748 results about "Data sorting" patented technology

A non-volatile memory organized into flash erasable blocks sorts units of data according to a temperature assigned to each unit of data, where a higher temperature indicates a higher probability that the unit of data will suffer subsequent rewrites due to garbage collection operations. The units of data either come from a host write or from a relocation operation. The data are sorted either for storing into different storage portions, such as SLC and MLC, or into different operating streams, depending on their temperatures. This allows data of similar temperature to be dealt with in a manner appropriate for its temperature in order to minimize rewrites. Examples of a unit of data include a logical group and a block.

Distributed data is not edited at a distributing end but is edited in a terminal apparatus, thereby reducing the cost. The terminal apparatus can quickly retrieve necessary information. In the apparatus, a cell ID extracting part (104) extracts all the cell IDs that can be currently received. A data receiving part (105) extracts distributed data. A data sorting part (106) compares the cell ID related to the distributed data with the cell IDs that can be currently received, and adds priorities to the distributed data in such a manner that a “high” priority is given to the distributed data related to the same cell ID as the local cell, while the lower priorities being given to the distributed data related to the cell IDs of other cells that are farther from the local cell. A display part (108) sequentially displays the distributed data in decreasing order of the priority.

Real-time fraud prevention software-as-a-service (SaaS) products include computer instruction sets to enable a network server to receive medical histories, enrollments, diagnosis, prescription, treatment, follow up, billings, and other data as they occur. The SaaS includes software instruction sets to combine, correlate, categorize, track, normalize, and compare the data sorted by patient, healthcare provider, institution, seasonal, and regional norms. Fraud reveals itself in the ways data points deviate from norms in nonsensical or inexplicable conduct. The individual behaviors of each healthcare provider are independently monitored, characterized, and followed by self-spawning smart agents that can adapt and change their rules as the healthcare providers evolve. Such smart agents will issue flags when their particular surveillance target is acting out of character, outside normal parameters for them. Fraud controls can therefore be much tighter than those that have to accommodate those of a diverse group.

Real-time fraud prevention software-as-a-service (SaaS) products include computer instruction sets to enable a network server to receive medical histories, enrollments, diagnosis, prescription, treatment, follow up, billings, and other data as they occur. The SaaS includes software instruction sets to combine, correlate, categorize, track, normalize, and compare the data sorted by patient, healthcare provider, institution, seasonal, and regional norms. Fraud reveals itself in the ways data points deviate from norms in nonsensical or inexplicable conduct. The individual behaviors of each healthcare provider are independently monitored, characterized, and followed by self-spawning smart agents that can adapt and change their rules as the healthcare providers evolve. Such smart agents will issue flags when their particular surveillance target is acting out of character, outside normal parameters for them. Fraud controls can therefore be much tighter than those that have to accommodate those of a diverse group.

Each of MIDI data, audio data, text data and image data to be received in a data receiving section is SMF-formatted data including event information and event-executing delta time. A data sorting section sorts data based on data type depending upon a delta time of each type of received data. The sorted data are respectively reproduced in a MIDI reproducing section, an audio reproducing section, a text reproducing section and an image reproducing section. The data reproduced in the MIDI reproducing section and audio reproducing section are mixed in a mixer and outputted as sound from a speaker, while the data reproduced in the text reproducing section and image reproducing section are mixed in a mixer and displayed as visual information on a display. Because each type of data is reproduced in the timing according to the delta time, synchronization can be easily provided between different types of data, for example as sound and images.

The invention provides a data classification and encryption method for cloud storage, and belongs to the field of computer storage safety. According to the method, the problems that the calculation amount is large and the efficiency is low due to the facts that an existing data encryption method does not identify data content and a single encryption algorithm is used for all data are solved. The method sequentially includes the steps of (1) security feature library establishment, (2) data classification, (3) data encryption according to classes and (4) data storage. According to the method, a high-security encryption algorithm is adopted only in the part of data related to privacy and needing high-security protection, and an encryption algorithm which is low in security and low in calculation complexity is adopted in general data accounting for the most part, high-security protection is performed on the privacy data, meanwhile, the calculation amount for encryption is lowered, and the efficiency is improved.

