393 results about "Particle position" patented technology

A method an apparatus for manipulating particles (micro, nano, and pico) having one or more characteristics with an optical trap formed by modulating a laser beam with a Diffractive Optical Element (DOE). At least one characteristic of the material is selected; and a laser beam having a selected wavelength corresponding to the at least one selected characteristic of the material is generated. Values of the DOE are calculated corresponding to the at least one selected characteristic of the material. The beam and the DOE are modulated to produce a holographic optical trap having properties corresponding to the at least one selected characteristic; the trap is focused to a beam focus or selected spot size; and the beam focus is located near a particle location for trapping the particle therein.

A method is provided for extracting the position of a particle, e.g., a moving or stationary particle that is excited or is emitting light. The method includes, among other steps, detecting and recording a signal based on, for example, the movement of the particle, a correlation step to eliminate noise and to create a transformed signal, a matching or fitting step to match the transformed signal to a fit function and an extracting or determining step to determine the position of the particle from the fit function. In one form, at least two particle positions are detected so that the distance between the subject particles can be determined.

A system and method of use for self-tuning a PID controller utilized with an exciter and generator is disclosed. The system includes a power source, an exciter electrically connected to the power source, a generator that is electrically energized by the exciter, and a processor that provides a PID controller that calculates an estimated exciter time constant and an estimated generator time constant using particle swarm optimization (“PSO”) to control exciter field voltage. The particle swarm optimization (“PSO”) technique includes increasing the voltage reference by a predetermined percentage over a predetermined time period, initializing each particle position of exciter time constant and generator time constant, calculating generator voltage, performing a fitness evaluation and then obtaining and updating best values. This is followed by repeating the steps of determining the generator voltage, the fitness evaluation, and the best values over a predetermined number of iterations.

The invention provides a power distribution network fault positioning method based on improvement of a binary particle swarm algorithm, the conventional binary particle swarm algorithm is improved, and the method is applied to positioning of power distribution network faults. The method comprises following steps: firstly, determining parameters including the particle swarm scale and the maximum iteration frequency etc.; then forming an expectation function of a switch according to fault information of the switch, and constructing a fitness function of power distribution network fault positioning; initializing a particle swarm, setting particle positions, and setting the speed of the particles as 0; calculating the fitness values of the particles according to the fitness function, and setting an initial global extremum; updating an individual extremum and the initial global extremum; updating the speed and position of the particle swarm; and stopping calculation when reaching the maximum iteration frequency, and outputting the global optimal position of the particle swarm, namely the practical fault state of each feed line section of a target power distribution network. According to the method, the problem of premature convergence of the conventional method can be overcome, and the convergence and the stability of the algorithm can be further improved.

The invention provides a three-dimensional printed minimally invasive guide template and a making method thereof. The making method comprises the steps that scanning is conducted on a target part to obtain a three-dimensional image of the target part; reconstruction is conducted according to the obtained three-dimensional image, and a three-dimensional model of the target part is obtained; needle insertion directions, needle insertion positions and needle insertion depths are planned according to the reconstructed three-dimensional model and requirements of doctors, and dosage is conformally distributed on source particle positions; a guide template digital model is built according to the reconstructed three-dimensional model and the planned needle insertion directions, needle insertion positions and needle insertion depths; the guide template digital model is printed into the three-dimensional printed minimally invasive guide template by means of a 3D printing technology. According to the three-dimensional printed minimally invasive guide template and the making method thereof, the reliability and effect of implanting treatment can be improved, operation labor intensity is reduced, the operation time is shortened, and a risk is reduced.

Aiming to solve the problem of test case suite expansion in a software regression test, the invention discloses a technology for reducing test case suite. The technology is inspired by the particle swarm optimization (PSO) and utilizes 0-1 coding manner to indicate test case suite. Different particles represent different selective schemes of test case suite; the adaptive value of each particle adopts two adaptive values to evaluate, wherein, one is coverage degree of the test case suite to a test demand set, and the other is redundancy of the test case suite satisfying the test demand set. The renewal of the particle position utilizes all test cases to randomly generate the next individual position taking the coverage rate of the rest demand as a probability, so as to get the particles with maximum coverage rate and minimum redundancy, which is the optimized test case suite reduction scheme. Compared with the prior test case suite reduction technology, the invention has no relation with initial values and has the advantages of simple operation, high calculation speed and excellent performance.

