Extraction of the key infrared radiation temperature features concerning stress and crack evolution of loaded rocks

The infrared radiation temperature(IRT)variation concerning stress and crack evolution of rocks is a critical focus in rock mechanics domain and engineering disaster warning.In this paper,a methodology to extract the key IRT features related to stress and crack evolution of loaded rocks is proposed.Specifically,the wavelet denoising and reconstruction in thermal image sequence(WDRTIS)method is employed to eliminate temporal noise in thermal image sequences.Subsequently,the adaptive partition temperature drift correction(APTDC)method is introduced to alleviate temperature drift.On this basis,the spatial noise correction method based on threshold segmentation and adaptive median filtering(OTSU-AMF)is proposed to extract the key IRT features associated with microcracks of loaded rocks.Following temperature drift correction,IRT provides an estimation of the thermoelastic factor in rocks,typically around 5.29×10^(-5) MPa^(-1) for sandstones.Results reveal that the high-temperature concentrated region in cumulative thermal images of crack evolution(TICE)can elucidate the spatiotemporal evolution of localized damage.Additionally,heat dissipation of crack evolution(HDCE)acquired from TICE quantifies the progressive failure process of rocks.The proposed methodology enhances the reliability of IRT monitoring results and provides an innovative approach for conducting research in rock mechanics and monitoring engineering disasters.

IUKF neural network modeling for FOG temperature drift

A novel neural network based on iterated unscented Kalman filter （IUKF） algorithm is established to model and com- pensate for the fiber optic gyro （FOG） bias drift caused by temperature. In the network, FOG temperature and its gradient are set as input and the FOG bias drift is set as the expected output. A 2-5-1 network trained with IUKF algorithm is established. The IUKF algorithm is developed on the basis of the unscented Kalman filter （UKF）. The weight and bias vectors of the hidden layer are set as the state of the UKF and its process and measurement equations are deduced according to the network architecture. To solve the unavoidable estimation deviation of the mean and covariance of the states in the UKF algorithm, iterative computation is introduced into the UKF after the measurement update. While the measure- ment noise R is extended into the state vectors before iteration in order to meet the statistic orthogonality of estimate and mea- surement noise. The IUKF algorithm can provide the optimized estimation for the neural network because of its state expansion and iteration. Temperature rise （-20-20℃） and drop （70-20℃） tests for FOG are carried out in an attemperator. The temperature drift model is built with neural network, and it is trained respectively with BP, UKF and IUKF algorithms. The results prove that the proposed model has higher precision compared with the back- propagation （BP） and UKF network models.

Development of Electron Temperature Measuring System by Silicon Drift Detector

The soft X-ray spectroscopy, laser Thomson scattering and electron cyclotron emission （ ECE ） are usually adopted for electron temperature measurement in fusion research of magnetic confinement. The particular soft X-ray spectroscopy has the very good spatial-temporal resolution and smaller measuring error than laser Thomson scattering, a close spatial-temporal resolution to ECE, absolute measurement ability, and smaller influence by suprathermal and runaway electrons than ECE.

Temperature Bias Drift Phase-Based Compensation for a MEMS Accelerometer with Stiffness-Tuning Double-Sided Parallel Plate Capacitors

This paper reports an approach of in-operation temperature bias drift compensation based on phase-based calibration for a stiffness-tunable MEMS accelerometer with double-sided parallel plate(DSPP)capacitors.The temperature drifts of the components of the accelerometer are characterized,and analytical models are built on the basis of the measured drift results.Results reveal that the temperature drift of the acceleration output bias is dominated by the sensitive mechanical stiffness.An out-of-bandwidth AC stimulus signal is introduced to excite the accelerometer,and the interference with the acceleration measurement is minimized.The demodulated phase of the excited response exhibits a monotonic relationship with the effective stiffness of the accelerometer.Through the proposed online compensation approach,the temperature drift of the effective stiffness can be detected by the demodulated phase and compensated in real time by adjusting the stiffness-tuning voltage of DSPP capacitors.The temperature drift coefficient(TDC)of the accelerometer is reduced from 0.54 to 0.29 mg/℃,and the Allan variance bias instability of about 2.8μg is not adversely affected.Meanwhile,the pull-in resulting from the temperature drift of the effective stiffness can be prevented.TDC can be further reduced to 0.04 mg/℃through an additional offline calibration based on the demodulated carrier phase representing the temperature drift of the readout circuit.

A precision ADC sampling system design

Introduction The high-energy photon source,which has been built in Huairou,Beijing,has high requirements on magnetic field dithering.Magnetic field dithering is mainly determined by the stability of the output current of the power supply.In order to ensure the stability of the output current of quadrupole magnet power supply,the power supply sampling control loop needs to be precisely designed.In this paper,a precision ADC sampling system based on internal temperature control is designed to carry out precise control of the sampling ADC.Materials In this design,precise ADC chip is used to complete the precise sampling of the system.The precise sampling system contains a DAC system for high-speed settings.Methods In order to verify the design of the system,high-precision quadrupolemagnet power supply is used for measurement.Conclusion The experimental results show that the temperature variation range of precision temperature control ADC system is±0.1°C.By using the precise temperature controlADCsystem,the output current stability of the high-precision quadrupole magnet power supply is effectively improved.

A novel SOI MOSFET electrostatic field sensor

A novel low temperature solid state electric field sensor is demonstrated as a promising sensor. The sensor is a type of constant voltage Wheatstone bridge whose resistors are four direct gate SOl MOSFET devices. It is demonstrated in theory that the output voltage signal is proportional to the electric field E, the temperature drift is about zero when the temperature is in the range from 200 to 400 K, and the doping concentration is in the range from 1 × 10^14 to 1 × 10^16 cm^-3. The experiment results indicate that the resolution of the sensor is about 3.27 mV for a 1000 V/m electric field at 300 K, and the voltage drift by an amount is about 47 V/m field signal when the degree temperature is in the range from 300 to 370 K, which is much smaller than the current drift of a single MOSFET which is about 10000 V/m field signal.