Nonlinear Techniques Research Articles

In‐Silico Optimization of a Bi‐Enzymatic Reactor for Mannitol Production Using Pareto‐Optimal Fronts

AbstractFor multi‐enzymatic cases, the determination of the batch reactor (BR) optimal operating policy often translates into a difficult multi‐objective problem. Exemplification is made here for the enzymatic reduction of D‐fructose to mannitol by using the mannitol dehydrogenase (MDH) enzyme and nicotinamide adenine dinucleotide (NADH) cofactor, with in situ regeneration of NADH at the expense of formate degradation by using the FDH enzyme. This paper presents an original rule to in silico generate the problem solution, by using the Pareto optimal‐front approach with accounting for pairs of competing economic goals and constraints. The optimal BR is then compared to an optimal fed‐BR (FBR), or a series of equal BRs (SeqBR). As proved, the Pareto‐optimal front alternative is an advantageous option, compared to the classical nonlinear programming technique, being simple to apply, by considering pairs of opposite objective functions. In the present case study, the Pareto‐optimal BR operating mode predicts an M‐productivity 1.5x better than those of an optimized FBR, with comparable enzymes consumption. The MDH consumption of this Pareto‐optimal BR is 10x smaller than an optimal SeqBR, and 130x smaller vs. heuristic (sub)optimal BR.

AQUILA OPTIMIZED NONLINEAR CONTROL FOR DC-DC BOOST CONVERTER WITH CONSTANT POWER LOAD

Constant power loads (CPL) can be operated by tight-controlled power electronic converters, which have negative impedance and absorb continuous power. Constant power load creates instability in an open-loop system because of its negative incremental impedance. To tackle this problem, a discrete sliding-mode (AQO-DSM) nonlinear control technique has been proposed for a DC-DC boost converter fed CPL based on Aquila optimization. In this paper, the proposed AQO-DSM controllers provide the system stability during a steady state and maintain output voltage regardless of input voltage or CPL variations. In this case, the DSM controller of a DC-DC boost converter's characteristics are adjusted using the Aquila optimization method (AQO). The Lyapunov stability concept is utilized to assess the system's overall stability. Under various system operating conditions, an experimental study and simulation are carried out to validate the proposed controller. The existing methods, such as sliding mode controller (SMC), fuzzy-based SMC (F-SMC), and super twisting algorithm (STA)-based SMC strategies, are compared with a proposed plan to demonstrate the superiority of the proposed AQO-DSMC. Simulated and experimental results have shown that the AQO-DSMC achieves the fastest convergence, the smallest steady-state, settling time under loaded conditions, and consistent chatter reduction compared with all contrasted control methods.

Nonlinear techniques for few-mode wavefront sensors

Nonlinear techniques for few-mode wavefront sensors

Measurement Technique for the Absolute Acoustic Nonlinearity Parameter Employing Only a Narrowband Pulser

In the nonlinear ultrasonic technique, the measurement of the absolute acoustic nonlinearity parameter is employed to obtain quantitative material nonlinearity. In the piezoelectric method, this measurement involves two processes: calibration and harmonic measurements. In the calibration process, a transfer function is obtained, and distortion of the propagating wave is measured in the harmonic measurement. In the conventional method for this measurement, a narrowband pulser is typically used for the harmonic measurement, while a broadband pulser is used for the calibration measurement. In this study, we propose a technique that utilizes a narrowband pulser for both harmonic and calibration measurements. First, we introduced the proposed method to measure the transfer function in the calibration measurement employing the narrowband pulser. Then, we compared the results of the transfer functions and the absolute acoustic nonlinearity parameters obtained using both a narrowband and broadband pulser in the calibration. Based on the experimental results, we found that the results obtained using both the proposed and conventional methods are highly similar, confirming the validity of the proposed measurement technique. In conclusion, even though we exclusively employ the narrowband pulser, we can accurately measure the absolute nonlinearity parameter, leading to a reduction in the required measurement equipment.

Quantifying bacterial efflux within subcellular domains of Pseudomonas aeruginosa.

