Intermittency Parameter Research Articles

Nonlinear Radial Consolidation Analysis of Vertical Drains Foundation under Cyclic Loadings

Aiming at the stability of storage facilities built in soft soil areas, this paper analyzes the influence of cyclic loading on consolidation during used stage. Firstly, the nonlinear compressibility and permeability of soil are introduced, the well resistance and three kinds of radial permeability coefficient modes are considered, the corresponding governing equations are obtained, and the corresponding analytical solution is obtained by the separate variables method. Then, the corresponding solution under the action of trapezoidal and triangular cyclic loading is obtained. The rationality of the solution in this paper is verified by the degradation method. Finally, the settlement of storage facilities during the using period is predicted by case analysis. The results show that the ratio of soil compression index to permeability index (Cc/Ck) and the radial permeability coefficient mode have no significant influence on the average consolidation degree. The load-related intermittent parameter β, the loading parameter α, and the parameter Nσ have a great influence on the average consolidation degree. The smaller the three parameters are, the more energy the load generates at the same time, and the greater the corresponding average degree of consolidation. The solution can be effectively applied to nonlinear radial consolidation analysis under trapezoidal and triangular cyclic loadings.

Temporal structure of blobs in NSTX

The time dependence of the blob pulse shape and the waiting time between blobs was found using data from the gas puff imaging diagnostic in NSTX. The database used was of 103 shots from 2010 as described in a previous paper (Zweben et al., Phys. Plasmas 29, 012505 (2022).]. The blob pulse shape was well fit by an exponential rise and fall where the average rise time was τr = 9.0 ± 2.7 μs and the average fall time was τf = 16.6 ± 5.8 μs. The waiting times between blob pulses above a threshold of three times the mean had a broad distribution with an average of τw = 1.2 ± 0.85 ms over the database. The blob intermittency parameter γb = τd/τw, where the blob pulse duration was τd = τr + τf, ranged from γb ∼1% to 5% for shots in this database and increased almost linearly with the blob fraction.

Intermittency in the Expanding Solar Wind: Observations from Parker Solar Probe (0.16 au), Helios 1 (0.3–1 au), and Voyager 1 (1–10 au)

Abstract We examine statistics of magnetic-field vector components to explore how intermittency evolves from near-Sun plasma to radial distances as large as 10 au. Statistics entering the analysis include autocorrelation, magnetic structure functions of the order of n (SF n ), and scale-dependent kurtosis (SDK), each grouped in ranges of heliocentric distance. The Goddard Space Flight Center Space Physics Data Facility provides magnetic-field measurements for resolutions of 6.8 ms for Parker Solar Probe, 6 s for Helios, and 1.92 s for Voyager 1. We compute SF2 to determine the scales encompassing the inertial range and examine SDK to investigate the degree of non-Gaussianity. Autocorrelations are used to resolve correlation scales. Correlation lengths and ion inertial lengths provide an estimate of effective Reynolds number (Re). Variation in Re allows us to examine for the first time the relationship between SDK and Re in an interplanetary plasma. A conclusion from this observed relationship is that regions with lower Re at a fixed physical scale have on average lower kurtosis, implying less intermittent behavior. Kolmogorov refined similarity hypothesis is applied to magnetic SF n and kurtosis to calculate intermittency parameters and fractal scaling in the inertial range. A refined Voyager 1 magnetic-field data set is generated.

Mathematical modeling of impulse island controller to safely store the energy from high‐voltage lightning impulse

AbstractExtricating the energy from lightning impulse is very difficult for researchers due to the uncontrollability of hazardous and intermittent parameters. This paper proposed a mathematical method to island the lightning impulse to provide safety for the impulse storage system. The proposed model also can intelligently force the impulse to be extricated for the conversion system of the lightning energy storage device. This islanding controller is designed to increase the efficiency and safety of the storage system. Six essential parameters, that is, breakdown voltage, current dependency, spark channel radius, spark channel conductivity, coil inductance, and coil voltage, are considered to design the mathematical model. These parameters are formulated to record the responses of the proposed controller. All the equations are validated by using state of the art experiments and prototype hardware setup. The proposed method, mathematical model, and analyzed responses may assist lightning researchers in advancing lightning energy harvesting.

