138 results about "Self correlation" patented technology

The invention relates to the technical field of power line communication, and discloses a frame synchronization method and device for broadband power line communication, and the method comprises the steps: carrying out the symbolization cross-correlation operation of a received signal, and obtaining a cross-correlation result sequence; performing secondary autocorrelation analysis and accumulation processing on the cross-correlation values in the sequence to generate an autocorrelation accumulated value sequence; screening candidate peak positions according to absolute values of the self-correlation accumulated values; defining a synchronous peak analysis window around the candidate peak position, and setting a noise reference window in an area far away from the window; verifying the validity of the candidate peak based on the difference between the autocorrelation accumulated values of the synchronization peak window and the noise reference window; and if the verification is valid, calculating and determining a final synchronization position through a mean value of a plurality of peak value positions in the synchronization peak window. According to the method, the problem of inaccurate synchronization peak positioning under the multipath effect and noise interference in the existing method is solved through the self-correlation accumulation and noise separation mechanism, and the reliability of frame synchronization is improved.

The invention discloses a power line carrier signal synchronization method and device, and belongs to the technical field of power line carrier communication. The method comprises the following steps: under the condition that a signal sequence is received, determining an autocorrelation result based on a known signal sequence; under the condition that the self-correlation result exceeds a dynamic threshold value, determining an effective correlation peak; the dynamic threshold is dynamically updated along with the change trend of the plurality of self-correlation results; and starting signal synchronization under the condition that the effective correlation peak and the historical effective correlation peak form a continuous correlation peak. According to the invention, through combination of cross-correlation and self-correlation, accurate detection of effective correlation peaks is realized, a dynamic threshold and continuity verification mechanism is introduced, the accuracy and robustness of signal synchronization are improved, the synchronization misjudgment rate can be significantly reduced, and the synchronization efficiency is improved.

The invention discloses a scraping surface morphology digital simulation method and system, and the method comprises the steps: 1, measuring a scraping surface, and obtaining the three-dimensional morphology data of the scraping surface; 2, calculating an overall surface parameter, a probability material ratio curve and an autocorrelation function of the three-dimensional shape data of the scraping surface; 3, obtaining three groups of surface height characteristic parameters according to the probability material ratio curve, generating a Gaussian surface by taking the parameters as input, and selectively superposing the Gaussian surface to obtain a surface with specified height characteristics; 4, calculating the power spectrum density of the scraping surface morphology through the autocorrelation function of the three-dimensional morphology data of the scraping surface; 5, generating a surface with a specified self-correlation function by taking the surface with the specified height feature and the power spectral density of the scraping surface morphology as input; step 6, reordering the surface with the specified height feature according to the sequence of the surface with the specified autocorrelation function so as to update the surface with the specified height feature; and 7, the step 5 and the step 6 are repeated until convergence is achieved, and the scraping surface with the specific height characteristic parameters and the specific space characteristic parameters is obtained. The method is suitable for digital simulation of scraping surfaces and multi-process surfaces.

The invention discloses an HRF signal synchronization method, device and system of a dual-mode carrier communication system, and belongs to the technical field of power line carrier communication. The method comprises the following steps: dividing a frequency offset range into a plurality of frequency offset hypothesis intervals based on an allowable maximum residual frequency offset range; performing frequency offset pre-compensation on the received signal according to each frequency offset hypothesis interval to obtain multiple paths of frequency offset compensation signals, and performing cross-correlation processing on the multiple paths of frequency offset compensation signals and a local reference sequence to obtain multiple paths of cross-correlation value sequences; in any cross-correlation value sequence, performing self-correlation processing on cross-correlation values of adjacent symbol intervals to obtain a corresponding self-correlation sequence; correlation peak values corresponding to the frequency offset hypotheses are extracted from the multiple paths of self-correlation sequences respectively, the maximum correlation peak value is determined from the correlation peak values, and if the maximum correlation peak value is larger than a synchronization judgment threshold value, the corresponding position is determined to be the synchronization position. According to the invention, high-precision wireless signal synchronization is realized, and high-quality signal transmission is ensured.

