153results about How to "Avoid premature" patented technology

The invention discloses a cascade hydropower station group optimized dispatching method based on an improved quantum-behaved particle swarm algorithm. The problems that local optimum happens to the quantum-behaved particle swarm algorithm at the later iteration period due to premature convergence for the reason that population diversity is decreased, and an obtained hydropower station group dispatching scheme is not the optimal scheme are mainly solved. The hydropower station group optimized dispatching method based on the improved quantum-behaved particle swarm algorithm is characterized by comprising the steps that first, power stations participating in calculation are selected, and the corresponding constraint condition of each power station is set; then, a two-dimensional real number matrix is used for encoding individuals; afterwards, a chaotic initialization population is used for improving the quality of an initial population, the fitness of each particle is calculated through a penalty function method, the individual extreme value and the global extreme value are updated, an update strategy is weighed, the optimum center location of the population is calculated, neighborhood mutation search is conducted on the global optimum individual, the positions of all the individuals in the population are updated according to a formula, and whether a stopping criterion is met or not is judged. The hydropower station group optimized dispatching method based on the improved quantum-behaved particle swarm algorithm is easy to operate, small in number of control parameters, high in convergence rate, high in computation speed, high in robustness, reasonable and effective in result, and applicable to optimized dispatching of cascade hydropower station groups and optimal allocation of water resources.

The invention discloses a multi-UAV (Unmanned Aerial Vehicle) cooperation path planning method based on an ant colony algorithm. The method comprises the following steps of (1) analyzing a UAV flight environment, and building environment modeling based on a Voronoi diagram; (2) calculating consideration of an edge in the environment modeling based on the Voronoi diagram; (3) utilizing the ant colony algorithm to plan an initial path for a UAV; (4) smoothing the initial path of each UAV to judge whether cooperation is achieved or not, and carrying out corresponding operation according to the results. According to the multi-UAV cooperation path planning method based on the ant colony algorithm provided by the invention, multiple different kinds of unmanned aerial vehicles are mutually cooperated, intelligently and autonomously adapt to complicated and changeable war environment factors, and dynamically adjust self-strategies so as to cooperatively accomplish the combat mission.

The invention discloses a reconfigurable assembly line sequencing method based on an improved genetic algorithm. The method comprises the following steps of: determining a population size according to a minimum production cycle of a reconfigurable assembly production line, and executing genetic encoding according to a standard of taking a chromosome as a full array of all tasks; calculating the idleness of the minimum reconfigurable assembly line, the quantity of unfinished work, the uniform parts use rate and the minimum production adjustment cost of the individual; executing a grading operation, executing a Pareto solution set optimization filtering operation, calculating the fitness of each grade, executing genetic operations according to the fitness, executing an elite reservation strategy, and obtaining a Pareto optimal solution set and a corresponding objective function value by judging whether convergence is realized or the pre-set maximum number of iteration is achieved. In the method, three major factors influencing the optimized sequencing of the reconfigurable assembly line are comprehensively considered, a plurality of technologies are used in the genetic operation, population diversity is ensured, algorithm prematurity is avoided, and global optimal search ability of the algorithm is enhanced.

The invention relates to a method for inverting anisotropy parameters using variable offset vertical seismic profile data. The method includes: calculating vertical longitudinal wave speed and vertical transverse wave speed of each layer of a vertical seismic profile; collecting symbol layer positions and determining intersections of each layer and well trajectory; building up a surface layer model and an observing system; randomly initiating the anisotropy parameters of layers to be determined and cubic coefficient of a top layer interface to be determined; forming anisotropy speed models to be optimized; screening receiver inter-layer points ; collecting shot first arrival to calculate polarizing angles; building up a target function using P wave first arrival travel time and the polarizing angles; using niche genetic algorithm to optimize the target function; finishing optimization of the anisotropy parameters of the layers to be determined and configuration of the top layer interface to be determined; calculating layer by layer 'from shallow deprocessing to deep deprocessing' to finish optimization of the anisotropy parameters of the whole model. By the method, a reflecting interface is close to actual stratum, linear convergence ray tracing is high in calculation efficiency, complicated internal relations of anisotropy media are simplified, iterative test ray tracing modeling is guided by guide search of the niche genetic algorithm, and the anisotropy parameters and interface configurations are inverted and optimized nonlinearly.

