89results about How to "Explanation of validity" patented technology

The utility model relates to a method which automatically can get the information on the administrative apanage of the website owner from the web page, comprising following steps: firstly, the database of the administrative regionalization is established, then the given website is visited to link the web page of the website get the web page information and match with the information available in the database of the administrative regionalization, until the information about the administrative apanage of the web page is obtained; the method plays an important role in the overall service of ''WHOIS inquiring'' provided by the internet; at the same time, the method can also offer direct help to know and study on the status quo and development trend of internet from the view of geographic distribution.

The invention relates to a quantum universal gravitation searching dynamic DOA estimation method under an impulsive noise environment. The quantum universal gravitation searching dynamic DOA estimation method is implemented together through a quantum universal gravitation searching mechanism and dynamic updating of a weighted signal covariance maximum likelihood equation, wherein the fact that robust tracking is conducted on the direction of arrival of a dynamic target is considered. The method comprises the steps that snapshot sampling and weighted processing are conducted on D source signals of space; the initial sates of all objects of the quantum universal gravitation searching mechanism are determined; a best objective function value and a worst objective function value are determined; the weight of each object in a tth iteration is calculated; an objective function value is calculated, whether the biggest number of iteration times is reached or not is judged; weighted processing is conducted on sampled data; the maximum tracking times are judged; a dynamic tracking result is output. The quantum universal gravitation searching dynamic DOA estimation method under the impulsive noise environment solves the problem of dynamic objective direction tracking under the impulsive noise environment, the designed quantum universal gravitation searching mechanism is used as a searching strategy, and the designed method has the advantages of being quick and high in robustness.

The invention relates to a method for testing an RCS (radar cross section) of a low-scattering conformal antenna based on two-dimensional microwave imaging, which is characterized by comprising the following steps: carrying out an RCS test on a metal envelope and a low-scattering conformal antenna in an installed state so as to obtain a two-dimensional microwave image; carrying out two-dimensional Fourier transform on the obtained new two-dimensional microwave image so as to obtain the data of a target spectral domain; carrying out an RCS test on a metal ball (the RCS of the metal ball is known) so as to obtain a two-dimensional microwave image, then carrying out two-dimensional Fourier transform on the obtained two-dimensional microwave image so as to obtain the data G0 (f, theta) of a scattered field of the metal ball, wherein the data of the scattered field varies with frequency and angle; and finally, obtaining the RCS of the conformal antenna, wherein the RCS of the conformal antenna meets the following formula: RCS= G1 (f, theta) (of the conformal antenna) - G0 (f, theta) (of the metal ball) + RCS (of the metal ball). The method provided by the invention is a method for testing an RCS (radar cross section) of a low-scattering conformal antenna in an installed state based on the two-dimensional microwave imaging technology, and after practicing, the obtained effect is good, therefore, the method provided by the invention has great engineering popularization value.

The invention aims at providing a multi-target spectrum allocation method based on an undisposal order preference quantum goose group algorithm, which comprises the following steps of: building a graph theory coloring model of cognitive radio spectrum allocation, initializing the position of the quantum geese and the quantum speed, carrying out the undisposal order preference and congestion degree calculation on individuals in the population according to the adaptability, sequencing the individuals with the same undisposal order preference levels in sequence from higher congestion degrees to lower congestion degrees, carrying out evolution on the population by a quantum goose group evolution rule, generating new quantum speed and position, carrying out undisposal order preference on obtained solutions in an elite solution set nonDomQGSAList and selecting the solutions with the undisposal solution level being 1 as the final Pareto front end solution set. The method solves the discrete multi-target optimization problem, designs the novel undisposal order preference quantum goose group algorithm as the evolution strategy and has the advantages that the convergence speed is high, and the precision is high. In addition, the method provided by the invention has a wider application range.

