51results about How to "Avoid precocity" patented technology

The invention discloses a track planning method for a multi-unmanned plane system in a three-dimensional environment, and belongs to the technical field of unmanned plane track planning. The method aims at the unmanned plane track planning characteristics. The method includes the steps of obtaining elevation and threatening information of an unmanned plane flying environment, generating a three-dimensional grid map of the flying environment, conducting multi-unmanned plane track planning with an improved heredity algorithm, conducting smooth processing for planned tracks by using a spline curve B, and finally forming a track that satisfies unmanned plane movement constraints. Optimal flying paths for multi-unmanned planes can be determined and planed through a heredity algorithm, and task completion efficiency is improved. The track planning method is applicable to the fields of unmanned plane reconnoiter, monitoring and rescue path planning.

The invention discloses an optimization method of the shortest emergency logistics path based on the fish swarm ant colony algorithm. The congestion factor is introduced into the basic ant colony algorithm to solve the optimization problem of the shortest emergency logistics path and enhance the search for emergency logistics. The ability to optimize the best solution for the path reduces the possibility of the basic ant colony algorithm falling into a local optimum. (1) Input the number of ants, the maximum number of iterations and the maximum load capacity of the vehicle, select the emergency situation, and input the importance of residual pheromone, the importance of heuristic pheromone, evaporation coefficient and intensity coefficient; (2) Find the shortest emergency logistics route : (1) Establish the model of the shortest emergency logistics route optimization problem; (2) The solution process of the model; (3) Display the distribution route results and the performance comparison results of the algorithm in solving the shortest emergency logistics route optimization problem. The optimization scheme for finding emergency logistics routes in the present invention is more efficient.

The invention discloses a multi-objective optimization improved genetic algorithm based on dynamic weight M-TOPSIS multi-attribute decision-making. The method includes the steps of first determining multi-objective optimization mathematical model and genetic algorithm parameters, and establishing a constrained feasible population and a population objective function matrix; then, calculating objective weights of objective functions by using an entropy weighting method, synthesizing the mixed dynamic weights of the objective functions, performing population individual sorting by using an M-TOPSIS method based on the dynamic weights, and obtaining a Pareto temporary solution set; assigning virtual fitness values to the individuals according to the sorting, and selecting an offspring population by using a proportional selection operator and a roulette method; next, performing crossing and mutation operations on the offspring population; finally, merging the Pareto temporary solution set and the offspring population after the mutation operation to generate a new population; obtaining an optimal solution and a Pareto optimal solution set until termination conditions of the algorithm is satisfied. The method of the invention can realize the multi-objective optimization and the multi-attribute decision-making process at the same time, provides a new solution for the multi-objective optimization problem, and has a high engineering practical value.

The invention relates to a PID controller parameter optimizing method, and especially discloses a PID controller optimizing design method based on a particle swarm membrane algorithm. The method comprises that an optimal particle is obtained from a swarm, different dimension values of the particle are successively assigned to to-be-optimized parameters Kp, Ki and Kd, a control system model is operated, performance indexes output by the system are obtained, and it is determined whether the evolution rule is used again to search for the optimal particle so that the optimal control effect can be obtained. The PID controller optimizing design method combines membrane computing with traditional PSO (Particle Swarm Optimization), fully utilizes the partition function and transport communication rules, can obtain a set of more proper PID control parameters, and finally enables a controlled system to achieve the optimal control effect. According the PID controller optimizing design method, the controlled system can obtain the set of more proper PID control parameters and achieves the optimal control effect under the condition that the controller structure is not changed.

The invention discloses an electric power inspection robot path planning method based on a simulated annealing ant colony algorithm. The method includes the steps: building a map according to an outdoor transformer substation environment, planning the running path and stop points of an electric power inspection robot and building a topological map according to coordinate information of the stop points; acquiring the local shortest path of two optional inspection points by the aid of a classical Dijkstra algorithm according to inspection tasks, and building an undirected graph; transforming a global optimal path planning problem into a multi-target traveling salesman problem according to path lengths and the number of passing stop points, and planning a global optimal path on the basis of the undirected graph by the simulated annealing ant colony algorithm; replacing a local path in the global optimal path by the local shortest path to obtain a complete final path. The method is high inconvergence rate and global searching ability and has the advantages of low complexity, high feasibility and high speed.

