96 results about "Data selection" patented technology

The application provides a base station traffic prediction method and related equipment, and relates to the field of communication, wherein the base station traffic prediction method comprises the following steps: determining the adjacent base stations of a to-be-predicted base station according to the geographical distance information between base stations; determining the neighbor base stations in the adjacent base stations, and the historical traffic records of the neighbor base stations and the historical traffic record of the to-be-predicted base station have a causal relationship; performing feature extraction on the historical traffic records of the to-be-predicted base station and the neighbor base stations to obtain feature data; and predicting the predicted traffic of the to-be-predicted base station according to the feature data. In the screening process of the feature data object, the spatial factor is fully considered to avoid the singularity of feature data selection, and the causal relationship between the base station traffics is fully considered, so that the blindness of feature data selection is avoided, the redundant feature interference is reduced, the effectiveness of feature selection is improved, and the base station traffic prediction accuracy and efficiency are improved.

The application discloses a transmission processing method and device, a terminal and a storage medium, and belongs to the technical field of communication. The transmission processing method of the application embodiment comprises: a terminal determining first resource information of first data, wherein the first resource information is used for re-evaluation or preemption of a target resource; wherein the first resource information is associated with first target information, and the first target information comprises at least one of the following: whether the first data is a periodic service, or whether the first data enables periodic reservation; whether periodic reservation is enabled in a resource pool; a periodicity configured by the resource pool; second resource information selected or reselected by the first data; a measurement result of the resource pool or a bandwidth part; a power saving mode of the terminal; a power level of the terminal; a discontinuous reception configuration of the terminal; third resource information configured or indicated in the resource pool; and related information of second data, wherein the second data is different from the first data.

Methods, apparatus, systems, and articles of manufacture to identify an episode number based on fingerprint and matched viewing information are disclosed. An example method includes processing meter data to identify a presented media based on a bumper included in a media, filtering the meter data based on the identification of the media, selecting a candidate episode, the candidate episode not associated with a known episode label, determining whether the candidate episode appears sequentially after a known episode for a threshold number of presentation locations, and labeling the candidate episode as the next sequential episode after the known episode in response to determining that the candidate episode appears sequentially after the known episode for the threshold number of presentation locations.

This application discloses a model evaluation method, apparatus, medium, and product, belonging to the field of computer science. The model evaluation method of this application identifies initial data to obtain first candidate data and the corresponding data type; filters the first candidate data based on the corresponding data type; inputs the filtered data into at least two target models to obtain second candidate data output by each target model; and evaluates the corresponding target models based on the second candidate data output by each target model to generate evaluation results. This application improves the accuracy of data selection and reduces labor costs by identifying initial data and filtering first candidate data; inputting the filtered data into multiple target models and evaluating the corresponding target models based on the second candidate data output by each target model enables a more comprehensive evaluation of the overall capabilities of the target models, making the evaluation data more realistic.

The present application provides a model fine-tuning data determination method and device, electronic equipment and storage medium, relating to the field of artificial intelligence and machine learning. The method is to obtain an original data set for downstream task fine-tuning and fine-tuning resource constraint conditions; based on the fine-tuning resource constraint conditions, a data selection strategy is constructed; based on the data selection strategy, a reference data subset is determined from the original fine-tuning data set; the reference data subset and the fine-tuning resource constraint conditions are used to fine-tune the target model to obtain fine-tuned model parameters; the fine-tuned model parameters are used to update the data selection strategy, and in the case that the fine-tuned target model meets the preset convergence condition, the updated data selection strategy is used to determine a target fine-tuning data subset from the original data set, thereby dynamically balancing the quality, quantity and distribution characteristics of the data subset, selecting data beneficial to the improvement of model performance, reducing the fine-tuning cost and improving the model convergence speed and verification performance.

The application relates to the technical field of transformer fault diagnosis, and discloses a TFD system and method based on spatiotemporal feature fusion and explainability enhancement. First, multi-source time series data selection and dynamic spatiotemporal feature construction are performed; then, multi-modal spatiotemporal features are extracted by adopting a double-channel, the double-channel output features are spliced, a cross-modal attention mechanism is introduced, the fusion weights of four categories of features are automatically calculated, and a weighted unified feature representation is generated; a bidirectional long short-term memory network is adopted to model the fused unified feature representation, capture the forward and backward time series dependence relationship of fault development, and output time series related feature encoding; finally, an explainable reasoning and output module is used to generate an explainable diagnosis report conforming to industry specifications and provide decision basis visualization. The application enhances early warning sensitivity, improves the comprehensiveness and context perception ability of fault diagnosis, and provides intuitive understanding model judgment basis for operation and maintenance personnel.