Vertical dispersion mechanism of long-range transported dust in Beijing: Effects of atmospheric turbulence

An extreme dust storm hit North China from March 14 to 16, 2021. With the enhanced observations in Beijing, the vertical dispersion of long-range transported dust was investigated from the perspective of atmospheric turbulence. A framework was developed by combining two types of turbulence intermittency parameter to distinguish intermittent or continuous turbulence; thus, the evolution of turbulence and its roles in dust dispersion were clearly illustrated on March 15, 2021. Initially, turbulence remained in the long quiescent period and turbulent exchange was suppressed significantly, which negligibly increased the dust mass concentration in the lower atmosphere, despite the dust mass being located at 300 to 800 m. Then, a short turbulence burst occurred as the cold front approached Beijing, and the elevated dust was mixed downward intensely and rapidly, leading to a sharp increase in the near-surface dust mass concentration. Finally, continuous turbulence dominated and diffused dust upward when the cold front fully occupied Beijing. Furthermore, the dissimilarity between the turbulent transport of dust and momentum-heat depends on the presence of dust sources (e.g., elevated dust mass and advection). When dust sources exist, there is efficient dust transport by large-scale turbulence, and thus the transport of dust and momentum-heat is similar. However, when there is no dust source, dust particles do not follow the air mass for small-scale turbulence and exhibit strong random movements, which confounds downgradient transport by large-scale turbulence and leads to low dust transport efficiency. In this condition, the conventional flux-gradient method is not applicable to estimate the dust flux especially by replacing the diffusion coefficient or universal function of dust mass concentration with that of momentum or heat.

Optimising Turning Operation in Precision Manufacturing Using Fused IoT Devices and Machine Learning Approach.

The two principal components of a turning operation on the machine tool are the cutting tool and workpiece. While the cutting tool condition alongside the machine tool condition, determines the surface finish of a machined part, the workpiece material influences the cutting tool condition. This paper proposes an intelligent precision manufacturing system that employs a machine learning algorithm on the data captured by fused IoT devices for intelligent decision making and condition identification. The data captured during the turning operation includes the thermal image, the vibration condition during the cutting operation and the intermittent wear parameters of the workpiece and cutting tool. Convolutional Neural Network (CNN) algorithm is used to classify, and correlate captured thermal images, while the vibration parameters and wear parameters are trained using Artificial Neural Network (ANN) for classifying the tool and workpiece wear conditions. Measured parameters of the finished part are correlated with the tool condition for optimizing the tool life. Classes indicating the tool and workpiece conditions are established from the trained network of measured data. The significance of the proposed method is that the tool wear condition is measured against the product finish requirement, hence, optimizing the operation by optimally utilizing the tool based on product requirement while reducing downtime due to intermittent condition monitoring.

Long term correlation and inhomogeneity of the inverted pendulum sway time-series under the intermittent control paradigm

In this study the extended detrended fluctuation analysis (EDFA) was applied to the sway data generated from an inverted pendulum (IP) model, intermittently controlled at the ankle. The time series taken into account was the center of pressure (COP), since it represents the widest used time series in posturography, and it constitutes a natural link between model and data-based analysis approaches for studying the dynamics of the human balance maintenance. COP time-series were obtained by varying the intermittent control parameters (ICP) in a uniform distribution range that ensures IP stability to quantify changes in the long-term correlation and inhomogeneity of the time-series. Globally, EDFA coefficients (α and β) showed to be sensitive to the variations of derivative control gain (D), whereas for proportional gain (P) and ρ parameters no significant trends were observed. However, relations between EDFA coefficients and ρ arose whether derivative gain is examined within a low and high regions of value. For low D gains, both α and β showed a significant correlation with ρ, which disappears when higher D values were considered. Thus EDFA coefficients can provide useful insights about the long-term correlation and local characteristics of COP timeseries, which are strictly related to the control policy adopted for maintaining balance. This supports the validity of the intermittent motor control paradigm for the human upright stance and suggests the use of EDFA in real posturography applications, in order to extract meaningful information regarding the properties of COP timeseries for different groups of patients.

