92 results about "Non linear behavior" patented technology

The invention discloses a spiral anchor foundation bearing effect evaluation method and system, and relates to the technical field of finite element analysis, and the method comprises the steps: building a soil body and spiral anchor finite element model, setting boundary conditions, and obtaining finite element data; according to the finite element data, a local damage variable is introduced based on a stress-strain degradation function, local shear band development is simulated, first damage data are generated, and the first damage data comprise a first stress-strain curve and a first local damage variable; layering the soil body, and optimizing the first damage data based on intra-layer constraints to obtain second damage data; calculating full-field stress, displacement and bearing capacity for effect evaluation based on a finite element solver according to the second damage data; the problem of inaccurate evaluation of the screw anchor effect caused by the nonlinear behavior and the layered heterogeneous effect of the soil body is solved.

The invention discloses a time-space dynamic weight-based building monitoring data accurate registration method, which comprises the following steps of: establishing a response model considering nonlinear behaviors of materials by analyzing deformation characteristics in multi-source monitoring data; constructing a dynamic weight function based on a structural stress state, and realizing self-adaptive distribution of sensor data reliability weight; geometric deformation correction is carried out by using the material nonlinear response model, and geometric distortion caused by material nonlinear behaviors is corrected; space-time registration of multi-source data is realized by adopting a weight fusion and deformation gradient constraint method; and parameter adaptive optimization is carried out through an online learning mechanism. According to the method, the technical problems of low geometric correction precision of a high-stress area, dynamic change of sensor reliability and the like are solved, and submillimeter-level precision building monitoring data registration is realized.

The invention relates to the field of civil engineering, and discloses a foundation pit finite element model parameterization rapid modeling method which comprises the following steps: constructing a finite element model through foundation pit geometric information and soil characteristics, simulating soil response by adopting a nonlinear constitutive relation and a random process, and realizing foundation pit stability optimization and dynamic adjustment; the invention further provides a foundation pit finite element model parameterization rapid modeling system which comprises an input module, a modeling module, a constitutive model module, a random process module, a simulation module, an optimization module, a mesh refining module and a real-time monitoring module. According to the method, the foundation pit finite element model is automatically generated, the Kachanov-Delace nonlinear soil constitutive model is introduced, the soil elastic modulus random field is generated based on the Gaussian process, and the model parameters are dynamically adjusted in real time, so that the modeling process is simplified, the calculation precision is improved, the nonlinear behavior and dynamic response of the soil are accurately simulated, and the method is suitable for large-scale popularization and application. And the stability and the safety in the foundation pit design and construction process are obviously improved.

The invention relates to the technical field of course analysis management, in particular to a course analysis management system based on deep learning. The method has the advantages that multi-modal data deep analysis realizes full-dimensional analysis of unstructured data by integrating a 3D-CNN model, a Transform architecture and a BERT model and synchronously extracting an attention hot area, a voice emotional state and a text knowledge point association network of a classroom video; nonlinear behavior modeling adopts an LSTM network and time convolutional network fusion model, a knowledge internalization path and forgetting curve prediction are dynamically generated, parameters are optimized in combination with incremental learning, and a transition rule across knowledge points is captured; a teaching scene-evaluation threshold mapping table is constructed based on a reinforcement learning algorithm through dynamic decision and resource collaboration, collaborative optimization under multi-campus data privacy protection is achieved in combination with a federated learning framework, GPU computing nodes are dynamically allocated through a heterogeneous resource scheduling engine, and the analysis efficiency is improved.

The invention belongs to the field of computational mechanics, and discloses a dynamic modeling method for parameters of a large-flexibility mooring anchor chain. On the basis of concentrated mass method modeling, the parameter variation principle is introduced, the nonlinear tension and the nonlinear seabed contact force of the mooring anchor chain are arranged into a unified expression containing parameters in a segmented expression form, and the nonlinear behavior of the anchor chain can be corrected by updating and iterating the parameters. The prediction of the motion state of the anchor chain at the next moment according to the motion state at the current moment in the traditional algorithm is avoided, the dynamic modeling simulation precision of the mooring anchor chain is greatly improved, and a foundation is laid for the subsequent dynamic algorithm solution of the mooring anchor chain.

