52 results about "Full matrix" patented technology

The invention belongs to the technical field of nondestructive testing, and particularly relates to a full-matrix microscopic ultrasonic leakage defect three-dimensional reconstruction method. According to the leakage defect three-dimensional reconstruction method, precise positioning and three-dimensional shape reconstruction of tiny complex defects in the austenite thin-wall stainless steel material are achieved, and technical support is provided for leakage micropore detection and three-dimensional representation of the austenite thin-wall stainless steel material. The leakage micropore three-dimensional reconstruction method comprises the following steps: recording an A-Scan original signal and position information of each path point to obtain full-matrix microscopic ultrasonic data; taking the minimum envelope entropy as an optimization target, performing variational mode decomposition and reconstruction on the A-Scan original signal of each path point, and calculating to obtain an optimal solution of a variational model; performing two-dimensional mask extraction on the processed full-matrix microscopic ultrasonic data to generate C scanning images with different depths; extracting to obtain two-dimensional defect edge masks under different depths; and carrying out splicing and volume rendering on a two-dimensional defect edge masking result.

The invention relates to a power network structure weakness detection method based on multi-diagonal-block matrix decomposition, and belongs to the technical field of super-large-scale integrated circuits. According to the method, an original large-scale sparse matrix is converted into a band edge diagonal block structure and divided into a plurality of sub-matrixes capable of being solved independently by constructing a layering and blocking strategy for eliminating tree drive, and redundancy of full-matrix calculation is avoided. Meanwhile, a local approximate inverse algorithm of column norm truncation is designed, and target elements are calculated on the premise that a preset error threshold value is met. According to the method, the parallel computing architecture of the multi-core processor is fully utilized, the computing complexity is reduced by 2-3 orders of magnitude, the computing efficiency is effectively improved, the memory occupation is remarkably reduced, and a high-precision and high-efficiency solution is provided for detecting the weakness of the power network structure of the super-large-scale integrated circuit.

The invention provides a cable lead sealing internal defect positioning method based on vector full focusing, and the method comprises the steps: obtaining a transmitting array element, a receiving array element and an echo signal when a one-dimensional ultrasonic array scans a cable lead sealing in a full-matrix capture mode according to an array element combination mode; generating a signal intensity distribution image according to the echo signal by using a full-focus algorithm; selecting a defect signal and a position coincidence condition from the signal intensity distribution image to obtain a defect point and a position; constructing a vector coordinate system and registering the vector coordinate system with the signal intensity distribution image to obtain the vector length and direction of the defect signal; obtaining the defect type of the defect point according to the vector length, direction and position coincidence condition of the defect signal; and a three-dimensional model of cable lead sealing is simulated on the global coordinate system, and automatic positioning and labeling are performed according to the position and the defect type of each defect point. By implementing the method, the problems of directional information loss, high small defect omission ratio, poor complex structure adaptability and the like which generally exist in the existing mode can be solved.

The embodiment of the invention relates to a processing method and device for a dense encoder realized based on a large language model, and the method comprises the steps: selecting a large language model which has completed pre-training and NLP task fine tuning and is realized based on a pure decoder architecture as a target model, the bidirectional encoders are obtained through a transformation mode of solidifying a causal mask matrix used by a target model decoder in a reasoning process into an all-one matrix, and the embedded encoding module of the target model and the plurality of bidirectional encoders are connected in sequence to form a dense encoder; performing first-stage fine adjustment on the dense encoder through a shielding word prediction task, and performing second-stage fine adjustment on the dense encoder through an unsupervised contrast learning mechanism; and after the fine tuning is finished, constructing a document vector library for a target document library appointed by the user by utilizing the dense encoder, and providing retrieval service for the target document library based on the document vector library and the dense encoder. The dense encoder is used for processing a text retrieval task, so that the retrieval accuracy can be improved.

