44 results about "Model decomposition" patented technology

The invention relates to an image restoring method, which comprises the following steps: firstly carrying out direction and experience model decomposition on a target image, allowing to draw damaged edge structure information in a given area to be restored according to the existing information, utilizing a frequency characteristic value obtained by the direction and experience model decomposition to calculate similarity so as to firstly finish edge structure restoration in the whole damaged area, then combining degree of confidence to calculate the priority of a residual filling area according to gradient characteristics of pixel points on the boundary of the area to be restored, and utilizing a frequency characteristic value obtained by the decomposition again to calculate similarity so as to finish the restoration of corresponding areas. The method not only can restore the structure and texture information in a larger-scale defective area, but also can restore the images with certain sheltering relation. The robustness in the restoring process is high, thus effectively preventing continuous expansion of error information.

The invention discloses a multi-AGV scheduling method based on petri network decomposition. The method comprises the following steps: step 1, converting a warehouse topological graph into a collision-free petri network model; decomposing the whole petri network model into sub-networks according to the drivable route of the single AGV; step 2, adopting a Dijkstra algorithm to realize global shortest path planning of the single AGV, and further adjusting the obtained algorithm according to the actual motion condition of the vehicle; step 3, establishing a target function; step 4, taking a staticshortest path obtained by a single AGV in each sub-network as a basis; using an external point penalty function method to carry out path iteration, obtaining collision and deadlock problems existingin the operation process of multiple AGVs, analyzing and locally optimizing the types of collision and deadlock, locally optimizing the path by taking time optimization as a principle, and generatingan optimal collision-free scheduling scheme. The method is high in operation efficiency, high in reaction speed and high in adaptability.

The invention discloses a fault tree collaborative analysis system based on authority management and model decomposition. The system mainly comprises the following seven modules which include a modeling target resolver, a modeling task decomposer, an authority manager, an authority mapper, a conflict inhibitor, a model combiner and a model analyzer. According to the system, a collaborative technology is applied from modeling target analysis and task definition to specific modeling processes, and fault tree modeling analysis can be achieved by cooperation of multiple persons and multiple roles under the action of authority management and mapping mechanism; a collaboration platform is provided for fault tree modeling staff, and fault tree modeling tasks of large complicated systems in fields of navigation, aerospace, nuclear power energy and the like can be achieved; requirements in terms of model scale and multidisciplinary approach in fault tree modeling of current large complicated systems can be met, and the task of fault tree modeling analysis can be efficiently finished by cooperation of modeling staff with different knowledge backgrounds and professional skills.

The invention relates to an oil / gas / water multi-phase flow type identification method based on main constituent analysis and supporting vector machine. The feature is that: taking collection to flow parameter signal of different flow type by using horizontal tubes, pressure sampling ring, pressure sampling tube, pressure difference transmitter, data collection card and computer; taking denoising processing to pressure difference fluctuation signal between the collected different pressure sampling distance; taking empiric model decomposition to the signal to gain the fixed modal function, and forming a matrix X from the modal functions, using the main constituent analysis to gain the eigenvector of the flow type; using the supporting vector machine to accomplish the mapping from feature space to flow type space to realize the final flow type identification. The eigenvector fusing the information from plural sensors and would fully reflect the information of flow type. It has the feature of rapid learning speed and strong classifying ability.

The invention provides a 3dsMax-based nuclear facility model radiation field dosage simulation method. The method comprises the following steps of: constructing a model by 3dsMax software according to determined nuclear facility parameters, and storing a file in a 3DS format; importing a 3DS nuclear facility model file and obtaining model parameters; decomposing the 3dsMax nuclear facility model into voxels by using an octree method; writing determined voxel parameters and material information into an input card; importing the input card into a point nuclear integration program; calculating an accumulation factor; calculating a mean free path, in a radiation field, of a gamma ray; establishing a flux rate-dosage rate conversion factor, quality decay factors of chemical elements and materials and a single-layer accumulation factor database by utilizing an SQLite database engine; carrying out combined operation on a box by using a Boolean connective operator, and constructing a complicated radiation field geometric structure; and calculating a three-dimensional radiation field dosage by using a point nuclear integration method. The method provided by the invention is capable of realizing radiation field dosage calculation of complicated 3dsMax nuclear facility models with sizes, materials and energy parameters.

