Measurement Noise Research Articles

Bridge damage location and quantification under the moving vehicle loads based on deep learning multi-objective regression

The extraction of damage-sensitive features based on deep learning for the vibration response caused by vehicle-bridge interaction (VBI) has become a mainstream method in damage identification. However, most studies remain at the stage of qualitative damage assessment, not achieving precise numerical quantification of damage. They also typically involve multiple frequent runs using only a single dedicated vehicle. In this study, utilizing a deep neural network designed for multi-objective regression tasks, a novel method for bridge damage localization and quantification is presented with a multi-objective regressor designed to create a direct mapping between the curve of bridge deflection in time domain and the damage information matrix. The final quantitative damage values are obtained through statistical analysis. The method is evaluated on a simulated data set generated from VBI simulation using various 3D heavy vehicle models. The results demonstrate that this method can accurately locate and quantify damage even with unseen vehicle load conditions, damage scenarios, and measurement noise. The structure and depth affect the effectiveness of extracting damage-sensitive features from time-domain deflection during training. The study shows potential for real-time damage identification under normal operating conditions of real bridges in the rapid development of intelligent transportation systems.

Passive Islanding Detection of Inverter-Based Resources in a Noisy Environment

Islanding occurs when a load is energized solely by local generators and can result in frequency and voltage instability, changes in current, and poor power quality. Poor power quality can interrupt industrial operations, damage sensitive electrical equipment, and induce outages upon the resynchronization of the island with the grid. This study proposes an islanding detection method employing a Duffing oscillator to analyze voltage fluctuations at the point of common coupling (PCC) under a high-noise environment. Unlike existing methods, which overlook the noise effect, this paper mitigates noise impact on islanding detection. Power system noise in PCC measurements arises from switching transients, harmonics, grounding issues, voltage sags and swells, electromagnetic interference, and power quality issues that affect islanding detection. Transient events like lightning-induced traveling waves to the PCC can also introduce noise levels exceeding the voltage amplitude by more than seven times, thus disturbing conventional detection techniques. The noise interferes with measurements and increases the nondetection zone (NDZ), causing failed or delayed islanding detection. The Duffing oscillator nonlinear dynamics enable detection capabilities at a high noise level. The proposed method is designed to detect the PCC voltage fluctuations based on the IEEE standard 1547 through the Duffing oscillator. For the voltages beyond the threshold, the Duffing oscillator phase trajectory changes from periodic to chaotic mode and sends an islanded operation command to the inverter. The proposed islanding detection method distinguishes switching transients and faults from an islanded operation. Experimental validation of the method is conducted using a 3.6 kW PV setup.

Scalable exact output synchronization of discrete‐time multi‐agent systems in the presence of disturbances and measurement noise with known frequencies

AbstractThis article aims to achieve scalable exact output and regulated output synchronization for discrete‐time multi‐agent systems in presence of disturbances and measurement noise with known frequencies. Both homogeneous and heterogeneous multi‐agent systems are considered, with parts of agents' states accessible in the latter case. The key contribution of this article is to establish scalable exact synchronization results under disturbances and measurement noise, which is achieved by using distributed protocols that only use information about agent models and no information about the communication network or the number of agents. The validity of the protocol is verified by numerical simulations with arbitrarily chosen number of agents.

Temperature control technology for PCR

AbstractIn order to solve the obvious nonlinear problem of temperature and complex structure of PCR instrument during nucleic acid amplification. In this paper, a new nucleic acid amplification device and temperature control algorithm were proposed. In the device, in order to improve the rise and fall rate and make the whole reaction device smaller and simpler, this paper uses a microfluidic chip for nucleic acid reaction. At the same time, in the warming and cooling module, the temperature is controlled by the semiconductor chilling plate, the air‐cooled cooling device and the heat sink structure, which greatly improves the speed of nucleic acid amplification. In the algorithm, a hybrid algorithm is designed, using Particle Swarm Optimization (PSO) to optimize PID algorithm parameters, and then based on fuzzy theory, according to the temperature control requirements of nucleic acid amplification, fuzzy rules are analyzed and fuzzy reasoning is carried out, and then combined with PID to achieve rapid response and overshooting control of temperature control. Finally, the measurement noise is filtered by Kalman filter. Finally, COMSOL and MATLAB software are used to simulate and compare, and it is proved that the device has a certain heat dissipation effect in the process of nucleic acid amplification. This algorithm can improve the accuracy and robustness of the control system, improve the response speed, reduce the overshoot, shorten the adjustment time, and restrain the interference.

