162 results about "Matrix element" patented technology

Systems or methods of the present disclosure may provide an integrated circuit system that includes a programmable logic device that includes a clock, one or more local controllers, programmable logic units implementing a systolic array to compute a matrix multiplication, and embedded memory blocks. The embedded memory blocks include a single port random access memory (SPRAM). The one or more local controllers are configured to, on a first set of alternating clock cycles of the clock, load matrix sub-elements from two rows of a matrix into corresponding matrix element of the SPRAM. The one or more local controllers are configured to, on a second set of alternating clock cycles of the clock, read out the matrix elements from the SPRAM to the systolic array to compute the matrix multiplication.

The invention provides a video processing method and device and electronic equipment, and the method comprises the steps: inputting a target video into a coding model, and determining the potential spatial features of each video frame through the coding model; constructing a similarity kernel matrix based on the potential spatial features; determining candidate sub-matrixes from the similarity kernel matrix, and determining the candidate sub-matrix with the maximum element difference among matrix elements in the candidate sub-matrixes as a target sub-matrix; and carrying out dimension reduction processing on the target sub-matrix, and outputting compressed data of the target video based on a dimension reduction result through a decoding model. In the mode, based on the potential spatial features of the target video determined by the encoder, the similar kernel matrix is constructed, representative samples are selected from the similar kernel matrix, dimension reduction is carried out on the samples, and the dimension reduction result is reconstructed again through the decoder, so that more redundant information can be removed on the premise of retaining data information, and the accuracy of the data information is improved. And data processing is performed in the potential space, so that the calculation cost is reduced, and the requirements of reducing storage and calculation resources are met.

Disclosed is an operation device. The operation device includes a memory storing at least one instruction; and a processor configured to execute the at least one instruction, and the processor, by executing the at least one instruction, may perform encoding or decoding for an approximate homomorphic ciphertext using a predetermined matrix having only a half of an element of a matrix corresponding to a canonical embedding function.

The invention provides a production line parameter real-time scheduling method based on reinforcement learning, and belongs to the technical field of production lines, and the method comprises the steps: collecting sensor data to form an original state vector, reducing the dimension of the original state vector into a low-dimensional feature state vector through a state compression encoder, inputting the low-dimensional feature state vector corresponding to a microcosmic scheduling unit into a parameter decision model, and carrying out the real-time scheduling of the microcosmic scheduling unit; the attention weight coefficient of the model is determined by the product of the historical scheduling success rate, the element value of the difference degree matrix and the maximum characteristic value of the inter-stage sensitivity matrix, after a scheduling instruction is output, simulation evaluation is performed in a digital twin platform, and after the scheduling instruction passes, a real production line is issued for execution and multi-time-scale deviation indexes are collected; and calculating a comprehensive reward value according to the indexes, and storing an experience sample to an experience playback buffer area for model online update training, thereby solving the technical problem that production line parameter scheduling is difficult to consider multi-level state feature recognition and dynamic decision weight optimization at the same time.

A computing system for performing runtime data handling optimization for generative models is provided. The computing system comprises at least one processor and memory comprising a first memory and a second memory, wherein the memory stores instructions that, when executed by the at least one processor, cause the at least one processor to execute a generative model. The computing system computes a first value matrix entry based upon the processing of an input to the generative model. The first value matrix entry is stored in a first memory wherein a first group of value matrix entries is identified. The computing system executes data quantization on the first group of value matrix entries which results in a first quantized value matrix. The first quantized value matrix is added to a second memory where it can be used during generation of the generative model. When the first group of value matrix entries is less than a group size parameter, data padding matrix values are generated and used during execution of the data quantization.

