Noise Errors Research Articles

A new method to determine the continuous refractive index of an absorbing film by Generalized Stockwell Transform

Generalized Stockwell Transform (GST) was adapted to analyze a transmittance signal to continuously determine the refractive index and extinction coefficient. The process is built by analyzing oscillation frequencies of the transmission signal where oscillations exist. The simulation studies clearly show the advantage of the locally referenced phase property of the presented method. The validity of the method was checked for a CdS thin film. The results determined by the GST method are consistent with the results determined by envelope and other signal analyzing methods, and the literature. The noise and relative error analysis of the method was also discussed.

Output-only structural damage identification based on Q-learning hybrid evolutionary algorithm and response reconstruction technique

Various structural damage identification methods have been developed and employed, while the absence of input excitation measurements may pose a huge challenge in their application since the input forces such as seismic load, traffic load, wind load, are not directly measurable. To address this issue, in the present paper, a novel output-only structural damage detection approach based on Q-learning hybrid evolutionary algorithm (QHEA) and response reconstruction technique is presented. On the one hand, the external excitation and structural acceleration responses are reconstructed with the aid of response reconstruction and Tikhonov regularization techniques in time domain. Structural damage identification can be formulated as an optimization-based inverse problem. On the other hand, a new optimization framework QHEA integrating Jaya algorithm, differential evolution, Q-learning algorithm is developed as search tool. The unknown structural parameters and unmeasured input force are iteratively updated by using two different measurement sets until the reconstructed acceleration responses agree well with the measured responses. Numerical examples involving a cantilever beam structure under single excitation and a simply-supported 51-bar truss structure under multiple excitations, as well as an experimental five-floor steel frame structure in the laboratory are carried out to validate the effectiveness of the proposed method. The final results demonstrate that the proposed method can accurately detect damage locations and quantify damage extents without the information of input excitation. In addition, the superiority of QHEA over other heuristic algorithms, the uncertainties of measurement noise and modeling errors on damage identification results are further examined.

Performance of a Modified ALLType NGS HLA Typing Assay with the Ion Chef/Ion S5/TypeStream Visual System

Abstract OBJECTIVE S: The commercial ALLType assay (One Lambda, West Hills, CA) enables accurate, high-resolution typing of 11 HLA genes using the Ion Chef/Ion S5/TypeStream Visual (TSV) system. The assay features a multiplexed PCR to enrich all target HLA genes in a sample, and up to 48 samples can be indexed and sequenced in a run. The assay is laborious due to the late pooling of indexed libraries. Early pooling significantly simplifies the workflow but may increase the background noise and typing errors. This project aims to examine the feasibility of early pooling immediately after the indexing step and evaluate the performance of such a modified ALLType protocol. METHODS A total of 75 unique samples with diverse HLA genotypes were included in this study, which comprised 52 proficiency testing samples of three different sources, 10 stem cell recipients, 7 stem cell donors (including 2 buccal swabs), and 6 deceased organ donors. Genomic DNA extracted from these samples was tested in four sequencing runs, with 15-31 samples per run, using the modified ALLType procedure (reagent lot: 15). The results were compared with the reference typings generated by the manufacturer-established protocol. All data were analyzed using TSV (version 3.0) and the IPD-IMGT/HLA library (version 3.47); the default analysis parameters were applied. The primary endpoint of the study was the concordance of 3-field typing results. The secondary endpoint was key health metrics, including allele balance, key-exon coverage, and background noise. We also evaluated the saving on hands-on time and reagents with the modified protocol. RESULTS The concordance rates of 3-field typing results were 100% for HLA-A, HLA-B, HLA-C, HLA-DPA1, HLA-DPB1, HLA-DQA1, HLA-DQB1, and HLA-DRB1, and 96.2% for HLA-DRB3/4/5. The three discrepant HLA-DRB3/4/5 results with the modified protocol were false positive allele calls due to a small number of contaminating reads, which were corrected by increasing the threshold for HLA-DRB3/4/5 in TSV. The allele balance and exon 2 coverage across all samples and genes were comparable between the modified and reference protocols, with R-squared values of 0.83 and 0.81, respectively. High background signals in exons were observed in only 0.6% of all samples with both protocols. The modified protocol reduced the hands-on time by 15%, from 6.5 to 5.5 hours, and reduced the use of library preparation reagents by 30%. CONCLUSION It is feasible to pool indexed libraries early during the library preparation process without increasing the background noise in the sequencing data. The modified ALLType protocol showed typing accuracy and health metrics comparable to the original protocol, with significant savings on time and reagents.

