Adaptive Rule Research Articles

Intelligent control of induction motor for photovoltaic water pumping system

This work aims to improve the performance of direct torque control (DTC) technique for induction motor based photovoltaic (PV) water pumping system (PVWPS). The innovative aspect of this work consists in introducing the adaptive fuzzy logic control and the fuzzy logic control techniques as alternative approaches to conventional DTC to control the PVWPS. To ensure a good operation of the PV array, a variable step size incremental conductance (VSS INC) is implemented. Simulation studies of the proposed topology based on intelligent approaches will be investigated using Matlab/Simulink under various operating conditions to validate the suitability of the proposed PVWPS. From the obtained results, the proposed control strategies appear to be very convenient for water pumping applications.Article HighlightsAn intelligent control based on the advanced techniques is proposed for PV water pumping system.An adaptive fuzzy logic PID approach and optimal fuzzy rules are proposed for better operation of PV system.The suggested PV water pumping system achieves better performance, in particular minimization of torque and flux ripples, reduction of torque overshoot and high dynamic response.

A structured seabird population model reveals how alternative forage fish control rules benefit seabirds and fisheries.

Fisheries for forage fish may affect the survival and reproduction of piscivorous predators, especially seabirds. However, seabirds have evolved life history strategies to cope with natural fluctuations in prey and it is difficult to separate effects of fishing on seabirds from impacts of natural variability. To date, potential impacts of forage fisheries on seabirds have mainly been explored using ecosystem models that simplify seabird-forage-fish dynamics. We sought to explore how different forage fish harvest policies affect seabirds, accounting for structured population dynamics, life history specifics, and variation in forage fish dependencies across life stages; and how impacts vary across seabird and forage fish life histories. To explore these impacts, we developed an age-stage structured seabird model that incorporates seabird diet specialization, foraging behavior, and reproductive strategy, as well as different functional responses between prey availability and adult survival, juvenile survival, reproductive success, and breeder propensity. We parameterized this model for two contrasting seabird life histories: (1) a low fecundity, limited foraging range, diet specialist ("restricted"); and (2) a high fecundity, wide ranging, diet generalist ("flexible"). Each was paired with two different forage fish prey archetypes that were fished under various control rules. The restricted seabird population was expectedly less robust to constant fishing pressure than the flexible seabird, and this sensitivity was mainly due to functional response parameterization, rather than other life history parameters. Particularly, the restricted seabird was highly sensitive to the relationship between prey availability and adult survival but was not sensitive to the relationship between prey and reproductive success. An adaptive biomass-limit harvest rule for forage fish resulted in substantially higher seabird abundance compared to constant fishing across all scenarios, with minimal trade-offs to the fishery (depending on fishery management objectives). However, mechanisms governing the impact of the forage fish fishery on the seabird varied by forage fish type. Therefore, tailoring forage fish management strategies to forage fish life history can lead to mutually acceptable outcomes for fisheries and seabirds. If data or time are limited, an adaptive control rule is likely a safe bet for meeting seabird conservation objectives with limited impacts to fisheries.

An Adaptive Material Interpolation for the Reconstruction of P-Wave Velocity Models with Sharp Interfaces using the Topology Optimization Method

A topology optimization (TO) approach is used to reconstruct P-wave velocity models with sharp interfaces. The concept of material model (interpolation), usually applied in TO to design structures and multi-physics devices, is explored in this work to solve this inverse problem. An adaptive interpolation rule is proposed to deal with the reconstruction problem as a transition from a single- to a multi-material approach combining the Solid Isotropic Material with Penalization (SIMP) and peak function material models. Data collected during the optimization process is used to find material candidates by means of a curve fitting strategy based on generalized simulated annealing (GSA), if this information is not available. The numerical analysis is carried out using a finite element (FE) approach in the frequency domain. Both forward and adjoint problems are solved aided by an open source Domain-Specific Language (DSL) framework and automated derivation tool, while the optimization problem is solved by using a BFGS algorithm. Numerical results for 2D examples demonstrated that proposed material interpolation can lead to solutions with sharper interfaces and improved resolution without including any type of regularization or extra constraint in the optimization problem.