The invention discloses an intelligent power distribution network fault recovery method based on a multi-target discrete particle swarm. The method comprises the following steps: 1), initializing parameters; 2), initializing a position and a speed of a discrete particle swarm optimization algorithm, and according to intelligent power distribution network island dividing and load recovery algorithms, calculating each fitness value fitness k relative to a multi-target function for each particle; 3), based on a fitness control concept, performing classification of a control population and a non-control population; 4), according to a corresponding updating rule, updating a particle position and a particle speed in the control population; 5), performing dynamic exchange between the control population and the non-control population; 6), detecting whether a maximum iteration frequency is reached, and if the maximum iteration frequency is reached, skipping to step (4), and otherwise, entering step 7); and 7), outputting a final optimization result.

An object of the invention is to provides a particle swarm optimization method based on a multi-strategy synergistic function. The technical scheme of the method comprises a first step of initializing a particle swarm, wherein NP particles are initialized, a second step of calculating fitness values of the NP particles, a third step of determining particle speeds and position change modes, a fourth step of executing Cauchy variation on positions of the particles, and a fifth step of determining a particle execution stop condition. The method is suitable for optimization solving of a function, takes the full advantage of elite reverse learning, and improves a function optimization speed and function optimization precision; and the particles are prevented from falling into a local optimal value by utilizing gauss variation, and variation is performed on the positions of the particles by utilizing the provided Cauchy variation a Cauchy distribution proportion parameter of which decreases linearly and progressively, so more excellent particles are generated to guide other particles to move toward a better solution direction, the function optimization precision is improved, and function optimization stability are also improved.

The invention provides an inertial navigation-based indoor positioning method which comprises the following steps: (1) acquiring gyroscope measured data and accelerometer measured data of a to-be-monitored carrier; (2) calculating the position of the to-be-monitored carrier; (3) judging whether a signal of a base station is detected; (4) calculating the particle propagation distance, direction of propagation and variation of the direction of propagation; (5) calculating particle propagation distance noise and direction noise, and calculating a particle position set; (6) calculating the particle weight according to the detected signal of the base station; and (7) resampling to obtain position estimation of the to-be-monitored carrier. The invention further provides an inertial navigation-based indoor positioning device which comprises a base station, an accelerometer, a gyroscope and a signal processing unit. A flexible positioning framework combining inertial positioning to be fused with base station data based on particle filtering is established, so that the inertial positioning and the base station data can be effectively fused, and the positioning accuracy and reliability are improved.

The present invention is a particle position solver that allows particles to reach an end of a fixed time, time step in an invalid state and which are then pushed toward a valid state. This allows the solver to continue the simulation while the simulation results move toward a valid behavior. Particle collision calculations are simplified by adopting a non-sequential model ignoring some collisions or combining them. The speed of the simulation is also improved by performing some operations outside the solver calculation loop and by using specialized data structures.

The invention discloses a magnetic styrene alkali anion exchange microsphere resin and a preparation method thereof. The resin comprises a basic framework and magnetic particles positioned inside the basic framework. The basic framework of the resin has the structure as follows: radical A is one or a plurality of the picture below, wherein n is 1, 2, 3 to 20, R1, R2 and R3 are alkyl, and X is C1 or OH. The method comprises the following steps of: adding a dispersant to a water phase; mixing reactants, a stabilizing agent and a pore-foaming agent to form an oil phase; adding an evocating agent to the oil phase for full dissolution; adding the magnetic particles to the oil phase, adding the oil phase to the water phase to obtain resin particles, filtering to remove water content, and washing or extracting the resin particles; and swelling the obtained resin particles, and adding an amine reagent. The invention simplifies traditional synthesis process, and prepared resin has magnetism and has higher alkali exchange capacity and can be used for separating or removing dissolved organic matters and a plurality of anions in various wastewater, drinking water or natural water.

The invention discloses a user discovery and resource allocation method based on social perception in D2D communication and belongs to the mobile communication field. The method specifically comprises the following steps of firstly, establishing a D2D communication scene which meets the limitation of the social relationship and the physical location; secondly, carrying out pairing and optimization on D2D communication users in the scene according to a discovery and pairing algorithm of the D2D communication users; thirdly, further computing the entire system throughput CTotal of a base station; fourthly, taking the system throughput CTotal as a target function, combining a limiting condition and adopting a quantum-behaved particle swarm optimization algorithm to solve to obtain an optimal particle position to carry out system subcarrier and power allocation; and lastly, carrying out simulation verification on user discovery and resource allocation based on the social perception in the D2D communication. The method has the advantages that an adaptive abdication under the conflict condition of the residual energy and the request of user nodes is considered; the subcarrier allocation and the power allocation are combined, a resource co-allocation method is provided, and a simulation result shows that the D2D discovery and pairing algorithm based on social perception has better performance.

