860 results about "Linear model" patented technology

The invention discloses an indoor temperature real-time regulation and control method of a heat distribution pipeline and a control system of the indoor temperature real-time regulation and control method, relates to the technical field of dynamic control of a heat distribution pipe network, and solves the problems of hydraulic oscillation and temperature control hysteresis caused by the fact that local valve regulation neglects whole-network coupling and a first-order linear model is difficult to describe multi-order thermal inertia and large heat capacity in the prior art. According to the scheme, on the basis of pipe network distributed PDE / lumped parameter hybrid modeling and in combination with extended Kalman filtering and unscented Kalman filtering on-line identification, a feedforward decoupling compensation item is generated through spectral decomposition, a self-adaptive multi-model predictive control and iterative learning compensation closed-loop structure is constructed, a control instruction is issued according to a pump-first and valve-second serialization strategy, and a self-adaptive multi-model predictive control and iterative learning compensation closed-loop structure is constructed. Meanwhile, the model weight and the prediction time domain are dynamically adjusted; according to the method, the global balance capability and the temperature tracking precision of heat distribution pipeline regulation and control are remarkably improved.

The invention relates to the technical field of fluid valve actuator equipment diagnosis, in particular to a fluid valve actuator monitoring and diagnosing system based on AI intelligence. According to the method, the target opening control instruction is generated through fluid medium characteristic data matching, and accurate regulation and control of the fluid valve are achieved; an opening degree change data set and a torque time sequence data set are captured in real time through a deployed sensor group, multi-dimensional dynamic fluctuation analysis is carried out, and intelligent recognition and screening of fluid valve abnormity are achieved; the medium temperature, the pipeline pressure and the medium flow velocity in the dynamic working condition parameters are substituted into a multiple regression linear model to generate reference torque and opening, and dynamic diagnosis of clamping stagnation abnormity is achieved by combining fusion calculation of a torque fluctuation coefficient and an opening deviation coefficient; through the correlation between the opening degree and the torque time sequence data and the traceability analysis of the current dynamic working condition parameters, the accurate positioning of the abnormal source is realized, and a systematic solution is provided for the state diagnosis and reliability evaluation of the fluid valve actuator.

The invention relates to an environment adaptive radio measurement calibration method and system. The method comprises the following steps: synchronously sampling temperature, humidity, air pressure and electromagnetic interference around radio equipment to obtain an environmental parameter vector; constructing a linear model and a nonlinear target model of a three-layer neural network structure according to the environmental parameter vector; solving an environmental parameter separation vector set by using the linear model and the nonlinear target model; executing one-dimensional iterative search optimization on the environment parameter separation vector set to obtain a target environment parameter vector; and inputting the target environment parameter vector into the nonlinear target model for calibration compensation calculation to obtain dynamic calibration compensation data. According to the method, the updating frequency is reduced when the environment is stable, the calibration precision is ensured, the system resource use is optimized, and the method is particularly suitable for wireless equipment with limited resources.

A method and apparatus for inter prediction in video coding system are disclosed. According to the method, input data associated with a current block comprising at least one colour block are received. A blending predictor is determined according to a weighted sum of at least two candidate predictions generated based on one or more first hypotheses of prediction, one or more second hypotheses of prediction, or both. The first hypotheses of prediction are generated based on one or more intra prediction modes comprising a DC mode, a planar mode or at least one angular modes. The second hypotheses of prediction are generated based on one or more cross-component modes and a collocated block of said at least one colour block. The input data associated with the colour block is encoded or decoded using the blending predictor.

A method and apparatus for video coding are disclosed. According to the method for the decoder side, a first syntax, related to whether the current block is coded in a CCLM related mode, is parsed from a bitstream comprising the encoded data for the current block. If the first syntax indicates the current block being coded in the CCLM related mode, a second syntax is parsed from the bitstream, wherein the second syntax is related to whether a multiple model CCLM mode is used or whether one or more model parameters are explicitly signalled or implicitly derived. The model parameters for the second-colour block are determined if the first syntax indicates the current block being coded in a CCLM related mode. The encoded data associated with the second-colour block is then decoded using prediction data comprising the cross-colour predictor for the second-colour block.

