47results about How to "Solve for stability" patented technology

The invention discloses a robot scene self-adaptive pose estimation method based on RGB-D camera. scene two-dimensional color image information of adjacent frames and space depth information corresponding to pixels of the two-dimensional color image are acquired based on a RGB-D camera; when the two-dimensional color image feature points are sufficient, ORB operators are adopted to extract features, the matching strategy provided by the invention is adopted to carry out accurate matching, and three-dimensional pose estimation is solved based on the pose estimation algorithm of the matching feature points; when the feature points are insufficient, the improved ICP algorithm provided by the invention is adopted to solve the three-dimensional pose estimation; then, a complete switching criterion is designed to fuse the two pose estimation methods; finally, pose estimation obtained through the two methods is optimized through a light beam adjustment algorithm, and finally smooth and accurate three-dimensional pose estimation is obtained. The three-dimensional pose estimation algorithm has the outstanding advantages of high robustness, high precision, small calculation amount, adaptability to different scenes and the like.

The invention provides a road network modeling method which takes road as a fundamental element, aiming at describing a traffic network by utilizing mutual relation among the roads with substitutive characteristics of the road in the traffic network taken into account. A data model matched with a conceptual model is given on the basis of improving a road network model which takes the road as the fundamental element, and data redundancy is reduced by blending related traffic information. A path searching algorithm suitable for the road network model is proposed on the basis of the road network model, and the path searching optimization is realized by utilizing road relation and road traffic attribute.

The invention provides a sparse characteristics based video image de-blurring method, comprising the following steps: a modeling step of establishing a de-fuzzy model of weighted total variation regularization constraint described in the figure wherein / x represents a restored clear image, B a fuzzy kernel, y a generated blur image, [miu] an adjustable parameter, W a diagonal matrix of weights, and Vx includes the first-order differentials in the transverse, longitudinal and diagonal directions; and a solving step where an alternating iterative method is used to obtain the updated pixel weights of the / x and repetitive iterations are performed until the / x is converged. According to the invention, the de-blurring of a blurred image can be realized for a restored clear image. Moreover, the non-convex constraint model is improved through the method of weight assignment, and the convex optimization model is established to make the first-order differential constraint model of the image sparser and the possibility for rapid solution for the model.

The invention provides an improved bat algorithm based method for solving a multi-objective active power dispatch problem of a power system. According to the invention, a bat algorithm for handling the multi-objective active power dispatch problem is proposed and is improved by utilizing an inertia weight coefficient and a global optimum guidance mechanism. The improved algorithm can handle the multi-objective problem effectively, can search an even-distribution Pareto Optimality front end and can search an optimal compromise solution by utilizing fuzzy subordination. According to the invention, the improved bat algorithm adopts congestion distance and non-dominated sorting for maintaining the even distribution of the Pareto Optimality front end and adopts the fuzzy mechanism to determinethe optimal compromise solution. The improved algorithm has a good optimization effect in an aspect of solving the multi-objective active power dispatch problem of the power system and is high in search efficiency and achieves even distribution of disaggregation of the Pareto Optimality front end.

The invention discloses a flexible multi-body robot modeling and solving method based on multi-dimensional reconstruction correction, This method is successively performed by: A mathematical model ofrigid component and a flexible component are respectively constructed and parameters are set, the holonomic constraint conditions of a flexible multi-body robot system are established, the static anddynamic equations of flexible multi-body robot system with holonomic constraints are obtained, According to the known state parameters of t-time, the state parameters of t + h-time are obtained by iterative solution of dynamic equations under complete constraints using multi-dimensional reconstruction and correction method, and the state parameters of t + h-time are obtained by repeated iterativeprocess until the end of the algorithm. The forward and inverse dynamic models of flexible multi-body robot with holonomic constraints are established by the modeling and solving method, and the dynamic equations are solved iteratively by using the multi-dimensional reconstruction and correction algorithm. The modeling is complete and the solving scale is small. Compared with the traditional solving method, the convergence condition is easier to be satisfied, and the efficiency of calculation and solution is improved.

