Residual Signal Research Articles

The use of circumferential guided waves to monitor axial cracks in pipes

Guided wave testing is in routine use to detect corrosion, particularly in the oil and gas sector, but the detection of axial cracking is difficult using the axially propagating waves commonly used for corrosion detection. This paper presents a novel guided wave monitoring technique for the detection of axial cracking in piping. Circumferential SH0 waves, travelling around the circumference were used in a monitoring configuration to detect axial defects in a 6 inch schedule 80 steel pipe. Finite Element (FE) analysis showed that both the defect reflection and the decline in the transmitted SH0 wave can be used for defect detection. Baseline subtraction was utilised to produce residual signals that can be better analysed for small defect reflections. The Root-Mean-Square (RMS) of the residual signal after baseline subtraction was found to be the most satisfactory means of monitoring defect progression. An amplification effect was identified where the residual signal is compounded by continued interaction with the defect on each revolution of the incident wave. The FE predictions were validated by an experiment in which an Electrical Discharge Machining (EDM) notch was grown in four stages. Temperature compensation using Location Specific Temperature Compensation (LSTC) was applied to the experimental data, allowing residuals to be compared for a ∼10 °C temperature swing over a monitoring period of over 2 months. It was determined that a 10 mm long (∼23% of wavelength), 5 mm deep (∼45% of the wall thickness) defect at an axial offset from the line of the transducers of 1.5 wavelengths (65 mm) would be readily detectable with a very high Probability of Detection (POD) and virtually no chance of a false call. Therefore, this novel guided wave monitoring system for axial crack detection is likely to be attractive for applications in a range of industries for its sensitivity to axial cracking combined with large coverage from a single transducer location.

Vibration Analysis of Two-Stage Helical Gear Transmission with Cracked Fault Based on an Improved Mesh Stiffness Model

The crack fault has an important influence on the vibration characteristics of the transmission system. This paper is devoted to analyzing fault mechanism and dynamic characteristics of transmission with gear crack, which provides the basis for crack diagnosis and monitoring of helical gear in two-stage transmission system. For this purpose, an improved calculation method of time-varying meshing stiffness with crack fault is proposed considering the axial force of the helical gear. The influence of crack depths and angles on stiffness is analyzed. Based on this, the dynamic model of the two-stage helical gear transmission system is established using the lumped mass method. The influence of crack parameters on the characteristics of the transmission system is studied. In order to better diagnose the crack fault of the helical gear, the statistical index in time domain is calculated and the sensitivity to crack fault is analyzed. It is shown that the stiffness decreases by 25%, and the vibration acceleration increases by 8.3% after the axial force component is considered into the stiffness of the crack gear pair. The meshing stiffness decreases with the increase of crack depths and crack angles. The dynamic model is verified by rig test. Periodic impact exists in the system. The time of entering meshing of the cracked tooth and the time of complete disengagement is observed in the residual signal. In the frequency domain, there appear sidebands on the mesh frequency of the first gear pairs and its harmonics, the second gear mesh frequency and its harmonic frequencies. There is a little influence on the vibration acceleration of the driven gear of the second-stage. According to the signals in the frequency domain and the time domain, we can tell at which stage the gear is cracked. While through the time required for the crack tooth from engagement to disengagement completely, we can tell which gear is cracked. The sensitivity of skewness, impulse factor and kurtosis to crack fault decreases successively. The results provide theoretical basis for crack fault diagnosis of a two-stage helical gear transmission system.

Arterial spin labeling signal in the CSF: Implications for partial volume correction and blood-CSF barrier characterization.

