654 results about "Non linear functions" patented technology

Systems and methods for controlling traffic flow along an arterial. The systems comprise a data processing apparatus configured to: monitor traffic light control signals (TLCSs) used to control traffic lights at intersection(s) and traffic delays occurring at intersection(s) at a time when the traffic lights are being controlled by TLCSs; access a hybrid model comprising a first linear term corresponding to an instance of traffic delays, a second linear term corresponding to a concurrent instance of TLCSs, and a nonlinear function defining a nonlinear relationship between the instance of the traffic delays and a previous instance of TLCSs; use the hybrid module to predict traffic delays at intersection(s) based on the traffic control signals and the traffic delays; determine, based on TLCSs and the traffic delays, traffic control signals that cause the predicted traffic delays to decrease; and cause the traffic lights to be controlled using the traffic control signals.

The invention discloses an AI-based bridge construction intelligent monitoring method, particularly relates to the field of data analysis, and comprises the steps of multi-modal sensing network construction, cross-domain characteristic parameter extraction, comprehensive state evaluation modeling and intelligent decision and regulation. According to the method, deformation, corrosion, vibration and other multi-physical field data are synchronously collected through a multi-modal sensing network, a cross-scale feature extraction algorithm is adopted to construct creep-corrosion collaborative factors and other composite indexes, the limitation of traditional single-dimensional parameter analysis is broken through, a dynamic evaluation model is fused with weighted integral and a nonlinear function, and the dynamic evaluation accuracy is improved. Precise early warning of risks such as bridge pier instability and cantilever cracking is achieved, the multi-field coupling effect quantification capacity is remarkably improved, an intelligent joint control mechanism automatically triggers instructions such as jacking reinforcement and damping tuning through a three-level emergency response system, a monitoring-analysis-regulation closed-loop system is constructed, the hysteresis of manual decision is effectively eliminated, and the safety and reliability of the system are improved. And all-weather self-adaptive safety guarantee is provided for bridge construction.

The invention relates to the technical field of chip testing, and discloses an LED driving chip testing method, device and equipment and a storage medium, and the method comprises the steps: carrying out the multi-point temperature excitation and constant-current output synchronous collection of an LED driving chip, and obtaining electrothermal coupling test data; performing thermal resistance network decomposition on the temperature field in the chip to obtain temperature distribution characteristic parameters; performing nonlinear function fitting on the temperature-constant current coupling relation to obtain an electrothermal coupling transfer coefficient; performing four-dimensional coupling calculation on the temperature distribution characteristic parameter and the electrothermal coupling transfer coefficient to obtain four-dimensional coupling response characteristic data; and partial differential sensitivity calculation is carried out based on the four-dimensional coupling response characteristic data, and an electrothermal coupling sensitivity analysis result is obtained.According to the method, the problem of test errors caused by time desynchrony in a traditional separated temperature-current test is solved, quantitative sensitivity analysis of electrothermal coupling parameters is achieved, and the sensitivity of the electrothermal coupling parameters is improved. Therefore, the test accuracy of the LED driving chip is improved.

An adaptive variable step-size LMS filter based on a hyperbolic tangent function and its filtering method and computer equipment belong to the field of filter technology, and solve the problem that the LMS algorithm improved based on a nonlinear function requires too many manual adjustments of parameters and that the convergence speed and convergence accuracy of the traditional LMS algorithm cannot be satisfied simultaneously. The method of the present invention includes: determining the filter tap length and the number of iterations; initializing the filter parameters; obtaining the filter input signal vector; determining the instantaneous expected signal of the filter; obtaining the instantaneous output signal; obtaining the instantaneous error signal of the filter and the accumulated amount of the error signal based on the instantaneous output signal; obtaining the instantaneous step size using a step-size update formula based on a hyperbolic tangent function; and continuously iterating the filter tap weight coefficient vector to construct an LMS filter using the filter tap weight coefficient vector obtained after the number of iterations. The present invention is suitable for improving the performance of the LMS filter, and the filter of the present invention can be widely used in various signal processing fields.

