399 results about "Iterative search" patented technology

Roughly described, a network interface device is assigned a maximum extent-of-search. A hash function is applied to the header information of each incoming packet, to generate a hash code for the packet. The hash code designates a particular subset of the table within which the particular header information should be found, and an iterative search is made within that subset. If the search locates a matching entry before the search limit is exceeded, then the incoming data packet is delivered to the receive queue identified in the matching entry. But if the search reaches the search limit before a matching entry is located, then device delivers the packet to a default queue, such as a kernel queue, in the host computer system. The kernel is then responsible for delivering the packet to the correct endpoint.

In described embodiments, a channel allocation system, such as a white space (WS) channel allocation system, employs a method to populate a WS database with channel availability information for areas with protected or otherwise registered service user information server. Communication characteristics of incumbent service providers licensed or otherwise registered to operate within certain frequency spectrum are plotted over a geographic area. The channel allocation system then divides the area into tiles, and determines whether each tile can be characterized for channel availability and, for those tiles that are uncharacterized, further divides the tiles into tiles of finer resolution. Based on information in the WS database, availability of channels in areas is pre-computed, and then an iterative search of areas with successively finer resolution identifies white space channels that might be included in the channel list in response to a query from a unlicensed or unregistered user.

The invention discloses a video target tracking method based on CAMSHIFT and Kalman filtering. The method takes a search window as a parameter and comprises the following steps of: predicting a search window Win by a Kalman filter; calling a CAMSHIFT algorithm by taking Win as a parameter to search for the target, and returning to the window targetWin containing candidate models; calculating similarity between the targetWin and the target model; if the similarity is greater than the set threshold, finding the target and returning to the target window; otherwise, taking the target window of the previous frame and the distance predicted by the Kalman filter as a window expanding parameter instead of enlarge, and calling an adaptive iterative search algorithm. By introducing the similarity judgment and the re-positioning technology, the method effectively eliminates the background interference and improves the tracking precision.

The invention discloses an improved MPPT (maximum power point tracking) control method based on initial rotation speed adjustment. The design principle of the method is as follows: the wind speed average value exactly reflects the wind speed and wind energy concentrated distribution interval, therefore the optimal rotation speed tracking interval and the average wind speed have a direct relation in quantity and further can be easily pre-estimated, and iteration search is not needed. Compared with an improved torque adjustment method, the improved method provided by the invention not only can be used for improving the wind energy capture rate, but also is simple and practicable. Through the simulative calculation and comparative analysis of a simulative wind speed sequence, the effectiveness and advancement of the method are verified.

The invention provides a mining lot exploiting and monitoring method based on interferometric synthetic aperature radar (InSAR) technology. The method includes the steps of obtaining a probability integral method model coefficient of a mining area adjacent to a mining area to be detected, using a radar sight line directional deformation field, obtained through the InSAR technology, of a mining lot to be detected, using the length, the width, the thickness, an exploiting depth, a trend azimuthal angle and a central point coordinate of a working face of the mining area to be detected as unknown numbers, and enabling the unknown numbers and a probability integral method model coefficient of the mining area adjacent to the mining area to be detected to be brought in the probability integral method model, then using a genetic algorithm to search and obtain a parameter value of the working face of the mining area to be detected, finally, using the working face parameter value obtained through the genetic algorithm as an initial value of a mode searching method, and obtaining the accurate working face parameter value of the mining area to be detected through iterative search. The mining lot exploiting and monitoring method overcomes the defects in the prior art that only an approximate exploiting location can be obtained in the process of exploiting monitoring to the mining area, and underground goaf detailed parameter information can not be obtained accurately, greatly expands the application space of the InSAR technology in the mining area, and provides a mining area exploiting fine monitoring method which is low in cost and large-scale.

A system may include a processor and memory. The processor may be configured to perform calibration measurements on the speaker even when the speaker is being used to conduct a live conversation. The processor may be configured to: provide a live output signal for transmission from a speaker; receive an input signal corresponding to the output signal; compute a midrange sensitivity and a lowpass sensitivity for a transfer function derived from a spectrum of the input signal and a spectrum of the output signal; subtract the midrange sensitivity from the lowpass sensitivity to obtain a speaker-related sensitivity; perform an iterative search for current parameters of a speaker model using the input signal spectrum, the output signal spectrum and the speaker-related sensitivity; and update averages of the speaker model parameters using the current parameter values. The parameter averages may be used to perform echo cancellation.

