11 results about "Matrix decomposition algorithms" patented technology

The invention relates to a three-component high-dimensional seismic data reconstruction method and device based on vector tensor reduced rank, a medium and equipment, and the method comprises the steps: firstly, placing three-component high-dimensional seismic data at three imaginary part positions of a quaternion array in a time domain through employing a quaternion tool, and setting a real part of the quaternion array to be 0 value; and realizing vector joint representation of the quaternion-based three-component seismic data. Quaternion representation can protect the nonlinear orthogonal relation of three component seismic data in the space direction, and vector field structural characteristics of seismic waves are kept. And secondly, transforming a time domain quaternion array into a frequency domain by using quaternion Fourier transform, extracting a spatial high-dimensional quaternion array corresponding to each frequency domain slice, and performing missing data restoration and reconstruction on the high-dimensional quaternion array by using a tensor reduced-rank reconstruction technology based on a quaternion parallel matrix decomposition algorithm. According to the method, the memory occupation amount is remarkably reduced, the calculation amount is small, the calculation cost is low, and the reconstruction calculation efficiency is high.

The invention provides a quantum circuit system based on an AES cryptosystem and an encryption method. The quantum circuit system based on the AES cryptosystem comprises a byte replacement module which adopts a pre-optimized S-box query circuit to realize non-linear byte replacement; the row shift module completes in-row byte cyclic shift through quantum bit exchange operation; the column confusion module realizes a matrix multiplication circuit based on finite field operation, and a quantum circuit of the column confusion module is generated by a depth-first matrix decomposition algorithm; and the round key adding module realizes XOR operation of a round key and a state by using a CNOT gate array, and a key required by the round key adding module is dynamically generated through a key expansion circuit. And a compact Z-shaped architecture is adopted to optimize time sequence scheduling and resource allocation of each module. On the premise of keeping the quantum bit resource usage amount equivalent to that of an original Z-shaped architecture, the quantum circuit depth is remarkably reduced, and higher fidelity and shorter execution time can be realized on NISQ equipment.

This invention provides a method, medium, and system for acoustic early warning of thermal runaway in lithium-ion batteries based on feature reconstruction, belonging to the field of lithium-ion battery technology. The invention constructs a positive sample set by collecting safety valve opening sounds through multi-condition thermal runaway experiments, and expands the samples using data augmentation. Multi-resolution Mel spectra are extracted from the audio signals. These Mel spectra are then input into a complex-domain phase-aware separation model for joint estimation of complex-domain amplitude masking and phase residuals. Physical prior corrections are applied to the reconstruction results using a sound source separation algorithm based on wave equation time-frequency inverse scattering and a low-rank sparse time-frequency matrix decomposition algorithm based on random matrix theory. The three corrected signals are weighted and fused to obtain a corrected Mel spectra, which are finally input into a temporal convolutional network to classify and identify the safety valve opening sounds and output a thermal runaway early warning signal. This invention solves the technical problem of insufficient accuracy in thermal runaway early warning caused by acoustic feature reconstruction distortion in complex noise environments.

The invention relates to a TSN (Time Sensitive Network) timestamp recovery method based on an FPGA (Field Programmable Gate Array), which belongs to the technical field of TSNs and comprises the following steps of: S1, collecting synchronous messages of different TSN nodes in parallel by multiple ports of the FPGA, and analyzing timestamp data by taking a message serial number as an anchor point; s2, constructing an incomplete timestamp matrix and a mask matrix by adopting a time sliding window mechanism, and automatically marking missing data; s3, a random index set is generated through LFSR, and matrix sparse sampling is achieved through a bitmap judgment unit; and S4, based on an FPGA accelerated CUR matrix decomposition algorithm, solving a pseudo-inverse matrix through parallel MGS-QR decomposition, and reconstructing a complete timestamp matrix through block parallel matrix chain multiplication. The FPGA parallel architecture, ping-pong cache and fixed-point number processing are adopted, microsecond-level low-delay recovery is achieved, hardware resource occupation is greatly reduced, robustness in a severe network environment is improved, and the method is suitable for a high-precision clock synchronization scene of an industrial TSN node.

The application discloses a transcription factor regulation network modeling method based on single cell transcriptome data, comprising the following steps: S1, extracting multi-phenotype single cell omics data, performing data cleaning, and integrating the cleaned data; S2, based on a biological knowledge base, analyzing the data processed in S1, and constructing a prior inter-molecular interaction relationship network; S3, based on a multi-factor non-negative matrix factorization algorithm, establishing a multi-dimensional molecular synergistic interaction relationship module according to the data processed in S1 and the prior inter-molecular interaction relationship network in S2; S4, calculating the interaction relationship module related to the phenotype of the multi-dimensional molecular synergistic interaction relationship module; and S5, visualizing and exporting the interaction relationship between the multi-dimensional molecular synergistic interaction relationship module and the phenotype. With the aid of single cell sequencing technology and prior biological knowledge, the application extracts multi-dimensional molecular synergistic regulation relationship in high-dimensional single cell omics data, and obtains a transcription factor-gene function synergistic regulation interaction mechanism closely related to phenotype changes.

