2397 results about "Data classification" patented technology

The invention discloses a three-dimensional city modeling method based on BIM and GIS, comprising the following steps: data measurement, data processing, establishing BIM model, establishing three-dimensional GIS model, model fusion, municipal simulation, element separation and optimization, model rendering and display; through BIM, the precise height of the building can be easily obtained, appearance size and interior space information, thus through combining BIM and GIS, buildings are modeled first, and then the architectural spatial information is shared with the surrounding geographic environment, and applied to urban 3D GIS analysis, so that the cost of building spatial information can be reduced. At that same time, after the data is collected, point cloud and aerial photograph data are calculated and simulated by data classification comparison, so that the elevation information of the ground object is perfect, the elevation precision of the building model is high, and the roof ismore fine. The invention does not need a large amount of manual interaction modeling, and the data are collected by the unmanned aerial vehicle and the unmanned vehicle, and the efficiency is high, the production period is short, and the timeliness is strong.

The invention relates to an infrared behavior identification method based on adaptive fusion of an artificial design feature and a depth learning feature. The method comprises: S1, improved dense track feature extraction is carried out on an original video by using an artificial design feature module; S2, feature coding is carried out on the extracted artificial design feature; S3, with a CNN feature module, optic flow information extraction is carried out on an original video image sequence by using a variation optic flow algorithm, thereby obtaining a corresponding optic flow image sequence; S4, CNN feature extraction is carried out on the optic flow sequence obtained at the S3 by using a convolutional neural network; and S5, a data set is divided into a training set and a testing set; and weight learning is carried out on the training set data by using a weight optimization network, weight fusion is carried out on probability outputs of a CNN feature classification network and an artificial design feature classification network by using the learned weight, an optimal weight is obtained based on a comparison identification result, and then the optimal weight is applied to testing set data classification. According to the method, a novel feature fusion way is provided; and reliability of behavior identification in an infrared video is improved. Therefore, the method has the great significance in a follow-up video analysis.

The present invention discloses a unified identification protocol for training and health, comprising: unified multipurpose identifier that serves to identify individuals and the relationships established with these individuals, such as relationships with entities, professionals, products, services and transactions, generation of a multipurpose identifier, unique, non-transferable and univocally distinguishable through a computer system. The computer system that identifies them universally to avoid identification errors, based on UUID, DID and/or other formats and unify the data of the same individual existing in different systems, stored in one or several electronic devices of the individual and/or their legal guardians and, additionally, in one or more cloud services, certify access permissions granted /revoked to the different data compartments in the profile of the individual and the authenticity of the data, carried out in one or more distributed data ledger technologies or blockchains.

The invention relates to the technical field of computers, in particular to a computer server cluster log monitoring method and monitoring platform. The monitoring method includes step A of monitoringmassive machine code commands generated by a main board during the running of each server in a computer server cluster, and using a real-time stream data collection frame in a big data technology tocollect the machine code commands generated by the main board in real time; step B of performing data classification and transfer operations on the collected data, including real-time filtering, processing and storage; step C of performing real-time analysis and display of the data processed in step B to obtain real-time analysis results and store the results; step D of performing analysis by combination with the real-time analysis results and stored historical analysis results, obtaining potential abnormal results of the computer server, and performing early warning and notification. The computer server cluster log monitoring method and monitoring platform have the advantages of real time, expandability, automation, high availability and rich API extension.

System and method for optimization of a design associated with a response function, using a hybrid neural net and support vector machine (NN/SVM) analysis to minimize or maximize an objective function, optionally subject to one or more constraints. As a first example, the NN/SVM analysis is applied iteratively to design of an aerodynamic component, such as an airfoil shape, where the objective function measures deviation from a target pressure distribution on the perimeter of the aerodynamic component. As a second example, the NN/SVM analysis is applied to data classification of a sequence of data points in a multidimensional space. The NN/SVM analysis is also applied to data regression.

