21 results about "High dimensional data sets" patented technology

The invention discloses a cardiovascular prediction method assisted by double-layer feature selection, and the method comprises the steps: collecting data sets which comprise a Cardiovascular data set, a Stalog data set, a Heart data set, and a Z-Alizadeh data set; preprocessing the data set; screening out the feature with the highest relevancy through double-layer feature selection; constructing a prediction model based on a machine learning algorithm; and inputting various health data of the patient into the cardiovascular prediction model to predict whether the patient has the risk of cardiovascular diseases. According to the method, a double-layer feature selection technology is introduced, so that the negative influence of redundant and irrelevant features in a high-dimensional data set on the model is avoided, and the prediction accuracy and stability are improved. Through automatic optimization of hyper-parameters, the model can automatically find an optimal parameter combination on different data sets, and the adaptability and prediction performance of the model are improved. In combination with precise training and optimization of a machine learning algorithm, the risk that a traditional model is prone to over-fitting or under-fitting on a small-scale data set is avoided, and the robustness of the model is ensured.

The application discloses a kind of personalized design intelligent interaction methods based on topology optimization, including S1, construct the user behavior monitoring system based on multi-modal data acquisition, generate high-dimensional data set containing time sequence characteristics;S2, based on adaptive deep neural network model, dynamically predict and hierarchical modeling to user personalized demand;S3, the optimal solution of design parameter is generated in real time by dynamic topology reconstruction algorithm;S4, through reinforcement learning mechanism, iteratively update design scheme generation rule;S5, construct neural network supported multi-objective design space exploration and optimization framework, quickly filter and optimize design parameter;S6, through multidimensional data mapping and parameterization control means, realize the dynamic update and local optimization of design scheme;S7, establish data-driven continuous learning and evolution mechanism, constantly optimize user demand prediction model and design optimization algorithm.The application has the advantages of strong dynamic adaptability, high intelligent level and high personalized satisfaction precision.

The application relates to the technical field of optical communication device testing and control, and discloses a DPMZM modulator fast calibration system and method based on deep learning, which solves the problems of slow speed of an existing DPMZM calibration method, periodic multi-value mapping of deep learning calibration, ambiguity of symmetry symbols, and mismatch of a loss function, and the like. The method constructs a high-dimensional data set through a segmented decoupling scanning strategy, performs physical symmetry correction and preprocessing on labels, designs a one-dimensional convolutional neural network with multi-scale feature aggregation, proposes a physical perception hybrid loss function and adopts a progressive training strategy, and finally embeds the trained model into a calibration system to realize parameter reasoning and physical voltage restoration, thereby completing fast and accurate calibration of the DPMZM.

This invention discloses an approximate nearest neighbor connection optimization method for encrypted databases based on PM# trees, belonging to the field of approximate nearest neighbor connection optimization technology for encrypted databases. It solves the problems of low retrieval efficiency and insecure retrieval processes in existing technologies. The method includes: projecting a high-dimensional dataset into a low-dimensional space using hash projection to obtain a low-dimensional dataset; encrypting the high-dimensional dataset to obtain an encrypted high-dimensional dataset; deleting redundant nodes generated during node splitting in the PM# tree to obtain a PM# tree, and using the PM# tree to build an index on the high-dimensional dataset to obtain an index file; encrypting the index file to obtain an encrypted index; and performing an approximate nearest neighbor connection query on the high-dimensional dataset based on the encrypted index, the low-dimensional dataset, and the encrypted high-dimensional dataset to obtain the query results. This achieves encryption of the high-dimensional dataset while reducing computational load, thus accelerating retrieval speed and improving retrieval quality.

The invention discloses an unsupervised dimensionality reduction visualization method for cell image data. The method comprises the following steps: acquiring an unlabeled cell image high-dimensional data set; processing the high-dimensional data set by adopting an unsupervised dimension reduction algorithm to generate low-dimensional embedding representation; generating a first visual chart based on the low-dimensional embedded representation to display the distribution of the data in the low-dimensional space; performing unsupervised clustering analysis on the data points in the low-dimensional embedding representation, and identifying at least one clustering center point; generating a second visual chart based on a clustering analysis result, and marking a clustering center point in an identifiable manner; according to the method, the clustering center point can be accurately identified, and a visual data distribution overview is provided for a user.

