148 results about "Selection (genetic algorithm)" patented technology

The invention relates to a multi-distribution-center open type vehicle path intelligent optimization method and system, and belongs to the technical field of logistics distribution optimization and intelligent transportation, and the method comprises the steps: firstly obtaining the input data of a multi-distribution-center vehicle path optimization problem, selecting a multi-distribution-center processing strategy according to the problem scale and constraint conditions, and carrying out the optimization of the multi-distribution-center vehicle path; a vehicle path optimization model is constructed, the vehicle path optimization model comprises a single-target model and a multi-target model, a multi-algorithm collaborative optimization framework is adopted for solving, and the multi-algorithm collaborative optimization framework comprises an ant colony algorithm, a variable neighborhood search optimization ant colony algorithm and a non-dominated sorting genetic algorithm; and outputting an optimal vehicle path scheme, wherein the optimal vehicle path scheme comprises a distribution route, a distribution sequence and a corresponding objective function value of each vehicle. According to the method, strategy adaptive selection and algorithm collaborative optimization are carried out, global exploration, local optimization and multi-target equalization are carried out by combining the advantages of the ant colony algorithm, the variable neighborhood search algorithm and the non-dominated sorting genetic algorithm, and the method is good in reproducibility, high in scene adaptability and high in decision support capability.

The invention relates to the technical field of air conditioner fan blade design optimization, and discloses an air conditioner fan blade optimization method and system based on a BP neural network and a GA algorithm. The method comprises the following steps: obtaining initial geometric parameters and performance data of a fan blade, and cleaning and standardizing the initial geometric parameters and the performance data to form a standard data set; a BP neural network is used for training to obtain a fan blade performance prediction model; a genetic algorithm is applied to optimize the prediction model, and a new design parameter population is generated through genetic operations such as selection, crossover and variation; the optimized parameters are input into a CAD system to generate a candidate fan blade model, numerical simulation is carried out, and performance indexes of the candidate fan blade model are calculated; and screening excellent individuals based on a multi-objective optimization method, iteratively executing optimization and simulation processes until convergence, and finally outputting an optimal fan blade design. According to the method, the fast prediction of the neural network and the global search capability of the genetic algorithm are combined, the dependence of traditional optimization on high-frequency numerical simulation is reduced, and the design efficiency and quality are improved.

The invention discloses an unmanned aerial vehicle cluster task allocation method based on a large language model optimization genetic algorithm, and belongs to the field of computers. The method comprises the following steps: setting a specific chromosome coding mode; generating a multi-constraint initial population; calculating fitness to quantify the advantages and disadvantages of individual genes of the population; when the optimal individual meets the requirement or the maximum iteration round is reached, ending; retaining the optimal individual as a filial generation; generating a batch of new filial generation individuals by the large language model, and fusing the new filial generation individuals with the current filial generation population; calling an optimized large language model to analyze individual chromosome semantics, and outputting an evolutionary potential score; obtaining an individual comprehensive selection probability by integrating the fitness and the evolution potential score, and executing a selection operation; and selecting individuals based on the individual comprehensive selection probability to carry out crossover and mutation operation to generate offspring. The large language model is embedded into the core link of the genetic algorithm, and the algorithm efficiency is improved by improving the population diversity of the genetic algorithm in the unmanned aerial vehicle cluster task allocation scene.

The invention discloses a polar region environment-oriented two-stage micro-siting optimization method for a wind generating set, and relates to the technical field of polar region renewable energy planning and wind power generation, the optimization method comprises the following steps: polar region environment parameter initialization and scene modeling; the first stage is grid primary selection based on mixed integer linear programming; the second stage is coordinate refinement based on a multi-population genetic algorithm; and scheme output and efficiency evaluation. According to the method, a sector reweighting mechanism of extremely cold correction parameters is realized through reweighting of biochemical parameters and scenes in an environment special for a polar region, the extremely cold correction parameters are directly embedded into a wind regime scene set and a power model, and the problem that a conventional land model can obviously underestimate or overestimate single-machine power and wake flow propagation distance under extremely low temperature and low roughness conditions, so that the reliability of the model is improved is solved. Therefore, the power prediction and wake flow loss evaluation are more practical under the polar region conditions such as the south pole, so that the engineering reliability of site selection decision and the accuracy of generating capacity prediction are improved.