A communication method and a communication apparatus in which a device may perform encoding or decoding based on an LDPC matrix, the LDPC matrix being determined based on an LDPC basis matrix, Zc, a, and b, a first region of the basis matrix including L types of elements, 2 < = L < = 2a * b, a and b not being 1 at the same time, Zc, a, and b being used to lift the elements in the first region to a (a * Zc) * (b * Zc) matrix, the matrix including (a * b) (Zc * Zc) matrices, each Zc * Zc matrix is an all-zero matrix or a cyclic shift matrix, the first sub-matrixes of the L elements are different, and the first sub-matrixes are matrixes formed by replacing the cyclic shift matrixes in (a * b) (Zc * Zc) matrixes of the matrix with the lifted corresponding elements with Zc * Zc unit matrixes. According to the method, the decoding performance of the LDPC code can be improved.

The invention relates to the technical field of new energy charging piles, in particular to a loaning distribution strategy based on a high-power platform semi-matrix model. A charging system is divided into a group A and a group B, a full-matrix model is adopted in each group, semi-matrix connection is formed between the groups through DC + / DC-double contactors, and cross-group power borrowing is achieved. The method comprises the following specific steps: calculating a preliminary power module demand table, generating and arbitrating a borrowing demand, and generating an action plan table after redistributing modules. Through priority dynamic allocation and an arbitration rule (priority is high and under-power magnitude is high), dynamic power demand conflicts of different vehicles and the same vehicle in different charging stages are balanced, and the module utilization rate and the charging efficiency are improved. Experiments show that the strategy improves the overall charging efficiency, simplifies the module switching logic, enhances the system stability, and gives consideration to the guarantee of high-priority users and the rights and interests of low-priority users.

The present invention discloses a three-phase calculation method, device and storage medium for a transformer based on matrix dimensionality reduction. Among them, the method includes: calculating the resistance value and reactance value of a three-phase transformer according to the nameplate information of the three-phase transformer; calculating the first turns ratio on the primary side of the three-phase transformer and the second turns ratio on the secondary side of the three-phase transformer according to the tap position and rated voltage of the three-phase transformer; determining the full matrix of the three-phase transformer based on the resistance value, reactance value, first turns ratio and second turns ratio of the three-phase transformer; performing dimensionality reduction processing on the full matrix of the three-phase transformer according to a preset matrix dimensionality reduction rule to obtain a dimensionality reduction matrix of the three-phase transformer; and superimposing the dimensionality reduction matrix of the three-phase transformer into the system matrix for three-phase calculation to perform three-phase power flow calculation and determine the three-phase power flow result of the three-phase transformer. The present invention can retain the physical meaning of transformer variables during the process of order reduction, which is beneficial to calculating various variables of the transformer, and at the same time reduces the amount of calculation.

A method of recovering transmitted symbols. The method receiving a signal comprising a codeword, the signal having been affected by channel effects including distortion and noise. A solution space for recovering the transmitted symbols is identified, including finding a sampling center using a full matrix W for minimum mean square error. A vector v′ is generated to generate noise. The vector has a variance ρσn2 where σn2 is a factor that defines power of added noise and where ρ is a scaling parameter that controls a size of a noise sampleable space. The vector v′ is applied to the solution space. Original samples are gathered from the solution space, which includes noise from the vector v′ and symbol information from the received signal to find probabilities for symbols. Using the probabilities, the symbols are recovered.

The invention relates to the technical field of artificial intelligence calculation models and intelligent decision control, and discloses a system based on an AI multi-dimensional model and an intelligent decision method, and the system comprises a feature perception module, a topology reconstruction engine, a feedback scheduling unit and a transformation processing unit. The feature sensing module extracts feature vectors of all components of the high-dimensional heterogeneous data stream, calculates a gradient fusion value and determines an active feature dimension; a topology reconstruction engine generates a dynamic mask based on the active feature dimensions, and performs edge pruning on the initial topological graph to construct a task-sensitive topological structure; the feedback scheduling unit uses a reinforcement learning operator to adjust a pruning threshold value and updates a dynamic mask coverage rate; and the conversion processing unit executes weighted mapping operation on the basis of the updated dynamic masks, full matrix operation is converted into sparse topological operation through feature guide logic pruning, and invalid electrical digital processing overhead is reduced.