Method and apparatus for the manipulation and / or control of the position of particles by means of time-variable fields of force. The fields of force can be of dielectrophoresis (positive or negative), electrophoresis, electrohydrodynamic or electrowetting on dielectric, characterized by a set of stable points of equilibrium for the particles.

The invention relates to the technical field of reactive powder optimization of a power distribution network of a power system, and particularly relates to a tabu particle swarm algorithm based reactive power optimization method of a power distribution network. According to the situation that a basic particle swarm algorithm in the optimization process can be easily trapped in local optimization, the invention discloses the improved method by the combination of a tabu search algorithm, and the defect that the particle swarm algorithm can be easily trapped in local optimum is overcome by utilizing the memory function and the characteristic of high climbing ability of the search algorithm; meanwhile, learning factors c1 and c2 which change as the increase of iterations and an inertia weight coefficient Omega are introduced in a particle position and a speed upgrading equation of the particle swarm algorithm, and the problem that the particle swarm algorithm can be easily trapped into the local optimum is further solved. By the combination of the two intelligent optimization algorithms, the optimization capability is improved greatly; the tabu particle swarm algorithm based reactive power optimization method is much suitable for departments relevant to a power system and the like to implement reactive power optimization of the power distribution network.

The invention discloses a laser range finder-based map establishing method of a snake-like robot. The laser range finder-based map establishing method comprises the following steps: starting the snake-like robot and a laser range finder, acquiring environmental information by the laser range finder along with motion of the snake-like robot, and preprocessing acquired laser data; initializing a map, and matching a current laser measured value with a current map; updating particle positions and weights of a robot particle set by using an Monte-Carlo algorithm, and calculating a probability center particle position of the robot particle set; updating the map by an improved Bresenham algorithm. The laser range finder-based map establishing method has the advantages as follows: the algorithms are simple and easy to understand; calculation is simple and high in accuracy; the laser range finder-based map establishing method is applicable to a fast-moving robot.

A method and apparatus for microencapsulating or electrodeposited coating of ferromagnetic and soft-magnetic sub-micron or nano sized powderized material comprising use of a rotary flow-through device assisted by an electromagnet within the electrode ring to alternately position the powder at the face of the cathode ring and electroplate the powder and reorient it prior to another repositioning. The invention is also of a process and apparatus for forming a strip, mesh, or film from magnetic powderized material in an organized bipolar arrangement, which is particularly useful for electroforming foils with the magnetic particles positioned in a monolayer within a multilayer metallic foil.

The invention discloses a pairwise overlay integrated software test case suite generating method regarding the generation problem of pairwise overlay integrated test case suite in an integrated software test. The method is inspired by discrete particle swarm optimization, and a particle represents a test case suite, wherein different particles represent different test case suite generation schemes, the overlaying condition of the test case suite on each factor integration is evaluated overall; regarding particle position updating, the individual position of next generation is generated by utilizing the number of occurring times of the discrete value of each factor in each test case suite as basis; and acquiring the test case suite which fulfills restriction and overlays all factor integrations. Compared with the conventional method, the invention provides the pairwise overlay integrated software test case suite generating method which is independent of the initial value, and has the advantages of high convergence speed, small calculation quantity, good stability and small integral scale of the generated test case suite, so that the software testing efficiency is improved, and the testing cost is reduced.

The invention discloses a wireless sensor node alliance generating method based on an improved particle swarm optimization algorithm, comprising the following steps of: 1, collecting the capacity information and the task information of each node and quantizing the capacity information and the task information of each node by using vectors; 2, dividing a particle swarm into m sub-swarms at t moment according to the number of tasks to be executed, and initializing the current position and setting the maximum iterations for each particle in each sub-swarm; 3, evaluating the benefit value of the current position of each particle by using the following utility functions for m sub-swarms; 4, comparing the benefit values a1 of the current positions of the particles with the preset benefit values a2 of the local optimum positions of the particles and the preset benefit values a3 of the optimum positions of swarm bodies and updating the local optimum positions of the particles and the optimum positions of swarm bodies, wherein the benefit values a1 is obtained in step 3; 5, calculating the particle speed vector and the particle position at the t+1 moment by using the particle swarm optimization algorithm; and 6, repeating step 3-step 5 to obtain the final optimum positions of the swarm bodies. The invention has the advantages of high executing efficiency and stability.

