129 results about "Linear approximation" patented technology

The invention relates to a vehicle and unmanned aerial vehicle combined scheduling method for wide-range low-cost inspection. The method comprises the following steps: acquiring prior information; modeling the unmanned aerial vehicle inspection problem of each target area according to the prior information to obtain a mixed integer non-convex optimization problem with the goal of minimizing the weighted sum of the total execution time and the energy consumption of all the inspection unmanned aerial vehicles; performing linearization on a non-convex bilinear term in the mixed integer non-convex optimization problem, and performing discretization processing on a nonlinear function by adopting piecewise linear approximation; an approximate mixed integer linear programming problem is obtained and solved, and an unmanned aerial vehicle scheduling strategy is obtained; modeling according to the unmanned aerial vehicle scheduling strategy and the prior information to obtain an inspection vehicle path planning problem taking the comprehensive driving cost as a target; the routing inspection vehicle path planning problem is converted and modeled into a Markov decision process, a routing inspection vehicle is used as an intelligent agent, a state, an action and a reward function are defined, and a routing inspection vehicle scheduling strategy is obtained. Therefore, combined inspection of the inspection vehicle and the unmanned aerial vehicle is realized, and the inspection range is expanded.

The invention discloses a rope-driven mechanical arm motion control method based on model predictive control, which comprises the following steps: taking a joint angle error as a state variable, taking a rope-driven rotation angle as an input variable, and establishing a nonlinear state-space equation of a rope-driven mechanical arm according to the state variable and the input variable; the nonlinear state-space equation is subjected to linearization processing, a linear approximation model is generated, and the linear approximation model comprises a linearization matrix; defining a prediction interval, expanding the linear approximation model into a multi-step prediction form, and generating a prediction model; constructing a cost function, and combining the prediction model into the cost function to solve and obtain an optimal control sequence; and extracting the optimal control increment at the current moment from the optimal control sequence, and updating the state variable, the input variable and the linearization matrix according to the optimal control increment to form closed-loop control. Accurate path tracking control can be carried out on the rope-driven mechanical arm under complex dynamic constraints, and it is ensured that the mechanical arm moves according to the planned trajectory.

The invention discloses an ARX password anti-differential and linear analysis security assessment method and system, and relates to the password technology, and the method comprises the steps: respectively constructing a differential distribution table CDDT of dependent carry of modular addition and a linear approximation table CLAT of dependent carry; respectively sorting the CDDT and the CLAT according to the differential probability and the linear probability; after sorting, searching the optimal differential feature and the linear feature of the ARX password based on a branch and bound algorithm; and after all plaintext differences and masks are traversed, obtaining the maximum difference and linear feature probability as the maximum difference and linear feature probability of the ARX password of the corresponding round number. The invention provides an ARX password anti-differential and linear analysis security assessment method based on a carry-dependent differential distribution table and a linear approximation table, and solves the problem of anti-differential and linear analysis security assessment in an ARX password design process.

The invention discloses a track query method and device based on adaptive space division, relates to the technical field of track data indexing, and mainly aims to solve the problem of low track data query efficiency. The method mainly comprises the following steps: constructing an index space layer of target trajectory data based on a data-driven adaptive hierarchical division strategy, and respectively constructing index time layers based on a multilayer piecewise linear approximation model for each space partition in the index space layer; in response to the trajectory data query instruction, obtaining a space range and a time range of a to-be-queried trajectory; identifying a target hierarchy matched with the spatial range and a target spatial partition under the target hierarchy from the index spatial layer, and identifying a track data storage position matched with the time range by utilizing an index time layer of the target spatial partition, and querying according to the track data storage position to obtain a track query result. The method is mainly used for constructing track indexes and querying track data.

