Disturbance Signals Research Articles

Anti-disturbance observer-based proportional-retarded control design for polytopic uncertain fractional-order systems

This study deals with the stabilisation problem of fractional-order time-delay systems with polytopic uncertainties and multiple disturbances with the use of Lyapunov stability theory. Notably, the multiple disturbances comprising both the known and unknown signals, are respectively characterised by the norm-bounded and exogenous system which signifies the harmonic signals with modelling perturbations. The control protocol is configured by the integration of proportional-retarded controller with gain perturbations and the output of an anti-disturbance observer. Precisely, the disturbance observer is taken into account for attenuating the influence of disturbance signals. Moreover, the implementation of a time-delay term in the feedback loop replicates the dynamic features of a derivative action that relies only on position measurements and its time differencing capabilities make it to be minimally sensitive against noise and provide a smoothing effect. By virtue of these circumstances, the asymptotic stability of the considered system is assured. With the aid of a suitable Lyapunov function, a set of robust order-dependent sufficient conditions is derived and then solved through the MATLAB LMI toolbox. As a result, the controller gain matrices can be computed and two numerical examples, including an electrical circuit model with simulation results are presented to certify the inherent potential of the theoretical outcomes.

Initial condition based real time classification of power quality disturbance using deep convolution neural network with bidirectional long short‐term memory

AbstractThe accurate classification of power quality disturbances (PQDs) is crucial for advancing real‐time monitoring and classification systems within the modern power grid. The proposed system must ensure dependable, safeguarded, and stable operating conditions amidst diverse power quality issues. This paper presents an approach to classifying power quality disturbances using a deep learning model that synergizes deep convolutional neural networks (DCNN) and Bidirectional Long Short‐Term Memory (BiLSTM). This amalgamation effectively extracts and classifies disturbance signals in real time, grounded on noise levels. The initial feature extraction from the signal is accomplished through a time‐frequency matrix. Subsequently, secondary extraction employs the BiLSTM layer to intricately and significantly classify disturbances in the power signal. This aids in transforming high‐dimensional matrices into a reduced set for enhanced performance. The detailed classification is facilitated by the softmax layer. The simulation results support the power quality evaluations under varied constraints and underscore the substantial classification of power quality disturbances through the DCNN‐BiLSTM algorithm, in comparison to alternative classification algorithms in terms of computational speed and accuracy.

Scanning Ion-Conductance Microscopy for Studying Mechanical Properties of Neuronal Cells during Local Delivery of Glutamate.

Mechanical properties of neuronal cells have a key role for growth, generation of traction forces, adhesion, migration, etc. Mechanical properties are regulated by chemical signaling, neurotransmitters, and neuronal ion exchange. Disturbance of chemical signaling is accompanied by several diseases such as ischemia, trauma, and neurodegenerative diseases. It is known that the disturbance of chemical signaling, like that caused by glutamate excitotoxicity, leads to the structural reorganization of the cytoskeleton of neuronal cells and the deviation of native mechanical properties. Thus, to investigate the mechanical properties of living neuronal cells in the presence of glutamate, it is crucial to use noncontact and low-stress methods, which are the advantages of scanning ion-conductance microscopy (SICM). Moreover, a nanopipette may be used for the local delivery of small molecules as well as for a probe. In this work, SICM was used as an advanced technique for the simultaneous local delivery of glutamate and investigation of living neuronal cell morphology and mechanical behavior caused by an excitotoxic effect of glutamate.

Hypertracking and Hyperrejection: Control of Signals Beyond the Nyquist Frequency

This paper studies the problem of signal tracking and disturbance rejection for sampled-data control systems, where the pertinent signals can reside beyond the so-called Nyquist frequency. In light of the sampling theorem, it is generally understood that manipulating signals beyond the Nyquist frequency is either impossible or at least very difficult. On the other hand, such control objectives often arise in practice, and control of such signals is much desired. This paper examines the basic underlying assumptions in the sampling theorem and pertinent sampled-data control schemes, and shows that the limitation above can be removed by assuming a suitable analog signal generator model. Detailed analysis of multirate closed-loop systems, zeros and poles are given, which gives rise to tracking or rejection conditions. Robustness of the new scheme is fully characterized; it is shown that there is a close relationship between tracking/rejection frequencies and the delay length introduced for allowing better performance. Examples are discussed to illustrate the effectiveness of the proposed method here.

