High Nonlinearity Research Articles

Linear and nonlinear optical properties of femtosecond laser inscribed waveguides into GLS glass

Gallium lanthanum sulfide (GLS) glass is a promising material for mid-infrared photonics due to its wide transmission window and its high nonlinear refractive index that is almost three orders of magnitude higher than that of fused silica. In this paper, we present the results of a detailed study into the linear and nonlinear optical properties of waveguides fabricated in GLS glass via ultrafast laser direct-inscription using three different techniques: cumulative heating in the thermal regime as well as multi-scan and half-scan in the athermal regime. Using quadriwave lateral shearing interferometry, we fully characterized the refractive index profiles of such inscribed waveguides and found no difference between half-scan and multi-scan writing which indicates the absence of laser-induced stress in this soft glass in stark contrast to fused silica. In terms of nonlinearity, we utilized self-phase modulation (SPM)-induced spectral broadening experiments at mid-IR wavelengths to demonstrate that waveguides fabricated in the athermal regime preserve the high intrinsic nonlinearity of the GLS bulk material, outperforming those written in the thermal regime based. These findings pave the way for the fabrication of fiber-coupled optical waveguide chips for nonlinear mid-infrared photonics.

Optimization of emergency frequency control strategy for power systems considering both source and load uncertainties

With the increasing integration of renewable energy sources and the presence of numerous controllable loads such as electric vehicles and energy storage in the modern power system, higher nonlinearities and uncertainty both sources and loads are introduced. These factors pose challenges in achieving fast and accurate emergency frequency control. Therefore, this paper addresses the issue of dual source-load uncertainties in power system and presents an optimization strategy based on the Soft Actor Critic (SAC) algorithm that involves the participation of controllable loads in emergency frequency control. Firstly, the spatio-temporal uncertainties of wind farm power output on power supply side and power demand on the load side are described using Weibull and normal probability distributions, respectively. Secondly, an improved Markov Decision Process (MDP) model for emergency frequency control is established, which considers the characteristics of the dual source-load uncertainties. Finally, an optimization of the SAC algorithm is conducted based on Deep Reinforcement Learning (DRL), aiming to achieve rapid system frequency recovery and minimize the cost of removing controllable loads. The presented approach in the paper enhances the emergency frequency control strategy for uncertain power systems and effectively addresses the issue of source-load uncertainty compounded by fault power shortages.

Investigation of linear and microscopic nonlinear optical responses of 1-indanone compounds in different environments based on polarity models

Molecular spectroscopic and nonlinear features can indicate positive changes by solvent molecules. In this work, DFT and spectroscopic techniques were used to study the polarity effects of different solvent environments. Polarity-based models were used for studying solvent induced interactions on the optical features of new groups of biomolecules. Despite the significant contribution of general effects on the molecular absorption spectra, there is considerable competition between general and specific environmental effects on the molecular emission properties. Under this condition, strong hydrogen bonds tend to increase molecular nonlinear responses. The same results were observed for the low order (first and second order) nonlinearity of biomolecules. Therefore, the studies on the environment effects on the biomolecules’ first order nonlinearity can give valuable information about higher-order optical responses. Moreover, 1-Indanone compounds with high nonlinearity can be considered as an effective element in designing optical devices.

Chaos and regularities in cavity assisted two-channel nonlinear coupler

This paper presents a study of the dynamical behavior in a cavity-assisted two-channel Kerr nonlinear directional coupler. Applying a deterministic periodic laser pump to a two-channel coupled system led to a range of dynamic behaviors, encompassing periodicity, quasiperiodicity, chaoticity and hyperchaoticity. This is confirmed by the exploration of the Lyapunov exponents, Poincaré sections and bifurcation diagrams for resonance and low cavity detuning. For low cavity detuning, chaos arises when the laser pump’s amplitude is large. This is due to higher interactions between photons and the cavity, resulting in more photons inside and higher nonlinearity. On the other hand, the reduced interactions in high cavity detuning displayed regular behavior which is explained qualitatively.

Generation of a MW-class tunable Raman soliton up to 3.8 µm in a fluorotellurite fiber.

We report the generation of MW-class tunable Raman solitons of 3.3-3.8 µm in a fluorotellurite fiber. The pump source of the mid-infrared fiber is a 2.15-2.3 µm Raman soliton source which has the highest peak-power Raman soliton (3.8 MW) ever achieved in a silica fiber. Fluorotellurite fiber is characterized by its short length and high nonlinearity, preventing the Raman soliton pulse broadening that occurs with fiber dispersion. The Raman soliton shifted to 3.8 µm while still maintaining an ultra-narrow pulse width of 70.4 fs with 1.36 W output power and 1.17 MW peak power. To the best of our knowledge, this is the highest peak-power Raman soliton obtained in a mid-infrared fiber.

