123 results about "Matrix analysis" patented technology

A method and system for calculating data traffic flow in a communications network are disclosed. The communications network comprises a plurality of nodes including a plurality of source nodes, a plurality of destination nodes, and a plurality of intermediate nodes. Each of the intermediate nodes includes at least one elbow comprising one ingress interface and one egress interface of the intermediate node. The method includes obtaining local data traffic measurements at each of the elbows, wherein the local data traffic measurements comprise data traffic arriving at the intermediate node via the ingress interface and leaving the intermediate node via the egress interface. The local data traffic measurements are used in calculation of the traffic flow and may be used, for example, to generate data traffic matrix information using data traffic matrix inference or data traffic matrix estimation.

At least one exemplary embodiment of the invention is directed to a molecular diagnostic device that comprises a cartridge configured to eject samples comprising genomic material into a microfluidic chip that comprises an amplification area, a detection area, and a matrix analysis area.

A fault analysis method includes: using a matrix of two sets of microgrid power distribution networks to analyze and solve a fault current, and for various types of faults of the distributed power distribution system, obtaining appropriate boundary conditions to calculate a variety of different types of single or simultaneous fault currents of load points. The present invention may be further applied to a situation where a bus or impedance or parallel loop is added. The present invention has good robustness and execution speed, and requires small memory space for calculation of analysis and identification of a power flow fault of the distributed power distribution system, and may be actually applied to an instrument control system for identification and analysis of a fault of a large-scalemicrogrid distribution system.

A method and implementation of wireless communication are disclosed in which wireless signals are exchanged between at least one remote client and a directional antenna array associated with a wireless network, wherein the directional antenna array includes a plurality of antenna elements. A statistical matrix analysis is performed for each of the at least one client and the antenna array, in order to locate angles associated with directions of each client with respect to the antenna array. Values are determined for weighting factors for RF signals of each of the respective plurality of antenna elements, so as to create predetermined phase differences between the signals of the plurality of antenna elements. The predetermined phase differences are used to direct at least one null toward at least one source of interference, so as to avoid signal interference.

A cooperative game method and trapezium-cloud model-based power distribution network operation state fuzzy and comprehensive evaluation method is characterized by comprising the steps of considering the purposiveness, the systematicness, the operability, the independence, the significance and the dynamism, and constructing a multi-dimension hierarchical power distribution network index system based on the safety, the high quality, the economy, the Intelligence, the environmental protection property and the sustainability; utilizing a weight method, an entropy weight method, a anti-entropy weight method and a compatibility matrix analysis method to calculate the power distribution network index weights separately; carrying out the cooperative game on the determined weights to obtain an optimal combined weight; carrying out the variable weight correction on the combined weight to obtain a final weight value; dividing the evaluation grades of a power distribution network to obtain an evaluation set; calculating a trapezium-cloud model parameter that each index corresponds to each evaluation grade, utilizing a trapezium-cloud model to calculate the membership degree values of the indexes of the to-be-evaluated power distribution network, and calculating a membership degree vector and an evaluation result of the whole power distribution network layer by layer and upwards on a fuzzyand comprehensive evaluation frame.

An encoder using LDPC (low density parity check) codes, and an encoding method. The encoder comprises a parity check matrix generator for generating a parity check matrix H; and a codeword generator for processing the parity check matrix H to generate a codeword, and the codeword generator comprises: an AB analyzer for analyzing the parity check matrix H into matrixes A and B; a pivoting unit for pivoting the parity check matrix H; a bit-reversing unit for bit-reversing the pivoted matrix; an LU analyzer for analyzing the matrix A into matrixes L and U; and a codeword generator unit for performing a logical operation on the matrixes A, B, L and U. A bit-reversing method is used to effectively generate a parity check matrix having a high girth by using a regular encoder.

The invention belongs to the technical field of shale gas development, and particularly relates to a segmentation multi-cluster fractured horizontal well yield prediction method for a shale gas reservoir. The method comprises the following steps that: establishing a physical model; obtaining an apparent permeability rate calculation method; according to the permeability rule and the pressure superposition principle of gas in a stratum, obtaining the overall pressure drop and crack grid traffic relational expression of all crack grid traffics for any point in a gas reservoir; according to the flowing relationship of gas in a crack and a boundary coupling relationship between the crack and the strum, obtaining a gas reservoir-crack coupling flowing equation; and adopting a matrix analysis method and a numerical iteration algorithm to obtain the horizontal well yield. By use of the method which is put forward by the invention, a blank of a segmentation multi-cluster fractured horizontal well yield prediction model which considers the complex and multiple migration mechanism is filled. The method has the characteristics of being convenient in calculation, high in operability, effectiveand practical, and a basis can be provided for optimizing the horizontal well segmentation multi-cluster fractured parameters of the shale gas.

