440 results about "Simulating annealing" patented technology

The invention relates to a novel image-reconstruction technique which is used to view multiphase flows using electrical capacitance tomography (ECT), which is based on non-linear heuristic global optimization methods involving simulated annealing and genetic algorithms. The inventive method consists in obtaining electrical capacitance data which are measured between electrodes positioned on the outer surface of pipeline, well or tank (electrically-insulating) containing fluids. The aforementioned data are dependent on the distribution of the fluids inside the pipeline, well or tank. Moreover, the data are processed in order to reconstruct an image of the spatial distribution of the relative electrical permittivity (also known as the dielectric constant) inside the tube, well or tank, which reflects the distribution of the different phases present in the flow.

A markerless motion capture system is provided for measurements accurate enough for biomechanical, clinical, sport, entertainment, animation, game and movie, design, ergonomics, surveillance applications. The system has multiple cameras distributed around a viewing volume. The cameras allow for the creation of three-dimensional mesh representations of an object dynamically moving within the viewing volume. A model of the object that incorporates specific morphological and kinematic model information (including soft joint constraints) is then matched to the captured three-dimensional mesh representations. The matching routine aims to embed the model into each of the three-dimensional representations using (i) iterative closest point or simulated annealing algorithms and (ii) using soft joint constraints. This unique combination of routines offers a simple, time-efficient, accurate and thus more meaningful assessment of movements. The system further offers feasibility of accurately and precisely measuring three-dimensional kinematics of the dynamically moving object or human.

The invention relates to a distributed power supply-containing microgrid multi-target optimization scheduling method. For a microgrid containing various distributed power supplies, a multi-target optimization scheduling model based on operating maintenance cost, pollution discharge processing cost and comprehensive benefit cost is established, conversion of the maximal fuzzy membership degree into non-linear simple target optimization is adopted, influences of various distributed power supply characteristics on the microgrid optimization scheduling are comprehensively considered, and according to corresponding operating scheduling strategies, and a self-adaptive simulated annealing genetic algorithm is adopted to obtain the optimal scheme of economic operation of the microgrid under multiple targets. Examples show that a multi-target model can reflect the real operating condition of the microgrid better than a simple target model, and achieves better environmental benefits with small operating economy under a precondition of comprehensively considering an economic property and an environmental protection property.

The invention provides an intelligent control method and system for electric equipment, the computer equipment and a medium. The method comprises the steps of A1 obtaining the current initial openingand closing time of the electric equipment and a preset continuous value of the opening and closing time; A2 performing simulated annealing algorithm iteration on the current initial opening and closing time to obtain new opening and closing time; A3 judging whether the predicted electricity consumption cost corresponding to the new opening and closing time of the electric equipment is smaller than the initial electricity consumption cost corresponding to the current initial opening and closing time or not; A4 if yes, storing the new opening and closing time and the corresponding predicted electricity consumption cost into a memory matrix; using the new turn-on and turn-off time as the current initial turn-on and turn-off time, and executing the step A2 again until the new turn-on and turn-off time of the electric equipment is continuously stored in the memory matrix for a preset number of times; and A5 selecting the opening and closing time corresponding to the minimum expected electricity consumption cost from the memory matrix as the final opening and closing time for controlling the electric equipment.

Optimal enhancement of 3G cellular network capacity utilizes two components of learning and optimization. First, a pair of learning approaches are used to model cellular network capacity measured in terms of total number of users carried and predict breakpoints of cellular towers as a function of network traffic loading. Then, an optimization problem is formulated to maximize network capacity subject to constraints of user quality and predicted breakpoints. Among a number of alternatives, a variant of simulated annealing referred to as Block Coordinated Descent Simulated Annealing (BCDSA) is presented to solve the problem. Performance measurements show that BCDSA algorithm offers dramatically improved algorithmic success rate and the best characteristics in utility, runtime, and confidence range measures compared to other solution alternatives. Accordingly, integrated iterative method, program, and system are described aiming at maximizing the capacity of 3G cellular networks by redistributing traffic from congested cellular towers to non-congested cellular towers.