Product recommendation is disclosed, including retrieving user behavior data associated with a predetermined statistical period; sorting the user behavior data into one or more groups of data corresponding to one or more types of products based at least in part on associated product identifiers; determining a plurality of interest levels associated with the predetermined statistical period for at least one or more groups of data; determining a plurality of purchase peak probabilities using at least the plurality of interest levels, wherein a purchase peak probability is associated with a predicted likelihood of user interest in receiving recommendations associated with a type of product; ranking at least a portion of the plurality of purchase peak probabilities in response to receipt of an indication to present recommendation information; and presenting recommendation information based at least in part on the ranked at least portion of the plurality of purchase peak probabilities.

A network device, which includes a plurality of network ports, a switching unit, a data classification unit, and a rate control unit, is provided. The plurality of network ports is configured to send and receive input data packets. The switching unit is coupled to the plurality of network ports and is configured to switch input data packets from a first port to a second port. The rate control unit is coupled to the switching unit and configured to control a data rate provided to each port of the plurality of network ports. The data classification unit is coupled to the switching unit and to the rate control unit. The data classification unit is configured to classify data packets based on their contents and output a classification to the rate control unit. The rate control unit is configured to perform rate control for input data packets based on the classification of each data packet.

Provided are an apparatus and method for supplying electronic programs for Video On Demand (VOD) services. The apparatus includes a service data receiving unit to receive service data about an image-based service, a service data sorting unit to sort the received service data according to a predetermined sorting condition, and a control unit to display the sorted service data according to the sorting condition through a predetermined graphical user interface.

The image data sorting device is configured with a characteristic value extraction unit that extracts from an image data piece a characteristic value of the image data piece; a relation evaluation unit that evaluates a mutual relation between the characteristic values extracted from plural image data pieces; a clustering unit that executes clustering to events expressed by the characteristic values of the plural image data pieces on the basis of the evaluation result of the above relation; and a sorting register unit that sorts the plural image data pieces on the basis of the result of the above clustering. The image data sorting device with the above configuration can provide a user with facility of use, whereby the appropriateness of the sorting can be secured without requiring any preprocessing, etc., and automatic sorting of the image data pieces can be carried out independently of the types of the image data pieces.

A computer-implemented data sorting method for sorting data at high speed with small memory capacity. The sorting method sorts an array of data where sorted data is constituted by a combination of a plurality of blocks where each block is produced separately from one another during the sorting process. In another aspect, a navigation method and system utilizes the data sorting method for quickly sorting and displaying requested information such as POIs sorted by distance with use of a small memory.

An optical metrology system includes model approximation logic for generating an optical model based on experimental data. By eliminating theoretical model generation, in which the fundamental equations of a test sample must be solved, the model approximation logic significantly reduces the computational requirements of the metrology system when measuring films formed on patterned base layers. The experimental model can be created by selecting an expected mathematical form for the final model, gathering experimental data, and compiling a lookup model. The lookup model can include the actual measurement data sorted by output (attribute) value, or can include “grating factors” that represent compensation factors that, when applied to standard monolithic model equations, compensate for the optical effects of grating layers.

A computer implemented system for genomic data sorting, comprising alignment and position mapping. The system maps each read to a position on the reference genome with which the read is associated, followed by sorting these reads by their mapped positions.

A data processing apparatus includes a receiving unit that receives a plurality of pieces of data, each of which having a different destination specified; a classifying unit that classifies the plurality of pieces of data into a predetermined number of groups; a sorting unit that extracts data from the groups in a parallel manner, and that sorts the data extracted according to a destination; and an output unit that outputs a set of the data sorted to a corresponding destination.

