79 results about "Coefficient optimization" patented technology

The embodiment of the invention provides a globally optimized staggered mesh finite-difference forward simulation method and device. The method comprises the steps that a staggered mesh finite-difference operator is obtained; a target function is established based on a maximized norm and is calculated to obtain a staggered mesh finite-difference decentralization coefficient for optimizing the staggered mesh finite-difference operator, wherein the staggered mesh finite-difference decentralization coefficient is calculated in the maximum wave number coverage range of the optimized staggered mesh finite-difference operator and under the maximum spectrum error margin of the optimized staggered mesh finite-difference operator; the calculated staggered mesh finite-difference decentralization coefficient is adopted to optimize the spatial staggered mesh finite-difference operator, and forward simulation of seismic waves is performed. By adopting the scheme, absolute spectrum errors of the optimized staggered mesh finite-difference operator are more flexibly controlled, numerical value frequency dispersion is more effectively constrained, and the seismic simulation precision is improved.

The invention provides an image inpainting method based on a tight frame feature dictionary. A discrete cosine transform DCT-II-type orthogonal matrix is used for constructing a DCT frame system with redundancy, the system is successfully applied to the image inpainting field, and texture structure information of the image can be effectively inpainted. The image is decomposed under a DCT small frame base, the obtained frame coefficients represent edge feature information of the image in different directions or different orders, a priori knowledge of the sparsity of the frame coefficients is used at the same time, a DCT frame coefficient optimization model based on a weighted l1 norm is built, and an iterative algorithm based on an approximation operator is put forward to acquire the solution of the model. Under the assumption of a probability model, a Laplasse probability distribution priori model is used for approximating the actual probability distribution for geometric frame coefficients, and under an assumed model noise Gauss distribution condition, an MAP technology is used for building an adaptive sparse soft threshold operator, geometric frame coefficient sparse representation is carried out on the image, and the noise can be filtered while the edge features can be protected.

The invention discloses a fabric defect detection method based on sparse representation coefficient optimization. The detection method comprises self-adaptive dictionary database study, sparse coefficient matrix optimization and image reconstruction as well as generation and segmentation of a vision saliency map and specifically comprises steps as follows: an image is partitioned into blocks, self-adaptive dictionary database study is performed, and a dictionary database is obtained; a sparse representation coefficient matrix is solved with an L2-norm minimization method, and abnormal coefficient elements in the obtained matrix are optimized; a fabric image is reconstructed with adoption of the obtained dictionary database and the optimized sparse representation coefficient matrix, the fabric image and a to-be-detected image are subjected to residual error processing, and a residual error saliency map is obtained; the saliency map is segmented with a maximum entropy threshold segmentation method, and a fabric defect detection result is obtained. Randomness of fabric textural features and diversity of defect varieties are overall considered, the to-be-detected fabric image is taken as a detection reference for a dictionary database studying sample and a defect area, the method has higher detection accuracy, no defect information is required to be extracted, and the self-adaptive capability is high; the computation speed is higher, and the method is suitable for online detection.

The invention provides a bridge impact coefficient optimization method based on the parameter identification technology, solving the problems in the calculation of impact coefficient and the optimization of impact coefficient sensitivity in stochastic dynamics, which can reduce the damage of bridge. The method is carried out in this manner: (1) defining a vehicle model, (2) deriving the equation of motion MU<..>+CU<.>+KU=F<g>+F<w>, according to the D'Alembert's principle; using the PIM method to solve deterministic response and using the PEM to calculate random response, U(t)=integral H(t-[tau], t)(F<g>([tau])+F<w>([tau])) differential [tau], t belongs to (0, t). The invention provides an effective optimization method for non-stationary random vibration of a vehicle-bridge coupling system, and improves comfort and safety of the vehicle and the bridge. The method of the invention has both accuracy and efficiency, and can provide better technical support for vehicle-bridge coupling vibration sensitivity analysis and optimization. The computational time required by the PEM-PIM numerical method is more than ten times that of the Newmark method when the computational accuracy is the same, and the method of optimizing the sensitivity of the self-identification design variables is proposed.

