39 results about "Statistical weight" patented technology

A novel and useful mechanism for the skinning of 3D meshes with reference to a skeleton utilizing statistical weight optimization techniques. The mechanism of the present invention comprises (1) an efficient high quality linear blend skinning (LBS) technique based on a set of skeleton deformations sampled from the manipulation space; (2) a joint placement algorithm to optimize the input skeleton; and (3) a set of tools for a user to interactively control the skinning process. Statistical skinning weight maps are computed using an as-rigid-as-possible (ARAP) optimization. The method operates with a coarsely placed initial skeleton and optimizes joint placements to improve the skeleton's alignment. Bones may also be parameterized incorporating twists, bends, stretches and spines. Several easy to use tools add additional constraints to resolve ambiguous situations when needed and interactive feedback is provided to aid users. Quality weight maps are generated for challenging deformations and various data types (e.g., triangle, tetrahedral meshes), including noisy, complex and topologically challenging examples (e.g., missing triangles, open boundaries, self-intersections, or wire edges).

A novel and useful mechanism for the skinning of 3D meshes with reference to a skeleton utilizing statistical weight optimization techniques. The mechanism of the present invention comprises (1) an efficient high quality linear blend skinning (LB S) technique based on a set of skeleton deformations sampled from the manipulation space; (2) a joint placement algorithm to optimize the input skeleton; and (3) a set of tools for a user to interactively control the skinning process. Statistical skinning weight maps are computed using an as-rigid-as-possible (ARAP) optimization. The method operates with a coarsely placed initial skeleton and optimizes joint placements to improve the skeleton's alignment. Bones may also be parameterized incorporating twists, bends, stretches and spines. Several easy to use tools add additional constraints to resolve ambiguous situations when needed and interactive feedback is provided to aid users. Quality weight maps are generated for challenging deformations and various data types (e.g., triangle, tetrahedral meshes), including noisy, complex and topologically challenging examples (e.g., missing triangles, open boundaries, self-intersections, or wire edges).

According to one aspect of the invention, a method of estimating geological location depths by converting horizon data from the time domain to the depth domain is provided, wherein the method comprises at least a data accumulation step and a data evaluation step, wherein the data evaluation step further comprises assignment of greater statistical weight to data derived from closer data acquisition points than to data derived from further data acquisition points using a weighted regression analysis, with distance being one of several weighting criteria. According to a further aspect of the invention, a method of estimating geological location depths is provided, wherein the method comprises a continuous, layer-by-layer computation of geological formation depths achieved by extracting values from horizon time data set at an exact geographic location of the next deeper horizon, thereby allowing interval computations to be carried out without the need for gridding. According to a still further aspect of the invention, a method of estimating geological formation depths is provided, wherein the method comprises estimating an expected error based on data scatter, and then allowing an alternative solution to be either partially or fully employed in areas where the accumulated data is insufficient to determine an acceptably reliable geophysical model.

A method of estimating geological location depths by converting horizon data from the time domain to the depth domain is provided wherein the method includes at least a data accumulation step and a data evaluation step, and wherein the data evaluation step further includes assignment of greater statistical weight to data derived from closer data acquisition points than to data derived from further data acquisition points using a weighted regression analysis, with distance being one of several weighting criteria. Layer-by-layer computation of geological formation depths is achieved by extracting values from horizon time data set at exact geographic locations in the next deeper horizon, thereby allowing interval computations to be carried out without the need for gridding.

There is provided a method and system for obtaining an enhanced estimate of bit error rate performance. A receiver module counts a predetermined number of bit errors and concurrently measures the time taken for the predetermined number of bit errors to occur. In this way an estimate of the bit error rate (BER) is obtained which has the same statistical weight regardless of the numerical value of the BER. The estimate of BER can subsequently be used to optimise the parameters of the system such that the true value of BER is at a minimum.

In a hierarchical profile, each node represents at least one feature of behavioral data collected about an entity profiled, with the topmost node selected as the “statistically most informative” feature of the data. A profile can cover numerous domains and be predictively very powerful in each domain. A number of observations can be “aggregated” together into a single datapoint. In use, the structure of the profile is compared against current information associated with the entity to produce a recommendation or prediction. If the profile represents at least some data aggregation, then new observations are folded into the profile based on statistical weights of the aggregations. Because of the way the profile is created and updated, its hierarchical structure maps the collected observations. Therefore, as new observations are incorporated, if the new observations change the profile's structure significantly, then it can be hypothesized that something “interesting” has happened to the entity.

