17 results about "Test target" patented technology

In certain embodiments, an ophthalmic system includes a light system, an imaging system, and a computer. The light system directs light towards a test target according to an expected parameter set to yield an actual spot on the test target. Each expected brightness parameter of the set describes an expected brightness of the actual spot. The imaging system generates a digital image of the actual spot. The computer determines pixel values of the pixels of the digital image and determines an actual brightness parameter set according to the pixel values. Each actual brightness parameter of the set describes an actual brightness of the actual spot and corresponds to an expected brightness parameter. The computer compares the actual brightness parameters to the corresponding expected brightness parameters and detects a deviation. The computer identifies an issue of the light system indicated by the deviation and provides an output associated with the issue.

The invention discloses an improved near-far field data transformation method and device based on a transfer matrix, and belongs to the field of RCS testing and data processing. The method comprises the following steps: according to the maximum size of a to-be-measured target on a rotation plane and the distance from the center of a measurement antenna to the scattering center of the target, calculating to obtain a synthetic aperture angle required by transformation calculation; performing one-dimensional gridding division processing on the area where the target to be tested is located along the transverse direction, and respectively establishing a near-field test observation matrix and a far-field test observation matrix of the target to be tested in the grid area obtained by division; according to the near-field test observation matrix and the far-field test observation matrix, calculating to obtain a transfer matrix for converting near-field scattering characteristic data into far-field scattering characteristic data; and updating the transfer matrix according to a preset test frequency and a conversion processing center angle, and calculating to obtain all far field scattering characteristic data of the to-be-tested target. The method can effectively reduce the scale of the observation matrix and improve the calculation efficiency.

The present application relates to the field of optical interferometry, and aims at the phase distortion problem in off-axis interferometric quantitative phase imaging, and provides an interferometric microscopic phase distortion elimination method based on PACUNeXt3+ network. The method comprises the following steps: 1. using Zernike polynomials and test target pictures to simulate and generate a data set; 2. establishing and training a PACUNeXt3+ neural network model; 3. inputting the interference graph I or of the sample to be measured into the trained neural network, and outputting the background interference graph I' r corresponding to the sample without sample information; 4. using the two interference graphs I or and I' r to reconstruct the sample phase distribution φ o (x, y) without phase distortion. The present application has high precision and speed, can eliminate the secondary or high-order phase distortion in interferometric quantitative phase imaging, and has great application prospect in the field of phase imaging.

The application discloses an automatic driving continuous testing method based on an adversarial attack, takes end-to-end automatic driving software as a testing object, takes a picture as an input, outputs a deflection angle for controlling a virtual car, sets a billboard area in the input picture as a testing area, sets an expected driving track as a testing target, positions a current position of the testing object, calculates an expected deflection angle of the testing object based on the testing target, positions the testing area in the input, adds Gaussian disturbance noise to simulate the influence of environmental factors, calculates a gradient about the input picture, updates the input of the testing object based on a sub-gradient iteration, extracts and uses affine transformation and interpolation transformation to convert the testing area image into a billboard size, inputs the testing area image into a virtual camera, and finally outputs the testing area image to the billboard in a testing scene, and completes the continuous testing based on feedback information and control information. The application improves the robustness and safety of automatic driving software based on machine learning and deep learning.

In certain embodiments, an ophthalmic system includes a light system, an imaging system, and a computer. The light system directs light towards a test target according to an expected parameter set to yield an actual spot on the test target. Each expected brightness parameter of the set describes an expected brightness of the actual spot. The imaging system generates a digital image of the actual spot. The computer determines pixel values of the pixels of the digital image and determines an actual brightness parameter set according to the pixel values. Each actual brightness parameter of the set describes an actual brightness of the actual spot and corresponds to an expected brightness parameter. The computer compares the actual brightness parameters to the corresponding expected brightness parameters and detects a deviation. The computer identifies an issue of the light system indicated by the deviation and provides an output associated with the issue.

