174 results about "Virtual sample" patented technology

A work state virtual simulation system for an electric haulage shearer based on different geological conditions belongs to automatic control and system simulation of shearers. The work state virtual simulation system comprises devices and a method, wherein the devices consist of a shearer work condition remote monitoring platform, a database, a remote controller, a visual basic system, an industrial Ethernet, a local controller and a machine-mounted detection control system. The method comprises the following steps of: acquiring shearer work condition parameters for the machine-mounted detection control system; carrying out data analysis on the shearer work condition parameters; finally, transmitting the shearer work condition parameters to the shearer monitoring platform for detecting and achieving through the local controller and the industrial Ethernet; on the basis of accessing an achieving database in real time, drawing fully mechanized coal faces under different geological conditions by applying a virtual real technology; and driving a shearer virtual sample machine to realize real reappearance of the work state of the shearer. A shearer virtual sample machine model is driven by using real-time work condition parameters provided by the machine-mounted detection control system, so that a shearer virtual simulation system has the field feeling entering the fully mechanized coal faces and favorable interaction.

For use in acoustic micro imaging, a method (and implementing apparatus) is disclosed for creating a 4D virtual sample data memory. The method comprises employing a pulsed ultrasonic microscope probe to interrogate a sample at three-dimensionally varied locations in the sample. Data developed by the microscope probe includes for each location interrogated a digitized A-scan for that location. The developed data is stored in a data memory.

A graphics system has a mode of operation in which primitive coverage information is generated for real sample locations and virtual sample locations for use in anti-aliasing pixels. An individual pixel has a single real sample with color information and at least one virtual sample. In one implementation each virtual sample within a pixel is a pointer that identifies whether the virtual sample belongs to the single real sample within the pixel or to a proximate neighboring pixel. The virtual sample information permits a blending weight to be determined for blending color values of a partially covered pixel with color values of neighboring pixels.

One embodiment of the present invention sets forth a technique for converting alpha values into pixel coverage masks. Geometric coverage is sampled at a number of “real” sample positions within each pixel. Color and depth values are computed for each of these real samples. Fragment alpha values are used to determine an alpha coverage mask for the real samples and additional “virtual” samples, in which the number of bits set in the mask bits is proportional to the alpha value. An alpha-to-coverage mode uses the virtual samples to increase the number of transparency levels for each pixel compared with using only real samples. The alpha-to-coverage mode may be used in conjunction with virtual coverage anti-aliasing to provide higher-quality transparency for rendering anti-aliased images.

The invention provides a virtual sample deep learning-based robot target identification and pose reconstruction method. The method comprises the steps of extracting a region of an operation target in a camera image by adopting a CNN region detector, and preliminarily determining relative positions of the operation target and a robot end camera; estimating an observation angle deviation of a current view angle and an accurate pose solving optimal view angle of the computer end camera by adopting a CNN pose classifier; controlling a robot motion by adopting a multi-observation view angle correction method to enable the end camera to be transferred to the accurate pose solving optimal view angle; and by adopting virtual-real matching of contour features and pose inverse solving at the optimal view angle, realizing accurate calculation of a target pose. According to the method, the problem in massive sample demands of a deep convolutional neural network is solved, and the problems of feature shielding and matching difficulty caused by an excessively large contour matching view angle deviation are solved; and the activeness of robot vision perception and the algorithm flexibility of target pose reconstruction are improved.

One embodiment of the present invention sets forth a technique for converting alpha values into pixel coverage masks. Geometric coverage is sampled at a number of “real” sample positions within each pixel. Color and depth values are computed for each of these real samples. Fragment alpha values are used to determine an alpha coverage mask for the real samples and additional “virtual” samples, in which the number of bits set in the mask bits is proportional to the alpha value. An alpha-to-coverage mode uses the virtual samples to increase the number of transparency levels for each pixel compared with using only real samples. The alpha-to-coverage mode may be used in conjunction with virtual coverage anti-aliasing to provide higher-quality transparency for rendering anti-aliased images.

An image evaluation device includes a storage unit that stores sample image data that represent a virtual sample image simulating a sample image included in a sample printout that is recognized as a non-defective printout; a reading unit that reads an inspection object image included in an inspection object printout obtained by printing the sample image on a recording medium by a printing device using image data representing the sample image; an extraction unit that extracts a line defect including a linear pattern formed in a specific direction from the inspection object image represented by inspection object image data, based on a difference value between the sample image data and the inspection object image data; and an evaluation unit that evaluates a visibility of the line defect extracted by the extraction unit.

The invention discloses an image processing method for expanding a data set under a small sample. In order to solve the problem of poor identification accuracy of a small sample image, the invention adopts a sample expansion technology to overcome the problem caused by insufficient samples, and provides a more general image processing method. The method comprises the steps that the original training samples are translated, rotated, mirrored, scaled and transmitted, the contrast and the brightness are transformed, the noise is added, the virtual training samples are generated, the number of training samples is increased by generating virtual samples, and then the virtual samples are fused with the original training samples. Through a large number of experiments, the method of the inventionhas excellent recognition effect on a small sample training set, and has better recognition performance. If the sample is insufficient, then the image features in the training phase are not enough toeffectively represent the image features changes, so that the difficulty of image recognition is increased, and even the phenomenon that the image can not be recognized appears.