Using deep learning and meteorological parameters to forecast the photovoltaic generators intra-hour output power interval for smart grid control

In recent years, the photovoltaic generation installed capacity has been steadily growing thanks to its inexhaustible and non-polluting characteristics. However, solar generators are strongly dependent on intermittent weather parameters, increasing power systems' uncertainty level. Forecasting models have arisen as a feasible solution to decreasing photovoltaic generators' uncertainty level, as they can produce accurate predictions. Traditionally, the vast majority of research studies have focused on the development of accurate prediction point forecasters. However, in recent years some researchers have suggested the concept of prediction interval forecasting, where not only an accurate prediction point but also the confidence level of a given prediction are computed to provide further information. This paper develops a new model for predicting photovoltaic generators' output power confidence interval 10 min ahead, based on deep learning, mathematical probability density functions and meteorological parameters. The model's accuracy has been validated with a real data series collected from Spanish meteorological stations. In addition, two error metrics, prediction interval coverage percentage and Skill score, are computed at a 95% confidence level to examine the model's accuracy. The prediction interval coverage percentage values are greater than the chosen confidence level, which means, as stated in the literature, the proposed model is well-founded.

Optimization of microenergy grid including adiabatic compressed air energy storage by considering uncertainty of intermittent parameters

AbstractIn recent years, zero‐carbon energy resources such as adiabatic compressed air energy storage with thermal energy storage have been interested due to growing concerns over global warming. This study proposes a microenergy grid including heat and power networks connected through adiabatic compressed air energy storage with thermal energy storage, which can be considered hybrid energy storage supplying power for both networks. The power network is supplied with the main grid and wind turbine systems, and the heat network is provided with heat pumps. The objective function minimizes power purchased from the main grid and power demand of heat pumps in the heat network. Since uncertainty plays a key role in the operation of integrated energy systems, the uncertainty of intermittent parameters such as active and reactive load and wind speed data has been considered in this study. Therefore, predicted values are used in the optimization problem instead of using deterministic values for such uncertain parameters. To do this, an efficient 2‐level corrective forecasting algorithm is proposed to have an accurate prediction for the day‐ahead operation of the microenergy grid. Different scenarios are presented to show the importance of the forecasting method and the utilization of adiabatic compressed air energy storage with thermal energy storage in the system’s structure. The results indicate that corrective actions on the predicted load and wind speed data decrease the operation cost of the microenergy grid from 57.13% to 13.21%. Also, it is found that neglecting adiabatic compressed air energy storage with thermal energy storage in the structure of the microenergy grid increases operation cost to 3423 US$. Other obtained results also indicate the importance of coutilization of compressed air energy storage with thermal energy storage and 2‐level corrective forecasting method leading to optimal operation of the microenergy grid.

Kratkoročno predviđanje brzine vetra bazirano na mrežama za dugotrajne-kratkoročne memorije (LSTM)

Power utilities, developers, and investors are pushing towards larger penetrations of wind and solar energy-based power generation in their existing energy mix. This study, specifically, looks towards wind power deployment in Saudi Arabia. For profitable development of wind power, accurate knowledge of wind speed both in spatial and time domains is critical. The wind speed is the most fluctuating and intermittent parameter in nature compared to all the meteorological variables. This uncertain nature of wind speed makes wind power more difficult to predict ahead of time. Wind speed is dependent on meteorological factors such as pressure, temperature, and relative humidity and can be predicted using these meteorological parameters. The forecasting of wind speed is critical for grid management, cost of energy, and quality power supply. This study proposes a short-term, multi-dimensional prediction of wind speed based on Long-Short Term Memory Networks (LSTM). Five models are developed by training the networks with measured hourly mean wind speed values from1980 to 2019 including exogenous inputs (temperature and pressure). The study found that LSTM is a powerful tool for a short-term prediction of wind speed. However, the accuracy of LSTM may be compromised with the inclusion of exogenous features in the training sets and the duration of prediction ahead.