For better quantification of perfusion with arterial spin labeling (ASL), partial volume correction (PVC) is used to disentangle the signals from gray matter (GM) and white matter within any voxel. Based on physiological considerations, PVC algorithms typically assume zero signal in the cerebrospinal fluid (CSF). Recent measurements, however, have shown that CSF-ASL signal can exceed 10% of GM signal, even when using recommended ASL labeling parameters. CSF signal is expected to particularly affect PVC results in the choroid plexus. This study aims to measure the impact of CSF signal on PVC perfusion measurements, and to investigate the potential use of PVC to retrieve pure CSF-ASL signal for blood-CSF barrier characterization. In vivo imaging included six pCASL sequences with variable label duration and post-labeling delay (PLD), and an eight-echo 3D-GRASE readout. A dataset was simulated to estimate the effect of CSF-PVC with known ground-truth parameters. Differences between the results of CSF-PVC and non-CSF-PVC were estimated for regions of interest (ROIs) based on GM probability, and a separate ROI isolating the choroid plexus. In vivo, the suitability of PVC-CSF signal as an estimate of pure CSF was investigated by comparing its time course with the long-TE CSF signal. Results from both simulation and in vivo data indicated that including the CSF signal in PVC improves quantification of GM CBF by approximately 10%. In simulated data, this improvement was greater for multi-PLD (model fitting) quantification than for single PLD (~1-5% difference). In the choroid plexus, the difference between CSF-PVC and non-CSF-PVC was much larger, averaging around 30%. Long-TE (pure) CSF signal could not be estimated from PVC CSF signal as it followed a different time course, indicating the presence of residual macrovascular signal in the PVC. The inclusion of CSF adds value to PVC for more accurate measurements of GM perfusion, and especially for quantification of perfusion in the choroid plexus and study of the glymphatic system.

Fault Diagnosis of Induction Motor Using D-Q Simplified Model and Parity Equations

Induction motors are the horsepower in the industrial environment, and among them, 3-phase induction motors (3PIMs) stand out for their robustness and standard 3-phase power supply. In the literature, there are many approaches to diagnose faults for the nonlinear 3PIM model, and the vast majority focus on a single motor fault, although others address more faults but at the cost of greater computational complexity. In this sense, one of the methods with less computational load and early detection is the parity equation approach, which is based on analyzing the discrepancy between the input and output signals of a real process and a linear mathematical model to generate a residual signal, which contains important information about the fault and is obtained through a suitable selection of a weighting matrix W to isolate the faults as much as possible. The problem in this case study is that the 3PIM model is a nonlinear system. In this work, the fault detection method based on the parity equations approach applied in the 3PIM is explored using a simplified and proposed model of the 3PIM working in the D-Q synchronous reference frame, which is matched with the direct current motor model to guarantee both the existence of the parity space and to ensure a large set of detectable faults in the 3PIM parameters. Simulation and experimental results validate the proposed scheme and confirm a very simple set of residual equations to guarantee both early detection and a large set of detectable faults in: Stator and rotor resistances, stator and rotor inductances, as well as current, voltage, and speed sensors. Additionally, development of human machine interface (HMI) is implemented to validate the proposed scheme.

Abstract 14778: Combined Shear Stress-Molecular Imaging for the Evaluation of Early Arterial Healing Responses Following Implantation of Bioresorbable Scaffold With Different Strut Thickness

Introduction and Hypothesis: Arterial healing following stent implantation involves a series of biological reactions such as thrombosis, inflammation, neointimal regeneration. Clinical evidences have shown favorable arterial healing after thin-strut stents, however, there has been a lack of in vivo imaging evidence on how strut thickness affect vascular healing process. We evaluated local endothelial shear stress (ESS) and healing response of a thin-strut bioresorbable scaffold (BRS, 100μm) compared to first-generation 157μm-thickness ABSORB. Methods and Results: BRS and ABSORB were simultaneously implanted in left coronary arteries of the same pigs (n=7). Detailed scaffolded-artery 3D model was reconstructed based on OCT and angiography. Scaffold-related inflammation response was serially assessed by dual-modal OCT-near-infrared fluorescence (OCT-NIRF) molecular imaging using macrophage-mannose receptor-Cy7 as an inflammation-targeting agent. On ESS analysis, regions with athero-prone low ESS were significantly greater in thick-ABSORB compared to BRS. Dual-modal OCT-NIRF imaging visualized arterial inflammation activity at peri-strut regions in vivo (Figure). Based on coregistered ESS-NIRF data, we found a significant inverse correlation was found between ESS and NIRF activity (p<0.0001), suggesting immune-modulating effect of ESS during early stent healing phase. Arterial inflammation, quantified as target-to-background ratio, was significantly lower in thin-strut BRS than ABSORB at 2 weeks (p<0.01). At week 3, unlike ABSORB with residual peri-strut NIRF signals, inflammation activity dissipated significantly in BRS. Ex vivo confocal fluorescence microscopy (Figure) and immunohistochemical analysis well corroborated the findings in vivo . Conclusions: Thin-strut BRS was associated with less inflammation and rapid resolution of inflammation compared to thicker-strut ABSORB. This study provides in vivo imaging insights into how strut thickness affects early arterial healing and the importance of ESS as an inflammatory modulator. Our comprehensive imaging strategy enabling evaluation of complex interplay between scaffold, ESS, and artery is expected to enhance understanding of stent healing.