The invention provides an urban groundwater inrush risk assessment method based on a three-dimensional grid model and an XGBoost algorithm, and belongs to the technical field of geological disaster early warning. The method comprises the following steps: acquiring geological, environmental and historical disaster data in real time through a multi-source heterogeneous data interface; constructing a heterogeneous grid unit based on three-dimensional geological modeling, and dynamically binding parameters such as underground water level, fault activity and lithologic permeability coefficient; an XGBoost algorithm is combined with second-order Taylor expansion to optimize gradient calculation, and a nonlinear risk assessment model is constructed through Bayesian hyper-parameter tuning and SHAP value feature interpretive analysis; identifying contiguous dangerous areas based on spatial autocorrelation analysis and an adaptive threshold segmentation algorithm; and a risk thermodynamic diagram, geological section superposition and real-time early warning are realized through a 3D GIS platform. According to the method, the problems that a traditional method depends on a linear model, data is single and local risk description is insufficient are solved, and the accuracy, efficiency and engineering practicability of risk assessment are remarkably improved.

Methods, apparatuses, and non-transitory computer-readable storage mediums are provided for video coding. In one method, a decoder obtains, for a current chroma block, a non-linear model (non-LM) mode and a linear model (LM) mode, and the LM mode includes a cross component linear model (CCLM) mode and a multi-model linear model (MMLM) mode. The decoder then combines the non-LM mode and the LM mode for a multi-hypothesis-based chroma prediction (MCP) for the current chroma block.

Systems, methods, and instrumentalities are disclosed for template based cross component linear model / multimode linear model (CCLM / MMLM) adjustment. In an example, a device, such as a video decoding device, or a video encoding device, may obtain a prediction model for predicting a coding block. The device may select an adjustment model, from multiple adjustment models, for adjusting the prediction model. The device may adjust the prediction model based on the selected adjustment model. The device may process (e.g., encode and / or decode) the coding block based on the adjusted prediction model.

A video signal processing method comprises the steps of: configuring a template including neighboring blocks of a current block; down-sampling luminance component samples of the neighboring blocks on the basis of a color format of a current picture including the current block; deriving a first linear model and a second linear model on the basis of the down-sampled luminance component samples; and on the basis of any one linear model from among the first linear model and the second linear model, predicting a chrominance component sample at a location corresponding to a location of a first sample from among the luminance component samples of the current block, wherein the any one linear model is determined by comparing a value of the first sample with a threshold value.

The invention discloses a TEC space-time prediction method based on fusion of feature analysis and a self-attention mechanism, and the method comprises the steps: screening strong effective features through analyzing the correlation between TEC historical data and each feature parameter, calculating the attention weight between data points on each time sequence through employing a self-attention coding mechanism, and carrying out the weighted summation to obtain the attention output. In an ionosphere total electron content prediction task, firstly, for sun and geomagnetic activity characteristic parameters, the correlation between each parameter and a TEC sequence in the same time period and a TEC sequence after a day interval is evaluated, strong correlation characteristics are screened, TEC sequences of adjacent grid points are used as characteristic quantities, multiple spatio-temporal characteristic matrixes of different time scales in a global range are established, and the total electron content of the ionosphere is predicted. And encoding the input feature information according to a self-attention encoding mechanism to obtain an attention output result, and decoding in a decoding layer through a linear model to obtain a TEC predicted value at a future moment. According to the method, efficient extraction of spatio-temporal information can be completed, and higher-precision prediction of the TEC is realized through a self-attention mechanism.