A sparse dot product and / or matrix multiply is computed by subdividing each vector and simultaneously performing operations to generate output matrix elements. In an embodiment, a bit mask is computed that includes one bit for each element of an input matrix, each bit indicating whether the element is non-zero or zero. In an embodiment, the element values are stored in a packed format, where all of the non-zero values are packed together and the remaining storage for the matrix contains zeros (or any other values). The bit mask can then be used to determine the location of each non-zero element in the packed storage. Rather than reading all of the elements, only the non-zero elements that will be multiplied by a non-zero element from the other input vector should be read. Any multiplication by a zero element from either input vector or matrix is unnecessary.

A Winograd algorithm multiplies a first matrix by a second matrix to generate a third matrix. A plurality of second accumulated value calculation units, accumulates second multiplication values obtained by multiplying each of paired element values of the second matrix, a second accumulated value output unit outputting selecting and outputting one of output values of adjacent second accumulated value calculation unit and an accumulated second multiplication value as a second accumulated value, a third accumulated value output unit including a plurality of third accumulated value calculation units and generating third accumulated value, and one or more row element value calculation units, of which one row element value calculation unit accumulates first matrix element multiplication values obtained by multiplying each of the paired element values of the first matrix, and calculates element values of the third matrix using the accumulated first matrix element multiplication value and the second accumulated value.

The application relates to a power data fusion method, comprising the following steps: acquiring time series data from multiple data sources respectively; calculating the similarity between each time series data to obtain a similarity matrix; accumulating the matrix elements in the same row or the same column in the similarity matrix to obtain the similarity accumulation value of each data source and other data sources; performing longitudinal parameter fusion on each data source with a similarity accumulation value higher than a first threshold value, comprising the following steps: performing data preprocessing on the time series data; calculating the grey correlation degree between each two time series data of the time series data after the data preprocessing; filtering out the time series data with a grey correlation degree lower than a third threshold value; performing data fusion on the time series data by using a BP neural network to obtain a first fusion result; and performing horizontal parameter fusion on each data source with a similarity accumulation value lower than the first threshold value.

The present application relates to the technical field of electric digital data processing, and especially relates to a hardware test resource allocation method based on dynamic topology adaptation, which comprises the following steps: step 1: constructing a dynamic topology-aware network; collecting electrical connection signals between devices on a test backplane in real time, generating and periodically updating a topology connection matrix, wherein the matrix element value represents the on-off state of the physical link between devices, and the matrix is updated immediately when a device plugging event is detected; step 2: quantifying resource and task attributes; step 3: performing topology-driven dynamic resource matching; step 4: responding to topology changes for non-interruption migration; when the topology connection matrix is updated, step 3 is re-executed for the affected tasks, and non-interruption switching is realized through parallel initialization of new and old resources and signal deviation fault tolerance verification. Through the dynamic topology adaptation technology of the present application, test resources can be dynamically adjusted and matched according to real-time topology and task requirements, ensuring efficient use of resources.

Presented herein are methods and systems for measuring the value of one or more parameters of interest, including changes in the value of one or more parameters of interest, based on measured spectral differences. A trained spectral difference-based measurement model determines changes in the value of one or more parameters of interest based on spectral difference measurements measured before and after one or more process steps. In some examples, the spectral difference measurements are determined based on differences in measured intensity, differences in harmonic signal values, or differences in the values ​​of one or more Mueller matrix elements. The spectral difference measurements can be expressed as difference values, scalar values, or a set of coefficients of a function that fits the difference values. The spectral difference measurements can be determined based on weighting the spectral differences according to wavelength.

A matrix multiply engine can include a first operand buffer and a second operand buffer, each of which can store multiple operand elements arranged in rows and columns. A cell array can be formed of cells, where each cell includes a memory and accumulator circuitry to receive operand elements column-wise from each of the first operand buffer and the second operand buffer, to compute a dot product of the received operand elements, and to accumulate the dot product into a corresponding tile state element in the memory. Matrix elements of the operand matrices to be multiplied can be loaded row-wise into rows of the operand buffers and read column-wise into the cells. The number of elements for which a dot product is computed can be selected depending on operand element width.

