Noise Analysis Techniques Research Articles

Characterizing the Background Noise Level of Rotational Ground Motions on Earth

Abstract The development of high-sensitive ground-motion instrumentation for Earth and planetary exploration is governed by so-called low-noise models, which characterize the minimum level of physical ground motions, observed across a very broad frequency range (0.1 mHz–100 Hz). For decades, broadband instruments for seismic translational ground-motion sensing allowed for observations down to the Earth’s low-noise model. Knowing the lowermost noise level distribution across frequencies enabled not only to infer characteristics of Earth such as the ocean microseismic noise (microseisms) and seismic hum, but also to develop highly successful ambient seismic noise analysis techniques in seismology. Such a low-noise model currently does not exist for rotational ground motions. In the absence of a substantial observational database, we propose a preliminary rotational low-noise model (RLNM) for transverse rotations based on two main wavefield assumptions: the frequency range under investigation is dominated by surface-wave energy, and the employed phase velocity models for surface waves are representative. These assumptions hold, in particular, for a period range of about 2–50 s and lose validity towards long periods when constituents produced by atmospheric pressure dominate. Because noise levels of vertical and horizontal accelerations differ, we expect also different noise levels for transverse and vertical rotations. However, at this moment, we propose a common model for both types of rotations based on the transverse RLNM. We test our RLNM against available direct observations provided by two large-scale ring lasers (G-ring and ROMY) and array-derived rotations (Piñon Flats Observatory array, Gräfenberg array, and ROMY array). We propose this RLNM to be useful as guidance for the development of high-performance rotation instrumentation for seismic applications in a range of 2–50 s. Achieving broadband sensitivity below such a RLNM remains a challenging task, but one that has to be achieved.

Anomaly detection in BWR fuel cell using neutron noise analysis techniques. Slow control rod detection

A two-step methodology based on noise analysis techniques was applied to detect and monitor a resonance to do with a control rod-related anomaly, notified as SLOW in a BWR-6 core. The methodology utilizes LPRM (Local Power Range Monitor) neutron flux signals. The first step involves applying traditional noise analysis to all available neutron detector signals. Both NAPSDs (Normalized Auto Power Spectral Density) and CPSDs (coherences and phases) were estimated. The second step uses EMD (Empirical Mode Decomposition) and VMD (Variational Mode Decomposition) in a sequence of successive decompositions which allow both the resonance under study to be isolated in only one IMF (Intrinsic Mode Functions) and the main resonance peak parameters (frequency, amplitude, and damping) to be estimated. On the one hand, it was possible to delimit the zone where perturbation takes place, though the phenomenon seemed to be quite local and not all detectors could provide information on it. On the other hand, intrinsic mode functions make it possible to observe traces of potential instrumentation tube impacts in the Hilbert Spectrum. The methodology proved to be very effective to monitor resonances even when these were not visible in the spectrum. The changes observed coincide with the corrective actions carried out by the plant.

