226 results about "Reference dataset" patented technology

The invention relates to a computer implemented method for defect detection comprising: obtaining an imaging dataset and a reference dataset of an object comprising integrated circuit patterns; and detecting defects in the imaging dataset using the imaging dataset and the reference dataset, wherein a machine learning model for defect highlighting is applied to the imaging dataset as input and generates a highlighted defect dataset as output, and wherein the machine learning model for defect highlighting comprises at least one attention mechanism. The invention also relates to computer programs, computer-readable media and corresponding systems.

The invention belongs to the field of data quality analysis, and particularly discloses a data set quality analysis and evaluation method, which performs integrity evaluation on a current data set, starts data confidence analysis only after the number of data groups reaches the standard, ensures that confidence evaluation is established on the basis of data with a complete structure, avoids erroneous judgment caused by data missing, and improves the quality of the data set. Meanwhile, during data confidence analysis, dynamic comparison between current data and a local time sequence neighborhood is implemented by constructing an adjacent data set of each data group; meanwhile, a historical reference data set is combined, longitudinal comparison with a historical fluctuation range is carried out, and dual verification of local consistency and historical comparability is fused, so that the anomaly detection sensitivity and the evaluation accuracy are improved to a great extent; and finally, establishing a closed-loop feedback path with an acquisition end when availability judgment is carried out based on a data set integrity and confidence evaluation result, and realizing self-adaptive adjustment of an acquisition strategy according to a risk positioning result.

System and methods are disclosed for matching extracted text data based on one or more similarity scores. The method may include receiving one or more documents from a plurality of data sources, utilizing an optical character recognition algorithm for extracting text data from the one or more documents, comparing, utilizing a fuzzy matching algorithm, the extracted text data to reference dataset(s) to determine one or more matches between the extracted text data and at least one of the reference dataset(s), wherein the one or more matches are based on at least one similarity score, inputting the determined one or more matches and the at least one similarity score into a trained machine-learning model to refine the one or more matches, and outputting a representation of the refined one or more matches and the at least one similarity score to a graphical user interface of a device.

A method for generating a segmentation mask of at least one image comprising generating a plurality of superpixels for the at least one image, automatically generating labels for the generated plurality of the superpixels, wherein the automatically generating labels comprises generating the labels for training a semantic segmentation model based on a plurality of segmentation masks by generating a label for each superpixel by identifying the most similar reference superpixel from a reference data set of reference superpixels, wherein each reference superpixel is associated with a class for supervised training of the semantic segmentation model, wherein the labeled superpixels form the segmentation mask of the at least one image, and computer-aided checking of the generated segmentation mask for correctness, wherein the checking for correctness comprises labeling unlabeled superpixels and correcting labels of incorrectly labeled superpixels by assigning the incorrectly labeled superpixels to the correct class.

The invention discloses an intelligent war game deduction decision-making method based on sample migration, and belongs to the technical field of artificial intelligence and military deduction games. The method comprises the following steps: determining environment information of wargame deduction migration, and constructing a reference data set; constructing a migration network model based on a CycleGAN network framework, training the migration network model by using the reference data set, and generating migration data by using the trained migration network model; constructing a soft constraint condition according to the migration data; under the constraint of the soft constraint condition, the migration data and exploration data in the current combat scene state are utilized to train the agent model, and a trained agent model is obtained; the trained agent model is used for intelligent wargame deduction decision making. The method has higher sample migration similarity and knowledge transitivity, meanwhile, the problem that an existing method is poor in training effect under non-ideal data is solved, and the robustness of an algorithm is improved.

The invention discloses an animal single cell data cell type annotation method, which is characterized by comprising the following steps: collecting Bulk RNA-seq data of purified cell types in various tissues and organs of a specific animal, and integrating the Bulk RNA-seq data into a reference data set after preprocessing; the method comprises the following steps: acquiring single-cell RNA-seq original data, screening the single-cell RNA-seq original data to obtain high-quality cells, screening high-variation genes from the high-quality cells, processing the high-variation genes, extracting principal components, performing dimensionality reduction on the principal components, and performing cell clustering based on a dimensionality reduction result to obtain cell clusters; and calculating expression similarity between the cell clusters and the reference data set based on the high-variation genes, determining initial cell types of the cell clusters according to a similarity result, and carrying out iterative tuning on the initial cell types with similar scores to obtain a final cell type annotation result. The method provides efficient and accurate technical support for animal single cell research.