Reconstruction of Communication Signal in Wireless Networks Based on Perturbation Compression Perception

<p>With the maturity and development of new generation communication technology, the demand for wireless network communication quality is getting higher and higher. However, the existing wireless network data transmission models are greatly affected by the signal quality in the data transmission process, which seriously affects the quality of data transmission. To address the problem of signal perturbation affecting communication quality in wireless network communication, this study designs an improved perturbation compression reconstruction algorithm that can be used to process block-structured data based on the block sparse structure in the original signal, and constructs a single-user wireless network communication data transmission model based on this algorithm. The performance simulation test of the model shows that the MSE values of the model are lower than those of the conventional and perfect schemes used as comparisons under the same noise conditions and the transmitted compressed data length conditions. When the variance of Gaussian white noise is 0.125, the MSEs of the research design model, the conventional scheme and the perfect scheme are 0.031, 0.048, and 0.047, respectively. Experimental data show that the improved disturbance compressed sensing reconstruction algorithm can better reconstruct the compressed information and reduce the information loss caused by the disturbance in the compression process on the premise of removing the signal disturbance. This research result has reference significance for improving the signal quality of wireless network communication.</p> <p> </p>

Spatio-temporal repolarization dispersion before and after exercise in patients with long QT syndrome type 1 vs controls - probing into the arrhythmia substrate.

Congenital long QT syndrome (LQTS) carries an increased risk for syncope and sudden death. QT prolongation promotes ventricular extrasystoles which in the presence of an arrhythmia substrate might trigger ventricular tachycardia degenerating into fibrillation. Increased electrical heterogeneity (dispersion) is the suggested arrhythmia substrate in LQTS. In the most common subtype LQT1, physical exercise predisposes for arrhythmia and spatio-temporal dispersion was therefore studied in this context. Thirty-seven patients (57% on beta-blockers) and 37 healthy controls (mean age 31 vs 35, range 6-68 vs 6-72 years) performed an exercise test. Frank vectorcardiography was used to assess spatio-temporal dispersion as Tamplitude, Tarea, the ventricular gradient (VG), and the Tpeak-end interval from 10s-signal averages before and 7±2 min after exercise; during exercise too much signal disturbance excluded analysis. Baseline and maximum heart rates as well as estimated exercise intensity were similar, but heart rate recovery was slower in patients. At baseline QT and heart rate corrected QT (QTcB) were significantly longer in patients (as expected), while dispersion parameters were numerically larger in controls. After exercise QTpeakcB and Tpeak-endcB increased significantly more in patients (18±23 vs 7±10ms and 12±17 vs 2±6ms; p<0.001 and p<0.01). There was, however, no difference in the change in Tamplitude,Tarea and VG between groups. In conclusion, while temporal dispersion of repolarization increased significantly more after exercise in LQT1 patients, there were no signs of exercise-induced increase in global dispersion of action potential duration and morphology. The arrhythmia substrate/mechanism in LQT1 warrants further study.

