46 results about "Self consistency" patented technology

The invention discloses a double chain-type cross chain trading Internet of blockchains model (golden monkey model) core algorithm. Different from the traditional centralized architecture, the model has a fully-distributed and multi-chain network architecture. Due to the fully-distributed architecture, easier expansion, easier extension and easier fault tolerance are realized than the existing architecture. Each chain maintains the self consistency, the consistency between chains does not need to be managed by a central organization but is maintained by a fully-distributed mechanism. Limitations from the previous centralized Internet of blockchains (chain network, in short) are broken, all chains can operate in parallel on the model, and the trading efficiency and the network operation speed are improved. The model has a new financial market architecture, extensibility is realized, a financial unit can randomly and easily enter or leave the network, and the large-scale network and a high transaction volume are supported. The model comprises participant blockchains and inter-chain blockchains, the participant blockchains or the inter-chain blockchains can be one or one group of financial institutions, the inter-chain blockchain has a double-chain structure, a trading record and a balance account are separated, and cross chain trading is realized. A core algorithm for trading between chains, particularly, a protocol algorithm of enabling the inter-chain blockchains to assist two or more than two participant blockchains for mutual trading is provided, no centralized mechanism is needed, and the consistency of the whole network is maintained.

The present invention provides a method for reliably detecting change in the 3-D shape of objects. It uses an estimate of the accuracy of the 3-D models derived from a set of images taken simultaneously. This accuracy estimate is used to distinguish between significant and insignificant changes in 3-D models derived from different image sets. In one embodiment of the present invention, the accuracy of the 3-D model is estimated using self-consistency methodology for estimating the accuracy of computer vision algorithms. In another embodiment of the present invention, resampling theory is used to compare the mean or median elevation for each change in the models. This methodology allows for estimating, for a given 3-D reconstruction algorithm and class of scenes, the expected variation in the 3-D reconstruction of objects as a function of viewing geometry and local image-matching quality (referred to as a “score”). Differences between two 3-D reconstructions of an object that exceed this expected variation for a given significance level are deemed to be due to a change in the object's shape, while those below this are deemed to be due to uncertainty in the reconstructions.

Self-consistency and/or differences between volume images and interpreted spatial/volumetric context may be exploited for improving seismic imaging and estimation of attributes of geobodies, in accordance with one or more embodiments. Exemplary embodiments allow exploitation of positional and/or shape discrepancies and/or similarities of geobodies in image volumes associated with a geologic model of a geologic volume of interest to improve the accuracy of the geologic model and/or the image volumes. Constraints associated with the geologic volume of interest may be determined and/or utilized to confirm and/or specify dependencies between attributes that are potentially associated with individual geobodies.

The invention relates to a multi-group section perturbation method for uncertainty analysis of reactor physics calculation. The multi-group section perturbation method comprises the following steps: (1), on the basis of ENDF/B, making a multi-group section database and a point-by-point section database at different temperatures by using a nuclear database section processing program NJOY, and making the point-by-point section database in a resonant reaction channel at a resonant energy section into an ultra-fine-group database according to the equal rib width; (2), perturbing the point-by-point section database in the energy section corresponding to a certain energy group in a certain reaction channel so as to obtain a perturbed point-by-point section database; (3), strictly transferring perturbation of the point-by-point section database into the multi-group section database by using a linear and nonlinear perturbation transferring method; and (4), reconstructing by adopting a reaction channel section self-consistency principle to obtain a perturbed multi-group section database. According to the invention, the section of a basic reaction channel can be finely sampled; approximate treatment in the multi-group section perturbation process can also be reduced; and thus, a precise multi-group section sample can be obtained.

The present invention discloses a false invoice issuance identification method based on parallel association rule mining. The method comprises the following steps: extracting an invoice record withina required accounting period, and constructing a transaction set; mining a frequent item set separately for a transaction subset of details in different industries to generate an association rule; andbased on the association rules of details in different industries, calculating self-consistency of the enterprise, and identifying the enterprise with the false invoice issuance behavior through a self-consistency calculation result. According to the method disclosed by the present invention, program design is performed based on the Spark parallel framework, by using an Apriori algorithm, association rule mining is performed on the industries to which the upstream and downstream enterprises of the enterprise belong, the abnormal industry combination is analyzed, the method can target the operating characteristics of the details in different industries in a fine-grained manner and has the characteristic of efficient parallelism, and the false invoice issuance behavior can be automatically,quickly and effectively identified.