The invention relates to a UAV group task allocation method based on a quantum crow group search mechanism. The UAV group task allocation method comprises the steps of: establishing a UAV group task allocation model from a plurality of start points to a plurality of tasks, wherein the UAV group task allocation model comprises UAV model numbers, start and end points, and an allocation model; initializing a quantum crow group; calculating the fitness of each quantum crow according to a fitness function, and storing a position of the quantum crow corresponding to the minimum value of the fitnessfunction as a globally optimal food position; updating a quantum position and the position of each quantum crow; and calculating the fitness of each quantum crow according to a fitness function, determining a hidden food position of each quantum crow, finding the optimal food position so far, outputting the globally optimal food position if the maximum iteration algebra is reached, and mapping themaximum iteration algebra into a task allocation matrix. The UAV group task allocation method solves the discrete multi-constrained objective function solving problem, designs a discrete quantum crowalgorithm as an evolution strategy, and has the advantages of fast convergence speed and high convergence precision.

The invention relates to an antenna array sparse construction and a directional diagram comprehensive method based on quantum bat searching. An overall optimal position of a scattered quantum bat group is obtained based on a quantum bat searching mechanism, and the overall optimal position is mapped as a sparse antenna array. On the basis of a constructed sparse antenna array, an overall optimal quantum position and an optimal mapping position of a continuous quantum bat group are obtained based on the quantum bat searching mechanism. Therefore, the optimal excitation amplitude of the antenna array is obtained.

The invention discloses a transverse time axis clustering method in generalized load modeling on the basis of time periods. According to the method, root bus data formed by wind power and loads of the whole year are obtained; the data are processed, all the processed data are connected end to end, the data in rows form the transverse continuous data which are divided into M sections according to transverse time units THN, and all the data are transversely clustered; based on the transverse cluster result, a sample data source to be analyzed is divided into q transverse classes, and each class is represented by the respective clustering center; feature vectors are intersected and matched. The classes to which samples belong are judged through intersecting and matching of the feature vectors, generalized load modeling is utilized for setting up an accurate model and testing the effectiveness of the clustering strategy, and the simulation result shows that generalized load modeling carried out after clustering analysis makes the model practical on the basis of meeting the requirement for accuracy, and is beneficial for improving the simulation accuracy and the simulation effectiveness of an electrical power system.

A global K-means clustering method based on a feature weight is disclosed. The implementation process of the method comprises the following steps of: firstly, clustering data into one class, wherein the optimal clustering centre is the centre of mass of all the data; secondly, figuring out the minimal data point of a target function through calculation and taking the data point as the initial clustering centre of a next class; thirdly, performing iterative updating by a K-means method with the feature weight to obtain the optimal clustering centre of clustering into two classes; and finally, performing updating iteration by orderly increasing the number of the clustering centres by the same method until the clustering of K classes predetermined is completed, thus completing the whole process of clustering all the data points into K classes. In the invention, the new global K-means clustering method based on the feature weight is established by combining the global K-means method with the K-means with the feature weight, and the clustering result is very stable; and in contrast with the experimental results of several K-means clustering methods, the effectiveness and the robustness of the clustering method disclosed by the invention are proved.

The invention discloses a wind power plant optimal control method based on a wake flow effect. Based on a Jensen wake flow model, wake flow air speed influence factors are analyzed, the wind turbine thrust coefficient is finally determined, and power output of a wind turbine can be optimized; and in particular, according to the wind turbine wake flow characteristic, a wind turbine wake flow speed stacking model is built, a numerical fitting method is then used for conducting numerical fitting on the wind turbine thrust coefficient and the wind-power utilization coefficient, fitting results are joined with the Jensen wake flow model, a wind turbine output power model is refined, and an optimal model of the wind turbine output power model is obtained. By means of the wind power plant optimal control method, a visualization method can be used for detecting change conditions of the wind power plant total output power at different wind speeds after a control optimization algorithm is added, and the effectiveness of the control algorithm is indicated compared with the total output power in the natural state.

The invention relates to a method for planning a coordinated path of a dual-arm space robot based on random sampling. According to the method, an initial feasible path of a dual-arm end effector in aCartesian space is planned through an RRT* algorithm, the initial path generated by the RRT* algorithm is smoothed by utilizing four splines, and a smooth path suitable for robot execution is obtainedby first-order, second-order and third-order differential continuous constraints satisfying the splines and the initial speed, end speed and acceleration constraints of the end effector of a mechanical arm. The method has the beneficial effects that the safe and feasible initial path of the tail end is quickly generated in an unstructured environment by utilizing the RRT* algorithm, the initial path generated by the RRT* is smoothed by utilizing four splines, the robot is ensured to execute the planned path in a good state, and the physical limit constraint of the mechanical arm is considered. A simulation result illustrates the effectiveness of the proposed coordinated path planning method. The method has important significance on motion planning of the mechanical arm, especially on capture path planning for a failed satellite.

