Noise Uncertainty Research Articles

Global reliability sensitivity analysis of cradle-type double rotary table

The rotary table is a crucial component of the machine tool, and its accuracy and reliability have an important impact on the machining accuracy and machining efficiency of the machine tool. In this paper, a global reliability sensitivity analysis method of the rotary table is proposed considering the random uncertainty for material parameters of each component. Firstly, the mechanical model of the rotary table is established based on the finite element method. Subsequently, the functional relationship between the parameters of each component and the deformation of the rotary table is reconstructed using the adaptive Kriging surrogate model. Finally, a global reliability sensitivity analysis of the rotary table is performed based on the Bayesian formulation. The results show that the proposed global reliability sensitivity analysis method of the rotary table demonstrates satisfactory efficiency and can provide theoretical guidance for the design and optimization of the rotary table.

TCP-UQ: two-color pyrometry uncertainty quantification using high speed color cameras

Abstract In this study, an uncertainty quantification method is developed for two-color pyrometry (TCP-UQ), using high-speed color cameras. A NIST traceable calibrated light source is used and the relative exposure is determined using spectrometer measurements and color camera response functions. The inherent digitization and noise uncertainty is estimated from a set of calibration images and characterized with probability distribution functions. These uncertainties are then propagated during TCP to two quantities of interest: the temperature and soot volume fraction measurements. TCP-UQ is applied to a polymethyl methacrylate slab burner experiment to determine the temperature and the soot volume fraction fields. Temperature measurements ranged from 2300 K to 2800 K , soot volume fractions are recorded between 0.5 and 10 ppm and are in good agreement with prior measurements reported in the literature. The newly developed TCP-UQ allows for uncertainty estimations in the temperature and the soot volume fraction measurements to be associated with variations in the pixel intensities. For the slab burner experiment, the average uncertainty in the temperature is between 120–140 K and the average uncertainty in the soot volume fraction is around 0.1 ppm. Generally, higher pixel intensities corresponded to lower uncertainty in the temperature measurement.

Atmospheric propane (C3H8) column retrievals from ground-based FTIR observations in Xianghe, China

Abstract. Propane (C3H8) is an important trace gas in the atmosphere, as it is a proxy for oil and gas production and has a significant impact on atmospheric chemical reactions related to the hydroxyl radical and tropospheric ozone formation. In this study, solar direct absorption spectra near 2967 cm−1 recorded by a ground-based Fourier transform infrared spectrometer (FTIR) were applied to retrieve C3H8 total columns between June 2018 and July 2022 in Xianghe in north China. The systematic and random uncertainties of the C3H8 column retrieval are estimated to be 18.4 % and 18.1 %, respectively. The mean and standard deviation of the C3H8 columns derived from the FTIR spectra in Xianghe are 1.80 ± 0.81 (1σ) × 1015 molec. cm−2. Good correlations are found between C3H8 and other non-methane hydrocarbons, such as C2H6 (R=0.84) and C2H2 (R=0.79), as well as between C3H8 and CO (R=0.72). However, the correlation between C3H8 and CH4 is relatively weak (R=0.45). Moreover, the FTIR C3H8 measurements in Xianghe are also compared against MkIV measurements at several sites around the world. The new FTIR measurements in Xianghe provide us with insight into C3H8 column variations and the underlying processes in north China.

Demand management and pricing in renewable integrated plug-in electric vehicle charging station using reinforcement learning

ABSTRACT The increasing adaption of Plug-in Electric Vehicles (PEVs) has increased the power demand for charging. Renewable generation can significantly help to meet this demand. The uncertainty of renewable generation and randomness in PEV charging makes the demand management of charging stations challenging. To address these challenges, this study proposes a novel demand management pricing strategy for renewable integrated charging stations. The proposed strategy uses reinforcement learning for charging coordination of PEVs. The renewable generation is estimated using weather data and accordingly PEVs are scheduled for demand management. The strategy decides the PEV power price based on real-time price, renewable tariff and PEV load. The strategy uses the bidirectional power flow with battery degradation cost. The distribution transformer operating cost and loading constraints are included to resemble the real-time environment. The PEV randomness and uncertainty of renewable generations are incorporated in the study. The results of the numerical case study show that the proposed strategy can manage the charging station load efficiently. The proposed strategy has optimized the cost of charging, discharging and charging station profit and increased the service capability of the charging station.

