53 results about "Hilbert space" patented technology

An improved method of partitioning and indexing multi-dimensional data that maps the data to one-dimensional values according to the sequence in which an approximation of a Hilbert space-filling curve passes through all of the points corresponding to potential multi-dimensional data in a data space. Data is partitioned into pages, each corresponding to a length of Hilbert curve. A page identifier is the sequence of the first point on its corresponding Hilbert curve section. The mapping orders data and also orders the data pages that contain data within a database. Mapping multi-dimensional data to one-dimensional values enables the data to be indexed using any one-dimensional index structure. The practical application of the indexing method is made viable and useful by the provision of a querying algorithm enabling data to be selectively retrieved in response to queries wherein all or some of the data that lies within a rectangular space within multi-dimensional space is required to be retrieved. The querying algorithm identifies pages whose corresponding curve sections intersect with a query region. The first intersecting page is found by calculating the lowest one-dimensional value corresponding to a possible multi-dimensional data value or point within the query region, and looking up in the index to find which page may contain this point. The next intersecting page, if it exists, is found by calculating the lowest one-dimensional value equal to or greater than the identifier of the next page to the one just identified. This new lowest one-dimensional, if found, is used to look up in the index and find the next page intersecting with the query region. Subsequent pages to be found, if any, are determined in a similar manner until no more are found. Pages found to intersect the query region can be searched for data lying within the query region.

A method of establishing a shared secret random cryptographic key between a sender and a recipient using a quantum communications channel is described. The method comprises: generating a plurality of random quantum states of a quantum entity, each random state being defined by a randomly selected one of a first plurality of bases in Hilbert space, transmitting the plurality of random quantum states of the quantum entity via the quantum channel to a recipient, measuring the quantum state of each of the received quantum states of the quantum entity with respect to a randomly selected one of a second plurality of bases in Hilbert space, transmitting to the recipient composition information describing a subset of the plurality of random quantum states, analysing the received composition information and the measured quantum states corresponding to the subset to derive a first statistical distribution describing the subset of transmitted quantum states and a second statistical distribution describing the corresponding measured quantum states, establishing the level of confidence in the validity of the plurality of transmitted random quantum states by verifying that the first and second statistical distributions are sufficiently similar, deriving a first binary sting and a second binary string, correlated to the first binary string, respectively from the transmitted and received plurality of quantum states not in the subset, and carrying out a reconciliation of the second binary string to the first binary string by using error correction techniques to establish the shared secret random cryptographic key from the first and second binary strings.

A detector and modulator of electromagnetic radiation is 3-dimensional structure made of substantially 2 dimensional high impedance metamaterial surfaces stacked one above the other with a dielectric layer in between and located above a conducting ground plane. Each 2 dimension surface may be formed by an open continuous conductive trace, such as metallic wire or a printed circuit line, which is cast or plated on or into a 2-D periodic arrangement of an element that belongs to the Hilbert space filling curves.

The invention discloses a cross-domain text sentiment classification method based on domain countermeasure self-adaption. The method comprises the following steps: inputting a word vector matrix, a category label and a domain label of a source domain sample and a target domain sample; Utilizing a feature extraction module based on a convolutional neural network to extract low-level features of thesample; constructing a constraint based on distribution consistency of a source domain and a target domain in a main task module, mapping a low-layer sample to a regeneration kernel Hilbert space, and learning a high-layer feature with transferability; inputting the high-level features of the source domain into a class classifier, and ensuring that the classifier has class discrimination on samples on the basis of reducing domain difference; a domain invariance constraint based on adversarial learning is constructed in an auxiliary task module, and low-level features are input into a domain classifier with adversarial properties, so that the classifier cannot judge the domain to which a sample belongs as much as possible, high-level features with domain invariance are extracted, and the migration problem of a source domain classifier to a target domain is effectively solved.