The invention discloses an image video super-resolution method and device. The method comprises the following steps: the input step: a low-resolution image is inputted; the super-resolution step which includes interpolation and deblurring, wherein interpolation is performed on the low-resolution image and deblurring is performed on the image after interpolation through a weighted total variation regularization constraint super-resolution model; and the output step: a high-resolution image is outputted. According to the method, the weighted total variation regularization model is used so that the curve of the model is enabled to be closer to the real first-order difference statistical result of the natural image, the non-convex phenomenon of an optimization function caused by certain conventional approximation priori can be improved and solving of the equation is enabled to be more stable and rapid. Besides, a rapid solving method is also designed for the model so that the model is enabled to be more practical. Meanwhile, the operation effect of the method is enabled to be more robust through excessive deblurring control.

A real-time soft tissue deformation method for simulating biomechanical properties is disclosed. The method comprises the steps of S1, generating soft tissue physical model data based on soft tissue organ three-dimensional visualization data; S2, generating collision detection model data based on the soft tissue physical model data; S3, loading the physical model data and the collision detection model data by a game engine for collision detection; and S4, calculating the state after soft tissue deformation through a constrained optimization method.

A camera response curve calibration method based on a single image includes the following steps of: (1) shooting one image for a 24-color standard color plate not in the situation of overexposure; and(2) solving the mean value of 24 color lump images and comparing with a true color value, constructing a camera response curve equation and using a least squares numerical method for solving. The method is simple in shooting and quick in calculation speed.

The invention discloses a joint task planning method for satellite imaging and transmission. The joint mission planning method is characterized by comprising the steps of 1, obtaining a two-dimensional imaging data sheet and a two-dimensional transmission data sheet; and 2, obtaining a planning result by using multi-algorithm hybrid iterative solution. According to the joint task planning method, the planning and computing can be carried out from the satellite imaging and transmission integrated global prospect, so that a more optimized automatic planning service can be provided.

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 discloses a multi-body-dynamics-equation solving method based on a Bathe integration strategy. The method includes: establishing a corresponding coordinate system in a multi-body dynamics system, and using a relative-coordinate method and a Lagrangian-multiplier method to obtain dynamics equations according to a virtual-power principle; seeking a second derivative of a constraint equation with respect to time to obtain differential-algebraic equations of an index -1, and adding constraint stability terms to form a general form of the dynamics equations with complete constraints;carrying out integration iteration solving on the general form of the dynamics equations with the complete constraints on a time step [t, t+h / 2] to obtain motion parameters of a moment t+h / 2; carryingout integration iteration solving on a time step [t+h / 2, t+h] to obtain the motion parameters of a moment t+h; and carrying out repeated iteration calculation, outputting values of generalized displacement, velocity and acceleration, which are of a system and are changed over time, when calculation time reaches set total simulation time, and thus analyzing motion status of the multi-body process-closed-loop dynamics system in certain time. The method can also achieve better precision in a case of larger integration step length.

The invention discloses an intelligent reservoir optimal operation method. According to the intelligent reservoir optimal operation method, based on the analysis on the defects of a traditional simulated annealing genetic algorithm in the aspect of reservoir optimal operation, improvement is conducted on the algorithm by introducing the ecological niche technology, adopting the self-adaptive crossover and mutation strategy and adopting the optimization saving strategy in the selection process, and a reservoir optimal operation nonlinear mathematical model with the maximum generating capacity as the target is solved according to the specific condition of a reservoir.

The invention discloses a distribution network reconstructing and modeling solving method capable of minimizing removal of a fault area. The method comprises the following steps: constructing an objective function containing load loss; establishing a voltage amplitude constraint model of an extended feasible region for all nodes; establishing a network constraint model for a distribution network;establishing a transmission capability model for a distribution line; and through the voltage amplitude constraint model, the network constraint model and the line transmission capability model, enabling the voltage amplitudes of a fault point and all nodes communicated with the fault point through a conduction line to fall in a fault voltage range, enabling the loads of the fault point and all nodes communicated with the fault point through the conduction line to be 0, and identifying the nodes with the loads being 0 as a fault removal area. By using the distribution network reconstructing method disclosed by the invention, the fault removal area and the load loss can be minimized. Through mixed integer linear programming representation, the problem that resolution cannot be realized andthe stability and reliability are poor in the prior art can be solved. The embodiment of the invention further discloses a device and a storage medium.