A method of classifying a digital document may include: identifying, by an automated data processor, a request for access to the digital document for a first user; determining user identifying information for the first user; obtaining, by the automated data processor, according to the user identifying information a first user characteristic including organizational affiliation of the first user or a job function of the first user; generating, by the automated data processor, based on the first user characteristic, a digital document classification for the digital document; associating the digital document classification with the digital document, by embedding the document classification in the digital document or logging the document classification in a log identifying the digital document. A user access determination for the digital document may be made according to the associated digital document classification.

Methods, systems and deep learning engines for content and context aware data classification by business category and confidentiality level are provided. The deep learning engine includes a feature extraction module and a classification and labelling module. The feature extraction module extracts both context features and document features from documents and the classification and labelling module is configured for content and context aware data classification of the documents by business category and confidentiality level using neural networks.

The invention relates to a real-time land cluster survey method. The method comprises the following steps: with an office data service platform as a center, firstly sending the filed data acquired by virtue of GPS/PDA (global positioning system/personal digital assistant) and GPS (global positioning system) data used for monitoring and positioning in a field data acquisition subsystem to a real-time communication service system in a standard data definition format by virtue of the TCP/IP (transmission control protocol/internet protocol) network protocol; classifying the obtained data by the office data server; and issuing spatial data and attribute data in the whole database by a service terminal, and browsing and analyzing to generate a report for use of a user, thus the real-time cluster survey monitoring on the land is realized finally.

The invention discloses a polarization SAR data classification method and system based on a mixed classifier. The polarization SAR data classification method includes the steps of obtaining polarization SAR data initial polarization characteristics in different classes, adopting a decision tree classifier to select the polarization characteristics for classification from the initial polarization characteristics, being based on the polarization characteristics for classification, and adopting an SVM classifier to classify polarization SAR data. According to the polarization SAR data classification method and system, the advantages of the decision tree classifier and the advantages of the SVM classifier are synthesized, the classification accuracy reaches the level of the SVM classifier, the classification calculation efficiency is equivalent to that of the decision tree classifier, and the polarization SAR data classification method and system are of great significances in polarization SAR data classification.

The invention relates to a TSN and non-TSN interconnection oriented industrial heterogeneous network scheduling method, which belongs to the field of industrial Ethernet and comprises the following steps that: in a network deployment stage, a user performs configuration management on a TSSDN controller according to application requirements through a terminal application program; the TSSDN controller performs data classification according to a delay demand; the TSSDN controller carries out scheduling priority management on the data; the TSSDN controller calculates the shortest path and schedules the priorities; the SDN switch performs flow table matching,and judges the congestion condition of the queue; and the feedback adjustment to queue congestion is carried out. According to the method,high-efficiency and high-quality scheduling is carried out in the industrial heterogeneous network with the TSN and the non-TSN interconnected, and low-delay and deterministic transmission of different types of data in the network is guaranteed.

A method of operating a file system in a host configured to store write data in a data storage device including a first region and a second region is disclosed, and includes; receiving a write data request for write data associated with a file, classifying the write data as hot data or cold data using file meta data for the file, and if the write data is classified as hot data, storing the write data in the first region, and otherwise if the write data is classified as cold data storing the write data in the second region.

A data classifier performs a data classification process using prototypes classified into clusters. A prototype map is formed using mapping means and clustering means. The mapping means forms, through learning, a prototype map by adjusting coupling weights between a plurality of prototypes provided in a map space based on a plurality of input data. The clustering means calculates a predetermined measure between the prototypes and classifies the prototypes into a plurality of clusters based on the measure.

The invention relates to the technical field of archive management, particularly relates to an archive management system and an operation process. The archive management system comprises a user queryterminal, an information collection platform, an archive processing system, an archive storage terminal and an automatic compression backup unit; the archive arrangement and filing unit comprises an archive data classification standard unit, an archive data identification unit and an archive data description unit, and the archive processing system comprises an archive retrieval module, an archivemanagement module, a retrieval service module, a system configuration module and a file configuration module. According to the scheme of the invention, archive information input by the information collection platform can be quickly sorted and stored in a classified manner; a user can conveniently input corresponding retrieval conditions in an operation interface to export corresponding archive data, the user with corresponding permission can edit, arrange and perform other related operations on archives, irrelevant personnel are prevented from forcibly changing archive information, informationleakage is prevented, the safety of the archives is guaranteed, and the archive arrangement and retrieval efficiency of the user is improved.

