the real-time load and the external weather of the equipment is carried out, and a matched prediction model is driven to carry out comprehensive analysis, so that sequential and personalized prediction of fault evolution is realized, the crossing from post-event alarm to pre-event early warning is completed, and the reliability of fault evolution is improved. And an operation and maintenance closed loop from accurate diagnosis to advanced prediction is also formed, and the adaptive capability of the system to different power transmission, power transformation and power distribution scenes is comprehensively enhanced.

Methods for a machine-learning network that provide efficient, scalable, and granular analyses during validation of a machine learning model are disclosed. Validation of models depends upon many factors, including the real-world application of the model, the type of model being trained, and the types of data samples it is being trained on. In order to provide relevant edge case information to users that pertains to their specific model, data slice finding techniques may be used to identify subsets of the dataset that are particularly problematic. By limiting a length of the slice description that the algorithm searches and by configuring the algorithm to target specific types of errors, users are provided with a more granular analysis that then allows them to determine how or if they need to retrain the model.

The invention relates to the technical field of large models, in particular to a reading recommendation large model generation method and system based on big data resources. The method comprises the following steps: acquiring a to-be-assessed text, collecting reading materials with multiple learning segments and multiple styles, labeling the reading materials, carrying out aggregation and consistency verification on labeling results, and constructing a verified recommendation database; performing low-rank adaptive fine tuning on the general pre-training large model based on the recommendation database to obtain a domain model, and performing performance and deviation monitoring and correction in the training process; performing multi-dimensional analysis on a to-be-evaluated text to generate a text feature vector, and performing mixed retrieval on a recommendation database in combination with semantic vector similarity retrieval; according to the invention, through the construction of the large reading recommendation model, the reading recommendation is more accurate and efficient.

Systems and methods for domain-specific content generation can include tuning a generative model on a domain-specific training dataset. The systems and methods can be tuned to train the generative model to generate domain-specific model-generated content items that include one or more domain-specific attributes. The domain-specific content items can include news articles, newsletters, research papers, or other content items with domain-specific structure and other domain-specific attributes.

The invention discloses a module schematic diagram automatic design method suitable for electronic product design, and belongs to the technical field of electronic design automation. The method aims at solving the problems that traditional manual schematic diagram design is low in efficiency, prone to errors and highly dependent on experience of engineers. The method comprises the following steps: firstly, carrying out structured information modeling on a design demand, a design template and a to-be-generated module; secondly, according to the design requirement after modeling, all candidate main chip sets meeting the conditions are screened out from a device library; then, aiming at each candidate main chip, automatically constructing a peripheral circuit of the candidate main chip through a template matching process; and finally, calculating the general device in the template according to the parameters of the main chip and replacing the general device with an actual device with a specific model. According to the method, various module schematic diagram schemes which conform to specifications and can be optimized can be quickly and accurately generated according to a single design requirement, the automation level and efficiency of electronic design are remarkably improved, and the design threshold is reduced.

The invention provides a double-layer adaptive fusion method and system for scientific and technical literature knowledge, and relates to the technical field of language processing, and the method comprises the steps: constructing a double-layer adaptive fusion architecture; receiving a scientific and technical literature text and extracting a text basic feature vector; the general knowledge layer processing module performs general high-dimensional feature vector mapping, and the professional knowledge layer processing module performs professional high-dimensional feature vector mapping; and the adaptive fusion module performs field confidence calculation, configures an adaptive fusion weight parameter, performs weighted fusion on the general-text high-dimensional feature vector and the professional text high-dimensional feature vector, and applies an output module to perform processing of the scientific and technical literature by using the fused feature vector. According to the method and the device, the technical problem of inaccurate knowledge fusion caused by poor adaptability of a general model, limited generalization ability of a field-specific model and lack of adaptability of static knowledge integration in the prior art can be solved, and the accuracy of knowledge fusion is improved by constructing a double-layer adaptive fusion architecture.

The invention discloses a round bar processing equipment multi-parameter cooperative control method based on artificial intelligence, and particularly relates to the field of round bar processing equipment control, which comprises the steps of data acquisition, data processing, data analysis and result judgment. According to data acquisition, a force sensor, a temperature sensor, a displacement sensor and a vibration sensor of specific models are deployed, data are acquired according to different set frequencies of a high-speed cutting process and a common cutting process, abnormal values of the acquired data are eliminated according to the Pauta criterion in data processing, and Kalman filtering denoising and normalization operation are performed. And in the data analysis step, processed data are substituted into a turning and milling cutting force model, an improved Archard tool wear model and a multiple regression machining precision model to be solved, and according to result judgment, the machining quality is evaluated according to a solving result comparison standard, and the equipment state is judged in combination with a threshold value. According to the method, multi-parameter collaborative optimization is realized, the processing quality stability and efficiency are improved, the dependence on artificial experience is reduced, and the downtime is shortened.