The invention relates to a quantitative control method for a nonlinear system, computer equipment and a storage medium. The method comprises the following steps of: performing sector bounded processing on the output of a static logarithmic quantizer to establish an expression for quantizing control input; constructing a nonlinear system with disturbance, time lag and parameter uncertainty, and constructing a T-S fuzzy linear model of the nonlinear system according to the expression; constructing a target function according to the T-S fuzzy linear model, and constructing an optimization problem on an infinite time domain by taking a system state constraint, a control input constraint and a disturbance constraint as conditions so as to realize minimum quantization control input under maximum disturbance; and performing stability analysis on the T-S fuzzy linear model by constructing an LKF, and solving an optimization problem under the condition of meeting the stability analysis to obtain quantitative control input. By adopting the method, stable control on a nonlinear system can be effectively realized.

The invention relates to a time sequence prediction method based on Transform, and belongs to the field of artificial intelligence time sequence prediction. The method comprises the following steps: preprocessing multivariable time series data, and performing season trend decomposition to obtain a season term and a trend term; predicting the trend term by adopting a linear model; a multi-layer perceptron (MLP) and a graph convolutional network (GCN) are adopted to model correlation among seasonal item variables; using convolution kernels of different scales to extract multi-scale features of seasonal items; constructing a position code and a timestamp code; a Transform-based encoder is constructed, and a sparse attention mechanism is adopted to capture a global dependency relationship of the time sequence; capturing a short-term dependency relationship of the time sequence by adopting a local attention mechanism; initializing a decoder input; and a decoder based on a Transform is constructed to realize prediction.

An adaptive engine and method of adaptive control. The adaptive control method comprises receiving a reference signal, a system output measurement, and a control output. The method also includes applying one or more estimation laws to estimate an estimated model parameter tensor, θ, for a nonlinear model; generating a possible control signal or an internal possible control signal using a control portion of the nonlinear model; receiving a linear time varying system corresponding to the reference signal, and the possible control signal or the internal possible control signal; and generating one or more of a predicted system output and an estimated system output using an estimation portion of the nonlinear model.

The invention relates to the technical field of industrial mechanism models, in particular to a shock absorber performance optimization control method based on model fusion, which comprises the following steps: extracting a low-frequency disturbance state variable and inputting the variable-topology industrial mechanism model to generate a nominal reference state trajectory; utilizing a depth state observation network fused with energy passivity constraint to calculate a non-linear model mismatch compensation amount and an adaptive weighting parameter; performing dynamic fusion on the nominal reference state trajectory and the compensation amount based on the adaptive weighting parameter to generate a generalized state estimation value; and executing dynamic multi-objective optimization based on the generalized state estimation value, and generating mixed mode control input acting on an execution end. According to the invention, through adaptive fusion of a mechanism model and a data driving method, physical consistency, calculation real-time performance and robustness of a control process are considered.

A method and apparatus for video coding system that uses intra prediction based on cross-colour linear model are disclosed. According to the method, model parameters for a first-colour predictor model are determined and the first-colour predictor model provides a predicted first-colour pixel value according to a combination of at least two corresponding reconstructed second-colour pixel values. According to another method, the first-colour predictor model provides a predicted first-colour pixel value based on a second degree model or higher of one or more corresponding reconstructed second-colour pixel values. First-colour predictors for the current first-colour block are determined according to the first-colour prediction model. The input data are then encoded at the encoder side or decoded at the decoder side using the first-colour predictors.

A method and system for power grid optimization control based on a linear time-varying model is provided. The system model is first estimated offline using historical data combined with a piecewise linear regression method that supports a vector regression model. For online applications, a time-varying linear model prediction control framework based on a piecewise linear model coordinates the optimization control of both fast and slow regulating devices in the power grid. This approach does not require precise system model parameters, instead learning the power grid model from historical data. The method optimizes voltage distribution, reduces operating costs, and addresses issues like bad data and collinearity in the historical data, improving the voltage quality and enabling optimal operation even with incomplete models.