Unscented-Kalman-Filter-Based Remote State Estimation for Complex Networks With Quantized Measurements and Amplify-and-Forward Relays.

In this article, the remote estimation problem is addressed for a class of discrete-time complex networks under the influence of probabilistic quantization and amplify-and-forward (AF) relays. The underlying complex network model, which is inherently nonlinear and stochastic, is affected by additive process and measurement noises. Owing to the limited bandwidth of the transmission channel, the measurement outputs are quantized by a probabilistic quantizer prior to transmission. To enhance the signal quality over long-distance transmissions, the quantized measurements are sent to AF relays and subsequently forwarded to the estimator. Utilizing the unscented Kalman filter approach, a novel state estimator is designed to minimize an upper bound on the estimation error covariance. Moreover, sufficient conditions are derived to ensure that the estimation error is exponentially bounded in the mean-square sense. Lastly, the efficacy of the proposed scheme is illustrated through numerical simulations.

ECD-CDGI: An efficient energy-constrained diffusion model for cancer driver gene identification.

The identification of cancer driver genes (CDGs) poses challenges due to the intricate interdependencies among genes and the influence of measurement errors and noise. We propose a novel energy-constrained diffusion (ECD)-based model for identifying CDGs, termed ECD-CDGI. This model is the first to design an ECD-Attention encoder by combining the ECD technique with an attention mechanism. ECD-Attention encoder excels at generating robust gene representations that reveal the complex interdependencies among genes while reducing the impact of data noise. We concatenate topological embedding extracted from gene-gene networks through graph transformers to these gene representations. We conduct extensive experiments across three testing scenarios. Extensive experiments show that the ECD-CDGI model possesses the ability to not only be proficient in identifying known CDGs but also efficiently uncover unknown potential CDGs. Furthermore, compared to the GNN-based approach, the ECD-CDGI model exhibits fewer constraints by existing gene-gene networks, thereby enhancing its capability to identify CDGs. Additionally, ECD-CDGI is open-source and freely available. We have also launched the model as a complimentary online tool specifically crafted to expedite research efforts focused on CDGs identification.

Conductive Atomic Force Microscopy—Ultralow-Current Measurement Systems for Nanoscale Imaging of a Surface’s Electrical Properties

One of the most advanced and versatile nanoscale diagnostic tools is atomic force microscopy. By enabling advanced imaging techniques, it allows us to determine various assets of a surface, including morphological, electrical, mechanical, magnetic, and thermal properties. Measuring local current flow is one of the very important methods of evaluation for, for instance, photovoltaic materials or semiconductor structures and other nanodevices. Due to contact areas, the current densities can easily reach above 1 kA/m2; therefore, special detection/measurement setups are required. They meet the required measurement range, sensitivity, noise level, and bandwidth at the measurement scale. Also, they prevent the sample from becoming damaged and prevent unwanted tip–sample issues. In this paper, we present three different nanoscale current measurement solutions, supported with test results, proving their performance.

Underwater noise measurements of a free-running ship model

ABSTRACT The high background noise and narrow widths of towing tanks, which cause sound wave reflections, make them unsuitable for noise measurements, leading to a lack of published hydroacoustic data. To address this, we developed a free-running test model of the benchmark DTC container ship at a 1/80 scale and conducted hydroacoustic tests in the artificial lake at Istanbul Technical University. The model was remotely controlled to pass a fixed water station equipped with a hydrophone. We present ship noise measurements at three different velocities, noting a strong correlation between the propeller rotation rates from self-propulsion tests and hydroacoustic signals at high speeds. However, accuracy decreases at lower speeds due to background noise interference.

