21 results about "Nonlinear algorithms" patented technology

The invention relates to the technical field of medical artificial intelligence and chronic kidney disease management crossing, in particular to a nonlinear algorithm system for precise chronic disease management of chronic kidney diseases. Through deep coupling of algorithms and in combination with two exclusive quantification formulas, transformation of CKD chronic disease management from standardized follow-up visit to precise and dynamic algorithm driving is realized, and the core pain points of large staging evaluation deviation, progress pre-judgment lag, poor intervention scheme adaptability, follow-up visit strategy homogenization and the like in traditional CKD management are solved. The method provides full-cycle decision support for nephrology doctors in the three-A hospital, customizes an individualized chronic disease management path for CKD patients, is suitable for CKD 1-5 period full-staging patients, and can be widely applied to CKD special management scenes of chronic disease diagnosis and treatment centers in the nephrology department of the three-A hospital, chronic disease management outpatient clinics in special nephropathy hospitals and high-end health management institutions.

The invention relates to the technical field of battery manufacturing, in particular to a confidence evaluation method and system for dynamic skipping of a capacity grading process. According to the method, the problem that the parameter interaction cannot be evaluated by a traditional method is solved by analyzing the association of the process processing parameters, the material characteristic parameters and the historical battery capacity and constructing the parameter-capacity mapping matrix, then the confidence reference interval and the design threshold are generated in combination with the process capability index, the adaptive adjustment of the evaluation standard is realized, and the evaluation accuracy is improved. Then combining static historical mapping with dynamic electrical performance through electrical characteristic parameters to obtain a confidence evaluation interval, and meanwhile, calculating an input disturbance coefficient and a quality risk index to realize decoupling judgment of data reliability and real risk, break through the limitation that a traditional method cannot distinguish a fluctuation root, and finally, integrating various indexes so as to realize the prediction of the data reliability and the real risk. And a nonlinear algorithm is adopted to calculate the dynamic skipping confidence coefficient, and flexible processing is performed on the critical-state battery, so that the precise control of the capacity grading process of the lithium ion battery is improved.

The invention relates to a method and a device for determining a signal detection network. The method comprises the following steps: determining sample communication parameters of a sample transmitting end and a sample receiving end communicating through a sample intelligent reflecting surface (IRS) (S101); and training an initial neural network based on a training sample set formed by the sample communication parameters to obtain a signal detection network, the input of the initial neural network being the sample communication parameters, and the output of the initial neural network being an estimated value of a transmission signal of the sample transmitting end (S102). According to the method and the device, the initial neural network can be trained based on the training sample set to obtain the signal detection network, and the neural network is a nonlinear algorithm and is not limited to operation of a linear relation, so that the method and the device can be effectively used for signal detection even in a complex communication environment with more nonlinear factors; and a high-precision estimation result is obtained for a transmitted signal.

The embodiment of the application provides a geological exploration method and device based on the radiation characteristics of the intrinsic subatomic wave of a substance, which comprises the following steps: obtaining remote sensing satellite data by setting the specific range of a target mining area, a scale, a shooting angle, a time period and a wave band, and determining the original negative of the remote sensing satellite corresponding to the exploration area; performing radiation operation on the original negative according to a preset subatomic field generator, performing image enhancement processing on the target substance radiation image according to a nonlinear algorithm, and determining the corresponding target substance digital map; determining the corresponding three directions according to the digital map, determining the corresponding three digital images according to the three directions respectively, performing target substance inversion calculation and target substance model establishment operation according to the three digital images, and determining the corresponding target substance mathematical model to realize the visualization of the target substance. The application can capture the subatomic radiation of the crust mineral elements based on the satellite, detect and visualize the minerals by using the subatomic characteristics, and improve the efficiency and accuracy of geological exploration.

The application discloses an advertisement optimization method, system, device and medium. The method comprises the following steps: obtaining an error value between a current advertisement effect and an expected effect; calculating a nonlinear PID control signal by using a nonlinear PID algorithm according to the error value; obtaining historical data of the effect of the advertisement, and predicting the error by using a long short-term memory network on the historical data to obtain error prediction data; converting the error prediction data by using a nonlinear function to obtain a feedforward compensation signal; and optimizing the advertisement according to the nonlinear PID control signal and the feedforward compensation signal. Compared with the related art, the application optimizes the PID algorithm by introducing a nonlinear function, and improves the response speed of the advertisement optimization system while improving the accuracy of the advertisement optimization system by combining the long short-term memory network (LSTM) for feedforward compensation prediction.

