55 results about "Sparse model" patented technology

The invention provides a large model lightweight reasoning deployment method under limited hardware resources, and the method comprises the steps: quantifying the weight importance of a large model through a composite index of gradient sensitivity and activation frequency, and carrying out pruning operation in combination with an improved index weighted moving average strategy, thereby obtaining a structured sparse model; the sparse model is divided into sub-networks by adopting double rules, a routing decision network is trained, and an adaptive feature shunting architecture model is constructed; a multi-precision weight set is generated through a nested quantization technology, quantization bit width is dynamically adjusted, and edge equipment hardware parameters are adapted to complete reasoning environment initialization; after a reasoning request is received, an optimal sub-network is selected based on the trained routing decision network, corresponding weights are loaded in parallel, and a reasoning result is fused and output; and converting a reasoning result format, and dynamically optimizing a scheduling strategy based on a system real-time monitoring index. The method is compatible with a mainstream large model and a hardware platform, and an efficient and universal deployment scheme is provided for end-side AI engineering landing.

The invention discloses a power adaptive digital pre-distortion method, system and device based on sparse GRU and a medium. The method comprises the steps that input and output signals under different powers are collected, a composite feature vector is constructed through amplitude normalization and phase alignment, and a pre-distortion training target is generated in combination with indirect learning; constructing a sparse GRU neural network, introducing L1 regularization constraint, and obtaining a sparse pre-distortion model through joint optimization of mean square error and sparse constraint; partial weight updating is realized based on power grading, and weight fusion output is carried out during power interval switching so as to complete power adaptive optimization; pruning and compressing the sparse GRU model and then deploying the sparse GRU model in a pre-distortion module to compensate the nonlinearity of the power amplifier; according to the method, the model complexity is reduced through rarefaction, the dynamic working condition stability is improved through a power adaptive mechanism, pruning compression adapts to low-power-consumption hardware, high precision, low complexity and adaptive capacity are considered, and the method is suitable for the low-power-consumption and high-performance requirements of a broadband wireless communication system.

The invention relates to the technical field of intelligent construction, and discloses a bent cap intelligent jacking construction progress intelligent prediction method, which comprises the following steps: collecting multi-source real-time data such as equipment, structure, personnel and environment in a construction process, and calculating a construction entropy for measuring system uncertainty; establishing a causal state evolution model, and predicting the evolution trend of the construction entropy in a future time period; and when it is predicted that a construction progress disturbance risk exists, performing anti-fact deduction based on the model or the dynamically trimmed sparse model thereof, and generating and recommending an optimal adaptive correction strategy. According to the method, the overall uncertainty of the construction system is quantified into the construction entropy, and the prospective deduction capability of the causal state evolution model is combined, so that the conversion from the risk passive response depending on experience to the data-driven beforehand active early warning and optimal decision recommendation is realized; and improvement of scientificity, foresight and timeliness of construction risk management is facilitated.

The invention provides a sparse Transform model reasoning acceleration method based on a GPU platform, and the method comprises the steps: building a first storage format in a sparse Transform model, and carrying out the coding of a sparse mask of any type through the first storage format; wherein the first storage format comprises an inner layer block and an outer layer block; an outer layer block adopts a block compression sparse row structure, and row pointers and column indexes of a completely effective block and a partially effective block are recorded; the inner layer block adopts an unsigned 64-bit integer value bitmap and is used for representing masks in the block; based on the sparse mask matrix coded by the first storage format, performing sparse calculation on the query, the key and the value, and outputting a corresponding context vector; compared with a traditional sparse mask storage structure in the prior art that calculation sparsity and memory access regularity are difficult to consider when the masks present random, global or mixed distribution characteristics, the method has the advantages that support for any type of masks is achieved, storage overhead is reduced, memory access continuity is improved, good GPU parallelism is kept, and thread divergence and memory access conflicts are avoided.