The invention discloses a method and system for judging the position of an automobile power window. The method comprises the steps that real-time sampling is started for the ripple current of a motor for controlling the window, and after at least two sampling periods are carried out, the continuous first ripple current sampling value, second ripple current sampling value and third ripple current sampling value are acquired in each sampling period; in each sampling period, according to comparison of the first ripple current sampling value, the second ripple current sampling value and the third ripple current sampling value, it is determined that the ripple current in the current sampling period is located on the wave trough or the wave crest or the non-wave-trough portion or the non-wave-crest portion, and the total wave crest number and the total wave trough number of the current sampling period are recorded after real-time sampling is started; and the position of the window is calculated according to the total wave crest number and the total wave trough number. According to the method and system, the features of the ripple current are extracted, the features of the wave crest and the wave trough of the ripple current are subjected to modeling decomposition and extraction, and therefore precise current ripple counting is obtained, and the position of the power window can be calculated accurately.

The invention relates to an industrial equipment remaining useful life prediction method and system and electronic equipment. The method comprises the following steps: a, performing normalization processing on original vibration signal data of equipment; b, after feature expansion is conducted on the normalized vibration signal data in an empirical mode decomposition mode, extracting data featuresof the vibration signal data; c, constructing a time sequence convolution network according to the extracted data features; and d, outputting a residual effective life prediction result of the equipment by using the time sequence convolution network. According to the method, the data characteristics of the original signals are decomposed, extracted and enriched through the empirical model, and then the sequential convolutional neural network is used for training and predicting to obtain the residual effective service life prediction model, so that the prediction speed and prediction precisionof the residual life of the industrial equipment can be greatly improved, and the method has realizability in the actual manufacturing process.

The invention relates to a recommendation prediction method based on attribute information reference self-leaning. Attribution information is utilized for slowing down cold start, the advantages of user and commodity collaborative filtering and model decomposition based on a matrix are combined, the training speed is high, and the interpretability is achieved; meanwhile, on the condition that rating data is sparse, the prediction precision is superior to that of user and commodity collaborative filtering and the matrix-based decomposition method.

The invention relates to a flexible mechanical arm sliding-mode control designing method based on a singular perturbation theory. The method includes four steps that (1) dynamics modeling of a flexible mechanical arm is performed; (2) partial differential equation (PDE) model decomposition is performed; (3) a control law is designed; (4) designing is finished. According to the flexible mechanical arm sliding-mode control designing method based on the singular perturbation theory, firstly, a Hamilton principle is utilized, and a PDE model of a whole system is determined; the original PDE model is decomposed into a concentrating slow subsystem which represents whole rigidity movement and a quick subsystem which describes system vibration by means of the singular perturbation theory; then a sliding-mode control law is designed by respectively aiming at the quick and slow subsystems, the designed control law is subjected to convergence analysis by the aid of a lyapunov function, and reasonability and stability of the control law are verified. Finally, a compound control law is obtained according to the singular perturbation theory.

The invention discloses a region saturation load prediction method based on a model family decomposition and integration technology. In the existing load prediction technology, single prediction methods are used, such as trend extrapolation, grey prediction and linear regression; and the prediction results for each prediction method are different greatly. The region saturation load prediction method includes the steps: extracting the main influence factors for region load growth; selecting a suitable single prediction method or model to predict region saturation load; and finally determining the weight of each single prediction method or model so as to perform combined prediction of the region saturation load scale. The region saturation load prediction method based on a model family decomposition and integration technology integrates with the advantages of each single saturation load prediction method, can perform optimized combination and comprehensive analysis, can comprehensively consider the influence of the indexes which are closely related with power consumption, integrates with the saturation load determination index set to predict the saturation scale and the saturation year of the region electric power consumption quantity, and is conductive to assisting long term of power grid planning in the region.

