High Nonlinearity Research Articles

A Dual-Stream Cross AGFormer-GPT Network for Traffic Flow Prediction Based on Large-Scale Road Sensor Data.

Traffic flow prediction can provide important reference data for managers to maintain traffic order, and can also be based on personal travel plans for optimal route selection. On account of the development of sensors and data collection technology, large-scale road network historical data can be effectively used, but their high non-linearity makes it meaningful to establish effective prediction models. In this regard, this paper proposes a dual-stream cross AGFormer-GPT network with prompt engineering for traffic flow prediction, which integrates traffic occupancy and speed as two prompts into traffic flow in the form of cross-attention, and uniquely mines spatial correlation and temporal correlation information through the dual-stream cross structure, effectively combining the advantages of the adaptive graph neural network and large language model to improve prediction accuracy. The experimental results on two PeMS road network data sets have verified that the model has improved by about 1.2% in traffic prediction accuracy under different road networks.

Modified genetic algorithms for generating S-boxes with high nonlinearity

This article discusses the use of modified genetic algorithms to generate S-boxes with high nonlinearity, which is critical for ensuring the security of cryptographic algorithms. S-boxes play a key role in providing resistance to cryptanalysis, in particular to linear and differential cryptanalysis attacks. In the course of the study, a series of experiments were conducted using a genetic algorithm modified by additional storage of the current population and the use of selection. This approach has significantly improved the efficiency of the algorithm compared to the classical genetic algorithm. The best results were achieved with the minimum number of instances in the population and the optimal number of mutations. It was found that the algorithm finds S-blocks with a probability of 99% with a nonlinearity of 104, which demonstrates its high efficiency. The results of the study showed that the modified algorithm is able to provide stable generation of S-boxes with the required attack resistance properties. The proposed method also proved to be flexible in parameter settings, which allows it to be adapted for various cryptographic applications. The paper also discusses the possibilities of further improving the algorithm, including the study of other mutation and selection methods, as well as parameter optimization to achieve even better results. Additionally, the possibility of using distributed computing to increase the speed of S-boxes generation is considered. The results of the study indicate the prospects of the proposed approach for creating attack-resistant cryptographic systems. This study considered the use of genetic algorithms to generate S-boxes with high nonlinearity. The proposed approach was based on the use of an objective function with optimal parameters, which allowed achieving high efficiency in generating bioactive S-blocks. The proposed algorithm showed high stability and efficiency in generating S-boxes with nonlinearity, which is confirmed by a 99% probability of achieving the goal. These results indicate the prospects of using genetic algorithms in cryptographic applications that require high resistance to attacks. In future research, it is planned to further improve the algorithm by exploring other methods of mutation and selection, as well as optimizing parameters to achieve even better results. In addition, it is possible to use distributed computing to further speed up the process of generating S-boxes.

A maximum power point tracking control strategy of tidal energy conversion system based on fixed-time observer technology

To achieve maximum tidal current energy capture in complex and harsh marine environments, this paper proposes a nonlinear control based on fixed-time observer (NCFTO) for tidal energy conversion system (TECS). The TECS operating in the ocean not only needs to face time-varying ocean current speed, but also needs to consider the adverse effects brought by parameter uncertainties and unknown disturbances in actual operation. Considering the high nonlinearity of TECS, conventional linear control systems may not necessarily provide satisfactory performance. Therefore, the NCFTO takes into account the TECS parameter uncertainties and unknown disturbances in its design. This mainly involves using a designed fixed-time observer to accurately estimate a concentrated disturbance term containing TECS nonlinearities, parameter uncertainties, disturbances in real time, and offsetting the adverse effects of actual disturbances on the control system. Meanwhile, a linear controller is designed based on the linearization of TECS to ensure satisfactory performance of NCFTO under time-varying ocean current speed, thus laying a solid foundation for achieving maximum tidal current energy capture. In addition, to verify the feasibility and effectiveness of NCFTO, three simulation testing scenarios have been set up, namely time-varying ocean current speed, rotor inertia uncertainty, and unknown disturbance torque.

