37results about How to "With parallelism" patented technology

The invention belongs to the technical field of intelligent robots and control and particularly relates to a mobile robot path planning method based on a variable-dimension particle swarm membrane algorithm. The mobile robot path planning method based on the variable-dimension particle swarm membrane algorithm includes the main steps of performing environment modeling, inputting path parameters, setting a fitness function, initializing parent particles and child particles of an effective path, classifying and re-classifying the path particles, evaluating the fitness function, updating the positions of d-dimension particles, judging whether the current particles have invalid dimensions or not, correcting the particles with invalid dimensions, removing redundant dimension information and the like. According to the mobile robot path planning method based on the variable-dimension particle swarm membrane algorithm, the optimum path suitable for a robot to walk along can be fast planned through global known or local position information of a complex environment and state parameters of an undetermined mobile robot.

The invention provides aircraft route planning method and device on the basis of a pigeon colony algorithm and belongs to the technical field of unmanned aerial vehicle route planning and multi-attribute decision making. The aircraft route planning method includes the steps of: 1) firstly establishing a trace prediction model including uncertainty, and determining a to-be-optimized route in a specified region; 2) with the pigeon colony algorithm, performing iteration to obtain an optimized route through operations with a map and a compass and landmark operation, and finally outputting parameters of the acquired optimized route. Correspondingly, the invention provides an aircraft route planning device on the basis of pigeon colony algorithm, which includes: an acquirement module, a construction module, a determination module, an optimizing module and a memory module. The method and device can deduct and calculate the trace prediction model, so that a route acquired by the model has good stability and has robustness and availability; in addition, through the pigeon colony intelligent optimization method, a problem of complex continuous optimization is solved. The calculation and searching process has the characters of parallelism, availability and high robustness.

The invention discloses a method for distributing thrust of multiple thrusters of an ocean engineering equipment dynamic positioning system. The method comprises the steps of establishing a multi-thruster thrust distribution mathematical model according to the application requirements of dynamic positioning in an actual project, wherein the method for establishing the multi-thruster thrust distribution mathematical model comprises the steps of obtaining an objective function and obtaining the constraint conditions of the characteristics of the thrusters; conducting optimization solving on the established multi-thruster thrust distribution mathematical model according to a group area searching algorithm. According to the method for distributing the thrust of the multiple thrusters of the ocean engineering equipment dynamic positioning system, due to the adoption of the group area searching algorithm, the problem of limitation of the optimization solving of the nonconvexity problem of the thrust distribution mathematical model of the dynamic positioning system is solved, and the solving range of the optimized thrust distribution is effectively widened; due to the adoption of the regional limitation type searching strategy, the optimized solving of the thrust distribution is achieved; due to the fact that the method has the advantages of being good in robustness and parallelism, simple and effective, the method is extremely beneficial to solving of optimized thrust distribution of the dynamic positioning system and can be applied to actual projects.

The invention discloses an ant colony optimization processing method of large-scale multi-target intelligent moving route selection; after data of NTSP target logistics delivery addresses, distances between every two addresses, and M price of cost for passing through each route are obtained, a route planning unit is solved by ant colony optimization technology so as to obtain a specific walking route for intelligent mobile-agent delivery, and the route is outputted to an executive mechanism for realization. When the method is used to solve the problem of large-scale multi-target intelligent moving route selection, the invention has good optimization performance, and has the advantages of parallelism, self-organization, strong robustness, and the like, and the obtained solutions are large in quantity, high in quality, and have strong approximation capability to the real Pareto solution set; the obtained solution set has uniform distribution; the calculation speed is high. The inventioncan be used in intelligent processing units of route planning systems in fields of logistics distribution, intelligent traffic, internet, robots, etc.

The present invention discloses a QIGA(quantum inspired genetic algorithm)-based discretized film system design method of a broadband EUV (extreme ultraviolet) multilayer film. The method comprises the following steps that: 1) the initial parameter values of the design method are inputted; and 2) quantum encoding is performed on the film layer deposition time of the multilayer film, so that an initial quantum group Q (t) can be generated; (3) observation states R (t) of quantum superposition states are constructed according to the quantum probability amplitude of the individuals in the initial quantum group Q (t); (4) whether a termination condition is satisfied is judged, if the termination condition is satisfied, the method stops and outputs an optimal solution, otherwise, the method continues; (5) the fitness of individuals in the R (t), which characterizes the deposition time of a discretized film system, is calculated, individuals corresponding the optimal deposition time of thefilm system are saved; (6) a quantum rotation gate is updated, and the updated quantum rotation gate is adopted to update the Q (t); (7) the Q (t) is observed, R (t) is generated and is evaluated, optimal individuals are retained; (8) the Q (t) is updated through a quantum NOT gate; and (9) an elite retention strategy is adopted, and the method shifts to step (4). With the method of the inventionadopted, problems such as low solution efficiency and high requirement for thickness control precision during film coating of an existing EUV multilayer film design method can be solved.