The invention relates to the technical field of network information security vulnerability monitoring, and discloses a network information security vulnerability monitoring method, system and device and a storage medium, and the method comprises the steps: obtaining network information security data; performing initialization processing on the network information security data to obtain a preprocessed data sequence; performing autocorrelation calculation on the preprocessed data sequence to obtain an autocorrelation sequence; performing cross-correlation calculation on the self-correlation sequence to obtain a cross-correlation sequence; screening out a suspicious parameter interval according to the self-correlation sequence and the cross-correlation sequence, and determining a suspected information leakage sequence; calculating based on the self-correlation sequence, the cross-correlation sequence and the suspected information leakage sequence to obtain the leakage probability of the sensitive data; and according to the leakage probability of the sensitive data, analyzing the security of the data information to obtain a corresponding security report. According to the method, the accuracy and efficiency of information monitoring can be improved, and the security and integrity of network data transmission are guaranteed.

The invention provides an ionosphere TEC prediction model integrating double mechanisms of self-attention and self-correlation, and belongs to the technical field of ionosphere delay prediction, and the model comprises a trend prediction module, a correlation analysis module and a sequence fusion module. Wherein the trend prediction module predicts the overall change trends of an ionized layer TEC sequence and space environment parameters by means of an autocorrelation operation mechanism; the correlation analysis module analyzes the mutual relation among the ionized layer TEC sequence, the space environment parameters and the longitude and latitude positions by using a self-attention mechanism; and the sequence fusion module performs further integration and optimization on the overall trend of the ionized layer TEC sequence by using the correlation information obtained by the correlation analysis module, and finally obtains an ionized layer TEC prediction result fusing the space environment and the latitude and longitude position information. According to the method, the spatial environment data and the longitude and latitude position information are fully utilized, and the accuracy of model prediction is greatly improved.

The invention relates to a sintering mixture moisture prediction method based on multivariable interaction correlation, and provides an interaction correlation door mechanism-based self-correlation door and mutual door closing algorithm aiming at ignoring a coupling relation and historical self-correlation between variables in an existing time sequence prediction method. An autocorrelation door and a mutual door are introduced to display the historical correlation of mining sequences and the cross correlation between process variables. In order to solve the problem that traditional time sequence prediction only takes the accuracy of a predicted value as an evaluation index, a model loss function fusing the predicted value and trend guidance is designed to provide feedback guidance for updating of model parameters, so that model information is more suitable for time sequence prediction and downstream trend prediction tasks. In conclusion, the method provided by the invention can accurately predict the moisture and the trend of the sintered mixture, has the advantages of high credibility, high accuracy and the like, and also provides an innovative idea for time sequence prediction.

The invention is applicable to the technical field of computer vision, and provides an autocorrelation domain adaptive image classification method based on dynamic balance, which comprises the following steps of: acquiring image data sets of two style fields, and processing the image data sets to obtain a source domain data set with a label and a target domain data set without a label; constructing a data weighting balance module, inputting the two image data sets into the data weighting balance module, and weighting according to the number of image samples in each domain; constructing an adversarial training module, and inputting the weighted image data set into an adversarial generative network multiplexed by a discriminator for training; constructing a dynamic balance parameter, and weighting by using the dynamic parameter to obtain a loss function; and based on the back propagation optimization generator and the discriminator, a classification model is obtained through multiple rounds of adversarial training. According to the method, a feature generator and a classifier are optimized by using balance parameters and adversarial loss, and a classification model capable of realizing reliable image classification in multiple domains is finally obtained.

The invention provides a residual electric quantity estimation method and device, electronic equipment and a storage medium, and relates to the technical field of power system energy storage control. The method comprises the following steps: determining a Kalman filter model according to target model parameters corresponding to a target battery; according to the innovation of a Kalman filter in the Kalman filter model, determining a corresponding relation among a target cross-correlation function, a target self-correlation function and an open-circuit voltage-residual electric quantity curve deviation; and determining the residual electric quantity of the target battery according to the corresponding relation. According to the method, accurate SOC estimation can be carried out on the battery under the condition that the open-circuit voltage-residual electric quantity curve has errors, so that the workload of continuously updating the open-circuit voltage-residual electric quantity curve by the battery management system is reduced.