A remote diagnostic system and method based on device data classification. Device diagnostic data with respect to a device can be acquired and a conditional probability look up table can be constructed for each fault code associated with the device diagnostic data by a classification module. A score function can then be created by summing the conditional probabilities and an occurrence of the fault code can be mapped to a service call category with a numerically highest score function. The fault occurrence data in association with a number of time stamps and device identifiers can be stored in a data warehouse. The occurrence of fault code can be matched with respect to a solution set which can be automatically dispatched to a customer via a communications link.

The invention relates to the field of pattern recognition, and discloses an unbalanced data classification method based on intuitive fuzzy integration and a system based on the method. The method comprises the following steps of: a) cleaning original data, and classifying original point-of-sale (POS) class samples according to intra-class positions to generate POS class artificial samples; b) training a base classifier by using different sample sets of inter-class approximate balance; c) converting the classification output equal utility of the base classifier into an intuitive fuzzy matrix; and d) integrating samples to be classified into the membership and the non-membership of the POS class and the negative (NEG) class by combining the weight of the base classifier, and making a classification decision. The invention has the advantages that: over learning is avoided by integrating over sampling and under sampling; the training samples of the base classifier are different, so that the difference of the base classifier is ensured; the base classifier is not specifically limited, so the method has good expandability; the intuitive fuzzy reasoning method quantitatively describes the uncertainty in classification so as to improve the performance of integrated learning; therefore, the system based on the method can better support the medical diagnosis decision and the like.

The invention discloses a medical big data classification method and system based on a generative adversarial network and semi-supervised learning, and the system comprises a data collection module which is used for collecting medical big data, and obtaining a large amount of medical data and medical images with high data dimension and high class mark uncertainty; The data processing module is used for preprocessing the acquired medical data and medical images; The algorithm application module is used for initializing and training the sub-learners, marking the unlabeled medical data and the unlabeled medical images, and expanding the labeled medical data and the labeled medical images; And the auxiliary decision module is used for classifying the medical big data of the test set. The dataprocessing module further comprises a medical data dimension reduction module, an image processing module, a data classification module and a medical data processing module; The algorithm applicationmodule further comprises a training sample generation module, a training module, a marking module, an expansion module and an integration module. And the accuracy of medical big data classification isimproved.

The invention discloses a hyperspectral remote sensing data classification method based on ensemble learning, belonging to the technical field of spectral data classification and aiming at solving the problem of low data classification precision caused by the fact that an existing hyperspectral data classification method is used for classifying data from the aspect of spectral dimensions. The hyperspectral remote sensing data classification method comprises the following steps: firstly, reading hyperspectral remote sensing data to obtain spectral characteristics and spatial characteristics of hyperspectral remote sensing data; integrating the spectral characteristics and the spatial characteristics to obtain a multiple characteristic set; determining a marking sample and selecting training samples and testing samples according to the multiple characteristic set; designing an Adaboost integration and classification frame of characteristic difference based on an ensemble learning method, and training with the training sample to obtain F weak classifiers; and classifying the testing samples by the F weak classifiers. The hyperspectral remote sensing data classification method is used for classifying hyperspectral remote sensing data.

The invention discloses a fault diagnosis method based on a semi-supervised learning deep adversarial network. The fault diagnosis method comprises the steps of: acquiring vibration signals of a bearing under different operation faults, and subjecting a vibration time-domain signal of the faulty bearing to wavelet transformation to form a two-dimensional image; and conducting supervised learning on a small amount of labeled data by a generative adversarial network, training on a large amount of unlabeled data in an unsupervised manner, extracting high-dimensional features by means of a convolutional neural network to achieve data classification, and therefore identifying a fault category of the bearing. According to the fault diagnosis method, a high-precision fault diagnosis model is obtained by training under the condition of limited labeled data, and a more precise discriminator is obtained, so that precise fault diagnosis can be carried out based on the vibration signals of the rolling bearing.