100 results about "Reference dataset" patented technology

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 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.

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 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.

Embodiments of the present disclosure relate to a method, an electronic device, and a computer program product for dataset updating. The method includes: acquiring a reference dataset comprising a plurality of reference images, wherein each reference image has a soft label, and the soft label indicates a probability that the each reference image belongs to each target class in a plurality of target classes. The method may further include: determining, according to a similarity between an input image received and the each reference image, an image soft label for the input image. The method further includes: adding the input image and the image soft label for the input image to the reference dataset to update the reference dataset.

The present application relates to the technical field of dynamic evaluation of spinal morphology, and particularly relates to a child spinal morphology intelligent evaluation method and system based on big data analysis. The method comprises the following steps: S1: obtaining static data and dynamic data, dividing the static data into a high-priority evaluation data set and a basic reference data set, and dividing the dynamic data into a key change data set and an auxiliary monitoring data set; taking the complete time period between the time point when the child spinal morphology first receives medical evaluation and the time point when the system confirms that the child spinal function and morphology reach a steady state as an evaluation and diagnosis cycle, and distributing the static data and the dynamic data to the evaluation and diagnosis cycle to obtain a data-stage distribution result. The present application constructs an intelligent evaluation system, accurately tracks child spinal development, dynamically optimizes data collection and stage distribution through error analysis and similarity comparison, and realizes individualized, efficient and self-adaptive closed-loop diagnosis management.

Methods, apparatus, systems, and articles of manufacture to estimate cardinality through ordered statistics are disclosed. In an example, an apparatus includes processor circuitry to selects a sample dataset from a first reference dataset of media assets and partitions the sample dataset into m mutually exclusive subsets of approximately equal size. The processor circuitry then estimates a ratio of a sample weighted average and empirical cumulative distribution of an approximately largest order statistic from at least one of the m subsets and generates an estimate of a total cardinality of the first reference dataset by multiplying the ratio by approximately m.

The application discloses a data processing method, device and computer readable storage medium. The method comprises the following steps: obtaining a first data set generated by a target object in a first time window; predicting a second data set corresponding to a second time window according to fluctuation amplitudes between data amounts corresponding to N time points; obtaining a first reference data set corresponding to a first historical time window; predicting a third data set corresponding to a third time window according to the first data set, the second data set and the first reference data set; and generating a predicted data set of the target object according to the second data set and the third data set. The application can improve the accuracy of the predicted data set.

Disclosed is wearable device for monitoring brain activity and for providing non-invasive stimulation, wearable device comprising: headwear arrangement comprising: electrode arrangement comprising electrodes configured to make electrical contact with skin of user; and flexible cushioning member supporting electrodes; input / output (I / O) arrangement configured to: receive electrical signals (ES) from electrode(s); transmit ES to data processing arrangement (DPA) to process ES for generating brain stimuli (BS); and generate and apply BS generated using a brain stimulation protocol (BSP) to electrode(s), BS applied on skin lying partially in mastoid region; DPA comprising processing unit configured to: receive ES from I / O arrangement; analyse ES with predetermined reference data set, by extracting signal feature(s); apply processing algorithm(s), to map signal feature(s) to stimulation parameter(s); generate BSP comprising stimulation parameter(s); and transmit BSP to I / O arrangement; and power unit(s) supplying electrical power to I / O arrangement and DPA.

The invention discloses an operation positioning management system and method for side channel tracking, and relates to the technical field of big data analysis, and the method comprises the steps: collecting the historical power consumption and electromagnetic signal data of normal operation of target equipment, adding a timestamp, and carrying out the preprocessing to form a reference data set; constructing a data standard library in combination with operation codes and timestamps extracted from the operation logs; comparing the real-time power consumption, the electromagnetic signal data and the operation code with a standard library, and positioning an attack starting node and a key operation node; quantitatively evaluating a node risk level; and generating a report list according to the risk level and dynamically tracking. The system comprises a reference data construction module, a data standard library construction module, an operation node positioning module, a risk level evaluation module and a dynamic tracking management module which are electrically connected in sequence. According to the method, accurate positioning, risk quantitative evaluation and dynamic tracking of attack nodes are realized, and clear targeting is provided for protection.

The invention discloses a data supervision system and method for targeted heavy metal removal in a fly ash washing process, and relates to the technical field of fly ash resource.The method comprises the steps that the heavy metal type, concentration, treatment object volume and period duration between two times of verification are periodically collected, the heavy metal content is calculated, and an accumulation table is generated; after data verification, grouping according to cycle duration, checking the average value of the heavy metal content, and constructing a standard reference table; determining a heavy metal conversion relation through deviation value analysis, and quantitatively generating a reference data set; and monitoring the sudden change of the concentration in real time, comparing with a reference value after conversion, and triggering verification early warning or process verification according to a threshold value. The system comprises a periodic data acquisition module, a heavy metal content accounting module, a standard reference table construction module, a reference value quantitative generation module and an abnormity monitoring and handling module, and the modules are in closed-loop connection. According to the method, full-process automatic supervision is realized, the abnormal response efficiency and the supervision accuracy are improved, the labor cost is reduced, and compliance guarantee is provided for resource utilization of the fly ash.