The invention discloses a video target tracking method, in particular a particle filter and light stream vector-based video target tracking method. The method comprises the following steps of: first, establishing an L-level Gaussian pyramid for an image at the moment t, and calculating the gray gradient of each level of Gaussian pyramid image along directions x and y; then, solving position deflection of M characteristic points of the image at the moment t-1 in the image at the moment t; next, resampling N particles for the image at the moment t-1 and redistributing the newly sampled N particles by using a random number or a light stream vector; later on, recalculating an RGB histogram of the N particles, calculating the weight of each particle according to the RGB histogram and performing weighted averaging for the N particle positions according to the weight to obtain an estimated position for the image of a tracking object at the moment t; and finally, performing stability comparison calculation on the estimated position to obtain a final position of the tracking object. The method can efficiently and accurately track a video target.

The invention discloses an image type smoke fire disaster detection method and system. The image type smoke fire disaster detection method comprises the following steps: filtering an obtained video image; subjecting the video image to differential operation to obtain an differential image, and generating a static image according to the video image and the differential image; dividing the differential image and the static image into a plurality of connected domains according to a preset color model, and processing the connected domains to obtain a potential smoke zone; dividing the potential smoke zone into a plurality of clusters, and obtaining the area information of each cluster, the average value of the HU monuments of the connected domains, the number of the connected domains, the fluttering feature information of each cluster and the particle position information of each cluster at the same time; and judging whether smoke exists in the potential smoke zone or not by using a Bayesian decision making device according to the area information of each cluster, the average value of the HU monuments of the connected domains, the number of the connected domains, the fluttering feature information of each cluster and the particle position information of each cluster.

A color electrophoretic display including a plurality of cells each containing electrophoretic particles. Each of the cells in the plurality is capable of displaying at least one of three selected primary colors, when the particles in the cell are moved from a first rest position to a second display position on the cell. An electrode is coupled to each of the cells and is operative when biased to move the particles from the first rest position to the second display position thereby displaying primary colors in the second display position and causing the display to provide full color capability according to particle position in the cells.

The invention discloses a PIC (Peripheral Interface Controller) model-based accelerator simulation method implemented by using a GPU (Graphic Processing Unit) in parallel. The method comprises the following steps: copying initialization information from a host to the GPU for calculating nodes; determining a corresponding relationship of particle positions and grids according to the initialization information; according to the corresponding relationship of the particle positions and the grids, calculating charge density weights, on the grids, of all particles in each grid to obtain the charge density distribution of the grid; calculating the potential distribution of the grids according to the charge density distribution of the grids, and calculating the electric field distribution of the grids according to the potential distribution of the grids; calculating the motion change of each particle under the action of an electric field, and updating the motion state of each particle; replacing the initialization information by the updated motion state of each particle, and iteratively carrying out the steps until the motion states of the particles satisfy design requirements. According to the method, the technical problems of being low in operation speed of a simulation algorithm, high in cost and the like in the existing PIC model-based accelerator can be solved.

The invention discloses a particle swarm algorithm based photovoltaic cell panel maximum-power tracking method. The method includes: firstly, setting initial power values to determine initial positions of particle positions and the number of particles; then taking the power values corresponding to the initial positions of the particles as the optimal particle values corresponding to the particles; finally, selecting out the maximum values as optimal swarm values of particle swarms by comparison of the optimal particle values and outputting the optimal swarm values. Output voltage of a photovoltaic cell plate can be acquired according to the particle swarm algorithm, duty ratio of PWM (pulse-width modulation) is taken as updating speed of the particles, and the output voltage of the cell plate is taken as objective functions used for judging the particle positions; the updating speed of the particles is taken as output to perform PWM on a switching tube of a Boost circuit to acquire the updated particle positions, and directions are given for updating of the particles with the selected optimal values; the optimal values of the particles are searched, and an MPPT (maximum power point tracking) objective is realized. The tracking method is high in intelligent degree and tracking precision, and the cell plate capable of tracking the maximum power value points without falling into locality is the optimal.

The embodiment of the invention provides a customer classification method and device based on an improved particle swarm optimization algorithm, and the method comprises the steps: initializing a particle speed and a particle position according to a classification number and a feature dimension, and setting an initial value, so as to build an initial population of a particle swarm; performing iterative updating operation on the inertia weight, the particle speed and the particle position of the population according to a preset fitness function including the customer characteristic data until apreset iteration frequency is reached; after the number of iterations is preset, respectively carrying out selection operation, crossover operation and mutation operation on the particle swarm according to a genetic algorithm after each update for next iteration update until the iteration update reaches the total number of iterations or meets a convergence condition; and obtaining a clustering center according to the particle swarm reaching the total number of iterations or meeting the convergence condition, and classifying the customers. According to the method, through organic fusion of thegenetic algorithm, falling into a local optimal solution can be avoided, the later convergence speed is increased, and the search precision is improved.