The invention relates to the field of artificial intelligence, particularly discloses a high-dimensional evolutionary feature selection method fusing filtering and packaging strategies, and aims at high-dimensional, small-sample and unbalanced complex data to realize minimum discriminant feature subset screening through a multi-stage hierarchical search framework from coarse to fine and from global to local. The method comprises the steps of generating a high-quality initial population by adopting core-marginal probability sampling and preferentially entering evolution based on double filtering type integrated score sorting of symmetric uncertainty SU and ReliefF; an environment-aware unimproved counter is introduced in the global search stage, the feature adding probability and the feature removing number are dynamically adjusted to balance exploration and development, and repeated evaluation is reduced in combination with fitness cache; and when search stops, binary particle swarm optimization neighborhood refinement is triggered, local high-strength discrete search is realized by Sigmoid mapping, and global optimum is backfilled and updated. Experiments prove that the method obtains a high F1-score and a significant compression feature number on a plurality of high-dimensional data sets.

The application relates to a high-dimensional perception data dimension reduction method in a multi-hop time delay sensitive network, which comprises the following steps: taking a certain specific time window as a benchmark, reading data from each device to form a high-dimensional data set; for the high-dimensional data of each device, a new feature set is formed; a PCA algorithm is used to reduce the dimension of the selected features; an adaptive feature selection (AFS) algorithm is used to further optimize the features after dimension reduction; and the feature data after adaptive feature selection is used for network transmission, so that the data dimension is reduced while the key features of the data are maintained, the transmission time delay and the network load are reduced, the PCA and the AFS are comprehensively used, the advantages of the PCA and the AFS in dimension reduction and feature selection are fully exerted, the data after dimension reduction retains important information and reduces the data volume, and an effective solution is provided for high-dimensional perception data transmission in a multi-hop time delay sensitive network.

The invention relates to an unsupervised feature selection method based on multi-stage learning optimization, and belongs to the technical field of high-dimensional data preprocessing. Performing standardization preprocessing on the obtained original high-dimensional data set, initializing a feature state vector for the high-dimensional data set after standardization preprocessing, and setting a feature state of a preset proportion as a selection state and the rest as an elimination state; setting the state of a preset number of features in the selected state as an elimination state, generating a candidate feature subset, carrying out multi-stage optimization learning, changing the feature state through iteration, and calculating the score of the candidate feature subset by using an unsupervised evaluation index to obtain an optimal feature subset; and performing clustering based on the optimal feature subset to obtain a clustering label, and performing a subsequent classification task based on the clustering label. The method aims at screening out a most representative low-dimensional feature subset from high-dimensional data, and the performance of an unsupervised learning task is improved while the data dimension is reduced.

The invention relates to the technical field of computer and big data management, and discloses a data interaction method of a handheld tablet personal computer, which comprises the following steps: receiving a big data query instruction, and obtaining a high-dimensional data set; a three-dimensional point cloud is generated through graph neural network dimension reduction embedding; constructing and rendering a dynamic interactive three-dimensional topological mapping model; responding to the multi-finger gesture operation to adjust the spatial layout and the clustering structure of the data entity in real time; and dynamically calculating and visually coding the association strength between the data. The system comprises a query receiving module, a data acquisition module, a dimension reduction processing module, a model construction module, a rendering module, a gesture recognition module, an association calculation module, a visual coding module and the like. According to the method, through three-dimensional space mapping and multi-finger gesture interaction, the information density and association visibility are remarkably improved, local detail expansion, spatial memory anchor points and multi-user cooperation are supported, and efficient and visual big data exploration is achieved.

The invention discloses an industrial large space temperature and humidity uniformity optimization method based on a POD-mGPR proxy model, and belongs to the field of industrial environment intelligent regulation and control, the method fuses computational fluid mechanics (CFD), intrinsic orthogonal decomposition (POD) and multivariate Gaussian process regression (mGPR), a high-dimensional data set is generated through CFD simulation, after a main mode is extracted through POD dimensionality reduction, the mGPR is used for constructing the proxy model, and the main mode is extracted through the mGPR; rapid and accurate prediction of the whole-space temperature and humidity field is realized; defining a non-uniformity coefficient, and establishing quantitative mapping of a control variable and a uniformity improvement effect; and finally, screening key variables through Sobol global sensitivity analysis, and solving an optimal control parameter combination in combination with a genetic algorithm. According to the method, the problems of insufficient perception, lack of quantitative feedback in control and low high-dimensional optimization efficiency of a traditional method are solved, the transformation from local experience control to global quantitative control is realized, and the temperature and humidity uniformity and the energy utilization efficiency are improved.