The application relates to a shape memory alloy soft driver control method based on data-driven modeling, which comprises the following steps: measuring and collecting input and output data of a shape memory alloy soft driver as sample data; designing an asymmetric activation function containing a hysteresis characteristic, constructing a neural network structure and selecting corresponding parameters, combining sample data and an improved gradient algorithm for iterative training, simultaneously introducing L1 regularization to generate a sparse model, and obtaining a soft driver theoretical model; inputting current soft driver corresponding input data into the soft driver theoretical model, outputting corresponding simulation output data, and controlling the soft driver according to the simulation output data. Compared with the prior art, the application adopts a neural network model, combines an improved nonlinear activation function and a gradient descent algorithm, improves the modeling accuracy and robustness of a hysteresis system, and effectively improves the subsequent control effect.

The embodiment of the invention provides a gene sequencing data analysis method and device, computing equipment and a readable storage medium, and belongs to the field of data processing. The gene sequencing data analysis method is applied to user equipment, and comprises the following steps: encoding gene sequencing data of a user to obtain a plurality of gene sequencing plaintexts corresponding to the gene sequencing data; encrypting each gene sequencing plaintext by using the public key to obtain a plurality of gene sequencing ciphertexts; sending the gene sequencing ciphertext to a server; receiving a gene sequencing data analysis result ciphertext sent by the server; and decrypting the gene sequencing data analysis result ciphertext by using the private key, and determining a gene sequencing data analysis result of the user. And the user equipment sends the plurality of encrypted gene sequencing ciphertexts to the server, so that the privacy leakage of the user is avoided. And the server performs data analysis by using the preset sparse model, and processes the to-be-detected gene sequencing ciphertext, so that the gene sequencing data analysis efficiency is improved.

The invention belongs to the technical field of GIS fault detection, and discloses a GIS latent insulation fault diagnosis method and system under strong noise, and the method achieves the omnibearing signal capture through synchronously obtaining optical, mechanical, electrical and acoustic signals, and solves a problem of weak signal leak detection. For strong noise interference, noise and fault characteristic frequency bands are effectively separated by using multi-modal signal difference and constructing a time domain and wavelet domain cascaded double-layer dictionary. A discriminative sparse model is combined with a K-SVD algorithm to carry out end-to-end iterative optimization, sparse features with higher discriminative force are automatically mined, and the problem of deep feature mining is solved. For the problem of weak generalization ability of small samples, a classifier error term is introduced into an objective function, a joint optimization objective function fusing reconstruction and classification errors is constructed, dictionary learning and classifier training are combined into one, the generalization ability of the model is significantly enhanced, and the method is suitable for large-scale popularization and application. Therefore, timely and accurate identification of the latent insulation fault under the conditions of strong noise and small samples is realized.

The invention discloses a sparse reasoning method, system, equipment and product for large language model reasoning, and relates to the technical field of artificial intelligence. The method comprises the following steps: firstly, acquiring a weight matrix of a large language model, and then carrying out pruning sparsification processing on the weight matrix to obtain a sparse matrix; the storage format of the sparse matrix is converted into a bitmap coding storage format which is suitable for tensor calculation core perception and adopts a multi-level block structure to respectively correspond to different calculation granularities in a GPU / NPU hardware architecture so as to obtain a sparse model, and then the sparse model is deployed to respond to a reasoning request to perform reasoning service; sparse matrix multiplication is completed through a sparse matrix multiplication kernel based on on-chip storage bitmap coding so as to perform reasoning, so that through an innovative sparse matrix storage format and calculation optimization, the storage efficiency and the calculation performance in the reasoning process are remarkably improved, and especially in a low-sparseness scene, the reasoning efficiency is greatly improved. And the performance blank of the existing sparse reasoning framework in the field is filled.

The invention discloses a personalized federal learning method and system based on client clustering and sparse pruning, and the method comprises the steps: firstly constructing a central server-multi-client frame, initializing a full-size cluster model, a channel mask and an incremental pruning rate, and configuring SGD training, network slimming compression and encrypted transmission; performing dynamic re-clustering in each round of training, executing channel-level pruning by the client by using cluster structure information, performing local fine tuning, and uploading a sparse model; and the server carries out channel fusion on the model, and arithmetic averaging is carried out on the shared channel weight to generate a unified cluster model to be issued. After the target pruning rate is reached, the client continues personalized training and uploads, the server only executes FedAvg in the shared channel, the private channel keeps the original weight, and heterogeneous aggregation is achieved. A lightweight model with shared knowledge and private features can be obtained through iteration, the communication and storage overhead is remarkably reduced, and the method is suitable for a privacy-friendly intelligent reasoning scene of resource-constrained edge equipment.

