1222 results about "Simulated annealing" patented technology

A method for the estimation of the state variables of nonlinear systems with exogenous inputs is based on improved extended Kalman filtering (EKF) type techniques. The method uses a discrete-time model, based on a set of nonlinear differential equations describing the system, that is linearized about the current operating point. The time update for the state estimates is performed using integration methods. Integration, which is accomplished through the use of matrix exponential techniques, avoids the inaccuracies of approximate numerical integration techniques. The updated state estimates and corresponding covariance estimates use a common time-varying system model for ensuring stability of both estimates. Other improvements include the use of QR factorization for both time and measurement updating of square-root covariance and Kalman gain matrices and the use of simulated annealing for ensuring that globally optimal estimates are produced.

Methods and systems are presented for the automatic optimal placement text or symbol labels corresponding to graphical objects on maps. Using a map server and a thin, typically mobile client, maps are dynamically drawn and displayed at the client. Optimized labeling of map objects is achieved by dividing the task into pipelined subtasks, some being performed off-line, and others being performed in real time, responsively to changing client requests, and changes in the map viewport. Optimization of candidate label positions is accomplished using simulated annealing.

A method for producing aperiodic gratings and waveguides with aperiodic gratings uses a simulated annealing process that starts with a random configuration of grating elements and iteratively computes a spectral response from a Fourier transform of the configuration of grating elements obtained in successive iterations. A cost function is computed as a convergence criterion. The aperiodic grating can be used, for example, as a filter in WDM applications.

A method includes: a) producing initial scheduling plans based on requests related to tasks to be performed within a time period by a remote sensing satellite; wherein each initial scheduling plan schedules respective tasks, which do not conflict with each other in time and in using satellite resources; and each task is scheduled in at least one of the initial scheduling plans; b) applying a genetic-algorithm-based processing to the initial scheduling plans to produce a genetic-algorithm-based scheduling plan which 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 is scheduled than in the genetic-algorithm-based scheduling plan.

The present invention relates to a method and a system for providing an estimate of the state of a contact. The method includes the steps of sensing the state of the contact; generating signals representative of the state of the contact; and processing the signals using a random search procedure to arrive at an estimate of the state of the contact. The random search procedure may employ the simulated annealing-based algorithm methodology or the genetic-based algorithm methodologies. The system includes sensors for sensing the state of the contact and a pre-programmed computer for generating the desired contact state estimates.

A system and method for making computer-generated maps includes a different scale factor for each road in a route. The scale factors are used to optimize the route map against a target function that considers factors such as the number of false intersections in the route and the number of roads falling below a minimum length threshold. A refinement technique such as simulated annealing is used to find a solution to the target function. Each road in the scaled map is rendered to provide a finished product having the appearance of a hand-drawn map. The finished product includes context roads that intersect the main route but are not part of the main route. Furthermore, the hand-drawn map is optimized to the characteristics of the viewport used to visualize the map.

An iterative method enabling a request plan to be established for an observation satellite. A plan consists in a succession of requests which are associated with pluralities of opportunities for satisfying said requests. The plan must also comply with a plurality of constraints. Each iteration k of the iterative method is made up of the following steps:new opportunity is selected;a provisional plan is derived from the preceding plan k-1 as calculated in-the preceding iteration, and from the new opportunity;provisional plan is verified for compliance with said plurality of constraints;the quality of said provisional plan is evaluated; andit is determined whether the provisional plan should be confirmed as plan k as a function of the quality of said provisional plan and of the quality of said preceding plan k-1.In the method, it is decided whether or not to confirm the provisional plan by a probabilistic metaheuristic of the simulated annealing type, and by the rule for constructing the provisional plan as a function of the newly selected opportunity.

