94 results about "Supervised clustering" patented technology

The invention discloses a data labeling method and system based on user behaviors and attention tracking, and the method comprises the steps: synchronously collecting multi-source behavior signals of a mouse, a keyboard, eye movement and the like of a doctor in real time, combining identity and interface metadata, and carrying out the standardized normalization, abnormality elimination and short time sequence behavior unit division. And extracting individual behavior micro-modes by using unsupervised clustering, and constructing a behavior portrait library. Through multi-modal time sequence modeling and a self-adaptive space-time attention mechanism, behavior characteristics, an interface area and a report text are deeply fused, a multi-level correlation probability is output, and high-precision automatic tagging of content and an image area is realized.

Systems, methods, and other embodiments associated with quasi-supervised clustering for activity pattern characterization and anomalous activity detection are described. In one embodiment, a method generates a first sparse similarity matrix for nearest neighbors of a plurality of data points. The data points each characterize a pattern of activity associated with an account. The method generates a second sparse similarity matrix for random neighbors of the plurality of data points. The method recursively clusters the plurality of data points based on the first sparse similarity matrix. The method quasi-supervises the recursive clustering based on the second sparse similarity matrix to stop the iterative clustering when the data points are split into N clusters. The value of N is not pre-determined. The method detects that the individual data point has changed clusters, indicating anomalous activity. And, the method generates an electronic alert that the anomalous activity is associated with the account.

The invention relates to the technical field of safety monitoring artificial intelligence, and discloses a safety management method and system based on artificial intelligence and big data. The method comprises the steps that multi-modal real-time monitoring data is collected through a distributed sensor array, and a standardized sequence is generated after preprocessing; the system accesses a historical security event library, adaptively divides risk category clusters by using an unsupervised clustering algorithm, and extracts key risk features and influence domains for each cluster. On the basis, a plurality of special self-adaptive security analysis engines are configured to perform parallel analysis on real-time data to generate a plurality of security analysis feature maps. And carrying out weighted fusion and enhancement on the feature maps through a feature fusion network to obtain a unified security situation feature vector, and finally outputting a security level and a management and control instruction through a decision model. According to the method, autonomous identification, dynamic evaluation and accurate response of complex risks are realized, and the intelligent level of safety management and the decision accuracy are improved.

Systems, methods, and other embodiments associated with quasi-supervised clustering for activity pattern characterization and anomalous activity detection are described. In one embodiment, a method generates a first sparse similarity matrix for nearest neighbors of a plurality of data points. The data points each characterize a pattern of activity associated with an account. The method generates a second sparse similarity matrix for random neighbors of the plurality of data points. The method recursively clusters the plurality of data points based on the first sparse similarity matrix. The method quasi-supervises the recursive clustering based on the second sparse similarity matrix to stop the iterative clustering when the data points are split into N clusters. The value of N is not pre-determined. The method detects that the individual data point has changed clusters, indicating anomalous activity. And, the method generates an electronic alert that the anomalous activity is associated with the account.

The invention provides a double-current comparison and DHI combined equipment fault detection method and system, and belongs to the technical field of power equipment state monitoring and fault diagnosis. The method comprises a time sequence perception adversarial enhancement module, a generative adversarial network (GAN) data expansion module, a double-flow contrast attention network (D-CAN) feature extraction module and a dynamic health index (DHI) calculation module. The core lies in that a fault sample with time sequence correlation is supplemented through a physically constrained GAN, robust features are extracted by using a ResNet1D and Transform fused double-flow network, and a health state is quantified in combination with unsupervised clustering and mahalanobis distance. Through simulation and experimental verification, zero-delay detection of the early fault of the transformer can be realized, the output health index and the 3D visualization result can provide an accurate basis for operation and maintenance of the transformer, and the diagnosis accuracy, the data utilization rate and the dynamic adaptability of state evaluation are remarkably improved.