AN EFFICIENT ALGORITHM FOR R PEAKS DETECTION OF ELECTROCARDIOGRAM SIGNALS

Efficient [Formula: see text] peaks detection is the key to the accurate analysis of electrocardiogram (ECG) signals which is a benefit to the early detection of cardiovascular diseases. In recent years, many effective [Formula: see text] peaks detection methods have been proposed, however, the false detection rate is relatively high when the noisy ECG signal is involved. Based on the property of MTEO that it could enhance the features of signal, a novel [Formula: see text] peaks detection algorithm is proposed in this paper to deal with ECG signals with low SNR. The algorithm includes two stages. In the first stage, a band-pass filter is used for eliminating noise, then the first-order forward differentiation and MTEO are used to transform the ECG signals, at last, the output of MTEO is smoothed with a Moving Averaging filter. In the second stage, the adaptive thresholds method and efficient decision rules are applied to detect the true [Formula: see text] peaks. The efficiency and robustness of the proposed method are substantiated on MIT-BIH Arrhythmia Database (MITDB), Fantasia Database and MIT-BIH Normal Sinus Rhythm Database. The testing of the proposed method on the MITDB showed the following results: Sensitivity [Formula: see text], Positive predictivity [Formula: see text] and Accuracy [Formula: see text]. On Fantasia Database involvement, [Formula: see text], [Formula: see text] and [Formula: see text]. On MIT-BIH Normal Sinus Rhythm Database involvement, [Formula: see text], [Formula: see text] and [Formula: see text]. Compared with other [Formula: see text] peaks detection methods, the proposed algorithm is simple, efficient and robust.

Distribution Networks Achieve Uniform Perfusion through Geometric Self-Organization.

A generic flow distribution network typically does not deliver its load at a uniform rate across a service area, instead oversupplying regions near the nutrient source while leaving downstream regions undersupplied. In this Letter we demonstrate how a local adaptive rule coupling tissue growth with nutrient density results in a flow network that self-organizes to deliver nutrients uniformly. This geometric adaptive rule can be generalized and imported to mechanics-based adaptive models to address the effects of spatial gradients in nutrients or growth factors in tissues.

Decentralized adaptive terminal sliding mode control design for nonlinear connected system in the presence of external disturbance

SummaryIn this article, a decentralized adaptive integral terminal sliding mode control is presented for a class of nonlinear connected systems. It is assumed that the system is also confronted by unknown disturbances, while the interconnections between subsystems are assumed unknown. An integral terminal sliding surface for each subsystem is locally considered to guarantee the stability of the closed‐loop system, and to increase the convergence speed during a tracking task. The unknown interconnections between subsystems are estimated using adaptive rules. An appropriate Lyapunov candidate is chosen to perform global stability analysis. In this regard, design parameters are chosen such that the closed‐loop stability is ensured. Performance of the proposed method for a mechanical connected system, including two chaotic subsystems, is shown through simulations.

Deriving adaptive long-term complementary operating rules for a large-scale hydro-photovoltaic hybrid power plant using ensemble Kalman filter

The complementary operating rules of hydro-photovoltaic (PV) hybrid power plants have been explored fully to operate reservoirs for complementing the PV output. However, prevailing operating rules were constant, which is incapable to be dynamically updated based on new observations. To address this issue, adaptive complementary operating rules were developed to deal with the future uncertainties of reservoir inflow and solar radiation using ensemble Kalman filter (EnKF). First, a long-term complementary optimization model was developed to derive the initial parameters of linear operating rules. Then, the optimal decisions under historical condition were used as the observation. Finally, six assimilation schemes were proposed, according to the two state transition equations (the same period of the previous year, the previous period) and three observations (the same period of the previous year, the previous period and their joint). China’s Longyangxia hydro-PV hybrid power plant was chosen as a case study. Results show that (1) the two state transition equations have comparative effects on operation results, (2) the choice of observation plays a decisive role. The assimilation scheme, in which the optimal operation decision of the same period in previous year is as observation, outperforms the stationary operating rules. Specifically, the total power generation and guaranteed rate are improved by 7.14% and 5.33%, respectively, (3) the deriving framework works effectively under the synthetic nonstationary reservoir inflow and solar radiation, and ensures the stability and security of energy system in the changing environment.

Social evolution as moral truth tracking in natural law.

Morality can be adaptive or maladaptive. From this fact come polarizing disputes on the meta-ethical status of moral adaptation. The realist tracking account of morality claims that it is possible to track objective moral truths and that these truths correspond to moral rules that are adaptive. In contrast, evolutionary anti-realism rejects the existence of moral objectivity and thus asserts that adaptive moral rules cannot represent objective moral truths, since those truths do not exist. This article develops a novel evolutionary view of natural law to defend the realist tracking account. It argues that we can identify objective moral truths through cultural group selection and that adaptive moral rules are likely to reflect such truths.