The invention relates to an AGV optimization scheduling method based on mixed particle swarm optimization. First of all, a mathematics model is abstracted from the work process of an AGV, and an object function of a scheduling scheme is determined, and secondly, the model is solved by use of the mixed particle swarm optimization based on a genetic algorithm, a stimulated annealing algorithm and ant colony optimization, and an optimization scheduling scheme is generated. According to the invention, a contrast analysis is made between the mixed particle swarm optimization and standard particle swarm optimization through examples, and the variation operation of the mixed optimization employs an ant colony optimization thinking mode, ensures intersection of individual best and group best in an intersection operation process, ensures the feasibility of the mixed particle swarm optimization and has the validity for solving large-scale scheduling tasks.

The invention discloses a method for the calculating ice coating thickness of a transmission line by utilizing video image processing technique, belonging to the technical field of digital video image processing or online monitoring of the transmission line. The method takes digital image intercepted from a video flowing of the transmission line which is transmitted into a surveillance center as the object of study and processes the image by methods of gradation of image, two-dimension image segmentation, filtration, regional mark and the like in advance. In the process of pretreatment, the image is segmented by adopting a new two-dimension varimax based on simulated annealing genetic algorithm, and the image of the transmission line is marked by adopting eight connected region marking method. Finally, by the contrast and calculation of the pixels of the images which are obtained before and after the ice coating of all the transmission leads, an average value is obtained, and the ice coating thickness is further calculated. When the ice coating thickness of any of the transmission leads exceeds the prescriptive safety range, alarm is given, so that deicing measure is adopted in time, thus providing security for the safe running of an electric power system.

The invention discloses an optimization method for solving an omni-channel logistics distribution problem. In a first stage, a Lagrange relaxation technology is used to solve a LAP (Location-allocation Problem) problem. In a second stage, adaptive large-scale neighborhood search is used for solving a multi-vehicle-type vehicle path problem, a feasible solution can be searched in a large range in a solution space through one group of simple destruction and reconstruction algorithms, and a situation of falling into local optimum can be effectively avoided. Meanwhile, a simulated annealing acceptance criteria realized in the adaptive large-scale neighborhood search can guarantee the quality of a solution and the convergence of the algorithm, a result is output after the algorithm is executed for an appointed iteration number, a time constraint requirement can be met, and a good vehicle distribution scheme is solved for enterprises in limiting time. The simpleness of the Lagrange relaxation technology is combined with the efficiency of the adaptive large-scale neighborhood search, so that the integral solving efficiency of a mixed algorithm is high, and the problem of omni-channel logistics distribution can be effectively solved.

The invention discloses an ultra-high-frequency partial discharge signal identification method for gas insulated switchgear (GIS). The method comprises a model training process and a defect identification process, and specifically comprises the following steps of: reprocessing partial discharge signals of the GIS; extracting discharge characteristic parameters such as average discharge amplitude, discharge amplitude standard deviation, discharge phase distribution, discharge polarity, discharge time interval mean, discharge time interval standard deviation; optimizing a weight and a threshold value of a back propagation (BP) neural network by utilizing a genetic simulated annealing tool; training samples by utilizing a BP neural network tool; establishing a corresponding gas statistic algorithm (GSA)-BP model; preprocessing the partial discharge signals to be identified of the GIS; and identifying the samples to be measured in a classified way according to the GSA-BP model after extracting the corresponding characteristic parameters. By the method, the efficiency and the accuracy of partial discharge fault diagnosis of the GIS are improved effectively; and the method is critical to evaluate the insulation state of the GIS and formulate a reasonable maintenance strategy.

The invention discloses a multi-mode intelligent configurable method for implementing optimization of a wireless network. Firstly, the network optimization demand and object are raised according to the user's own network and then the demand and object are analyzed and used as a basis for determining the network optimization mode and establishing a simple model of wireless network, and the network optimization plan and configuration network optimization parameters are prepared. Subsequently, the network is optimized by the cost functions (such as capacity, coverage and network quality) at five different angles (antenna, power, address, frequency and load balance) in combination with different optimization algorithms. The optimization algorithms include three heuristic algorithms (simulated annealing, particle bee colony and ant colony) and the conventional greedy algorithm, and increase the network performance to the ideal level. Finally, the optimization results of the wireless network are sorted to provide a network optimization plan to the user for reference and reality basis. The method is intelligent and configurable, can meet reasonable requirements of users, can also achieve the purpose of optimizing 2G/3G dual-network coexistence, has strong flexibility, and provides a good reference for the present network performance.

