1113 results about "Singular value" patented technology

A term-by-document matrix is compiled from a corpus of documents representative of a particular subject matter that represents the frequency of occurrence of each term per document. A weighted term dictionary is created using a global weighting algorithm and then applied to the term-by-document matrix forming a weighted term-by-document matrix. A term vector matrix and a singular value concept matrix are computed by singular value decomposition of the weighted term-document index. The k largest singular concept values are kept and all others are set to zero thereby reducing to the concept dimensions in the term vector matrix and a singular value concept matrix. The reduced term vector matrix, reduced singular value concept matrix and weighted term-document dictionary can be used to project pseudo-document vectors representing documents not appearing in the original document corpus in a representative semantic space. The similarities of those documents can be ascertained from the position of their respective pseudo-document vectors in the representative semantic space.

A computer-based information search and retrieval system and method for retrieving textual digital objects that makes full use of the projections of the documents onto both the reduced document space characterized by the singular value decomposition-based latent semantic structure and its orthogonal space. The resulting system and method has increased robustness, improving the instability of the traditional keyword search engine due to synonymy and / or polysemy of a natural language, and therefore is particularly suitable for web document searching over a distributed computer network such as the Internet.

A system and method are described for dynamically adapting the communication characteristics of a multiple antenna system (MAS) with multi-user (MU) transmissions (“MU-MAS”). For example, a method according to one embodiment of the invention comprises: transmitting a training signal from each antenna of a base station to each of a plurality of wireless client devices, each of the client devices analyzing each training signal to generate channel characterization data, and receiving the channel characterization data at the base station; computing a plurality of MU-MAS precoder weights based on the channel characterization data, the combined MU-MAS precoder weights comprising a MU-MAS channel matrix; determining instantaneous or statistical channel quality (“link quality metric”) for the wireless client devices using mutual information of MU-MAS links or singular values of the MU-MAS composite channel matrix; determining a subset of users and a MU-MAS transmission mode based on the link quality metric; precoding data using the MU-MAS precoder weights to generate precoded data signals for each antenna of the base station; and transmitting the precoded data signals through each antenna of the base station to each respective client device within the selected subset.

The statistical analysis described and claimed is a predictive statistical tree model that overcomes several problems observed in prior statistical models and regression analyses, while ensuring greater accuracy and predictive capabilities. Although the claimed use of the predictive statistical tree model described herein is directed to the prediction of a disease in individuals, the claimed model can be used for a variety of applications including the prediction of disease states, susceptibility of disease states or any other biological state of interest, as well as other applicable non-biological states of interest. This model first screens genes to reduce noise, applies k-means correlation-based clustering targeting a large number of clusters, and then uses singular value decompositions (SVD) to extract the single dominant factor (principal component) from each cluster. This generates a statistically significant number of cluster-derived singular factors, that we refer to as metagenes, that characterize multiple patterns of expression of the genes across samples. The strategy aims to extract multiple such patterns while reducing dimension and smoothing out gene-specific noise through the aggregation within clusters. Formal predictive analysis then uses these metagenes in a Bayesian classification tree analysis. This generates multiple recursive partitions of the sample into subgroups (the “leaves” of the classification tree), and associates Bayesian predictive probabilities of outcomes with each subgroup. Overall predictions for an individual sample are then generated by averaging predictions, with appropriate weights, across many such tree models. The model includes the use of iterative out-of-sample, cross-validation predictions leaving each sample out of the data set one at a time, refitting the model from the remaining samples and using it to predict the hold-out case. This rigorously tests the predictive value of a model and mirrors the real-world prognostic context where prediction of new cases as they arise is the major goal.

In a technique for video segmentation, classification and summarization based on the singular value decomposition, frames of the input video sequence are represented by vectors composed of concatenated histograms descriptive of the spatial distributions of colors within the video frames. The singular value decomposition maps these vectors into a refined feature space. In the refined feature space produced by the singular value decomposition, the invention uses a metric to measure the amount of information contained in each video shot of the input video sequence. The most static video shot is defined as an information unit, and the content value computed from this shot is used as a threshold to cluster the remaining frames. The clustered frames are displayed using a set of static keyframes or a summary video sequence. The video segmentation technique relies on the distance between the frames in the refined feature space to calculate the similarity between frames in the input video sequence. The input video sequence is segmented based on the values of the calculated similarities. Finally, average video attribute values in each segment are used in classifying the segments.