Laser Heterodyne Detection Based on Photon Time–Domain Differential Detection Avoiding the Effect of Decoherence Phase Noise

Laser heterodyne detection (LHD) is a key velocimetry technique that provides better accuracy and sensitivity than direct laser detection. However, random phase noise can be introduced by the surface topography of the moving target undulation or atmospheric turbulence during transmission. The random phase noise causes the target echo to undergo decoherence, resulting in degradation of the signal-to-noise ratio (SNR). Here, we propose a novel LHD method based on photon time–domain differential detection. It can infer the heterodyne spectrum of the target echo and the local oscillator light from the time intervals of the photon arrival. The time interval statistic is a relative quantity, which can effectively avoid the effect of random phase noise in LHD. With our method, the SNR of LHD can be improved in application scenarios where the target echo is decoherent. We developed a complete solution model for acquiring the heterodyne spectrum based on photon time–domain differential detection and performed proof-of-principle experiments. The experimental results show that in the presence of random phase noise, the SNR and velocity measurement error of our method are significantly better than that of the conventional method, and the larger the phase noise is, the more the SNR and velocity measurement error of our method are improved. Moreover, along with the increase in phase noise, the SNR of our method is basically unchanged, which also indicates that our method is not affected by random phase noise. This advantage is significant for photon-level weak echoes that require long detection times to be detected.

A Gene Selection Method Considering Measurement Errors.

The analysis of gene expression data has made significant contributions to understanding disease mechanisms and developing new drugs and therapies. In such analysis, gene selection is often required for identifying informative and relevant genes and removing redundant and irrelevant ones. However, this is not an easy task as gene expression data have inherent challenges such as ultra-high dimensionality, biological noise, and measurement errors. This study focuses on the measurement errors in gene selection problems. Typically, high-throughput experiments have their own intrinsic measurement errors, which can result in an increase of falsely discovered genes. To alleviate this problem, this study proposes a gene selection method that takes into account measurement errors using generalized liner measurement error models. The method consists of iterative filtering and selection steps until convergence, leading to fewer false positives and providing stable results under measurement errors. The performance of the proposed method is demonstrated through simulation studies and applied to a lung cancer data set.

Magnetic noise analysis for small magnetically shielded room in different environmental magnetic fields

The compact high-performance magnetically shielded room (MSR) provides near-zero magnetic field environment for low-cost magnetoencephalography (MEG) measurements. Predicting the level of magnetic noise in the MSR under different magnetic field holds significant importance for the deployment of MEG systems. In this paper, the magnetic noise of MSR in different operational conditions is studied. The magnetic noise in MSR is modeled as residual environmental noise, material one, and additional one. A quantitative model for magnetic noise calculation is established, leading to the definition of the Weak Shielding Factor (WSF), which establishes a relationship between the environment magnetic noise and MSR one. Verification experiments are conducted using a compact MSR, a magnetically shielded box, and a cylindrical magnetically shielded bucket. The average magnetic noise error between calculated and measured values in 1–100 Hz range for the three shielding structures are 8.29%, 12.33%, and 7.50%, respectively, which shows the effectiveness of the proposed method.

Evaluation of Landweber Coupled Least Square Support Vector Regression Algorithm for Electrical Capacitance Tomography for LN2–VN2 Flow

The electric capacitance tomography (ECT) technique has been widely used in phase distribution reconstruction, while the practical application raised nonideal noise and other errors for cryogenic conditions, requiring a more accurate algorithm. This paper develops a new image reconstruction algorithm for ECT by coupling the traditional Landweber algorithm with the least square support vector regression (LSSVR) for cryogenic fluids. The performance of the algorithm is quantitatively evaluated by comparing the inversion images with the experimental results for both the room temperature working medium with the dielectric constant ratio close to cryogenic fluid and the cryogenic fluid of liquid nitrogen/nitrogen vapor (LN2-VN2). The inversion images based on the conventional LBP and Landweber algorithms are also presented for comparison. The benefits and drawbacks of the developed algorithms are revealed and discussed, according to the results. It is demonstrated that the correlated coefficients of the images based on the developed algorithm reach more than 0.88 and a maximum of 0.975. In addition, the minimum void fraction error of the algorithm is reduced to 0.534%, which indicates the significant optimization of the LSSVR coupled method over the Landweber algorithm.