A new extragradient algorithm with adaptive step-size for solving split equilibrium problems

He (J. Inequal. Appl. 2012:Article ID 162 2012) introduced the proximal point CQ algorithm (PPCQ) for solving the split equilibrium problem (SEP). However, the PPCQ converges weakly to a solution of the SEP and is restricted to monotone bifunctions. In addition, the step-size used in the PPCQ is a fixed constant μ in the interval (0, frac{1}{ | A |^{2} } ). This often leads to excessive numerical computation in each iteration, which may affect the applicability of the PPCQ. In order to overcome these intrinsic drawbacks, we propose a robust step-size { mu _{n} }_{n=1}^{infty } which does not require computation of | A | and apply the adaptive step-size rule on { mu _{n} }_{n=1}^{infty } in such a way that it adjusts itself in accordance with the movement of associated components of the algorithm in each iteration. Then, we introduce a self-adaptive extragradient-CQ algorithm (SECQ) for solving the SEP and prove that our proposed SECQ converges strongly to a solution of the SEP with more general pseudomonotone equilibrium bifunctions. Finally, we present a preliminary numerical test to demonstrate that our SECQ outperforms the PPCQ.

Adaptive Multimodal Image Fusion with a Deep Pyramidal Residual Learning Network

Multimodal medical imaging is an indispensable requirement in the treatment of various pathologies to accelerate care. Rather than discrete images, a composite image combining complementary features from multimodal images is highly informative for clinical examinations, surgical planning, and progress monitoring. In this paper, a deep learning fusion model is proposed for the fusion of medical multimodal images. Based on pyramidal and residual learning units, the proposed model, strengthened with adaptive fusion rules, is tested on image pairs from a standard dataset. The potential of the proposed model for enhanced image exams is shown by fusion studies with deep network images and quantitative output metrics of magnetic resonance imaging and positron emission tomography (MRI/PET) and magnetic resonance imaging and single-photon emission computed tomography (MRI/SPECT). The proposed fusion model achieves the Structural Similarity Index Measure (SSIM) values of 0.9502 and 0.8103 for the MRI/SPECT and MRI/PET MRI/SPECT image sets, signifying the perceptual visual consistency of the fused images. Testing is performed on 20 pairs of MRI/SPECT and MRI/PET images. Similarly, the Mutual Information (MI) values of 2.7455 and 2.7776 obtained for the MRI/SPECT and MRI/PET image sets, indicating the model’s ability to capture the information content from the source images to the composite image. Further, the proposed model allows deploying its variants, introducing refinements on the basic model suitable for the fusion of low and high-resolution medical images of diverse modalities.

Reliability-based optimization of ship structure based on interest subdomain dynamic surrogate model

Objectives Aiming at the difficulty of ensuring the fitting accuracy and optimization efficiency of surrogate models due to the high nonlinearity in ship structure reliability-based optimization design, a reliability-based ship structure optimization method based on the interest subdomain dynamic surrogate model is proposed. Methods This method puts forward the concept of the interest subdomain based on the sequential optimization and reliability assessment (SORA) method, determines the range of interest subdomains and formulates adaptive spatial reduction rules based on information entropy function H, then proposes an adaptive spatial reduction sequential sampling strategy based on interest subdomains, thereby constructing a dynamic Kriging surrogate model that highly fits the subdomain of interest locally with as few sample points as possible, and embedding the surrogate model and multi-island genetic algorithm (MIGA) in the SORA method to undertake reliability-based optimization.This study proposes a probability constraint feasibility checking method to reduce unnecessary reliability assessment processes. A mathematical example is given to verify the reliability-based optimization method. Results The relative error between the optimal solution and theoretical solution is 0.066 8%, and the number of function calls is 40.6% less than those of the optimal method in the references, which proves the accuracy and efficiency of this method. Conclusions When the proposed method is applied to the reliability optimization design of a cabin structure, the total cabin mass is reduced by 0.511% compared with the references, and 94 fewer finite element calculations are required, proving the efficiency and applicability of this method.

Adaptive type‐II fuzzy nonsingular fast terminal sliding mode controller using fractional‐order manifold for second‐order chaotic systems

AbstractA novel fuzzy adaptive finite‐time controller is designed for synchronization and control of the Duffing–Holmes and the gyroscope systems in the presence of uncertainty and external disturbance. The fractional‐order fast nonsingular terminal sliding mode manifold is used to ensure the finite‐time control, speed up the convergence rate, and address the singularity problem of the conventional terminal sliding mode controller. Also, the novel control law successfully reduces the chattering phenomenon. Adaptive biased type‐II fuzzy inference system is used to determine the optimal values of the switching control term. The adaptation rules of the novel fuzzy inference system are achieved based on Lyapunov stability theorem. Finally, by using numerical simulations, the effectiveness and superiority of the proposed control scheme are verified against some other methods.