Various embodiments of the present disclosure provide prompt engineering and iterative, feedback-based generative techniques that improve traditional LLM technology, including extractive LLM techniques. The techniques may include selecting one or more simple annotated question-answer pairs for an input data object comprising an input question and an input document from a reference dataset. The techniques may include selecting one or more complex annotated question-answer pairs from the reference dataset. The techniques may include generating a few-shot prompt based on the one or more simple annotated question-answer pairs and the one or more complex annotated question-answer pairs. The techniques may include providing the few-shot prompt to a large language model (LLM) to receive a predictive output for the input question.

The invention relates to a method for predicting the lifespan of an individual, comprising I. providing biological input data of the subject, ii. providing a list of confounding variables, iii. predicting an age-related mortality or disease, preferably the time to death and / or the mortality risk and / or the risk of age-related disease, for the subject by analyzing the data with an algorithm, wherein the algorithm comprises a neural network, which is trained on at least one reference dataset comprising biological data of at least one reference subject by applying: a) a selector layer to filter input data of i., and b) an adversarial learning framework, that removes from the input data the information related to the confounding variables, wherein preferably the adversarial learning framework, is a neural network comprising three elements: a feature extractor (FE), a predictor (P), and a confounder predictor (C). The invention further relates to a device or system comprising means for carrying out the steps of the method according to the invention, a computer program and a computer-readable storage medium.

The invention relates to a computer implemented method for defect detection comprising: obtaining an imaging dataset of an object comprising integrated circuit patterns; obtaining a reference dataset of the object; registering the imaging dataset and the reference dataset by obtaining at least one transformation field pair comprising an input transformation field and a corresponding reference transformation field, wherein the input transformation field or the reference transformation field can be zero; and detecting defects in the imaging dataset using the at least one obtained transformation field pair. The invention also relates to a computer-readable medium, a computer program product and a system for detecting defects.

Systems and methods are provided to generate improved sets of reference data that are ML model-agnostic. The system initiates an imbalance analysis on a training dataset (e.g., text, image, time series, etc.) that includes determining a set of classes in the data. Using the set of classes, the system processes mutual information (MI) across the data segments to generate a set of matrices from extracted partition-level mutual information. In some examples, the system may generate baseline reference data from the set of matrices and provide the baseline reference data for implementation with anomaly detection or model explainability in external machine learning (ML) models.

The invention discloses a building component autonomous inspection system and method based on a BIM (Building Information Modeling) and a VLM (Visual Language Model), and relates to the technical field of intelligent inspection of building engineering. Generating a spatial reference data set, a semantic tag set and a component semantic embedding vector; the method comprises the following steps: collecting multi-modal visual and spatial data, carrying out semantic segmentation and description generation by utilizing a visual language large model, calculating a semantic ambiguity coefficient and carrying out confidence evaluation; through BIM and visual semantic embedding vector comparison and spatial deviation analysis, calculating a cross-modal consistency index and generating a deviation report; calculating a comprehensive path cost coefficient and performing path optimization; and in combination with semantic recognition, cross-modal consistency and path cost, obtaining inspection performance evaluation indexes, realizing adaptive optimization through reinforcement learning, and writing back to a BIM database to realize continuous improvement of data and decision ability.

The invention provides a fault judgment method of a wind driven generator and computer equipment. The fault judgment method comprises the following steps: acquiring a first sample data set of the wind driven generator of a to-be-judged wind driven generator set; based on the data distribution of each piece of first sample data in the first sample data set, screening the first sample data set to obtain a second sample data set; comparing the second sample data set with a reference data set to obtain a fault discrimination condition for discriminating the fault of the wind driven generator; and based on the operation data of the wind driven generator and the fault discrimination condition, carrying out fault discrimination on the wind driven generator. The problem that efficient and accurate fault judgment of a lightweight algorithm is difficult to realize is solved, fault judgment can be carried out on the wind driven generator by screening and optimizing sample data and flexibly determining fault judgment conditions, and efficient and accurate fault judgment can be carried out through the lightweight algorithm.

A three-dimensional (3D) mapping system can be configured to generate a 3D map of a real-world environment using annotation of large image data sets, in which terrestrial imagery can be programmatically labeled with accurate labels using remotely sensed overhead image data. The 3D mapping system can implement photogrammetry to create a point cloud. Each pixel in the point cloud can be classified based on a consensus of each frame. The point cloud can be co-registered to a remotely sensed reference dataset to provide precise spatial coordinates for each pixel. Different patches of point clouds can be stitched together to provide a complete 3D map for a given area, such as a downtown area of a city.