The invention discloses a Loran-C system ground wave propagation ASF statistical property prediction method and related equipment, and the method comprises the steps: firstly obtaining the mean value and standard deviation of the electric field intensity in a random stratum-atmosphere waveguide environment based on a PCE-FDTD method, and on this basis, further positioning the position of a forward zero crossing point in a specific carrier period of a mean value time domain signal; therefore, the mean value of the ground wave propagation ASF of the Loran-C system is accurately calculated. Then, a linear approximation method is introduced, two sampling moments are selected at adjacent positions of mean value signals in the same period, and the propagation standard deviation of the ground wave propagation ASF signals of the Loran-C system on the flat terrain is rapidly obtained; finally, according to the method, the ASF mean value and the standard deviation of the Loran-C signal under the random medium background can be successfully obtained only through single simulation, and the solving efficiency is remarkably improved. The research provides a valuable insight for improving the accuracy of a PNT system and enhancing the stability and reliability of a communication system.

The invention discloses a method and a device for calculating Gamma correction in an RISC-V processor, which can improve the execution efficiency and the calculation precision of a Gamma correction algorithm on the RISC-V processor. Gamma correction is divided into six levels of pipeline structures, wherein the first level is composed of a floating point adder and a floating point shifter, and input pixel values in an integer range from 0 to 255 are mapped to a real number range from 0 to 1; the second stage is used for carrying out reciprocal operation on the Gamma value; in the third stage, logarithm operation is carried out on input pixel values, and a lookup table and piecewise linear approximation method is adopted; the fourth stage uses a floating point multiplier to perform product operation on the result of reciprocal operation and the result of logarithm operation; the fifth stage is used for performing exponential operation on the result output by the fourth stage; and the sixth level is composed of a floating point shifter and a floating point subtractor, and image pixel values after Gamma correction are remapped to an integer range of 0-255.

The invention discloses a squeeze film damper active control method based on RBF neural network PID optimization. The method comprises the following steps: acquiring state parameters of a rotor system through a data acquisition module; inputting the state parameters into an RBF neural network, and performing online optimization on PID control parameters by using the RBF neural network; based on the optimized PID parameters, a piezoelectric ceramic actuator is driven through a piezoelectric ceramic controller, the damping force of the extrusion oil film damper is adjusted, and active control over vibration of the rotor system is achieved. According to the method, RBF neural network nonlinear approximation and PID control stability are fused, the hysteresis and locking problems of traditional passive control are solved, active suppression of rotor system vibration is achieved, and the control precision and reliability are greatly improved.

The invention discloses a real-time solution reconstruction planning method for a man-machine interaction rope traction parallel robot, which belongs to the field of robot configuration planning, and comprises the following steps of: 1, constructing kinematics and dynamics models according to the position relationship between a rope leading-out point and a movable platform, and establishing an admittance model; 2, according to the dynamics and admittance model, a hyperplane movement method is used for representing a force feasible condition, and an optimization problem objective function is set according to the force feasible condition; 3, expressing a rope leading-out point solving problem as a nonlinear optimization problem through constraint, approximating the nonlinear optimization problem as a linear optimization problem through linear approximation, and solving an approximate optimal solution of the linear optimization problem; and 4, an artificial potential field is set according to dynamic characteristics, the approximate optimal solution is corrected, the solved rope leading-out point position is not located at the boundary of a solution space while the solving speed is guaranteed, and reconstruction planning of real-time solving of the robot rope leading-out point is completed. According to the method, the solving instantaneity and the man-machine interaction stability can be ensured.

This invention proposes a GQA-LUT method, utilizing a genetic algorithm and LUT-based circuit to efficiently approximate non-linear operators in Transformers. It adaptively finds optimal solutions for various non-linear functions, outperforming conventional neural network methods. A novel rounding mutation (RM) algorithm enhances approximation accuracy during quantization, improving low-bit integer precision. The invention also introduces a LayerNorm folding strategy as a near-memory computing principle, reducing IO and energy overheads with a two-stage memory hierarchy. Additionally, an additive partial sum quantization method is proposed to reduce energy consumption by quantizing accumulated PSUMs in matrix multiplication, alongside a PSQ-APSQ grouping strategy and floating-point regularization.

The invention provides a GQA-LUT method. According to the GQA-LUT method, a non-linear operator in a converter is effectively approximated by utilizing a genetic algorithm and an LUT-based circuit. The GQA-LUT method can adaptively find optimal solutions of various nonlinear functions, and is superior to a conventional neural network method. A novel rounding variation (RM) algorithm enhances approximation accuracy during quantization, thereby improving low order integer precision. In the invention, a LayerNorm folding strategy is also introduced as a near memory calculation principle, so that IO and energy overhead of the hierarchical structure of the two-stage memory is reduced. Furthermore, an additive partial and quantization method is proposed to reduce energy consumption by quantizing accumulated PSUM in matrix multiplication as well as a PSQ-APSQ grouping policy and floating point regularization.