The invention discloses a PID optimization control method of a four-rotor aircraft. The method includes the following steps that power performance modeling is carried out on a PID controller; the PID controller is designed based on a power performance model; parameters of the PID controller are optimized through the particle swarm optimization algorithm; the parameters of the PID controller are further optimized through the combination of an improved particle swarm optimization algorithm and the genetic algorithm. According to the method, the improved particle swarm optimization algorithm is adopted to optimize the parameters of the PID controller, the speed and position of a particle are changed through comprehensive learning of surrounding particles, and better performance can be easily achieved through sufficient learning. A first particle can be updated to an optimal position after comprehensive learning of the surrounding particles; to avoid local optimum, especially for multi-peak functions which are prone to local optimum, the particles are recombined through selection, crossing and mutation of the genetic algorithm, and the prematurity phenomenon of the particles is avoided.

The invention belongs to the logistics distribution site selection technical field and relates to an adaptive Levy distribution hybrid mutation improved artificial fish swarm algorithm-based distribution center site selection optimization method. The method includes the following steps that: (1) relevant parameters are initialized, and a distribution center site selection optimization model is established; (2) the distribution center site selection optimization model is solved through using the optimization method according to which adaptive Levy distribution hybrid mutation is utilized to improve an artificial fish swarm algorithm; and (3) a distribution center site selection result is compared with the result of using the adaptive Levy distribution hybrid mutation to improve the artificial fish swarm algorithm in solving a distribution center site selection problem. According to the method of the invention, Levy mutation and chaotic mutation are introduced into the basic fish swarm algorithm, so that the diversity of artificial fish states in the basic artificial fish swarm algorithm can be increased, the capability of the basic artificial fish swarm algorithm to jump out of local optimum can be improved, and the optimization of distribution center site selection can be enhanced.

The invention discloses a high-voltage switch cabinet insulator electric field optimization method based on a quantum genetic algorithm. The method comprises the following steps that 1) a high-voltage switch cabinet insulator geometric model is built; 2) the high-voltage switch cabinet insulator model is subjected to electrostatic field simulation to obtain the maximum electric field intensity value, and structural factors for influencing the electric field distribution and the maximum field intensity are determined through changing variable structural parameters; 3) a population is initialized; 4) an objective function is determined, and a fitness degree function is calculated, wherein the objective function of an individual is the electric field intensity corresponding to the parameters; 5) for the individual population consisting of binary gene codes, the variation is carried out after the selection and the full-interference crossing; and 6) whether the quantum genetic operation stop condition is met or not is judged, if the stop condition is not met, the operation returns to the first step, and if the stop condition is met, the corresponding response value is calculated according to the optimized structure parameters obtained in the fifth step, and the maximum electric field intensity value is obtained. The method can realize the optimization on the high-voltage switch cabinet insulator electric field.

The invention discloses a load model identification method based on transformer substation measurement. The load model identification method comprises the steps of performing homology cluster division on transformer substations in a power grid, constructing load models of equivalent induction motors, constructing an identification criterion function, optimizing load parameters to be identified in the load models according to a response between input voltage and an output of an actual system by a particle swarm algorithm, and counting the load models of different stations to obtain a regional power grid load model parameter library. According to the load model identification method, an initial active power proportional coefficient Kpm and a rated initial load rate coefficient Mlf are introduced into the identified parameters, so that the influence caused by the time-varying characteristic of a load amplitude value is eliminated, and the load models are more accurate; due to the arrangement of a speed adjustment factor and an inertia factor of the particle swarm algorithm, the convergence precision of the algorithm is improved.

The invention discloses a fruit-fly-optimization-algorithm-based multi-station assembling sequence planning method. The method comprises: a priority constraint relationship between parts is expressed by using a priority sequence graph; a priority relationship matrix, an integrated interference matrix, a station capability list, and an assembling information table are constructed; and an interference relationship between parts as well as a relationship between stations is described. A coding system of a fruit fly algorithm is provided; and three searching stages including an odour searching stage, a vision searching stage, and a cooperative searching stage of the fruit fly are designed by considering the local and global searching capabilities of the fruit fly algorithm. A fitness function expression close to practical engineering is put forward by considering influences of an assembling operation cost, a replacement tool cost, a clamping changing cost and a transport cost comprehensively. According to a priority sequence matrix, initial sequence evolution is guided; and a product assembling sequence and a station assembling sequence are optimized by using the fruit fly optimization algorithm.

