Search Results (1,870)

In this study, the key challenges faced by global navigation satellite systems (GNSSs) in the field of security are addressed, and an eye diagram-based deep learning framework for intelligent classification of interference types is proposed. GNSS signals are first transformed into two-dimensional eye diagrams, enabling a novel visual representation wherein interference types are distinguished through entropy-centric feature analysis. Specifically, the quantification of information entropy within these diagrams serves as a theoretical foundation for extracting salient discriminative features, reflecting the structural complexity and uncertainty of the underlying signal distortions. We designed a hybrid architecture that integrates spatial feature extraction, gradient stability enhancement, and time dynamics modeling capabilities and combines the advantages of a convolutional neural network, residual network, and bidirectional long short-term memory network. To further improve model performance, we propose an improved coati optimization algorithm (ICOA), which combines chaotic mapping, an elite perturbation mechanism, and an adaptive weighting strategy for hyperparameter optimization. Compared with mainstream optimization methods, this algorithm improves the convergence accuracy by more than 30%. Experimental results on jamming datasets (continuous wave interference, chirp interference, pulse interference, frequency-modulated interference, amplitude-modulated interference, and spoofing interference) demonstrate that our method achieved performance in terms of accuracy, precision, recall, F1 score, and specificity, with values of 98.02%, 97.09%, 97.24%, 97.14%, and 99.65%, respectively, which represent improvements of 1.98%, 2.80%, 6.10%, 4.59%, and 0.33% over the next-best model. This study provides an efficient, entropy-aware, intelligent, and practically feasible solution for GNSS interference identification. Full article

Political polarization in Western democracies has accelerated in the last decade, with negative social consequences. Research across disciplines on antecedents, manifestations and societal impacts is hindered by social systems’ complexity: their constant flux impedes tracing causes of observed trends and prediction of consequences, hampering their mitigation. Social physics models exploit a characteristic of complex systems: what seems chaotic at one observation level may exhibit patterns at a higher level. Therefore, dynamic modeling of complex systems allows anticipation of possible events. We use this approach to anticipate 2024 US election results. We consider the highly polarized Democrats and Republicans, and Independents fluctuating between them. We generate average group-stance scenarios in time and explore how polarization and depolarization might have affected 2024 voting outcomes. We find that reducing polarization might advantage the larger voting group. We also explore ways to reduce polarization, and their potential effects on election results. The results inform regarding the perils of polarization trends, and on possibilities of changing course. Full article

This paper investigates the nonlinear dynamics of the wheel-side planetary reducer, considering the tooth wear effect. The tooth wear model based on the Archard adhesion wear theory is established, and the impact of tooth wear on meshing stiffness and piecewise-linear backlash of the planetary gear system is discussed. Then, the torsional vibration model and dimensionless differential equations considering tooth wear for the wheel-side planetary reducer are established, in which meshing excitations include time-varying mesh stiffness (TVMS), piecewise-linear backlash, and transmission error. The dynamic responses are numerically solved using the fourth-order Runge–Kutta method. On this basis, the nonlinear dynamics, such as the bifurcation and chaos properties of the wheel-side planetary reducer with tooth wear, are analyzed. Simulation results demonstrate that the existence of tooth wear reduces meshing stiffness and increases backlash. The reduction in the meshing stiffness changes the bifurcation path and chaotic amplitude of the system, inducing chaotic phenomena more easily. The increase in the gear backlash causes a higher amplitude of the relative displacement and more severe vibration. Full article

This study investigates the dynamic behavior of a discrete epidemic model as affected by media coverage through integrated analytical and numerical methods. The main objective is to quantitatively assess the impact of media coverage on disease outbreak models through mathematical modeling. We use the central manifold theorem and bifurcation theory to perform a rigorous analysis of the periodic solutions, focusing on the coefficients and conditions governing the flip bifurcation. On this basis, state feedback and hybrid control are utilized to control the system chaotically. Under certain conditions, the chaos and bifurcation of the system can be stabilized by the control strategy. Numerical simulations further reveal the bifurcation dynamics, chaotic behavior, and control techniques. Our results show that media coverage is a key factor in regulating the intensity and chaos of disease transmission. Control techniques can effectively prevent large-scale outbreaks of epidemics. Notably, enhanced media coverage can effectively increase public awareness and defensive behaviors, thus contributing to mitigating disease spread. Full article