The invention relates to an intelligent decision-making method and system for server customization demand matching. The method comprises the steps of obtaining a server customization demand input by a user; constructing a multi-dimensional demand portrait based on the server customization demand, wherein the demand portrait comprises a quantitative index and a configuration constraint condition; according to the demand portrait, executing configuration compatibility check, and identifying whether a compatibility problem exists or not; when the compatibility check is passed, a candidate configuration scheme is generated by adopting a multi-target genetic algorithm based on the demand portrait passing the compatibility check; and sorting and screening the candidate configuration schemes based on user preference data in the demand portraits passing the compatibility check to obtain a recommended scheme list, and transmitting the recommended scheme list to a display interface for a user to select and confirm, the recommended scheme comprising a component list, a price, a performance index and a recommendation reason. The method has the effect of improving the accuracy of server customization recommendation.

The invention discloses a database index optimization method, system and equipment based on artificial intelligence. The method specifically comprises the steps of collecting performance data of a database in real time; performing preprocessing and feature extraction on the performance data to obtain a spatial-temporal feature matrix; taking the spatial-temporal characteristic matrix as input, performing index combination selection by using a hybrid solver formed by a quantum annealing algorithm and a classical genetic algorithm, and generating an index optimization strategy; on the basis of an index optimization strategy, potential performance risks are predicted according to an index change influence evaluation model, and an optimization decision explanation is generated through an SHAP value; and executing index change operation according to the index optimization strategy, evaluating database performance after index change, and feeding back to the hybrid solver for updating and optimization. According to the method, the database index is automatically and intelligently optimized, the database performance is improved, and the manual intervention cost is reduced.

The invention relates to the technical field of big data mining and analysis, and discloses a charging pile intelligent site selection method and system based on big data analysis, and the method comprises the steps: obtaining traffic space-time big data, and generating a road network topological structure; determining a shortest path matrix based on the road network topological structure, generating an importance score in combination with a graph theory algorithm, and comparing the importance score with a preset score threshold; carrying out simulation evolution based on a comparison result, generating a propagation coefficient and an aggregation coefficient of the node, and obtaining a transmission influence value; acquiring spatial load data, and outputting a site selection scheme set in combination with a genetic algorithm; obtaining road network structure data of the scheme set, and determining a preliminary ranking; acquiring data of the ranking scheme to determine a to-be-optimized scheme, and updating by using the prediction model; and calculating a time expectation value based on the updated data, and outputting a final site selection scheme by combining a preset time threshold value for judgment. According to the method, the road network integrity and the dynamic association effect are quantitatively evaluated through big data analysis, and global optimization of charging pile site selection is realized.

The invention relates to a multi-process-route-oriented production scheduling integrated optimization method, and relates to the technical field of flexible workshop multi-target scheduling. The method comprises the steps that firstly, a production line process planning and production scheduling integrated optimization model is constructed, the model adapts to a machining sequence flexible scene, and optimal process route determination and procedure machining equipment distribution are synchronously achieved; secondly, double optimization targets are set, namely, the maximum completion time is minimized, and the equipment utilization rate is maximized; thirdly, designing an optimization solution process based on a genetic algorithm framework, generating a chromosome population containing a process route selection machine allocation process sequence, and fusing double targets through an adaptive fitness evaluation mechanism; and finally, developing a visual interaction system in a matched manner, and dynamically outputting an optimal process route scheme and an equipment scheduling plan. The method can effectively solve the problems of long processing task completion time and unbalanced equipment utilization rate in a multi-process route scene, is suitable for a scheduling scene of a flexible job shop, and improves the overall operation efficiency of a production line.

The invention provides an automobile foot mat multi-style mixed flow production scheduling method based on customer order data, and the method comprises the steps: carrying out the standardization and feature vector generation of order process parameters, combining with the real-time state collection of production equipment, building a static process similarity and dynamic equipment response cost evaluation model, and fusing multi-source data to analyze the dynamic switching cost. The method comprises the following steps: establishing a scheduling sequence, realizing scheduling sequence optimization by using an improved NSGA-II multi-target genetic algorithm, comprehensively considering switching cost, equipment utilization rate and production line rhythm, finally dynamically selecting an optimal scheduling scheme through a TOPSIS decision method and a workshop load rate, and continuously correcting and evaluating model parameters by using an online incremental learning algorithm, thereby realizing scheduling sequence optimization. According to the method, the production scheduling efficiency and the resource utilization rate are improved, the equipment switching loss is remarkably reduced, and flexible production self-adaptive optimization is realized.