The application provides a text interaction traffic robustness automatic testing method and device, the method comprises the following steps: based on the test target, different scenes are configured, different scene configurations are combined to obtain test cases covering different scenes, multiple parameters of the test cases are configured, the test cases are grouped and prioritized based on the test requirements, the configuration parameters required for testing are processed to obtain configuration parameters that meet the parameter receiving requirements of the large model firewall system, the configuration parameters are sent to the large model firewall system, the large model firewall system is preconfigured based on the configuration parameters, simulated test traffic is sent to the large model firewall system based on the test case parameters for traffic detection, the traffic detection results returned by the large model firewall system are received, the target test data of the whole test process is collected and processed, the core evaluation indexes are calculated based on the processed target test data, and the visual test report is generated based on the core evaluation indexes.

The invention discloses a lightweight target detection method for a UAV aerial photography scene, and the method comprises the steps: selecting a data set, dividing the data set into a training set, a verification set and a test set, and carrying out the preprocessing; constructing a target detection network model OGCF-YOLO: taking YOLOv8n as a baseline model, eliminating redundant P5 down-sampling branches and detection heads thereof, and introducing a high-resolution P2 detection head; inserting a space-channel collaborative attention module at the tail end of the backbone network and in front of the SPPF layer; an orthogonal gating feature fusion mechanism is provided, and a C2f module of the Neck network is replaced by an OGCF-C2f module; a dynamic up-sampling module based on point sampling is used for replacing traditional interpolation up-sampling; and inputting the shot image into the tested target detection network model OGCF-YOLO to obtain a detection result. By reconstructing the backbone network and the feature fusion path, the significant rise of the detection precision is realized.

The application discloses a kind of electromagnetic scattering data determination method and device of test target, belong to target electromagnetic scattering characteristic test field.The method comprises: constructing semi-empirical model of test target;First scattering geometric parameter known to test target and electromagnetic scattering characteristic data corresponding to first scattering geometric parameter are input into semi-empirical model, and the model parameter of semi-empirical model is obtained;Second scattering geometric parameter known to test target is input into semi-empirical model with determined model parameter, and the electromagnetic scattering characteristic data corresponding to second scattering geometric parameter is obtained;Wherein, the bistatic angle corresponding to second scattering geometric parameter is greater than the bistatic angle corresponding to first scattering geometric parameter.The present application can improve the accuracy and reliability of large bistatic angle test on rough ground (such as land, cement ground).

An object is to provide a technique capable of increasing efficiency of executing a fuzzing test. A restart controller restarts a test target apparatus when a monitoring part monitors that the test target apparatus is crushed on fuzzing using a plurality of fuzzes, the fuzzing execution part executes the fuzzing using one or more fuzzes after the test target apparatus is restarted, and one or more fuzzes including a fuzz crushing the test target apparatus is specified based on a monitoring result of the monitoring part on the fuzzing using one or more fuzzes.

This utility model provides an endoscopic camera module testing device, belonging to the field of camera module production equipment. The device includes a frame with a transmission mechanism inside. A target testing box is located at one end of the frame. The transmission mechanism drives a module clamping mechanism to move along a preset straight path towards the target testing box. The module clamping mechanism includes an adapter base, a support base, and a limiting and fixing module. The adapter base is connected to the transmission mechanism, and the support base is connected to the adapter base. The support base has multiple module mounting positions. The limiting and fixing module covers the upper part of the support base and is used to press the module firmly into the module mounting position. The multi-positioning structure effectively avoids module position shift during testing, ensuring clear imaging and accurate positioning of the test target, providing strong assurance for the reliability of the test data.

The embodiment of the application provides an MTF testing method and device for an AR projection light machine, comprising: acquiring a projection test target, wherein the projection test target is a cross slit bright line; collecting a test projection image corresponding to the projection test target; based on the test projection image, scanning along the direction perpendicular to the extension direction of each bright line in the cross slit bright line respectively, and determining the brightness data of each corresponding scanning position; performing brightness feature extraction on the brightness data of each corresponding scanning position to obtain an effective brightness data sequence; based on the effective brightness data sequence, constructing a line spread function; and based on the line spread function, determining a test result. Through the cross slit bright line test target combined with directional scanning, brightness feature extraction and line spread function construction, the imaging quality of the AR projection light machine is accurately tested, and the reliability and applicability of the test result are improved.

