29 results about "Nonlinear gain" patented technology

The invention belongs to the technical field of image processing, and particularly relates to a composite material ultrasonic scanning image defect feature extraction method and system, and the method comprises the steps: obtaining an ultrasonic scanning image of a composite material, and carrying out the filtering processing; constructing a gradient outer product matrix of neighborhood pixel points of the pixel points and accumulating to obtain a local structure matrix; performing characteristic decomposition on the local structure matrix to obtain a characteristic value, and calculating a structure coherence factor according to the characteristic value; coupling potential energy is constructed in combination with a gray value and a structural coherence factor, and a low-gray and disordered defect signal is highlighted through nonlinear gain; and performing region segmentation by using the high-coupling potential energy points as anchor points, and extracting defect blocks. According to the method, the problem of low-contrast defect leak detection under the strong texture background is effectively solved by utilizing the essential difference between the background texture and the defect in the physical topology, and the defect feature extraction precision is remarkably improved.

The invention relates to the technical field of precision machining control, and discloses an intelligent control method for a cutting path of a scribing machine, which comprises the following steps: acquiring a load current signal of a main shaft servo motor in real time and storing the load current signal into a sliding window data queue; performing weighted differential calculation to obtain a real-time load change rate representing a load trend, and calculating an average absolute deviation to obtain an oscillation state quantity representing load dispersion; querying based on a preset nonlinear gain scheduling relationship to obtain a first speed regulation coefficient and an oscillation suppression coefficient; according to the method, an orthogonal dual-channel feed-forward control mechanism is constructed in the same data window, and multiplication cooperation of weighted differential and dispersion statistics is utilized, so that a feed speed set value is corrected in real time; the problems of phase lag and response mismatch of conventional feedback control in the case of transient impact and microcosmic oscillation of hard and brittle materials are solved.

The invention discloses a PID (Proportion Integration Differentiation) tracking control method based on error perception nonlinear gain, which is used for high-precision tracking control of an MIMO (Multiple Input Multiple Output) nonlinear system. Comprising the following steps: 1) constructing an adaptive gain by using current error information, historical error information and prediction error information; 2) realizing intrinsic integral saturation resistance through nonlinear integral design; and 3) complete autonomous operation can be realized without accurate system dynamics priori knowledge or manual adjustment. According to the method, the nonlinear compensation capability and the robust adaptive performance are enhanced while the structural simplicity of the PID controller is kept. Through rigorous Lyapunov analysis, the stability of the method to a non-linear system in a general form is verified, and the method shows excellent performance in both adjustment and time-varying reference tracking scenes. The method can be widely applied to the fields of linear motor servo systems, sea surface ship control, unmanned vehicle dynamics control and the like needing high-precision tracking control.

The invention discloses a servo motor disturbance suppression method based on variable gain control, and the method comprises the steps: adding a nonlinear branch for disturbance suppression on the basis of a conventional position closed-loop control structure; the nonlinear branch is formed by connecting a phase extractor and a gain regulator in series, the phase extractor extracts effective phase information according to a tracking error signal of the servo system, and the gain regulator outputs a corresponding nonlinear compensation signal according to the phase information. After the compensation signal is superposed to the position loop, the loop gain of the system at a low frequency band can be dynamically improved, so that the disturbance suppression capability is remarkably enhanced. The nonlinear branch is essentially a switching function, but is different from a traditional switching function or a fixed gain control mode, the gain regulator adopts a reset integral mechanism to generate nonlinear gain, and the gain regulator has excellent low-frequency disturbance suppression performance and anti-noise capability. The device has the advantages of simple structure, quick response and the like.

This invention relates to a position synchronization control method for multiple permanent magnet synchronous motors based on common-mode and differential-mode separation, belonging to the field of multi-motor cooperative drive and control technology. This invention decouples the system control objective in terms of topology by constructing common-mode and differential-mode loops. The common-mode loop uses a unified speed controller to generate a common-mode current reference value based on a weighted average feedback speed. The differential-mode loop uses a nonlinear synchronization controller for each motor, which generates a differential-mode current reference value based on the synchronization deviation input, combined with an adaptive nonlinear gain and a recovery coefficient. The adaptive gain is the maximum value between a first gain based on mechanical clearance constraints and a second gain based on disturbance rejection requirements, and the recovery coefficient is dynamically adjusted in segments according to the deviation and its rate of change. This invention achieves complete decoupling of speed tracking and position synchronization, significantly improving synchronization accuracy and disturbance rejection capability under current saturation constraints, and is suitable for high-performance multi-motor cooperative drive applications such as space servo mechanisms and precision transmissions.

