65results about How to "Avoid repeated searches" patented technology

The invention discloses a semi-supervised multi-spectral remote sensing image segmentation method based on spectral clustering; the segmentation process includes that: (1) the characteristics inputted to the multi-spectral sensing image are extracted; (2) N points without labels and M points with labels are randomly and evenly sampled from a multi-spectral sensing image with S pixel points to form a set n which is the summation of N and M, wherein M points with labels are used for creating pairing limit information Must-link and Cannot-link sets; (3) the sampled point set is analyzed through semi-supervised spectral clustering to obtain the class labels of the n (n=N+M) points; (4) the sampled n (n=N+M) points are used as the training sample to classify the rest (S-N-M) points through nearest-neighbor rule, each pixel point is assigned with a class label according to the class of the pixel point and is used as the segmentation result of the inputted image. Compared with prior art, the invention has good image segmentation effect, strong operability, improves the classification accuracy, avoids searching the optimum parameters through repeated test, has small limit on image size and is better applicable to the segmentation of multi-class multi-spectral sensing images.

The invention relates to the field of unmanned aerial vehicle algorithm calculating, in particular to a multi-unmanned aerial vehicle traversal search algorithm based on sub-region division. According to the designed multi-unmanned aerial vehicle traversal search algorithm based on sub-region division, a specific region division mode and a multi-unmanned aerial vehicle route planning and task allocating mode are given, and a practical and feasible scheme is provided for unmanned aerial vehicle traversal searching. It is verified that the algorithm is valid through a specific algorithm generating living example, no special demand is needed for the shape of a searching region, and the algorithm is suitable for multiple irregular regions and has the universality. Compared with other division algorithms, the region division algorithm has the advantages that achievement is easy, repeated searching of unmanned aerial vehicles can be effectively avoided, the consumed time of the unmanned aerial vehicles during sub-region connection is shortened, and a larger region can be searched for under the condition of the same property and number of the unmanned aerial vehicles.

The invention relates to a sub module capacitance and voltage balancing and optimizing method for a modularized multi-level converter. The system used in the method is a modularized multi-level conversion system, and a flexible direct current power transmission system valve base control system framework taking a distributed parallel processing mode as a framework is established. The method comprises the following steps of (1) determining a control target of the balancing control for the sub module capacitance and voltage; (2) establishing a sub module state decision optimizing model; (3) performing balancing and equalizing on the sub module capacitance and voltage; (4) verifying the balancing and equalizing on the sub module capacitance and voltage. The distributed computation framework is adopted, the processing capability is greatly improved, and the aim of the application of a complex algorithm and a complex strategy in a high-voltage large-capacity flexible direct current power transmission system is ensured.

The invention relates to a four-rotor UAV (unmanned aerial vehicle) attitude control parameter tuning method based on an improved fish swarm algorithm. Based on a standard artificial fish swarm algorithm, the invention provides a strategy for dynamically adjusting an artificial fish moving step length, an elitist preservation and reproductive behavior and an external fishing behavior are introduced, and defects of series reduction of a later convergence speed, low convergence accuracy and easy falling into local optimum of a standard artificial fish swarm algorithm in the prior art are overcome. The method can be used for solving problems of aircraft design, flight control parameter tuning and path planning and has good problem solving accuracy and efficiency.

The invention discloses a multi-UAV cooperation target searching method based on a multi-group ant colony algorithm, and the method comprises the steps: carrying out the modeling of a search environment of an UAV; and carrying out the multi-UAV target searching according to the modeling environment through employing the multi-group ant colony algorithm. One ant colony plays a role in mutual guide,and different ant colonies achieve the mutual repulsion, so the method can improve the diversity of solutions and the cooperation performance of multiple UAVs, and also can select the direction wherea target is probably located as much as possible, thereby avoiding the greedy selection and improving the selection diversity. The method also can achieve the dynamic adjustment of the search benefitafter the discovery of a target, avoids the repeated search of the target because of the impact from a target expectation factor, selects an optimal path of each generation for pheromone updating, and promotes the algorithm to approach to a better path. The method achieves the path cooperation of the UAV when the UAVs perform the searching, avoids the phenomena of the excessive paths and repeatedsearching of the target caused by the impact from the pheromone and target inspiring factors, reduces the search cost, and improves the search efficiency.

