123 results about "Data discrimination" patented technology

Methods and systems for creation, processing, and use of compound features during data analysis and feature recognition are disclosed herein. In a preferred embodiment, the present invention functions to apply a new level of data discrimination during data analysis and feature recognition events such that features are more easily discerned from the remainder of the data pool using processing techniques that are more conducive to human visualizations, perceptions, and/or interpretations of data. This is accomplished using an example tool that allows previously processed and identified features (hereafter “known features”) to be aggregated so as to aid the system in recognizing abstract data features, preferably using Boolean operators and user-assigned hit weight values across desired cluster ranges surrounding analyzed data elements.

A phase comparison circuit has (1) a data discriminator to which a data signal is input for discriminating the data signal using a clock signal, (2) a logic circuit for outputting the exclusive-OR data of the data signal and a data discrimination signal output by the data discriminator and inputting the result of the exclusive-OR as a phase-difference signal to a clock generator which generates the clock signal, (3) an erroneous-synchronization phase detector for detecting whether the phase difference between the data signal and the clock signal resides within a range of phases for which there is a possibility of erroneous synchronization, and (4) an output fixing unit which holds the phase-difference signal at a fixed value if the phase difference resides within the above-mentioned range of phases, thereby it is possible to eliminate erroneous synchronization in which the clock generator is locked to a phase other than a target phase.

The invention discloses an implementation method of distributed cooperation intrusion detection system data security transmission, which belongs the computer network security technology field. By adopting data code, message exchange and key management of the intrusion alarm information, the method ensures the security of information transmission from end to end in distributed cooperation intrusion detection system, solves the problems of maintenance and key management for data encryption and data discrimination, and data integrality, realizes an security transmission of intrusion detection system alarm information, and provides a reliable transmission service for monitoring cooperation.

The invention discloses a cached data loading method and apparatus. The method comprises the steps of scanning a service access layer and identifying an interface containing preset automatic loading information; loading data, corresponding to the identified interface containing the automatic loading information, in a database according to preset cache parameter information, and performing caching; and when data of an accessed business is the data corresponding to the interface containing the automatic loading information, reading the cached data to realize business access processing, wherein the cache parameter information includes preset data discrimination identifier information, caching condition information and/or expiration time. According to embodiments of the cached data loading method and apparatus, the data is cached through the automatic loading information and the cache parameter information, so that system avalanche caused by large amounts of concurrent operations during cache failure is avoided; and furthermore, the cached data is sorted and a cache is judged, so that the utilization efficiency of the data in the cache and the reading efficiency of the cached data are improved.

The invention provides a massive data processing method and a massive data processing device, and aims to solve the problem of unremarkable data discrimination effects caused by higher sparsity of original data. The method comprises the following steps of storing the massive original data into an (m*n)th-order original matrix A, wherein m and n are positive integers, and the original data is operating data for each user; when the original matrix A is subjected to singular value decomposition, distributing the original data in the original matrix A to a plurality of processing nodes for processing; reconstructing a first unitary matrix U, a first diagonal matrix S and a second unitary matrix V, which are obtained by the singular value decomposition, to obtain a corresponding reconstructed matrix B; and clustering data in the reconstructed matrix B to discriminate the data of different types. According to the method and the device, the problem of higher sparsity of the original data is radically solved, so that higher data discrimination performance during subsequent massive data processing is ensured.

The invention provides a method and a device, which are applicable to technical field of data storage of memories, for cold-hot data discrimination management in a flash memory device. The method includes: creating an address field data sheet for recording logic address fields storing hot data and a weight data sheet for recording weight corresponding to each logic address field; receiving an operation instruction, carrying logic address field information, of a mainframe system; judging whether the logic address fields carried in the operation instruction exist in the address field data sheet or not; if so, performing hot data operation, and adding 1 to the weights corresponding to the address fields carried by the operation instruction after operation; and if not, performing cold data operation, updating the logic address fields carried in the operation instruction to the address field data sheet, and recording the weights corresponding to the logic address fields into the weight data sheet.

The invention discloses a game log data processing method and apparatus. The method comprises the following steps of: S1, a data discrimination step of including game logs of physical game servers in different monitoring groups according to server types and/or player group types; S2, a data acquisition and storage step of acquiring and storing the game logs of the monitoring groups separately; and S3, a data processing step of performing processing analysis on game data in the game logs of the monitoring groups separately. According to the game log data processing method and apparatus, the game data remarkably different in characteristic can be discriminately monitored according to different monitoring groups, more concrete data can be provided, and economic system and currency data can be systematically monitored.