The invention discloses an intelligent steel cord conveyer belt defect identification method and an intelligent steel cord conveyer belt defect identification system. The identification method includes the following steps: (1) electromagnetic loading; (2) defect signal acquisition; (3) feature extraction; (4) training sample obtainment; (5) class priority determination; (6) multi-class model establishment; (7) multi-class model training; (8) real-time signal acquisition and synchronous class: electromagnetic detection units are adopted for real-time detection, detected signals are synchronously inputted into a data processor, features are extracted and then sent into established multi-class models, and the defect class of a detected conveyer belt is automatically outputted. The identification system comprises an electromagnetic loader, a plurality of electromagnetic detection units, the data processor and an upper computer, the data processor can automatically output the defect class of the detected conveyer belt, and the upper computer bidirectionally communicates with the data processor. The design of the invention is reasonable, the invention is easy to operate and convenient to put into practice, moreover, the using effect is good, the practical value is high, the reliability of conveyer belt defect detection is enhanced, and the efficiency of defect identification is increased.

A semiconductor photodiode (18) is formed as a pn-junction between a region (2) of a first conductivity type and a region (6) of a second conductivity type. The region (6) of the second conductivity type is approximately hemispherical. A mini guard ring (8), i.e. a ring of the second conductivity type having a junction depth that is much smaller than the junction depth of the region (6) preferably surrounds the region (6) in order to prevent surface trapping. The photodiode (18) is operated with a high reverse bias so that light falling on the photodiode (18) produces the avalanche effect.

The invention relates to the technical field of power distribution networks, in particular to a method for planning a power distribution network comprising photovoltaic power supply. The method comprises the following steps: (1) establishing a target function of social cost; (2) determining a constraint condition; and (3) determining the planning method with a particle swarm algorithm containing chaos optimization, outputting the optimal planning scheme and determining the optimal access position and capacity of the photovoltaic power supply. According to the method, the optimal access position and capacity of the photovoltaic power supply are calculated, so that the network loss of the power distribution network is reduced; the operating cost of the power distribution network is reduced; the voltage of a system is improved; the load capacity of the system is enhanced; safe and effective operation of the power distribution network can be ensured; and the economic benefits can also be further improved.

The invention relates to a static reactive power compensation device based on an energy method and a control method thereof, belonging to the technical field of power transmission and distribution. The invention comprises a mutual inductor group, a filtering and signal conditioning module, an A/D conversion module, a computing module, a memory module, a phase-locked loop circuit module, a communication module and a display and keyboard operation module. The invention can evaluate and analyze possibly potential dangers of a power grid by adopting energy function method, predict the influence on the stable operation of the power grid possibly resulted from faults by establishing an anticipated accident list, carry out scientific, effective and real-time analysis and evaluation on the power grid state and carrying out integral cognition on the power grid performance as complete as possible, thereby quantitatively evaluating the operation state level of the power grid; and the invention effectively avoids an algorithm from falling to local optimum by adopting an improved particle intelligent swarm algorithm based on a fuzzy control theory.

The present invention discloses a sliding mode robust control method of a discrete time delay uncertain system. A multi-population chaotic simulated annealing particle swarm optimization algorithm is used to optimize a support vector regression machine (SVR) structure parameter, a combined kernel function support vector regression machine is used to give a system prediction model, a control law is solved through a novel sliding mode reaching law, and the reachability of a sliding mode can be guaranteed. The method is used for the robust control of uncertain discrete system with state time delay.

The invention discloses a method for optimizing a disaggregated model by adopting genetic algorithm. The method comprises the following steps of: 1, acquiring a training sample, wherein the acquiring process includes signal acquisition, characteristic extraction and sample acquisition; 2, selecting kernel function: radial basic function is selected as the kernel function of a disaggregated model needing to be established, and the disaggregated model is a support vector machine model; and 3, determining penalty parameter C and kernel parameter gamma: a genetic algorithm is adopted to optimize the penalty parameter C of the disaggregated model needing to be established and the kernel parameter gamma of the selected radial basic function and the optimization process includes population initialization, calculation on the fitness value of each individual in the initialized population, selection operation, interlace operation and variation operation, calculation on the fitness value of each individual in the offspring, selection operation and judgment on whether the termination condition is met. The method is reasonable in design, simple and convenient in operation, convenient to realize and good in use effect and high in practical value; the classification precision of the obtained disaggregated model is high, the training speed is high and the number of support vectors is less.