The invention discloses an unsupervised silicon wafer surface defect detection method and device based on hierarchical clustering, relates to the field of image processing and computer vision, and utilizes an unsupervised clustering technology to detect silicon wafer surface defects. The method comprises the following steps: extracting multi-scale features from a plurality of normal silicon wafer images through a pre-trained convolutional neural network; integrating the multi-scale features of the plurality of pictures by adopting a hierarchical fusion strategy; constructing a feature memory library by using a clustering algorithm, and generating a clustering center; generating patch features for the test image through a consistent fusion process; through comparative supervision, normal patches are close to a clustering center, and abnormal patches are pushed away; calculating the distance between the patch and the clustering center, and judging abnormity; and mapping the position of the abnormal patch, and positioning scratches, cracks, pollution or recesses. According to the method, through an unsupervised mode, a large amount of defect labeling data is not needed, the data preparation cost is effectively reduced, and the accuracy and robustness of silicon wafer surface defect detection are remarkably improved.

The invention discloses an intelligent calibration method and system for the electric quantity of an instrument, and relates to the technical field of electric energy metering of an electric power system.The intelligent calibration method comprises the steps that an electric meter network diagram with a main meter and sub-meters as nodes is constructed, and robust composite correlation indexes are calculated on the basis of reading increments in a sliding time window; the interference of common background load and instantaneous noise is suppressed by using a threshold clipping and main table purification mechanism, so that the depiction capability of the correlation graph on a physical connection relationship is improved fundamentally, and the phase to which the sub-table belongs can be identified more accurately in a stock area with unknown phase or incomplete topology; a graph neural network model is introduced based on the network graph, electric meter embedding representation with physical meaning is obtained through neighborhood information aggregation, phase grouping is achieved in combination with unsupervised clustering, split-phase energy balance loss is designed in the training stage, and the difference between the reading of a main meter and the sum of the reading of sub-meters in each phase group is used as a constraint. Alignment of the embedding space and the energy conservation relation is pushed in a self-supervised mode.

The invention discloses an academic community discovery and analysis method driven by a citation network, and the method comprises the steps: constructing the citation network, constructing an adjacent matrix A and a node attribute matrix X according to the citation network, and extracting a core sub-network information matrix S based on a k-core algorithm; constructing a dual-channel sparse graph attention auto-encoder, respectively taking (X, A) and (S, A) as input of the two channels to learn low-dimensional embedding of the two channels, and obtaining joint embedding Z through a dynamic weighted fusion strategy; reconstructing adjacent matrix information by adopting an inner product decoder, reconstructing node attribute characteristics and core sub-network information, and calculating reconstruction loss; and inputting the joint embedded Z into a self-supervised clustering module, performing joint optimization on the information embedding and reconstruction module and the self-supervised clustering module, and finally outputting a group division result of the papers / authors. According to the method, complementary fusion of attributes and a core structure is realized, robustness in noise and sparse scenes is enhanced, and discriminability and stability of group division are improved.

The invention discloses an automatic driving test scene generation method and device based on a text collision report, equipment and a medium, and relates to the technical field of automatic driving, and the method comprises the steps: obtaining an unstructured text collision report, converting the unstructured text collision report into a high-dimensional numerical vector through preprocessing and feature extraction, and obtaining a high-dimensional numerical vector; a typical collision mode is identified and classified by using an unsupervised clustering algorithm, a logic scene is further constructed in a parameterized manner, physical feasibility is verified through a kinematics verification module, risk levels are divided, and a risk scene library is formed. And finally, generating an automatic driving test case set by adopting a stratified sampling strategy. By using the collision text data, real, diversified and physically feasible test scenes are generated, and the efficiency and accuracy of automatic driving safety assessment are improved.

The invention discloses a geothermal fluid source discrimination method based on geochemical index and machine learning fusion, and relates to the technical field of geothermal resource intelligent exploration, and the method comprises the steps: firstly collecting and calculating multi-dimensional geochemical indexes, and constructing a standardized feature sequence; establishing an end member feature library by using unsupervised clustering; identifying the source type of the fluid through a discrimination model fused with an attention mechanism; aiming at the mixed source fluid, constructing an optimization model embedded with physical and chemical constraints, and quantitatively inverting the contribution proportion of each end member; and finally, on the basis of the time sequence proportion data obtained by inversion, predicting a future evolution trend by adopting a time convolutional network-long and short-term memory network model fused with an attention mechanism. According to the geothermal fluid source discrimination method based on geochemical index and machine learning fusion provided by the invention, the whole-process intelligent analysis of the geothermal fluid source from qualitative identification to quantitative prediction is realized.