Development of physics-guided neural network framework for acid-base treatment prediction using carbon dioxide-based tubular reactor

Accurate acid-base treatment prediction is necessary to achieve the required yield, given the inherent complexity, high nonlinearity, and restricted availability of data samples; to address this challenge, a data-driven approach was developed. However, the technique is constrained by the need for sufficient data to construct an accurate model and lacks both process insight and physical consistency. Therefore, this study introduces a physics-guided neural network model for acid-base treatment prediction in a dynamic tubular reactor using the fundamental physical intermediate variables obtained through the derivation process of the reaction schematic. By integrating batch experimental data, which provides key intermediate variables such as residence time and hydroxide ion concentration, the model addresses the challenge of high nonlinearity and limited data availability. The result shows that the physics-guided potential of a hydrogen predictor had outstanding performance in terms of prediction accuracy (greatest coefficient of determination value of 0.9381). The proposed model demonstrated an average improvement of 24.92% in pH prediction accuracy compared to traditional models without physical guidance, with a maximum improvement of up to 64.95% under limited data conditions. Moreover, downsampling tests revealed that the proposed model maintained robust performance with minimal accuracy reduction even when data was limited without overfitting implication.

Constructions of plateaued correctors with high correction order and good nonlinearity via Walsh spectral neutralization technique

Constructions of plateaued correctors with high correction order and good nonlinearity via Walsh spectral neutralization technique

A time-varying non-probabilistic reliability analysis method using surrogate model for the reinforcement effectiveness of damaged concrete gravity dam

Evaluation of the effectiveness of reinforcement measures for the diseased concrete gravity dam is a critical issue in the field of hydraulic structural health control. The traditional probabilistic reliability analysis method is constrained by factors such as the randomness of the uncertain parameters, the high nonlinearity of the performance function, etc., when it is applied to the evaluation of the effectiveness evolution of reinforcement measures for the diseased concrete gravity dam. Therefore, a time-varying non-probabilistic reliability (NR) analysis method is proposed in the research. To characterize the uncertainty of the parameters, we adopt the form of interval numbers to present parameters and construct the interval parameter-based reliability analysis framework. Specifically, combining interval mathematics and non-probabilistic reliability theory, the NR calculation model for the gravity dam is proposed. In addition, considering of the aging of the structure, the time-varying NR (TV-NR) calculation model utilized to evaluate the long-term reinforcement effectiveness is established. To tackle the issue of expensive computation existed in the TV-NR model, response surface method (RSM) is coupled in the TV-NR calculation model to replace the original high nonlinearity performance function. Ultimately, the proposed method is verified in engineering examples that it can detect the most possible initial locations and failure processes of the gravity dam varying with time and can efficiently and accurately evaluate the reinforcement effectiveness of the gravity dams. The results indicate that the use of reinforcement measures can provide an improvement for the safety service of gravity dams.

Koopman Modeling for Optimal Control of the Perimeter of Multi-region Urban Traffic Networks

The purpose of perimeter control is to regulate the transfer flow between the perimeters of the urban traffic network, so that the vehicle aggregation in each region is maintained at a desired level. It is regarded as one of the most important methods to solve traffic congestion in urban road networks. Accurate mathematical modeling of perimeter controlled traffic dynamics remains a challenge due to its high nonlinearity and uncertainty. Machine learning methods have been used to learn traffic dynamic models for perimeter control. However, these existing techniques lack generalization and interpretability. In this paper, we propose a Koopman modeling approach (i.e., a two-stage method) that uses a new eigenfunction of the Koopman operator based on a novel L0 norm approximation to construct an interpretable finite-dimensional approximation of the Koopman operator, which is a linear operator that describes how eigenfunctions evolve along the trajectory of a specific nonlinear dynamical system. The main advantage of utilizing the Koopman operator is that it can characterize the nonlinear system in a global linear lifted feature space. Furthermore, an algorithm based on the Koopman modeling method and model predictive control is proposed for real-time perimeter control of urban road networks. Some experiments are carried out to demonstrate the effectiveness of the proposed algorithm.