The invention discloses a passing device control method based on bluetooth distance sensing and a control system thereof. The method comprises the following steps: an authorization electronic key is obtained by an intelligent terminal, the intelligent terminal automatically receives a first signal emitted by a passing device, when a practical distance is shorter than or equal to a first distance, the intelligent terminal automatically sent the authorization electronic key to the passing device, the passing device performs authentication though the authorization electronic key, a lock is opened by the passing device after the authentication is successful; and the system comprises a cloud platform for Internet of Things, an intelligent terminal and the passing device by integrating beacon equipment. The unlocking operation is realized through the authorization of the passing device in indoor complex environment, automatic induction and direct passing without stay can be realized when closing to the passing device, a traditional physical card is substituted, operation for opening cellphone application is omitted for users, so that, the user experience and on-site management efficiency are greatly increased.

The invention discloses a path planning method and system for an unmanned vehicle. The method comprises the following steps: randomly generating a plurality of paths from a starting point to a targetpoint, wherein the paths are obtained by connecting a plurality of path points; encoding the path by using the serial numbers of the path points through which the path sequentially passes from the starting point to the target point; dividing paths with the same number of path points into the same sub-population; performing parallel genetic operation on each sub-population; selecting the sub-population with the optimal moderate value from the sub-populations after genetic manipulation, and recording the sub-population as the optimal sub-population; and performing genetic manipulation on each individual in the optimal sub-population, determining an optimal path in combination with the fitness value of each individual in the optimal sub-population, wherein the fitness function, the crossoverprobability function and the mutation probability function in the method are all adaptive functions. According to the method, the algorithm can be prevented from falling into local optimum, the algorithm performance is further improved, the adaptability is higher, and the use experience of vehicle passengers is effectively improved.

The invention relates to a TSP problem path planning method. The method comprises the following steps: initializing; reading the position and calculating the distance; initializing a population through a greedy algorithm; replacing the worst individuals with randomly generated individuals; calculating the fitness; selecting; crossing; performing variation; randomly performing simulated annealing on the plurality of individuals; calculating the fitness; giving the contemporary optimal solution and the variant solution thereof to the first individual and the second individual respectively; and iterating until the termination condition is met. The population generated by the greedy algorithm has randomness and high quality, and optimization can be accelerated. A plurality of worst individualsare replaced by randomly generated individuals, so that the influence of differential solutions is reduced, and precocity is avoided. Some better solutions can be found through simulated annealing, and precocity and local optimization are avoided. The storage of the optimal solution and the variant solution of the optimal solution retains excellent information and increases population diversity.According to the invention, a shortest access path can be effectively and quickly planned, so that the method is an effective method capable of providing path planning for the TSP problem.

The invention discloses an RBF (Radial Basis Function) neural network-based coal and gas outburst prediction method. The method comprises the steps of 1, performing dimensionality reduction on feature data of coal and gas outburst, and performing normalization processing on the feature data subjected to the dimensionality reduction to obtain normalized feature data; 2, clustering the normalized feature data by using a K-mean algorithm and calculating the center of an RBF; 3, training an RBF neural network on the normalized feature data, and introducing an optimal expansion factor and an optimal weight when the number of neurons in a hidden layer is determined by an adaptive differential evolution algorithm; 4, increasing the number of the neurons in the hidden layer, and repeating the steps 2-3 to obtain global optimal parameters of the RBF neural network, thereby determining a prediction model of the RBF neural network; and 5, predicting test data by using the prediction model of the RBF neural network. According to the method, the optimal parameter adaptive problem of the RBF neural network can be solved, so that the coal and gas outburst can be quickly and accurately predicted.

