145 results about "Data generator" patented technology

The invention provides a three-dimensional visual model construction method and system for a mine slope, and relates to the technical field of data processing, and the method comprises the steps: obtaining the inclined image data of the mine slope; performing error correction on the inclined image data; performing live-action modeling according to the inclined image data after error correction to obtain a three-dimensional grid model and a point cloud data file; generating a digital terrain model according to the point cloud data file; extracting a contour line of the digital terrain model, and exporting a contour line file; generating a three-dimensional terrain curved surface by using a terrain data generator according to the contour line file; the three-dimensional terrain curved surface extends downwards by a preset depth, and a three-dimensional entity slope model is generated; physical parameters of side slope rock soil are set, and a Moire-Coulomb intensity criterion is given to the three-dimensional entity side slope model so as to simulate shear failure characteristics of the rock soil; on the basis of shear failure characteristics of rock and soil, grid division is carried out on the three-dimensional solid slope model; and generating a three-dimensional visual model of the mine slope.

The invention relates to the crossing field of industrial control system (ICS) safety and artificial intelligence, and particularly discloses a PLC high-interaction honeypot system based on multi-agent task splitting and RAG enhancement, which adopts a localized multi-agent collaborative architecture based on edge computing and is composed of a high-simulation equipment layer and an intelligent decision-making layer. The high-simulation equipment layer comprises a PLC dynamic mirror image, an HMI interface and a sensor data generator, and an active trapping environment is constructed through protocol fingerprint confusion and virtual and real data fusion technologies. The decision-making layer deploys a multi-agent task scheduling engine, integrates four kinds of agents including protocol analysis, behavior analysis, threat assessment and response generation, and realizes attack context perception and strategy dynamic generation based on a local RAG knowledge base. The load balancing agent dynamically allocates tasks according to equipment resources, and cooperates with offline knowledge update (USB flash disk encryption synchronization threat features) to form a closed-loop defense system, thereby ensuring physical isolation of an industrial network and realizing high-fidelity active defense.

The invention discloses a test data intelligent generation and privacy compliance desensitization method, which belongs to the technical field of electric digital data processing, and comprises the following steps: monitoring the structure change of a preset regulation update source and a target application system, and obtaining compliance dynamic information; analyzing the compliance dynamic information to generate a dynamic compliance rule base; based on the dynamic compliance rule base, generating an initial test data set through a preset intelligent data generator; desensitizing the initial test data set, and outputting a desensitized test data set; scanning the desensitized test data set in real time for compliance verification, and generating a compliance verification report; and dynamically optimizing the generation strategies of the dynamic compliance rule base and the intelligent data generator based on the compliance verification report. Dynamic rule base construction, intelligent separated data generation, closed-loop verification optimization and desensitization audit tracing technologies are adopted, laws and regulations and system changes can be automatically adapted, and the service validity, privacy security and law compliance of test data are synchronously guaranteed.

Control system (1) comprising: a sensor (44) that detects an acceleration based on a vibration of a robot (3); an interpolator (222) that interpolates a plurality of sensor data sets detected by the sensor (44); and a data generator (223) that calculates a correlation coefficient based on a plurality of interpolation data sets interpolated by the interpolator (222), combines the plurality of interpolation data sets based on the correlation coefficient, and averages composite data with fine sampling intervals resulting from the combination of the plurality of interpolation data sets.

Proposed is a calibration method for a camera and a lidar using a calibration board having a specific pattern. The method may include extracting one frame for an image captured by a camera and point cloud data acquired by a lidar, by a data generator, identifying a feature point for a calibration board included in one extracted frame for the image and the lidar, by the data generator, and performing calibration for the camera and the lidar based on the identified feature point, by the data generator. The present method is a technology developed with support from the Ministry of Trade, Industry and Energy / Korea Planning and Evaluation Institute of Industrial Technology (Project No. 20022003 / Project name-Automotive industry technology development project / Project name-Development of industrial autonomous driving and safety assurance technology based on longitudinal and lateral interconnection).