Compensation of system nonlinearity in the measurement of acoustic nonlinearity parameters

The acoustic nonlinearity parameter is determined from the amplitudes of the fundamental and second harmonic component of the acoustic wave propagating through the material. However, the generally used through-transmission based measurements with PZT transducers contain high system nonlinearity, so that the received second harmonic component includes an extra component caused by the transducer, which significantly decrease the reliability of measurements. In this study, we proposed a novel method to reduce the system nonlinearity in the conventional through-transmission based measurements by adding a simple process in which the transmitting and receiving transducers are in direct contact without a specimen. The proposed method was experimentally validated for two materials, Al6061-T6 and Fused Silica. For both materials, several specimens with different thickness were prepared, and the measurement results showed that the magnitude of the second harmonic was proportional to the thickness, but there was an offset due to system nonlinearity. On the other hand, after applying the proposed technique, the offsets were greatly reduced, and furthermore, this performance was maintained even when the transducer setup was changed, and the ratio of nonlinearity parameters measured for the two materials was in good agreement with the known literature value.

PGNN-Net: Parallel Graph Neural Networks for Hyperspectral Image Classification Using Multiple Spatial-Spectral Features

Hyperspectral image (HSI) shows great potential for application in remote sensing due to its rich spectral information and fine spatial resolution. However, the high dimensionality, nonlinearity, and complex relationship between spectral and spatial features of HSI pose challenges to its accurate classification. Traditional convolutional neural network (CNN)-based methods suffer from detail loss in feature extraction; Transformer-based methods rely too much on the quantity and quality of HSI; and graph neural network (GNN)-based methods provide a new impetus for HSI classification by virtue of their excellent ability to handle irregular data. To address these challenges and take advantage of GNN, we propose a network of parallel GNNs called PGNN-Net. The network first extracts the key spatial-spectral features of HSI using principal component analysis, followed by preprocessing to obtain two primary features and a normalized adjacency matrix. Then, a parallel architecture is constructed using improved GCN and ChebNet to extract local and global spatial-spectral features, respectively. Finally, the discriminative features obtained through the fusion strategy are input into the classifier to obtain the classification results. In addition, to alleviate the over-fitting problem, the label smoothing technique is embedded in the cross-entropy loss function. The experimental results show that the average overall accuracy obtained by our method on Indian Pines, Kennedy Space Center, Pavia University Scene, and Botswana reaches 97.35%, 99.40%, 99.64%, and 98.46%, respectively, which are better compared to some state-of-the-art methods.

Study of a novel capacitive pressure sensor using spiral comb electrodes

Abstract For traditional capacitive pressure sensors, high nonlinearity and poor sensitivity greatly limited their sensing applications. Hence, an innovative design of capacitors based on spiral comb electrodes is proposed for high-sensitivity pressure detection in this work. Compared to traditional capacitive pressure sensors with straight plate comb electrodes, the proposed sensor with the spiral comb electrodes increases the overlap areas of electrodes sufficiently, the pressure sensitivity can thus be greatly improved. Moreover, the capacitance variation of the proposed sensor is dominated by the change of the overlap area of the electrodes rather than the electrode's distance, the linearity can also thus be improved to higher than 0.99. Theoretical analysis and COMSOL-based finite element simulation have been implemented for principle verification and performance optimization. Simulation results show that the proposed design has a mechanical sensitivity of 1.5 ×10-4 m/Pa, capacitive sensitivity of 1.10 aF/Pa, and nonlinear error of 3.63%, respectively, at the pressure range from 0 to 30 kPa. An equivalent experiment has been further carried out for verification. Experimental results also show that both the sensitivity and linearity of capacitive pressure sensors with spiral comb electrodes are higher than those with straight comb electrodes. This work not only provides a new avenue for capacitor design, but also can be applied to high-sensitivity pressure detection.

Diffusive Memristor with CuS Nanoparticles Embedded in Polymeric Film as Artificial Nociceptor.

The threshold behavior and the ion diffusion dynamics in diffusive volatile memristors have a very uncanny resemblance to the transduction process of biological nociceptors. Hence, the diffusive memristors are considered the most suited for making artificial nociceptive systems. To facilitate their widespread adoption, it is imperative to develop polymeric or organic-inorganic hybrid material-based diffusive memristors that are economical, biocompatible, and easily processable. In this study, we present a cluster-type polymeric diffusive memristor where copper is used as the active top electrode. The switching medium comprises copper(II) sulfide (CuS) nanoparticles embedded in poly(ethylene oxide) (PEO). The devices show electrochemical metalization (ECM)-type and bidirectional diffusive volatile memory with high nonlinearity (104) and turn-on slope (5.6 mV/dec). They reliably remain diffusive volatile with up to 10 wt % CuS in PEO and for a wide range of compliance (10-6 to 10-2 A) without transitioning to the bipolar nonvolatile type. The low reduction potential of CuS and optimal segmental dynamics of PEO work synergistically to ensure stable and reproducible diffusive memory. The CuS nanoparticles act as bipolar electrodes, undergoing local oxidation and reduction under the influence of the bias. The switching of resistance states in the CuS-PEO memristors is attributed to the formation of cluster-type filaments between CuS nanoparticles within the PEO matrix supported by the participation of copper ions from the top Cu electrode. The observation of low filament temperature and the independence of on-state resistance with respect to the device area and temperature further corroborate the cluster-type filament in CuS-PEO memristors. Using a 5 wt % CuS-based device, an artificial nociceptor is realized, which successfully mimics most of the nociceptive plasticities such as threshold, relaxation, no adaptation, and sensitization.