Effects of cross flow on the launching process of submarine-launched vehicle considering the 6-DOF motion at barrel-exit stage

Submarine-launched vehicle (SLV) is a research hotspot due to its strong strike capability, among which underwater launching technology is a key issue hindering its development. In this paper, a numerical model is proposed to simulate the launching problem of SLV. The high nonlinearity induced by the coupling of multi-degree-of-freedom motions and the interaction between fluid and structure is solved. Specifically, the 6-DOF motion model of structure considering its strong added-mass effect, cavitation model and collision model are adopted. This numerical model is firstly validated by simulating the launching of SLV in still water. And then, the applicability of this model is discussed, as well as the effect of cross flow and initial launching velocity on the launching process. The results indicate that proposed numerical model could be used under both low-speed and high-speed cross flow situations. Cross flow changes the attitude and locomotion state of SLV. When flow velocity exceeds 1.25 m/s, the collision occurs between SLV (with the initial launching velocity of 40 m/s) and its barrel. This obtained locomotion of SLV after leaving its barrel causes the launching failure. Besides, a method of increasing launching velocity is introduced to promote the success rate of launching in high-speed cross flow situation.

Study on optical properties of hexagonal lattice photonic crystal fibers infiltrated with heavy water

In this paper, we analyzed a PCF made from fused silica glass, with a core filled with heavy water. The guiding properties of proposed fibers in terms of effective refractive index, attenuation, and dispersion of the fundamental mode were studied and optimized setups were selected and analyzed in detail. After 25 simulations, we determined two structures possessing optimal dispersion with the lattice constant (Ʌ) and the filling factor as follows: Ʌ = 1.1 µm, d/Ʌ = 0.92 for #F1 and Ʌ = 1.4 µm, d/Ʌ = 0.92 for #F2. Besides, high nonlinearity and low confinement loss are also outstanding points in our model. Thanks to these advantages, the proposed fibers have been targeted for flat and smooth broadband supercontinuum (SC) generation for near-infrared applications.

A silicon nitride (Si3N4) filled PCF containing high birefringence and nonlinearity for optical applications

A silicon nitride (Si3N4) filled PCF containing high birefringence and nonlinearity for optical applications

PM2.5 concentration prediction based on EEMD-ALSTM

The concentration prediction of PM2.5 plays a vital role in controlling the air and improving the environment. This paper proposes a prediction model (namely EEMD-ALSTM) based on Ensemble Empirical Mode Decomposition (EEMD), Attention Mechanism and Long Short-Term Memory network (LSTM). Through the combination of decomposition and LSTM, attention mechanism is introduced to realize the prediction of PM2.5 concentration. The advantage of EEMD-ALSTM model is that it decomposes and combines the original data using the method of ensemble empirical mode decomposition, reduces the high nonlinearity of the original data, and Specially reintroduction the attention mechanism, which enhances the extraction and retention of data features by the model. Through experimental comparison, it was found that the EEMD-ALSTM model reduced its MAE and RMSE by about 15% while maintaining the same R2 correlation coefficient, and the stability of the model in the prediction process was also improved significantly.

Enhanced efficiency of correlated photon pairs generation in silicon nitride with a low-loss 3D edge coupler

We demonstrate the generation of correlated photon pairs by using a hybrid integrated quantum photonic platform, where the dual-layer platform consists of a high-index doped silica glass (HDSG) layer to accommodate low-loss linear components and an SiN-based layer to accommodate the photon source. Leveraging the low-loss fiber coupling to the HDSG waveguide and the high nonlinearity of the SiN waveguide, we experimentally realize integrated source of photon pairs with high heralding efficiency. The directly measured photon pair rate is up to 87 KHz (corresponding to 1.74 × 10−3 pairs per pulse) when the coincidence-to-accidental ratio is greater than 10. The raw heralding efficiency can reach 18%. If the filtering loss is excluded, the heralding efficiency can further reach 29%.