Molecular efflux is a mechanism through which bacteria actively expel undesirable substances. This is a crucial line of defense against toxic chemicals in harsh environments. Understanding how efflux works is critical for designing antimicrobial strategies. Though much is already known about efflux proteins, important details about the mechanisms of efflux (e.g., importance of specific subcellular domains and ejection rates) have yet to be experimentally quantified. Herein, we use the nonlinear optical technique, second harmonic light scattering, to simultaneously measure the efflux rates from the periplasm and cytosol of a Gram-negative bacterium. The influence of efflux on the uptake kinetics of a mild antimicrobial, malachite green (MG), by Pseudomonas aeruginosa was quantified. It is observed that efflux primarily occurs from the periplasm and is two orders of magnitude faster than from the cytosol. Efflux pumps activate to maintain MG concentrations in the periplasm below 1 µM, while efflux from the cytosol maintains MG concentration below 0.1 µM. Efflux pumps are shown to saturate when exogenous MG concentrations are greater than 25 µM, while the cytosol efflux function saturates at >15 µM. Finally, efflux pumps can simultaneously eject different compounds, as proven by experiments with both MG and hexane, a known effluxable compound.IMPORTANCEMolecular efflux pumps are a crucial defense mechanism that protects bacteria from an otherwise unchecked influx of toxic molecules present in the extracellular environment. The efflux functions constitute a significant hindrance to antimicrobial efficacy. While much is now known regarding the structure of these channels, knowledge of the influence of efflux in individual subcellular domains and the associated ejection rates is still lacking. Using the nonlinear optical technique, second-harmonic light scattering, we have measured the threshold concentrations for pump activation, saturation concentrations, and efflux rates from both the periplasm and cytosol in living Gram-negative bacteria. The quantified efflux data in the different subcellular compartments not only provide a clear mechanistic understanding but also are critical for developing antimicrobial strategies.

Research on the Constructive Narrative Mode of Film based on the Embodied Imagination Theory

Psychoanalytic theory can be used to analyze the narrative mode of the film to explain psychological problems from the perspective of dreams. Based on the theory of embodied imagination, this study uses the text analysis method to analyze the film constructive narrative mode embodied in the film Deep Sea through the case study. The film constructs the sand play narrative space and uses nonlinear narrative techniques to reflect the three major characteristics of the film’s constructive narrative mode based on the theory of embodied imagination: the integration of authenticity and illusion in image construction, the penetration of emotional emergence and the process of image experience, and the realization of healing effects both on and off-screen. In the research process, the advantages and disadvantages of film works are analyzed. The advantages of free narrative logic and immersive movie-watching experience are accompanied by the disadvantages of increasing the threshold of movie-watching and large investment risks. This study provides a reference for the construction of the theoretical system of film constructive narrative mode.

The Influence of Dopaminergic Medication on Regularity and Determinism of Gait and Balance in Parkinson’s Disease: A Pilot Analysis

Background/Objectives: Understanding how dual-tasking and Parkinson’s disease medication affect gait and balance regularity can provide valuable insights to patients, caregivers, and clinicians regarding frailty and fall risk. However, dual-task gait and balance studies in PD most often only employ linear measures to describe movement regularity. Some have used nonlinear techniques to analyze PD performances, but only in the on-medication state. Thus, it is unclear how the nonlinear aspects of gait or standing balance are affected by PD medication. This study aimed to assess how dopaminergic medication influenced the regularity and determinism of joint angle and anterior–posterior (AP) and medial–lateral (ML) center of pressure (COP) path time-series data while single- and dual-tasking in PD. Methods: Sixteen subjects with PD completed single- and dual-task gait and standing balance trials for 3 min off and on dopaminergic medication. Sample entropy and percent determinism were calculated for bilateral hip, knee, and shoulder joints, and the AP and ML COP path. Results: There were no relevant medication X task interactions for either the joint angles series or the balance series. Instead, the results supported independent effects of medication, dual-tasking, or standing with eyes closed. Balance task difficulty (i.e., eyes open vs. eyes closed) was detected by the nonlinear analyses, but the nonlinear measures yielded opposing results such that standing with eyes closed simultaneously yielded less regular and more deterministic signals. Conclusions: When juxtaposed with previous findings, these results suggest that medication-induced functional improvements in people with PD might be accompanied by a shift from lesser to greater signal consistency, and the effects of dual-tasking and standing with eyes closed were mixed. Future studies would benefit from including both linear and nonlinear measures to better describe gait and balance performance and signal complexity in people with PD.