One embodiment provides a graphics processor comprising an instruction cache to store an instruction and a compute block configured to perform multiply-accumulate operations in response to execution of the instruction. The compute block includes a scheduler to schedule a plurality of threads for execution of the instruction and multiply-accumulate circuitry configured to execute the instruction via the plurality of threads, wherein the multiply-accumulate circuitry includes a plurality of functional units configured to process, in parallel via the plurality of threads, a corresponding plurality of matrix elements to multiply a first matrix and a second matrix, and to multiply the first matrix and the second matrix includes to multiply data elements in a row of the first matrix by corresponding data elements in a column of the second matrix to generate a plurality of products.

The invention relates to an iron-nickel-based medium-temperature electrothermal alloy and a preparation method thereof, and belongs to the technical field of alloy preparation. The electrothermal alloy comprises the following chemical components in percentage by mass: 0.01%-0.03% of C, 0.70%-1.50% of Al, less than or equal to 0.80% of Cr, less than or equal to 0.003% of S, less than or equal to 0.020% of P, 51%-53% of Ni, 0.04%-0.20% of rare earth, 0.80%-1.50% of Ti and a matrix element Fe, wherein the rare earth comprises at least one of Y, La and Ce. Alloy elements such as Al, rare earth and Ti are introduced, so that the oxidation resistance of the alloy is improved, and the service life of the alloy in a medium-temperature environment is greatly prolonged. And meanwhile, on the basis of ensuring excellent mechanical properties, the resistivity of the alloy material is effectively improved, and the resistance temperature coefficient of the alloy material is optimized, so that the alloy material can better meet the performance requirement of the positive temperature coefficient effect, and finally, the applicability, design flexibility and integration degree of the electrothermal alloy in a limited design space are enhanced.

The invention discloses a nickel-based corrosion-resistant alloy and a processing method thereof, and belongs to the technical field of nickel-based corrosion-resistant alloys, and the nickel-based corrosion-resistant alloy comprises the following chemical components in percentage by weight: 0.01-0.018% of carbon, 0.07-0.15% of silicon, 0.08-0.15% of manganese, 0.007-0.015% of phosphorus, less than or equal to 0.005% of sulfur, 26.5-27.5% of chromium, 8.5-9.6% of molybdenum, 0.22-0.37% of aluminum, 3.2-3.8% of niobium, 5.3-6.5% of iron, 0.01-0.018% of nitrogen and less than or equal to 0.002% of oxygen. The trace impurities are inevitably introduced in the production process, the trace impurities are derived from raw material introduction and environmental contact in smelting and processing links, the total content of the trace impurities is smaller than or equal to 0.5%, and the content of a single impurity element is smaller than or equal to 0.05%. According to the mode, by reasonably adjusting the element ratio, on the basis that it is ensured that nickel serves as a matrix element to form a stable austenite structure (the cold and hot machining performance and the use performance of the alloy are stable), the nickel content is properly reduced, the use amount of precious metal elements is reduced, and therefore the alloy cost is reduced on the raw material level; the problem that wide application of existing alloy is limited due to the high proportion of precious metal is solved.

The invention provides a matrix distribution and calculation method and system for multiply-add units, and a storage medium. The method comprises the following steps: determining the number of rows and columns of an array formed by a plurality of multiply-add units; a matrix needing matrix operation is obtained by inputting a request source, and the number of rows and columns of matrix elements obtained through operation is determined in advance; determining the number of multiplication and addition units needing to be occupied according to the row and column number of the calculated matrix elements, and endowing the multiplication and addition units with corresponding row and column numbers and occupation indexes; the first matrix elements participating in the operation are sequentially distributed to the multiplication and addition units of the corresponding column numbers according to columns, and the second matrix elements participating in the operation are sequentially distributed to the multiplication and addition units of the corresponding row numbers according to rows; based on a multiply-add unit, matrix calculation is carried out according to the distributed matrix elements, and an intermediate result is obtained; and through an output request, taking out intermediate results from the multiply-add unit and sequentially splicing the intermediate results to obtain a matrix operation result. According to the invention, the resource utilization rate of the multiply-add unit array can be improved.

