32 results about "Signature matrix" patented technology

The invention provides a method and system combining functional genomic and genetic, proteomic, anatomic neuroimaging, functional neuroimaging, behavioral and clinical measurements and data analyses for autism pediatric population screening, diagnosis or prognosis. More specifically, the invention provides a weighted gene and feature test for autism which uses a weighted gene signature matrix for comparison to a reference database of healthy and afflicted individuals. The invention also provides normalized gene expression value signatures for comparison to a reference database. The invention additionally combines either the weighted gene or the normalized gene analysis with comparisons to a gene-networks signature matrix, a multi-modal signature matrix, and a collateral features signature matrix for improved accuracy in screening, diagnostic and prognostic relevance for autism, particularly for newborns, babies ages birth to 1 year, toddlers ages 1 to 2 years, toddlers ages 2 to 3 years and young children ages 3 through 4 years.

The invention discloses a multi-label rolling bearing fault diagnosis method based on meta-learning, and the method comprises the steps: constructing a multi-label fault data set of a rolling bearing,and dividing the multi-label fault data set into a training set and a test set according to fault types; extracting a time-frequency signature matrix feature T-FSMs of the fault signal; establishinga multi-label convolutional neural network model MLCML based on meta-learning; training an MLCML model by using the training set sample; verifying the trained MLCML model by using a test set sample; and carrying out fault diagnosis on the small-sample multi-label rolling bearing by utilizing the trained model. According to the method, multiple semantics contained in the rolling bearing fault sample are fully utilized, the fault diagnosis result is more accurate, meanwhile, the problem of small samples in actual fault diagnosis of the rolling bearing can be better solved through the time-frequency signature matrix characteristics and the meta-learning strategy, design is reasonable, operation is easy and convenient, and wide application value is achieved.

A method for monitoring a servo-control loop (3) of an actuator system (2) for actuating variable-geometry components of a turbojet, said method comprising:an estimation step of estimating a plurality of monitoring parameters from operating data of the servo-control loop (2);an evaluation step of evaluating a plurality of indicators from the monitoring parameters;an evaluation step for evaluating at least one signature matrix, each signature matrix being representative of the values of at least some of the indicators; anda detection and location step of detecting and locating a degradation affecting the servo-control loop as a function of said at least one signature matrix.

The problem of modeling and designing a structure including a planar element(s) (e.g., antenna, RF circuit, etc.) arranged in association with a non-planar element(s) (e.g., non-planar packaging, a non-planar scattering element, a complex meta material, a periodic array element, etc.), in a computationally efficient and rigorous manner is solved by (1) modeling each of the planar elements using both conventional space-spectral analysis and complex space-spectral analysis to obtain an electro-magnetic (EM) signature matrix for each planar element, (2) determining (e.g., using a brute force modeling based on Maxwell's equations, by measuring experimentally, or by reading one from a stored library) an EM signature matrix (e.g., reflection and scattering matrices) compatible with those of the planar elements, for each of the non-planar elements, and (3) combining the EM signatures of the elements using matrix analysis to obtain a complete EM signature the structure.

The invention provides a hyperspectral mixed pixel classification method based on neighbor cooperation enhancement, and the method comprises the steps: calculating a spectrum signature matrix of a plurality of target ground features through marked sample ground features; designing a multi-class classifier based on spectrum characteristics, and carrying out the classification of the ground features; carrying out the fusion of spatial structure features in a classification result, and extracting neighbor pixels; carrying out the class marking of unmarked hyperspectral ground features through the neighbor pixels; carrying out the classification and marking of the unmarked hyperspectral ground features through an interaction method; carrying out the further fusion of the spatial features of the target ground features in a mode of neighbor expansion, and completing the final classification and marking. According to the invention, the multi-class classifier is used for the simultaneous classification of ground features, and a problem that a conventional classification method cannot carry out the classification of background features is solved. Moreover, a mode of neighbor cooperation enhancement is employed for marking the unmarked ground objects step by step, thereby achieving the effective fusion of the spectrum features and spatial features of the ground features. The classification effect is good.