The invention discloses a method for optimizing grid-connected unit excitation difference adjustment coefficient based on minimum overall network loss in the technical field of power system analysis and control. The method includes the steps: building a load flow calculation model affecting the generator excitation difference adjustment coefficient; determining a load flow constraint equation; building a grid-connected unit excitation difference adjustment coefficient optimization object function aiming at the minimum overall network loss, and determining constraint conditions of the optimization object function; gaining the optimal solution of the optimization object function according to the load flow calculation model and the load flow constraint equation to obtain the optimized excitation difference adjustment coefficient. The method overcomes the shortcoming that a traditional method determines the difference adjustment coefficient by experience, stable voltage is ensured, and the economic benefit of a power grid is the highest.

The invention discloses a multi-machine system difference adjustment coefficient optimization method based on sensitivity analysis, and the method comprises the steps: firstly obtaining the track sensitivity of a difference adjustment coefficient R, an inertia time constant M and a load damping coefficient D to the frequency deviation of a power system, analyzing the impact on the maximum frequency deviation from the track sensitivity of all parameters, and determining a dominant parameter of the maximum frequency deviation as a difference adjustment coefficient R; establishing a mathematicalmodel of difference adjustment coefficient R optimization, and converting a nonlinear optimization problem of the established mathematical model into a linear programming problem by adopting a sensitivity analysis method; and optimizing and sequencing the units according to the sensitivity of the difference adjustment coefficients of the units of the power system, and optimizing the difference adjustment coefficients of the units in sequence. The method can guarantee the output stability of the synchronous unit while meeting the system frequency stability, and fully excavates the frequency modulation capability of the synchronous unit.

The invention provides a mixed domain Fourier finite difference prestack depth migration imaging method based on the coefficient optimization in order to improve the earthquake migration imaging accuracy of a complex high steep structure region and forcefully guide accurate prediction and exploitation of oil-gas resources. According to the method, wave field extrapolation operators are expanded through a pade approximation rational function, expanded coefficients are optimized through a Chebyshev polynomial, new wave field extrapolation operators are obtained trough deduction, relative errors between the new wave field extrapolation operators and wave equation accurate wave field extrapolation operators are reduced, the approach degree of the wave field extrapolation operators is increased, and the earthquake migration imaging accuracy of the complex high steep structure region is improved while computational efficiency is guaranteed. The imaging effect of the improved mixed domain Fourier finite difference migration method on the Marmousi model migration section is compared with that of a conventional Fourier finite difference migration method on the Marmousi model migration section, and it is proved that the mixed domain Fourier finite difference prestack depth migration imaging method obtained based on coefficient optimization has higher migration imaging accuracy. The method has important significance in improving the earthquake migration imaging accuracy of the complex high steep structure region.

The invention provides a droop control coefficient optimization method. The method is realized by using an intelligent-control PSO-IHS (Particle Swarm Optimization-Improved Harmony Search) hybrid algorithm based on a micro-grid control model and a small-signal dynamic model. The PSO-IHS hybrid algorithm can effectively escape from local optimum to quickly find the global optimum, and is higher in robustness. The PSO-IHS hybrid algorithm is adopted for optimization, so that the optimal value of a droop control coefficient can be obtained, the method is an effective droop control coefficient optimization method, and the operation of a micro-grid can be more stable and efficient.

The invention provides an automobile wind resistance coefficient optimization method based on a self-adaptive agent model. The automobile wind resistance coefficient optimization method comprises thefollowing steps: conducting CFD simulation calculation on an automobile initial model; selecting a design variable and determining a change range of the design variable; constructing a parameterized model; selecting a sample point, calculating a wind resistance coefficient calculation value corresponding to the sample point, and storing the sample point into a sample point database; judging whether design variables need to be screened or not; constructing an agent model; optimizing the agent model to obtain an optimized solution and an optimized value; calculating a wind resistance coefficientcalculation value corresponding to the optimal solution; calculating the distance between the optimization solution and the sample point and the absolute value of the difference between the corresponding wind resistance coefficient calculation values, and obtaining an increase point; and calculating the error between the minimum wind resistance coefficient calculation value and the correspondingoptimization value until the precision requirement is met. The method has the beneficial effects that on the premise of ensuring the precision, the calling frequency of a complex real simulation modelis reduced, the automobile wind resistance coefficient optimization efficiency is improved, the time cost is reduced, and the research and development period is shortened.