Blob interactions in 2D scrape-off layer simulations

Interaction of coherent structures known as blobs in the scrape-off layer of magnetically confined plasmas is investigated. Isolated and interacting seeded blobs, as well as full plasma turbulence, are studied by two-dimensional numerical simulations. The features of the blobs (position, size, amplitude) are determined with a blob tracking algorithm, which identifies them as coherent structures with amplitudes above a chosen particle density threshold, and their velocities are compared to a conventional center of mass approach. We find that the theoretical velocity-size scaling dependence for isolated blobs is correctly resolved by the blob tracking method. The benchmarked approach is then extended to a population of interacting plasma blobs with statistically distributed amplitudes, sizes, and initial positions for different levels of blob interaction. We observe a correlation between the level of blob interaction and the number of blobs deviating from size–velocity scaling laws of perfectly isolated blobs. This is found to be caused by the interaction of blobs with the electrostatic potential of one another, leading to higher average blob velocities. We introduce a model specific intermittency parameter, quantifying the degree of blob interaction. For interacting blobs, we estimate the deviation from the picture of perfectly isolated blobs as a function of the intermittency parameter. For full plasma turbulence simulations, we observe a strong correlation between the blob amplitudes, sizes, and velocities estimated by the blob tracking algorithm.

Numerical simulation of turbulent thermal convection based on LBM

The two-dimensional (2D) turbulent thermal convection is numerically investigated by using Lattice Boltzmann Method. The 2D turbulence is considered as 2D channel flow where the flow is forced by the arrays of adiabatic cylinders placed in the inlet and wall boundary of 2D channel, which is heated uniformly from the inlet as to inspire the paradigmatic motion of thermal convection. It is found that the spacing vortex number density distribution in the large-scale range [Formula: see text], based on the Liutex vortex definition criterion, which is in fair agreement with the Benzi prediction. The energy spectrum of the Liutex field [Formula: see text]. The scaling behavior of full-field energy spectrum in the large scale is [Formula: see text]. The temperature spectrum in the large-scale range is found to be approximate to [Formula: see text], which is according with the Bolgiano theory of 2D buoyancy driven turbulence. The energy flux cascades to the large scale, the enstrophy cascades to small scale. The moments of the energy dissipation field [Formula: see text] coarse grained at the scale [Formula: see text] have the power-law behaviors with the scale [Formula: see text]. The velocity intermittency measured by PDF exists in large-scale range of 2D turbulent thermal convection. The measured scaling exponents [Formula: see text] are determined by a lognormal formula. The measured intermittency parameter is [Formula: see text], which denotes the strong intermittency in the large-scale range of 2D turbulent thermal convection.

Comparison between mirror Langmuir probe and gas-puff imaging measurements of intermittent fluctuations in the Alcator C-Mod scrape-off layer

Statistical properties of the scrape-off layer plasma fluctuations are studied in ohmically heated plasmas in the Alcator C-Mod tokamak. For the first time, plasma fluctuations as well as parameters that describe the fluctuations are compared across measurements from a mirror Langmuir probe (MLP) and from gas-puff imaging (GPI) that sample the same plasma discharge. This comparison is complemented by an analysis of line emission time-series data, synthesized from the MLP electron density and temperature measurements. The fluctuations observed by the MLP and GPI typically display relative fluctuation amplitudes of order unity together with positively skewed and flattened probability density functions. Such data time series are well described by an established stochastic framework that models the data as a superposition of uncorrelated, two-sided exponential pulses. The most important parameter of the process is the intermittency parameter, $\gamma = {\tau _{d}} / {\tau _{w}}$ , where ${\tau _{d}}$ denotes the duration time of a single pulse and ${\tau _{w}}$ gives the average waiting time between consecutive pulses. Here we show, using a new deconvolution method, that these parameters can be consistently estimated from different statistics of the data. We also show that the statistical properties of the data sampled by the MLP and GPI diagnostic are very similar. Finally, a synthetic GPI signal using only plasma parameters sampled by the MLP shows qualitatively different fluctuation statistics from the measured GPI signal.