A review of deep learning techniques for enhancing spectrum sensing and prediction in cognitive radio systems: approaches, datasets, and challenges

Cognitive radio (CR) is an emerging wireless technology designed to optimize frequency band usage and address spectrum shortages. Spectrum sensing and prediction are crucial for cognitive radios to make intelligent spectrum access decisions and dynamically alter transmission parameters based on real-time spectrum conditions. These techniques contribute to the efficient use of the spectrum. However, they encounter significant obstacles such as noise uncertainty, low signal-to-noise ratios, channel fading, and more, necessitating robust and intelligent solutions. Deep learning has attracted much attention and displayed great potential in various fields in recent years. This review paper provides a thorough examination of the use of deep learning techniques in spectrum sensing and prediction in the context of cognitive radio. It examines the available literature in depth, highlighting the techniques, the assessment keys, datasets, and limitations of the investigations. The article seeks to provide significant insights and guidance for future developments in harnessing deep learning for better cognitive radio spectrum management by critically assessing the present state of research.

GNSS-assisted optimal alignment method for low-cost SINS motion of vehicle

Abstract To improve the alignment accuracy and environmental applicability of the micro-electromechanical system (MEMS)-based strapdown inertial navigation system (SINS), this study proposes a global navigation satellite system (GNSS)-assisted multi-vector determination of the attitude optimal indirect coarse alignment. Using the inertial information output from the inertial measurement unit and the velocity information from the GNSS, a simplified velocity observation vector is constructed and the velocity lever arm stemming from the mounting position inconsistencies of the GNSS and SINS is fed back into the velocity of the carrier system. When constructing the observation vectors, the integration interval is shortened by reconstructing the two-vector integration formula for reducing the cumulative error of the inertial device. The attitude matrix problem is defined as the Wahba problem, which is solved using the singular value decomposition method. Based on the relationship between gyro zero bias and misalignment angle, the corresponding state and measurement equations are designed. Furthermore, owing to the measurement noise uncertainty, the adaptive traceless Kalman filtering algorithm is introduced to realize the effective adaptation processing of the measurement noise. More accurate attitude matrix estimates are obtained by continuously correcting the carrier system transformation matrix. The running car experiment results show that the proposed method exhibits higher alignment accuracy and environmental applicability than the current MEMS strapdown inertial navigation coarse alignment method and the traditional optimization-based alignment method.

Low-uncertainty wave tank testing and validation of numerical methods for floating offshore wind turbines

Abstract. Accurate simulation of the loads and motions of floating offshore wind turbines (FOWTs) in operation is key to the commercialisation of this technology. To improve such load predictions, a critical assessment of the capabilities and limitations of simulation methods for FOWTs is mandatory. However, uncertainties arise during the whole validation process of a numerical method. These can drastically impair the quality of the validation. In the case of FOWTs, the interaction between aerodynamic, hydrodynamic and mooring loads on the one hand and platform motions on the other hand causes a high level of uncertainty in the measurement data acquired in model tests. This also applies to comparing a numerical model to the test data, as these interactions make the distinction between cause and effect challenging. To address these challenges, several improvements to the validation process aiming to reduce the uncertainties are proposed and evaluated in this work. The major improvements are the measurement of the rotor thrust force excluding the tower top inertia loads, the wind field quality in the wave tank, a comparison of the rotor aerodynamics in the wind tunnel and wave tank, and the utilisation of hybrid simulations based on the measured platform motions. These steps are applied to wave tank tests of a FOWT utilising a single-point mooring and the subsequent validation of the numerical panel method panMARE. The improvements allowed for a considerable decrease in the random and systematic uncertainty in the model tests and made a valuable contribution to the distinction between cause and effect regarding the deviations between measurements and simulations.

