Sensor Saturation Research Articles

Event-based nonfragile state estimation for memristive neural networks with multiple time-delays and sensor saturations

This article investigates the issue of nonfragile state estimation (SE) for memristor-based neural networks (MNNs) with leakage delay and proportional delay. In actual engineering, a multitude of usefulness data are transmitted to the estimator through the networks, which stress the burden on communication bandwidth. A dynamic event-triggered mechanism (DETM) that relies on incomplete measurements is utilised to select valuable data. A novel delay-dependent criterion for the existence of the event-based state estimator is derived in terms of a convex optimisation problem by means of the Lyapunov theory and some integer inequalities technique. In the end, two numerical simulations are shown to illustrate the validity of the proposed theoretical methods.

Fault detection for saturated nonlinear system subject to redundant channel and additive time‐varying delays

AbstractUnder the influence of sensor saturation and packet dropout, this paper is concerned with the fault detection (FD) problem for a class of nonlinear systems with two additive time‐varying delay components. In the addressed measurement model, the sensor is assumed to have two communication channels, which can contribute to improving the probability of successfully delivering the measurements. A FD filter is designed to generate residual signals, and the sufficient criteria are established to ensure the stochastic finite‐time stability (SFTS) of the residual system with performance constraint by constructing new Lyapunov‐Krasovskii functionals. Based on the established criteria, the explicit expression of the desired FD filter is obtained via solving a set of linear matrix inequalities (LMIs). Finally, the usefulness of the proposed FD scheme is verified by a simulation example.

Distributed Fault Estimation for Nonlinear Systems With Sensor Saturation and Deception Attacks Using Stochastic Communication Protocols

Distributed Fault Estimation for Nonlinear Systems With Sensor Saturation and Deception Attacks Using Stochastic Communication Protocols

Quantized Nonfragile State Estimation of Memristor-Based Fractional-Order Neural Networks with Hybrid Time Delays Subject to Sensor Saturations

Quantized Nonfragile State Estimation of Memristor-Based Fractional-Order Neural Networks with Hybrid Time Delays Subject to Sensor Saturations

Detection of Signal Integrity Issues in Vibration Monitoring Using One-Class Support Vector Machine

AbstractThis paper presents an analysis of the common signal integrity issues in vibration monitoring caused by sensor saturation and signal distortion, or sensor loosening and detachment, and the development of a method of detecting the occurrence of vibration signal integrity issues using a one-class support vector machine. For this, vibration signals with distortions due to sensor saturation and/or sensor detachment are analysed to determine parameters sensitive to common integrity issues. These features are then extracted from training data of good quality vibration signals. Principal Component Analysis is used to dimensionally reduce the parameters which are then used in the training of the one-class support vector machine. The proposed method was validated on trained and untrained health conditions. Additionally, model sensitivity was also tested on trained health conditions with varying defect sizes. The method successfully detected all signal integrity issues tested proving to be an effective pre-processing step in condition monitoring systems for increased reliability and accuracy.

Computational Tool for Aircraft Fuel System Analysis

Fuel level gauging in aircraft presents a significant flight mechanics challenge due to the influence of aircraft movements on measurements. Moreover, it constitutes a multidimensional problem where various sensors distributed within the tank must converge to yield a precise and single measurement, independent of the aircraft’s attitude. Furthermore, fuel distribution across multiple tanks of irregular geometries complicates the readings even further. These issues critically impact safety and economy, as gauging errors may compromise flight security and lead to carrying excess weight. In response to these challenges, this research introduces a multi-stage project in aircraft fuel gauging systems, as a continuum of studies, where this first article presents a computational tool designed to simulate aircraft fuel sensor data readings as a function of fuel level, fuel tank geometry, sensor location, and aircraft attitude. Developed in an open-source environment, the tool aims to support the statistical inference required for accurate modeling in which synthetic data generation becomes a crucial component. A discretization procedure accurately maps fuel tank geometries and their mass properties. The tool, then, intersects these geometries with fuel-level planes and calculates each new volume. It integrates descriptive geometry to intersect these fuel planes with representative capacitive level-sensing probes and computes the sensor readings for the simulated flight conditions. The method is validated against geometries with analytical solutions. This process yields detailed fuel measurement responses for each sensor inside the tank, and for different analyzed fuel levels, providing insights into the sensors’ signals’ non-linear behavior at each analyzed aircraft attitude. The non-linear behavior is also influenced by the sensor saturation readings at 0 when above the fuel level and at 1 when submerged. The synthetic fuel sensor readings lay the baseline for a better understanding on how to compute the true fuel level from multiple sensor readings, and ultimately optimizing the amount of used sensors and their placement. The tool’s design offers significant improvements in aircraft fuel gauging accuracy, directly impacting aerostructures and instrumentation, and it is a key aspect of flight safety, fuel management, and navigation in aerospace technology.

