Noise Sensitivity Analysis Research Articles

Physics Parameter Identification of Annular Tuned Liquid Damper for the Structure‐Damper‐Coupled System Using a Mechanics‐Enhanced SSI Method

Tuned liquid damper (TLD) is one of the main technologies for passive control. The fundamental modal parameters of a TLD contain natural frequency and damping ratio, which are primarily related to the liquid height in the TLD device. Although the liquid height of the TLD is calibrated before installation, it is still necessary to identify the physics parameters of the TLD and the main structure during the service period to prevent the detuning of the TLD. A physics parameter identification method for the structure‐damper‐coupled system based on mechanics‐enhanced stochastic subspace identification (SSI) is proposed in this paper, illustrated through annular TLD (ATLD). By extracting the state matrix of the first controlled mode of the main structure, the natural frequency and damping ratio of the ATLD are identified, thereby determining the liquid height of the ATLD. Numerical models of structure‐ATLD‐coupled systems with different degrees of freedom are constructed, and their simulated acceleration responses under different excitations are obtained. Sensitivity analysis of environmental noise is performed to verify the accuracy and robustness of the proposed parameter identification method. A dynamic test was designed for a steel chimney model to further verify the practicality of this method. The results show that when the noise level of the measurement noise is below 5.0%, the average relative error in identifying the ATLD liquid height does not exceed 10%. The identification error in the damping ratio of the structure‐ATLD‐coupled system will lead to a decrease in the accuracy of ATLD liquid height estimation. The proposed method can effectively identify changes in the ATLD liquid height within the optimal frequency ratio range by analyzing the experimental data from the chimney model. The proposed method can effectively estimate the modal parameters of the coupled system, providing reliable data support for evaluating the working condition of the ATLD during its service period.

Modal identification of wind turbine tower based on optimal fractional order statistical moments

AbstractIn vibration testing of civil engineering structures, the first two vibration modes are crucial in representing the global dynamic behavior of the structure measured. In the present study, a comprehensive method is proposed to identify the first two vibration modes of wind turbine towers, which is based on the analysis of fractional order statistical moments (FSM). This study offers novel contributions in two key aspects: (1) theoretical derivations of the relationship between FSM and vibration mode; and (2) successful use of 32/7‐order displacement statistical moment as the optimal FSM to identify wind turbine tower modes, by combining with noise resistance analysis, sensitivity analysis, and stability analysis, respectively. Using the proposed method, the FSM was first used to identify the modal vibration of wind turbine towers. By obtaining the response of the structure on the same vertical line, FSM was then calculated to estimate the corresponding structural modal vibration. Considering other influencing factors in the field test, the modal identification results of this index under different excitation forms and noise conditions were analyzed based on numerical simulation and verified with field wind tower test data. The results of the evaluation show that the proposed statistical moments of can accurately identify the first two vibration modes of wind turbine towers. This presents a new robust method for modal vibration identification, that is, simple and effective in its implementation.

Sensitivity analysis of landing and takeoff noise for conceptual low-boom supersonic aircraft using variable noise reduction system

This paper presents the results of an analysis of the sensitivity of the use of a variable noise reduction system (VNRS), considered in the new landing and takeoff (LTO) noise standard for supersonic aircraft, using a Japan Aerospace Exploration Agency (JAXA)-designed 66-ton twin-engine conceptual low-boom supersonic airliner as an example. The analysis was conducted using a tool developed by JAXA and examined the sensitivity of low-speed aerodynamic performance, the sensitivity of takeoff reference speed, the sensitivity of thrust control and flap control duration time ranges, and the sensitivity of noise level to takeoff profile uncertainty. The analyses showed that improving low-speed aerodynamic performance and increasing the takeoff reference speed significantly impact noise levels and the use of VNRS could reduce noise by 2.1 EPNLdB while maintaining the performance of the aircraft. Because the effective noise reduction method by changing the takeoff procedure considered differs for each noise measurement point, it is important to design a takeoff procedure that incorporates an appropriate VNRS. Uncertainty of the takeoff profile was also investigated using Monte Carlo simulations, and it was shown that noise reduction by VNRS is effective even with the assumed uncertainty in the takeoff profile.