The invention relates to artificial intelligence, and provides a service system anomaly detection method and device, computer equipment and a storage medium, and the method comprises the steps: constructing a multi-scale signature matrix according to the multivariate time sequence data of each index generated by a service system; inputting the multi-scale signature matrix into a convolution layerto encode a spatial mode of the multi-scale signature matrix, and outputting a spatial feature map; inputting the spatial feature map into an attention-based convolutional long-short-term memory network layer, and updating the hidden state of the spatial feature map through the attention-based convolutional long-short-term memory network layer to obtain an updated spatial feature map; inputting the updated spatial feature map into a deconvolution layer to decode and reconstruct the updated spatial feature map to obtain a reconstructed signature matrix; comparing the reconstructed signature matrix with the multi-scale signature matrix, and determining an abnormal index of the service system. In addition, the invention also relates to a blockchain technology, and the multivariate time seriesdata can be stored in the blockchain. By adopting the method, the anomaly detection accuracy can be improved.

Disclosed is a method of automated speaker identification, comprising receiving a sample speech input signal from a sample handset; deriving a cepstral covariance sample matrix from the first sample speech signal; calculating, with a distance metric, all distances between the sample matrix and one or more cepstral covariance signature matrices; determining if the smallest of the distances is below a predetermined threshold value; and wherein the distance metric is selected from d5⁡(S,Σ)=A+1H-2,d6⁡(S,Σ)=(A+1H)⁢(G+1G)-4,d7⁡(S,Σ)=A2⁢⁢H⁢(G+1G)-1,d8⁡(S,Σ)=(A+1H)(G+1G)-1,d9⁡(S,Σ)=AG+GH-2,fusion derivatives thereof, and fusion derivatives thereof with ⁢d1⁡(S,Σ)=AH-1.

The present invention relates to a system and method for the ultra-precise analysis and characterization of RF propagation dynamics in complex wireless communication networks. The invented system includes sub-systems for the collection of network specific performance and environmental data, the consolidation of said data into representative signature elements, and the organization of said signature elements into a relational matrix. Through the invented methodology, RF performance and environmental composition data are closely correlated in uniformly weighted signature elements. These signatures, arranged in a relational matrix, represent a multiplicity of propagation pattern extrema. Limited RF data is compiled and formed into fractional signature elements. Fuzzy logic based reconstructive techniques are used to integrate these fractional elements into the normal signature matrix, allowing rapidly gathered and severely abbreviated data to produce extremely detailed and accurate characterization of RF propagation in localized coverage zones.

A method of using magic square signature to identify true or false of status includes generating a magic square for each article, adding elements of generated magic square on elements of signature magic square to achieve model for obtaining a signature matrix and using this matrix as identification data of article status, sending signature matrix to identifying device with signature magic square for calculating model difference of said matrix to signature magic square and verifying whether matrix is magic square or not, confirming that status having signature matrix is true or otherwise it is false if matrix is magic square.

A method is described for generating a minimal set of Analytical Redundancy Relations representing a system to which a plurality of sensors is associated for the observation of variables indicative of operating conditions and adapted to enable detection and isolation of faults.The minimal set of Analytical Redundancy Relations is derived from a complete set of Analytical Redundancy Relations in implicit form, for which an associated binary Fault Signature Matrix is specified, and comprises the relations associated to a minimal subset of rows of the original matrix, which has the same number of non-zero columns and the same number of distinct columns as the original matrix.

Methods and systems for anomaly detection and correction include generating original signature matrices that represent a state of a system of multiple time series. The original signature matrices are encoded using convolutional neural networks. Temporal patterns in the encoded signature matrices are modeled using convolutional long-short term memory neural networks for each respective convolutional neural network. The modeled signature matrices using deconvolutional neural networks. An occurrence of an anomaly is determined using a loss function based on a difference between the decoded signature matrices and the original signature matrices. A corrective action is performed responsive to the determination of the occurrence of the anomaly.

The invention provides a construction data self-checking method and system, and the method comprises the steps of obtaining all construction data in a construction scene, and constructing a construction data matrix; calculating relevancy between data fields in the construction data matrix to obtain a signature matrix; performing feature extraction on the signature matrix by using a pre-trained data anomaly detection model to obtain an output matrix; calculating a mean square error between the output matrix and the signature matrix; and according to the mean square error, determining a self-inspection result of all construction data in the construction scene. According to the construction data self-checking method and system provided by the invention, the association relationship between the construction data in the same construction scene is extracted, the data anomaly detection model is constructed, automatic detection of the abnormal data is realized according to the mean square error between the input and the output of the model, false data and invalid data are prevented from existing, the self-checking precision of the construction data is effectively improved, and the self-checking efficiency is greatly improved.