The invention relates to a radar waveform design method for suppressing an autocorrelation sidelobe based on multi-carrier phase coding. The method comprises the following steps: acquiring a chaos sequence with pseudo-randomness; according to the chaos sequence, an OFDM method and a phase encoding method are adopted for processing, and an MCPC radar signal is obtained; according to the MCPC radar signal, the power difference between a maximized target and a sidelobe peak value, the minimum peak value sidelobe level and the minimum integral sidelobe level are accurately measured for processing, and a pulse compression sidelobe of the radar signal is obtained; pulse compression sidelobes of the radar signals are processed in a subcarrier amplitude windowing mode, and radar signals of multi-carrier phase coding suppression self-correlation and cross-correlation sidelobes are obtained; the self-correlation sidelobe is taken as an optimization target, and a genetic algorithm is adopted to optimize a carrier weighted window function to reduce the self-correlation sidelobe. The radar signal designed by the method has good anti-interference performance and sidelobe reduction performance.

The invention discloses an AD gait anomaly detection method based on wavelet transform and ARMA modeling. The AD gait anomaly detection method comprises the following steps that gait data and HRV data of a patient are collected and preprocessed; performing feature extraction on the preprocessed gait data by using a wavelet transform function; an ARMA model is constructed by combining the self-correlation function and the partial self-correlation function with the HRV data of the patient, a fusion model is constructed based on the ARMA model and an LSTM neural network, the AD gait anomaly degree of the patient is detected through the fusion model, and different levels of risk signals are sent according to the detection result. The method has the beneficial effects that the problems of noise sensitivity, short prediction period, insufficient specificity and the like in AD gait detection are solved through a three-order technical chain of wavelet transform-ARMA modeling-nonlinear judgment. More accurate AD gait anomaly detection is realized, so that a patient can receive early warning and carry out intervention in an AD early stage in real time.

The invention relates to the technical field of brake wear analysis, in particular to a service life prediction method and system for a brake disc. The method comprises the following steps: firstly, acquiring a real-time road condition difference degree according to a difference condition between pedal moving speeds corresponding to a current time period and a historical time period; integrating the braking habit influence coefficient and the braking habit wear coefficient to obtain an overall habit influence index; according to the pedal moving distance sequence of the reference time period, obtaining an autocorrelation coefficient; correcting the autocorrelation coefficient according to the overall habit influence index to obtain a corrected autocorrelation coefficient; and predicting the service life of the brake disc according to the pedal moving distance sequence of the reference time period and the corrected autocorrelation coefficient. The method fully considers the influence of the real-time driving habit on the wear of the brake disc, reasonably sets the corrected autocorrelation coefficient, and improves the prediction accuracy of the service life of the brake disc.

The invention belongs to the related technical field of satellite communication ranging, and particularly relates to a low-orbit satellite communication ranging integrated signal generation and analysis method, which comprises the following steps of: generating a pilot signal according to an ideal self-correlation sequence, and mapping the pilot signal and a modulated constellation symbol to an OFDM (Orthogonal Frequency Division Multiplexing) resource unit; generating a transmitting signal, performing Doppler frequency shift pre-compensation on the receiving signal at a receiving end, sampling to obtain a discrete signal, and generating a local sampling sequence; and respectively carrying out self-correlation operation on the received discrete signal and the local sequence in the time domain and the frequency domain to estimate time delay so as to obtain a corresponding transmission distance. According to the method, an optimization function of system communication and ranging performance can be further established, a communication error rate and a ranging error are minimized based on a genetic algorithm, and an autocorrelation sequence and a pilot frequency distribution matrix are obtained. By using the method provided by the invention, the system can realize the distance measurement function while reliable communication is realized, and the communication performance and the distance measurement performance can be balanced.

The invention provides a signal transmission method and device. The method comprises the following steps: determining a first signal; the first signal comprises a preamble signal or a postamble signal determined according to a first sequence; the first sequence is a sequence in a candidate sequence set, each sequence in the candidate sequence set comprises a first part and a second part, the number of elements included in the first part is the same as the number of elements included in the second part, and the number of element jumps in the first part is greater than the number of element jumps in the second part; and sending the first signal. Because each sequence satisfies the above characteristics, the self-correlation performance of each sequence in the candidate sequence set is good, so that better timing precision can be achieved, the success rate of demodulating the first signal by the receiving end is improved, and the system performance is improved.