The invention provides a simulated annealing-based WSN hierarchical routing method. The method is based on a hierarchical network topology. The hierarchical structure is maintained by cluster nodes, cluster head nodes and sink nodes in a network. The method comprises the steps of firstly, dividing levels of the WSN, constructing the hierarchical structure of nodes, and then conducting data transmission. In the constructing phase of the hierarchical structure of nodes, cluster head nodes are elected and backed up according to energy. In the data transmission phase, a combination of a negotiation mechanism, a multi-hop energy multi-path routing mechanism and a simulated annealing algorithm is used, cluster nodes use the negotiation mechanism to transmit data to cluster head nodes, after data fusion is conducted by cluster head nodes, the data are transmitted to sink nodes, the multi-hop energy multi-path routing mechanism is used between cluster head nodes and sink nodes, the simulated annealing algorithm is used for seeking out an optimal path, and the data are transmitted from cluster head nodes to sink nodes along the optimal path.

The invention relates to a method for tracking multi-target vehicles by adopting the MCMC (Markov Chain Monte Carlo) algorithm, comprising the following steps: modeling the multi-target vehicle tracking process by adopting the MCMC algorithm, establishing various preselection states, traversing the preselection states by using the Metropolis-Hastings sampling based simulated annealing algorithm, and selecting the preselection state with the maximum connection probability as the optimal solution. In the method, a limit is set to the transfer equivalence for the first time, and the parameters in the data association are estimated by the experimental data regression fitting, thus the optimal vehicle motion trajectory with maximum posteriori probability significance is obtained, and the vehicles are tracked. The invention solves the problems of frequent shielding splitting of the multi-target vehicles and has the advantages of high multi-target vehicle tracking precision and good real-time performance.

The invention discloses a service request distribution method oriented to an edge computing environment. The method comprises the steps of (1) monitoring an available resource and a service request execution situation of each edge apparatus in a system in real time; (2) collecting service requests collected by all edge apparatuses in the system; (3) selecting an optimal edge server or a cloud server for all service requests by use of a GASD (Globally Aligned Spatial Distribution) algorithm; and (4) performing service request scheduling on all edge servers. Compared with the prior art, from twoaspects of distribution and scheduling of the service requests, the service response time is optimized to the utmost extent; meanwhile, a heuristic method is adopted, a temperature control mechanismin a simulated annealing method is introduced into a genetic algorithm, the convergence rate of the algorithm can be slowed down at the initial phase of the algorithm, thereby effectively avoiding local optimization; and the convergence rate is accelerated at the termination phase of the algorithm, thereby improving the efficiency of the algorithm.

The SATME method and system estimates source-to-destination traffic matrices using a simulated annealing algorithm, the traffic matrix estimation being represented as a probability distribution over the set of all possible matrices that satisfy a set of given constraints. The constraints explicitly encode information that the user knows about the network traffic as components of an objective function (a fitness function), that is then minimized using simulated annealing. With the method according to the invention, arbitrary constraints of any form can be included, and the case where there are no feasible solutions can be diagnosed by the objective function not converging to zero.

The invention discloses a control system performer fault diagnosis method based on multi-swarm cooperative chaos simulated annealing particle swarm optimization-support vector machine (MCCSAPSO-SVM). The method comprises the steps of: by means of combined noise reduction and an improved empirical mode decomposition (EMD) method, carrying out feature extraction on collected output signals of a performer; utilizing MCCSAPSO to optimize structural parameters of the support vector machine; adopting a combined Kernel function to ensure good generalization capability and learning capability of the SVM; and utilizing training data to construct a binary tree SVM, and converting a complex polytomous problem into a plurality of dichotomous problems. According to the invention, common state signals, which can be easily obtained, of the control system are processed, whether the control system performer breaks down can be effectively judged in real time according to the output of the binary tree SVM, and the fault type can be accurately determined when the control system performer breaks down. The method is used for real-time fault diagnosis of a high-precision system.

The present invention discloses a main user positioning method based on sensor and quantum intelligent computing, which is achieved based on a wireless sensor network assisting a cognitive radio network, and includes the following steps: step 1, a network deploy stage; step 2, a positioning information collecting stage; step 3, a distance measurement stage, wherein a data fusion center averages the sampled signal strength as a received signal strength RSS of an anchor node, and estimates the distance between a main user and the anchor node according to the RSS in a lognormal shadow path loss wireless broadcasting environmental model; and step 4, a positioning stage, wherein the positioning problem is converted into an optimization problem, and the optimization problem is solved by using a quantum genetic simulated annealing algorithm, thereby achieving positioning the location of the main user in a two-dimensional space. On the premise that a good positioning performance is ensured, the present invention can achieve the effect of reducing complexity of the algorithm and saving the energy consumption of the battery at the same time; and accurate location information of the main user can be obtained via the positioning method based on the quantum genetic simulated annealing algorithm.