Vehicle posture wheelbase preview control of in-wheel motors drive intelligent vehicle on potholed road

The in-wheel motors drive intelligent vehicle (IMDIV) has a strong ability of dynamic control, which makes it be a major development path of intelligent vehicles in the future. However, its stability is easily deteriorated by the impact of the road when driving on potholed road. To tackle this issue, a vehicle posture control method based on road feedback and elevation recognition is proposed. Firstly, an elevation recognition algorithm of adaptive Kalman filter (AKF) based on vehicle dynamic response is proposed after considering the flooded road and the system noise uncertainty. Secondly, a vehicle posture controller of fuzzy active disturbance rejection control (FADRC) is designed, with the function of dynamically adjusting the active disturbance rejection control (ADRC) parameters. It is designed to implement front suspension feedback control and rear suspension wheelbase preview control. At last, the road elevation recognition algorithm and vehicle posture control method are proved by Simulink simulation and off-line hardware in the loop test. The results revealed that the AKF can effectively recognize both white noise pavement and potholed road, and the recognition accuracy is greater than 90%. The FADRC controller realizes accurate front suspension feedback control and rear suspension wheelbase preview control by adjusting the real-time online parameters of ADRC. The proposed vehicle posture control method can effectively control the pitch and roll motion, which significantly improves the stability of IMDIV on potholed road. This study can serve as a reference for the posture stability control of IMDIV on potholed road.

Adaptive performance optimal control for flexible-joint robots with random noises: Design and experiment

This study developes a flexible performance optimal control scheme via reinforcement learning strategy and event-triggered mechanism for flexible-joint robots with random noise and non-affine input. It is notable that an event-triggered optimization mechanism is developed, which meets the optimality principle and saves communication resources. Nevertheless, the existing event-triggered strategy is unable to handle non-affine input, which limits the applicability of this method. To overcome the above problems, a modified event-triggered mechanism is proposed. At the same time, the optimal solution of the system is given by an optimized control algorithm based on the improved performance index function. In the controller design, neural network is used to deal with random disturbances and uncertainties, and an adaptive law is designed to replace the identifier structure. Besides, a flexible prescribed performance function is constructed to yield multiple prescribed performance behaviors by adjusting the key parameters, while the tracking error is stayed within a prescribed boundary. Finally, the effectiveness of the proposed control scheme is further demonstrated by simulation and the experiment of the 2-link flexible-joint robot on the Quanser platform.

Exploring Gene Expression Biclustering with Integrated PSO-SA-Fuzzy Logic Methodology and its Application to Air Pollution Analysis

<p style="line-height:150%;margin-bottom:10.0pt;text-align:justify;"><span style="font-family:"Times New Roman",serif;font-size:12.0pt;line-height:150%;" lang="EN-US">Gene expression data analysis is crucial for understanding complex biological mechanisms, yet current biclustering techniques struggle with noise, unpredictability, and intrinsic uncertainty. This study proposes an innovative biclustering method combining particle swarm optimization (PSO), simulated annealing (SA), and fuzzy logic to improve gene expression analysis. By integrating natural language processing (NLP) semantic similarity into the PSO framework, the method enhances the capture of intricate gene interactions. Additionally, environmental factors, particularly air pollution, are incorporated to explore their impact on gene expression patterns. The approach leverages the complementary strengths of PSO's exploration capabilities, SA's exploitation efficiency, and fuzzy logic's ability to handle data ambiguity. Comparative assessments on benchmark datasets reveal that this integrated strategy significantly outperforms individual methods in accuracy and resilience. The results demonstrate the PSO-SA-Fuzzy Logic method's superior capacity to detect nuanced and context-dependent gene expression patterns, offering a robust solution to the limitations of existing biclustering techniques. The method not only improves precision and computational efficiency but also enhances the detection of significant gene expression patterns under varying conditions, including environmental stressors. This advancement represents a notable contribution to computational biology, providing a more effective tool for gene expression data analysis.</span></p>