The invention discloses a complex system designing method based on the resampling particle swarm optimization algorithm. The method comprises the following steps: (I) establishing a fitness function of specific optimization problems; (II) initializing the particle swarm; (III) performing resampling operation of the particle swarm; (IV) updating the particle position and speed; (V) updating the historical optimal position and swarm optimal position of each particle; and (VI) if the precision requirement is not met and the maximum number of iterations is not reached, adding one to the number of iterations and returning to the step (III); and otherwise, recording and outputting the result. In the invention, practical problems in the optimal design of a complex system can be effectively dealt with; and by introducing the resampling technology, the convergence speed is increased, the efficiency is improved, the global search ability is enhanced, the optimization precision is improved, and an aim of efficiently and accurately solving the problems in the optimal design of the complex system is finally achieved.

The invention relates to an improved particle swarm optimization method based on flow production scheduling of a fuzzy due date. The optimal solution of the existing algorithm can not be found easily. The method comprises the following steps: using the penalty function for neighborhood block design of key lines based on the needs of the flow shop scheduling of the fuzzy due date, establishing a taboo table, and adopting the neighborhood search strategy, thereby enhancing the effect of optimal solution of the improved particle swarm algorithm. Since the stagnation can easily occur to the particle swarm optimization, the concepts of exchange operators and exchange sequences are introduced to reconstruct the particle location formula and the speed optimization formula of the particle swarm optimization, and the introduction of the taboo search algorithm helps the particle swarm optimization to search the best solution in the local area. Simulation experiments on the flow production scheduling of the fuzzy due date demonstrate that the improved particle swarm optimization method facilitates the overall solution.

The invention provides a wafer detecting method and a wafer detecting device. The wafer detecting method provided by the invention comprises the following steps: measuring light is produced; a detecting light spot is formed by the measuring light on a wafer to be detected; the wafer to be detected is scanned by the detecting light spot; the measuring light is scattered by particles positioned within the range of the detecting light spot, so as to form scattered light; the scattered light is detected, and a corresponding time-related scattered light signal is formed; and distribution information of the particles on the wafer to be detected is obtained on the basis of the time-related scattered light signal. The wafer detecting device comprises a light source for providing the measuring light, a moving and rotating platform for bearing the wafer to be detected and enabling the wafer to move or rotate, a photoelectric detector for detecting the scattered light at a certain frequency, and a data processing unit for obtaining the distribution information of the particles on the wafer to be detected according to the time-related scattered light signal detected by the photoelectric detector. The wafer detecting method provided by the invention has the advantage of relatively high efficiency, and the wafer detecting device provided by the invention has the advantage of relatively low design difficulty.

The invention discloses a graph-theory-based intelligent optimization method for failure recovery of a smart distribution grid. The method comprises the following steps of 1) inputting network parameters including an original structure of a distribution network, line parameters of each branch, load of each node, DG (Distributed Generation) data and other parameters; 2) inputting a number of the current faulty line and zeroing the corresponding switching state thereof; 3) setting dimensionality, iterations and corresponding parameter values of a quantum particle swarm optimization algorithm; 4) initializing a position value xk, a quantum bit, a rotation angle, a local optimal vector xp and a global optimal vector xg of each particle; 5) correcting the position value of each particle based on a graph theory; 6) updating a rotation angle guiding value, a quantum rotation angle and a bit of each quantum particle in sequence; 7) updating the position value xk of each quantum particle; 8) updating the local optimal vector and the global optimal vector of each particle; 9) carrying out convergence test; and 10) outputting an optimal particle position value x to obtain a corresponding failure recovery strategy.

The invention discloses a sensor node positioning method. The method comprises the following steps of: collecting the position information of each beacon node in a communication range at the node to be positioned to obtain the positioning information set of each beacon node; broadcasting the position information set to each beacon node by the node to be positioned; respectively independently operating a particle group algorithm by each beacon node according to the position information set to respectively obtain the corresponding the optimum particle position information; detecting the optimum particle position information by the node to be positioned to fix the coordinate position of the node to be positioned. The invention can obviously reduce the calculation consumption of the node to be positioned.