122 results about "Mean square" patented technology

The invention discloses a pilot optimization method for large-scale MIMO channel estimation based on structural compressed sensing. The method comprises the steps of establishing a channel estimation model for a large-scale MIMO-OFDM (Multiple-Input-Multiple-Output-Orthogonal Frequency Division Multiplexing) system when pilots are placed in an overlapping mode; simplifying the channel estimation model for the large-scale MIMO-OFDM system, thereby enabling the channel estimation model to correspond to a structural compressed sensing model; and obtaining an optimum pilot matrix through utilization of a pilot optimization algorithm. Through adoption of the optimum pilot matrix, according to the channel estimation of the large-scale MIMO system based on structural compressed sensing, the mean square errors MSEs of the channel estimation are clearly reduced, and the channel estimation performance is improved.

The invention discloses a missile erection filtering method, system and device and a readable storage medium, and relates to the technical field of inertial navigation and the field of initial alignment. Based on the reference inertial unit pitch angle before erection, the reference inertial unit pitch angle at the current moment, the inertial unit angular velocity on the missile and the trajectory launching inclination angle, whether the missile is in any one of the non-target states of the non-erection state, the just erection state, the to-be-erected in-place state and the erection completion state or not is judged; if yes, measurement updating is not carried out in the Kalman filtering calculation process of the current moment, and time updating is carried out based on the state transition matrix of the current moment, the state vector of the previous moment, the mean square error matrix of the previous moment and the process noise variance matrix; and attitude information output by the inertial navigation system at the current moment is used as a missile attitude result for calculation at the next moment. According to the method, shaking between the guided missile and the launching box in the erecting process before launching is considered, and the attitude precision of initial alignment of the guided missile is improved.

The invention discloses a multivariable time sequence prediction method and system based on an implicit neural network. The method comprises the following steps: 1) collecting data and preprocessing the data; 2) performing window division on the standardized or normalized multivariable time sequence and determining the length of a to-be-predicted window; 3) performing variable correlation coding on the input window to obtain a variable-level feature vector; 4) the implicit neural network based on time attention predicts target parameters by using the variable features in the step 3), and implicit neural representation of the target sequence is modeled through the parameters; 5) taking the output of the implicit nerve representation and the original input window as the input of the multi-head attention predictor, and obtaining a prediction result through cross-sequence cross attention calculation performed in the implicit space and multi-layer perceptron conversion output dimension; and 6) training and optimizing model parameters, calculating a mean square error of a prediction result and a real result, taking the mean square error as a loss function, carrying out back propagation to optimize trainable parameters of the variable correlation coding module, the implicit neural network module multi-head attention predictor and the multi-layer perceptron, and then repeating the steps 3) to 6) to obtain the multi-head attention predictor. Until the preset number of iterations is reached or the error of the model on the verification set meets the requirement of early stop; and 7) performing prediction by using a model of training convergence, and performing reverse normalization on a prediction result to obtain a final prediction result. The method has good generalization, and meanwhile, the interpretability of the attention mechanism is remarkably improved by generating the hidden space characteristics of the trend component and the season component.

The invention discloses a high-precision quantization method for non-uniformly distributed data, and aims to solve the problems that reconstruction errors are remarkably increased and codebook resources are wasted when a traditional uniform quantization algorithm processes complex data distribution. According to the scheme, firstly, a dynamic programming algorithm is used for carrying out local homogenization processing on data, and optimal segmentation is achieved by minimizing the sum of squares of a weighting range; and then introducing a particle swarm optimization algorithm to carry out iterative tuning on the end points of the quantization interval, and constructing an efficient codebook by taking the minimization of a mean square error (MSE) as a target. Experimental results show that the quantization errors of the method on a 128-dimensional data set and a 420-dimensional data set are respectively reduced by 93.52% and 98.05% compared with those of a traditional method, meanwhile, hardware compatibility is kept, and the method is suitable for scenes such as edge computing nodes, AI chips and the like which have strict requirements on compression efficiency and real-time performance.