Robust H∞ Control for Autonomous Underwater Vehicle’s Time-Varying Delay Systems under Unknown Random Parameter Uncertainties and Cyber-Attacks

This paper investigates robust H∞-based control for autonomous underwater vehicle (AUV) systems under time-varying delay, model uncertainties, and cyber-attacks. Sensor and actuator cyber-attacks can cause faults in the overall AUV system. In addition, the behavior of the system can be affected by the presence of complexities, such as unknown random uncertainties that occur in system modeling. In this paper, the robustness against unpredictable random uncertainties is investigated by considering unknown but norm-bounded (UBB) random uncertainties. By constructing a proper Lyapunov–Krasovskii functional (LKF) and using linear matrix inequality (LMI) techniques, new stability criteria in the form of LMIs are derived such that the AUV system is stable. Moreover, this work is novel in addressing robust H∞ control, which considers time-varying delay, cyber-attacks, and randomly occurring uncertainties for AUV systems. Finally, the effectiveness of the proposed results is demonstrated through two examples and their computer simulations.

Bayesian Updating Method Under Random and Interval Hybrid Uncertainties

Bayesian updating is an effective method for refining the random distribution model of the system when new observational information is gathered. To deal with the problem with multisource uncertainty, a Bayesian updating method under random and interval hybrid uncertainties is originally established in this paper. In the proposed method, the likelihood function is firstly built by considering the error of the observation and the hybrid uncertainties of the model prediction. Then, the posterior information of the random model inputs can be refined based on the defined likelihood function and the prior information. Finally, to efficiently obtain the posterior information of the random model inputs, an adaptive kriging method is developed. In this method, different learning functions are employed to accurately predict the extrema and classify the candidate samples, respectively. The significant novelty of the proposed adaptive kriging method is to design a posterior statistical information-oriented stop criterion. Four computational examples are used to illustrate the feasibility and rationality of the proposed Bayesian updating method.

Distribution-free prediction intervals with conformal prediction for acoustical estimation.

Acoustical parameter estimation is a routine task in many domains. The performance of existing estimation methods is affected by external uncertainty, yet the methods provide no measure of confidence in the estimates. Hence, it is crucial to quantify estimate uncertainty before real-world deployment. Conformal prediction (CP) generates statistically valid prediction intervals for any estimation model using calibration data; a limitation is that calibration data needed by CP must come from the same distribution as the test-time data. In this work, we propose to use CP to obtain statistically valid uncertainty intervals for acoustical parameter estimation using a data-driven model or an analytical model without training data. We consider direction-of-arrival estimation and localization of sources. The performance is validated on plane wave data with different sources of uncertainty, including ambient noise, interference, and sensor location uncertainty. The application of CP for data-driven and traditional propagation models is demonstrated. Results show that CP can be used for statistically valid uncertainty quantification with proper calibration data.

Influence of Noise Uncertainty Source and Model on the SNR Wall of Energy Detection

Influence of Noise Uncertainty Source and Model on the SNR Wall of Energy Detection

Stochastic analysis of combined primary-secondary structures subjected to imprecise seismic excitation

The imprecise stochastic process model is able to incorporate both random and epistemic uncertainties, leading to a more accurate description of environmental excitations, such as earthquakes or wind loads, when limited data are available. Epistemic uncertainties in the loading model may significantly affect the performance of structural systems.The present paper addresses the stochastic analysis of combined primary-secondary structures subjected to imprecise seismic excitation modeled as a zero-mean stationary Gaussian random process, characterized by an interval-valued Power Spectral Density (PSD) function. The power and energy content of the imprecise ground motion acceleration may vary, even for the same soil category, affecting the complex dynamic behavior of combined structures and the vibration control capacity of secondary substructures under different frequency tuning conditions. The main purpose of this study is to develop a framework for investigating both the influence of epistemic uncertainties in the loading model and the interaction effects between the subsystems on the seismic performance of combined primary-secondary structures. To this aim, the stochastic analysis of combined structures under imprecise ground motion acceleration is addressed in both the frequency and time domains by an efficient approach capable of decoupling the propagation of interval and random uncertainties. Seismic safety assessment is performed in the framework of the first-passage theory by interval extension.

