Disturbance Signals Research Articles

Deep Learning Soft-Decision GNSS Multipath Detection and Mitigation.

A technique is proposed to detect the presence of the multipath effect in Global Navigation Satellite Signal (GNSS) signals using a convolutional neural network (CNN) as the building block. The network is trained and validated, for a wide range of C/N0 values, with a realistic dataset constituted by the synthetic noisy outputs of a 2D grid of correlators associated with different Doppler frequencies and code delays (time-domain dataset). Multipath-disturbed signals are generated in agreement with the various scenarios encompassed by the adopted multipath model. It was found that pre-processing the outputs of the correlators grid with the two-dimensional Discrete Fourier Transform (frequency-domain dataset) enables the CNN to improve the accuracy relative to the time-domain dataset. Depending on the kind of CNN outputs, two strategies can then be devised to solve the equation of navigation: either remove the disturbed signal from the equation (hard decision) or process the pseudoranges with a weighted least-squares algorithm, where the entries of the weighting matrix are computed using the analog outputs of the neural network (soft decision).

Augmented Two-Stage Hierarchical Controller for Distributed Power Generation System Powered by Renewable Energy: Development and Performance Analysis

The sustainable development of an area is highly reliant on a reliable electrical energy supply. Microgrids are important in integrating distributed energy resources (DERs) using power electronic converters. However, microgrid control becomes challenging with the increasing number of distributed generators and loads. With the conventional droop control method, power contributions from DER converters cannot be accurately shared due to a mismatch of line impedances. In this paper, an augmented hierarchical control mechanism is proposed to solve the issues mentioned above. This hierarchical control mechanism consists of primary and secondary controllers. The primary stage utilized the droop controller to improve optimal power flow, mainly for the resistive network. The secondary stage is based on an improved methodology to compensate for the voltage and frequency variations during small and large signal disturbances. Moreover, the modelling and analysis for PMSG-based wind energy conversion systems are also presented. The response of the primary controller for active and reactive power sharing is investigated. The analysis emphasizes the demonstration of optimal power-sharing under normal and abnormal conditions for the considered load. Finally, the suggested robust controller’s performance is evaluated in a MATLAB environment, and simulation results show the proposed scheme’s superiority under different operating conditions. The frequency is stable at 50 Hz after a 50 KW load is added.

Induced ℒ∞ control for continuous-time nonlinear systems

In this paper, the problem of induced L ∞ control in the presence of disturbance signals in the control output of continuous-time nonlinear systems is considered. Under the condition that the system control output has a disturbance signal, the expected design conditions of the controller that satisfy the asymptotic stability and the specified performance are given. The sufficient conditions for such an induced L ∞ controller are expressed in terms of linear matrix inequalities (LMIs). Finally, the effectiveness and feasibility of the proposed induced L ∞ control design strategy is illustrated by a tunnelling diode circuit.

Disturbance and vegetational structure in an urban forest of Indian Siwaliks: an ecological assessment.

Urban forests face multiple human-mediated pressures leading to compromised ecosystem structure and functioning. Therefore, understanding ecosystem structure in response to ongoing pressures is crucial for sustaining ecological integrity and human well-being. We aim to assess the disturbance and its effects on the vegetation structure of urban forests in Chandigarh using a combination of remote sensing techniques and vegetation surveys. The disturbance was evaluated as a change in NDVI (Normalised Difference Vegetation Index) from 2001 to 2021 by applying the BFAST (Breaks For Additive Season and Trend) algorithm to the MODIS satellite imagery data. A vegetation survey was conducted to compare the species composition, taxonomic and phylogenetic diversity as measures of forest vegetational structure. While signals of disturbance were evident, the changes in vegetation structure were not well established from our study. Further, this analysis indicated no significant differences in vegetation composition due to disturbance (F1,12 = 0.91, p = 0.575). However, the phylogenetic diversity was substantially lower for disturbed plots than undisturbed plots, though the taxonomic diversity was similar among the disturbed and undisturbed plots. Our results confirmed that disturbance effects are more prominent on the phylogenetic than taxonomic diversity. These findings can be considered early signals of disturbance and its impact on the vegetation structure of urban forests and contribute to the knowledge base on urban ecosystems. Our study has implications for facilitating evidence-based decision-making and the development of sustainable management strategies for urban forest ecosystems.