The application belongs to the technical field of image processing, and particularly relates to a composite material ultrasonic scanning image defect feature extraction method and system, which comprises the following steps: obtaining an ultrasonic scanning image of a composite material and performing filtering processing; constructing a gradient outer product matrix of neighborhood pixel points of a pixel point and accumulating to obtain a local structure matrix; performing feature decomposition on the local structure matrix to obtain an eigenvalue, and calculating a structure coherence factor according to the eigenvalue; combining a gray value and the structure coherence factor to construct a coupling potential, highlighting a low-gray and disordered defect signal through a nonlinear gain; and using a high coupling potential point as an anchor point to perform region segmentation and extract a defect block. The application effectively solves the problem of missing detection of low-contrast defects in a strong texture background by using the essential difference between background texture and defects in physical topology, and significantly improves the precision of defect feature extraction.

The invention relates to the technical field of industrial equipment automatic control, in particular to a cooperative operation control method and system for a multi-effect evaporator. The method comprises the following steps: training a GRU-based reference feedforward model by using multi-source time sequence data in a clean state of equipment; secondly, an observation total heat exchange coefficient and a theoretical total heat exchange coefficient are obtained in real time, and a cleanliness evaluation factor is calculated through a filter to represent the scaling degree; secondly, performing nonlinear gain correction on the reference feed-forward signal based on the factor to obtain a self-adaptive feed-forward signal for compensating the scaling influence; and finally, generating a dynamic cooperative weight according to the volatility of the current working condition, and carrying out weighted fusion on the self-adaptive feedforward signal and the feedback control signal. According to the method, the problem that the static model is mismatched along with equipment scaling is solved, and stable and efficient control of the whole period is guaranteed.

The invention relates to the technical field of image processing, in particular to an automobile high-voltage line surface defect online identification method based on machine vision, which comprises the following steps: acquiring a current frame image containing a high-voltage line main body; calculating a vibration compensation factor of the current frame image, wherein the vibration compensation factor is in positive correlation with the sum of the displacement of the plurality of feature points in the current frame image relative to the previous frame; and calculating a non-periodic disturbance factor of the current frame image. According to the method, the non-periodic disturbance factor based on the historical mean value is constructed, so that the interference of inherent mechanical vibration of the production line on defect signals is effectively stripped, and the problem of misinformation under a dynamic background is solved; meanwhile, a defect energy response model is constructed by utilizing a normal included angle of the cylinder, nonlinear gain compensation is performed on an edge illumination attenuation area, the contrast ratio of edge weak defects is remarkably enhanced, the problem of dead angles in detection of the edge of the cylinder is solved, and full-coverage high-precision online detection is realized.

The application belongs to the technical field of optical computing, and discloses a silicon-based integrated all-optical deep neural network chip, a training method thereof and an intelligent device. The chip comprises an input layer, an output layer and a plurality of fully connected layers. Each fully connected layer and the output layer comprise a beam splitter, a microcavity array and a nonlinear gain unit. The beam splitter divides each wavelength signal output by the previous layer into M parts. Different rows of the microcavity array are used to load weights on the different parts of the divided signals. The nonlinear gain unit is used to perform nonlinear optical amplification calculation on each column to generate a regenerated signal. Each fully connected layer uses a wavelength division multiplexing unit to multiplex the regenerated signal onto one waveguide and transmit it to the next layer. The output layer uses a photodetector array to perform photoelectric conversion on the regenerated signal to obtain an inference result. Based on the above structure, multi-layer high-dimensional optical matrix calculation can be realized, the problem of multi-layer cascaded optical energy attenuation can be solved, and the function of a multi-layer cascaded nonlinear activation function can be realized.