Signal quality evaluation of single-channel respiratory sEMG recordings

This work presents a learning-based approach to respiratory surface electromyography (sEMG) quality evaluation. For this purpose, we define the signal-to-disturbance ratio (SDR), which quantifies the relative impact of disturbances on affected signal components. The SDR values for different disturbance types are estimated based on signal and disturbance characteristics and serve as a measure of signal quality. For the multivariate regression task, a fully connected neural network with three layers is trained based on standard and handcrafted signal features. The features are extracted before and after removing cardiac artifacts. This integration of domain-specific knowledge enables us to leverage shallow neural networks, which contributes to the interpretability of the method. For training and testing, artificially disturbed signals are generated based on undisturbed clinical sEMG recordings of mechanically ventilated patients and different disturbance models. This includes single and combined disturbances with different SDRs. The results show that the root-mean-square error (RMSE) of the estimated to the applied SDR regarding powerline (2.3dB) and spike-like disturbances (2.1dB) is smaller than the RMSE referring to high-frequency disturbances (3.6dB) and motion artifacts (5.8dB). Our findings also indicate that the SDR is estimated similarly well regardless of whether one or more contaminants are present simultaneously. Overall, the SDR can be determined with an RMSE of 3.8dB and a correlation coefficient of 0.97. This work contributes to automatically quantifying the impact of disturbances and objectively assessing the quality of sEMG signals of the respiratory muscles.

An all-pass based internal model principle controller for galvanometer mirror steering

Galvanometer is a critical component for beam steering in additive manufacturing, profilometry, and medical imaging. In these applications, the quality of the process relies on the precision motion control of the galvanometer to either track or reject narrow-band signals. From the internal model principle, this can be accomplished by incorporating the dynamic model of the reference or disturbance signal in the feedback loop. This paper proposes an innovative internal model principle controller based on numerically robust all-pass filters. The main concept revolves around converting the controller design into a phase response design problem with all-pass filters. For the proposed approach, the targeted frequencies of the internal model can be arbitrarily placed without sacrificing the performance. Further, it produces lower-order controller and is robust against quantization effect. When combining with frequency estimation algorithm and adaptive filter, it can be readily applied to unknown and time-varying disturbance rejection. The advantages of the proposed method are demonstrated by comparing with conventional approaches through analysis, simulation, and experimentation.

Modulation of Notch Signaling by Small-Molecular Compounds and Its Potential in Anticancer Studies.

Notch signaling is responsible for conveying messages between cells through direct contact, playing a pivotal role in tissue development and homeostasis. The modulation of Notch-related processes, such as cell growth, differentiation, viability, and cell fate, offer opportunities to better understand and prevent disease progression, including cancer. Currently, research efforts are mainly focused on attempts to inhibit Notch signaling in tumors with strong oncogenic, gain-of-function (GoF) or hyperactivation of Notch signaling. The goal is to reduce the growth and proliferation of cancer cells, interfere with neo-angiogenesis, increase chemosensitivity, potentially target cancer stem cells, tumor dormancy, and invasion, and induce apoptosis. Attempts to pharmacologically enhance or restore disturbed Notch signaling for anticancer therapies are less frequent. However, in some cancer types, such as squamous cell carcinomas, preferentially, loss-of-function (LoF) mutations have been confirmed, and restoring but not blocking Notch functions may be beneficial for therapy. The modulation of Notch signaling can be performed at several key levels related to NOTCH receptor expression, translation, posttranslational (proteolytic) processing, glycosylation, transport, and activation. This further includes blocking the interaction with Notch-related nuclear DNA transcription. Examples of small-molecular chemical compounds, that modulate individual elements of Notch signaling at the mentioned levels, have been described in the recent literature.

Modeling compound hydrologic disturbances in the Rio Grande Headwaters

AbstractIn recent decades, the western United States (U.S.) has experienced increasing magnitudes and frequencies of natural land cover disturbances that impact water budget partitioning. Post‐disturbance hydrologic response is often variable at the stream outlet and is difficult to detect and quantify with traditional before–after control–impact studies. This study uses a modified version of the U.S. Geological Survey's Monthly Water Balance Model (MWBM) to simulate and separate the hydrologic response to several forest disturbances, including (1) wildfire, (2) forest conversion (subalpine to mid‐elevation forest) and (3) a climate that is hotter and drier than present in the Rio Grande Headwaters (RGH) in Colorado, U.S. (this climate scenario was derived from an ensemble of climate scenarios from the Coupled Model Intercomparison Project phases 3 and 5, which were selected based on water stress potential in the state of Colorado). We leverage historic post‐disturbance vegetation data in the RGH to add quantitative vegetation representation to the MWBM, then modeled synthetic future (2021–2050) streamflow scenarios as both single and compound disturbances. Relative to a baseline scenario, modeled scenarios predict several changes to average annual water trends over the final simulation decade (2041–2050); (1) decreases in average annual water yield under a hot and dry climate (−14%), except during the rising limb of annual snowmelt; (2) increases in average annual water yield (+32%) and peak runoff under a fire simulation; and (3) increases in average annual water yield (+24%) along with earlier and higher peak runoff under compound (fire + hot/dry) conditions. These findings show the strengths of hydrologic models in separating compound disturbance signals at the stream outlet and a need for quantitative vegetation representation within models to adequately represent dynamic disturbance conditions.