The invention relates to a non-linear behavior-considered user graph-driven electric quantity prediction method and system, and the method comprises the following steps: S1, obtaining user historical power consumption data, meteorological time data, user portrait data and time feature data, and carrying out the preprocessing; s2, constructing a dynamic user similarity map; s3, based on the user dynamic similarity map, using a map attention network to capture a non-linear relationship between the user and a neighbor node thereof, and generating a map embedding feature; s4, based on the meteorological time data, a time sequence model is adopted, and meteorological time data time sequence features are obtained; and S5, constructing a power prediction model, and obtaining a final user power prediction value by combining user portrait data and time feature data based on the obtained graph embedding features and meteorological time data sequence features. According to the invention, omnibearing modeling of the power consumption behavior of the user is realized, and the accuracy and robustness of prediction can be obviously improved.

The invention relates to the technical field of bridge engineering data processing, and particularly provides an online correction method and system for a bridge anti-seismic model based on parameter identification, and the method comprises the steps: continuously collecting a multi-source mixed response signal of a bridge in real vibration; automatically generating self-adaptive feature slices according to a vibration energy transfer path and a structure local nonlinear behavior, and outputting a recombined feature set; constructing a non-uniform evolution trajectory for each independent fluctuation unit in the recombination feature set; generating a directional disturbance vector field in a bridge anti-seismic model parameter space; and the directional disturbance vector field takes the sensitive domain intensity as a disturbance weight, and asymmetric fine tuning is carried out on original bridge anti-seismic model parameters. The system comprises a lossless stripping and recombination module, a time evolution trajectory modeling module and a parameter space directional disturbance module. According to the method, the time domain limitation of traditional offline correction is broken through, and the anti-seismic model ecological system with the autonomous evolution capability is formed through dynamic coupling of response feature-parameter disturbance.

The invention discloses a control system based on a composite controller and an optimization control method, and relates to the technical field of advanced control. The problems that an existing nonlinear time delay system generally exists in a complex industrial control and automation system, and a traditional control strategy is difficult to effectively deal with time delay and nonlinearity are solved. According to the method, the Smith predictor is designed to eliminate the time lag influence in the system, and meanwhile, the fractional order controller is introduced to enhance the adaptive capacity of the system to nonlinear behaviors. The Smith predictor and the fractional order controller are combined to form a composite controller, and the dynamic response performance and the control precision of the system can be remarkably improved. In order to further optimize controller parameters, an improved artificial rabbit optimization algorithm is adopted, and the algorithm improves the global search capability and convergence speed by introducing a dynamic adjustment factor, a Levy flight strategy and a boundary control strategy.

The invention discloses a modeling method of a radio frequency MOS (Metal Oxide Semiconductor) device, and relates to the technical field of MOS device modeling. Electromagnetic simulation and thermal field simulation are combined, a dynamic parasitic parameter library is established, parasitic effects under complex working conditions are described, the dynamic parasitic parameter library is taken as a core basis, the dynamic response capability of a model is remarkably improved, and the modeling efficiency of the radio frequency MOS device is improved. Dynamic parasitic parameters are introduced into a small-signal equivalent circuit model, dynamic changes of the parasitic parameters are explicitly embodied, a partition modeling method is introduced, dynamic behaviors of a grid electrode, a source electrode and a drain electrode are refined into independent contributions, local dynamic characteristics of the radio frequency MOS device can be described more accurately, modeling precision is enhanced, large-signal excitation and nonlinear polynomial description are introduced, and the dynamic characteristics of the radio frequency MOS device can be described more accurately. The method comprises the steps of capturing nonlinear behaviors under a high-power condition, establishing a unified behavior model from linearity to nonlinearity, accurately describing high-order nonlinearity by utilizing a Volterra series method, and solving key problems of signal frequency aliasing and dynamic distortion in nonlinear analysis in combination with dynamic parasitic parameters.