An optical receiver includes one or more photodetectors and an analog front end (AFE) configured to accept input signals from the one or more photodetectors. The AFE includes a non-linear gain amplifier (NLGA). The NLGA includes a piecewise linear gain stage configured to apply a piecewise linear transfer function to the input signals to form amplified signals. The AFE also includes a DC offset stage configured to apply a DC offset to the amplified signals. A related method of operation and vehicle are also disclosed.

The invention belongs to the technical field of special optical fibers, and particularly discloses an ultralow nonlinear gain optical fiber and a preparation method thereof. The optical fiber comprises a fiber core, a core-cladding junction area and an inner cladding which are sequentially arranged from inside to outside in the radial direction, and the fiber core is air; the core package junction region is a rare earth doped region; the inner cladding comprises a plurality of glass thin walls which are periodically distributed around the core cladding junction area, and the glass thin walls are filled with air. The rare earth ions are doped in the core-cladding junction area of the band gap optical fiber, gain amplification of a fiber core fundamental mode is achieved through coupling of a core mode and a surface mode, light is limited in an air fiber core for transmission based on the photonic band gap light guide principle, and the nonlinear effect of the optical fiber can be effectively reduced.

The application provides a chip-on nonlinear amplification gain waveguide structure, which comprises, in sequence, an input waveguide, a first mode converter, a second mode converter, a nonlinear waveguide, a third mode converter and a fourth mode converter; a first signal light is input from the input waveguide to the fourth mode converter, is converted and output as a second signal light by the fourth mode converter, is input from the fourth mode converter to the first mode converter, is converted and output as a third signal light by the second mode converter, is input from the second mode converter to the third mode converter, and is converted and output to an output waveguide by the third mode converter. The application adopts a mode circulation type nonlinear gain amplification waveguide structure composed of multiple mode conversion devices, improves the space utilization of the chip-on waveguide, increases the action length of the optical field and the waveguide, thereby improving the generation efficiency of the nonlinear effect in a unit area, and realizing the effect of amplification gain.

The invention provides an automatic synchronous exposure method and system based on a flat panel detector, and the method comprises the steps: carrying out the line-by-line scanning of pixels of the flat panel detector, obtaining the line pixel data of the flat panel detector, and obtaining the summation value of the line pixel data; comparing the summation value of the current row of pixel data with an exposure trigger threshold value in real time, and if the summation value of the current row of pixel data is greater than the exposure trigger threshold value, judging that exposure is started and immediately stopping scanning; after the scanning is stopped, enabling the flat panel detector to enter a silent integral state, calculating the maximum safe exposure time, monitoring the signal change rate in real time, and triggering the flat panel detector to end the silent integral; after silent integration is finished, scanning again to obtain an original exposure image, and processing the original exposure image according to the virtual dark field image data to obtain a net signal image; and performing nonlinear gain correction on the net signal image to obtain a final exposure output image. Through the method, the synchronous exposure performance of the flat panel detector is obviously optimized.

The invention relates to the field of audio recognition and processing, in particular to a game voice noise reduction and enhancement method based on AI voiceprint analysis, which comprises the following steps: firstly, decoupling audio data into two groups of orthogonal feature streams, and carrying out parallel calculation on spectrum features for neural network input and trigger features for state judgment; and then, switching between a steady-state self-adaption mode and a transient locking mode according to the spectral flux change rate and the envelope saturation by utilizing a finite-state machine containing asymmetric hysteresis logic. In the steady-state mode, the system updates the dynamic background noise model in real time to execute denoising standardization; and in the transient mode, freezing statistic update and inhibiting high-frequency clipping distortion through a nonlinear gain function. And finally, coding the state flag into a priori gating signal, and injecting the priori gating signal into a neural network attention layer. According to the method, pollution of burst high-energy signals to a background model is effectively prevented, mistaken truncation of long-tail reverberation is avoided, and speech enhancement robustness and response speed in a complex acoustic environment are remarkably improved.