A term-by-document matrix is compiled from a corpus of documents representative of a particular subject matter that represents the frequency of occurrence of each term per document. A weighted term dictionary is created using a global weighting algorithm and then applied to the term-by-document matrix forming a weighted term-by-document matrix. A term vector matrix and a singular value concept matrix are computed by singular value decomposition of the weighted term-document index. The k largest singular concept values are kept and all others are set to zero thereby reducing to the concept dimensions in the term vector matrix and a singular value concept matrix. The reduced term vector matrix, reduced singular value concept matrix and weighted term-document dictionary can be used to project pseudo-document vectors representing documents not appearing in the original document corpus in a representative semantic space. The similarities of those documents can be ascertained from the position of their respective pseudo-document vectors in the representative semantic space.

The invention discloses a face detection and recognition method based on skin color segmentation and template matching. A face is segmented with the use of a face skin mixing Gauss model according to the clustering characteristics of face skin color in an YCbCr color space and a region of which the color is close to face skin color is separated from an image so as to achieve the purpose of quickly detecting the outer face of a face. A light compensation technology is used to overcome the influence of brightness on face detection and recognition. Adaptive template matching is used to overcome the influence of a skin-color-alike background on face detection and recognition. A secondary matching algorithm is adopted to reduce the amount of computation during the matching process, and singular value features of a face image are extracted to recognize a face and achieve the purpose of reducing the number of dimensions of a characteristic value matrix in the process of face feature extraction. The face detection and recognition method can quickly detect a face, improves the accuracy of face detection and recognition, is strong in practicability and has strong popularization and application values.

Methods and systems of analyzing an operating environment of wireless communication equipment in a wireless communication system are provided. A stimulus in the operating environment at a location of the wireless communication equipment is sensed and linearly expanded in Slepian sequences using a multitaper spectral estimation procedure. A singular value decomposition is performed on the linearly expanded stimulus, and a singular value of the linearly expanded stimulus provides an estimate of interference at the location of the wireless communication equipment.

The present invention provides a rolling bearing fault diagnosis method in various working conditions based on feature transfer learning, and relates to the field of fault diagnosis. The objective ofthe invention is to solve the problem that a rolling bearing, especially to various working conditions, is low in accuracy of diagnosis. The method comprise the steps of: employing a VMD (VariationalMode Decomposition) to perform decomposition of vibration signals of a rolling bearing in each state to obtain a series of intrinsic mode functions, performing singular value decomposition of a matrixformed by the intrinsic mode functions to solve a singular value or a singular value entropy, combining time domain features and frequency domain features of the vibration signals to construct a multi-feature set; introducing a semisupervised transfer component analysis method to perform multinuclear construction of a kernel function thereof, sample features of different working conditions are commonly mapped to a shared reproducing kernel Hilbert space so as to improve the data intra-class compactness and the inter-class differentiation; and employing the maximum mean discrepancy embedding to select more efficient data as a source domain, inputting source domain feature samples into a SVM (Support Vector Machine) for training, and testing target domain feature samples after mapping. Therolling bearing fault diagnosis method in various working conditions has higher accuracy in the rolling bearing multi-state classification in various working conditions.

A system and method are described for dynamically adapting the communication characteristics of a multiple antenna system (MAS) with multi-user (MU) transmissions (“MU-MAS”). For example, a method according to one embodiment of the invention comprises: transmitting a training signal from each antenna of a base station to each of a plurality of wireless client devices, each of the client devices analyzing each training signal to generate channel characterization data, and receiving the channel characterization data at the base station; computing a plurality of MU-MAS precoder weights based on the channel characterization data, the combined MU-MAS precoder weights comprising a MU-MAS channel matrix; determining instantaneous or statistical channel quality (“link quality metric”) for the wireless client devices using mutual information of MU-MAS links or singular values of the MU-MAS composite channel matrix; determining a subset of users and a MU-MAS transmission mode based on the link quality metric; precoding data using the MU-MAS precoder weights to generate precoded data signals for each antenna of the base station; and transmitting the precoded data signals through each antenna of the base station to each respective client device within the selected subset.