On the factor ambiguity of MCR problems for blockwise incomplete data sets

Multivariate curve resolution (MCR) methods are sometimes faced with missing or erroneous data, e.g., due to sensor saturation. In some cases, an estimation of the missing data is possible, but often MCR works with the largest submatrix without missing entries. This ignores all rows and columns of the data matrix that contain missing values. A successful approach to deal with incomplete data multisets has been proposed by Alier and Tauler (2013), but it does not include a factor ambiguity analysis. Here, the missing data problem is addressed in combination with a factor ambiguity analysis. An approach is presented that minimizes the factor ambiguity by extracting a maximum of spectral information even from incomplete rows and columns of the spectral data matrix. The method requires a high signal-to-noise ratio. Applications are presented for UV/Vis and HSI data.

Event-based nonfragile state estimation for memristive recurrent neural networks with stochastic cyber-attacks and sensor saturations

Abstract This paper addresses the issue of nonfragile state estimation for memristive recurrent neural networks with proportional delay and sensor saturations. In practical engineering, numerous unnecessary signals are transmitted to the estimator through the networks, which increases the burden of communication bandwidth. A dynamic event-triggered mechanism, instead of a static event-triggered mechanism, is employed to select useful data. By constructing a meaningful Lyapunov–Krasovskii functional, a delay-dependent criterion is derived in terms of linear matrix inequalities for ensuring the global asymptotic stability of the augmented system. In the end, two numerical simulations are employed to illustrate the feasibility and validity of the proposed theoretical results.

Anti-laser interference methods for compressive spectral imaging based on grayscale coded aperture

Coded aperture snapshot spectral imaging (CASSI) captures three-dimensional (3D) spectral images by one or several snapshots two-dimensional (2D) measurements. At present, in practical application of spectral imaging systems, artificial interference such as laser irradiation is wide-spread. When CASSI system is irradiated by intense laser, optoelectronic image sensor will inevitably be dazzled, leading to the loss of compressive sampling information, thereby reducing the reconstruction accuracy of spectral images. This paper establishes the imaging model of CASSI system under laser interference and proposes a grayscale coding design method. Through the design of a reasonable and feasible feedback model, the grayscale coded aperture is calculated according to the measurements of CASSI system in real time feedback. The intensity of the incident light of the system is modulated to reduce the power density on the sensor, thus avoiding sensor saturation, and improving the anti-laser interference threshold of the CASSI system. Simulation results show the improvement of reconstruction accuracy when grayscale coded apertures are employed.

Proportional-Integral Observer-Based State Estimation for Markov Memristive Neural Networks With Sensor Saturations.

This article investigates the resilient proportional-integral observer (PIO) problem for Markov switching memristive neural networks (MSMNNs) with randomly occurring sensor saturation within a finite-time interval. The Markov switching of memristive neural networks is regulated by a higher level deterministic switching signal, whose transition probabilities are piecewise time-varying and can be depicted by the average dwell-time strategy. Meanwhile, a Bernoulli stochastic process associated with an uncertain packet arriving rate is adopted to describe the randomly occurring sensor saturation. The aim is to design a resilient PIO such that the augmented dynamic has the property of stochastic finite-time boundedness while meeting the desired performance index. By applying the Lyapunov method and the average dwell-time scheme, sufficient criteria are established for MSMNNs, and a unified design method is presented for the existence of the PIO. Lastly, the attained theoretical results are validated via a numerical simulation.

Anti-windup scheme-based control for fractional-order systems subject to actuator and sensor saturation

This paper addresses the problem of dissipative-based stabilization for fractional-order nonlinear control systems with uncertainties, time-delay, external disturbances, actuator faults, actuator and sensor saturations. Specifically, a non-fragile reliable dynamic output feedback controller is designed for the purpose of stabilization of the addressed system. Due to the effect of saturation phenomena occurring in both the actuator and sensor, the anti-windup loops framed with the help of the observer design are incorporated into the controller design. Some sufficient conditions are formulated in terms of linear matrix inequalities by choosing a suitable Lyapunov function thereby assuring the dissipative-based stabilization. The derived results are thus validated by two numerical examples including the practical model of two-stage chemical reactor.

