Intrinsic Nonlinearity Research Articles

Comprehensive reconstructions and predictive control for quadrotor UAV information gathering tracking missions based on fully actuated system approaches

This paper introduces a novel approach to the comprehensive reconstruction and predictive control (PC) of the quadrotor UAV for information-gathering missions, employing fully actuated system (FAS) approaches. Unlike conventional PC methods applied to a quadrotor UAV with hybrid constraints, our work integrates reconstructions of the system model, hybrid constraints, and the receding horizon performance index into to an integrated tracking control scheme within the FAS-PC framework. Specifically, the under-actuated quadrotor UAV model is reconstructed into a full-actuated model to inject full-actuation properties. And the implicit hybrid constraints that arise from the model reconstruction are explicitly transformed and decoupled. Simultaneously, the cascaded predictive algorithm is established that the new time-varying input constraints are solved in each predictive horizon, and then the nonlinear optimization problem is decoupled into four linear convex optimization problems subject to the corresponding decoupled linear constraints and the pre-addressed input constraints. Within this framework, the intrinsic complexities, nonlinearities, and interdependencies of the quadrotor UAV system model, along with hybrid constraints and the optimization dilemma, are considerably diminished. This reduction significantly eases computational demands, enabling satisfactory real-time performance. Furthermore, the selection of predictive parameters guarantees the stability of the resultant tracking error closed-loop system. Finally, the efficacy of the proposed method is validated through two sets of flight missions, conducted via simulation and practical experimentation, respectively.

Vortex Quantum Droplets under Competing Nonlinearities

This concise review summarizes recent advancements in theoretical studies of vortex quantum droplets (VQDs) in matter-wave fields. These are robust self-trapped vortical states in two- and three-dimensional (2D and 3D) Bose–Einstein condensates (BECs) with intrinsic nonlinearity. Stability of VQDs is provided by additional nonlinearities resulting from quantum fluctuations around mean-field states, often referred to as the Lee–Huang–Yang (LHY) corrections. The basic models are presented, with emphasis on the interplay between the mean-field nonlinearity, LHY correction, and spatial dimension, which determines the structure and stability of VQDs. We embark by delineating fundamental properties of VQDs in the 3D free space, followed by consideration of their counterparts in the 2D setting. Additionally, we address stabilization of matter-wave VQDs by optical potentials. Finally, we summarize results for the study of VQDs in the single-component BEC of atoms carrying magnetic moments. In that case, the anisotropy of the long-range dipole-dipole interactions endows the VQDs with unique characteristics. The results produced by the theoretical studies in this area directly propose experiments for the observation of novel physical effects in the realm of quantum matter, and suggest potential applications to the design of new schemes for processing classical and quantum information.

Technology transfer for green investments: exploring how technology transfer through foreign direct investments can contribute to sustainable practices and reduced environmental impact in OIC economies.

Estimating the asymmetrical influence of foreign direct investment is the primary goal of the current study. In addition, further controlled variables affect environmental degradation in OIC nations. Due to this, current research employs the asymmetric (NPARDL) approach and the data period from 1980 to 2021 to estimate about viability of the EKC (environmental Kuznets curve) theory. The study utilized greenhouse gas (GHG) including emissions of carbon dioxide (CO2), nitrous oxide (N2O), methane (CH4), and ecological footprint as substantial parameters of environmental quality. A nonlinear link between foreign direct investments, trade openness, economic growth, urbanization, energy consumption, and environmental pollution with CO2, N2O, CH4, and ecological footprint in the OIC nations is confirmed by the study's outcomes, which however reveals inconsistent results. Furthermore, the results also show that wrong conclusions might result from disregarding intrinsic nonlinearities. The study's conclusions provide the most important recommendations for decision-makers.