A system for evaluating CAD models automatically and transmitting the results of a series of algorithm-based checks. An evaluation model is read in by the system and its parameters are evaluated for self-consistency and conformance to a set of diagnostic rules. Results are returned or displayed interactively upon viewing, transmitting, or inspecting a CAD component model, or placement of a CAD component model in an EDA software tool. The evaluation produces a result, which comprises the pass/fail results of the individual checks, and an indication of quality level. The system may additionally receive from an administrator a plurality of rules, suppressions or quality levels. The set of diagnostic rules comprises the plurality of EDA rules that are not associated with one of the plurality of suppressions.

Data quality measurement is provided for use in a data processing stream, which comprises at least one upstream data processing system and at least one downstream data processing system. An input alert component can be used to provide a measurement of data prior to its input to a data processing system (e.g., a downstream data processing system or an upstream data processing system). An output alert component can be used to provide a measurement on data output by a data processing system. A self-consistency component can be used to measure consistency between items of input, or output data. An end-to-end component can be used to measure data quality using data items from both input data and output data. These components can be used in some combination, or independent of the other, and in any order. In addition, the data quality measurements can be performed separate from that processing performed by either the upstream or downstream processing system.

Methods, metrology modules and target designs are provided, which improve the accuracy of metrology measurements. Methods provide flexible handling of multiple measurement recipes and setups and enable relating them to landscape features that indicate their relation to resonance regions and to flat regions. Clustering of recipes, self-consistency tests, common processing of aggregated measurements, noise reduction, cluster analysis, detailed analysis of the landscape and targets with skewed cells are employed separately or in combination to provide cumulative improvements of measurement accuracy.

The invention relates to a ring surface harmonic-analysis method on the basis of satellite gravity gradient observation data, which includes the following steps: preprocessing the satellite gravity gradient observation data, integrally reducing the satellite gravity gradient observation data to the same spherical surface and performing gridding process on the same to obtain regular grid average value; mapping the grid value on the spherical surface to the ring surface by means of mapping relation of the spherical surface to the ring surface, and obtaining circular regular grid value; perform spline analysis on the gravity gradient data on the ring surface, converting the regular grid average value on the ring surface into continuous spline approximating function; performing two-dimensional Fourier analysis on the gravity gradient on the ring surface, and converting spline approximation to determine Fourier coefficient; and computing the two-dimensional Fourier spectral coefficient of the ring surface into harmonic expansion coefficient. High-accuracy and high-efficiency self-consistency harmonic analysis of the satellite gravity gradient observation data is realized, the error caused by inaccuracy and the like of smoothing factors in existing harmonic analysis is overcome, and the ring surface harmonic-analysis method has the advantages of high computing speed and accuracy.

The invention discloses a remote diagnosis method for the motion behavior of a protection element at a dispatching terminal. The remote diagnosis method is characterized in that the method comprises following steps: S01: establishing an association model of primary equipment and relay protection; S02: transmitting and collecting relay protection intermediate logic node information, SOE information, protection motion information, telecommand deflection information, and fault record files from a substation to the dispatching terminal; S03: determining whether a power grid has faults; S04: determining a fault point via protection motion information, breaker deflection, and protection fault range-measuring information and obtaining association relay protection if the power grid has faults; S05: extracting intermediate logic node files of association relay protection; S06: comparing and analyzing each protection member according to self-consistency and consistency of each protection member and motion strategy; and S07: determining whether all the protection member motions are correct. According to the method, hidden problems of the protection members are discovered in advance, and necessary data is provided for remote operation and maintenance of relay protection.

The invention discloses a parallel magnetic resonance imaging high quality reconstruction method based on self-consistency and containing combined total variation; the method is based on a SPIRiT frame, and provides the high quality reconstruction method aiming at parallel imaging reconstruction problems containing JTV and JL1 composite regular terms; firstly a constraint reconstruction problem is converted into an unconstrained reconstruction problem; then a data fidelity term and a self-correction term are simplified; then the operator split technology converts the simplified reconstruction problem into a gradient calculating problem and a denoising problem containing the JTV and JL1 composite regular terms, and the composite regular terms denoising problem is solved through a whole novel design algorithm based on Split Bregman technology; finally FISTA can accelerate the speed. The reconstruction performances of the novel algorithm and other normal algorithms are compared; test simulation shows that the convergence speed of the novel algorithm is close to a POCS algorithm, yet reconstruction image SNR can be largely improved.