The invention discloses an AUV (Autonomous Underwater Vehicle) target searching method. The AUV target searching method comprises the steps of establishing a sonar detection model, establishing an environment sensing map and searching a target based on an improved attraction source. According to the invention, surrounding environment information is detected in real time through the sonar sensor inan unknown underwater environment without prior information; various environment sensing maps are created and updated; a pheromone release mechanism is improved; therefore, the AUV can perform returnvisit on the area with lower target existence probability under the condition of higher coverage rate of the search area; the target omission caused by the detection probability problem is avoided; the AUV is prevented from repeatedly searching in the searched area; according to the method, to-be-activated attraction sources are arranged at opposite corners of a search area, targets at corners can be searched more easily by activating the to-be-activated attraction sources, updating formulas and search income functions of all environment perception maps are formulated, an AUV is made to makea maximum income movement decision, and the search efficiency is improved while the search reliability and stability are guaranteed.

The invention relates to a historical experience and real-time adjustment combination-based particle swarm optimization algorithm. The objective of the invention is to eliminate the defect of local optimum under a condition that a standard particle swarm optimization algorithm (PSO) is utilized to optimize a high-dimensional complex function. According to the historical experience and real-time adjustment combination-based particle swarm optimization algorithm, previous bad experience is considered in a speed update process, so that particles can be prevented from repeatedly searching previously-found poorest positions; the individual optimal information of each particle is fully utilized, so that the search ability of the algorithm can be improved; and the inertia weight of each particle is adaptively changed by using the adaptive value of each particle, and therefore, global and local search capacity can be adjusted effectively in real time. When the algorithm of the invention is used to optimize four standard test functions, the algorithm can effectively speed up global convergence rate and improve global optimization accuracy compared with the other three algorithms.

The invention discloses a dynamic hierarchical integrated data accessing method capable of guaranteeing semantic correctness and aims to solve the problem about how to provide data access with completely correct semantics under condition of integration of large-scale relation databases. The technical scheme includes that the dynamic hierarchical integrated data accessing method includes firstly, expanding to obtain a description logic subset DL-Lite, then layering the ontology TBox on the basis of the description logics, and establishing LAV (local as view) mapping and O-GAV (object-global as view) mapping between the relation database and the ontology; expanding and rewriting query request by means of SuperRef algorithm according to the T<Q> in layering of the TBox, and establishing a dynamic ABox A<Q> according to query results; and finally, refining the ABox A<Q>, and feeding back query response results. The dynamic hierarchical integrated data accessing method can provide data with the completely correct semantics for users and meet the requirement for correctness of the semantics in integrated data access. Further, calculating complexity can be reduced and expanding, query and rewriting efficiency can be improved.

The invention belongs to the technical field of computer application, and discloses an Internet of Things oriented micro-service framework and a service combination method thereof. For characteristicsand requirements of heterogeneous mass services, layered and domain-divided deployment, application cross-domain collaboration and the like of the Internet of Things, a flat micro-service architecture of the multi-domain Internet of Things is provided, and dynamic sensing and distributed cross-domain management of mass differential micro-service states are realized. The micro-service architecturecan be compatible with cross-domain differential Internet of Things network elements and is expanded to various information system services. Independent deployment of mass multi-level services is achieved, and loose coupling and high fault tolerance are achieved and support is provided for application cross-domain collaboration. Based on the characteristics and requirements of difference of Internet of Things application requirements, service combination diversification and the like, the association relationship of multi-domain Internet of Things mass services is studied, and a multi-domain mass service relationship graph construction and evolution model is provided. A multi-dimensional cross-domain service dynamic discovery mechanism and a demand-driven scene self-adaptive service flexible combination method is provided, so that the Internet of Things service can be provided as required.