The utility model discloses a full-matrix charging power distribution system, which comprises a main control module, a charging power module, a power distribution module and charging piles, and is characterized in that the main control module is connected with the charging power module and the power distribution module, the charging power module is connected with the power distribution module, and the power distribution module is connected with the charging piles; the power distribution module comprises a plurality of power distribution units, the charging pile comprises a plurality of charging guns, and the output ends of the power distribution units in the same sequence in the power distribution module are correspondingly connected with the charging guns in the same sequence in the charging pile; any power distribution unit comprises a plurality of input ends, the charging power module comprises a plurality of charging power units, and the input ends of the same sequence in each power distribution unit are correspondingly connected with the charging power units of the same sequence based on a charging bus. According to the charging power distribution structure based on the full matrix, the flexible distribution requirement of the charging power of the charging pile is met, and the charging efficiency and reliability of the charging pile are effectively improved.

Embodiments of the present specification disclose a two-party joint data processing method and device. In the method, two parties securely calculate secret sharing fragments of a target vector involved in an online service, so that the first party and the second party correspondingly obtain a first vector fragment and a second vector fragment; the second party reads a second product fragment determined in an offline stage as a second target fragment of this operation, wherein the second product fragment is a self fragment of a secure matrix multiplication operation of a random vector generated by the second party and a target matrix held by the first party, and a corresponding first product fragment is held by the first party; the second party determines a third vector fragment based on the second vector fragment and the random vector; and the first party determines a first target fragment of a product result of the target vector and the target matrix based on the target matrix, the first product fragment, the first vector fragment, and the third vector fragment received from the second party.

Examples of this topic provide techniques for compressed sampling of acoustic data. The probe can sample in a compressed mode, so that the entire matrix is ​​not sampled at full temporal or spatial resolution. Thus, the amount of initial data captured by the probe is reduced, enabling the use of lower-density hardware (e.g., fewer analog-to-digital conversion channels or related analog front-end hardware) at lower data rates.

The application discloses a kind of fusion social network graph and hypergraph information recommendation system matrix filling method, comprising the following steps: S1, using generative model modeling generation social network graph information and hypergraph information;S2, the generation and input of incomplete recommendation matrix;S3, through three-stage algorithm, using social network hypergraph information and incomplete matrix information are carried out matrix filling, output the recommendation matrix after completion, and then complete recommendation task;S4, collect user feedback information, optimize recommendation algorithm: according to the feedback of user, improve social network information and then optimize recommendation algorithm.Compared with prior art, the present application uses mixed graph and more complex hypergraph for modeling, can better make full use of complex social network data, on this basis, through three-stage algorithm, using social network hypergraph information auxiliary matrix filling, can carry out more accurate matrix reply, and then enable recommendation system to realize more efficient recommendation.

The application relates to the field of ultrasonic nondestructive testing, and discloses a downhole thread digital modeling method and system. The method comprises the following steps: firstly, wrapping a thread area with an adaptive coupling module; secondly, fixing an ultrasonic phased array probe on the adaptive coupling module, performing a compound motion, and acquiring full matrix data; then, performing imaging processing on the full matrix data to obtain a two-dimensional image; then, extracting a thread three-dimensional feature point cloud based on the two-dimensional image; and finally, reconstructing a three-dimensional digital model of the thread based on the obtained three-dimensional feature point cloud. The method has the sound beam deflection and focusing capacity of the phased array technology, can better adapt to threads of different specifications and surface states in combination with flexible coupling, has strong universality, and in addition, the method can not only judge whether a defect exists, but also accurately reconstruct a three-dimensional form of the defect, thereby providing unprecedented data support for safety evaluation and service life prediction.