The invention provides a planar antenna array sparse method based on a quantum spider population evolution mechanism. The planar antenna array sparse method comprises the steps of 1, establishing a planar antenna array sparse model; 2, setting system parameters; 3, performing evaluation on advantages and disadvantages of each spider coding position in a population by a fitness function, and taking the optimal position of the fitness function as the global optimal position of the whole population; 4, dividing genders of spiders in the population; 5, calculating weight of each spider; 6, updating quantum positions of female spiders by adopting an analog quantum vector rotation door rotation based on the updated quantum vector rotary angle; 7, updating quantum positions of male spiders by adopting an analog quantum vector rotation door rotation based on the updated quantum vector rotary angle; 8, updating the respective historical optimal positions; and 9, judging whether the maximum number of iterations is reached or not. By adoption of the planar antenna array sparse method, the difficulty existing in multi-constraint planar array antenna sparsity is solved, and various requirements on the planar sparse array are satisfied.

The invention provides a loop antenna array spare method based on a multi-objective quantum spider population evolution mechanism. The method comprises the following steps that: establishing a loop antenna array spare model, setting a proper system parameter, and initializing the quantum position and the [0,1] coding position of each spider in a population in a solution space; designing a multi-objective fitness function; calculating the weight of each spider in the population, and dividing the gender of the spiders according to the weight; according to the initial population, generating an initial elite solution set; selecting a global optimal solution and a suboptimum solution from the elite solution set; then, independently updating the quantum positions of the female spider and the male spider, and converting the quantum position into the [0,1] coding position through a measurement way; updating the elite solution set, and updating the weights of all spiders in the population; finally, judging whether a maximum iteration number is achieved or not is judged, and outputting the elite solution set if the maximum iteration number is achieved; and otherwise, returning to carry out iteration. By use of the method, a problem that high-dimension discrete multi-objectives are subjected to spare construction by a multi-objective loop antenna array is solved.

The present invention discloses an adaptive guided filtering-based digital vein image enhancement method. Based on the defects of an original guided filter, a novel local adaptive weighted guided filtering method is put forward based on an edge detection operator under a condition that the linear complexity of the guided filter is not changed. The quality of an image enhanced by using the method provided by the invention is better than the quality of an image enhanced by using a guided filtering-based enhancement method in three objective image quality indicators, namely, contrast, sharpness and local structure similarity. As indicated by a recognition experiment, the method of the invention is also advantageous in recognition rate. Thus, the effectiveness of the adaptive guided filtering-based digital vein image enhancement method is fully embodied.

The invention discloses a translation form self-acquiring method and device in the computer science technique domain, which comprises the following steps: a) predisposing the language material; b) extracting Chinese word graph and English word graph separately; c) inducting the grammar of Chinese part and English part; d) aligning the extracted phrase structure; e) comparing the result of different similarity function to select optimum similarity function; f) disposing the aligned result to gain the needed mechanic translation model. The device contains Chinese word classification device, Chinese grammar induction device, English grammar induction device, phrase structure alignment device and after-treatment device.

The invention relates to a case public opinion abstract generation method based on case element sentence association graph convolution, and belongs to the technical field of natural language processing. According to the method, case elements and words are fused to construct a sentence association graph based on the case elements to represent an association relationship between sentence informationand sentences; an association relationship between sentence information and sentences is learned by using a graph convolutional neural network, and the sentences are classified to obtain an importance score of each sentence; sentences are sorted according to importance, and abstracts are obtained through duplication elimination and sequential relation adjustment. According to the invention, important information is obtained from a plurality of news texts related to a specific case and is used as a brief abstract of the news texts. The method plays an important role in quickly mastering the public opinion situation by related personnel, and solves the problem of how to integrate domain knowledge and cross-document sentence association relationships for case public opinion abstracts.