The invention discloses an intrusion detection method based on a parallel multi-artificial bee colony algorithm and a support vector machine. Firstly, an original artificial bee colony algorithm is redesigned, including: design of a nectar source coding scheme, initial design of a population, construction of a fitness evaluation function, a neighborhood search method of a nectar source and calculation of the probability of recruiting observed bees, and in such a way, the problems such as precocious algorithm, poor diversity of solutions, easy fall into local optimum, slow later convergence speed and the like are overcome. Secondly, a multi-bee colony information exchange and collaboration mechanism is designed, and by use of a parallel execution technology, a dual-loop multi-bee colony collaborative optimization model is proposed for synchronous optimization of characteristics and support vector machine model parameters. Then, based on the collaborative optimization model, an intrusion detection method and model based on a parallel multi-artificial bee colony algorithm and a support vector machine are given.

The invention relates to a chaos genetic BP neural network image segmentation method based on Arnold transformation. The method comprises BP neural network optimization through adoption of a chaos genetic algorithm and image segmentation by utilizing a trained neural network. The specific process of the chaos genetic algorithm is as follows: (1) a swarm is initialized, two swarms x, y are generated through chaos mapping, the small swarm x is taken as an initial swarm, and the large swarm y is standby; (2) individual fitness values in the initial swarm x are calculated; and individuals the number of which is set after the individual fitness values in the initial swarm x are replaced with individuals in the large swarm y, and fitness values of the individuals after replacement are calculated; and (3) according to the calculated individual fitness values, selection, intersection and chaotic variation operation are performed on the individuals in the initial swarm x until the fact that the maximum evolution frequency is reached or the maximum fitness is not changed, and then the algorithm stops. Ergodicity of a swarm evolution process can be effectively ensured, neural network training process is speeded up, and an image segmentation effect is enhanced.

The invention relates to a three-particle cooperative optimization method applied to speaker recognition, which is an optimization design method for a vector quantization speaker codebook model and comprises the following steps: dividing an initial population of six particles into two sub-populations, wherein each particle represents a codebook and each sub-population consists of three particles named three-particle; adopting different particle update parameters by two three-particles so as to realize global exploration and local exploration; executing updating of speed and position of PSO and operation of an LBG algorithm with an iteration number of three by the particles in each iteration; when the iterations reach mixing update times, mixing and dividing the particles into new three-particles to realize global information exchange and cooperative evolution; when a condition of the maximum iteration number of the initial population is met, selecting two particles from the two three-particles to continue searching until the maximum iteration number of the elite particles is achieved; and using the optimized one as the speaker codebook model. The three-particle cooperative optimization method applied to the speaker recognition better solves the problem that an initial codebook influences the optimization result, and obviously improves the performance of short-voice speaker recognition.

The invention discloses a path planning method for multiple unmanned aerial vehicles to arrive at a designated place simultaneously in a three-dimensional environment, and the method comprises the steps of building a population, wherein a chromosomes represents a comprehensive flight path, and the comprehensive flight path is formed by the series connection of the flight paths of all unmanned aerial vehicles; setting the fitness of the genetic algorithm, and comprehensively evaluating the set violation degree W of the maneuverability constraint of the unmanned aerial vehicle by the total tracklength L of all unmanned aerial vehicles, the difference degree T of arrival time of different unmanned aerial vehicles and the track; and optimizing the chromosome by adopting the genetic algorithm,and obtaining an optimal chromosome after the iteration of the genetic algorithm, and decomposing the optimal chromosome to obtain the track of each unmanned aerial vehicle. By using the method, thetechnical purposes of gathering multiple unmanned aerial vehicles and reaching the task points at the same time can be achieved, and meanwhile time errors are reduced as much as possible.

The invention discloses an oil-immersed transformer fault diagnosis method based on a self-adaptive genetic algorithm. The oil-immersed transformer fault diagnosis method comprises a subsystem execution step, a subsystem evaluation step and a rule discovery and optimization step. By analyzing and building the structure of a model system, a classifier superior to the existing classifier can be generated, so as to improve the overall system performance. On the basis of credit allocation, an excellent classifier rule is searched to prevent premature of the algorithm and obviate the trap of localoptimization. Due to the global searching characteristics of the genetic algorithm, the last fault rule searched by the system is the optimal rule, so that the transformer fault diagnosis method can achieve high accuracy in practical application. In addition, the method has simple and clear rule, is definite in determining the fault mechanism, and can provide great convenience for transformer equipment maintenance.