The invention provides an efficient data acquisition method and system for an HPLC (High Performance Liquid Chromatography) dual-mode communication module based on edge calculation, and relates to the technical field of HPLC dual-mode communication data acquisition. The method comprises the steps of collecting multi-dimensional resource data of an HPLC dual-mode communication module in real time, performing feature extraction by adopting a local linear embedding algorithm, constructing a resource state evaluation model based on an extreme learning machine, evaluating a resource load state of the communication module in real time, and establishing a multi-target optimization model. Solving by adopting a non-dominated sorting genetic algorithm III to obtain an acquisition strategy solution set, selecting an optimal acquisition strategy, converting the optimal acquisition strategy into a control instruction, and adjusting data acquisition parameters in combination with predictive control and a feedback correction mechanism. According to the method, the problems of resource waste, single index and the like of an existing scheme are effectively solved by performing adaptive denoising, feature extraction and resource state accurate evaluation on the multi-dimensional data and combining multi-objective optimization of data space-time association mining and acquisition efficiency, energy consumption and reliability.

The invention discloses a photovoltaic power station site selection method and system based on multi-source space factor weight adaptive optimization, and belongs to the technical field of new energy and space decision. The method aims at solving the technical problems that subjective and objective weight distribution is rigid, the model adaptability is poor and the decision-making process is opaque in an existing site selection method. The core of the method is that firstly, subjective weights and objective weights of evaluation indexes are obtained through an analytic hierarchy process and a random forest algorithm; innovatively introducing a genetic algorithm, and performing dynamic nonlinear fusion optimization on subjective and objective weights by taking the prediction performance of the site selection evaluation model as an optimization target to obtain an optimal comprehensive weight; and finally, based on the comprehensive weight, carrying out spatial weighted stack analysis in a GIS platform, and generating a site selection suitability distribution diagram. Through an AHP-RF-GA cooperation mechanism, deep optimal fusion of expert experience and a data rule is realized, the precision, robustness and interpretability of site selection evaluation are remarkably improved, and efficient decision support is provided for scientific planning of a photovoltaic power station.

The application provides a high-dimensional multi-objective evolutionary method based on an improved dominance criterion, which is used for optimizing the wiring design of a very large scale integrated circuit physical design and comprises the following steps: step one, convergence of a non-dominated solution set is ensured according to a defined convergence index, and an adaptive parameter based on a genetic algorithm niche is combined to control the diversity of the solution set, the radius target and the line length target of the wiring design are optimized by minimizing MOP, and the dominance criterion is improved; step two, a convergence index and a diversity index are designed, the two indexes jointly constitute a dynamic fitness function, and individuals with better convergence and diversity are adaptively reserved to perform MaOEA-IDR environment selection; and step three, an adaptive t distribution crossover operator is provided, which can balance the global search capability of a Cauchy operator and the local exploration capability of a Gaussian operator in a high-dimensional space, and the ASDX adaptive distribution crossover operator is adaptively adjusted. t The application can effectively solve the wiring problem of the very large scale integrated circuit physical design.

The application discloses a kind of multi-star on-orbit cooperation observation task planning method and system, by designing heuristic initial solution generation strategy, roulette selection algorithm based on Boltzmann selection probability, adaptive crossover operator and mutation operator based on population dispersion degree, stop condition based on population convergence coefficient, from initial solution, selection operator, crossover operator, mutation operator, stop condition etc. Various angles improve the genetic algorithm for multi-star cooperative observation task planning, compared with the genetic algorithm based on circle next cross, variation, significantly improve the execution benefit of observation task and the observation number of target point, construct the scheduling system of constellation cloud computing system, reach the purpose of on-orbit cooperation multi-star efficient execution observation task.

