73 results about "Graph generator" patented technology

An attribute collector may collect an attribute set for each test entity of a plurality of test entities associated with a software test executed in a software environment. An attribute analysis signal handler may receive an attribute analysis signal associated with a change in the software environment, and a view generator may provide an attribute-based view associated with an affected attribute set associated with the change, the attribute-based view identifying an affected test entity that is affected by the change.

The invention provides a combined anti-counterfeit label which comprises a two-dimensional code, a blank area and a dot matrix graph, wherein the two-dimensional code, the blank area and the dot matrix graph are arranged on the same plane. A method for generating the combined anti-counterfeit label comprises the steps of obtaining a two-dimensional code pattern converted from plaintext information and ciphertext information via a two-dimensional code generator, generating a dot matrix graph pattern with the area not greater than that of the blank area via a dot matrix graph generator, and superimposing the dot matrix graph pattern on the blank area in the two-dimensional code pattern, wherein the dot matrix graph is to be implanted into the blank area of the two-dimensional code pattern. A device for generating the combined anti-counterfeit label comprises the two-dimensional code generator, a dot matrix graph ID (identification) database, the dot matrix graph pattern generator and a merging device. The label is formed by combining the two-dimensional code and the dot matrix graph; even if the label is imitated by a counterfeiter, information bound with the anti-counterfeit label cannot be acquired, and the anti-counterfeit label has high-grade anti-counterfeiting performance.

The invention provides a combined-type anti-counterfeiting mark based on a same ID. The combined-type anti-counterfeiting mark is composed of a quick response code and a raster graph. The quick response code and the raster graph all comprise the same ID number extracted from an ID number database during a generating process. The method for generating the combined-type anti-counterfeiting mark comprises the steps that by using a quick response code generator, the ID number, parameter information of the quick response code, plaintext information and preset ciphertext information memory area are converted into the quick response code, wherein the ID number, the parameter information of the quick response code and the plaintext information are extracted from the ID number database; by using a raster graph generator, the raster graph is generated by the ID number which is same as the quick response code. The device for generating the combined-type anti-counterfeiting mark comprises the quick response code generator and the raster graph pattern generator. The quick response code and the raster graph comprise the same ID number, and therefore the quick response code and the raster graph have relevancy, due to the fact that quick response code can only be recognized and read through dedicated equipment, the raster graph cannot be recognized once again after being copied and scanned, and therefore the high level anti-counterfeiting performance is achieved.

A method and apparatus for analyzing a joint. The apparatus comprises a graph generator and a model generator. The graph generator forms a first graph based on a plurality of points within a region. The plurality of points represent a plurality of locations relative to a structure for a plurality of fastener devices configured to be installed in the structure to form a joint. The graph generator is configured to identify primary points and secondary points based on the first graph. The graph generator is further configured to form a second graph using the primary points, the secondary points, and the first graph. The model generator is configured to generate a two-dimensional model comprised of triangular sectors using vertices of the second graph. The two-dimensional model is configured for use in forming a mesh for use in performing finite element analysis for the joint.

The invention provides a two-dimensional splicing treatment method for an inclined scanning type photoetching machine during stepping type exposure. The two-dimensional splicing treatment method comprises the following steps: calculating a unit bitmap size of a graph to be exposed, which is split for each time; splitting the graph to be exposed into a plurality of unit bitmaps and filling a black graph into an insufficient part; rotating the unit bitmap and extending the unit bitmap to the size of a view field of a graph generator; and moving a precision movement platform and gradually finishing exposure. With the adoption of the two-dimensional splicing treatment method, the problem that the inclined scanning type photoetching machine cannot normally splice at the spliced part in the stepping type exposure process is solved; when the exposed graph is split, conversion treatment is carried out on the exposed graph according to a fixed included angle between the graph generator and the precision movement platform; and by controlling the precision movement platform, all the unit bitmaps are combined to one complete exposed graph, and all the splicing positions of the finally-etched graph have the good splicing effect, so that the quality of the stepping type exposure of the inclined scanning type photoetching machine is improved.