Data stored in non-volatile storage is decoded using iterative probabilistic decoding. An error correcting code such as a low density parity check code may be used. In one approach, initial reliability metrics, such as logarithmic likelihood ratios, are used in decoding sensed states of a set of non-volatile storage elements. The decoding attempts to converge by adjusting the reliability metrics for bits in code words which represent the sensed state. Simulated annealing using an adjustable temperature parameter based on a level of error in the data read from the system can be performed to assist the iterative decoding process. The simulated annealing can introduce randomness, as noise for example, into the metric based decoding process. Moreover, knowledge of the device characteristics can be used to guide the simulated annealing process rather than introducing absolute randomness. The introduction of a degree of randomness adds flexibility during the iterative decoding that permits possible faster convergence times and convergence in situations where data may otherwise be uncorrectable.

A method that facilitates identification of features in a scene enables enhanced detail to be displayed. One embodiment incorporates a multi-grid Gibbs-based algorithm to partition sets of endmembers of an image into smaller sets upon which spatial consistency is imposed. At each site within an imaged scene, not necessarily a site entirely within one of the small sets, the parameters of a linear mixture model are estimated based on the small set of endmembers in the partition associated with that site. An, enhanced spectral mixing process (SMP) is then computed. One embodiment employs a simulated annealing method of partitioning hyperspectral imagery, initialized by a supervised classification method to provide spatially smooth class labeling for terrain mapping applications. One estimate of the model is a Gibbs distribution defined over a symmetric spatial neighborhood system that is based on an energy function characterizing spectral disparities in both Euclidean distance and spectral angle.

The invention relates to a method for predicting dynamic risk and vulnerability at fine scale and belongs to the scientific field of global information. The method is mainly characterized in that an optimized Bayesian network is searched from multi-source heterogeneous spatiotemporal data on the basis of a grid format with certain resolution at fine scale; domain knowledge is combined to improve the network; therefore, the uncertain estimation of disaster risk and the vulnerability, namely probability estimation, is carried out. In the method, a nuclear density method is put forward to train a sample according to a sample derivative grid; an optimized discretization method is put forward to discretize continuous variables so as to provide discrete state space input for the network; a simulated annealing optimization algorithm is adopted to search an optimized network structure; and a method of accurate reasoning combined with approximate reasoning to predict the probabilities of risk and the vulnerability is adopted. The method provided by the invention can position the positions of the disaster risk and the vulnerability in real time at the fine spatial scale, estimate the spatial distribution of the risk probability and has important theoretical significance and practical value for improving the effects on the reduction and relief of disaster and building an intelligent public emergency pre-warning system by the state.

The invention relates to a dynamic adjustment-based express delivery distribution optimization method. The method includes the following steps that: express delivery dynamic information is obtained in real time, wherein the express delivery dynamic information contains the locating information of express delivery vehicles, goods delivery point information, goods taking point information and real-time traffic information; with minimum total distribution costs adopted as an objective function, a mixed integer programming model is adopted to construct a current distribution path according to the express delivery dynamic information, wherein the minimum total distribution costs contain the transportation costs of the vehicles, the fixed costs of the enabling of the vehicles, additional costs caused by the exceeding of maximum travel and penalty costs caused by the violation of time window constraints; and a simulated annealing hybrid genetic algorithm is adopted to optimize the current distribution path. According to the dynamic adjustment-based express delivery distribution optimization method of the invention, the current distribution path is optimized and dynamically adjusted according to the express delivery dynamic information, and therefore, the speed of express delivery distribution is improved, customer demands can be quickly responded, the distribution costs of an enterprise can be decreased, the utilization rate of the vehicles can be improved, urban traffic congestion can be reduced, and the operation efficiency of express delivery business can be benefitted.

