Abstract: A conical workpiece length measurement method is provided. Two laser displacement sensors are symmetrically arranged at opposite sides of a to-be-measured conical workpiece or a tooling loaded with the to-be-measured conical workpiece. Distance X0 from each displacement sensor to a bottom plane of the to-be-measured conical workpiece is calibrated. An elongated base plate is arranged at a tip of the to-be-measured conical workpiece, and the two displacement sensors measure their respective distances to the base plate. The total length of the to-be-measured conical workpiece is calculated as follows: X=X0+(X1+X2)/2, where X1 represents distance from one of the two displacement sensors to the base plate, and X2 represents distance from the other of the two displacement sensors to the base plate. Factors influencing the length measurement include calibration of the fixed length, measurement accuracy of the displacement sensor and a tilt error of the base plate.

Abstract: A device for measuring length and center of mass of conical workpiece, including an inner frame, an outer frame, a base plate, two laser displacement sensors and a plurality of weighing mechanisms. The inner frame is rotatably connected with the outer frame through a pin shaft. A bottom surface of the conical workpiece can be fixedly connected with an inner bottom surface of the inner frame. The base plate is removably arranged on the inner frame, and abuts against a top of the conical workpiece. The base plate is perpendicular to an axis of the conical workpiece. The laser displacement sensors are fixedly arranged at a position of the inner frame below the top of the conical workpiece. The weighing mechanisms are provided at a bottom of the outer frame, and can bear the inner frame, the outer frame and the conical workpiece.

Abstract: A method for assessing test adequacy of deep neural networks based on element decomposition is provided. The network testing is divided into black box testing and white box testing, of which key elements are decomposed and defined. Network parameters including a weight matrix and a bias vector are extracted. Importance values of neurons in individual layers of the deep neural network are calculated and clustered, and an importance value hot map of neurons in each layer is generated based on clustering results. Mutation testing, and index calculation and evaluation are performed.

Abstract: A method for locking repetition rate of an optical frequency comb is provided. A cavity length adjusting actuator is adopted to lock the repetition rate of the optical frequency comb. When a locking state of the cavity length adjusting actuator is in a critical threshold state, an optical delay line of an optical frequency comb optical system is adjusted to adjust the repetition rate of the optical frequency comb, so as to keep locking the repetition rate of the optical frequency comb. A locking device of the repetition rate is also provided, in which an optical delay line control system can judge the locking state of a cavity length adjusting actuator control system in real time, such that the repetition rate can be slowly adjusted within a wide range, and the locking state of the optical frequency comb can be maintained.

Abstract: A system for automatically measuring an exhaust area of a turbine guide vane, including a data acquisition module configured to measure a three-dimensional point cloud coordinate of a contour of a throat; a positioning module configured to automatically adjust a relative spatial position between the data acquisition module and the turbine guide vane; and a data processing module configured to fit a three-dimensional contour of the throat according to the three-dimensional point cloud coordinate measured by the data acquisition module. A method for automatically measuring an exhaust area of a turbine guide vane using the system is also provided.

Abstract: Disclosed is a single star-based orientation method using a dual-axis level sensor, which includes a calibration process and an actual calculation process.

Abstract: A method for coordinate error correction with a three-dimensional (3D) lidar scanner. The error source that affects the measurement accuracy of the three-dimensional coordinate is determined by building an error model, and then the error is modified to improve the measurement accuracy of the three-dimensional lidar scanner. The error correction method includes: building a theoretical calculation model, analyzing the source of measurement error, building an error model, solving the error model and implementing coordinate correction. During building the error model, 26 error factors are considered to obtain a calculation expression of the three-dimensional Cartesian coordinate. The calculation expression includes the amount of errors, the azimuth angle, the pitch angle and the distance.

Abstract: The present invention relates to a method for correcting measuring errors of a long-distance scanning laser radar, comprising: S1, establishing a measuring model and acquiring a positional relationship between a measured point and a coordinate origin; S2, acquiring an actual positional relationship between the measured point and a laser radar and establishing error models of three major error sources; S3, performing a sub-parameter measuring experiment on the laser radar to acquire major sample data of the three major error sources; S4, analyzing probability density distribution of the three major error sources with a statistical method to obtain error correction samples of the three major error sources in a three-dimensional coordinate system; S5, acquiring three-dimensional coordinate samples according to the error correction samples of the three major error sources and the measuring model; and S6, correcting a three-dimensional coordinate measuring point in real time.

Abstract: The present invention relates to a method for correcting measuring errors of a long-distance scanning laser radar, comprising: S1, establishing a measuring model and acquiring a positional relationship between a measured point and a coordinate origin; S2, acquiring an actual positional relationship between the measured point and a laser radar and establishing error models of three major error sources; S3, performing a sub-parameter measuring experiment on the laser radar to acquire major sample data of the three major error sources; S4, analyzing probability density distribution of the three major error sources with a statistical method to obtain error correction samples of the three major error sources in a three-dimensional coordinate system; S5, acquiring three-dimensional coordinate samples according to the error correction samples of the three major error sources and the measuring model; and S6, correcting a three-dimensional coordinate measuring point in real time.