Improved mountain gazelle optimizer based interactive distributed strategy to solving large scale OPF

This paper focuses on solving the multi-objective optimal power flow of large-scale power systems under critical loading margin stability with accuracy using a novel improved mountain gazelle optimizer (IMGO)-based flexible distributed strategy. Multi-shunt compensator-based flexible alternative current transmission systems (FACTS), such as SVC and STATCOM devices, are integrated at specified locations to exchange reactive power with the network. Several metaheuristic methods can solve the standard OPF related to small and medium test systems. However, by considering large-scale electric systems based on FACTS devices and renewable energy and by considering the operation under loading margin stability, the majority of these techniques fail to achieve a near-global solution because of the high dimension and nonlinearity of the problem to be solved. This study proposes the Multi-Objective OPF-Based Distributed Strategy (MO-OPFDS), a new planning strategy that optimizes individually and simultaneously various objective functions, in particular the total power loss (T∆P), and the total voltage deviation (T∆V). Standard MGO search is enhanced by automatically balancing exploration and exploitation throughout the search. The robustness of the proposed variant was validated on a large electric test system, the IEEE 118-Bus, and on the Algerian Network 114-Bus under normal conditions and at critical loading margin stability. The obtained results compared with several recent techniques clearly confirm the high performance of the proposed method in terms of solution accuracy and convergence behavior.

Pump and probe wave mixing for imaging nonlinear features embedded into a massive concrete block

Abstract Nonlinear ultrasonic parameters are known to be highly sensitive to the presence of damage in materials, and numerous methods have already been reported. This study proposes a way to image a nonlinear defect embedded into a nonlinear medium. The nonlinear defect is mimicked by a Berea sandstone sphere (high nonlinearity). It is placed into a large concrete block (average nonlinearity) during casting. The proposed pump and probe propagating wave interaction method allows imaging the presence of the highly nonlinear region. The image contrast is magnified by a factor 8 in the defective zone. The results are validated using a model and the individual mechanical properties of both concrete and Berea Sandstone. These findings open perspectives for application to civil engineering concrete structures such as the assessment of durability distresses involving internal swelling.

Survey of Advanced Nonlinear Control Strategies for UAVs: Integration of Sensors and Hybrid Techniques.

This survey paper explores advanced nonlinear control strategies for Unmanned Aerial Vehicles (UAVs), including systems such as the Twin Rotor MIMO system (TRMS) and quadrotors. UAVs, with their high nonlinearity and significant coupling effects, serve as crucial benchmarks for testing control algorithms. Integration of sophisticated sensors enhances UAV versatility, making traditional linear control techniques less effective. Advanced nonlinear strategies, including sensor-based adaptive controls and AI, are increasingly essential. Recent years have seen the development of diverse sliding surface-based, sensor-driven, and hybrid control strategies for UAVs, offering superior performance over linear methods. This paper reviews the significance of these strategies, emphasizing their role in addressing UAV complexities and outlining future research directions.

Cryptanalysis and construction of keyed strong S-Box based on random affine transformation matrix and 2D hyper chaotic map

As the only nonlinear module, S-Box (substitution box) is widely used in stream cipher, hash function, and so on. However, there exist some weaknesses in existing S-Boxes, such as fixed point, reverse fixed point and short iteration period rings. Furthermore, the S-Boxes in AES and SM4.0 were fixed, which makes them vulnerable to attack. To address these weaknesses, first, a non-degenerate 2D chaotic map (2D-NDCM) with ergodicity in phase space was constructed. Second, based on 2D-NDCM, the seed S-Boxes with high nonlinearity were constructed in batches by affine transformation and Boolean function, and then three possible weaknesses were eliminated to obtain strong keyed S-Boxes. Statistical analysis demonstrated the effectiveness and practicability of the proposed S-Box batch generating algorithm.

Enhancing electric field calculation in HTS tape simulations for currents exceeding the critical limit using full HTS tape modeling