The invention discloses a mathematical model for optimal configuration of power distribution network filtering devices. Under the condition that harmonic voltage, condenser capacity and the like meet constraint conditions, in terms of system average voltage total distortion and investment cost, a comprehensive objective function is given in a linear weighting manner, a penalty function is added to the objective function so that a constrained optimization problem is converted into a unconstrained optimization problem, and an improved simulated annealing-particle swarm optimization (namely introduction of an adaptive inertia coefficient and a memorizer) is used for solving. The model can be adapted to the random variation of a harmonic source and network parameters, can ensure that the harmonicration and average voltage total harmonic distortion of each node of the network are in the specified limits, can optimize the installation type, installation location, installation quantity, capacity parameter and the like of active filters and passive filters in a centralized manner in the whole network; and by the mathematical model, the investment cost of filtering devices in the whole network is reduced to the minimum.

The invention provides a network security situation prediction method based on an SA _ SOA _ BP neural network. The method comprises the steps of collecting network security data information as experimental data for preprocessing; determining a network structure of the BP neural network by using a trial-and-error method according to the input quantity and the output quantity in the experimental data; introducing a simulated annealing algorithm into the crowd search algorithm to obtain an improved crowd search algorithm; initializing a simulated annealing algorithm, finding an optimal individual by adopting an improved crowd search algorithm, calculating the fitness value of the individual through a fitness function, and optimizing the connection weight and threshold of the BP neural network; and substituting the test sample into the BP neural network to obtain a predicted value of the network security situation. According to the method, the simulated annealing algorithm is introduced into the crowd search algorithm, so that the problems that the crowd search algorithm is easy to fall into local optimum and slow in convergence are solved; and the BP neural network is optimized and improved by utilizing the advantages of the improved crowd search algorithm in speed and global search.

An on-load tap-changer fault identification method based on vibration signal characteristic extraction is disclosed. The method is characterized by using a simulated annealing algorithm to carry out parameter selection; and adaptively selecting a modal number K and a second penalty factor alpha according to a vibration signal to obtain an optimal parameter combination. The effect of vibration signal modal decomposition is improved, which is good for extracting more significant characteristics and is convenient for subsequent state monitoring and fault diagnosis.

The invention relates to a building and calibrating method for a construction material wireless propagation loss parameter database, which includes the following steps: firstly, building a construction material wireless propagation loss parameter database; secondly, building a three-dimensional space model for a target building; thirdly, selecting a plurality of testing points inside the building, and measuring wireless signal intensity information of the obtained testing points on site; fourthly, in virtue of the structure of the building and the information of the construction material wireless propagation loss parameter database, based on an indoor distributing system model inside the building, and through the utilization of an improved ray tracing propagation model, predicating the wireless signal intensity information of the testing points; fifthly, adjusting the construction material wireless propagation loss parameter database through the simulated annealing algorithm to obtain the minimum error of mean square of the predictive value and the measured value, so as to obtain the more accurate and perfect construction material wireless propagation loss parameter database.

The invention discloses a distribution network energy storage optimization configuration method considering whole life cycle cost. A two-stage optimization solution method is adopted to solve the problems of distribution network energy storage addressing and constant volume; from the aspects of energy storage to stabilize voltage fluctuations caused by distributed power and load, with the whole life cycle cost of energy storage as an optimization goal, a two-stage optimization model is established; in a first stage, candidate nodes are screened through a genetic algorithm and a simulated annealing algorithm, and energy storage required mounting nodes and the energy storage operation strategy and the minimum mounting capacity of each node are outputted finally; and in a second stage, the service life of the energy storage is considered based on the optimization result in the first stage, and the energy storage capacity configuration with the lowest whole life cycle cost is optimized through the genetic algorithm and the simulated annealing algorithm. The method considers the random output fluctuation of the distributed power and the whole life cycle cost of the energy storage system, and can obtain a more economical energy storage location and a capacity allocation scheme for the distribution network, and a reference is provided for the distribution network energy storage optimization configuration method.

The invention discloses a simulated annealing particle swarm algorithm based hybrid power automobile parameter optimization method. A threshold value in a hybrid power automobile control strategy is converted into a group of particles to be optimized, the automobile fuel consumption rate and emissions are utilized as an optimization objective function, the simulated annealing process is performed on the particles in a parallel mode, the new state of every particle is selectively accepted according to the Metropolis criterion in the annealing process, the local optimum is jumped out through the jumping characteristic of a simulated annealing particle swarm algorithm, and the global optimal solution is achieved through convergence finally. According to the simulated annealing particle swarm algorithm based hybrid power automobile parameter optimization method, the problems that the setting process of hybrid power automobile control parameters is based on the experience and an optimal threshold value cannot be obtained are solved, the optimal threshold value can be obtained rapidly, and the vital significance is brought to the automobile energy conservation and emissions reduction and the theory research of hybrid power automobiles.