The invention relates to a fault diagnosis method of a rolling bearing under variable working conditions. The method comprises the steps of: extracting a characteristic vector of a signal by means of Hilbert-Huang conversion, carrying out EMD (Empirical Mode Decomposition) to a signal to obtain a plurality of IMFs (Intrinsic Mode Function), and selecting useful IMFs to carry out Hilbert conversion respectively to obtain analytic signals Hi(t) and taking envelopes respectively to form a characteristic vector w; then, carrying out singular value decomposition for w by a singular value decomposition method, and using a singular value matrix as the final fault characteristic vector; using the singular value matrixes in four modes of the rolling bearing as input and four matrixes corresponding to the four modes as output to train an Elman neural network. When the rolling bearing fails, whether the rolling bearing is faulted or not can be judged and direction of faults can be accurately positioned through analysis and judgment of a signal at t moment by the trained Elman neural network. According to the invention, classification of fault modes of the rolling bearing under variable working conditions can be effectively finished, and higher precision is maintained.

Abstract of Disclosure A computer-based system is described that provides users with the ability to develop high-fidelity digital quantitative representations of physical and chemical phenomena, and to employ an optimization-based approach to control associated physiochemical processes. The system includes a computational environment, intuitive user interface(s), integrated software libraries, analytical tools, and visualization / rendering engine that together provide an integrated framework for nanoscale modeling, analysis, simulation, and synthesis. Additionally, the system includes an optimal linear control synthesis methodology that incorporates a first order dynamic mathematical representation (of the conceptual molecular system) suitable for applying various pragmatic control system techniques including optimization of structured singular values, linear quadratic performance functions, Lyapunov criteria, or similar, for the purposes of nanoscale fabrication and molecular assembly.

The invention relates to an electroencephalogram feature extracting method based on brain function network adjacent matrix decomposition. The current motion image electroencephalogram signal feature extraction algorithm mostly focuses on partially activating the qualitative and quantitative analysis of brain areas, and ignores the interrelation of the bran areas and the overall coordination. In light of a brain function network, and on the basis of complex brain network theory based on atlas analysis, the method comprises the steps of: firstly, establishing the brain function network through a multi-channel motion image electroencephalogram signal, secondly, carrying out singular value decomposition on the network adjacent matrix, thirdly, identifying a group of feature parameters based on the singular value obtained by the decomposition for showing the feature vector of the electroencephalogram signal, and fourthly, inputting the feature vector into a classifier of a supporting vector machine to complete the classification and identification of various motion image tasks. The method has a wide application prospect in the identification of a motion image task in the field of brain-machine interfaces.

Techniques for deriving eigenvectors based on steered reference and used for spatial processing. A steered reference is a pilot transmission on one eigenmode of a MIMO channel per symbol period using a steering vector for that eigenmode. The steered reference is used to estimate both a matrix Σ of singular values and a matrix U of left eigenvectors of a channel response matrix H. A matrix Ũ with orthogonalized columns may be derived based on the estimates of Σ and U, e.g., using QR factorization, minimum square error computation, or polar decomposition. The estimates of Σ and U (or the estimate of Σ and the matrix Ũ) may be used for matched filtering of data transmission received via a first link. The estimate of U or the matrix Ũ may also be used for spatial processing of data transmission on a second link (for reciprocal first and second links).

A wireless communication system noncollaborative, multiple input, multiple output (MIMO) space division multiple access (SDMA) system determines subscriber station combining and weighting vectors that yield a high average signal-to-interference plus noise ratio (SINR). Each subscriber station independently transmits information to a base station that allows the base station to determine a weight vector wi for each subscriber station using the determined combining vector of the subscriber station. The ith combining vector corresponds to a right singular vector corresponding to a maximum singular value of a channel matrix between a base station and the ith subscriber station. Each subscriber station transmits signals using a weight vector vi, which corresponds to a left singular vector corresponding to a maximum singular value of a channel matrix between the ith subscriber station and the base station. The base station uses the weight vector wi to determine the signal transmitted by the ith subscriber station.

Specifically, a method and system that performs MIMO and beamforming at a base station based on an uplink channel sounding (ULCS) from only one of the mobile station antennas and closed-loop multiple input, multiple output (MIMO) schemes based on the singular value decomposition (SVD) of the channel matrix. The ULCS is limited to sounding and the channel uses fewer than an optimal number of transmit antennas (e.g. one for WiMAX). The base station arrays may be configured for a full array transmitting mode or a sub-array transmitting mode.