The invention provides a self-adaptive ultrasonic detection and defect identification method for a composite material, and belongs to the technical field of ultrasonic detection.The self-adaptive ultrasonic detection and defect identification method for the composite material comprises the specific steps that firstly, preparation is conducted before detection; 2, collecting full-matrix data of the one-way plate; 3, calculating a core elastic constant of the composite board; 4, deducing the velocity distribution of each layer group of the composite board; 5, collecting full-matrix data of the composite board; step 6, adaptively correcting imaging delay; and 7, performing full-focusing imaging and judgment on the defects of the composite board. According to the method, defect subtle features which are difficult to recognize by human eyes in the full-focus imaging picture can be automatically mined through the deep learning model, the problems of missing detection and false detection caused by subjective judgment deviation are effectively avoided, rapid and accurate classification and positioning of various defects can be realized through the deep learning model, the detection period is greatly shortened, and the detection efficiency is improved. And reliable technical support is provided for high efficiency and precision of ultrasonic detection of the composite material.

The invention relates to the technical field of power control, in particular to a full-matrix power distribution module, unit and equipment. The full-matrix power distribution module enables input control parts of the relay units in the same row to share lines among different relay groups; meanwhile, the power parts of the adjacent relays in each relay group are connected in a back-to-back manner, so that the power output parts of the adjacent relays share lines, multi-path power distribution full-matrix output is realized, and the problem of low density of traditional full-matrix power distribution is solved. Meanwhile, at the output end, the bus output port of each relay group is provided with an output external wire in a step mode, so that front installation and assembly are facilitated.

This invention relates to the field of artificial intelligence computing models and intelligent decision control technology, and discloses a system and intelligent decision-making method based on an AI multi-dimensional model, including a feature perception module, a topology reconstruction engine, a feedback scheduling unit, and a transformation processing unit. The feature perception module extracts feature vectors of each component of a high-dimensional heterogeneous data stream, calculates the fusion value of the gradient, and determines the active feature dimension. The topology reconstruction engine generates a dynamic mask based on the active feature dimension and performs edge pruning on the initial topology graph to construct a task-sensitive topology structure. The feedback scheduling unit uses reinforcement learning operators to adjust the pruning threshold and update the dynamic mask coverage. The transformation processing unit performs weighted mapping operations based on the updated dynamic mask. This invention transforms full matrix operations into sparse topology operations through feature-guided logical pruning, reducing the overhead of invalid digital processing.

The utility model discloses a full-matrix power distribution topological structure based on charging piles, which belongs to the technical field of circuit electronics and comprises a plurality of power module groups, a plurality of power distribution units and a plurality of charging terminals. The power module group comprises a combination of one or more power modules, and the technical problem that the cost is high due to the fact that an existing power distribution topological structure needs to be provided with a plurality of power distribution units and a plurality of contactors is solved.

The invention discloses a construction method of a power transmission fitting dynamic matrix defect focusing algorithm. The construction method comprises the following steps: performing full-matrix capture through a phased array probe, acquiring FMC data, dividing a detection area into a plurality of pixel points, and setting a focusing point; carrying out delay correction on the signal based on propagation time calculation, and carrying out dynamic weight distribution and focusing superposition on the signal after delay correction based on signal quality to generate a signal intensity matrix; and carrying out multi-frequency signal fusion and multi-mode signal conjoint analysis on the superposed signal matrix, generating a three-dimensional imaging graph based on the optimized signal, and carrying out defect feature extraction and classification labeling by using a deep learning model. According to the invention, the power transmission fitting defect detection efficiency and precision are significantly improved, and powerful technical support is provided for safe operation of a power transmission line.

The invention relates to a dynamic sparse ultrasonic full-focusing method for a large-wall-thickness structural member, belongs to the field of nondestructive testing, and solves the problems of low full-focusing imaging efficiency, insufficient deep defect detection sensitivity and unbalanced imaging quality at different depths caused by a fixed sparse rate during ultrasonic testing of the large-wall-thickness structural member (the thickness is greater than or equal to 300mm) in the prior art. The method comprises the following steps: S1, acquiring medium sound velocity and total thickness of a detected component and determining array parameters of the detected component; s2, collecting a full-matrix signal covering a detection depth range; s3, establishing an acoustic beam diffusion model, and calculating acoustic beam diffusion widths at different depths; s4, dynamically distributing the array element sparse rate of each depth; and S5, selecting an effective array element according to the sparse rate, calculating the delay time from the effective array element to the focus point, and reconstructing and imaging to obtain a full-focus detection image covering the whole detection depth. Efficient and accurate nondestructive testing of large-wall-thickness structural members such as pressure containers and heavy pipelines is achieved, and imaging quality and detection efficiency are both considered.