The application discloses a kind of soft measurement modeling methods of fermentation process based on time sequence diagram network, belongs to the technical field of soft measurement of fermentation process.It includes the following steps: (1) data acquisition and integration: the penicillin fermentation process under different conditions is obtained, and the data is divided, collected and integrated;(2) data selection: the data is selected, the redundant useless data is removed and the causal diagram between variables is established;(3) modeling training: algorithm model is constructed, and learning training is carried out;(4) model prediction: the trained algorithm model is used for prediction, and the prediction result is given.The application proposes a kind of soft measurement modeling method of fermentation process based on time sequence diagram network, improves the prediction accuracy of key product quality of fermentation process;The method uses graph convolution network and long short-term memory, extracts data in time and space dimensions, increases the generalization of model;The method can accurately measure the key product quality of different fermentation processes.

The measurement and evaluation of backlash caused by wear and tear are performed during the operation of the equipment to improve the equipment's operating rate. [Solution] The present invention comprises an electric motor 22 that drives a small gear 24, an operation control device 21 that controls the electric motor 22, and a backlash measuring device 43 that measures the backlash that occurs between a large gear 25 and a small gear 24. The backlash measuring device 43 consists of an angle torque measuring unit 13 that measures the rotation angle and torque of the electric motor 22 based on data obtained from an input shaft angle sensor 12 that acquires data on the rotation angle of the electric motor 22 and data obtained from the operation control device 21, a data selection unit 14 that selects data on the rotation angle and torque of the electric motor 22 during operation from the angle torque measuring unit 13, and a backlash evaluation unit 15 that calculates a measured value of the backlash that occurs between the large gear 25 and the small gear 24 based on the data selected by the data selection unit 14.

This invention provides a ground electronic unit device and transmission method for a train control system. The device includes a chassis, a power supply module, a recording module, a main control module, and a C-interface module. The main control module determines the operating mode according to a configuration file. When the operating mode is transparent message transmission, it receives data sent by the target device and transmits it to the C-interface module. When the operating mode is message selection, it selects a message based on the target device's status data and sends it to the C-interface module, so that the C-interface module converts the message into a transponder C-interface signal and sends it to the active transponder. This solution is compatible with multiple LEU types to adapt to different application scenarios and supports autonomous redundancy switching based on its own operating status, ensuring rapid switching to backup equipment in case of primary equipment failure, thereby significantly improving system availability. This device allows users to flexibly configure each functional module according to their needs, possessing extremely high adaptability and scalability, enhancing the reliability and availability of the system.

Various aspects relate to a memory module including: a memory interface; a storage device for persistently storing data; a non-volatile memory device providing a memory storage for persistently storing data and providing a write buffer configured to receive, via the memory interface, and to store corresponding data to be written to the memory module; and a memory controller configured to write the corresponding data selectively from the write buffer to the storage device or to the memory storage.

This invention relates to the field of terminal data transmission technology, specifically providing an optimization method, terminal, and medium for intelligent fusion terminal reporting and supplementary reporting. The method includes: polling pending reporting tasks, determining whether the reporting task is a range reporting task, and further determining whether it is a low-frequency reporting task; if so, generating a corresponding supplementary reporting task based on the reporting task, modifying the execution cycle of the pending reporting task and the supplementary reporting task to achieve same-day supplementary reporting for low-frequency reporting tasks; if the reporting task is not a low-frequency reporting task, determining whether the end time of the data selection range of the reporting task is later than the task execution time; if so, compensating the execution cycle of the data selection range; executing the data reporting of the reporting task; if a supplementary reporting task has already been generated, executing the data reporting of the supplementary reporting task simultaneously. Correction is performed when the end time of the data selection range of the reporting task is later than the current execution time, avoiding future time issues and ensuring the integrity of the reported collected data.

A method for constructing a seismic image by attenuation of internal multiples, including: receiving a seismic dataset from a data processing platform; determining a current working swath and a plurality of neighboring swaths in a target swath area to determine a data library for predicting internal multiple events in the target swath area; reducing redundant seismic data in the data library by excluding seismic data outside the target swath area and decimating seismic data based on a predetermined decimation parameter; selecting a raw seismic trace from a seismic source to a seismic receiver; predicting, using a data-driven internal multiple elimination process and the data library, an internal multiple event from the seismic source to the seismic receiver; determining a processed seismic trace by subtracting the internal multiple event from the raw seismic trace; and outputting the processed seismic trace to the data processing platform for imaging a subsurface structure.