In Ukrainian

Brownian motors belong to the class of nanoscale devices that use the thermal noise of the environment as one of the necessary components in the mechanism of their operation. Today, there are a lot of practical implementations of such nanomachines, both inorganic, fairly simple mechanisms produced artificially, and more complex ones created from separate biological components available at the cellular level. One of the options for implementing the mechanism of straightening the chaotic thermal noise of the environment into unidirectional motion is the presence of a motor particle in the field of action of an asymmetric periodic stationary potential, which undergoes certain small disturbances (fluctuations) periodically over time. To describe such asymmetric one-dimensional structures (for example, dipole chains or fibers of the cytoskeleton) in the theory of Brownian motors, two model potentials are most often used: piecewise linear sawtooth and double sinusoidal. In this work, within the framework of the approximation of small fluctuations, a model of a pulsating Brownian motor with a stationary double sinusoidal potential and a disturbing small harmonic signal is considered. A new method of parametrization of such a problem is proposed, which allows to separate the contributions from various factors affecting the operation of the ratchet, and the numerical procedure for calculating the average speed of the directional movement of nanoparticles for the selected type of model potentials is specified. A number of numerical dependences of the average speed on the main parameters of the system were obtained. Peculiarities of the behavior of the motor as dependent on the parameter responsible for asymmetry and the number of potential wells on the spatial period of the stationary potential have been investigated. It is shown that the direction of the generated flux of nanoparticles depends not only on the phase shift between the stationary and fluctuating components of the potential, but also on the temperature of the system and the frequency of fluctuations, i.e., a possibility of temperature-frequency control of the direction of movement in the considered model has been found. Diagrams have been constructed that allow you to choose the ratio between the parameters of the nanomotor to create a flux of particles in the desired direction.

Context-aware voltage sag waveform decomposition method using hybrid models for complex power quality detection

To analyze the voltage sag characteristics of distribution networks, automotive companies need an accurate and efficient non-stationary signal analysis tool to extract voltage sag feature information from power quality monitors and fault recorders, which is crucial for automotive companies to analyze grid characteristics and improve localized charging strategies. We innovatively propose a hybrid decomposition method to decompose voltage sag waveforms from composite disturbance data. Subsequently, we design a context-aware mechanism to adapt the proposed decomposition method to the complex power quality data in different regions. In addition, tests were conducted on an integrated power quality simulation system to verify that this method can accurately decompose the voltage sag waveform from the composite disturbance signals. Finally, the experimental simulation results also show the better performance of the method in decomposing voltage sag waveforms with different causes in different composite disturbance signals compared with other mainstream methods.

Transcriptome response of a marine copepod in response to environmentally-relevant concentrations of saxitoxin

Paralytic shellfish toxins (PSTs) can pose a serious threat to human health. Among them, saxitoxin (STX) is one of the most potent natural neurotoxins. Here, the copepod Tigriopus japonicus, was exposed to environmentally relevant concentrations (2.5 and 25 μg/L) STX for 48 h. Although no lethal effects were observed at both concentrations, the transcriptome was significantly altered, and displayed a concentration-dependent response. STX exposure decreased the copepod's metabolism and compromised immune defense and detoxification. Additionally, STX disturbed signal transduction, which might affect other cellular processes. STX exposure could inhibit the copepod's chitin metabolism, disrupting its molting process. Also, the processes related to damage repair and protection were up-regulated to fight against high concentration exposure. Collectively, this study has provided an early warning of PSTs for coastal ecosystem not only because of their potent toxicity effect but also their bioaccumulation that can transfer up the food chain after ingestion by copepods.

Disturbance Observation and Suppression in an Airborne Electro-Optical Stabilized Platform Based on a Generalized High-Order Extended State Observer.