Simplified Laser Frequency Noise Measurement Using the Delayed Self-Heterodyne Method

Here, we report on a simplified laser frequency noise measurement technique employing an acousto-optic modulator, a delay line, and a real-time oscilloscope. The technique is a slight modification of the typical delayed heterodyne method. Instead of using a swept frequency spectrum to analyze the laser emission spectrum, the waveform captured on a real-time oscilloscope is used to directly calculate the laser frequency noise. The oscilloscope bandwidth and sampling requirements can be kept modest by choosing a modulator driven at a few hundred megahertz, making this technique attractive for a large number of laboratories. We show the frequency noise measurements of two different lasers with linewidths at 2.7 kHz and 2 MHz. We took the opportunity to investigate the noise floor of the frequency noise measurement system, and we found that the noise floor of the frequency noise measurement depends on the power level of the laser that is being characterized, with the kilohertz linewidths laser requiring more power to reduce the noise floor to acceptable levels.

State-of-health estimation for lithium-ion batteries based on Kullback–Leibler divergence and a retentive network

Accurate state-of-health (SOH) estimation is crucial for the lithium-ion battery industry, as it underpins the safety, durability, and reliability of lithium-ion batteries. Currently, most researchers use various methods of health indicator (HI) extraction for the SOH estimation of batteries. However, these methods may require certain expertise and prior knowledge to achieve accurate modeling, being affected by measurement noise and other factors. To solve the abovementioned problems, three Kullback–Leibler (KL) divergence features based on partial voltage sequences are proposed as new HIs that are independent of prior knowledge and strongly correlated with SOH. Moreover, a modified retentive network is proposed to enhance SOH estimation accuracy and better utilize HIs than traditional deep learning methods, which have high training costs and insufficient accuracy. To ensure consistent extraction of KL divergence features across various experimental conditions and time intervals, a B-spline algorithm is utilized for interpolation. The effectiveness of the proposed method is validated through analysis of Pearson correlation coefficients and experiments conducted in four dimensions. Additionally, the potential of using the proposed method to compress data on the cloud-side is explored.

Lithium battery state of charge estimation based on improved variable forgetting factor recursive least squares method and adaptive Kalman filter joint algorithm

As one of the core functions of the battery management system, battery state of charge (SOC) estimation is crucial to battery life and safety. As the traditional recursive least squares method cannot adapt well to complex variable current conditions, this paper proposes a SOC estimation method that combines the improved variable forgetting factor recursive least squares (IVFFRLS) and the adaptive extended Kalman filter (AEKF). First, IVFFRLS removes the rounding operation of the original algorithm to improve the sensitivity to errors and uses a genetic algorithm to optimize the upper and lower bounds of the variable forgetting factor and the sensitivity coefficient to improve the accuracy of the VFFRLS algorithm. Meanwhile, AEKF introduces a noise covariance adaptive update link based on the covariance matching method to correct the system's process noise Q and measurement noise R in real time to improve the SOC estimation accuracy. Based on the second-order RC equivalent circuit model, the method proposed in this paper has good accuracy and robustness under four different working conditions. The experimental results show that compared with traditional methods, the proposed IVFFRLS-AEKF algorithm has higher SOC estimation accuracy and better robustness.

Bridge Profile and Vehicle Property Determination Using Vehicle Fleet Monitoring Concept

Over the past 20 years, drive-by monitoring, using data from sensors in passing vehicles, has become increasingly popular due to its low cost and high efficiency at a network level. Despite significant advances, the dynamics of vehicles remains one of the main challenges to overcome before accurately identifying critical information about the infrastructure being crossed, bridges or road pavements. This paper introduces a novel approach to estimating bridge profiles (i.e. road surface profiles on the bridges) and vehicle properties using a fleet of passing vehicles. In this method, bridge profiles are first calculated using the innovative Inverse Newmark-beta integration method, and the cross-entropy optimization algorithm is employed to solve the problem. Numerical results demonstrate that the proposed approach is highly effective in extracting bridge profiles and predicting vehicle properties, even in scenarios with significant levels of measurement noise.