Noise sensitivity analysis of focal scanning light field imaging.

Light field imaging can simultaneously record spatial and angular information of light signals to provide various computational imaging functions. However, traditional microlens array-based light field cameras usually suffer from a trade-off between spatial and angular resolutions. In contrast, focal scanning light field imaging (FSLFI) can digitally modulate an incident light field through an image stack captured at different focal planes and then utilize the transport-of-intensity property to computationally recover the full-resolution light field. This paper presents a unified light field reconstruction algorithm framework, which involves different types of algorithms, such as back-projection reconstruction and additive/multiplicative iterative reconstruction, for FSLFI. Based on the unified algorithm framework, we systematically analyze and investigate the FSLFI performance on noise sensitivity. Light fields are reconstructed at different noise levels to quantitatively analyze the FSLFI performances with different types of algorithms. Both simulation and actual experimental results demonstrate that the noise sensitivity and reconstruction accuracy are constrained by each other for FSLFI. Back-projection reconstruction is appropriate in high-efficiency light field reconstruction, while additive/multiplicative iterative reconstruction is suitable for high-accuracy light field imaging at high/low noise levels. These conclusions can apply to any FSLFI method covered by the unified algorithm framework, in which appropriate algorithms can be selected for high-quality light field imaging and measurement according to specific application scenarios.

Overview of the new capabilities in the Monte-Carlo particle-transport code NECP-MCX V2.0

NECP-MCX is a Monte-Carlo particle-transport code developed by the Nuclear Engineering Computational Physics (NECP) Lab. of Xi’an Jiaotong University in 2018. The first version of NECP-MCX is focused on addressing the challenge of deep-penetration radiation-shielding problems. In recent years, new capabilities of unstructured-mesh geometries, dose engine for Boron Neutron Capture Therapy (BNCT), neutron noise analysis, sensitivity analysis, depletion and activation calculation, simulation of randomly dispersed media, homogenization and gamma dose calculation based on point-kernel integral method are implemented in NECP-MCX V2.0. A user interface of NECP-MCX V2.0 is also under active development. This paper will introduce these new capabilities.

Nonlinear state feedback-synergetic control for low frequency oscillation suppression in grid-connected pumped storage-wind power interconnection system

Aiming at enhancing the performance of suppressing low frequency oscillation in grid-connected pumped storage-wind power interconnection system (PS-WPIS), the nonlinear state feedback-synergetic control (NSF-SGC) governor is studied. Firstly, the nonlinear mathematical models of grid-connected pumped storage power system (PSPS) and PS-WPIS are established. Then, the NSF-SGC strategy and macro variable determination method are proposed, and the NSF-SGC governor equation is derived. Furthermore, the action mechanism, regulation and damping characteristics of NSF-SGC governor in grid-connected PSPS and PS-WPIS are explored. Finally, the sensitivity and robustness of NSF-SGC governor are illuminated. The results show that, the proposed NSF-SGC strategy is superior to the proportional-integral, NSF and SGC strategies in regulation quality, and can significantly suppress low-frequency oscillation. The stability of grid-connected PSPS can be prominently improved by selecting smaller linear and time parameters, and a larger absolute value of nonlinear parameter of NSF-SGC governor. The NSF-SGC governor with dual macro variables enhances the damping characteristics of grid-connected PS-WPIS. Appropriate parameters of NSF-SGC governor can enhance the regulation performance of grid-connected PS-WPIS. Noise and parameter sensitivity analysis provide a guidance for improving system stability. The NSF-SGC governor has strong robustness under the variations of hydraulic, mechanical and electrical parameters.