Active disturbance rejection control (ADRC) is widely used in airborne optoelectronic stabilization platforms due to its minimal reliance on the mathematical model of the controlled object. The extended state observer (ESO) is the core of ADRC, which treats internal parameter variations and external disturbances as total disturbances, observes the disturbances as extended states, and then compensates them into the control loop to eliminate their effects. However, the ESO can only achieve a precise estimation of constant or slowly varying disturbances. When the disturbance is periodically changing, satisfactory results cannot be obtained. In this paper, a generalized high-order extended state observer (GHOESO) is proposed to achieve the precise estimation of known frequency sinusoidal disturbance signals and improve disturbance suppression levels. Through numerical simulations, a traditional ESO and GHOESO are compared in terms of disturbance observation capability and disturbance suppression ability for single and compound disturbances based on our prior knowledge of disturbance frequency. The effectiveness of the proposed GHOESO method is verified. Finally, the algorithm is applied to an airborne optoelectronic stabilization platform for a 1°/1 Hz swing experiment on a space hexapod swing table. The experimental results demonstrate the superiority of the GHOESO proposed in this paper.

A network-based mobile positioning system using an optimization model

The expansion of cellular network coverage facilitates the advancement of research on network-based positioning. We are interested in the signal fingerprinting method to predict the location of a mobile device. By this method, the device must be within the fingerprint coverage to have a successful location prediction. However, any disturbance in the signal propagation would decrease the prediction accuracy. We propose an optimization model based on generalized triangulation combined with a signal fingerprint which is treated more adaptively in responding to any signal disturbance. The triangulation method determines the most likely region where the device is located. The solution provides the estimated longitude and latitude of the device. An illustration of the implementation of the model is presented. The model is assessed using the Indosat cellular network in three distinct testbeds in Indonesia, which are: South Jakarta, a metropolitan area; South Tangerang, a buffer area adjacent to the metropolitan area; and Malang, a city surrounded by rural areas. The most favorable outcome yields an average prediction error of 39.6 m, a maximum error of 197.08 m, a minimum error of 0.05 m, and a standard deviation of error of 39.22 m.

Molecular evidence of sterile tissue damage during pathogenesis of the pododermatitis aseptica hemorrhagica circumscripta is associated with disturbed epidermal-dermal homeostasis

Podermatitis aseptica hemorrhagica circumscripta is associated with metalloproteinase 2 weakening of distal phalangeal suspensory structures and sinkage of the distal phalanx in the claw capsule. Pressure from the tuberculum flexorium on the sole epidermis and dermis produces hemorrhagic tissue injury and defective horn production appearing as yellow-red, softened claw horn in region 4 of the sole. A model of the MAPK/ERK signal cascade orchestrating epidermal-dermal homeostasis was employed to determine if sterile inflammatory responses are linked to disturbed signal transduction for epidermal homeostasis in sole epidermis and dermis. The objective was to assess shifts in target genes of inflammation, up- and downstream MAPK/ERK signal elements, and targeted genes supporting epidermal proliferation and differentiation. Sole epidermis and dermis was removed from lateral claws bearing lesions of podermatitis aseptica hemorrhagica circumscripta, medial claws from the same limb and lateral claws from completely normal limbs of multiparous, lactating Holstein cows. The abundance levels of targeted transcripts were evaluated by real-time QPCR. Lesion effects were assessed by ANOVA, and mean comparisons were performed with t-tests to assess variations between mean expression in ulcer-bearing or medial claw dermis and epidermis and completely normal lateral claw dermis and epidermis or between ulcer-bearing dermis and epidermis and medial claw dermis and epidermis. The lesions were sterile and showed losses across multiple growth factors, their receptors, several downstream AP1 transcription components, CMYC, multiple cell cycle and terminal differentiation elements conducted by MAPK/ERK signals and β 4, α 6 and collagen 17A hemidesmosome components. These losses coincided with increased cytokeratin 6, β 1 integrin, proinflammatory metalloproteinases 2 and 9, IL1B and physiologic inhibitors of IL1B, the decoy receptor and receptor antagonist. Medial claw epidermis and dermis from limbs with lateral claws bearing podermatitis aseptica hemorrhagica circumscripta showed reductions in upstream MAPK/ERK signal elements and downstream targets that paralleled those in hemorrhagic lesions. Inhibitors of IL1B increased in the absence of real increases in inflammatory targets in the medial claw dermis and epidermis. Losses across multiple signal path elements and downstream targets were associated with negative effects on targeted transcripts supporting claw horn production and wound repair across lesion-bearing lateral claws and lesion-free medial claw dermis and epidermis. It was unclear if the sterile inflammation was causative or a consequence of these perturbations.