The invention relates to the technical field of optimization and setting of generator excitation difference adjustment coefficients, and in particular relates to a generator excitation difference adjustment coefficient optimization setting method considering voltage stabilization. According to the method, the voltage stability margin is used as a constraint, and the minimum value of one norm of the reference voltage correction amount of the generator is used as a target, the characteristic that the power generator cannot maintain the reference voltage due to the limitation of the reactive capacity is simulated, the relation between the reactive power, the voltage and the difference adjustment coefficient of the generator is analyzed and expressed, and an optimized setting model of the difference adjustment coefficient is established. A user can give the setting value of the difference adjustment coefficient according to the set stability margin. Compared with an existing method, the method disclosed by the invention has the advantages that the calculation efficiency is high, the actual requirements such as voltage stabilization are comprehensively considered, and the method has relatively strong practical value.

The invention discloses a method for optimizing and determining the coefficient of a pipeline diversion point for process industry production scheduling, including: (1) establishing a process industry production scheduling logic network; (2) defining a function equation expression about profit expectations; (3) using Genetic algorithm, optimized to determine the pipeline diversion point coefficient. The present invention can determine and optimize large-scale pipeline diversion point coefficients by combining simulation modeling technology with the heuristic algorithm cutting-edge multi-population genetic algorithm, effectively improving the efficiency of production scheduling in the process industry, reducing the cost of production scheduling, and reducing the difficulty of calculation , to improve the performance index of production scheduling, the principle is simple, the implementation is convenient, the portability is strong, and it is suitable for different production scheduling environments.

The invention discloses a parameter optimization method based on power transmission line state estimation. The device is characterized by comprising a base, the method comprises the steps of basic particle swarm parameter optimization, weight coefficient optimization, learning factor optimization, power transmission line state estimation parameter optimization and the like. According to the method, a traditional swarm intelligence parameter optimization method can be overcome; the problem of insufficient local and global search capability balance is solved; particularly, when the search spacerange is relatively large and the search nodes are difficult to traverse, the global and local search capabilities of the balance algorithm are optimized by adopting the nonlinear weight coefficient and the learning factor collaboratively, and the global optimal solution is quickly converged in the evolution process, so that the optimal state estimation parameter is determined, and the state estimation accuracy is improved. The method is scientific and reasonable, and can be suitable for parameter optimization of various state estimations.

The invention relates to a coal supply method of optimizing a fire power plant by real-time coal firing heat value coefficient. The method includes monitoring various main parameters of a running unit in a certain time, multiplying and dividing relevant variable integral to obtain a dimensionless coefficient which can directly indicate relative capacity of real-time coal-fired heating quantity ofthe unit and provide operation basis for adjustment and control of an automatic power generation unit. By the coal supply method, an expensive coal-quality online analyzer can be substituted, online computing of real-time coal-fired heating quantity of the power generation unit is realized, and a coat-fired heating value coefficient is obtained. The coat-fired heating value coefficient can indicate data relation between real-time coal-fired heating quantity and designed value, which on one hand, can provide running references for monitors and can provide important operation data for an automatic power generation control system on the other hand. The coal supply method realizes real-time load adjustment of the unit and provides important insurance for safe and stable running of the unit and high-efficient peak pitching and frequency modulation of the power grid.

