10 results about "First order derivative" patented technology

The application discloses a component fatigue micro-damage early identification method based on a nonlinear static component, and particularly relates to the technical field of material nondestructive testing and structural health monitoring, which comprises the following steps: arranging an excitation probe and a receiving probe on the surface of a component to be measured; selecting an excitation frequency based on a frequency dispersion curve and generating a Hanning window modulated wave; collecting a propagation response signal; extracting a static component amplitude A and a fundamental wave amplitude A through time-frequency processing; calculating a nonlinear static parameter beta=A / A²; further calculating a first derivative S(N) and a second derivative ΔS(N) of beta with respect to a fatigue loading cycle N; and identifying a dislocation dominant stage, a dislocation saturation or rearrangement stage, a micro-crack initiation stage and a macro-crack formation stage according to the first derivative S(N) and the second derivative ΔS(N). The application can be used for early stage identification of the evolution process of component fatigue micro-damage.

The present application relates to the technical field of biomedical image processing, and discloses a multi-scene video intelligent clipping method and system, comprising the following steps: obtaining time-synchronized original microscopic video stream and structured experimental operation log; for each operation event, constructing an event neighborhood reference window in the original microscopic video stream and performing spatial registration to generate a locally video segment after spatial correction; generating a spatial attention weight mask according to the operation space coordinate vector, calculating a structure disturbance response value sequence of each frame of the local video segment in combination with a structural similarity index, and obtaining a phase significance score sequence through smoothing filtering; generating an original candidate segment based on the discrete first-order derivative of the phase significance score sequence and baseline period statistical characteristics; according to the preset priority weight of the operation action type label corresponding to the candidate segment, performing cutting and transition processing on the time overlapping segment, and outputting an abstract video file; and generating a metadata index file associated with the abstract video file.

The application discloses a complex conduit first-order continuous center line reconstruction method based on scanning point cloud data and belongs to the field of engine geometric parameter digitization detection. After obtaining scanning point cloud data of an outer surface of a conduit, straight pipe data and curved pipe data of the complex conduit are segmented. For the segmented straight pipe and curved pipe data, random sampling cylinder fitting and circle fitting operation based on intersection of a vector point and a main plane are respectively performed to obtain trend lines of the straight pipe and the curved pipe. For the trend lines of the straight pipe and the curved pipe, center points of different pipe sections based on equally-spaced vertical section planes are solved, and linear fitting is respectively performed on the center points of the different pipe sections to obtain a plurality of discontinuous center line sections of the complex conduit. A cubic spline curve is adopted to perform first-order derivative interpolation fitting between adjacent straight line sections and circular arc section center lines to obtain a complex conduit first-order derivative continuous center line. The reconstructed center line is used as a reference for calibration of a pipe special-purpose measuring device measuring capacity.

This invention provides a method for measuring the thickness of silicon carbide epitaxial layers based on the principle of infrared interferometry, belonging to the field of semiconductor material parameter measurement technology. It proposes a two-beam interferometry mathematical model combining the Sellmeier equation, quantifying the dispersion characteristics of silicon carbide refractive index into the calculation of epitaxial layer thickness, establishing a precise quantitative correlation between epitaxial layer thickness and wavenumber, incident angle, and refractive index, solving the problem of errors caused by neglecting dispersion effects in existing two-beam models. An improved first-order differential extremum detection algorithm is designed, using a first-order derivative threshold to screen candidate points and a second-order derivative to determine the extremum type, effectively filtering out noise and false extrema, improving the extraction accuracy of key interferometric parameters. A complete algorithm flow of data preprocessing, model calculation, interferometric judgment, correction and optimization, and result verification is constructed, realizing automated and high-precision measurement of silicon carbide epitaxial layer thickness, filling the gap in the existing technology system.

The application relates to the technical field of agricultural Internet of Things application services, and discloses a non-antibiotic poultry breeding management system and method, which comprises a data acquisition module, a logic processing module and an intervention control module; the logic processing module processes drinking water frequency time sequence and environmental temperature and humidity sampling data sets obtained by the data acquisition module, calculates phase delay characteristics of the drinking water frequency time sequence and an environmental heat stress index sequence in a phase space, then compares the phase delay characteristics with a dynamic metabolism benchmark to extract a representation deviation parameter, and when the representation deviation parameter penetrates a preset threshold, determines an injection concentration parameter of an adjusting agent according to a first derivative of the representation deviation parameter; the application captures the dynamic evolution law of the physiological buffering capacity of a biological organism through the phase delay characteristics, and realizes accurate identification of surface physiological heterogenization in the early stage of subclinical infection.

The application provides a gas chromatograph peak separation method and system based on wavelet transform and multi-derivative characteristics, and relates to the technical field of gas chromatography analysis. The technical scheme provided by the application realizes the automatic identification of the peak starting point, the peak top point and the peak ending point by comparing the numerical relationship between the first derivative data, the second derivative data and the determination threshold value in the process of traversing the time sequence of the target chromatographic data. Based on the accurate identification of the peak starting point, the peak top point and the peak ending point, the vertical line peak separation method is used to separate the chromatographic peaks, the accurate separation of complex peak types such as overlapping peaks and shoulder peaks is realized, and the analysis accuracy of the chromatographic data is improved.

The specification provides a fracturing wellhead pressure prediction method and device based on multivariate data regression. Multivariate fracturing construction data of a target well in a target area is acquired; a crack initiation point and a pump stop point are determined according to a first-order derivative of a displacement and a first-order derivative of a tubing pressure in the multivariate fracturing construction data, and effective operation data from the crack initiation to the pump stop are determined by using the crack initiation point and the pump stop point; the effective operation data is mapped to a preset sampling step length by using a preset linear interpolation rule to determine a fixed-length feature sequence meeting a preset dimension; a final multivariate feature tensor is determined according to the fixed-length feature sequence by using a preset feature derivation calculation rule; and a full-sequence wellhead pressure prediction value of the target well is determined by using a preset pressure prediction model and the final multivariate feature tensor. Thus, full-sequence wellhead pressure prediction of the target well is realized, effectively solving the problems of insufficient prediction accuracy and poor sequence integrity in the prior art.

The application discloses a high-robustness cuffless continuous blood pressure estimation method based on multi-view joint modeling, and comprises the following steps: 1, collecting signals of a subject and corresponding blood pressure labels, and performing pretreatment to obtain blood pressure signal samples; 2, constructing a blood pressure prediction neural network, comprising a channel interaction feature extraction module, a time sequence feature coding module, a multi-scale sparse fusion module and a regression module, and processing the blood pressure signal samples to obtain predicted systolic pressure and diastolic pressure; 3, constructing a double-target adaptive balance loss function to optimize the blood pressure prediction neural network to obtain an optimal blood pressure prediction model. The application utilizes signals, first-order derivatives and second-order derivatives thereof, realizes multi-view information complementation, and utilizes a double-target adaptive balance loss function to relieve regression deviation problems, thereby improving the accuracy and robustness of blood pressure prediction.