46 results about "Nonlinear noise" patented technology

The invention discloses a signal processing method based on deep learning and adaptive filtering fusion. The method comprises the following steps: S1, collecting and preprocessing an original signal; s2, carrying out adaptive filtering; s3, constructing a deep neural network model to perform deep feature extraction on the filtered signal; s4, signal fusion and interference elimination are carried out; S5, a real-time closed-loop feedback mechanism is established, and dynamic parameter adjustment is carried out; s6, performing an end-to-end dynamic optimization process; s7, performing iterative updating and high-confidence sample judgment; and S8, processing a newly collected signal by using the trained adaptive filtering module and the deep neural network model, and generating a reconstructed signal through signal fusion. According to the method, through effective fusion of adaptive filtering and the deep neural network, accurate suppression and high-dimensional feature extraction of a multipath effect, nonlinear noise and transient interference in a complex wireless signal are realized, so that the precision, robustness and real-time performance of signal processing are remarkably improved.

The invention discloses a precise and synchronous collaborative data signal analysis method based on dual-channel optical fiber sensing, and relates to the field of collaborative data signal analysis, polarization compensation and nonlinear noise decoupling analysis are carried out on time domain optical signals collected by independent A and B sensing channels to obtain polarization dimension-increasing coherent collaborative signals, and the collaborative data signal analysis is carried out on the time domain optical signals. Performing quaternion mapping and transformation compensation analysis to obtain a pure polarization state of strain immunization, performing fractional differential, HOSVD decomposition and time varying Jacobian matrix propagation disturbance based on the pure polarization state of strain immunization to obtain a non-equilibrium entanglement feature vector, combining quantum Bayesian update and a KLD weight model, generating a particle set through state sampling, and obtaining a non-equilibrium entanglement feature vector. And performing directional transfer entropy calculation on the particle set, constructing a weighted Dirichlet clustering model and directional causal energy flow analysis, comparing dual-channel energy values to obtain a channel fault judgment result, and performing channel fault judgment timely and accurately.

The invention relates to the technical field of optical communication, in particular to a wavelength adaptive configuration method of a CWDM / DWDM hybrid wavelength division system, which comprises the following steps of: acquiring physical layer parameters of an optical fiber link in real time; outputting the OSNR margin and the nonlinear noise prediction value of each wavelength position; dynamically adjusting the reservation proportion of the channel; collaboratively optimizing bandwidth, time delay, damage and cost constraints in path calculation through a reinforcement learning algorithm; splitting the CWDM channel into virtual sub-channels, or combining the virtual sub-channels to generate a super-channel; compared with the prior art which depends on an empirical formula to estimate transmission damage and has the defects of extensive modeling and cross-band crosstalk missing detection, the method provided by the invention has the advantages that a power spectrum, a temperature gradient and nonlinear interaction are analyzed in real time through an LSTM-GNN combined architecture, and a mixed channel damage map covering a full frequency band is constructed; the method has the advantages that cross damage of a CWDM wide channel and a DWDM dense area is accurately quantified, and high-crosstalk wavelength combination is dynamically avoided.

The invention discloses a swimming skeleton point coordinate data denoising method and device based on spatio-temporal topological structure learning, and belongs to the field of data processing. The method comprises the following steps: acquiring and splicing a swimming video of a target object; detecting a target object in the video, and obtaining skeleton point coordinates and corresponding confidence scores of the target object by adopting a posture estimation model; preprocessing the coordinates to obtain feature vectors; adjacency information of each skeleton point is obtained based on the human body skeleton topology, the feature vectors of all the skeleton points are input into a spatial domain noise removal network, the adjacency information of the skeleton points is aggregated, multi-scale pooling is carried out, and denoised spatial domain features are obtained; sampling the high-dimensional spatial-temporal characteristics by adopting a deformable time convolutional network; and carrying out deformable convolution operation and decoding operation on the sampling features to obtain skeleton point coordinates after time-space domain denoising. Linear and nonlinear noise in data is processed in a time-space domain in a cooperative manner, so that the limitation of a traditional low-pass filter method and an existing ST-GCN method is effectively overcome.