Application of the complex differentiation method to the sensitivity analysis of aerodynamic noise

The complex differentiation method (CDM) is applied to the sensitivity analysis of the noise generated by two-dimensional mixing layers, simulated by Direct Numerical Simulation (DNS), in order to investigate its capabilities to highlight the effects of a key parameter on the aerodynamic noise. For this purpose, simulations are carried out using the CDM for different flow Mach numbers, Reynolds numbers and mesh spacings. In each case, the derivatives of the noise levels with respect to one of the three parameters are obtained using the CDM, implemented by adding a small imaginary perturbation to the parameter under study. In most cases, vortex pairings occur in the mixing layers and produce acoustic waves at a single frequency. The derivatives of the acoustic intensity obtained using the CDM show that the sound radiation is stronger and less directed downstream as the Mach number increases, in agreement with dimensional analyses. They also indicate that the radiation is more intense and directive as the Reynolds number increases. The magnitude of the derivatives of the acoustic intensity with respect to the mesh size decreases for finer meshes, showing that the grid sensitivity of the radiated noise is reduced for the latter meshes, as expected. In all cases, the derivatives obtained using the CDM are in good agreement with results from parametric studies. This suggests that the CDM can be used to describe the effects of physical parameters and of the grid resolution on the sound produced by a high-speed flow.

Deterministic and stochastic methods for sensitivity analysis of neutron noise

Neutron noise calculated from the neutron noise equation in the frequency domain is governed by the cross section and kinetic parameters. Deterministic and stochastic methods to obtain the sensitivity coefficient of neutron noise with respect to the abovementioned parameters are proposed. As a deterministic method, a diffusion equation for the first derivative of neutron noise with respect to a cross section or kinetic parameter is derived by differentiating the neutron noise diffusion equation. As a stochastic method, the differential operator sampling method, which is a well-established Monte Carlo technique, is applied to calculate the sensitivity coefficient. Neither method requires adjoint mode calculations and can be expanded to higher-order derivatives. Based on verifications performed in this study, it is discovered that these techniques yield accurate sensitivity coefficients. The methods developed in this study eliminates a large number of calculations that need to be performed in the random sampling method.

Noise sensitivity for the top eigenvector of a sparse random matrix

We investigate the noise sensitivity of the top eigenvector of a sparse random symmetric matrix. Let v be the top eigenvector of an N×N sparse random symmetric matrix with an average of d non-zero centered entries per row. We resample k randomly chosen entries of the matrix and obtain another realization of the random matrix with top eigenvector v[k]. Building on recent results on sparse random matrices and a noise sensitivity analysis previously developed for Wigner matrices, we prove that, if d≥N2∕9, with high probability, when k≪N5∕3, the vectors v and v[k] are almost collinear and, on the contrary, when k≫N5∕3, the vectors v and v[k] are almost orthogonal. A similar result holds for the eigenvector associated to the second largest eigenvalue of the adjacency matrix of an Erdős-Rényi random graph with average degree d≥N2∕9.

Gunshots Localization and Classification Model Based on Wind Noise Sensitivity Analysis Using Extreme Learning Machine

Gunshots Localization and Classification Model Based on Wind Noise Sensitivity Analysis Using Extreme Learning Machine

An inverse method for mechanical characterization of heterogeneous diseased arteries using intravascular imaging

The increasing prevalence of finite element (FE) simulations in the study of atherosclerosis has spawned numerous inverse FE methods for the mechanical characterization of diseased tissue in vivo. Current approaches are however limited to either homogenized or simplified material representations. This paper presents a novel method to account for tissue heterogeneity and material nonlinearity in the recovery of constitutive behavior using imaging data acquired at differing intravascular pressures by incorporating interfaces between various intra-plaque tissue types into the objective function definition. Method verification was performed in silico by recovering assigned material parameters from a pair of vessel geometries: one derived from coronary optical coherence tomography (OCT); one generated from in silico-based simulation. In repeated tests, the method consistently recovered 4 linear elastic (0.1 ± 0.1% error) and 8 nonlinear hyperelastic (3.3 ± 3.0% error) material parameters. Method robustness was also highlighted in noise sensitivity analysis, where linear elastic parameters were recovered with average errors of 1.3 ± 1.6% and 8.3 ± 10.5%, at 5% and 20% noise, respectively. Reproducibility was substantiated through the recovery of 9 material parameters in two more models, with mean errors of 3.0 ± 4.7%. The results highlight the potential of this new approach, enabling high-fidelity material parameter recovery for use in complex cardiovascular computational studies.