The invention relates to a state labeling method in the field of electrical power systems, in particular to a power distribution network reliability assessment state labeling method based on segments. The state labeling method is characterized by being based on the segments and combined with transferring supply after a distribution network faults, enumerating the faults in each segment, conducting state labeling on all segments and internal elements of the segments according to the faults in each segment, and saving labeling results in a vector data structure corresponding to a fault segment. The state labeling method comprises the following steps of (1) conducting reliability assessment state labeling pretreatment based on the segments; and (2) calculating reliability indexes in a circulated mode. The number of the segments in the power distribution network is fewer than that of feeder segments, so that search based on the feeder segments is only carried out when first depth search is carried out, then transversal is carried out on all segments according to the faults of each segment, a complex process that each feeder segment fault traverses all feeder segments is avoided, and the state labeling method is high in efficiency.

The invention relates to the technical field of communication, and discloses a network search method and a network search device. The network search method comprises the following steps: frequency bands of radio access technology (RAT) used by different channels are merged; a radio frequency device used in each band of each RAT is redistributed, and an RAT network search sequence is generated; parallel network search is carried out according to the network search sequence, and the distributed radio frequency device is used for parallelly receiving the network search data of the corresponding RAT; and if any RAT network search succeeds, the searched network is recovered. Thus, the network search process by each RAT in turn in the case of no network can be quickened, the radio frequency device opening time is compressed, the network search power consumption can be effectively reduced, and the terminal standby time is prolonged. When any RAT network signal appears in the environment, the time from a non-network environment to network recovery by the terminal is effectively shortened, and the user experience is improved.

The invention relates to a method and a system for a coordinating search for active targets based on unmanned flying robots. The method for the coordinating search for the active targets based on the unmanned flying robots comprises the steps of building a searching map model on a searching area where the targets are located, namely carrying out discretization inside two-dimensional space, providing a route planning method based on the map model, therefore constructing a navigation cost function, planning a flying path of each flying robot, and achieving the coordinating search which is low in cost and high in efficiency. According to the system for the coordinating search for the active targets based on the unmanned flying robots, a GPS positioning instrument, a wireless communication device, a signal direction-finding instrument, a flying control device and a main control unit are installed on each of the two unmanned robots, and during a searching process of the flying robots, real-time communication, map sharing and target information sharing are achieved. The method and the system for the coordinating search for the active targets based on the unmanned flying robots can be used for searching and detecting various targets with radiation sources inside the area, the automatic coordinating search by the two flying robots are used, the targets with the radiation sources can be rapidly and effectively found, and the rapid and high-efficiency search can be carried out on the targets inside the area.

The invention discloses a path optimization method for material distribution of a multi-objective discrete assembly workshop. The method comprises the following steps: taking distribution equipment transportation time consumption, distribution service satisfaction and distribution equipment load as problem optimization objectives, and carrying out overall modeling on a discrete assembly workshop material distribution path optimization process; encoding the material distribution paths of the N distribution devices with the load constraints, and randomly generating a material distribution path initial solution of the target model; performing local search on the neighborhood space of the initial solution by using a probability-based multi-neighborhood random local search method, and solving amaterial distribution path optimal solution of the target model; carrying out tabu operation on the search process by utilizing a tabu table, and recording a searched neighborhood space structure; and repeating the steps until a search termination condition is met, and outputting an optimal workshop material distribution path scheme. The method is short in operation time in a target problem optimization process, and can deal with a problem scene with real-time state update changes.

The invention relates to a hub-and-spoke logistics network junction station site selection and distribution method based on a probability tabu algorithm. The method comprises the following steps that: firstly, adopting a random greedy strategy to construct an initial junction station site selection and distribution scheme; from the initial scheme, searching a neighbourhood based on junction station and non junction station exchange to determine an optimal junction station site selection; and in an exchange neighbourhood search process, in order to avoid repeated search, adopting a probability tabu search strategy to make an effective improvement on a traditional tabu object and a traditional ban lifting mechanism. On the basis of a situation that the junction station site selection is determined, the neighbourhood based on non junction station movement is subjected to deep search, and optimal junction station distribution is determined. By use of the method, an optimal junction station site selection and distribution scheme in a large-scale hub-and-spoke logistics network is determined in acceptable time, and good economic benefits are obtained.