The invention relates to the technical field of civil aircraft maintenance and repair, in particular to a flap health state prediction method based on transferable trend grouping autocorrelation comparative learning, which comprises the following steps of: firstly, mining a typical degradation trend from total data by utilizing a clustering algorithm; dividing the individual samples with differentiation into a plurality of trend data groups with high consistency; secondly, introducing an autocorrelation comparison mechanism into each group, capturing common time sequence dependence crossing a long period among samples, and constructing a plurality of pre-training models capable of representing a specific decline mode; then, dynamic matching is carried out according to the characteristics of the flap to be detected, and the most adaptive trend category and the pre-training model are selected; and finally, on the basis of a migration fine tuning strategy, a small amount of data is used for carrying out directional fine tuning on the pre-trained model, so that an exclusive prediction model of the flap is efficiently obtained, compared with the prior art, a model does not need to be built for each aircraft from zero, migration can be carried out on the basis of a limited model library, and effective management of the health state of the civil aircraft is achieved.

The invention provides a heterogeneous data intelligent fusion method, system and device based on a multi-modal sensor and a medium, and belongs to the technical field of sensor induction data fusion. Self-correlation and cross-correlation functions are calculated through recursive average, and then the variance of each sensor is estimated; calculating a trend correction factor based on the variance sliding average value, and determining the optimal weight of each sensor; calculating an average value of measured values of the sensor at each moment to form a sequence; a polynomial fitting order is adaptively selected through an AIC criterion, and a fitting function is established; and finally, outputting a final fusion result in combination with the weighted fusion value and the fitting value. According to the method, the accuracy, the stability and the adaptive capacity of data fusion of the multi-source heterogeneous sensor are effectively improved, and the anti-interference performance and the robustness of the system are enhanced.

The invention belongs to the technical field of industrial artificial intelligence, and particularly relates to an unsupervised multi-mode industrial anomaly detection method based on self-correlation learning. The method comprises the following steps: acquiring a to-be-detected RGB image and depth map data, and generating a multi-modal pseudo sample based on Berlin noise and texture filling so as to enhance the discrimination capability of a model for abnormity; rGB and deep modal features are fused by using comparative learning, and interaction information between modals is extracted; calculating local association and global association of the features by using a learnable Gaussian kernel function and a self-attention mechanism respectively; and finally, the association difference between the local association and the global association is used as a discrimination standard, and the reconstruction error is combined to calculate an anomaly score, so that the tiny anomaly on the surface of the industrial product is accurately detected and positioned. According to the method, the self-correlation learning framework which fuses the multi-modal features and uses the global-local correlation difference as the anomaly discrimination basis is constructed, so that high-precision anomaly positioning is realized under the condition of lack of anomaly sample labeling.

The invention discloses a complex environment recognition and intelligent interaction system for blind person navigation, and aims to provide safer and humanized travel assistance for blind persons or visually impaired persons. Based on a travel route planned by map software, a YOLOv8 target detection algorithm added with a CBAM attention mechanism is combined with a visual large model to carry out complex scene recognition, and a recognition result is transmitted to the large language model. And the large language model generates an active warning text or answers questions of the user according to the recognition result, and feeds back the questions to the user in a voice form to realize intelligent interaction. Meanwhile, on the basis of the average step length calculated by the monocular vision technology and the self-correlation analysis algorithm, the broadcasting unit is converted from'meter 'to'step', and the humanization of voice broadcasting is improved. According to the invention, front and back end information transmission is combined, and complex environment identification, voice interaction and humanized guidance technologies are introduced, so that the safety, interactivity and user experience of blind person navigation assistance are remarkably improved.

The present invention provides an ionospheric TEC prediction model that combines self-attention and self-correlation dual mechanisms, belonging to the technical field of ionospheric delay prediction. The model includes: a trend prediction module, a correlation analysis module, and a sequence fusion module. Among them, the trend prediction module predicts the overall change trends of the ionospheric TEC sequence and the space environment parameters respectively by means of a self-correlation operation mechanism. The correlation analysis module uses the self-attention mechanism to analyze the mutual connections among the ionospheric TEC sequence, the space environment parameters, and the latitude and longitude positions. The sequence fusion module then uses the correlation information obtained by the correlation analysis module to further integrate and optimize the overall trend of the ionospheric TEC sequence, and finally obtains the ionospheric TEC prediction result that fuses the space environment and the latitude and longitude position information. The present invention makes full use of the space environment data and the latitude and longitude position information, greatly improving the accuracy of model prediction.