The invention relates to a KV cache compression method based on attention alignment, and belongs to the technical field of large language models. Comprising the following steps: a target model self-generates dialogue data reply, and constructs training data; adding a soft token into a word list of the target model, and carrying out soft token random initialization; finely adjusting specified parameters, splicing soft tokens at the tail of an original input sequence, and transmitting the soft tokens to the model; and respectively calculating the soft token and the attention distribution of the self-generated reply, calculating the mean square error of the soft token and the attention distribution of the self-generated reply as a loss function, and completing training. According to the method, a limited number of soft tokens playing an auxiliary role are introduced into original input, and importance discrimination of KV cache elements is realized and expelling is completed by aligning attention distribution of the soft tokens and real generated content; according to the method, better compromise is achieved between sequence length compression and performance loss, and the loss of model performance is better controlled while it is guaranteed that KV cache video memory space occupation is reduced.

The invention belongs to the field of water-wind-light multi-energy complementary optimal scheduling, and particularly discloses a water-wind-light optimal scheduling method and system based on a two-stage dual-population evolutionary algorithm, and the method comprises the steps: taking the water level of a reservoir as a decision variable, taking the maximum power generation amount and the minimum residual load mean square deviation as objective functions, and constructing a multi-objective scheduling model; the two populations are initialized, and the water-wind-light multi-target scheduling model is solved; the two populations are initially in a fixed division stage, and operators A and B are respectively adopted for iterative updating; when a switching condition is met, switching to a self-adaptive cooperation stage for iterative updating, determining the selection probability of the operators A and B according to population performance at the moment, and selecting the operators based on the selection probability for iterative updating; the switching condition is that after the population is iteratively updated each time, if the population diversity is smaller than a threshold value or the evaluation frequency reaches the threshold value, stage switching is carried out. According to the method, the contradiction between convergence and diversity in water-wind-light multi-objective optimization can be solved, and accurate optimization is realized.

The invention discloses a method and a system for predicting the service life of a rolling bearing in multiple failure modes based on Weibull distribution, and relates to the technical field of rolling bearing service life prediction. In order to solve the problem that the service life discreteness of a rolling bearing is large due to multiple failure modes such as early failure, random failure and fatigue failure under different working conditions, the method comprises the following steps: firstly, determining first prediction time by using a random convolution kernel binary regression model, and dividing failure time data based on the first prediction time; a plurality of failure modes are identified by using Weibull distribution; secondly, through a particle filter (PF) dynamic updating mechanism and a soft resampling technology, improving a cell gate and a hidden state of a long and short term memory network (LSTM), constructing a particle filter and long and short term memory network (PF-LSTM) fusion prediction model, and enhancing adaptive capacity to complex working conditions; and finally, constructing a multi-criterion loss function by using the probability density function of the Weibull distribution and the mean square error again, and further improving the prediction precision of the PF-LSTM by integrating the traditional mean square error and the priori knowledge of the Weibull distribution. Experiments prove that the method provided by the invention can predict the service life of the rolling bearing in multiple failure modes, compared with a traditional LSTM method, the average absolute error is reduced by 13.69%, and in addition, compared with a CNN-LSTM model, the prediction average score is improved by 0.169.

The invention discloses a motor multi-objective optimization design method and system based on a Kriging agent model. Comprising the following steps: constructing an initial data set through an experimental design method and finite element simulation calculation, so as to train an initial Kriging agent model which takes a motor design variable combination as an input variable and takes motor performance as an output response; the proxy model serves as a target function, a multi-target optimization problem is solved, the proxy model is updated, and an updating strategy is as follows: after each round of optimization is finished, a high-error solution in a current Pareto solution set is screened based on a Kriging model prediction mean square error, a finite element response of the high-error solution is obtained and supplemented to a data set, and the proxy model is updated and trained; and repeating the process until the optimization result converges. According to the method, high-uncertainty region samples are selectively supplemented, the calculation cost is remarkably reduced while the local prediction precision of the proxy model at the Pareto leading edge is improved, and the method has good practicability and economical efficiency.