The invention provides a bearing fault diagnosis method based on Fisher criterion adaptive optimization RCMFDE and CAOA-SVM, and the method comprises the steps: firstly obtaining a vibration acceleration signal of a rolling bearing, and discretizing a continuous time sequence into sample segments based on a non-overlapping sliding window technology; secondly, constructing a fractional order divergence entropy of a nonlinear gain characteristic, obtaining a fine composite multi-scale fractional order divergence entropy based on a fine composite multi-scale strategy, taking the fine composite multi-scale fractional order divergence entropy as a feature quality evaluation function based on a kernel Fisher discrimination criterion, adaptively searching an optimal fractional order in a preset interval, and extracting a fault feature vector with high discrimination; and inputting the extracted feature vector into a fault diagnosis model, and outputting the fault category of the rolling bearing. According to the method, the problems of blind parameter selection and difficult weak feature extraction in bearing fault diagnosis are solved, and the diagnosis efficiency and generalization ability are improved.

This invention relates to the field of optoelectronic payload imaging control technology, specifically providing an optoelectronic payload target tracking control method and system, comprising: acquiring an image of the target; calculating the miss distance of the target based on the pixel coordinates of the target in the image and the coordinates of the field of view center; obtaining the lag time of the miss distance relative to the actual position of the target; constructing a nonlinear continuously variable gain smoothing adaptive function using the miss distance, and generating a real-time dynamically adjusted tracking control gain using the nonlinear continuously variable gain smoothing adaptive function; acquiring N sets of angular velocity sequences at a fixed period, weighting the angular velocity sequence data using a forgetting factor to form a compensation model, and using an angle compensation amount to perform feedforward compensation for the miss distance; inputting the angle compensation amount and the tracking control gain into a tracking control module to track the optoelectronic payload. This invention improves tracking accuracy by constructing a nonlinear gain function and performing feedforward compensation for the miss distance.

The invention relates to a neural worthland system for digital pre-distortion. Aspects of the present disclosure relate to a neural wolfram actuator. In an embodiment, a neural Wollaston actuator comprises: a first processing block comprising an artificial neural network; a second processing block including a nonlinear gain block; a multiplier; a connection circuit configured to adjust an electrical connection to an input of a multiplier of the multipliers; and a combiner that generates a combined output signal based on the output signal from the multiplier. The combined output signal may be a digitally pre-distorted version of the input signal received by the Neural Wollaston actuator. Related methods and systems are also disclosed.

The application relates to the technical field of precision machining control, and discloses an intelligent control method for a cutting path of a scriber, which comprises the following steps: collecting a load current signal of a main shaft servo motor in real time and storing the load current signal into a sliding window data queue; performing weighted differential calculation to obtain a real-time load change rate representing a load trend, and calculating an average absolute deviation to obtain a shock state quantity representing load discreteness; obtaining a first speed adjustment coefficient and a shock suppression coefficient based on a preset nonlinear gain scheduling relationship respectively; and multiplying the first speed adjustment coefficient and the shock suppression coefficient to couple the first speed adjustment coefficient and the shock suppression coefficient to each other, and then correcting a feed speed setting value in real time. The application constructs an orthogonal double-channel feedforward control mechanism in the same data window, and utilizes multiplication synergy of weighted differential and discreteness statistics to solve the problems of phase lag and response mismatch of conventional feedback control when facing transient impact and micro oscillation of hard and brittle materials.

A bionic hand adaptive anti-slip control method based on multi-source sensor fusion includes: establishing a kinematic forward and inverse kinematic model based on the geometric parameters of the metal linkage mechanism in the bionic hand, calculating the real-time Jacobian matrix, and constructing the system state-space equation; establishing a finger dynamics model, calculating the loss current through a benchmark compensation function, and establishing an operating space impedance control compliance benchmark based on the desired stiffness matrix and desired damping matrix; initiating dual-link monitoring when the bionic hand finger approaches the target; after establishing contact, outputting the slip probability reflecting the micro-slip state of the object using the nonlinear feature extraction capability of a deep neural network model; constructing a nonlinear gain function based on the slip probability to dynamically correct the desired stiffness matrix and desired damping matrix; and switching to a virtual self-locking holding mode after entering the steady-state grasping stage. This invention solves the computational explosion problem of existing technologies, reduces perception lag, and balances the compliance and anti-slip capability of grasping.

