240 results about "Adaptive value" patented technology

The present invention is a method, system and apparatus for maintaining transactional integrity within an adaptive value chain involving cross enterprise interactions. In the present invention, transactional integrity can be maintained in a cross-enterprise business process management system by managing business transformation operations among cross-enterprise interactions to produce an adaptive value chain. At the same time, atomicity can be enforced among the business transformation operations and the cross-enterprise interactions.

Aiming to solve the problem of test case suite expansion in a software regression test, the invention discloses a technology for reducing test case suite. The technology is inspired by the particle swarm optimization (PSO) and utilizes 0-1 coding manner to indicate test case suite. Different particles represent different selective schemes of test case suite; the adaptive value of each particle adopts two adaptive values to evaluate, wherein, one is coverage degree of the test case suite to a test demand set, and the other is redundancy of the test case suite satisfying the test demand set. The renewal of the particle position utilizes all test cases to randomly generate the next individual position taking the coverage rate of the rest demand as a probability, so as to get the particles with maximum coverage rate and minimum redundancy, which is the optimized test case suite reduction scheme. Compared with the prior test case suite reduction technology, the invention has no relation with initial values and has the advantages of simple operation, high calculation speed and excellent performance.

The invention discloses a wireless sensor network clustering routing method based on particle swarm optimization and ant colony optimization, and belongs to the technical field of wireless sensor networks. The method is characterized in that the method comprises the steps that the optimal cluster quantity of an animal breeding environment detection system is decided dynamically; factors influencing energy consumption are analyzed earnestly; an adaptive value function is constructed; clustering is optimized by using a particle swarm optimization algorithm; and then routing between clusters is optimized by operating an ant colony algorithm in a cluster head node. Therefore, the energy consumption of a sensor node in a network is equalized; the survival time of the network is prolonged; a path with small communication delay is selected; the flow of the network is equalized; and a utilization ratio of a wireless sensor network for animal breeding environment detection is increased.

The invention develops a software test data evolution generation system facing a path, which has the advantages that the test data passing a target path can be automatically generated by using a genetic algorithm according to the test path selected by users, in addition, various optional genetic algorithm individual coding modes, adaptive value calculation methods, selection modes, crossing modes, variation modes and the like can be provided in the process of generating test data by using the genetic algorithm, and users can conveniently select different test data generation methods according to the tested program in different types. In addition, the system also provides the traditional random method test data generation method as the comparison, and the automation generation of the software test data is really realized. When the system disclosed by the invention is utilized, the software test efficiency is greatly improved, a large number of software development resources are also saved, the technical support is provided for the credible software research and development of the national relevant plan and the engineering, and the development of software industry in China is promoted.

The invention discloses an optimal configuration method for a power distribution automatic terminal type. A target function of a two-remote and three-remote power distribution automatic terminal optimal configuration model is established, and the target function includes cost generated by adopting a life-cycle period method and comprehensive social benefits generated by reducing of the outage cost; the average power supply availability of a system serves as the reliability constraint condition of the two-remote and three-remote power distribution automatic terminal optimal configuration model, an analytical method is used for conducting reliability assessment, the constraint condition is used for converting a target function through a penalty function method, and a fitness function is formed; the particle swarm optimization is adopted for conducting the optimal solution, adaptive values of particles are calculated, the adaptive value of each particle is compared with the current entity extreme value and the global extreme value, and the optimal entity value and the optimal global value are determined and updated; whether the maximum number of iterations is reached or not is inspected. The optimal configuration method can solve the problem of the number and the position configuration of two-remote and three-remote power distribution automatic terminals.

The invention discloses a reactive power optimization method of a power distribution network. The method comprises the steps of 1), building a mathematical model of a fan; 2), initializing the node voltage; 3), building a reactive power planning mathematical model and converting into a multi-objective optimization problem; 4), inputting original data; 5), forming an initial particle swarm and initializing the speeds and the positions of particles; 6), performing load flow calculation and obtaining adaptive values and a current optimum value of the particles; 7), correcting the speeds and the positions of the particles by using an ecological niche weight flight time method; 8), calculating the adaptability of the whole population, obtaining the adaptability value and updating the current optimum solution pBestid; 9), adjusting a control variable of a border crossing point of an individual particle and modifying a border crossing state variable; and 10), determining whether a requirement of an end condition is met, if the requirement of the end condition is met, outputting the result, otherwise, returning to the 6).