The invention discloses a method and device used for power load aggregation. The method and device used for power load aggregation comprise S1, acquiring sample data of n transformer substation comprehensive load static characteristics; S2, mapping the sample data to a Hilbert space through a gaussian kernel function and acquiring samples; S3, confirming an initial aggregation center by selecting K samples in the samples; S4, performing aggregation calculation on mapped samples in a core space by adopting a k-means algorithm, and assigning each sample to closest-type upper and lower approximations according to an upper and lower approximation method; S5, dynamically adjusting weights omega1, omegabnr according to a current iteration; S6, calculating a Jomega value according to an arithmetic convergence criteria, and judging whether |Jomega(t)- Jomega(t-1)|<= epsilon or t>=tmax, if yes, then generating a final collection and finishing, if not, then entering into S7; and S7, enabling t=t+1, reconfirming an aggregation center and transferring to S4. The method and device used for power load aggregation is simple, easy, fast and effective, aggregation results are reasonable, and has important significance on practicability of load modeling research.

The present invention relates to a method and a system for matching MR image feature points before and after the nonlinear deformation of a biological tissue. According to the technical scheme of the invention, a feature point automatic detection method based on a depth-cascaded convolutional neural network is provided. According to the method, firstly, a general region of feature points is obtained through the first layer of the depth convolutional network. Secondly, the position of a target feature point is approximated step by step in the second and third layers of the cascade convolutional network, so that the detection rate of feature points is further improved. The method aims to solve the problem in the prior art that the feature point distinguishing ability is reduced due to the image nonlinear deformation of existing feature point descriptors. In this way, a Riemannian manifold is combined with the kernel method to construct a nonlinear deformation feature point descriptor for robustness. The three-dimensional feature points of a magnetic resonance image are mapped into a four-dimensional Riemannian manifold space. Meanwhile, the feature points are further mapped into a higher-dimensional Hilbert space based on the kernel method, so that a richer description of data distribution is obtained. Meanwhile, a real geometric distance between feature points is obtained, so that the feature points are matched.

The invention discloses a load balancing method for geospatial data on a cloud computing platform. The method is characterized by comprising the following steps of: performing Hilbert space filling curve sorting on the geospatial data, sampling data blocks according to the data blocks divided by the geospatial data and number of map nodes during cloud platform processing to obtain the sampled data blocks; judging whether the sampled data blocks are suitable for a mean value method, if so, directly solving and dividing, otherwise judging whether the sampled data blocks are suitable for backtracking, if so, directly solving and dividing, otherwise dividing the sampled data blocks and the map nodes into two parts according to a bisection method, and repeating the previous operations on each part until all the sampled data blocks are correspondingly distributed to each map node; and finally, distributing an adjacent data block which corresponds to each sampled data block to each map node for processing.

The invention discloses a method for extracting features of a crack acoustic emission signal of a drawing part. The method comprises the following steps of: first, preprocessing an acquired original acoustic emission signal in a computer; then, performing empirical mode-based decomposition on the preprocessed acoustic emission signal to obtain n intrinsic mode function components and a residual component; next, performing Hilbert transform on each intrinsic mode function component and expressing the amplitude of the signal as a local wave time-frequency spectrum in Hilbert space; later on, dividing the plane of the local wave time-frequency spectrum into m regions equally, respectively calculating local energy of a time-frequency domain of each region, normalizing the local energy of the time-frequency domain of each region and taking the normalized local energy of the time-frequency domain as an initial feature parameter; and finally, performing a genetic algorithm operation on the initial feature parameter after a plurality of iterations, and obtaining an optimal feature parameter by realizing automatic reorganization and optimization on the initial feature parameter. By using the method, interferences caused by other components are eliminated; the signal-to-noise ratio is improved; the optimal feature parameter can be searched quickly; the diagnostic time can be shortened remarkably; and the diagnostic efficiency can be improved.

A wave-based tomographic imaging method and apparatus based upon one or more rotating radially outward oriented transmitting and receiving elements have been developed for non-destructive evaluation. At successive angular locations at a fixed radius, a predetermined transmitting element can launch a primary field and one or more predetermined receiving elements can collect the backscattered field in a "pitch/catch" operation. A Hilbert space inverse wave (HSIW) algorithm can construct images of the received scattered energy waves using operating modes chosen for a particular application. Applications include, improved intravascular imaging, bore hole tomography, and non-destructive evaluation (NDE) of parts having existing access holes.