The image generation method and system generates an image using a predetermined iterative reconstruction technique from cone beam data that has been expanded by adding additional data, and an instance of the iteration process is weighted according to a corresponding validation weight during the reconstruction. Optionally, an instance of the iteration process is weighted according to a combination of weights during the reconstruction. The predetermined combination of the weights includes axial weights based upon a validity value of the expanded data and statistical weights.

An embodiment of the invention provides a method and a device for performing histogram equalization processing on an image. The method mainly comprises steps as follows: calculating the distance between each pixel point in the image and a center pixel point; calculating a histogram statistical weight of each pixel point in the image according to the distance between each pixel point in the image and the center pixel point; performing weighted grey level histogram statistics of each grey level on the image respectively according to the histogram statistical weight of each pixel point in the image; performing histogram equalization processing on the image according to a weighted grey level histogram statistic result of each grey level of the image. According to the embodiment, different statistical weights are adopted for the pixel points according to spatial distribution positions of the pixel points in the image, so that a vision concerned area of people, namely, a center area of the image, can be stressed in the processed image, differentiated contrast enhancement processing is performed on different areas of the image, and the contrast ratio of the vision concerned area in the image is effectively enhanced.

A word vector representation method based on Chinese language element and pinyin joint statistics comprises the following steps of 1, collecting internet text information to construct a corpus, and conducting text cleaning and word segmentation processing on the constructed corpus; (2) carrying out word segmentation processing on the Chinese corpus, converting the processed Chinese corpus into pinyin information which does not reserve tone information, and then respectively carrying out statistical weights TFc, IDFc, TFp and IDFp on word frequency statistics and inverse document probability ofthe morphemes and pinyin characteristics in the training set corpus and the whole document; (3) constructing a Chinese single morpheme representation vector based on a Chinese word representation model of contextual morpheme and pinyin joint statistics; And (4) training a three-layer neural network on the basis of the step (3) for predicting the central target word. According to the method, the adaptability of an offline dictionary and the corpus data scale can be met, large-scale unlabeled internet information text data can be directly learned, the consideration of a conventional word embedding model on Chinese language difference characteristics can be improved, and the representation and recognition accuracy of wrongly written words can be improved.

The invention relates to a construction method for a gray-level information content histogram, comprising the following steps: acquiring the scale space expressions of an input image under different scales, and folding the input image with Gausses of different scale factors to obtain the multiscale space expression of the input image, namely a Gaussian pyramid image; acquiring the DoG pyramid multiscale expression of the input image, detecting a scale space extreme point on the basis, and storing the corresponding scale information; revising the statistical weight of each pixel, assigning 1 for the increment of the statical weight which is in a local high information area and is detected by the scale space, assigning 0 for the increment of the statical weight outside the area, and adopting a progressive scanning mode to revise the statistical weights of all the pixels in the local high information area; and respectively accumulating the statistical weights of the gray levels of the pixels, thus obtaining the measured result of the information content born by each gray level, namely the gray-level information content histogram. The invention comprehensively considers the quantity and space position distribution, so that the statistical result can better reflect the role of each gray level in describing information.

The invention proposes a method for implementing a consensus mechanism of a blockchain system. The method comprises the following steps: creating a system node weight table when the blockchain systemis initialized; when the current production node in the blockchain system is producing a block, querying the ip address of each node according to the system node weight table to determine whether there is a dropped node in the blockchain system; if there is a dropped node, decrementing the node weight value of the dropped node by 1, and updating the system node weight table; and when production nodes are generated based on the dpos consensus mechanism, selecting production nodes according to the number of votes of each node and the node weight value of each node. Therefore, the method providedby the invention links the offline time of the nodes with the statistical weight of the node voting number, so that even if the nodes that are frequently dropped have a high number of votes, the statistical weight of the voting number is not too high, thereby greatly reducing the possibility that the nodes that are often dropped become block production nodes, and ensuring that the blockchain system operates efficiently.

The invention discloses a network identity relationship reliability calculation method, comprising obtaining a plurality of second network identities associated with a plurality of first network identities to be queried, and utilizing the scene to which each of the second network identities belongs as a connection to form a network identity relation graph; and obtaining the scene statistical weight of adjacent network identity relations in the network identity relation graph to calculate the reliability of any pair of network identity relations in the network identity relation graph. Through building the network identity relation graph, the method can more rapidly calculate the reliability of a pair of network identity relations, and further be in favor of network data safety and maintenance.