Active Control Algorithm of Hybrid Vibroacoustic for Vehicle Interior Sound Quality

Background: In some applications, retaining the residual noise at a specified frequency can provide a better vehicle interior sound quality for occupant’s driving experience. The hybrid vibro-acoustic adaptive noise equalizer (HVA-ANE) algorithm introduces a sound retention factor on the basis of the hybrid vibro-acoustic filter-u, and filter-x least mean square (HVA-FUXLMS) algorithm to achieve control of the sound quality, but it is not possible to retain varying degrees of different frequency bands. The hybrid vibro-acoustic empirical-mode-decomposition frequencydomain block adaptive noise equalizer (HVA-EBANE) algorithm introduces Empirical-Mode- Decomposition (EMD) and frequency domain block methods to achieve different degrees of retention of noise in different frequency bands but cannot deal with the irrelevant frequency components in the error signal. Objective: This paper proposes an improved active control algorithm for vehicle interior sound quality, the so-called hybrid vibro-acoustic EMD frequency-domain block FELMS (HVA-EBFELMS) algorithm. Methods: Based on the filtered error, the least mean square algorithm and the residual filter of the specific frequency band are designed according to the psychoacoustic parameters of each component. The filter filters the irrelevant frequency components in the residual signal, and only retains the frequency components that have a great influence on the sound quality so as to achieve the regulation of the desired sound quality. Results: In order to verify the effectiveness of our algorithm, we used the noise and vibration in the vehicle at idle speed as the object and compared the noise control effect with HVA-ANE and HVAEBANE algorithms considering the sound quality. The simulation experiment shows that our algorithm reduces the loudness of the ear-side noise by more than 40% while taking into account the noise and vibration control. Conclusion: The loudness control effect of hybrid vibro-acoustic EMD frequency-domain block FELMS (HVA-EBFELMS) algorithm is better than HVA-ANE and HVA-EBANE algorithms; it has a better effect of improving loudness as well.

Bearing Fault Diagnosis usingEnhanced Envelope Analysis

Early fault detection and diagnosis of bearing is of paramount importance in wind turbines as it contributes to around 70% of gearbox and 21%-70% of generator failure. This paper presents an enhanced envelope analysis technique for bearing fault identification using vibration measurements. The raw vibration signal measured from the outer casing of the WT gearbox with faulty bearing has not only fault frequency component but also resonant frequency, discrete frequency (gear and shaft) and high frequency background noise. The present work diagnose bearing faults through a three-step process: i) using Auto- Regressive(AR) time series model to remove discrete frequency components of gear and shaft ii) fault-sensitive frequency and demodulation band selection using spectral kurtosis. The spectral kurtosis is expected to higher around the region of resonance frequencies when fault occurs otherwise the fault signal is modulated. iii) envelope analysis to obtain spectrum by Hilbert Transform (HT) and retrieval of fault information. The order of the AR model is chosen such that the residual signal exhibits maximum kurtosis and spectral kurtosis is computed through short time Fourier Transform (STFT). The enhanced envelope analysis technique for bearing fault diagnosis is verified and validated using numerical simulations and Korea Aerospace University bearing damage benchmark datasets.