An apparatus, system and method for providing a may include at least a phacoemulsification surgical console having a customizable non-linear custom aspiration mode. The console may include at least an aspirator; a foot pedal; and a non-transitory computing code resident on a computing memory associated with a computing processor which, when executed by the processor, causes to be executed the steps of: receiving a percentage actuation of the foot pedal; calculating, including from a non-linear algorithm, a percentage actuation for the aspirator corresponded to the received percentage foot pedal actuation; and dictating the calculated percentage aspirator actuation to the aspirator.

The application relates to the technical field of grating coupler design, and discloses a design method and system of a grating coupler, computer equipment and a storage medium, which comprise the following steps: a topological prediction model is constructed based on a deep neural network, and the topological prediction model is trained by using a preset method; the response spectrum of the grating coupler is input into the trained topological prediction model, so that the structure parameters of the functional area of the grating coupler are obtained; and the topological structure of the functional area of the grating coupler is determined according to the structure parameters, so that the design of the grating coupler is realized. Through the approximate processing of the training data set, the simple network structure is combined with the complex nonlinear algorithm, the model power requirement is reduced, the prediction efficiency and effect of the model are improved, the prediction accuracy of the model is further improved through the training method combining the forward direction and the reverse direction, so that the design of the grating coupler with high coupling efficiency at any angle is realized, and the device performance of the grating coupler is ensured.

The application discloses a kind of laser volume direct writing optical lens additive manufacturing system and method, first by single point printing experiment to photosensitive resin obtains the relationship between irradiation time and solidification shape and depth, and obtains solidification growth function model by fitting and correction;Second, the residence time required by design surface is calculated by linear convolution and nonlinear algorithm;Residence time is processed by two-dimensional Gaussian filtering again, finally the corresponding conversion between residence time and laser irradiation energy is realized by 3D printing equipment, and the additive manufacturing of precision optical parts is realized.The application combines growth function and 3D printing technology, and proposes a new laser direct writing volume forming 3D printing method with high customization.The forming speed, precision and optical performance of the optical lens printed by the application are far superior to the optical lenses processed by other additive manufacturing equipment of the same price, so that additive manufacturing has further breakthrough in the field of precision optical manufacturing.

The invention discloses a phase difference method wavefront detection method combined with image sensor noise suppression, and the method comprises the steps: building an ideal light spot image of an image surface and a defocus position based on a phase difference method phase recovery system diffraction model; calculating a noise suppression factor of each defocus position according to the average noisy image of each defocus position and the image sensor parameters, and applying the noise suppression factor to the corresponding average noisy image to obtain a corrected noisy image of each defocus position; and finally, substituting the ideal light spot image at each out-of-focus position and the corrected noise-containing image into a target function, optimizing through a nonlinear algorithm, repeatedly updating the Zernike coefficient and recalculating the ideal image, and finally obtaining a restored wavefront phase. According to the invention, the wavefront detection precision can be significantly improved.

The invention relates to the technical field of geophysics, and discloses a method and a device for quantitatively inverting fracture density and fluid type based on seismic frequency variation, and a medium. The method comprises the following steps: establishing an HTI medium two-phase medium model based on a rock physical model, reservoir section geology and logging description characteristics; calculating a longitudinal wave reflection coefficient of the HTI medium; processing a seismic forward synthesis record of the seismic convolution model based on wavelets, the wavelets extracting actual seismic data from frequency division and angle division; according to the correlation between the seismic convolution model data and the actual seismic data, constructing a multi-objective function of different frequencies; and performing quantitative inversion on the multi-objective function by adopting a nonlinear algorithm to obtain the fracture density and the fracture fluid-containing type. According to the technical scheme, the rock physical model considers the influence of fracture density and fluid type changes on seismic response, the objective function is established through model forward modeling and actual data correlation, nonlinear quantitative inversion of the fracture density and the fluid type is achieved, and the effect of accurate quantification is achieved.

The invention relates to the technical field of digital publishing, in particular to a cultural media digital publishing content auditing method and system, which comprises the following steps: receiving to-be-audited content, deconstructing the to-be-audited content into a text stream and a visual frame, and mapping the text stream and the visual frame to a knowledge graph to construct a semantic association vector and a visual symbol tensor; and calculating cosine similarity through a cross-modal model and generating a symbol imbalance index, executing nonlinear coupling operation in combination with a network propagation popularity coefficient to quantify a public opinion risk probability value, generating an adaptive threshold according to historical distribution, and comparing and outputting a blocking instruction or issuing a voucher in compliance. According to the method, a multi-modal culture semantic mapping space is constructed, narrative features and visual features are projected to a unified plane for alignment analysis, a non-linear algorithm is utilized to accurately calculate a symbol imbalance index of cross-modal content so as to identify metaphor conflicts, and a discrimination threshold value is adaptively adjusted in combination with a real-time propagation environment, so that the resolution of the metaphor conflicts is improved. And intelligent capture and dynamic defense of private risk contents in a complex context are realized.

