19 results about "Y-Coordinate" patented technology

The invention discloses a method for automatically measuring the center position of a five-axis rotary table cradle, and relates to the technical field of five-axis machining tools. The method comprises the following steps: S1, placing a standard ball at a preset position of a five-axis turntable, and enabling a probe to contact and obtain a contact point coordinate; s2, fitting by using a least square method to obtain a sphere center coordinate and a radius; s3, controlling the C shaft to rotate, changing the position of the standard ball, obtaining the contact point again and determining the position of the standard ball; s4, rotating the standard ball to a second position, calculating the center coordinate of the ball, and estimating the center X and Y coordinates of the rotary table; s5, accurately measuring a plurality of sphere center coordinates, and calculating X and Y coordinates and a deviation angle of the center of the rotary table; and S6, controlling the A axis to rotate, calculating a Z coordinate, and finally obtaining a three-dimensional coordinate of the center of the rotary table. Through automatic accurate measurement and optimization calculation, the three-dimensional coordinates of the rotary table can be efficiently and accurately determined, and stable positioning and high consistency in a complex working environment are ensured.

The invention relates to the technical field of bridge deformation monitoring, in particular to a highway bridge deformation monitoring method and system based on laser measurement, and the method comprises the steps: obtaining a slope difference of a fitting straight line and a curvature difference of all edge points based on x and y coordinates of all edge points between two side edge clusters; de-noising various point clouds in combination with the local change trend of the curvature of all edge points in each side cluster; fitting each plane point and all plane points in the neighborhood of the plane point, analyzing the error condition of the fitting process, and screening out a feature cluster by combining the dispersion degree of the curvature of each plane point and all plane points in the neighborhood of the plane point; all matching point pairs between the feature clusters of different feature point clouds are obtained, key indexes are determined by analyzing the distance between the matching point pairs and the difference of the concave-convex coefficients between the matching point pairs, and deformation monitoring is carried out on the highway bridge. The invention aims to improve the precision of monitoring the deformation of the highway bridge.

The application discloses a kind of experimental correction method and system of optical deformation of density interface in density stratified fluid, method steps: S1, obtains the continuous original image of spherical particle cross-interface, control adjacent image particle displacement is not more than average pixel radius, set particle falling direction as y axis positive direction;S2, select the image i before particle enters interface, get y coordinate by profile, build the mapping array of corrected y' and original y;S3, select the i+n (n≥1, indicate that it is processed n image) image, get y coordinate and particle center by profile, determine particle center correction position according to mapping array, obtain its corrected y' by formula for specific line, add mapping array;S4, repeat S3 to obtain complete mapping array;S5, according to complete mapping array, correct subsequent image.The application constructs mapping array in stages to realize accurate correction, and can improve the reliability of related experimental image measurement data.

The invention discloses a cube chartlet texture cache allocation method based on X and Y coordinates and surface label optimization, and the method comprises the steps: unfolding a cube chartlet into a plane, taking the left upper corner as an original point, and reallocating the XY coordinates and surface label information of each pixel, thereby achieving the dual optimization. According to the method, cache conflicts during bilinear sampling are effectively reduced, the effect is particularly remarkable in small-size cube mapping, and the texture sampling efficiency is remarkably improved. The optimized cache allocation strategy enables distribution of texture data in the cache to be more reasonable, complexity and performance overhead caused by coordinate rotation are reduced, hardware resources are fully utilized, the service life of equipment is prolonged, and the overall performance of the system is improved. The method is suitable for cube chartlets of various sizes, has high universality and flexibility, and solves the problems of high cache conflict rate and low sampling efficiency in the prior art.