A method, system, and transitory computer-readable media for providing rapid deployment of Deep Neural Networks (DNNs) for Edge Computing using Structured Pruning at Initialization (SPaI). An input of a dense model and a pruning amount is received. Unstructured Pruning (UP) of the input model is performed by the pruning amount to generate a sparse model pruned by the pruning amount. The sensitivity of each layer of the sparse model to pruning is evaluated by contrasting a sparsity of the sparse model with an average sparsity of a global model to generate a structured pruning plan. The structured pruning plan is applied to the resilient layers. A remaining sensitive layers are reinitialized to produce an initialized model pruned by the pruning amount. The initialized model pruned by the pruning amount is provided as an output.

The application relates to a deep learning channel estimation method and device suitable for an underwater acoustic sensor network. The method utilizes OFDM pilot data to establish a sparse signal model, and obtains a real-value sparse model by performing real-value transformation on the sparse signal model. An approximate message passing (AMP) estimation framework is introduced to realize preliminary recovery of channel information. Further, a model-driven deep learning framework is proposed. An ST-LAMP network based on AMP without considering sparse prior and a GGM-LAMP network considering sparse prior are respectively established. Network training is respectively performed according to a predetermined strategy. Optimal matching parameters are learned through data learning and are updated and fixed. Correct channel estimation values can be output through input of new measurement values. The problem that traditional empirical parameter setting cannot be applied to high complexity of an underwater acoustic channel, leading to deviation of channel estimation from an actual channel, is solved. The precision of channel estimation is improved. The channel estimation method has the characteristics of low complexity and strong self-adaptability.

The invention discloses an image registration method based on grouped motion estimation and neighborhood refining sampling. The method comprises the following steps: constructing an image registration network comprising a shared feature extraction module, a sparse motion module and a dense motion module; a source image and a target image are obtained to train the image registration network, and shared feature extraction, grouping motion estimation and neighborhood refining sampling are sequentially carried out until a loss function converges to complete training; a to-be-registered source image and a target image thereof are processed through a network, and finally a registration motion stream is obtained to register the target image to the source image. According to the method, sparse modeling is carried out on input features by introducing grouping motion estimation, the local deformation expression ability is enhanced in combination with an adaptive fusion mechanism, meanwhile, a neighborhood refining sampling method is designed, a learnable local weighted correction mechanism is introduced in the motion flow sampling process, and the local deformation expression ability is improved. The boundary definition and detail precision of the dense motion field are effectively improved, and the precision and robustness of image registration are remarkably improved.

Examples including a system described herein implement a novel liquid time constant neural network (LTC-NN) based architecture to recover an underlying model of physical dynamics from real world data. The automatic differentiation property of LTC-NN nodes overcomes problems associated with low sampling rate, the input dependent time constant in the forward pass of the hidden layer of LTC-NN nodes creates a massive search space of implicit physical dynamics, the physics model solver based data reconstruction loss guides the search for the correct set of implicit dynamics, and drop out in dense layer ensures extraction of the sparsest model. Further, to account for perturbation timing error, the LTC-NN based architecture of the system utilizes dense layer nodes to search through input shifts that results in the lowest reconstruction loss.

The present invention provides a communication-efficient federated learning method based on compressed sensing, which can be summarized as follows: first, dictionary learning is performed using a quasi-validation dataset held by the server to learn a sparse representation of the model parameters; then, an adaptive compression ratio selection algorithm is used to determine the appropriate compression ratio based on the model training loss; finally, the linearity of compression is exploited on the server side to reduce the computational cost of global model recovery from n times to one execution of the reconstruction algorithm; in addition, the computational cost of compression is reduced through layered compression. With the present invention, non-sparse model parameters can be effectively compressed and accurately reconstructed, supporting not only uplink and downlink compression but also reducing overall communication costs without compromising test accuracy. Experiments on three image classification tasks demonstrate that our method consistently outperforms existing methods, achieving high reconstruction accuracy even when using a small quasi-validation dataset to learn the sparse dictionary.