The invention relates to the technical field of computers, in particular to a low-temperature economizer digital twinborn body construction method, and aims to solve the problems that an existing model statically solidifies, multi-source data fusion is difficult, and degradation recognition lags. The method comprises the following steps: constructing a multi-physical field reference model covering fluid, heat transfer and corrosion mechanisms; collecting and carrying out time-space alignment on temperature, pressure, flue gas components and ash deposition data, and combining wavelet and median filtering to carry out de-noising; introducing extended Kalman filtering to correct model state variables on line, and realizing dynamic updating of parameters; a CNN-GRU hybrid network is embedded to extract time sequence degradation features, and unsupervised clustering is combined to identify working condition states; and when the detection is abnormal, triggering local high-fidelity CFD re-simulation, and completing closed-loop optimization and state synchronous mapping. Through the mechanism, the internal physical field error lt of the equipment is realized; 5% high-precision dynamic mapping, more than 95% of anomaly detection rate and rapid deployment of a new unit within 72 hours are realized, the prediction accuracy and preventive maintenance capability are remarkably improved, and the service life of equipment is prolonged by 15%-20%.

The invention relates to an intelligent color box printing color analysis control method, and provides a reinforcement learning decision closed-loop control method integrating distributed color detection, space-time multi-modal data processing, feature extraction and self-supervised clustering and multi-scale reward modeling. Multi-variable normalization and synchronization are completed through multi-point real-time collection of color, process and environment data, color change meta-state features are extracted based on a self-encoding neural network, and a meta-state distribution model related to space and working conditions is established through self-supervised clustering. And the color risk is evaluated in combination with a multi-scale reward function, so that color parameter optimal adaptive adjustment and cooperative control driven by reinforcement learning are realized. In an actual control link, the system can dynamically optimize a meta-state aggregation and strategy network structure based on a feedback signal, a stable and self-adaptive color consistency closed-loop control system is constructed, and the color consistency and process intelligence level of a printed product under a complex working condition are effectively improved.

The invention provides a scene complexity quantitative characterization method for testing an automatic driving automobile, and belongs to the technical field of automatic driving testing. The method comprises the following steps: establishing a scene model and a rare target object model based on data-mechanism hybrid modeling; controllable reproduction of a complex scene is realized through physical simulation technologies such as video injection and echo simulation; constructing a causal graph structure to extract scene high-dimensional features; self-adaptive extraction, derivation and generalization of a scene are realized by using unsupervised clustering, a generative adversarial network and an optimization search algorithm; developing a data conversion algorithm to generate a standardized test scene library; analyzing complexity mapping relations between scene elements and perception, decision and execution systems, and establishing scene complexity quantitative representation; and finally, constructing a multi-dimensional evaluation system fusing safety and anthropomorphism, and realizing integrated performance evaluation of the automatic driving automobile fusing scene complexity. The scene complexity is converted into a quantifiable index from a qualitative concept, and the scientificity of testing and the fairness of evaluation are improved.

The invention discloses an intelligent automatic drawing system and method, and relates to the technical field of agricultural remote sensing monitoring. The method comprises the following steps: acquiring a multi-temporal remote sensing image of a farmland area, performing preprocessing and spatial alignment, dividing the farmland into spatial units, extracting time sequence spectrum and vegetation index characteristics of the spatial units, and forming a visual characteristic vector sequence; analyzing the sequence by using unsupervised clustering, and automatically identifying the time sequence evolution trajectory categories representing different growth states; and finally, the system automatically marks the space units which are judged to be abnormal categories on a map, generates early warning and outputs a farmland abnormity thematic map. The system comprises a data preprocessing module, a data preprocessing module, a track classification module and an abnormal graph generation module. According to the invention, full-automatic and intelligent dynamic monitoring and abnormity identification of the farmland growth state are realized, the limitation of traditional single-temporal analysis or artificial visual interpretation is overcome, and the monitoring efficiency and objectivity are significantly improved.