An adaptive boundary element method for the transmission problem with hyperbolic metamaterials

In this work we present an adaptive boundary-integral equation method for computing the electromagnetic response of wave interactions in hyperbolic metamaterials. The indefiniteness of the permittivity tensor gives rise to preferential wave radiation within the propagating cone for the hyperbolic media, and this induces sharp transition for the solution of the integral equation across the cone boundary when waves start to decay or grow exponentially. In order to avoid a global refined mesh over the whole boundary, we employ a two-level a posteriori error estimator and an adaptive mesh refinement procedure to resolve the singularity locally for the solution of the integral equation. Such an adaptive procedure allows for the reduction of the number of the degrees of freedom significantly for the integral equation solver while achieving desired accuracy for the solution. In addition, to resolve the fast transition of the fundamental solution and its derivatives accurately across the propagation cone boundary, adaptive numerical quadrature rules are applied to evaluate the integrals for the stiffness matrices. Finally, to formulate the integral equations over the boundary we also derive the limits of layer potentials and their derivatives in the hyperbolic media when the target points approach the boundary.

An Adaptive Assistance Controller to Optimize the Exoskeleton Contribution in Rehabilitation

In this paper, we present a novel adaptation rule to optimize the exoskeleton assistance in rehabilitation tasks. The proposed method adapts the exoskeleton contribution to user impairment severity without any prior knowledge about the user motor capacity. The proposed controller is a combination of an adaptive feedforward controller and a low gain adaptive PD controller. The PD controller guarantees the stability of the human-exoskeleton system during feedforward torque adaptation by utilizing only the human-exoskeleton joint positions as the sensory feedback for assistive torque optimization. In addition to providing a convergence proof, in order to study the performance of our method we applied it to a simplified 2-DOF model of human-arm and a generic 9-DOF model of lower limb to perform walking. In each simulated task, we implemented the impaired human torque to be insufficient for the task completion. Moreover, the scenarios that violate our convergence proof assumptions are considered. The simulation results show a converging behavior for the proposed controller; the maximum convergence time of 20 s is observed. In addition, a stable control performance that optimally supplements the remaining user motor contribution is observed; the joint angle tracking error in steady condition and its improvement compared to the start of adaptation are as follows: shoulder 0.96±2.53° (76%); elbow −0.35±0.81° (33%); hip 0.10±0.86° (38%); knee −0.19±0.67° (25%); and ankle −0.05±0.20° (60%). The presented simulation results verify the robustness of proposed adaptive method in cases that differ from our mathematical assumptions and indicate its potentials to be used in practice.

Bayesian seismic inversion: a fast sampling Langevin dynamics Markov chain Monte Carlo method

SUMMARY In this study, we aim to solve the seismic inversion in the Bayesian framework by generating samples from the posterior distribution. This distribution incorporates the uncertainties in the seismic data, forward model, and prior information about the subsurface model parameters; thus, we obtain more information through sampling than through a point estimate (e.g. maximum a posteriori method). Based on the numerical cost of solving the forward problem and the dimensions of the subsurface model parameters and observed data, sampling with Markov chain Monte Carlo (MCMC) algorithms can be prohibitively expensive. Herein, we consider the promising Langevin dynamics MCMC algorithm. However, this algorithm has two central challenges: (1) the step size requires prior tuning to achieve optimal performance and (2) the Metropolis–Hastings acceptance step is computationally demanding. We approach these challenges by proposing an adaptive step-size rule and considering the suppression of the Metropolis–Hastings acceptance step. We highlight the proposed method’s potential through several numerical examples and rigorously validate it via qualitative and quantitative evaluation of the sample quality based on the kernelized Stein discrepancy (KSD) and other MCMC diagnostics such as trace and autocorrelation function plots. We conclude that, by suppressing the Metropolis–Hastings step, the proposed method provides fast sampling at efficient computational costs for large-scale seismic Bayesian inference; however, this inflates the second statistical moment (variance) due to asymptotic bias. Nevertheless, the proposed method reliably recovers important aspects of the posterior, including means, variances, skewness and 1-D and 2-D marginals. With larger computational budget, exact MCMC methods (i.e. with a Metropolis–Hastings step) should be favoured. The results thus obtained can be considered a feasibility study for promoting the approximate Langevin dynamics MCMC method for Bayesian seismic inversion on limited computational resources.