Investigation on the induced electrical wakes generated by underwater vehicles

The induced electrical wake generated by underwater vehicles in the conductive seawater subject to the geomagnetic field can offer significant information for non-acoustic detection. However, the crucial demand in actual detection requires the sophisticated detection technology to capture the variations in the wake, which has not been experimentally explored. In this paper, an underwater measurement platform, which is capable of accurately capturing the weak electrical signature, is developed to verify the detectability of the induced electric field in the wake. Besides, a towing system is designed and established to generate the wake driving the motion of the conducting seawater in the current experiment. The results indicate that the induced electric field distributions in the wake demonstrate prominent peak characteristics. The detectable intensity of the measured electric signatures in the wake can range from 1 to 3 μV/m, primarily concentrated within the extremely low frequency band below 1 Hz. Additionally, the induced electric field intensity is positively correlated with the towing velocity and demonstrates a similar evolutionary trend. Subsequently, the numerical simulations are performed to not only cross-validate the measurement results but also further demonstrate the detailed distribution of the electrical wake evolution. These findings illustrate the feasibility of detection based on the induced electric field disturbance signal in the wake, which holds potential for non-acoustic detection and tracking of underwater targets.

Optimization technology of hydroelectric power plant unit speed control based on the constriction coefficient-based particle swarm gravitational search algorithm fusion model

In recent years, the integration of wind power into hydropower networks has become a trend. But after wind power is connected to the grid, its unstable power will have an impact on the stability of the power grid. In order to improve the impact of wind power grid connection on the stability of the power supply network, a water motor speed control fusion model considering wind power interference is proposed. Firstly, analyze the impact of wind power grid connection and construct a speed regulation model for grid connected units. Secondly, the gravity search algorithm is introduced to optimize the unit speed control parameters. Considering that the algorithm is prone to local convergence, the convergence factor particle swarm optimization algorithm is adopted to optimize the gravity search algorithm. In addition, considering the impact of wind power interference on unit regulation, the Hopf bifurcation theory is introduced to analyze the dynamic regulation parameter range of hydropower units, in order to meet the requirements of stable operation of the power grid. The research contribution is mainly reflected in suppressing wind power grid connection interference through the speed regulation of hydroelectric units. At the same time, considering the impact of multiple interferences on grid connection, an intelligent model is introduced to optimize the speed control parameters of the unit, thereby improving the stable impact of wind power grid connection on the water conservancy grid. In the simulation analysis of hydroelectric unit speed regulation, after adding 5 % disturbance signal, the proposed model under rigid water hammer disturbance could stabilize the unit's stable control within 15 s, which is superior to other models. In the simulation analysis of speed regulation under wind power disturbance, the proposed model could achieve the fastest and most stable speed regulation under rigid water hammer step wind power disturbance, with a duration of 60 s. The proposed model could respond quickly to small overshoot and overshoot in elastic water hammer slope type wind power disturbances. Therefore, the proposed speed regulation technology for hydropower units has excellent application effects in practical scenarios, which will provide effective technical references for wind power grid connection and hydropower unit regulation.

Interference fading suppression with a multi-subcarrier pulse in a distributed acoustic sensor

A low-complexity multi-subcarrier pulse generation scheme is proposed to suppress the interference fading in a phase-sensitive optical time-domain reflectometer (Φ-OTDR) based distributed acoustic sensor (DAS) with heterodyne coherent detection. The multi-subcarrier pulse is generated in the digital domain based on the proper clipping operation of a sine signal. The localization and recovery of the disturbance signal are realized by the spectrum extraction and rotated vector sum (SERVS) method. The experimental results show that the occurrences of interference fading can be significantly reduced. The intensity fluctuation is reduced from ∼75 dB to ∼25 dB. Multiple disturbance signals are successfully demodulated to verify the effectiveness of the proposed method.

Valine induces inflammation and enhanced adipogenesis in lean mice by multi-omics analysis.