The invention discloses a resource allocation method and device, a computer program product and electronic equipment. The method comprises the following steps: acquiring project information of a target project; multiple evaluation indexes of the target project are determined according to the project information, index weights of the evaluation indexes are determined based on an index judgment matrix, and matrix elements of the index judgment matrix are used for reflecting the importance proportion between every two evaluation indexes; the feasibility level of the target project is determined based on a fuzzy relation matrix and the index weight of each evaluation index, and matrix elements of the fuzzy relation matrix are used for representing the membership degree of each evaluation index to comments of different levels; and determining a cloud resource allocation strategy of the target project according to the project information and the feasibility level, and allocating corresponding cloud resources to the target project according to the cloud resource allocation strategy. The technical problems of unreasonable resource allocation and untimely risk response existing in a cloud resource scheduling strategy for formulating a project based on static information are solved.

The application discloses a kind of self-adapting coding imaging positioning method, its steps include:1) according to the actual coding pattern of the machining form of coding board, and according to the coding pattern, the dimension number of imaging detector projection and the dimension number of expected imaging generation decoding matrix;2) the projection matrix of the imaging detector is generated, and the corresponding pixel index position of the bad pixel and the corresponding pixel of slit dead zone of imaging detector in projection matrix is obtained;3) according to pixel index position, the pixel value corresponding to the corresponding matrix element in decoding matrix is set to 0, represents that ray does not pass;When the coded pattern projection is formed on imaging detector by the modulation of the coded board after the ray bundle emitted by the ray source, the coded pattern projection is decoded and calculated using the corrected decoding matrix, and the decoding image is generated;4) the position and azimuth angle of the corresponding hotspot of the ray source are positioned from the decoding image.The application eliminates artifact, and improves positioning accuracy.

The invention belongs to the technical field of geological exploration, and particularly discloses a symmetrical excitation anti-explosion geological exploration system and method, and the method comprises the steps: receiving array multi-channel signals at two sides, and carrying out the time alignment correction of all channel signals, so as to eliminate the time delay; a multi-channel cross-correlation matrix between the left array and the right array is constructed based on the aligned signals, and matrix elements are obtained through integral cross-correlation operation of array channel signals on the two sides; performing low-rank decomposition on the cross-correlation matrix, and extracting the first k main components as public noise modes; carrying out inversion calculation on the contribution of each noise mode in each acquisition channel, and reconstructing a channel noise component; and subtracting the reconstructed noise component from the original signal, and outputting a purified geological reflection signal. The method does not need special hardware, can accurately suppress the homologous low-frequency noise of the tunnel, improves the signal-to-noise ratio of the signal and the geological imaging resolution, and is suitable for geological detection of closed spaces such as the tunnel.

The application relates to the technical field of aero-engine aerodynamic acoustic detection, and discloses a pipe-in-sound mode integrated detection method considering abnormal gain of a microphone, which comprises the following steps: a microphone array unit, a rotating speed detection unit and a signal acquisition module are built to realize data acquisition; a core algorithm unit is built, a joint sparse model is constructed based on a blade passing frequency calculated from rotating speed data, and super parameter adaptive updating is completed through an EM algorithm until the super parameter meets a convergence condition; a mode coefficient matrix and an abnormal gain matrix are extracted based on the converged super parameter, elements of the abnormal gain matrix are arranged in ascending order, and the position of an abnormal microphone is determined according to a preset threshold; subsequently, channel data corresponding to the abnormal microphone is removed, and the mode coefficient is re-inverted by using a Bayesian compressive sensing method; and a standardized detection report is output by the system. The method realizes high-precision mode recognition and abnormal detection, does not need manual intervention, and improves detection efficiency and robustness.