The invention relates to an environmental governance process monitoring system and a monitoring method thereof. The system comprises a field data acquisition module, a data storage and transmission module, a cloud data processing platform and a cloud supervision platform which are connected in sequence, wherein the cloud data processing platform includes a data receiving module, a data discrimination module, a data analysis module and a data processing module, and the cloud supervision platform includes an authority management module, a model calculation module, an instruction management module, a process processing module, a data monitoring module and a data statistics module. The system and the method depend on a wireless communication technology, an Internet of things technology, an electric power parameter sensing technology, a data transmission technology and cloud computing, continuously collect power utilization data of an enterprise main incoming line loop, an enterprise production equipment power utilization loop and an enterprise pollution treatment equipment power utilization loop for 24 hours, and timely find out an abnormal work condition of the environmental protection treatment equipment. And production states of limited production and a shutdown renovation enterprise are monitored in real time.

The invention discloses an intelligent traffic time period dividing method based on a sliding average algorithm. The method comprises the following steps: 1) the road traffic flow information at an intersection is acquired; 2) the sliding average algorithm is used for smoothing the acquired data; 3) difference calculation is carried out through the smoothed data, and a corresponding derivative, that is, the change trend of the traffic flow, is obtained; and 4) according to the different change characteristics of the traffic flow derivative, further data discrimination is carried out, the traffic state of a day is divided to multiple time periods, and a basis is provided for the design of a timing scheme in the next step. The data are smoothed by the sliding average algorithm, the data derivative is approximated by the difference result, the time periods are divided, the traffic flow data change trend can be reflected more sensitively, the traffic condition of the road network can be recognized more accurately, and the effectiveness of the timing scheme is improved.

The invention relates to a data scheduling method used in an EPON terminal system and based on absolute priority. The EPON terminal system is a point-to-multipoint network system, comprises three parts: an end OLT, a terminal ONU and a passive optical divider, and is characterized in that: a. FIFOs used for data caching are added in the terminal ONU, and the number of the FIFOs is determined by the current priority number for data discrimination; b. address spaces are allocated in an external memory according to the priority; c. the terminal ONU transmits data to the end OLT, a transmission time slot for report frames used for bandwidth application is reserved at the tail part of a transmission time slot, and the number of data remained in the current system and to be transmitted can be accurately calculated when every time slot transmission is ended; d. when a higher priority comes, the current transmitted data is confirmed to be consistent with the data applied to be transmitted; and e. when end OLT allocates bandwidth more than the applied bandwidth to the terminal ONU, absolute priority scheduling can be selected or round-robin scheduling is selected to equally allocate the excessive bandwidth. Through practical operation and testing, the invention has the advantages that the effect is good, the bandwidth utilization ratio is high and the data transmission requirement of the EPON system is satisfied.

The present invention discloses a hyperspectral data subspace projection and classification method based on a fuzzy label mainly for solving the problems of wrongly classified ground objects and poor data discrimination performance caused by the mixed pixels and noise in a hyperspectral image. The method comprises the steps of 1, dividing a remote sensing database sample set into a training sample and a labeled sample set; 2, calculating a discrimination term generated by the labeled sample set after the subspace projection; 3, constructing a Laplace regularization term determined by the fuzzy label of the training sample; 4, obtaining an optimal projection matrix and the fuzzy label by maximizing the difference of the discrimination term and the regularization term to realize the effective dimensionality reduction and the high-precision classification simultaneously. According to the present invention, the discrimination term is constructed by a method of discriminating the subspace projection, the data is projected to the low-dimensional space, the data discrimination performance is enhanced, and then the fuzzy label is introduced to construct the Laplace regularization, thereby solving the wrong classification problem brought by the mixed pixels, and realizing the dimensionality reduction and the high-precision classification simultaneously.

The invention discloses an abnormal data discrimination method based on multi-criterion fusion. The method comprises the following steps: S1, preprocessing a sample data set; S2, according to four detection models in the abnormal data discrimination model, establishing sample data sets corresponding to the four detection models respectively; S3, inputting the input data set of the deep learning method into the long-short-term neural network model for training, and obtaining the trained long-short-term neural network model; S4, respectively inputting the sample data sets into the correspondingmodels, discriminating abnormal values, and obtaining the probability that each data point is discriminated as an abnormal point by the corresponding model; and S5, fusing the discrimination result probabilities of the data points discriminated by the corresponding models, and judging a fusion result according to a set judgment criterion to obtain a final abnormal data discrimination result. The data abnormal point discrimination precision is improved, the data accuracy and the available value are also improved, and accurate data guarantee is provided for operation detection business.

Disclosed is a mobile display module capable of displaying optimal still images or moving pictures depending on characteristics of images to be displayed. The mobile display module includes a graphic memory for storing image data, a data latch unit for latching the image data outputted from the graphic memory, a driver for applying the image data to a liquid crystal panel, a data discrimination unit for determining whether the image data inputted into the graphic memory is still image data or moving picture data, and a gamma voltage generating unit receiving control signals to output first gamma voltage adaptable for the moving picture data to the driver if the image data are the moving picture data and to output second gamma voltage adaptable for the still image data to the driver if the image data are the still image data.