In one embodiment, an apparatus comprises a log circuit to: identify an input associated with a logarithm operation, wherein the logarithm operation is to be performed by the log circuit using piecewise linear approximation; identify a first range that the input falls within, wherein the first range is identified from a plurality of ranges associated with a plurality of piecewise linear approximation (PLA) equations for the logarithm operation, and wherein the first range corresponds to a first equation of the plurality of PLA equations; compute a result of the first equation based on a plurality of operands associated with the first equation; and return an output associated with the logarithm operation, wherein the output is generated based at least in part on the result of the first equation.

This specification pertains to the field of refining and chemical production processing technology, and particularly relates to a method and apparatus for optimizing refining and chemical production plans for a target secondary processing unit. The method includes: obtaining parameters of the secondary processing unit under a refining and chemical production optimization scenario; constructing a nonlinear constraint equation for product output based on the parameters of the secondary processing unit; performing an equivalent transformation on the nonlinear constraint equation to obtain an equivalent transformation equation; extracting the nonlinear constraint terms and performing a Taylor expansion; transforming the Taylor expansion formula into a linear approximation equation based on the proportion of the feed material to the output material of the blending tank, the feed material quantity to the blending tank, and the feed property values ​​of the blending tank, according to the property balance constraints of the blending tank; and then substituting these into the nonlinear constraint equation for product output, thereby transforming the nonlinear constraint equation for product output into a linear approximation equation containing only material information variables. This method effectively ensures model convergence and shortens the solution time.

The invention provides a dual-domain attack detection method and system oriented to source-load side distributed resources. According to the implementation scheme, linear approximation is carried out based on a historical modulation input signal time sequence and a historical output current time sequence of an inverter switching network in a distributed power grid system, and a state space matrix of the inverter switching network is obtained; adding a random excitation signal to the modulation input signal of the inverter switching network so as to input the modulation input signal into the inverter switching network, and obtaining an output current detection value of the inverter switching network; based on the state space matrix of the inverter switching network, verifying the output current detection value of the inverter switching network to determine whether the on-way conversion process of the inverter switching network for the random excitation signal is normal; and under the condition that the on-way conversion process is abnormal, determining that the inverter switching network is attacked. According to the invention, whether the inverter switching network is attacked can be accurately detected.

A computer-implemented method, computer program product, and computer system for automated model predictive control. The computer system trains multiple step look-ahead regression models, using historical states and historical actions for a to-be-optimized system, for each timestep of a past time horizon. Regression models may be either linear or nonlinear in order to capture process dynamics and nonlinearity. The computer system generates optimization constraints for each timestep of a future time horizon. The computer system generates optimization variables, based on the multiple step look-ahead regression models, for each timestep of the future time horizon. The computer system constructs a mixed integer linear programming based optimization model that includes an objective function, the optimization constraints, and the optimization variables. Nonlinear regression models are converted into piecewise linear approximation functions. The computer system solves the optimization model to produce actions for the to-be-optimized system, over the future time horizon, and recommend commitment-look-ahead actions.

The invention discloses a reservoir group medium-and-long-term power generation optimization scheduling method based on gallery iteration static optimization, and the method comprises the steps: firstly calibrating convex hull parameters based on a planar convex hull linear approximation method, constructing a scheduling model, and solving an initial solution (including the storage capacity and the output flow of each time period); dividing the whole output feasible region into small-scale regions in a high-density manner, and respectively calibrating convex hull parameters of each region; comparing the initial solution with the small-scale region boundary period by period, determining the region to which the initial solution belongs, and updating the convex hull parameter of the corresponding period; constructing a time period corridor by taking the initial solution as a reference, and constructing a new model in combination with the updated parameters to solve a current iterative solution; and if the target function value change is smaller than the threshold value, converging and outputting the optimal solution, otherwise, updating the scale parameter and repeating iteration. According to the method, the optimal solution is approximated through static calibration-parameter selection, linear errors are corrected, efficient solution can be achieved, operation of the cascade hydropower stations can be guided, and coordinated utilization of water resources is optimized.