Model-based Reinforcement Learning for Ship Path Following with Disturbances

Modelling and control of ships is a challenging task due to their intrinsic nonlinearities and high uncertainty. In the complex and dynamic sea environment, the efficacy of well-designed motion controllers diminishes significantly. To achieve optimal performance, high-performance motion control systems must possess the ability to adapt to diverse working conditions, repel external disturbances, and incorporate learning capabilities. In addressing critical computational challenges encountered in the real-world deployment of autonomous driving agents, Reinforcement Learning (RL) methods have been effectively employed. This paper proposed a reinforcement learning control method for ship path following based on an environmental disturbance model. RL is utilized to learn the dynamic properties of the system to resist environmental disturbances. Experimental results show that the method can follow the path with acceptable accuracy and high robustness.

Crescimento da linhaça oleaginosa descrito por modelo não linear logístico

ABSTRACT Knowledge on plant-atmosphere interactions is essential to understand the growth and development of agricultural crops. Thus, fitting growth curves is an important methodology to model plant growth and phenological stages. The study aimed to describe the growth of four oilseed flax materials cultivated in six agricultural years and with different sowing dates through the nonlinear logistic model. Nine experiments were carried out in Curitibanos, SC, Brazil, between 2014 and 2020, considering different sowing dates. Throughout the crop cycle, the number of leaves, number of secondary stems, plant height and total dry mass were measured. Nonlinear logistic model was fitted to the data, with the growth variables as the dependent variables and the accumulated thermal sum as the independent variable. Model fit and parameter estimation were obtained by ordinary least method, using a Gauss-Newton algorithm. The goodness of fit was measured by intrinsic and parametric nonlinearity, adjusted coefficient of determination, random standard error, standard deviation of fit, Akaike information criterion, and Bayesian information criterion. The performance of the nonlinear logistic model differed between the varieties and cultivars studied, in different years and sowing times. However, the use of the nonlinear logistic model improves inferences about the growth of oilseed flax, and the estimates of its parameters and critical points allow a biological and practical interpretation to assist in crop planning. Furthermore, the study suggests that the oilseed flax cycle is directly related to genotype × environment interactions, and when sown at later times, the materials tend to shorten their cycle.

Tracking Control of Heterogeneous Multiagent Systems With Intrinsic Nonlinear Dynamics in Noisy and Time-Delayed Environments.

This article investigates the tracking control problem of heterogeneous multiagent systems (MASs) with intrinsic nonlinear dynamics in noisy and time-delayed environments. First, a stability criterion for nonlinear stochastic delay systems with multiplicative noise and time-varying delay is proposed by applying the appropriate Lyapunov-Krasovskii functional. Then, based on the proposed stability criterion, sufficient conditions are derived for mean square (m.s.) and almost sure (a.s.) tracking of heterogeneous MASs with intrinsic nonlinear dynamics. Afterward, the above sufficient conditions further degenerate to integrator heterogeneous MASs. In particular, when the time-delay vanishes, the explicit conditions are obtained for the integrator heterogeneous MASs in the form of scalar inequalities, which can intuitively reflect the relationship between noise intensity and control gains. Finally, simulation results validate the effectiveness of the proposed control protocol.

Superior optical limiting performance of lead-free metal halide perovskites nano-crystals embedded chalcogenide glass

The nonlinear optical properties of metal halide perovskites (MHP) are highly attractive, making them an emerging hotspot in the field of perovskite photonics. In this study, the third-order optical nonlinear properties of lead-free MHP (CsSnCl3) nano-crystals (NCs) embedded in a chalcogenide glass (GeS2-Sb2S3) substrate were characterized using the Z-scan technique. The spectral range examined was from 750 to 900 nm. Experimental results revealed that the incorporation of CsSnCl3 NCs enhanced the nonlinear refraction and nonlinear absorption behavior of the GeS2-Sb2S3 glass. Furthermore, the magnitude of these effects increased with the growth size of the CsSnCl3 NCs. Importantly, the MHP glasses exhibited strong two-photon absorption due to the large multiple-absorption cross-section of MHP crystals and the intrinsic high optical nonlinearity of the chalcogenide glass. The superior optical limiting performance induced by this process was successfully demonstrated.