The present invention provides a method for marking animal or plant living body crust under the condition of no damaging living body and its equipment for using said method. The described equipment to formed from laser generator, light-conducting arm, X and Y vibrating mirror, graph generator, F theta lens and light-conducting cylinder. The laser beam produced by laser generator is reflected or refracted by means of one group of lens and focussed on the living body material on the working table under the control of graph generator and a certain pattern is formed on it. Said method is simple and easy to implement, and the mark formed by said method is not easy to be fallen off when it is worked at high temp. or soaked in water.

Branch sequences for branch prediction performance test are generated by performing the following steps: (i) generating a branch node graph, by a branch node graph generator machine logic set, based, at least in part, upon a set of branch traces of a workload or benchmark code; (ii) generating a first assembly pattern file, for use with a first instruction set architecture (ISA)/microarchitecture set, by an assembly pattern generator machine logic set, based, at least in part, upon the branch node graph so as to mimic the control-flow pattern of the workload or benchmark code; and (iii) running the assembly pattern file on the first ISA/microarchitecture set to obtain first execution results.

A spherical lithography system with area differentiation comprises a light source, an illumination system, a mask graph generator, a computer control system, a projection imaging system and an object table. The light source provides illumination light which illuminates the mask graph generator after being parallel uniformized by the illumination system; the computer control system controls the mask graph generator to provide mask graphs, and subdivides an exposure field into a plurality of units according to the curvature of a sample and the focus depth of the projection imaging system, so that the mask graph generator does not defocus on a single-binning imaging; the projection imaging system develops the mask graphs provided by the mask graph generator onto the sample mounted on the object table; and the computer control system identifies the swing length and the bidimensional movement of the object table according to the curvature of the spherical sample, and differentiates the area of the plane mask, thereby realizing the surface splicing lithography on the samples with different curvatures. The invention has great flexibility and maneuverability in the projection exposure lithography of the spherical samples, and has indigenous advantage in the exposure lithography of the spherical samples with different curvature radiuses.

A system for measuring performance of an enterprise architecture includes an enterprise collaborative recommender engine that provides recommendations for an account within the enterprise architecture based upon enterprise assessments; a health control process engine that triggers rebuilding of the heat map topology view that provides recommendations for updating the enterprise architecture towards increased maturity in response to recommendations from the enterprise collaborative recommender engine; and an enterprise heat map generator that presents a heat map providing maturity information of the enterprise architecture for each domain.

The invention provides an online diagnosis method and system for the internal insulation performance of a capacitor voltage transformer, and the method comprises the steps: building a source domain data set and a target domain data set of a CVT diagnosis model, and determining the personalized features and common features in CVT data; according to a CVT internal insulation performance degradation mechanism, extracting related parameters of the internal insulation fault CVT as common characteristics; estimating a common feature distribution model through a Dirichlet process Gaussian mixture model; constructing an internal insulation fault virtual CVT generation model to generate an internal insulation fault virtual CVT sample; based on the number of normal-state CVT samples and the number of virtual CVT samples in a graph generator balance target domain, nodes on a balance network are distinguished through a training graph convolutional network recognizer, and a fault diagnosis model is obtained; and performing internal insulation fault diagnosis on the to-be-tested CVT sample through the fault diagnosis model. Through the scheme, the sample data demand can be reduced, the fault diagnosis model is constructed based on the generated virtual sample, and the recognition accuracy of the fault diagnosis model can be effectively guaranteed.

Provided in the invention is a multiplying power calibration method of a zooming objective lens of a write-through photoetching machine. The multiplying power calibration method comprises: multiplying powers of a marking graph formed on a substrate along an X shaft direction and Y shaft direction of a precision motion platform at a CCD observation light path from a zooming objective lens to a CCD are calculated respectively; and multiplying powers of a graph generator along the X shaft direction and Y shaft direction of the precision motion platform at the light path from the zooming objective lens to the substrate and then from the substrate to the CCD through the zooming objective lens are respectively calculated. With the method, a multiplying power calibration problem of the write-through photoetching machine between the graph generator and the precision motion platform projection light path can be solved; and on the basis of the specific optical structure, an actual zooming multiplying power can be obtained according to a specific location relation of the CCD camera, the graph generator, and the precision motion platform. The method is easy to realize; and the calibration precision is high.