The application discloses a lightweight target detection method for UAV aerial photography scenes, comprising the following steps: selecting a data set, dividing the data set into a training set, a verification set and a test set, and performing pretreatment; constructing a target detection network model OGCF-YOLO: taking YOLOv8n as a baseline model, eliminating a redundant P5 downsampling branch and a detection head thereof and introducing a high-resolution P2 detection head; inserting a space-channel collaborative attention module before an SPPF layer at the end of a backbone network; proposing an orthogonal gate feature fusion mechanism, and replacing a C2f module of a Neck network with an OGCF-C2f module; replacing a traditional interpolation upsampling with a dynamic upsampling module based on point sampling; inputting a photographed image into the tested target detection network model OGCF-YOLO to obtain a detection result. Through the reconstruction of the backbone network and the feature fusion path, the application realizes a significant leap in detection accuracy.

In certain embodiments, an ophthalmic laser system includes a laser system, an imaging system, and a computer. The laser system accesses a planned test pattern of a planned laser spot and directs a laser beam toward a test target located at a target plane according to the planned test pattern to produce an actual test pattern of an actual laser spot on the test target. The actual laser spots correspond to the planned laser spots. The imaging system has a digital camera that generates a digital image of the actual test pattern. The computer analyzes the digital image to: compare the actual test pattern to the planned test pattern; detecting a deviation between the actual test pattern and the planned test pattern; identifying a problem indicated by the deviation; and providing an output in response to the identified question.

This application discloses an endoscope assembly fixture and inspection method, relating to the field of endoscope assembly technology. It is used for assembling the lens and chip of a high-resolution endoscope. The endoscope assembly fixture includes: a base plate, a light source, a first movable component, a gripper mechanism, a second movable component, a conductive fixture, a third movable component, and a test target. The light source is movably disposed on one side of the base plate with a gap between it and the base plate; the gripper mechanism is movably disposed between the light source and the base plate, and grips the lens; the conductive fixture is movably disposed between the light source and the base plate, and electrically connected to the chip for external imaging; the test target is disposed on the side of the light source facing the base plate. This application allows for real-time observation of image effects, ensuring that the alignment accuracy of the lens and chip remains within a high range.

The invention relates to the technical field of space debris protection, in particular to a satellite cable space debris protection test device which comprises a first clamping plate, a second clamping plate, a cable and a protection structure. The protection structure and the cable are clamped and fixed by means of the first clamping plate and the second clamping plate so as to form the test target. And the aluminum bullets are launched through the second-stage light-gas gun. And a projectile passes through the projectile penetration hole and impacts on the protection structure, so that a satellite cable space debris protection test is completed on the ground. The protection capability of the protection structure is evaluated through test device manufacturing, test method designing and test result failure analysis. And according to a specific failure criterion adopted by the satellite cable, judging whether the test parameters adopted by the impact cause cable failure or not.

An anomaly detection device (1) comprises: an inspection target feature amount extraction unit (121) for generating a first feature amount vector set containing feature amount vectors of a plurality of sub-areas of an inspection target image as elements; an inspection target feature amount conversion unit (122) for converting the first feature amount vector set into a second feature amount vector set, which is the same feature space as that of a normal feature amount vector set; a normal distance calculation unit (123) for calculating a normal distance per corresponding set element between the normal feature amount vector group set and the second feature amount vector set; an anomalous distance calculation unit (124) for calculating an anomalous distance per corresponding set element between an anomalous feature amount vector group set and the second feature amount vector set;and a determination unit (125) for determining whether each element of a set corresponding to the test target image is normal or anomalous, based on the normal distance theorem and the anomalous distance theorem.;

The invention relates to the technical field of chips, and provides a wafer reliability control method which comprises the following steps: making a test plan, and determining items, parameters and test conditions required to be tested for wafer reliability control; obtaining a target chip with the same manufacturing process as the wafer and a hierarchical structure of the target chip; determining a test target structure of each item and a level where the test target structure is located; removing the target chip layer by layer to obtain a target layer of the target chip, and positioning a target structure, needing to be tested, of the target layer and a target position of the target structure on the target layer; according to the determined target position, setting a test condition, and collecting characteristic parameters of the target structure by using a nanoprobe imaging analysis system; and obtaining a reliability control performance index of the target chip according to the characteristic parameters of the target structure, and establishing a reliability control scheme of the new wafer manufacturing process according to the reliability performance index of the target chip. According to the method provided by the invention, a wafer reliability control scheme of a new manufacturing process can be quickly established.