The invention discloses a battery SOC prediction method fusing online identification and an adaptive algorithm, relates to the technical field of power battery monitoring, and aims to solve the problem of insufficient SOC prediction precision of an electric vehicle power battery under a dynamic working condition. The prediction method comprises the steps of obtaining lithium battery charging and discharging experiment data, establishing a dual-polarization equivalent circuit model to simulate a strong nonlinear behavior in the battery, and fitting an OCV-SOC curve based on the experiment data; performing online parameter identification on the equivalent circuit model by using a forgetting factor recursive least square method based on grid search-real-time voltage difference improvement; sOC prediction is carried out by combining online parameter identification with a self-adaptive expansion Kalman filtering algorithm; and the proposed prediction algorithm is verified under a mixed pulse power characteristic test working condition. According to the method, the accuracy and the real-time performance of SOC prediction are remarkably improved, and the method has a wide application prospect.

The invention relates to the technical field of bolt connection, in particular to a method for predicting the working condition of a bolt connection structure based on representation of pretightening force relaxation. The invention provides a double-exponential attenuation function, parameter identification of different functions is carried out, the double-exponential function which is simple in structure and good in characterization is obtained, the fitting precision of the double-exponential attenuation function reaches 99.3%, and the bolt pretightening force relaxation rule can be accurately characterized. According to the method, a pre-tightening force relaxation rule function is introduced into an Iwan model to represent the Iwan model under the relaxation condition meeting the actual condition, the relation between various parameters and pre-tightening is analyzed, a mathematical expression is constructed by adopting a least square method, the pre-tightening relaxation rule in a period is equivalent to the pre-tightening relaxation rule of tangential displacement and loading time, and the pre-tightening force relaxation rule is obtained. And constructing an Iwan model density function under a relaxation condition based on various variable parameters. According to the method, nonlinear behaviors such as energy dissipation and rigidity degradation can be predicted, and particularly, a relatively accurate method is provided for predicting various indexes under the working condition of bolt pretightening force relaxation.

The invention relates to a machine learning method of a dielectric elastomer film on-line modeling under equal biaxial prestretching. The method comprises the following steps of: 1, acquiring an experimental data set for generating input data of the physical information neural network in the step 2 and the Kolmogorov-Arnold network in the step 3; 2, building a physical information neural network; the prediction module is used for predicting a mapping relation between invariants I1 and I2 and a strain energy function, and is used for optimization training of the Kolmogorov-Arnold network in the step 3; step 3, constructing a Kolmogorov-Arnold network, and carrying out the construction of the network; an explicit expression used for predicting invariants I1 and I2 and a strain energy function, and generating a constitutive model capable of reflecting the nonlinear behavior of the dielectric elastomer film; and step 4, carrying out joint training of a physical information neural network and a Kolmogorov-Arnold network on the basis of an experimental data set of voltage and stretch ratio obtained by applying voltage after equal biaxial pre-stretching of the dielectric elastomer film obtained in the step 1, and finding out optimal network parameters. According to the invention, the mechanical-electric coupling characteristic of the dielectric elastomer material can be reflected.

The invention relates to the technical field of data prediction, in particular to a virtual prediction method for mechanical behaviors of a composite material. The method comprises the following steps: acquiring a material level data set; performing data preprocessing on the material level data set to generate a multi-level material processing data set; random field defect analysis is carried out on the multi-level material processing data set, and material microdefect distribution data is generated; carrying out nonlinear behavior factor extraction on the material microdefect distribution data to generate a composite material microcosmic nonlinear behavior factor; therefore, through the multi-level data set processing and interface mechanical modeling technology, the problems that traditional material design depends on experimental verification and is long in period and high in cost are solved, and the precision and efficiency of performance optimization of the composite material are improved.

An electronic device may include wireless circuitry with a processor, a transceiver circuit, a front-end module, and an antenna. The front-end module may include amplifier circuitry such as a low noise amplifier for amplifying received radio-frequency signals. The amplifier circuitry may include non-linearity suppression circuitry that suppresses or reduces non-linear behavior of components such as a core amplification transistor and a degeneration inductor within the amplifier circuitry. The non-linearity suppression circuitry may include an intermodulation distortion suppression circuit having an auxiliary amplification transistor. The non-linearity suppression circuitry may include a frequency-selective filter coupled to the degeneration inductor.