The invention relates to the technical field of power system operation and control, and discloses a primary and secondary frequency modulation cooperative control system of a hybrid energy storage coupling wind generating set, which comprises a wind power generation unit, a hybrid energy storage unit and a central cooperative controller. A total power demand including inertia response and primary and secondary frequency modulation is synthesized, a primary and secondary frequency modulation cooperative distribution module generates a dynamic adjustment weight by using an S-type nonlinear function based on a frequency change rate and frequency deviation coupling relationship, and an energy relay compensation mechanism is introduced to fill a power gap in a switching process. And the SOC adaptive constraint and execution module applies direction selective boundary constraint to the state of charge, and controls hybrid energy storage to perform internal energy self-balancing in a frequency modulation dead zone. According to the invention, seamless connection of multi-time scale frequency modulation is realized, frequency secondary drop is effectively prevented, and the frequency support capability and the self-recovery capability of the system are improved.

The invention relates to the technical field of electrical testing, in particular to a battery testing method and system, and the method comprises the steps: obtaining a voltage data sequence and historical log data of each cell in a to-be-tested battery pack; determining a dynamic instability score of each battery cell according to the parameter volatility index of each battery cell and the spatial correlation weight between each battery cell and the adjacent battery cell; on the basis of the historical log data, analyzing a repeated cumulative effect of all historical damage events corresponding to each battery cell, and determining a historical damage degree of each battery cell; performing deep fusion on the dynamic instability score and the historical damage degree through a nonlinear function to obtain a comprehensive risk index of each battery cell; and based on the extreme values and distribution of the comprehensive risk indexes of all the cells, determining a fault degree capable of representing the overall health risk of the battery pack, and determining a health test result of the battery pack according to the fault degree. According to the method, the accuracy and reliability of battery pack testing are improved.

The invention discloses a self-adaptive safety control method of a nonlinear MIMO system under intermittent DoS attack, which comprises the following steps: establishing a nonlinear MIMO system model suffered from intermittent DoS attack, and the system model is in a strict feedback form and comprises an unknown nonlinear function and bounded external disturbance; the DoS attack causes periodic interruption of a communication link from a sensor to a controller, so that actual measurement data is unavailable in an attack activation period; designing a switching type adaptive state estimator; coordinate transformation is carried out by introducing a filter, and a system error is decomposed into a tracking error and a dynamic surface filtering error; a Lyapunov function is constructed; performing stability analysis in stages; designing an adaptive law to update unknown parameter estimation values of the FLS; and a final event triggering controller is designed, and the event triggering controller is combined with a virtual controller to track an error compensation term and an FLS approximation term. The problem that the state cannot be measured is solved.

The invention belongs to the related technical field of multi-agent control, and particularly discloses a predetermined performance consistency tracking control method and system for a nonlinear multi-agent system, and the method comprises the steps: constructing a Lyapunov function based on the consistency tracking error of the multi-agent system, and obtaining a control law; consistency tracking control of the multi-agent system is realized according to the control law; wherein the system state quantity in the control law is estimated through a distributed state observer; the distributed state observer is designed through the following method: according to a universal approximation principle, a nonlinear function in a multi-agent system dynamic model is approximated based on a fuzzy logic system, and then the distributed state observer is obtained by combining the multi-agent system dynamic model. According to the method, the precision and robustness of the consistency tracking process of the multi-agent system can be remarkably improved.

The invention discloses a mechanical equipment lightweight intelligent fault diagnosis method based on a Kolmogorov-Arnold network, and the method comprises the steps: firstly obtaining original vibration monitoring data of mechanical equipment under different fault types, and constructing an equipment vibration monitoring fault data set; a KAN-based fault diagnosis model is constructed, and a learnable nonlinear function is adopted to replace a fixed linear weight in convolution; then, the output is converted into a one-dimensional feature vector through KAN convolution operation; inputting the one-dimensional feature vector into a KANLinear layer so as to realize the mapping of the high-dimensional feature to different fault types; then calculating training loss, taking the total loss as an optimization target of a training stage, and updating model parameters by using a gradient descent method; iteratively optimizing the fault diagnosis model; and finally, testing the fault diagnosis model by using the test set. According to the method, high diagnosis precision can be maintained, meanwhile, the model parameter quantity is remarkably reduced, the calculation cost is reduced, and therefore the overall calculation efficiency is improved.

