149results about How to "Simplify the optimization process" patented technology

The invention discloses an antenna downward inclination angle adjusting method and device based on an LTE network. The antenna downward inclination angle adjusting method and device based on the LTE network are used for solving the problems that in the prior art, due to manual intervention, resources are wasted, data are inaccurate, antenna downward inclination angles are arranged unreasonably, and the reliability of wireless transmission cannot be ensued. The method comprises the steps that a cell weak coverage coefficient and a cell overlapping coverage coefficient of each cell are obtained through simulation results of a propagation model, problem cells are screened out according to the cell weak coverage coefficients and the cell overlapping coverage coefficients of all cells, and lastly, the antenna downward inclination angles of all the problem cells are adjusted in sequence according to priorities of all the problem cells and the coverage areas of all the cells. In this way, the manual intervention is reduced, the resources are saved, the data are obtained through simulation calculation instead of experience values, the optimization process is simplified, optimization efficiency is improved, the accuracy of the antenna downward inclination angle is improved, and transmission reliability is ensured.

Disclosed is a power distribution unit and power management architecture employing such power distribution unit. The power management architecture includes a remote power management system for managing a plurality of power appliances, and a plurality of power distribution units each of which is provided with wireless network connectivity. With the wireless network connectivity, the remote power management system and the power distribution units constitute a mesh link network for two-way data transmission. In operation, the remote power management system transmits a first profile to the power distribution units to allow the first control unit of the power distribution unit to real-timely control the operation of the power distribution units according to the management policy and/or the management principle recorded in the first profile.

The invention relates to a dynamic response topological optimization method implemented by application of improved bi-directional evolutionary structural optimization (BESO) to an equivalent static load method. In the method, improved BESO is applied to the equivalent static load method to form an optimized iteration method for replacing an original variable density method, and establish a new convergence condition in order to improve a dynamic response topological optimization flow. By adopting the method, optimization time of a design circulation stage of which k is greater than 1 can be reduced effectively, and CPU (Central Processing Unit) total time needed by optimization in two examples is reduced by 21.8 percent and 25.3 percent compared with an original method; the method has the characteristics of high computation speed and simple optimization flow; along with the increase of external load acting points and the expansion of the optimization scale, design circulation times needed by optimization are increased, and the method has greater advantages.

The invention relates to a stochastic model modification method based on uncertainty of a stochastic response surface estimated parameter, which comprises the following steps: 1, representing an uncertain parameter of a structure system as a function of a standard stochastic variable and representing a response of the structure system as a polynomial chaos expansion which uses the standard stochastic variable as an independent variable and is based on a Hermite polynomial, and solving an undetermined coefficient of the polynomial chaos expansion so as to establish a stochastic response surface model of the response of the structure system and calculate a statistical characteristic value of the response of the structure system; 2, utilizing an error function of a statistical characteristic value of the stochastic response surface model and the statistical characteristic value of the actually measured response to establish a target function required by stochastic model modification; 3, utilizing the target function to construct optimization inverse problems and modifying a parameter statistical characteristic value step by step; and 4, on the basis of the parameter statistical characteristic value obtained by stochastic modification, utilizing a stochastic response surface to calculate and obtain the statistical characteristic value of the response of the structure system. The method ensures modification accuracy when improving modification efficiency.

The invention relates to random model updating method based on an interval response surface model. The method is characterized by including the steps of firstly, building a second-order polynomial response surface model without cross terms according to experiment design and regression analysis; secondly, using a square completing method to convert a polynomial response surface expression into perfect square; thirdly, substituting interval parameters into the response surface expression to allow the definite response surface model to be changed into the interval response surface model; fourthly, performing interval calculation on the interval response surface model to obtain predicted structural response intervals, and combining the predicted structural response intervals with actual response intervals to build a target function; fifthly, building a optimization inversion problem to identify interval distribution of parameters. By the method, the expansion problem of interval calculation is avoided, fast calculation of structural response intervals is considered, finite element analyzing calculation and sensitivity matrix building during (interval) random model updating are avoided, a large amount of calculation time and cost is saved, and ill-conditioned optimization is avoided as much as possible.