A novel wind power forecasting system integrating time series refining, nonlinear multi-objective optimized deep learning and linear error correction

Wind power prediction is crucial for successfully integrating large-scale wind energy with the grid and achieving a carbon-neutral energy mix. However, previous studies encountered challenges to noise reduction, information extraction, error handling, and uncertainty estimation. To address these limitations, this study proposed a novel wind power forecasting system, systematically enhancing forecasting capability by refining the raw time series using horizontal denoising and vertical granulation methods, utilizing a bidirectional deep learning model for capturing nonlinear features and optimizing its core hyperparameters with a newly developed multi-objective enhanced version of metaheuristic algorithm to achieve Pareto optimal solutions, employing the traditional statistical approach for residual linear feature extraction from error series and effective correction, and integrating quantile regression for interval prediction. The predictive performance was evaluated based on wind power, speed, and direction data from two different countries, the system yielded correlation coefficients of 0.9796, 0.9943, 0.9795, and 0.9780 in spring, summer, autumn and winter for China, and 0.9950 for Turkey, demonstrating its pronounced advantages over comparative models. Overall, the proposed system contributes to the advancement of wind-power prediction theory and holds substantial potential for practical applications.

A Minimum Joint Error Entropy-Based Localization Method in Mixed LOS/NLOS Environments

In this paper, we address the time-of-arrival (TOA) based source localization problem in mixed light-of-sight (LOS)/non-light-of-sight (NLOS) environments, where localization accuracy is degraded by both measurement noise and NLOS error. A novel exponential optimization problem is formulated based on new minimum joint error entropy criteria and statistical characteristics of measurement noise. After that, a two-step relaxation method is proposed. In the first step, the original problem is relaxed into a non-exponential problem which maintains the consistency of the solution. The second step is to transform the non-exponential problem into a convex problem. Furthermore, we extend the method to asynchronous networks where the source and anchors are not time synchronized. Numerical results show that the proposed method can provide significant robust performance in synchronous or asynchronous networks, whether in sparse or dense NLOS scenarios.

RUL Prognostics

With the rising complexity of manufacturing processes, resulting from rapid industrial development, the utilization of remaining useful lifecycle (RUL) prediction, based on failure physics and traditional reliability, has remained limited. Although data-driven approaches of RUL prediction were developed using machine learning algorithms, uncertainty-induced challenges have emerged, such as sensor noise and modeling error. To address these uncertainty-induced problems, this study proposes a stochastic ensemble-modeling concept for improving the RUL prediction result. The proposed ensemble model combines artificial degradation patterns and fitness weights, which incorporate formulas reflecting failure patterns and various reliability function data with the observed degradation factor. Furthermore, a recursive Bayesian updating technique, reflecting the difference between expected and observed remaining life sequentially, was leveraged to reduce the prediction uncertainty. Moreover, we comparatively studied the predictive performance of the proposed model (recursive Bayesian ensemble model) against an existing baseline method (exponentially weighted linear regression model). Through simulation and case datasets, this experiment demonstrated the robustness and utility of the proposed algorithm.