The invention discloses a distribution network overcurrent protection method with a distributed power supply, and a fixed value optimization method and system, and belongs to the technical field of distribution network relay protection. When a fault occurs, a data acquisition system acquires a fault current flowing through each inverse time limit overcurrent relay in the distribution network witha distributed power supply; according to the inherent characteristics of the fault current and the inverse time limit overcurrent relay and the selectivity, the sensitivity and reliability requirements of the relay protection, a mathematical model is established, wherein the mathematical model comprises an objective function and a constraint condition; a particle swarm optimization based on crowdsearch is employed to perform particle optimizing of the time setting coefficient and starting current of the inverse time limit overcurrent relays; and according to the optimal particles, the time setting coefficient and starting current of each inverse time limit overcurrent relay are subjected to re-assigning, and faults are cut off in the shortest time. The problem is solved that the relay fixed value is improperly set after the distributed power supply is accessed into the distribution network.

The invention discloses a wind power system reactive power planning method based on the golden section cloud particle swarm optimization algorithm. The wind power system reactive power planning method comprises the steps that a reactive power planning mathematic model is built, and a target function is determined; original data of a wind power system is input, and therefore an initial population is formed; all particles are generated randomly, a golden section judging criterion is used for dividing a particle swarm into three parts according to the self-fitness value of the particle swarm, and different inertia weight is set for each part of particles; new positions and speeds of the particles are obtained through the particle swarm optimization algorithm, the particles are divided into three parts and iterated repeatedly according to the method before an the end condition is met, an optimal solution is searched, and therefore reactive power planning of the wind power system is achieved. According to the wind power system reactive power planning method based on the golden section cloud particle swarm optimization algorithm, the node voltage level of the wind power system is effectively improved, network loss of a power network is reduced, the diversity of the particles is kept according to the algorithm, the prematurity phenomenon which easily occurs during optimization searching is avoided, and convergence rate in the optimization searching process is improved. In addition, the wind power system reactive power planning method based on the golden section cloud particle swarm optimization algorithm is small in calculated amount, and higher in operability.

An ultra-short-term wind power plant power prediction method combined with meteorological factors belongs to the technical field of power generation power prediction of a power system, and comprises the following steps: step 1, preprocessing wind power historical data and NWP meteorological data, supplementing missing data and modifying abnormal data; 2, generating a wind power prediction model; and 3, carrying out future wind power prediction by utilizing the trained model and future NWP meteorological data. According to the method, new firefly individuals are periodically added into the population by using a chaos strategy in the iteration process, so that the prediction precision of the ultra-short-term wind power is improved, and a favorable basis is provided for scheduling personnel of a power grid department to perform short-term scheduling decision arrangement.

The invention provides a construction project multi-objective optimization method. The construction project multi-objective optimization method comprises the following steps: determining a mathematical model and genetic algorithm parameters of multi-objective optimization; establishing a population with feasible constraints and a population target function matrix; calculating an objective weight of the target function by adopting an entropy weight method according to the target function matrix, and synthesizing a hybrid dynamic weight of the target function; sorting the population by adoptinga method based on dynamic weight to obtain a Pareto temporary solution set; attaching virtual fitness values to individuals according to population individual sorting, and selecting a filial generation population by adopting a proportional selection operator and a roulette method; performing crossover operation on the filial generation population; performing mutation operation on the filial generation population after the crossover operation; combining the Pareto temporary solution set with the filial generation population after mutation operation to generate a new population; and if the algorithm termination condition is met, terminating the algorithm, otherwise, returning. According to the method, the problem of ambiguity between an original multi-objective optimization algorithm and engineering application is well solved, and the method has better engineering applicability.

The invention discloses an accurate modeling method of an electromechanical actuation system friction pair; the method employs a Stribeck friction model and a simulation annealing heredity algorithm,and belongs to the electromechanical system modeling technical field; the method comprises the following steps: 1, online test; 2, model selection; 3, target function selection; 4, iteration search identification. The iteration search identification step comprises the following 7 substeps: 1, random generation of initialization population; 2, individual fitness calculation; 3, employing a random traversal sampling method to form a new generation population; 4, simulation annealing selection operation; 5, simulation annealing intersect operation; 6, simulation annealing mutation operation; 7, iteration operation termination determination. Compared with the prior art, the electromechanical actuation system modeling method is faster in convergence speed and higher in modeling precision.