Distributed Probabilistic Fuzzy Rule Mining for Clinical Decision Making

INTRODUCTION: With the growing size, complexity, and distributivity of databases, efficiency and scalability have become highly desirable attributes of data mining algorithms in decision support systems. OBJECTIVES: This study aims for a computational framework for clinical decision support systems that can handle inconsistent dataset while also being interpretable and scalable. METHODS: This paper proposes a Distributed Probabilistic Fuzzy Rule Mining (DPFRM) algorithm that extracts probabilistic fuzzy rules from numerical data using a self-organizing multi-agent approach. This agent-based method provides better scalability and fewer rules through agent interactions and rule-sharing. RESULTS: The performance of the proposed approach is investigated on several UCI medical datasets. The DPFRM is also used for predicting the mortality rate of burn patients. Statistical analysis confirms that the DPFRM significantly improves burn mortality prediction by at least 3%. Also, the training time is improved by 17% if implemented by a parallel computer. However, this speedup decreases with increased distributivity, due to the added communication overhead. CONCLUSION: The proposed approach can improve the accuracy of decision making by better handling of inconsistencies within the datasets. Furthermore, noise sensitivity analysis demonstrates that the DPFRM deteriorates more robustly as the noise levels increase.

Seismic velocity analysis in the presence of amplitude variations using local semblance

ABSTRACTObtaining accurate velocity models plays a crucial role in many routine seismic imaging algorithms. Seismic velocity models are normally made through seismic velocity analysis workflows. The routine workflows are not capable of dealing with polarity variations across moveout curves. We address this limitation by proposing a straight‐forward and robust semblance‐based workflow, which is a modified version of the conventional semblance function. The coherency function applies semblance analysis on separate clusters of receivers followed by averaging the corresponding coherency measures from all the clusters. The proposed approach is suitable for any case of amplitude variations including attenuation and any class of amplitude‐versus‐offset effects. The ability of the proposed workflow is demonstrated to two synthetic data as well as two field‐recorded common‐midpoint gathers. We perform accuracy analysis by comparing the results from the proposed approach with the results achieved from conventional velocity analysis, and another semblance‐based algorithm that is developed to address the polarity variation task. We also studied noise sensitivity analysis by computing and comparing mathematical expectations between theory and practice.

Vibration-based detection of skin-stiffener debonding on composite stiffened panels using surrogate-assisted algorithms

Stiffened panel is a common application of fiber reinforced polymer (FRP) laminates in engineering industries, particularly those in mechanical and aerospace domains. However, laminated structures such as stiffened FRP panels are sensitive to impact and frequently suffer from invisible inter-laminar defects, for instance, in the bonding interface between the stiffener and the skin plate, which results in a considerable reduction of structural load capability and service lifetime. In this paper, a vibration-based approach is applied to assess skin-stiffener debonding on carbon fiber reinforced polymer (CFRP) stiffened panel using changes in natural frequencies. Three computational intelligence approaches, namely artificial neural network (ANN), genetic algorithm (GA) and Jaya algorithm, are employed as inverse algorithms for predicting the type, location and size of debonding damage. Validations of the proposed inverse algorithms are firstly conducted through numerical test cases. Thereafter, experimental modal testing is conducted using scanning laser Doppler vibrometer on CFRP stiffened panel specimens, and the measured frequency changes are then fed into inverse algorithms for the experimental validation. The performances of three inverse algorithms in prediction accuracy, running speed and reliability are compared. Lastly, a noise sensitivity analysis is conducted for evaluating the robustness of algorithms’ performance for this application.