The invention provides a method capable of comprehensively evaluating the intelligent degree of household intelligent home equipment, and the method comprises the steps: reasonably selecting architecture parameters capable of reflecting the intelligent degree; data required by architecture parameters can be effectively obtained by collecting and analyzing multi-dimensional data such as categories, quantity and scores of household intelligent household equipment and App application use conditions, and meanwhile, intelligent degree scores required by model training are set in a reasonable mode, so that the intelligent degree of the intelligent household equipment is improved. And finally, a linear model is adopted to construct a scoring model, so that the development difficulty can be effectively reduced, the evaluation accuracy can be improved, and a convenient and effective evaluation means is provided for subsequent users, operators and the like. According to the method, whether the user uses the intelligent equipment or not can be judged through the App accessed by the user, a relatively reasonable score is made for a family of which the equipment model cannot be captured according to the App, and two sets of rating standards are further adopted, so that the matching between the score and the rating can be effectively ensured, and the reliability of an output result is improved.

Provided are an image encoding / decoding method and apparatus. An image decoding method may comprise deriving chroma reference samples located around a chroma block and luma reference samples corresponding to the chroma reference samples based on an intra prediction mode of the chroma block being a cross-component linear model (CCLM) mode, deriving CCLM parameters based on the chroma reference samples and the luma reference samples, and generating a prediction sample of the chroma block based on the CCLM parameters. The chroma reference samples may be obtained from two or more chroma reference lines adjacent to the chroma block.

The invention discloses a hydraulic engineering operation and management cooperative control system based on digital twinning, and the system comprises the following steps: a data assimilation module which collects observation station, remote sensing and meteorological data and assimilates the data to obtain an assimilation estimator; the dictionary and lifting module is used for generating a lifting variable sequence according to hydrodynamic force prior; the structure identification and uncertainty module is used for identifying a lifting linear model and recursively predicting uncertainty under the constraints of conservation, monotonicity, dissipation and spectral radius; the terminal security module constructs a positive invariant terminal security set according to a support function; the opportunity constraint module is used for setting a total out-of-limit probability budget and generating an opportunity constraint substitution set; the Koopman rolling optimization module is used for solving the control sequence and executing the first control quantity; and the online updating and correcting and deformation and verification module is used for executing residual error triggering low-rank correction, projection return and support surface parameter online deformation. According to the invention, risk-controllable and stable and efficient collaborative scheduling is realized.

The invention relates to the technical field of power system automatic control, and discloses a primary frequency modulation method and device for controllable load resources to participate in a power system, and the method comprises the steps: obtaining the load state data of the power system, carrying out the multi-dimensional load clustering of the load state data, and obtaining a load feature time sequence data set; based on the load characteristic time sequence data set, establishing a Koopman operator high-dimensional linear model of the power system by using an extended dynamic mode decomposition algorithm; performing optimization solution on the load state of the power system by using the Koopman operator high-dimensional linear model of the power system to obtain an optimal load control sequence; and performing frequency adjustment on controllable load resources participating in the power system by using the optimal load control sequence. According to the method, the stable prediction of the load state of the power system and the accurate adjustment of the frequency in the controllable load resources participating in the power system are realized, and the operation efficiency and the stability of the power system are improved.

A method for using Extrapolation Intra Prediction (EIP) to code pixel blocks is provided. A video coder receives data to be encoded or decoded as a current block of pixels of a current picture of a video. The video coder inherits a previously used linear model for extrapolation intra prediction (EIP) that is derived based on samples in a template region neighboring a previously coded block, the template region occupying an area that extends beyond at least one of top and left edges of the previously coded block. The video coder applies the inherited linear model to generate a predictor of the current block by using reconstructed samples or predicted samples of the current block as input to a filter to generate samples of the predictor. The video coder encodes or decodes the current block by using the predictor.