The invention discloses a subcutaneous vein three-dimensional reconstruction method based on a hybrid matching strategy and aims to obtain the three-dimensional information of veins. The method includes the steps of firstly, using IUWT and Hessian matrix analysis to respectively obtain the blood vessel segmentation result and the related blood vessel feature image in each view; secondly, extracting and dividing blood vessel central lines through morphology and a blood vessel tracking algorithm to obtain the radius and blood vessel direction of each central line branch; thirdly, using epipolar constraint to calculate the candidate point set, of points in the central line branch of single view, in another view; respectively extracting SURF in the blood vessel similarity image of each view, completing SURF feature point matching, and using a Ransac method to calculate the homograph matrix among views; fifthly, using the layered matching strategy from part to whole to realize point-point matching between the blood vessel central lines of the views of two eyes according to the homograph matrix and the candidate matching point set, and completing the optimization of the homograph matrix during matching; sixthly, completing the three-dimensional reconstruction of the matching central line points according to the principle of triangular measuring, and recovering a three-dimensional blood vessel surface according to two-dimensional blood vessel diameter information.

The invention provides a method for testing mobile application in batch and equipment used in the method. The method for testing the mobile application in batch has the beneficial effects that much equipment can be tested at the same time, and development and maintenance cost is greatly saved; besides, parallel test on mobile application is emphasized, namely mobile equipment is tested in a multi-platform, multi-resolution and multi-language environment, and saving of testing time is more emphasized; and compared with the condition that analysis is carried out by virtue of a test matrix, a tester can adopt the method provided by the invention for greatly improving testing efficiency, the method provided by the invention can help the tester to realize high coverage rate, and on the basis of high testing coverage rate, the tester does not need to consider a risk, caused by adopting the matrix analysis, of choice of test content; meanwhile, the method provided by the invention also can be adopted by a mobile phone manufacturer, and the sampling rate of mobile equipment during sample testing before the mobile equipment leave a factory can be improved.

The invention discloses a high efficiency accurate license plate identification method comprising the following steps: carrying out license plate compactification treatment on an input license plate image so as to obtain a compactification license plate image; using a pre-trained all convolution nerve network model to carry out license plate integral identification, thus obtaining prediction results of each pixel point character class in the compactification license plate image, and forming a label score matrix; parsing the label score matrix so as to obtain the final license plate string information. The method needs no character segmentation, thus solving the problems that an existing method is poor in character segmentation robustness; in addition, the method uses the all convolution nerve network model to carry out license plate integral identification, only one identification process is needed, so the algorithm efficiency is high; the complete context information of the license plate can be combined in the identification process, thus greatly improving the identification accuracy and robustness; finally, in label matrix analysis, the method can carry out difference treatments on different license plate and characters, thus further improving the algorithmic robustness and applicability.

The invention discloses an internet open information-based event occurrence risk prediction and early-warning method. The method comprises the following steps: (1), carrying out spam filtering on webpage information; (2), parsing words expressing location in the filtered webpage information to obtain a place name word; and carrying out processing on the parsed webpage information based on an established information body and classifying the webpage information into a matching region; (3), filtering the webpage information so as to obtain webpage information related to food safety, and carrying out processing on the filtered webpage information by using a regression analysis model and determining an object type related to each piece of webpage information; (4), determining webpage information sets of events of a set region and a set object and establishing event feature parameters and calculating parameter values regularly, and carrying out early warning on an event whose feature parameter exceeds a set threshold value; and (5), on the basis of a matrix analysis and a regression prediction model, carrying out different early warning on a risk of set event occurrence at a target region. According to the invention, the efficiency of risk early warning is improved.

