79 results about "Linear algebra" patented technology

An active set algorithm exploits a ‘hot start’ for the set of binding constraints at optimality along with efficient linear algebra to make rapid progress towards the solution. The linear algebra is designed to deal with degenerate constraints as the required factorizations are performed and as degeneracy emerges, and not via a mostly unnecessary pre-process step. Combined together, these novel approaches enable solution of the control problem in real-time.

The contamination level estimation method for high voltage insulators collects samples of naturally contaminated insulators and builds an image data set for the collected insulators. Flashover voltages of several insulators samples are measured. ESDD levels of the collected insulators are estimated. Images are input to image processing algorithms to extract representative features. The images are segmented. Transforming the image from RGB color space into grayscale model excludes the background from the image. Subsequently, the segmented images are transferred back to RGB color space model using matrix manipulation. Since contaminants on the insulator surface affect the color of the insulator, the segmented image is transformed from RGB to HSV color space which is used for extracting statistical and linear algebraic features from the hue image. A trained artificial neural network correlates the extracted features to the contamination levels enabling testing of other contaminated insulators.

A computerized method (and structure) of linear algebra processing on a computer having a plurality of processors for parallel processing, includes, for a matrix having elements originally stored in a memory in a rectangular matrix AR or especially of one of a triangular matrix AT format and a symmetric matrix AS format, distributing data of the rectangular AR or triangular or symmetric matrix (AT, AS) from the memory to the plurality of processors in such a manner that keeps all submatrices of AR or substantially only essential data of the triangular matrix AT or symmetric matrix AS is represented in the distributed memories of the processors as contiguous atomic units for the processing. The linear algebra processing done on the processors with distributed memories requires that submatrices be sent and received as contiguous atomic units based on the prescribed block cyclic data layouts of the linear algebra processing. This computerized method (and structure) defines all of its submatrices as these contiguous atomic units, thereby avoiding extra data preparation before each send and after each receive. The essential data or AT or AS is that data of the triangular or symmetric matrix that is minimally necessary for maintaining the full information content of the triangular AT or symmetric matrix AS.

The execution engine is a new organization for a digital data processing apparatus, suitable for highly parallel execution of structured fine-grain parallel computations. Possible applications include many types of digital signal processing computations, such as filtering, convolution, and deconvolution, as well as many types of linear algebra operators, such as iterative and direct solvers, singular value decomposition, and constraint optimization. The invention improves energy efficiency of these structured parallel operators as compared to a regular data flow or von Neumann computer.

The invention belongs to the technical field of cyberspace security and particularly relates to a PEKS (public-key encryption with keyword search) method for lattice-based cloud stored cyphertext data under standard model. The method of the invention has no need for a random oracle so that the security of the designed PEKS method can more truly reflected; a designed cryptographic algorithm is based on assumption of LWE (learning with errors) hard problems, quantum computer attacks can be resisted effectively. The method of the invention has a need for specifying a unique cloud server to perform testing and return corresponding search results, so that no malicious servers are able to execute search test operation, and malicious server attacks are partly avoided accordingly. In addition, the algorithm can ensure that cyphertext is undistinguishable. Furthermore, in keyword cyphertext generation phase, the method of the invention needs no computing of high-overhead modular exponentiation and bilinear pairing operation, but the computing of finite linear algebraic operation, and accordingly is highly worthy of practical application in post-quantum communication environments.

The present invention recognises and eliminates from a biomagnetic measurement signal interferences whose source is disposed in the direct vicinity of an object being measured. The invention utilises the SSS method that can be used to separate from one another the signals associated with the internal and external sources of a set of measurement sensors by calculating two series developments. The sources to be examined in the invention and disposed in the so-called intermediate space produce a component to both of the developments, and can, therefore, be detected by means of an analysis to be performed in a time domain. This division into components can be made using the Principal Component Analysis (PCA), the Independent Component Analysis (ICA) or the Singular Value Decomposition. Finally, the clarified interferences in the intermediate space can be eliminated from the measured signal using, for example, the linear algebraic orthogonal projection.

The inherent smoothing in bending stiffness measurement of wood boards as occurs in the machine grading of lumber can mask the effect of knots and other local characteristics affecting structural value. Improved estimates of local (pointwise) stiffness will be useful in decisions about further processing and use of a tested board. Measured compliance is reciprocally related to measured stiffness and is the convolution of local compliance and a “span function”. Span function is specific to the bending span configuration used and can change during measurement of a board. A general procedure for computing span function, which heretofore has been known only for simple bending spans, is disclosed. A Kalman filter uses this and other available information to optimally estimate local compliance from an observed relationship between local compliance and state variables of a state-space model. Method for linear algebraic determination of local compliance also depends on span functions and is disclosed.