A signal processing circuit having a data sync signal detector and a disk device. Input data read from a magnetic disk is input to a data discriminator. A data discrimination output constituting a code bit output discriminated by the data discriminator is input to a post-coder the output of which is input to a decoder and a (1+D) processing unit. The processed output of the processing unit is input to an error detection/correction unit and separated into bit strings of odd numbered bits and even numbered bits, divided into groups. An error detection/correction output is input to a data sync signal detector, and matched against a sync pattern. When the number of coincident groups is greater than a threshold value, a sync signal is output and upon detection causes the decoder to demodulate the data.

Certain embodiments relate to systems and methods for performing data discrimination using dimensionality reduction techniques. Particularly the Sparse Matrix Transform (SMT) is applied to more efficiently determine the Fischer Discrimination vector for a given dataset. Adjustments to the SMT facilitate more robust identification of the Fischer Discrimination vector in view of various resource constraints.

Provided is a data collecting apparatus based on store receipts. The collecting apparatus provides accurate and efficient acquisition of the turnover data from the stores of every lessee and achieves rapid and efficient turnover management of the lessees. The collecting apparatus is characterized in that: the receipts issued by each store are clamped in the sheets (20) indicating the identification code of the store, with the images thereof being read by a reader; the images being read are analyzed; a store information special designated part (12) specifies the stores according to the identification code of the stores; a receipt data discrimination part (13), according to the pattern and item information of the receipt of each store stored in the store information storage part (112), picks out the item information of the receipts with image thereof being read by the reader (10), thereby identifying the printed value according to each item, and a bill data processing part (14) is used for making the bill data required in the turnover management of the lessees from recognized receipt data and storing the data in the turnover DB (114).

The invention discloses a hyperspectral remote sensing image classification method and device. The method provided by the invention comprises the following steps: firstly, acquiring rich shallow features by using a convolution layer; then sequentially learning attention weights in spectrum and space through dual-channel attention to assign attention weights to the hyperspectral remote sensing images, amplifying useful information, inhibiting useless information to make the data discrimination degree higher; and finally, taking the weighted 3D data blocks as the input of the dense residual error network to extract spectral spatial features for classification. The attention module fully considers the characteristics of the hyperspectral remote sensing images, learns spectral attention and spatial attention in sequence and assigns attention weights to the hyperspectral remote sensing images in sequence, so that the subsequent dense residual network is facilitated to extract spectral-spatial characteristics with discrimination.

The invention provides a power line communication power consumption data analysis method and system based on clustering analysis. The power consumption data analysis is realized based on a power consumption information acquisition system. The data acquisition is conducted continuously according to a preset acquisition time, including a power consumption data acquisition process and a power consumption data feature extraction and saving process. For a current acquisition time, abnormal data discrimination is performed in real time, including a power consumption data reading process at the current acquisition time and an abnormal data discrimination process based on K-Means clustering analysis. The invention proposes the technical scheme of abnormal power consumption data identification based on the clustering validity for the first time by using a power line as a communication carrier to request and transmit the power consumption data, so as to achieve the purpose of improving the operation reliability of the power consumption information acquisition system.

A multi-class discriminating device for judging to which class a feature represented by data falls. The device has a first unit for generating plural first hierarchical discriminating devices for discriminating one from N, and a second unit for combining score values output respectively from the plural first hierarchical discriminating devices to generate a second hierarchical feature vector and for entering the second hierarchical feature vector to generate plural second hierarchical discriminating devices for discriminating one from N. When data is entered, the plural first hierarchical discriminating devices output score values, and these score values are combined together to generate the second hierarchical feature vector. When the second hierarchical feature vector is entered, the second hierarchical discriminating device which outputs the maximum score value is selected. The class corresponding to the selected second hierarchical discriminating device is discriminated as the class, into which the feature represented by the entered data falls.

The requested data is read from a DVD disk by interruption in ST46. Then, the ID information for indicating the leading end of the data, and the result of the identification by use of the EDC for indicating whether or not the data is correct are stored in ST47. When the disk is identified as a DVD-video disk and the flag is set in ST 48, and also when the number of times N of retries is identified as being larger than NR in ST50 and the read data is not identified as control data in ST51, data transmission is performed. When no flag is set, the disk identification is performed based on whether or not the data at a predetermined position on the logical format has predetermined data in ST49. When it is identified that an error has been detected in ST52 and also when the number of times N of retries becomes larger than NS in ST53, the occurrence of an error is announced.