The invention discloses a multi-target direct positioning method without signal source number estimation, which comprises the following steps of: performing data acquisition on a plurality of targets by using a distributed receiving station, and constructing a joint receiving signal model of each observation station in a distributed array scene by using receiving signals of the targets; constructing a space-time correlation matrix by using the autocorrelation coefficients of the target signal at different time intervals and the joint space steering vector of each receiving station to the target according to the joint signal receiving model of each observation station; setting interference parameters of position estimation, and constructing a direct positioning cost function by using the interference parameters, the space-time correlation matrix and the joint space steering vector at the assumed position; and performing grid point division on the effective monitoring area of the receiving station, solving the direct positioning cost function for different grid points, and obtaining a position estimation result of each target on the grid points. According to the method, the problems of low resolution, poor robustness, priori information dependence and the like of a classic algorithm in a current multi-target positioning scene are solved.

The invention relates to a related information enhanced two-dimensional angle estimation method based on tensor decomposition, which comprises the following steps: constructing tensor output of whole array receiving data, then obtaining a covariance tensor of the tensor output, and the covariance tensor is obtained on the whole array tensor output, so that the covariance tensor comprises cross-correlation and self-correlation information of array sub-area arrays. The method comprises the following steps: combining {1, 3} dimensions and {2, 4} dimensions of a covariance tensor to obtain a reconstructed matrix, rearranging elements in the reconstructed matrix to obtain an output matrix corresponding to a uniform array, synthesizing the matrix and a conjugate tensor of the matrix according to a third dimension to obtain a new combined tensor, performing spatial reconstruction on the combined tensor, performing tensor decomposition to obtain a combined factor of the tensor, and obtaining a final tensor. In conclusion, the method has the advantages that the spatial domain information and the time domain information of the received data are expressed in different dimensions, the angle estimation precision is improved, and the target radiation source is positioned more accurately.

Various embodiments of the present disclosure provide a method for beamforming. The method which may be at a network node and includes: obtaining channel information of a terminal device; determining a self-correlation factor of the terminal device according to the channel information; determining main lobe width according to a relationship between self-correlation factor and main lobe width; determining a weight vector on at least one transmitting antenna according to the determined main lobe width; and transmitting a message using the weight vector on the at least one transmitting antenna, to the terminal device.

The embodiment of the invention provides a frame synchronization method and device based on a WI FI system, and the method comprises the steps: carrying out the delay self-correlation of a short training sequence of a main channel and a secondary channel, carrying out the bandwidth detection of a current WI FI frame according to the delay self-correlation result of the main channel and the secondary channel, and when the bandwidth detection result of the current WI FI frame is greater than a specified bandwidth, carrying out the bandwidth synchronization of the current WI FI frame. And coarse synchronization is carried out according to a delay self-correlation result of the main channel and the secondary channel to obtain long training sequences of the main channel and the secondary channel, and a correlation peak is searched for a cross-correlation result of the long training sequences of the main channel and the secondary channel and a long training sequence of a local sequence to carry out fine synchronization so as to determine a symbol boundary of the current WIFI frame. According to the method and the device, the problems that only a main 20M channel is used, other channels are not used, and timing errors are deteriorated due to the length of the PPDU are solved, and the effects of improving the frame synchronization precision of the WI FI system and reducing the error code probability caused by the overlong length of the PPDU are achieved.

A method of processing an electrocardiosignal capable of accurately obtaining heartbeat information, a device for processing an electrocardiosignal, a computer-readable storage medium, and a computer program product. The method of processing an electrocardiosignal includes: a detection signal acquisition step of acquiring a detection signal by detecting an electrocardiosignal of a living body by a sensor; a signal preprocessing step of dividing the detection signal into a plurality of segmented signals with a prescribed time length, calculating an average value of a plurality of signals included in each of the segmented signals, and obtaining a preprocessed signal by subtracting the average value of the segmented signal from each signal sample point included in the segmented signal; and a self-correlation calculation step of calculating a self-correlation function of the preprocessed signal.