DATA FUSION OF ULTRASONIC SENSORS FOR DISTANCE MEASUREMENT

Distance measurement plays a fundamental role in contemporary technology, exerting a significant influence on applications across a myriad of sectors, ranging from autonomous navigation and industrial automation to robotics. Ultrasonic sensors, celebrated for their simplicity, cost-efficiency, and adaptability, have grown into essential instruments for gauging distances. Nevertheless, individual ultrasonic sensors are not exempt from constraints, which encompass inaccuracies stemming from environmental conditions, sensor noise, and measurement errors. This article delves into the advanced practice of data fusion, a method that entails amalgamating data from multiple ultrasonic sensors, bolstered by intricate algorithms like artificial intelligence (AI) and statistical techniques. The fusion of ultrasonic sensor data surpasses mere amalgamation; it harnesses the potency of AI to refine data integration. Consequently, this doesn't only enhance measurement precision but also equips systems to function dependably in dynamic and demanding environments. The article explores the importance of ultrasonic sensors, the necessity for data fusion, procedures for data preprocessing, the extraction of relevant features, the application of fusion algorithms, and the handling of uncertainties. It investigates the utilization of ultrasonic sensor data fusion in diverse domains such as autonomous vehicles, robotics, industrial automation, healthcare, and environmental monitoring. Moreover, it confronts issues associated with data quality, environmental variations, privacy concerns, and ethical deliberations. The article concludes by shedding light on the encouraging avenues that lie ahead in the field, which include the development of advanced algorithms, the seamless integration of different sensor types, real-time data processing, and a strong emphasis on safety-critical applications. Data fusion from ultrasonic sensors signifies an evolving and transformative technology with profound consequences for various sectors. The responsible advancement and deployment of this technology are of paramount importance to ensure the realization of its full potential, while maintaining the principles of safety, ethics, and reliability in a continuously evolving technological landscape. Keywords: Complex measurement, Multi-sensor distance measurement, Ultrasonic sensors, Data fusion, Measurement accuracy.

Hierarchical Bayesian calibration of deck deflection models using distributed fiber optic strain data

A naval hovercraft was outfitted with a distributed fiber optic sensing system (FOSS) on its cargo deck in which two fibers were placed along orthogonal axes. A series of trucks were loaded onto the cargo deck to simulate operational loading conditions and the resulting strain profiles were measured by the distributed FOSS. The aim of this study is to quantify the deck deflections that correspond to the measured strains under different vehicular loads. This is accomplished by idealizing the cargo deck as a rectangular plate and using the data to estimate the associated plate model parameters. In order to account for measurement noise and model prediction error, as well as the inherent variability of inferences made with different data sets, a hierarchical Bayesian scheme that considers both the model parameter uncertainty and the prediction error variance is utilized to derive the posterior distributions of the model hyper-parameters, i.e., their mean and variance. Given the volume of FOSS strain data available, different approaches were taken for incorporating the data into the hierarchical approach in order to determine whether the inclusion of all available strain data meaningfully reduced the uncertainty compared to using only time-averaged strain. Comparisons between the different approaches are explored in the context of 95% confidence intervals for strain profiles, deck deflection envelopes, and reliability indices based on allowable deflections.

High-resolution conductivity reconstruction by electrical impedance tomography using structure-aware hybrid-fusion learning

BackgroundElectrical impedance tomography (EIT) has gained considerable attention in the medical field for the diagnosis of lung-related diseases, owing to its non-invasive and real-time characteristics. However, due to the ill-posedness and underdetermined nature of the inverse problem in EIT, suboptimal reconstruction performance and reduced robustness against the measurement noise and modeling errors are common issues. ObjectivesThis study aims to mine the deep feature information from measurement voltages, acquired from the EIT sensor, to reconstruct the high-resolution conductivity distribution and enhance the robustness against the measurement noise and modeling errors using the deep learning method. MethodsA novel data-driven method named the structure-aware hybrid-fusion learning (SA-HFL) is proposed. SA-HFL is composed of three main components: a segmentation branch, a conductivity reconstruction branch, and a feature fusion module. These branches work in tandem to extract different feature information from the measurement voltage, which is then fused to reconstruct the conductivity distribution. The unique aspect of this network is its ability to utilize different features extracted from various branches to accomplish reconstruction objectives. To supervise the training of the network, we generated regular-shaped and lung-shaped EIT datasets through numerical calculations. ResultsThe simulations and three experiments demonstrate that the proposed SA-HFL exhibits superior performance in qualitative and quantitative analyses, compared with five cutting-edge deep learning networks and the optical image-guided group sparsity (IGGS) method. The evaluation metrics, relative error (RE), mean structural similarity index (MSSIM), and peak signal-to-noise ratio (PSNR), are improved by implementing the SA-HFL method. For the regular-shaped dataset, the values are 0.119 (RE), 0.9882 (MSSIM), and 31.03 (PSNR). For the lung-shaped dataset, the values are 0.257 (RE), 0.9151 (MSSIM), and 18.67 (PSNR). Furthermore, the proposed network can be executed with appropriate parameters and efficient floating-point operations per second (FLOPs), concerning network complexity and inference speed. ConclusionsThe reconstruction results indicate that fusing feature information from different branches enhances the accuracy of conductivity reconstruction in the EIT inverse problem. Moreover, the study shows that fusing different modalities of information to reconstruct the EIT conductivity distribution may be a future development direction.