Almost output regulation for turbofan switched systems under multi‐source disturbances and time delay

AbstractIn allusion to the multi‐variable robust control for the turbofan engine with the large envelope, an almost output regulation (AOR) problem is discussed for a class of turbofan switched systems under multi‐source disturbances and time delay. The addressed disturbances include the regulated disturbance signal, the modeled disturbance from the input channel and the energy‐bounded disturbance. First of all, a disturbance observer is constructed to estimate the unknown disturbance in the system input. Then, by combining the output regulation method and the disturbance observer‐based control (DOBC) method, a switched composite regulation controller is developed to deal with the AOR problem of the turbofan switched systems and reject the adverse effects of disturbances. Through the multiple Lyapunov function method, a set of sufficient conditions are obtained to guarantee the AOR property with the performance and disturbance suppression ability. Finally, the validity of the proposed method is demonstrated by applying the composite anti‐disturbance regulator to the regulation of the speed and pressure ratio for the turbofan model.

Sparse Adversarial Attacks against DL-Based Automatic Modulation Classification

Automatic modulation recognition (AMR) serves as a crucial component in domains such as cognitive radio and electromagnetic countermeasures, acting as a significant prerequisite for the efficient signal processing of receivers. Deep neural networks (DNNs), despite their effectiveness, are known to be vulnerable to adversarial attacks. This vulnerability has inspired the introduction of subtle interference to wireless communication signals—interference so minuscule that it is difficult for the human eye to discern. Such interference can mislead eavesdroppers into erroneous modulation pattern recognition when using DNNs, thereby camouflaging communication signal modulation patterns. Nonetheless, the majority of current camouflage methods used for electromagnetic signal modulation recognition rely on a global perturbation of the signal. They fail to consider the local agility of signal disturbance and the concealment requirements for bait signals that are intercepted by the interceptor. This paper presents a generator framework designed to produce perturbations with sparse properties. Furthermore, we introduce a method to reduce spectral loss, which minimizes the spectral difference between adversarial perturbation and the original signal. This method makes perturbation more challenging to monitor, thereby deceiving enemy electromagnetic signal modulation recognition systems. The experimental results validated that the proposed method significantly outperformed existing methods in terms of generation time. Moreover, it can generate adversarial signals characterized by high deceivability and transferability even under extremely sparse conditions.

Analysis of signal cable noise currents in nuclear reactors under high neutron flux irradiation

Cables are indispensable in nuclear power plants for transmitting data measured by various types of detectors, such as self-powered neutron detectors (SPNDs). These cables will generate disturbing signals that must be accurately distinguished and eliminated. Given that the cable current is not very significant, previous research has focused on SPND, with little attention paid to cable evaluation and validation. This paper specifically focuses on the quantitative analysis of cables and proposes a theoretical model to predict cable noise. In this model, the reaction characteristics between irradiated neutrons and cables were discussed thoroughly. Based on the Monte Carlo method, a comprehensive simulation approach of neutron sensitivity was introduced and long-term irradiation experiments in a heavy water reactor (HWR) were designed to verify this model. The theoretical results of this method agree quite well with the experimental measurements, proving that the model is reliable and exhibits excellent accuracy. The experimental data also show that the cable current accounts for approximately 0.2% of the total current at the initial moment, but as the detector gradually depletes, it will contribute more than 2%, making it a non-negligible proportion of the total signal current.