The second data release from the European Pulsar Timing Array

Aims.The nanohertz gravitational wave background (GWB) is expected to be an aggregate signal of an ensemble of gravitational waves emitted predominantly by a large population of coalescing supermassive black hole binaries in the centres of merging galaxies. Pulsar timing arrays (PTAs), which are ensembles of extremely stable pulsars at approximately kiloparsec distances precisely monitored for decades, are the most precise experiments capable of detecting this background. However, the subtle imprints that the GWB induces on pulsar timing data are obscured by many sources of noise that occur on various timescales. These must be carefully modelled and mitigated to increase the sensitivity to the background signal.Methods.In this paper, we present a novel technique to estimate the optimal number of frequency coefficients for modelling achromatic and chromatic noise, while selecting the preferred set of noise models to use for each pulsar. We also incorporated a new model to fit for scattering variations in the Bayesian pulsar timing package temponest. These customised noise models enable a more robust characterisation of single-pulsar noise. We developed a software package based on tempo2 to create realistic simulations of European Pulsar Timing Array (EPTA) datasets that allowed us to test the efficacy of our noise modelling algorithms.Results.Using these techniques, we present an in-depth analysis of the noise properties of 25 millisecond pulsars (MSPs) that form the second data release (DR2) of the EPTA and investigate the effect of incorporating low-frequency data from the Indian Pulsar Timing Array collaboration for a common sample of ten MSPs. We used two packages, enterprise and temponest, to estimate our noise models and compare them with those reported using EPTA DR1. We find that, while in some pulsars we can successfully disentangle chromatic from achromatic noise owing to the wider frequency coverage in DR2, in others the noise models evolve in a much more complicated way. We also find evidence of long-term scattering variations in PSR J1600-3053. Through our simulations, we identify intrinsic biases in our current noise analysis techniques and discuss their effect on GWB searches. The analysis and results discussed in this article directly help to improve the sensitivity to the GWB signal and they are already being used as part of global PTA efforts.

Noise signal review and analysis on a digital accelerometer in a real-world circuit system

In recent years, the applications of MEMS inertial sensors have expanded significantly. The majority of applications challengesthe accuracy, stability, and sensitivity of inertial sensors. Noise characteristic is a crucial performance indicator for sensors. though diverse methods for analyzing and classifying sensor noise from various perspectives are proposed, t his study examines a prominent noise analysis technique known as Allan Variance (AVAR). According to the results of several computations, this approach classifies inertial sensor noise into five groups. In addition, a set of algorithms are proposed to minimize noise in order to limit sensor noise's impact on the system. In general, these algorithms disregard the type of noise and assume that it is random. In this paper, a quick introduction to these algorithms will be provided. A comprehensive investigation of accelerometer noise will conclude this paper. The noise signal is gathered under various conditions for comparison. Typically, this research illustrates a potential difficulty with microcontroller noise signal reception accuracy. The impact and potential explanation of such a problem on noisy signal reception will also be discussed.

Effect of heat treatments on metastable pitting of 316L stainless steel fabricated by selective laser melting

Effect of heat treatments on metastable pitting of 316L stainless steel fabricated by selective laser melting (SLM) was studied by scanning electron microscopy, potentiodynamic polarization, electrochemical noise (EN) and transient analysis techniques. The results show that the selective laser melted (SLMed) 316L stainless steel was composed of honeycomb cells with different orientations, and a small number of lack-of-fusion (LOF) pores were randomly distributed. After heat treatment, the honeycomb-like cellular structure transformed into austenite structure, and the non-metallic inclusions coarsened and grew. Non-metallic inclusions can be divided into two types according to the composition difference, one was manganese silicate enriched by Mn, Si and O and the other was MnS coexisted with the manganese silicate. The potentiodynamic polarization results show that the pitting potential of SLMed 316L stainless steel decreased after heat treatment. During the EN tests, three kinds of current transient were found, which corresponded to the metastable pitting initiated by the manganese silicate oxide inclusions, MnS coexisted with the manganese silicate and pore defects, respectively. Statistical analysis results of the transient signals show that the heat treatment reduced the nucleation rate of metastable pitting, and increased the transient amplitude, pit radius and pit stability product. The decreased pitting performance after heat treatment can be attributed to the low pit growth resistance.