The invention provides a battery formation detecting system. The battery formation detecting system comprises a comparison circuit, a sampling device, an insulated gate bipolar transistor (IGBT) module and a pulse width modulation digital processing unit. The IGBT module comprises an IGBT arranged on an upper bridge arm, an IGBT arranged on a lower bridge arm and two absorption and protection circuits. A source electrode of the IGBT arranged on the upper bridge arm is connected with a drain electrode of the IGBT arranged on the lower bridge arm, and grid electrodes of two IGBTs are connected with the pulse width modulation digital processing unit. Each absorption and protection circuit comprises a buffer capacitor, a fast recovery diode and an absorbing resistor. The invention further provides a parameter optimization selection method of the absorption and protection circuits in the battery formation detecting system through parallel self-correcting and multiple target genetic algorithm. According to the battery formation detecting system and the protection circuit parameter selection method thereof, a major loop is small in inductance, and transient voltage can be controlled well.

The invention belongs to a license plate recognition system, and discloses a license plate character recognition method based on an SIFT operator and a chaos genetic algorithm. The method is characterized in that Chinese characters and alphanumeric characters of a license plate are separately recognized, that is, the Chinese characters are recognized by using an SIFT operator feature extraction and template matching method; and the alphanumeric characters are recognized by using a thirteen-point feature extraction method and a support vector machine, and the problem of low overall recognition rate of the license plate characters due to that most of the existing license plate recognition systems adopt a unified character feature extraction and recognition method for recognition is solved. Meanwhile, in order to improve the classification capability of the support vector machine, the method adopts the chaos genetic algorithm to optimize radial basis function parameters and penalty factors, license plate images of different backgrounds are collected and tested and simulated on matlab software, the overall recognition rate of the characters can reach more than 99%, and the chaos genetic algorithm has a higher character recognition rate and a faster convergence rate than a traditional genetic algorithm.

The invention provides a Sigma-Delta modulator self-adaptive mixing optimization method for improving the signal to noise ratio. The method includes creating a noise transfer function of a Sigma-Delta modulator and performing dimensionality reduction on noise transfer function parameters, optimizing the noise transfer function parameters subjected to dimensionality reduction by a differential evolution method based on self-adaptive Cauchy distribution and chaotic mapping, calculating to acquire optimal values of the noise transfer function parameters according to to-be-optimized optimal parameter values, determining the optimal noise transfer function to complete self-adaptive mixing optimization of the Sigma-Delta modulator, taking a sinusoidal signal output by an interpolation filter of a Sigma-Delta digital-to-analog converter as the input of the Sigma-Delta modulator with the optimized noise transfer function, transforming an output value of the Sigma-Delta modulator to a frequency domain, and calculating the signal to noise ratio of the Sigma-Delta modulator. According to the method, ergodicity of chaotic mapping and high disturbance of self-adaptive Cauchy distribution are fully utilized, a target function is created and is optimized by the mixing differential evolution method, and the signal to noise ratio is remarkably increased while the stability of the modulator is kept.

The invention discloses a low-carbon logistics distribution management system and method based on machine learning and interference management, and relates to the technical field of machine learning and internet application management. The enterprise-level user side comprises a login management module, a client management module, a vehicle management module and a scheduling management module. According to the invention, an improved quantum ant colony algorithm adjustment strategy is adopted, so that the interference scheduling plan deviation of the logistics distribution system is minimum and the interference event is timely and accurately processed. The improved quantum ant colony algorithm provided by the invention improves the convergence speed of the optimal solution, increases the search range of the global optimal solution, avoids the premature phenomenon of the algorithm, reduces the operation time and cost of the low-carbon logistics distribution system, improves the operation efficiency of the system, and shortens the response time.

The invention discloses a method and a system for optimizing and predicting dissolved oxygen based on hybrid QPSO-DE, and the method comprises the steps: carrying out the optimization design of parameters of a support vector machine through employing a QPSO-DE algorithm, setting the support vector machine obtained through optimization design as an optimization prediction dissolved oxygen model, and obtaining dissolved oxygen prediction data. Compared with a standard differential evolution algorithm, the invention has the advantages that the search capability is stronger, the performance is better, individuals entering the next generation in each iteration have better fitness values, the premature phenomenon of the algorithm can be effectively avoided, that is, the algorithm falls into local optimum and cannot jump out, and the global search capability of the algorithm is improved.