The invention discloses a diagnosis method for the fault position and the performance degradation degree of a rolling bearing, belonging to the technical field of fault diagnosis for bearings, and solving the problems of low accuracy of diagnosis for fault position and performance degradation degree, and high time consumption of training existing in an intelligent diagnosis method for a rolling bearing in the prior art. A white noise criterion is added in the disclosed integrated empirical mode decomposition method, so that artificial determination for decomposition parameters can be avoided, and the decomposition efficiency can be increased; and via the disclosed nuclear parameter optimization method based on a hypersphere centre distance, the small and effective search region of nuclear parameters in a multi-classification condition can be determined, so that training time is reduced, and the final state hypersphere model of a classifier is given. The intelligent diagnosis method based on parameter-optimized integrated empirical mode decomposition and singular value decomposition, and combined with a nuclear parameter-optimized hypersphere multi-class support vector machine based on the hypersphere centre distance is higher in identification rate compared with the existing diagnosis method. The diagnosis method disclosed by the invention is mainly applied to intelligent diagnosis on the fault position and the performance degradation degree of the rolling bearing.

Output-only modal testing of an object. Vibrations are excited in said object and measured by a number of vibration sensitive detectors. From the data of the measurements, a spectral density matrix function is determined. From this density matrix, auto spectral densities for the individual modes are identified performing a decomposition based on the Singular Value Decomposition technique. From the auto spectral densities of the individual modes, natural frequencies and damping ratios for the modes can be estimated, and from the singular vectors of the Singular Value Decomposition, the modes shapes can be estimated.

The invention provides a clustering collaborative filtering recommendation technology based on a singular value decomposition algorithm. The clustering collaborative filtering recommendation technology based on the singular value decomposition algorithm comprises firstly classifying users by using user attributive character values provided by the clustering collaborative filtering recommendation technology based on the singular value decomposition algorithm, and reducing dimension of a user-commodity grade matrix; improving a singular value decomposition (SVD) algorithm which is frequently used in image processing and natural language processing, and using the improved SVD algorithm in a recommendation system; decomposing a grade matrix in a cluster where users are located, and aggregating the decomposed grade matrix so as to fill predicted scores of non-grade items in the grade matrix, calculating similarity of the users in the same cluster by using the filled grade matrix, calculating final predicted scores of a commodity by applying collaborative filtering technologies based on the users and widely applied in the recommendation system, and carrying out final recommendation. The clustering collaborative filtering recommendation technology based on the singular value decomposition algorithm has the advantages of being capable of improving recommendation efficiency of the recommendation system, solving the problems such as data sparsity of the recommendation system, and meanwhile being capable of improving accuracy rate of recommendation of the recommendation system.

The invention discloses a fault positioning method for a transmission line and belongs to the technical field of power systems. The fault positioning method comprises the following steps: arranging a Rogowski coil-based broadband core-through traveling wave detection device on the high-voltage transmission line; acquiring a current traveling wave signal of a three-phase line; performing singular value detection by applying the phase mode conversion and wavelet mode maximum principle; accurately identifying the traveling wave line mode wave head, and accurately determining the wave head arrival time by using a GPS high-precision clock; and measuring the wave speed of real-time traveling wave mode on line according to different wave head time difference information to realize accurate fault positioning. The fault positioning method has the advantages of effectively avoiding two main factors influencing the traveling wave fault positioning precision, along with high positioning precision and accurate and reliable fault positioning result.

An image pickup apparatus capable of capturing a still image while a high sharpness is being maintained and a reduction in image quality due to the existence of a focus detecting pixel is being suppressed. An image pickup device includes an image pickup pixel group and a focus detecting pixel group. A calculation unit calculates a singular value in the signal values of pixels existing around the focus detecting pixel group. An adjustment unit adjusts a gain of signal values in the focus detecting pixel group. A correction unit corrects an image data corresponding to the position of the focus detecting pixel group based on the signal values in the image pickup pixel group. A determination unit determines the amount of adjusting the gain of signal values in the focus detecting pixel group and the amount of correction based on the results calculated by the calculation unit.