The invention provides a programmable photon integrated circuit phase configuration optimization method and system based on a neural network, and relates to the technical field of photon signal processing. The method comprises the following steps: firstly, randomly generating multiple groups of phase configurations in a chip, measuring to obtain a corresponding transmission matrix, and constructing a sample data set of phase configuration-matrix elements; training a full-connection neural network based on the data set to enable the full-connection neural network to accurately fit transmission characteristics under a component error condition; taking the phase configuration to be optimized as a parameter to be optimized, iteratively calculating loss through a gradient descent algorithm, and updating the parameter; and when the loss meets a preset condition, outputting the target phase configuration and loading the target phase configuration to the chip, thereby realizing high-precision, low-cost and directly applicable phase configuration optimization of the MZI network.

The present application relates to the field of automation, and discloses an anti-pinch early warning method and apparatus, and an electronic device. The method comprises: determining distance values between sensing points of a sensor module and the sensor module, and constructing a target distance matrix on the basis of the distance values; acquiring an operating state value of a movable end, and determining a target template matrix on the basis of the operating state value; performing differential calculation on the target distance matrix and the target template matrix according to a preset rule to obtain a target difference matrix, and grouping matrix element values in the target difference matrix on the basis of connectivity of the matrix element values in the target difference matrix, so as to obtain Q matrix element value sets; and determining the number of elements in each matrix element value set, and determining an anti-pinch early warning result on the basis of the number of elements. The present application solves the problem in the related art of a high risk of a user being pinched due to the fact that an anti-pinch operation can be started only upon detecting that a movable end of a target device is in contact with a foreign matter.

The invention relates to a matrix type differential passivity index determination method and device, equipment and a storage medium. The method comprises the following steps: acquiring a linear system model corresponding to target equipment in a power system, wherein the power system is a multiple-input multiple-output system; determining a corresponding differential passivity equivalent system model according to the linear system model; based on the linear matrix inequality, a matrix type passivity index of the differential passivity equivalent system model is solved, the matrix type passivity index is used as a matrix type differential passivity index of the linear system model, the matrix type differential passivity index is in a matrix form, and the matrix type differential passivity index is used as a differential passivity index of the linear system model; matrix elements in the ith row and the jth column in the matrix represent coupling passivity characteristics between an input channel i and an output channel j in the power system. By adopting the method, the passivity characteristics in the MIMO dynamic system can be represented more accurately and comprehensively.

The application belongs to the field of aero-engine clamp design, and is a method for establishing a discrete model of a pipeline clamp using a matrix unit. A corresponding physical model is first established, and then node 1 and node 2 are set to simulate the two ends of the clamp in the physical model. A mechanical pipeline is set, thereby establishing a mechanical model. Then, a coordinate system corresponding to the mechanical model is established, the corresponding relationship between the coordinate system and the coordinate system in the physical model is obtained, and then the node 1 and the node 2 are both set to have 6 degrees of freedom, and a 12x12 order matrix is established, so as to convert the mechanical model into a mathematical model. The stiffness characteristics and the damping characteristics in each direction can be simultaneously equivalent, the modeling is simple and fast, and the calculation accuracy can be improved. When nonlinear simulation is performed, the values of each row and each column of the matrix unit are adjusted as appropriate, the nonlinear characteristics of the clamp can be simulated, the nonlinear calculation of the pipeline system is supported, and the simulation efficiency is greatly improved.

The invention relates to the technical field of vehicle electronic and electrical architecture, in particular to a routing configuration method and device, electronic equipment and a storage medium. The method comprises the following steps: acquiring signal routing mapping information and message routing mapping information based on a preset communication matrix; constructing a target mapping relation between a preset communication matrix element and a preset protocol stack configuration element based on the signal reason mapping information and the message route mapping information; and based on the target mapping relationship, generating configuration information of a signal route in a preset communication layer, and / or generating configuration information of a message route in a preset message layer. Therefore, the routing relationship in the preset communication matrix is automatically scanned, and the mapping relationship between the routing relationship scanned in the communication matrix and the configuration elements defined in the communication protocol stack of the configuration tool is constructed, so that the problems of long time consumption, easiness in making mistakes, difficulty in maintenance and the like existing in a traditional manual configuration mode are solved; the routing configuration efficiency is effectively improved; and the configuration accuracy is remarkably improved.