The invention provides a production scheduling method and system based on a hybrid parallel inheritance and variable neighborhood algorithm, a storage medium and electronic equipment, and relates to the field of production scheduling. The method includes: adopting a heuristic algorithm to obtain each workshop production scheduling scheme of each individual in an initialized population, and taking the individual with the highest fitness value as a global optimal solution; searching a new solution in a neighborhood structure; the updated global optimal solution is migrated to each sub-group; according to the updated fitness value of the individual in each sub-group, adopting a selection operator, a crossover operator and a mutation operator to obtain a next-generation sub-group; and selecting an individual with the highest fitness value in the current group, and updating the globally optimal solution. An approximate optimal solution is found through iteration of mixed coarse-grained parallel inheritance and a variable neighborhood search optimization algorithm, the premature phenomenon of a genetic algorithm is avoided, and the convergence degree of the algorithm is increased; the efficiency improvement caused by the machine processing deterioration effect and the resource investment is considered, and the problems of production scheduling decision and resource configuration decision are considered.

The invention provides an intelligent vehicle scheduling method and device based on a genetic algorithm, electronic equipment and a storage medium. The method comprises steps of the number K of vehicles required for distribution being determined according to a current distribution task of a distribution center and the load capacity of a single vehicle, and 2N vehicle path schemes for executing the current distribution task by using K vehicles being determined; taking the 2N vehicle path schemes as 2N chromosomes, calculating the fitness of each chromosome, and constructing an initial population by using the N chromosomes with optimal fitness; setting a crossover probability and a mutation probability in stages, carrying out staged iterative operation on the initial population by adopting a genetic algorithm according to the crossover probability and the mutation probability until a preset maximum number of iterations, and determining the vehicle path scheme corresponding to the chromosome which has the highest fitness and is not overloaded in the population after evolution as the vehicle path scheme of the distribution center. According to the method, a premature phenomenon of a modern heuristic algorithm can be well avoided, the global optimization capability of the algorithm is enhanced, and an optimal solution with relatively high quality can be solved.

The invention provides a lower limb prosthesis road condition recognition method based on surface electromyogram signals. The method comprises the following steps of 1, collecting and preprocessing the lower limb surface electromyogram signals of a thigh amputation patient under different road conditions; 2, extracting a characteristic value sample set of road condition recognition of the preprocessed lower limb surface electromyogram signals; 3, optimizing classification parameters of the extreme learning machine through a backbone particle swarm algorithm to obtain an optimal ELM classifier,and realizing lower limb prosthesis road condition identification and classification. According to the lower limb prosthesis road condition recognition method based on surface electromyogram signals,an extreme learning machine classifier is constructed by using the optimal hidden layer node number and the kernel function parameters. The road condition recognition accuracy is high. The backbone particle swarm algorithm has global search capability, is easy to implement and high in search speed. The premature phenomenon can be effectively avoided on the premise of ensuring the accuracy. The road condition recognition accuracy is effectively improved.

The invention relates to a method for predicting short-term wind power generation output power, which is technically characterized by comprising the following steps of: acquiring input data and outputdata of wind power generation, and normalizing the data; of an improved optimal foraging algorithm and a support vector machine model; running the improved optimal foraging algorithm to obtain an optimal penalty factor in the support vector machine model and an optimal parameter of a kernel function in the support vector machine model; substituting the optimized optimal parameters into a supportvector machine model, and training the support vector machine model optimized by the improved optimal foraging algorithm; and inputting the prediction data into a support vector machine model optimized by an improved optimal foraging algorithm to obtain a prediction result, and performing reverse normalization on the prediction result. According to the method, the reliable and high-precision prediction function on the short-term wind power generation output power is realized, the hidden danger existing in the operation of the wind power generation access power grid is effectively handled, andthe defect of low prediction precision of the existing short-term wind power generation output power prediction method is also overcome.