A Self-Calibration Method for Robot End-Effector Using Spherical Constraints

A self-calibration method utilizing spherical constraints is proposed for calibration of robot end-effectors. The method establishes a mathematical model to account for both the geometric errors of the robot and the deformation errors of the end-effector. A nonlinear least-squares parameter identification technique based on spherical constraints is employed to achieve autonomous calibration of the end-effector. Contrasted with methodologies relying on point plane or distance constraints, this novel technique delivers superior positioning accuracy, streamlined operational procedures and enhanced efficiency. Both simulation and experimental validation confirm that the self-calibration method using spherical constraints improves the positioning accuracy of the robot end-effector from 3 mm to 0.3 mm, showing the effectiveness of the method.

D-Band 4.6 km 2 × 2 MIMO Photonic-Assisted Terahertz Wireless Communication Utilizing Iterative Pruning Deep Neural Network-Based Nonlinear Equalization

In this paper, we explore the enhancement of a 4.6 km dual-polarization 2 × 2 MIMO D-band photonic-assisted terahertz communication system using iterative pruning-based deep neural network (DNN) nonlinear equalization techniques. The system employs advanced digital signal processing (DSP) methods, including down-conversion, resampling, matched filtering, and various equalization algorithms to combat signal distortions. We demonstrate the effectiveness of DNN and iterative pruning techniques in significantly reducing bit error rates (BERs) across a range of symbol rates (10 Gbaud to 30 Gbaud) and polarization states (vertical and horizontal). Before pruning, at 10 GBaud transmission, the lowest BER was 0.0362, and at 30 GBaud transmission, the lowest BER was 0.1826, both of which did not meet the 20% soft-decision forward error correction (SD-FEC) threshold. After pruning, the BER at different transmission rates was reduced to below the hard decision forward error correction (HD-FEC) threshold, indicating a substantial improvement in signal quality. Additionally, the pruning process contributed to a decrease in network complexity, with a maximum reduction of 85.9% for 10 GBaud signals and 63.0% for 30 GBaud signals. These findings indicate the potential of DNN and pruning techniques to enhance the performance and efficiency of terahertz communication systems, providing valuable insights for future high-capacity, long-distance wireless networks.

Enhanced Fatigue Crack Detection in Complex Structure with Large Cutout Using Nonlinear Lamb Wave

The large cutout structure is a key component in the bottom skin of an airplane wing, and is susceptible to developing fatigue cracks under service loads. Early fatigue crack detection is crucial to ensure structural safety and reduce maintenance costs. Nonlinear Lamb wave techniques show significant potential in microcrack monitoring. However, nonlinear components are often relatively weak. In addition, a large cutout structure introduces complex boundary conditions for Lamb wave propagation, making nonlinear Lamb wave monitoring more challenging. This article proposes an integrated data processing method, combining phase inversion with continuous wavelet transform (CWT) to enhance crack detection in complex structures, with phase-velocity desynchronization adopted to suppress the material nonlinearity. Experiments on a large cutout aluminum alloy plate with thickness variations were conducted to validate the proposed method, and the results demonstrated its effectiveness in detecting fatigue cracks. Furthermore, this study found that nonlinear components are more effective than linear components in monitoring closed cracks.