The invention discloses an optomechanical integrated analysis method for pre-processing interface program and mirror surface shape optimization method. The method comprises the steps of establishing a mirror finite element model, numbering all finite element mesh nodes in the mirror finite element model, based on the node number extracting coordinate information of finite element mesh nodes, calculating the weighted factor of the mirror node area of the finite element mesh node, based on the weighted factor and coordinate information of the mirror node area, using the Zernike Zernike polynomials as the basis functions for fitting, acquiring the relationship between the displacement of the mirror rigid body and the displacement of the Zernike Zernike polynomial and the mirror mesh node, conducting optomechanical interface pre-processing of the linear relation to generate the finite element analysis pre-processing file, introducing the finite element analysis pre-processing file into the finite element model for the optomechanical system dynamics integration analysis and mirror surface shape optimization. The optomechanical integrated analysis method for pre-processing interface program and mirror surface shape optimization method solves the problem that a large amount of fitting data leads to a large amount of calculation and a failure of fitting in an optomechanical program post-processing.

The invention discloses a method and device of building a decision-making tree and a method and device of network performance optimization. The method of the network performance optimization comprises the steps of obtaining preset current parameter data of wireless parameters in a wireless network to be optimized; forecasting a network performance level of the wireless network according to a wireless parameter decision-making tree of the wireless network and the obtained parameter data; judging whether the wireless network needs performance optimizing or not according to the forecast network performance level; and if a judging result is yes, the parameter data of the wireless parameters conduct adjusting so as to optimize network performance of the wireless network. By the adoption of the technical scheme, the problems that in the prior art, flexibility of network performance optimization is low, and a process is complex and lags are solved.

The invention discloses a method and device for building a neural network so as to solve a technical problem that a neural network built in the prior art is high in optimization difficulty and low in efficiency. The method comprises the steps of building an initial neural network, wherein a plurality of specific structures preset in the initial neural network are respectively provided with corresponding sparse scaling operators, and the sparse scaling operators are used for scaling the output of the corresponding specific structures; training a weight of the initial neural network and the sparse scaling operators of the specific structures by adopting preset training sample data, and acquiring an intermediate neural network; deleting specific structures with the sparse scaling operators being zero in the intermediate neural network, and acquiring a target neural network. The neural network built by adopting the technical scheme of the invention is simple in optimization, easy to implement and high in training efficiency.

The invention provides a grid fault diagnosing method based on the combined type cross entropy algorithm. The method comprises the steps of collecting the action messages of a protector and a breaker by means of a data acquiring and monitoring control system and a relaying information system, marking off faulty sections and determining a suspended fault element set, establishing an objective function based on suspended fault elements and solving the function by means of the cross entropy algorithm, identifying a fault element through the obtained optimal solution, and outputting a diagnosing result. According to the grid fault diagnosing method based on the combined type cross entropy algorithm, a fault diagnosing model is established according to the action messages of the protector and the breaker, the optimal solution of the model is obtained by means of the cross entropy algorithm, and then the fault element is identified. The grid fault diagnosing method based on the combined type cross entropy algorithm can be used for on-line grid fault diagnosis.

The invention provides a design method of woven fabric composite material properties based on controllable microstructure. The design method utilizes universal single-cell thought and establishes mesoscopic/ macroscopic heat transfer/ mechanical an analysis model; based on mesh deformation method, through the meso-scale of typical periodic cell elements and the change in the form of the cell, and combined with multiscale analysis and multi-objective optimization, the design method is capable of quickly effectively meeting the requirements of targeted design and the 'lightweight' pursuit. The technology has the advantages of being low in cost and short in design period to gain physical property parameter which suits the need of 2D, 2.5D, 5D, and 3D woven fabric composite material.

