12 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.

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 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.

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.