High Nonlinear Characteristics Research Articles

Highly coherent supercontinuum generation in a photonic crystal fiber based on GeSe2–As2Se3–PbSe chalcogenide in the mid-infrared region

We numerically demonstrated highly coherent mid-infrared (IR) super-continuum (SC) generation using all-normal dispersion (AND) GeSe2−As2Se3−PbSe chalcogenide photonic crystal fiber (PCF). The structural parameters of this PCF were optimized to obtain an ultra-flat-AND region and high nonlinearity characteristics using the full-vectorial finite difference method with the perfectly matched layer boundary conditions to calculate the linear properties of the fiber. We show that, pumping a light source at 9.8 µm pump wavelength with an input pulse energy of 3.5 nJ and pulse width of 200 fs, corresponding to the peak power of 17.5 kW, into a 10-cm-long fiber, an ultra-flat-coherent mid-IR SC extended from 2851.8 nm to 13538.8 nm (bandwidth of 10687 nm) is obtained. This ultra-wide and flattened spectrum has excellent stability and coherence properties that can be used for mid-IR applications such as biological imaging, optical coherence tomography, and frequency metrology.

Multi-XGB: A multi-objective reliability evaluation approach for aeroengine turbine discs

Multiple failure sites often co-existed in aeroengine turbine discs, its reliability performance contains complex characteristics of high-nonlinearity and multiple-output, leading to the insufficient reliability evaluating accuracy/efficiency of traditional direct simulation or surrogate separate methods. To address this problem, a multi-XGB model is presented by fusing the linkage sampling technique to extract a multi-input multi-output dataset, the XGBoost series ensemble platform to build the modelling architecture, and the Bayesian optimization algorithm to find the optimal model hyperparameters. Moreover, a multi-XGB driven multi-objective reliability evaluation framework is presented as well. Regarding a typical aeroengine high-pressure turbine disc with multiple failure sites (i.e., disc core, disc rim) as an example, the probabilistic distributions, correlation relationships, and reliability degree for each/all frail sites in turbine disc are acquired by applying the presented multi-XGB model. Through the method comparisons, the presented method is verified to hold high computing accuracy and efficiency in evaluating the multi-objective reliability of turbine discs. The current efforts shed light on the theoretical development for reliability evaluation, design, and optimization of high-end equipment like aeroengine.

Time series clustering based on polynomial fitting and multi-order trend features

Time series clustering serves as a potent data mining method, facilitating the analysis of an extensive array of time series data without the prerequisite of any prior knowledge. It finds wide-ranging use across various sectors, including but not limited to, financial and medical data analysis, and sensor data processing. Given the high dimensionality, non-linearity, and redundancy characteristics associated with time series, conventional clustering algorithms frequently fall short in yielding satisfactory results when directly applied to this kind of data. As such, there is a critical need to judiciously select suitable feature extraction methods and dimension reduction techniques. This paper introduces a time series clustering algorithm, drawing primarily from polynomial fitting derivative features as a wellspring for feature extraction to achieve effective clustering results. Initially, Hodrick Prescott (HP) filtering comes into play for the processing of raw time series data, thereby eliminating noise and redundancy. Subsequently, polynomial curve fitting (PCF) is applied to the data to derive a globally continuous function fitting this time series. Next, by securing multi-order derivative values via this function, the time series is transformed into a multi-order derivative feature sequence. Lastly, we designed a polynomial function derivative features-based dynamic time warping (PFD_DTW) algorithm for determining the distance between two equal or unequal granular length time series, and subsequently a hierarchical clustering method anchored on the PFD_DTW distances for time series clustering after computing interspecies distances. The effectiveness of this method is corroborated by experimental results obtained from several practical datasets.