The present application discloses a method, device, terminal and medium for constructing a digital twin simulation model of a power device. The solution provided by the present application first constructs a geometric model of the target power device, then establishes multiple physical field models related to the target power device, determines the type of coupling relationship between different physical field models according to the field quantity change relationship between different physical field models, and then constructs a physical field coupling relational expression according to different coupling relationship types to achieve the purpose of coupling multiple physical fields. Then, through the simulation test method, the material change characteristic data of the target power device is obtained to construct the material characteristic function of the target power device, and the material characteristic function is subjected to Taylor expansion and linear approximation processing to complete the material characteristic mapping, so as to obtain the digital twin simulation model of the target power device, realize the lightweight reconstruction of the power device in the multi-physical field coupling scenario, and solve the technical problem of low modeling efficiency of the existing digital twin model of the power device.

The application discloses a wireless resource allocation method for a LEO satellite-ground fusion uplink communication system, which comprises user base station selection, power and carrier allocation and power and carrier allocation methods for base station to satellite link, first establishes a system energy efficiency maximization model for subcarrier and power allocation; then quotes a slack variable to convert the model into a target function lower bound model; uses a symbolic function to couple power allocation variables and carrier allocation variables; and further decomposes the target function lower bound model into a base station access and carrier allocation optimization model and a power allocation optimization model; linear approximation method and successive convex approximation method are respectively used for solving, so that the wireless resource allocation of the uplink communication system is realized. By using the method, 0-1 integer optimization variables are removed, the calculation complexity is low, and higher uplink system energy efficiency is obtained.

The invention discloses a linear approximation attention operator acceleration method based on NPU, and the method combines linear approximation attention with an Ascend C programming language, and achieves an Ascend C operator for calculating the linear approximation attention. According to hardware characteristics of the mercuration NPU, optimization of vector calculation is carried out, the number of instructions used for line-by-line summation and division is reduced, and time consumption is reduced; according to hardware characteristics of the mercuric chloride NPU, pipeline parallel optimization is carried out, large matrix multiplication is decomposed into multiple small matrix multiplication and asynchronous matrix multiplication, execution of matrix multiplication and vector calculation tasks can be coordinated more efficiently, and the utilization rate of hardware resources is maximized; cache occupation is optimized according to hardware characteristics of the mercuric chloride NPU, and the number of blocks of matrixes such as Q, K, P, Z and the like can be reduced under some input scales, so that the number of cycles of calculation and carrying is reduced, and the operation time of operators is shortened.

The present application relates to the technical field of power system, in order to solve the problem that the current reactive power flow calculation result cannot be applied to the actual operation system, provide a kind of power system optimal reactive power flow calculation method and system.Power system optimal reactive power flow calculation method includes with the active power network loss in power system as minimum to construct objective function, then combine linearized power flow equation, the reactive power output limit value corresponding to synchronous generator in power system and the linearization approximation constraint converted by the constraint of the internal potential and output current of inverter, construct optimal reactive power flow model and solve to obtain the new operation mode of power system;After obtaining the new operation mode of power system, recalculate the power flow distribution of power system, obtain the active power network loss of power system under new operation mode, in the process of successive linear approximation, the active power network loss is continuously reduced.It can obtain the optimal reactive power flow calculation result, guarantee the stability of power system.

The application discloses a corona discharge load state detection method based on an oscillation circuit voltage ratio relationship and relates to the technical field of corona discharge detection. An ideal Royer circuit is constructed; a peak value protection circuit is used for collecting a peak voltage Vm; a direct current input voltage of a sine wave self-excitation oscillation boost circuit is Vin; a ratio relationship of Vm and Vin in the ideal Royer circuit under no-load is calculated to obtain a first relationship formula; a ratio relationship of Vm and Vin in the ideal Royer circuit under load is calculated to obtain a second relationship formula; first-order linear approximation fitting is performed on the first relationship formula and the second relationship formula to obtain a target relationship formula; and the corona discharge load state is determined according to the target relationship formula. Through establishing a quantitative relationship between the ratio of the peak voltage of the primary side of the Royer circuit which is easy to measure and the input voltage and the secondary direct current high voltage and the load current reflecting the needle tip state, safe, quantitative and adaptive contamination detection is realized.

