13results about How to "Add nonlinearity" patented technology

The invention discloses a wave pitching motion control method and system for an offshore floating type wind turbine generator, and belongs to the technical field of offshore wind power equipment control. The method comprises the following steps: signal acquisition: acquiring unit operation state, marine environment and pitching motion state parameters in real time; wave load prediction: predicting a future wave load based on the improved LSTM neural network model; evaluating a pitching motion state, pre-judging a motion trend through a kinetic model, and judging whether the motion trend exceeds a safety threshold value or not; cooperative control instruction generation: generating cooperative control instructions for fine tuning of a variable pitch angle, a generator torque and a cabin yaw angle based on an MPC algorithm; and control execution and feedback correction: driving an execution mechanism to act and dynamically correcting a control instruction. By accurately predicting the wave load, cooperatively adjusting multiple controllable components and dynamically correcting, efficient and accurate suppression of pitching motion is achieved, and the unit operation stability, the wind energy capturing efficiency and the equipment safety are improved.

This invention provides an audio dynamic range control circuit and an audio device. The audio dynamic range control circuit includes an audio zero-crossing detection circuit, a preprocessing circuit, a first control circuit, a first gain selection circuit, a gain multiplication processing circuit, a second control circuit, a third control circuit, and a second gain selection circuit. The audio dynamic range control circuit provided by this invention achieves an effect similar to the existing RMS algorithm by performing absolute value processing on the linear domain sampling data of the audio signal, while simplifying the hardware circuit implementation. Simultaneously, by detecting the zero-crossing position of the audio signal through the audio zero-crossing detection circuit, and updating the gain value at subsequent zero-crossing positions when a sampling point with high energy is detected, the nonlinearity of the output signal can be greatly improved and auditory discomfort can be eliminated, effectively improving audio quality.

The invention relates to establishment of a rotary table servo system model, in particular to an iterative identification method and a nonlinear disturbance data acquisition method of a linear model of a rotary table servo system. In order to solve the defects that in the prior art, data of a non-linear disturbance part is difficult to obtain and even cannot be measured, the iterative identification method for the linear model of the rotary table servo system comprises the steps that (1) an input signal of a first amplitude is used for exciting the rotary table servo system to identify and obtain the linear model; 2) respectively exciting the turntable servo system and the linear model by using input signals with the same frequency and the amplitude smaller than the first amplitude to obtain two output signals, and the difference value of the two output signals is a non-linear compensation value; the method comprises the steps of (1) obtaining a nonlinear compensation value, (2) obtaining a nonlinear compensation value, (3) obtaining a linear model through re-identification according to the difference value between the output signal in the step (1) and the nonlinear compensation value, (4) repeating the step (2) to obtain a new nonlinear compensation value, and (5) repeating the step (3) and the step (4) until the error of the linear model meets a set threshold value, and outputting the linear model.

This application provides a deep learning-based method and system for evaporator defect detection, relating to the field of anomaly detection. The method includes: acquiring multi-view image data of the evaporator; extracting multi-scale features, including the number of fins, their arrangement direction, and the spatial location of notches, through the backbone network layer of a deep learning target detection model; nonlinearly enhancing the multi-scale features through the model's feature enhancement network layer to obtain enhanced multi-scale features; and then extracting reconstructed features through a hybrid attention mechanism layer; based on the reconstructed features, obtaining the detection results of evaporator fins and notches through the model's detection layer, and comparing them with preset structural parameters to obtain the defect detection results. This application, used in evaporator defect detection, solves the technical problem of existing technologies being unable to stably identify evaporator defects under changing environmental and evaporator structural parameters.

This invention belongs to the field of low-light image enhancement, specifically relating to a low-light image enhancement method based on spectral interaction. The method includes constructing a low-light image enhancement model, which comprises a spatial feature extraction module, a spectral real part interaction module, and a brightness-perceived fusion module. The model is trained, and the trained low-light image enhancement model is used to enhance low-light images. This invention employs a two-branch fusion strategy, which effectively utilizes the advantages of both branches while avoiding their respective shortcomings, further improving image quality and clarity.

This invention discloses an optical-SAR image translation method based on a scattering feature enhancement strategy. The method proposes an image translation framework, SFEG, which mainly includes an SCG module and a GFE module. The SCG module first extracts edges from the input image, generates an edge mask by combining target annotation information, and models the scattering field using prior knowledge of the SAR target scattering characteristics. Finally, the generated scattering field and the corresponding optical image are input into the generator. This module can effectively restore the scattering characteristics of the target in the input image. The GFE module includes a channel attention branch and a spatial attention branch, which can enhance the input features from the channel and spatial dimensions respectively, and combine nonlinear functions to achieve feature recombination, further enhancing the nonlinear and refined output capabilities of SFEG. This module can improve the actual effect of generating SAR images.

The application provides a filter, a duplexer, a multiplexer and a communication device. The filter comprises an input terminal, an output terminal, one or more series resonators and one or more parallel resonators, wherein at least one resonator in the one or more series resonators and / or the one or more parallel resonators is split into two series split resonators, and a phase delay factor PDC of the series split resonators satisfies a predetermined range. Through the processing scheme of the present disclosure, the non-linear characteristic of the filter is improved.