The invention discloses a hybrid evolutionary optimization method based on a generative adversarial network model, which mainly solves the problem that the traditional evolutionary algorithm is difficult to process high-dimensional and non-convex optimization and the like when facing an optimization problem, and comprises the following implementation steps of: (1) initializing a population; (2) calculating fitness values of the individuals according to a fitness criterion; (3) selecting dominant individuals; (4) performing crossover and mutation operation on the dominant individuals to obtainnew individuals; (5) taking the dominant individuals as samples, and generating new individuals by training the generative adversarial network model; (6) combining the new individuals obtained after the crossover and mutation operation with the new individuals generated through the generative adversarial network to form a new filial generation population; and (7) judging whether to terminate: outputting the optimal value of the target function after the algorithm is terminated, otherwise, returning to the step (2). According to the method, the global search capability and the convergence speedof the evolutionary algorithm are improved, and the method can be used for solving the complex high-dimensional optimization problem.

The invention discloses a CT image denoising method based on wavelet transformation, belongs to the technical field of medical image processing, and is particularly suitable for CT image denoising of new crown pneumonia. The CT image is susceptible to the interference of Gaussian noise in the transmission and acquisition process, and the wavelet transform can effectively remove the interference of the Gaussian noise. In order to solve the problems that early-stage lesions of a new crown CT image are not obvious in change, the number of the lesions is small, the range of the lesions is small, the density is low, and missed diagnosis of early-stage new crown patients is easily caused, the contrast ratio of the new crown lesions is improved, namely, an arc tangent improved self-adaptive wavelet threshold function of index adjustment and an improved threshold based on contraction factors are provided; the arc tangent function changes quickly near the zero point and changes slowly away from the zero point, the exponential function is adjusted to adapt to different layer threshold functions, more high-frequency detail information in the lung CT image is obtained, the detail edge is reserved, and fuzziness is reduced. The selection of wavelet threshold function parameters is a key factor for determining distortion and errors after image denoising, and the optimal adjustment parameters are searched through the improved particle swarm optimization of sine and cosine fusion normal distribution, so that the threshold optimization effect is greatly improved.

The invention relates to a neutron spectrum real number solution genetic algorithm with linear constraint, a constraint penalty function is introduced in the algorithm process, priori information of afew groups can be fully utilized, and the accuracy and the calculation speed of the spectrum solution method can be effectively improved by using valuable priori information. Solving the problems that simple genetic algorithm is not accurate, and cannot effectively use the initial information problem and the solution space discrete. At the same time, solving the SAND-II iterative algorithm requires accurate cluster corresponding initial spectrum problem and the SAND-II iterative algorithm is heavily dependent on the initial spectrum.

The invention provides an equipment testability strategy optimization method and device, and the method comprises the steps of obtaining a test population which comprises a plurality of test sets for testing equipment; determining the test cost, the basic reliability cost and the testability performance benefit of the equipment based on the test population; taking the test cost and the basic reliability cost as first constraint parameters, and taking the testability performance benefit as a first test index to construct a first testability optimization model; and / or, taking the test cost, the basic reliability cost and the testability performance benefit as second constraint parameters, and taking the equipment comprehensive benefit as a second test index to construct a second testability optimization model; and performing testability strategy optimization calculation by adopting a fitness function based on the first testability optimization model and the second testability optimization model. According to the invention, the problems of single optimization target and easiness in falling into local convergence in the existing equipment testability optimization process in related technologies are solved.

The invention provides an optimal retention strategy-based genetic algorithm wave impedance inversion method and belongs to the field of geophysical inversion, which particularly relates to a wave impedance inversion technology in the oil-gas geophysical exploration field. The invention provides an improved genetic algorithm-based wave impedance inversion method, wherein the early-maturing convergence problem of a standard genetic algorithm in wave impedance inversion is solved. As a result, an obtained inversion result is more reliable. The method mainly comprises the following steps of (1) constructing a target function of wave impedance inversion according to a convolution model; (2) estimating the seismic wavelet through the homomorphic theory; (3) coding the wave impedance in a binarycoding mode; (4) calculating the fitness value of each individual by using a target function, and carrying out quantitative evaluation on the individual; (5) generating a new-generation population according to the selection mode of the optimal reservation strategy; (6) according to designed crossover and mutation operators, carrying out the genetic operation; (7) converting individual genotypes into phenotypes according to corresponding decoding modes and realizing algorithm circulation; and (8) adopting a recursion method for calculating the wave impedance.