The application provides a lunar surveying radar data alignment processing method, device, equipment and medium, and relates to the technical field of lunar surveying radar. The method comprises the following steps: S1, reading the lunar surveying radar data to be processed, performing walking data selection and splicing on the lunar surveying radar data, and obtaining a radar data matrix; S2, performing shape-preserving segmented cubic interpolation on the radar data matrix; and S3, performing cross-correlation operation between two signals in the interpolated radar data matrix, and aligning the data by combining the delay amount between the signals. The application utilizes the correlation of radar data to efficiently and accurately perform alignment processing on the lunar surveying radar data.

Systems and methods include processors for receiving training data for a user activity; receiving bias criteria; determining a set of model parameters for a machine learning model including: (1) applying the machine learning model to the training data; (2) generating model pre- diction errors; (3) generating a data selection vector to identify non-outlier target variables based on the model prediction errors; (4) utilizing the data selection vector to generate a non-outlier data set; (5) determining updat- ed model parameters based on the non-outlier data set; and (6) repeating steps (1)-(5) until a censoring performance termination criterion is satisfied; training classifier model parameters for an outlier classifier machine learning model; applying the outlier classifier machine learning model to activity-related data to determine non-outlier activity-related data; and applying the machine learning model to the non-outlier activity-related data to predict future activity-related attributes for the user activity. 20 24 27 85 34 18 D ec 2 02 4 2 0 2 4 2 7 8 5 3 4 1 8 D e c 2 0 2 4 M A C H I N E L E A R N I N G M O D E L S A B S T R A C T

The application provides a method and device for selecting seismic data and a computer device, and belongs to the technical field of seismic exploration. The method comprises the following steps: obtaining seismic data of a work area to be studied; obtaining a first attribute parameter of each shot point, a second attribute parameter of each receiver point, and a third attribute parameter of each bin point; selecting at least one target shot point, at least one target receiver point, and at least one target bin point from a plurality of shot points, a plurality of receiver points, and a plurality of bin points based on the first attribute parameter of each shot point, the second attribute parameter of each receiver point, and the third attribute parameter of each bin point; and determining target seismic data based on first seismic data of each target shot point, second seismic data of each target receiver point, and third seismic data of each target bin point, wherein the target seismic data is used for underground structure imaging. The method improves the accuracy of the target seismic data used for underground structure imaging.

Example embodiments of the present disclosure provide for an example method for prompt element generation for use as input into generative models. The example method includes obtaining user input data including initial prompt data. The example method includes selecting one or more suggested prompt elements based at least in part on the initial prompt data. The example method includes transmitting the one or more suggested prompt elements to be presented for display as selectable user interface elements via a user interface. The example method includes obtaining second user input data including data indicative of a selection of the one or more suggested prompt elements. The example method includes responsive to obtaining the second user input data, providing the second user input to an image generation model to generate an output image including a visual representation associated with the one or more suggested prompt elements.

Provide a system. 【Solution means】 Data collection means, Data processing means for integrating the collected data and performing preprocessing, Model selection means for selecting and applying an analysis model based on the preprocessed data, Result generation means for generating and visualizing the analysis result, Proposal creation means for generating a proposal for business improvement based on the analysis result, Interface means for providing the proposal to the user and collecting responses, Response processing means for improving the model based on the response, Planning optimization means for assisting in the handling plan of articles in the warehouse, Anomaly detection means for detecting anomalies using the information and sending warnings, A system including.

This application provides a method and system for early debris flow identification based on SAR technology combined with a 3D model. Interferometric SAR data is selected as the wide-area coverage data for the target area, and fully polarimetric SAR data is selected as the local refined data for the target area. Phase unwrapping is performed on the wide-area coverage data to generate a wide-area digital elevation model (DEM). Polarization decomposition and phase optimization are performed on the local refined data to generate a local DEM. The wide-area DEM and the local DEM are fused to obtain a hybrid DEM. Based on the hybrid DEM, topographic factors are extracted to generate DEM spatial data to support debris flow susceptibility analysis. Early debris flow identification is performed based on historical debris flow data, historical precipitation data, regional soil moisture content, and DEM spatial data, yielding identification results. A differential monitoring and early warning system based on SAR technology and a 3D model is established. Remote sensing data generated by SAR technology is applied to debris flow disaster identification, improving the early identification capability of debris flow hazards and conducting debris flow hazard deformation prediction analysis.

