19 results about "Nonlinear smoothing" patented technology

This invention relates to a hybrid altitude measurement method employing dynamic integral Kalman filtering. First, a SiA200 MEMS accelerometer is used to measure the carrier's acceleration, and the acceleration is integrated twice to obtain inertial altitude information. An FYC-LG24-800 radio altimeter is used to measure the carrier's radio altitude information. A GPS device is used to measure the GPS altitude information of the carrier. A second-order square root nonlinear smoothing filter is designed to obtain smoothed signals from the three measured altitude signals. Then, a dynamic integral Kalman filter is designed based on the altitude error after the combined Kalman filtering and the nonlinear integral to adjust the variance information, thereby solving for the pairwise Kalman filtered signals of the three altitude smoothing signals. Finally, the error signal is superimposed with its nonlinear integral signal to generate a dynamic combined weighting factor in real time. The three Kalman filtered altitudes are then optimized and combined to obtain the final total altitude output signal.

The application discloses a point cloud rendering method and system, and relates to the technical field of computer graphics, and comprises the following steps: acquiring point cloud data of a detected object; S2, dynamically dividing a three-dimensional space into multiple rendering levels based on the real-time distance from the acquired point cloud data to a current observation point; based on each rendering level, respectively establishing a nonlinear smooth continuous function mapping relationship between rendering sampling probability and the observation distance of a rendering point radius, and respectively inputting a preset representative observation distance at each layer to obtain the rendering parameters of each layer; and applying the corresponding rendering parameters of the point cloud of different rendering levels to perform differentiated rendering operations. Advantageous effects: the hierarchical mechanism provides a structured framework for subsequent nonlinear parameter mapping, so that the adjustment of the rendering parameters can be based on an ordered spatial division closely related to visual perception, which is a prerequisite for the intellectualization of the whole method.

This invention relates to the field of satellite navigation and positioning and multi-source information fusion technology, specifically a low-cost, high-precision GNSS navigation method for complex urban environments. The method constructs a dual-channel adaptive weighting model based on scene classification results. Pseudorange observations are weighted using a nonlinear smooth reduction based on the Sigmoid function, while carrier phase observations are weighted using a two-way reward / penalty system based on signal-to-noise ratio and environment type. This invention achieves a smooth transition of pseudorange weights through the Sigmoid nonlinear function, avoiding filter divergence caused by hard cutoff. A unique open-field carrier reward mechanism improves positioning accuracy in ideal environments. Furthermore, without increasing hardware costs, only algorithm upgrades can significantly improve the navigation performance of low-cost chips in complex urban environments, demonstrating high engineering application value.

The application provides a kind of based on optimal solution Seismic Fault Feature Enhancement Identification Method and system, it is related to Seismic Fault Feature Identification technical field.The method includes obtaining Seismic Fault Image Data, feature seed point is picked up, and forms Seismic Fault Attribute Image;Seismic Fault Attribute Image is carried out nonlinear smoothing processing, and forms preprocessed fault image;Each feature seed point in preprocessed fault image is carried out dynamic programming, and the corresponding optimal surface path is determined;Different optimal surface paths are smoothed, and optimal selection score is determined;Optimal selection score graph is constructed according to optimal selection score;According to optimal selection score graph, fault feature information is extracted.The method enhances Seismic Fault Attribute Image, suppresses noise features irrelevant to fault, and at the same time makes fault features more clear and continuous, so as to improve the accuracy and efficiency of fault interpretation.

The invention discloses a gesture smoothing method, wearable equipment and a readable storage medium, and relates to the technical field of computer vision, the method comprises the following steps: receiving to-be-smoothed hand skeleton point data, and evaluating a current hand motion state according to the hand skeleton point data; if the motion state indicates that the hand slowly moves, selecting a preset linear smoothing algorithm as a target smoothing algorithm; if the motion state indicates that the hand rapidly moves, selecting a preset nonlinear smoothing algorithm as a target smoothing algorithm; and performing smoothing processing on the hand skeleton point data through a target smoothing algorithm to obtain smooth hand skeleton data so as to complete gesture smoothing processing. According to the invention, the robustness of the gesture smoothing effect is improved.