The invention relates to the technical field of video test, and particularly relates to a video frame rate test method. The video frame rate test method can be used in whole process image signal test of a digital end-to-end device, and the digital end-to-end device comprises a graph generator provided with an LED lamp array; a plurality of LED lamps on the LED lamp array along a preset direction are selected to serve as a test lamp group, the test lamp group comprises a plurality of light emitting units, and each light emitting unit comprises a plurality of adjacent LED lamps; each light emitting unit is turned on in sequence, and the lighting frequency of the light emitting units is gradually increased; and an image displayed by the test lamp group is obtained by an image capturer in real time, and when the obtained image of the light emitting unit is static, the lighting frequency value on the graph generator at the moment is used as the value of the video frame rate of the digital end-to-end device, and then the whole process image signal test is completed. Since the video frame rate test method in the technical scheme is simple, easy to implement and is relatively high in test efficiency and reliability, the whole process image signal test can be completed quickly.

The invention provides a large-scale array fast adaptive anti-interference method based on a convolutional neural network, mainly solving the problems of large calculation amount and poor beam conformality in large-scale phased array adaptive beam forming in the prior art. The method in the implementation scheme comprises the following steps of constructing a phased array radar receiving signal model, a training sample set and a test sample set, establishing an array element weight prediction sub-network W to generate an array element weight, generating a directional diagram Q by using a directional diagram generator P according to the array element weight, and cascading W and P to form a directional diagram zeroing conformal network H, setting a directional diagram zeroing conformal network loss function L composed of a zeroing loss function L1 and a conformal loss function L2, training H by using the training sample set and adopting a gradient descent method, and inputting the test sample set into the trained weight prediction network to obtain a large-scale array anti-interference result. Due to the fact that the influence of interference null on the directional diagram is avoided, the calculation amount of the self-adaptive anti-interference algorithm is reduced, and the method can be used for target detection.

A system and method for rapid workflow design utilizing machine learning includes a scanner, a convolutional neural network, a converter, a graph generator, and a controller. The scanner may convert a paper copy of a process flow into a pixelated image. The convolutional neural network is configured to segment the pixelated image into objects including at least one of an action, a decision, a connector, or a combination thereof. The converter is configured to transform the objects into a connectivity matrix. The graph generator is configured to transform the connectivity matrix into a rectilinear graph. The controller is configured to identify automated actions, semi-automatic actions, and actions requiring operator input from graphical icons provided on the paper copy and, based on the rectilinear graph, to serialize the automated actions, the semi-automatic actions, and the actions requiring operator input as control commands to a printing system.

Provided are a search system and a search method capable of searching an unknown combination of material property parameters having a significant relationship based on already known relationships from among multiple arbitrary combinations of material property parameters. The search system includes a database, a graph generator, and a graph searcher, and is configured as follows: the database stores the plurality of pairs of material property parameters having mutual relationships, and the graph generator generates a graph in which material property parameters stored in the database are nodes, and an inter-node corresponding to the pairs of material property parameters stored to have mutual relationships is an edge. The graph searcher searches the graph generated by the graph generator based on a given search condition and outputs a search result.

The invention provides a depression recognition method and device based on a face video and a storage medium. The depression recognition method comprises the following steps: extracting a pre-acquired target spatial-temporal feature of each to-be-recognized face sub-video through a pre-trained target depression recognition model; outputting a depression degree recognition score of each to-be-recognized face sub-video through a target depression recognition model, and determining a thermodynamic video of each to-be-recognized face sub-video through a thermodynamic diagram generator; determining a first depression recognition result of the target person based on the depression degree recognition score of each to-be-recognized face sub-video; determining a second depression recognition result of the target person based on the thermodynamic video; and determining a final depression recognition result of the target person based on the first depression recognition result and the second depression recognition result. In this way, the final depression recognition result of the target person is determined based on the depression degree recognition score obtained based on the spatial-temporal features of the to-be-recognized face sub-video and the thermal video, and the accuracy of the depression recognition result can be effectively improved.