A surface water body extraction system and method based on an artificial intelligence algorithm, the system comprising a backend processing system and a front-end operating system; the method: a user registers and logs in through a user login module of the front-end operating system; the user selects data and corresponding parameters through a data selection module, selects a band combination of a remote sensing image and an image stretching method; a model selection module is used to determine a water body extraction model, the backend processing system adjusts the band combination of the remote sensing data according to the corresponding parameters, calls the corresponding model in a database storage module and processes the data using an algorithm processing module to obtain a water body extraction result; whether to verify the water body extraction result is selected through a verification module, if verification is selected, the accuracy of different results is evaluated using precision, recall, F1 score and average intersection over union ratio indexes, if no verification is selected, the water body extraction result is directly output; the present application has the advantages of high water body extraction accuracy and strong technical robustness.

A computing device records feature embeddings of each document as a document node of a knowledge graph and connects each document node of the knowledge graph with one or more engagement edges based on engagement telemetry data indicating a measure of engagement with the documents stored in the document datastore. The computing device trains a graph neural network using the knowledge graph populated with each document node and the one or more engagement edges. The computing device may generate a feature embedding for the document query and classify one or more documents from the document datastore as relevant to the document query using the graph neural network based on the feature embedding of the document query.

The application relates to an energy coordination control method, device, equipment and medium for a hybrid energy storage system. The method comprises the following steps: acquiring total power instruction data of the hybrid energy storage system, state of charge parameters of energy storage units of the hybrid energy storage system, and real-time delay observation data between the energy storage units; selecting other energy storage units as neighborhood energy storage units of the energy storage unit; obtaining a neighborhood charge state prediction value set of the energy storage unit based on the state of charge parameters, the real-time delay observation data and a preset state prediction model; quantifying delay effects of each neighborhood energy storage unit of the energy storage unit based on the real-time delay observation data to obtain a delay adaptive weight set; and distributing the total power instruction data to each energy storage unit based on the state of charge parameters, a preset rated power capacity set, the neighborhood charge state prediction value set and the delay adaptive weight set to obtain power control instructions of each energy storage unit. The method can improve the timeliness and accuracy of energy control.

This application relates to the fields of computer vision and autonomous driving perception technology, and particularly to a method, apparatus, device, medium, and program product for generating panoramic parking space data. The method includes: acquiring an original AVM image and preprocessing it to obtain a clean background image; generating parameterized parking spaces based on a preset set of candidate parking space contour primitives and preset multi-dimensional parameters, and mapping the parameterized parking spaces to the clean background image based on the coordinate system of the original AVM image to obtain an initial parking space image set; calculating the corner detection loss of each parking space image in the initial parking space image set based on a preset parking space corner detector, and generating a final parking space image set based on the corner detection loss of each parking space image from the initial parking space image set. This solves the problems of difficulty in simultaneously achieving background realism and foreground controllability, and low efficiency of data selection mechanisms in related technologies, thus improving the realism of the synthesized data.

A computer-implemented method of training an intelligent agent system for influencing the operation of a physical system to achieve an objective relating to an intended state of the physical system is described. The method comprises performing a respective reinforcement learning process for an intelligent agent, the reinforcement learning process being conducted within an environment that provides a representation of the physical system, the agent being trained to achieve the objective within the environment by carrying out one or more actions selected from a respective set of available actions, the training being based on a reward that is determined by the reinforcement learning process for each of the training steps. The reinforcement learning process comprises providing, to the agent, a set of observations of the environment of that agent, the agent being configured to select the one or more actions in dependence on the set of observations to achieve the objective, the set of observations being provided in a data structure in which the set of observations are arranged in an order which is dependent on a starting position of the agent and / or a position relating to the objective, within the environment.

