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203 results about "Model representation" patented technology

Method of isomorphic singular manifold projection still/video imagery compression

Methods and apparatuses for still image compression, video compression and automatic target recognition are disclosed. The method of still image compression uses isomorphic singular manifold projection whereby surfaces of objects having singular manifold representations are represented by best match canonical polynomials to arrive at a model representation. The model representation is compared with the original representation to arrive at a difference. If the difference exceeds a predetermined threshold, the difference data are saved and compressed using standard lossy compression. The coefficients from the best match polynomial together with the difference data, if any, are then compressed using lossless compression. The method of motion estimation for enhanced video compression sends I frames on an "as-needed" basis, based on comparing the error between segments of a current frame and a predicted frame. If the error exceeds a predetermined threshold, which can be based on program content, the next frame sent will be an I frame. The method of automatic target recognition (ATR) including tracking, zooming, and image enhancement, uses isomorphic singular manifold projection to separate texture and sculpture portions of an image. Soft ATR is then used on the sculptured portion and hard ATR is used on the texture portion.
Owner:PHYSICAL OPTICS CORP

Internet of Things data uncertainty measurement, prediction and outlier-removing method based on Gaussian process

The invention relates to an Internet of Things data uncertainty measurement, prediction and outlier-removing method based on the Gaussian process. The method is a dynamical system method of estimating and collecting the standard deviation of Internet of Things perception sensor measurement errors and combining the Gaussian process modeling theory with autoregression model representations; prediction values and uncertainty measurement of observation data effective time sequence data are given, whether the data are missing values or outlier data is judged according to the information, and data supplement is correspondingly carried out. The method is a non-parameterized probability prediction method. Due to the fact that training set learning has the feature of tracing system dynamic states, judgment, early-warning and data supplement can be carried out on data exception and data missing phenomena in time according to the prediction value uncertainty and the sensor calibration standard deviation, the prediction error is small, and the accuracy is high. The Internet of Things data uncertainty measurement, prediction and outlier-removing method is used for controlling the quality of Internet of Things automatic observation data, and can ensure accuracy of collected data.
Owner:SHANDONG AGRICULTURAL UNIVERSITY

Cross-domain knowledge transfer tag embedding method and apparatus

The invention relates to a cross-domain knowledge transfer tag embedding method and apparatus. The method comprises the steps of obtaining text data of a source domain and a target domain, performing model representation, solving word vector parameters of keywords in the source domain and the target domain, and performing transfer of keyword tags from the source domain to the target domain; obtaining nearest neighbors of labeled keywords in the source domain and the target domain, performing weight assignment on keywords of the nearest neighbors by keyword tags of the labeled keywords to obtain extended keyword tags; performing user-level keyword tag labeling according to extracted user-level text data; dynamically optimizing parameters of user-level keyword tag parts according to click and / or access data information of a user based on the word vector parameters of the keywords and the user-level keyword tags; and obtaining new user-level text data from the target domain, performing user-level keyword tag labeling prediction and sorting, and outputting a result. According to the method and the apparatus, the accuracy and high efficiency of tag labeling can be taken into account and business demands of business personnel are met.
Owner:BEIJING BLUEFOCUS BRAND MANAGEMENT CONSULTANTS CO LTD

Point cloud enhancement method based on subsection resampling and surface triangularization

The invention discloses a point cloud enhancement method based on subsection resampling and surface triangularization. The method comprises the specific steps that an input point cloud (imag file=' 2013107425845100004dest-path-image001. TIF' wi='13' he='32'/) is divided to obtain the set (img file='752571des t-path-image002. TIF' wi='37' he='24'/) of the subsets of the point cloud (img file='835430dest-path-image001. TIF' wi='13' he='32'/), each subset (img file='2012107425845100004dest-path-imag003. TIF' wi='20' he='32'/) is resampled, data noise is filtered out, and new point sets (img file='dest-path-image005. TIF' wi='23' he='32'/) which are more even in space distribution are obtained; the sets (img file='250417dest-path-image006. TIF' wi='40 he='32/) of all the resampled new point sets are combined, a new point cloud (img file='dest-path-mage007. TIF' wi='16' he='32'/) is obtained, the surface triangularization is carried out on the new point cloud (img file='114468dest-path-mage007. TIF' wi='16' he='32'/), and a triangular mesh model ((img file='397682dest-path-image008. TIF' wi='15' he='32'/) is obtained. According to the method, an environmental structure is restored accurately, and meanwhile the original edges and corners of the environment are prevented from being smoothed by mistake; different sampling densities are selected according to different model surface shape change intensity degrees, and model representation is more efficient. The point sets are projected to a two-dimensional plane to be triangulated at the part of the model, and the calculation efficiency ratio is higher than that of triangularization directly carried out in a three-dimensional space.
Owner:HANGZHOU JIAZHI TECH CO LTD

A structure frequency response dynamic model correction method based on deep learning

The invention discloses a structure frequency response dynamic model correction method based on deep learning. The method includes transforming the frequency response value of the experimental measured structure and the frequency response value of the dynamic model simulation into an image mode for storing and feature extraction, taking the corresponding parameters of the training sample images aslabels of the training set, and establishing the training sample sets of multi-frequency points, multi-observation points and multi-observation directions, and based on this, building a deep neural network and other processes. The method combines the advantages of deep learning in the field of image recognition, establishes the fast mapping relationship between the dynamic output and the parameters to be corrected, inputs the experimental measurement image into the trained neural network, outputs the model correction result, and effectively solves the problems of poor model representation ability of the manual feature extraction method and the like. In addition, considering the possibility of over-fitting caused by less training samples, the method of adding fast connection structure in the network forward transmission and adding noise expansion samples are adopted to reduce the parameter correction error.
Owner:BEIHANG UNIV
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