The invention provides a predetermined time distributed optimal inclusion control method for an unmanned aerial vehicle cluster, and belongs to the technical field of collaborative decision of an unmanned cluster system. The method comprises the steps that unmanned aerial vehicles in an unmanned aerial vehicle cluster are divided into virtual leader unmanned aerial vehicles and follower unmanned aerial vehicles; according to the single integral linear model of the virtual leader unmanned aerial vehicle, generating a preset formation pattern through a preset time distributed optimization theory; constructing a nonlinear model of a follower unmanned aerial vehicle in the unmanned aerial vehicle cluster; and designing an inclusion control protocol of the follower unmanned aerial vehicle based on a distributed inclusion control algorithm according to the nonlinear model of the follower unmanned aerial vehicle, so that the follower unmanned aerial vehicle converges to a preset formation. Through a distributed optimal formation control algorithm and a distributed inclusion control algorithm, a virtual leader unmanned aerial vehicle formation generation strategy within a preset time is realized, follower unmanned aerial vehicles converge to a preset formation, and thus unmanned aerial vehicle cluster security cooperative control under a complex sea condition is completed.

An intra prediction method by using cross component liner prediction mode (CCLM), includes: determining a luma block corresponding to a current chroma block; obtaining luma reference samples of the luma block based on determining L available chroma reference samples of the current chroma block, wherein the obtained luma reference samples of the luma block are down-sampled luma reference samples; calculating linear model coefficients based on the luma reference samples and chroma reference samples that correspond to the luma reference samples; and obtaining a prediction for the current chroma block based on the linear model coefficients and values of a down-sampled luma block of the luma block.

The ELM-based frequency safety constraint linearization optimization scheduling method comprises the following steps: establishing a fire-wind-light multi-energy complementary frequency dynamic response model, and deducing a time domain analytical expression of a frequency safety index by taking a frequency maximum change rate and a frequency maximum deviation as the frequency safety index of a system; considering a frequency lowest point security constraint and a frequency maximum change rate security constraint, and establishing a frequency security constraint-containing optimization scheduling model; a maximum bearable disturbance power linearization mapping method based on ELM is provided, nonlinear frequency security constraints are converted into a series of linear constraint sets, and linearization processing of the frequency security constraints is achieved; and piecewise linearization is carried out on quadratic terms in the established objective function containing the frequency security constraint optimization scheduling model, the overall model is converted into a mixed integer linear model, and a Gurobi solver is used to solve the linearly processed model. The scheduling method can ensure that the system frequency is safe and stable, so that the established optimal scheduling model gives consideration to both economy and frequency safety.

The invention relates to a dynamic grid-based foundation pit temperature field-seepage-stress field coupling calculation method, which comprises the following steps of: establishing a nonlinear dynamic bidirectional coupling constitutive relationship of a temperature field, a seepage field and a stress field containing a two-dimensional foundation pit cofferdam, and obtaining a three-field full coupling equation set; in combination with a GPU acceleration solver, solving the three-field full-coupling equation set by adopting a sub-domain decoupling-asynchronous iteration strategy; and performing real-time early warning on the cofferdam construction period according to a solving result. The method has the advantages that compared with a traditional linear model, the precision of the established three-field full-coupling equation set is improved, and the interaction influence relation of three fields can be well simulated.

A straight line fitting method for image detection in a complex interference environment comprises the following steps: firstly, carrying out interference point deletion on a feature point set of an environment image, then carrying out multiple times of random sampling, forming a total point set by using associated points through distance operation, obtaining a plurality of straight line models through initial fitting, and carrying out linear fitting on the linear models; and then calculating an interior point proportion in the total point set according to the linear model, finally selecting the total point set with the maximum number of interior points, and continuously reducing a residual threshold in multiple iterative calculations to improve an interior point proportion threshold by dynamically adjusting a feedback mechanism of the residual threshold when the interior points are calculated. In the initial stage of iteration, more points can enter the model to ensure the diversity and robustness of the model, and the threshold value is gradually tightened along with the convergence of the model, so that the interior points conforming to the model are screened out more accurately. And finally, performing secondary sampling on the inner points and performing straight line fitting on an inner point set subjected to secondary sampling again to eliminate potential error inner points and further improve the fitting precision.