The invention relates to a distributed photovoltaic power generation maximum consumption capability calculation system based on an active power distribution network. The system comprises an input module, an initialization module, a load flow calculation module, a particle swarm operation module and an output module; the input module is used to obtain a distribution network parameter, a photovoltaic power generation system parameter, a photovoltaic time sequence characteristic parameter, a load time sequence characteristic parameter, the number of various typical days in a year and an adaptive chaotic particle swarm algorithm parameter; the initialization module initializes a population by utilizing a chaotic algorithm, and each particle in the population represents a distributed photovoltaic access scheme; the load flow calculation module performs a load flow calculation considering active management and obtains adaptive values of the particles in a time section t; the particle swarm operation module performs a loop computation through adoption of an adaptive chaotic particle swarm algorithm and obtains a particle having an optimal total adaptive value; and the output module outputs an optimal distributed photovoltaic access scheme and the maximum year consumption amount under the distributed photovoltaic access scheme. Compared with the prior art, the system has the advantages of high calculation efficiency, high integration and the like, and is closely combined with the actual.

The invention discloses a two-stage particle swarm optimization algorithm including independent global search. The two-stage particle swarm optimization algorithm comprises the following steps: species initializing; adopting the chaotization method for initializing the positions X and the speeds V of particles; adopting the fitness function (fitness) to calculate the adaptive values of all current particles, and initializing the record optimal position (pbesti) of each particle and the global optimal position (gbest) of all the particles; carrying out the first stage iterative-global search; carrying out the second stage iterative-local search. The two-stage particle swarm optimization algorithm has the benefits that during each iteration of the first stage iterative-global search, one non-self particle is randomly selected from all the particles for learning, and the random selection guarantees that the species is prevented from tracking the specific particle and that the aggregate phenomenon is avoided; the second stage iterative-local search can quickly converge and can obtain solutions high in accuracy, the accuracy of the optimal solution is increased, and the prematurity defect is remarkably improved.

The invention discloses a face recognition method based on a particle swarm optimization BP network. The method includes that an image is preprocessed to eliminate external disturbance; information of the preprocessed image is projected to a feature space by means of mapping transformation and by selecting different feature extraction modes; in the training or recognition process of neural networks, each feature corresponds to one input node of each neural network, output nodes are equal to classes in number, and one output node corresponds to one class. Therefore, a fully-connected BP network is designed, wherein the number of neurons in an input layer corresponds to the number of the features of the image, the number of neurons in an output layer is the number of swarm classes, the number of neurons in a hidden layer is set as the following formal, network weight is initialized as a random value between 0 and 1, and each particle corresponds to one neuron network. According to adaptive values of the particles and variable quantities of the adaptive values, inertia weight of each particle is regulated in real time, a global optimal solution can be rapidly found out, and efficiency and accuracy of face recognition are improved finally.

Enhanced adaptive profiling of ranges of values in a stream of events includes identifying a set of contiguous ranges of the values and corresponding access frequencies in the stream of events. The enhanced adaptive profiling uses a merge threshold value and a split threshold value. The set of contiguous ranges spans an entire range space of the values. Periodic traversal of the set of contiguous ranges of values and corresponding access frequencies identifies a target set of ranges of the values having corresponding access frequencies above a predetermined threshold access frequency. The target set of ranges of values has a total number of ranges less than or equal to a predetermined number of ranges. The target ranges of values span at least some of the entire range space of values. A first operation uses the target set of ranges of values.

The adaptive system includes a power toothbrush (10) which determines bristle force or pressure applied by the user against the teeth. The toothbrush includes a processing system (28) for comparing the determined force with a threshold value of excessive force and a threshold adaptive value which is below the excessive force threshold but serves as a warning for the user. The system is capable of raising the initial adaptive value threshold (64) following the user exceeding the adaptive value for a selected number of brushing events and then further for decreasing the adaptive value (66) back toward the initial adaptive value when the increased adaptive value is not exceeded for a selected number of brushing events, thus providing the ability to encourage/coach the user toward a brushing force which is below the threshold adaptive level and into a safe region of operation.

The invention discloses a multi-energy complementary microgrid energy storage optimized configuration method based on double layers of optimization models. The method comprises the steps of (1) constructing an upper layer optimization model with the economy of the energy storage device of a multi-energy complementary microgrid system in an off-grid state as a goal and the consideration of maximum output power constraint and ramp rate constraint, (2) constructing a lower layer optimization model with the economy and environmental protection of the multi-energy complementary microgrid system in a grid-connected state as a goal and the consideration of factors of power balance constraint, power constraint and the like, and carrying out single objective processing on a double-goal problem by using a random weighted method, and (3) carrying out normalization processing on the adaptive values obtained by the upper layer optimization model and the lower layer optimization model, and facilitating the weighted calculation of a final result. According to the method, the energy storage configuration requirement of multi-energy complementary system in an off-grid mode and the operation state in a grid-connected mode are considered at the same time, the improvement of the efficiency and economy and energy storage and capacity configuration are helped, and a microgrid energy storage optimized configuration model with the comprehensive consideration of off-grid and grid-connected operation modes is established.