The application provides a quantum circuit system and encryption method based on AES encryption system, which comprises a byte substitution module, a row shift module and a column confusion module; the byte substitution module uses a pre-optimized S-box query circuit to realize nonlinear byte substitution; the row shift module completes the cyclic shift of the bytes in the row through a quantum bit exchange operation; the column confusion module realizes a matrix multiplication circuit based on finite field operation, wherein the quantum circuit of the column confusion module is generated by a depth-first matrix decomposition algorithm; the round key addition module uses a CNOT gate array to realize the XOR operation of the round key and the state, and the required key of the round key addition module is dynamically generated through a key expansion circuit. The timing scheduling and resource allocation of each module are optimized by using a compact Z-shaped architecture. Under the premise of keeping the quantum bit resource usage equivalent to the original Z-shaped architecture, the quantum circuit depth is significantly reduced, and higher fidelity and shorter execution time can be realized on NISQ devices.

The application discloses a high-capacity vehicle clustering method and system based on an information-physical dual network, which comprises the following steps: firstly, constructing a physical network topology graph corresponding to the current network of vehicles, and calculating a vehicle mobility correlation index; then, constructing a physical network topology graph corresponding to the current network of vehicles, and calculating a link transmission quality; further, superimposing the physical network and the information network by weighted summation, calculating a correlation density under a comprehensive network, and further establishing an optimal clustering model based on the correlation density; finally, using a matrix decomposition algorithm to approximately solve, and obtaining a final clustering result. In the calculation process, the channel fading under the LOS and NLOS environments is considered, and a channel link capacity calculation method is given through a matrix mother function. The method can reflect the stability of the physical topology, and can also reflect the link transmission quality of the whole channel, so that the VC construction with a more efficient transmission structure can be realized.

This invention discloses an objective fatigue state recognition method based on EEG classifier-assisted annotation, belonging to the field of human-computer interaction and biosignal processing technology. The method includes: simultaneously acquiring EEG, EMG, and eye-tracking video signals from the subject; weighting the EEG channels and extracting frequency domain features using a non-smooth, non-negative matrix factorization algorithm to construct a MobileNetV2 EEG classifier; extracting eye-tracking features using an improved Unet algorithm incorporating an attention mechanism and elliptic fitting error loss; automatically annotating the eye-tracking data using the EEG classifier and setting a threshold for fuzzy sample exclusion; and fusing the outputs of the EEG classifier and the eye-tracking sub-classifier at the decision level using D-S evidence theory to construct a comprehensive fatigue recognition model and form a closed-loop optimization paradigm. This invention solves the bottleneck problem of high-quality annotation of cross-modal fatigue data, significantly improving the objectivity and accuracy of fatigue recognition, and can be widely applied in safety-critical fields such as aviation, rail transportation, and long-distance driving.

The application relates to an airborne dual-frequency high-resolution SAR image fusion method, device, equipment and medium. The method comprises the following steps: performing multi-scale transformation on a first source image and a second source image which have been accurately registered to obtain low-frequency sub-band coefficients and high-frequency sub-band coefficients in each direction corresponding to the first source image and the second source image; obtaining an observation matrix in the form of additive noise according to the low-frequency sub-band coefficients corresponding to the first source image and the second source image, decomposing the observation matrix by using a non-negative matrix decomposition algorithm to obtain a feature matrix, performing non-negative matrix decomposition on the feature matrix to obtain low-frequency fusion coefficients; combining the high-frequency sub-band coefficients in each direction by using an improved Laplace energy sum to obtain high-frequency fusion coefficients; and performing inverse transformation on the low-frequency fusion coefficients and the high-frequency fusion coefficients to obtain a fusion image. The method can obtain a high-frequency and low-frequency SAR fusion image with higher resolution, more comprehensive information and smaller distortion.

The embodiment of the invention relates to a model processing method and device, equipment and a medium. The method comprises the steps that a target task model and a basic model are acquired; wherein the model parameters of the basic model are adjusted based on the target task data sample to obtain a target task model; obtaining a weight difference model matrix of the target task model and the basic model; and decomposing the weight difference model matrix according to a preset matrix decomposition algorithm to obtain a low-rank matrix, and associatively storing the low-rank matrix and the target task identifier for executing the target task. By adopting the technical scheme, the low-rank matrix is obtained by decomposing the weight difference model matrix of the target task model and the basic model, and the extracted low-rank matrix is ensured to have high fidelity and practicability, so that the low-rank matrix of the target task model can be stored, and the accuracy of the extracted low-rank matrix is improved. And the storage and deployment cost is greatly reduced while the model effect is maintained.

A service soldier training and certification system includes a processing module that performs the following steps: collecting and preprocessing multimodal data to obtain skill increment feature components; establishing a digital twin roulette model, adjusting the weights of corresponding skill points in the digital twin roulette model according to the skill increment feature components and / or external feedback components, and obtaining a skill point weight matrix; obtaining a standardized course feature matrix, calculating the cosine similarity between the skill point weight matrix and the standardized course feature matrix to obtain a course content matching score; obtaining a service soldier-course cross-feature matrix and processing it based on a matrix factorization algorithm to obtain a course prediction score; linearly weighted summing to obtain the final course score, sorting the courses in the database according to the final course score, generating a course recommendation list for each registered service soldier, and pushing it out. This application can provide service soldiers with dynamic, personalized, and quantifiable training paths.