The branched-chain amino acids (BCAAs) are essential to mammalian growth and development but aberrantly elevated in obesity and diabetes. Each BCAA has an independent and specific physio-biochemical effect on the host. However, the exact molecular mechanism of the detrimental effect of valine on metabolic health remains largely unknown. This study showed that for lean mice treated with valine, the hepatic lipid metabolism and adipogenesis were enhanced, and the villus height and crypt depth of the ileum were significantly increased. Transcriptome profiling on white and brown adipose tissues revealed that valine disturbed multiple signaling pathways (e.g., inflammation and fatty acid metabolism). Integrative cecal metagenome and metabolome analyses found that abundances of Bacteroidetes decreased, but Proteobacteria and Helicobacter increased, respectively; and 87 differential metabolites were enriched in several molecular pathways (e.g., inflammation and lipid and bile acid metabolism). Furthermore, abundances of two metabolites (stercobilin and 3-IAA), proteins (AMPK/pAMPK and SCD1), and inflammation and adipogenesis-related genes were validated. Valine treatment affects the intestinal microbiota and metabolite compositions, induces gut inflammation, and aggravates hepatic lipid deposition and adipogenesis. Our findings provide novel insights into and resources for further exploring the molecular mechanism and biological function of valine on lipid metabolism.

Lipid metabolism is dysregulated in endocrine glands upon autoimmune demyelination

Disturbance in neuroendocrine signaling has been consistently documented in multiple sclerosis (MS), a chronic autoimmune disorder of the central nervous system (CNS) representing the main cause of non-traumatic brain injury among young adults. In fact, MS patients display altered hormonal levels and psychiatric symptoms along with the pathologic hallmarks of the disease, which include demyelination, neuroinflammation and axonal injury. In addition, we have recently shown that extensive transcriptional changes take place in the hypothalamus of mice upon the MS model experimental autoimmune encephalomyelitis (EAE). We also detected structural and functional aberrancies in endocrine glands of EAE animals. Specifically, we described the hyperplasia of adrenal glands and the atrophy of ovaries at disease peak. To further expand the characterization of these phenotypes, here we profiled the transcriptomes of both glands by means of RNA-seq technology. Notably, we identified fatty acid and cholesterol biosynthetic pathways as the most dysregulated molecular processes in adrenals and ovaries, respectively. Furthermore, we demonstrated that key genes encoding neuropeptides and hormone receptors undergo distinct expression dynamics in the hypothalamus along disease progression. Altogether, our results corroborate the dysfunction of the neuroendocrine system as a major pathological event of autoimmune demyelination and highlight the crosstalk between the CNS and the periphery in mediating such disease phenotypes.

Nonlinear dynamical system approximation and adaptive control based on hybrid‐feed‐forward recurrent neural network: Simulation and stability analysis

AbstractWe proposed an online identification and adaptive control framework for the nonlinear dynamical systems using a novel hybrid‐feed‐forward recurrent neural network (HFRNN) model. The HFRNN is a combination of a feed‐forward neural network (FFNN) and a local recurrent neural network (LRNN). We aim to leverage the simplicity of FFNN and the effectiveness of RNN to capture changing dynamics accurately and design an indirect adaptive control scheme. To derive the weights update equations, we have applied the gradient‐descent‐based Back‐Propagation (BP) technique, and the stability of the proposed learning strategy is proven using the Lyapunov stability principles. We also compared the proposed method's results with those of the Jordan network‐based controller (JNC) and the local recurrent network‐based controller (LRNC) in the simulation examples. The results demonstrate that our approach performs satisfactorily, even in the presence of disturbance signals.

Artificial neural network‐based adaptive control for nonlinear dynamical systems

SummaryThis research article presents an artificial neural network (ANN)‐based indirect adaptive control method for nonlinear dynamical systems. In this article, a modified Elman recurrent neural network (MERNN) is proposed as an identifier and controller for controlling nonlinear systems. The architecture of the proposed controller is a modified form of the existing Elman recurrent neural network. The parameter training of ANN‐based controllers is obtained by using the most popular optimization algorithm which is known as the back‐propagation algorithm. A comparative study includes Elman, Diagonal, Jordan, feed‐forward neural network (FFNN), and radial basis function network (RBFN)‐based controllers to compare with the proposed MERNN controller. To determine the controller's robustness, parameter variations, and disturbance signals have been considered. The performance analysis of the proposed controller is illustrated by two simulation examples. The simulation results reveal that MERNN can not only identify the unknown dynamics of the plant but also adaptively control it compared to the others.