The invention discloses a pH (potential of hydrogen) value predicting method of a BP (back propagation) neutral network based on a simulated annealing (SA) algorithm optimization. The pH value predicting method comprises the following steps: step one, selecting a sample according to a sample selecting strategy and inputting; step two, according to the BP theorem, determining the structure of the BP neutral network; step three, according to a network training strategy, applying the simulated annealing algorithm to optimize the BP network weight parameter; training the BP network by using the input sample, and determining the optimal weight and optimal hidden node number of the BP network; step four, according to the well trained BP neutral network, structuring a predicting model of the pH value. The pH value predicting method overcomes the randomness of the BP network in terms of weight selection, improves the rate of convergence and study ability of the BP neutral network. Besides, the method optimizes the selection of the training sample and the network hidden neutral element number, and improves the generalization ability of the BP neutral network. Moreover, the pH value predicting method is high in predicting accuracy of pH value and good in nonlinear fitting ability.

The invention discloses a PID controller parameter setting algorithm based on an improved PSO (particle swarm optimization) algorithm, and the algorithm comprises the following steps: 1, initializing the algorithm parameters; 2, switching to an iterative loop, and carrying out the updating of the position and speed of each particle; 3, randomly searching a new position in the neighborhood of a current position; 4, calculating the adaptability difference between two positions, and judging whether to accept the new position or not through a simulated annealing mechanism when the adaptability of the new position is inferior to the adaptability of an original position but is superior to the adaptability of a global optimal position; 5, updating the global optimal position of a population, carrying out the natural selection operation, carrying out the arrangement of all particles according to the adaptability values, and employing the information of a part of better particles to replace the information of the other half particles; 6, judging whether to stop the iteration or not; 7, outputting PID controller parameters or executing step 2 again. The method can achieve the automatic setting of control parameters, irons out a defect that a conventional PSO algorithm is very liable to be caught in local optimization, achieves the complementation of the simulated annealing operation and a natural selection strategy, improves the convergence precision of the algorithm under the condition that the number of convergence times of the algorithm is guaranteed, is higher in robustness and precision, and enables the PID controller to generate a more excellent control effect.

The invention discloses a route planning method for pesticide application aided by multiple unmanned aerial vehicles. The method comprises the following steps: determining the course and spraying width of the unmanned aerial vehicles and dividing a pesticide application area into a plurality of parallel sub-areas accordingly; then determining optimization standards and constraint conditions and accordingly establishing a target function for the route of pesticide application aided by the multiple unmanned aerial vehicles; and optimizing the established target function by using an optimization method which integrates genetic algorithm and simulated annealing algorithm into one so as to eventually obtain an optimal flight route for the unmanned aerial vehicles. According to the invention, the dual optimization scheme of a combination of GA and SA is employed, and the route for coordinated flight of the multiple unmanned aerial vehicles is provided on the basis of the optimization scheme, so the technical problem that common agricultural machines cannot accurately accomplish pesticide application in agriculture is overcome.

A high-speed rail train driving method based on the section profile passenger flow includes the following steps that high-speed rail station information is obtained through a high-speed rail network operating system firstly, and a high-speed rail network is divided into a plurality of sections; the stop modes of high-speed rail trains are determined according to multiple grades; the O-D (origin-destination) demands of each section are classified according to the stop modes; the historical data about the actual O-D demands of each section are obtained, and the time varying demand of each type of O-D in each section is obtained through a linear second exponential smoothing prediction method; on the basis, the driving scheme unit of each section is made according to the sequence of the trains from the high grade to the low grade; all the driving scheme units are spliced and combined to form an initial train driving scheme; and finally, the scheme is adjusted to meet various restraint conditions and optimized through a simulation annealing algorithm, and each high-speed rail train is driven according to the optimization result. The high-speed rail train driving method based on the section profile passenger flow takes the benefits of the high-speed rail system and the passenger demands into account, and can improve the running benefits of the high-speed rail system.