The method for automatic identification, classification and development trend analysis of net red villages based on multi-source data fusion and natural language processing comprises the following steps: UGC data is crawled from Xiaohongshu and Douyin through a distributed master-slave architecture, de-duplicated based on SimHash, and normalized in time and coding format; a text semantic fingerprint is generated, and multi-level semantic cache fingerprint matching is performed; for unassigned text, its complexity is calculated, and a large language model API is adaptively called to automatically complete and extract five-level administrative divisions; weights are determined based on the analytic hierarchy process, interaction indicators such as likes, comments, collections and forwards are integrated, and a comprehensive network heat index of the village is obtained; an external text mining tool is connected, and batch word frequency analysis, semantic network analysis and sentiment tendency evaluation are performed; a document-term matrix is constructed, TF-IDF weighting is performed, and unsupervised clustering algorithm is used for clustering analysis of village characteristics; cross-dimension analysis is performed on the clustering results, and a development portrait, advantage mining and operation suggestion warning are automatically generated in combination with the SWOT model.

The application provides a charging facility fault early warning model construction method based on multi-source data perception, and belongs to the technical field of charging facility fault early warning.The application forms a multi-source time series data set by collecting charging pile operation state data and environmental data and performing time series alignment, expands fault samples by using an adversarial generative network and a semi-supervised clustering algorithm to form a balanced sample data set, constructs a fault early warning model containing a time series memory pool encoding layer and a frequency domain convolution feature extraction layer, cooperatively optimizes early warning accuracy and response time delay by using a double-layer game optimization framework to obtain an optimal parameter combination, deploys the optimized model to an edge computing module to realize real-time fault early warning, establishes an online incremental learning mechanism based on sliding time window detection data distribution offset, and uses an elastic weight consolidation technology to update model parameters and retain historical knowledge, thereby solving the problem of early warning performance degradation of charging facility fault early warning in a data distribution evolution environment.

The application relates to a medical term normalization method, and belongs to the technical field of data processing. The method solves the problems that coverage cannot be guaranteed and new variations cannot be dynamically adapted in the prior art. The method comprises the following steps: acquiring historical unmatched original words to construct an original word set; acquiring terms labeled for part of the original words in the original word set; adopting a supervised clustering algorithm to cluster the original word set to obtain original word clusters; for each original word cluster, an improved genetic algorithm is adopted to obtain a regular expression for mapping the original word cluster to a term corresponding to the original word cluster. The method realizes the improvement of coverage, can automatically discover new variations, and dynamically adapts new variations.

The invention relates to an open set fault diagnosis method based on a two-stage entropy perception consensus domain confrontation framework, and the method comprises the following steps: S1, obtaining a training data set of a rotating machine, the training data set comprising a source domain data set and a target domain data set; s2, constructing a model framework, wherein the model framework comprises a feature extractor and loss functions of five modules; the feature extractor comprises a convolutional neural network, a bidirectional long-short-term memory network and a multi-head attention mechanism which are arranged in sequence; the five modules comprise an original classifier module, a two-stage entropy sensing module, an auxiliary classifier module, a self-supervised clustering module and a consensus domain confrontation module; inputting the training data set into a feature extractor, and adjusting parameters of a model framework by using an optimization algorithm to complete training of the whole framework; and S3, obtaining a test data set, inputting the test data set into the trained model framework, and outputting a fault diagnosis result. The problem that an existing domain self-adaption method cannot effectively process unknown faults is solved, and the open set fault diagnosis method is achieved.

The invention discloses a user behavior prediction method and device based on multi-source feature fusion, equipment and a storage medium, and the method comprises the steps: collecting user behavior data from different data sources, carrying out the feature extraction of the user behavior data, and obtaining the user behavior features corresponding to each data source; determining an importance weight corresponding to each data source according to a current prediction demand, and performing weighted fusion on each user behavior feature based on the importance weight to obtain a fused user feature; a preset unsupervised clustering algorithm is adopted, dynamic grouping processing is carried out according to the fused user features, and a plurality of user groups are generated; and constructing a corresponding behavior prediction model based on each user group, wherein the behavior prediction model is used for performing behavior prediction on the new user based on the current prediction demand. By dynamically adjusting the weight and fusing the multi-source features, the accuracy of dynamic grouping of the users can be improved, and the prediction model is specifically constructed based on different groups, so that high-precision user behavior prediction is realized.