The application discloses a kind of based on ST-GAN's multi-source data dynamic risk early warning method and system, the method includes the following steps: S1. constructing the spatiotemporal generation confrontation network suitable for spatiotemporal data characteristics, utilize the network to generate extreme precipitation data, realize spatiotemporal unbalanced data reduction, obtain the equalization data of final output;S2. the equalization data of final output with high-dimensional space variable is carried out multi-source data fusion, and nonlinear spatiotemporal information conversion equation, and by local linearization obtains linear approximation model, to predict future time series;S3. under the present situation that extreme rainfall data amount is relatively insufficient, neural network based on dual learning theory accurately learns the parameter of nonlinear spatiotemporal conversion, estimates extreme weather event.The application is through spatiotemporal generation confrontation network (ST-GAN) and multi-source data fusion engine, significantly improves the precision and efficiency of natural disaster warning.

The invention belongs to the technical field of micro-grid optimization scheduling, provides a micro-grid model prediction control method and system based on bilateral opportunity constraint, and aims at solving the problem of micro-grid model prediction control optimization operation control by combining historical data of renewable energy prediction errors and updated data in real-time operation. A non-parametric Bayesian model and an incremental Gaussian mixture model are respectively adopted to establish and update a Gaussian mixture model of a renewable energy prediction error, and bilateral chance constraints of a storage battery energy storage level are introduced in a micro-grid model prediction control optimization operation process, so that safe operation of a micro-grid is ensured. A piecewise linear approximation method and an algorithm for accelerating the solving process are provided, so that the opportunity constraint can be efficiently solved by using a commercial solver; compared with the prior art, the method has the advantages that the calculation efficiency and the operation economy and conservative property are well balanced.

This invention claims protection for a CNN-LSTM algorithm neural network accelerator based on FPGA. The CNN hardware implementation includes a data input line buffer module, a convolution calculation module, a ReLU activation function module, an intermediate result buffer module, and a pooling calculation module. The LSTM hardware implementation includes an LSTM control module, a gate function calculation module, and a sigmoid activation function linear approximation module. The FC hardware implementation includes an FC control module, a fully connected layer calculation module, a ReLU activation function module, and a data output buffer. The purpose of this invention is to design a high-performance, low-power, and highly flexible CNN-LSTM neural network accelerator tailored to specific application scenarios. The innovation lies in the fact that, compared to traditional neural network accelerators, this invention uses a parallel pipelined design method to implement a CNN-LSTM algorithm neural network accelerator, which significantly improves the low power consumption and data throughput of the neural network accelerator. Furthermore, the parallel processing capabilities of the FPGA enable the algorithm to run at a faster speed.

The invention relates to a computer-implemented method for creating a model for detecting an anomaly or an operating state or a change in an operating state in a technical system (1), comprising the following steps: - Providing (S1) time series of operational variables, each indicating a temporal progression of an operational variable, as training data; - Performing (S2) a piecewise linear approximation on the time series of the operating variables to obtain segments of concatenated linear functions for each of the operating variables, obtaining change points (T) as time points between the segments of all operating variables, and summarizing the change points (T) of all operating variables to define state segments between two temporally successive state segments that define time intervals of the time series of the operating variables; - Determining (S3) characteristics of the time-dependent behavior of the operating variables for each of the state segments in order to form a state vector for each (S4); - Training (S6) a classification model based on the state vectors; - Implement (S7) the classification model in a control unit of the technical system (1).