The invention relates to the technical field of intelligent warehousing, and particularly discloses an intelligent warehousing information acquisition method and system, and the method comprises the steps: obtaining the cargo amount at regular time according to a sensor installed at a warehousing space; arranging the cargo quantity according to the time information to obtain a cargo quantity array, and synchronously calculating an autocorrelation coefficient and a lag time difference of the cargo quantity data; comparing the self-correlation coefficient and the lag time difference of each storage space to obtain the similarity between any two storage spaces; and for any storage space, determining the image acquisition frequency according to the similarity between the storage space and all other storage spaces. According to the method, the goods change information of each storage space is periodically analyzed, the goods change similarity between each storage space and other storage spaces is determined according to the periodic analysis result, the importance degree of each storage space is evaluated, then the data acquisition frequency is adjusted, and the traditional fixed acquisition frequency is optimized.

The application discloses a water supply network sensor optimization arrangement method based on graph signal processing, and comprises the following steps: acquiring topological structure information of a water supply network, and establishing a user node weighted graph without direction and connection according to the topological structure information to determine a self-correlation localized graph information value of each user node and a cross-correlation graph information redundancy value between different user nodes; wherein the self-correlation localized graph information value of the user node is: the user node v i The cross-correlation graph information redundancy value between the user nodes v j According to the self-correlation localized graph information value of the user node and the cross-correlation graph information redundancy value between different user nodes, a target function is determined: user nodes meeting the target function are screened out as sensor nodes one by one until the number of sensors reaches the requirement.

The invention relates to an obstacle recognition method and device, electronic equipment and a storage medium. The obstacle recognition method comprises the steps that the aftershock value of a current detection period and the aftershock value of a historical detection period of an ultrasonic sensor are acquired; according to the aftershock value of the current detection period and the aftershock value of the historical detection period, calculating an aftershock average value of the whole operation period and an aftershock moving average value of the latest m detection periods; wherein the whole operation period is the operation time after the ultrasonic sensor is powered on; calculating an autocorrelation coefficient between the aftershock value of the current detection period and the aftershock value of the whole operation period according to the aftershock value of the current detection period, the aftershock average value and the aftershock moving average value; the self-correlation coefficient is compared with a preset self-correlation coefficient threshold value, whether the obstacle exists in the detection blind area or not is judged according to the comparison result, and the obstacle in the detection blind area can be effectively recognized when the obstacle and the probe are both static.

The application discloses a method for self-interference multipath channel estimation based on zero correlation zone sequence. The method comprises the following steps: in the channel estimation stage of the simultaneous full duplex communication, a zero correlation zone sequence is sent; by using the good autocorrelation and cross-correlation characteristics of the zero correlation zone sequence, the receiver uses the same zero correlation zone sequence to slide correlate with the received signal, and the self-interference multipath channel can be accurately decomposed into several single-path channels; and the single-path channel parameters are estimated by using the correlation peak and the known zero correlation zone sequence. The application can more accurately estimate the self-interference multipath channel parameters, and ensures the self-interference cancellation ability in the multipath environment.

A periodic detection method based on a time sequence comprises the following steps: acquiring the time sequence, preprocessing and encrypting the time sequence, and respectively setting a noise threshold value and a trend component proportion threshold value; carrying out self-correlation function calculation on the originally obtained time sequence; calculating the similarity difference of each sub-sequence of the autocorrelation sequence A; obtaining a period length T, a noise degree and a trend component proportion based on a minimum value of the similarity difference sequence; the noise degree and the trend component proportion of the sequence are calculated, and encryption is carried out after correction is carried out based on a preprocessing result; by means of the method, the problems that the period detection efficiency and accuracy of the time sequence period with the low signal-to-noise ratio and the trend component are low can be solved, and the operation and maintenance information processing efficiency and accuracy are improved.

The invention relates to the technical field of communication, and discloses a communication method, device and system. The method comprises the following steps: a first communication device generates a synchronization signal according to a first sequence and sends the synchronization signal; wherein the first sequence is ideal autocorrelation, the value set of elements of the first sequence is {-A, 0, A}, and A is a constant. By adopting the method, the first sequence comprises at least one element with the value of 0, and the first sequence is ideal in self-correlation, so that the detection complexity of the synchronization signal can be reduced while the detection performance of the synchronization signal is ensured.