The invention discloses a method and device for removing glasses in a face image. The method comprises the following steps: judging whether the face image to be processed is a face image of a person with glasses according to a pre-trained glasses judger; when the face image to be processed is the face image of the person with glasses, performing iterative search on the glasses in the face image to be processed according to a deformable model algorithm, thereby obtaining a glasses frame; and removing the glasses frame in the face image to be processed by using an image remediation algorithm. The method and the device, which are disclosed by the invention, are used for removing glasses in the face image.

A speculative start point selection for motion estimation iterative search improves the efficiency and quality of the integer-pel motion estimation iterative search by speculatively selecting the start position of the iteration. The start position is selected by comparing the Sum of Absolute Differences (SAD) value of a 0 motion vector, a predicted motion vector and a global motion vector (GMV) and selecting the position with the smallest SAD value. A refinement scheme with a threshold improves the efficiency and quality of the motion estimation iterative search by performing several comparisons to ensure the proper motion vector is selected. Applications of this improved motion estimation search include stabilizing an image as well as many other applications where motion vectors are used.

The invention discloses a human face feature positioning method based on an ASM algorithm, belongs to the technical field of image processing, and mainly relates to human face recognition technology in biological feature authentication. The method comprises the following steps: firstly, performing manual calibration on a group of human face sample images to extract feature points; secondly, adopting a Procrustes Analysis algorithm to carry out registration on a sample set to obtain an average human face model; thirdly, adopting a strategy of performing contour search on the images under three resolution ratios to establish a statistic grayscale search model; and finally, performing a matching operation on the statistic grayscale search model and a local grayscale model of a human face image Ys to be positioned, and performing cyclic iterative search positioning on the human face image Ys to be positioned. The human face feature positioning method combines the ASM algorithm and the Procrustes analysis method, can effectively improve human face positioning speed and accuracy, and has strong commonality.

Resource management using an iterative real-time auction with user rules is provided. Users of a community, such as a multi-user development, establish user rules including conditions for operating a resource-consuming device based on an incentive. A utility manager receives the user rules and integrates them with respect to an incentive value. The aggregate resource demand of the community can be estimated based on the integration of the user rules and can be compared with a desired demand. By iteratively changing the incentive value and estimating the demand with respect to the incentive value, the utility manager can control resource demand and consumption while taking into account user preferences. In other words, users of the community provide bids for a resource auction based on incentives provided by the utility manager. Resources can include electricity and incentives can include financial incentives, carbon credits, or third party offers.

Embodiments of the invention disclose a multi-target-based improved gray wolf optimization algorithm which is used to solve the technical problems that a standard gray wolf algorithm falls into a local optimal value easily and has a low convergence speed and other defects while processing a multi-target optimization problem in the prior art. The method of the embodiments comprises the following steps: S1, setting a wolf pack initialization parameter and a direction correction probability, and randomly initializing wolves' individual positions; S2, calculating an adaptability value of each wolf individual according to a solving target, and selecting the three wolf individuals ranking top; S3, optimizing the wolves' individual positions of a wolf pack, generating moderate wolves, and updating a wolf pack position; S4, executing direction correction operation on the updated wolf pack, controlling the upgraded wolf pack to participate correction of the size of dimensions according to the direction correction probability, and obtaining a corrected wolf pack position; and S5, determining whether an iteration frequency reaches a preset maximum iteration frequency, outputting the corrected wolf pack position as a final optimization result if the iteration frequency reaches the preset maximum iteration frequency, and, if the iteration frequency does not reaches the preset maximum iteration frequency, turning to the S3 so as to continue performing iteration searching.

The invention discloses a particle-swarm-based rapid optimization deployment method for wireless network sensors. The method is capable of improving a global optimization effect of a particle swarm optimization algorithm (PSO) and reducing particle resource use and greatly speeding up a solution speed. The method is as follows: using a feasible solution of each kind of deployment of the wireless network sensors as a particle to establish a particle swarm and initiating the speed and position of each particle and setting an iteration number and using the PSO to perform iterative search on the particles, wherein a disturbance term is added when the speed of the particles is updated; after the iterative search is performed for the set iteration times, obtaining a final optimization result so as to realize particle swarm optimization; the PSO using an effective coverage rate of a wireless network as a fitness; and the disturbance being the product of a disturbance amplitude and a random number selected through standard normal distribution and increasing or decreasing the proportion of the disturbance term through changing the disturbance amplitude. The particle-swarm-based rapid optimization deployment method for the wireless network sensors is suitable for the PSO to obtain a comparatively satisfactory solution under a condition that the number of particles is significantly small.