The invention provides a lithium battery capacity online prediction method based on K-means clustering and an Elman neural network. The method comprises the following steps: firstly, determining the model of a lithium ion battery to be tested, carrying out cyclic charging and discharging experiment by utilizing a battery with the same model as the battery to be tested, recording a lithium batterydischarging time sequence, carrying out K-means clustering on the lithium battery discharging time sequence, and establishing a data model; and then, introducing a simulated annealing genetic algorithm to optimize initial weight and threshold of the Elman neural network, training the Elman neural network by using the constructed data model, and establishing a lithium ion battery actual capacity prediction system offline. When capacity prediction is carried out online, the collected actual discharging time sequence data of the lithium ion battery to be tested is input into the prediction system, and the actual capacity of the battery is predicted while the normal work of the lithium ion battery is not influenced. According to the invention, online accurate prediction of the actual capacityof the lithium ion battery can be realized.

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 configured control optimization method for high-speed aircrafts based on pneumatic nonlinearity and coupling and aims to solve the technical problem that the existing configured control optimization method for high-speed aircrafts is poor. According to the technical scheme, the method includes: firstly, defining pneumatic nonlinearity evaluation indicators and pneumatic coupling evaluation indicators; secondly determining configured control optimization target functions; and thirdly establishing a configured control optimization model. Optimization is targeted at the pneumatic nonlinearity evaluation indicators and pneumatic coupling evaluation indicators of aircrafts, and a multi-target configured control optimization algorithm in coordination of ideal point method and simulated annealing algorithm is adopted. Pneumatic nonlinearity and coupling features of the aircrafts are decreased by continuously changing the appearances of the aircrafts, so that a linear model is more similar to a nonlinear model, a decoupling model and a coupling model and the pneumatic nonlinearity and coupling features of the aircrafts are optimal under certain limitations.

The invention relates to a satellite scheduling method, processing system and software program product. The method includes: a) producing initial scheduling plans based on requests related to tasks tobe performed within a time period by a remote sensing satellite; b) applying a genetic-algorithm-based processing to the initial scheduling plans to produce a genetic-algorithm-based scheduling planwhich is for mission objectives, and complies with constraints related to the satellite resources, to the tasks, and to the time period; and c) applying a simulated-annealing-based processing to the genetic-algorithm-based scheduling plan to produce a simulated-annealing-based scheduling plan that fits the mission objectives, complies with the constraints, and in which a larger number of tasks isscheduled than in the genetic-algorithm-based scheduling plan.

The invention provides a multi-agent adversarial decision-making method based on cooperative reinforcement learning and transfer learning, and the method is characterized in that the method comprisesthe following steps: defining the state space S = {s1, s2,..., sn} of an agent; setting an action space A to be equal to {a1, a2,..., an}; setting a value function matrix of the agent reinforcement learning model; calculating a value function sequence corresponding to the current state st by using an action evaluator, and selecting a corresponding action at through an action selector based on simulated annealing and softmax strategy; meanwhile, the state of the intelligent agent is changed, and the intelligent agent is transferred to the next state st + 1. After the action at is executed, theintelligent agent obtains a reward signal rt from the environment; the loss of experience storage can be reduced through a weight sharing mode, and the adversarial decision-making efficiency is improved. Through the migration learning method based on the attenuation function, the agent can reuse previous experience with a gradually decreasing probability, and the migration learning migrates the previously trained action evaluator weight to more adversarial decision scenes, thereby improving the generalization of the learning model.

An AGV scheduling optimization model is built with goods cabinets, sorting tables and AGVs as scheduling research objects and minimum time for AGVs to complete a specified task as an optimization objective. The AGV scheduling optimization model is solved using a two-stage multi-population parallel genetic algorithm and a simulated annealing algorithm, and the algorithms are solved by means of parallel computation to get an optimal scheduling scheme of AGVs for a specified task. Populations can be prevented from converging too early. The computational efficiency is improved. The algorithms can deal with a large-scale scheduling optimization problem.