The present invention belongs to but is not limited to the field of motion control technology, and in particular relates to a high-order full-drive system control method and system for a networked multi-axis motion system, including: S1, analyzing a networked multi-axis motion system model with time delay, system uncertainty and nonlinearity, noise and external interference, giving its differential equation, discretizing it so that the system meets the full-drive conditions, and obtaining its high-order full-drive system model; S2, designing a high-order full-drive system control law, establishing a system linearized state space equation, normalizing the system time delay, system uncertainty and nonlinearity, and external interference, and processing them into system lumped interference; S3, designing a filter-based equivalent input interference estimator to achieve a good estimation of the lumped interference, and then designing a controller with interference elimination function to ensure that the networked multi-axis motion system with time delay, system uncertainty and nonlinearity, noise and external interference has control performance.

The invention discloses a graph signal blind source separation method in a white noise environment, and belongs to the technical field of signal processing. Aiming at the problem that the traditional blind source separation performance is reduced due to additive white noise, the invention provides a joint optimization scheme combining blind compression nonlinear noise suppression and image smoothing regularization. Firstly, a blind compression function is applied to a noisy observation image signal for preprocessing; secondly, carrying out mean value removal and whitening processing on the compressed signal; then, constructing a graph Laplacian matrix as a smoothing operator, establishing a joint diagonalization objective function fusing graph autocorrelation, a FastICA item and a graph smoothing regular item, and adopting a Givens rotation algorithm to iteratively optimize and solve a separation matrix; and finally, reconstructing a source signal through multiplication of the separation matrix and the whitening data. According to the method, noise interference is effectively suppressed through blind compression, signal structure consistency is maintained by using graph smoothing prior, and separation robustness, precision and convergence stability in a strong noise environment are remarkably improved.

A code error rate estimation device included in an optical transmission system using a direct detection receiver, the code error rate estimation device including: a transmission path model creation unit that creates a physical model of a transmission path for each candidate path for performing communication between user devices that perform communication; a propagation waveform calculation unit that generates an electric field signal waveform to be output from a transmitter assumed in the physical model of the transmission path and generates a reception signal waveform at the time of direct detection by using linear fiber propagation simulation; a nonlinear noise calculation unit that calculates nonlinear noise light intensity of light on the basis of the physical model of the transmission path; a noise intensity conversion unit that converts the calculated nonlinear noise light intensity of the light into noise in an electrical stage; a reception signal waveform calculation unit that calculates a Gaussian distribution of each symbol or each sample in a reception signal waveform at the time of the direct direction on the basis of the reception signal waveform at the time of the direct detection and the converted noise in the electrical stage; and a code error rate calculation unit that calculates a code error rate on the basis of the Gaussian distribution of each symbol or each sample.

Data signals transmitted over cable networks experience degradation, such as frequency-dependent loss, spectral tilt, signal-dependent nonlinear noise, distortion, and compression, which vary with cable characteristics, temperature, electrical loading, and traffic conditions. The present invention relates to improving network performance by estimating cable signal-degradation using physics-based nonlinear system modeling and telemetry data and applying corrections to operating parameters of cascading amplifiers to offset the signal degradation, thereby improving overall network efficiency and reliability.

To enable GOSNR obtained by adding nonlinear noise power to be measured from signal light.SOLUTION: According to the present disclosure, there is provided a GOSNR measuring device comprising a calculation processing unit (14) that: using Stokes parameters acquired by propagating signal light through an optical fiber transmission path (93), calculates a total noise power (Ptotal-noise) obtained by adding together a nonlinear noise power (Pnl) and an ASE noise power (Pase) in the optical fiber transmission path; and measures a GOSNR using the total noise power.SELECTED DRAWING: Figure 1

The application provides an air conditioning system active noise reduction method and system based on adaptive order, relates to the active noise reduction technical field, and comprises the following steps: collecting environmental noise signals and pre-processing to obtain input signals; the input signals are subjected to adaptive order Volterra nonlinear expansion to obtain expanded signals; the expanded signals are input into a non-normal FIR filter, the filter output signals are obtained through non-normal FIR filter processing, and the filter output signals pass through a secondary path to obtain reverse noise signals; the reverse noise signals are input into a noise environment for noise cancellation, and residual error signals after noise cancellation are collected; filter parameters of the non-normal FIR filter are updated according to the residual error signals. The foregoing steps are repeatedly executed until a preset noise reduction requirement is reached. The application improves the real-time performance, stability and nonlinear noise suppression capacity of the air conditioning system active noise reduction under complex working conditions.