The invention relates to the technical field of cattle behavior recognition, in particular to a cattle behavior recognition method based on a multi-scale weighted adaptive sequential network, which comprises the following steps: acquiring a cattle behavior sensor signal data set; constructing an MWATNet model comprising a DFW module, an MTFE module and an ADC module, and dynamically extracting key channel information by using the DFW module; modeling time sequence dependence and nonlinear behavior characteristics on a time scale by using a convolution and interaction mechanism of an MTFE module; and a classification strategy and a discrimination boundary are dynamically adjusted by using a category sensing mechanism of the ADC module. The invention solves the problem that the rumination and feeding category identification capability of the needle cattle needs to be further improved in the existing method.

The invention provides a nonlinear modal data drive identification method based on physical fusion deep learning, and the method employs a deep learning technology, and can identify a nonlinear modal only depending on the response data of a nonlinear system. The process strictly follows the definition of a nonlinear regular mode, the physical interpretability of the recognition process is ensured, and the method is suitable for various nonlinear systems. Compared with a traditional non-linear modal recognition method, the method effectively solves the problems that non-linear modal recognition is high in calculation complexity, the prior knowledge of non-linear behaviors depends on, and the recognition method lacks physical interpretation.

To provide a fracture analysis device capable of performing limit evaluation for a wide range of fracture phenomena, including shear fracture.SOLUTION: A fracture analysis device 1 for calculating a generation amount and a growth amount of voids based on a GTN model, which is a damage mechanics model, comprises a damage mechanics model calculation unit 13 having a convergence calculation unit 15 that sets a volumetric plastic strain increment Δεp to 0 under a condition where no tensile stress acts (σh≥0) and performs plastic correction calculation for nonlinear behavior using an iterative method only for a deviation component Δεq of a plastic strain increment.SELECTED DRAWING: Figure 3

A method of non-linear acoustic non-destructive testing of a testpiece (3) comprising (i) determining a resonance frequency of a testpiece (3) by applying at least one excitation vibration to the testpiece (3) and analysing a vibrational response of the testpiece (3) to the at least one excitation vibration, (ii) applying at least one excitation vibration to the testpiece (3) at the determined resonance frequency and / or over a range of frequencies that includes the determined resonance frequency, and (iii) determining if the testpiece (3) has one or more flaws by analysing a vibrational response of the testpiece, to the at least one excitation vibration in step (ii), for non-linear behaviour. An apparatus (1) for non-linear acoustic non-destructive testing of a testpiece (3) according to the method.

The present application relates to a kind of mountainous large-span bridge transverse damping system parameter solving method and system, method includes: obtaining bridge structure, establish spatial finite element model, obtain target design displacement, set the initial damping parameter of each position damper of bridge;Actual displacement is calculated based on the damping parameter at this time, and the adjusted vector of damper is obtained according to the difference between target design displacement and actual displacement, combined with adjusted vector and influence matrix, calculate the application vector, the adjusted damping parameter is calculated based on application vector, it is judged whether the adjusted damping parameter satisfies design requirement, if satisfy, then the adjusted damping parameter is regarded as final damping parameter, otherwise, repeat this step.Compared with prior art, the basic principle of influence matrix is used, the multi-tower / pier coupling effect in transverse damping system and the strong nonlinear behavior of damping measure are considered, and the reasonable design parameters of damping system at each tower / pier position can be determined by the iterative calculation of fast and stable convergence.

The invention discloses a digital self-interference cancellation method based on transceiver nonlinear behavior modeling, and belongs to the technical field of electronic reconnaissance and interference. The digital self-interference cancellation method comprises the following steps of performing linear combination on memory components of a broadband signal, and generating cross terms with different phase structures by using a nonlinear effect so as to construct a Wiener model; establishing a multipath self-interference channel model; nonlinear modeling is carried out on linear components of interference signals received by a radio frequency front end at a receiver, and nonlinear component modeling is a linear model; for a nonlinear model constructed by a linear component in an interference signal received by a radio frequency front end, adopting dynamic reduction measurement to reduce the complexity of the model; parameter preliminary estimation is carried out by using the characteristics commonly represented by the intermediate variables, and parameter estimation is optimized by combining a two-step iterative algorithm; and reconstructing the transmitting signal in the digital domain according to a parameter estimation result to generate a reference signal, and realizing interference cancellation in the digital domain of the receiving end.