Product manifold optimization-based joint beamforming for RIS-aided MIMO systems: A fairness based approach

Reconfigurable intelligent Surface (RIS) can effectively improve the communication quality and extend the signal coverage range by reconfiguring the wireless communication transmission environment, which is very suitable for millimeter wave (mmWave) multiple-input multiple-output (MIMO) systems. In this paper, a product manifold optimization-based beamforming design (PMO-BD) algorithm for a RIS-assisted multi-user MIMO system is proposed to enhance fairness among users. We establish a lower bound for the minimum signal-to-interference-plus-noise ratio (SINR) among users as the optimization objective, which is subjected to multiple constraints. By maximizing the lower bound to ensure the quality of service (QoS) for each user, fairness among users get improved. The PMO-BD algorithm exploits the internal structure of the product manifold to deal with the high dimension and nonlinear characteristics of the problem effectively, thereby reducing algorithm complexity and enhancing convergence rates. Simulation results verify the advantages of the proposed PMO-BD algorithm in comparison to other algorithms.

An interpretable deep learning multi-dimensional integration framework for exchange rate forecasting based on deep and shallow feature selection and snapshot ensemble technology

Accurate exchange rate forecasting is important for the better realization of international economic transactions and international currency investments. However, it is challenging to forecast exchange rates due to their high volatility, nonlinearity, and noisy characteristics. This paper creatively proposes a novel interpretable deep learning multi-dimensional integration framework for foreign exchange rate forecasting based on shallow and deep feature selection and snapshot ensemble technology. The main factors affecting exchange rates are considered at a deep and shallow level, and the feature selection project is implemented. A cluster-based multidimensional learning paradigm is proposed. The framework clusters data and constructs predictive sub-models and then embeds snapshot ensemble techniques into models to improve the robustness of single models. Finally, the results are nonlinearly integrated by a deep learning model. In addition, three datasets and twelve comparative models were used to demonstrate the performance of the models. The empirical results show that the proposed model has the smallest mean absolute percentage error (0.407859%, 0.47134%, and 0.470167%, respectively).

Adaptive RBFNN‐based predictive control for the nanopositioning of an electrostatic MEMS actuator

AbstractElectrostatic micro electro mechanical system (MEMS) as an actuator is one of the most widely used actuators in micro dimension systems. Due to the high nonlinear characteristics in the micro actuator's model, nanopositioning has been a challenging problem for researchers. In addition, the system is subject to uncertainties associated with the system's unknown parameters and un‐modelled dynamics. Thus, to overcome these problems and achieve precise positioning a robust non‐linear control method is required. In this research, an optimal voltage control algorithm is initially designed utilizing the nonlinear predictive control approach. The Taylor expansion sequence is used to estimate the final position of the parallel plate actuator. To approximate unknown disturbances, a radial basis function neural network (RBFNN) has been designed. The estimations made were used in updating the control input in the previous step, and the obtained results showed an increase in the robustness of the designed controller. The stability of the closed‐loop system is also analyzed. Finally, a comparison is accomplished between the potential of the proposed controller and that of the feedback linearization and sliding mode controllers. The obtained results indicate better performance and robustness of the suggested controller.

Distributed low-carbon operational optimization model of an integrated energy system based on ladder carbon trading and integrated demand response