The invention relates to the technical field of communication transmission, and provides an equivalent simulation method and device for transmission performance evaluation of an optical fiber communication system, and the method comprises the steps: generating a signal power density spectrum of each span according to a link parameter; calculating a nonlinear noise power density spectrum according to the link parameters and the signal power density spectrum of each span; and taking the signal power density spectrum as an input parameter of the equivalent simulation model, and calculating a nonlinear damage index at the tail end of the optical transmission system to obtain transmission performance evaluation. A simulated signal power density spectrum is used for replacing a tested signal power density spectrum, the nonlinear damage simulation precision is still kept under the condition that model parameter dependence is reduced, and a nonlinear model is more flexibly applied to various scenes. According to the calculation method, the algorithm is optimized and upgraded from the two aspects of operation efficiency and input parameter dependency, the problems that a non-linear noise algorithm is low in operation speed and input parameters are difficult to obtain are solved, and the algorithm implementation complexity is reduced while the algorithm simulation precision is guaranteed.

The invention provides an unsupervised small sample internal defect detection method, and belongs to the technical field of intelligent manufacturing, and the method comprises the following steps: based on a pulse thermal imaging system, regulating thermal imaging parameters, and obtaining time sequence thermal image sequences of different defect samples; carrying out time domain compression and reconstruction on the time sequence thermal image sequence by adopting a depth self-encoder, eliminating nonlinear noise and reserving defect features; and inputting the image denoised by the depth auto-encoder into a Swin Transformer Wnet for extracting potential defect features from a spatial domain, performing post-processing on the potential defect features, and outputting a pixel-level defect segmentation result. According to the method provided by the invention, the limitation of traditional supervised defect detection can be avoided, potential space images are generated through a continuous coding and decoding structure, and the feature images are optimized into an image segmentation result, so that unsupervised internal defect detection under the condition of scarce samples is realized.

The present invention discloses a real-time ship track prediction method based on a combination of linear and nonlinear filters, including: S1, collecting the target historical trajectory as a data set and performing data preprocessing to obtain a data set with noise removed; S2, using an N-order polynomial fitting to fit the data set to obtain N + 1 polynomials for prediction, and using K-fold cross-validation to select the optimal prediction curve from the N + 1 prediction polynomials to obtain a preliminary prediction result; S3, using median filtering to process the prediction result to obtain a prediction result with nonlinear noise removed; A real-time ship track prediction method based on a combination of linear and nonlinear filters proposed by the present invention is used to predict the future trajectory of a target ship. According to the historical trajectory data in the short term, the combined filtering prediction model is updated in real time, with low cost, no need for the inherent parameters of the ship where the intelligent water cannon equipment is located and the external environment parameters, and good generalization performance.

The invention discloses an industrial fault detection method based on joint sparse low-rank double-dictionary learning, relates to the technical field of engineering fault detection, and is technically characterized in that sparse representation and low-rank representation are combined, single-dictionary learning is expanded, and the industrial fault detection method based on double-dictionary learning is constructed; according to the method, the problems of nonlinearity, noise interference, high-dimensional data and the like in a complex industrial process are solved, and the fault detection rate of the system is remarkably improved; in a high-dimensional complex industrial process including noise pollution and a multivariable coupling relationship, the scheme can effectively detect system faults.