Superconducting devices are an interesting technological option for improvement of efficiency of energy systems. High-temperature superconducting tapes are among the best choices for power applications. Because of their high non-linearity, the design of devices with superconducting tape requires specific simulation models and methods to correctly represent the superconducting behavior. The J-A formulation has been investigated as an efficient tool to represent the electromagnetic behavior of such devices. It is based on the current density (J) and magnetic vector potential (A), and able to represent both superconducting materials and ferromagnetic materials. With regards to the tapes, the use of thin-film approximation can be successful with the J-A formulation. Usually, only representations considering the superconducting layer of tapes have been investigated. This becomes a problem for operations where the current in the superconductor surpasses the critical current, for example in transient analysis. This paper presents an analysis of the effects the inclusion of all of the tapes’ layers have on simulations with the J-A formulation with thin-film approximations. By considering all layers of the tape, one represents the transition between superconducting and high-loss states of the tape. A simple system, consisting of a single tape with applied current, has been studied as benchmark and simulated with J-A and the more established T-A formulation in two cases: considering all layers; and only the superconducting layers. Results show that results for the J-A and T-A formulations are compatible and the inclusion of all layers improves the stability of the simulation and provides correct assessment of the electric field in the tapes.

Detection of data tampering attack in FIR system identification with binary-valued observations

This paper addresses the security detection of data tampering attack in identification of finite impulse response systems with quantized observations. First, we designed a detection algorithm for data tampering attack based on the prior information of the system and gave its online detection form. Second, the introduction of the concepts of the false judgment rate and the missed judgment rate gave a specific evaluation index for the performance of the detection algorithm. Considering the high nonlinearity of binary quantized and the difficulty of obtaining distribution function, we provided calculation methods for two indexes and gave expressions under large samples. Then, the effects of the data length, the prior information of the system parameter, and the attack strategy on the detection algorithm are discussed in this paper. Finally, the theoretical results are verified by numerical simulation.

Numerical Analysis of Optical Properties Using Octagonal Shaped Ge15As15Se17Te53 Chalcogenide Photonic Crystal Fiber

A novel Ge15As15Se17Te53 chalcogenide-based photonic crystal fiber with an octagonal cladding was designed which exhibits low confinement loss and high nonlinearity. The dispersion properties were investigated over a wide wavelength range of up to 11 µm. Based on preliminary simulation results, three fibers with suitable characteristic quantities are proposed for supercontinuum generation. Among these fibers, fiber #F1 (Ʌ = 3.1 µm, d/Ʌ = 0.3) exhibits all-normal dispersion with a small value of −0.374 ps/[nm.km]. At the same time, the highest nonlinear coefficient of 2876.861 W−1.km−1 at the pump wavelength of 5.45 µm is also found. The #F2 fiber (Ʌ= 4.5 µm, d/Ʌ = 0.3) has anomalous flat dispersion with one zero dispersion wavelength. This fiber has the smallest dispersion and lowest confinement loss values of 0.281 ps/[nm.km] and 1.360×10−9 at 5.7 µm pump wavelength, respectively. With the anomalous dispersion, #F3 fiber (Ʌ = 3.5 µm, d/Ʌ = 0.3) offers a small effective mode area of 20.892 µm2 and a low confinement loss of 4.974×10−8 dB/m at 5.25 µm wavelength. The proposed fibers can be a low peak power, broadband supercontinuum source, suitable for application fields such as optical communication and biomedical sensors.

Mitigating waveguide loss in Ge–Sb–Se chalcogenide glass photonics

Minimizing propagation loss within waveguides remains a central objective across diverse photonic platforms, impacting both linear lightwave transmission and nonlinear wavelength conversion efficiencies. Here, we present a method to mitigate waveguide loss in Ge28Sb12Se60 chalcogenide glass, a material known for its high nonlinearity, broad mid-infrared transparency, and significant potential for mid-IR photonics applications. By applying a sacrifical oxide layer to eliminate etching residues and a subsequent waveguide thermal reflow to smooth lithography-induced line edge roughness, we successfully reduced the waveguide loss down to 0.8 dB cm−1 at 1550 nm wavelength. This represents the best result in small-core and high-index-contrast Ge28Sb12Se60 channel waveguides. Our approach paves the way for low-loss, on-chip chalcogenide photonic devices.