The invention discloses an EMD (Empirical Mode Decomposition) endpoint effect suppression method based on HMM (Hidden Markov Model) correction and neural network extension. The method comprises the steps of A) using a sensor to obtain a signal; B) using a neural network extension algorithm to estimate partial known data in a signal endpoint, calculating estimation errors and predicting data outside the endpoint; C) using an HMM algorithm to establish a model for the estimation errors and using the parameters of the model to predict the extension errors of the used extension algorithm; D) using the predicted error data to correct the extended data to obtain final extended data; E) performing empirical model decomposition to the extended signal and abandoning the extended data at two ends to obtain IMF (Intrinsic Mode Function) components of the original signal; and F) extracting signal features by analyzing the IMF components after endpoint effect suppression. The EMD endpoint effect suppression method based on HMM correction and neural network extension has the advantages that the neural network extension algorithm can be corrected, the errors existing in a data extension method are reduced, and the endpoint effect in empirical model decomposition is effectively suppressed.

The invention relates to an intelligent inventory early warning and management system based on a BIM (Building Information Modeling) model. The method comprises the following steps: 1, a BIM model is imported to the system; 2, a cargo requirement task plan based on the BIM model is generated in the system, and the cargo requirement task plan is sent to a factory; 3, the BIM cargo requirement plan is subjected to model decomposition, and the material amount for needed component types is counted; 4, the system judges the current inventory; 5, the system judges an insufficient component material amount, if the cargo requirement plan time is smaller than needed time T, first-level inventory early warning is triggered, or otherwise, second-level inventory early warning is triggered; 6, coordinated purchasing is carried out on component list materials; and 7, the system detects whether the current inventory meets enterprise safety inventory. The informatization operation level of inventory management of a construction enterprise is enhanced.

The invention discloses a multicore parallel solving method for the computation of polymer molecular weight distribution, comprising the following steps of: establishing a mechanism model of free radical polymerization dynamic simulation to solve the polymer molecular weight distribution by taking the free radical polymerization dynamic simulation as an object; dividing the mechanism model into a small-scale free radical polymerization model and a large-scale free radical polymerization model through a decoupling method; solving the small-scale free radical polymerization model by utilizing a variable step-size and order backward difference method; and solving the large-scale free radical polymerization model to accelerate the solution of the computation of the polymer molecular weight distribution through a parallel sequential method by taking a multicore computer system as a computing platform. Compared with the traditional non-parallel solving method of the computation of the polymer molecular weight distribution, the invention can increase the solving speed of the mechanism model by sufficiently utilizing the multicore characteristics of the multicore computer system; and in addition, the multicore parallel solving method has simple and clear principle and is convenient to implement on any multicore computer system.

The invention provides a complete polarization synthetic aperture radar target decomposition method for adaptive selection unitary transformation. The method comprises the following steps: (1) performing two unitary transformations for singh for data coherence T matrix of the complete polarization synthetic aperture radar to obtain the matrix, (the formula is as shown in specification); (2) performing other two unitary transformations for the coherence T matrix to obtain the matrix T(omega), wherein the first unitary transformation is used for performing the spiral angle compensation and restraining the volume scattering excessive estimation of model decomposition, the second unitary transformation is used for further restraining the volume scattering excessive estimation and reducing one degree of freedom of the coherence T matrix; (3) comparing with element (the formula is as shown in specification) of the matrix (the formula is as shown in specification) with the element T33(omega) of the matrix T(omega); if (the formula is as shown in specification), and (the formula is as shown in specification), otherwise, T is equal to T(omega); (4) performing three-component model decomposition on coherence T matrix. The two unitary transformations for singh in the step (1) or the two unitary transformations in the step (2) can be selected for the coherence T matrix in a self-adaption mode by the method according to the real situation of the object, and the volume scattering excessive estimation problem of the model decomposition can be effectively restrained.