ABSTRACT Integrated energy system (IES) has become significant in the global energy revolution because of its unique advantage of promoting clean energy use and improving energy efficiency. However, its low-carbon operational optimization and capacity allocation challenges have sharply increased because, as its scale continuously grows, its inherent strongly coupled multi energy flow and high nonlinearity characteristics have become more prominent. This study proposes a distributed low-carbon operational optimization mechanism for an IES using integrated demand response (IDR) and ladder carbon trading (LCT). First, a low-carbon IES was modeled as a coupled model between energy operators (EO) that contain combined cooling, heating, and power systems (CCHP) and flexible users (FU) based on the Stackelberg game. Second, the operational optimization model was formulated as a source-load synergistic operation scheduling mechanism based on IDR and the LCT. Finally, the strategy of each stakeholder was obtained using a modified distributed algorithm of genetic algorithm nested quadratic programming (GA-QP). The effectiveness of the proposed model was verified through a case study. The economic benefits for each stakeholder improved, as confirmed through the experimental results. The energy cost of the FU decreased by 4.42%, and the revenue from energy sales of the EO increased by 11.9%; renewable energy was prioritized for consumption, with a consumption rate of 100%; the total output of the core devices for energy cascade utilization increased by 15.6%, significantly improving energy supply efficiency; the total carbon emissions decreased by 8.64%; and power and heat loss rates were 0% and 0.00059%, respectively. Highlights With the rapid development of renewable energy, The integrated energy system relies on its inherent operational advantages of high flexibility, high toughness, and multi energy coupling cascade utilization, demonstrates unique advantages in serving as a clean and renewable energy carrier and adapting to the development of distributed energy The energy cost of flexible users has decreased by 4.42%, and the revenue from energy sales of energy operators has increased by 11.9%; Renewable energy is prioritized for consumption, with a consumption rate of 100%; The total output of the core devices for energy cascade utilization has increased by 15.6%, significantly improving energy supply efficiency; The total carbon emissions decreased by 8.64%, both power and heat loss rates are 0%, the error rate of energy supply is 0.00059% Based on the above research, this paper is established a distributed low-carbon operation optimization model for Integrated energy system coupled the EO that contain CCHP and the FU based on Stackelberg game In view of the gradual deepening of the coupling degree of the IES, the increasingly obvious characteristics of multi-agent, the difficulty of centralized scheduling to protect the interests of each independent entity, and the inadequacy of the system’s energy cascade utilization advantages, this paper establishes a bilateral distributed operation optimization model based on Game theory In response to the insufficient utilization of the advantages of multi energy coupling in existing research on IES, insufficient attention paid to the interaction between supply and demand on both sides of the system, and insufficient enthusiasm of users as independent entities to participate in system operation scheduling, this article adopts a integrated demand response as the load side operation scheduling strategy

Intelligent train stopping control for railways: A deep learning approach

AbstractStation parking accuracy is an important indicator for the automatic control of high‐speed trains; however, it is subject to many influencing factors, such as the characteristics of high nonlinearity and large time delays in the train control model, time‐variant humidity, and uncertain weather conditions, leading to unsatisfying performance with existing feedback control algorithms. This study first proposes an intelligent train stopping control approach based on deep learning for high‐speed railways. By collecting a large amount of historical train operation data from the Beijing–Shenyang high‐speed railway, three data‐driven models are developed for train stopping control. The first model is based on a deep‐layered feedforward neural network (DNN), which predicts the exact train stopping position with dynamic train running states (position, velocity etc.) as input. Taking advantage of the physical train control models used in practice, the DNN to a convolutional neural network (CNN) is then improved by customizing the convolutional layers of the CNN. To overcome the issues arising from the incompleteness of data samples, a few‐shot convolutional neural network (FSCNN) is further developed to enhance the prediction performance of the CNN. Compared with that of the current method used in practice, the simulation experiments show that the train station parking error can be decreased by 38.6%, 42.8%, and 49.2% by our developed DNN, CNN, and FSCNN, respectively.

Improved deep residual shrinkage network on near infrared spectroscopy for tobacco qualitative analysis

Accurate qualitative analysis of near-infrared (NIR) spectroscopy of tobacco is one of the primary challenges faced by the tobacco industry. The NIR spectrum has the characteristics of high dimensionality, high redundancy, and non-linearity, which leads to the low prediction accuracy of the model. In this paper, we developed an improved deep residual shrinkage network (DRSN) model for enhancing the accuracy of NIR qualitative analysis. The method used gramian angular summation fields (GASF) to convert the NIR spectrum into a two-dimensional image, so that the one-dimensional spectral data was encoded into the geometric structure of the two-dimensional image. The attention module was embedded into the DRSN, which improved the attention of the network to the local bands and peaks of the spectrum. Two adjustment factors were also introduced to achieve adaptive selection of thresholds while reducing the interference of noise in the spectra. In order to investigate the effectiveness and stability of the model, a comparison with support vector machine (SVM), random forest (RF), convolutional neural networks (CNN) and DRSN was provided. The results of the proposed method show improvement in prediction accuracy for both the tobacco grades and producing areas. It is also valuable for the promotion of online analysis and any other applications of qualitative analysis.