The invention relates to a multi-channel multi-mode space-time 3D convolution and Transform fused satellite-borne GNSS-R wave height inversion model, and aims to solve the problems that a traditional empirical model is poor in adaptability to complex oceans and existing deep learning cannot fully excavate space-time correlation. According to the method, three modules are innovatively fused for cooperative processing: firstly, a 3D CNN-ConvLSTM is utilized to extract local spatial-temporal characteristics of multichannel GNSS-R data and capture a dynamic time sequence; secondly, performing deep coding on the sea surface environment parameters through a multi-head attention mechanism of Transform, and establishing global dependence between features; and finally, realizing cross-modal fusion by adopting a weighted summation and feature splicing strategy. And through training of an Adam optimizer and control of an early stop strategy, optimization is carried out by taking a mean square error as a loss function. Experiments show that compared with a traditional model and a machine learning model, the method has the advantages that the significant wave height estimation error is reduced by 40-53%, the correlation coefficient reaches 0.84-0.91, the precision and generalization ability under the complex sea condition are remarkably improved, and reliable support is provided for ocean remote sensing monitoring and disaster early warning.

The present invention discloses a method for denoising GNSS monitoring data of open-pit mine slopes by integrating multiple source indicators. The method comprises the following steps: S1, collecting GNSS monitoring data of the slopes and detecting and interpolating large gross errors using the 3σ method, then performing wavelet decomposition to obtain low-frequency coefficients and high-frequency coefficients; reconstructing the trend term Q using the low-frequency coefficients, solving the mean square error m using the first and second layer high-frequency coefficients, and then detecting gross errors again; S2, performing empirical mode decomposition on the GNSS monitoring data of the slopes to obtain a number of slope IMFs; screening to obtain high-noise modes, low-noise modes, and residuals, reconstructing the low-noise modes and residuals to obtain a preliminary denoised slope GNSS signal result EMD_A; and S3, processing the high-noise modes of the slopes using the interval soft threshold method and synthesizing them with the slope EMD denoising result EMD_A to obtain the final slope GNSS denoising result. The present invention achieves accurate denoising of GNSS monitoring data of open-pit mine slopes, providing more reliable data support for slope stability monitoring in mining areas.

The invention discloses a current transformer calibration method and device based on minimum mean square M estimation and a medium, and belongs to the technical field of current transformer calibration. The method comprises the steps that response signals are extracted from a to-be-calibrated transformer and a standard transformer, and response signal vectors are established; parameters of an N-order RVSSLMSM adaptive filter are initialized, the initial parameters and a to-be-calibrated mutual inductor response signal vector are substituted into the adaptive filter to obtain filter output, an error vector of an adaptive filter output signal vector and a standard mutual inductor response signal vector is calculated, and therefore a variance value is updated; and according to the variance variation, updating the parameters of the adaptive filter, substituting the response signal vector of the to-be-calibrated mutual inductor into the updated adaptive filter, and repeating the steps until the residual sum is converged or reaches a certain number of iterations, thereby obtaining the optimal coefficient of the filter and completing the calibration. And the calibration precision and the system robustness are effectively improved.

The invention provides a PMU harmonic data missing filling method and device based on multi-measuring-point data correlation, and aims to provide a higher-precision solution for a power grid harmonic monitoring system. When the harmonic data of the target station are missing, PMU measurement data of the target station and the adjacent monitoring stations in the same time period are extracted, and the PMU measurement data are divided into a known data set and a missing data set according to the integrity of the harmonic data; and performing strong correlation screening on the known data set: calculating type-by-type Pearson's correlation coefficients of harmonic data of the target station and PMU data of the target station and PMU data of other stations, removing data types lower than a threshold value, and retaining strong correlation data. Strong correlation data is used as an input feature, a target harmonic real value is used as an output label, a CNN-LSTM model (CNN extracts spatial features and LSTM captures time dependence) is adopted for training, and parameters are optimized through mean square errors until the error of a verification set reaches the standard. And finally, predicting a harmonic value of the missing data set by using the model, and finishing data restoration by taking a prediction result as a filling value.

The various implementations described herein include methods and systems for coding video. In one aspect, a video bitstream includes a current coding block of an image frame and signals a syntax element for a cross-component prediction (CCP) mode. When the CCP mode is enabled, a computing system identifies a reference area of the current coding block and downsamples the reference area to identify samples of a subset of reference area. A plurality of model parameters used in the CCP mode are determined for a first chroma sample of the current coding block based on the samples of the subset of reference area. The computing system combines a set of one or more luma samples (e.g., of a reference coding block) using the plurality of model parameters to generate the first chroma sample. The image frame is reconstructed based on the current coding block including the first chroma sample.