Approaches are described for addressing artifacts associated with iterative reconstruction of image data acquired using a cone-beam CT system. Such approaches include, but are not limited to, the use of asymmetric regularization during iterative reconstruction, the modulation of regularization strength for certain voxels, the modification of statistical weights, and / or the generation and use of synthesized data.

Methods and systems are disclosed to associate input data with status, location, and technical attributes to arrive at a uniquely identified candidate object by obtaining a set of initial candidate objects based on input data being compared to known technical attributes of uniquely identified objects, obtaining best-known object location and status history for each initial candidate object using previously known location and status information of each initial candidate object, assigning a statistical weight to each initial candidate object based on its maximum calculated speed and speed capabilities of each initial candidate object, generating an association probability of each of the initial candidate objects to the input data to arrive at a set of final candidate objects, and re-evaluating any of the above with data considered to be current that was not used, wherein the set of final candidate objects are uniquely identified candidate objects arrived at with current data.

In a hierarchical profile, each node represents at least one feature of behavioral data collected about an entity profiled, with the topmost node selected as the “statistically most informative” feature of the data. A profile can cover numerous domains and be predictively very powerful in each domain. A number of observations can be “aggregated” together into a single datapoint. In use, the structure of the profile is compared against current information associated with the entity to produce a recommendation or prediction. If the profile represents at least some data aggregation, then new observations are folded into the profile based on statistical weights of the aggregations. Because of the way the profile is created and updated, its hierarchical structure maps the collected observations. Therefore, as new observations are incorporated, if the new observations change the profile's structure significantly, then it can be hypothesized that something “interesting” has happened to the entity.

The invention discloses a density calculation method of plasmas in a gun barrel in a gunpowder combustion process. The density calculation method comprises the following steps: step 1, obtaining gun bore structure parameters and explosive filling parameters; step 2, establishing a gunpowder combustion equation set; step 3, solving the gunpowder combustion equation set; obtaining functions of p, l and phi relative to t by taking the t as an independent variable; step 4, calculating a gas temperature T: T=Sp(l+lphi) / omegaphiR, wherein R is a gas constant; and step 5, calculating a plasma density ne: ne=no / ni*(2mnokT)<1.5> / h<3>*2gi / g0exp(-eEi / kT), wherein Ei is an ionization potential of ions, go is a statistical weight of an atomic ground state, gi is a statistical weight of an ion ground state, me is the mass of electrons, ni is the ion density and no is the atom density. The density calculation method of the plasmas in the gun barrel in the gunpowder combustion process, disclosed by the invention, can accurately calculate the density of the plasmas in the gun barrel, and provides theoretical supports for the density of the plasmas in the gun barrel.

In a hierarchical profile, each node represents at least one feature of behavioral data collected about an entity profiled, with the topmost node selected as the “statistically most informative” feature of the data. A profile can cover numerous domains and be predictively very powerful in each domain. A number of observations can be “aggregated” together into a single datapoint. In use, the structure of the profile is compared against current information associated with the entity to produce a recommendation or prediction. If the profile represents at least some data aggregation, then new observations are folded into the profile based on statistical weights of the aggregations. Because of the way the profile is created and updated, its hierarchical structure maps the collected observations. Therefore, as new observations are incorporated, if the new observations change the profile's structure significantly, then it can be hypothesized that something “interesting” has happened to the entity.

In accordance with the teachings of the present invention, a computer-implemented apparatus and method is provided for determining the impact of certain actions on the performance of a pre-specified or modeled system is provided. A manifest variable database is utilized for storing manifest variable data relating to user interaction with a system of interest. An imputation module may be coupled to the manifest variable database for calculating any missing manifest variables. Embodiments of the invention may further include a statistical weights calculator for determining strength of correlation among manifest and latent variables, a latent score calculator, a fuzzy clustering module that derives clusters or segments that have their own impacts and scores for a fitted model and constraining impact calculator that determines the impact of certain operations on the fitted model.

The image generation method and system generates an image using a predetermined iterative reconstruction technique from cone beam data that has been expanded by adding additional data, and an instance of the iteration process is weighted according to a corresponding validation weight during the reconstruction. Optionally, an instance of the iteration process is weighted according to a combination of weights during the reconstruction. The predetermined combination of the weights includes axial weights based upon a validity value of the expanded data and statistical weights.