This invention relates to the field of data representation technology, and discloses a semi-supervised clustering method and its open-ended question answer text encoding method. The semi-supervised clustering method includes: acquiring a dataset to be clustered, its labeled dataset, and its unlabeled dataset; mapping the dataset to be clustered to a spatial density map and / or a topological density map; and using the labeled and unlabeled datasets to cluster the data in the dataset to be clustered into several clusters, wherein each data point in each cluster has a cluster label, which can be an existing label or a new label. This invention uses a semi-supervised clustering method to efficiently and accurately cluster open-ended question answer text data, and can discover new classes with limited prior knowledge. After clustering, keywords can be extracted and encoded from the open-ended question answer text of each class, facilitating a quick and accurate understanding of the situation of the interviewed group and improving the efficiency and quality of diagnosis and treatment.

The invention discloses an oral lichen planus molecular subtype based on a serum protein marker. The molecular typing is based on a multi-center clinical queue, a new molecular typing is obtained by using transcriptome sequencing data through a Seed-Kmeans semi-supervised clustering algorithm, and a serum marker is further screened out through multiple omics data including the transcriptome sequencing data, proteome sequencing data and serum enzyme-linked immunosorbent assay experimental data. The method can be used for further judging molecular typing. The molecular typing can obviously improve the curative effect of the oral lichen planus medicine, and is beneficial to the realization of OLP individualized precision medical treatment.

A method of creating a machine learning clustering meta learning model for use in solving a machine learning clustering problem includes obtaining a plurality of information related to the machine learning clustering problem, wherein the plurality of information includes classification datasets, machine learning transformers and clustering estimators, creating a set of clustering datasets using the classification datasets, generating trained clustering pipelines by training the set of unsupervised clustering pipelines responsive to the clustering datasets, processing the trained clustering pipelines to generate internal scores and external scores for the set of clustering datasets, creating an encoded clustering pipeline by encoding the trained clustering pipeline using the external score as a label and generating a trained supervised machine learning model by combining the internal scores and the encoded clustering pipelines.

The application relates to a software defect prediction method based on a sampling algorithm optimization and unsupervised clustering, which comprises the following steps: 1) data set oversampling: according to original data set category proportion information and a set balance coefficient, the data amount required to be generated by the minority class samples is calculated and generated, and a balanced data set is obtained; 2) data set standardization: the balanced data set is subjected to standardization processing, and a standard data set is obtained; 3) unsupervised clustering: a clustering model is constructed, the standard data set is input into the clustering model for clustering operation, and the standard data set is divided into two categories; 4) clustering result confirmation: the two categories divided by clustering are subjected to confirmation category processing, and whether the software represented by the data has defects is judged; and 5) prediction evaluation index: a model evaluation index is constructed, model evaluation index information is acquired, and the accuracy of software defect prediction is measured. The method process design is reasonable, convenient to use, and compared with traditional algorithms, has the advantages that sampling effect is better and clustering result is more accurate.

The invention relates to the field of scene self-adaptive control, in particular to a new energy commercial vehicle self-adaptive control method and system based on scene awareness, and the method comprises the steps: extracting vehicle real-time operation features and inputting the vehicle real-time operation features to a scene recognition model which fuses semi-supervised clustering and a domain knowledge graph; and outputting the current scene category and the associated performance demand. The model has an online updating capability, and when it is detected that a current real-time feature vector significantly deviates from a known scene, iterative optimization is automatically triggered. Based on the recognized scene and performance requirements, a strategy reasoning engine is called, the engine conducts multi-target reasoning and optimization calculation by traversing the logic relation in the knowledge graph and combining real-time data, and an accurate vehicle control parameter set matched with the current scene is generated. And the parameter set is output to each control system of the vehicle, so that self-adaptive closed-loop control of an electric driving system, a battery management system and a thermal management system is realized, and the energy efficiency, the performance and the intelligent level of the whole vehicle in different operation scenes are improved.

The application provides an unsupervised monocular three-dimensional target detection method based on a video sequence and a pre-trained instance segmentation. The main steps are as follows: a camera with a known internal parameter is used to shoot a video sequence frame of a certain length in a certain scene, a monocular depth network is self-supervisedly trained by using the projection relationship between the sequence images, and the monocular depth estimation value of the sequence images is learned; then, an instance segmentation result of the image is directly predicted by using a pre-trained instance segmentation network, the instance segmentation result is combined with the camera internal parameter and the learned monocular depth estimation value, and is back-projected into a three-dimensional space to obtain pseudo-radar data of each instance; finally, an unsupervised clustering method is used to filter out outliers, a minimum circumscribed rectangle of the point set is solved in a bird's eye view (x-z plane), a maximum-minimum height difference of the point set is solved in a y-axis direction, and finally a three-dimensional target detection bounding box of the object is obtained. The application can complete three-dimensional target detection of any new scene without manual annotation by using the sequence information of the front and rear frames of the video and the pre-trained instance segmentation network, and can significantly reduce the manual annotation cost required for learning three-dimensional target detection of a new scene.