A kind of double-agent model optimization method of wind snow-removal robot pipeline structure parameter, specific steps are as follows: (1) according to the initial design of wind snow-removal robot pipeline, determine the design variable and value range of pipeline structure parameter;(2) establish high-dimensional data set;(3) establish low-dimensional data set;(4) select a proxy model to obtain high-dimensional proxy model A and low-dimensional proxy model B;(5) generate active learning candidate sample pool;(6) calculate the under-learning degree score of each candidate learning point;(7) establish active learning point set;(8) carry out CFD simulation to obtain updated data set, continue to train model A, when the prediction accuracy of model A, the prediction accuracy of model B and the inconsistency degree of model A model B prediction result all reach specified threshold, stop active learning.The present application provides an efficient, reliable and engineering applicable method for the optimization of complex pipeline parameters with multiple structural variables and multiple outlet flow responses.

The application relates to the technical field of text dimension reduction, and discloses a local analysis text data dimension reduction method based on complete supervision, wherein a high-bit data set is segmented through a K-neighbor rule to form a plurality of mutual overlapping local parts, geometric information of different category data in a local neighborhood is explored, a complete supervision local target function is constructed through a complete supervision local analysis algorithm, a local arrangement matrix is solved, same data points in a local part are as close as possible, different categories are as separated as possible in the dimension reduction process, the local neighbor geometric structure can be effectively learned, the system classification capability is improved, global projection matrixes are obtained by comprehensively considering geometric arrangement information of each local part and using an eigenvalue solving mode, and thus low-dimensional data after dimension reduction of the high-dimensional data set can be solved, so that the problem of external samples can be effectively solved.

The application provides an information entropy calculation method and device for wafer processing machine stability evaluation. The method comprises the following steps: obtaining full high-dimensional data set collected by FDC system, each data point in the full high-dimensional data set corresponding to a set of process parameter values, and having multiple dimension characteristic information; performing normalization preprocessing on the full high-dimensional data set to eliminate the dimensional difference between parameters; traversing the full high-dimensional data set, taking each data point as a target data point, calculating the distance between the target data point and the remaining data points, and selecting the K nearest data points to form a neighbor set of the target data point; and calculating the information entropy value of the full high-dimensional data set based on the neighbor set of all target data points. The application uses information entropy as a measurement index, converts the complex high-dimensional parameter running state into a single comparable numerical value, and can intuitively and quantitatively represent the wafer processing machine running stability.

The application discloses a video advertisement putting effect intelligent analysis management system based on big data analysis, and relates to the technical field of data analysis.The system comprises: a data processing part, which collects a multi-dimensional data set after advertisement putting, maps the multi-dimensional data set to a high-dimensional characteristic space by using a kernel function, obtains a high-dimensional data set, adopts Laplace characteristic mapping to reduce the dimension of implicit characteristic representation, and obtains a low-dimensional embedding representation; a user behavior prediction model part, which adopts a bidirectional long short-term memory network to capture time dynamic characteristics of user behavior according to the low-dimensional embedding representation, and predicts future user behavior; and a putting effect management part, which defines a multi-target evaluation function, formulates a putting action, and makes the putting strategy parameter vector approach a target putting strategy parameter vector, so that the putting effect of the advertisement is maximized.The application can evaluate the advertisement putting effect, improve the advertisement putting strategy, and improve the precision and intelligence of the advertisement putting effect.

A hybrid approximate neighbor search method fusing clustering guide partition and graph structure pruning belongs to the field of database and information retrieval and comprises the following steps: 1, performing KMeans + + coarse clustering on a large-scale high-dimensional data set to obtain a plurality of clusters and calculating a centroid; 2, initializing a candidate neighbor graph in each cluster and executing alpha-angle pruning to obtain a sparse candidate graph; local Beam Search is executed in parallel on the sparse candidate graph to obtain a k-CNA candidate set, and a high-quality intra-cluster local sub-graph is obtained through diversity pruning; 3, selecting representative points and boundary points from the local sub-graphs of each cluster to form a global representative point set, and constructing a lightweight upper-layer routing structure according to the global representative point set; and 4, during online query, firstly selecting clusters in an upper layer graph route, then carrying out parallel retrieval on a target cluster and an adjacent cluster set, and outputting a Top-K approximate nearest neighbor result. According to the method, the performance bottleneck caused by global graph construction is avoided, and the method has good engineering practicability and popularization and application value.