The invention discloses a pollution source tracing method based on a three-dimensional fluorescence spectrum, and relates to the technical field of water quality monitoring, a water sample of an upstream specific pollution discharge unit is collected as a pollution source, a mixed water sample formed by mixing different pollution source water samples according to different volumes or proportions is prepared, and a pollution source database adapted to parallel factor analysis is established; according to the pollution source tracing method, a good linear model between a pollution source and a three-dimensional fluorescence spectrum characteristic value is established, a shortest path algorithm is proposed to quantitatively solve the volume or proportion of the pollution source in a monitored water sample, and the pollution source is traced to a specific pollution discharge unit. According to the invention, the sewage three-dimensional fluorescence spectrum database established by mixing upstream pollution source drainage improves the application range of parallel factor analysis, so that the three-dimensional fluorescence spectrums of sewage of different industrial systems can be subjected to effective parallel factor analysis together; compared with a parallel factor analysis-Tucker congruence coefficient method, the method has the advantage that the pollution source can be traced to a more specific level.

The invention relates to the technical field of prediction models, in particular to a battery energy storage life prediction method and system based on multiple time scales, and the method comprises the following steps: deploying three groups of short-term, medium-term and long-term parallel data windows, obtaining battery capacity and internal resistance parameters, sliding window initialization parameters, synchronous baseline calibration, and Kalman filtering correction parameters, and extracting a variation amplitude and rate, an output capacity mean value and an internal resistance variance, calculating a residual sequence, performing three-time linkage on the capacity residual exceeding a threshold to trigger an abnormity, performing weighted fusion after the abnormity, and outputting the residual life. According to the invention, through multi-time scale parallel data window dynamic monitoring, sliding window synchronous calibration base line, Kalman filtering dynamic correction parameters, capacity and internal resistance residual error linkage determination mechanism establishment, dual-attenuation coupling model fusion multi-dimensional attenuation characteristics and multi-scale collaborative analysis of battery aging, the limitation of a linear model is broken through; the anomaly detection sensitivity and robustness are enhanced, the life prediction accuracy and timeliness are improved, and the misjudgment risk is reduced.

The invention discloses a plant extraction method and system based on image processing, and relates to the technical field of image processing. The method comprises the following steps: acquiring and standardizing a plant raw material image; extracting feature vectors of the images by using a neural network convolutional layer algorithm, comparing the feature vectors with a plant raw material standard feature database, and screening out qualified images of the plant raw materials; qualified images are processed through multispectral image analysis and an image segmentation algorithm, and an effective component spatial distribution diagram is obtained; constructing a partial least squares regression linear model, extracting spectral feature vectors of the effective components, inputting the spectral feature vectors into the model to obtain concentration values, and calculating quantitative data of the effective components; establishing an extraction process rule base, selecting a process parameter combination according to a spatial distribution diagram and quantitative data, and extracting to obtain a primary extracting solution and plant residues; and calculating an effective component residual error rate by matching and mapping to a qualified image coordinate, and if the effective component residual error rate is greater than a preset threshold, performing secondary extraction to obtain a secondary extracting solution. And quantitative basis is provided for extracting process parameters through the spatial distribution diagram of the effective components.

The invention relates to an electric vehicle wireless charging system LPV-Hammerstein model identification method based on an alternating least square iteration strategy, and belongs to the technical field of power electronic system modeling and parameter identification. The method comprises the following steps: firstly, constructing an LPV-Hammerstein model structure based on a WPT system circuit topology and a working mechanism, and then collecting discrete data of a system input control signal U, an output DC voltage Vo and a scheduling variable Req; then, alternately fixing other parameters by adopting an alternate least square iterative algorithm, and converting a to-be-estimated parameter problem into a linear least square sub-problem to be solved; and when a convergence condition is satisfied, a final LPV-Hammerstein model parameter estimation result is obtained. According to the method, the common nonlinear and parameter change coupling characteristics in the wireless charging system of the electric vehicle can be effectively processed, a systematized and efficient way is provided for obtaining a high-precision system dynamic model, and the limitation that the system cannot be accurately described by a traditional linear model or a simple nonlinear model is overcome.