The application discloses a kind of industrial robot dynamics identification method with nonlinear viscous friction, it is related to robot dynamics modeling and control field.The method is considered in robot modeling, nonlinear term of viscous friction, different from traditional coulomb viscous friction model, its content includes robot friction model construction, excitation trajectory model selection, parameter optimization and dynamics parameter solving.In excitation trajectory selection optimization, the trajectory model of five order Fourier series and five polynomials is selected, and the genetic algorithm with penalty function is used to optimize the parameters of excitation trajectory;In solving process, based on interior point method, iteration fitting is carried out, and complete dynamics parameters of robot are obtained.The friction model considered in the application has higher modeling accuracy, and the problem of system jitter at start and end position is overcome, the efficiency and accuracy of robot dynamics parameter identification are improved, and can be used in robot mobile handling, polishing and polishing and heavy load hydraulic industrial scene.

The application relates to the technical field of quantifiable test indexes of new energy vehicles, and particularly discloses a method for constructing a typical road spectrum working condition based on a genetic algorithm and new energy platform data, data acquisition, invalid data section elimination rules including static data section elimination and abnormal continuous duration elimination; data abnormal value identification rules: speed abnormal values are defined and a correction method is set; data smoothing and denoising rules: a median filter is used to preliminarily denoise a speed sequence; characteristic parameter calculation: including kinematic characteristic parameters and new energy light truck characteristic parameters; construction of an initial section library; multi-objective genetic algorithm optimization; road spectrum synthesis and post-processing: optimal sections filtered by the algorithm are spliced, and smoothing processing is carried out to ensure the continuity of a speed curve; multi-objective genetic algorithm selection: selecting an NSGA-III algorithm as a multi-genetic objective algorithm condition; the application realizes rapid construction and iteration of the road spectrum, and is high in efficiency.

The invention relates to the technical field of software development, and discloses a method, a device and equipment for arranging and testing laboratory orders, and a storage medium. The method comprises the following steps: according to a current order arrangement strategy generated based on a reinforcement learning model, performing chromosome coding on current laboratory order information, current experimenter information and current experimental equipment information to obtain a corresponding initial population; the fitness of individuals in the initial population is evaluated based on a current fitness function, the initial population is iteratively evolved by selecting and improving crossover and mutation operation of chromosomes, and the current fitness function is obtained based on examination parameters of laboratory orders predicted by a first AI model; and under the condition that a set genetic termination condition is met, an optimal laboratory order scheduling and testing scheme is obtained. Therefore, through the reinforcement learning model and the AI model and in combination with the genetic algorithm, comprehensive optimization of laboratory order arrangement and measurement is realized, and the order arrangement efficiency and the resource utilization rate are improved.

The application provides a nitrogen oxide emission prediction method and device based on a genetic algorithm and a convolution network. The method is based on historical operation data, converts variable selection, time lag characteristics of variable influence, and a prediction model optimization process into an integrated function optimization problem, optimizes parameters of each link by using a genetic algorithm, determines a model structure according to a function solution space corresponding to an optimal solution, solves a local optimal solution problem caused by variable selection and model optimization due to measurement time lag, and enables the prediction model to more fully mine NOx generation rules, thereby providing more accurate support for NOx removal control.

This invention discloses an automatic calibration method for airborne lidar placement deviation based on voxel sharpness. The method includes: selecting a natural calibration site containing building facades, linear markers, and point targets; collecting point cloud and POS data via a UAV flying in multiple headings; modeling the placement deviation to be calibrated using six parameters; optimizing the parameters using a genetic algorithm; using voxel sharpness as the fitness function; voxelizing the point cloud in the region of interest; counting the number of points within each voxel and calculating the sharpness score; a higher sharpness score indicates a more pronounced clustering of point clouds in the feature region; and iteratively optimizing the method through selection, crossover, and mutation operations to output the optimal placement deviation parameters. This invention uses an ordinary basketball court as a natural calibration site, eliminating the need for artificial targets. After calibration, the point cloud sharpness is improved by more than 150%, and the positioning accuracy reaches the centimeter level.

The invention belongs to the technical field of drilling fluid deironing equipment, and provides a drilling fluid demagnetizing device parameter optimization method based on a genetic algorithm and a related device.The method comprises the steps that structure and process parameters of a drilling fluid demagnetizing device are obtained and converted into individual codes of the genetic algorithm, design parameters of the drilling fluid demagnetizing device are obtained and serve as gene codes, and the genetic algorithm is used as the individual codes of the drilling fluid demagnetizing device; each individual represents a device configuration parameter; constructing a fitness function based on the adsorption efficiency, the processing capacity and the cost of the drilling fluid demagnetizing device; and based on the fitness function, evaluating the fitness of each individual, performing selection, crossover and mutation operation on the population to generate a new generation of individuals, and when a predetermined evolution algebra is reached or the fitness reaches a predetermined threshold, completing optimization. Various parameters are allowed to be adjusted according to actual conditions by adopting a parameter optimization algorithm so as to adapt to different types of fluids containing magnetic substances, the flexibility is high, and the application field is wider.

