9 results about "Model order" patented technology

A method for identifying a model for modeling cable stress relaxation dynamics of an imprint head is provided. The method includes performing a non-contact imprint test run for a predetermined number of wafers. Data from a force trace and a position trace of the imprint head during the non-contact imprint test run are collected to compute cable stress relaxation forces. A model having a plurality of different model orders is generated. A set of parameters of the model for each model order is identified. Residual sum of squares for wafer average error for each of the model orders is calculated based on the obtained cable stress relaxation forces. The set of parameters of one of the plurality of model orders may be based on a difference in the residual sum of squares for wafer average error between the one of the plurality of model orders and a next higher model.

This invention provides a radar motion parameter estimation method and apparatus based on information criterion values. It acquires raw radar echo data, performs range processing and target extraction to obtain a target slow-time complex sequence. Based on the target slow-time complex sequence, it constructs multi-order motion candidate models, calculates the residual sum of squares, and calculates the information criterion value based on the residual sum of squares. The optimal model order is determined based on the information criterion value, and the target radial motion parameters are inverted and output based on the phase coefficients corresponding to the optimal model order. Instead of pre-fixing the use of a first-order, second-order, or third-order model, it determines the optimal model order after constructing multi-order candidate models and evaluating them using the calculated information criterion value. This approach adaptively determines the appropriate motion model order for the target based on the phase evolution characteristics of the signal itself, and completes motion parameter estimation accordingly. This ensures estimation accuracy while suppressing unnecessary model complexity, thus improving its engineering application value.

This invention discloses a method for verifying and confirming numerical prediction thrust spectrum results of propellers, belonging to the field of computational fluid dynamics (CFD) result verification and confirmation. While maintaining consistency between the physical model and boundary conditions, this invention encrypts the CFD calculation settings with joint discrete parameters and quantitatively characterizes simulation errors and uncertainties based on Richardson extrapolation. This invention employs the Lomb-Scargle periodogram method to directly process non-uniformly sampled data, avoiding spectral distortion caused by resampling interpolation and improving the accuracy of spectrum estimation. This invention automatically selects the optimal AR model order through information criteria, models colored noise backgrounds in actual engineering, and achieves adaptive noise modeling. This invention simultaneously evaluates the statistical significance of the global maximum peak value and each expected harmonic frequency in a single Monte Carlo simulation cycle. This invention uses a block bootstrap method to calculate the frequency band power confidence interval, maintaining the local correlation of the time series and achieving reliable confidence interval quantification.

The application discloses a hybrid reduced-order control method for large-scale dynamic systems and relates to the technical field of system reduced-order control.The application builds a multi-time-scale state space model, quantitatively divides fast and slow subsystems based on a preset time scale threshold value, accurately screens dominant state variables by using eigenvalue dominant factors and participation factor analysis, performs energy-optimal reduction on the slow subsystem by adopting balance truncation and singular perturbation principles, and introduces a closed-loop deviation evaluation and automatic optimization mechanism, thereby solving the problems that the division of fast and slow subsystems is subjective in the prior art, dominant modes are easily lost, transient accuracy is poor, decoupling and reduction links are mutually fragmented and depend on artificial trial and error, and realizing automation, high precision and strong universality of the reduction process, so that the model order can be reduced while the dominant modes and transient characteristics of the system are completely retained, and an efficient and reliable technical scheme is provided for analysis, simulation and control design of large-scale dynamic systems.

The application discloses a modal identification method based on an unsupervised optimization covariance stochastic subspace method, and comprises the following steps: arranging vibration sensors on a to-be-measured structure with noise interference or weak excitation modes, and acquiring structural dynamic response data; calculating a covariance matrix and constructing a toply matrix; calculating an extended observable matrix and a controllable matrix based on the weighted toply matrix; defining a toply matrix row block number and a model order parameter range, and iteratively calculating a parameter optimization index; calculating a cumulative contribution rate based on a singular entropy increment of the toply matrix and determining a critical model order; calculating a cumulative parameter optimization index in an interval from a minimum model order to the critical model order under different toply matrix row block numbers, and determining an optimal parameter combination through an average minimum value; substituting the optimal parameter combination into a covariance stochastic subspace algorithm to identify a system matrix and calculate structural modal parameters; and automatically identifying each order physical mode from candidate modes by using a DBSCAN clustering method. The application further discloses a system and an electronic device.

The present invention belongs to the technical field of hydraulic pump anomaly detection. Disclosed is a mining area load spectrum anomaly detection method based on canonical variate analysis. The method comprises: S1, performing data preprocessing to obtain standardized data, and in combination with operating conditions of an excavator, constructing a typical operating condition data set of a hydraulic pump; S2, constructing a historical vector and a future vector, and on the basis of the historical vector and the future vector, constructing a historical observation matrix and a future observation matrix; S3, constructing a Hankel matrix on the basis of an auto-correlation matrix and a cross-correlation matrix, decomposing the Hankel matrix, and determining a model order; S4, mapping original data into a canonical variate space and a residual space, and evaluating the total variation of canonical variates in a state space and the sum of squared variation errors in the residual space; and S5, determining an evaluation threshold value, and if a control limit is exceeded, determining that the hydraulic pump operates abnormally. In the present invention, pressure pulsation data of a hydraulic pump is used to perform anomaly detection on the basis of canonical variate analysis, and the method in the present invention is sensitive to the internal operating state of the pump, is not prone to the impact of an external environment, and enables early warning of faults in the hydraulic pump.

The present application relates to a kind of artificial intelligence driven new energy vehicle power system energy efficiency optimization method, the present application constructs vehicle dynamics model to determine total torque demand;Based on mechanical power and adaptive fitting motor loss, the motor power of motor is obtained;Order prediction model is according to current data density and noise level adaptive prediction motor loss model order, after determining order, using square sum programming to adopt least square fitting monotone increasing and positive definite nature quasi-convex function, modeling motor loss;Each motor consumption electric energy and whole vehicle motor consumption electric energy are obtained;Collect whole vehicle operating condition in the process of driving, and the weight of each optimization subgoal of total optimization target is adjusted according to whole vehicle operating condition by pre-training multi-objective decision model, in the satisfaction vehicle total torque demand, with total optimization target optimal to optimize each motor torque distribution.Considering actual operating condition, power system energy efficiency optimization is realized.

The application discloses a laser communication coarse tracking system comprehensive modeling and multi-target complementary control method, and belongs to the field of laser communication technology control. The laser communication coarse tracking system is used for control, comprising linear modeling of the coarse tracking system, combination of low frequency bands of sinusoidal sweep signals and middle and high frequency bands of pseudo-random signals to obtain system frequency response, and system transfer function obtained through Hankel matrix identification method; nonlinear modeling of the coarse tracking system, establishment of a Stribeck friction model, and design of a feedforward compensation algorithm based on the Stribeck friction model to obtain a multi-target complementary control method for controlling the coarse tracking system. The application realizes accurate identification of model order and parameters in the linear part, significantly improves low-speed tracking performance in the nonlinear part, effectively solves the contradiction between system performance and robustness, and significantly improves tracking precision and anti-interference ability.