The invention provides a detection method for producing areas of Xihulongjing tea based on genetic algorithm optimization. The method is characterized in that an electronic nose is utilized to acquire fingerprints of the smell of Xihulongjing tea from different producing areas, sensors having special contribution to modeling of the producing areas are screened out based on analysis of average fingerprints and a principal component score chart through three key techniques of genetic algorithm, i.e., a roulette selection method, a single-point crossover method and a simple mutation method, with an integral recognition rate as a fitness function, optimization is carried out so as to reduce the amount of sensors needed in modeling, and fingerprint response values of the sensors are subjected to matrix analysis through an F value obtained through variance analysis and principal component load.

A system and method is provided for detecting anomalies in turbine engines emanating from the main shaft and / or main shaft bearings. The anomaly detection system includes a sensor data processor and a matrix analysis mechanism. The sensor data processor receives engine sensor data, including main engine speed data during spin down, and formats the engine sensor data into an appropriate matrix. The matrix analysis mechanism receives the sensor data matrix and performs a singular value analysis on the sensor data matrix to detect potential anomalies in the turbine engine main shaft and / or bearings. The output of the matrix analysis mechanism is passed to a diagnostic system where further evaluation of the anomaly detection determination can occur.

The invention discloses a matrix decomposition recommendation method based on Bayesian probability with social relations and project content. The method includes the steps that a PMF method is used for performing hidden matrix analysis on an observation evaluation matrix to obtain a hidden user characteristic matrix and a hidden project characteristic matrix; a BPMFSR method or a BPMFSRIC method is used for performing Gibbs sampling on the hidden user characteristic matrix and the hidden project characteristic matrix to obtain the hidden user characteristic matrix after sampling and the hidden project characteristic matrix after sampling; according to the hidden user characteristic matrix after sampling and the hidden project characteristic matrix after sampling, a forecast evaluation matrix is calculated, and recommendation is performed based on the forecast evaluation matrix. The method is efficient in calculation, can be applied to a recommendation system which has a large-scale data set and is based on trust or content, has a larger convergence rate, obtains more accurate recommendation results compared with other matrix decomposition methods and solves the problems of data sparseness and cold start better than other methods.

The invention relates to the technical fields of computer vision and target tracking, and aims to solve the problem on how to ensure the robustness of target tracking in different background interference situations, improve the robustness and adaptability of an algorithm and effectively overcome constant change of the dimension and direction of a target in the process of tracking. According to the technical scheme employed in the invention, an adaptive Mean Shift target tracking method based on LBP features comprises the following steps: (1) generation of a target model; (2) similarity measurement: the similarity between the target model and a target candidate model is measured with a Bhattacharyya coefficient; and (3) target dimension and direction estimation: Mean shift iteration is performed on a target area to make the target area converge to the spatial location of a candidate target, matrix decomposition is performed on a target candidate area weight map integrating texture and color features, and the dimension and direction of the target candidate area are calculated through matrix analysis. The method is mainly used in target tracking occasions.

The invention discloses a fan authentication test system analysis platform. For fan authentication test analysis software developed based on FAMOS, BLADED and MATLAB, five modules, namely a data analysis module, a power test module, a performance test module, a load test module and a system identification module are distinguished; mainly aimed at professional equipment based on GL / IEC standards, data formats and IEC test standard specification requirements are tested; and according to the GL / IEC standards, a test report is automatically made. The platform can realize not only functions of test data calibration, captured matrix analysis, sector screening, scattered point curve graph analysis, load rain-flow analysis, time-domain spectral analysis, test and simulation analysis comparison, theoretical simulation model system identification and the like but also automated generation of a fan performance test analysis report, a power test analysis report and a load test analysis report in a GL-D professional format. Moreover, the wind generator unit authentication test data processing is more systematized and modularized.

A system and method is provided for detecting anomalies in turbine engines emanating from the main shaft and / or main shaft bearings. The anomaly detection system includes a sensor data processor and a matrix analysis mechanism. The sensor data processor receives engine sensor data, including main engine speed data during spin down, and formats the engine sensor data into an appropriate matrix. The matrix analysis mechanism receives the sensor data matrix and performs a singular value analysis on the sensor data matrix to detect potential anomalies in the turbine engine main shaft and / or bearings. The output of the matrix analysis mechanism is passed to a diagnostic system where further evaluation of the anomaly detection determination can occur.