Reliable Fuzzy Sampled-Data Control for Nonlinear Suspension Systems Against Actuator Faults

This article investigates the reliable fuzzy sampled-data control issue of nonlinear vehicle suspension systems subject to actuator faults. Considering the high nonlinear characteristics in the springs and the shock absorbers, the Takagi–Sugeno fuzzy method is utilized to describe the nonlinear suspension system. To deal with the discrete phenomenon of nonuniform sampling, an input-delay is employed to convert the continuous-discrete suspension system into a continuous-time form. Motivated by the above system model, a Lyapunov functional candidate is employed to obtain sufficient conditions that can meet system stability and performance requirements simultaneously. Furthermore, a novel two-step optimization algorithm is proposed for the sampled-data controller design in the structure of output feedback. Finally, experimental tests are implemented via an active suspension test rig, and the test results illustrate that the proposed method contributes greatly to improve the ride comfort, handling performance, and road holding capability simultaneously.

Retention Secured Nonlinear and Self-Rectifying Analog Charge Trap Memristor for Energy-Efficient Neuromorphic Hardware.

A memristive crossbar array (MCA) is an ideal platform for emerging memory and neuromorphic hardware due to its high bitwise density capability. A charge trap memristor (CTM) is an attractive candidate for the memristor cell of the MCA, because the embodied rectifying characteristic frees it from the sneak current issue. Although the potential of the CTM devices has been suggested, their practical viability needs to be further proved. Here, a Pt/Ta2 O5 /Nb2 O5- x /Al2 O3- y /Ti CTM stack exhibiting high retention and array-level uniformity is proposed, allowing a highly reliable selector-less MCA. It shows high self-rectifying and nonlinear current-voltage characteristics below 1µA of programming current with a continuous analog switching behavior. Also, its retention is longer than 105 s at 150°C, suggesting the device is highly stable for non-volatile analog applications. A plausible band diagram model is proposed based on the electronic spectroscopy results and conduction mechanism analysis. The self-rectifying and nonlinear characteristics allow reducing the on-chip training energy consumption by 71% for the MNIST dataset training task with an optimized programming scheme.

A Network Intrusion Detection Model Based on Principal Component Analysis and Random Forest

Network intrusion data has the characteristics of high dimension, nonlinearity and redundancy. To solve the problem of low detection rate of traditional dimensionality reduction and detection methods, a network intrusion detection method based on principal component analysis combined with random forest is proposed. Firstly, the data dimension of network intrusion is reduced by principal component analysis to eliminate redundant information between data, and then the processed data is classified and trained by using random forest classifier. The algorithm is verified by the network intrusion NSL_KDD dataset. The experimental results show that compared with other network intrusion detection methods, the method has fast learning speed, high detection accuracy, low false negative rate and low false positive rate. An efficient, real-time and good network intrusion detection method.

Coupled Indirect Torque Control and Maximum Power Point Tracking Technique for Optimal Performance of 12/8 Switched Reluctance Generator-Based Wind Turbines

Maximum power point tracking (MPPT) techniques for wind turbines have a significant effect on renewable energy production. Therefore, the association of the indirect torque control of the switched reluctance generator (SRG) with the wind turbine considering the MPPT technique has been developed in this work. The proposed strategy has a great impact on the production of renewable energy using an SRG machine. The main steps to achieve the object of this work are: First, the wind turbine was modeled and simulated according to the MPPT. In the second step, the indirect torque control strategy, based on the hysteresis current control for SRG 12/8, was realized. This was undertaken using a proportional integral regulator and the hysteresis controller for the torque in order to obtain the appropriate switching based on an asymmetric bridge converter. Moreover, the SRG has high nonlinear characteristics. Thus, the modeling results of this kind of machine are obtained by the use of the finite element method, with its dynamic study performed by the unique estimation of the electromagnetic torque in its generator mode functioning. Finally, the indirect torque control technique of the SRG has also been associated with the MPPT technique to maximize the efficiency power coefficient. The obtained results approve and validate the efficiency of a proposed MPPT of the wind turbine associated with the SRG. This illustrates, simultaneously, the remarkable effects of the turn-switching angles on the operating performances and the high quality of the produced energy. The importance of the effect of varying the turn-switching angles is also presented and discussed.