The invention discloses a multi-stage pipe network leakage positioning method and system based on a hydraulic model sensitivity matrix and sparse inversion, and relates to the field of monitoring and leakage detection of an urban water supply pipe network, and the method comprises the steps: S1, linearizing a pipe network continuity equation and an energy equation at a normal operation point of a baseline working condition, obtaining a sensitivity matrix of the leakage rate and the demand disturbance to the pressure residual error, and establishing a linear approximation relation; s2, constructing an optimization problem with L1 regularization based on the relationship; eliminating a demand disturbance item, converting into a non-negative LASSO problem, and completing suspicious node positioning; s3, convex combination approximation of sensitivity of nodes at the two ends is introduced for the suspicious pipe section, an optimization target is constructed, and the internal leakage continuous position and the leakage amount of the pipe section are estimated; and S4, a joint optimization problem is constructed based on the pressure residual data of the multiple time periods, consistent selection of leakage positions of the multiple time periods is kept, and final leakage positioning is realized. According to the method, pipe network leakage positioning is accurately and efficiently completed through multi-stage progressive positioning.

An example device includes a multiplexer configured to receive a first digital output value indicating whether a first inequality condition with respect to first and second input values is true or false, and a second digital output value indicating whether a second inequality condition with respect to the first and second input values is true or false. Such device further includes calculation circuitry coupled to the multiplexer and configured to receive the first and second input values and calculate an output value representative of a linear combination of the first and second input values as specified by a select signal that is based on the first and second digital output values.

To efficiently evaluate an error of a quantum gate.SOLUTION: Acquiring measurement data indicating an execution result of a quantum computer for a quantum circuit in which a quantum gate array including a first quantum gate is repeated N times (N is an integer of 1 or more); determining, from a first matrix indicating an ideal value of the quantum gate array, a period k (k is an integer of 1 or more) at which the first matrix raised to the power of k becomes a unit matrix; Processing for generating a linear approximation function for linearly approximating the influence of an error on measurement data by approximating the repetition of a quantum gate array and processing for estimating the error by using the linear approximation function and the measurement data are executed.SELECTED DRAWING: Figure 1

The invention discloses an elevator energy consumption control system based on multi-objective optimization, and the system comprises a parameterization module which generates a self-adaptive non-uniform B-spline control point set and a corresponding non-uniform node vector; the elevator discrete nonlinear dynamics module is used for constructing an elevator discrete nonlinear dynamics model; the index function construction module is used for constructing an elevator finite time domain nonlinear optimal control performance index function; the iteration module constructs a linear approximation system; the Newton-GMRES module is improved, and the optimal correction amount of the adaptive non-uniform B-spline control point set is obtained; the updating module is used for generating an updated self-adaptive non-uniform B-spline track; the motor drives the signal generation module to drive the elevator to run along the updated self-adaptive non-uniform B spline track; and the circulation module is used for repeatedly executing the steps from the index function construction module to the motor driving signal generation module. According to the method, the speed and precision of track generation and adaptive adjustment are remarkably improved.

The application provides a secant approximation method for nonlinear constraints of a redundant drive system, and belongs to the technical field of dynamic control distribution of the redundant drive system. The method comprises the following steps: firstly, according to the control input model of the redundant drive system with any pair of constraint components being nonlinear constraints, a closed region formed by the intersection of a rectangle and an ellipse in a geometric plane is obtained; then, after the closed region is divided into the union of the rectangle and the ellipse triangle, the approximation result of the closed region is obtained by performing the approximation of the rectangle and the triangle combination on the ellipse triangle, so that the linear approximation of the pair of nonlinear constraint components is realized. The application jointly uses the triangle and the rectangle to perform the approximation on the region surrounded by the nonlinear constraints, converts the nonlinear constraints into multiple linear constraints, converts the nonlinear constraint set into a linear constraint set, effectively solves the problem that the control reachable set cannot be determined due to the nonlinear constraint relationship between the actuators in the redundant drive system, and is helpful to realize the real-time control of the redundant drive system.

A battery diagnosis apparatus includes a voltage sensor to generate a voltage signal indicating a battery voltage of a battery, a current sensor to generate a current signal indicating a battery current of the battery and a control circuit. The control circuit determines a capacity curve indicating a relationship between the battery voltage and a charge capacity in a set voltage range based on the voltage signal and the current signal collected at each unit time for a constant current charging period. The control circuit determines a differential curve indicating a relationship between the battery voltage and a differential capacity in the set voltage range based on the capacity curve. The control circuit determines an approximate straight line of the differential curve using a linear approximation algorithm, and determines whether lithium deposition is present in the battery based on the approximate straight line.