The present application belongs to the field of automatic control technology of power system, and particularly relates to a method and system for automatic regulation and control of power grid frequency disturbance fast response participated by a low-rotational-inertia energy storage system.S1, real-time frequency and system reference capacity data are collected, frequency deviation and frequency change rate are calculated, energy storage system energy storage state of charge and power ramp rate data are collected;S2, disturbance working conditions are determined, disturbance intensity coefficient is dynamically set, disturbance correction coefficient is preset, virtual inertia coefficient and frequency droop control coefficient are calculated;S3, frequency deviation-frequency change rate coupling coefficient is dynamically set, basic frequency modulation power is calculated, nonlinear smoothing correction coefficient is determined, final frequency modulation power is calculated;S4, according to the power ramp rate data, power quantization distribution of the hybrid energy storage system is carried out, whether to start or exit frequency modulation is determined, frequency modulation is carried out according to the final frequency modulation power, and frequency modulation is smoothly exited after frequency stabilization.The present application realizes fast automatic response and accurate frequency modulation of power grid frequency disturbance based on a hybrid energy storage system.

The present invention discloses a method for measuring the packet loss rate of a tire pressure monitoring system, which relates to the technical field of packet loss rate measurement. The method includes: constructing a physical communication model of the tire pressure monitoring system; comparing the sequence numbers of the original data packet and the received data packet with the expected sequence numbers to calculate a first packet loss rate; constructing a channel compensation model based on vehicle status data; calculating the channel interference error based on the physical communication model and the channel compensation model, compensating for the first packet loss rate, and generating a second packet loss rate; statistically analyzing the second packet loss rate using an outlier detection algorithm, identifying and correcting measurement errors caused by transient interference or external factors, performing smoothing processing to generate a third packet loss rate, and displaying the third packet loss rate as the tire pressure monitoring system packet loss rate. The channel compensation model is constructed by combining the physical communication model with vehicle status data, and an anomaly detection and correction algorithm is further employed. Combined with smoothing processing, continuous nonlinear smoothing of the packet loss rate is achieved, ensuring the authenticity of the final output data.

The present application relates to a kind of radar post-processing trajectory reconstruction method with outlier robustness.The method includes step 1, trajectory missile kinematics equation and radar measurement equation are integrated, and mathematical model for trajectory reconstruction is established;Step 2, according to the mathematical model established in step 1, the forward filtering process of nonlinear smoothing algorithm is designed based on maximum correlation entropy criterion and iterative solution method;Step 3, based on the forward filtering process, the backward estimation process of nonlinear smoothing algorithm is designed in combination with quadratic index, and high-precision smoothing estimation of missile position and velocity in radar post-processing stage is realized.The present application can be applied to missile target tracking and after recovery, trajectory characteristic analysis, trajectory model verification and other fields, enhance the robustness of trajectory estimation for radar measurement outlier, improve the precision of trajectory reconstruction.

Additional in-loop filtering can be using Gausian smoothing, anisotropic nonlinear smoothing, and / or enhancement. Two ideas are proposed. As an additional input to CC-ALF use filtered Chroma reconstruction Chroma buffers as an additional input to CC-ALF. New additional filter coefficients are trained by encoder and are signaled to the bit stream. From the decoder side signaled coefficients are read and CC-ALF applied; or As an additional input to Chroma ALF use filtered Chroma reconstruction Chroma buffers as an additional input to Chroma ALF. New additional filter coefficients are trained by encoder and are signaled to the bit stream. From the decoder side signaled coefficients are read and Chroma ALF applied.

This invention discloses a panchromatic sharpening method based on a deep recurrent network of a degradation model. The main steps include: analyzing and constructing a degradation model for high-resolution multispectral images; obtaining maximum a posteriori probability expressions for panchromatic and low-resolution multispectral images based on global nonlinear smoothing constraints according to the degradation model; constructing residual group convolutional neural network modules to replace various components in the expressions for panchromatic and low-resolution multispectral images respectively; alternately cascading the constructed panchromatic and low-resolution multispectral image expression networks to form a deep recurrent fusion network; uniformly training the deep recurrent fusion network using a training dataset, and outputting the final fusion result. The method described in this invention achieves good subjective and objective results and makes the network interpretable to a certain extent. Therefore, this invention is an effective panchromatic sharpening method based on a deep recurrent network of a degradation model.