The invention relates to the technical field of wireless communication protection, in particular to a wireless communication self-adaptive safety protection method and system based on edge computing. Capturing physical layer channel state information when the Internet of Things terminal equipment accesses the network gateway for the first time, a functional area semantic identifier of the network gateway and a current timestamp, and generating an anonymous session voucher; tracking operation track data of the anonymous session voucher, and constructing a track operation fragment; obtaining a privacy risk assessment value based on trajectory uniqueness, spatial information sensitivity and behavior association analysis of the trajectory operation fragment; when the privacy risk assessment value exceeds a preset risk threshold value, triggering a track link breaking mechanism; obtaining privacy enhanced trajectory data based on privacy protection processing and a trajectory broken link mechanism; training a virtual data generation model through the privacy enhancement trajectory data; and after the virtual data generation model converges, driving a virtual data generator in the virtual data generation model to generate a virtual operation and maintenance data stream so as to realize self-adaptive protection of wireless communication data.

The invention discloses a photovoltaic-energy storage-electric vehicle collaborative V2G optimization scheduling method adaptive to a building scene, and relates to the technical field of building energy management and vehicle network interaction, and the method comprises the steps: training to obtain a data generator; outputting sample data of new energy output and building load scenes through a data generator, and generating a plurality of typical scenes by using a clustering algorithm and a Nash equilibrium model; in each typical scene, planning vehicle network interaction facility configuration, and carrying out cooperative scheduling on a source, a load, a storage and a vehicle; selecting participating automobiles, and scheduling the participating automobiles in combination with battery loss and a compensation mechanism; and comprehensive evaluation of economy and operation quality is carried out on the collaborative scheduling result of the source, the load, the storage and the vehicle, the planning result of vehicle network interaction facility configuration and the scheduling result of the participating vehicle. According to the method, the uncertainty scene serves as input, cost minimization is achieved through the two-stage model, and the economical efficiency and feasibility of the scheme are verified through evaluation.

An apparatus for sharing data according to one embodiment includes a first combined data generator configured to generate first combined data by combining target data to be shared and a random bit string, a data encryptor configured to generate a ciphertext for the first combined data, a key encryptor configured to generate one or more ciphertexts for a decryption key of the ciphertext for the first combined data using one or more public keys, a second combined data generator configured to generate second combined data by combining the ciphertext for the first combined data, the one or more ciphertexts for the decryption key, and the random bit string, and a data discloser configured to disclose the second combined data to one or more entities to share the target data to be shared.

The invention belongs to the technical field of artificial intelligence and distributed machine learning, and discloses a personalized federal learning data generation method and a related device. The personalized federal learning data generation method comprises the following steps: acquiring a local data similarity distribution vector of a selected client; acquiring a trained local model of the selected client and sending the trained local model to a server in federal learning; and obtaining a global data generator issued by the server, training based on the local sample similarity distribution vector to obtain a local data generator, and finally generating personalized federal learning data of the selected client by using the local data generator. The technical scheme disclosed by the invention specifically relates to a personalized federated learning data generation scheme based on adversarial training and similarity evaluation, and can effectively obtain a generator and generate personalized federated learning data with relatively high reliability.

The invention discloses a digital annotation quality automatic verification method and system, and relates to the technical field of data processing. The method comprises the following steps: identifying and classifying original annotation data to obtain an identification task level; task matching is carried out according to the identified task level and the multi-level processing path, and task import connection is established; obtaining a distribution verification target through task import connection, calling an adversarial data generator to create semantic and quality dimension variants, comparing the annotation consistency of original data and variant data, and obtaining annotation vulnerabilities and potential error information; and configuring an identification tolerance threshold according to the identification task level, configuring an identification constraint parameter based on the vulnerability and the potential error information, executing annotation verification in the matching processing path based on the identification constraint parameter, and outputting a verification result. The technical problems of low efficiency and insufficient accuracy caused by the fact that digital labeling quality verification depends on manual sampling inspection in the prior art are solved, and the technical effect of improving verification efficiency and accuracy is achieved.