The invention discloses a chaotic block encryption method and system based on model random selection, and a medium, belongs to the technical field of information security, and is used for solving the problems that an existing chaotic encryption algorithm is easy to crack and maliciously attack when being independently used, depends on internal characteristics of a nonlinear function, does not expand a secret key space, and cannot be used for encrypting a chaotic block. And the improvement of the security of the secret key is limited. The method comprises the following steps: constructing a chaotic system model library based on a preset chaotic system; the chaotic system model library comprises a plurality of chaotic subsystems; generating a secret key initial field for the original image based on binary coding; selecting a corresponding target chaotic subsystem in the chaotic system model library according to the key initial field; and encrypting the original image through the target chaos subsystem to obtain an encrypted image. The line-level image chaotic encryption algorithm is designed, and the image encryption speed is increased. And the length of the secret key is increased, the space of the secret key is expanded, and the safety of the secret key is improved.

The invention discloses a GNSS interference identification method based on spatial transformation, multi-scale feature fusion and KAN. The method comprises the steps of forming an interference signal data set; obtaining a time-frequency image training set and a time-frequency image test set; constructing a GNSS interference identification classifier composed of a spatial transformation module, a multi-scale feature fusion module and a KAN module; a trained GNSS interference identification classifier is obtained; and obtaining a final GNSS interference identification classifier. And finally, outputting the category of the GNSS interference signal acquired in real time by the GNSS interference identification classifier. According to the invention, the spatial transformation module is used to carry out translation, scaling and other operations on the interference signal time-frequency image, and the number of training samples is increased. And the multi-scale feature fusion module adopts parallel convolution kernels of different scales to extract multi-scale spatial features of the time-frequency graph, so that the richness of feature representation is increased, and the recognition accuracy is further improved. The KAN module uses a KAN to replace a traditional multi-layer perceptron to execute a final classification task, and a more complex nonlinear function can be fitted by using fewer parameters.

The invention discloses a nonlinear multi-agent system sampling consistency control method and system and a medium, and the method comprises the following steps: S101, obtaining system model parameters, including a nonlinear function # imgabs0 #, a pulse gain # imgabs1 #, a sampling period # imgabs2 #, a communication topological graph # imgabs3 # and a leader-follower kinetic equation; s102, verifying an assumed condition; s103, constructing a Lyapunov function # imgabs4 #, and analyzing the stability of an error system in combination with pulse disturbance; s104, deriving a consistency condition through a Lyapunov method and a pulse theory, calculating key parameters, and determining a pulse gain and a sampling period constraint; s105, selecting parameters meeting conditions; s106, constructing a sampling controller containing pulse disturbance; and S107, implementing the controller and performing numerical simulation. According to the invention, the anti-pulse disturbance capability is strong: through the design of the pulse gain # imgabs5 #, the instantaneous interference of an input channel is effectively counteracted, and the robustness of the system is improved; a sampling data control strategy is adopted, a control signal is updated only at a discrete sampling moment, and the communication burden is reduced.

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 provides a nonlinear data generation and processing method based on light quantum and a light quantum computer, and the method comprises the steps: obtaining a nonlinear function and attribute information of a nonlinear layer; according to the frequency spectrum number and the attribute information of the nonlinear function, constructing an initial light quantum circuit; measuring the initial light quantum circuit, and determining a target function corresponding to the initial light quantum circuit according to a measurement result; loss information of the target function is determined, whether the initial light quantum circuit is applied or not is determined according to the loss information, and if yes, the initial light quantum circuit serves as a target light quantum circuit; and performing operation on input data of the nonlinear layer based on the target light quantum circuit. According to the method, the low-energy-consumption characteristic of photon calculation, the nonlinear characteristic of light quantum calculation and the general programmable capability of electronic calculation can be fused, and high-energy-efficiency calculation can be carried out on input data of a nonlinear layer. In addition, the method also has the advantages that the complexity of a quantum circuit is smaller, and the participation of a nonlinear optical device is not needed.

