4623 results about "Genetic algorithm" patented technology

The present invention provides systems and methods for dynamic detection and prevention of electronic fraud and network intrusion using an integrated set of intelligent technologies. The intelligent technologies include neural networks, multi-agents, data mining, case-based reasoning, rule-based reasoning, fuzzy logic, constraint programming, and genetic algorithms. The systems and methods of the present invention involve a fraud detection and prevention model that successfully detects and prevents electronic fraud and network intrusion in real-time. The model is not sensitive to known or unknown different types of fraud or network intrusion attacks, and can be used to detect and prevent fraud and network intrusion across multiple networks and industries.

In general, the invention is directed to a technique for selection of parameter configurations for a neurostimulator using genetic algorithms. The technique may be employed by a programming device to allow a clinician to select parameter configurations, and then program an implantable neurostimulator to deliver therapy using the selected parameter configurations. In operation, the programming device executes an electrode configuration search algorithm to guide the clinician in the selection of electrode configurations. The search algorithm relies on a genetic algorithms to identify potential optimum electrode configurations within an electrode set. The genetic algorithms provide guidance in the electrode configuration selections process, interactively guiding the clinician by suggesting the configurations that are most likely to be efficacious given the results of tests already performed during an evaluation session.

To provide a region detecting method capable of setting a proper threshold independently of a photographing condition and moreover, quickly and accurately detecting a specific region, which uses an image data storing section 10 for storing a sample image 12 and a purposed image 11, a first computing means 1 for obtaining the extraction-region-identifying threshold data for a plurality of sample images including a common extraction region but having average color values different from each other in accordance with a genetic algorithm and generating a threshold table 8 for the average color values, a second computing means 2 for adaptively computing the extraction-region-identifying threshold data for the purposed image 11 in accordance with the average color value of the purposed image and the threshold table 8, and a third computing means 3 for detecting an extraction region in accordance with the threshold data computed by the second computing means 12.

A route finder means and method for finding routes to satisfy a plurality of connection requests in a communications network comprising a plurality of nodes connected by a plurality of links. A cost is assigned to each network link. Arrays of eight shortest paths of links between each pair of nodes in the network are created. Bit strings comprising for example a 3 bit binary number for each point-to-point connection request are generated. Each 3 bit number is an index to one element of the shortest path array for each connection request's source and destination nodes. The bit strings are assembled into population members which are manipulated by genetic algorithms. The fitness of the population members is evaluated by calculating the cost of traversing the routes represented by the bit strings. The route finder means and method has an ability to split traffic over multiple routes, and to handle different traffic types, eg different bit rate traffic types. The route finder means and method is generic to a plurality of different communications network types.

Disclosed are a computer-readable code, system and method for generating candidate novel concepts in one or more selected fields. The system operates to generate strings of terms composed of combinations of word and optionally, word-group terms that are descriptive of concept elements in such field(s), and uses a genetic algorithm to find one or more high fitness strings, based on the application of a fitness metric which quantifies, e.g., the number occurrence of pairs of terms in texts in a selected library of texts. The highest- score string or strings are then applied in a database search to identify one or more pairs of primary and secondary texts whose terms overlap with those of a high fitness string.

Online fraud prevention including receiving a rules set to detect fraud, mapping the rules set to a data set, mapping success data to members of the rules set, filtering the members of the rules set, and ordering members of the data set by giving priority to those members of the data set with a greater probability for being fraudulent based upon the success data of each member of the rule set in detecting fraud. Further, a receiver coupled to an application server to receive a rules set to detect fraud, and a server coupled to the application server, to map the rules set to a data set, and to map the success data to each members of the rules set. The server is used to order the various members of the data set by giving priority to those members of the data set with a greatest probability for being fraudulent.

The invention provides methods for enterprise business visibility that transform any of marketing, e-commerce and transactional from a plurality of legacy and other databases into resource description framework (RDF) syntax. This information can be time-stamped (e.g., with expiration dates) and stored in a central data store. Answers to queries are discerned by applying genetic algorithm-based search techniques to the holographic store, with the confidence levels of those answers is based in part, for example, on the time-stamps of the triples.