The invention discloses an electric power system optimal support set positioning method based on anticipated fault decomposition. In order to stabilize the frequency of a power grid, multiple units having decisive effects on safety and reliability of the power grid need to be selected. The method comprises steps of by considering different anticipated faults of the power grid, establishing an electric power system safety restraining optimal power flow mode by taking the minimization of the climbing ability of generator sets as an objective; adopting an anticipated fault decomposition method to decompose the model into a main problem under a normal operation mode and sub problems under each anticipated fault operation mode; through iteration solving of the main problem under the normal operation mode and the sub problem under each anticipated fault operation mode, achieving solving of above electric power system safety restraining optimal power flow mode; and according to the steps, solving the smallest climbing ability transformation quantity of the generator sets, carrying out progressively decreased ranking on the generator sets with the non-zero transformation quantity and successively recording the generator sets to be the optimal support sets with progressively decreased priorities according to the sequences to be output as the results. The method is excellent in applicability and actual requirements are well met.

The invention discloses an included-angle-based multi-model decomposition and control method of a Hammerstein-Wiener system. multi-model decomposition is carried out on a Hammerstein-Wiener system byusing an included angle and a linear model set of an approximate Hammerstein-Wiener system is obtained; a linear controller is designed based on the model set; and according an included-angle-based weighting method, weighted fusion is carried out on the obtained linear controlled and optimized controlling of the Hammerstein-Wiener system is carried out. Therefore, defects that the traditional nonlinear inverse control method can only be applied to a static reversible Hammerstein-Wiener system and the closed loop performance is reduced because the input or output nonlinearity of the system cannot be considered into the controller designed can be overcome.

The invention discloses a time mark separation aircraft elastomer control method based on nonlinear information, belongs to the field of aircraft control, is especially suitable for a hypersonic-velocity aircraft elastomer, and is used for solving a problem that a conventional elastomer hypersonic-velocity aircraft cannot achieve the rigid-flexible mode separation control. The method comprises thesteps: carrying out the kinetic analysis of a hypersonic-velocity aircraft elastomer kinetic model, and clearly determining the coupling mode of rigid-flexible modes; carrying out the quick-slow timemark decomposition of the model through the singular perturbation theory, and enabling a rigid mode and a flexible mode in the kinetic model to be separated; designing a control strategy based on thelinear information for a slow variable time mark part for representing the rigid mode of the system, and enabling a nonlinear item obtained after model decomposition to be directly substituted into acontroller; designing a sliding-mode control strategy for a quick variable time mark part for representing the flexible mode of the system; finally integrating the two types of control input into onetype of control input as the general rudder deviation, thereby achieving the effective control of the rigid-flexible modes of an aircraft.

The invention discloses a polarization SAR model decomposition evaluation method based on electromagnetic scattering simulation. The method comprises the steps of (S1) setting surface electromagneticgeometric parameters, aircraft flight parameters and radar satellite position parameters and calculating a surface real beta value of a target scene by using a classical forward model, (S2) processingthe polarization SAR simulation data of the target scene by using a polarization SAR model decomposition method to be evaluated and carrying out inversion to obtain an inversion beta value, and (S3)calculating a mean square root error of the inversion beta value and the surface real beta value, and evaluating the polarization SAR model decomposition method to be evaluated with a principle that the smaller the mean square root error is, the better the effect of the decomposition method is. According to the scheme of the invention, the electromagnetic scattering simulation theory and the polarization SAR model decomposition theory are organically combined, a decomposition algorithm is evaluated from the perspective of electromagnetic wave simulation and model decomposition, the method is fair and just, and a reference can be provided for the selection of an excellent model decomposition method for different application scenes.