This application relates to the field of wind power generation and discloses a micro-site selection method for polar wind farms based on the wake and icing characteristics of complex terrain. The method includes: modeling the terrain scene using spatial environmental parameters for each meteorological time zone, and initializing the meteorological environmental parameters; discretizing the wind farm area into a grid, and using mixed-integer linear programming for global optimization to determine the coarsely selected grid area where the wind turbines are located; and using a multi-stage adaptive hybrid heuristic algorithm that integrates multiple population genetic algorithms and particle swarm optimization to search in the continuous coordinate space corresponding to the coarsely selected grid area, and optimizing the wind turbine layout scheme by solving the fitness function. This eliminates the overestimation of the actual wind farm capacity under extreme blizzard conditions by traditional general models, ensuring the reliability of polar energy system planning.

This invention relates to the field of precision drive and positioning technology, and discloses a three-dimensional macro-micro composite positioning stage, which includes a stage body, an X-axis motion component, a Y-axis motion component, a Z-axis motion component, and a mobile terminal platform connected to the X-axis motion component, Y-axis motion component, and Z-axis motion component respectively. The X-axis motion component includes an X-axis macro-drive and an X-axis micro-drive; the Y-axis motion component includes a Y-axis macro-drive and a Y-axis micro-drive; the Z-axis motion component includes a Z-axis macro-drive and a Z-axis micro-drive, used to jointly drive the mobile terminal platform to linear motion along the X-axis, Y-axis, and Z-axis. This invention balances large stroke and high precision, achieving three-dimensional positioning with millimeter-level stroke and nanometer-level precision. The X-axis, Y-axis, and Z-axis micro-drives use piezoelectric ceramic direct drive, resulting in high positioning accuracy and fast response speed. The overall structure of this invention is reasonable and compact, and its operation is reliable.

The invention is suitable for the field of power load prediction, and discloses a load time sequence mixed quantum fusion prediction method, system, device and medium, and the method comprises the steps: obtaining multi-channel historical load time sequence data, carrying out the sequence embedding processing, obtaining an initial sequence, and inputting the initial sequence to a plurality of layers of star aggregation-distribution modules which are sequentially connected in series; wherein the star aggregation-distribution module adopts a first quantum parallel hybrid network to carry out network processing on an initial sequence to obtain a multi-sequence representation; performing random pooling operation through multi-sequence representation to obtain core representation; the core representation and the multi-sequence representation are spliced, a second quantum parallel network is adopted for fusion, and an output sequence is obtained; the output sequence of the previous module is used as the input of the next module, and the final output sequence is used as a power load prediction result. According to the method, the parallel mixed classical quantum regression network is introduced into the star aggregation-distribution module, so that complex time sequence data is effectively processed, and the time sequence prediction performance is improved.

The application discloses a near-transform limit nanosecond pulse mode-locked fiber laser with periodic bifurcation and a producing method. The laser is a fiber ring cavity which comprises a wavelength division multiplexer, an erbium-doped fiber, a first polarization controller, a polarization detector, a dispersion control unit, an output coupler, a fiber isolator and a second polarization controller which are connected in sequence in a closed loop. The method comprises the following steps: coupling pump continuous light into the laser through the wavelength division multiplexer; absorbing the pump continuous light by the erbium-doped fiber and radiating gain pulses in a long-wave band; oscillating the generated pulses in the fiber laser cavity; through the cooperation of the first polarization controller, the polarization detector and the second polarization controller, generating equivalent saturated absorption effect by using the nonlinear polarization rotation generated when the gain pulses propagate in the fiber, and realizing pulse mode-locking; and making the fiber laser work in an abnormal dispersion region so that the generated pulses satisfy the soliton area theorem and near-transform limit pulses are generated. The application can realize nanosecond pulse periodic bifurcation output of a short-cavity fiber laser.

The present application relates to a kind of recrystallization nucleation site prediction method and system based on multi-factor scoring model, the method includes: the electron backscattering diffraction scanning of cold deformation metal sample is carried out, and the electron backscattering diffraction data containing crystal orientation information is obtained;Based on the electron backscattering diffraction data, local orientation difference field is calculated;From local orientation difference field, the spatial distribution of the grain boundary information, including large-angle grain boundary and coincident site lattice grain boundary is extracted;Based on the morphological characteristics and spatial distribution of local orientation difference field, shear band region is automatically identified;Multi-factor weighted scoring model is established, and the nucleation tendency score of each pixel position is calculated, wherein multi-factor includes local energy storage factor, large-angle grain boundary distance factor, shear band factor and coincident site lattice grain boundary factor;According to the score threshold value, determine the preferential nucleation site, output nucleation site spatial distribution diagram and statistical result.Compared with prior art, the present application realizes the automatic and accurate prediction of nucleation site.

This invention discloses a structurally variable coupled chaotic system and its applications, belonging to the field of information security technology. The coupled chaotic system is formed by the coupling of multiple discrete chaotic systems. During system evolution, the coupling structure between different subsystems is dynamically changed in real time by manipulating the coupling matrix. The coupled system exhibits complex dynamic behavior and high nonlinearity, and the dynamic changes in structure cause continuous abrupt changes in the system's dynamics, resulting in non-stationary chaotic sequences. This system is resistant to attacks from statistical analysis, algebraic analysis, and other methods, exhibiting extremely high security. Furthermore, this system is applicable to various chaotic systems and complex chaotic networks of arbitrary scale. It can also generate complex dynamics based on simple discrete chaotic systems. Under the same complexity and security requirements, controlling structural changes results in lower overhead. It can be used to design high-performance chaotic cryptographic systems and as a component in network security applications.