Hydraulic fracturing distributed acoustic sensing monitoring data source mechanism inversion: A Hessian-based method

Distributed acoustic sensing (DAS) microseismic monitoring during hydraulic fracturing provides microseismic data with high spatial samplings for fracturing zones. However, sources located perpendicular to the single horizontal well of the DAS acquisition system have limited source-receiver geometries with extremely poor azimuthal coverage, resulting in high uncertainty in source mechanism inversion. To address this problem, we introduce the Hessian matrix, which governs the blurring effect caused by the source-receiver geometry, into the DAS microseismic source mechanism inversion. Our Hessian-based source mechanism inversion method consists of three main steps: (1) construct the Hessian matrix of the source mechanism based on the source-receiver geometry, (2) obtain an initial source mechanism using a conventional source mechanism inversion method, and (3) update the initial source mechanism using a Hessian-based [Formula: see text]-regularized least-squares algorithm. We assess the robustness of our method using synthetic DAS microseismic data with the consideration of noise, source location error, and different regularization parameters, and we compare the results with those of the conventional method. The results demonstrate that the Hessian-based method has a remarkable ability to mitigate the blurring effect of the Hessian matrix caused by limited DAS source-receiver geometry with poor azimuthal coverage, thereby reducing the uncertainty of the inverted source mechanism even in the presence of real noise and/or source location error. Finally, we use our method to invert the source mechanism of a real DAS microseismic event acquired during hydraulic fracturing. Our Hessian-based method provides low uncertainty in the source mechanism inversion of the real DAS microseismic data.

Analysis and suppression of magnetic noise of cylindrical nanocrystalline shield in atomic sensors

Magnetic field interference poses a major challenge to the proper functioning of electronic equipment, electronic instruments and ultra-sensitive sensors, so magnetic field shield is a vital component. Particularly for ultra-sensitive atomic sensors, there is a pressing need for magnetic shields with superior performance and reduced magnetic noise. This paper introduces the application of nanocrystalline alloy magnetic shields in ultra-sensitive atomic sensors. We have analyzed the calculation of the longitudinal and transverse magnetic noise of nanocrystalline magnetic shields using a loss separation based approach and establish a magnetic noise model for nanocrystalline alloy magnetic shields. Meanwhile, we investigate the impact of structural parameters on longitudinal and transverse magnetic noise to effectively minimize magnetic noise in the shield. Additionally, we present an error model for magnetic noise, providing valuable theoretical support for mitigating magnetic noise errors in K-Rb-21Ne SERF co-magnetometers. This study contributes to the advancement of magnetic shield technology for ultra-sensitive atomic sensors, improving their overall performance and accuracy.

Effective uncertain fault diagnosis technique for wind conversion systems using improved ensemble learning algorithm

This paper introduces a pioneering fault diagnosis technique termed Interval Ensemble Learning based on Sine Cosine Optimization Algorithm (IEL- SCOA), tailored to tackle uncertainties prevalent in wind energy conversion (WEC) systems. The approach unfolds in three integral phases. Firstly, the establishment of interval centers and ranges, employing upper and lower bounds, effectively manages the inherent uncertainties arising from noise and measurement errors intrinsic to the wind system. Subsequently, the dataset undergoes processing via the Sine-Cosine Optimization Algorithm (SCOA), enabling the extraction of the most pertinent attributes. The culmination of predictive precision and classification performance is achieved through the integration of the refined dataset into an ensemble learning paradigm, harmonizing bagging, boosting techniques, and an artificial neural network classifier. The principal aim of the IEL-SCOA approach is to discern the spectrum of operational conditions within WEC systems, encompassing a healthy mode alongside six distinct faulty modes. These anomalies, encompassing short circuits, open circuits, and wear-out incidents, are deliberately induced at diverse locations and facets of the system, notably the generator and grid sides. Empirical results underscore the robustness and efficiency of the proposed methodology, showcasing an exceptional accuracy rate of 99.76 %. These outcomes definitively establish the IEL-SCOA approach as a potent and efficacious tool for precise fault diagnosis in uncertain WEC systems.