A simple yet powerful Bayesian model of linear regression is disclosed for methods and systems of machine learning. Unlike previous treatments that have either considered finding hyperparameters through maximum likelihood or have used a simple prior that makes the computation tractable but can lead to overfitting in high dimensions, the disclosed methods use a combination of linear algebra and numerical integration to work a full posterior over hyperparameters in a model with a prior that naturally avoids overfitting. The resulting algorithm is efficient enough to be practically useful. The approach can be viewed as a fully Bayesian version of the discriminative regularized least squares algorithm.

Computer-readable media having computer-executable instructions and apparatuses categorize documents or corpus of documents. A Tensor Space Model (TSM), which models the text by a higher-order tensor, represents a document or a corpus of documents. Supported by techniques of multilinear algebra, TSM provides a framework for analyzing the multifactor structures. TSM is further supported by operations and presented tools, such as the High-Order Singular Value Decomposition (HOSVD) for a reduction of the dimensions of the higher-order tensor. The dimensionally reduced tensor is compared with tensors that represent possible categories. Consequently, a category is selected for the document or corpus of documents. Experimental results on the dataset for 20 Newsgroups suggest that TSM is advantageous to a Vector Space Model (VSM) for text classification.

A method (and structure) of linear algebra processing, includes processing a (real or complex) matrix data having elements originally stored in one of a triangular format and a symmetric matrix format in a subroutine designed to process matrix data in a full format. The processing uses a hybrid full packed data structure, which provides a rectangular space characteristic of the full format. The rectangular space is defined by a leading dimension (LD). Inside of the rectangular space are stored a plurality of entities that include all elements of the matrix data originally stored in the triangular or symmetric format.

A method for multiplying a first sparse matrix by a second sparse matrix in an associative memory device includes storing multiplicand information related to each non-zero element of the second sparse matrix in a computation column of the associative memory device; the multiplicand information includes at least a multiplicand value. According to a first linear algebra rule, the method associates multiplier information related to a non-zero element of the first sparse matrix with each of its associated multiplicands, the multiplier information includes at least a multiplier value. The method concurrently stores the multiplier information in the computation columns of each associated multiplicand. The method, concurrently on all computation columns, multiplies a multiplier value by its associated multiplicand value to provide a product in the computation column, and adds together products from computation columns, associated according to a second linear algebra rule, to provide a resultant matrix.

The invention discloses a method for lightening of a BIM. The method comprises the steps that information data is extracted; in BIM engineering modeling under an existing system, model information data is extracted from an original BIM through use of legal and compliant technical manners; with regard to all the extracted component lists as well as race information data used by each component list, math methods of linear algebra and space analytic geometry will be used to analyze components of the same type in the BIM; and component template data formed by a reusable technologies and global coordinate system matrix data in the BIM are obtained through analysis. According to the invention, the ultra-large data size of the BIM is reduced; bottlenecks of the BIM in storage and transfer are solved; and the BIM is lightened.

The present invention recognises and eliminates from a biomagnetic measurement signal interferences whose source is disposed in the direct vicinity of an object being measured. The invention utilises the SSS method that can be used to separate from one another the signals associated with the internal and external sources of a set of measurement sensors by calculating two series developments. The sources to be examined in the invention and disposed in the so-called intermediate space produce a component to both of the developments, and can, therefore, be detected by means of an analysis to be performed in a time domain. This division into components can be made using the Principal Component Analysis (PCA), the Independent Component Analysis (ICA) or the Singular Value Decomposition. Finally, the clarified interferences in the intermediate space can be eliminated from the measured signal using, for example, the linear algebraic orthogonal projection.

The invention discloses a document hierarchical desensitization encryption method. The invention discloses a document desensitization technology. The document classification technology and the document encryption technology are combined to realize document confidentiality; the method has the advantages that a computable linear algebraic model of the sensitive information is constructed according to the requirements on integrity, availability, safety sharing, document classification and work under special conditions, quick and accurate positioning of the sensitive information and multi-information association determination are achieved, national confidentiality is prevented from being leaked, and the method plays a positive role in guaranteeing the national safety. For persons with different levels, the hierarchical decryption access control technology of the confidential document is realized, i.e., the persons with different security levels can obtain different pieces of decryption information when decrypting the same confidential document. Implementing hierarchical decryption capabilities. And the access control flexibility and security of the encrypted file in the transmission and sharing scheme are improved. The hierarchical desensitization encryption method is suitable for safety office requirements of banks, military units, party and government agencies and the like.