A method and a device for monitoring a field of view of a stationary sensor. The method includes: a first step for receiving recordings of surroundings of the sensor, which are generated at different times within a predefined measuring time frame with the aid of the sensor; a second step for ascertaining, based on the recordings, measuring points, which each represent positions and / or dimensions of objects in the surroundings of the sensor; a third step for grouping the ascertained measuring points based on a predefined similarity metric, where each group of measuring points is represented by a prototype; and a fourth step for ascertaining a change of content within the field of view of the sensor based on a comparison of the prototypes with a reference data set, which includes reference prototypes, whose underlying measuring points are ascertained in a reference state of the sensor and of the surroundings.

A method executed by an on-board unit of a vehicle includes receiving real-time data from vehicles in a convoy, wherein the data includes an indicator regarding a vehicle type within the convoy, providing a policy with a set of rules and comparing the data with the policy, providing a set of reference data corresponding to the convoy, wherein the set of reference data includes a trend of previous data indicating normal operation of the convoy, comparing the set of data with the set of reference data, wherein a determination of whether the set of data deviates from the trend of previous data is based on operation of one of the vehicles in the convoy, and initiating an operation when there is both a violation of the policy and a deviation within the set of data from the set of reference data is detected.

System (10) and method for computer-aided pattern recognition and sequential-adaptive question generation based on user responses (22) to data-based classification questions (23) for a compatibility check between user data and reference datasets, the system comprising: a data input interface (1) that transmits the user responses (22) to all data-based classification questions (23) and a classification result (24a, 24b, 24c) resulting from all user responses (22) and belonging to these user responses (22); a processor unit (2) that transforms the user responses (22) into data points (D), wherein the data points (D) take on values ​​in a predefined range; receives the data points (D) and the classification result (24a, 24b, 24c) belonging to each of these data points (D);in a training phase of the system (10) a decision tree learning-based machine learning model (3) that automatically induces decision trees (20) from the data points (D), executes (...);

A computer-implemented method for detecting reference data standardization gaps in data sets is disclosed. The method comprises identifying at least one reference data candidate in a data set, using an index for values of the identified at least one reference data candidate, and determining a difference between an earlier version of a reference data set relating to the reference data candidate and a current version of the reference data set. Furthermore, the method comprises comparing the determined difference with values of the index, and identifying entries in the at least one reference data candidate having a value identical to a value of the difference as reference data standardization gap.

One or more systems, devices, computer program products and / or computer-implemented methods of use provided herein relate to optimizing performance of TLS biases. For example, a system can comprise a memory that can store computer executable components and a processor that can execute the computer executable components stored in the memory. The computer executable components can comprise a measurement component that can obtain a reference dataset of measurements. The computer executable components can further comprise a training component that can determine, from the reference dataset of measurements, a set of parameters and a set of hyperparameters of a machine learning model. The computer executable components can further comprise a prediction component that can predict, via the machine learning model configured with the set of parameters and the set of hyperparameters, a set of TLS biases of a quantum system that minimizes a cost function based on prior measurements.

A computer-implemented method for operating a magnetic resonance facility for recording a magnetic resonance data set may include, after the recording of all the reference data, before the establishment of the calibration data, an intensity adjustment of the reference data with respect to the different temporal position of time portions in which they were recorded is performed for the recording of the associated slice scan data. The intensity adjustment may include establishing a representative intensity value for each reference data set of a slice, forming an intensity progression for each time portion from the intensity values of the reference data sets recorded in the time portion, establishing a scaling of the intensity progressions relative to one another, and performing the intensity adjustment based on the scaling.

This application relates to the field of digital humanities technology and discloses a method, apparatus, and device for historical verification analysis. The method includes: acquiring target data to be verified and determining modal data matching the target data, the modal data including first modal data and second modal data; querying target reference data matching the modal data to form a set of reference data matching the modal data; performing confidence screening on the reference data set to determine a set of candidate data matching the modal data, the candidate data set including a first candidate set and a second candidate set; performing cross-modal semantic consistency matching on the first and second candidate sets; constructing a text-image association graph based on the matching results; correcting the edge weights of the text-image association graph; and determining the historical verification information of the target data based on the corrected text-image association graph. The technical solution provided by this application can improve the reliability and accuracy of historical verification.

The invention discloses a carbon dioxide and oil phase interface distribution condition determination method and device, electronic equipment and a storage medium. The method comprises the following steps: determining a reference value set and a reference data set; determining first target data, and determining a first attenuation coefficient according to the first target data; determining second target data, and determining a second attenuation coefficient according to the second target data; performing subtraction on the first attenuation coefficient and the second attenuation coefficient to obtain an attenuation coefficient difference value, and dividing the attenuation coefficient difference value by the voxel porosity to obtain a third target distribution diagram; and according to the third target distribution diagram, the reference value set and the reference data set, determining the distribution condition of the carbon dioxide and the oil phase in the target rock core. According to the method, the third target distribution diagram is determined through the attenuation coefficient, the third target distribution diagram is analyzed by considering the reference value set and the reference data set, and the accuracy of the distribution condition of the carbon dioxide and the oil phase in the target rock core can be improved.