A table saw fence with an adjustment mechanism is provided. A fence assembly configured to guide a workpiece with respect to a blade of a saw device includes a fence that is supported by rails for sliding movement relative to a work surface. Opposed ends of the fence are supported on the rails via rail engagement members. The fence assembly includes a fence angle adjustment mechanism associated with a housing of one of the rail engagement members. The fence angle adjustment mechanism includes biasing elements working in conjunction with an adjustment screw to adjust the position of the fence with respect to parallelism to the blade.

The present invention discloses a QIGA(quantum inspired genetic algorithm)-based discretized film system design method of a broadband EUV (extreme ultraviolet) multilayer film. The method comprises the following steps that: 1) the initial parameter values of the design method are inputted; and 2) quantum encoding is performed on the film layer deposition time of the multilayer film, so that an initial quantum group Q (t) can be generated; (3) observation states R (t) of quantum superposition states are constructed according to the quantum probability amplitude of the individuals in the initial quantum group Q (t); (4) whether a termination condition is satisfied is judged, if the termination condition is satisfied, the method stops and outputs an optimal solution, otherwise, the method continues; (5) the fitness of individuals in the R (t), which characterizes the deposition time of a discretized film system, is calculated, individuals corresponding the optimal deposition time of thefilm system are saved; (6) a quantum rotation gate is updated, and the updated quantum rotation gate is adopted to update the Q (t); (7) the Q (t) is observed, R (t) is generated and is evaluated, optimal individuals are retained; (8) the Q (t) is updated through a quantum NOT gate; and (9) an elite retention strategy is adopted, and the method shifts to step (4). With the method of the inventionadopted, problems such as low solution efficiency and high requirement for thickness control precision during film coating of an existing EUV multilayer film design method can be solved.

The invention relates to a method for optimizing a beer production formula. A conventional production formula is determined by a method which combines human experience and tests. The method comprises the following steps of: obtaining main raw materials in a beer formula; establishing a comprehensive production performance index estimation model according to the parameters of the raw materials, wherein main considered comprehensive performance indexes comprise saccharifying power, wort total nitrogen, wort alpha amino nitrogen, wort beta glucan and the thickness of a distilled grain layer of afilter tank; and optimizing a beer formula model by using a variable-metric ant colony optimization method and finally obtaining a production formula with the lowest cost. The method of the inventionhas the advantages of openness, robustness, parallelism and global convergence.

The invention relates to an integrated optimization method for production planning and scheduling. At present, the solution to production planning and scheduling problems is mainly the traditional optimization method. The optimization result is difficult to be the optimal solution, and it is likely that it cannot be realized in the process, resulting in the failure of the expected production target and the failure of resource allocation. . The steps of the invention are: obtaining the specific data of the production planning layer and the dispatching layer; respectively establishing the planning layer expense model and the dispatching layer expense model according to the specific data; using the improved collaborative optimization method to optimize the planning and dispatching expense models, and finally obtaining the lowest cost production plan. The present invention aims at some difficult problems in production planning and scheduling optimization, and proposes an optimization method with strong global optimization ability. The optimization method has openness, robustness, parallelism, global convergence and a mathematical form for problems No special requirements and other characteristics.

The present invention provides an intelligent substation equipment state early warning method based on ICA algorithm, which includes the following steps: (1) information data collection; (2) information data transmission; (3) information data processing: the control center in the control room reads in real time Collect the data set sent by the collector, and use the ICA algorithm to clean up the collected data set, data integration and conversion, data dimensionality reduction, data discretization, etc.; (4) Early warning diagnosis: the control center of the station control room uses Bayeux The Sri Lankan algorithm analyzes the data processed in step (3), and through the methods of data classification, association and prediction, trains and models historical data and integrates the existing experience of staff to obtain strategies, and outputs corresponding early warning signals. The invention can simply and quickly extract effective data information, and provide more accurate early warning for the operation status of the primary / secondary equipment of the substation, so that the monitoring and early warning of the substation is more timely and accurate. The invention is suitable for risk assessment and early warning of all communication equipment in the power system.