This invention provides a converter impedance measurement method that overcomes the influence of nonlinearity. By injecting voltage disturbance signals of different frequencies and amplitudes multiple times, the impedance is determined based on the voltage and current responses of the voltage disturbance signals. The method also takes into account the voltage and current responses caused by circuit disturbances, which can effectively avoid the influence of nonlinear behavior in the process of determining the impedance group of the converter under test, ensure the final measurement accuracy, and provide accurate data reference for subsequent impedance stability analysis and the formulation of impedance stabilization strategies.

This invention provides a battery model and construction method based on electrochemical impedance spectroscopy that considers the nonlinear behavior of batteries. These methods belong to the field of battery state monitoring technology. The model adopts the topological structure of the Thevenin model, improves and simplifies the solid-phase diffusion equation, and rederives the Buter-Volmer equation to describe the nonlinear behavior of batteries at low states of charge and high current rates, respectively. The embodiments of the invention provide a battery model suitable for low states of charge and high current rates. This solves the problem of significant loss of accuracy of traditional equivalent circuit models at low states of charge and high current rates, provides a scientific basis for the application of batteries under such extreme conditions, and improves the accuracy of the battery model.

The RC column concentrated plastic hinge model research method based on lateral stiffness relates to the structural seismic technology field; the method aims to solve the problem that the simulation precision of the existing concentrated plastic hinge model is influenced by the value of the multiplier n, and the function relationship between the spring rotation stiffness and the component lateral stiffness is established, without defining the multiplier n; the specific steps include deducing the concentrated plastic hinge series model unit stiffness matrix of the cantilever column, establishing the function relationship between the rotation stiffness and the lateral stiffness, designing the state updating algorithm based on the displacement loading control, verifying the accuracy of the new model, and comparing the precision of the existing model; the experimental results show that the simulation precision of the model is improved significantly, the nonlinear behavior of the RC column can be accurately simulated without the multiplier n, and the method has important practical significance; the method has been successfully applied to the seismic analysis of a plurality of typical RC frame structures, not only simplifies the calculation process, but also improves the calculation efficiency, provides a more reliable and convenient tool for the structural seismic design, and has high popularization value.

The application discloses a kind of based on the detection result prediction method of electric network reserve pool, belong to neural network technical field, the method includes: the voltage signal sequence corresponding to historical detection result is passed into the electric network reserve pool based on the input node of multiple type electric characteristic neuron, to map voltage signal sequence into high-dimensional output state matrix;Compared with the dimension increasing method of pure mathematical operation of traditional reserve pool algorithm, this method is completely based on the I-V intrinsic electric characteristic of semiconductor device, for the spontaneous nonlinear behavior of electric network formed by the connection of multiple electric characteristic neurons, it is highly explainable, and it is convenient to physically realize.Further, the output state matrix of electric network reserve pool is dimensioned again, the future detection result is predicted by the fitting of historical detection result, thereby solve the technical problems that extrapolation prediction based on historical data in existing detection technology is difficult to realize.

The invention provides a temperature-stress-based welding finite element rapid stiffness matrix assembly solving method, and relates to the technical field of welding process finite element simulation, and the method optimizes a stiffness matrix updating strategy through double variables of temperature and stress based on local heat input characteristics of a welding process. In the elastic area, the calculation strategy is flexibly adjusted according to the temperature change amplitude, and in the plastic area, iterative correction is carried out aiming at the nonlinear behavior of the material. By intelligently identifying the key area and avoiding unnecessary global updating, the algorithm greatly reduces the calculation amount and significantly improves the simulation speed. Besides, in combination with a sequential solution method of thermal-mechanical coupling, the algorithm can provide rapid and accurate stress solution for a complex welding process on the premise of ensuring precision, and is particularly suitable for efficient simulation analysis of large weldments. The problem that in the welding process of large weldments, the rigidity matrix assembling efficiency is low is solved.