Fetal ECG Signal Extraction From Long-Term Abdominal Recordings Based on Adaptive QRS Removal and Joint Blind Source Separation

Despite wearable electronic devices having enormous potential applications, the recording quality has not sufficiently met the requirements for fetal disease detection. In this article, we aim to propose a novel framework for fetal electrocardiography (ECG) extraction, where the hybrid approach mainly considers the combination of a novel adaptive maternal QRS removal (AQRSR) algorithm and a tensor-based joint blind source separation (JBSS) approach. To adapt to the dynamic change of the ECG signal and suppress the powerful maternal heartbeat, AQRSR is explored with an adaptive template, which is generated by a model consisting of a generator, discriminator, and transformer. By replacing the oldest segmentation with the newest cycle, the approach is capable of quickly adapting to the estimated output to match the new input. In addition, due to the significant crossover between the target signal and the noise, even if the maternal ECG removal processing is implemented, the residual signal inevitably contains fetal ECG, part of maternal ECG, and noise. As a consequence of this, a novel JBSS that incorporates tensor decomposition is formulated to separate fetal heartbeat from long-term recordings contaminated with maternal movements noise and heartbeat interference. By dividing the collected data into multiple segments, this method can extract fetal ECG signals by exploiting the signals that are not only statistically independent within each segment but can be dependent on different segments. Experimental results show that even if the recorded signals are contaminated by the noise, the proposed framework has comparable better performance on fetal ECG extraction.

The Impact of Plate Motions on Long‐Wavelength InSAR‐Derived Velocity Fields

AbstractInterferometric Synthetic Aperture Radar (InSAR) measurements are increasingly being used to measure small amplitude tectonic deformations over large spatial scales. Residual signals are often present at these scales and are interpreted to be noise of indeterminate origin, limiting studies of long‐wavelength deformation. Here, we demonstrate the impact of rigid motion by the Earth's tectonic plates on velocity fields derived from InSAR. The range‐dependent incidence angle of the InSAR observations, coupled with plate velocities of centimeters per year, can induce long‐wavelength spatial gradients of millimeters per year over hundreds of kilometers in InSAR velocity fields. We show that, after applying corrections, including for the ionosphere and troposphere, plate motion represents the dominant source of long‐wavelength secular velocity gradients in multi‐year time series for several study areas. This signal can be accounted for using plate motion models, allowing improved detection of regional tectonic strain at continental scales.

Spatial-digital joint self-interference cancellation method for in-band full-duplex underwater acoustic communication

The in-band full-duplex underwater acoustic communication (IBFD-UWAC) mode has twice the information throughput of the traditional half-duplex communication mode, significantly increasing the communication efficiency. Extracting the weak desired signal from the high-power self-interference signal without distortion remains a challenging problem in implementing IBFD-UWAC systems. This paper proposes a spatial-digital joint self-interference cancellation (SDSIC) method for IBFD-UWAC. We first perform spatial self-interference cancellation (SSIC) and propose an improved wideband constant-beamwidth beamformer to overcome the problem of direction- and array-dependent interference in IBFD-UWAC systems. Convex optimization is used to maintain a constant beam response in the main flap and cancel the self-interference signal from a fixed direction, thus increasing the signal-to-interference ratio of the desired signal. Subsequently, we perform digital self-interference cancellation (DSIC) on the residual self-interference signal, and propose a variable-step-size least-mean-squares algorithm based on the spatial noise threshold. This algorithm modifies the least-mean-squares step-size adjustment criterion according to the noise level after SSIC and the desired signal, resulting in better DSIC. A series of simulations are implemented in a hardware-in-the-loop platform to verify the practicality and real-time performance of the proposed SDSIC method. The results show that the self-interference signal power can be reduced by 41.5 dB using the proposed method, an improvement of 13.5 dB over the conventional SIC method.