The invention discloses an edge collaboration cache arrangement method based on a drosophila optimization algorithm. The method comprises the following steps: (1) obtaining a popular video set and a user demand vector of an area according to the historical request information of users in the area; (2) according to the popular video set and the user demand vector, establishing a target optimizationproblem with the maximization of a total video transmission delay reduction amount in the area as the target, and solving the target optimization problem based on the drosophila optimization algorithm to generate a cache arrangement decision; (3) allocating a video cache task for each cache node according to the cache arrangement decision; and (4) when a user request arrives on the cache node, ifthe cache node does not cache corresponding contents, downloading the contents from a neighbor cache node that caches the contents and has the minimal delay, and if all cache nodes in the area have no cache response contents, downloading the cache response contents from a remote server. By adoption of the edge collaboration cache arrangement method disclosed by the invention, the cache hit rate can be improved, the average video transmission delay is reduced, and the user experience quality is improved.

The invention discloses a whale optimization algorithm-based multicast routing optimization method and application of the whale optimization algorithm-based multicast routing optimization method to aSpark platform. According to the method, as for a specific multicast routing optimization problem, improvements are made on a whale optimization algorithm, so that the whale optimization algorithm canbe applied to solving the multicast routing optimization problem; the whale optimization algorithm that optimizes continuous problems is mapped to a discrete search space through the binary codes ofposition information, and the ideas of algorithms such as individual crossover, mutation, and taste concentration are introduced into a position update strategy, so that the method can be more suitable for solving the multicast routing problem; and therefore, the optimization process of multicast routing can be simplified, convergence speed can be increased. The invention also discloses the application of the whale optimization algorithm-based multicast routing optimization method to the Spark platform, and therefore, the parallelization of the whale optimization algorithm-based multicast routing optimization method can be realized on the Spark platform, and the execution speed of the algorithm is greatly accelerated.

The invention discloses a method for fixing a turn-on angle and optimizing a turn-off angle of a switched reluctance generator, belonging to the technical field of control of the switched reluctance generators. The method is as follows: 1. a phase current expression is firstly derived according to a voltage equation of the switched reluctance generator, an average electromagnetic power expression in a cycle is calculated by integration based thereon, and a factor which is only related to the turn-on angle and the turn-off angle is separated therefrom. 2. The turn-on angle is then fixed, the turn-off angle changes in a reasonable range, the factor is taken as a single-variable function of the turn-off angle, and the turn-off angle which is corresponding to the maximum value is solved by three situations according to the features of a high order derivative thereof. The method is based on the theory, simple and easy to operate and good in universality.

A cheap and high performance 1.5 transistor-type flash memory highly compatible to external of memory region is provided. The flash memory has sacrifice film formed on substrate. U-shaped groove is formed on sacrifice film, where multiple insulating film is laminated. Multiple insulating film includes silicon nitride film as charge storage layer. Low resistive material is disposed on multiple insulating film to form control gate. Select gate is formed on insulating film on side of control gate in self-aligned manner. Semiconductor regions opposite in conductivity to substrate on both sides of adjoining control gate and select gate to form source and drain, respectively. Thus, a 1.5 transistor-type flash memory is formed with adjoining control gate and select gate between source and drain. In MOS-type transistor with control gate, threshold voltage is changeable according to injection/emission of charge to silicon nitride as charge storage layer, and thus work as non-volatile memory.

The invention discloses a multi-train speed curve collaborative optimization method considering network loss. The method comprises the following steps: acquiring basic data of trains, lines, timetables, traction networks and the like; calculating an energy-saving speed curve of a preceding train running in each section, introducing a posterior train, dividing the running process of the multi-trainsystem into sub-sections, establishing a traction network equivalent circuit model based on multiple trains, establishing and solving a traction substation total energy consumption optimization model, and outputting a multi-train energy-saving optimization speed curve. According to the invention, the running speed curve of a single train in each section is optimized such that train traction energy consumption is reduced, the utilization rate of regenerative braking energy is increased by cooperatively optimizing speed curves of multiple trains, single-train optimization and multi-train optimization are combined, meanwhile, train traction energy consumption is reduced, the utilization rate of regenerative braking energy is increased, and finally the total energy consumption of a traction substation is reduced.