The invention relates to a particle swarm optimization method based on a complex network. The particle swarm optimization method is used for solving the multiobjective optimization problem in the real world. The particle swarm optimization method based on the complex network comprises the steps that the population network topology is established according to a scale-free network generation mechanism, the optimization space, the population size, the positions of particles and the speeds of the particles are determined, the adaptive value is calculated according to a fitness function, the historical best position of each particle, the historical best position of the corresponding neighbor particle and the global historical best position of the particles are recorded, the positions and the speeds of the particles are updated in an iteration mode every time, the adaptive value is calculated again until iteration is completed, and the global best position is output. The particle swarm optimization method based on the complex network further provides four indexes for evaluating the optimal performance of center particles and non-center particles, the influence in neighborhood, the information transmission capacity, the advantages and disadvantages of the adaptive value and the capacity for maintaining population activeness. By means of the particle swarm optimization method based on the complex network, the local optimum can be effectively avoided, and the convergence rate and the optimization effect for resolving targets are balanced through the application of the particle swarm optimization algorithm.

The invention relates to a least squares-support vector machine (LS-SVM) prediction method based on an adaptive particle swarm. According to the method, the inertia weight is adjusted according to the degree of convergence of groups and the adaptive values of individuals. Training is speeded up. A matrix equation appearing in an LS-SVM is iteratively calculated using the algorithm, and matrix inversion is avoided. Memory is saved. The optimal solution is obtained. By using the method, the training sample can be simplified effectively, and the training speed is improved. Moreover, the method has the advantages of high classification accuracy, fast convergence and good generalization ability. The problems in prediction like high feature dimension, redundancy between samples and a limited number of samples are solved.

The invention relates to a control method of a light-type direct-current transmission system converter of an offshore wind power station, belonging to the technical field of power transmission. In the invention, a PID (Piping and Instruments Diagram) neural network controller is designed based on a particle group optimizing method, the traditional PI (Piping and Instruments) regulator is substituted, step input is used to train a neural network, that is to say, multi-group particles are set to search in the neural network weight space of searching, and the position and speed of the particles are continuously updated according to an adaptive value function of the neural network to obtain the optimum weight of the neural network. The invention adopts the method of using the optimum weight value obtained by training in combination with the error forward broadcast of the neural network to substitute the traditional PI regulator to control the operation of the system, thereby reducing the parameter to be regulated and improving the transient response performance of the system; the neural network weight value is obtained by training a nonlinear model of a controlled system so as to approach to a true system. Only the forward broadcast process of the PID neural network is controlled in the operation process of the system. The method is relatively simple and is easy to achieve.

The invention relates to a dynamic economical dispatch method for a microgrid system on the basis of an improved particle swarm optimization. The dynamic economical dispatch method for the microgrid system on the basis of the improved particle swarm optimization comprises the following steps: setting a particle swarm optimization; generating an initial particle swarm; setting the upper and lower limit constraint of the speed of particles; determining the adaptive values of the particles; comparing the adaptive values of the particles, finding the local optimal value and the position of each particle, and also finding a particle which achieves a global optimal value and the position of the particle; updating the position and the speed of each particle; judging whether the position and the speed of each particle are out of limit; determining the transmitting power of PCC (point of common coupling) serving as a swing bus; carrying out acceleration processing to the speed of the particles by an adaptive algorithm; and if the speed of the particles achieves iterations, stopping iteration, and obtaining a final result. According to the dynamic economic dispatch method for the microgrid system on the basis of the improved particle swarm optimization, the speed of the particles is regulated, a targeted search can be carried out when a search becomes a local search; meanwhile, the PCC used for connecting a microgrid with a major network is used as the swing bus; and the PCC is used for balancing when on-line load and the power output of a distributed power supply are not matched.