The invention discloses a characteristic guided wave modal enhancement method and system based on full-matrix data phase coherence. The method comprises the following steps: arranging a plurality of signal monitoring points in the circumferential direction of a detected structure; characteristic guided wave signals are collected and summed; and according to the number of the minimum semicircle phases corresponding to different sampling moments, obtaining test coefficients and signal scaling coefficients at different sampling moments. And weighting the joint received signal by using the signal scaling coefficients at different sampling moments to obtain a modal enhanced signal. According to the invention, a plurality of signal monitoring points are used for mutual signal excitation and reception to obtain a plurality of independent characteristic guided wave signals, and the signals are summed to obtain a joint receiving signal; the amplitude of the joint receiving signal at the moment of non-uniform phase distribution is increased by using the difference of the distribution conditions of the guide wave signals with different characteristics; due to the fact that the interference mode and the noise phase have the characteristic of approximate random uniform distribution, energy enhancement of the attention mode with weak frequency dispersion and large group velocity can be achieved.

A method for multiplying a sparse matrix (SM) with a full matrix, includes receiving a SM, left aligning each row based on a maximum number of non-sparse cells in each said row, generating a left aligned matrix, displacing at least one cell from an original row into another row of the left aligned matrix to thereby generate at least one new sparse column in said SM to thus reduce sparsity by discarding said at least one new sparse column, thus generating a critical-path-reduced matrix, and wherein the at least one displaced cell includes a flag identifying said cell has been displaced from the original row in the SM, and multiplying the critical-path-reduced matrix with the full matrix using a multiplier, wherein the product from each cell having a displacement flag is moved and accumulated to an accumulator within the multiplier associated with remainder of data for said original row.

A method of recovering transmitted symbols. The method receiving a signal comprising a codeword, the signal having been affected by channel effects including distortion and noise. A solution space for recovering the transmitted symbols is identified, including finding a sampling center using a full matrix W for minimum mean square error. A vector v′ is generated to generate noise. The vector has a predefined variance. The vector v′ is applied to the solution space. Original samples are gathered from the solution space, which includes noise from the vector v′ and symbol information from the received signal to find probabilities for symbols. Using the probabilities, the symbols are recovered.

A system and method for an adaptive full focus method (TFM) includes obtaining full matrix capture (FMC) data of a region of interest of an article, the region of interest being made of a material; for each unit of a set of units in the article, evaluating the FMC data to obtain a parameter for relating to a material property of the material; associating the parameters of each cell in the set of cells with a state model of the material to determine an anisotropic state of each cell in the set of cells in the article; generating a distribution parameter model of the material anisotropy of the product by using the anisotropy state of each unit in the group of units in the product; and operating the beamformer with the distributed parameter model using the TFM to image the region of interest.

A communication method and a communication apparatus. In the method, a device may perform encoding or decoding on the basis of an LDPC matrix, wherein the LDPC matrix is determined on the basis of an LDPC base matrix, Zc, a, and b; a first region of the base matrix comprises L types of elements, wherein 2≤L≤2a*b, and a and b are not both 1 at the same time; Zc, a, and b are used for lifting each element in the first region into an (a*Zc)*(b*Zc) matrix, wherein the matrix comprises (a*b)(Zc*Zc) matrices, and each Zc*Zc matrix is an all-0 matrix or a cyclic shift matrix; first sub-matrices corresponding to the L types of elements are all different; and each first sub-matrix is a matrix formed by replacing, with an identity matrix of Zc*Zc, a cyclic shift matrix within the (a*b)(Zc*Zc) matrices of the matrix lifted by a corresponding element. The method can improve the decoding performance of LDPC codes.