The invention relates to a flight path planning method based on a sparse A* algorithm and a genetic algorithm and belongs to the technical field of flight path planning of an unmanned aerial vehicle (UAV). According to the characteristics of flight path planning, the method comprises the following steps: planning an initial reference flight path by the utilization of the sparse A*search (SAS) algorithm, wherein constraint conditions are combined into algorithm search, so that useless nodes in a search space can be effectively deleted and the search time is shortened; and when emergent threat occurs during real-time flight of the UAV, performing real-time flight path planning by the utilization of the genetic algorithm and generating a flight path with local optimum or approximate optimum until the threat disappears and the UAV returns the original global optimum reference flight path and continues flying. The method provided by the invention has high real-time performance and rapidity; the searched flight path is closer to the actual UAV optimal flight path; and the method can be applied to the technical fields of robot path planning, urban vehicle path planning and the like under complex environments.

A route finder for point to multi-point connection requests in a communications network comprising a plurality of nodes connected by a plurality of links. A cost is assigned to each network link. For each connection request a set of all network nodes not included in its source node or its plurality of destination nodes are selected. An array of bits is created with an array element corresponding to a selected node element having a value of 1 if the node is steiner vertex for a steiner tree of nodes not selected, otherwise the array element has a value of 0. Each array is treated as a bit string and considered as population members which are manipulated by genetic algorithms. The fitness of the population members is evaluated by calculating the cost of traversing the routes represented by the bit strings. The method is capable of routing a plurality of multi-point connection requests, and selecting an overall optimum solution.

A genetic algorithm (GA) approach is used to adapt a wireless radio to a changing environment. A cognitive radio engine implements three algorithms; a wireless channel genetic algorithm (WCGA), a cognitive system monitor (CSM) and a wireless system genetic algorithm (WSGA). A chaotic search with controllable boundaries allows the cognitive radio engine to seek out and discover unique solutions efficiently. By being able to control the search space by limiting the number of generations, crossover rates, mutation rates, fitness evaluations, etc., the cognitive system can ensure legal and regulatory compliance as well as efficient searches. The versatility of the cognitive process can be applied to any adaptive radio. The cognitive system defines the radio chromosome, where each gene represents a radio parameter such as transmit power, frequency, modulation, etc. The adaptation process of the WSGA is performed on the chromosomes to develop new values for each gene, which is then used to adapt the radio settings.

The invention discloses a power system load modelling method, including the steps that: A, load classification based on daily active load curve; B, load modelling based on statistic synthesis method; C, load model parameter sensitivity analysis and range analysis based on simulation analysis; D, load model parameter node identification based on genetic algorithm, namely node identification is carried out by taking fitted local disturbance data as target, setting low sensitivity parameter value as statistic synthesis method surveying parameter value and adopting the genetic algorithm, so as to obtain multiple groups of load model parameters; E, load model parameter wide area check based on WAMS, namely load model parameter check is carried out by taking the multiple groups of solutions identified by node as initial value and taking accurate fitting on system wide area dynamic characteristic as target, so as to obtain unique solution capable of reflecting wide area dynamic characteristic when system is subject to disturbance. The invention solves the problems of multiple solutions and identifiability of overall measuring identifying method by load point classification and load model parameter aggregation and has popularization and application values.

The invention discloses a vision-inertia integrated SLAM (Simultaneous Localization and Mapping) method based on a genetic algorithm. The method comprises the following steps: integrating a vision navigation coordinate system with an inertia navigation coordinate system, and calibrating parameters of a binocular camera so as to solve a three-dimensional space coordinate according to an image pixel coordinate; independently calculating by inertia navigation; calculating by vision navigation; integrating vision navigation information with inertia navigation information by using an extended Kalman filter, and building a system filter model; and observing global feature point road signs by using the binocular camera by taking localization locality into account, carrying out data association on map features based on the genetic algorithm, and feeding extended state vectors back to a filter. The method is capable of carrying out long-time and high-accuracy localization; the genetic algorithm is added for improving the data association of the map, so that the simultaneous mapping accuracy is greatly improved.