A fault detection and isolation method for independent metering control system based on adaptive robust observer

The independent metering control system (IMCS) demonstrates high control precision and energy efficiency for advanced electrohydraulic applications but tends to be faulty. Current model-based fault diagnosis methods face challenges in robustness and adaptability to high uncertainties and high nonlinearities of the IMCS. To enhance the safety and reliability of the IMCS, a novel fault detection and isolation method based on adaptive robust observer (ARO) is proposed. To reduce estimation errors, the designed ARO combines the advantages of the robust filter and parameter adaption, which can overcome the impact of uncertainties and nonlinearities. To detect the fault robustly and sensitively, three types of adaptive thresholds are designed according to the spool displacement residuals. To isolate the fault, a fault space is established, and spatial fault points are calculated. The verification experiments show that the proposed method can well estimate the spool displacement while accurately detecting and isolating faults of the IMCS.

Enhanced crystallinity of (Sr,Ba)Nb2O6 films on sapphire and alumina substrates

Barium-strontium niobate in thin film form can be considered as a prospective ferroelectric material for microwave applications due to high dielectric nonlinearity. Its performance is linked to film structure quality, and one method to achieve high-quality films on microwave substrates goes through the process of annealing. (Sr,Ba)Nb2O6 films of good crystallinity were formed on sapphire and alumina substrates by magnetron sputtering. The processes of recrystallization of films on single- and polycrystalline substrates under the conditions of ex situ annealing at temperatures of 1100–1200 °C were studied. Highly (00l) oriented films on sapphire substrate were obtained by the deposition in oxygen atmosphere at a substrate temperature of 900 °C with post-growth annealing in air at an annealing temperature of 1150 °C.

Evaluation of advanced control strategies for PMSG in wind turbines: backstepping fuzzy logic control and adaptive neural network control approaches

This study presents a comparative analysis of two advanced control strategies for Permanent Magnet Synchronous Generators (PMSGs) in wind turbines: Backstepping Fuzzy Logic Control and Adaptive Neural Network Control. The aim is to evaluate their effectiveness in handling structured and unstructured uncertainties and enhancing system performance. Backstepping Fuzzy Logic Control integrates backstepping techniques with fuzzy logic regulation to improve flexibility and robustness. This method addresses the limitations of traditional PI controllers by enhancing adaptability to parameter variations and external disturbances. In contrast, Adaptive Neural Network Control employs neural networks with dynamic compensators to address high uncertainties and nonlinearities, aiming to surpass the performance of conventional PI controllers. The study involves simulations to compare these control strategies based on several criteria: robustness in managing parameter changes and external disturbances, flexibility in adapting to dynamic conditions, and overall performance including efficiency and stability. Simulation results indicate that both strategies offer significant improvements over traditional PI controllers. Backstepping Fuzzy Logic Control demonstrates strong adaptability and robustness, while Adaptive Neural Network Control shows superior efficiency and optimality, particularly in high-uncertainty environments. This comparative analysis provides insights into the strengths and limitations of each approach, offering guidance for selecting the most suitable control strategy based on specific operational requirements. The findings contribute to advancing control methodologies in wind energy systems and suggest directions for future research and practical implementation.

Disturbance-observer-based terminal sliding mode control of the quadrotor with thrust deviation fault

This paper presents a novel approach utilizing a sliding mode controller to control a quadrotor effectively in the presence of structural faults. While previous research has extensively explored fault-tolerant control for UAVs, particularly addressing faulty actuators and sensors, there remains a significant gap in addressing control methods for quadrotors experiencing structural faults. We focus specifically on scenarios where a fault in one of the quadrotor’s rotors diverts thrust from the vertical axis, complicating traditional control methods. Our proposed method involves two key steps: firstly, identifying the fault vector using a terminal sliding mode observer, and secondly, calculating appropriate control inputs using a sliding mode method based on the estimated fault vector. Simulation results demonstrate the robustness of this approach, showing accurate control performance even in the presence of multiple faults and disturbances. The system’s reliability hinges on the convergence of the disturbance observer’s estimation error to zero within a finite time frame. In conclusion, the developed controller offers a dependable solution for safely controlling a faulty quadrotor amidst high nonlinearity and uncertainty.