Corner table creation for graphics processing is described. An example of an apparatus includes corner data generator circuitry, including a circuit to generate a plurality of vertex-corner lists for a plurality of portions of a triangle mesh, wherein a vertex-corner list for a portion includes an index for a vertex, a count of corners for the vertex, and a list of corners associated with the vertex, and a circuit to receive the vertex-corner lists and generate one or more edge hash maps, each edge hash map including corner indices for edges of the triangle mesh.

The application provides a visual language large model adaptive bias evaluation method for evaluating bias performance of a target model on a specific task, and the method comprises the following steps: S1, obtaining an initial evaluation dataset, wherein the initial evaluation dataset comprises a plurality of evaluation data; S2, fine-tuning a preset large language model using the initial evaluation dataset in a preset fine-tuning manner to obtain an initial data generator; and S3, generating evaluation data based on the initial data generator to perform multiple rounds of bias evaluation on the target model, wherein in each round of bias evaluation, based on all evaluation data corresponding to the last round of bias evaluation, a data generator corresponding to the last round of bias evaluation is iteratively updated according to a preset objective function to obtain a current round data generator, and the current round data generator is used to generate a current round evaluation dataset to perform bias evaluation on the target model.

A virtual reality (VR)-based tour route providing apparatus, which creates a tour route of a corresponding tourism area through a VR experience in which a tourism area of users is implemented according to one embodiment of the present invention, includes an area information receiver, a zone information receiver, a VR data generator, a tracking data generator, a zone visit information generator, a zone-of-interest selection unit, and a tour route information generator.

The invention relates to a system (100) for optimizing the configuration of crash tests and simulations for vehicle development. The system (100) comprises a data preparation module (200) that splits a data set (210) containing crash test and simulation data into a first training data set (210), a validation data set (230), and a test data set (240); a training module (300) with a first AI model (350) for creating configurations of physical crash tests and virtual simulations, wherein the first AI model (350) is trained with the first training data set (220); and a data value module (400) that calculates data values ​​(φ(i)) for the data points (i) of the crash test and simulation data of the training data set (220) with respect to the validation data set (230).a model optimization module (500) comprising a second AI model (550) and a training data generator (570), wherein the training data generator (570) creates a second training data set (580) which is used to train the second AI model (550) to predict the relationship between input parameters (p) and the computed data values ​​(φ(i)); a parameter module (700) which uses the trained second AI model (550) to find crash test parameters with a maximum data value (φ(i)).

Proposed is a calibration method for a lidar and an IMU using features according to the shape of a specific object included in point cloud data. The method may include placing point cloud data acquired by a lidar mounted on a vehicle on a predefined world coordinate system, by a data generator, extracting a region to be used for calibration from the placed point cloud data, by the data generator, identifying at least one object included in the extracted region, by the data generator, and performing calibration on the point cloud data by fitting point cloud included in the at least one identified object to a pre-stored model, by the data generator. The present method is a technology developed with support from the Ministry of Land / KAIA (Project No. RS-2021-KA160637).

The invention discloses a wireless data transmission system and method using a compression technology, and the system comprises a data generator which is used for generating to-be-transmitted wireless data and transmitting the to-be-transmitted wireless data to a compression module, the compression module is used for compressing the to-be-transmitted wireless data into compressed data by using a self-adaptive differential pulse code modulation algorithm, and the compressed data is transmitted to the compression module through the self-adaptive differential pulse code modulation algorithm; the gateway end comprises a gateway protocol end and a transmission module, and the gateway protocol end is used for receiving the compressed data packet and sending the compressed data packet to the transmission module for transmission; and the server is used for receiving and decompressing the compressed data packet transmitted by the transmission module so as to realize the transmission of the wireless data to be transmitted. According to the invention, low-cost, high-efficiency and high-real-time wireless data transmission under a large field domain can be realized.

A modular adversarial training data generator (“generator”) generates adversarial training data for a machine learning (ML) model to detect malicious protocol data units (PDUs). The adversarial training data mimics high volume cyberattacks by perturbing PDUs to bypass malicious detection systems. For Hypertext Transfer Protocol (HTTP) PDUs, the generator rearranged, replaces, and grid searches values of HTTP header fields to generate the adversarial training data. The generator further biases grid search based on metrics for values of HTTP header fields that quantify impact of replacing the values on malicious verdicts by ML models trained on the adversarial training data.