A present novel and non-trivial system, module, and method for constructing a flight path used by an avionics system are disclosed. A processor receives flight plan data and object data associated with terrain and obstacles. Free cells are extracted above the objects using a recursive space decomposition technique, and a reference path is formed through traversable free space determined from the availability of free cells. In an additional embodiment, threat data associated with hostile military weaponry and significant meteorological conditions could affect the availability of free cells. A genetic algorithm applying genetic operators which include mutators is employed with aircraft kinematic constraints to refine the reference path used to form a population of best path candidates. When a best path is reached after cycling through a re-generation process of path candidates, flight path data representative of the best path is generated and provided to at least one avionics system.

A system generates optimal global set points for an environmental management system. The system comprises a system model for modeling components of a thermal plant, an objective function for modeling a parameter of the thermal plant, and an optimization engine for optimizing the parameter modeled by the objective function. The system model is coupled to an input data collector for receiving building data and weather data corresponding to a particular site. The system model includes models for thermal plant system components that may be implemented using classical models or artificial intelligence models. Classical models are those models that are implemented using linear programming, unconstrained non-linear programming, or constrained non-linear programming methodologies. The artificial intelligence models are those models that may be implemented using a fuzzy expert control system with crisp and fuzzy rules, genetic algorithms for optimization, or neural networks.

A genetic algorithm (GA) approach is used to adapt a wireless radio to a changing environment. A cognitive radio engine implements three algorithms; a wireless channel genetic algorithm (WCGA), a cognitive system monitor (CSM) and a wireless system genetic algorithm (WSGA). A chaotic search with controllable boundaries allows the cognitive radio engine to seek out and discover unique solutions efficiently. By being able to control the search space by limiting the number of generations, crossover rates, mutation rates, fitness evaluations, etc., the cognitive system can ensure legal and regulatory compliance as well as efficient searches. The versatility of the cognitive process can be applied to any adaptive radio. The cognitive system defines the radio chromosome, where each gene represents a radio parameter such as transmit power, frequency, modulation, etc. The adaptation process of the WSGA is performed on the chromosomes to develop new values for each gene, which is then used to adapt the radio settings.

The invention discloses a three-dimensional formation reconfiguration method for multiple unmanned aerial vehicles based on particle swarm optimization and genetic algorithm. The method considers the position of the unmanned aerial vehicle in the ground coordinates and the speed, track angle and course angle of the unmanned aerial vehicle when establishing a formation model, carries out subsection linear disposal of the control input of each flying unit in the unmanned aerial vehicle, replaces the approximate subsection linear control input with the continuous control input, then carries out global search by the genetic algorithm, subsequently carries out partial searching by the particle swarm optimization algorithm, on the base thereof, the particle swarm optimization is used to guide the genetic algorithm to search a global optimum solution so as to figure out the subsection linear control input. Compared with the traditional method, the method provided by the invention has good real-time performance and rapidity and can be used for solving the formation reconfiguration problem of multiple space robots under complex and dynamic environment.

The invention discloses a mixed robot dynamic path planning method which is capable of being applied on the condition that a part of environment information is known, and unknown dynamic and static obstacles exist at the same time. Aiming at the conditions, the mixed robot dynamic path planning method comprises the following steps: utilizing a genetic algorithm (GA) as a global planning method to obtain global paths, and then conducting local planning by means of an improved artificial potential field method. According to the mixed robot dynamic path planning method, in local planning, speed and acceleration information of a robot and a barrier is considered, and therefore the mixed robot dynamic path planning method has better effects on processing dynamic path planning. In addition, effects of global planning path for local planning are also considered. The mixed robot dynamic path planning method is ease to achieve, robot paths obtained are more optimized, and meanwhile good flexibility is shown in the application.