The invention relates to an unsupervised clustering automatic T cell subtype flow-type circling method and an unsupervised clustering automatic T cell subtype flow-type circling system. The method comprises the following steps: S1, data preprocessing; s2, automatically setting a door and clustering; s3, statistical analysis; and S4, generating and outputting a door information report. Through designed full-process automatic processing, unsupervised learning and biomarker specific expression, the problems that traditional door setting is low in efficiency and high in subjectivity and an existing automatic method cannot process spectrum overlapping are effectively solved, and the method has the advantages that accuracy, stability and automation level are improved, and the method is kept consistent with clinical practice standards and the like; the whole process is free of supervised learning and intelligent data statistics, manual gate setting is omitted, multi-channel marker data and density clustering are combined, the gate accuracy is ensured, the method is suitable for large-scale streaming data samples, batch gates and parallel output are supported, visual gate graphs and analysis reports can be automatically generated, and manual auditing and reproduction are facilitated.

This invention discloses a data annotation method and system based on user behavior and attention tracking. It synchronously and in real-time collects multi-source behavioral signals from doctors, including mouse, keyboard, and eye movements. Combined with identity and interface metadata, the data undergoes standardization, anomaly removal, and short-term behavioral unit segmentation. Unsupervised clustering is used to extract individual behavioral micro-patterns, and a behavioral profile library is constructed. Through multimodal temporal modeling and an adaptive spatiotemporal attention mechanism, behavioral features, interface regions, and report text are deeply integrated to output multi-level correlation probabilities, achieving high-precision automatic labeling of content and image regions.

The invention belongs to the technical field of intelligent control, and particularly relates to an axle surface electrophoretic coating control method based on artificial intelligence. According to the method, an axle surface image after electrophoresis is collected through machine vision, color features are extracted by using an improved convolutional neural network, and a color uniformity index is generated. In combination with process parameter time sequence data, large-scale sample labeling amplification is realized through semi-supervised clustering and parameter calibration transformation, an incidence relation between process parameters and color uniformity indexes is established, and key process parameters are screened by using mutual information entropy and correlation analysis. And after the uniformity index prediction model is trained, the key parameter control range is taken as a constraint, a genetic algorithm is adopted to optimize the key process parameters, and an optimal parameter combination corresponding to the target color quality is obtained. According to the invention, the intelligent and optimal control of the electrophoretic coating process of the axle surface is realized.

Techniques are disclosed herein that encompass image pre-processing and a semi-supervised clustering for optimization and analysis of immune-enriched single-cell proteomics data generated via multiplexed imaging technologies. This is achieved through an image pre-processing pipeline, which converts image data contained in one type of file (e.g., .mcd) into another type of file (e.g., .tiff) and removes artifact signals from the image data using various algorithms to generate improved image data. Thereafter, a semi-supervised clustering pipeline analyzes the improved image data using various techniques, including implementing a supervised algorithm to identify metaclusters such as general immune phenotypes (e.g., CD4−T-cells, Macrophages, Neutrophils, etc.) as well as non-immune phenotypes while implementing an unsupervised algorithm that enables the identification of specific subclusters and a more in-depth cellular status characterization.

The application discloses a method for accurately identifying a copy number variation (CNV) breakpoint and application thereof. The method comprises the following steps: expanding a globally identified candidate CNV region, performing window merging on the expanded CNV region, and identifying a CNV accurate breakpoint based on the read depth of the merged window in the expanded CNV region. The application can ensure the accuracy of the breakpoint identification in the local CNV identification process by dynamically expanding the candidate CNV region. The machine learning unsupervised clustering method is innovatively used to cluster and merge adjacent fragments according to the fragment characteristics, so that the accurate CNV fragment can be finally identified, the identification error is lower than that of the global CNV identification, the identification accuracy can be adjusted according to the local CNV identification window merging parameter, and the sensitivity is higher and the operation is simpler.