A Dynamic DNA Color Image Encryption Method Based on SHA-512.

This paper presents a dynamic deoxyribonucleic acid (DNA) image encryption based on Secure Hash Algorithm-512 (SHA-512), having the structure of two rounds of permutation–diffusion, by employing two chaotic systems, dynamic DNA coding, DNA sequencing operations, and conditional shifting. We employed the SHA-512 algorithm to generate a 512-bit hash value and later utilized this value with the natural DNA sequence to calculate the initial values for the chaotic systems and the eight intermittent parameters. We implemented a two-dimensional rectangular transform (2D-RT) on the permutation. We used four-wing chaotic systems and Lorentz systems to generate chaotic sequences and recombined three channel matrices and chaotic matrices with intermittent parameters. We calculated hamming distances of DNA matrices, updated the initial values of two chaotic systems, and generated the corresponding chaotic matrices to complete the diffusion operation. After diffusion, we decoded and decomposed the DNA matrices, and then scrambled and merged these matrices into an encrypted image. According to experiments, the encryption method in this paper not only was able to withstand statistical attacks, plaintext attacks, brute-force attacks, and a host of other attacks, but also could reduce the complexity of the algorithm because it adopted DNA sequencing operations that were different from traditional DNA sequencing operations.

Multichannel Parallel Testing of Intermittent Faults and Reliability Assessment for Electronic Equipment

Many connections exist in complex electronic equipment, which leads to a long duration of intermittent fault test and difficulty in achieving comprehensive coverage of intermittent fault. The intermittent fault mechanism and the multichannel intermittent fault parameter parallel capture method are investigated. A smaller module covers multiple intermittent connection faults in electronics and prevents missed detections. Based on the test results, a reliability assessment of the connection link with intermittent faults is needed in the next step. The correlation between the intermittent fault parameters and the reliability of connection link is analyzed, and the intermittent fault parameters are used to evaluate the connection link reliability. An aviation electrical connector and solder joint are used for testing. The results show that increases in the intermittent fault parameters are consistent with the trend of reliability degradation, which indicates that the proposed method can be applied to intermittent fault testing and reliability assessment for the connection link of electronic equipment.

Fine Spike Timing in Hippocampal-Prefrontal Ensembles Predicts Poor Encoding and Underlies Behavioral Performance in Healthy and Malformed Brains.

Spatial working memory (SWM) is a central cognitive process during which the hippocampus and prefrontal cortex (PFC) encode and maintain spatial information for subsequent decision-making. This occurs in the context of ongoing computations relating to spatial position, recall of long-term memory, attention, among many others. To establish how intermittently presented information is integrated with ongoing computations we recorded single units, simultaneously in hippocampus and PFC, in control rats and those with a brain malformation during performance of an SWM task. Neurons that encode intermittent task parameters are also well modulated in time and incorporated into a functional network across regions. Neurons from animals with cortical malformation are poorly modulated in time, less likely to encode task parameters, and less likely to be integrated into a functional network. Our results implicate a model in which ongoing oscillatory coordination among neurons in the hippocampal-PFC network describes a functional network that is poised to receive sensory inputs that are then integrated and multiplexed as working memory. The background temporal modulation is systematically altered in disease, but the relationship between these dynamics and behaviorally relevant firing is maintained, thereby providing potential targets for stimulation-based therapies.

Oxygen desaturation rate as a novel intermittent hypoxemia parameter in severe obstructive sleep apnea is strongly associated with hypertension.