Synchronization in Networks With Heterogeneous Adaptation Rules and Applications to Distance-Dependent Synaptic Plasticity

This work introduces a methodology for studying synchronization in adaptive networks with heterogeneous plasticity (adaptation) rules. As a paradigmatic model, we consider a network of adaptively coupled phase oscillators with distance-dependent adaptations. For this system, we extend the master stability function approach to adaptive networks with heterogeneous adaptation. Our method allows for separating the contributions of network structure, local node dynamics, and heterogeneous adaptation in determining synchronization. Utilizing our proposed methodology, we explain mechanisms leading to synchronization or desynchronization by enhanced long-range connections in nonlocally coupled ring networks and networks with Gaussian distance-dependent coupling weights equipped with a biologically motivated plasticity rule.

Synchronization in Networks With Heterogeneous Adaptation Rules and Applications to Distance-Dependent Synaptic Plasticity

This work introduces a methodology for studying synchronization in adaptive networks with heterogeneous plasticity (adaptation) rules. As a paradigmatic model, we consider a network of adaptively coupled phase oscillators with distance-dependent adaptations. For this system, we extend the master stability function approach to adaptive networks with heterogeneous adaptation. Our method allows for separating the contributions of network structure, local node dynamics, and heterogeneous adaptation in determining synchronization. Utilizing our proposed methodology, we explain mechanisms leading to synchronization or desynchronization by enhanced long-range connections in nonlocally coupled ring networks and networks with Gaussian distance-dependent coupling weights equipped with a biologically motivated plasticity rule.

Changing readiness to mitigate SARS-CoV-2 steered long-term epidemic and social trajectories

Societal responses crucially shape the course of a pandemic, but are difficult to predict. Mitigation measures such as social distancing are here assumed to minimize a utility function that consists of two conflicting sub-targets, the disease related mortality and the multifaceted consequences of mitigation. The relative weight of the two sub-targets defines the mitigation readiness H, which entails the political, social, and psychological aspects of decision making. The dynamics of social and behavioral mitigation thus follows an adaptive rule, which in turn is mediated by a non-adaptive dynamics of H. This framework for social dynamics is integrated into an epidemiological model and applied to the ongoing SARS-CoV-2 pandemic. Unperturbed simulations accurately reproduce diverse epidemic and mitigation trajectories from 2020 to 2021, reported from 11 European countries, Iran, and 8 US states. High regional variability in the severity and duration of the spring lockdown and in peak mortality rates of the first SARS-CoV-2 wave can be explained by differences in the reconstructed readiness H. A ubiquitous temporal decrease of H has greatly intensified second and third waves and slowed down their decay. The unprecedented skill of the model suggests that the combination of an adaptive and a non-adaptive rule may constitute a more fundamental mode in social dynamics. Its implementation in an epidemic context can produce realistic long-term scenarios relevant for strategic planning, such as on the feasibility of a zero-infection target or on the evolutionary arms race between mutations of SARS-CoV-2 and social responses.

Transient circulant clusters in two-population network of Kuramoto oscillators with different rules of coupling adaptation.

We considered a network consisting of two populations of phase oscillators, the interaction of which is determined by different rules for the coupling adaptation. The introduction of various adaptation rules leads to the suppression of splay states and the emergence of each population complex non-stationary behavior called transient circulant clusters. In such states, each population contains a pair of anti-phase clusters whose size and composition slowly change over time as a result of successive transitions of oscillators between clusters. We show that an increase in the mismatch of the adaptation rules makes it possible to stop the process of rearrangement of clusters in one or both populations of the network. Transitions to such modes are always preceded by the appearance of solitary states in one of the populations.

Planar Learning to Forecast Market Games

In a system where expectations and realisations of a price feed back to each other, it has been found that the sign and strength of this feedback is an important predictor of the market stability. In this paper we contribute to the generalisation of this result to a two dimensional system, where the expectations and realisations of two prices affect each other. We conduct a Learning to Forecast Experiment and show that eigenvalues can be used as predictors of stability in such a higher dimensional framework. We investigate eigenvalues of positive real part, and found that complex eigenvalues with a polar angle of 45◦ lead to more stable dynamics than real eigenvalues with the same absolute value. For the real eigenvalues we find a change from stable to unstable dynamics inside the unit circle, which is in line with the findings from the one dimensional case. In order to reproduce the decisions being made in individual time steps, simple models like an adaptive rule are often sufficient. In order to reproduce long run dynamics, we develop a two dimensional Heuristic Switching Model. We use this model to predict the stability of systems with eigenvalues which we did not test experimentally.