The invention puts forwards a 3D (Three-dimensional) printing-oriented model decomposition and arrangement method. The method comprises the following steps: giving a model S and a printing space PV, calculating the decomposition of the initial pyramid attribute blocks of the model S, carrying out voxelization on the pyramid attribute blocks to generate a first initial intermediate solution isolu0; initializing an intermediate solution set ISolu={isolu0}, and initializing an optimal complete solution P*; for each intermediate solution isolui in the intermediate solution set ISolu, calculating one series of candidate intermediate solutions generated by the intermediate solutions, and solving an optimal decomposition and arrangement solution of the model S; and carrying out local optimization on a three-dimensional grid of a plurality of extracted blocks, causing the blocks to be more tightly arranged through the translation of each block along the negative direction of a Z axis, and therefore, obtaining the optimal decomposition and arrangement about the model S. The model is decomposed into few blocks, the few blocks are effectively arranged into a printing space, and therefore, a printing process is effectively carried out.

The invention discloses a gap multi-model weighting function parameter self-tuning method, which is characterized in that the characteristics of a nonlinear system are analyzed, scheduling variables capable of reflecting system operating conditions are selected, a model set of an approximate nonlinear system is constructed by using a multi-model decomposition algorithm, a sub-controller is designed based on each sub-model in the model set, a weighting function of the sub-controller comprises a reciprocal of a gap distance between the nonlinear system and each sub-model at a current moment, theweighting function has a unique tuning parameter, and an optical value of the tuning parameter is obtained by optimizing an integral absolute error value between closed-loop output and reference input of the nonlinear system. The gap multi-model weighting function parameter self-tuning method has the advantages of avoiding the cumbersomeness of manual parameter tuning of the weighting function, reducing the dependence of parameter tuning on prior knowledge, and optimizing the control performance of a closed-loop system.

The invention provides a satellite orbit determining method and device based on maximum model decomposition. The satellite orbit determining method comprises the steps that based on the KAM principle, a Fourier series expansion model of position coordinates of a satellite is established when the satellite operates on an orbit; according to the descending order of amplitudes, the Fourier series expansion model of the position coordinates is decomposed, and a decomposed similar series expansion model of the position coordinates is obtained; based on the actual observation values of the position coordinates of the satellite when the satellite operates on the orbit, the amplitudes and the corresponding frequencies in the similar series expansion model of the position coordinates are determined; through the similar series expansion model of the position coordinates, the position coordinates of the satellite on the orbit at any moment are predicated. The satellite orbit is determined more rapidly and conveniently by the adoption of the satellite orbit determining method and device based on maximum model decomposition, the satellite orbit determining method and device based on maximum model decomposition is practical, only the frequencies and the amplitudes of the satellite orbit need to be computed and stored in the predication process, storage resources are saved, and the processing efficiency is greatly improved.

The invention relates to a power grid minimum newly-increased reactive compensation capacity calculation method. Based on a power gird load predicated value, the method takes a minimum power grid newly-increased reactive compensation capacity as a target function; meanwhile, reactive compensation minimum capacity out-of-limits of a capacitor and an electric reactor in Power System Voltage and Reactive Power Technology Guides, and constraint conditions including voltage upper and lower limits, line active tides and the like of a transformer substation bus under three operation manners including a highest load, a lowest load and a normal load are considered, and a whole model is decomposed into an upper-layer model capable of being directly solved by a linear planning algorithm and a lower-layer optimization model composed of non-linear constraint conditions through a layered optimization concept; a final optimized result meets the related technology guides through the continuous optimization between the two layers of models, and the voltage operation requirements of an electric power system are met under a plurality of load levels.

The invention discloses a sea wave height analysis method. The sea wave height analysis method comprises a fast band-pass filtering method for signal mode decomposition; a model decomposition trend is used for substituting for a sectioned polynomial fitting trend in an MF-DFA (multifractal detrended fluctuation analysis) method. A partition function method for multifractal analysis and the MF-DFA method are applied to sea wave height testing data analysis to indicate that a wave height sequence has a weak multi-fractal characteristic. The sea wave height analysis method makes improvements aiming at the problems existing in the MF-DFA method and establishes an MF-DFA method based on signal mode decomposition; testing data verification indicates that the improved method has the capability of avoiding the defects of the original method and has certain advantages on the basis of well meeting an elimination trend of the original method.