Anatomy of Continuous Mars SEIS and Pressure Data from Unsupervised Learning

ABSTRACT The seismic noise recorded by the Interior Exploration using Seismic Investigations, Geodesy, and Heat Transport (InSight) seismometer (Seismic Experiment for Interior Structure [SEIS]) has a strong daily quasi-periodicity and numerous transient microevents, associated mostly with an active Martian environment with wind bursts, pressure drops, in addition to thermally induced lander and instrument cracks. That noise is far from the Earth’s microseismic noise. Quantifying the importance of nonstochasticity and identifying these microevents is mandatory for improving continuous data quality and noise analysis techniques, including autocorrelation. Cataloging these events has so far been made with specific algorithms and operator’s visual inspection. We investigate here the continuous data with an unsupervised deep-learning approach built on a deep scattering network. This leads to the successful detection and clustering of these microevents as well as better determination of daily cycles associated with changes in the intensity and color of the background noise. We first provide a description of our approach, and then present the learned clusters followed by a study of their origin and associated physical phenomena. We show that the clustering is robust over several Martian days, showing distinct types of glitches that repeat at a rate of several tens per sol with stable time differences. We show that the clustering and detection efficiency for pressure drops and glitches is comparable to or better than manual or targeted detection techniques proposed to date, noticeably with an unsupervised approach. Finally, we discuss the origin of other clusters found, especially glitch sequences with stable time offsets that might generate artifacts in autocorrelation analyses. We conclude with presenting the potential of unsupervised learning for long-term space mission operations, in particular, for geophysical and environmental observatories.

Critical Analysis of Tess Transit Photometric Data: Improved Physical Properties for Five Exoplanets

Abstract We present improved physical parameters for four hot Jupiters: KELT-7 b, HAT-P-14 b, WASP-29 b, WASP-95 b, and a hot Neptune: WASP-156 b, by performing critical and rigorous analysis of the time-series observations from the Transiting Exoplanet Survey Satellite (TESS). Being a space-based telescope, the transit photometric data obtained by TESS are free from any noise component due to the interference of Earth’s atmosphere. In our analysis of the observed data, we have used critical noise reduction techniques, e.g., the wavelet denoising and Gaussian process regression, in order to effectively reduce the noise components that arise from other sources, such as various instrumental effects and the stellar activity and pulsations. The better quality of photometric data from TESS, combined with our state-of-the-art noise reduction and analysis technique, has resulted in more accurate and precise values of the physical properties for the target exoplanets than that reported in earlier works.

Editorial

Editorial

Ricardo Miledi. 15 September 1927—18 December 2017

For nearly five decades, Ricardo Miledi was among the foremost researchers in elucidating how nerves transmit signals across synapses. Born in Chihuahua, Mexico, he qualified as a medical doctor, obtained a PhD with Arturo Rosenblueth and then, while in Canberra with John Eccles FRS, was invited by Bernard Katz FRS to join the Biophysics department at University College London, where he stayed from 1958 to 1984. Both independently and with Katz, he demonstrated that influx of calcium into the presynaptic nerve terminal is the essential trigger for the release of the neurotransmitter that carries signals across to the postsynaptic cell. He found that cutting the nerve to a frog's muscle increased the number and distribution of its muscle acetylcholine (ACh) receptors, which he purified and established as membrane proteins. Together with Katz, he introduced the technique of membrane noise analysis to determine the properties of the individual ion channels opened by ACh, providing the first functional characterization of a single receptor with integral ion channel. With Eric Barnard (FRS 1981), he pioneered a new approach facilitating the study of neurotransmitter receptors and ion channels by ‘transplanting’ them from brain and other tissues into largeXenopusoocyte cells by injection of messenger RNA. After moving to the University of California, Irvine, in 1984, he helped to establish the Mexican Institute for Neurobiology at Querétaro. Working in Irvine and Mexico he extended this oocyte expression technique to incorporate transplanted brain membranes, particularly from patients with epilepsy or other neurological disorders. He received many honours for his work, including the Royal Medal (1998), but was happiest working in his lab applying his extraordinary technical skills and imagination to study synaptic transmission and inspiring a generation of neuroscientists.

