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.

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.