The present application belongs to the field of base station calibration, and is a base station calibration method based on fusion of TOF and AOA method. A base station is selected as an original base station to establish a coordinate system. With the original base station as the origin, a first adjacent base station A is found in a counterclockwise or clockwise direction, the distance r between the original base station and the base station A is measured n , the spatial coordinates of the base station A are calculated, and the azimuth of the base station A to be determined is distinguished by azimuth. The distance r between the original base station and a base station B adjacent to the base station A is measured n , the azimuth and the pitch angle are calculated, the base station A and the base station B are projected onto the xoy plane of the original base station, a spherical equation is established, two sets of spatial coordinate solutions of the base station B are obtained by solving the spherical equation, and the azimuth of the base station B is used to determine the final solution. The calibration method improves the x and y coordinate accuracy of the base station. Through distance measurement of three base stations, mutual constraint is achieved, and the calibration accuracy is improved.

The application provides a tunneling machine cutting track planning method based on an intelligent interaction interface, provides a roadway section parameter and a cutting process path planning function through an intelligent monitoring software interaction interface; two number axes perpendicular to each other and having a common origin are established in the interface to form an X, Y axis rectangular coordinate system, a region in the coordinate is subjected to a gridding treatment to establish a gridding map to divide a to-be-cut roadway section; in the gridding region, a starting point and an ending point coordinate data of each section in a cutting process path are taken by using an arbitrary selected point mode, after the selected point of each cutting track section is completed, a two-point coordinate line is automatically generated, and this is repeated until the planning of the whole cutting process path is completed; in the selected point coordinate process, an automatic recognition gridding intersection point function is built in the interaction interface, and when a diagonal line or an irregular cutting process path coordinate system appears, a control strategy of taking a cutting head radius as a minimum step distance to segmentally solve X, Y coordinate data is adopted to realize the optimal solution treatment on the cutting track.

The invention provides a method for determining the optimal position of a wellhead. The method comprises the steps that the position of a reservoir dessert is obtained; according to the sweet spot position, the target entering point and the well bottom of the horizontal reservoir section serve as the first target spot position and the second target spot position respectively; the full-angle change rate and the target front distance of the horizontal well are obtained; determining an azimuth angle according to the difference value of each axis of the target spot I and the target spot II; determining the X-axis coordinate of the deflecting end point of the horizontal well according to the X-axis coordinate of the target point I, the difference value of each axis of the target point II and the target point I and the pre-target distance; determining the Y-axis coordinate of the deflecting end point according to the Y coordinate of the target spot I, the difference value of each axis of the target spot II and the target spot I and the target front distance; the X-axis coordinate of the wellhead of the horizontal well is determined according to the X-axis coordinate of the deflecting end point, the difference values of all axes of the deflecting end point and the second target point and the full-angle change rate; and the Y-axis coordinate of the wellhead of the horizontal well is determined according to the Y-axis coordinate of the deflecting end point, the difference values of all the axes of the deflecting end point and the second target point and the full-angle change rate. The problem that there is no method for determining the optimal position of the wellhead in the prior art is solved.

A convolution method for high speed deep learning according to the present invention includes (a) a step in which a feature map receiving unit of the convolution system receives a feature map configured by N channels; (b) a step in which a main controller of the convolution system selects a “0”-th channel from the feature map configured by N channels; (c) a step in which the main controller confirms a coordinate in which x, y coordinate is “0”, from the feature map of the “0”-th channel; (d) a coarse step in which a convolution calculating unit of the convolution system performs a convolution operation and a rectified linear unit (ReLU) operation while shifting by 2 in a horizontal direction and a vertical direction in the feature map; (e) a step in which the channel switching unit of the convolution system switches the channel to a subsequent channel when the coarse step is completed for the feature map of the “0”-th channel; (g) a step in which the main controller determines whether the switched channel is greater or less than N; and (g) a step in which if the channel switched in step (0 is greater than N, the main controller determines that the convolution operation for all channels has been completed and outputs the feature map by means of a feature map output unit. By doing this, the convolution operation which occupies most of the convolution neural network is reduced to increase inference speed in the deep learning.