The invention discloses an ODE-PDE-based formation control method and system, and belongs to the field of multi-agent cluster control, and the method comprises the steps: building a heterogeneous multi-agent system model of a to-be-controlled system, and calculating an ODE tracking error system model of each agent, and an ODE-PDE error system model shared by the sparse-dense distributed intelligent agents is obtained through partial continuous processing. And at any moment, fitting a nonlinear function of a sparse distribution intelligent agent by using the first RBF neural network, calculating a control signal, substituting the control signal into a corresponding ODE tracking error system model, fitting a nonlinear function of a dense distribution intelligent agent by using the second RBF neural network, calculating a control signal, and substituting the control signal into a corresponding PDE tracking error system model. If the ODE-PDE tracking error system converges, the formation control is ended; otherwise, continuing to update the control signal and enabling the entity object to execute the corresponding action. According to the invention, the cooperative control of the multi-agent system can be effectively utilized to realize the formation control of any scale and any distribution system.

The invention provides a financial risk dynamic prediction method based on deep learning, and relates to the field of financial risk dynamic prediction. The financial risk dynamic prediction process provided by the invention comprises the steps of constructing a financial risk prediction data set, calculating a deviation absolute value sequence of a factor data time sequence, and fusing and mapping the time sequence and the deviation absolute value sequence into a high-dimensional embedded vector sequence through a weighted nonlinear function; constructing a finite span directed graph by taking each time step as a graph node; introducing a structure resonance factor; carrying out weighted coding and weighted fusion on a path to form an aggregation context vector; calculating a risk score; overall feature representation of factor data is extracted in combination with a bidirectional long-short-term memory network, and finally a financial risk prediction value is calculated by using a full-connection neural network. According to the method, local deviation and global time sequence information are fused, and the accuracy of financial risk prediction is improved.

According to the double-pulse co-switching optimal orbital transfer method for the lunar large elliptical orbit, firstly, a double-pulse co-switching orbital transfer control strategy based on a two-body nonlinear model is calculated according to the known initial state of the lunar orbit and the state of a target orbit, and through two times of pulse control in the tangential direction, the optimal orbital transfer control strategy is obtained; meanwhile, the average semi-major axis, the average eccentricity rate and the average latitude argument of the large elliptical orbit are adjusted, the speed increment minimum problem is converted into the one-dimensional nonlinear function minimum problem to solve the optimal orbital transfer strategy, and accurate transfer between any two elliptical orbits is achieved; then, a two-body orbit model is designed as an initial value, numerical integration is carried out on the average orbit elements under the influence of perturbation force, the two-body model is corrected, and then a real orbital transfer control strategy of the average orbit parameters under the influence of perturbation force is obtained; the problem that the problem becomes a time-varying problem and is difficult to solve due to the fact that the short-cycle term introduced by directly adopting instantaneous orbit element description is solved, and the engineering practical value of the algorithm is improved.

The invention discloses an anti-interference control method for a longitudinal model in a ground effect area of a ground effect aircraft, and the method comprises the steps: firstly arranging a transition process for system expected input and system actual output through a tracking differentiator, and enabling the transition process to reduce the initial value deviation as much as possible; secondly, a new nonlinear function is introduced to improve the extended observer, so that the anti-interference capability of the system is greatly enhanced, and the robustness of the system is enhanced; finally, a new nonlinear function is introduced, a nonlinear feedback law is improved, a system control law is obtained, and the control precision of the system is greatly improved.

A data storage device has an inference engine that may infer a read threshold based on a non-linear function reflecting an input to a current memory and a data condition. The read threshold may be used in reading word lines in the memory. Using a machine learning-based approach to infer read thresholds may provide significant improvements in read threshold accuracy, which may reduce bit error rates and also improve latency, throughput, power consumption, and quality of service.

Analog system and method for implementing an iterative neural network based model, the system comprising analog vector-by-matrix multiplication circuitry encoding a matrix of weights of an iterative neural network-based model, and analog nonlinearity circuitry encoding a non-linear function arranged in a feedback loop configured to return the output signals from the nonlinearity circuitry as inputs to the vector-by-matrix multiplication circuitry, wherein the system is configured to output a solution vector of values of the iterative neural network based model on convergence of the system.