NEUTRON NOISE SPECTRAL FEATURES OF SIMULATED MECHANICAL AND THERMAL-HYDRAULIC PERTURBATIONS IN A PWR CORE

KWU-PWR reactors (SIEMENS design) are commonly exhibiting high neutron noise levels that can lead to costly operational issues. Recent analysis seems to indicate that, coolant flow, temperature oscillations, and mechanical vibrations have a key impact on neutron noise phenomena. In order to advance in understanding this phenomenon, the transient nodal code SIMULATE-3K (S3K) has been already utilized to simulate scenarios with individual or combined types of perturbation sources: mechanical vibrations of fuel assemblies and thermal-hydraulic fluctuations at the core inlet. In this work, new simulations are performed with all the perturbations applied simultaneously. The simulated neutron detectors responses are then analyzed with noise analysis techniques. All the simulated spectral features of neutron noise are compared to those obtained from real plant data. Results show that the simulated neutron noise phenomenology behaves similarly to that obtained from real plant data by increasing the fluctuation amplitude in the inlet coolant flow in the S3K calculations.

Measurement of kinetic parameter of the Jordan research and training reactor

The kinetic parameter of the Jordan Research and Training Reactor (JRTR) was measured during commissioning. Neutron noise analysis techniques of correlation, power spectral density (PSD), and variance to mean ratio (VTMR) were applied to determine the prompt neutron decay constant (α) at several zero-power subcritical states. The ratio of the effective delayed neutron fraction to prompt neutron reproduction time (βeff/Λ) corresponding to α at a critical state was estimated by a linear extrapolation of α values as a function of inverse count rate. The βeff/Λ value determined by using the time series neutron data from the BF3 detectors was 54.76–60.32 s-1 and was compared to the calculated one of 61.70 s-1 by the McCARD code with the ENDF/B-VII.0 nuclear library. The relative difference between the measurement and the calculation was within 13%, regardless of the noise analysis techniques.

Frequency Adjusting Technique for Transient Noise Analysis of Nonlinear Integrated Circuits

Frequency Adjusting Technique for Transient Noise Analysis of Nonlinear Integrated Circuits

Neutron noise analysis of simulated mechanical and thermal-hydraulic perturbations in a PWR core

KWU pre-Konvoi PWRs (SIEMENS design) are commonly exhibiting high neutron noise levels which can lead to costly operational issues (i.e. activation of SCRAM system, operation of unrated core power, etc.). The frequency region of interest of neutron noise is below 1 Hz, which is the typical frequency range of thermal-hydraulic phenomena. This feature seems to indicate that, coolant flow and temperature oscillations can have a key impact on neutron noise phenomena. Moreover, an increasing neutron noise trend (in term of normalized root mean square) was recently observed in many KWU-PWRs. This increasing trend has been speculated to be correlated with the introduction of a new fuel design type in the KWU-PWRs. This fact indicates that there should be some correlation between neutron noise spectral characteristics and fuel assemblies’ performance. In order to advance in understanding this phenomenon, the transient nodal code SIMULATE-3K (S3K) has been used to simulate mechanical vibrations of fuel assemblies and thermal-hydraulic fluctuations of the core inlet flow and temperature. The simulated neutron detectors responses are analysed with noise analysis techniques and compared to real plant data. This analysis indicates that the cross-feedback between the mechanical and thermal-hydraulic disturbances complicate the identification of the origin of the perturbation source. The simulated results indicate that the neutron noise spectral characteristics can be associated separately to different causes. In this sense, the results of this work seem to indicate that the spectral features of the neutron noise are a consequence of both mechanical perturbations and thermal-hydraulic fluctuations.

Statistical short-time analysis of electrochemical noise generated within a proton exchange membrane fuel cell