The application discloses a snore detection and pressure control method and device in the technical field of respiratory medical equipment and a breathing machine. The method extracts snore disturbance by multiplexing a breathing flow rate detection link and performing band-pass filtering on a breathing flow rate signal, and introduces a double sliding time window to calculate a flow rate change trend feature, thereby effectively distinguishing snore from non-target interference at a feature level. Then, snore indexes of an inspiration phase and an expiration phase are calculated and integrated to obtain a total snore index. Further, a nonlinear smooth mapping relationship between the index and an output pressure adjustment amplitude is established through a Gaussian function, and adaptive closed-loop automatic pressure control is realized. The scheme solves the problems of insufficient detection stability, complexity of implementation and disconnection with pressure control in the prior art, significantly improves the accuracy and anti-interference ability of snore detection without increasing hardware cost, and enables the detection result to directly and smoothly link output pressure regulation, thereby enhancing the intelligence and effectiveness of the breathing machine.

The invention discloses a self-adaptive nonlinear clutter suppression method based on MUSIC high-resolution spectrum estimation, relates to the technical field of radar signal processing, and aims to solve the problem of undersuppression or over suppression caused by the fact that spectral shape features of miscellaneous wave crests cannot be distinguished generally in an existing method, and the self-adaptive nonlinear clutter suppression method based on MUSIC high-resolution spectrum estimation is obtained by performing quantitative analysis on morphological parameters of the miscellaneous wave crests in a Doppler spectrum to generate an asymmetric notch filter. And targeted suppression of complex multi-peak clutters is realized. A nonlinear flat wave suppression mechanism is introduced into the notch, peak and spectrum leakage is effectively removed, and meanwhile, real energy distribution of a dynamic target is reserved; and in combination with a guard band strategy, a low-speed target is prevented from being mistakenly suppressed in a near-zero-frequency region. And the problem of under-suppression or over-suppression in the prior art is avoided.

The invention discloses a hyperspectral image compression and reconstruction method based on graph tensor structure sparse constraint, and belongs to the technical field of image processing and spectral remote sensing, and the method comprises the steps: 1, constructing a basic hyperspectral image compression and reconstruction model based on regular term constraint, and obtaining a compression measurement value of a hyperspectral image; 2, constructing a sparse constraint regular term based on a tensor structure; step 3, constructing a non-linear smooth regular term based on the graph tensor; 4, fusing a tensor structure sparse constraint regular term and an image tensor nonlinear smooth regular term, replacing a sparse regular term of the basic hyperspectral image compression and reconstruction model in the step 1, and forming a final hyperspectral image compression and reconstruction model; and 5, solving the hyperspectral image compression reconstruction model constructed in the step 4 by adopting an alternating optimization method, and finally outputting a reconstructed hyperspectral image. The method can effectively solve the problems that a hyperspectral image is large in data size and difficult to store and transmit, and an existing compression reconstruction method neglects multi-dimensional structure information.

The invention relates to the technical field of desulfurization island bed pressure and material level control, and discloses a desulfurization island bed pressure and material level cooperative control method. In order to solve the problems of large bed pressure fluctuation, non-uniform material level, poor system stability and high operation energy consumption caused by dependence on manual operation in the prior art, flue gas air volume, bed pressure drop and ash hopper material level signals are collected in real time, an optimal bed pressure target value is calculated through a dynamic bed pressure setting model, a total regulating variable is generated through bed pressure main control, and the total regulating variable is calculated through a dynamic bed pressure control model. According to the control method, the adjusting quantity is intelligently distributed to each ash bucket valve in combination with a material level balanced distribution algorithm, automatic cooperative control of stable bed pressure and uniform material level is realized by adopting nonlinear smooth adjustment and a self-adaptive optimization technology, the system stability is remarkably improved, and meanwhile, the operation energy consumption is reduced by optimizing bed pressure setting.