Multidisciplinary design optimization of an autonomous underwater vehicles based on random uncertainty

Herein, discipline decomposition was performed for an autonomous underwater vehicle (AUV) based on multidisciplinary design optimization (MDO), and its design parameters were determined. Parameterized analysis of the hull-form and structure disciplines was performed, and an approximate model of these disciplines was established with a high fitting accuracy. An analysis model of propulsion, energy, and system disciplines was developed based on the formula method. Based on collaborative optimization as well as discipline and system level models, a deterministic MDO framework for AUVs was established. Subsequently, an optimized solution was obtained. By considering the random uncertainty of design variables, an MDO framework for the uncertainty of AUVs was established and optimized solutions were obtained. The distribution of solutions obtained from uncertainty optimization was more concentrated and the distribution range was smaller than that obtained using deterministic optimization. Robustness analysis was performed on the initial scheme, typical deterministic optimization schemes, and typical uncertainty optimization schemes. Results showed that fluctuations in design variables may lead to constraints that exceed boundary conditions in the deterministic optimization design scheme. Using uncertainty and objective function optimization, the robustness of the overall scheme of AUVs was improved.

Airborne Toxicity in Don DeLillo’s White Noise

Abstract In the wake of the COVID-19 pandemic, people across the world came to realize the significant relationship between air and human health. This pandemic, which changed the course of many lives, demonstrated how air serves as a transmitter of viruses. However, this quality of air is not new, with air acting as a significant tool in transmitting diseases, pollution, and even death. It is crucial to understand that airborne diseases include but are not limited to epidemics or pandemics such as the black death, influenza, or COVID-19. Since the Chernobyl disaster, it is perceived that the previously feared disasters were replaced by new and human-made hazards such as toxicity and radioactivity. Environmental disasters such as the Bhopal disaster, Donora Smog, and the Chernobyl disaster emphasize the impact of toxic chemicals on humans and more-than-human lives. These disasters show that toxic substances that threaten the lives of all living things imperceptibly seep into the soil, water, and air, causing harm to ecosystems, and entering into human and more-than-human bodies. Exposure to toxicity and radioactivity can happen in the blink of an eye, transmitted through the air we breathe. Don DeLillo’s novel White Noise presents a significant example of toxicity through its striking portrayal of an airborne toxic event. This event, the appearance of a cloud of the fictional chemical Nyodene D., presents an environmental crisis through which relationships between air and environments can be explored. Similar to the issues and reflections experienced after the emergence of the COVID-19 pandemic, the characters in White Noise experience chaos, uncertainty, and fear following the abrupt occurrence of an airborne toxic event.

Estimation of the error in determining the motion parameters of maneuvering objects

The study contains the definition of errors in estimating the motion parameters of maneuvering objects that arise due to the discrepancy between the real model and the model used to build the filter. In the real model, along with noise and stochastic uncertainties, non-stochastic uncertainties act. The solution to the problem of estimation errors is based on the use of convex polyhedra. The statement of the research problem is given. It is shown that since the observer uses the motion model and the measurement equation, the source of estimation errors is not only the above, but also the fact that along with the random component of the measurement noise, there is also a non-stochastic component that is not taken into account by the observer. At the same time, the statistical characteristics of the latter do not exist or are unknown. It is shown that the use of a filter, for example, Kalman, will lead to errors. The conditions that determine the average filtering error and the total standard error of the estimation are described. It is noted that the obtained calculation results allow us to estimate the maximum permissible values used in the design of the filter for the most unfavorable case of evaluation. The relationship between the filtration quality and the parameters of the effects is determined. Expressions are obtained regarding the filtration quality for the worst and the best for object maintenance. The asymptotic properties of filtering errors are considered. It is shown that the "convergence" of the filter means its final "memory", since errors at the next step do not depend on errors from all previous steps, but only on steps from a certain value. The conditions under which the potential accuracy of the filter is achieved are also determined. The solution of extreme problems is described. You can determine the maximum filtering quality by iterating over the values on a set of corner points or using extremum conditions. Numerical simulation of extreme values of filtering errors is given. The asymptotic properties of filtering errors are considered. It is shown that the "convergence" of the filter means its final "memory", since errors at the next step do not depend on errors from all previous steps, but only on steps from a certain value. The conditions under which the potential accuracy of the filter is achieved are also determined. The solution of extreme problems is described. You can determine the maximum filtering quality by iterating over the values on a set of corner points or using extremum conditions. Numerical simulation of extreme values of filtering errors is given. Expressions for the set of average values of filtering errors are found, asymptotic and extreme properties of errors and sets are considered. It is shown that the conducted analysis of the accuracy of the filtering algorithm is evaluative in nature and is designed for the worst case. If the accuracy is not satisfactory, it is necessary to either change the model or synthesize a minimax-stochastic filter.