A Magnetoelastic Twist on Magnetic Noise: The Connection with Intrinsic Nonlinearities

AbstractIntrinsic magnetic noise limits the functionality of all magnetic field sensors and related devices using magnetic films as sensing elements. A novel origin of magnetic noise due to ferromagnetic material's magnetostriction is revealed by implementing a comprehensive multi‐level signal‐noise model and thoroughly validating it experimentally on magnetoelectric composite ΔE‐effect sensors. From electrical measurements and operando magnetic domain visualization, magnetic contributions are shown to dominate the noise floor and limit the overall performance of multi‐domain magnetoelastic sensors. The newly introduced noise contribution is correlated with the nonlinearity of the magnetostrictive properties. The effect on sensor output is particularly pronounced in magnetoelastic and magnetoelectric composite devices, but the results generally apply to all sensor devices incorporating magnetic materials. Therefore, the identified magnetic noise source makes a significant contribution to understanding the limitations of magnetic sensors and provides important guidelines for optimizing future magnetic sensors for low detectivity.

Adaptive optimal secure wind power generation control for variable speed wind turbine systems via reinforcement learning

As the utilization of wind energy continues to grow, it is crucial to prioritize the identification of vulnerabilities, raise awareness, and develop strategies for cybersecurity defense. False data injection (FDI) attacks, if targeted at the communication between the rotor speed sensor and the wind turbine (WT) controller, can potentially disrupt the normal operation of the system. These attacks have the capability to overload the drive-train and significantly reduce the power generation efficiency of the wind turbine. So, this note presents an adaptive optimal secure control strategy entailing reinforcement learning (RL) neural network (NN) using the filter error to compensate the detrimental effects of FDI attack as well as actuator fault for WT systems. The Hamilton–Jacobi–Bellman (HJB) equation is constructed and solved to obtain the optimal control policy. Since the HJB is inextricably intertwined with intrinsic nonlinearity and complexity, solving this equation is quiet challenging. To tackle this issue and also approximate the solution of the HJB, an actor–critic-based reinforcement learning (RL) strategy is used, in which actor and critic NNs are applied to execute control action and assess control performance, respectively. To detect FDI attack, an anomaly detection is developed using a nonlinear observer/estimator. Stability analysis is performed using Lyapunov theory which guarantees semi-global uniformly ultimately bounded (SGUUB) of the error signal. Finally, simulation results verify the effectiveness of the proposed control approach.

Interpreting gas sorption isotherms in glassy polymers using a Bayesian framework: A view on parameter uncertainty propagation into mixture sorption predictions

Investigation of mixed-gas sorption is necessary for robust design and optimization of membrane-based processes. While sorption models for glassy polymers are well-established, they often deviate from observed mixture data. In doing so, however, parameters of these models are typically estimated via traditional least-squares optimization methods, and so parametric uncertainty is often ignored in mixture sorption predictions. As an alternative, we use Bayesian Inference (BI) to estimate probability distributions for sorption models' parameters and thus provide statistically meaningful mixture sorption forecasts that reflect parameter uncertainty rigorously. To this end, we exploit molecular sorption simulations, combining Grand Canonical Monte Carlo (GCMC) and Equilibrium Molecular Dynamics (EMD), to focus on two different glassy polymer systems (i.e., single- and mixed-gas sorption of CO2/CH4 in a fluorinated polyimide and CH4/H2 in a polymer of intrinsic microporosity) and three popular mixture sorption models (the Dual-Mode Sorption model, Non-Equilibrium Thermodynamics for Glassy Polymers model, and the Ideal Adsorbed Solution Theory) to demonstrate the benefits of this technique for uncertainty quantification and propagation in membrane applications. We show that observed sorption data at typical working pressures (e.g., 0−25atm) are often insufficient to accurately estimate model parameters, and consequently, these models often fail to represent observed mixture data adequately. Furthermore, we show that sorption data (e.g., high-pressure data), able to capture intrinsic isotherm nonlinearities of sorption models, are critical to considerably improve parameter inference from collective model-data fits and thus accurately predict mixture sorption.