The invention provides an intelligent setting method for unmanned aerial vehicle attitude control parameters based on a quantum firefly algorithm, and belongs to the field of automatic control. The method comprises the steps of building an unmanned aerial vehicle attitude motion model, designing a fractional order PID controller, determining a to-be-set parameter, and selecting an error index function as an objective function; setting parameters of the quantum firefly algorithm; executing the quantum firefly algorithm to perform controller parameter setting optimization, and obtaining the optimal controller parameter and the objective function value of the current setting; judging whether the objective function value meets requirements or not; if the objective function value meets the requirement, determining that the firefly position is the optimal attitude controller parameter, and ending the setting; otherwise, returning to the step 2, resetting the parameters of the quantum fireflyalgorithm, and executing the steps 2-4. According to the method, on the basis of a standard firefly algorithm, improvement is carried out by utilizing a quantum theory, elite retention and mutation behaviors, and the defects that the later convergence speed of the standard firefly algorithm is seriously reduced, the convergence precision is not high, and local optimum is likely to happen in the prior art are overcome.

A genetic programming algorithm based on local search for dynamic job shop scheduling is applicable to the field of job shop scheduling. According to the technical scheme adopted by the invention, the algorithm comprises the following steps: first, solving the scheduling optimization problem by use of scheduling rules; second, automatically designing scheduling rules by use of a genetic programming method; third, using a heuristic method of local search; fourth, perturbing the current optimal solution of local search; and fifth, introducing a taboo search strategy in the process of local search. The search mechanism of the algorithm achieves balance between development and exploration. Compared with the existing algorithm, compact enough and better scheduling rules can be obtained in a smaller computing time range.

The invention relates to data intelligent information processing in the technical field of computers, in particular to a bidirectional clustering detection method for local similarity submatrices in a data matrix and the implementation of the method. The bidirectional clustering detection method for the local similarity submatrices in the data matrix is beneficial for finding out important knowledge and rules existing in the data matrix. A new bidirectional clustering detection technical framework system is provided, and an integral local similarity submatrice detection process based on the clustering results is designed and achieved. Traditional clustering algorithms (such as K-means and FCM) and the local similarity submatrice detection are separated into two independent sequential processing processes, and the local similarity submatrice detection is based on the clustering results of the traditional clustering algorithms. The bidirectional clustering detection method has the advantages that the previous clustering work can be completed by flexibly adopting the corresponding traditional clustering algorithms along with the development of definition of the local similarity submatrices, the existing algorithms are well and organically connected with the new technology, and continuation and consistency of the knowledge hierarchy are achieved.

The invention provides a Beidou navigation satellite selection method under a tabu search artificial bee colony algorithm, and the method comprises the steps: calculating the elevation angle and azimuth angle of each Beidou navigation satellite at a ground observation point through the obtained position information of the ground observation point and the real-time position information of the Beidou navigation satellite; screening the Beidou navigation satellites according to the shielding angles at the ground observation points to obtain a visual satellite set; using a tabu search artificial bee colony algorithm, using a geometric accuracy factor GDOP of a satellite combination scheme as a target function, optimizing and solving the satellite combination scheme in a solution space, and obtaining the satellite combination scheme with the minimum GDOP. According to the method, the artificial bee colony algorithm based on tabu search is applied to the Beidou navigation satellite selectionprocess, the advantages that the artificial bee colony algorithm is few in preset parameter and high in optimizing capacity, the optimizing speed is high and the like are brought into full play, theefficiency of the satellite selection process in Beidou navigation application is effectively improved, and the method has high value and practicability.

The invention relates to an improved cultural gene algorithm for solving a flexible job shop scheduling method with transportation time, which can effectively solve a flexible job shop scheduling scheme with transportation time. According to the technical scheme, the method comprises the following steps; 1, setting parameters; 2, generating an initial population through three initialization methods; 3, calculating a current optimal solution; 4, judging whether the algorithm is terminated or not; 5, executing a mutation operator; 6, forming a new population; 7, calculating the maximum completion time of the population individuals. The encoding mode is simple and easy to implement, the algorithm operation efficiency is improved, repeated search of neighborhood solutions of excellent individuals is avoided through the elite library and the mutation probability, and the search efficiency is improved.