The invention provides a satellite navigation receiver simulation anti-jamming testing method. An anti-jamming testing system is established, fast conversion of wired testing and wireless testing of a testing evaluation system is achieved, and anti-jamming capacity testing and evaluating under the wired testing situation are achieved. By means of the testing method, the anti-jamming capacity of a tested receiver can reach the product performance index, and meanwhile effectiveness of the method is verified.

The invention relates to a CEEMD-improved wavelet threshold denoising-based transformer winding ultrasonic detection three-dimensional imaging method, and belongs to a transformer winding deformationdetection method. The method comprises the following steps: firstly, performing CEEMD decomposition on a target signal to obtain a multi-order IMF component, then calculating a correlation coefficientof the IMF component, performing improved wavelet threshold processing on a high-frequency component with a relatively low correlation coefficient, and finally reconstructing a denoised component, alow-frequency component and a residual component to obtain a denoised signal. According to the method, most noise is suppressed while low-amplitude effective information and high-frequency effective information are reserved, and the denoising effect is ideal. In an ultrasonic detection three-dimensional imaging system, the denoised transformer winding state diagram is better in visual effect and clearer in fault position, and it is shown that the transformer winding state diagram denoising method has a good denoising effect.

The invention relates to a multi-target frequency spectrum allocation method based on a quantum flower pollination search mechanism in a cognitive heterogeneous network, and the method is realized based on a multi-target quantum flower pollination search mechanism. The method comprises the steps that the wireless access network perception module in a base station perceives network information; (2) a network reconstruction management module performs multi-granularity channel division on the frequency spectrum resource; (3) a quantum pollen set including P quantum pollens is initialized; (4) each individual pollen in the pollen set is mapped as a frequency spectrum allocation matrix to be corrected; (5) the transformation probability of cross-pollination and self-pollination in the quantum flower pollination search mechanism is set; (6) the new generation of pollen set and the previous generation of pollen set are mixed; (7) the appropriate pollens are selected from Pareto front-end solutions and mapped as the frequency spectrum allocation matrix; and (8) the network reconstruction management module partitions the optimal allocation matrix. The problem of multi-target frequency spectrum allocation can be solved so as to enhance the utilization rate of the frequency spectrum.

The invention provides a small-scale corpus DNN-HMM acoustic model. For small-scale corpus speech recognition in a DNN-HMM acoustic model, feature extraction is carried out on an inputted small-scalecorpus speech; the DNN-HMM acoustic model is trained by using the extracted feature and the DNN-HMM acoustic model is obtained; a language model is trained by using text information corresponding to the small-scale corpus speech to obtain a small-scale corpus language model; and a decoder is obtained by using the acoustic model, the language model and dictionary construction, so that an overall small-scale corpus speech recognition frame is obtained.

The invention relates to a dynamic frequency spectrum distribution method of a cognitive heterogeneous network. A probability HMCR carrying out selection from a quantum harmony set is set and a quantum-harmony-based dynamic frequency spectrum method is initialized; a quantum tone of each quantum harmony is measured to obtain a harmony base x and a harmony memory base H, and fitness calculation is carried out on each harmony and the corresponding fitness values are respectively stored in a Fit and a Fit 1; if rand is less than HMCR, the quantum tone of the last-generation quantum harmony is inherited and a quantum rotating gate is started to generate a new solution; if the rand is larger than the HMCR, a tent pseudo chaos sequence is generated randomly to generate a new quantum harmony; the quantum tone of each quantum harmony is measured successively to obtain a corresponding harmony, and fitness calculation is carried out to replace the corresponding fitness value in the Fit 1; and a global optimum harmony in the harmony memory base H is outputted in a loop iteration mode and global optimum harmony mapping is carried out to obtain a frequency spectrum distribution matrix A that is the optimum frequency spectrum distribution scheme of the cognitive heterogeneous network.