A covariance-clustering algorithm for partitioning a graph into sub-graphs (clusters) using variations of the pseudo-inverse of the Laplacian matrix (A) associated with the graph. The algorithm does not require the number of clusters as an input parameter and, considering the covariance of the Markov field associated with the graph, algorithm finds sub-graphs characterized by a within-cluster covariance larger than an across-clusters covariance. The covariance-clustering algorithm is applied to a semantic graph representing the simulated evidence of multiple events.

The invention belongs to the fields of navigation, and measurement and control, and relates to a division-of-amplitude angle sensing design method and device for carrying out navigation by using sky scattering light polarization information. The method comprises the following steps: a) detecting an incident light polarization state by adopting a division-of-amplitude multi-channel synchronous detection method; b) achieving light-concentration and directional measurement functions by a small-field telescope; c) analyzing a vibration azimuth angle of a linear polarized electric field vector by a stokes vector and a matrix analysis method; d) carrying out system optimization design on indexes and debugging states of internal elements of a sensing device by taking an instrument matrix condition number as an evaluation factor; and e) accurately calibrating an instrument matrix of the sensing device by a benchmark polarization generator, and calibrating and accurately checking the sensing device. A new technological approach is provided for autonomous navigation by using an optical means; the method and the device can be widely applied to navigation targets of equipments such as vehicles, ships and warships, low-altitude aircrafts and autonomous robots, also can be applied to the fields such as polar research, outdoor adventure and geologic assay.

The invention relates to an on-chip internet fault-tolerance routing method based on channel dependency graphs, which comprises the steps of: firstly carrying out analysis on the communication characteristics of an application program, building a communication relation bipartite graph of an application program module, generating an application traffic matrix ATM, simultaneously generating a channel dependency directed graph CDG under the faultless condition, generating a faulty channel dependency directed graph FCDG under the fault condition by rough and fine granulation fault detection, and finally generating a corresponding acyclic fault channel dependency directed graph AFCDG and a corresponding flow consistency matrix FCM by applying a turning model; and secondly, carrying out matrix analysis on the ATM and FCM, selecting communicated AFCDG with single VC (virtual channel) or multiple VCs, setting the VC sequence, and finally obtaining an optimal load balancing routing plan by comparison. According to the method, the available resources are utilized to the greatest extent by a rough and fine granulation fault detection method, and the purposes of deadlock avoidance and load balancing are realized by constructing weighted acyclic fault channel dependency directed digraph AFCDG based on single VC or multiple VCs.

The invention relates to a method for predicting compound carcinogenic toxicity based on complex sampling and an improved decision forest algorithm. The method is suitable for calculating carcinogenic toxicity evaluation and virtual selection on a compound according to an organic small molecular structure, and comprises the following steps: firstly, adopting a relative force field to molecules with the molecular structure to carry out optimization and charge calculation, carrying out complex sampling to the compound with centralized original training to be used for generating a training subset, and fixing various relative descriptors in composition calculation molecules of descriptors according to a complex sampling algorithm result; secondly, optimizing a descriptor pool by using a method based on relative matrix analysis and factor analysis; and finally, carrying out data mining on carcinogenic toxicity data and corresponding chemical characteristics thereof of training set molecules by using the improved decision forest method to obtain a classified prediction confidence interval, a carcinogenic toxicity prediction mold and a judgment rule. The method has favorable application prospect in high throughput virtual selection and calculating the carcinogenic toxicity evaluation.

A method and implementation of wireless communication are disclosed in which wireless signals are exchanged between at least one remote client and a directional antenna array associated with a wireless network, wherein the directional antenna array includes a plurality of antenna elements. A statistical matrix analysis is performed for each of the at least one client and the antenna array, in order to locate angles associated with directions of each client with respect to the antenna array. Values are determined for weighting factors for RF signals of each of the respective plurality of antenna elements, so as to create predetermined phase differences between the signals of the plurality of antenna elements. The predetermined phase differences are used to direct at least one null toward at least one source of interference, so as to avoid signal interference.