The invention discloses a semi-buried pipeline corrosion state monitoring method and system and a medium, and belongs to the technical field of pipeline corrosion monitoring and digital twinning. Comprising the following steps: generating a multi-factor environmental load spectrum simulating a soil and atmosphere alternate coupling effect according to a target semi-buried pipeline service environment spectrum, collecting environment, corrosion morphology and mechanical data through an accelerated corrosion test, and constructing a high-dimensional data set; based on the data set, respectively establishing an environment-time coupled global corrosion rate prediction model and a mechanical-chemical coupled local crack propagation rate prediction model; deploying a sensing network in a pipeline risk section, and constructing a pipeline digital twin integrated with the model; real-time monitoring data is used for driving the digital twinborn body, and online evaluation and future prediction of the corrosion state of the pipeline are achieved; according to the invention, accurate simulation and intelligent early warning of the corrosion behavior of the semi-buried pipeline are realized.

The application discloses a BOW graph matching method and system based on spectral clustering, and the method comprises the following processes: extracting node features and topological features of a citation network graph; using an optimized K-means++ algorithm obtained by combining a spectral clustering algorithm with a genetic algorithm to optimize K values, to convert node features and topological feature descriptors of the citation network graph into words, and realizing construction of a dictionary; using a local constraint coding mode to code features of the dictionary, to obtain a visual vocabulary histogram; and classifying the visual vocabulary histogram, to realize the BOW graph matching method based on spectral clustering. The application uses the spectral clustering algorithm to cluster high-dimensional data sets, and then uses the K-means algorithm to perform two-stage clustering in a low-dimensional solution space, so that the problems of poor processing effect on high-dimensional data and low classification effect are solved.

The present application belongs to the field of composite computer aided engineering and intelligent manufacturing technology, specifically a kind of SFRP composite material multi-objective optimization design and performance prediction method based on CAE-ML. Including the following steps: S1, modeling based on multi-angle performance index system and cross-scale parameterization;S2, simulation calculation and high-dimensional data set construction based on CAE automatic simulation;S3, the establishment and training of multi-task machine learning agent model fused with micro morphology;S4, multi-angle performance collaborative optimization based on multi-objective optimization algorithm;S5, optimal design scheme decision and performance prediction. The present application integrates CAE simulation, machine learning and multi-objective optimization algorithm to construct a closed-loop intelligent design system, realizes the collaborative design and rapid prediction of material formula, process parameters and component performance.

The general problem of pattern change discovery between high-dimensional data sets is addressed by considering the notion of the principal angles between the subspaces is introduced to measure the subspace difference between two high-dimensional data sets. Current methods either mainly focus on magnitude change detection of low-dimensional data sets or are under supervised frameworks. Principal angles bear a property to isolate subspace change from the magnitude change. To address the challenge of directly computing the principal angles, matrix factorization is used to serve as a statistical framework and develop the principle of the dominant subspace mapping to transfer the principal angle based detection to a matrix factorization problem. Matrix factorization can be naturally embedded into the likelihood ratio test based on the linear models. The method may be unsupervised and addresses the statistical significance of the pattern changes between high-dimensional data sets.

The invention discloses an efficient and rapid whole genome association analysis method based on a multi-site mixed model, and belongs to the field of plant and human disease gene mining, the method comprises the following steps: step 1, constructing a single-site mixed linear model, and determining candidate single nucleotide polymorphism sites potentially associated with target traits; step 2, constructing a multi-site mixed linear model by utilizing the obtained candidate single nucleotide polymorphic sites to determine a remarkably associated QTN (Quantitative Trait Networks); 3, determining the obtained SNP with the corrected P value smaller than or equal to the threshold value as the QTN significantly associated with the target character, and outputting the QTN. According to the method, unit point scanning and multi-site mixed model construction are integrated, so that rapid correlation analysis of a large-scale high-dimensional data set is realized, the calculation efficiency and the statistical effect of QTN detection are optimized, and the calculation speed and the detection robustness are balanced.

This application discloses a deep learning-based intelligent frequency harnessing control method, relating to the fields of clock synchronization and high-precision timekeeping. It includes: constructing a high-dimensional dataset comprising a time deviation measurement sequence, an ambient temperature sequence, and a control voltage sequence; inputting the dataset into an aging and frequency drift prediction model to obtain a predicted frequency drift value; inputting the current time deviation measurement value, the time deviation measurement value sequence prior to the current time, the innovation sequence, the ambient temperature sequence, the control voltage sequence, and the predicted frequency drift value into a deep learning noise identifier, outputting Kalman filter noise parameters; inputting the noise parameters into the Kalman filter to obtain the current time deviation; using the current time deviation as the input to a PID controller to calculate the control voltage and apply it to the local clock, completing closed-loop control. This solves the problem of accuracy degradation in traditional frequency harnessing control algorithms, making it difficult to achieve sub-nanosecond level locking.