The invention discloses a drug clinical comprehensive evaluation method based on the real world. The method includes the steps of establishing an evaluation object, establishing an index system, determining an index weight, establishing an evaluation methodology variable model, analyzing and summarizing results and the like. According to the method, a linear algebra method is used in the operation process, the mathematical principle is rigorous, the operation method is easy to understand and apply, system analysis and calculation are simplified, decisions are kept consistent easily, and the method is applicable to qualitative decision analysis or qualitative and quantitative decision analysis, convenient and rapid to use, easy to operate and suitable for application and popularization.

A multi-temporal, multi-angle, automated target exploitation method is provided for processing a large number of images. The system geo-rectifies the images to a three-dimensional surface topography, co-registers groups of the images with fractional pixel accuracy, automates change detection, evaluates the significance of change between the images, and massively compresses imagery sets based on the statistical significance of change. The method improves the resolution, accuracy, and quality of information extracted beyond the capabilities of any single image, and creates registered six-dimensional image datasets appropriate for mathematical treatment using standard multi-variable analysis techniques from vector calculus and linear algebra such as time-series analysis and eigenvector decomposition.

The invention discloses a quality quantitative evaluation method for a large-scale complex aerospace system. The quality quantitative evaluation method comprises the steps of: firstly, extracting a plurality of elements from the aerospace system research and development process; secondly, establishing an incidence matrix C according to correlation between the elements; thirdly, establishing at least three levels of evaluation criteria separately for evaluation key points of the plurality of elements, and acquiring a quality level value matrix P of each element according to the evaluation criteria; and finally, calculating a quality comprehensive level value L of the aerospace system by adopting a formula shown in description. The quality quantitative evaluation method conducts preliminary evaluation on development quality of the plurality of elements of the aerospace system, calculates the more scientific and reasonable final quality comprehensive level value of the large-scale complex aerospace system according to the incidence matrix relation between the elements and utilizing a weighted average method of linear algebra, makes the final quality comprehensive level value closer to the real situation of overall quality of the aerospace system, and is conducive to research and improvement of the aerospace system.

Systems and methods that facilitate dimensional transformations of data points are disclosed. In particular, the subject invention provides for a system and methodology that simplifies dimensional transformations while mitigating variations of a distance property between pairs of points. A set of n data points in d dimensional space is represented as an n×d input matrix, where d also corresponds to the number of attributes per data point. A transformed matrix represents the n data points in a lower dimensionality k after being mapped. The transformed matrix is an n×k matrix, where k is the number of attributes per data point and is less than d. The transformed matrix is obtained by multiplying the input matrix by a suitable projection matrix. The projection matrix is generated by randomly populating the entries of the matrix with binary or ternary values according to a probability distribution. Unlike previous methods, the projection matrix is formed without obtaining an independent sample from a Gaussian distribution for each entry in the projection matrix, without applying a linear algebraic technique to generate the projection matrix and without employing arbitrary floating point numbers. Processes and / or algorithms can utilize the reduced transformed matrix instead of the larger input matrix to facilitate computational efficiency and data compression.

The invention discloses a public-verifiable linear algebra entrusted calculation system. The public-verifiable linear algebra entrusted calculation system comprises an entrusting module and a third-party verifying module. Through the mode, the private verifiability of entrusted calculation is expanded to public verifiability by the public-verifiable linear algebra entrusted calculation system disclosed by the invention, so that not only does a consignor have the verifying capability, any third party can efficiently verify results. As a result, the accidental calculation errors of a cloud-side server during cloud calculation and the malicious deception behavior of a cloud calculation service provider can be basically detected, and a matrix inversion scheme is indirectly realized through the utilization of quartic matrix multiplication. Besides, high verification efficiency is realized by pseudo-random functions having enclosed effectiveness.

Computer-readable media having computer-executable instructions and apparatuses categorize documents or corpus of documents. A Tensor Space Model (TSM), which models the text by a higher-order tensor, represents a document or a corpus of documents. Supported by techniques of multilinear algebra, TSM provides a framework for analyzing the multifactor structures. TSM is further supported by operations and presented tools, such as the High-Order Singular Value Decomposition (HOSVD) for a reduction of the dimensions of the higher-order tensor. The dimensionally reduced tensor is compared with tensors that represent possible categories. Consequently, a category is selected for the document or corpus of documents. Experimental results on the dataset for 20 Newsgroups suggest that TSM is advantageous to a Vector Space Model (VSM) for text classification.

A simple yet powerful Bayesian model of linear regression is disclosed for methods and systems of machine learning. Unlike previous treatments that have either considered finding hyperparameters through maximum likelihood or have used a simple prior that makes the computation tractable but can lead to overfitting in high dimensions, the disclosed methods use a combination of linear algebra and numerical integration to work a full posterior over hyperparameters in a model with a prior that naturally avoids overfitting. The resulting algorithm is efficient enough to be practically useful. The approach can be viewed as a fully Bayesian version of the discriminative regularized least squares algorithm.