Monitoring of bolt loosening using vibro-acoustic modulation (VAM)

Detection of bolt loosening using vibro-acoustic modulation (VAM) has been increasingly investigated in the past decade. However, conventional nonlinear coefficients, derived from theoretical analysis, are usually based on the assumption of ideal wave-surface interactions at the joint interfaces. Such coefficients show a poor correlation with the tightening torque when the joint is under the combined influences from structural and material nonlinearities. A reliable inspection method of residual bolt torque was proposed in this study using Support Vector Regression (SVR) with acoustic features from VAM. By considering material intrinsic nonlinearity (MIN) and dissipative nonlinearity (DN), responses of aluminum-aluminum and composite-composite bolted joints during the VAM test were accurately simulated. SVR were subsequently established based on the database built by a mixing of simulated and experimental nonlinear spectral features when the joints were inspected at different scenarios. The results show that the evaluation of residual torque using the SVR models driven by the acoustic nonlinear responses show a higher accuracy compared with the conventional nonlinear coefficients. Requiring limited experiment data, the proposed method can achieve reliable inspection of bolt torque by including the simulated data into machine training.

Predicting the performance and emissions of an HCCI-DI engine powered by waste cooking oil biodiesel with Al2O3 and FeCl3 nano additives and gasoline injection – A random forest machine learning approach

This study presents a novel method for predicting the performance and emissions of a Homogeneous Charge Compression Ignition-Direct Injection (HCCI-DI) engine fuelled by waste cooking oil biodiesel (WCOB) blended with Aluminum oxide (Al2O3) and Ferric chloride (FeCl3) nano additives and premixed with gasoline. The test fuels considered were pure diesel, WCOB, B50 (a 50–50 blend of diesel and WCOB), and the nano-additives at concentrations of 50 ppm and 100 ppm. All experiments were performed on a 4.4 kW HCCI-DI engine operating at 1500 rpm. The results revealed that, utilization of WCOB showed a substantial reduction in emissions. Hydrocarbon (HC), carbon monoxide (CO) and smoke emissions decreased by 54.17 %, 50 %, and 22.69 %. Nevertheless, oxides of nitrogen (NOx) emissions increased by 18.32 %. When introducing gasoline (20 %) as a premix in HCCI-DI engines, a favourable shift in emission and efficiency metrics was observed. The brake thermal efficiency (BTE) increased 4.23 %. Moreover, NOx and smoke emissions decreased by 4.3 % and 39.21 %. Furthermore, compared to conventional diesel-fueled combustion, the integration of the nano additives manifested promising results. After introducing Al2O3 into neat fuel, The BTE improved by 11.27 % for direct injection (DI) combustion and 18.31 % for HCCI-DI combustion. Additionally, there was a marked decrease in exhaust emissions. FeCl3 nano additive into the test fuel significantly reduced HC, CO, and smoke emissions. Furthermore, the random forest machine learning approach demonstrated an extensive accuracy in forecasting both engine performance and emissions for the HCCI-DI engine. The innovative combination of cutting-edge machine learning techniques and combustion technology used. This model to understand the complicated correlations between input parameters and engine outputs and account for the system's intrinsic non-linearities and interactions. This dramatically improves standard prediction approaches, often assuming linear correlations or disregarding variable interdependencies.

Geometry-informed dynamic mode decomposition in Kresling origami dynamics

Origami structures often serve as the building block of mechanical systems due to their rich static and dynamic behaviors. Experimental observation and theoretical modeling of origami dynamics have been reported extensively, whereas the data-driven modeling of origami dynamics is still challenging due to the intrinsic nonlinearity of the system. In this study, we show how the dynamic mode decomposition (DMD) method can be enhanced by integrating geometry information of the origami structure to model Kresling origami dynamics in an efficient and accurate manner. In particular, an improved version of DMD with control, that we term geometry-informed dynamic mode decomposition (giDMD), is developed and evaluated on the origami chain and dual Kresling origami structure to reveal the efficacy and interpretability. We show that giDMD can accurately predict the dynamics of an origami chain across frequencies, where the topological boundary state can be identified by the characteristics of giDMD. Moreover, the periodic intrawell motion can be accurately predicted in the dual origami structure. The type of dynamics in the dual origami structure can also be identified. The model learned by the giDMD also reveals the influential geometrical parameters in the origami dynamics, indicating the interpretability of this method. The accurate prediction of chaotic dynamics remains a challenge for the method. Nevertheless, we expect that the proposed giDMD approach will be helpful towards the prediction and identification of dynamics in complex origami structures, while paving the way to the application to a wider variety of lightweight and deployable structures.