The invention relates to a big data sharing-based 3D printing platform and a working method thereof. The 3D printing platform comprises a cloud server, a 3D printer and an upper computer connected with the cloud server, wherein the upper computer is suitable for inputting printing modules and storing decomposed data of to-be-printed models to the cloud server, suitable for obtaining decomposed data of corresponding printing models from the cloud server, and further suitable for carrying out 3D printing on the obtained decomposed data of the printing models through the 3D printer; data decomposition is carried out on the input printing models through the upper computer and the decomposed data is stored in the cloud server, so that the data can be conveniently called by other upper computers; and a big data sharing distribution mechanism is further combined, so that sharing distribution is carried out on the decomposed data of the printing models, and data search and matching are carriedout by using pictures of the printing models.

The invention provides a workflow system-oriented test case generation method and device. The method comprises the following steps of: establishing a Petri network model on the basis of a workflow system with an input-output constraint; dividing the Petri network model into a plurality of fragments according to a splitter-aggregator pair; if the splitter-aggregator pair exists in the divided fragments, continuously dividing the fragments to obtain a model decomposition tree corresponding to the Petri network model; traversing the model decomposition tree and generating a test sequence set corresponding to the model decomposition tree; and generating a test case according to input-output information included in each test sequence in the test sequence set. The workflow system-oriented test case generation method and device provided by the invention can solve the problem that the test technology in the prior art is not strong in pertinence and low in workflow system test sufficiency, so as to improve the system test efficiency.

The invention relates to a structural parameter identification method for a two-degree-of-freedom based on a vibration response signal. The content of the structural parameter identification method comprises the steps of performing structural excitation, and acquiring a vibration displacement signal; acquiring a natural frequency and a natural angular frequency of each order of the system by applying FFT (Fast Fourier Transform); decomposing the vibration displacement signal by using a rapid empirical model decomposition method so as to acquire a plurality of intrinsic mode function (IMF) components; solving an instantaneous frequency of each IMF component, comparing the instantaneous frequency of each IMF component with the natural frequency acquired by FFT so as to screen out IMF components capable of representing the natural frequency of the system; processing the screened IMF components by applying Hilbert transform, acquiring each slowly-varying amplitude and slowly-varying phase angle of a vibration response, and performing parameter correction; and substituting known parameters into an identification model to solve, solving an average value, and acquiring structural parameters of the system. The structural parameter identification method can simultaneously utilize time-frequency domain information of data, is high in anti-noise capacity and can directly utilize vibration response data to perform parameter identification.

The invention discloses a method for classifying power quality mixed disturbances based on time-frequency domain multi-feature quantities. Classify voltage sags, voltage swells, voltage short-time interruptions, pulse transients, oscillation transients, harmonics and flicker power quality disturbances and their combined disturbances. The specific implementation steps first use clustering The empirical model decomposition method EEMD and the improved incomplete S-transform MIST process the disturbance signal, and extract 9 time-frequency domain feature values; then, the feature quantities are input into the block-based automatic classification system for disturbance identification. This method fully considers the mutual interference between single disturbances, and effectively suppresses them through complementary time-frequency domain feature quantities. The simulation results show that under certain noise conditions, the method can effectively classify power quality disturbances such as voltage sags, voltage swells, voltage short interruptions, pulse transients, oscillation transients, harmonics and flicker and their combinations. into a mixed disturbance.

Decomposing a process model in an Enterprise Intelligence (‘EI’) framework, including: receiving, by a process model decomposer, a process model for an EI assembly; identifying, by the process model decomposer in dependence upon an EI framework metamodel, component parts of the process model; and generating, in dependence upon the identified component parts, a taxonomy breakdown comprising a definition of a process classification.