ENHANCING CNN PERFORMANCE WITH RIGHT-NEIGHBOR DEVIATION (RND) POOLING: A FOCUS ON MULTI-CLASS BRAIN TUMOR CLASSIFICATION IN MRI IMAGES

In recent years, the field of image processing and computer vision has been deeply transformed by the advent of Convolutional Neural Network (CNN). These deep learning models, inspired by biological processes, have demonstrated remarkable success in tasks such as image classification, object detection, and semantic segmentation. A critical component of this success is feature extraction, where the pooling layer within CNN serves as a nonlinear down-sampling technique. This layer reduces the spatial size of the convolved feature map through operations like average or maximum pooling, significantly impacting the model’s performance in complex tasks. This research introduces a novel pooling algorithm, Right-Neighbor Deviation (RND), designed to enhance the performance of CNN. The RND pooling method aims to capture detailed spatial features and hierarchical representations more effectively, thereby improving the accuracy and resilience of deep learning models. A dataset of multi-class brain tumor classification, incorporating MRI images from the BR35H dataset, is utilized to explore the potential benefits of RND pooling compared to traditional methods. The effectiveness of the proposed method is evaluated using performance metrics including accuracy, precision, recall, and F1 score, achieving generalized values of 98.86%, 98.84%, 98.80%, and 98.80%, respectively. The results highlight the potential of RND pooling to significantly advance the state of the art in brain tumor classification using CNNs, potentially leading to improved diagnosis and treatment outcomes.

Entire loosening stage monitoring of bolted joints via nonlinear electro-mechanical impedance spectroscopy

This article proposes a nonlinear electro-mechanical impedance spectroscopy (NEMIS) methodology for the entire loosening stage monitoring of bolted joints. Unlike the conventional electro-mechanical impedance spectroscopy (EMIS) implemented in the frequency domain, NEMIS utilizes a temporal interrogating signal to procure the developed nonlinear impedance spectrum. It can harness the structural resonance information akin to the conventional EMIS, while concurrently capturing the contact acoustic nonlinearity (CAN) features with only one solitary Piezoelectric Wafer Active Sensor (PWAS). To elucidate the underlying principles of NEMIS, a comparative analysis juxtaposing the conventional EMIS, nonlinear ultrasonic techniques, and the proposed NEMIS was conducted. Subsequently, a reduced-order analytical model was established to simulate the rough contact interfaces of the bolted joints under various looseness states. Parametric studies were carried out to demonstrate the resonance deviation and nonlinear features resulting from the bolt looseness. Ultimately, the experimental implementation of NEMIS for the entire loosening stage monitoring of the bolted joint was performed compared with the conventional EMIS method. It was proved that NEMIS could detect both the incipient bolt loosening at an embryo stage and severe loosening of the bolted joint at the terminal period, integrating the continuous monitoring capability of conventional EMIS and the sensitivity of nonlinear ultrasonic methodology. The quantification on the severity of bolt loosening was accomplished via both linear and nonlinear damage indices, exhibiting a consistent trend with the gradations of bolt looseness. The article culminates in a summary, concluding remarks, and suggestions for future work.

Insights into the phase behavior at interfaces using vibrational sum frequency generation spectroscopy.

Phase separation is ubiquitous at the interface between two distinct phases. Physical transformation during phase separation often plays a crucial role in many important mechanisms, such as lipid phase separation, which is fundamental for transport through biological membranes. Phase separation can be complex, involving changes in the physical state and the reorganization of molecular structures, influencing the behavior and function of materials and biological systems. Surface-sensitive vibrational sum frequency generation (VSFG) spectroscopy provides a powerful tool for investigating these interfacial processes. As a non-linear optical technique, VSFG spectroscopy is sensitive to changes in molecular orientation and interactions at interfaces, making it an ideal method for studying phase separation processes. Here, we review the molecular interaction mechanisms underlying phase separation. We also explore the application of VSFG spectroscopy in studying phase separation processes at different interfaces. In particular, we focus on oil-water interfaces, which are relevant in environmental and industrial contexts; polymer and lipid surfaces, important for materials science and biological membranes; and intrinsically disordered protein systems, which play key roles in cellular function and disease.