The invention discloses an automobile powertrain system rotational inertia synthetic method. The method comprises the steps of inputting the known parameters including the mass, center of mass, inertia and eulerian angle of an engine and a gear box into an EXCEL so as to facilitate data import and correction; obtaining the coordinates of the main center of mass obtained after synthesis according to the mass and center of mass of the engine and the gearbox by means of the method for solving the center of mass in theoretical mechanics; establishing a coordinate system the orientation of which is parallel to that of a main coordinate system according to the coordinates of the main center of mass; expressing vectors with a coordinate matrix, establishing the relationship between the coordinate matrixes of the same vectors under different coordinate systems, and deducing the relationship between inertia matrixes under different coordinate systems by means of correlation theoretical methods of the linear algebra, namely establishing the relationship between rigid body rotation inertia and the product of inertia under different coordinate systems; calculating a synthesis result by means of a corresponding computer language compile program according to a deduced matrix expression. According to the method, deduction is conducted in the matrix form, briefness and visualization are achieved, the conclusion is in a quite brief form, and a solution can be obtained easily by means of the computer language.

A heating, ventilation and air-conditioning (HVAC) system for a building is optimized while maximizing a comfort of occupants and minimizing energy consumption. The building is modeled as a network of nodes and edges, wherein the nodes represent rooms, and the edges represent walls. Dynamics of temperatures and humidity in the rooms and the temperature of the walls and the building are modeled using differential equations and the network. The comfort of the occupants is modeled by a predicted mean vote (PMV). The minimizing is formulated as an optimal control problem, which is discretized using an integration technique to obtain a finite dimensional optimization problem. Then, the finite dimensional optimization problem is solved using sparse linear algebra until convergence.

Methods and algorithms are presented that implement linear algebraic techniques for rapidly estimating chemical similarities for several popular measures. The methods of the present invention reflect source similarity measures for both Tanimoto calculation and rank ordering. After a precalculation step on a database, the methods of the present invention afford several orders of magnitude of speedup in database screening. The present invention also provides an asymptotic speedup for large similarity matrix construction problems, reducing the number of conventional slow similarity evaluations required from quadratic to linear scaling.

A method and structure of increasing computational efficiency in a computer that comprises at least one processing unit, a first memory device servicing the at least one processing unit, and at least one other memory device servicing the at least one processing unit. The first memory device has a memory line larger than an increment of data consumed by the at least one processing unit and has a pre-set number of allowable outstanding data misses before the processing unit is stalled. In a data retrieval responding to an allowable outstanding data miss, at least one additional data is included in a line of data retrieved from the at least one other memory device. The additional data comprises data that will prevent the pre-set number of outstanding data misses from being reached, reduce the chance that the pre-set number of outstanding data misses will be reached, or delay the time at which the pre-set number of outstanding data misses is reached.

A system and method for waveform compression includes preprocessing a collection of waveforms representing cell and / or interconnect response waveforms and constructing a representative waveform basis using linear algebra to create basis waveforms for a larger set of waveforms. The collection waveforms are represented as linear combination coefficients of an adaptive subset of the basis waveforms to compress an amount of stored information needed to reproduce the collection of waveforms. The representation of coefficients may be further compressed by, e.g., analytic representation.

The invention discloses a branch vector determinant and matrix feedback loop calculation method, which belongs to a calculation method of system dynamics. The method is a branch vector feedback loop calculation method, which comprises four parts, namely a branch vector determinant calculation method of all feedback loops of a system flowchart, the branch vector determinant calculation method of a new feedback loop of the system flowchart, a branch vector matrix calculation method of the new feedback loop of the system flowchart and the branch vector matrix calculation method of all second-order feedback loops of the system flowchart. The method has the advantages that: the branch vector determinant and matrix feedback loop calculation method is provided and perfect combination of graph theory and linear algebra in the research of a complex system dynamic feedback problem is realized, and the analysis process can be realized by means of a computer program; all the feedback loops of a complex flowchart structure model are calculated, the feedback loop set of the flowchart structure model is established, the system feedback change rule can be disclosed and the analysis of a quantitative simulation result is facilitated; the feedback structure change of a new countermeasure can be favorably disclosed and implemented and the effect of the new countermeasure can be disclosed by calculating the new feedback loop for increasing an in-tree; and all second-order feedback loops of the complex flowchart can be calculated, so that the method is favorable for generating various basic modes by using the second-order feedback loop.