The invention discloses a magnetotelluric forward modeling method and device based on a ground-air decomposition strategy and a storage medium. The method comprises the following steps: constructing ageoelectric geometric model for solving a three-dimensional MT of a region according to digital terrain data (DEM); discretizing the geoelectric geometric model into a series of tetrahedron units; obtaining an integral weak solution form satisfied by A-phi-psi coupling potential three-dimensional MT forward modeling based on a ground-air decomposition strategy, and performing node discretization-based conversion on the integral weak solution form to obtain a sparse linear equation set; loading a right-end term of the incident field placed in the sparse linear equation set and solving the sparse linear equation to obtain a vector bit A, a scalar bit phi and a scalar bit psi of each node of the tetrahedron unit in the solving area; calculating parameters of the observation point according to the vector bit A, the scalar bit phi and the scalar bit psi. The method has a promotion effect on large-scale three-dimensional magnetotelluric method inversion research, and has important practicalsignificance and value for promoting subject development and improving the academic level of electromagnetic method data interpretation.

The invention relates to a dieless hydraulic quasi-static thin-plate bulging deformation limit strain and load graphic calculation method. The method comprises the steps of taking dimensionless deflection as horizontal coordinates, taking an equivalent stress and an equivalent strain as left and right longitudinal coordinates, and drawing a united curve based on geometric and physical relationships, a curve cluster based on a static balance relationship and a strain-dimensionless deflection curve based on a geometric relationship in sequence; finding deformation, stress, strain and the like corresponding to an action static pressure load through a curve intersection point; and through a curve tangent point which is a tension instability point, obtaining a limit load corresponding to a balance relationship curve first and then finding a limit strain in the right longitudinal coordinates in combination with the strain-dimensionless deflection curve. The method has the beneficial effects that (1) solving is stable; (2) the method is suitable for other constitutive model materials; (3) the method can be used for solving deformation, stress and strain in a loading process; (4) the calculation process is simple and clear and is definite in physical significance; and (5) the maximum deflection and the maximum stress value can be obtained.

The invention relates to a three-dimensional tomography method based on seismic wave in-phase axis slope information. The method comprises the steps that 1) effective data picking is carried out on athree-dimensional common shot point gather and a common detection wave point gather to acquire horizontal component information of ray parameters at shot points and detection wave points; 2) a three-dimensional slope tomographic matrix is established according to the acquired horizontal component information of the ray parameters at the shot points and detection wave points; 3) a regularization term is added into the linear equations of the established three-dimensional slope tomographic matrix to acquire large-scale sparse linear equations; and 4) the large-scale sparse linear equations are solved to acquire a macroscopic velocity model of slope tomographic inversion, and the model is used for three-dimensional migration imaging or as an initial model of three-dimensional full waveform inversion. The method provided by the invention can be widely applied in the field of exploration seismology.

The invention discloses a Stephenson-Newton iteration method based on gear shaving and modifying and engagement-angle calculating, which is applied to the technical field of the modifying of the gear shaver for the first time. The optimal solution of the engagement angle can be obtained by adopting the existing general approximate calculating method of the end surface engagement angle, the calculation error of the length of the engagement line, which is caused by the calculation error of the engagement angle, can be avoided, the modifying position of the gear shaver can be calculated more accurately, and the process effect of removing the concave shape of the shaved gear can be guaranteed effectively.

The invention discloses a multi-b-value diffusion magnetic resonance imaging optimization method based on signal-to-noise ratio weighting. The method comprises the following steps of through multi-b-value weight data, establishing a fitting model; through the multi-b-value weight data, estimating a signal to noise ratio; according to the signal to noise ratio, calculating a residual error correction weight alpha i and establishing a fitting model initial value; according to the residual error correction weight alpha i, calculating a fitting model residual error correction value; if the fitting model residual error correction value accords with a convergence condition, through an optimal solution of the fitting model, calculating and increasing stability; otherwise, returning to a step4 and continuously seeking the optimal solution of the fitting model. In the method, aiming at multi-b-value diffusion weighting, the fitting model is constructed; through optimization of a least square algorithm, stability of model optimization searching solution is increased so that estimation of a parameter image in MRI imaging is stable; and generation of a singular value or a meaningless value is reduced so as to further promote development and application of a MRI imaging technology.