The embodiments of the present invention disclose a method and apparatus for joint optimization of multi-UAV task assignment and path planning. The method comprises: obtaining the location information of a plurality of UAVs and a plurality of target points, the dispersion of groundspeed course angle, and motion parameters of each UAV and wind field; constructing an initial population based on the location information, the dispersion of groundspeed course angle and a preset genetic algorithm; determining the flight status of each UAV and the flight time taken by each UAV to complete a path segment of the corresponding Dubins flight path based on the initial population and the motion parameters, obtaining the total time taken by all the UAVs corresponding to each chromosome to complete the task based on the flight time of the path segment; and subjecting the chromosomes in the initial population to crossover and mutation based on the genetic algorithm and, when a predetermined number of iterations is reached, selecting the optimal Dubins flight path as the joint optimization result. In the embodiments of the present invention, the UAV flight path planning problem is combined with the actual flight environment of the UAV, so that the optimal flight path obtained is superior to the solution in which the UAV speed is constant.

Systems and methods are provided for receiving order information based on an order of a customer; and determining an offer for the customer based on the order information and at least one of a genetic program and a genetic algorithm.

A wavelet-based band difference-sum ratio method reduces the computation cost of classification and feature extraction (identification) tasks. A Generalized Difference Feature Index (GDFI), computed using wavelets such as Daubechies wavelets, is employed in a method to automatically generate a large sequence of generalized band ratio images. In select embodiments of the present invention, judicious data mining of the large set of GDFI bands produces a small subset of GDFI bands suitable to identify specific Terrain Category / Classification (TERCAT) features. Other wavelets, such as Vaidyanathan, Coiflet, Beylkin, and Symmlet and the like may be employed in select embodiments. The classification and feature extraction (identification) performance of the band ratio method of the present invention is comparable to that obtained with the same or similar data sets using much more sophisticated methods such as discriminants, neural net classification, endmember Gibbs-based partitioning, and genetic algorithms.

The invention discloses a method for the fault diagnosis of wind turbines on the basis of a genetic neural network, in particular to a method for modeling the fault diagnosis of wind turbines on the basis of the genetic neural network capable of learning the operating data of the wind turbines in the history, more particularly a method for judging the probability that faults occur to a gearbox, a generator and a yaw system by reading the real-time operating data of the wind turbines online and calling the diagnosis model of the genetic neural network to carry out the analysis on the real-time data, thus judging the fault state of the wind turbines. Based on the method combining the genetic algorithm with the neural network, the invention can achieve the algorithm complementation, improve the model convergence and diagnostic capacity and ensure higher robustness; and the method capable of carrying out the online monitoring and fault diagnosis on the operating state of the wind turbines on a real-time basis to take maintenance measures as soon as possible can improve the reliability of the wind turbines and reduce the maintenance cost.

Systems and methods are provided for receiving order information based on an order of a customer; and determining an offer for the customer based on the order information and at least one of a genetic program and a genetic algorithm.

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.

A system for generation of business executables using artificial intelligence or rules distributed among multiple hardware devices, including: a memory element of a specially programmed general-purpose computer storing a first rule or an artificial intelligence (AI) program; a generating element, in a processor for the specially programmed computer, arranged to generate at an executable using at least one of the first rule or the AI program; an interface element of the specially programmed computer, arranged to receive a second rule from a first wireless communications device (WCD), or from a general-purpose computer associated with a business entity and to store the second rule in the memory element; and a modifying element, in the processor, arranged to modify the executable using the second rule and to transmit, using the interface element, the modified executable to a wireless communications network for transmission to a second WCD.

The invention relates to an 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 method and system managing radio frequency identification (RFID) systems. The system monitors the performance of systems that use RFID tags and readers. It operates at multiple levels to ensure the optimal performance of readers, tags, antennae, and the information processing systems that acquire and convey tag data. The management system may employ artificial intelligence techniques such as genetic algorithms, fuzzy logic, neural networks, Bayesian networks, support vector machines or statistical methods to develop, maintain and exploit models of RFID system behavior. By comparing the actual performance of RFIDs and related components, the management system can detect and report failures and partial failures of components. The management system may also send signals to components to enhance performance of the overall RFID system.

The invention provides an optimization device for a unitary apparatus that can obtain optimum characteristics as a combined apparatus, without losing user's selectivity and unitary apparatus's versatility. The optimization device includes an optimization process section that optimizes dynamic characteristics of the unitary apparatus using genetic algorithms and fuzzy inference, with functional characteristics of a combined apparatus as evaluation reference.