Electrochemical noise analysis (ENA) technique has been performed for the diagnosis of proton exchange membrane fuel cell (PEMFC) under various operating conditions. The interest of electrochemical noise (EN) measurements relates with its non-invasive character and the possibility of online diagnosis of commercial fuel cells without interruption of the system. A new approach for the interpretation of electrochemical noise (EN) measurements has been proposed. This approach is based on internal intermittence of the recorded fluctuating signal (cell voltage). Namely, statistical descriptors in time domain (standard deviation, skewness, flatness), calculated for small time windows (short-time analysis), are rather unstable. This phenomenon can be called the internal intermittence of EN. Our experiments show that the level of internal intermittence is very sensitive to water management and increases drastically when a fuel cell meets either flooding or drying conditions. This conclusion has been confirmed using three statistical moments (standard deviation, skewness, and flatness), four different relative humidities, and three operation points (OCV, 2.5 A, 8 A). The level of internal intermittence can be detected easily by different ways and can be used for the characterization of possible faults of water management in practical applications as commercial fuel cell stacks. From a practical point of view, the measurement of the level of the internal intermittence is rather simple and avoids a time drift. In our knowledge, the internal intermittence of EN has not been used previously for studies of fuel cells. It will be interesting to apply this approach for other electrochemical systems and processes.

Noise in Neuronal and Electronic Circuits: A General Modeling Framework and Non-Monte Carlo Simulation Techniques.

The brain is extremely energy efficient and remarkably robust in what it does despite the considerable variability and noise caused by the stochastic mechanisms in neurons and synapses. Computational modeling is a powerful tool that can help us gain insight into this important aspect of brain mechanism. A deep understanding and computational design tools can help develop robust neuromorphic electronic circuits and hybrid neuroelectronic systems. In this paper, we present a general modeling framework for biological neuronal circuits that systematically captures the nonstationary stochastic behavior of ion channels and synaptic processes. In this framework, fine-grained, discrete-state, continuous-time Markov chain models of both ion channels and synaptic processes are treated in a unified manner. Our modeling framework features a mechanism for the automatic generation of the corresponding coarse-grained, continuous-state, continuous-time stochastic differential equation models for neuronal variability and noise. Furthermore, we repurpose non-Monte Carlo noise analysis techniques, which were previously developed for analog electronic circuits, for the stochastic characterization of neuronal circuits both in time and frequency domain. We verify that the fast non-Monte Carlo analysis methods produce results with the same accuracy as computationally expensive Monte Carlo simulations. We have implemented the proposed techniques in a prototype simulator, where both biological neuronal and analog electronic circuits can be simulated together in a coupled manner.

Autocorrelation Analysis as a Technique to Study Physical Mechanisms of MOSFET Low-Frequency Noise

A low-frequency noise (LFN) analysis technique is introduced, based on the autocorrelation of the LFN spectra in terms of frequency, biasing, and temperature. This technique reveals information about the mechanisms behind 1/ ${f}$ noise that is difficult to obtain otherwise. These correlation analyses provide strong evidence that the LFN of both nMOS and pMOS transistors is mainly composed of the superposition of thermally activated random telegraph signals (RTSs). The methodology enables quantification and validation of old and new LFN models, and, therefore, helps to clarify the relation between RTS and 1/ ${f}$ that is still frequently debated in the literature.

Evaluation of the Effect of Molybdenum on the Pitting Corrosion Behavior of Austenitic Stainless Steels Using Electrochemical Noise Technique

Electrochemical noise (EN) studies were conducted on three austenitic stainless steels (SSs) with different molybdenum contents, 0.02 wt% Mo (Type 304LN SS), 2.53 wt% Mo (Type 316LN SS), and 3.58 wt% Mo (Type 317LN SS), in 0.01 M FeCl3 solution at the corrosion potential (Ecorr) and at a sampling frequency of 1 Hz. The EN data were analyzed using shot noise and wavelet analysis techniques. Current transient analysis showed that the total number of current transients, as well as transients with high current amplitude, decreased with increase in Mo content indicating increased resistance to pitting corrosion. Shot noise analysis revealed higher normalized characteristic charge (q)N at low frequency in Type 304LN SS as compared to Type 316LN SS and Type 317LN SS, implying increase in pitting corrosion resulting from the absence of Mo in this steel. Pit current decreased substantially with increase in Mo content. These results were supported by the standard deviation of partial signal (SDPS) values generated ...