D-region ionospheric disturbances due to the December 2019 solar eclipse observed using multi-station VLF radio network

We present the low-latitude D-region (60–90 km) ionospheric disturbances due to the December 2019 annular solar eclipse observed using the multi-station Very Low Frequency (VLF) radio network in West Bengal, India. VLF signals from communication transmitters were received from ten places using low-cost VLF radio receivers. The receivers were capable of recording the amplitudes of VLF signals mainly from the transmitters VTX, India at 18.2 kHz and NWC, Australia at 19.8 kHz. During the solar eclipse, the VTX signal experienced considerable positive, negative, and mixed-type amplitude variations depending on the propagation lengths between the transmitter and receivers, whereas the NWC signal displayed a relatively small amplitude response. We have also found that the highest VLF signal disturbances in each propagation path occurred after the time when the solar obscuration was maximum over the entire path. We used the observations of the VTX signal to formulate the relationship between solar obscuration and D-region electron density reduction during the solar eclipse. On the basis of this, we presented the electron density distribution of the D-region ionosphere during the solar eclipse across the Indian subcontinent. This study shows a useful method for integrating VLF observations and simulations to estimate the D-region electron density distribution over a vast area during a solar eclipse.

Power quality disturbances classification with imbalanced/insufficient samples based on WGAN-GP-SA and DCNN

The accurate identification of power quality disturbance signals (PQDs) is of great importance to ensure the safe and stable operation of power systems. Considering that there are insufficient samples and imbalance samples of power quality disturbance signals in real systems, the hybrid classification method based on WGAN-GP-SA (Wasserstein generative adversarial networks with gradient penalty and Squeeze-and-Excitation block with atrous spatial pyramid pooling module) and DCNN (deep convolutional neural network) is proposed for recognition of PQDs. Firstly, time-frequency features are extracted by combining improved Kalman and continuous wavelet transforms (CWT); secondly, the original training time-frequency maps are expanded by using WGAN-GP-SA, which can learn the distribution of real data and generate similar samples, and PSNR, SSIM, FID and other indexes are introduced for evaluation of generated data; finally, classification experiments are conducted on two data sets using DCNN, and simulation results show that the accuracy is improved by 2.82 % compared with the original data without augmentation. Furthermore, t-distributed stochastic neighbor embedding algorithm (t-SNE) is introduced for visually analysing of classification performance of various datasets, and the visualization indicates the superiority of the hybrid classification method.

Sensitivity of Sentinel-1 Backscatter to Management-Related Disturbances in Temperate Forests

The rapid and accurate detection of forest disturbances in temperate forests has become increasingly crucial as policy demands and climate pressure on these forests rise. The cloud-penetrating Sentinel-1 radar constellation provides frequent and high-resolution observations with global coverage, but few studies have assessed its potential for mapping disturbances in temperate forests. This study investigated the sensitivity of temporally dense C-band backscatter data from Sentinel-1 to varying management-related disturbance intensities in temperate forests, and the influence of confounding factors such as radar backscatter signal seasonality, shadow, and layover on the radar backscatter signal at a pixel level. A unique network of 14 experimental sites in the Netherlands was used in which trees were removed to simulate different levels of management-related forest disturbances across a range of representative temperate forest species. Results from six years (2016–2022) of Sentinel-1 observations indicated that backscatter seasonality is dependent on species phenology and degree of canopy cover. The backscatter change magnitude was sensitive to medium- and high-severity disturbances, with radar layover having a stronger impact on the backscatter disturbance signal than radar shadow. Combining ascending and descending orbits and complementing polarizations compared to a single orbit or polarization was found to result in a 34% mean increase in disturbance detection sensitivity across all disturbance severities. This study underlines the importance of linking high-quality experimental ground-based data to dense satellite time series to improve future forest disturbance mapping. It suggests a key role for C-band backscatter time series in the rapid and accurate large-area monitoring of temperate forests and, in particular, the disturbances imposed by logging practices or tree mortality driven by climate change factors.