An Implicit Neural Representation for the Image Stack: Depth, All in Focus, and High Dynamic Range

In everyday photography, physical limitations of camera sensors and lenses frequently lead to a variety of degradations in captured images such as saturation or defocus blur. A common approach to overcome these limitations is to resort to image stack fusion, which involves capturing multiple images with different focal distances or exposures. For instance, to obtain an all-in-focus image, a set of multi-focus images is captured. Similarly, capturing multiple exposures allows for the reconstruction of high dynamic range. In this paper, we present a novel approach that combines neural fields with an expressive camera model to achieve a unified reconstruction of an all-in-focus high-dynamic-range image from an image stack. Our approach is composed of a set of specialized implicit neural representations tailored to address specific sub-problems along our pipeline: We use neural implicits to predict flow to overcome misalignments arising from lens breathing, depth, and all-in-focus images to account for depth of field, as well as tonemapping to deal with sensor responses and saturation - all trained using a physically inspired supervision structure with a differentiable thin lens model at its core. An important benefit of our approach is its ability to handle these tasks simultaneously or independently, providing flexible post-editing capabilities such as refocusing and exposure adjustment. By sampling the three primary factors in photography within our framework (focal distance, aperture, and exposure time), we conduct a thorough exploration to gain valuable insights into their significance and impact on overall reconstruction quality. Through extensive validation, we demonstrate that our method outperforms existing approaches in both depth-from-defocus and all-in-focus image reconstruction tasks. Moreover, our approach exhibits promising results in each of these three dimensions, showcasing its potential to enhance captured image quality and provide greater control in post-processing.

Motion Reconstruction of Fast-Rotating Rigid Bodies

Abstract Motion reconstruction and navigation require accurate orientation estimation. Modern orientation estimation methods utilize filtering algorithms, such as the Kalman filter or Madgwick's algorithm. However, these methods do not address potential sensor saturation, which may occur within short time periods in highly dynamic applications, such as, e.g., particle tracking in snow avalanches, leading to inaccurate orientation estimates. In this paper, we present two algorithms for orientation estimation combining magnetometer and partially saturated gyrometer readings. One algorithm incorporates magnetic field vector observations and the full nonlinearity of the exponential map. The other, computationally more efficient algorithm builds on a linearization of the exponential map and is solved analytically. Both algorithms are then applied to measurement data from four different experiments, with two of them being snow avalanche experiments. Moreover, Madgwick's filtering algorithm was used to validate the proposed algorithms. The two algorithms improved the orientation estimation significantly in all experiments. Hence, the proposed algorithms can improve the performance of existing sensor fusion algorithms significantly.

Security-guaranteed filter design for discrete-time Markovian jump delayed systems subject to deception attacks and sensor saturation

This work is devoted the problem of a security-guaranteed filter design for a class of discrete-time Markov jump systems that are vulnerable to stochastic deception attacks and have random sensor saturation. Deception attacks, in particular, are taken into account in the filter when the attacker attempts to modify the broadcast signal in communication networks by inserting some misleading information data into the assessment output. The Bernoulli distribution is satisfied by two sets of introduced stochastic variables. It shows the likelihood that the broadcaster’s data transmissions will be the focus of deception attacks and sensor saturation. The Lyapunov functional technique is established, and criteria are derived to ensure that the system is mean-square stable. Furthermore, explicit expression of the filter gains is obtained by solving a set of linear matrix inequalities. Lastly, two simulation examples including a synthetic genetic regulatory network are provided to further demonstrate the validity and efficiency of the suggested theoretical results.

Nonfragile observer-based event-triggered fuzzy tracking control for fast-sampling singularly perturbed systems with dual-layer switching mechanism and cyber-attacks

This paper investigates the problem of nonfragile observer-based tracking control for a class of fuzzy fast-sampling singularly perturbed systems (SPSs) with sensor saturation, which subject to event-triggered scheme and random cyber-attacks. The switching topology of the fast-sampling SPSs is dominated by a dual-layer switching mechanism, in this case, the Markov process in the underlying system is nonhomogeneous and its time-varying transition probabilities (TPs) are governed by a persistent dwell-time switching signal. First, in order to avoid asynchronous phenomenon of premise variable between the fuzzy fast-sampling SPSs and the fuzzy controller under network environments, the non-parallel distributed compensation (non-PDC) technique is applied to improve the design flexibility and reduce the conservativeness of the results. Furthermore, a novel fuzzy tracking control design scheme is proposed, in which integrating the fuzzy nonfragile observer and reference model signal into the synthetic design of the tracking controller can be expected to reduce the tracking error. Second, by constructing a well-designed time-delay and mode-dependent Lyapunov function, some sufficient conditions are derived such that the closed-loop system is mean-square exponential stable and with extended dissipative tracking performance. Finally, the effectiveness and applicability of the proposed control protocol are illustrated by two simulation examples.