An example method includes (a) obtaining an object detection from a perception system that describes an object in an environment of the autonomous vehicle; (b) obtaining, from a reference dataset, a label that describes a reference position of the object in the environment; (c) determining a plurality of component divergence values respectively for a plurality of divergence metrics, wherein a respective divergence value characterizes a respective difference between the object detection and the label; (d) providing the plurality of component divergence values to a machine-learned model to generate a score that indicates an aggregate divergence between the object detection and the label, wherein the machine-learned model includes a plurality of learned parameters defining an influence of the plurality of component divergence values on the score; (e) evaluating a quality of a match between the object detection and the label based on the score.

The invention relates to the field of nuclear power safe operation, in particular to a lightning arrester resistive current data classification method and a lightning arrester working state judgment method. The classification method comprises the following steps: acquiring humidity data and resistive current data of the lightning arrester to form an original data set; improving a bandwidth formula, generating B resistive current reference data sets according to a KDE kernel density estimation function, and forming a reference data set by the resistive current reference data sets and the humidity data; calculating the sum of squares of classification distance errors of an original data set of all data sets and a reference data set generated after processing under different clustering center numbers k, calculating Gap statistics, and selecting an optimal Kbest value; and performing K-means clustering analysis on the original data set based on the optimal Kbest value, and dividing the original data set into a rain class and a sunny class. The method is accurate in classification, effectively improves the online monitoring precision of the lightning arrester, avoids the misjudgment of the working state of the lightning arrester, and achieves the stable operation of a power system.

The invention relates to the technical field of riding route planning, and particularly provides a riding route AI planning method based on user demands, which comprises the following steps: receiving user riding preference information and basic travel information, and carrying out integration quantitative preprocessing to form a structured user demand data set; acquiring multi-source reference data such as preset route database data and real-time dynamic data, and performing fusion preprocessing to generate an adaptive reference data set; inputting the two types of data sets into a trained AI planning model to calculate an optimal path, and embedding adaptive point locations based on preference constraints to generate an initial path; and performing multi-dimensional adaptability optimization by combining basic constraints, generating a personalized scheme with multiple versions, setting an output priority, completing route optimization according to a user interaction instruction, and outputting a final executable riding route. The method deeply adapts to multiple demands of the user, improves the planning accuracy and flexibility, and provides a personalized riding route service giving consideration to experience and practicability for the user.

The invention relates to a video generation method and device, computer equipment and a storage medium. The method comprises the following steps: acquiring audio data information and multi-role video frame information; performing dynamic three-dimensional mask processing on the multi-role video frame information to obtain role mask information; combining the audio data information and the role mask information based on multi-modal diffusion to generate role mouth shape action information; acquiring a reference data set, and training a video generation model according to the reference data set based on a preset staged training strategy; and inputting the role mouth shape action information and the multi-role video frame information into the video generation model to generate an effective fusion video. The method and the device can be applied to application scenes of a financial service system and a digital medical system, and can effectively realize video generation with high audio synchronization rate in a multi-role scene according to the input audio data and the multi-role video frame.

The invention discloses a provincial road network emergency resource scheduling path optimization method based on big data analysis, and relates to the field of path optimization, and the method comprises the following steps: obtaining corresponding resource data, and constructing a data storage library according to the obtained resource data; obtaining element information of corresponding resource data in the data storage library, setting a data storage stereoscopic model, and obtaining a corresponding data analysis set; analyzing and processing the data analysis set to obtain importance data between corresponding element information combinations, and setting an element importance evaluation data table; acquiring emergency event information, and setting a reference data set according to the emergency event information; setting basic resource scheduling path information corresponding to the corresponding emergency event information according to the reference data set; performing optimization processing on the basic resource scheduling path information based on the element importance evaluation data table to obtain optimized resource scheduling path information; according to the method, the efficiency and the accuracy of the path optimization process are improved to a certain extent.