Non-Monte Carlo Analysis of Low-Frequency Noise: Exposition of Intricate Nonstationary Behavior and Comparison With Legacy Models

Modeling and analysis of low-frequency noise, such as 1/ ${f}$ and burst noise, with time-varying bias conditions is a long-standing open problem in circuit simulation. In this paper, we offer a solution for this problem and present a computational model for low-frequency noise. The merits of our model are twofold. First, it is fully nonstationary. It can represent noise processes with time-varying statistics that are tightly coupled to the circuit variables in a stochastically correct manner. Second, its mathematical structure allows the utilization of well-established, non-Monte Carlo noise analysis techniques which are orders of magnitude faster than their Monte Carlo counterparts. We first provide an overview of our noise model along with the legacy modeling techniques. We verify that, when used with fast non-Monte Carlo analysis methods, our noise model produces results with the same accuracy as computationally laborious Monte Carlo simulations. We then proceed to contrast our nonstationary noise model with the legacy modulated stationary model. As a typical instance, where the results produced by these two models differ significantly due to intricate nonstationary behavior, we analyze the switched biasing technique that was proposed in order to reduce low-frequency noise. As a case study, we examine the 1/ ${f}$ noise behavior in the previously proposed coupled sawtooth oscillator circuit and show that, whereas simulations conducted with the legacy modulated stationary model suggest that no noise reduction is obtainable with switched biasing, our nonstationary noise model predicts the correct amount of noise reduction observed in previously published experimental data.

Study on the measurement accuracy of coolant flowrate in a nuclear fuel test rig

To measure the heat generation rate of nuclear fuel rods during an irradiation test, the temperature deviation of the coolant that passes by the fuel rod and its flow velocity need to be measured. Although the temperature of the coolant can be measured by thermocouples instrumented in the nuclear fuel test rig, the flowrate of the coolant is difficult to measure owing to the narrow space of the test rig. Therefore, a noise analysis technique using fluctuation signals from thermocouples installed at both end parts of the fuel rod has been developed by several advanced groups. Although the noise analysis technique was also developed at the Korea Atomic Energy Research Institute, out of pile test results showed that the accuracy of the coolant flow measurement was more than 40%. In this study, the ground pattern of the control board and earth connection are enhanced to eliminate external noise. In addition, a heater unit is installed in the coolant flow simulator to obtain a strong fluctuation signal from the coolant. Thus, the coolant flow simulator is improved, and the accuracy of the coolant flow measurement shows an error of less than 5%.

Corrosion inhibition effect of novel methyl benzimidazolium ionic liquid for carbon steel in HCl medium

In the present investigation, the corrosion inhibition property of the newly synthesized3-(4-chlorobenzoylmethyl)-1-methylbenzimidazoliumbromide ([BMMB]+Br−) ionic liquid on carbon steel was investigated in 1M HCl solution using electrochemical studies. Polarization study revealed that [BMMB]+Br− is a mixed type of inhibitor. The impedance to metal dissolution was increased not only with increasing inhibitor concentration but also with temperature. The diminished current amplitude waves obtained in the electrochemical noise analysis (ENA) technique implied high noise resistances thereby indicating the formation of passive inhibitor layer. Adsorption of [BMMB]+Br− ionic liquid on carbon steel was spontaneous and followed a Langmuir adsorption isotherm. The surface examination of the coupons before and after engrossed in inhibited medium was analyzed using Attenuated total reflectance – Fourier transform infrared spectroscopy (ATR-FTIR) and scanning electrochemical microscope with energy dispersive X-ray (SEM/EDX) studies and confirmed that the surface of carbon steel was protected from corrosion by the adsorption of [BMMB]+Br− ionic liquid. Thermodynamic properties revealed that [BMMB]+Br− has both physical and chemical adsorption behavior. In addition, density functional theory (DFT) calculations were carried out to give atomic level understandings to the experimental results by deducing the electronic properties of inhibitor in proportion to inhibition efficiency in aggressive medium.