The invention relates to a particle swarm and fuzzy means clustering based cell image segmentation method. On the basis of an intuitive fuzzy means clustering theory, an intuitive fuzzy membership degree form is improved aiming at cell images and combined with fractional order particle swarms to realize alternate optimization, and a morphological method is adopted for result improvement to realize accurate segmentation of the cell images. The method includes steps: firstly, selecting appropriate parameters, initial particle swarms and relevant data; secondly, starting alternate iterative optimization of the fractional order particle swarms and intuitive fuzzy clustering; thirdly, comparing results with standard fuzzy means clustering results, and selecting a morphological processing method through differential images; finally, subjecting result images to range conversion and watershed segmentation to obtain a final result. The particle swarm and fuzzy means clustering based cell image segmentation method can be widely applied to various cell image based application systems and has a promising market prospect and a high application value.

The invention provides an annular antenna array sparse method based on a quantum chicken evolution mechanism. The method comprises the following steps that: 1: establishing an annular antenna array sparse model; 2: setting an initial parameter; 3: designing a fitness function; 4: calculating the fitness value of each chick in a population, distinguishing the category of the chicken, and dividing sub-populations; 5, 6, and 7: independently constructing the quantum vector rotation angle update formula of roosters, hens and chicken, updating the quantum vector rotation angle, and updating a quantum position; 8: through a measurement way, converting into a [0,1] coding position, calculating the fitness value of the [0,1] coding position, and updating the individual historical optimal solutionand the global optimal solution of each chick; and 9: judging whether a maximum iteration is achieved or not. The method is higher in rate of convergence and higher in accuracy of convergence, and hasa good spare effect on solving the problem of annular antenna array sparse construction, the complexity and the cost of antenna array system are greatly lowered, and an expectant requirement is achieved.

The invention relates to the field of pharmaceutical preparation, in particular to a bifendate self-emulsifying preparation and a method for preparing the same. The bifendate self-emulsifying preparation is characterized by adopting the mixture of polyethylene glycol-12-hydroxy-stearic acid ester and polyoxyethylene hydrogenated castor oil or the mixture of the polyethylene glycol-12-hydroxy-stearic acid ester and polyoxyethylene castor oil as an emulsifier. The bifendate self-emulsifying preparation is emulsified spontaneously to form micro-emulsion droplets of which the particle diameter is less than 200 nanometers after oral administration, thereby enhancing the solubility and the dissolving speed of the bifendate so as to improve the bioavailability of bifendate. At the same time, the bifendate self-emulsifying preparation prepared by adding a solid adsorption material into a liquid self-emulsifying preparation not only effectively improves the defects of liquid preparations in production, storage, administration and the like, but also significantly improves the stability of drugs with a simple and practicable preparation process.

The invention discloses a quantization-based set value Kalman filtering algorithm. Compared with the prior art, the algorithm has the advantages that the problem in remote state estimation of a dynamic random system in a wireless sensor limited in bandwidth is solved; original measurement values are quantized into messages, and the messages are transmitted to a remote estimator from a local sensor; and a set region of the original measurement values is represented by utilizing information contained in a quantization policy, and a closest ellipsoid approximation method of the region is given. Therefore, the quantization-based set value Kalman filtering algorithm is proposed. Three algorithms are compared and analyzed in computer simulation to explain the validity of the algorithm proposed by the invention.

The invention relates to a satellite remote sensing image color equalization method based on block adjustment. The method comprises steps of S1, extracting an image overlapping region according to thespatial position of an image, dividing the image overlapping region into uniform grids, extracting grid features through employing an HOG feature extraction algorithm, and carrying out the optimization screening of grids with the same name through employing the grid features; S2, taking a polynomial as an adjustment model, introducing a reference image for a coefficient array rank depletion problem, introducing virtual control for a pixel value distribution nonuniformity problem, constraining a difficult region with weak connection by adopting an integral gray average value of an overlappingregion, and carrying out integral solution of an introduced weight on color conversion parameters of each image in a measurement region; and S3, performing color correction on each image by utilizingthe color transformation parameters obtained in the S2 to obtain a color-balanced image sequence. Compared with the prior art, the method is advantaged in that brightness and color difference betweenadjacent images can be effectively processed, so color transition in the whole measurement area is natural, and a better effect is achieved.