The invention relates to patient occupancy detection. An apparatus is provided for processing data collected from an area within a facility for transmission to a cloud-based processing system for processing at the cloud-based processing system. The device comprises an input unit, a point cloud data generator and an output unit. The input unit is configured to receive data collected from an area in a health care institution, the data to be sent to a cloud-based processing system. The acquired data includes one or more of video data, image data, and ranging data. The point cloud data generator is configured to process the collected data to generate point cloud data that can be used to provide point cloud rendering of the region in the health care institution. The output unit is configured to communicate with a network to transmit the generated point cloud data and the corresponding metadata to the cloud-based processing system.

The application discloses an electric energy meter informationization evaluation calibration model measurement and evaluation verification system and method, and the system comprises an electric energy meter informationization evaluation calibration model measurement uncertainty evaluation and control module, a load data generator based on a digital twin mixed reality technology and an electric energy meter informationization evaluation calibration model result measurement and evaluation verification method research and measurement and evaluation verification platform. The application evaluates and verifies the electric energy meter informationization calibration model in theory and practice, improves the informationization measurement accuracy and reliability to a new height, and also solves the problems of low work efficiency, high operation and electric energy meter replacement cost, high manual labor intensity, incapability of real-time monitoring of electric energy meters and timely fault reporting and early warning, incapability of effectively controlling the measurement faults and error power and the like existing in the currently generally adopted traditional manual field calibration or disassembly laboratory calibration mode.

The invention provides a large-model multi-modal and multi-dimensional data augmentation method and device based on a man-machine interaction mode, and the method comprises the steps: collecting multi-modal and multi-dimensional agricultural data, carrying out the preprocessing and formatting unification, and obtaining the processed agricultural data; obtaining a multi-mode demand instruction input by a user through the man-machine interaction interface; calling a data augmentation model based on the multi-modal demand instruction and the processed agricultural data to obtain augmented agricultural data; performing cross-modal consistency verification on the augmented agricultural data, and fusing the augmented agricultural data passing the verification with the processed agricultural data to obtain an augmented agricultural data set; the data augmentation model comprises a large language model in the agricultural field, a CLIP model in the agricultural field, an agricultural knowledge graph and a data generator corresponding to multiple modal types associated through an attention mechanism of a shared semantic space. And therefore, high-quality multi-modal augmented data which is consistent in semantics and reasonable in agriculture can be generated.

A photoplethysmogram (PPG) sensor includes: a pixel array that collects light; a pixel sampler that converts the light collected by the pixel array into a plurality of pixel data; a valid area determiner that determines a valid area and an invalid area of the pixel array based on the pixel data; a power controller that is operable to cut off power supply to the invalid area of the pixel array; and a PPG data generator that generates PPG data according to the pixel data corresponding to the valid area in the pixel data.

A setting system includes a setting input section, an image data generator, and a control data generator. The setting input section is configured to receive an input of setting data on a setting of operation of a plurality of facilities. The plurality of facilities includes a lighting fixture and a shielding apparatus. The image data generator is configured to render, based on model data, image data representing a light environment corresponding to the setting data of the building. The control data generator is configured to generate control data for executing control corresponding to the setting data over the plurality of facilities.

The application relates to the technical field of image processing, and discloses a light-weight single-pixel imaging method based on frequency domain-space domain modulation attention, which comprises a single-pixel image data generator and a calculation reconstruction generator; an initial inversion estimation is obtained through inverse mapping of a measurement matrix; then, the initial inversion estimation is input into a dual-domain collaborative attention deep reconstruction network; the network is composed of a plurality of dual-domain collaborative attention module cascades connected in series, a multi-head spectrum calibration module in the module realizes long-distance information reconstruction through dual-domain feature information fusion interaction; a multi-scale-depth dynamic convolution fusion module extracts features in a space domain, accurately processes high-frequency edges, and significantly enhances the reconstruction capability of texture details; and the features are corrected in combination with a residual error guiding update module. Through the dual-domain collaborative mechanism, the reconstruction precision and the detail recovery capability of single-pixel imaging are effectively improved while the network parameter quantity and the calculation complexity are greatly reduced, and the application is suitable for edge deployment.