The invention relates to the technical field of sensor measurement, discloses a multi-modal sensor azimuth measurement fusion system, and aims to solve the problems of insufficient time synchronization precision, limited sensor anomaly detection and compensation capability and fusion performance reduction in a nonlinear noise environment in multi-modal sensor azimuth measurement. The system comprises a sensor array module, a space-time alignment preprocessing module, a multi-modal feature extraction and coding module, a self-adaptive weighted fusion decision module and a system output and feedback control module. Through hardware synchronous sampling, phase-locked loop clock compensation, external parameter calibration, adaptive feature extraction, sensor health degree evaluation and improved volume Kalman filtering fusion and in combination with a closed-loop correction mechanism, high-precision, robust and adaptive azimuth measurement is realized, and the state estimation precision and the system stability are effectively improved.

This invention relates to the field of sensor measurement technology and discloses a multimodal sensor orientation measurement fusion system, aiming to solve the problems of insufficient time synchronization accuracy, limited sensor anomaly detection and compensation capabilities, and degraded fusion performance under nonlinear noise environments in multimodal sensor orientation measurement. The system includes a sensor array module, a spatiotemporal alignment preprocessing module, a multimodal feature extraction and encoding module, an adaptive weighted fusion decision module, and a system output and feedback control module. Through hardware synchronous sampling, phase-locked loop clock compensation, extrinsic parameter calibration, adaptive feature extraction, sensor health assessment, and improved capacitive Kalman filter fusion, combined with a closed-loop correction mechanism, it achieves high-precision, robust, and adaptive orientation measurement, effectively improving state estimation accuracy and system stability.

The invention provides a signal processing method based on a Brillouin optical time domain reflectometer system, which comprises the following steps: filtering an input signal to obtain an output signal, comparing an expected signal with the output signal to obtain an error signal, iteratively updating a weight coefficient of an adaptive filter, and repeatedly executing filtering operation by using the iteratively updated weight coefficient. Enabling an error signal to approach 0; and performing square operation on the filtered and denoised signal to obtain a square filtered and denoised signal, and performing envelope demodulation processing on the square filtered and denoised signal to obtain a root-mean-square envelope demodulation signal to determine a mutational sensing optical fiber signal. And according to the suddenly changed sensing optical fiber signal, determining a suddenly changed sensing optical fiber detection point. According to the method, the carried sensing information can be effectively extracted from a Brillouin optical time domain reflectometer signal containing a large amount of nonlinear noise, and demodulation and extraction of event points by a Brillouin optical time domain reflectometer system under a low signal-to-noise ratio are realized.

The invention provides a slow wave structure and a terahertz traveling wave tube frequency doubling amplifier. According to the invention, the slow-wave structure designed in a segmented manner is adopted, and independent modulation and amplification of the fundamental frequency signal and the harmonic signal are combined, so that the problem of non-uniform energy distribution of electron beams in a traditional traveling wave tube is effectively solved. The first sub-slow-wave structure carries out shallow modulation on a fundamental frequency signal, excessive consumption of electron beam energy is avoided, meanwhile, a harmonic amplification section in the second sub-slow-wave structure can effectively amplify a harmonic drive signal, and the output power of higher harmonics is ensured. The electron beam gradually-changing drift channel is designed through gradually-changing diameters, energy transfer of fundamental frequency and harmonic wave sections is smoothly transited, the system efficiency, bandwidth and stability are further improved, and meanwhile nonlinear noise and oscillation risks are reduced.

The present application relates to a kind of method and device for calculating the nonlinear noise of Raman amplification scene.The method part mainly includes: obtaining the equivalent in-fiber optical power spectrum of each channel according to Raman coupling equation or obtaining the equivalent in-fiber optical power spectrum of each channel according to Raman analytical equation;According to the equivalent in-fiber optical power spectrum of each channel and related fiber parameters, the equivalent nonlinear noise signal ratio of each channel is obtained by calculation, and the equivalent signal noise ratio caused by nonlinearity in Raman amplification scene is finally obtained.The present application can solve the problem that traditional model is too complex and backward Raman amplification scene is difficult to solve, and is more practical.