The invention relates to a multi-agent game incremental distribution network source network load cooperative planning method, which comprises the following steps: respectively establishing planning decision models of a plurality of investment subjects; analyzing the static game behavior between DG investment operator and distribution network company according to the transfer relationship of threeinvestment agents, establishing a static game model when the game is in equilibrium; using robust optimization to deal with the uncertainty of DG output, and introducing the virtual game player Nature. According to the dynamic game behavior between the virtual game player and the distribution network company, the game reaches the equilibrium state, and the dynamic game model is established on thisbasis. Considering the multi-agent game of DG investment operators, distribution network companies, power users and Nature, a dynamic-static joint game layout for incremental distribution network planning is formed and a dynamic-static joint game model is built on the basis. The iterative search method is used to solve the Nash equilibrium point, and the programming decision model based on game theory is solved to get the final planning scheme.

The invention discloses a project constraint parameter optimizing method based on an improved artificial bee colony algorithm. According to the method, the problem of the project constrained parameter optimization is described by the adoption of an objective function and an equality / non-equality constraint; an artificial bee colony is initialized according to the value range of parameters; partial parameters in a parameter vector is selected according to the probability M to serve as the adjusted object, and step size in search is adjusted in a self adaptive mode, so that a guide bee can search nectar sources randomly in an intra area; according to the corresponding cost function value fi of the nectar sources, the fitness function value fiti is acquired through fi, the probability Pi of follow bees being transferred to the nectar sources is further acquired, and whether position updating is conducted or not is judged; the current optimal solution is recorded in every iterative search process, and the optimized estimated value of the parameters is acquired through the finite iterative search. The step size in search changes in a self adaptive mode with the times of search, on the premise that search accuracy is not affected, search time is reduced effectively, and search efficiency is improved.

The invention discloses a wireless sensing network routing method based on an ant colony algorithm. The method comprises the following steps of (10) network initialization, including: a wireless sensing network is divided into grids, messages and hop counts are broadcasted, and different message structures are defined for a forward ant and a backward ant; (20) path searching, including: the forward ant starts to move to a next node to determine an importance degree of heuristic information; (30) determination of a pheromone value, including: the pheromone value released by the forward ant while passing through a path is determined; (40) establishment of routing, including: iterative searching is performed on the pheromone on the path, the backward ant returns to a source node along a reverse pheromone table, and thus the routing is successfully established; and (50) data transmission, including: each node acquires the situation of the routing between a neighbor node and a sink node, periodically broadcasts routing table information of the neighbor node of each node, and performs data transmission. The routing method provided by the invention is high in data transmission efficiency, balanced in consumption of network energy, and longer in network life.

The invention discloses a method for navigation positioning by using a gravitation vector and gradient tensor. The method comprises the following steps: firstly, establishing a background field three-dimensional information databank of a target area according to beforehand observation, wherein basic elements comprise position coordinates of different points, gravitation vector invariants and gravitation gradient tensor invariants, subsequently observing and calculating according to the gravitation and the gravitation gradient in real time so as to obtain three invariants, judging whether navigation positioning can be performed directly or not by judging whether a resolving matrix is of full rank, if navigation positioning can be performed directly, performing iterative computation to obtain the position of a point to be positioned according to a least squares algorithm, if navigation positioning cannot be performed directly, performing matching search with the combination of an inertial navigation positioning technique, and performing iterative search for multiple times so as to obtain the position of the point to be positioned. The method has the outstanding characteristics that the gravitation vector invariants and gravitation gradient tensor invariants are adopted, and not only is contribution of all components of gravitation field information considered, but also the components are not related to the posture, so that compared with a conventional algorithm, the method can greatly reduce the influence of posture errors.