Effect of dielectric pocket for controlling ambipolar conduction in TFET and analysis of noise and temperature sensitivity

An n+ pocket-doped silicon on insulator (SOI) tunnel field effect transistor (TFET) along with a dielectric pocket (DP) at channel drain junction has been proposed and investigated in this article. The merged impact of both lateral and vertical tunneling due to n+ pocket in the gate–source overlap region enhances the ON current and provides steeper subthreshold swing (SS). The dielectric pocket at channel drain junction depletes the drain region at channel drain interface. Consequently, the minimum tunneling width at channel drain interface is enhanced to offer significant suppression of ambipolar conduction in a TFET. The proposed TFET structure offers a high ON/OFF current ratio of 1.57 × 1010 and considerably low SS of 8 mV/dec along with reduced ambipolar conduction up to a larger negative bias region. The impact of parametric variation of the proposed structure is studied and optimized accordingly. Noise characteristics of the proposed SOI TFET are investigated to realize the reliability issues of the device. Besides, the impact of elevated temperature on transfer characteristic and various RF parameters including transconductance (gm), total gate capacitance (Cgg), gate to drain capacitance (Cgd), cutoff frequency (ft), gain bandwidth product (GBP) and intrinsic delay (τ), respectively, have been investigated. The device performance has been upgraded by the rise in cutoff frequency and drop in intrinsic delay at high temperature.

Application of Machine Learning Techniques in Mineral Classification for Scanning Electron Microscopy - Energy Dispersive X-Ray Spectroscopy (SEM-EDS) Images

Mineral classification and segmentation is time-consuming in geological image processing. The development of machine learning methods shows promise as a technique in replacing manual classification. In this study, performances of five shallow machine classification algorithms and a deep learning algorithm were compared for the goal of pixel-level mineral classification of Scanning Electron Microscopy - Energy Dispersive X-Ray Spectroscopy (SEM-EDS) images. Five machine learning models, including Logistic Regression (LR), Linear Support Vector Machine (SVM), k-Nearest Neighbor (k-NN), Random Forest (RF), and Artificial Neuron Networks (ANN), and a deep learning CNN U-Net model were used in this study. Thirteen mineral phases were classified on SEM-EDS images of a shale sample taken from the Bakken Formation. Hyperparameters of models were tuned using a grid-search method. Randomly selected balanced dataset was used for the shallow models, while the original cropped images were used for the U-Net.The experimental results showed that all classification algorithms resulted in high F1 scores ranging from 0.86 to 0.92. The RF demonstrated the best performance among the five machine learning models, with an F1 score of 0.92. Additionally, sensitivity analysis on the size of dataset demonstrated that the LR algorithm and the SVM were less sensitive to the dataset reduction, while the models of k-NN, RF, and ANN were more influenced. Sensitivity analysis of noise suggested that noises added on the element of Silicon, Aluminum, Magnesium, Calcium, Potassium, and Iron would decrease the performance of the RF. Furthermore, the noise in Silicon had the greatest effect on the prediction result compared to the other minerals. In addition, those non-linear classifiers showed a larger performance score drop when the noise was simultaneously included into all the elements density. Though U-Net shows poor performance on the segmentation of minority classes due to the negative effect of imbalanced dataset the U-Net model still outperformed the RF model when it comes to unseen shale samples.

Homogenization of a stochastically forced Hamilton-Jacobi equation

We study the homogenization of a Hamilton-Jacobi equation forced by rapidly oscillating noise that is colored in space and white in time. It is shown that the homogenized equation is deterministic, and, in general, the noise has an enhancement effect, for which we provide a quantitative estimate. As an application, we perform a noise sensitivity analysis for Hamilton-Jacobi equations forced by a noise term with small amplitude, and identify the scaling at which the macroscopic enhancement effect is felt. The results depend on new, probabilistic estimates for the large scale Hölder regularity of the solutions, which are of independent interest.