The application discloses an optical OFDM modulation method combining a probability shaping technology and a subcarrier grouping transmission technology, and belongs to the technical field of optical communication, and comprises the following steps: optimizing a QAM constellation diagram based on a probability shaping method, acquiring optimal probability distribution of constellation points, mapping the optimal probability distribution of the constellation points based on a constant distribution matcher, and obtaining OFDM symbols; performing frequency domain symbol transformation on the OFDM symbols, generating transformed frequency domain signals; converting the transformed frequency domain signals into LED group signals based on a filtering block; and processing the LED group signals based on a DCO modulation mode, and completing OFDM modulation. Different subcarriers are modulated on multiple LEDs, so that the peak-to-average power ratio of the OFDM signals modulated on each LED is reduced, and the nonlinear noise interference of the system is further resisted.

This invention discloses an active noise reduction algorithm based on a convolutional-fuzzy neural network without secondary path sensors. The algorithm establishes a correlation model between noise sources at different locations within a vehicle and the noise in the target noise reduction area using a convolutional-fuzzy neural network. It calculates and fits a virtual error noise signal for the target noise reduction area, replacing the error signal obtained through secondary path sensors in traditional methods. This reduces the dependence of active noise reduction in large vehicle interior spaces on multiple secondary path sensors. Furthermore, the invention uses the virtual error noise signal as input to the fuzzy layer, iteratively calculating and identifying secondary paths, and outputting the optimal noise cancellation signal in real time, improving computational efficiency and enhancing the ability to handle nonlinear noise.

This application discloses a noise joint processing method, apparatus, electronic device, and readable storage medium. The noise joint processing method includes: extracting a first sub-band feature signal and a first speech signal from a nonlinear noise residual signal, and extracting a second sub-band feature signal and a second speech signal from a far-end signal; fusing the first sub-band feature signal and the second sub-band feature signal to obtain a first input feature signal, and fusing the first speech signal and the second speech signal to obtain a second input feature signal; fusing the first input feature signal and the second input feature signal to obtain a fused feature signal; determining a target sub-band gain and target speech filter coefficients based on the fused feature signal, the first input feature signal, and the second input feature signal; and obtaining a sound signal based on the target speech filter coefficients and the target sub-band gain. This application solves the technical problem of difficulty in jointly processing echo, noise, and reverberation while achieving good noise reduction effect.

The application discloses a transmitting power optimization method and system of an improved simulated annealing algorithm, and the method comprises the following steps: modeling self-emission noise of an amplifier of an ultra-wideband wavelength division multiplexing system, obtaining linear noise of the ultra-wideband wavelength division multiplexing system, and obtaining nonlinear noise of the ultra-wideband wavelength division multiplexing system according to an ISRS GN model; determining the relationship between the throughput of the ultra-wideband wavelength division multiplexing system and the channel transmitting power through a Shannon formula and the linear noise and the nonlinear noise; and optimizing the transmitting power of the ultra-wideband wavelength division multiplexing system through the improved simulated annealing algorithm based on the relationship between the throughput of the ultra-wideband wavelength division multiplexing system and the channel transmitting power. The application can improve the convergence speed of the algorithm while searching for the optimal transmitting power distribution.

The invention discloses a pod rapid self-alignment method based on multi-sensor joint estimation, and relates to the technical field of safety management, and the method comprises the following steps: I, initializing a multi-source sensor and collecting environment information; iI, constructing an adaptive filtering structure and estimating the initial attitude of the pod; iII, analyzing the causal relationship of the multi-source observation data and evaluating the credibility of the multi-source observation data; iV, correcting the postures of the pods and eliminating redundant information in the multi-source observation data; the method can significantly shorten the overall alignment time, improve the system starting efficiency, avoid the influence of single sensor failure or noise increase on the system, improve the data fusion effect, dynamically adjust the filtering structure according to the actual operation condition, improve the adaptability to the complex environment, and improve the reliability of the system. A non-linear noise mode which is difficult to find by a traditional statistical method can be identified, abnormal data are stripped through a noise index positioning and data reconstruction mode, and the observation data quality is improved.