Fault Detection and Fault Tolerant Control for Anti-lock Braking Systems )ABS) Speed Sensors by Using Neural Networks

This paper proposed neural networks to continuously provide alternative constructed signals for vehicle and wheel speed sensors utilized for the Anti-Lock Braking System (ABS), which serves as the fault tolerant control method. These alternative constructed signals are used for two purposes. The first is to generate residual signals, and the second is to be adopted instead of isolated faulty signals. The residual signal is generated by extracting the difference between the alternative constructed signals and the corresponding actual signals. These residual signals serve as an indication of fault occurrence and to express that fault severity. Whenever a fault occurrence is detected and diagnosed in one of the sensor’s signals, the faulty signal is isolated and replaced by the corresponding constructed signal to maintain the system's normal behavior under a faulty condition. The range of data covered under the proposed estimating neural networks is huge, continuous in time, and not sampled. In this work, the range of the data lies between [50 to 120 km/h] when the braking is started. That cannot be performed by any available method. These models' training process is based on the Levenberg-Marquardt (LM) algorithm, implemented and tested by MATLAB/Simulink. The results show that these models can accurately map the measured data into the desired output through the best-fit functions. The fast response of the trained models makes them suitable for real-time alternative signals for fault-tolerant purposes for speed sensors during hard or panic braking.

Single-modulator Based Multi-Format Switchable Signal Generator Without Background Noise

A photonic multi-format background-free binarily modulated microwave signal generator using a single dual-polarization dual-parallel Mach–Zehnder modulator is proposed and demonstrated. The proposed generator features good reconfiguration and switchability. By simply adjusting the direct current biases of the modulator, amplitude shift keying (ASK), phase shift keying (PSK) and frequency-shift keying (FSK) microwave signals can be generated. Benefiting from the complementary intensity modulation, there is no residual baseband modulated signal in the generated binarily modulated signals. The system does not involve any optical or electrical filters, which ensures good tunability of the generator. By changing the frequency of the driven microwave carrier and bit rate of the electrical coded signal, the frequency and rate of the generated multi-format signals can be adjusted accordingly. The multi-format switchable signal generator has great potential in multi-functional communication and radar systems.

Improvement of an Industrial Robotic Flaw Detection System

Rotary encoders are commonly used for dynamic control and positioning of industrial robots. Results of this study suggested that rotary encoder signal can also be used to monitor the efficiency of industrial robot systems effectively after proper processing. A novel strategy using singular spectrum analysis (SSA) integrated with hierarchical hyper-Laplacian prior prototype (HHLP) is proposed in this study for defect detection in industrial robots. Sparsity-assisted techniques are efficient flaw-based extraction techniques that have been extensively investigated in recent years. However, the selection of an appropriate sparse prior from the point of view of probability theory remains unverified. First, SSA enables the separation of complicated encoding signals to several interpretable components, including a trend, a set of cyclic oscillations, and residual oscillations (noise). Second, we describe HHLP by maximizing posterior probability of robot flaw diagnosis. HHLP is proposed to extract noise interference, cyclic pulse, and harmonic interference from the residual signal using the SSA technique. We infer that the hyper-Laplacian prior in the prototype may present a more efficient prototype flaw than the Laplacian prior. In addition, HHLP incorporates physical characteristics necessary to distinguish harmonic intervention. This study primarily establishes a modern prototype that represents the former dispersed from the perspective of maximizing the probability of the latter. Meanwhile, generalized minimax-concave regularization inductive and kurtosis-based weighted sparse prototypes are compared and spectral kurtosis is used to confirm the efficacy of HHLP. Note to Practitioners—This study aims to solve the problem of industrial robot fault diagnosis during the operation process to avoid production delays. Rotary encoder sensor is attached to each joint to collect raw information and identify the robot position. Data from the rotary encoder sensor can also be used for the efficient health status assessment of the performance of the industrial robot system after proper processing. Therefore, a new approach using singular spectrum analysis combined with hierarchical hyper-Laplacian pre-induced prototype is proposed in this study. The residual signal extracted using the singular spectrum analysis method for processing in a hierarchical hyper-Laplacian pre-induced prototype is used to improve the weak fault feature. We describe a hierarchical hyper-Laplacian preprototype by maximizing the posterior probability of the flaw diagnosis. We introduce a hierarchical hyper-Laplacian prior that integrates physical characteristics to distinguish between harmonic interferences. The distribution of coefficients acquired through other dictionaries or transformations and selection of the optimal priority for the extraction of flaw characteristics will be the foci of future investigations.