An improved smoothed particle hydrodynamics method for modeling multiphase flows

Multiphase flows are prevalent in both natural and engineered systems. The study of multiphase flow problems using numerical simulation is challenging due to the presence of high nonlinearities and moving interfaces. In this paper, an improved multiphase smoothed particle hydrodynamics (SPH) model is proposed for simulating multiphase flows. In the improved multiphase SPH model, an improved interface repulsive force model is proposed to reduce the interpenetration of particles at the multiphase interface and make the multiphase interface smooth and clear, and an improved kernel gradient correction is introduced to optimize the computational results. In addition, the particle shifting technology is applied to make the particle distribution uniform. Five numerical examples including the Rayleigh–Taylor instability, non-Boussinesq lock-exchange problem, square droplet deformation, single bubble rise, and circular droplet oscillation are investigated to verify the correctness and effectiveness of the improved multiphase SPH model. The results demonstrate that the improved multiphase SPH approach is effective in modeling multiphase flows.

Multivariate ocean waves dynamics in North Sea and Norwegian Sea by Gaidai reliability method

Current study presents state-of-the-art approach for evaluating spatiotemporal multivariate environmental risks, especially suitable for complex environmental systems that have been either numerically simulated or physically observed. Advocated methodology provides accurate hazard risk forecasts, based on real-time in situ environmental data. Design of offshore structures requires multivariate risk assessment – offshore and naval operations require both short- and long-term risk and reliability analyses. Contemporary risk evaluation techniques often struggle with raw timeseries multivariate data due to intrinsic multidimensionality and nonlinear interconnections among critical system’s dimensions/components. In the current study effectiveness of the Gaidai multivariate risk evaluation method is illustrated by utilizing significant wave heights dataset, measured within two offshore zones: Heidrun and Troll Norwegian oil fields. Analyzing offshore waves being particularly challenging due to their complexity, high nonlinearity, multidimensionality, yet dynamic inter-correlations. Global warming being among several significant factors, affecting ocean and sea wave heights. For naval, offshore and marine structures operating in harsh weather conditions, robust multivariate environmental risk evaluation methods being crucial for both design and safe operations. Current study aims to validate and benchmark state-of-the-art methodology, enabling extraction. Advocated approach allows for efficient yet accurate assessment of global damages, failures or hazard risks for a wide range of complex nonlinear multivariate environmental and energy systems. Primary advantage of presented multivariate reliability methodology lies within its ability to treat complex systems with practically unlimited number of dimensions, while existing reliability methods being mostly limited to univariate and bivariate analyses.

Multivariate statistical analysis and bespoke deviation network modeling for geochemical anomaly detection of rare earth elements

Rare earth elements (REEs), a significant subset of critical minerals, play an indispensable role in modern society and are regarded as “industrial vitamins,” making them crucial for global sustainability. Geochemical survey data proves highly effective in delineating metallic mineral prospects. Separating geochemical anomalies associated with specific types of mineralization from the background reflecting geological processes has long been a significant subject in exploration geochemistry. The processing of high-dimensional, non-linear geochemical survey data necessitates a systematic framework to address common issues, including missing values, the closure effect, the selection of appropriate multivariate analysis methods, and anomaly detection techniques in order to detect geochemical anomalies associated with mineral occurrences. The Curnamona Province in South Australia is considered an emerging REE province with significant REE mineralization potential. In this study, we use data from this region to evaluate the performance of a novel machine learning-based framework that incorporates data pre-processing, multivariate statistical analysis, and anomaly recognition to address challenges such as missing data, noise interference, data imbalance and high non-linearity. We utilize lithogeochemical data to map potential greenfield regions of REE mineralization. The primary advantages of our framework lie in its provision of an effective random forest-based data imputation method, utilization of isometric log-ratio transformation to eliminate the closure effect, and reduction of the impact of outliers on data interpretation through robust principal component analysis. Additionally, the framework utilizes a deviation network to learn anomaly scores from complex, non-linear data under imbalanced data conditions, identifying geochemical anomalies associated with REE occurrences by leveraging prior knowledge rather than those caused by data noise or anthropogenic factors. The anomalous areas identified by this framework delineate all known REE deposits and extend to the surrounding regions. Furthermore, a close spatial coupling relationship exists between these strongly anomalous areas and the felsic granite intrusions. The comprehensive workflow for processing geochemical data proposed in this study can effectively address common challenges in the geochemical exploration of critical minerals. The identified geochemical anomalies can provide important clues for subsequent exploration.