Activation Function Dynamic Averaging as a Technique for Nonlinear 2D Data Denoising in Distributed Acoustic Sensors

Abstract

This work studies the application of low-cost noise reduction algorithms for the data processing of distributed acoustic sensors (DAS). It presents an improvement of the previously described methodology using the activation function of neurons, which enhances the speed of data processing and the quality of event identification, as well as reducing spatial distortions. The possibility of using a cheaper radiation source in DAS setups is demonstrated. Optimal algorithms’ combinations are proposed for different types of the events recorded. The criterion for evaluating the effectiveness of algorithm performance was an increase in the signal-to-noise ratio (SNR). The finest effect achieved with a combination of algorithms provided an increase in SNR of 10.8 dB. The obtained results can significantly expand the application scope of DAS.

A Non-Destructive Study of Optical, Geometric and Luminescent Parameters of Active Optical Fibers Preforms

“…Above the rail, on a static bracket in a V-shaped groove (5), a fiber optic multimode patch cord (7) is laid. One of its ends is equipped with a collimator directed vertically downward into the preform under investigation, and the other is connected to a semiconductor pump diode (4) (BWT K976AB2RN-3.000 W, Beijing, China) with a stabilization wavelength of 976 nm and a power of 3 W. Directly below the diode collimator is a splitter-scatterer (19), which splits off part of the pump radiation to the Thorlabs PDA05CF2 reference photodetector (Newton, NJ, USA) (8). This detector is needed to monitor fluctuations in the optical power of the pump diode.…”

“…Practice has shown that the maximum time for such a wait is about 20 min, and about 7 min on average. The pump radiation is transmitted through a multimode fiber to the collimator and, after passing through the splitter-scatterer (19), enters through the side surface first into the reflective cladding of the optical fiber preform and then into the core, where, among other reagents, active erbium is contained. The active metal begins to luminesce at a wavelength of 1530 nm.…”

Advancements in Battery Monitoring: Harnessing Fiber Grating Sensors for Enhanced Performance and Reliability

“…This includes addressing the coupling of temperature and strain, electrolyte RI, and other influencing factors, such as the solid electrolyte layer versus the electrochemical interfacial layers. (2) Effective signal processing techniques can significantly reduce the impact of noise on optical signals, thereby enhancing the accuracy of battery condition measurements [142][143][144][145][146][147][148]. For instance, the Activation Function Dynamic Averaging (AFDA) algorithm outperforms the Frequency Domain Dynamic Averaging (FDDA) algorithm by processing signals eight times faster and improving the SNR by 3.7 dB independently, and by 10.8 dB when combined with MA-MD [143].…”

“…(2) Effective signal processing techniques can significantly reduce the impact of noise on optical signals, thereby enhancing the accuracy of battery condition measurements [142][143][144][145][146][147][148]. For instance, the Activation Function Dynamic Averaging (AFDA) algorithm outperforms the Frequency Domain Dynamic Averaging (FDDA) algorithm by processing signals eight times faster and improving the SNR by 3.7 dB independently, and by 10.8 dB when combined with MA-MD [143]. Additionally, a nonlinear distortion correction algorithm can rectify charge coupled device (CCD) measurement spectra, eliminating errors that may arise during various Time of INTegration (TINT) measurements [142].…”

Fiber-Optic Hydraulic Sensor Based on an End-Face Fabry–Perot Interferometer with an Open Cavity

“…Firstly, careful work is needed to stabilize the sensor parameters, which includes: improving the interferometer manufacturing technology (for example, controlling fuse effect parameters, using special optical fibers, monitoring cavity parameters using optical reflectometry in the frequency domain, stabilization of the parameters of the receiving and transmitting parts of the system, digital signal processing, design of a sensitive membrane. It may be reasonable to start with algorithmic methods, since in our practice we have already encountered a significant improvement in the parameters of acoustic sensors due to signal processing [47]. Secondly, after stabilizing the sensor parameters, it is necessary to evaluate the range of its possible applications.…”