Collaborative-prediction-based recursive filtering for nonlinear systems with sensor saturation under duty cycle scheduling

In this paper, a recursive filtering problem is analyzed for nonlinear systems with sensor saturation under duty cycle scheduling (DCS). The sensor saturation is taken into account to describe practical engineering better. The DCS is introduced to conserve energy by alternating sensor nodes between active and dormant states. The considered problem aims to design a collaboration-prediction-based recursive filtering algorithm for nonlinear systems with sensor saturation such that, under the sparse measurements due to DCS, satisfactory filtering performance is guaranteed. By solving a set of matrix difference equations, the upper bound on the filtering error covariance is first obtained, and then the gain matrix of the filter that minimizes the upper limit is calculated. In addition, the boundedness of the upper bound of the filtering error covariance is analyzed. Finally, the effectiveness of the proposed collaboration-prediction-based recursive filtering algorithm is verified by the simulation example.

Time domain design of PI-C controllers

Abstract PI-C (Proportional-Integral-Compensating) controllers are observer-based compensators with integral action containing an inverse model of the plant. Originally developed for windup prevention in case of sensor saturation they are also useful in the presence of actuator saturation. They allow to achieve a nearly perfect disturbance attenuation while the loop gain satisfies the Circle Criterion. It has also been demonstrated that PI-C controllers can solve the windup problem in case of joint input and output saturation. The design of PI-C controllers has only been formulated in the frequency domain so far which, in the MIMO case, requires the manipulation of polynomial matrices. Here, the state-space design of PI-C controllers is presented.

Recursive Estimation for System With Random Transmission Loss and Censored Measurement

Censored measurements are frequently occurring phenomena in sensor systems with the characteristics of output dead-zone or sensor saturation. In censoring sensor network systems, random transmission loss is inevitable, which results in the two measurement uncertainties occurring simultaneously. In this study, we address this problem and attempt to present an efficient solution. First, the random transmission losses are described by a set of Bernoulli random variables, and then the censored random matrix and translation transformation are used to modify the traditional Tobit censored measurement model, which includes calculating the probability that the latent measurement is not censored when the transmission is lost and is only pure noise. Then, an optimal recursive filtering algorithm for a system with the two uncertain measurements is derived by using the innovation analysis method and the full expectation rule. The effectiveness and superiority of the algorithm is verified by simulating an oscillator model and comparing with other existing algorithms.

Secure P2P Nonfragile Sampled-Data Controller Design for Nonlinear Networked System under Sensor Saturation and DoS Attack

Secure P2P Nonfragile Sampled-Data Controller Design for Nonlinear Networked System under Sensor Saturation and DoS Attack

Event-Triggered H∞ Filtering for Cyber–Physical Systems Against DoS Attacks

This article mainly focuses on the problem of resilient <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$H_{\infty}$</tex-math> </inline-formula> filtering for cyber–physical systems (CPSs) subject to denial-of-service (DoS) attacks and sensor saturation. A new event-triggered mechanism (ETM) considering both DoS attacks and limited network bandwidth is put forward to guarantee the secure performance of the filter. Under this mechanism, inherent periodical transmission attempts are generated during DoS active periods, by which the latest measurement output of the system can be successfully transmitted to the filter after the end of the DoS attack; while during DoS sleep periods, the ETM degenerates into a traditional one. Furthermore, a valid DoS attack model is proposed to further characterize the following two scenarios occurring between adjacent sampling instants: 1) the end of the DoS attack and 2) both the start of the DoS attack and the end of the DoS attack. Sufficient conditions for designing the secure filters of CPSs against DoS attacks are achieved by using the piecewise Lyapunov–Krasovskii functional approach. Finally, the validity of our designed approach is manifested by an illustration of quarter-vehicle suspension systems (SSs).