The invention discloses an on-chip optical network mapping method based on a growth type annular SOM neural network. The method comprises the following steps: acquiring an IP core connectivity relation matrix in a core graph and a node communication loss matrix in a network topology; training the growth type annular SOM neural network by using a row vector in the node communication loss matrix to obtain a trained growth type annular SOM neural network; respectively acquiring a winning neuron index number of each row vector in the node communication loss matrix and a winning neuron index number of each row vector in the IP core connectivity relation matrix by using the trained growth type annular SOM neural network; and according to the winning neuron index number of the IP core connectivity relation matrix and the winning neuron index number of the node communication loss matrix, obtaining a mapping matrix between the IP core and the network topology node in the core graph. According to the method, the complexity of the mapping method is reduced and the solving time is shortened in a self-adaptive manner under the condition of ensuring the stability of the result.

The invention relates to a regional integrated energy system equipment capacity optimization method based on improved NSGA-III. The method comprises the following steps: establishing a regional integrated energy system model composed of photovoltaic, wind power, CCHP and electric energy storage equipment; from the perspective of a carbon target, determining a target function and constraint conditions of the regional integrated energy system model; the method comprises the following steps: constructing an improved NSGA-III algorithm based on a self-adaptive SBX-NBX hybrid crossover operator; the method comprises the following steps: simulating cooling, heating and power loads of a research area, collecting annual temperature, solar radiation intensity and wind speed data, and dividing the whole year into three typical days of summer, winter and transition season by adopting a K-means clustering method; and by utilizing an improved NSGA-III algorithm, according to a target function and constraint conditions of the model, in combination with typical daily data of a research region, solving capacity configuration of the regional integrated energy system equipment, and obtaining an optimal capacity configuration scheme. Compared with the prior art, the method provided by the invention can accurately and stably solve the optimal scheme for the equipment capacity configuration of the low-carbon regional integrated energy system.

The invention discloses a combination learning super-resolution method based on dual constraints, which mainly solves the problem of image blurring caused by the traditional neighborhood embedding super-resolution method. The combination learning super-resolution method based on the dual constraints comprises the following steps of: (1) generating a training set by using a high-resolution image and establishing a group block pair of a low-resolution image block and a high-resolution image block; (2) extracting an eigenvector of the low-resolution image block and searching the best matched group block pair in the training set; (3) jointly learning eigenvectors of the low-resolution image block and the high-resolution image block in the group block pair, constructing a projection matrix andgenerating a joint characteristic subspace; (4) estimating high-resolution image blocks by using the neighborhood embedding in the generated joint characteristic subspace; (5) synthesizing all the estimated high-resolution image blocks into a high-resolution image; and (6) improving the image quality of the synthetic high-resolution image by using global reconstructing constraints and a back projection algorithm. An experimental result indicates that the invention has stronger super-resolution ability and can be used for magnifying low-resolution natural images.

The invention relates to an inverse kinematics solving method suitable for a two-section six-degree-of-freedom continuum mechanical arm, and aims to solve the technical problem of how to realize accurate motion control for a full-degree-of-freedom continuum mechanical arm. A virtual connecting rod method is adopted, a continuum mechanical arm formed by connecting two sections in series is equivalent to a connecting rod type mechanical arm, and constraints between kinematic pairs are given; physical description of the inverse kinematics model of the mechanical arm is converted into mathematical description; and the mathematical description of the model is converted into a function expression, and solving is performed.

The invention provides a video image deblurring method based on sparse characteristics, which includes the following steps: a modeling step, establishing a deblurring model with weighted total variation regularization constraints, expressed as where, represents the restored clear image, and B represents the blur kernel , y represents the generated blurred image, μ is an adjustable parameter, W is the diagonal weight matrix, Vx includes horizontal, vertical and oblique first-order differences; the solution step is to obtain the updated pixel weight through alternating iteration method , iterate multiple times until convergence. According to the present invention, the deblurring process of blurred images is realized and clear images are restored. Moreover, the non-convex constraint model is improved through the weight distribution method, a convex optimization model is established, and the first-order difference constraint model of the image is made sparser, while fast solution of the model is achieved.