Partial shading conditions for photovoltaic system using artificial neural networks technique

Partial shading condition in solar photovoltaic (PV) systems is an inevitable problem due to the behavior of high nonlinear and unpredictable characteristics due to different shading states. However, several scientific works and research aimed to find approximate and expressive models of this nonlinear behavior using modern methods and techniques to allow researchers to find effective solutions to these critical situations. This paper aims to obtain the appropriate model for partial shading cases using artificial intelligence techniques through machine learning of neural network technology, based on experimental data of PV characteristics for different cases. This model allows for diagnosing the state of faults of Partial shading (PV) systems. Moreover, it allows the development of appropriate algorithms in order to maintain, perform, and prevent the complete shutdown of the systems. All results of the model photovoltaic partial shading characteristics for different situations based on the machine learning process confirm the effectiveness of the adopted technique after comparing it with the real data with a very acceptable margin of error.

Design of Asynchronous Motor Controller Based on Controlled Lagrangians Method

Asynchronous motor system has the characteristics of high order, strong coupling, and nonlinearity. From the dynamical model, it is the underactuated mechanical system, which means that the dimension of its input space is fewer than the degree of freedom. Following this perspective, the energy based nonlinear control technology-CL (controlled Lagrangians) method is used to solve the control problem in this paper. Based on the expected controlled energy and its derivative with respect to time, controlled Lagrangians and generalized force are constructed, and they produce the controlled equations. In order to ensure the complete matching between the controlled equation and the original equation, the gyroscopic forces containing the first-order term of velocity are innovatively introduced into the generalized force, and the matching conditions are obtained. By solving the matching conditions composed of some partial differential equations, the nonlinear smooth feedback control law can realize the global asymptotic stabilization of not only velocity but also position. Finally, the controlled energy is selected as the Lyapunov function, and the stability is proved according to the LaSalle invariant theorem. The effectiveness of the designed control law is demonstrated in the results of the simulation.

The PSR-FA-NAR model for assessing and forecasting environmental impacts: An empirical analysis of Changning–Weiyuan shale gas play in China

The largest ever investment in shale gas resources has induced potential environmental threats in China. The assessment and forecasting of environmental impacts associated with shale gas production is highly challenging due to the characteristics of high uncertainty, nonlinearity, and complexity. This paper proposes a new hybrid model by combining the pressure-state-response (PSR) framework with the firefly algorithm (FA) and a nonlinear auto-regressive (NAR) dynamic neural network (the PSR-FA-NAR model) to detect and forecast the state of the environment as well as send warning signals for shale gas production. Then, an empirical sample, the Changning-Weiyuan national-level shale gas pilot zone that produces more than 50% of Chinese shale gas output, is used to test the effectiveness of the proposed model. The results show that Changning play will predictably face severe environmental threats imposed by rapid development, and the model is not only able to capture nonlinearity time-series and present cause-effect relationships but is also able to improve the predictive performance and forecasting accuracy. It proves that the PSR-FA-NAR model can effectively address the problems with high dimensionality, complexity, and nonlinearity and provides a practical methodology to quantify and identify the potential environmental impacts of unconventional oil and gas production.

A Novel Virtual Optical Image Encryption Scheme Created by Combining Chaotic S-Box with Double Random Phase Encoding.

The double random phase encoding (DRPE) system plays a significant role in encrypted systems. However, it is a linear system that leads to security holes in encrypted systems. To tackle this issue, this paper proposes a novel optical image encryption scheme that combines a chaotic S-box, DRPE, and an improved Arnold transformation (IAT). In particular, the encryption scheme designs a chaotic S-box to substitute an image. The chaotic S-box has the characteristics of high nonlinearity and low differential uniformity and is then introduced to enhance the security of the DRPE system. Chaotic S-boxes are resistant to algebraic attacks. An IAT is used to scramble an image encoded by the DRPE system. Meanwhile, three chaotic sequences are obtained by a nonlinear chaotic map in the proposed encryption scheme. One of them is used for XOR operation, and the other two chaotic sequences are explored to generate two random masks in the DRPE system. Simulation results and performance analysis show that the proposed encryption scheme is efficient and secure.