The application relates to a small-sample residual life prediction method based on task incremental meta-learning, which comprises the following steps: acquiring a multi-element sensor failure signal and an actual residual life of an airplane engine, and pre-processing and selecting the signal; constructing a dual network, including a meta network and an auxiliary network, wherein the meta network regards the mapping of sensor data of each device to the residual life as a regression task, and automatically learns a task-specific model of each device by using meta learning; the auxiliary network generates pseudo labels for unlabeled samples of non-failed devices; the dual network is trained based on labeled data of failed devices and unlabeled data of non-failed devices by using a task incremental training algorithm; the pseudo labels of test device samples are predicted by using the auxiliary network, the meta network is updated to a task-specific model based on a pseudo label support set, and the residual life of the test device is predicted by using the task-specific model. Compared with the prior art, the application can realize accurate residual life prediction under a small number of failure samples.

Examples of the present disclosure describe systems and methods for using AI to identify regions of interest (ROI) in medical images. In aspects, medical reports and images may be provided to a second environment. The second environment may use the medical report data / medical images to train a natural language processing (NLP)-based algorithm to identify the location in images of ROI described in the medical report data. The output of the NLP-based algorithm may be stored in an ROI repository in the second environment. After the NLP-based algorithm has been trained, a request to train a user-specific model may be received in a first environment. Data objects for the requested user-specific model may be provided to the second environment, which uses the ROI repository to train the model. The trained model may be provided to the first environment, where the trained user-specific model / algorithm may be tested and stored.

The invention discloses a multi-modal large model adaptive iterative reasoning method and system for a long video, and belongs to the technical field of multi-modal artificial intelligence and reinforcement learning training. According to the method, a self-adaptive multi-round iterative reasoning framework is adopted, a model autonomously selects a direct answer according to a current reasoning state, and fine-grained time positioning or representation optimization of self-extraction visual clues is executed. Wherein in the fine-grained time positioning, a key time slice related to a task is dynamically selected for an output target interval through discrete marking; according to representation optimization, visual clues are extracted and injected into visual representation, and the model is supported to integrate dispersed multi-modal information in a long time range. Meanwhile, a reinforcement learning framework based on an entropy guided branch exploration mechanism and a composite reward mechanism is adopted, and an adaptive decision strategy of the model is enhanced. The method does not depend on a specific model structure, has good universality and expansibility, and can significantly improve reasoning integrity, stability and overall efficiency in a long video understanding task.

The invention discloses a post-excavation stratum reinforcement grouting effect visualization method based on multi-field coupling, and the method comprises the steps: firstly obtaining the stratum condition, rock mechanics data and grouting parameters of a grouting region, and carrying out the simulation calculation of the distribution condition of effective principal stress after excavation through a specific model; based on the distribution of the effective principal stress in the mechanical field in the porous medium, simulating and calculating the porosity distribution and the permeability coefficient after formation fracturing; analyzing a slurry diffusion path by adopting a specific model, and obtaining the overall change conductivity of the porous medium and the slurry through analog calculation; parameters of a resistivity hole lane combined monitoring system are determined according to the changing conductivity, and the potential value of each point position in the grouting reinforcement process is actually measured through the monitoring system; finally, the actually measured data and the model calculation data are compared and analyzed, the diffusion path and the reinforcement effect of the grout in the grouting reinforcement process are continuously obtained, and dynamic visualization of the stratum reinforcement grouting effect before and after excavation is achieved through comprehensive analysis of the multi-physics field coupling condition.

Disclosed is a computer-implemented method and system for training a subject-specific machine learning model to infer inherent subject features from recorded or live video data. The system preprocesses the visual and audio channels, converting audio to text, and employs multiple pre-trained extraction models to generate feature embeddings. Ground truth data is obtained to guide training, where weights are assigned to produce and combine predicted feature values. Model performance is optimized by minimizing error. The trained feature extraction models are deployed on an edge device, while the subject-specific model resides in the cloud. A lightweight edge model, derived via knowledge distillation and model compression, supports local inferencing with reduced reliance on cloud resources. Synchronization ensures iterative updates for sustained accuracy.

The application discloses an optical module, comprising: a first shell and a second shell, the first shell and the second shell surround to form a containing space, the containing space is used for containing a circuit board, a boss is arranged in the second shell, and the boss is located in the containing space, an adhesive is arranged on the boss, and the adhesive is used for fixedly connecting the circuit board and the second shell. The optical module does not need to additionally increase a jig, and the jig does not need to be maintained, so that equipment investment and maintenance cost can be reduced, the positioning precision of the jig can be avoided from being affected due to long-term use and abrasion, and the production rhythm can be avoided from being affected due to additional time required for installation and adjustment of the jig, so that the production efficiency can be improved. In addition, the jig is only suitable for a specific model, and the jig needs to be redeveloped when product design is changed, so that the development period is affected, and the boss can be compatible with circuit boards of different size specifications, and the production line flexibility is improved.

The invention discloses an on-demand data cleaning method and system oriented to a support vector machine, relates to the technical field of data cleaning, and provides a new on-demand data cleaning normal form for solving the problem that an existing data cleaning method is low in reliability of a cleaning result. The data and the downstream task model are regarded as a whole, and the data quality oriented to the specific model is further optimized on the basis that the downstream task effect and the generalization requirement are met. The normal form breaks through the limitation that a traditional method only pays attention to the quality of data, and the reliability of a data cleaning result is greatly improved.