Fuzzy-proportional-integral-derivative hybrid controller design for ultra-high temperature milk processing

In the ultra-high temperature (UHT) process, fluid temperature is raised above 135 °C for a short period of time (typically 4 seconds) and then quickly cooled ensuring no microbes remain in the final product. To have better quality processed milk, a stringent temperature control system is necessary. To solve this problem a detailed control-oriented mathematical model of the heating system for UHT application is developed and a detailed block diagram is established by identifying various systems and signals. To draw the merits of a feedforward controller (transfer function or fuzzy logic based) and proportional-integral-derivative (PID) feedback compensator, a fuzzy PID hybrid controller is designed and simulated in a MATLAB environment. Findings of the simulation results indicate that the fuzzy-PID hybrid compensator concatenates the benefits of both controllers. PID controller processes the error signal and tracks the setpoint whereas the feedforward controller (transfer function or fuzzy) effectively rejects the disturbance signal’s effect on the controlled variable. The fuzzy-PID hybrid controller performs better than the individual PID or fuzzy controller.

Methodology for Designing Cyber-Physical Multi-Operation Robot Systems Operating in the Conditions of Digital Robust Control

Abstract The article proposes an original interdisciplinary approach to the design and construction of cyber-physical robot systems for mechanical processing. From a methodological aspect, the goal is the unification of modeling/synthesis and simulation software of a robot system for mechanical processing operating under the conditions of digital robust control. The system includes industrial robots; modules for implementing technological operations and transport systems. Adherence to the principle of modular construction, reconfiguration, and multi-operation ensures high flexibility and quick response when readjusting the system, and the optimization criteria – minimizing idle moves of the robot, leads to a reduction in the work cycle. The robust control, simultaneously in the instrumental, configurational, and system direction, is a counteraction in the mode of uncertainty, both to external signal disturbances and to possible constantly acting, system reparametrizing factors. This creates prerequisites for maintaining and implementing in online mode both the technological and geometric parameters of the details processed.

The Extended H∞ Particle Filter for Attitude Estimation Applied to Remote Sensing Satellite CBERS-4

An extension of the linear H∞ filter, presented here as the extended H∞ particle filter (EH∞PF), is used in this work for attitude estimation, which presents a process and measurement model with nonlinear functions. The simulations implemented use orbit and attitude data from CBERS-4 (China–Brazil Earth Resources Satellite-4), making use of the robustness characteristics of the H∞ filter. The CBERS-4 is the fifth satellite of an advantageous international scientific interaction between Brazil and China for the development of remote sensing satellites used for strategic application in monitoring water resources and controlling deforestation in the Legal Amazon. In the extended H∞ particle filter (EH∞PF) the nature of the system, composed of dynamics and noises, seeks to degrade the state estimate. The EH∞PF deals with this by aiming for robustness, using a performance parameter in its cost function, in addition to presenting an advantageous feature of using a reduced number of particles for state estimation. The justification for the application of this method is because the non-Gaussian uncertainties that appear in the attitude sensors impair the estimation process and the EH∞PF minimizes in signal estimation the worst effects of disturbance signals without a priori knowledge of them, as shown in the results, in addition to presenting good precision within the prescribed requirements, with 100 particles representing a processing time 2.09 times less than the PF with 500 particles.