The invention discloses a correction method for saturation spectral line intensity in a laser spectrum, and relates to the technical field of spectral data analysis, the method comprises the following steps: identifying saturation characteristics in the spectrum, determining a saturation point based on an intensity threshold, and recording the original intensity of the saturation point; determining a target spectrum peak wavelength range containing a saturation point; eliminating saturation points from the wavelength range of the target spectrum peak, and retaining unsaturation points; fitting the unsaturated point through a nonlinear function to obtain the maximum fitting strength; if the maximum fitting strength is lower than the original strength, performing strength correction by adopting a linear extension mode by taking an unsaturated point as a reference, and otherwise, performing strength correction directly based on a fitting model; and outputting the corrected spectral intensity data, and calculating the maximum intensity of the spectral peak and the integral area of the spectral peak. According to the method, the accuracy of saturation judgment and the reliability of a correction result are improved, and a direct intensity data basis and core derivative parameters are provided for quantitative analysis.

The invention discloses a chaos encryption and decryption method and system based on random weight, belongs to the technical field of information security, and is used for solving the technical problems that an existing chaos encryption and decryption algorithm seriously depends on internal characteristics of a nonlinear function, does not expand a secret key space, is limited in improvement of secret key security and is easy to crack. The method comprises the following steps: in an encryption stage, injecting an original image into different subsystems of a Lorentz chaotic system to obtain a plurality of initial ciphertexts; performing linear combination on the plurality of initial ciphertexts through random weights to obtain ciphertext data of the original image; in a decryption stage, generating a weight matrix and an auxiliary ciphertext based on the number of the subsystems; and based on the weight matrix and the auxiliary ciphertext, decrypting the encrypted image to obtain the original image. According to the design method, the weight is added into the initial value key of the original scheme, so that the key length is increased, and the security is ensured.

The invention discloses a product forging data intelligent management system and method based on 5G cloud computing, and relates to the field of intelligent manufacturing. According to the system, multi-source data such as temperature, stress, load and images are collected through a process data module and weighted labeling is carried out; the multi-model reasoning module selects support vector regression, a random forest and a convolutional neural network for parallel prediction according to data features; the prediction fusion module generates a unified prediction result by adopting a nonlinear function based on confidence score; the cloud-edge collaborative scheduling module determines cloud or edge deployment in combination with task complexity, edge computing power and a network state; the parameter optimization module adopts an EPQ-Opt algorithm to optimize process parameters based on energy consumption prediction and residual score; and the security guarantee module adjusts the strategy through consistency detection and robustness evaluation and guarantees data security. The method significantly improves the prediction precision and response speed, reduces the energy consumption, enhances the system safety, and is suitable for the field of high-end forging and intelligent manufacturing.

The invention relates to a transformer comprehensive safety assessment method and system based on symbol regression. The method comprises the following steps: acquiring monitoring variables of an electrical signal dimension, an equipment state dimension and an operation environment dimension of a transformer; based on the maximum mutual information coefficient, screening the dimension monitoring variables to obtain a plurality of monitoring variable sets with closely associated variables; performing sign regression on the plurality of monitoring variable sets to obtain a nonlinear function expression between any reference monitoring variable and other monitoring variables in the monitoring variable sets, namely a mutual check function, and realizing sign regression by using a genetic algorithm; establishing a single-variable fault probability function according to the influence of the multi-dimensional monitoring variable on the transformer equipment; and building a system dynamics model based on the mutual verification function and the fault probability function to realize comprehensive safety evaluation of the transformer. Compared with the prior art, the method has the advantages of improving the safety assessment level of the transformer, being high in interpretability and the like.