Engine response curves (RCs) can be used for streak compensation for printed documents. A feedback control paradigm can be included to effect RC compensation. Singular Value Decomposition (SVD) can be used to represent each RC in the collection of spatial RC data as a linear combination of basis vectors. RCs are approximated by selecting the first few basis vectors, the approximation aiding in noise rejection and reducing computation in the controller by reducing dimensionality of the RC data from gray levels to the number of SVD bases selected. An optimal subset of RCs is selectable from the set of approximated RCs by clustering the SVD weights, the clustered SVD weights producing TRCs that span all engine response RCs generated by a printer. Compensation RCs are constructible using reduced number of bases and clustered SVD weights

The invention discloses a non-contact contact line geometrical parameter detecting method. The method includes a first step of collecting high-definition images at equal time intervals by means of collection of control signals and finishing image preprocessing by means of the median filtering technique, the image graying technique and the like, a second step of locating a laser spot center point and extracting a coordinate of the center point by means of the iterative thresholding algorithm and a method of removing isolated noise in mathematical morphology, a third step of extracting a matched target region and detecting a transverse gray singular value of the target region, a fourth step of giving the wire height and a stagger value of a contact line by means of conversion from an image coordinate system to a camera coordinate system and from the camera coordinate system to a detection vehicle coordinate system, and compensating vibration of a vehicle body, and a fifth step of giving precise detection values of the wire height and the stagger value and displaying information of a plurality of parameters in a developed graphic monitoring interface. The method effectively improves detection efficiency of geometrical parameters of a contact net, simplifies the algorithm, improves precision of fault detection, and specifically improves safe reliability of the contact net of a high-speed train.

A method and apparatus are provided for automatic data rate control in wireless communication systems, such as wireless LANs. A wireless communication device according to the present invention includes a data rate controller that adapts a transmission rate based on a channel correlation measure. The channel correlation measure may be, for example, eigenvalues or singular values of a channel matrix. The data rate controller may also consider the signal quality, channel delay spread or both in determining a data rate.

A wireless communication system noncollaborative, multiple input, multiple output (MIMO) space division multiple access (SDMA) system determines subscriber station combining and weighting vectors that yield a high average signal-to-interference plus noise ratio (SINR). Each subscriber station independently transmits information to a base station that allows the base station to determine a weight vector wi for each subscriber station using the determined combining vector of the subscriber station. The ith combining vector corresponds to a right singular vector corresponding to a maximum singular value of a channel matrix between a base station and the ith subscriber station. Each subscriber station transmits signals using a weight vector vi, which corresponds to a left singular vector corresponding to a maximum singular value of a channel matrix between the ith subscriber station and the base station. The base station uses the weight vector wi to determine the signal transmitted by the ith subscriber station.

An estimation apparatus for estimating a channel response of a radio propagation path using a received signal including a first known signal, comprises a generator which generates a reference signal matrix, a calculator which calculates a generalized inverse matrix of the reference signal matrix including singular values which exceed a preset threshold value, an estimation unit configured to estimate an impulse response of the radio channel modeled using a transversal filter based on the first known signal and the generalized inverse matrix, and a converter which converts the estimated impulse response into a frequency-domain signal to acquire a frequency transfer function of the channel.

A hydroelectric generating set vibration signal noise reduction method based on mode autocorrelation analysis of SVD and VMD comprises the steps of constructing a Hankel matrix of a set vibration signal and performing singular value decomposition (SVD), selecting an effective singular value based on a mean value filtering strategy for reconstructing the signal, and realizing pre-filtering; performing decomposition through variational mode decomposition (VMD) for obtaining a series of mode functions, calculating an autocorrelation function of each mode component, selecting effective mode components according to an energy set of the autocorrelation function, and obtaining a signal after noise reduction through adding all effective mode components. According to the hydroelectric generating set vibration signal noise reduction method provided by the invention, a noise reduction experiment is carried out through simulation analysis and actual measurement of a vibration signal; and a result represents a fact that the method has relatively high noise reduction performance and can effectively improve hydroelectric generating set vibration signal analysis precision.

The present invention includes a wireless communication system. The wireless communication system includes a plurality of transceiver antennae. Each transceiver is spatially separate from at least one other transceiver antenna. Each transceiver antenna includes a transceiver antenna polarization. At least one transceiver antenna has a polarization that is different than at least one other transceiver antenna. Each transceiver antenna transmits a corresponding data stream. The wireless communication system further includes a plurality of receiver antennae. The receiver antennae receive at least one data stream. The transceiver antenna polarization of each transceiver antenna is pre-set to optimize separability of the received data streams. A transmission channel between the transceiver antennae and the receiver antennae can be estimated with a channel matrix. The pre-set transceiver antenna polarization of each transceiver antenna can be determined by minimizing a singular value spread of the channel matrix. A pre-set receiver antenna polarization of each receiver antenna can also be determined by minimizing a singular value spread of the channel matrix.