According to the subject-matter of the present disclosure, there is provided a computer-implemented method of determining if a medical data sample requires referral for investigation for a disease. The method comprises: performing a pairwise comparison of the medical data sample against each example of medical data in a reference data set using one or more machine learning algorithms to determine a difference in severity of the medical data compared to each example medical data in the reference data set; and flagging the medical data sample as requiring referral for investigation for the disease based on results of the pairwise comparisons, wherein the reference data set includes a plurality of medical data examples, the plurality of medical data examples ranked according to their degree of severity of disease

A method of detecting anomalies in data, includes receiving a dataset with a plurality of multidimensional data points (MDDPs) wherein a portion of the plurality of MDDPs are labeled and wherein other MDDPs of the plurality of MDDPs are unlabeled; based on a neighborhood size k of the plurality of MDDPs, computing a neighborhood radius ox for each MDDP in a reference dataset computed for the plurality of MDDPs; and generating a locally adaptive similarity (LAS) kernel of a newly arrived MDDP (NAMDDP) based on the neighborhood radius ox. The method additionally includes applying a random walk model to the LAS kernel to determine a probability of the NAMDDP being an anomaly; and if the NAMDDP is an anomaly, outputting data associated with an alarm or notification responsive to the detection of the anomaly.

The invention discloses a single cell annotation method and system based on gene sensitivity. The core process of the method comprises the following steps: firstly, extracting a related Marker gene list from a gene database to construct a reference data set; establishing an undirected network based on a protein interaction relationship of an effective Marker gene in a to-be-annotated cell, quantifying a gene comprehensive network weight by means of a network centrality index, performing probabilistic conversion on gene expression data, calculating gene divergence in combination with expression distribution, and determining a scarcity index according to the occurrence frequency of a reference data set. And then integrating the three to obtain a gene sensitivity index, introducing machine learning to fuse gene importance weight to eliminate divergence when annotation divergence of a cell and a clustering level exceeds a threshold value, and finally determining an annotation classification result according to comprehensive matching score of the cell to a known type.

In this computer-implemented method for classifying an event in an elongate textile test material, at least one measured value of at least one event parameter is measured (901) for the event. At least one reference data set is provided (902) containing, for a respective reference event, at least one reference value of the at least one event parameter and a classification of the reference event. The at least one measured value is compared with the at least one reference value of all the reference data sets (904). The event is classified (908) according to the classification of the reference event, at least one reference value of the reference event being most similar to the at least one measured value according to a predefined similarity criterion. The method achieves high quality and at the same time high productivity.

The virtual item effectiveness evaluation method, apparatus, storage medium, and computer equipment provided in this application, by responding to the activation command of the virtual item, acquire item identification information and current game scene data, and continuously acquire player operation data. By comprehensively considering the item identification information, current game scene data, player operation data, and a preset reference dataset, the predicted effectiveness result of the virtual item is dynamically evaluated. Compared to traditional single-dimensional judgment logic, this application can fully consider the characteristics of the virtual item itself, subtle differences in player operation behavior, and the specific needs of the current game scene, making the item effectiveness result more personalized and scene-adaptable.

The invention provides a pruning method for constructing a crystal material property reference data set, and the method comprises the steps: firstly obtaining an original crystal material training set, carrying out the deep evidence regression of the original crystal material training set through a pre-training agent model, and obtaining the uncertainty quantitative score of each data point; meanwhile, geometric feature extraction is carried out on the data set, and a geometric feature fingerprint vector set is generated; calculating the cosine distance of the vector to obtain a corresponding structure weight factor set; and the uncertainty quantitative score and the structure weight factor are fused to generate a comprehensive influence score set, and finally pruning construction is performed on the original data set based on the score set to obtain a pruned data set after scale reduction. According to the crystal material data pruning method for benchmark prediction, data uncertainty and structural features are combined, the data set scale is compressed, meanwhile, the accuracy and generalization ability of benchmark prediction are effectively improved, and the calculation overhead of subsequent model training is reduced.

Systems and methods are provided for generating static and articulated 3D assets, the core of which includes a 3D automatic decoder. The 3D automatic decoder framework embeds attributes learned from the target dataset into potential space, which may then be decoded into volumetric representations to render view-consistent appearances and geometries. Appropriate intermediate volume potential spaces are then identified and robust normalization and anti-normalization operations are performed to learn 3D diffusion from 2D images or monocular videos of rigid or articulated objects. These methods are sufficiently flexible, may use existing camera supervision or do not use camera information at all, but rather effectively learn camera information during training. The results generated are proved to be superior to the most advanced alternatives in various reference datasets and indices, including multi-view image datasets of synthetic objects, real field videos of moving persons, and real video datasets of large-scale static objects.