The invention discloses a high-resolution image reconstruction method based on sparse samples, belonging to the field of image processing. The method comprises the following realization processes: firstly taking the original low-resolution images as low frequencies and null matrices as high frequencies in three directions to carry out inverse wavelet transformation to complete image initialization, then utilizing contourlet to decompose the initial images, optimizing the coefficients in a decomposition domain, building neighbourhood vectors around the optimized coefficients and finally utilizing the mapping functions of the high/low-resolution sparsity coefficient neighbourhood vectors obtained in the training stage and neighbourhood fusion to obtain high-resolution contourlet coefficients and obtaining the high-resolution reconstructed images through inverse contourlet transformation. The method has the following advantages: the reconstruction accuracy of fine image textures is improved by utilizing the directionality of the contourlet coefficients, the reconstruction speed of the images is improved by utilizing the sparsity of the contourlet coefficients and possible noise jamming is inhibited by optimization of the contourlet coefficients; and through the comparison of several image reconstruction methods, the reconstruction method disclosed by the invention has the advantages of the highest peak signal to noise ratio and the best visual effect.

The invention discloses a user maximum sum rate optimization method in an environmental backscatter access NOMA (Non-Orthogonal Multiple Access) system, which is suitable for a system comprising a base station, two users and a backscatter, and all equipment is provided with a single antenna. A base station allocates power to two users according to a power allocation principle, backscattering is carried out on the two users under the condition that a scattering coefficient is greater than 0 and less than 1, a joint optimization problem of realizing the optimal sum rate of the two users is established, and the optimization problem is a double-target optimization problem of the power allocation coefficient and the backscattering coefficient of the base station. A scattering coefficient is fixed, a Lagrange function is established by utilizing a KKT condition to obtain a power distribution coefficient optimization value, the power distribution coefficient optimization value is substitutedinto an original target function, the optimization value of the scattering coefficient is solved according to the same method, and finally, the two solved optimization values are substituted into a sum rate formula to obtain the optimized sum rate.

The invention discloses an air conditioner energy saving control method and device. The method comprises the steps that an air conditioner cooling comprehensive performance coefficient optimization objective function is constructed based on the acquired target parameters; the constraints are configured for the cooling comprehensive performance coefficient optimization objective function; the objective function after the constraints are configured is optimized, and the optimal solution is obtained; and energy-saving control is carried out on the air conditioner based on the optimal solution. Byconstructing the cooling comprehensive performance coefficient optimization objective function of the air conditioner and configuring the corresponding constraints, the objective function after the constraints are configured is optimized, the optimal solution under the optimal condition of the air conditioner cooling comprehensive performance coefficient is obtained, and the energy saving controlis carried out on the air conditioner based on the optimal solution, so that the comfort of a user is taken into consideration while the power consumption of the air conditioner is reduced effectively, the effects of energy saving and scientific use of the air conditioner are achieved, and the technical problem that energy-saving control and user comfort of an existing air conditioner cannot be balanced is solved.

The invention discloses a method for designing a bilateral compensation structure-based strapdown inertial navigation attitude coning compensation algorithm. In allusion to the problem that the strapdown inertial navigation attitude algorithms at present are low in efficiency when compensating coning errors and maneuver errors at the same time, a novel bilateral coning compensation structure is disclosed; after that, the transformational relation and constrained relation from the traditional uncompressed attitude coning compensation structure coefficient to the novel bilateral coning compensation structure coefficient are established, and the error description on the basis of the traditional uncompressed attitude coning compensation structure under common maneuver conditions is deduced; on the basis, a novel attitude coning compensation structure coefficient optimization design method is disclosed and the optimization design of the attitude coning compensation structure coefficient is implemented, so that a bilateral compensation structure-based strapdown inertial navigation attitude coning compensation algorithm which is capable of effectively compensating the coning error and the maneuver error is designed.