The invention belongs to the technical field of GNSS data processing and ionosphere monitoring, and discloses a high-precision GNSS ionosphere TEC observation value extraction method. Precise single point positioning calculation of the station-by-station standard mode is performed on a GNSS observation network by using the original observation data of GNSS observation network through combination of the high-precision track, clock error and antenna information provided by the IGS, the wide lane ambiguity and the narrow lane ambiguity of the phase observation value are fixed and the fixed wide lane integer ambiguity and the narrow lane integer ambiguity are substituted in the no-geometric-distance phase combination observation value so as to extract the ionosphere TEC observation value. Theproblem of low extraction accuracy of the ionosphere TEC observation value in the prior art can be solved so that the ionosphere TEC observation values of different observation stations in the whole network are enabled to have great self-consistency and consistency, the influence of arc segment length and multipath error on the extraction result is low and thus the extraction accuracy of the ionosphere TEC observation value can be effectively enhanced.

The invention relates to a self-consistent parallel magnetic resonance imaging reconstruction method combining total variation Lp pseudo norms based on feature vectors, and belongs to the technical field of medical magnetic resonance imaging. The invention provides an ESPIRiT parallel imaging reconstruction algorithm containing a joint total variation Lp pseudo norm regularization term based on aniteration self-consistency parallel imaging reconstruction ESPIRiT framework of a feature vector. The method comprises the following steps: firstly, decomposing a reconstruction problem into a gradient calculation problem and a denoising problem containing sparse regular terms by adopting an OS technology; secondly, denoising is carried out by applying an MM algorithm; and finally, FISTA is usedfor acceleration. Experimental results show that compared with a traditional reconstruction algorithm using L1 regular terms, the proposed new algorithm using LpJTV regular terms based on the ESPIRiTreconstruction model can more effectively improve the reconstruction quality of the image.

The invention discloses a self consistency based parallel magnetic resonance imaging quick reconstructing method. Based on an SPIRiT frame, aiming at a JTV and JL1 composite regular term contained parallel imaging reconstruction problem, the invention proposes a quick reconstructing method employing Descartes sampling. In the prior art, scanning time can be reduced distinctively by adopting the magnetic resonance imaging technology; however, the reconstructing time of a prior reconstructing algorithm is long. The method provided by the invention utilizing a split Bregman technology for dissolving a prior problem into a plurality of sub problems that are easy to solve. Experiment simulation results show that the new algorithm is greatly improved in convergence speed under the premise of ensuring reconstruction quality compared with a prior NLCG algorithm.

The invention relates to an iterative self-consistency parallel imaging reconstruction method based on transformation learning and joint sparsity, and belongs to the technical field of medical magnetic resonance imaging. Based on an iteration self-consistency parallel imaging reconstruction problem, the invention provides a Cartesian iteration self-consistency parallel magnetic resonance imaging reconstruction method combining transformation learning and sparse regularization terms. and carrying out solution by using a variable separation (VS) technology and an alternating direction multipliermethod (ADMM) technology. The method comprises the following steps of: carrying out solution by using an ADMM method, Simulation experiments on the two actual data sets show that compared with othercomparison methods, the new algorithm provided by the invention can obtain better reconstruction quality.

The invention discloses a chitosan-containing polyether-type polycarboxylate superplasticizer and a preparation method thereof. Functional modification is carried out on chitosan by using an epoxy group-containing unsaturated compound, then a copolymerization reaction is carried out on the chitosan subjected to the functional modification with acrylic acid monomers, methylacrylic acid monomers andpolyoxyalkylene-based ether unsaturated macromonomers, and the product is the chitosan-containing polycarboxylate superplasticizer; or a copolymerization reaction is carried out on the epoxy group-containing unsaturated compound with acrylic acid monomers, methylacrylic acid monomers and polyoxyalkylene-based ether unsaturated macromonomers, then a chitosan solution is added into the product forpolymerization, a modification reaction is carried out, and the chitosan-containing polycarboxylate superplasticizer is obtained. The superplasticizer prepared by the invention has high water reducingefficiency, can be used alone or in combination with a polycarboxylate superplasticizer, and can endow commercial concrete with the self consistency of workability and slump retaining performance.