The invention provides a short-wavelength residual static correction method based on a target reconstruction algorithm, and belongs to the technical field of seismic data processing. Comprising the following steps: modeling seismic data, expressing the seismic data as a form of a plurality of waves, conforming to a target sparse model, and assuming that the target sparse model contains a plurality of targets and is accompanied by noise; target decomposition is carried out on the seismic data containing the noise and reconstruction is carried out by adopting an iteration method, one target is reconstructed each time, and residual errors after decomposition are continuously decomposed until reconstruction of all targets is completed; the minimization problem of the target reconstruction process is converted into a regularization optimization problem with constraint conditions; and solving a quadratic optimization problem about the target to obtain a minimum value depending on a shift vector, and completing short-wavelength residual static correction by solving the maximum weight from the vertex 1 to the vertex N + 1 in the K approximate graph and reconstructing the shift vector. The invention provides the residual static correction method with robustness in combination with the properties of the seismic data under the condition that the calculation cost is not increased.

The invention provides a costume design system and method based on artificial intelligence, and relates to the field of artificial intelligence, and the method comprises the steps: collecting multi-dimensional design data through a cloud collaborative architecture of costume design resources; performing dynamic hierarchical allocation on the multi-dimensional design data to obtain a hierarchical allocation result, and performing sparse processing on the multi-dimensional design data to obtain a sparse model of the costume design resources; performing strategy training based on deep reinforcement learning on the sparse model according to a hierarchical distribution result and historical collaborative data to obtain a sharing strategy and an optimized migration parameter; constructing a knowledge migration component of the cloud collaborative architecture based on the sharing strategy and the optimized migration parameters; knowledge migration optimization is carried out on the collaborative process of the costume design resources based on the knowledge migration component through the cloud collaborative architecture, the costume shared resources for intelligent generalization are generated, dynamic hierarchical distribution and knowledge migration optimization can be carried out on the costume design resources, and intelligent generalization sharing of the costume design resources is achieved.

The embodiment of the invention provides a data transmission method and device, a data processing method and device and electronic equipment, and aims to improve the processing efficiency during data transmission and parallel computing. The method comprises the following steps: for each to-be-transmitted data packet, performing structured sparse processing on the to-be-transmitted data packet through a data sparse model deployed at an initiating node; evaluating the data quality of each processed to-be-transmitted data packet to obtain the quality score of each to-be-transmitted data packet; the quality score is used for representing the proportion of effective data in the to-be-transmitted data packet; according to the quality score corresponding to each to-be-transmitted data packet, sequentially transmitting each to-be-transmitted data packet to the corresponding path node; and for each path node, determining respective semantic importance of each to-be-transmitted data packet received by the path node, and transmitting each to-be-transmitted data packet to a corresponding destination node according to each semantic importance.

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 application discloses a kind of training after sparse benchmark evaluation method and device.The method constructs a precise evaluation system by five indexes: sparse distribution mode, reconstruction technology, model architecture accuracy, size robustness and different task accuracy. After the original model is sparsified using the standardized sparse paradigm, the sparsification performance of the model is evaluated using these indexes. This method can compare different post-training sparse models horizontally, comprehensively evaluate the performance of the model from both the algorithm and the model, and distinguish the impact of sparsification and reconstruction on performance, as well as the impact of architecture, size or task on sparsification performance. Using the present application, not only the model compression efficiency is improved, but also an important reference is provided for model selection and optimization.

The invention relates to a motor fault diagnosis method based on sparse Bayesian and a one-dimensional neural network. The method comprises the following steps: S1, collecting a motor stator current signal, and carrying out filtering and preprocessing; s2, performing Hilbert transformation on the current signal to obtain an analysis signal; s3, constructing a sparse signal model in the truncated frequency domain and performing frequency grid division; s4, solving the sparse model based on sparse Bayesian learning, and correcting the deviation between the fault frequency and the grid by adopting an off-grid model to obtain an envelope spectrum; and S5, inputting the envelope spectrum into a one-dimensional convolutional neural network to output a motor fault diagnosis result. The method has higher frequency recovery precision, stronger robustness and fault recognition accuracy exceeding 98% in a complex environment.