The invention discloses a revocable palm print feature generation method and system based on a minimum signature. The method comprises the following steps of obtaining an original palm print image, extracting a palm print region of interest (ROI) in the original palm print image, and extracting the orthogonal features of the palm print ROI; randomly generating a chaotic matrix as a secret key, andperforming XOR calculation on the chaotic matrix and the orthogonal matrix to obtain an initial characteristic matrix; and randomly generating a plurality of hash functions to generate a first signature matrix, carrying out scanning calculation on the initial feature matrix according to the hash functions, and replacing the maximum value in the signature matrix with the minimum value to obtain asecond signature matrix serving as the revocable palm print feature; and finally, carrying out scanning calculation on the initial feature matrix by utilizing irreversibility of a Hash function, and generating a final minimum Hash signature matrix as a final revocable palm print feature. According to the present invention, the security and the privacy of palm print biological characteristics are effectively protected, and the security and the privacy can be improved under the condition that the recognition rate is ensured.

The invention provides a block chain consensus method and system based on propagation activeness and asset attestation, and the consensus method comprises the following steps: improving the structure of an existing block, and enabling the improved block to comprise a block head, a transaction set and a signature matrix; expanding transaction types on the block chain, wherein the transaction types on the expanded block chain comprise transfer transactions and punishment transactions; and selecting a candidate group by using the signature matrix, determining a final bookkeeper in the candidates according to the number of assets, broadcasting winning information by the bookkeeper, and releasing a new block to the main chain to achieve one consensus. According to the method, invalid resource consumption caused by computing power competition can be avoided, and meanwhile, the bookkeeping right can be prevented from being mastered by hands of a few persons; the propagation delay can be reduced, the message coverage speed of the distributed system is increased, and the possibility of network partitioning is effectively reduced; a self-monitoring function is provided for system safety, and the application requirement of high throughput can be met.

The invention discloses a classification method for multivariable medical sensing data streams. The method comprises the following steps that firstly, a signature matrix which can capture the correlation among time sequences and represent the time sequences, and has robustness to noise is constructed; then, for individual unbalanced categories, an auxiliary classifier is adopted to generate an adversarial network ACGAN to generate enough signature matrixes corresponding to the categories; finally, a bidirectional convolution long-term and short-term memory (BPCLSTM) lightweight network classification model based on an attention Attention mechanism is constructed to realize accurate classification of multivariable medical sensing data streams, and the classification model not only can improve the classification accuracy, but also can reduce the scale of an original classification model.

The invention discloses a block rapid extension method based on smallest-structure singular subsets and belongs to the technical field of multi-physical-field modeling. The method comprises the steps of step one, converting a sparse high-order DAE system generated by multi-physical-field models into a signature matrix; steps two, performing block upper triangular matrix decomposition on the signature matrix; step three, processing each independent block upper triangular matrix through the block rapid extension method to obtain an optimum offset vector of each matrix; step four, forming a whole optimum offset vector of the signature matrix through local optimum offset vectors of all matrixes, converting the DAE system into an ODE system, and finding a constraint equation of consistent initial point values. According to the method, the DAE system is converted into the ODE system, so that attribute reduction of the sparse DAE system attribute reduction is achieved rapidly and accurately, and structural information such as optimum offset vectors of the DAE system is directly obtained.

The invention relates to a combination optimizing method based on a Lucene index section, and belongs to the technical field of the computer index. The method comprises the following steps: combiningcurrent node load information and section information of index, building a combination analyzing module to judge whether to meet a combination condition or not; according to a dictionary file contained in each index section, to obtain a characteristic matrix in the index with respect to an index section, processing by combining a minHash algorithm and a minimum hash signature algorithm, so as to calculate the signature matrix of the index section; through combining the signature matrix of the index section and a Jaccard similarity principle, calculating a similarity coefficient between the index sections, and according to the similarity coefficient, dividing the index sections into different similar sets; and using a similarity evaluation model to grade each similar set, and sorting according to a set score, selecting one or more sets with the highest score to be combined by a combination thread. The optimizing method is capable of reducing the effect of combination operation to performance of an index function and a search function and effectively improving a search speed.

A method for generating a minimal set of Analytical Redundancy Relations representing a system to which a plurality of sensors is associated for the observation of variables indicative of operating conditions and adapted to enable detection and isolation of faults. The minimal set of Analytical Redundancy Relations is derived from a complete set of Analytical Redundancy Relations in implicit form, for which an associated binary Fault Signature Matrix is specified, and comprises the relations associated to a minimal subset of rows of the original matrix, which has the same number of non-zero columns and the same number of distinct columns as the original matrix.