The application discloses an iterative classification matching data association method suitable for complex environment, and comprises the following steps: performing primary association; the characteristic group of primary association success constitutes a data set Z t + and F t + ; the characteristic group of primary association failure constitutes a data set Z t ‑ and F t ‑ ; solving a least square matching vector Θ according to the data set Z t + and F t + ; updating the data set F t ‑ by the least square matching vector Θ to obtain an updated data set, combining the data set Z t ‑ and F into new input, and performing iteration until a mean square error detection is satisfied. The application optimizes the data association method, improves the consistency of algorithm estimation, and reduces the algorithm calculation complexity, and makes up for the deficiency of ICNN and JCBB algorithms in large-scale underwater environment.

The invention discloses an interpretable data-knowledge-free distillation method based on a class activation graph, and the method comprises the following steps: inputting random noise to a generator, and generating a composite image similar to the distribution of a target data set; respectively inputting the synthesized image into a teacher model and a student model which are subjected to structure adjustment; respectively generating corresponding class activation diagrams through the teacher model and the student model; inputting the teacher class activation graph and the student class activation graph into a pre-trained scale mapping network, and performing cross-scale scaling on the class activation graphs; performing regularization processing on the scaled class activation diagram to obtain a standardized teacher class activation diagram and a student class activation diagram; calculating mean square error loss based on the teacher type activation graph and the student type activation graph to realize knowledge transmission; and finally, optimizing the generator in combination with information entropy loss, one-time heat loss and activation loss. According to the method, effective knowledge distillation can be realized under the condition of no real data, and the interpretability and stability of the distillation process are improved.

The invention provides an uplink MIMO-NOMA (Multiple Input Multiple Output-Non-Orthogonal Multiple Access) system optimization method with a maximized sum rate, which comprises the following steps: firstly, in order to suppress interference between a transmission cluster and a reflection cluster, providing an inter-cluster SIC method: clustering users according to transmission characteristics, adopting space division multiple access in each cluster, and performing inter-cluster SIC of NOMA transmission so as to reduce the interference between the clusters; secondly, a non-convex optimization problem aiming at maximizing the total rate of the system is formulated and solved by using an alternating optimization framework, in the framework, a receiving equalizer is optimized according to a minimum mean square error standard, and transmitting power control of a user is processed through successive convex approximation, so that sub-problems are easy to process; and finally, the beam forming of the active STAR-RIS adopts a sequential rotation design. According to the invention, the dual-fading effect is effectively overcome by adjusting the phase shift and amplitude of electromagnetic waves, an alternating optimization framework is also provided to solve the described non-convex problem, user power is controlled through continuous convex approximation, and active STAR-RIS beam forming is realized through sequential rotation.

In examples, a filter used to denoise shadows for a pixel(s) may be adapted based at least on variance in temporally accumulated ray-traced samples. A range of filter values for a spatiotemporal filter may be defined based on the variance and used to exclude temporal ray-traced samples that are outside of the range. Data used to compute a first moment of a distribution used to compute variance may be used to compute a second moment of the distribution. For binary signals, such as visibility, the first moment (e.g., accumulated mean) may be equivalent to a second moment (e.g., the mean squared). In further respects, spatial filtering of a pixel(s) may be skipped based on comparing the mean of variance of the pixel(s) to one or more thresholds and based on the accumulated number of values for the pixel.

The invention provides a load prediction method and related equipment. The method comprises the following steps: carrying out preprocessing and characterization processing on parameters correspondingly obtained on a day to be predicted to obtain feature data; inputting the feature data into a trained mean square error prediction model to obtain a mean square error value corresponding to the day to be predicted; the mean square error value is a mean square error between the to-be-predicted day and each historical day; determining a candidate day based on the mean square error value; calculating the trend similarity between each candidate day and the day to be predicted, and determining at least one similar day based on the trend similarity; and determining the load value of the to-be-predicted day based on the load value of the similar day and the mean square error value. According to the embodiment of the invention, through characterization processing of the to-be-predicted day parameters, in combination with the trained mean square error prediction model and similar day analysis, the accuracy and reliability of load prediction are effectively improved, and the method is suitable for complex and changeable practical application scenes.