The present disclosure pertains to a power amplifier system that promotes enhanced signal linearity and overall system efficiency. The system includes a power amplifier with an amplification path for a radio frequency (RF) signal, and detector circuitry operationally linked to sample locations along this path. The detector circuitry captures and transmits signal characteristics of the RF signal. A voltage standing wave ratio (VSWR) quadrant data generator in communication with the detector circuitry generates VSWR quadrant data based on the detected signal characteristics. The baseband circuitry, comprised of a memory unit preconfigured with digital pre-distortion (DPD) coefficients and a DPD processor, controls the shaping of pre-distortion applied to the RF signal based on the VSWR data, thereby enhancing signal linearity. The components of the system interconnect and collaboratively function to optimize the performance and efficiency of the power amplifier system.

The invention relates to the technical field of data enhancement, and discloses an infrastructure resource library data enhancement method, system and device based on artificial intelligence and a medium, comprising: constructing an infrastructure contract format and a noise knowledge engine, obtaining contract data, initiating through an adversarial noise generation agent, and based on the infrastructure contract format and the noise knowledge engine, obtaining the contract data; generating antagonism data under the fact constraint; and constructing a target contract key clause identification model to obtain a contract identification result and a loss value, generating an instant reward signal or a delay penalty signal of the adversarial noise generation agent, and performing strategy updating on the adversarial noise generation agent. According to the method, a closed-loop online data enhancement architecture based on fact constraint and antagonistic learning is introduced, so that the fidelity and rationality of generated data are remarkably improved, the efficiency and pertinence of data enhancement are greatly improved, and co-evolution of a data generator and a data learner is realized.

Embodiments of the present disclosure disclose an image-to-hyperspectral data conversion method, device, electronic equipment and medium. A specific implementation of the method comprises: obtaining image data from a pre-constructed image set; preprocessing the obtained image data to obtain pre-processed image data; generating hyperspectral data according to a hyperspectral data generator and the pre-processed image data; generating a performance analysis result corresponding to the hyperspectral data according to a performance analyzer and the hyperspectral data; generating optimization parameter data according to the performance analysis result; performing multi-stage optimization on the hyperspectral generator and the performance analyzer according to the optimization parameter data to obtain an optimized hyperspectral generator and an optimized performance analyzer; and generating target hyperspectral data according to the optimized hyperspectral generator and the optimized performance analyzer. The implementation generates hyperspectral data, performs multi-stage optimization and performance evaluation, and obtains target hyperspectral data of higher quality.

Systems and methods for super sampling and viewport shifting of non-real time 3D applications are disclosed. In one embodiment, a graphics processing unit includes a processing resource to execute graphics commands to provide graphics for an application, a capture tool to capture the graphics commands, and a data generator to generate a dataset including at least one frame based on the captured graphics commands and to modify viewport settings for each frame of interest to generate a conditioned dataset.

A stepwise waveform generation data generator is configured to generate stepwise waveform generation data having a data value corresponding to each display gradation within each horizontal scanning period, and a time length corresponding to the number of pixels of each of the display gradations for generating a stepwise waveform signal, based on a gradation value of each of the display gradations and start time of the display gradation period for each of the display gradations. A grayscale-transformed video data generator is configured to generate grayscale-transformed video data, which is video data obtained by transforming pixel values of each pixel in the horizontal scanning periods of video data into pixel values that are dispersed so as to correspond to the selection periods corresponding to the display gradations.

A transmitter includes a data generator, a serializer, a transmission driver and a feedback circuit. The data generator generates a retimed data signal and retimed test data by adjusting a delay amount of each of an input data signal and a test data based on adjusted clock signals. The serializer generates a serial data signal by serializing the retimed data signal based on multi-phase clock signals. The transmission driver generates an output data signal based on the serial data signal and transmits the output data signal through a channel. The feedback circuit detects the setup margin and the hold margin of the retimed test data through a separate path different from a path of the retimed data signal and generates the adjusted clock signals by adjusting delay amounts of the multi-phase clock signals based on the detected setup margin and hold margin of the retimed test data.