The invention designs a near-infrared subcutaneous vein segmentation method based on multi-feature clustering, and can realize the multi-feature extraction and the automatic clustering of a vein. The method comprises the following steps:1) adopting a NiBlack and a morphology algorithm to realize the segmentation of a skin area and edge mirror extension; 2) obtaining a vein similarity diagram, a vein directional diagram, a vein scale diagram and an initial segmentation vein through multiscale IUWT (Isotropic Undecimated Wavelet Transform) and Hessian matrix analysis; 3) extracting and repairing a vein branch center line by adopting the initial segmentation vein and the vein directional diagram, and correcting the position and the direction of the branch center line by adopting a segmentation spline fitting method; 4) on the basis of a vein branch direction, calculating a coordinate mapping relationship between an artwork and a branch outline image, and extracting normalized second-order Gaussian characteristics and vein similarity characteristics after an IUWT enhanced image and the vein similarity image are independently mapped to outline image space; and 5) utilizing the obtained vein characteristics to cluster the outline image into three types including skin, vein and a fuzzy region by adopting a K-means algorithm.

The invention discloses an event occurrence risk prediction and early warning method targeted to a specific zone. The method comprises the following steps: (1), carrying out filtering on crawled webpage information; (2), parsing words expressing location in the webpage information to obtain a place name word; and carrying out processing on the webpage information based on an established information body and classifying the webpage information into a matching region; (3), carrying out processing on the webpage information by using a regression analysis model and determining an object type related to each webpage; (4), obtaining webpage information sets of events of a set region and a set object according to a located zone of the webpage as well as the related object type, and establishing event feature parameters and calculating parameter values regularly, and carrying out early warning on an event whose feature parameter value exceeds a set threshold value; and (5), on the basis of a matrix analysis and a regression prediction model, carrying out different early warning on a risk of the set event occurrence at the target zone on under the circumstances of occurrence of set object event early warning at a certain zone. According to the invention, the efficiency of risk early warning is improved.

The invention relates to a matrix analysis method of power system network topology, which still utilizes the advantages of a matrix method to obtain a full-access matrix by two matrix multiply operations and improves matrix analysis speed. The matrix analysis method comprises the following steps of: optimizing and numbering nodes according to the number of closed switches connected with the nodes from big to small and optimizing and numbering a bus according to the number of branch circuits connected with the bus from big to small, thereby reducing the matrix multiply operand; and preserving values of a communication matrix and the full-access matrix by directly using adjacency matrixes without needing a special sandwich matrix in order to save a half of a computer storage space and reduce matrix assignment operation. Compared with the traditional matrix method, the matrix analysis method has smaller calculation amount and the more obvious efficiency the more top points.

The invention discloses an analyzing and processing method and an analyzing and processing system of traffic flow direction based on application. The analyzing and processing method of the traffic flow direction includes that: a traffic matrix analysis system (FMAS) obtains traffic flow direction data of all data flows in an Internet protocol (IP) backbone network according to flow direction information of Internet data center (IDC) data flows reported by an egress router of the IDC and border gateway protocol (BGP) routing information reported by a router of the IP backbone network; an application extractor (AE) which is disposed on an exit link of the IDC detects net load information of each data package on the IDC exit link thoroughly and obtains application information of each data flow on the IDC exit link combining characteristic fields of each application, wherein the application information includes IP address, application type and application name of each data flow; the AE reports the application information of each data flow on the IDC exit link to the FMAS; the FMAS analyzes traffic flow direction data of all data flows in the IP backbone network and the application information of all data flows on the IDC exit link and obtains traffic flow direction data of each application in the IDC. The analyzing and processing method is capable of obtaining data traffic flow direction conditions of applications in the network.

The invention provides a blind recognition method for RS code and punctured convolutional code cascaded code parameters and belongs to the technical field of channel code blind recognition. Firstly, a punctured convolutional code parameter blind recognition method based on a Walsh-Hadamard algorithm which is called a WH algorithm for short is adopted for recognizing the code length, the starting point, the puncture mode and generated matrixes of punctured convolutional codes; then, a Viterbi decoding algorithm is used for decoding; next, according to a decoded sequence, a matrix analysis method is adopted for recognizing the interweaving width and the interweaving length, and interweaving is performed according to interweaving parameters; finally, according to an interwoven sequence, RS code parameters are recognized through a traversal method and a fast Fourier transform method in the Galois field. The method for solving the blind recognition problem of the RS code and punctured convolutional code cascaded code parameters is provided, and the RS code small-order recognition rate is improved while recognition data size is reduced.

A system and method is provided for predicting consumer behavior for selected products. The method includes providing a first matrix associated with N products evaluated by a plurality of consumers, providing a second matrix associated with the N products characterized by at least one of an analytical profile or an evaluation by a plurality of experts and correlating the first matrix to the second or / and the third matrix to produce a relationship model.