A novel and useful mechanism for the skinning of 3D meshes with reference to a skeleton utilizing statistical weight optimization techniques. The mechanism of the present invention comprises (1) an efficient high quality linear blend skinning (LBS) technique based on a set of skeleton deformations sampled from the manipulation space; (2) a joint placement algorithm to optimize the input skeleton; and (3) a set of tools for a user to interactively control the skinning process. Statistical skinning weight maps are computed using an as-rigid-as-possible (ARAP) optimization. The method operates with a coarsely placed initial skeleton and optimizes joint placements to improve the skeleton's alignment. Bones may also be parameterized incorporating twists, bends, stretches and spines. Several easy to use tools add additional constraints to resolve ambiguous situations when needed and interactive feedback is provided to aid users. Quality weight maps are generated for challenging deformations and various data types (e.g., triangle, tetrahedral meshes), including noisy, complex and topologically challenging examples (e.g., missing triangles, open boundaries, self-intersections, or wire edges).

The invention discloses a statistical analysis method based on home sensors. The statistical analysis method comprises the following steps of: obtaining the state information of a smart home device, and storing the information in a server database according to the user classification; the server obtaining correlation coefficients between different home sensors based on the statistical analysis operation; setting correlation statistical weights according to the correlation coefficients, and establishing a relationship list according to the correlation statistical weights; and reminding the userwhether the corresponding operation needs to be performed when the operation of the user does not appear the corresponding operation expected in the relationship list. According to the statistical analysis method based on the home sensors and system, the automatic scene recognition, the automatic generation, and the automatic reminding are realized by the correlation operation that the smart homesensors collect the state of the smart home device. The method is more intelligent than the current user manually setting the scene, and the real full automation is achieved.

A system and method include acquisition of projection data from a scanned object, the set of projection data comprising a plurality of projection measurements. The system and method also include calculation of a set of modified statistical weights from the projection data, wherein a respective modified statistical weight of the set of modified statistical weights comprises a deviation from an inverse variance of a corresponding projection measurement of the projection data. The system and method further include reconstruction of an image of the scanned object using the set of modified statistical weights as coefficients in an iterative reconstruction algorithm.

The invention provides an address book text classification method and device and electronic equipment, and the method comprises the steps: carrying out the word segmentation of a source address book text according to a preset word segmentation model, carrying out the filtering of a word segmentation result, and calculating the statistical weight of each segmented word in a filtered word segmentation result; obtaining segmented words of which the statistical weights are greater than a preset weight threshold value to serve as primary selected segmented words; performing regularization processing on the primarily selected segmented words according to priori knowledge to obtain selected segmented words; generating a keyword vector list according to the selected segmented word so as to obtaina clustering result related to the keyword vector list; and dividing and sorting the risk levels of the clustering results according to a preset risk label statistical result. According to the method,the address book texts can be classified, risk grade division can be provided, more comprehensive address book portrait information can be obtained in the subdivision fields of marketing, client management, risk control and the like, and effective support is provided for specific services by further combining the risk information.

In a hierarchical profile, each node represents at least one feature of behavioral data collected about an entity profiled, with the topmost node selected as the “statistically most informative” feature of the data. A profile can cover numerous domains and be predictively very powerful in each domain. A number of observations can be “aggregated” together into a single datapoint. In use, the structure of the profile is compared against current information associated with the entity to produce a recommendation or prediction. If the profile represents at least some data aggregation, then new observations are folded into the profile based on statistical weights of the aggregations. Because of the way the profile is created and updated, its hierarchical structure maps the collected observations. Therefore, as new observations are incorporated, if the new observations change the profile's structure significantly, then it can be hypothesized that something “interesting” has happened to the entity.

The invention discloses a sample expansion-based unbalanced data multi-dimensional parameter estimating method and is applied to the field of airborne equipment reliability. The sample expansion-basedunbalanced data multi-dimensional parameter estimating method combines multiplex samples and derivative samples to effectively expand the sample space; the multiplex samples have certain initial damage before accelerated lift test is implemented, so that data statistics should be implemented after accumulated damage caused by an initial load spectrum is converted; the load spectrum of the derivative samples is different from that of normal samples, so that data acquired under different load strength should not apply the unified data statistical weight but be provided with different weights according to different load strengths; by means of the methods of grouped resampling and load strength coefficient normal weighting, different types of samples can be comprehensively parameter-estimated, so that he problem of unbalanced data small sample parameter estimation can be solved.