The present invention presents methods and apparatus for predicting a joint quantum state of subjects, such as human beings, modulo an underlying proposition that revolves about an object, a subject or an experience while deploying a quantum representation of the situation. The joint quantum state is built from a transmit subject qubit |Tx assigned to a transmitting subject that broadcasts a measurable indication and also a receive subject qubit |Rx that is assigned to a receiving subject that is capable of receiving the measurable indication. The subjects share a common internal space represented by a Hilbert space ^(TR). The joint quantum states admit of representation by symmetric and anti-symmetric wave functions depending on the quantum statistics (Bose-Einstein or Fermi-Dirac) corresponding to consensus and anti-consensus forming types exhibited by the qubits when considered modulo the proposition.

The invention discloses a human body behavior recognition method based on kernel sparse coding, and belongs to the technical field of digital image processing. The method comprises the steps: firstly dividing an inputted video into video segments which have a fixed length and are mutually overlapped; secondly extracting the gradient and a light stream characteristic covariance or shape characteristic covariance of each video segment; and thirdly carrying out the dimension reduction of a covariance matrix through employing a symmetric positive definite matrix dimension reduction method. On the basis of the Stein kernel, the method proposes a sparse maximization symmetric positive definite matrix dictionary learning, and proposes a Riemann sparse solver which enables Riemann manifold to be embedded into a kernel Hilbert space. The method is used for the recognition of human body behaviors in a video, is simple in processing, is low in calculation complexity, and is robust for behavior difference, view change and low resolution.

A method of displaying a cardiac cycle of a heart into a three set colorable waveform comprising a plurality electrocardiography data taken over a period of time, said cardiac cycle information comprising a P wave, a PR segment, a QRS complex, and a ST segment, the method comprising generating a first wave hump by recalculating said P wave by using the Time-Frequency domain to determine an influxes of data compensation; generating a second wave hump by recalculating said PR segment by using integral formulaic expressions; generating a third wave hump by recalculating said QRS complex and said ST segment by using Hilbert space in the inner product.

The invention discloses a drift suppression method for an electronic nose of a domain self-adaptive extreme learning machine (ELM) based on domain correction. The drift suppression method comprises the following steps: mapping source domain data and target domain data which are inconsistent in data distribution into a high-dimensional Hilbert space from the point of view of data distribution, andminimizing domain distance between a source domain and a target domain in the space; meanwhile, preserving the data properties of an original source domain and the target domain to the maximum extent,and obtaining the source domain data and the target domain data after domain correction, thus suppressing drift from the data level; and incorporating migration samples and unlabeled samples in the target domain into ELM for learning to obtain the domain self-adaptive ELM and improve the robustness of a predictive model from the decision level. The drift suppression method disclosed by the invention has the advantages that data distribution is adjusted without samples being added; and in addition, the unlabeled samples in the target domain are incorporated into the learning of a classifier, so that the drift is suppressed from two levels, i.e., the data level and the decision level, and the robustness of the model is improved.

The invention provides a Hilbert space multi-kernel function multiplication based wind speed prediction method which comprises the following steps that: pulsation wind speed samples of vertical space points are generated by utilizing ARMA model simulation, and the pulsation wind speed sample of each space point is divided into two parts, namely a training set and a test set; a kernel function theory of a support vector machine is discussed in complete inner product space, both linear addition and product operation belong to closed operation in Hilbert space, and the results of linear addition and product operation still belong to the Hilbert space; and a multiplicative combination kernel function based on a global kernel function and a local kernel function is created, a model of a least-square support vector machine (LSSVM) using the multiplicative combination kernel function is established, model parameters are optimized by adopting particle swarms, and the pulsation wind speed of a single point is predicted by utilizing the model. A test sample is compared with a pulsation wind speed result predicted by the PSO-LSSVM using the multiplicative combination kernel function to ensure the accuracy for pulsation wind speed prediction.

The invention discloses a three-dimensional face identification method based on a multi-scale covariance descriptor and local sensitive Riemann and sparse classification. The method comprises the following steps of carrying out automatic preprocessing on original G database set face models and P test set face models; according to the database set face models and the test set face models after theautomatic preprocessing in the step (1), establishing a scale space and carrying out multi-scale key point detection and neighbor area extraction; extracting a d*d dimension local covariance descriptor from each key point neighbor area under each scale, and carrying out multi-scale fusion on the local covariance descriptors so as to construct the multi-scale covariance descriptor; and mapping thelocal covariance descriptors to a renewable Hilbert space, and providing the local sensitive Riemann and sparse expression to carry out classification identification on a three-dimensional face. In the invention, a description capability of a single-scale local covariance descriptor can be effectively increased, simultaneously, the local sensitive Riemann and sparse classification can effectivelyuse locality of the multi-scale descriptor.