Nonlinear optical research on 2D NbSe2 nanosheets and their ultrafast photonics applications

Two-dimensional (2D) NbSe2 is a new material with a variety of excellent properties. In this article, 2D NbSe2 nanosheets are prepared using liquid phase exfoliation (LPE) and spin coating methods. At the same time, the properties of NbSe2 were calculated by using density functional theory (DFT), exploring the changes in the electronic band structure of NbSe2 with the number of layers, and studying the optical properties of NbSe2. The nonlinear optical properties caused by the Pauli blocking effect and the absorption spectra are studied through typical nonlinear testing techniques and an ultraviolet–visible-near-infrared (UV–VIS-IR) spectrophotometer. In addition, 2 µm solid-state pulse lasers have important applications in a variety of fields. For the first time, 2D NbSe2 nanosheets are prepared as saturable absorbers (SA) and applied them to solid-state lasers as nonlinear optical modulation devices, successfully achieving the generation of ultra-short pulse lasers with a pulse duration of 445.4 ps in 2 µm band. Our research results prove that 2D NbSe2 nanosheets is a promising nanomaterial, can be prepared into nonlinear optical modulation devices with excellent performance, and show great application potential as ultrafast photonic devices. It is beneficial to the miniaturization of solid-state pulse lasers in subsequent applications.

Genetic algorithm inspired optimal integrated nonlinear control technique for an electric power steering system

Genetic algorithm inspired optimal integrated nonlinear control technique for an electric power steering system

Online kernel sliced inverse regression

Online dimension reduction techniques are widely utilized for handling high-dimensional streaming data. Extensive research has been conducted on various methods, including Online Principal Component Analysis, Online Sliced Inverse Regression (OSIR), and Online Kernel Principal Component Analysis (OKPCA). However, it is important to note that the exploration of online supervised nonlinear dimension reduction techniques is still limited. This article presents a novel approach called Online Kernel Sliced Inverse Regression (OKSIR), which specifically tackles the challenge of dealing with the increasing dimension of the kernel matrix as the sample size grows. The proposed method incorporates two key components: the approximate linear dependence condition and dictionary variable sets. These components enable a reduced-order approach for online variable updates, improving the efficiency of the process. To solve the OKSIR problem, we formulate it as an online generalized eigen-decomposition problem and employ stochastic optimization techniques to update the dimension reduction directions. Theoretical properties of this online learner are established, providing a solid foundation for its application. Through extensive simulations and real data analysis, we demonstrate that the proposed OKSIR method achieves performance comparable to that of batch processing kernel sliced inverse regression. This research significantly contributes to the advancement of online dimension reduction techniques, enhancing their effectiveness in practical applications.

Nonlinear control of the permanent magnet synchronous motor PMSM using input-output linearization control and sliding mode control

Today, electric machines are becoming increasingly important in all sectors (industrial drives, the automotive industry, aviation, traction systems, and agriculture, etc.). Among these machines, permanent magnet synchronous machines (PMSMs) hold a significant place in the control of industrial mechanisms, automated systems, and in the fields of renewable energy like (wind energy, solar energy, and hybrid systems, etc ). Currently, in variable speed drives, the use of PMSM, especially for low power and certain special industrial applications, is replacing DC motors and asynchronous motors because they offer higher efficiency, power factor, and torque density. Moreover, PMSM do not have an excitation circuit in the rotor, which leads to reduced maintenance demands. The control of permanent magnet synchronous machines (PMSM) presents significant challenges due to their nonlinear behavior. Classical linear controllers, such as PI, perform well with linear systems that have constant parameters. However, for nonlinear systems with varying parameters, the conventional control methods may prove insufficient and exhibit a lack of robustness. To address the issues and achieve decoupled machine control, various methods have been proposed. Nonlinear robust control techniques have been extensively explored within the domain of power electronics and drive systems. Included in these, sliding mode control and nonlinear input-output feedback linearization (IOFL). Sliding mode control (SMC) is a control technique known for its excellent dynamic performance in PMSM drives. It offers high robustness and straightforward implementation in both software and hardware. However, a significant drawback of this method is the chattering phenomenon. By the way the input/output linearization control can provide good behavior in steady and dynamic regimes and also provides good decoupling between system variables. This article presents a design of tow control laws based on sliding mode and feedback linearization controller based on input-output feedback linearization to adjust the speed of a permanent magnet synchronous motor (PMSM). To highlight the effectiveness of the designed controllers, a comparison was carried out Matlab Simulink, revealing that the feedback linearization controller outperforms the SMC.