The present application relates to a kind of infrared touch screen real touch point recognition method and infrared touch screen, method includes: the slope k of all n pieces of blocked light in first coordinate system is calculated with intercept b, obtain n groups of numerical combinations;N groups of numerical combinations are filled into kob second coordinate system, obtain n coordinate points;Identify alternative touch area in first coordinate system;The number of internal blocked light of each alternative touch area centroid point coordinate value is calculated, from the alternative touch area that is not selected, the number of internal blocked light most is selected as target alternative touch area, the centroid point coordinate value thereof is mapped as a virtual straight line in second coordinate system, all coordinate points in the range of the virtual straight line and both sides are marked as cleared, stop until the coordinate point marked as cleared reaches preset proportion, with the X coordinate value and Y coordinate value of the virtual straight line each corresponding as the coordinate of each point, realize the coordinate of each real point when multi-point touch is quickly and accurately judged.

This invention discloses a method for analyzing the behavior patterns of flight trajectory points based on deep learning image processing, comprising the following steps: Step 1, converting the latitude and longitude of the flight trajectory points into x and y coordinates on the image using the Universal Transversal Mercator method; Step 2, using the rol-traj-yolov5 algorithm to adaptively rotate bounding box detection to predict the position of a specific trajectory and identify its pattern; Step 3, analyzing the specific behavior patterns of the trajectory to determine the specific aircraft model that formed the trajectory, and the specific mission or flight route. This invention, by identifying the trajectory patterns of a specific region of the flight trajectory point, can analyze useful information such as the aircraft model and the currently executed mission or flight route.

This invention discloses a method for JRC estimation based on Hausdorff distance. The method involves extracting the coordinates of 10 standard profiles of Barton's structural surfaces with a sampling interval of dx. Based on the extracted x and y coordinate data, the cumulative slope of each standard profile is calculated, forming a cumulative slope data sequence of standard curves. The structural surface curves are then measured in the field, and the coordinates of the measured curves are extracted with a sampling interval of dx. The cumulative slope of the measured curves is calculated, forming a cumulative slope data sequence of measured data. The Hausdorff distance between the cumulative slope of the measured curves and the cumulative slope of the standard curves is calculated. The standard profile of the structural surface most similar to the measured curve is identified, and its corresponding JRC value is assigned to the measured curve, ultimately estimating the JRC value of the measured curve. This invention achieves JRC value estimation based on the self-similarity of the cumulative slope of the curves, avoiding the arbitrariness and subjectivity in determining the JRC value caused by comparing visual observation with standard profiles.

Provided is a method for evaluating an edge profile of a wafer, which is capable of quantitatively evaluating a degree of similarity between edge profiles at two locations. The edge profiles at the two locations are measured (step S1). X-Y coordinates of two items of edge profile data are acquired (step S2). The X-Y coordinates are converted into Rθ polar coordinates (step S3). The two items of edge profile data are displayed on an Rθ orthogonal coordinate plane with the horizontal axis as θ and the vertical axis as R (step S4). On the Rθ orthogonal coordinate plane, the average value of each R is subtracted from each item of edge profile data (step S5). On the Rθ orthogonal coordinate plane, a difference R' of R is taken between the two items of edge profile data (step S6). As an example, a root mean square RMS of R' is calculated (step S7). The degree of similarity between the edge profiles is evaluated on the basis of the RMS (step S8).

An inspecting unit for use in a laser processing apparatus includes a camera configured to image a spot of a laser beam, and a processing unit configured to generate a light intensity at two-dimensional X-Y coordinates corresponding to a two-dimensional image imaged by the camera. The processing unit includes a determining unit configured to generate a three-dimensional approximate curve or an approximate curved surface such that the light intensity corresponding to the two-dimensional coordinates is set as a Z-coordinate, and determine that there is dirt, a flaw, or an abnormality in an optical system on an optical path on the laser oscillator side of a position at which the spot is imaged when a difference between the first light intensity of the Z-coordinate corresponding to the two-dimensional coordinates and a second light intensity indicated by the approximate curve or the approximate curved surface exceeds an allowable value.

