329 results about "Specific model" patented technology

The invention relates to the technical field of advertisement putting and intelligent decision making, in particular to a favorite advertisement putting system for predicting the advertisement click rate, which comprises a context awareness intelligent adaptation unit and a deep enhancement decision making unit. By means of deep semantic analysis and situational inference engine processing, interest keywords are extracted by constructing a specific model, user situational portraits are constructed in combination with multi-source data, a preliminary advertisement set is screened by matching with an advertisement material library, a multi-agent architecture is constructed by a deep reinforcement decision unit, a master agent performs overall planning, slave agents are responsible for different advertisement types, and a user can perform multi-agent interaction. A multi-dimensional reward function system is designed, each agent collects feedback data such as operation of a user on an advertisement page, a reward value is calculated according to the feedback data, a strategy network is updated, a main agent integrates information to optimize an overall advertisement pushing strategy, accurate pushing of advertisements is achieved, and the advertisement click rate and the putting effect are effectively improved.

The invention belongs to the technical field of intelligent agriculture, and discloses a canopy-atmospheric environment information-based soil water content prediction method and system, and the method comprises the steps: setting a canopy and atmospheric environment monitoring unit in a field, and synchronously collecting the canopy temperature, wind speed, relative humidity, and six-dimensional environment parameters of atmospheric temperature, wind speed and relative humidity; the crown temperature difference is calculated after preprocessing; the method comprises the following steps: constructing a training set by utilizing measured data of soil relative water content, constructing a soil water content prediction model by adopting a random forest algorithm, screening and confirming an optimal prediction model through grid search and cross validation, and screening key features by combining an SHAP value; the water demand is automatically calculated according to a pre-established crop whole-growth-period drought stress threshold table and a dynamic irrigation decision formula, and meanwhile, the adaptation of a general model to a regional specific model is realized by constructing an environmental parameter-actually measured water content database and transfer learning, so that the soil water content state is accurately predicted, and the soil quality is improved. And a scientific basis is provided for precise irrigation.

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.

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.

A system and method for multiple fraud type detection includes anti-injection attack system that has a layered architectural approach that uses a includes combination of different specific models to detect the attacks in combination with image processing techniques, device signals and liveness checks to detect the variety of different types of fraud attacks or repeat fraud attacks. The anti-injection attack system applies the analysis of the tools used to create deepfake, face morph and face swap attacks to define the elements of its layered architecture that can these various types of attacks.

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 invention relates to the field of large language models, and provides a safety guarantee method for text generation of a large language model. The existing watermarking technology can detect whether a text comes from a specific model or not, but cannot trace the identity of a user, so that responsibility can not be traced when the text is spread or tampered for the second time. According to the main scheme, a statistical signal is embedded in a generated text based on a relaxed red-green dictionary method, and a model tends to select a green list token by adjusting the token sampling probability when the language model is generated; the user information is a mobile phone number or an identity card number, Base4 coding is carried out, and offset information is obtained; in the basic watermark text, starting from the first green token, generating a synonym list based on the context every two continuous token positions, and selecting corresponding synonyms for replacement according to the offset so as to implicitly embed user information; the method comprises the following steps: detecting a basic watermark, recovering a text by using a Transform-based sequence denoising auto-encoder, and extracting user information according to a replacement position and an offset.

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.

The invention discloses an intelligent automatic generation system and method for coral species semantic samples, relates to the technical field of computer vision and marine ecological monitoring, and aims to efficiently generate high-precision semantic annotation samples from underwater coral images. The system adopts a hierarchical feature extraction architecture, a common model and a field-specific model CoralSCOP are collaboratively optimized, an SAM series provides a high-precision geometric segmentation region through zero sample learning, and the CoralSCOP realizes multi-granularity label distribution through parallel semantic branches. The method innovatively introduces a two-stage mask optimization mechanism: in the initial stage, a semantic segmentation model is utilized to generate a 50 + category semantic mask; in the post-processing stage, boundary details are extracted through SAM / CoralSCOP, semantic and geometric features are matched through a region-level fusion strategy, and the segmentation precision and efficiency are improved. The system provides a high-quality marking benchmark for coral reef monitoring, the expert dependence is remarkably reduced through the zero-sample and multi-granularity characteristics, and the system is suitable for marine ecological research and AI model training.