The application relates to an intracavity all-fiber Raman laser based on a fused-taper wavelength division multiplexer, which comprises a pump source, a first fundamental light high-reflection grating, a mid-infrared active optical fiber, a fused-taper wavelength division multiplexer, a Raman nonlinear gain optical fiber, a first Raman laser grating, a second fundamental light high-reflection grating and a second Raman laser grating; the fused-taper wavelength division multiplexer is made of the mid-infrared active optical fiber and the Raman nonlinear gain optical fiber through a fused-taper process; pump laser generated by the pump source is input to the mid-infrared active optical fiber; the first fundamental light high-reflection grating and the second fundamental light high-reflection grating are respectively arranged on the first mid-infrared active optical fiber and the first Raman nonlinear gain optical fiber; and the first Raman laser grating and the second Raman laser grating are respectively arranged on the first Raman nonlinear gain optical fiber and the second Raman nonlinear gain optical fiber.

The application discloses a kind of based on super surface cascade nonreciprocal nonlinear transmission implementation method, it is related to electromagnetic transmission technical field.The method includes:1) gain super surface preparation and nonlinear gain debugging;2) time modulation super surface preparation and harmonic control;3) double super surface cascade establishment;4) nonreciprocal nonlinear transmission control;5) performance verification.The method presented in the application is by time modulation super surface and gain super surface along electromagnetic wave propagation direction space cascade, using the periodic harmonic generation characteristics of time modulation super surface and the nonlinear amplification characteristics of gain super surface, solve the problem of large loss, complex structure, linear response limitation of traditional nonreciprocal transmission implementation method, realize no magnetic bias, amplitude and frequency two-dimensional nonreciprocal transmission, with the advantages of control flexibility, compact structure.

The invention relates to the technical field of unmanned ship autonomous navigation control, and discloses a large inertia unmanned ship autonomous navigation control method comprising the following steps: in each sampling period, generating a mixed sampling candidate trajectory cluster comprising a linear trajectory and an arc trajectory; performing multi-constraint fusion evaluation on each candidate track; according to the total evaluation value, selecting an optimal track from the candidate track cluster, and taking the navigational speed and the navigational direction of the first time step in the optimal track as planning results; the planning result is input into a nonlinear gain sliding mode controller to be adjusted to generate a final control result, the nonlinear gain sliding mode controller comprises a nonlinear gain PID controller and a sliding mode controller, in proportion and differential terms of the nonlinear gain PID controller, a nonlinear function is added, sliding mode control is combined, the calculated amount is low, real-time performance is good, and the control precision is high. And the response speed, the course stability and the anti-interference capability of the large-inertia unmanned ship are remarkably improved.

The invention relates to a digital background calibration scheme for calibrating linear and third order nonlinear gain errors of a residual amplifier (RA) in a pipelined analog-to-digital converter (ADC). According to the scheme, a customized convolutional neural network (CNN) is adopted to extract linear and third-order nonlinear gain error information of the RA, and rapid convergence is realized by injecting a jitter signal. Compared with a traditional calibration algorithm based on correlation, the method has the advantages that the convergence speed and the robustness of resisting jitter capacitor mismatch are remarkably improved. Compared with a previous calibration technology (generally used for foreground calibration) based on a neural network, the method can be operated in the background, adapts to error changes, and has no signal fidelity risk.

The present application relates to the field of audio recognition and processing, in particular to a game voice noise reduction and enhancement method based on AI voiceprint analysis. First, the audio data is decoupled into two groups of orthogonal feature streams, and the spectral features for neural network input and the trigger features for state determination are calculated in parallel. Then, a finite state machine containing asymmetric hysteresis logic is used to switch between steady-state adaptation and transient locking mode according to the spectral flux change rate and the envelope saturation. In the steady-state mode, the system updates the dynamic background noise model in real time to perform noise reduction normalization; in the transient mode, the statistical quantity update is frozen and the high-frequency clipping distortion is suppressed through a nonlinear gain function. Finally, the state flag is encoded into a priori gating signal and injected into the neural network attention layer. The present application effectively prevents the pollution of sudden high-energy signals to the background model, avoids the mis-truncation of long-tail reverberation, and significantly improves the voice enhancement robustness and response speed in complex acoustic environments.