The present application discloses a high-resolution time-varying spectrum simulation method, device, electronic device, and medium. The method may include: performing a Gabor transform on seismic data to obtain a time-frequency amplitude spectrum and a time-frequency logarithmic spectrum; performing nonlinear smoothing on the time-frequency logarithmic spectrum to obtain a mean-filtered time-frequency logarithmic spectrum; obtaining the attenuation trend of the time-frequency logarithmic spectrum in the time direction and calculating the time-varying wavelet time-frequency logarithmic spectrum; calculating the time-varying wavelet amplitude spectrum based on the time-varying wavelet time-frequency logarithmic spectrum; calculating the frequency band widening at the low-frequency and high-frequency ends of the time-varying wavelet to determine the expected output of the time-varying wavelet; calculating a high-resolution operator based on the time-varying wavelet amplitude spectrum and the expected output of the time-varying wavelet; and calculating the high-resolution time-varying spectrum result. The present invention designs a reasonable high-resolution operator based on the time-varying signal-to-noise ratio characteristics of the data, achieving the purpose of reasonably improving the resolution of shallow, medium, and deep data and easily controlling parameters, thereby providing a high-resolution data foundation for subsequent inversion and interpretation.

The invention relates to the technical field of intelligent retrieval and knowledge service, and discloses an intelligent retrieval and reference material generation method and system for report compiling. The method comprises the following steps: receiving at least one original query keyword input in a report compiling scene, and generating a keyword set based on a subject analysis demand; vectorizing and aggregating the data into a comprehensive semantic vector representing the intention of the report; retrieving by taking the keyword set and the comprehensive semantic vector as a mixed query condition to obtain an initial material; on the basis of the requirement of the report on the credibility of the material, calculating a correlation score by using a rearrangement model, and obtaining a standardized material confidence score through normalization and nonlinear smooth mapping processing; and according to a scoring result, outputting a sorting list which can be used as the alternative reference materials of the report. According to the method, the problems of low efficiency of manual searching and reference material screening and difficulty in credibility verification in report compiling are solved, and the support efficiency and quality of an intelligent retrieval system based on big data in professional content generation are improved.

The application relates to a high-precision automatic gain control feedback adjustment method and system, which comprises the following steps: calculating a real-time power deviation value of an input signal; taking the power deviation value as an index to query a lookup table to obtain a coarse gain adjustment factor; using state monitoring logic to dynamically switch a filtering strategy according to the state of the deviation value to perform nonlinear smoothing processing on the coarse gain to output a fine gain adjustment factor; and using a multiplier to perform closed-loop gain compensation. The application replaces hardware division operation by table mapping, and uses three-state control logic including locking, rapid suppression and smooth recovery to reduce hardware resource consumption, consider noise suppression in a steady state and rapid response in a transient state, and realize high-precision gain locking.

The embodiment of the invention belongs to the technical field of laser plane processing, and relates to a signal noise reduction method and device, processing equipment and a storage medium, and the method comprises the steps: receiving a signal noise reduction instruction carrying original signal data; performing local linear regression fitting on the original signal data to obtain high-frequency change feature data; calculating residual data of each data point according to the high-frequency change characteristic data and the original signal data; calculating the interpolation bandwidth of each data point according to the residual data; performing interpolation on each data point of the original signal data according to the interpolation bandwidth to obtain interpolation signal data; and performing first nonlinear smoothing processing on the interpolation signal data according to a first bandwidth preset value to obtain noise reduction signal data. According to the invention, the problem of over-smoothing or under-smoothing of a traditional signal processing mechanism can be effectively solved.

The application discloses a three-dimensional geological structure intelligent modeling method and system based on deep learning, relates to the technical field of geological structure modeling, and comprises the following steps: data feature extraction and mapping are completed through gravity-magnetic collaborative inversion deep network to generate a geological physical parameter field; topological relationship construction and coding conversion are completed by using a space topological constraint three-dimensional coding model to obtain a topological feature matrix; then, a stratigraphic interface nonlinear smoothing fitting algorithm is used to process interface feature points to obtain continuous stratigraphic interface data; subsequently, a multi-scale rock mass integrated intelligent modeling simulation platform is used to complete rock mass layering, parameter calibration and generate a preliminary model. Through the collaborative cooperation of the deep network, topological coding, nonlinear fitting and integrated simulation platform, the application improves the geological topological expression, stratigraphic fitting and overall modeling precision, reduces manual operation, and adapts to intelligent modeling work under complex geological scenes.