The invention discloses a kalman filtering and sparrow search optimization-based fall detection method for elderly people living alone. The method comprises the steps of S1, obtaining calibrated original motion data; s2, inputting the calibrated original motion data into a Kalman filtering algorithm to obtain attitude feature data; s3, training an action recognition model by using a pre-established labeled reference data set and the attitude feature data, and outputting preliminary action category judgment sequence data; s4, collecting historical motion behavior data, judging sequence data in combination with the preliminary motion category, initializing a sparrow search optimization algorithm, and forming a self-adaptive motion recognition model; and S5, inputting the real-time posture feature data into the self-adaptive action recognition model, generating fall event judgment result data, and when the fall event judgment result data indicates that a fall event occurs, triggering an alarm mechanism. According to the method, the problem that the transition states of suspicious falling and slow falling cannot be recognized clearly in a traditional method is effectively solved.

An example method includes (a) obtaining an object detection from a perception system that describes an object in an environment of the autonomous vehicle; (b) obtaining, from a reference dataset, a label that describes a reference position of the object in the environment; (c) determining a plurality of component divergence values respectively for a plurality of divergence metrics, wherein a respective divergence value characterizes a respective difference between the object detection and the label; (d) providing the plurality of component divergence values to a machine-learned model to generate a score that indicates an aggregate divergence between the object detection and the label, wherein the machine-learned model includes a plurality of learned parameters defining an influence of the plurality of component divergence values on the score; (e) evaluating a quality of a match between the object detection and the label based on the score.

The present application relates to a terminal device for blood sugar management during pregnancy and its cloud server, the terminal device collects physiological data and blood sugar data of a target user including the gestation period, the cloud server obtains a plurality of blood sugar data of reference users from a plurality of terminal devices and forms an overall reference data set in a manner associated with gestational age, the cloud server calculates basic statistical indicators for each group in the overall reference data set, combines the specific percentile position of the user's blood sugar data in its own group distribution, judges the current blood sugar state of the user by comparing the user's blood sugar data with the preset healthy blood sugar range, and generates a graded diet suggestion accordingly, the diet suggestion optimizes the diet structure of the target user by selecting food categories with different GI values; the terminal device receives and visually displays personalized diet suggestions matching the blood sugar status of the target user to the target user.

This invention relates to a method for designing and utilizing a broad RNA modification library that includes all surrounding sequences of the RNA modification. By introducing multiple types of RNA modifications, this invention enables the construction of RNA modification libraries that encompass all possible combinations of surrounding sequences flanking the modified RNA. Therefore, it differs from existing reported methods for constructing RNA modification libraries. Furthermore, using the RNA modification library of this invention for single-molecule RNA sequencing generates a high-quality reference dataset containing all possible combinatorial motifs. If this dataset is used as training data for deep learning (or machine learning), it overcomes the limitations of existing software that is limited to specific RNA modifications or has low prediction accuracy for other modifications. This allows for the development of novel, more accurate RNA modification detection software and the simultaneous detection of multiple RNA modifications.

The invention provides a method and device for predicting the lifetime of a tumor patient, computing equipment and a storage medium. The method comprises the following steps: acquiring multi-dimensional medical data related to the condition of a tumor patient, wherein the multi-dimensional medical data at least comprises data of at least two dimensions; determining a similar dataset of the multi-dimensional medical data from a reference dataset, the reference dataset including reference multi-dimensional medical data and corresponding lifetime of each of a plurality of reference patients having reached the end of life at different time points; and obtaining a prediction result of the lifetime of the tumor patient based on the multi-dimensional medical data and the similar data set by using a fine-tuned large language model, the prediction result indicating the lifetime of the tumor patient and an explanatory text of information on which the prediction result is based.

Embodiments of the present invention disclose a data expansion method, apparatus, and storage medium. The method includes: obtaining a reference data set in a target business scenario and a data set to be expanded in an associated business scenario associated with the target business scenario; determining reference feature data of each data in the reference data set and determining target feature data in the reference feature data; selecting first data to be expanded having the target feature data from the data set to be expanded, and using the first data to be expanded as target expansion data for the target business scenario. The technical solution in the embodiments of the present invention realizes more rapid and effective data expansion, thereby reducing the time consumed for data expansion and further improving the efficiency of data expansion.

The invention relates to the technical field of power battery management, and discloses a power battery generative dataset quality evaluation method, which comprises the following steps: mapping a power battery generative dataset to be evaluated to a standardized feature space to obtain a target generative dataset; performing first data processing on the target generation type data set to obtain a multi-dimensional evaluation result, including: calculating a statistical distribution closeness degree of the target generation type data set and a real reference data set to obtain a authenticity evaluation result; calculating the distribution breadth of the target generation type data set in the feature space to obtain a diversity evaluation result; calculating the multi-variable synchronization relation consistency of the target generation type data set under the same or similar working condition labels to obtain a consistency evaluation result; calculating the coverage integrity of the evaluation target generation type data set on the key working condition area to obtain a representative evaluation result; and performing second data processing on the multi-dimensional evaluation result to obtain a comprehensive evaluation result. According to the method, the power battery generation type data quality can be measured in multiple dimensions.

