7 results about "Initial curve" patented technology

The application discloses a roll shape determination method, determines a roll surface initial curve; adopts a high-order reverse curve to perform edge processing on the initial curve to obtain a smooth superimposed curve; determines a slope curve on an operation side, connects the slope curve with the smooth superimposed curve to form a slope body combined curve; and performs fitting smoothing on the slope body combined curve to obtain a roll shape curve. The roll shape determination method provided by the application adopts high-order edge superimposed smoothing, conical design, curve intercepting combination, final smoothing and control rack use on the basis of the initial curve, guarantees the conical roll to reduce the edge drop effect, considers the body crown control at the same time, controls the middle wave problem, improves the profile quality and rolling stability, and provides strong support for super flat material rolling. The cost is not increased, and the existing process is not changed.

This application provides a Saber-based inverter simulation method, which includes: generating an initial simulation test circuit; performing time-domain simulation based on the initial simulation test circuit to obtain the total loss, switching loss, and conduction loss of the power device model; transforming the initial simulation test circuit to obtain a replacement simulation test circuit, and performing sensitivity testing on the obtained replacement simulation test circuit based on the total loss, switching loss, and conduction loss of the power device model to obtain the sensitivity of each initial curve; and adjusting the power device design based on the obtained total loss, switching loss, conduction loss of the power device model, and the sensitivity of each original curve. This method can guide the design of power devices and other parameters in the circuit, effectively improving R&D efficiency and quickly achieving the goals of improving inverter efficiency, reducing losses, and meeting system efficiency requirements.

Embodiments of the present disclosure provide a method, device, electronic equipment and storage medium for visualizing and presenting while-drilling telemetry decoding data, wherein the method for visualizing and presenting while-drilling telemetry decoding data comprises: drawing an initial curve of each type of target while-drilling telemetry decoding data according to a classification result of a plurality of acquired target while-drilling telemetry decoding data; determining a calibration gray value of each point in the initial curve of each type of target while-drilling telemetry decoding data based on a preset calibration rule; re-coloring the corresponding points by using the calibration gray value based on a correspondence between the points in the initial curve of each type of target while-drilling telemetry decoding data and the calibration gray value; and pushing the initial curve of each type of target while-drilling telemetry decoding data after re-coloring to a user terminal. By using the method of the present disclosure, the simultaneous display of parameter values and accuracy values in while-drilling telemetry decoding data can be achieved, thereby providing better visual effects for drilling exploitation sites or subsequent research.

The application discloses a multi-row gas film cooling efficiency superposition prediction method based on discrete Fourier transform, and belongs to the field of thermal protection of high-temperature components of an aero-engine. The method firstly acquires the geometric and aerodynamic parameters of a gas film hole and a single-row hole cooling efficiency curve, and calculates an initial superposition curve based on a Sellers formula; then the initial curve and working condition parameters are input into a pre-trained three-branch neural network model, wherein a time domain branch extracts time domain features through a convolution network, a frequency domain branch performs discrete Fourier transform on the curve to extract frequency domain features, and a residual branch extracts residual correction features; finally, the three features are fused to output a high-precision prediction curve. The application introduces discrete Fourier transform to extract physical priori, and combines a three-branch network structure, so that the defects of low precision, poor generalization and dependence on large data of a pure data-driven model are effectively overcome, and high-precision and strong extrapolation prediction of the cooling efficiency of the multi-row gas film is realized under limited samples.

This invention relates to the field of control, and in particular to a method and system for power regulation of a cotton harvester using multi-source data fusion. The method includes: constructing a multi-dimensional operational scenario state vector; inputting the state vector into a forward prediction model to generate an initial power demand curve; training a reverse correction model using historical data and actual power consumption; outputting an error gradient vector and calculating a prediction deviation scalar; correcting the initial curve based on the error gradient to obtain a corrected power demand curve; generating an initial power allocation scheme for the drive, harvesting, and cleaning systems; iteratively adjusting the power ratio of harvesting and cleaning when the overlap between the operating frequency and mechanical resonance frequency of the initial scheme exceeds a threshold; recording the vibration damping adjustment cost until the overlap reaches the target; calculating the system's basic confidence level based on the sensor data dispersion and cross-correlation; fusing the vibration damping adjustment cost function value to generate the system confidence level; adjusting the power allocation scheme accordingly; and outputting execution commands.

This application provides an evaluation method for curve mask process correction based on SEM image contour extraction. The method includes: acquiring an initial curve pattern and its corresponding standard SEM image contour; constructing a simulation prediction model using a machine learning model based on the initial curve pattern and the standard SEM image contour; obtaining a corrected curve pattern of the initial curve pattern using a curve mask method, and extracting the corrected SEM image contour corresponding to the corrected curve pattern; inputting the corrected curve pattern into the simulation prediction model to generate a corrected simulation image of the corrected curve pattern; calculating the correction difference value between the corrected SEM image contour, the initial curve pattern, and the corrected simulation image; and analyzing the correction difference value to obtain a correction evaluation result. This application can more comprehensively and objectively reflect the contour accuracy and stability after curve mask process correction.

The application provides a wall double-unknown thermal parameter inversion method and system based on a physical neural network, and belongs to the technical field of building energy saving, and comprises the following steps: obtaining relevant environmental parameters and infrared image data of a wall to be measured at an inversion moment; inputting the infrared image data into a convolutional neural network model to obtain temperature field information of an outer wall surface of the wall to be measured; setting an initial curve of an inner wall surface temperature based on the smooth characteristics and moderate lagging effect of indoor air temperature, and giving an initial value of a heat conductivity coefficient; jointly constructing a physical neural network model according to the Fourier heat conduction law and the boundary conditions of inner and outer surface convective heat exchange; inputting the relevant environmental parameters and the temperature field information of the outer wall surface into the physical neural network model to perform forward solving to obtain the heat transfer coefficient of the wall to be measured and the inner wall surface temperature. The application can obtain stable and reliable wall heat conductivity coefficients and inner wall surface temperatures.