Deep Learning CNN-GRU Method for GNSS Deformation Monitoring Prediction

Hydraulic structures are the key national infrastructures, whose safety and stability are crucial for socio-economic development. Global Navigation Satellite System (GNSS) technology, as a high-precision deformation monitoring method, is of great significance for the safety and stability of hydraulic structures. However, the GNSS time series exhibits characteristics such as high nonlinearity, spatiotemporal correlation, and noise interference, making it difficult to model for prediction. The Neural Networks (CNN) model has strong feature extraction capabilities and translation invariance. However, it remains sensitive to changes in the scale and position of the target and requires large amounts of data. The Gated Recurrent Units (GRU) model could improve the training effectiveness by introducing gate mechanisms, but its ability to model long-term dependencies is limited. This study proposes a combined model, using CNN to extract spatial features and GRU to capture temporal information, to achieve an accurate prediction. The experiment shows that the proposed CNN-GRU model has a better performance, with an improvement of approximately 45%, demonstrating higher accuracy and reliability in predictions for GNSS deformation monitoring. This provides a new feasible solution for the safety monitoring and early warning of hydraulic structures.

Design and Experimental Study of an Embedded Controller for a Model-Based Controllable Pitch Propeller

The controllable pitch propeller hydraulic system has high constraints and nonlinearity. Due to these inherent deficiencies, the proportional–integral–derivative (PID) control algorithm cannot meet the control accuracy requirements of nonlinear systems. A control law based on a model predictive control (MPC) algorithm is designed in this paper. The gain parameters of the predictive control are optimized. The MPC and PID control systems are compared and simulated to verify the MPC controller’s effectiveness. Subsequently, the embedded controller of a controllable pitch propeller is developed. The support package for the embedded circuit board target containing an underlying driver for each interface is written by introducing the C-MEX S-Function and TLC programming language. A semi-physical simulation experiment is performed. The results show that the established controllable pitch propeller with an embedded controller displays reliable running performance, good anti-interference, and the capacity to fulfill the control function of the pitch propeller under various working conditions.

Modeling gas flow in low-permeability formations: An efficient combination of mixed finite elements and high order time integration schemes

Numerical simulation of gas flow in low permeability formations is a challenging task due to the high nonlinearity induced by (i) the compressibility of the gas, (ii) the Klinkenberg slippage effect and (iii) the Langmuir adsorption of the gas on the pore surface. Because of these nonlinearities, modeling gas flow in low permeability formations requires a great deal of computational effort. In this work, we develop an efficient numerical model using advanced spatial and temporal discretization methods for a simultaneous solution of the coupled equations of gas flow, cubic Peng-Robinson equation of state, slippage effect and Langmuir adsorption. The spatial discretization is performed with the lumped hybrid formulation of the mixed finite element method which is well adapted for fluid flow in heterogeneous porous media. The time integration is performed with high-order methods via the method of lines (MOL) which allows large time steps and efficient solution of the nonlinear system of equations. Numerical experiments, performed for gas extraction in a heterogeneous domain, point out the high efficiency of the new model since it can be until 10 times more efficient than the classical first-order time discretization method. Results of global sensitivity analysis show that the intrinsic gas-phase permeability and the Klinkenberg factor are the most influential parameters controlling the pumping rate in the case of gas extraction in a homogeneous domain.

Distributed consensus and formation control of multi-AUV systems under actuator faults and switching topology

This note presents the distributed consensus and formation control for a group of AUVs, comprising one leader and three followers arranged in a diamond formation. The study addresses significant control challenges, including external disturbances, noise, model uncertainties, actuator faults, stochastic switching topologies, time-varying communication delays, and positional information between agents. Stochastic switching topologies are assumed to follow a Markov chain. To effectively address these challenges and ensuring satisfactory performance levels, two control architectures have been formulated to facilitate the imposition of desired trajectories upon the system states. The initial architecture amalgamates sliding mode control methodology with adaptive algorithms, designed explicitly for tracking missions in the presence of fully functional actuators. In response to potential actuator failures, the second control architecture integrates passive fault-tolerant techniques, significantly enhancing system reliability under such circumstances as well as switching topology. The proposed framework demonstrates its effectiveness in managing the high nonlinearity and coupled dynamics of AUVs. Simulation results validate the efficacy of the developed strategy. It successfully establishes a desired consensus and formation among agents, reduces chattering phenomena, accurately tracks reference trajectories, handles disturbances and uncertainties within an unknown domain, and achieves finite-time convergence.