A Comparative Study of Geometric Phase Change- and Sideband Peak Count-Based Techniques for Monitoring Damage Growth and Material Nonlinearity.

This work presents numerical modeling-based investigations for detecting and monitoring damage growth and material nonlinearity in plate structures using topological acoustic (TA) and sideband peak count (SPC)-based sensing techniques. The nonlinear ultrasonic SPC-based technique (SPC-index or SPC-I) has shown its effectiveness in monitoring damage growth affecting various engineering materials. However, the new acoustic parameter, "geometric phase change (GPC)" and GPC-index (or GPC-I), derived from the TA sensing technique adopted for monitoring damage growth or material nonlinearity has not been reported yet. The damage growth modeling is carried out by the peri-ultrasound technique to simulate nonlinear interactions between elastic waves and damages (cracks). For damage growth with a purely linear response and for the nonlinearity arising from only the nonlinear stress-strain relationship of the material, the numerical analysis is conducted by the finite element method (FEM) in the Abaqus/CAE 2021 software. In both numerical modeling scenarios, the SPC- and GPC-based techniques are adopted to capture and compare those responses. The computed results show that, from a purely linear scattering response in FEM modeling, the GPC-I can effectively detect the existence of damage but cannot monitor damage growth since the linear scattering differences are small when crack thickness increases. The SPC-I does not show any change when a nonlinear response is not generated. However, the nonlinear response from the damage growth can be efficiently modeled by the nonlocal peri-ultrasound technique. Both the GPC-I and SPC-I techniques can clearly show the damage evolution process if the frequencies are properly chosen. This investigation also shows that the GPC-I indicator has the capability to distinguish nonlinear materials from linear materials while the SPC-I is found to be more effective in distinguishing between different types of nonlinear materials. This work can reveal the mechanism of GPC-I for capturing linear and nonlinear responses, and thus can provide guidance in structural health monitoring (SHM).

Enhanced control of grid-connected multi-machine wind power generation systems using fuzzy backstepping approaches

This research paper presents an approach for enhancing the performance of a multi-machine wind power generation system (WPGS) through the combination of nonlinear and intelligent control techniques. The suggested approach focuses on optimizing the WPGS, which utilizes permanent magnet synchronous generators (PMSGs) interconnected with the grid. To enable grid connection, a back-to-back configuration is employed for both the rectifier and inverter in a fifteen-switch configuration, with control achieved through pulse width modulation. The designed control algorithm is designed to effectively regulate the system and reduce the active and reactive power ripples. Initially, the WPGS model is provided. Subsequently, the fuzzy backstepping control (FBC) law is detailed, incorporating the Lyapunov stability technique. Fuzzy logic is used to adapt the gains in the backstepping approach, ensuring the control system can accommodate disturbances and system variations. As a result, this adaptive control approach enables optimal effusiveness of the entire system in both static and dynamic operational regimes. Experimental tests were conducted using MATLAB to compare the efficacy of the designed strategy against the traditional backstepping control approach. The obtained results demonstrated the robustness and effective reference tracking capabilities of the designed FBC approach, along with the complete elimination of speed overshoot across diverse wind conditions. These findings validate the designed control approach's effectiveness and superiority over the BC approach under varying conditions.

A Non-Linear Control Technique for Single Phase qZSI

Quasi-Z-source inverters, or qZSIs, are a common type of inverter used in transformer-less systems because they can boost or buck input voltage without a requirement for a DC- DC converter. A transformer is not necessary for the qZSI's single-stage conversion to operate as a grid-tie converter. In transformer-less designs common mode current is the major problem, though, because there is no galvanic isolation. To reduce the common mode current, this article offers controlling the qZSI using a modified pulse width modulation (PWM) approach two additional semiconductor switches. The developed methodology provides a practical means of integrating solar photovoltaic systems into the grid. what the recommended topology's attributes should be: 1)It uses the phase-leg shoot through to boost the direct current and voltage to required level by eliminating the need for a second dc-dc converter. 2) Freewheeling is achieved while removing PWM deadtime by employing extra linked switches.