The invention discloses a distributed sparse model fingerprint method based on dual-key driving, and the method comprises the following steps: inputting an original model, an owner secret key and an identity label into a computer system; generating a seed based on an owner secret key, calculating a global index set in one or more stable target convolution layers pre-selected in the model, and selecting a weight subset as a fingerprint carrier; constructing a random projection matrix based on the identity label; the weight vector and the random projection matrix generate an original model fingerprint; verifying the suspicious model, repeating the steps to obtain a fingerprint to be detected, and calculating the cosine similarity between the original model fingerprint and the fingerprint to be detected; and judging whether an illegal derivative relationship is formed based on a calibrated threshold value, and outputting an ownership judgment result. According to the method, identity and pseudo-random processes are bound through double keys, and distributed sparse sampling and normalized random projection are combined, so that the uniqueness and robustness of fingerprints are improved, various attack scenes can be effectively resisted, and the ownership of the model is accurately judged.

The invention provides an Internet of Things large-scale multi-user detection method based on accurate structure learning. The method comprises the following steps: acquiring a base station receiving signal and an equivalent channel matrix, and initializing a sparse proportion, a noise variance and a maximum iteration number; executing signal estimation under the constraint of a user frame sparse model, wherein the user frame sparse model requires that the active user sets of all time slots are the same; the method comprises the following steps of: (a) executing accurate structure learning based on a memory linear estimation result, and updating a user active probability through full-time-slot Bayesian fusion; (b) generating a detection output in the non-linear estimation; (c) when iteration continues, optimizing the intermediate variable by adopting dynamic damping control; (d) performing multi-time-slot joint optimization on the sparse proportion and updating noise parameters; and (e) judging an iteration state based on the reconstruction error: if a termination condition is met, outputting a detection result, otherwise, returning to the step (a).

This invention describes methods and systems for utilizing blockchain-verified sparse language models (SLMs) in enterprise applications. Sparse models address the limitations of large language models (LLMs) by reducing overfitting, bias, and complexity, while blockchain integration ensures result verification and maintains immutable records of model operations. The system employs smart contracts for automated validation and distributed consensus mechanisms to verify model outputs. This comprehensive approach leads to improved transparency, trust, and cost-efficiency through both model sparsification and blockchain-based accountability, making these verified SLMs particularly suitable for high-risk applications. The invention's combination of sparse modeling techniques with blockchain verification creates a robust framework for deploying trustworthy and efficient language models in critical enterprise environments.

This invention discloses an image restoration method based on a hybrid structured sparse model, specifically including the following steps: initializing the restored image and setting the number of iterations; constructing a matrix of similar image patch groups; establishing a hybrid structured sparse model; using the hybrid structured sparse model to sparsely encode each similar image patch group; reconstructing each similar image patch group; and restoring the entire image based on all reconstructed similar image patch groups. The image restoration method of this invention better reconstructs details such as edges and textures, and effectively suppresses unwanted visual artifacts, further improving the image restoration effect.

This invention proposes a multi-target detection method based on group sparsity OTFS communication sensing integration. The implementation steps are as follows: initializing parameters; constructing a sparse signal recovery problem based on the OTFS communication sensing integrated group sparsity model; solving the sparse signal recovery problem; and obtaining multi-target detection results. This invention constructs an OTFS communication sensing integrated group sparsity model and uses this model to construct a sparse signal recovery problem for N channels. During the iterative solution of the sparse signal recovery problem, the channel gain parameters and error terms are gradually updated, avoiding the impact of forced convergence on the channel gain estimate. Furthermore, the previously estimated channel gain parameters are used to gradually eliminate interference from other targets, thereby significantly reducing interference between multiple targets and avoiding the defect that interference between multiple targets increases with the number of targets, further improving target detection accuracy and robustness.