The application discloses a path planning method based on deep reinforcement learning-fast exploration random tree, comprising the following steps: S1, obtaining a starting point and an ending point; S2, calculating candidate path points and selecting an action in an action state according to a Q value; S3, calculating a reward value and a new action state after the action is executed; S4, storing the action state, the action, the reward value and the new action state to an experience pool, in response to the number of stored experience values in the experience pool being greater than a batch size, randomly selecting experience values of the batch size, and updating the Q value and a time difference error through a policy network; S5, updating policy network parameters through a mean square error loss, and calculating a target network update step according to the time difference error; S6, judging whether a searched path reaches the ending point or satisfies a set maximum path point search number, if yes, outputting a current path, and if not, returning to S2. The application improves the search efficiency of the algorithm without increasing the search time of the algorithm.

The application discloses a current transformer calibration method and device based on minimum mean square M estimation, and a medium, and belongs to the technical field of current transformer calibration. The method extracts response signals from a to-be-calibrated transformer and a standard transformer, and establishes a response signal vector. Parameters of an N-order RVSSLMSM adaptive filter are initialized, the initial parameters and the response signal vector of the to-be-calibrated transformer are brought into the adaptive filter to obtain filter output, an error vector of the adaptive filter output signal vector and the response signal vector of the standard transformer is calculated, and a variance value is updated. According to the size of the variance change amount, the adaptive filter parameters are updated, the response signal vector of the to-be-calibrated transformer is brought into the updated adaptive filter, and the cycle is repeated until the residual error converges or a certain number of iterations is reached, the optimal coefficient of the filter is obtained, the calibration is completed, and the calibration accuracy and the system robustness are effectively improved.

The invention discloses a method for predicting an impact displacement time history curve of a concrete filled steel tubular column under the influence of defect coupling, comprising the following steps: S1, acquiring a data set: acquiring test data and verified finite element numerical simulation data, the data set sample comprising structural characteristic parameters of a component and a corresponding displacement time history curve under transverse impact, s2, data preprocessing: extracting geometric, material, defect and impact working condition parameters of the component to form an input feature vector, standardizing input features, performing time alignment and resampling on a displacement time history, and constructing a time sequence input tensor of a unified dimension; s3, a CNN-LSTM-Attention model is established, and the CNN-LSTM- S4, training and optimizing the model by using sample data, and taking a mean square error as a loss function; s5, target component parameters are input, and an impact displacement time history prediction result is output. According to the method, the impact displacement time history of the concrete filled steel tubular column containing multiple defects can be accurately and efficiently predicted, and the impact resistance evaluation efficiency of the structure is improved.

The application relates to the technical field of high-dimensional data, in particular to a high-dimensional data fitting algorithm and system based on an improved gradient descent, the algorithm is deeply coupled with a long short-term memory (LSTM) network, a gated differential adaptive momentum variable gradient descent (AM-VGD) is designed for high-dimensional time series data, high-precision fitting of high-dimensional data is realized, the gated differential adaptive momentum variable gradient descent (AM-VGD) is proposed, different momentum factors and gradient decoupling items are designed according to the gradient characteristics of the LSTM forget gate, input gate and output gate, the gradient propagation efficiency is improved by 40% under high dimension, the model convergence iteration number is reduced to <=500 times, the convergence speed is improved by more than 2 times, a stacked auto-encoder (SAE) is fused to decouple and extract high-dimensional features, a high-dimensional decoupling regular term of the gradient descent is combined, the collinearity interference between features is eliminated, the fitting determination coefficient R2 of high-dimensional time series data is greater than or equal to 0.98, the mean square error (MSE) is reduced by more than 60%, and there is no overfitting / underfitting phenomenon.

The invention discloses a lightweight attention-based long-term time series prediction method and system for an MLP architecture. The method comprises the following steps of 1, preprocessing time series data; 2, data grouping reconstruction is carried out; 3, performing feature extraction and representation on the obtained time sequence feature vector to obtain a time sequence feature; 4, processing the time sequence feature vector to obtain a prediction time sequence generated based on the training data set; step 5, training to obtain a time sequence prediction model PCOTS; step 6, obtaining a prediction time sequence generated based on the test data set; 7, calculating MAE and MSE between the prediction sequential sequence obtained in the step 6 and the prediction sequence in the test data set; 8, repeating the steps 2 to 7 until the MAE obtained in the step 7 and the mean square error MSE are not reduced any more, and obtaining an optimal time sequence prediction model PCOTS; and 9, inputting an input sequence given by a prediction task into the optimal time sequence prediction model PCOTS obtained in the step 8 to obtain a generated prediction time sequence.