The invention discloses an efficient identification method for parameters of an equivalent circuit model of a photovoltaic module, and the method comprises the steps: improving the development capability of a population through a parameter adjustment strategy for correcting a wormhole existence probability WEP based on an enhanced dynamic elite multivariate universe algorithm; a parameter adjustment strategy for adjusting the travel distance rate TDR by relying on the current population quality is introduced to prevent search from falling into local optimum; the travel based on a dynamic elite strategy is adopted to improve the success probability of each universe travel, and model parameters can be rapidly, accurately and stably solved under the minimum calculation amount all the time. Besides, an accurate photovoltaic module mathematical model is established, so that the output characteristics of the system are accurately evaluated, and the method is of great significance to system performance evaluation, fault analysis and operation and maintenance guidance.

The invention discloses a radioactive power distribution network reconstruction method with a variable number of connected areas, and the method comprises the steps: constructing a phase angle minimization sub-optimization problem with a phase angle lower bound, and converting the phase angle minimization sub-optimization problem into a KKT condition; constructing a zero phase angle identificationconstraint, and solving a phase angle minimization sub-optimization problem from a KKT condition to obtain the number of zero phase angles; taking the number of the zero phase angles as the number ofdegrees of freedom of the voltage phase angles of the connected districts, and constructing a power distribution network radioactivity constraint; and embedding or merging the radioactive constraintsof the power distribution network into the optimization model of the power distribution network reconstruction problem to obtain a power distribution network reconstruction optimization model with the radioactive constraints of the power distribution network. According to the method, the defect that the network radioactivity condition cannot be listed before the number of the connected areas is obtained in the prior art can be effectively overcome, and the network radioactivity condition can be embedded into the variable-number distribution network reconstruction optimization problem of the connected areas in an explicit mode. The embodiment of the invention further discloses a radioactive power distribution network reconstruction device with the variable number of connected areas.

The invention relates to an integrated energy system equipment capacity optimization method based on an improved particle swarm algorithm. The method comprises the following steps: establishing an integrated energy system model composed of photovoltaic, wind power, CCHP and electric energy storage equipment; determining an objective function and constraint conditions of the integrated energy system model; based on reverse learning and elite promotion, constructing an improved dynamic multi-population speed-item-free particle swarm algorithm; solving the capacity configuration of the integrated energy system equipment by utilizing an improved dynamic multi-population speed-item-free particle swarm algorithm in combination with a target function and a constraint condition of the integrated energy system model to obtain an optimal capacity configuration scheme. Compared with the prior art, the method has the advantages that by improving the particle swarm optimization, the position updating process of the particles can be effectively simplified, the particles can be prevented from missing the globally optimal solution, and therefore the equipment capacity configuration optimal scheme of the comprehensive energy system can be rapidly, accurately and stably solved.

The robust target tracking method based on adaptive simultaneous sparse representation of the present invention comprises the following steps: S1, according to the size of Laplacian noise energy, adaptively establishes a simultaneous sparse tracking model; S2, to the established tracking model Solving; S3, updating the template. The tracking method provided by the invention has good tracking and recognition effect, strong anti-interference ability, can realize more accurate and real-time target tracking, and the tracking method is relatively stable.

The invention discloses a multi-objective optimization solving method based on the congestion degree, which comprises the following steps: firstly, calculating the congestion degree of a Pareto front edge and the congestion degree of a Pareto solution set, then obtaining a new congestion degree by utilizing the congestion degree of the Pareto front edge and the congestion degree of the Pareto solution set, analyzing the obtained new congestion degree to obtain the next search direction of an optimization algorithm, and gradually obtaining the final Pareto front edge. According to the method, on the basis of Pareto frontier analysis, the congestion degree analysis of the Pareto solution set is added, the congestion degree of the Pareto solution set is obtained, the defects in the prior art are overcome, and the solving method is more accurate and faster; the invention also discloses a system for realizing the method.