68 results about "Regularization algorithm" patented technology

The invention discloses a high-precision near-field acoustic holography algorithm adopting a weighted iteration equivalent source method, which is characterized in that a holographic plane H is arranged in a sound source near-field radiation area, and sound pressure PH on the holographic plane H is measured; an equivalent source plane Se is arranged at the side, which is away from the holographic plane H, of an object reconstruction plane T, and equivalent sources are arranged on the equivalent source plane Se; a relation between the sound pressure PH and each equivalent source is established by using a sound pressure transfer matrix between the equivalent source and the holographic plane H; and the source intensity Q of each equivalent source is solved by adopting a new iterative regularization algorithm with a posteriori weighted norm constraint penalty term, and then sound field data on the object reconstruction plane T is calculated by using the solved source intensity Q and the transfer matrix between the equivalent source and the object reconstruction plane T. According to the invention, the source intensity of each equivalent source is precisely solved by using the new iterative regularization algorithm with the posteriori weighted norm constraint penalty term, thereby avoiding source intensity energy leakage caused by a 2-norm penalty term in the Tikhonov regularization process. Compared with conventional equivalent source based near-field acoustic holography, a calculation result acquired by the method disclosed by the invention is more accurate.

A method of acquiring scatter data and image projection data in computed tomography is provided that includes attenuating a radiation source using a pattern of blockers arranged to provide blocked and unblocked regions of the radiation source, and acquiring image data and scatter data of a target using an imaging device. A scatter map in the projection image can be estimated by interpolation and / or extrapolation of the projection image using an appropriately programmed computer, subtracting the estimated scatter map from the projection image to obtain scatter-corrected projections, reconstructing a CBCT volume using a total variation regularization algorithm, and applying an iterative regularization process to suppress the noise level on the reconstructed CBCT volume. Reconstructing a CBCT volume can include using a total variation regularization algorithm and applying an iterative regularization process to suppress the noise level on the reconstructed CBCT volume, where scatter-induced artifacts are corrected in the projection image.

The invention relates to a voice evaluation method and system based on voice similarity and the method comprises the following steps: providing a training data set; using the training data set to train the dynamic time regularization algorithm and the support vector sequential regression algorithm to obtain a similarity scoring model; providing reference voice information; recording, mocking and reading the mocked voice information for the reference voice information; extracting a set of reference voice feature sequences in the reference voice information and a set of mocked voice feature sequences in the mocked voice information; and inputting the set of reference voice feature sequences in the reference voice information and the set of mocked voice feature sequences in the mocked voice information into the similarity scoring model to obtain and output the similarity scoring value between the mocked voice information and the reference voice information. On the basis of evaluating the correctness of sound making, the similarity evaluation method of the invention is added by the evaluation of the mocking of speech-making so as to assist a user to accomplish targeted mocking practice and improve their speech-making level.

The invention discloses a lithium battery SOC prediction method of a bayes regularization LM-BP neural network. The lithium battery SOC prediction method comprises the following steps: a, establishinga BP neural network model; b, establishing a bayes regularization LM-BP neural network algorithm; c, acquiring sample data and calculating sample SOC; and d, performing the normalization processing of data. The neural network has good nonlinear fitting capacity and does not need to consider a complicated chemical structure inside the battery, dynamic characteristics of the lithium battery can bewell fit, by combining the bayes regularization algorithm, the generalization capacity of the network can be improved, by combining the LM algorithm, the convergence rate of the network can be increased, and the approximation accuracy can be improved; and therefore, the lithium battery SOC prediction method of the bayes regularization LM-BP neural network has the characteristics of high predictionprecision, high convergence speed, and high generalization capacity and is suitable for various power batteries.

The invention relates to a road abnormality detection model based on window partition and dynamic time regularization, comprising the following steps: 1) carrying out threshold detection and sliding window processingon z-axis acceleration data, and screening fragments to be determined; 2) comparing the acceleration data of the segment to be determined with the segments of several known abnormal types and normal road segments through a dynamic time regularization algorithm to obtain a difference degree vector; 3) determining the abnormal type of the segment to be determined. The road abnormality detection model has the beneficial effects that the abnormal section of the road can be intercepted completely, and the experiment result also proves that the abnormal condition of the road can be detected more accurately on different data sets, and the effect of the method is better than that of the existing method in terms of the two-classification or the multi-classification.

The invention discloses a social network event timing relation analysis method, which comprises the following steps: obtaining event detection result data, extracting event short text cluster time series, dynamically adjusting time series, and constructing a quantile-Quantile diagram to analyze the temporal relationship of events. At first, the invention extract the event short text cluster time series from the event short text cluster set, matches the time series of the event with a dynamic time regularization algorithm, then quantitatively calculates the time sequence distance of the time sequence correspondence relationship between the event short text clusters according to the matched result, and quantiles the time sequence distance of the event short text clusters according to the time sequence distance of the event short text clusters. Quantile graph visualization method qualitatively analyzes the temporal relationships among event short text clusters, which can significantly improve the recognition accuracy of event time series relationships in social networks.