The invention discloses a photovoltaic array parameter identification method based on measured data. The method includes the steps that a feasible solution range of parameters to be acquired are determined in advance through a conventional parameter identification method; parameter identification is conducted on the measured data of a photovoltaic array through a genetic algorithm and a simulated annealing genetic algorithm. The parameter identification method is high in practicality. By compressing a region of search, identification efficiency of the genetic algorithm is effectively improved, and complexity of the algorithm is reduced; meanwhile, the measured parameters are identified by combining the genetic algorithm and the simulated annealing genetic algorithm, and therefore accuracy of identification results is effectively improved.

A nodule registration system useful for tracking lung nodules in computed tomography (CT) scans is presented. The system registers a small sphere centered on a detected nodule in one scan with another scan under a rigid transformation assumption using a fast registration scheme. The registration scheme employs very fast simulated annealing (VFSA) with constraints to maximize a tunable cross-correlation (TCC) coefficient, enabling the system to register, with minimal registration error, all nodules within their maximum diameter. The system achieves an average registration time of 10 seconds or less on a 3.06 GHz computer programmed to implement the present invention.

A methodology for optimizing the placement of network “Points of Presence” (POPs) across the carrier's entire network (i.e., a “global” solution”) utilizes carefully constructed customer clustering and simulated annealing methodology to create a cost-efficient solution. The customer base is first partitioned into a plurality of clusters such that the customers within each cluster are closer to its centroid than the centroid of any other cluster (e.g., applying the k-means clustering algorithm or any other suitable method of partitioning the customer base). A linear algorithm process is used to minimize the costs associated with the number of placement of POPs within each cluster. A simulated annealing (SA) process is then used to iterate the entire set of potential POP locations until a compact, steady-state solution is achieved (or, alternatively, until a given number of iterations has been performed). In a preferred embodiment, a number of iterations are performed at each “temperature” in the simulating annealing process to further improve the result (this iterative process referred to in the art as “intensification”).

The invention discloses a firefighting movable robot path planning method and system based on an improved artificial potential field method. The method comprises the steps that a fire point is acquired; the coordinate point of a firefighting movable robot near the fire point is acquired; a target point is set, and a corresponding relation between the target point and the firefighting movable robotis set up; environmental information is read, and resultant force corresponding to the firefighting movable robot is calculated through the improved artificial potential field method; whether the firefighting movable robot path gets into a local minimum point or not is judged, and if yes, the firefighting movable robot path gets away from the local minimum point through a simulated annealing algorithm, and then the resultant force is calculated again; if not, the calculated resultant force is sent to the corresponding firefighting movable robot, and whether the moved firefighting movable robot reaches the corresponding target point or not is judged; if the firefighting movable robot reaches the target point, fire extinguishing work is carried out; and if not, the resultant force is calculated again. According to the firefighting movable robot path planning method and system, the problem that during artificial potential field method path planning, the target cannot be reached, the firefighting movable robot gets into the local minimum point is solved, and meanwhile the firefighting movable robot avoids an obstacle and reaches the target point faster.

Described herein are systems and methods for classifying a data set using an ensemble classification technique. Classifiers are iteratively generated by applying machine learning techniques to a training data set, and training class sets are generated by classifying the elements in the training data set according to the classifiers. Objective values are computed based on the training class sets, and objective values associated with different classifiers are compared until a desired number of iterations is reached, and a final training class set is output.

The invention relates to a location method applicable to improvement of micro-earthquake location reliability. The method is characterized in that in view of shortcomings of a successive mesh generation micro-earthquake algorithm, in combination with a very fast simulated annealing algorithm, a micro-earthquake location algorithm is developed, which is based on joint very fast simulated annealing and successive mesh generation. According to the method, the very fast simulated annealing algorithm is employed to search for a highly energy-focused circular region in a three-dimensional target area, and then the successive mesh generation method is employed to search for a maximum energy-focused point in the area; thus, the problem of wrong location of the successive mesh generation method due to oversize primary mesh generation during location of a high-frequency seismic wave is effectively solved, and the location reliability is improved while the calculation efficiency is ensured.

The invention discloses an underground logistics system node grading and site selection system and method. Underground logistics nodes are classified into first-level nodes and second-level nodes; twofactors of node distance and freight volume are considered; the Euclidean distance weighted quadratic sum of a traditional fuzzy C clustering method is corrected; a genetic algorithm and a simulatedannealing algorithm are combined, a good clustering effect is obtained, and the grading of the nodes and the difference of grading attributes are considered at the same time, so that the network transportation state of the underground logistics system can be reflected more truly, and a good data mining model can be provided for subsequent research of the underground logistics network.