The invention provides an electric field sensing signal denoising method fusing Kalman filtering and a neural network, and relates to the field of signal denoising, and the method comprises the steps: carrying out the back propagation training based on a first frequency domain signal, and obtaining a recurrent neural network (RNN); receiving the second frequency domain signal, performing feature extraction to obtain a first feature vector, and performing nonlinear distortion modeling to generate a first distortion observation signal; performing Kalman filtering state estimation to obtain a first estimation state; mapping the first estimation state back to a signal space through a two-layer full-connection network to obtain an output mapping signal; and converting the output mapping signal from the frequency domain to the time domain through inverse short-time Fourier transform, and performing inverse normalization processing to obtain a target electric field sensing signal. According to the method, the Kalman filtering and the improved recurrent neural network are fused for joint optimization, the nonlinear noise characteristics of the electric field sensor signal are estimated and captured through the full-connection network, the nonlinear modeling capability is enhanced, and efficient and high-precision denoising of the electric field sensor signal is realized.

The application discloses a signal clustering decision method and system, and relates to the technical field of digital signal processing. The signal clustering decision method comprises the following steps: according to the signal modulation format and the nonlinear noise size, adjusting the proportion of the modulus difference value and the angle difference value of the constellation point and the current clustering center when calculating the distance, so as to calculate the distance between the constellation point and the current clustering center; according to the distance between the constellation point and the current clustering center, determining the final clustering center and the constellation points included in each final clustering center through iterative calculation; and based on the symbol decision attempt, taking the symbol relationship corresponding to the minimum bit error rate as the decision symbol to which each final clustering center and the included constellation point belong. The application can perform clustering on the constellation diagram with large linear noise and nonlinear noise.

The invention discloses a nonlinear noise suppression method and system based on a hybrid model laser vibration measurement system, and belongs to the technical field of laser vibration measurement, and the method comprises the steps: carrying out the phase modulation and demodulation of a vibration signal, and obtaining an original vibration signal with noise; the modulation phase radian of the noisy vibration signal is calculated according to the phase displacement relation, and a divide-and-conquer threshold value is determined; based on the relationship between the modulation phase radian of the noisy vibration signal and the divide-and-conquer threshold, selecting a corresponding model from the mixed model to perform ellipse parameter fitting; de-noising the original vibration signal with noise based on the ellipse parameter fitted by the hybrid model to obtain a vibration signal without nonlinear noise; wherein the hybrid model comprises a least square model and a Lissajous deep fitting network model. According to the idea of division and treatment, the problem of ellipse fitting denoising under two conditions of violent vibration and weak vibration is simplified, and full-scale nonlinear noise suppression from violent vibration and nanoscale weak vibration is realized.

The application discloses an enhanced model day-ahead electricity price prediction method based on dynamic feature selection, which extracts features from the original data set through a multi-head attention mechanism for multidimensional correlation analysis, generates a feature weight vector in real time to realize dynamic screening of the optimal feature subset, thereby suppressing redundant information and nonlinear noise features at the source; then, an enhanced XGBoost model is used to deeply mine the structured relationship of power market data, simultaneously combined with a Transformer model to capture the long-range time series dependence characteristics of the electricity price sequence, and through a gating mechanism to perform dynamic weighted fusion, to realize the complementary integration of spatial features and time series features; finally, a multi-objective Bayesian optimization algorithm is introduced to obtain a trained enhanced model through multi-objective collaborative optimization, and then prediction is performed. The application significantly improves the prediction accuracy and robustness, effectively solves the deployment problem of the model in a resource-limited environment, and has high engineering application value and power market prediction practicality.

A SOA arrangement (100) including a first polarization-diversity SOA pair (102) and a second polarization-diversity SOA pair (104) arranged on a common optical path with a correlation reducing optical arrangement 106 The correlation reducing optical element is configured to receive a first and a second input component from the first SOA pair and to mix the first and second input components into a first and second output components. The first output component is a combination of the first and second input component from the first polarization-diversity SOA pair. The second output component is a combination of the first and second input component from the first polarization-diversity SOA pair. The SOA arrangement thereby being arranged to reduce the correlation between the nonlinear noise generated by the first SOA and the second SOA.