The application discloses a ship power system optimization scheduling method and system based on a genetic algorithm, and relates to the technical field of ship power control. The method comprises the following steps: acquiring real-time operation parameters of a ship power system; constructing an initial population of the genetic algorithm based on the operation parameters; calculating a comprehensive fitness value of each individual based on energy consumption characteristic indexes and emission characteristic indexes; performing selection operation, crossover operation and mutation operation on the initial population to generate a new population; repeatedly performing population iteration optimization until a preset condition is met; outputting a combination of ship speed adjustment parameters, a generator set start-stop decision sequence and a power distribution scheme; and generating a ship power system control instruction set. The ship power system optimization scheduling method and system based on the genetic algorithm improve the operation efficiency of the ship power system, reduce energy consumption and reduce carbon emissions.

The invention discloses a relay protection starting point intelligent selection and efficient setting method based on quantum heuristic recombination optimization and a dynamic adaptive genetic algorithm, and the method comprises the following steps: 1) constructing a protection cooperation importance comprehensive index, so as to quantify the influence of misoperation caused by protection mismatch on a power grid; 2) based on a protection cooperation importance comprehensive index, constructing a starting point optimization and setting calculation model based on the minimum mismatch importance and the protection adjustment number; and 3) solving the starting point optimization and setting calculation model based on the minimum mismatch importance degree and the protection adjustment number to obtain a relay protection starting point setting result. According to the method, the calculation efficiency of relay protection setting is remarkably improved, the consumed time of starting point optimization and setting calculation is shortened from several hours to tens of seconds through the QIRO-DAGA fusion algorithm, and the problems that manual setting is slow in response and long in period are effectively solved.

A system for extracting tabular data from an electronic document including an extraction engine that extracts tabular data from the electronic document using a plurality of data extraction services to provide a plurality of individual datasets, a population engine configured to generate a dataset population by arranging each individual dataset into a key-value format, and a selection engine configured to derive a final dataset based on the dataset population using a genetic algorithm.

The application relates to the technical field of aluminum alloy structure design, and discloses an aluminum alloy net shell structure design method and system based on a genetic algorithm. The method receives design requirement parameter input by a user, including span, height and load conditions of the net shell structure; divides the net shell structure into multiple design units corresponding to nodes or rod pieces; obtains historical performance data of aluminum alloy material, such as strength, rigidity and durability, from a database; determines the importance level and constraint level of each design unit based on the above data; generates an initial population containing multiple design schemes by using a genetic algorithm; iteratively executes selection, crossover and mutation operations to optimize the design individual; collects structure response data such as stress, strain and displacement in real time in the iteration; confirms whether a preset constraint condition is violated based on the data, and adjusts the genetic algorithm parameter if the constraint condition is violated; and outputs an optimal design result and generates a structure analysis image for the user to view.

The application discloses a kind of unmanned plane path planning method and related device based on triangle segmentation technology, method includes: based on the preset triangle route method obtains multiple unmanned plane inspection routes, forms unmanned plane inspection route set;With unmanned plane inspection route set as initialization particle group, route optimization calculation is carried out using preset genetic algorithm, obtain optimal unmanned plane route, preset genetic algorithm is based on the preset triangle segmentation algorithm optimization route selection, based on preset collision crossover strategy cross operation.Not only can select suitable genetic individual, but also can reduce calculation amount, while guaranteeing the reliability of route planning, also can minimize calculation pressure.Therefore, the application can solve the technical problems that the route planning method based on genetic algorithm in the prior art is limited by large calculation amount or large error, resulting in poor efficiency in engineering application process.