The application relates to the field of civil engineering, and discloses a foundation pit finite element model parameterization rapid modeling method: a finite element model is constructed through foundation pit geometric information and soil characteristics, soil response is simulated by adopting a nonlinear constitutive relation and a random process, foundation pit stability optimization and dynamic adjustment are realized, and the application further provides a foundation pit finite element model parameterization rapid modeling system: an input module, a modeling module, a constitutive model module, a random process module, a simulation module, an optimization module, a grid refinement module and a real-time monitoring module. The application realizes simplified modeling process, improved calculation precision, accurate simulation of soil nonlinear behavior and dynamic response, and significantly improves the stability and safety in the foundation pit design and construction process by automatically generating a foundation pit finite element model, introducing a Kachanov-Delale nonlinear soil constitutive model, generating a soil elastic modulus random field based on a Gaussian process, and dynamically adjusting model parameters in real time.

Methods, systems, and devices for wireless communications are described. A component of a user equipment (UE) may experience non-linearity behavior. Using a digital post distortion (DPOD) process, the UE may estimate non-linear distortion caused by the non-linearity behavior and may use the estimated non-linear distortion to cancel or mitigate distortion at a received downlink signal. To improve the non-linear distortion estimation, a network entity may determine and transmit to the UE recommended kernel set sizes to use at a DPOD algorithm of the DPOD process. The recommended kernel set sizes may be based on a quantity of receive antenna in use at the UE and an instantaneous SNR measurement at the UE. The UE may utilize one of the recommended kernel set sizes in performing the DPOD process on a received downlink signal to mitigate or cancel the non-linear distortion.

The invention provides an equivalent thin layer unit simulation method and system for an eVTOL aircraft threaded connection structure. And respectively calculating the axial rigidity of the internal thread under the unit length, the radial rigidity of the internal thread under the unit length, the axial rigidity of the external thread under the unit length and the radial rigidity of the external thread under the unit length. And calculating the axial total rigidity and the radial total rigidity of threaded connection, and determining the axial modulus and the radial modulus of the equivalent thin-layer material. In the finite element model, establishing a thin layer unit at a threaded connection interface, and endowing the thin layer unit with an axial modulus and a radial modulus; and connecting the thin layer unit and the two parts in threaded connection through binding constraint, and applying a pre-tightening force and a dynamic load to complete construction of a simulation model. Therefore, the simulation precision is improved, the dynamic response error is reduced, and the nonlinear behavior of the thread under multiple working conditions can be accurately simulated.

The application provides a SiC MOSFET behavior modeling method based on physical mechanism optimization, comprising: for a SiC MOSFET output characteristic curve, constructing a static electrical model structure of the SiC MOSFET on the basis of considering a drain-induced barrier lowering effect of the SiC MOSFET; based on the static electrical model structure, adopting a voltage function to describe nonlinear behavior of an interelectrode capacitance varying with a bias voltage, and constructing a dynamic characteristic model structure of the SiC MOSFET; adopting a method combining experimental data and structure function analysis to perform physical mechanism optimization on the static electrical model structure and the dynamic characteristic model structure, and obtaining a final required SiC MOSFET behavior model. The application can accurately reflect static and dynamic characteristics of the SiC MOSFET device, improve precision, applicability and convenience of parameter extraction of the model, and meet design requirements of a new generation of high-performance power electronic systems.

The invention discloses a permanent magnet synchronous linear motor optimal current control method based on a magnetic equivalent circuit model, and the method comprises the steps: considering the nonlinear behavior of a motor, building an accurate MEC model suitable for a PMLSM for a stator part, an air gap part and a rotor part through the electromagnetic quantity of the motor, and building an electromagnetic parameter and thrust balance equation; therefore, an accurate motor thrust expression is obtained, and an optimal current control strategy considering thrust accuracy and coil copper loss minimization is further designed based on the motor thrust expression. According to the MEC model and the corresponding optimal current control strategy, the calculation burden can be reduced on the basis that the model precision is ensured, meanwhile, accurate control over the position and thrust is achieved, the optimal current strategy can further reduce copper loss and improve the efficiency of the system, and high research value and application value are achieved.