Enhanced defect detection in NDE using registration aided heterogeneous data fusion

The degradation of systems in service, including pipelines, over time highlights the critical need for reliable and accurate defect detection to ensure safe operations. However, single modality-based Nondestructive Evaluation (NDE) data used in practical applications often suffers from noise contamination and errors caused by various factors like lift-off/standoff distances, probe drift, scanning speed, variation in data acquisition rates, and poor sensor sensitivity. The presence of agnostic noise types poses a challenge in extracting defect signals. To address these challenges, this paper presents an automated NDE theory-based data fusion framework aimed at enhancing the detection of surface and near-surface defects in magnetizable and conductive specimens. The Magnetic Flux Leakage (MFL) and Eddy Current (EC) based NDE sensing methods demonstrate the highly heterogeneous nature of noise distributions. Given the heterogeneity of the inspection methods, a screening rule is proposed to determine the conditions under which fusion would be beneficial. An important aspect of the proposed fusion method is registration, which ensures accurate alignment of multi-sensor image data. Two registration methods are proposed in this study as performing blind fusion without registration leads to erroneous results. The first registration method is translational, whereas the second method is registration based on linear optimal transport (OT) which proves to be effective in the boundary conditions. Finally, the registered source images from the EC and MFL modalities are fused using pixel-based fusion algorithms, including transform domain and spatial domain-based methods. Qualitative and quantitative assessments demonstrate that the registered fusion results exhibit higher accuracy and reliability compared to unregistered fused results and source images. Although the fusion method is applied to MFL and EC data in this paper, it is also suitable for other NDE modalities.

Precision of sinewave amplitude estimation in the presence of additive noise and quantization error

Abstract This research paper delves into a comprehensive investigation concerning the impact of additive noise and quantization error on the precision of amplitude, offset, and phase estimates of a sine wave fitted to a set of data points acquired by a waveform digitizer. Simulation results are used to validate the expressions presented.

A Methodology for Predicting Improved Dipole Source Configurations From Near-Field Scan Data

Equivalent sources found from near-electric and magnetic field scans are often used to predict and solve interference problems. While there are many ways to represent the source, the user lacks the ability to determine which of the many possible source configurations is more likely to represent the “true” source, and thus accurately represent field data outside the measurement area and in the presence of typical measurement errors. A methodology is proposed for estimating which of many possible dipole source representations of near-field scan data are likely to give better estimates of fields outside the measurement scan plane when utilizing imperfect measurement data. A quality metric for determining better configurations is proposed, which utilizes the statistical variation of the global difference measure (GDM) in the predicted and measured fields. Equivalent sources are estimated when adding noise to measurement data, and prediction statistics are generated for multiple instantiations of measured noise. Results demonstrate that the better configurations minimize the average plus standard deviation of the GDM. The ability of the technique to identify robust source configurations was tested when measurements were subject to additive measurement noise, cross-field coupling, and systematic errors in probe position, and was evaluated based on its ability to predict fields at points above and to the side of the measurement plane. The method consistently identified better source configurations using both simulated and measured data.

Joint Moving Target and Antenna Localization for Distributed MIMO Radar With a Calibration Object

This paper investigates the problem of simultaneously locating the moving target and refining erroneous antenna parameters with a calibration object in distributed multiple-input multiple-out (MIMO) radars. Albeit traditional methods achieve moving target localization with antenna parameter errors, they still have some disadvantages, such as the high computational complexity of joint localization or the high noise sensitivity of estimating the target parameters. To overcome the drawbacks of existing algorithms, we first formulate a new localization model that joint target and antenna localization with a calibration object and then present an algebraic solution. Unlike previous work, the proposed estimator has additional unknowns of antenna parameter errors and presents new matrix equations by incorporating the calibration measurements. The proposed method is shown theoretically to attain the Cramer–Rao lower bound (CRLB) accuracy both in theory and simulations under small measurement noise and antenna error levels. Furthermore, simulation results show that the proposed method significantly improves the localization performance compared with existing methods.