This application provides an intelligent noise removal method and apparatus based on multi-feature deep fusion, relating to the field of petroleum geophysical exploration data processing technology. The method includes: designing an observation system according to a pre-defined geological task; dividing seismic gather data into N regions and selecting low signal-to-noise ratio (SNR) shots; identifying data above the first arrival time window as the first type of noise data; selecting labeled shots and standardizing and quality-evaluating the labeled shot data; adding noise data from a noise database to the labeled shots according to a set weight ratio, forming noisy shot gather data through data fusion technology, adding gain, and standardizing; constructing a deep neural network model; generating training samples based on the labeled shot data through data augmentation and sample partitioning techniques; and removing noise through feature analysis and learning of the input data using the model to obtain high-quality data. The modular process and flexible parameter configuration of this application facilitate integration with existing processing systems, demonstrating strong engineering practicality and promotional value.

A computer system is described for conducting interactions of a simulated event for users in a virtual environment. The computer system includes a processor programmed to: (i) present the virtual environment including an item of a first user associated with a first user device; (ii) receive sensor data from one or more sensors associated with the first user device, the sensor data including data related to the item; (iii) select a simulated event involving the item; (iv) generate the simulated event involving the item and using the received sensor data for display within the virtual environment; and (v) cause the simulated event involving the item to be displayed within the virtual environment to the first user device and a second user device, wherein the first user and the second user visually experience the simulated event involving the item.

This invention provides an information processing system, information processing method, and program that can contribute to reducing reliance on individual expertise in ordering operations at each store. [Solution] In the acquisition step, the information processing device acquires sales performance indicators for each store and each product. In the selection step, for each store and each product, based on the sales performance indicators for that product, it determines whether or not to select a demand forecasting algorithm from among multiple algorithms as the algorithm to be used for ordering that product, thereby selecting a recommended algorithm. In the ordering step, it executes the ordering process using the recommended algorithm. The multiple algorithms include a demand forecasting algorithm that determines the order quantity based on demand forecasts derived from past sales performance, and a conditional algorithm that determines the order quantity based on whether past sales or inventory levels meet certain conditions.

Systems and techniques for training a machine learning model to identify content labels within a video catalog using multimodal inputs are described. The techniques include receiving a multimodal input including the content. The techniques include determining a first selection of video data in data clusters based on the input and determining metadata indicating a correlation between the first selection and the attribute. Subsequently a second selection is selected based on the metadata and the first selection that may be used as a training dataset. A machine learning model is trained using the training data to determine instances of the attribute and build a content repository that summarizes the video data using the attribute labels.

A page buffer includes a charging circuit, first and second storage circuits, and a selection circuit. The charging circuit charges a bit line during a precharge period. The first storage circuit determines and stores data corresponding to a state of a selected memory cell among memory cells connected to the bit line when the charging circuit charges the bit line. The second storage circuit, which is a circuit separate from the first storage circuit, determines and stores data corresponding to the state of the selected memory cell after the precharge period. The selection circuit outputs a control voltage that controls a switching element connected between the bit line and the charging circuit, and determines a magnitude of the control voltage during the precharge period based on the data stored in the first storage circuit.

The present application belongs to the field of lithium ion battery remaining useful life prediction, and particularly relates to a lithium ion battery remaining useful life prediction method, system, device and storage medium, and especially relates to a lithium ion battery remaining useful life prediction method based on SFOA-VMD-Transformer-BiLSTM and a corresponding system. The method comprises data selection and preprocessing; global optimization of key parameters of variational mode decomposition by a sea star optimization algorithm; VMD decomposition of a standardized capacity sequence by optimal parameters; input of VMD decomposition results into multiple independent deep time series prediction model branches and training; capacity prediction result data fusion and reconstruction of each branch; output of actual capacity prediction results; and calculation of remaining useful life RUL. The present application significantly improves the prediction accuracy of lithium ion battery RUL, and effectively solves the problems of noise interference, low prediction accuracy and insufficient result stability.

Disclosed are a slave node device in a multimedia transmission system and the multimedia transmission system. The slave node device comprises: a receiving module configured to receive a data frame from an upstream node; a sending module configured to send the data frame to a downstream node; an analyzing module configured to analyze the received data frame and store the analyzed data into a bus data buffer area; an encoding module configured to read data from the bus data buffer area or a local data buffer area according to a data selection control signal and output the encoded data; a control signal generation unit configured to generate the data selection control signal according to a preset strategy and a data transmission state; and a data path switching module configured to take the output of the receiving module or the encoding module as the input of the sending module according to the data selection control signal. The slave node device selects the best data path for the entire data frame or each part of the data frame decomposed from the data frame and transmits the data frame by using the best data path, thereby reducing the node delay of the slave node device.