The present application provides an ultrasonic tomography method, comprising: S1, using a ring array to perform full-matrix data acquisition on a target to be measured to determine observation data of N frequency points; S2, selecting observation data of the i-th frequency point; S3, encoding the observation data of two adjacent sound sources at the i-th frequency point to obtain first data; S4, encoding the two adjacent sound sources in an inversion process at the i-th frequency point based on the Green's function to obtain second data; S5, determining a target function based on the first data and the second data; the target function is used to quantify the difference between the observation data of the i-th frequency point and the synthetic data predicted based on the initial sound velocity parameter image; S6, iterating the initial sound velocity parameter image m0 to determine the sound velocity parameter image of the i-th frequency point; S7, using the sound velocity parameter image as the initial sound velocity parameter image at the i+1-th frequency point, executing the S2-S6 process, traversing the observation data of the N frequency points, and obtaining an ultrasonic tomography image.

Mechanisms are provided for fault-tolerant computing. The mechanisms solve, by a controller computing device in a controller-worker computer architecture, a sparse algebraic linear system of equations with incomplete matrix-vector products. The solving includes: A set of worker computing devices computing matrix-vector products Ax=b, wherein A is a sparse matrix, x is a vector being multiplied, and b is a resultant vector; a controller determining that a worker has not returned a result of its computation within a threshold length of time; and in response to the determination, assuming, by the controller computing device, that the result of the worker computing device computation is zero and continuing the computing of a solution to the sparse algebraic linear system. The controller computing device provides, to a subsequent computing operation, the solution to the sparse algebraic linear system of equations as a basis for performing the subsequent computing operation.

The invention relates to the field of ultrasonic non-destructive testing, and discloses an underground thread digital modeling method and system.The method comprises the steps that firstly, a thread area is wrapped with a self-adaptive coupling module; secondly, an ultrasonic phased array probe is fixed to a self-adaptive coupling module to do composite motion, and full-matrix data are obtained; then, performing imaging processing on the full-matrix data to obtain a two-dimensional image; then, extracting a thread three-dimensional feature point cloud based on the two-dimensional image; and finally, reconstructing a three-dimensional digital model of the thread based on the obtained three-dimensional feature point cloud. The method has the acoustic beam deflection and focusing capability of a phased array technology, can better adapt to threads of different specifications and surface states by combining flexible coupling, is high in universality, can judge whether defects exist or not, can accurately reconstruct the three-dimensional form of the defects, and provides unprecedented data support for safety evaluation and life prediction.

This invention relates to a secure computation method and system for nonlinear functions based on unintentional shuffling and splitting outsourcing, belonging to the field of computer software technology. The method includes: generating dynamic permutation parameters for inference requests via a client; performing unintentional shuffling on the ciphertext through two mutually distrustful servers based on the dynamic permutation parameters; splitting the unintentionally shuffled data into a first part and a second part, with each server reconstructing the first and second parts respectively to obtain partial plaintext; calculating a nonlinear function using the partial plaintext and merging the results; and performing inverse unintentional shuffling on the merged results to obtain the final nonlinear layer output. This invention transforms expensive online secure matrix multiplication into low-cost vector addition and single-round communication operations, achieving "one-time pad" dynamic permutation at extremely low cost, fundamentally resisting statistical inference attacks.

The invention discloses a full-matrix signal conversion method for driving apodization plane wave signals to a synthetic emission aperture (STA) by a self-supervised convolutional neural network (CNN) for high-quality two-dimensional ultrasonic imaging, an ultrasonic imaging system and a medium. The method comprises the following steps: designing a self-supervised learning CNN model based on a linear acoustic superposition principle; training the CNN model by using the simulation STA full matrix signal; an optimized binary weight is extracted from a CNN model encoder convolutional layer to serve as emission apodization, and plane wave signals are collected; inputting the apodization plane wave signal into a CNN model decoder to convert and generate an STA full-matrix signal; and performing beam forming based on the generated STA full-matrix signal so as to reconstruct a high-quality B ultrasonic image. According to the method, apodization plane wave signals can be well converted into STA full-matrix signals so as to carry out high-quality two-dimensional ultrasonic imaging, and the problem that the plane wave imaging quality is poor can be solved to a certain extent.