3 results about "Random drift" patented technology

The invention relates to a dynamic adjust gyroscope life forecasting method based on gray model. By data collection of vibration effective value, random drift and environmental temperature parameter which are preprocessed using radial neural networks, influence of environmental temperature on vibration effective value and random drift is eliminated and random drift and effective value just related to time are obtained by subtracting drift constant value term, then trend term of vibration effective value and random drift are extracted by using wavelet transformation and gray model are built separately for their trend term. The smaller data in two values of life predicted of dynamic adjust gyroscope unless two predicted values exceeding performance parameter limitation when dynamic adjust gyroscope is considered losing effect. The invention uses performance parameter of life probative period of product to predict its life, showing discipline of performance parameter and life of dynamic adjust gyroscope. It is easy and convenient economical and reliable.

This application provides a method, apparatus, terminal, and medium for calculating navigation heading angle based on a combination of GNSS and IMU, including: acquiring the real-time heading angle and real-time forward speed of a mobile device at the current moment to construct a motion model of the mobile device; obtaining the state-space equation of the motion model based on the state transition matrix, velocity increment matrix, and white noise sequence; obtaining a Kalman filter measurement model based on the state-space equation of the motion model and using GNSS navigation and positioning results as observation vectors; and obtaining the optimal navigation heading angle at the current moment based on the Kalman filter measurement model, Kalman gain, GNSS navigation and positioning results, and IMU kinematic calculation results. This invention effectively controls the influence of large GNSS noise, and the use of incremental data to calculate prior values ​​also avoids the cumulative divergence phenomenon of IMU random drift errors.

The application discloses an underground pipeline intelligent leakage detection method and device, relates to the field of intelligent leakage detection, collects a high-frequency pressure sequence, establishes a time-temperature equivalent equation to calculate a moving factor, maps the high-frequency pressure sequence to a reduced time axis by using the moving factor, obtains a temperature equivalent pressure sequence, and is converted into a full history stress-strain memory field tensor, constructs a neural lag operator network, takes the full history stress-strain memory field tensor as input, carries out non-local memory characteristic and hysteresis loop analysis, carries out point-by-point difference processing on the high-frequency pressure sequence and the pipe material intrinsic nonlinear hysteresis response signal, extracts a fidelity fluid dynamics abnormal residual error, constructs a spatiotemporal evolution Poincare section, inputs into a convolutional neural network, and determines real leakage negative pressure waves and sensor random drift through attractor shape difference on the spatiotemporal evolution Poincare section, so that periodic false alarms caused by material rheological characteristics are eliminated.