Each of an image coding apparatus and an image decoding apparatus uses a motion compensated prediction using virtual samples so as to detect a motion vector for each of regions of each frame of an input signal. Accuracy of virtual samples is locally determined while the accuracy of virtual samples is associated with the size of each region which is a motion vector detection unit in which a motion vector is detected. Virtual samples having half-pixel accuracy are used for motion vector detection unit regions having a smaller size 8×8 MC, such as blocks of 8×4 size, blocks of 4×8 size, and blocks of 4×4 size, and virtual samples having ¼-pixel accuracy are used for motion vector detection unit regions that are equal to or larger than 8×8 MC in size.

Each of an image coding apparatus and an image decoding apparatus uses a motion compensated prediction using virtual samples so as to detect a motion vector for each of regions of each frame of an input signal. Accuracy of virtual samples is locally determined while the accuracy of virtual samples is associated with the size of each region which is a motion vector detection unit in which a motion vector is detected. Virtual samples having half-pixel accuracy are used for motion vector detection unit regions having a smaller size 8×8 MC, such as blocks of 8×4 size, blocks of 4×8 size, and blocks of 4×4 size, and virtual samples having ¼-pixel accuracy are used for motion vector detection unit regions that are equal to or larger than 8×8 MC in size.

The invention discloses an SAR image data enhancement method and device and a storage medium, and the method comprises the following steps: carrying out the electromagnetic simulation of an SAR targetimage, and obtaining an SAR electromagnetic simulation image; processing the SAR electromagnetic simulation image and the SAR target image through a generative adversarial network to obtain an SAR target image virtual sample set. According to the method, the SAR target image is subjected to electromagnetic simulation to obtain the multi-azimuth SAR electromagnetic simulation image, so that the defect that the traditional SAR target image sample acquisition is insufficient is overcome, and sufficient data input is provided for subsequently solving the problem of training data scarcity in the deep learning training process; the mapping relation between the SAR electromagnetic simulation image and the SAR target image is learned through the generative adversarial network, the data richness of the SAR electromagnetic simulation image is improved, and therefore the SAR target image with the missing angle is expanded, and powerful support is provided for subsequent SAR target image recognition and detection and other work.

An image evaluation device includes a storage unit that stores sample image data that represent a virtual sample image simulating a sample image included in a sample printout that is recognized as a non-defective printout; a reading unit that reads an inspection object image included in an inspection object printout obtained by printing the sample image on a recording medium by a printing device using image data representing the sample image; an extraction unit that extracts a line defect including a linear pattern formed in a specific direction from the inspection object image represented by inspection object image data, based on a difference value between the sample image data and the inspection object image data; and an evaluation unit that evaluates a visibility of the line defect extracted by the extraction unit.

Each of an image coding apparatus and an image decoding apparatus uses a motion compensated prediction using virtual samples so as to detect a motion vector for each of regions of each frame of an input signal. Accuracy of virtual samples is locally determined while the accuracy of virtual samples is associated with the size of each region which is a motion vector detection unit in which a motion vector is detected. Virtual samples having half-pixel accuracy are used for motion vector detection unit regions having a smaller size 8×8 MC, such as blocks of 8×4 size, blocks of 4×8 size, and blocks of 4×4 size, and virtual samples having ¼-pixel accuracy are used for motion vector detection unit regions that are equal to or larger than 8×8 MC in size.

The present invention discloses a virtual sample generation method comprising the following steps: firstly, acquiring a limited quantity of high dimensional real samples by adopting means of signal acquisition and corresponding devices, then constructing a feasibility based planning (FBP) model by adopting a partial least square (PLS) algorithm, a genetic (GA) algorithm and a back propagation neural network (BPNN) algorithm; secondly, generating an input of a virtual sample on the basis of future knowledge of the known real sample; thirdly, inputting PLS extracted potential features of the virtual sample into FBP, and acquiring an output of the virtual sample on the basis of the future knowledge; and finally, combining input vectors and output vectors of the virtual sample according with a preset rule to acquire a complete virtual sample. Therefore, the virtual sample that can be used for predicting high dimensional data is generated relatively accurately.

The invention relates to the technical field of materials, in particular to a GISSMO material failure model parameter optimization method which comprises the following steps: S1, determining a real stress-strain curve of simulation input; s2, comparing the simulation result of the uniaxial stretching virtual sample with the test result to determine the initial range of the WF; s3, under the condition that no keyword * MAT_ADD _ EROSION exists, optimizing the material parameter WF by adopting an interval reduction sequence based on a meta-model; and S4, adding * MATs _ ADD _ EROSION, namely a GISSMO failure model, and optimizing GISSMO failure model parameters by adopting an optimization method consistent with the step S3 and a target function. According to the method, based on a GISSMO failure model provided in commercial finite element software LS-DYNA, GISSMO failure model parameters are reversely solved and calibrated according to material mechanical property test data parameters; by adopting the LS-OPT, material parameters can be quickly identified, so that output engineering stress-strain curves of simulation and test can obtain relatively good consistency, and a reference canbe provided for establishment of a quick, automatic and high-precision failure material library.