The invention provides a track quality prediction method and system based on an improved gray combination model, and the method comprises the steps: preprocessing original data, and also comprises thefollowing steps: constructing an improved non-equal-time-interval gray IGM prediction model; optimizing the weight and the threshold of the Elman network by using a PSO algorithm, and constructing aPSO-Elman neural network correction model; and constructing an IGM-PSO-Elman model by integrating the advantages of the improved non-equitime-interval grey IGM model, the particle swarm optimization PSO algorithm and the Elman neural network. The invention provides the orbit quality prediction method and system based on the improved grey combination model; improvement is carried out on the basis of a traditional gray model, a weight coefficient optimization first-order accumulation generation process is introduced, weight matrix optimization model parameter solving is added, calculation of a difference equation background value is optimized by utilizing an integral area, and a PSO-Elman residual error correction model can reduce data fluctuation and reduce an improved non-equal-time-interval gray prediction residual error.

The invention discloses a wind power plant active power off-line prediction controller design method considering dynamic wake flow, and mainly relates to the technical field of wind power plant active power control. The method includes the following steps: S1, establishing a dynamic wake flow model of the whole wind power plant; s2, establishing a wind power plant active power prediction model through a state equation based on a wind power plant whole-field dynamic wake flow model; s3, constructing an active power optimization model of the wind power plant based on the dynamic wake flow model of the whole wind power plant; s4, performing wind power plant active power optimization solution by adopting a PSO algorithm; s5, establishing a mapping relation between the wind power plant system state and the yaw angle optimization value and between the wind power plant system state and the axial thrust coefficient optimization value through a CNN-GRNN hybrid network; and s6, constructing an active power off-line prediction controller of the wind power plant. According to the invention, the online calculation time of the wind power plant active power model prediction controller can be reduced, and the control precision is ensured.

The invention discloses an alternating iteration-based two-channel approximate orthogonal graph filter bank coefficient optimization method. According to the method, aiming at the problem of minimizing and maximizing filter coefficients of a graph filter bank, an optimal graph filter bank coefficient is obtained by determining a reconstruction error value of the graph filter bank, alternately optimizing an obtained objective function, fixing one group of coefficients to optimize the other group, and fixing the optimized group of coefficients to optimize the non-optimized group of coefficients. According to the invention, the coefficients of each graph filter bank are optimized mainly, and meanwhile, the optimization independence among the coefficients is ensured only for an approximate orthogonal graph filter bank structure, so that the maximum reconstruction error value is reduced, and the expected optimization target is achieved. Compared with a traditional optimization method, independent optimization of coefficients in the optimization process and the degree of freedom of design are guaranteed. Reconstruction errors can be reduced in the high-order and low-order filter coefficient optimization process, and a good effect is achieved.

The invention discloses a dimension optimization method of failure impact evaluation based on discrimination coefficient, according to the corresponding relationship between the aspects involved in the process failure impact and the type of enterprise responsibility, according to the property right boundary theory, perceived risk theory and manufacturing maturity theory , to construct a dimensional system for evaluating the severity of manufacturing process failure with systematic theoretical basis and completeness. Then, create a discriminatory coefficient calculation formula that can be used for fuzzy risk assessment. Finally, with the goal of minimizing the number of dimensions and maximizing the information content of the system, and taking the requirements of the principal component analysis method for the completeness of the system as constraints, a multi-objective mathematical optimization model based on the weight of each dimension and the evaluation data of each dimension is established. Optimize the selection of dimensions to find the best balance between the completeness and simplicity of the dimension system.

The present invention provides a microgrid economy and stability optimization method considering renewable energy source randomness. The method comprises the following steps: the step 1: performing output prediction of renewable energy source power; the step 2, establishing a microgrid economy optimization model; the step 3, establishing a microgrid stability evaluation index system; the step 4, determining a microgrid active power control strategy; and the step 5, performing coordination optimization of the microgrid economy and stability. Based on a droop control method, the microgrid economy and stability optimization method considering the renewable energy source randomness improves the isolated study of microgrid economy and stability in the prior art, effectively combines the microgrid economy and stability study, is suitable for changing of different operation modes, and performs optimization solution of droop coefficients through a differential evolution algorithm to realize good dynamic stability of the microgrid while good economy of the microgrid, and the established model method is reasonable and efficient.