The invention relates to the technical field of alfalfa screening, discloses a high-quality and high-yield alfalfa hybrid combination screening method fusing multiple technologies, provides a multi-dimensional coding and double-elite evolution mechanism, constructs an improved non-dominated sorting genetic algorithm-II, and provides a high-quality and high-yield alfalfa hybrid combination screening method by coding a hybrid combination into a continuous real number vector. A chromosome expression mode decoupled from a problem structure is constructed, a double-elite mechanism of elite retention and offset mating is introduced, the retention and heredity ability of a high-quality solution is improved, offset crossover and jitter operation is used, premature convergence is avoided, efficient global search of a strain combination space is achieved, and a high-quality solution is obtained. Meanwhile, a multi-omics association model of genes, metabolites and phenotypes is provided, transcriptome, metabolome and molecular marker data are comprehensively utilized, a multi-dimensional character prediction model is constructed, early-stage accurate screening of filial generations is achieved through correlation analysis and marker verification, the error selection rate is reduced, and the breeding period is shortened.

The application discloses a greening plant selection method, system, device and storage medium, and technical scheme points thereof are as follows: soil analysis data and plant physiological data corresponding to each candidate plant in a sub-region are acquired; a structural equation model is constructed according to a congestion delay index, soil analysis data and plant physiological data of the sub-region, and significant factors of each candidate plant affected by traffic are determined according to the structural equation model; a first fitness of each candidate plant is calculated according to the congestion delay index of the sub-region and the significant factors of each candidate plant in the sub-region; a plant selection model corresponding to each sub-region is constructed based on a genetic algorithm, candidate plants are substituted into the plant selection model of each sub-region, and target plants corresponding to each sub-region are obtained, wherein, an adaptive function of the plant selection model is constructed based on the first fitness, the congestion delay index and the significant factors. The application can select plants with high adaptability and strong carbon fixation capacity based on traffic congestion.

The invention discloses a disease progress prediction method and system based on frequency domain characteristics, and the method comprises the steps: carrying out the modeling of multi-dimensional frequency domain characteristics, and converting an original physiological signal into a global frequency domain characteristic; self-adaptive frequency band selection: selecting an optimal frequency band combination in the global frequency domain characteristics by using a genetics algorithm; extracting a frequency domain template of the disease, constructing a disease specific frequency domain feature library (DSFP), and calculating a matching score; training a time sequence prediction model according to the optimal frequency band combination and the matching score, and performing disease progress prediction by using the trained prediction model; and using the trained prediction model to predict the disease process. The system comprises a time sequence prediction model training module and a real-time prediction model application module. According to the method, the frequency domain and multi-scale time-frequency structure features are fully mined, and dynamic matching with disease pertinence is realized through the constructed disease specific frequency domain feature library (DSFP).

The application provides a kind of electromagnetic sensor resource scheduling method for spectrum monitoring area coverage optimization, comprising the following steps: selecting sensing resource scheduling optimization target, the sensing resource scheduling optimization target includes: sensing resource load and task completion effect;Analysis sensing resource scheduling optimization target, establish multi-objective optimization sensing resource scheduling model;Using the genetic algorithm based on fitness optimization designed according to the characteristics of spectrum monitoring task completion effect, find the optimal sensing resource selection scheme under the conditions of meeting sensing task requirements.In the case of meeting the requirements of spectrum monitoring area coverage, as far as possible to optimize the utilization efficiency of electromagnetic sensing resource, the multi-objective optimization model proposed can be compatible with the cooperative scheduling requirements of other sensing tasks and be easy to extend, and the time-consuming link in the scheduling process is improved to optimize the convergence efficiency of algorithm.Implementation of sensing resource scheduling automation and improve the utilization rate of sensing resource.

The invention provides a satellite task scheduling method and system, and relates to the technical field of satellite task scheduling, and the method comprises the following steps: obtaining a satellite and a satellite task set, and determining a decision variable of satellite task scheduling according to the satellite and the satellite task set; constructing a satellite task scheduling model based on the decision variable of satellite task scheduling; solving the satellite task scheduling model by adopting a multi-target genetic algorithm to obtain a satellite task scheduling solution; and generating a scheduling instruction based on the satellite task scheduling solution, and performing task scheduling on the satellite according to the scheduling instruction. According to the method, a multi-population coevolution mechanism, an active auxiliary population selection mechanism and a dynamic Epsilon constraint relaxation mechanism are embedded in a traditional constraint multi-objective evolutionary algorithm framework, and the optimization efficiency, solution set quality and scheme feasibility of satellite task scheduling under complex constraints are effectively improved.