The present invention relates to a method and system for constructing a spindle simulation component considering damage gradient. First, based on the simplified model of the spindle structural component, the damage gradient path at the critical point is determined, as well as the target damage values of each center point on the damage gradient path. Then, a spindle simulation component is constructed based on the structure at the critical point, and the reciprocal of the mean square error between the simulated damage values and the target damage values of each center point on the damage gradient path is used as the fitness function. The genetic algorithm is adopted to optimize the parameters of the spindle simulation component to make it consistent with the damage gradient of the spindle structural component.

The invention provides a music time coding method for artificial neural network modeling. The method aims at the coding requirement of music beat time, molecules are decomposed according to a fixed denominator for multi-bit independent classification coding, and the method comprises the following steps: setting the fixed denominator as a composite number and decomposing the composite number into a prime factor combination; the molecules are mapped into vectors, the value range of each bit is determined by a cardinal number of a prime factor combination, and the vectors are converted into one-hot coding sub-vectors with redundant components removed. The technical scheme of the invention solves the problem that the traditional mean square error (MSE) ignores the internal correlation of scores and has high sparsity in the topology recognition task of an optical music recognition system (OMR), and compared with a traditional coding method adopting floating point numerical output and a mean square error loss function under the conditions of the same data set, model architecture and training hyper-parameters, the method provided by the invention has the advantages that the coding efficiency is greatly improved; according to the method, the comprehensive error index is reduced by about 21%, and the model precision and the training efficiency are improved.

The invention discloses a self-adaptive BFP quantization method and device for deep neural network acceleration, and the method comprises the steps: calculating the mean square error of all blocks when a current tensor carries out the BFP quantization through employing a current block size, and if the mean square error is smaller than or equal to a quantization error threshold value, carrying out the BFP quantization. If not, performing BFP quantization on the current tensor according to the current block size, ending and exiting; otherwise, the current tensor is divided into a subset meeting the quantization error threshold value and a subset not meeting the quantization error threshold value, the subset meeting the quantization error threshold value is directly subjected to BFP quantization according to the current block size, and the subset not meeting the quantization error threshold value serves as a new current tensor; and reducing the size of the block to obtain a new current block size, and continuing iteration. The method aims at better balancing the model precision and the hardware overhead, and fundamentally reducing the dependence on floating point operation, so that the hardware cost is reduced.

The application discloses a channel estimation method and related device, wherein the method comprises: performing minimum mean square error (MMSE) channel estimation based on the product of the first amplitude factor and the first noise power of the first dimension, the first channel autocorrelation matrix of the first dimension, and the first diagonal matrix to obtain the channel estimation value matrix of the first dimension; wherein the channel estimation value matrix of the first dimension is obtained by performing MMSE channel estimation in the non-last step of the multi-dimensional step-by-step MMSE channel estimation; the first amplitude factor is a positive number less than or equal to 1; and performing MMSE channel estimation based on the channel estimation value matrix of the first dimension, the second channel autocorrelation matrix of the last dimension, the second noise power and the second diagonal matrix to obtain the multi-dimensional channel estimation value matrix. The method can improve the performance of the multi-dimensional step-by-step MMSE channel estimation.

The present invention discloses an evaluation method, device, system and storage medium for upper limb motor ability rehabilitation. The method includes: after the user to be evaluated completes a painting test game and a grip strength game using a rehabilitation ball, obtaining an initial painting image and a current painting image corresponding to the painting test game, as well as pressure information and game completion information corresponding to the grip strength game; determining the painting completion quality evaluation of the user to be evaluated based on the initial painting image and the current painting image, and determining the user muscle strength level of the user to be evaluated based on the pressure information and the game completion information, where the painting completion quality evaluation includes mean square error, peak signal-to-noise ratio and covariance; determining the upper limb motor ability rehabilitation evaluation information of the user to be evaluated based on the mean square error, peak signal-to-noise ratio, covariance and user muscle strength level, and the upper limb motor ability rehabilitation evaluation information includes muscle strength level and wrist joint damage condition. The present invention realizes the evaluation of comprehensive upper limb motor ability rehabilitation.