There is provided a flash memory apparatus for storing data aggregate having a plurality of types of data in and reproduce the data aggregate from a flash memory via a plurality of ports. The flash memory apparatus includes a plurality of access request units configure to request to write data in one block of the flash memory by aligning a writing position of one block data with a page unit on a data type basis of the flash memory when the data classified by type that are inputted via the ports corresponding to the access request units on the one-to-one basis are stored to reach an amount relative to one block of the flash memory, and an access controller configured to write the data in the flash memory during time division allocated per port based on the requests incited by the respective access request units.

The invention discloses a data security control method and system based on data classification and grading. The method includes the following steps of finding, wherein data sets in the management range are vectorized; classifying, wherein found and vectorized documents are classified according to a classifying model; important service data identifying, wherein whether the documents are important service data or not is judged according to an important service data identifying model for the classification results; grading, wherein for identified important service data, the security level of the documents is defined according to a grading model; labeling, wherein document important characteristics in the important service data obtained after grading are recorded, identification ID and the document important characteristics are recorded in a document header, and detailed content of the labeled data is stored in a third-party database; the important service data formed by the important service data perception step are analyzed and controlled. By means of the scheme, the data security control system is constructed from the visual angle of combining security management and data management, the system is evaluated subjectively and accurately, the security control result is quantized in an index assessment mode to guide control improvement.

The invention discloses a hyperspectral remote sensing data classification method based on deep learning, and belongs to the technical field of hyperspectral data classification. The invention aims to solve a problem of low classification precision of a method for classifying hyperspectral remote sensing data with nonlinear characteristics. The hyperspectral remote sensing data classification method comprises the following steps: firstly, processing hyperspectral original data to obtain the spectral feature vector and the spatial feature information of the hyperspectral original data; then, integrating the spectral feature vector with the spatial feature information; confirming labeled samples by hyperspectral integrated data, selecting a training sample and a test sample from the labeled samples; Pre-training a multi-layer restricted Boltzmann machine which forms a deep network by the training sample; carrying out supervised learning to the network formed by the multi-layer restricted Boltzmann machine through the training sample; and inputting the test sample into the trimmed network formed by the multi-layer restricted Boltzmann machine to realize hyperspectral remote sensing data classification. The invention is used for the hyperspectral remote sensing data classification.

The invention discloses an RUDP-based real-time video transmission method under internet condition. By adopting a client/server (C/S) structure model, the technical scheme comprises specific steps of video data distribution, packet loss detection, report receiving, packet loss recovery, receiving end data sorting, decoding and display and the like. According to the RUDP-based real-time video transmission method under internet condition, problems that video receiving is delayed and paused, mosaic appears on the picture, the screen is blurred and the like due to network congestion, data packet loss and the like can be solved under the internet condition. The RUDP-based real-time video transmission method under the internet condition has the main advantages that existing network conditions of the user can be improved without extra spending and the user can make use of the existing network condition to obtain clearer and smoother video communication effects.

The invention provides red date intelligent selection equipment. The red date intelligent selection equipment comprises a bottom supporting frame, a transmission supporting frame, a control box, a hopper, a feeding transmission device, a horizontal conveying device, a pneumatic mechanism and a division box. The transmission supporting frame is fixed to the upper portion of the bottom supporting frame. The horizontal conveying device is arranged in the transmission supporting frame and provided with a horizontal conveying belt composed of rollers. The hopper is arranged on one side of the bottom support frame. An inclined feeding plate is arranged in the hopper, and the feeding plate is connected with the feeding transmission device. The division box is fixed to the other side of the horizontal conveying belt. The control box is fixed to the middle of the transmission supporting frame. The pneumatic mechanism is arranged at the position, between the control box and the division box, of the transmission supporting frame. Industrial cameras and an industrial personal computer are arranged in the control box. An optical fiber sensor is arranged on one side of a chain wheel of the horizontal conveying device. According to the red date intelligent selection equipment, good dates and bad dates are separated through the image processing technology and the pneumatic mechanism, the detection precision is high, the red data sorting efficiency is improved, and the labor intensity is reduced.