The invention discloses a motion noise detection method based on a photoplethysmography signal, which lays a foundation for the follow-up work of heart rate measurement. In this method, multiple photoplethysmogram signals and acceleration signals in the same time period are collected by reflective photoelectric sensors and motion sensors; the acceleration signals are processed by principal component analysis to generate reference signals related to motion noise, and combined with The least mean square adaptive filter eliminates part of the motion noise; then multiple photoplethysmography signals and acceleration signals are processed to form a spectrum matrix, and the sparse structural features of the spectrum matrix rows are extracted to construct a sparse signal reconstruction model; finally, the regularization algorithm is used to Optimize the sparse signal reconstruction model to obtain the spectral peak positions of motion noise in the spectrum of multiple photoplethysmography signals. The invention can accurately detect the motion noise in the photoplethysmography signal, and realize the high-precision measurement of the heart rate.

The invention provides a seismic source wavelet optimal setting method and device. The seismic source wavelet optimal setting method comprises the following steps that seismic wavelets are extracted from seismic data to serve as a target wavelet and an initial seismic source wavelet; the initial seismic source wavelet is updated by means of the target wavelet and the initial seismic source wavelet through a regularization inverse algorithm; when a preset convergence condition is met, optimized seismic source wavelets are output. According to the seismic source wavelet optimal setting method and device, the optimized seismic source wavelets are obtained through regularization algorithm inversion, the initial seismic wavelets extracted from the seismic data can directly serve as the seismic source wavelets to be output to a two-dimensional migration and inversion process, and therefore the reliable seismic source wavelets can be provided for numerical simulation, migration imaging and full waveform inversion of acoustic waves and elastic waves.

The invention provides a method for identifying a lateral driving condition of a vehicle. The method includes the following steps: identifying a vehicle steering process; calculating condition identification data in the identified steering process, wherein the condition identification data includes the sum of weighted distance among a heading angle change angle, a heading angel history curve and apreset typical lane change template curve, maximum yaw angular velocity and lateral displacement, and the sum of weighted distance is obtained based on a dynamic regularization algorithm; and determining a driving condition to which the steering process belongs based on the calculated condition identification data, wherein the driving condition includes a turning driving condition, a U-turn driving condition and a lane changing driving condition. The invention also provides a system for identifying the lateral driving condition of the vehicle. Since the lane changing driving condition in thevehicle lateral driving condition is identified by combining the dynamic time regularization algorithm, the identification method has good adaptability and high identification accuracy, and the accuracy rate of lane changing conditions is more than 90%, thereby more accurately determining the driver's control condition of the vehicle.

The invention provides an air pollution prediction method. The method comprises the following steps: (1) acquiring air pollution meteorological data; (2) converting the air pollution meteorological data into a pixel matrix and performing data filling; (3) carrying out time-space domain-oriented spatial-temporal feature unified modeling through a three-dimensional convolutional neural network model; (4) taking the output of the three-dimensional convolutional neural network model as the input part of a convolutional long-short term memory network, and carrying out long-time and short-time dependent modeling; (5) generating an air pollution prediction model based on a space-time dynamic advection method; and (6) optimizing the air pollution prediction model through an orthogonal regularization algorithm, and then carrying out environment prediction. The decoupled three-dimensional convolution is used to carry out spatial-temporal feature unified modeling, the representation capability of the spatial-temporal feature extraction is enhanced, the fusion of the spatial-temporal features is truly realized, and the training cost is reduced and the training speed is improved while the spatial-temporal convolution performance is improved.

A load identification method based on numerical operation and an improved regularization algorithm comprises the following steps: step 1, establishing a discrete finite element model of a system, andutilizing an explicit Wilson-theta based load identification method to obtain a load identification model of the structure system through a load identification algorithm; 2, applying a dynamic load tothe structure, and measuring the response of the dynamic load; Step 3, constructing a load identification regularization model; Step 4, determining regularization parameters of the load identification model by using an L curve method, and substituting the regularization parameters into the load identification model to carry out calculation of load identification; 5, calculating is finished, and outputting a load identification result. The method is simple and convenient to operate in practical application, only the parameter data of the structure needs to be known, a corresponding load identification regularization model is established, and an unknown dynamic load can be identified by utilizing a response signal obtained through measurement; In addition, a traditional Tikhonov regularization method is improved, and the dynamic load identification precision of the improved regularization method is higher.

The invention discloses a method for adaptively measuring film thickness through white light reflectance based on a Bayesian regularization algorithm. The method is characterized by carrying out learning fitting on signals polluted by noise in dynamic measurement through a BP nerve network using the Bayesian regularization algorithm to obtain a fit WLRS curve; and obtaining characteristic values of the fit WLRS curve, and carrying out fast and accurate measurement of the thickness of a film to be measured. Through actual measurement, the method is proved to have better anti-interference capability and a certain adaptability.