Short-Term Power Load Forecasting: An Integrated Approach Utilizing Variational Mode Decomposition and TCN–BiGRU

Accurate short-term power load forecasting is crucial to maintaining a balance between energy supply and demand, thus minimizing operational costs. However, the intrinsic uncertainty and non-linearity of load data substantially impact the accuracy of forecasting results. To mitigate the influence of these uncertainties and non-linearity in electric load data on the forecasting results, we propose a hybrid network that integrates variational mode decomposition with a temporal convolutional network (TCN) and a bidirectional gated recurrent unit (BiGRU). This integrated approach aims to enhance the accuracy of short-term power load forecasting. The method was validated on load datasets from Singapore and Australia. The MAPE of this paper’s model on the two datasets reached 0.42% and 1.79%, far less than other models, and the R2 reached 98.27% and 97.98, higher than other models. The experimental results show that the proposed network exhibits a better performance compared to other methods, and could improve the accuracy of short-term electricity load forecasting.

Modulational instability and droplet formation in Bose-Bose mixtures with Lee-Huang-Yang correction and polaron-like impurity

This letter studies the excitation of nonlinear patterns on the one-dimensional (1D) quantum droplet in the presence of polaron-like impurity under modulational instability (MI) activation. Analytically, it is shown that strong coupling between the impurity and Bose species expands the instability domain, especially in the quantum droplet regime, while pronounced self-repulsive nonlinearity tends to restrict the MI to regions of strong interspecies interactions. In the process, the wavenumber spectrum, capable of supporting MI, also shrinks. Direct numerical simulations confirm the existence of coupled boson-impurity wave patterns, mainly governed by trains of solitary waves of different profiles for the impurity and quasi-droplet waveforms for the boson. Additionally, the two components are mutually trapped, which enhances energy transfer between them, leading to the formation of quasi-droplet structures under a suitable balance between the back action of the impurity on the boson, the Lee-Huang-Yang term, as well as the intrinsic self-repulsive nonlinearity.

Quantum illumination based on cavity-optomagnonics system with Kerr nonlinearity.

Quantum illumination is a quantum optical sensing technique, which employs an entangled source to detect low-reflectivity object immersed in a bright thermal background. Hybrid cavity-optomagnonics system promises to work as quantum illumination because a yttrium iron garnet (YIG) sphere can couple to microwave field and optical field. In this paper, we propose a scheme to enhance the entanglement between the output fields of the microwave and optical cavities by considering the intrinsic Kerr nonlinearity of the YIG. We investigate the difference between intrinsic Kerr nonlinearity and optomagnonical parametric-type coupling on improving entanglement. Our result show that the large value optomagnonical parametric-type coupling does not mean the large entanglement, nevertheless, the large value of Kerr nonlinearity does monotonously improve the entanglement for our group of parameters. Consequently, under feasible parameters of current experiment, the signal-to-noise ratio and probability of detection error can be improved after considering the magnon Kerr nonlinearity.

STOCHASTIC DYNAMICS BETWEEN THE IMMUNE SYSTEM AND CANCER CELLS WITH ALLEE EFFECT AND IMMUNOTHERAPY

In this work, we use continuous-time Markov jump processes and the corresponding zero fluctuation ordinary differential equations to analyze the relation between immune response and cancerous cells. We incorporate the Allee effect into our model to show that intrinsic stochasticity and nonlinearity may interact in elimination, equilibrium, and escape mechanisms in the low-count regime. Later, we consider the effect of immunotherapy through a pulse injection term and the Tau-Leaping algorithm. We show using the model state variables and parameters that the cancer cell population at its threshold level gets into the elimination phase for high antigenicity values.