The invention provides a monitoring method of a photovoltaic power generation system and a system thereof. The method comprises the classified collection, classified storage, service provision and classification application. The classified collection comprises the real-time data collection, periodic data collection and the event data acquisition of equipment in a system. According to the data, the provided service comprises state management, equipment control, exclusive management, alarm recognition, task management and log management. The operated application comprises operation monitoring, remote control, alarm notification, video surveillance, analysis and diagnosis, comprehensive inquiry, statistical reporting and equipment management. According to the monitoring method and the system, the real-time monitoring and control of the main equipment in a solar photovoltaic power station is carried out, the operation of the power station is rapidly grasped through various styles of charts and data, the completely reliable and stable operation of a solar photovoltaic power generation system is ensured through a user-friendly interface, powerful analysis functions and perfect fault alarm, and remote off-site maintenance and multi-people cooperation are facilitated to realize secondary configuration development.

The invention provides a relevance vector machine-based multi-class data classifying method, which mainly solves the problem that the traditional multi-class data classifying method cannot integrally solve classifying face parameters and needs proximate calculation. The relevance vector machine-based multi-class data classifying method comprises a realizing process comprising the following steps of: partitioning a plurality of multi-class data sets and carrying out a normalizing pretreatment; determining a kernel function type and kernel parameters; setting basic parameters; calculating the classifying face parameters; calculating lower bounds of logarithms and solving variant values of the lower bounds of the logarithms and adding 1 to an iterative number; if the variant values of the lower bounds of the logarithms are converged or the iterative number reaches iterating times, finishing updating the classifying face parameters, and otherwise, continuing to updating; and obtaining a prediction probability matrix according to the updated classifying face parameters, wherein column numbers corresponding to a maximum value of each row of the matrix compose classifying classes for testing the data sets, and samples which have the prediction probability less than a false-alarm probability and the detection probability corresponding to a false-alarm probability value set in a curve are rejected. The relevance vector machine-based multi-class data classifying method has the advantages of obtaining classification which is comparable to that of an SVM (Support Vector Machine) by using less relevant vectors and rejecting performance and can be used for target recognition.

A system for classifying data includes an access monitor, a compliance processor and a classifier. The access monitor monitors access to files in a documentation system. The compliance processor categorizes the files according to pre-determined rules wherein the rules are based on at least one of: access to the files and at least one file property of the files. The classifier classifies the files according to the results of the compliance processor.

An apparatus and a method are provided that can easily distribute material suitable for each person or each place to which the material is distributed compared to conventional apparatuses and methods. A material delivery server is provided with a box for each user, a print data sorting portion for making the box for a user to whom material is delivered memorize print data of the material to be delivered, a print conditions memorizing portion for memorizing print conditions information for each user, a transmission data generation portion for generating print data for transmission by setting the print conditions information of a user relating to the box for the print data memorized in the box, and a print data transmission portion for transmitting the print data generated from the print data memorized in the box to a material reception terminal of the user relating to the box.

The invention provides an attitude and orbit control data analysis method based on a decision tree. The method comprises the following steps: attitude and orbit control data preprocessing, i.e., finishing remote measurement data deduplicating, remote measurement data sequencing, remote measurement data extraction and remote measurement data outlier rejection through the data preprocessing; hierarchical modeling of an attitude and orbit control, i.e., establishing an information and control flow chart of the attitude and orbit control, determining remote measurement variables related to current faults of the attitude and orbit control, and taking the variables as input variables for decision tree analysis; establishing a flow chart of the decision tree analysis; and decision tree modeling, i.e., creating a decision tree C5.0 algorithm model, defining a model name in the model, boosting algorithm test frequency, and trimming attributes and a minimum recording number of each sub branch. The method provided by the invention solves the problem of difficulties in satellite attitude and orbit control complex data analysis and has a certain guidance effect on satellite fault identification, diagnosis and anticipation.