To investigate the effects of different intermittent hypoxemia properties on blood pressure (BP) and short-term blood pressure variability (BPV) in severe obstructive sleep apnea (OSA) patients. Nocturnal BP was continuously monitored by measuring pulse transmit time. Apnea-related systolic BP elevation values were used to reflect BPV. Beat-to-beat R-R interval data were incorporated in polysomnography for heart rate variability analysis. The low-frequency/high-frequency band ratio was used to reflect sympathovagal balance. The rate of pulse oxyhemoglobin saturation (SpO₂) decrease was counted as the change in the percentage of SpO₂ per second after obstructive apnea and expressed as the oxygen desaturation rate (ODR). Patients with severe OSA (n = 102) were divided into 2 groups according to the median ODR: faster ODR (FODR group: ODR > 0.37, n = 50) and slower ODR (ODR ≤ 0.37, n = 52). Comparisons between the 2 groups showed significantly higher systolic BP (SBP) values in the FODR group than in the slower ODR group (awake SBP 149.9 ± 18.3 vs 131.8 ± 15.6 mm Hg; asleep SBP: 149.6 ± 19.9 vs 128.7 ± 15.6 mm Hg; both P < .001), as well as short-term BPV (15.0 ± 4.8 vs 11.6 ± 3.6 mm Hg; P < .001), and the prevalence of hypertension (74.0% vs 26.9%; P < .001). Multiple linear regression analyses revealed that after adjusting for body mass index, functional residual capacity, expiratory reserve volume, and baseline SpO2, ODR, as assessed by ΔSpO₂/Δt, had the strongest association with both BP and short-term BPV. Correlation analysis showed that ODR was positively correlated with the low-frequency/high-frequency band ratio (r = .288, P = .003). ODR, as a novel hypoxemia profile, was more closely associated with the elevation of BP and BPV in patients with severe OSA. FODR might be associated with enhanced sympathetic activity. Registry: ClinicalTrials.gov; Name: Characteristics of Obstructive Sleep Apnea Syndrome Related Hypertension and the Effect of Continuous Positive Airway Pressure Treatment on Blood Pressure; URL: https://clinicaltrials.gov/ct2/show/NCT03246022; Identifier: NCT03246022.

When will we ditch the AHI?

When will we ditch the AHI?

Synchronization for stochastic hybrid coupled controlled systems with Lévy noise

In this paper, synchronization for stochastic hybrid‐delayed coupled systems with Lévy noise on a network (SHDCLN) is investigated via aperiodically intermittent control. Here time delays, Markovian switching and Lévy noise are considered on a network simultaneously for the first time. After that, by means of Lyapunov method, graph theory, and some techniques of inequality, some sufficient conditions are derived to guarantee the synchronization for SHDCLN. In addition, the designed range of aperiodically intermittent controller parameters is shown. Meanwhile, the coupling strength and the perturbed intensity of noise have a great impact on the intensity of control. Then, we investigate synchronization for stochastic hybrid delayed Chua's circuits with Lévy noise on a network as a practical application of our theoretical results. Finally, a numerical example is given to illustrate the effectiveness of the theoretical results.

Guaranteed Cost Finite-Time Control of Uncertain Coupled Neural Networks.

This article investigates a robust guaranteed cost finite-time control for coupled neural networks with parametric uncertainties. The parameter uncertainties are assumed to be time-varying norm bounded, which appears on the system state and input matrices. The robust guaranteed cost control laws presented in this article include both continuous feedback controllers and intermittent feedback controllers, which were rarely found in the literature. The proposed guaranteed cost finite-time control is designed in terms of a set of linear-matrix inequalities (LMIs) to steer the coupled neural networks to achieve finite-time synchronization with an upper bound of a guaranteed cost function. Furthermore, open-loop optimization problems are formulated to minimize the upper bound of the quadratic cost function and convergence time, it can obtain the optimal guaranteed cost periodically intermittent and continuous feedback control parameters. Finally, the proposed guaranteed cost periodically intermittent and continuous feedback control schemes are verified by simulations.