The invention discloses a grid path recommending method based on internet of vehicle and belongs to the field of information retrieval. The grid division method is adopted, a grid OD matrix is built, a search grid of a sudoku is divided with an initial site and a destination input by a target user as a search center, the individual movement feature and the static feature and dynamic feature of a grid path are considered only in the search grid, a path overhead function is linearly combined, the path nearest neighbor set of the target user is obtained according to the path overhead function, a more accurate neighbor set of the target user is provided in an iterative search mode for the search radius, and the accuracy of a recommendation result is improved. The method reduces the time complexity for computing the grid, solves the problem that a traditional path feature is simplex in computation, and redefines constitutes of the path feature and the computation rule. The grid path recommending method based on internet of vehicle can be widely applied to related areas such as traffic and social networking services.

Disclosed are a variable weight grey wolf algorithm optimization method and an application. According to the method, social classes are set and act on the whole search and predation processes of a grey wolf population, and the grey wolf population surrounds a target in the search process and surrounds the target in the center in the predation process; and in the iterative search process, the positions of an alpha grey wolf, a beta grey wolf and a delta grey wolf with high social classes in the population are firstly, secondly and thirdly close to the target all the time, and in the iterative process, the positions of the grey wolfs in the population are described by combination of variable weight functions of the alpha grey wolf, the beta grey wolf and the delta grey wolf, wherein the weight w1 of the position of the alpha grey wolf is gradually reduced to 1 / 3 from 1, the weight w2 of the beta grey wolf and the weight w3 of the delta grey wolf are gradually increased to 1 / 3 from 0, and the use of w1, w2 and w3 meets the requirements that the sum of w1, w2 and w3 is equal to 1, w1 is greater than or equal to w2, and w2 is greater than or equal to w3. The method has the advantages that the search process is remarkably accelerated and the optimization calculation can be finished more quickly.

The invention discloses a cooperative service deployment and service allocation method for a regional edge computing internet of things, and relates to the field of edge computing and internet of things. In order to improve the resource effectiveness in the edge computing Internet of Things service deployment and reduce the network deployment cost, an edge server cooperation mode in the regional edge computing Internet of Things is firstly constructed, and then the service deployment problem with the minimum deployment cost in the edge servers is modeled into a vector encasement problem; the minimum number of servers and an initial service allocation result are determined through a minimum resource proportion increment heuristic algorithm; and then, in order to balance the network load andreduce the forwarding load of the network management unit, an iterative search mobile switching heuristic algorithm is utilized to redistribute the service to a locally deployed edge server, and thenumber of service calculation tasks processed by the local edge server is maximized.

The invention discloses a novel bat optimization algorithm system (Iterated local search and stochastic inertia weight bat algorithm system, ILSSIWBAS), comprising: a bat algorithm operation module, a disturbance module and an iterative local search module, a judgment global optimal solution module, a global optimal solution module, and a global optimal solution module. The optimal solution storage module; through the bat algorithm operation module, run the bat algorithm to obtain the local optimal solution; on the basis of the local optimal solution, through the disturbance module, add disturbance, according to the local iterative part iterative search module, use the iterative local search algorithm The global optimal solution of the population position is searched, and when the judgment conditions of the global optimal solution are met, the global optimal solution is obtained, and the global optimal solution is stored in the global optimal solution storage module. The system proposed by the invention mainly solves the problems that the existing bat optimization algorithm is easy to fall into local optimum and the optimization result is unstable, and improves the optimization accuracy of the optimization algorithm and the stability of the optimization result.

The invention relates to a laser ceilometer dynamic threshold selecting method based on pulse echo forms, and belongs to the field of laser remote sensing. The laser ceilometer dynamic threshold selecting method based on the pulse echo forms solves the problems that in an existing threshold selecting method, only extreme point positions or fifty-percent peak point positions or dual-threshold positions of pulse echoes are adopted to confirm transition time of laser pulses, the selected thresholds can be only suitable for specific measurement conditions or plane targets of small slopes. According to the laser ceilometer dynamic threshold selecting method based on the pulse echo forms, a mathematic model of the laser ceilometer pulse echo forms, noise standard deviation and threshold rising edge moment variance is regarded as a theoretical basis, the range error minimization of a laser ceilometer serves as a basis, and the optimization selection of the threshold coefficient of the laser ceilometer is achieved through a parameter iterative search method. The influences of noise and pulse widening introduced by gap title effects of the plane targets are fully considered in the threshold value selection method, range errors caused by threshold setting can be reduced, and the satellite-borne laser ceilometer can complete high-precision laser ranging of the plane targets of different slopes under different measurement conditions.