The BINGO project

Context. A new and promising technique for observing the Universe and study the dark sector is the intensity mapping of the redshifted 21 cm line of neutral hydrogen (H I). The Baryon Acoustic Oscillations [BAO] from Integrated Neutral Gas Observations (BINGO) radio telescope will use the 21 cm line to map the Universe in the redshift range 0.127 ≤ z ≤ 0.449 in a tomographic approach, with the main goal of probing the BAO. Aims. This work presents the forecasts of measuring the transversal BAO signal during the BINGO phase 1 operation. Methods. We used two clustering estimators: the two-point angular correlation function (ACF) in configuration space, and the angular power spectrum (APS) in harmonic space. We also used a template-based method to model the ACF and APS estimated from simulations of the BINGO region and to extract the BAO information. The tomographic approach allows the combination of redshift bins to improve the template fitting performance. We computed the ACF and APS for each of the 30 redshift bins and measured the BAO signal in three consecutive redshift blocks (lower, intermediate, and higher) of ten channels each. Robustness tests were used to evaluate several aspects of the BAO fitting pipeline for the two clustering estimators. Results. We find that each clustering estimator shows different sensitivities to specific redshift ranges, although both of them perform better at higher redshifts. In general, the APS estimator provides slightly better estimates, with smaller uncertainties and a higher probability of detecting the BAO signal, achieving ≳90% at higher redshifts. We investigate the contribution from instrumental noise and residual foreground signals and find that the former has the greater impact. It becomes more significant with increasing redshift, in particular for the APS estimator. When noise is included in the analysis, the uncertainty increases by up to a factor of ∼2.2 at higher redshifts. Foreground residuals, in contrast, do not significantly affect our final uncertainties. Conclusions. In summary, our results show that even when semi-realistic systematic effects are included, BINGO has the potential to successfully measure the BAO scale at radio frequencies.

Adaptive Event-Triggered Finite-Frequency Fault Detection With Zonotopic Threshold Analysis for LPV Systems.

This article investigates a class of multiobjective optimization fault detection observer design problems for linear parameter varying (LPV) systems considering the unknown but bounded disturbance with an adaptive event-triggered scheme. In this study, the actuator faults are considered in the low-frequency domain. First, to save the communication bandwidth and improve communication efficiency, an adaptively adjusted event-triggered (AAET) mechanism is proposed. Then, in order to make the designed observer gain satisfy both fault sensitivity and disturbance robust conditions, an H-/L∞ multiobjective optimization problem is proposed and solved by appropriate linear matrix inequalities. Next, the upper and lower bounds of the generated residual are calculated by the zonotope method when considering the estimation uncertainty. Fault detection can be achieved by judging whether the zero value belongs to the generated range of the residual signal. Finally, a simulation case is used to verify the effectiveness of the proposed method.

Dynamic Reduced-Order Observer-Based Detection of False Data Injection Attacks With Application to Smart Grid Systems

This article investigates the problem of attack detection of false data injection attacks for a class of large-scale smart grid systems in the context of cyber–physical systems. First, by exploiting the graph theory to decompose the considered system into multiple interconnected subsystems, a bank of dynamic reduced-order observers are delicately constructed to generate residual signals for the attack detection task. Then, a novel decentralized attack detection scheme is proposed based on the adaptive detection thresholds with prescribed performance. Compared with the existing results, the proposed detection scheme has less conservative thresholds and enhanced robustness against process disturbance and measurement noise, such that the detectability is improved. Finally, the effectiveness and availability of the proposed scheme are verified by two simulation examples and the experimental results from IEEE 30-bus system built in the OPAL-RT real-time simulator.

Model and Data-Driven Combination: A Fault Diagnosis and Localization Method for Unknown Fault Size of Quadrotor UAV Actuator Based on Extended State Observer and Deep Forest.