The present disclosure describes techniques of performing machine unlearning in a recommendation model. An unlearning process of the recommendation model may be initiated in response to receiving a request for deleting a fraction of user data from any particular user. The recommendation model may be pre-trained to recommend content to users based at least in part on user data. Values of entries in a matrix corresponding to the fraction of user data may be configured as zero. The matrix may comprise entries denoting preferences of users with respect to content items. Confidence values associated with the fraction of user data may be configured as zero to block influence of the fraction of user data on performance of the recommendation model. The unlearning process may be implemented by performing a number of iterations until the recommendation model has converged.

The invention discloses a deflection calculation method of a steel-concrete composite beam under uniformly distributed loads, and relates to the technical field of high-speed railway bridge construction. The method comprises the following steps: establishing a differential equation of the continuous steel-concrete composite beam and carrying out Laplace transformation to obtain an expression of a non-zero real solution; inverse transformation is carried out on the equation obtained after Laplace transformation, an analytical expression of the deflection of the composite beam is obtained, and the expression comprises a plurality of unknown quantities. Substituting the structure parameters and the material parameters of the composite beam into the non-zero real number solution expression to solve Laplace transformation parameters; the parameters and the boundary conditions are substituted into the deflection analytical expression, and matrix elements used for solving unknown quantities are obtained; and constructing a linear equation set, solving an unknown quantity, substituting the unknown quantity into a deflection analytical expression, and finally obtaining a complete expression for solving the deflection of the continuous composite beam. The complete expression of the deflection of the continuous composite beam provided by the invention is suitable for analyzing the deflection of a single-span or continuous steel-concrete composite beam, and has relatively high calculation precision.

A device (400) for optical matrix-vector multiplication is provided. A x → = y → , encompassing: n*m optically coupled cells (i,j) (330-339) representing the elements of an (n × m) matrix A, an input vector x → , whose components x i (221-226) are represented by n waveguides which are coupled to the rows of matrix A, forming an output vector y → , whose components y j (231-236) are represented by m waveguides coupled to the columns of matrix A, each cell (i,j) (330-339) containing an optical element (350) which is a matrix element a ijmodeled, further comprising: a set of optical amplifiers (411 - 416) connecting a g-th column of cells with a g+1-th column of cells in the row direction, wherein each optical amplifier (411 - 416) connects a cell (i,g) with its neighboring cell (i,g+1), and / or a set of optical amplifiers (421 - 426) connecting an h-th row of cells with an h+1-th row of cells in the column direction, wherein each optical amplifier (421 - 426) connects a cell (h,j) with its neighboring cell (h+1,j).

The invention provides a security authentication method and device for logging in a self-service terminal, equipment, a storage medium and a program product, which can be applied to the technical field of big data. The method comprises the following steps: in response to a code scanning operation of a mobile terminal on a self-service terminal, displaying a login interface on the mobile terminal; acquiring login information of a user and biological characteristic information of n dimensions; comparing the biological characteristic information of the n dimensions with biological characteristic information in a user biological characteristic database, and determining L users closest to each piece of biological characteristic information; mapping the biological characteristic information of n dimensions into n rows, mapping L users closest to each piece of biological characteristic information into L columns of matrix elements, and obtaining an n * L matrix; calculating confidence coefficients of k users represented by all matrix elements according to a preset matrix column weight; determining a target user according to the confidence coefficient; and verifying the consistency between the pre-stored information of the target user and the login information to obtain a security authentication result.