A method and device for mapping lookup between a cache and a main memory; an optical comparator realizes a logical operation relationship xor by utilizing the above relation of a micro ring size in an optical field instead of negating a control signal A and an input signal B in an electric field; because of the parallelism of light, the optical comparator is capable of parallelism calculation and has small delay, and can be used to search the cache for the position of the data required by the CPU; and compared with a logic circuit implementing fully-associative mapping in the prior art, the logic circuit of the fully-associative mapping implemented by the optical comparator has a simpler structure, and the comparison delay does not increase with the increase of the number of compared bits.

The present application provides a data-driven micro-fault diagnosis method and device, wherein the method includes: preprocessing the acquired test samples of the test aircraft to obtain the feature data set of the test aircraft, and the feature data The collection is input into the pre-trained fault diagnosis model to obtain the fault probability of various fault types of the aircraft to be tested. The fault diagnosis model includes an input layer, a time convolutional network layer and a Softmax classification layer. The maximum failure probability is selected in the failure probability, and the failure type corresponding to the maximum failure probability is determined as the target failure type of the aircraft under test. In this way, this application realizes the capture of long-term information through the introduction of time convolution network, which has the characteristics of parallelism and low memory, and helps to improve the speed of fault diagnosis of hypersonic aircraft.

The invention discloses an identity authentication method and an authentication system based on electroencephalogram features, wherein the method includes five steps of model training, password setting, alpha wave testing, electroencephalogram feature extraction and classification, and model updating. Among them, the brain wave feature extraction and classification method is composed of three parts: layer-by-layer pre-training, network fine-tuning, and classification. The present invention first utilizes the characteristic that α waves fluctuate greatly when a person tells a lie to carry out the first re-authentication on the tester, improves the existing method at the same time, establishes a feature extraction method based on a deep belief network, and uses the immune leapfrog algorithm to It is optimized to perform feature extraction and classification on the tester's brain waves when observing the password pattern, so as to perform the second verification. The two-layer verification can not only ensure the safety of users, but the improved deep belief network can also improve the existing recognition accuracy, reduce the error rate, avoid a large number of false alarms, and bring a good experience to users.

The invention belongs to the field of intelligent robot and control technology, and in particular relates to a path planning method for a mobile robot based on a variable-dimensional particle swarm algorithm. The steps of the present invention mainly include: modeling the environment, inputting path parameters, setting the fitness function, initializing effective path parent particles and sub-particles, classifying and reclassifying path particles, evaluating the fitness function, updating the position of the d-dimensional particle, and judging Whether the current particles have invalid dimensions, correct the particles with invalid dimensions and remove redundant dimension information, etc. The invention can quickly plan the optimal path suitable for walking of this type of robot by using the globally known information of the complex environment or the information of the local position of the complex environment and the state parameters of the undetermined mobile robot.

The invention discloses a single-band matrix type DEM data progressive refined transmission method and system. The method comprises: segmenting DEM data, performing singular value decomposition, analyzing sub-slices, extracting effective singular vectors and singular values layer by layer, then performing compression, performing transmission and storage from a sending end to a receiving end layerby layer, and performing recovery at the receiving end. The system comprises a segmentation module, a decomposition module, an analysis and extraction module, a compression module, a transmission module and a receiving module. According to the method and the system, refined transmission under the control of precision parameters can be realized according to the requirements of actual precision, andthe specific requirements of numerical DEM data in the aspect of precision are met. After the DEM data fragments are compressed, the DEM data fragments do not need to be directly recovered to be in amatrix form at the receiving end and can continuously exist in a vector form, and the compression effect is reserved. When the data is recovered, the used calculation is basic calculation, so that the calculation burden is not increased, and the method has parallelism.

The invention discloses a Gamma adjusting and recalibrating method and a Gamma adjusting and recalibrating device of an OLED module based on a multi-objective relational mathematic model. The method comprises the following steps: (1) establishing an objective function; and (2) establishing a constraint condition, wherein R, G and B register values of all the binding points of the module are in positive linear or negative linear dependence, the multi-objective relation optimization is converted into single-objective optimization, the double-objective optimization is converted into single-objective optimization, and finally, the R, G and B register values corresponding to F are solved and are output to R, G and B registers corresponding to the gray scale binding point m in a module chip according to setting. According to the technical scheme, the diversified mode of gamma adjustment is supported, the features of platformization and parallelism are realized, and the method and the systemhave the advantages of efficiency and stability in adjusting and good gradual change effect. The experimental result shows that with the provided mathematic model, the complexity can be effectively reduced, the conventional algorithm is converted into algorithm and calculation, the adjusting and recalibrating efficiency is very ideal, and the problems including that the module is poor in consistency are solved.