The embodiment of the invention provides a multi-modal general-purpose model collaborative reasoning method based on a dynamic routing mechanism, and the method comprises the steps: receiving a task instruction and multi-modal data corresponding to the task instruction, determining a task type corresponding to the task instruction, and extracting the data features of the multi-modal data, fusing the data features through a task general model to generate general fusion features, matching a task special model of the current task type, a modal processing flow and a fusion feature weight according to a preset processing mapping relation, and transmitting the data features and the general fusion features to the task special model through a routing node, and enabling the task special model to process the data features and the general fusion features according to the modal processing flow and the fusion feature weight to obtain special fusion features, and processing the special fusion features through a task execution component to obtain a task result corresponding to the task instruction. According to the method, the defects of lack of model flexibility and the like during multi-modal data processing can be effectively solved, and the convenience degree of semantic fusion is remarkably improved.

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.

The invention discloses an industry exclusive customization model generation method based on a large model, and the method comprises the following key steps: firstly, collecting structured and semi-structured multi-modal data containing key entities, concepts and relationships in a target industry; then, constructing a knowledge graph through a deep learning method; in a model construction stage, carefully selecting a pre-trained large language model, and carrying out personalized adjustment on feature engineering and a model architecture according to industry characteristics; on the basis of the constructed knowledge graph, a retrieval enhancement technology based on the knowledge graph is adopted, the answer content of the large language model is optimized, and target industry domain knowledge is injected; and finally, by using a reinforcement learning algorithm, optimizing model output by training a reward model. According to the method, specialized customization of the industry exclusive model is realized, the question and answer ability of the large language model in the target industry field is remarkably improved, and high accuracy and high correlation of the model when the model answers related questions of the target industry are ensured.

The invention discloses a remote sensing image small target feature enhancement refined classification method based on dynamic mixed knowledge distillation, and the method comprises the steps: inputting a low-resolution remote sensing image of a to-be-classified small target into a pre-trained classification model, and obtaining the specific model information of the target; the classification model comprises a trunk low-resolution image student network branch, and in the training stage of the classification model, an auxiliary high-resolution image teacher network branch consistent with the student network branch in structure and a dynamic mixed knowledge distillation module are introduced, the dynamic mixed knowledge distillation module is deployed between a trunk low-resolution image student network branch and an auxiliary high-resolution image teacher network branch, and high-resolution image intermediate features and output features learned by the teacher network branch are used as priori knowledge to be distilled to the student network branch through the dynamic mixed knowledge distillation module; and training of the student network branches is guided, so that the feature representation capability of the student network branches is enhanced, and a trained classification model is obtained.

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.

The invention relates to the technical field of government affair data analysis, and discloses a government affair SaaS architecture and method based on cloud edge collaboration, and the government affair data processing method based on cloud edge collaboration comprises the steps: obtaining government affair scene feature data, and generating scene semantic representation; the scene semantic similarity is calculated, and the knowledge migration potential is evaluated; analyzing semantic features of a target scene, and screening clients participating in training based on scene similarity; processing the client model, and dynamically adjusting the aggregation weight according to the scene similarity and the model contribution degree; global model knowledge is extracted, and a target scene local model is optimized; through organic combination of federated learning technologies of scene semantic understanding and scene perception, the limitation that data privacy protection and scene specialization model training cannot be realized at the same time in the traditional technology is overcome, and the technical problems of cross-scene knowledge integration and scene specialization service in government affair data analysis are effectively solved.

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.

The invention provides a multi-modal power price analysis method, system and device, and a storage medium. The method comprises the following steps: dividing obtained data of different modals into a training data set and a test data set; constructing a ViT initial model, carrying out LAION-2B data set training based on the ViT initial model, then carrying out training of the training data set, carrying out comparative learning by adopting a self-supervised learning method, and carrying out pre-training to obtain a multi-modal data processing model; preprocessing the test data set to obtain multi-modal data, and performing iterative training on a multi-modal data processing model based on the multi-modal data to obtain an optimal prediction model; and inputting the obtained current different modal data into the optimal prediction model to predict and obtain power price data. According to the method, data processing is carried out on different modes through the prediction model, the trouble of designing a specific model for each mode is avoided, the development and training cost is reduced, and thus the prediction precision is improved.

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 discloses a BiGRU-GCN structure-based power system fault positioning method, device and system and a medium. The method comprises the steps of constructing a power system power distribution network model comprising a high-proportion inverter; preprocessing simulation data of the power distribution network model of the power system by adopting a sliding window technology; performing multi-scale feature extraction on the preprocessed data by using an encoder to obtain local features and global features; performing feature fusion and fault position reduction on the local features and the global features by using a decoder; and in combination with fault feature distribution and confidence degree evaluation output by the decoder, the fault occurrence position and confidence degree information are determined, sorting is carried out according to confidence degrees, and the position with the highest confidence degree is found out and is the fault position. According to the method, the advantages of the neural network in the aspect of data processing and the advantages of a specific model solving method are fully utilized, and the problem that after a high-proportion inverter is connected to a power distribution network, a fault section is difficult to accurately locate through traditional relay protection is solved.