The embodiment of the invention provides a method for obtaining object track similarity. The method comprises the following steps: obtaining a spatial-temporal characteristic matrix corresponding to each target object; performing dimension reduction operation on the spatial-temporal characteristic matrix to obtain a signature matrix corresponding to the spatial-temporal characteristic matrix; performing similarity calculation on the signature matrixes corresponding to any two spatial-temporal feature matrixes to obtain the signature matrix similarity between any two target objects; and selecting two target objects of which the signature matrix similarity is greater than a preset similarity threshold, and determining the trajectory similarity between the two selected target objects based on the spatial-temporal feature matrixes corresponding to the two selected target objects. According to the method, dimension reduction operation is carried out on the target spatial-temporal characteristic matrix corresponding to the target object, so that the dimension of data is greatly reduced, the data volume is greatly reduced, the track similarity obtaining efficiency of the target object is improved, the tracking response speed of the tracked object is high, and the real-time performance is good.

The invention discloses a terminal access method and device, and the method and device are used for solving a problem that the system performance of a conventional SCMA multi-terminal access mechanism is reduced because of collision, and a possible retransmission problem of a system. The method comprises the steps that a terminal is determined to enter a competitive access stage; the terminal determines a first signature according to a random number seed and a signature matrix during the transmission of binary data at each time, and enabling the binary data to be mapped to a multi-dimensional symbol for transmission according to the first signature, wherein the first signatures selected during the transmission of binary data at each time are different.

The invention discloses an identification database deduplication method and system for an MES system, and the method comprises the steps: enabling each to-be-processed identification database to serve as a slave node, enabling an idle operation node to serve as a master node, and sequentially labeling the slave nodes; dividing the data in all the slave nodes into N parts by using an SNM algorithm; calculating a minimum signature matrix of each data set; calculating the similarity between every two data elements in each data set according to the minimum signature matrix of each data set; and according to the similarity between every two data elements in each data set, performing deduplication processing on the data source in each data set. According to the method and system, time consumed in the data transmission process is shortened in a data set division mode, the minimum signature matrix is constructed, interaction is conducted on the minimum signature matrix, similarity calculation is conducted, interaction of data elements is not needed, the data privacy is guaranteed, and the time consumed in the data transmission process is further shortened.

The invention relates to a method for merging and optimizing based on Lucene index segments, and belongs to the technical field of computer indexing. It includes the following steps: combining the load information of the current node and the segment information of the index, constructing a merge analysis module to judge whether the merge condition is satisfied. According to the dictionary files contained in each index segment, the feature matrix of the index segment in the index is obtained, and then combined with the minHash algorithm and the minimum hash signature algorithm to calculate the signature matrix of the index segment. Combined with the signature matrix of the index segment and the Jaccard similarity principle, the similarity coefficient between each index segment is calculated, and the index segment is divided into different similar sets according to the similarity coefficient. Use the similarity evaluation model to score each similar set, and sort according to the set score, and select one or more sets with the highest score to be merged by the merge thread. The optimization method of the invention can reduce the impact of the merge operation on the performance of the index function and the retrieval function and can effectively improve the speed of retrieval.

The invention discloses a signature-based matrix dual encryption method. The method comprises a data encryption method and a data decryption method. The encryption process is used in a data to-be-transmitted stage, and the decryption process is used in a data completion stage; the data is encrypted through a secret key, then the secret key is converted into a signature matrix through an agreed matrix, and the purpose of data encryption transmission is achieved; and after receiving signature data and encrypted data, a server can decrypt original data through the signature data. In the data transmission process, the safety of the data is ensured, and the possibility of information leakage is greatly reduced.

The invention relates to the technical field of artificial intelligence, and discloses a resume duplicate checking method and device, equipment and a medium. The method comprises: acquiring a to-be-duplicated resume; performing word segmentation according to the to-be-duplicated resume, and performing hash signature matrix calculation on a word segmentation result to obtain a to-be-duplicated hashsignature matrix; according to the to-be-duplicated hash signature matrix, and carrying out similar resume query from a resume library according to information classification to obtain a candidate resume set; respectively constructing a resume pair feature vector for the to-be-duplicated resume and each resume in the candidate resume set to obtain a plurality of to-be-predicted resume pair feature vectors; inputting the to-be-predicted resume pair feature vectors into the classification prediction model for similarity probability prediction to obtain probability prediction values of the to-be-predicted resume pair feature vectors; and determining a target repeated resume pair according to the probability prediction values of the plurality of to-be-predicted resume pair feature vectors. According to the invention, the duplicate checking efficiency is improved, similar rules do not need to be set manually, and the accuracy of determining the target repeated resume pairs is guaranteed.