The present invention relates to a computer-implemented method and apparatus for data processing for the purpose of blind separation of nonnegative correlated pure components from smaller number of nonlinear mixtures of mass spectra. More specific, the invention relates to preprocessing of recorded matrix of mixtures spectra by robust principal component analysis, trimmed thresholding, hard thresholding and soft thresholding; empirical kernel map-based nonlinear mappings of preprocessed matrix of mixtures mass spectra into reproducible kernel Hilbert space and linear sparseness and nonnegativity constrained factorization of mapped matrices therein. Thereby, preprocessing of recorded matrix of mixtures mass spectra is performed to suppress higher order monomials of the pure components that are induced by nonlinear mixtures. Components separated by each factorization are correlated with the ones stored in the library. Thereby, component from the library is associated with the separated component by which it has the highest correlation coefficient. Value of the correlation coefficient indicates degree of pureness of the separated component. Separated components that are not assigned to the pure components from the library can be considered as candidates for new pure components. Identified pure components can be used for identification of compounds in chemical synthesis, food quality inspection or pollution inspection, identification and characterization of compounds obtained from natural sources (microorganisms, plants and animals), or in instrumental diagnostics—determination and identification of metabolites and biomarkers present in biological fluids (urine, blood plasma, cerebrospinal fluid, saliva, amniotic fluid, bile, tears, etc.) or tissue extracts.

The invention discloses an image classification method based on sparse nonlinear subspace migration. The method comprises the steps of mapping data from an original space to a regenerated kernel Hilbert space via a kernel method, mapping target domain training data XT to a preset subspace via predefined fundamental transformation P in the kernel Hilbert space to obtain target data PXT, mapping source domain training data XS to the preset subspace via the fundamental transformation P to obtain P[XS, XT], transforming the source domain data P[XS, XT] via a spare matrix Z, and distributing the P[XS, XT] and the PXT in the preset subspace in a sharing mode. The method has the advantages of improving the migration accuracy of image data in the preset subspace and being applicable to transformation of nonlinear data.

The invention relates to a Gaussian fuzzy clustering computing method for differentiating and diagnosing chronic bronchitis. The method comprises the steps of obtaining inspection data of diagnosed chronic bronchitis patients from an electronic medical record system; calculating the initial clustering number through utilization of a hierarchical clustering algorithm; randomly selecting a clustering center according to the initial clustering number; mapping the clustering center and samples to a Hilbert space through mapping; calculating a membership matrix of the samples according to the clustering center in the Hilbert space; calculating a new clustering center through utilization of the calculated membership matrix; continuously and iteratively calculating the membership matrix and the clustering center until the change of the clustering center is smaller than a threshold value; calculating clustering granularity values according to the obtained clustering centers; circulating all initial clustering numbers and carrying out the steps; and taking the clustering center with the minimum granularity value as a final clustering result. The method can be used for finely classifying chronic bronchitis symptoms, and certain facilitation for diagnosing the chronic bronchitis is provided.

The invention discloses a voice identification algorithm of a modular robot. The voice identification algorithm comprises the following steps: S1, performing interval sampling on a voice feature function f(x) to obtain m function values (X0(E), X1(E), L, Xm(E)); S2, converting the feature function f(X)={(X0(E), X1(E), L, Xm(E)} in a time domain into points in an m-dimensional Hilbert space by taking X0, X1, L and Xm as coordinates in the Hilbert space, and converting a voice sampling function family F(x) into a series of points in the Hilbert space; S3, analyzing a similarity relation among the series points of F(x) by taking the Hilbert space as a new feature space of a voice signal, and obtaining a mode identification module by applying a high-dimensional hypersphere covering method; S4, performing mode identification on the voice signal in the Hilbert space to complete voice identification. The voice identification algorithm of the modular robot has the most important characteristics that no FFT is provided, the complexity is low, and the algorithm is simple; the voice identification algorithm is suitable for a voice control system of a smaller-sized modular robot.