The invention belongs to the technical field of artificial intelligence, and discloses a meta-cognitive nonlinear emergence system and method based on man-machine symbiosis, and the system comprises a problem formatting and information extraction module, a core processing engine, and a result output and interaction interface. Wherein the core processing engine is composed of a multi-dimensional feature element cognitive analysis engine, a multi-dimensional feature nonlinear algorithm engine, a multi-dimensional feature analysis scanning engine, a lever point positioning engine, a man-machine collaborative learning engine and the like. The multi-dimensional feature analysis scanning engine performs parallel feature extraction and quantitative evaluation on the complex problem through six preset dimensions, and outputs a six-dimensional feature vector; the lever point positioning engine calculates and determines the most efficient intervention point based on the vector; the man-machine collaborative learning engine realizes deep collaboration and co-evolution of machines and human beings in the cognitive level. According to the method, panoramic analysis can be carried out on complex problems, broken points are accurately positioned, efficient intelligent support is provided for complex decisions, and the man-machine symbiosis mode can continuously optimize system performance and user thinking ability.

The invention discloses a construction method of a highway engineering carbon emission measuring and calculating model, which comprises the following steps: integrating calibration parameters of each stage of the full life cycle of a highway, constructing a carbon flow analysis model infrastructure, and establishing an ecological disturbance carbon emission response evaluation model input parameter system through field monitoring ecological related data; then, a nonlinear algorithm is adopted to quantify the coupling effect of the carbon emission influence factors, a carbon footprint full-chain tracing platform is built based on the block chain technology, and data full-process encryption storage and tracing are achieved; the carbon flow analysis result, the ecological disturbance response parameter and the nonlinear carbon emission incremental data are accessed to a platform, and multi-source data standardized mapping and collaborative association are completed; and finally, combining highway engineering characteristic parameters to complete construction of a measurement and calculation model. According to the method, efficient integration of multi-source data is realized, and the comprehensiveness and accuracy of highway engineering carbon emission measurement and calculation are improved.

The invention relates to a cross-rail squint equivalent along-rail interference flow measurement method. The method comprises the following steps: step 1, acquiring original echo data of a cross-rail double-antenna squint SAR (Synthetic Aperture Radar); step 2, carrying out imaging processing on the original echo data by adopting a nonlinear CS algorithm to obtain two SAR (Synthetic Aperture Radar) images; 3, performing high-precision image registration on the two SAR images, and performing interference processing to obtain an interference phase; step 4, carrying out flat-ground phase suppression; and 5, extracting the velocity of the flow field, and completing the cross-rail squint equivalent along-rail interference flow measurement. According to the invention, the height and speed information of the flow field can be measured at the same time, or the height and speed of the single flow field can be measured.

The invention provides a sensorless robust prediction-backstepping double-loop control method and device for a permanent magnet synchronous motor, and solves the problems that in an existing sensorless control system of the permanent magnet synchronous motor, a PI control structure is difficult to give consideration to fast dynamic response and robust stability, and a complex nonlinear algorithm is poor in real-time performance and insufficient in realizability. According to the method, a sliding-mode observer based on a Sigmoid function is constructed to estimate induced electromotive force, and a phase-locked loop is combined to extract a smooth rotor speed and position estimation value; a robust prediction controller based on a proportion-integral type error trend index is adopted in a rotating speed outer ring, an integral type backstepping controller is adopted in a current inner ring, and the motor is driven to operate through SVPWM modulation. According to the invention, through prediction-backstepping double-loop cooperative control, the dynamic response speed, anti-disturbance capability and stability of the system are substantially enhanced, the structure is simple, and engineering realization is easy.

A multi-module matrix control system dynamically manages the operation of electrolyzers across modules to optimize power allocation and maximize efficiency. Each module operates as an independent unit with multiple electrolyzers and corresponding power sources, utilizing linear and nonlinear algorithms for load distribution. The system includes innovative rotation strategies to balance wear and ensure longevity, fault prediction for proactive maintenance, and real-time adjustments to adapt to power input fluctuations. With three operational modes—Peak Performance, Dynamic Equilibrium, and Energy Conservation—the system seamlessly transitions between states based on power availability. This control approach enhances hydrogen production scalability, minimizes energy consumption, and ensures stable, efficient operation under varying conditions, making it ideal for applications ranging from megawatt to gigawatt-scale systems.