The invention discloses a vehicle scheduling algorithm based on greedy tabu search, and the algorithm comprises the steps: establishing a vehicle scheduling basic model based on dynamic planning, solving, and obtaining a vehicle scheduling initial solution; establishing a loss function based on a tabu search algorithm to serve as a basis for iteration of an initial solution; performing local iterative search improvement based on a neighborhood structure thought to generate a plurality of reliable candidate solutions; in order to prevent local search from falling into a local optimal solution,adding various destruction operator disturbance initial solutions to enable the initial solutions to have relatively high quality; and filtering the candidate solutions according to a loss function adopted by a tabu search algorithm to obtain an optimal solution in the candidate solutions, and generating a final scheduling scheme. Cost of vehicles and drivers is considered, a scientific model algorithm, namely a greedy tabu search algorithm and a scheduling scheme for searching the minimum number of vehicles and conforming to scheduling habits, is applied, effectiveness of the algorithm is verified, and the algorithm is applied to practice.

The invention discloses a multi-target cloud workflow scheduling method based on an improved non-dominated genetic algorithm. By introducing a scoring mechanism idea and considering the influence of the current population and historical population information on individual dominant information, the accuracy of population individual evaluation is improved, and the efficiency of iterative search isimproved. The method comprises the following steps of constructing population hierarchy so as to directly depict diversity and optimality of an optimal solution traversed by algorithm search, and dynamically updating a population hierarchical structure according to the degree of approaching Pareto optimality of an offspring individual in an iteration process by improving a parent individual selection mode, so that the possibility that the found solution approaches Pareto optimality is improved. Meanwhile, a search direction self-adaptive adjustment strategy based on optimal level individual monitoring is provided.By setting local optimum and divergence detection parameters, relevant parameters can be adjusted in time when the search is trapped in the local optimum or tends to be divergent,and the optimization direction is changed to jump out of the local optimum or regression convergence.

The invention relates to a double-end range finding method for a single-phase earth fault of an overhead-cable mixed line. The method comprises: according to positive-sequence components of three-phase voltages and currents of a front end and a tail end in a normal condition of an overhead-cable mixed line, non-synchronized angles delta of currents at the two ends of the overhead-cable mixed line are calculated; power frequency components of the three-phase voltages and currents at the front end and the tail end of the overhead-cable mixed line after a single-phase earth fault are extracted by using a differential fourier algorithm; symmetric component conversion is carried out on the power frequency components of the three-phase voltages and currents at the front end and the tail end of the overhead-cable mixed line after the single-phase earth fault to obtain sequence components of the voltages and currents at the front end and the tail end of the overhead-cable mixed line after the single-phase earth fault; a sequence component of the single-phase earth fault current of the overhead-cable mixed line is calculated; and the fault point position is determined based on a one-dimensional iterative searching algorithm. Compared with the prior art, the provided method has characteristics of accurate range finding and high efficiency.

The invention aims to provide a robust general steganalysis method for image content awareness. The method of the invention can better utilize the advantage of the CS technology to process digital images, and further improve the data processing efficiency and the classification judgment precision of the steganalysis. The method comprises the following steps: (1) setting the size N, a block size B and a measured value M of a digital image X; (2) determining a 9 / 7-Haar DWT sparse matrix Psi; (3) determining a Hadamard measurement matrix PhiB of the BCS; (4) dividing a digital image into B*B blocks and calculating a measurement vector yi and the measurement data Y of the entire image X; calculating the measurement vector yi of the i-th image block and the measurement data Y of the entire image X according to the Hadamard measurement matrix PhiB ; and (5) dividing the image into a smooth part and a non-smooth part as a perceived result; (6) a training process; 7) a classification process; 8) a judgment and analysis process; and 9) repeating iterative search until the entire image data is detected.

Computational techniques for mapping a continuous variable objective function into a discrete variable objective function problem that facilitate determining a solution of the problem via a quantum processor are described. The modified objective function is solved by minimizing the cost of the mapping via an iterative search algorithm.