The invention provides a service simulation-perception-evaluation method and system. The method comprises the following steps: acquiring the levels of grids in each cell service area and the levels of adjacent cells in a network, segmenting the levels of the adjacent cells according to the upper threshold values and lower threshold values of the levels of the adjacent cells, and counting probabilities P1, P2 and P3 that the level of each adjacent cell is positioned in three intervals; calculating the service occurrence probability of each grid; calculating the service occurrence probability of the entire network; simulating telephone traffic scattering in a network area according to the service occurrence probability of the entire network by using Monte Carlo simulation; simulating a new site according to the telephone traffic simulation scattering of each cell in the network; and performing service perception and evaluation on the simulated new site. Compared with the prior art, the service simulation-perception-evaluation method and system have the advantages that comprehensive optimization of service perception can be performed by making full use of MR (Measurement Result) data; service scattering, the determination and evaluation of the new site and network optimizing flow can be greatly simplified; and manpower and material resources are greatly saved.

The invention discloses an adaptive finite element GPR frequency domain forward modeling method. The method comprises the steps of setting parameters of a forward modeling method; calculating a numerical solution and a local error indication value of the current grid; marking a unit set needing to be refined; refining the marked grid; and carrying out numerical solution to obtain a final self-adaptive finite element GPR forward modeling result. The invention provides a self-adaptive finite element GPR forward modeling method. Based on a posteriori error estimation strategy, a posteriori errorestimation strategy is acquired. A self-adaptive finite element method is adopted to carry out GPR frequency domain forward modeling, EPML boundary conditions are introduced into frequency domain forward modeling calculation, the parameter optimization process is simplified, a good absorption effect can be achieved under the condition that the thickness of an absorption layer is small, the degreeof freedom of nodes can be reduced, and the calculation cost is saved to a great extent.

The invention discloses an ultrasonic echo signal extraction method based on multi-scale matching tracking. The method comprises the following steps: step one, ultrasonic echo signal acquisition and synchronous uploading; step two, time frequency parameter value range compression: compressing value ranges of time frequency parameters of a Garbo atom used for performing sparse decomposition on a to-be-processed signal f(t) by adopting the data processing equipment, wherein the time frequency parameters comprise a scale parameter, a displacement parameter, a frequency parameter and a phase parameter; step three, signal extraction as follows: step 301, signal sparse decomposition based on optimization algorithm; and step 302, signal reconstruction. The method disclosed by the invention is simple in step, reasonable in design, convenient for realization, and good in use effect; the value range of each parameter in the time frequency parameters is compressed, and then the optimal matching atom is searched by adopting the signal sparse decomposition method based on the optimization algorithm, the signal extraction speed can be greatly accelerated, and the signal extraction precision canbe effectively improved.

The invention discloses a grey box model parameter identification method and system for a robot system. The method comprises the steps that multiple times of closed-loop excitation are carried out onthe robot system at each operation point selected in a working space to obtain time domain input and output data of each time of closed-loop excitation of the operation points; nonlinear suppression and discrete Fourier transform are carried out on the time domain input and output data to determine a test frequency response function; according to state data corresponding to the operation points, and rigid body parameters, joint rigidity parameters and joint damping parameters of the robot system, an extended flexible joint dynamical model of the robot system is constructed, and a grey box model frequency response function is determined; a target optimization function is constructed according to a logarithmic error between the test frequency response function and the grey box model frequency response function; and the target optimization function is solved to obtain an optimal joint rigidity parameter and an optimal joint damping parameter. According to the method, the accurate identification of unknown joint elastic parameters in the dynamical model of the robot system can be realized.