Electro-optic transduction in silicon via gigahertz-frequency nanomechanics

Interfacing electronics with optical fiber networks is key to the long-distance transfer of classical and quantum information. Piezo-optomechanical transducers enable such interfaces by using gigahertz-frequency acoustic vibrations as mediators for converting microwave photons to optical photons via the combination of optomechanical and piezoelectric interactions. However, despite successful demonstrations, efficient quantum transduction remains out of reach due to the challenges associated with hybrid material integration and increased loss from piezoelectric materials when operating in the quantum regime. Here, we demonstrate an alternative approach in which we actuate 5-GHz phonons in a conventional silicon-on-insulator platform. In our experiment, microwave photons resonantly drive a phononic crystal oscillator via the electrostatic force realized in a charge-biased narrow-gap capacitor. The mechanical vibrations are subsequently transferred via a phonon waveguide to an optomechanical cavity, where they transform into optical photons in the sideband of a pump laser field. Operating at room temperature and atmospheric pressure, we measure a microwave-to-optical photon conversion efficiency of 1.72±0.14×10−7 in a 3.3 MHz bandwidth. Our results mark a stepping stone towards quantum transduction with integrated devices made from crystalline silicon, which promise efficient high-bandwidth operation and integration with superconducting qubits. Additionally, the lack of need for piezoelectricity or other intrinsic nonlinearities makes our approach applicable to a wide range of materials for potential applications beyond quantum technologies.

Experimental demonstration of a skyrmion-enhanced strain-mediated physical reservoir computing system

Physical reservoirs holding intrinsic nonlinearity, high dimensionality, and memory effects have attracted considerable interest regarding solving complex tasks efficiently. Particularly, spintronic and strain-mediated electronic physical reservoirs are appealing due to their high speed, multi-parameter fusion and low power consumption. Here, we experimentally realize a skyrmion-enhanced strain-mediated physical reservoir in a multiferroic heterostructure of Pt/Co/Gd multilayers on (001)-oriented 0.7PbMg1/3Nb2/3O3−0.3PbTiO3 (PMN-PT). The enhancement is coming from the fusion of magnetic skyrmions and electro resistivity tuned by strain simultaneously. The functionality of the strain-mediated RC system is successfully achieved via a sequential waveform classification task with the recognition rate of 99.3% for the last waveform, and a Mackey-Glass time series prediction task with normalized root mean square error (NRMSE) of 0.2 for a 20-step prediction. Our work lays the foundations for low-power neuromorphic computing systems with magneto-electro-ferroelastic tunability, representing a further step towards developing future strain-mediated spintronic applications.

Scaling for Saturated Moist Quasigeostrophic Turbulence

Abstract Much of our conceptual understanding of midlatitude atmospheric motion comes from two-layer quasigeostrophic (QG) models. Traditionally, these QG models do not include moisture, which accounts for an estimated 30%–60% of the available energy of the atmosphere. The atmospheric moisture content is expected to increase under global warming, and therefore, a theory for how moisture modifies atmospheric dynamics is crucial. We use a two-layer moist QG model with convective adjustment as a basis for analyzing how latent heat release and large-scale moisture gradients impact the scalings of a midlatitude system at the synoptic scale. In this model, the degree of saturation can be tuned independently of other moist parameters by enforcing a high rate of evaporation from the surface. This allows for study of the effects of latent heat release at saturation, without the intrinsic nonlinearity of precipitation. At saturation, this system is equivalent to the dry QG model under a rescaling of both length and time. This predicts that the most unstable mode shifts to smaller scales, the growth rates increase, and the inverse cascade extends to larger scales. We verify these results numerically and use them to verify a framework for the complete energetics of a moist system. We examine the spectral features of the energy transfer terms. This analysis shows that precipitation generates energy at small scales, while dry dynamics drive a significant broadening to larger scales. Cascades of energy are still observed in all terms, albeit without a clearly defined inertial range. Significance Statement The effect of moist processes, especially the impact of latent heating associated with condensation, on the size and strength of midlatitude storms is not well understood. Such insight is particularly needed in the context of global warming, as we expect moisture to play a more important role in a warmer world. In this study, we provide intuition into how including condensation can result in midlatitude storms that grow faster and have features on both larger and smaller scales than their dry counterparts. We provide a framework for quantifying these changes and verify it for the special case where it is raining everywhere. These findings can be extended to the more realistic situation where it is only raining locally.