Self-consistency and/or differences between volume images and interpreted spatial/volumetric context may be exploited for improving seismic imaging and estimation of attributes of geobodies, in accordance with one or more embodiments. Exemplary embodiments allow exploitation of positional and/or shape discrepancies and/or similarities of geobodies in image volumes associated with a geologic model of a geologic volume of interest to improve the accuracy of the geologic model and/or the image volumes. Constraints associated with the geologic volume of interest may be determined and/or utilized to confirm and/or specify dependencies between attributes that are potentially associated with individual geobodies.

The invention discloses a pencil beam dose algorithm based on self consistency. An original three dimensional pencil beam dose distribution is decomposed into a product of two functions, wherein the two functions respectively represent two dimensional dose distributions of a beam with finite width and infinite length. The algorithm uses two dimensional data to obtain a three dimensional dose distribution of a pencil core, which means a dose distribution of a rectangular pencil beam is processed to be a product of a depth dose distribution and two transverse distributions, wherein the depth dose distribution represents a dose value along the center axis of the pencil beam (denoted as a Z direction) and is a one dimensional distribution, and the transverse distributions represent the relative dose distributions in XZ and YZ planes and are two dimensional distributions. The algorithm reduces storage requirements, and the form of the algorithm is simple which facilitates the calculation and improves the calculation speed. Meanwhile, the decomposition form can satisfy requirements of symmetry and self consistency, which means uniform dose distributions can be produced under uniform field intensity.

The invention provides a nonlinear hyperspectral image mixed pixel decomposition method and device. The mixed pixel decomposition precision can be improved. The method comprises the following steps: taking end-member spectral information in a mixed pixel as a non-perturbation item, taking interaction among different spectrums as a perturbation item, carrying out nonlinear mathematical descriptionon the mixed pixel, and constructing a nonlinear spectral correlation mixing model meeting self-consistency; By taking the spectral cluster as an impurity, mapping the nonlinear spectral correlation mixing model to an impurity model, and constructing a superlattice local Green function of the impurity model for solving to obtain an end member component and an end member abundance estimation result; And according to the obtained end member components and the end member abundance estimation result, clustering the estimation end members by using a density peak clustering method, and merging the same kind of estimation end members. The invention relates to the technical field of remote sensing image processing.

Self-consistency and/or differences between volume images and interpreted spatial/volumetric context may be exploited for improving seismic imaging and estimation of attributes of geobodies, in accordance with one or more embodiments. Exemplary embodiments allow exploitation of positional and/or shape discrepancies and/or similarities of geobodies in image volumes associated with a geologic model of a geologic volume of interest to improve the accuracy of the geologic model and/or the image volumes. Constraints associated with the geologic volume of interest may be determined and/or utilized to confirm and/or specify dependencies between attributes that are potentially associated with individual geobodies.

The invention provides an MOS capacitor measuring method based on a five-element model. The MOS capacitor measuring method comprises the steps of: establishing a five-element equivalent circuit model of an MOS capacitor; adopting a two-element parallel model for carrying out C-V measurement and I-V measurement on the MOS capacitor at two different frequencies; extracting an auxiliary characteristic equation of the MOS capacitor according to the five-element equivalent circuit model and measurement results; and solving the auxiliary characteristic equation to obtain a capacitance value of the MOS capacitor. The MOS capacitor measuring method is applied to an MOS structure of a non-dispersive dielectric, the obtained reasonable MOS capacitance does not change along with the two selected frequencies (namely, non-dispersive), other four elements have reasonable numerical values, and the reasonability and self-consistency of the MOS capacitor measuring method based on the five-element model are shown.

The present invention provides a method for measuring the self-consistency of inference algorithms. The present invention provides a method for measuring the accuracy of an inference algorithm that does not require comparison to ground truth. Rather, the present invention pertains to a method for measuring the accuracy of an inference algorithm by comparing the outputs of the inference algorithm against each other. Essentially, the present invention looks at how well the algorithm applied to many of the different observations gives the same answer. In particular, the present invention provides a method that is not time and labor intensive and is cost effective.