The invention discloses a multi-dimensional layered current limiting method based on spatio-temporal feature fusion. The method comprises the following steps: acquiring a hardware limit speed, and generating a global quota based on a reinforcement learning model; calculating a variable coefficient, and if the variable coefficient exceeds a preset critical value, introducing a conservative coefficient and generating a weight factor through nonlinear function mapping to suppress the micro network jitter; constructing a priority factor matrix based on the service priority and the service type, and obtaining a service priority factor; correcting the average bandwidth of the node by integrating the multi-dimensional dynamic weight factor and the service priority factor to obtain a suggested speed, inputting a historical performance index into the LSTM model to predict the load of the next period, and if the historical performance index exceeds a critical value, triggering connection migration before executing current limiting to realize active avoidance; otherwise, comparing and selecting the minimum value of the global quota, the suggested speed and the hardware limit speed as the final current-limiting speed. The problems of current limiting strategy lag and wide oscillation can be solved, and the bandwidth utilization rate and the system stability are improved.

A computer implemented method for obtaining one or more optimal solutions to a given classical constraint linear or non-linear optimization problem is provided. Each solution is specified and identified by a set of parameters and the one or more optimal solutions provide optimal values of a cost function that is a linear or non-linear function of one or more decision variables. The one or more optimal solutions fulfill one or more constraints comprising a set of equality constraints and / or a set of inequality constraints, where each equality constraint and each inequality constraint comprises a linear or non-linear complex functional form. A quantum computation component and a classical computation component of the hybrid quantum-classical computer system are used together to solve the classical constraint optimization problem by iteration. While performing the method, the quantum computation component executes a parameterized quantum circuit with surrogate phase operators and with the set of parameters.

A data storage device has an inference engine that can infer a read threshold based on a non-linear function of inputs that reflect current memory and data conditions. The read threshold can be used in reading a wordline in the memory. Using a machine-learning-based approach to infer a read threshold can provide significant improvement in read threshold accuracy, which can reduce bit error rate and also improve latency, throughput, power consumption, and quality of service.

The invention provides a grid-connected inverter grey box impedance identification method and device suitable for multiple working points. The method comprises the following steps: determining a sequence impedance analytical expression of a grid-connected inverter considering frequency coupling characteristics based on a control scheme containing double-loop control; extracting a first nonlinear function relation and a second nonlinear function relation; respectively replacing the first nonlinear function relationship and the second nonlinear function relationship with a feedforward neural network, and constructing a first neural network and a second neural network; injecting voltage disturbance to the grid-connected point to obtain a first data set and a second data set; and training the first neural network and the second neural network based on the first data set and the second data set to obtain a grid-connected inverter grey box impedance model adapted to multiple working points, and calculating a positive sequence impedance amplitude and a phase angle of the grid-connected inverter considering frequency coupling characteristics. The accuracy of neural network model training can be improved under the condition of limited data volume, and accurate identification of the impedance of the grid-connected inverter under multiple working points is realized.

The invention belongs to the technical field of information security, and particularly relates to an unmanned surface ship fusion filtering method with sensor saturation under FDI attack. Constructing a discrete time multi-sensor network system model, constructing a real number matrix inequality optimization filter design based on linear and nonlinear functions, and enabling filtering to show exponential stability under the mean square meaning; and according to local filter parameters, a distributed fusion filter is designed to resist FDI attacks. The anti-interference capability and the data fusion precision of the unmanned surface ship in a severe environment are improved by using an optimization design and a distributed strategy.

The invention relates to a rapid guidance method for an unmanned ship, which comprises the following steps of: establishing a parameterized reference path, determining a position and attitude relationship of the unmanned ship on a horizontal plane, designing a finite time drift angle observer for accurately observing an unknown drift angle of the unmanned ship, defining an estimated value of the drift angle, an adjusting parameter and an error term, and determining the position and attitude relationship of the unmanned ship on the horizontal plane. Deriving a derivative expression of the error term, proving that the error term converges to zero in finite time, establishing a drift angle estimation formula, and proving that the observation error converges to zero in finite time; designing a virtual speed and an updating law thereof; designing a nonlinear function for accelerating the convergence speed of the lateral error; establishing a line-of-sight tracking angle formula; and by parameterizing the reference path and accurately calculating the error between the unmanned ship and the virtual target point and combining the derivative expression of the error, the unmanned ship can be more accurately controlled to sail along the preset path, and the path tracking precision is remarkably improved.