The invention relates to a robust multi-user detection method based on a quantum Hopfield neural network and a quantum fish swarm algorithm in an impact noise environment. The method comprises the steps that a robust multi-user detection model is established; the quantum Hopfield neural network is activated, and a second-best solution is generated; a quantum fish swarm is initialized; an evolution rule of the quantum artificial fish swarm algorithm is used for carrying out evolution on a population; according to a food concentration function, food concentration values are computed for all new positions; and an obtained global optimum position is sending data for detecting a plurality of users, and detecting results are output. Robust multi-user detection in the strong impact noise environment is well achieved, the designed quantum Hopfield neural network and the quantum fish swarm algorithm are used as evolution strategies, and the designed method has the advantages of being high in convergence rate and high in convergence precision.

The invention discloses a dual-jump full-duplex DF relay system optimization power distribution method for total power limitation, and relates to the technical field of wireless transmission. The dual-jump full-duplex DF relay system optimization power distribution method solves the problem that the total power of a dual-jump full-duplex DF relay system is limited. The method comprises the steps that the end-to-end outage probability Po (gamma th) is solved; when the total transmitted power limitation of nodes is Ptotal, a dual-jump full-duplex DF relay optimization power distribution strategy is equivalent to nonlinear optimization; according to the process of optimization power distribution, a cost function J (Po, P1, gamma) is set, the gamma is an intermediate parameter, the gradient of the cost function J (Po, P1, gamma) is obtained, an iteration precision threshold value epsilon is set, initialization is carried out on the Po and the P1 by utilizing equal power distribution, and when the equations that |Po-Po*|<epsilon, Po=Po* and P1=Ptotal-Po* are met, a dual-jump full-duplex DF relay optimization power distribution result for the total power limitation is obtained. The dual-jump full-duplex DF relay system optimization power distribution method is suitable for optimization power distribution of the dual-jump full-duplex DF relay system, so that the end-to-end outage performance of the system achieves the optimization.

The invention discloses a tooth profile deviation measurement method based on DP-PSO-SVR, and belongs to the technical field of precision testing. The invention helps resolve problems of high measurement cost, low precision and short service life. The method comprises the following steps: measuring tooth profile data of a to-be-measured gear as to-be-measured data, measuring tooth profile data ofa test gear as sample data, and carrying out coordinate transformation processing on theoretical tooth profile data of the to-be-measured gear; establishing a tooth profile deviation mathematical model; establishing an SVR model; optimizing and training parameters in the SVR model by using a DP-PSO algorithm and the sample data to obtain a DP-PSO-SVR model; fitting the to-be-measured data by usingthe DP-PSO-SVR model to obtain an actual involute of the to-be-measured gear, fitting theoretical tooth profile data by using a least square method to obtain a theoretical involute, and calculating to obtain a tooth profile deviation between the actual involute and the theoretical involute through the tooth profile deviation mathematical model. According to the invention, the measurement cost canbe reduced, the measurement precision can be improved, and the anti-interference performance is good.

The invention discloses a longitudinal time axis clustering method in generalized load modeling on the basis of seasonality. According to the method, root bus data formed by wind power and loads of the whole year are obtained; transverse time axis pre-clustering is carried out, single day time re-clustering is carried out, longitudinal time unit clustering is carried out, longitudinal time axis clustering results of the whole year are output, a longitudinal time axis clustering method is utilized for analyzing the data of the whole year, and categorical data considering the seasonality are obtained for accurate modeling. An AP algorithm and a longitudinal time axis clustering strategy considering the seasonality are utilized, the large sample data actually measured can be partitioned reasonably, and the simulation result shows that compared with a traditional modeling method, generalized load modeling carried out after clustering analysis makes a model practical on the basis of meeting the requirement for accuracy, and is beneficial for improving the simulation accuracy and the simulation effectiveness of an electrical power system.

The invention discloses a node matching model based on an adjacent matrix and an evolutionary solving method. The model and the method aim to find node corresponding relations of an individual with multiple identities in different networks when associated networks are comprehensively analyzed, and therefore network data are mined. Firstly, the node matching model based on the adjacent matrix is established, evaluation functions for machining advantages and matching disadvantages of two networks are created, the network node matching problem is converted into a function optimization problem, and therefore the problem is simplified; secondly, the evolutionary solving method for the node matching problem is given; at last, three types of common complex networks are utilized to carry out matching experiments. Thus, effectiveness of the provided methods is revealed and detected.