Automatic Recognition of Vertical-Line Pulse Train from China Seismo-Electromagnetic Satellite Based on Unsupervised Clustering

Pulse signals refer to electromagnetic waveforms with short duration and high peak energy in the time domain. Spatial electromagnetic pulse interference signals can be caused by various factors such as lightning, arc discharge, solar disturbances, and electromagnetic disturbances in space. Pulse disturbance signals appear as instantaneous, high-energy vertical-line pulse trains (VLPTs) on the spectrogram. This paper uses computer vision techniques and unsupervised clustering algorithms to process and analyze VLPT on very-low-frequency (VLF) waveform spectrograms collected by the China Seismo-Electromagnetic Satellite (CSES) electric field detector. First, the waveform data are transformed into time–frequency spectrograms with a duration of 8 s using the short-time Fourier transform. Then, the spectrograms are subjected to grayscale transformation, vertical line feature extraction, and binarization preprocessing. In the third step, the preprocessed data are dimensionally reduced and fed into an unsupervised K-means++ clustering model to achieve automatic recognition and labeling of VLPTs. By recognizing and studying VLPT, not only can interference be recognized, but the temporal and spatial locations of these interferences can also be determined. This lays the foundation for identifying VLPT sources and gaining deeper insights into the generation, propagation, and characteristics of electromagnetic radiation.

Design of a single input control signal for fault-tolerant synchronization of fractional order hyperchaotic Lu system

ABSTRACT The authors of this paper used an adaptive-sliding mode controller (A-SMC) to synchronise two fractional-order hyperchaotic (FOHC) Lu systems in the presence of external disturbance and bounded parametric uncertainty with unknown bounds. They employed the Riemann–Liouville fractional order and defined a new fractional-order sliding surface for the FOHC Lu system to determine a proper active control. Additionally, they utilised adaptive laws to estimate uncertainty bounds and unknown disturbance signals. The authors proved the stability of the closed-loop control system using the Lyapunov theory. Simulation results in MATLAB demonstrated the desirable performance of this method in the presence of disturbance and parametric uncertainty.

Big data-based method for automatic localization of power quality disturbance signal of PV access distribution network

At present, the conventional automatic localization method of the power quality disturbance signal of the distribution network mainly extracts the feature vector of the disturbance signal. It constructs the target localization function, which leads to poor localization accuracy because the noise part of the disturbance signal is ignored. In this regard, the automatic localization method of PV access power quality disturbance signal based on big data is proposed. By using the wavelet decomposition algorithm, the noise part of the power quality disturbance signal is removed, and the measurement matrix and reconstruction function are combined with compressing and reconstructing the disturbance signal. Finally, the automatic positioning of the disturbance signal is achieved by extracting the characteristics of the disturbance signal data and clustering analysis processing. In the experiment, the designed signal localization method is tested for the localization effect. The results can prove that when the proposed method is used to automatically localize the disturbed signal, the localization results are consistent with the spatial feature distribution of the signal and have a more desirable automatic localization effect.

Audiogenic Seizures in the Streptozotocin-Induced Rat Alzheimer's Disease Model.

Alzheimer's disease (AD) is a neurodegenerative and progressive disorder with no cure and constant failures in clinical trials. The main AD hallmarks are amyloid-β (Aβ) plaques, neurofibrillary tangles, and neurodegeneration. However, many other events have been implicated in AD pathogenesis. Epilepsy is a common comorbidity of AD and there is important evidence indicating a bidirectional link between these two disorders. Some studies suggest that disturbed insulin signaling might play an important role in this connection. To understand the effects of neuronal insulin resistance in the AD-epilepsy link. We submitted the streptozotocin (STZ) induced rat AD Model (icv-STZ AD) to an acute acoustic stimulus (AS), a known trigger of seizures. We also assessed animals' performance in the memory test, the Morris water maze and the neuronal activity (c-Fos protein) induced by a single audiogenic seizure in regions that express high levels of insulin receptors. We identified significant memory impairment and seizures in 71.43% of all icv-STZ/AS rats, in contrast to 22.22% of the vehicle group. After seizures, icv-STZ/AS rats presented higher number of c-Fos immunopositive cells in hippocampal, cortical, and hypothalamic regions. STZ may facilitate seizure generation and propagation by impairment of neuronal function, especially in regions that express high levels of insulin receptors. The data presented here indicate that the icv-STZ AD model might have implications not only for AD, but also for epilepsy. Finally, impaired insulin signaling might be one of the mechanisms by which AD presents a bidirectional connection to epilepsy.