The invention provides an anti-counterfeiting code figure with identification points. The code figure is provided with at least one identification point printed by adopting special ink or an identification pattern formed by multiple identification points. When excitation lines shine on the surface of the code figure or the code figure is checked through different perspectives, the identification point or the identification pattern presents a color different from the code figure. The code figure generation method comprises steps: an electronic pattern of the needed code figure is generated; according to the size of the electronic pattern, multiple virtual grids with printing identification point implantation regions are divided; after basic parameters of the identification point or the identification pattern are set, the ink type is selected and a specified virtual grid is selected, the identification point is printed on the code figure via the virtual grid. The code figure generation device comprises a code figure generator, an image processor, a size comparison module and a parameter setter. Before the code figure is scanned, through identifying whether an identification point or an identification pattern exists in the code figure, the source can be known.

A video processing unit can include a non-object-based region-of-interest detection neural network, a threshold selection module and a region-of-interest map generator. The non-object-based region-of-interest detection neural network can be configured to receive a video frame and generate a plurality of candidate non-object-based region-of-interest blocks. The threshold selection module can be configured to receive the plurality of candidate non-object-based region-of-interest blocks and identify a plurality of selected region-of-interest blocks based on a predetermined threshold. The region-of-interest map generator can be configured to receive the selected non-object-based region-of-interest blocks and generate a region-of-interest map.

The invention provides a breast tissue classification and identification method based on transfer learning. The method comprises the following steps: firstly, obtaining a suspected patient breast tissue slice pathological image through a tissue slice, then carrying out format adjustment and a series of image processing to obtain a breast tissue slice preprocessed pathological image, and manually marking the preprocessed image pathological category; constructing a transfer learning breast tissue classification model, sequentially inputting the preprocessed images into the model, predicting a pathological category through the model, constructing a loss function model in combination with an actual pathological category of the images, and performing optimization training to obtain an optimized transfer learning breast tissue classification model; and predicting the pathological category of the suspected patient through the optimized model, and combining the pathological category with a heat map generator to obtain a thermodynamic diagram corresponding to the evidence region. The invention has the advantages that the method is suitable for an application environment with a small data set, the robustness is enhanced in training, and the credibility of a result is improved in prediction.

The invention discloses a memory chip, a data processing circuit thereof and a data processing method thereof. The data processing circuit arranged in the memory chip comprises a data graph generator,a parallel-serial conversion circuit and a port driving module. In a testing mode, the data graph generator automatically starts a preset test command and generates test data based on the preset testcommand, and the test data is subjected to parallel-serial conversion through the parallel-serial conversion circuit to generate a first data signal, and the first data signal is used for controllingthe port driving module to output a first result, and power consumption parameters of the memory chip are monitored based on the first result. Data does not need to be written in advance, so that test time is shortened; and the test sampling interval is not limited by the maximum address upper limit of the data, so that the test difficulty is reduced, and the test precision is improved.

The invention provides a progressive training fine-grained visual classification method based on puzzle arrangement learning, and the method comprises the steps: cutting an image data set into images with different granularities through a puzzle generator, employing a residual network (ResNet50) as a feature extractor, enabling a puzzle solver to act on each stage of feature image extraction, and carrying out the extraction of a feature image. In each stage of a residual network (ResNet50), local information of different particle sizes is learned, multi-scale features are fused, and at each stage, a jigsaw puzzle solving module is used to learn a jigsaw puzzle arrangement matrix of disorganized images so as to learn global information of an object. Fine-grained visual classification is carried out by learning global information of objects while learning local information of different granularities in each stage, and a complementary relationship between local detail information of objects of different granularities and the global information of the whole object is utilized at the same time in each stage of the model. And the final classification performance of the model reaches the most advanced level at present.