Noise-sensitivity Analysis and Improvement of Automatic Retrieval of Temperature and Emissivity Using Spectral Smoothness

There are numerous algorithms that can be used to retrieve land surface temperature (LST) and land surface emissivity (LSE) from hyperspectral thermal infrared (HTIR) data. The algorithms are sensitive to a number of factors, where noise is difficult to handle due to its unpredictability. Although there is a lot of research regarding the influence of noise on retrieval errors, few studies have focused on the mechanism. In this study, we selected the automatic retrieval of temperature and emissivity using spectral smoothness (ARTEMISS) algorithm—the representative of the iterative spectral smoothness temperature-emissivity separation algorithm family—as the research object and proposed an improved algorithm. First, we analyzed the influence mechanism of noise on the retrieval errors of ARTEMISS in theory. Second, we carried out a simulation and inversion experiment and analyzed the relationship between instrument spectral resolution, noise level, the ARTEMISS parameter setting and the retrieval errors separately. Last, we proposed an improved method (resolution-degrade-based spectral smoothness algorithm, RDSS) based on the mechanism and law of the influence of noise on retrieval errors and provided corresponding suggestions on instrument design. The results show that RDSS improves the accuracy of temperature inversion and is more effective for thermal infrared data with a high noise level and high spectral resolution, which can reduce the LST inversion error by up to 0.75 K and the LSE median absolute deviation (MAD) by 31%. In the presence of noise in HTIR data, the RDSS algorithm performs better than the ARTEMISS algorithm in terms of temperature-emissivity separation.

Bargmann-Fock percolation is noise sensitive

We show that planar Bargmann-Fock percolation is noise sensitive under the Ornstein-Ulhenbeck process. The proof is based on the randomized algorithm approach introduced by Schramm and Steif ([30]) and gives quantitative polynomial bounds on the noise sensitivity of crossing events for Bargmann-Fock. A rather counter-intuitive consequence is as follows. Let $F$ be a Bargmann-Fock Gaussian field in $\mathbb {R}^{3}$ and consider two horizontal planes $P_{1},P_{2}$ at small distance $\varepsilon $ from each other. Even though $F$ is a.s. analytic, the above noise sensitivity statement implies that the full restriction of $F$ to $P_{1}$ (i.e. $F_{| P_{1}}$) gives almost no information on the percolation configuration induced by $F_{|P_{2}}$. As an application of this noise sensitivity analysis, we provide a Schramm-Steif based proof that the near-critical window of level line percolation around $\ell _{c}=0$ is polynomially small. This new approach extends earlier sharp threshold results to a larger family of planar Gaussian fields.

Tracer kinetic models as temporal constraints during brain tumor DCE-MRI reconstruction.

PurposeTo apply tracer kinetic models as temporal constraints during reconstruction of under‐sampled brain tumor dynamic contrast enhanced (DCE) magnetic resonance imaging (MRI).MethodsA library of concentration vs time profiles is simulated for a range of physiological kinetic parameters. The library is reduced to a dictionary of temporal bases, where each profile is approximated by a sparse linear combination of the bases. Image reconstruction is formulated as estimation of concentration profiles and sparse model coefficients with a fixed sparsity level. Simulations are performed to evaluate modeling error, and error statistics in kinetic parameter estimation in presence of noise. Retrospective under‐sampling experiments are performed on a brain tumor DCE digital reference object (DRO), and 12 brain tumor in‐vivo 3T datasets. The performances of the proposed under‐sampled reconstruction scheme and an existing compressed sensing‐based temporal finite‐difference (tFD) under‐sampled reconstruction were compared against the fully sampled inverse Fourier Transform‐based reconstruction.ResultsSimulations demonstrate that sparsity levels of 2 and 3 model the library profiles from the Patlak and extended Tofts‐Kety (ETK) models, respectively. Noise sensitivity analysis showed equivalent kinetic parameter estimation error statistics from noisy concentration profiles, and model approximated profiles. DRO‐based experiments showed good fidelity in recovery of kinetic maps from 20‐fold under‐sampled data. In‐vivo experiments demonstrated reduced bias and uncertainty in kinetic mapping with the proposed approach compared to tFD at under‐sampled reduction factors >= 20.ConclusionsTracer kinetic models can be applied as temporal constraints during brain tumor DCE‐MRI reconstruction. The proposed under‐sampled scheme resulted in model parameter estimates less biased with respect to conventional fully sampled DCE MRI reconstructions and parameter estimation. The approach is flexible, can use nonlinear kinetic models, and does not require tuning of regularization parameters.