Joint state and process inputs estimation for state-space models with Student’s t-distribution

This paper proposes a joint state and unknown inputs (UIs) discrete-time estimation method for industrial processes, represented by a state-space model. To cope with the outliers in process data, the measurement noise is characterized by the Student’s t-distribution. The identification of UIs is accomplished through the recursive expectation–maximization (REM) approach. Specifically, in the E-step, a recursively calculated Q-function is formulated by the maximum likelihood criterion, and the states and the variance scale factor are estimated iteratively. In the M-step, UIs are updated analytically together with the degree of freedom is updated approximately. The effectiveness of the proposed algorithm is validated using a quadruple water tank process and a continuous stirred tank reactor. It shows that the proposed method significantly enhances the robustness and estimation accuracy of state and UIs in industrial processes, effectively handling outliers and reducing computational demands for real-time applications.

Joint Bayesian estimation of process and measurement noise statistics in nonlinear Kalman filtering

Bayesian filtering aims at sequentially estimating the states and parameters in nonlinear dynamical systems, and finds applications in a variety of engineering fields. Extended and unscented Kalman filter are popular choices for this purpose as they typically achieve a good trade-off between computational complexity and accuracy, while still quantifying posterior uncertainty. However, the accuracy of the identified states and parameters, both in terms of their posterior mean and variance, is heavily influenced by the assumed statistics of both the process and measurement noise terms. Therefore, learning the noise characteristics becomes an essential aspect of Bayesian filtering, but is non-trivial especially in cases where process and measurement noise statistics are jointly non-identifiable. This article proposes a methodology that decomposes measurement and process noise learning in a fully Bayesian setting. For measurement noise estimation, we improve upon a Bayesian method that leverages conjugacy of the inverse-Wishart distribution with respect to the Gaussian likelihood model. For process noise estimation, Bayesian model averaging is coupled with a mutation scheme to compare the performance of competing models while exploring high-probability regions in the space of the unknown process noise variance. The algorithm is first tested on simple numerical models for both state estimation and parameter learning, highlighting the superior accuracy of the proposed measurement noise correction and the benefits of decoupling process and measurement noise learning in non-identifiable settings. Then we demonstrate the usefulness of this algorithm in more challenging problems, namely identification of a multi-degree-of-freedom system under unmeasured excitation, and modeling of the highly nonlinear response of a full-scale bridge pier using experimental data.

An assessment of controlled source EM for monitoring subsurface CO2 injection at the wyoming carbonSAFE geologic carbon storage site

We evaluate if electromagnetic (EM) geophysical methods for monitoring geologic carbon storage (GCS) efforts at the Wyoming CarbonSAFE project adjacent to the Dry Fork Station power plant near Gillette, Wyoming. This first involved acquiring both electric and magnetic fields at eleven different locations ranging in distance from immediately adjacent to 4 km from the plant. Passive EM measurements were made to provide spectral EM noise measurements generated by electricity production at the plant and to determine if useful magnetotelluric (MT) data can be successfully collected in the region. The processed data indicate that useful MT data can be collected as long as the site is located more than 2km away from the power plant as well as active roads and rail lines. Controlled source EM data were collected using three different source configurations, two of which connected to steel casings used to complete the injection wells. Comparing the EM noise measurements to the CSEM data show measurable electric and magnetic field signals at all sites. Next a series of three-dimensional (3D) numerical models were built that simulate resistivity changes caused by the proposed CO2 injection at depths ranging from 2.4 to 3.0km. These models were used to simulate various EM measurement configurations. The modeling shows that casing-source CSEM monitoring can provide sensitivity to the injected CO2 if source electrodes are connected to the bottom of one or both of the injection wells.

Identifying chaotic dynamics in noisy time series through multimodal deep neural networks

Abstract Chaos detection is the problem of identifying whether a series of measurements is being sampled from an underlying set of chaotic dynamics. The unavoidable presence of measurement noise significantly affects the performance of chaos detectors, as discerning chaotic dynamics from stochastic signals becomes more challenging. This paper presents a computationally efficient multi-modal deep neural network tailored for chaos detection by combining information coming from the analysis of time series, recurrence plots and spectrograms. The proposed approach is the first one suitable for multi-class classification of chaotic systems while being robust with respect to measurement noise, and is validated on a dataset of 15 different chaotic and non-chaotic dynamics subject to white, pink or brown coloured noise.