An apparatus is provided that executes instructions to move an item within a scan zone to specific X-Y coordinates of the scan zone. The item is placed on a platform and is rotated 360 degrees at each X-Y coordinate within the scan zone. Item images are captured by cameras at each X-Y coordinate and for each rotation at the corresponding X-Y coordinate. The item images are labeled and retained. The item images are used as input to a Machine-Learning Model (MLM) to train the MLM to recognize item codes for the items when subsequent images are captured for the item during a checkout. In an embodiment, during a checkout unknown item images are flagged and labeled with the corresponding item code when the corresponding item's barcode is scanned during the checkout; the labeled item images are also retailed for training the MLM for item recognition.

A flame detection system is disclosed. The flame detection system includes one or more processors coupled to at least one flame detector and at least one image capture device. The one or more processors identify a location of the flame by determining a base Y-coordinate, a tip Y-coordinate, a leftmost X-coordinate, and a rightmost X-coordinate of a plurality of pixels associated with the flame; determining that the variation in the base Y coordinate of the most current image in the sequence of images is less than a predefined limit of the height of the flame; and determining that the change in the tip Y coordinate of the most current image in the sequence of images is greater than a predefined limit of the height of the flame, or the change in the leftmost X coordinate or the rightmost X coordinate of the most current image in the sequence of images is greater than a predefined limit of the width of the flame.

The invention discloses an experimental correction method and system for optical deformation of a density interface in density stratified fluid, and the method comprises the steps: S1, obtaining a continuous original image of spherical particles passing through the interface, controlling the displacement of particles in adjacent images not to exceed the average pixel radius, and setting the falling direction of the particles as the y-axis positive direction; s2, selecting an image i before particles enter an interface, extracting a contour to obtain a y coordinate, and establishing a mapping array of a corrected y'and an original y; s3, selecting an (i + n) th image (n is greater than or equal to 1 and represents the processed n images), extracting a contour to obtain a y coordinate and a particle center, determining a particle center correction position according to the mapping array, obtaining a corrected y'of a specific line through a formula, and adding the corrected y 'into the mapping array; s4, repeating S3 to obtain a complete mapping array; and S5, correcting a subsequent image according to the complete mapping array. According to the method, the mapping array is constructed in stages to realize accurate correction, and the reliability of related experimental image measurement data can be improved.

The application provides a method for measuring and correcting the deviation of a goniometer table of a transmission electron microscope, comprising the following steps: S1, collecting a Ronchigram image and extracting the accurate coordinates of the center of the image; S2, collecting a plurality of data points based on a polycrystalline FIB sample, wherein the data comprises the corresponding alpha and beta tilt angle data of a plurality of sample grains when the sample grains are tilted to the normal axis, so as to ensure the reliability and repeatability of the data statistics in the calibration process; S3, calculating the corresponding x and y coordinates of the grains on the image, namely Pixelx and Pixely, when the grains are tilted to the normal axis; S4, constructing a mathematical model for describing the geometric relationship between the actual tilting system of the transmission electron microscope and the image acquisition system; S5, performing fitting processing and solving parameters on the two groups of data of the corresponding Pixelx and Pixely data in relation to the actually collected alpha and beta tilt angle data; and S6, calculating the tilt angle of the electron microscope rotation shaft operation based on the calculation result of step S5; the application has the advantages of high precision, good stability and strong repeatability.

The invention discloses a high-quality factor nearly perfect chiral response metasurface suitable for multiple environments, which comprises a quartz substrate and silicon columns with square cross sections, an x-y coordinate system is established by taking two diagonal lines of each silicon column as an x axis and a y axis and an intersection point of the two diagonal lines as an original point, the silicon columns are periodically arranged on the quartz substrate along the x and y directions, and the silicon columns are arranged on the quartz substrate along the x and y directions; and a square notch is formed in any right angle of the silicon column. The method has a nearly perfect chiral response with a high quality factor, regulation and optimization of CD in a range from-1 to + 1 can be realized only by adjusting an incident angle, and in different refractive index environments, the incident angle corresponding to the maximum CD can be predicted by calculating the position of a point C in an inverted space, so that the accuracy of the optimal CD is improved. And the incident angle is accurately regulated and controlled to realize chiral response with the quality factor Q being greater than 10000 and the circular dichroism CD being approximately equal to 1.