The rotor is an essential actuator of quadrotor UAV, and is prone to failure due to high speed rotation and environmental disturbances. It is difficult to diagnose rotor faults and identify the fault localization simultaneously. In this paper, we propose a fault diagnosis and localization scheme based on the Extended State Observer (ESO) and Deep Forest (DF). This scheme can accurately complete the fault diagnosis and localization for the quadrotor UAV actuator without knowing the fault size by combining the model-based and the data-driven methods. First, we obtain the angular acceleration residual signal of the quadrotor UAV by using ESO. The residual signal is the difference between the observed state of ESO and the true fault state. Then, we design the residual feature analysis method by considering the position distribution of the quadrotor UAV actuator. This method can embed the actuator fault localization information into the fault data by simultaneously considering pitch and roll of the quadrotor UAV. Finally, we complete the fault diagnosis and localization of the quadrotor UAV actuator by processing the fault data by using DF. This scheme has the advantages of straightforward observer modeling, strong generalization ability, adaptability to small sample data, and few hyperparameters. Our simulation results indicate that the accuracy of the proposed scheme reaches more than 99% for the unknown size of the quadrotor UAV actuator fault.

False Data Injection Attacks Against State Estimation Without Knowledge of Estimators

This article investigates the stealthy false data injection attack design problem for a class of cyber physical systems equipped with state estimators and attack detectors. The objective is to worsen the estimation performance without triggering any alarm. First, a necessary and sufficient condition for the existence of perfect attacks, which alter the state estimate without affecting residual signals, is provided. It is shown that the estimation error can be arbitrarily large under the well-designed perfect attacks. Second, if the perfect attacks do not exist, the existence of nonperfect attacks, which worsen the estimation performance with a degree influence on residual signals, is analyzed. It is shown that the desired nonperfect attack sequence can be designed by analyzing the maximum eigenvalue and the corresponding eigenvector of an auxiliary matrix. Compared with the existing methods, in this article, attacks are designed without knowledge of estimators and can be injected from any time point. Finally, a numerical simulation is provided to illustrate the effectiveness of the proposed methods.

Fault-tolerant control with high-order sliding mode for manipulator robot

<p>The safety of manipulators systems is receiving a lot of attention these days. Faults have the ability to carry out harmful activities that harm equipment, the environment, or persons. As a result, it is critical to detect and diagnose problems as soon as possible, as well as to incorporate fault tolerance to avoid performance deterioration and harmful circumstances. Robust sensor fault detection and isolation (FDI) as well as fault-tolerant control (FTC) for a robotic system are discussed in this work. The goal of this research is to create an FDI method that uses a super-twisting third-order sliding mode (STW-TOSM) observer to estimate residual signals. A suggested system based on a higher-order sliding mode (HOSM) observer/controller approaches is used to accomplish this. In comparison to an active FTC technique, the test results demonstrate high level of performance. Finally, simulation results illustrate the efficacy of the presented techniques.</p>

The genomic and bulked segregant analysis of Curcuma alismatifolia revealed its diverse bract pigmentation

Compared with most flowers where the showy part comprises specialized leaves (petals) directly subtending the reproductive structures, most Zingiberaceae species produce showy “flowers” through modifications of leaves (bracts) subtending the true flowers throughout an inflorescence. Curcuma alismatifolia, belonging to the Zingiberaceae family, a plant species originating from Southeast Asia, has become increasingly popular in the flower market worldwide because of its varied and esthetically pleasing bracts produced in different cultivars. Here, we present the chromosome-scale genome assembly of C. alismatifolia “Chiang Mai Pink” and explore the underlying mechanisms of bract pigmentation. Comparative genomic analysis revealed C. alismatifolia contains a residual signal of whole-genome duplication. Duplicated genes, including pigment-related genes, exhibit functional and structural differentiation resulting in diverse bract colors among C. alismatifolia cultivars. In addition, we identified the key genes that produce different colored bracts in C. alismatifolia, such as F3′5'H, DFR, ANS and several transcription factors for anthocyanin synthesis, as well as chlH and CAO in the chlorophyll synthesis pathway by conducting transcriptomic analysis, bulked segregant analysis using both DNA and RNA data, and population genomic analysis. This work provides data for understanding the mechanism of bract pigmentation and will accelerate breeding in developing novel cultivars with richly colored bracts in C. alismatifolia and related species. It is also important to understand the variation in the evolution of the Zingiberaceae family.