The present invention discloses an augmented reality head-up display system and a display method thereof, which relate to the field of assisted driving technology. In response to complex road conditions and diversified driving needs, the system achieves a comprehensive integrated analysis of the driving environment and driver status by sequentially executing four steps: environmental perception and multi-source data acquisition, situational analysis and information complexity determination, dynamic content screening and display strategy generation, and real-time monitoring and adaptive feedback closed loop. First, the multi-dimensional sensor data is labeled and normalized to form environmental factors and driving load factors, and the current information complexity is determined by a nonlinear algorithm. Then, the most critical content is screened from the information pool according to priority, and the display position and style are finely arranged. If the monitoring detects a HUD configuration deviation, a dynamic strategy update is executed, so that the AR-HUD always guarantees the accuracy and conciseness of the displayed information in different scenarios, thereby improving driving safety and user experience.

This invention discloses a blockchain-based information security detection system, relating to the field of information security technology. It includes: an off-chain monitoring node that uses a non-linear algorithm to calculate the dynamic reputation score of the monitored entity in real time. A trigger signal is generated only when the reputation score crosses a preset reporting threshold. This trigger signal then backtracks to the local cache and extracts the Merkle root hash of key events and related data, assembling them into a standardized evidence package and constructing a notarization transaction. The notarization smart contract responds to this transaction, recording the evidence package in the blockchain state ledger and reading the current block height as the unique temporal trust benchmark for the evidence package for associated storage. This invention, through the combination of off-chain dynamic risk threshold triggering and on-chain consensus time anchoring, achieves an automated, high-fidelity conversion from dynamic risk states to tamper-proof on-chain evidence, and establishes a network-wide consensus time trust root that does not rely on a single node's clock.

The present application relates to the technical field of electronic paste intelligent manufacturing and semiconductor packaging detection, and discloses a conductive copper paste online detection system oriented to AI computing power, which realizes full-process and full-dimension online collection of copper paste from wet film to solidification through a double-station multi-sensing module for wet film nondestructive testing and final inspection after sintering, breaks through the limitations of single-station detection lag and single dimension of traditional schemes, synchronously realizes defect identification, high-temperature failure risk prediction and interlayer matching evaluation, completes the management and control upgrade from post-elimination to pre-prediction, and has a cross-process closed-loop architecture with a built-in multi-field coupling nonlinear algorithm, can generate single substrate exclusive sintering process parameters, overturns traditional isolated control logic, can realize feedforward compensation of printing deviation without interrupting production, and realizes full-link data sedimentation, model iteration and product full-life-cycle quality traceability through end-side cloud cooperation.

To improve the usability of models that have intermediate variables. [Solution] The modeling method involves selecting a portion of the constituent components 41a of the intermediate variable C to determine a selected variable D, thereby estimating a first estimation model Ms1 that starts from the explanatory variable Vi, goes through the selected variable D corresponding to each intermediate variable C to the target variable Vo, and connects each of the 41 variables with a first kind intervariate model M1 based on a decision tree algorithm. Then, the intermediate variable C, with the selected variable D set, is dimensionally reduced in the component direction based on a recoverable linear or nonlinear algorithm to set a reduction variable E for each intermediate variable C, and a second kind intervariate model M2 is generated and output, which takes the selected variable D or explanatory variable Vi as input and outputs the reduction variable E connected to the terminal side of the input.

The invention discloses a mobile agent closed-loop dynamic positioning optimization method based on digital twinning. The method is suitable for dynamic positioning of unmanned vehicles, mobile robots and the like in complex and high-noise environments. The method comprises the following steps of: simulating signal multi-path propagation characteristics through a digital twin system, extracting characteristic coefficients such as path propagation time delay, attenuation and frequency shift, calculating a dynamic difference degree of a virtual signal and an actually measured signal, and establishing a joint posterior probability model in combination with a node motion state, so as to realize dynamic position estimation based on Bayesian reasoning. According to computing resource conditions, the method supports three optimization modes of convex optimization relaxation, a nonlinear Levenberg-Marquardt algorithm and a Kalman filtering fusion mechanism, real-time high-precision positioning is realized, recursive correction is performed on a prediction state and an observation error, and time sequence continuity and positioning precision are improved. According to the method, the problem of poor indoor static positioning precision caused by multi-path interference and non-line-of-sight propagation in a complex industrial environment is solved, and the anti-fluctuation capability and the estimation stability of the system are remarkably improved.