The present application relates to the technical field of machine vision and image data, and particularly relates to a method for detecting defects on the surface of galvanized layer of electric iron accessories based on machine vision. The method comprises the following steps: obtaining a surface image of the electric iron accessories, constructing a multi-scale Gabor filter bank and calculating the real part and the imaginary part of the convolution response, and constructing a ridge line phase asymmetry index for each pixel point of the surface image; constructing a local structure tensor to determine the dominant direction of the neighborhood, and combining the gradient distribution to construct a neighborhood texture topological consistency coefficient; using the neighborhood texture topological consistency coefficient to construct a nonlinear gain weight, modulating the comprehensive amplitude energy of the pixel points determined by the Gabor filter bank to generate a defect saliency map, and determining the defect detection result through double-threshold hysteresis segmentation. The method can effectively distinguish linear defects from zinc flower grain boundary texture from the physical mechanism, suppresses the background interference while retaining weak defects, and improves the accuracy of the surface defect detection of the galvanized layer of the electric iron accessories.

The present application relates to the technical field of industrial numerical control, and more particularly to a numerical control system adaptive vibration suppression method, system, device and medium, the method constructs a static stiffness attenuation factor representing the spatial posture of the machine tool, establishes a process damping speed threshold model in combination with the instantaneous cutting depth; the benchmark frequency is modified by introducing the thermal drift and the force-induced softening amount for multi-physical field coupling, and the notch depth is dynamically solved by using a nonlinear gain function according to the speed deviation, and the filter is reconstructed synchronously; the technical problems that the existing technology cannot identify the physical stability boundary, leading to false triggering of the filter, and ignoring the thermal-force coupling, leading to frequency off-target, are solved; the low-speed process damping characteristics are used to actively bypass the filter in the self-stable zone, the dynamic bandwidth of the servo system is maximized, and the frequency deviation caused by temperature rise and load can be compensated in real time, ensuring that the vibration suppression strategy accurately locks the resonance point, and significantly improving the machining surface quality and production efficiency under complex working conditions.

This invention relates to the field of control optimization technology, and discloses a method for optimizing the pH control of generator stator cooling water. The method includes: constructing a two-dimensional operating phase space and extracting nonlinear gain features from the pH regulation characteristic data of the generator stator cooling water; online identification of a time-varying object model of the pH-controlled process of the generator stator cooling water; controller parameter optimization based on adaptive evaluation of operating conditions, corrosion risk constraints, and feedforward search boundary contraction; verification of optimization results, local correction, and parameter distribution based on continuous actual disturbance playback; and online operation of the pH regulation of the generator stator cooling water. This invention introduces an adaptive forgetting factor driven by the rate of change of cooling water conductivity in the online identification of the object model. This enables the recursive least squares algorithm to accelerate model tracking during buffer capacity migration and suppress parameter jitter when the operating conditions are stable, thereby providing a time-varying object model that is closer to the current actual dynamics for subsequent optimization.

An optical receiver includes one or more photodetectors and an analog front end (AFE) configured to accept input signals from the one or more photodetectors. The AFE includes a non-linear gain amplifier (NLGA). The NLGA includes a piecewise linear gain stage configured to apply a piecewise linear transfer function to the input signals to form amplified signals. The AFE also includes a DC offset stage configured to apply a DC offset to the amplified signals. A related method of operation and vehicle are also disclosed.

This application relates to the field of automated control technology for printing machinery, and particularly to an automatic registration compensation method, system, and device for a flexographic printing press. The method includes: a deviation acquisition step: during the printing process, the real-time positional deviation value of the color mark printed by the subsequent printing unit relative to a reference mark is acquired in real time through the vision detection system, the reference mark being the mark printed on the substrate by the first printing unit; an adjustment step: the real-time positional deviation value is processed by the motion controller, including validity verification, filtering, and nonlinear gain adjustment, to generate a corresponding plate roller phase adjustment command; and a correction step: the plate roller phase adjustment command is applied to the plate roller of the corresponding printing unit through a superimposed positioning method to correct the phase of the plate roller relative to the spindle, thereby achieving registration correction. This application can effectively correct the phase of the plate roller relative to the spindle, achieving registration correction and improving printing quality.