Random and systematic uncertainty in ship‐based seawater carbonate chemistry observations

AbstractSeawater carbonate chemistry observations are increasingly necessary to study a broad array of oceanographic challenges such as ocean acidification, carbon inventory tracking, and assessment of marine carbon dioxide removal strategies. The uncertainty in a seawater carbonate chemistry observation comes from unknown random variations and systematic offsets. Here, we estimate the magnitudes of these random and systematic components of uncertainty for the discrete open‐ocean carbonate chemistry measurements in the Global Ocean Data Analysis Project 2022 update (GLODAPv2.2022). We use both an uncertainty propagation approach and a carbonate chemistry measurement “inter‐consistency” approach that quantifies the disagreement between measured carbonate chemistry variables and calculations of the same variables from other carbonate chemistry measurements. Our inter‐consistency analysis reveals that the seawater carbonate chemistry measurement community has collected and released data with a random uncertainty that averages about 1.7 times the uncertainty estimated by propagating the desired “climate‐quality” random uncertainties. However, we obtain differing random uncertainty estimates for subsets of the available data, with some subsets seemingly meeting the climate‐quality criteria. We find that seawater pH measurements on the total scale do not meet the climate‐quality criteria, though the inter‐consistency of these measurements improves (by 38%) when limited to the subset of measurements made using purified indicator dyes. We show that GLODAPv2 adjustments improve inter‐consistency for some subsets of the measurements while worsening it for others. Finally, we provide general guidance for quantifying the random uncertainty that applies for common combinations of measured and calculated values.

Detection of Internal Hemorrhage via Sequential Inference: An In Silico Feasibility Study.

This paper investigates the feasibility of detecting and estimating the rate of internal hemorrhage based on continuous noninvasive hematocrit measurement. A unique challenge in hematocrit-based hemorrhage detection is that hematocrit decreases in response to hemorrhage and resuscitation with fluids, which makes hemorrhage detection during resuscitation challenging. We developed two sequential inference algorithms for detection of internal hemorrhage based on the Luenberger observer and the extended Kalman filter. The sequential inference algorithms use fluid resuscitation dose and hematocrit measurement as inputs to generate signatures to enable detection of internal hemorrhage. In the case of the extended Kalman filter, the signature is nothing but inferred hemorrhage rate, which allows it to also estimate internal hemorrhage rate. We evaluated the proof-of-concept of these algorithms based on in silico evaluation in 100 virtual patients subject to diverse hemorrhage and resuscitation rates. The results showed that the sequential inference algorithms outperformed naïve internal hemorrhage detection based on the decrease in hematocrit when hematocrit noise level was 1% (average F1 score: Luenberger observer 0.80; extended Kalman filter 0.76; hematocrit 0.59). Relative to the Luenberger observer, the extended Kalman filter demonstrated comparable internal hemorrhage detection performance and superior accuracy in estimating the hemorrhage rate. The analysis of the dependence of the sequential inference algorithms on measurement noise and plant parametric uncertainty showed that small (≤1%) hematocrit noise level and personalization of sequential inference algorithms may enable continuous noninvasive detection of internal hemorrhage and estimation of its rate.