The invention provides a hyperspectral mixed pixel classification method based on neighbor synergistic enhancement, comprising: calculating the spectral signature matrix of multi-target features using marked sample features; designing a multi-category classifier based on spectral features to classify the features Classification; integrate the spatial structure features in the classification results, and extract the neighboring pixels; use the neighboring pixels to collaboratively classify the unmarked hyperspectral objects; use the iterative method to gradually classify and label the unmarked objects; The method of domain extension further fuses the spatial characteristics of the target object to complete the final classification and labeling. The invention uses a multi-category classifier to simultaneously classify the object categories, which solves the problem that the traditional classification method cannot classify the background objects; and uses the method of neighbor synergistic enhancement to gradually mark the unmarked object objects, effectively It combines the spectral characteristics and spatial characteristics of ground objects, and the classification effect is better.

A method for continuously determining mutually corresponding image points between a first camera image and a second camera image, having the steps of capturing (10) the first camera image (101); capturing (11) the second camera image (102); ascertaining (40) at least one first signature matrix element (203) of a first signature matrix (201) on the basis of the first camera image (101); assigning (60) first coordinates to an ascertained signature value of the first signature matrix element (203) in a signature value position table (300); ascertaining (70) at least one signature value of a second signature matrix element (203) having second coordinates of a second signature matrix (202) on the basis of the second camera image (102); and determining (80) at least one element (503) of a correspondence matrix (500, 501, 502) on the basis of the signature value position table (300), the cyclically ascertained signature value of the second signature matrix element (203) and the second coordinates of this second signature matrix element (203), wherein at least one concatenation matrix element (403) of a concatenation matrix (400) is ascertained (50) on the basis of the first signature matrix (201) and the signature value position table (300), and the element (503) of the correspondence matrix (500, 501, 502) is additionally determined (80) on the basis of the ascertained concatenationmatrix (400), the first camera image (101) and the second camera image (102).

This application provides a block chain consensus method and system based on communication activity and asset certification. The consensus method includes the following steps: improving the structure of the existing block, and the improved block includes block header, transaction set and signature Matrix; expand the transaction types on the blockchain. After the expansion, the transaction types on the blockchain include transfer transactions and penalty transactions; use the signature matrix to select the candidate group, and determine the final bookkeeper among the candidates according to the amount of assets , the bookkeeper broadcasts the winning information and releases a new block to the main chain to reach a consensus. This application can avoid the waste of invalid resources caused by computing power competition, and at the same time, it can avoid the power of bookkeeping in the hands of a few people; it can reduce the propagation delay, speed up the message coverage speed of the distributed system, and effectively reduce the possibility of network partition; Provides self-monitoring function for system security, which can meet the application requirements of high throughput.

A transmitter device including a processor in a wireless communication system. In one embodiment, the processor is configured to receive at least one modulated data message and to spread the at least one modulated data message into the transmission signal using a low density signature matrix. The low density signature matrix is ​​an acyclic signature matrix. The receiver device is configured to receive said transmission signal and to detect at least one modulated data message within the received transmission signal. The processor is configured to detect at least one modulated data message in one iteration using the acyclic signature matrix. Such an acyclic signature matrix is ​​constructed as a concatenation of a phase-rotated identity matrix and an all-zero matrix.

The embodiment of the present invention discloses an information processing method and a server. The method includes: obtaining the first historical behavior data obtained by the first user performing operations on the terminal; performing feature construction according to the first historical behavior data to generate the first feature matrix, the first feature matrix is ​​used to represent the correspondence between the first user and the first information; the first feature matrix is ​​compressed according to the first preset rule to generate a second signature matrix, and the second signature matrix is ​​used to represent The minimum hash value corresponding to the second historical behavior data, the second historical behavior data is obtained after the first historical behavior data is compressed; the second signature matrix is ​​processed according to the second preset rule to generate the first model , the first model is used to represent the degree of correlation between the second historical behavior data; the current behavior data of the second user is obtained, and the current behavior data is input into the first model to obtain the associated data associated with the first user.