A comparative study of force models in monitoring the flank wear using the cutting force coefficients

Difficult-to-machine materials, such as nickel-based alloys, are widely utilized in aerospace industry, while their low thermal conductivity and high temperature strength lead to the rapid wear of cutting tool. Tool wear monitoring is regarded as one of the useful methods to guarantee the product quality and maximize the tool utilization. Due to the high nonlinearity and stochastic characteristics of tool wear process, it is difficult to establish a general tool wear monitoring model. This work contributes to find out the most suitable cutting force model by comparing their ability and performance in monitoring the flank wear. The tool wear monitoring is realized through developing the relationship between cutting force coefficients and tool wear, and the coefficients are calculated based on the average cutting forces in different feed rates. Then, correlational analysis is performed to select sensitive coefficients. Finally, the selected coefficients are normalized and then trained by the feed-forward backprop neural network. Experiments are conducted to compare the four different models in cutting force prediction and tool wear monitoring by three criteria. The cutting force model including the shearing forces, the edge forces, and forces due to the tool wear gives the best results. The obtained results also show great suitability for different cutting conditions.

Frequency conversions of nine peaks based on dispersive waves and solitons in a tellurite microstructured optical fiber.

We demonstrate the generation of broadband dispersive waves (DWs) and solitons in an 80-cm tellurite microstructured optical fiber (TMOF) designed and fabricated with 78TeO2-5ZnO-12LiCO3-5Bi2O3 (TZLB) glass. A 1810-nm femtosecond laser is used as the pump source with an average pump power ranging from 33 mW to 175 mW, where the tunable frequency range is 211.1 THz, which corresponds to the tunable wavelength range of 1742.9 nm. At 175 mW, the trapped multiple DWs are located at 923.8 nm, 1039.2 nm, 1121.6 nm, and 1204.6 nm and the multiple solitons are located at 2666.7 nm, 2426.1 nm, 2165.9 nm, 1952.7 nm, and 1842.1 nm. The experimentally obtained maximum DW conversion efficiency is 14%, and the maximum soliton conversion efficiency is 43%. The experimental and theoretical results of pulse evolution in the TMOF agree very well. To the best of our knowledge, this is the first time that nine peaks of frequency conversions have been realized simultaneously in non-silicon fibers. The exceptionally high nonlinearity and broadband-tunable characteristics of the proposed TMOF are promising components for the development of compact and highly efficient tunable mid-infrared fiber lasers, wavelength converters, and time-frequency metrology.

A Novel Support-Vector-Machine-Based Grasshopper Optimization Algorithm for Structural Reliability Analysis

Aiming at the characteristics of high computational cost, implicit expression and high nonlinearity of performance functions corresponding to large and complex structures, this paper proposes a support-vector-machine- (SVM) based grasshopper optimization algorithm (GOA) for structural reliability analysis. With this method, the reliability problem is transformed into an optimization problem. On the basis of using the finite element method (FEM) to generate a small number of samples, the SVM model is used to construct a surrogate model of the performance function, and an explicit expression of the implicit nonlinear performance function under the condition of small samples is realized. Then, the GOA is used to search for the most probable point (MPP), and a reasonable iterative method is constructed. The MPP information of each iteration step is used to dynamically improve the reconstruction accuracy of the surrogate model in the region that contributes most to the failure probability. Finally, with the MPP after the iteration as the sampling center, the importance sampling method (ISM) is used to further infer the structural failure probability. The feasibility of the method is verified by four numerical cases. Then, the method is applied to a long-span bridge. The results show that the method has significant advantages in computational accuracy and computational efficiency and is suitable for solving structural reliability problems of complex engineering.