48 results about "Matrix compression" patented technology

A bioreactor device, and a method and system for fabricating tissues and growing cells and tissues in the bioreactor device, accommodates less than about 1 mL (or less than about 200 μL) of local medium volume but sample sizes of about 100 μL or greater. The bioreactor device includes a bioreactor chamber for containing a sample, where sample growth in response to mechanical, electrical, and biofactor stimulation is monitored through one or more optical ports. Embedded sensors are provided for measuring fluid pressure, pH, temperature, and oxygen tension. The bioreactor device can receive different types of mechanical loadings, including fluid shear, hydrostatic pressure, matrix compression, and clinorotation.

Associative matrix compression methods, systems, computer program products and data structures compress an association matrix that contains counts that indicate associations among pairs of attributes. Selective bit plane representations of those selected segments of the association matrix that have at least one count is performed, to allow compression. More specifically, a set of segments is generated, a respective one of which defines a subset, greater than one, of the pairs of attributes. Selective identifications of those segments that have at least one count are stored. The at least one count that is associated with a respective identified segment is also stored as at least one bit plane representation. The at least one bit plane representation identifies a value of the at least one associated count for a bit position of the count that corresponds to the associated bit plane.

The invention belongs to the technical field of data mining, and specifically provides a text semantic representation method based on an aggregation weighting matrix compression algorithm. According to the method, through constructing a global smooth context matrix, influences of pragmatic habits on word semantics are weakened; through utilizing a word vector aggregation algorithm to weight global smooth contexts, a word vector weight measurement method is corrected; and finally, texts are represented through utilizing an infinite norm compression algorithm to convert global smooth contexts into vectors. Compared with the existing method, the method has the advantages that the highest possible value of each dimension of the vectors is reserved and the text information is represented more fully.

The invention discloses a base vector matrix compression device based on a high-speed systolic array and Givens transformation. The base vector matrix compression device is applied to a base vector matrix V with the dimensionality of M*N (M>=N) and comprises a preprocessing module and a compression module, wherein the preprocessing module is composed of a first-kind preprocessing unit and second-kind preprocessing units; the compression module is composed of real number processing units, Givens rotation processing units and simplified Givens rotation processing units. The base vector matrix compression device can guarantee the regularity of hardware implementation and parallel flow processing of data, is high in universality and can achieve compression of any base vector matrix with the dimensionality of M*N.

The invention discloses an electromagnetic characteristic analysis method and system for an electrically large-size target with a complex structure, and the method comprises the steps: carrying out the surface triangulation of the target, setting a regional decomposition seed, completing the regional autonomous decomposition of the target based on the decomposition seed, obtaining a plurality of decomposition parts, and numbering the decomposition parts; creating a plurality of parallel threads, specifying a main thread and sub-threads, and distributing the number and serial number of decomposition parts responsible for each sub-thread; for each sub-thread, constructing a comprehensive function for all responsible decomposition parts, compressing the self / mutual impedance matrix and the excitation matrix of the decomposition parts by utilizing the comprehensive function, and reporting a result to a main thread; the main thread detects connection boundaries between different decomposition components, and calculates a self / mutual impedance matrix and an excitation matrix of the connection boundaries; and constructing a target overall compression matrix equation, solving the equation,and combining with the comprehensive function definition to obtain the electromagnetic characteristics of the target in space. The method solves the problems of low processing efficiency and the likein the prior art, and greatly improves the comprehensive processing efficiency of the SBFM.

The invention discloses a word vector matrix compression method and device and a word vector acquisition method and device, which relate to the technical field of data processing, and are beneficial to saving the storage space of client equipment. The word vector matrix compression method comprises the following steps of generating a word vector matrix for representing a to-be-processed vocabularybased on a word vector model, and taking the generated word vector matrix as a to-be-compressed word vector matrix, wherein one row or one column of the to-be-compressed word vector matrix is a wordvector, and one word vector in the to-be-compressed word vector matrix is used for representing one word in the to-be-processed vocabulary; classifying the word vectors included in the to-be-compressed word vector matrix according to the semantic information of the to-be-processed vocabulary to obtain at least two categories; and compressing at least one of the at least two categories, constructing a compressed word vector matrix according to the word vector obtained by compressing the at least one category, and storing the compressed word vector matrix.

The invention discloses a connected piece identification method based on association matrix compression and branch pointer vector update, which solves the problem of how to efficiently and reliably perform network connected piece identification. The invention comprises the following steps: step 1, generating a branch-node association matrix of the network according to the network branch-node related information; step 2, searching the zero-column of the branch-node association matrix to identify an isolated node; step 3, "compressing" the branch-node association matrix by rows, leaving only thecolumn labels of non-zero elements to obtain a plurality of binary connected sets; step 4, performing column scan on the branch-node association matrix and updating the branch pointer vector; and step 5, merging and growing the binary connected set according to the final branch pointer vector to obtain the final connected slice set. The method avoids a large number of graph search and logic operations, with no need to perform matrix decomposition operation, and is efficient and reliable to be suitable for advantages of accelerating by using sparse technology, etc.

The invention discloses a multi-layer matrix compression method for analyzing the mutual coupling of microstrip packaging interconnection lines. The steps are: extract the grid file of microstrip packaged interconnection lines, mark the interconnection line ports of mutual coupling; perform octree grouping on the analyzed interconnection line grid according to the number of discrete edges in the finest layer group, and group The distance between the centers divides the near-field action area and the far-field action area of ​​each group containing discrete edges; establishes the mixed potential integral equation and the right excitation vector corresponding to the interconnection line feed, and for the dense matrix generated by the integral equation discretization, The moment method is used in the near-field action area, and the multi-layer matrix compression method is used in the far-field action area; the current coefficient of the equation is determined by the GMRES iteration method of ILU preconditioning, and the relationship between the interconnection lines is solved according to the marked interconnection lines. coupling. The method of the invention has the advantages of less memory consumption and high efficiency, and realizes fast and effective estimation of the mutual coupling of the entire microstrip packaging interconnection line.

The invention belongs to the technical field of data mining, and specifically provides a text semantic representation method based on an aggregation weighting matrix compression algorithm. According to the method, through constructing a global smooth context matrix, influences of pragmatic habits on word semantics are weakened; through utilizing a word vector aggregation algorithm to weight global smooth contexts, a word vector weight measurement method is corrected; and finally, texts are represented through utilizing an infinite norm compression algorithm to convert global smooth contexts into vectors. Compared with the existing method, the method has the advantages that the highest possible value of each dimension of the vectors is reserved and the text information is represented more fully.

The invention discloses a matrix compression algorithm for calculating the shortest path of an expressway. The matrix compression algorithm comprises the following steps of: 1, calculating a matrix graph of the path with the minimum cost of the expressway by using a Dijkstra algorithm; 2, performing matrix row compression, establishing a row tag array Flag [N] (initialized as full false), traversing a preorder matrix row by row, and compressing each row in the Pre array into a two-tuple set RowListi (column number c, value v); 3, matrix column compression: traversing the matrix column by column, compressing each column in the Pre array into a triple set ColListk (a starting value s, an end value e and a value v), and compressing and neglecting special points and repeated points in the matrix; and 4, completing matrix compression, and compressing the preorder array through a matrix compression algorithm, so that the required memory is greatly reduced, and the retrieval efficiency is not reduced too much.