The application discloses a structural damage identification method based on an overlapping group sparse model, and comprises the following steps: arranging a sensor to collect structural excitation and response information; establishing a finite element model of a healthy structure; taking a unit stiffness change coefficient as a design variable; establishing a sensitivity matrix according to the sensitivity of a modal parameter to the change amount of the design variable; performing modal analysis on a damaged structure; obtaining the change amount of the modal parameter of the damaged structure and the finite element model; setting the number of overlaps and the number of non-overlaps of a sparse group according to the number of units of the structure; constructing a corresponding grouping matrix and rearranging the sensitivity matrix; solving a target function by using a split Bregman algorithm; considering the damage characteristic constraint design variable; iterating until convergence; and positioning and quantifying the damage according to the identification result. The method provided by the application is suitable for the case that the structural unit is divided into more units, can realize accurate positioning and quantification of the damage, and has high engineering application value.

The application discloses a direct positioning method and system based on a single-bit signal, and the method comprises the following steps: a single-bit model and a space sparse model of a received signal are established; a probability density function of a single-bit received signal in the single-bit model of the received signal is obtained according to a prior distribution of noise, a prior distribution of observation data and a Laplace prior distribution; a mean value and a variance expression of a space sparse signal and a maximum posterior probability density function of a target radiation source position are obtained; an optimal target function of a hyperparameter is obtained; the optimal target function of the hyperparameter is solved to obtain an updating expression of the hyperparameter; the updating expression of the hyperparameter and the mean value and the variance expression of the space sparse signal are alternately iterated and solved to obtain the mean value and the variance of the space sparse signal, and the space position of the target radiation source is obtained according to the mean value of the space sparse signal. The application can reduce signal transmission cost and effectively improve the positioning speed and precision of multi-target radiation sources.

The invention discloses a sparse model parameter-oriented target task adaptive parameter optimization transfer learning method, which is suitable for medical image classification and model optimization training. The classification contribution degree of each convolution kernel in a pre-trained CNN model to each target category of a target domain is calculated based on a feature attribution method, the convolution kernels are sorted according to the classification contribution degree, the convolution kernels with the correlation degree larger than a threshold gamma are screened out for optimization and fine tuning, and other parameters are frozen; and self-adaptive low-rank fine tuning: performing fine tuning on the frozen convolution kernel parameters by adopting low-rank adapters, including calculating function values to determine the rank of each adapter, and performing self-adaptive low-rank fine tuning on the low-correlation convolution kernels by allocating different ranks.

The invention provides a two-stage fine-grained structured sparse training traffic signal control method based on reinforcement learning, and the method comprises the following steps: S1, constructing a traffic signal control model based on deep reinforcement learning, and the traffic signal control model comprises a strategy network and a value network; s2, performing two-stage sparse model training on the traffic signal control model, and outputting a customized sparse model suitable for local features of each intersection; and S3, deploying the customized sparse model suitable for the local features of each intersection in the corresponding edge control device, and enabling each edge control device to operate the customized sparse model suitable for the local features of each intersection according to the real-time traffic observation data so as to generate a traffic signal control instruction. According to the invention, through structure recombination and a training mechanism, the resource efficiency is obviously improved innovatively.

The invention discloses a forging path optimization method and system and control equipment. The method comprises the following steps that S1, multi-source process data during forging are collected, and a multi-channel time sequence tensor is constructed; s2, determining a stress prediction result, a strain prediction result, a temperature rise prediction result, a grain average size prediction result and a corresponding confidence index of each position in the next time step based on a multi-channel time sequence tensor and a multi-scale sparse Transform model; s3, whether forging path optimization needs to be carried out or not is determined according to the result of S2; and S4, when it is determined that the forging path needs to be optimized, optimizing population iteration through a sparrow search algorithm to generate an optimal forging path. According to the method, the prediction precision of the model is improved, the average error of whole-field prediction of the next time step is reduced for a dynamic process scene of complex section turning and a local high-gradient region, real-time physical field feedforward information can be provided for forging path optimization, and a better process parameter combination or path can be found in combination with a sparrow search algorithm.