The invention relates to an automatic pilot and genetic algorithm-based method for monitoring the hovering range of an unmanned aerial vehicle. According to the method, when the unmanned aerial vehicle malfunctions or rolls over, an automatic control instruction is generated for an automatic pilot; and the automatic pilot and an genetic algorithm can be combined together so as to perform unmanned aerial vehicle flight hovering control; when it is monitored that sharp pitching or rollover of the unmanned aerial vehicle is not brought about by a flight instruction of a task, the automatic pilot immediately adopts a flight attitude adjustment parameter as a local initialization population value; the genetic algorithm performs iterative optimization on the adjustment quantity of an adaptive value according to the requirements of adaptability conditions in the initialization value; an adjustment solution of a control instruction when the flight attitude of the unmanned aerial vehicle tends to be stable can be obtained through iterative selection; and therefore, stable control on the attitude of the unmanned aerial vehicle can be realized, and the unmanned aerial vehicle can hover or land in a certain space area range.

The invention discloses a tire vulcanization workshop energy consumption optimization scheduling method based on the hybrid genetic algorithm, and belongs to the technical field of tire vulcanization workshop scheduling and production energy conservation. The method includes the procedure steps of conducting mathematical modeling, conducting matrix type coding on machines and machined workpieces, conducting population initialization on the basis of the LPP rules, determining the fitness function F(i), conducting selective operation on individuals in a roulette mode, selecting the individuals meeting the conditions and conducting interlace operation according to the probability Pc, selecting the individuals meeting the conditions and conducting mutation operation according to the probability Pm, conducting adaptive value evaluation, and judging whether the conditions are met or not. The method is simple in procedure, the speed for obtaining the optimal scheduling scheme is increased by 5 times, the production cost of each machine is reduced by 8.7%, energy can be saved for enterprises, the production cost can be reduced for enterprises, and timely delivery can be ensured for enterprises.

The invention provides a power transformer risk assessment method based on a fault tree. The power transformer risk assessment method comprises the following steps that 1) according to the given fault tree, fuzzy judgment matrixes R are built by using the fuzzy analytic hierarchy process; 2) according to the fuzzy judgment matrixes R, a constraint programming problem equation set is written out, wherein the severity weights omega i of various fault causes are obtained according to the constraint programming problem equation set, and the severity weights vector W satisfying the equation that W=[omega 1, omega 2,...omega n](T) is obtained; 3) according to the severity weights vector W, the optimal solution of the severity weight omega i enabling an adaptive value Z to be minimum is obtained; 4) the optimal solution of the severity weight omega i is substituted into the formula (4) to obtain the risk coefficients of the fault causes of a transformer. According to the power transformer risk assessment method, matrixes achieving fuzzy judgment consistency do not need to be built, and risk of the transformer can be assessed under the circumstance that the fuzzy judgment matrixes are not consistent; compared with the method in which the matrixes achieving fuzzy judgment consistency have to be built in the prior art, the power transformer risk assessment method is simple in algorithm, rapid and relatively high in precision.

The invention discloses an adaptive bilateral filtering algorithm for a polarized SAR image. The adaptive bilateral filtering algorithm for a polarized SAR image is mainly used for solving the problem of adaptive values for filtering parameters according to the structural characteristics and the noise situation of the image during the process for bilateral filtering of a polarized SAR image. The implementation process of the adaptive bilateral filtering algorithm is that 1) executing a polarized total power graph; 2) adaptively selecting the space variance; 3) estimating the noise variance of the image; 4) calculating the gray scale variance according to the noise variance; 5) polarizing bilateral filtering; and 6) utilizing pauli RGB to decompose the filtering result to obtain a pseudo colour graph. The adaptive bilateral filtering algorithm can save the work for manually setting the filtering parameters, and also the obtained filtering parameters are optimized and reliable, compared with a traditional polarized filtering algorithm; and the adaptive bilateral filtering algorithm can be used for filtering processing on the polarized SAR image.

The invention discloses a method for improving a standard shuffled frog leaping algorithm.The method comprises the steps of initializing parameters; calculating the adaptive value of each frog individual, and finding the adaptive value and position of the global optimum frog individual of a frog population; conducting optimum drawdown ranking on the frog population; conducting dividing for obtaining frog sub-populations; finding the positions of the optimum and the worst frog individual of each frog sub-population; conducting updating operation on the position of the worst frog individual of each frog sub-population; calculating the adaptive value of the frog individual with the position updated in each frog sub-population, and finding the global optimum adaptive value and the position of the frog population at this moment; implementing prediction of the global optimum adaptive value of the frog population obtained after iteration is completed next time, and furthermore adjusting the movement step-length variable coefficient dj and skip among steps; judging whether the ending conditions are met or not.By means of the method, the defects that at the later stage, the convergence rate of the standard shuffled frog leaping algorithm is severely lowered, convergence precision is insufficient, and the algorithm is prone to getting into local optimum are overcome.