The embodiment of the invention relates to the technical field of wind measurement data processing, and provides a wind measurement data correction method and device, electronic equipment and a medium, and the method comprises the steps: obtaining equipment wind measurement data which is first time period measurement data containing a time sequence, and obtaining second time period wind data of a position where the equipment is located; extracting a first reference data set, a second reference data set and a third reference data set from the first time period measurement data and the second time period wind data; respectively carrying out matrix processing on the first reference data set, the second reference data set and the third reference data set to obtain a first wind frequency matrix, a second wind frequency matrix and a third wind frequency matrix; and determining a correction result of the wind measurement data based on the first wind frequency matrix, the second wind frequency matrix and the third wind frequency matrix. And high-precision data correction comprehensively considering two-dimensional characteristics of wind speed and wind direction is realized.

The invention provides a heat exchange tube folding control method and system based on folding state analysis, and the method comprises the steps: obtaining the real-time folding state information and stress information of a heat exchange tube through various types of sensors, and generating complete data through preprocessing operation; determining a plurality of influence factors influencing the folding of the heat exchange tube, constructing a heat exchange tube folding enthusiasm prediction model, and obtaining an optimal factor group of the folding of the heat exchange tube through the enthusiasm prediction model; calling reference data related to the optimal factor group from a historical database, respectively generating a to-be-adjusted data set and a reference data set, and calculating a similarity value of the two data sets; and according to a comparison result of the similarity value and a preset threshold value, power source output during folding of the heat exchange tube is adjusted, and the folding state is controlled. According to the method, the similarity between the current state and the historical optimal state of the heat exchange tube is calculated by utilizing the optimal factor group, so that the folding of the heat exchange tube is accurately controlled, and the folding process of the heat exchange tube is optimized.

The present invention relates to the technical field of power distribution. More specifically, the present invention relates to a power distribution method for a power supply system in a computer room. The method includes: obtaining a reference data set of parameter items at each acquisition moment in the time series of operation data of bus-connected devices, where the parameter items include current and / or voltage; calculating the trend and prediction deviation of the parameter items at each acquisition moment, normalizing the product of the prediction deviation of the parameter items, a preset error factor, and the trend to obtain the error factor of the parameter items; using the error factor in the HTFE algorithm to obtain the predicted value of the parameter items at the next acquisition moment, and obtaining the power required by the device at the next acquisition moment through the product of the predicted values of each parameter item to achieve power distribution, effectively improving the accuracy of power distribution in the computer room power supply system.

The application discloses a data processing method, which comprises the following steps: assembling an attribute set by referring to the attributes of the data of each category in a reference data set; screening a candidate attribute subset from the attribute set; wherein the candidate attribute subset is a subset in the attribute set that satisfies a mutual information condition with a value domain set composed of the values of the data of each category; analyzing the association rules between each attribute subset in the candidate attribute subset based on the classification identifiers of each category, and locating a frequent item set with a target association rule; wherein the support degree of each item set in the frequent item set with the target association rule is greater than a support degree threshold, and the confidence degree of each item set is greater than a confidence degree threshold, and each item set comprises at least two attribute subsets. The application also discloses a data processing device, an electronic device and a computer readable storage medium.

The application relates to a special variable user load feature extraction method, device, equipment and medium based on multi-dimensional time sequence data. The method comprises the following steps: preprocessing an original data set collected to obtain a reference data set, obtaining a to-be-repaired data point in the reference data set, respectively obtaining a spatial repair value and a time repair value of the to-be-repaired data point, fusing the spatial repair value and the time repair value based on a fusion weight to obtain a data repair value, updating a data value corresponding to the to-be-repaired data to the data repair value to obtain a repaired data set, performing time sequence alignment on a load repaired data set and an environment repaired data set to obtain a multi-dimensional time sequence data set, extracting a time sequence feature set of the multi-dimensional time sequence data set, screening the time sequence feature set to obtain a target feature set, and constructing a special variable user load space-time feature sample library based on the target feature set. The method can improve data processing precision.