Resolving the BWR Stability Paradox of Space-Dependent Decay Ratios

Noise signals obtained from local power range monitors and average power range monitors are routinely used for extracting stability information for boiling water reactors. The stability parameters of decay ratio (DR) and natural frequency are produced by signal processing algorithms. While theoretically a dynamical system like a reactor core composed of coherently coupled components possesses a unique DR, noise measurements from different detectors have been reported in several published works to produce different DRs, creating the impression that a DR is not unique at a given operating state but rather is space dependent. This paper is an attempt to reconcile theory with measurements and resolve the space-dependent DR paradox that was encountered afresh in the course of designing a new high-fidelity online stability monitor. As such, the issue of space dependence could not be overlooked as attributable to variability within the uncertainty of noise analysis algorithms.

Criteria for selection of point-ahead angle compensation strategy in intersatellite laser ranging interferometry of TianQin based on research of tilt-to-length coupling noise

TianQin is expected to deploy three spacecraft in Earth orbit at the altitude of about 1 × 108 m to form an equilateral triangular constellation with arm lengths of about 1.7 × 108 m. It is aimed at detecting gravitational waves in the frequency range from 0.1 mHz to 1 Hz by means of high sensitive laser ranging interferometry with pathlength measurement noise below 1 pm/Hz1/2. The long-range propagation of beam in the intersatellite laser ranging interferometry between the three spacecraft results in a non-negligible time delay of 0.58 s. Therefore, there is an angular difference between the optimal outgoing and incoming laser beam of laser ranging interferometry on each spacecraft, which is denoted as the point-ahead angle. Due to the relative motion between the three spacecraft, the point-ahead angle of TianQin constellation is approximately 22.96 μrad with a dynamic variation range of ±24.71 nrad. Point-ahead-angle mechanism is a fine steering mirror used for point-ahead angle compensation to diminish the beam pointing error, thus to maintain the intersatellite laser link as well as to diminish the tilt-to-length coupling noise, which are vital to the gravitational wave detection. Both the static and dynamic point-ahead angle compensation strategies can be used in TianQin and the selection of the two is based on minimizing the total point-ahead-angle-related tilt-to-length coupling noise. In this paper, a criteria for selection of the two strategies are proposed by establishing a model of point-ahead-angle-related tilt-to-length coupling noise as well as a corresponding evaluation function for calculation and comparison. The results indicate that the dynamic compensation strategy is the optimal choice only when the first-order tilt-to-length coupling coefficient of point-ahead-angle mechanism is less than 6.4 × 10−9 m/rad, otherwise the static compensation strategy should be used. The proposed criteria, as well as the model and the requirement for point-ahead-angle mechanism, can serve as a common theoretical basis in laser ranging interferometry applications.

Measurement and analysis of intra-urban environmental noise in Melbourne, Australia

Measurement and analysis of intra-urban environmental noise in Melbourne, Australia

Dynamic data reconciliation based on elman neural network and particle filter

In the process of modern industries, complex nonlinear dynamic systems present high requirements for measured data. In the actual industrial process system, the measurement data obtained by sensors will inevitably be subject to noise disturbances from the equipment itself or from the outside environment. These noise disturbances will deteriorate the dynamic performance of the system to a certain extent and affect the industrial production. Particle filter (PF) can be used to infer the accurate outputs of nonlinear dynamic system from the contaminated measurement data, but PF is limited to the pre-known state space model. In the actual industrial process, it is difficult to summarize the internal behavior of the system and obtain the pre-known state space model. Therefore, it is impossible to directly use PF in the nonlinear dynamic system with unknown model. In order to solve the above problems, this paper proposes a dynamic data reconciliation method called ENN-PF, which combines Elman neural network (ENN) data-driven modeling with PF. In this method, ENN is used for data-driven modeling, that is, the system model is dynamically identified by using the input and output data of the system, and then the dynamic data reconciliation is carried out by using PF according to the model identified by ENN. Finally, the proposed ENN-PF method is validated by simulations and practical experiments to effectively reduce the interference of measurement noise and improve the dynamic performance of the system.