The swinging sticks pendulum is an intriguing physical system that exemplifies the intersection of Lagrangian mechanics and chaos theory. It consists of a series of slender, interconnected metal rods, each with a counterweighted end that introduces an asymmetrical mass distribution. The rods are arranged to pivot freely about their attachment points, enabling both rotational and translational motion. Unlike a simple pendulum, this system exhibits complex and chaotic behavior due to the interplay between its degrees of freedom. The Lagrangian formalism provides a robust framework for modeling the system’s dynamics, incorporating both rotational and translational components. The equations of motion are derived from the Euler–Lagrange equations and lack closed-form analytical solutions, necessitating the use of numerical methods. In this work, we employ the Bulirsch–Stoer method, a high-accuracy extrapolation technique based on the modified midpoint method, to solve the equations numerically. The system possesses four fixed points, each one associated with a different level of energy. The fixed point with the lowest energy level is a center, around which small perturbations are studied. The other three fixed points are unstable. The maximum energy used for the perturbations is $0.001\%$ larger than the lowest equilibrium energy. When the system’s total energy is low, nonlinear terms in the equations can be neglected, allowing for a linearized treatment based on small-angle approximations. Under these conditions, the pendulum oscillates with small amplitudes around a stable equilibrium point. The resulting motion is analyzed using tools from nonlinear dynamics and Fourier analysis. Several trajectories are generated and examined to reveal frequency interactions and the emergence of complex dynamical behavior. When a small initial perturbation is applied to one rod, its motion is characterized by a single frequency with significantly greater amplitude and angular velocity compared to the second rod. In contrast, the second rod displayed dynamics that involved two frequencies. The present study, to the best of our knowledge, is the first attempt to describe the dynamical behavior of this pendulum. Full article

This study examines a discrete-time predator–prey model constructed via piecewise constant discretization of its continuous counterpart. Through comprehensive qualitative and dynamical analyses, we reveal a rich set of nonlinear phenomena, encompassing Neimark–Sacker bifurcation, flip bifurcation, and codimension-two bifurcations corresponding to 1:2, 1:3, and 1:4 resonances. Rigorous analysis of these bifurcation scenarios, conducted via center manifold theory and bifurcation methods, establishes a robust mathematical framework for their characterization. Numerical simulations corroborate the theoretical predictions, exposing intricate dynamical phenomena such as quasiperiodic oscillations and chaotic attractors. Our results demonstrate that resonance-driven bifurcations are potent drivers of ecological complexity in discrete systems, acting as key determinants that orchestrate the emergent dynamics of populations—a finding with profound implications for interpreting patterns in real-world ecosystems subject to discrete generations or seasonal pulses. Full article

With the rapid development of big data and artificial intelligence, the problem of image privacy leakage has become increasingly prominent, especially for images containing sensitive information such as faces, which poses a higher security risk. In order to improve the security and efficiency of image privacy protection, this paper proposes an image encryption scheme that integrates face detection and multi-level encryption technology. Specifically, a multi-task convolutional neural network (MTCNN) is used to accurately extract the face area to ensure accurate positioning and high processing efficiency. For the extracted face area, a hierarchical encryption framework is constructed using chaotic systems, lightweight block permutations, RNA cryptographic systems, and bit diffusion, which increases data complexity and unpredictability. In addition, a key update mechanism based on dynamic feedback is introduced to enable the key to change in real time during the encryption process, effectively resisting known plaintext and chosen plaintext attacks. Experimental results show that the scheme performs well in terms of encryption security, robustness, computational efficiency, and image reconstruction quality. This study provides a practical and effective solution for the secure storage and transmission of sensitive face images, and provides valuable support for image privacy protection in intelligent systems. Full article

Deterministic chaos in optically injected semiconductor lasers (OILs) has attracted significant attention due to its relevance in secure communications, entropy generation, and photonic applications. However, existing studies often rely on low-resolution parameter sweeps or include noise contributions that obscure the intrinsic nonlinear dynamics. To address this gap, we investigate a noise-free OIL model and construct high-resolution chaos maps across the injection strength and frequency detuning parameter space. Chaos is characterized using two complementary approaches for computing the largest Lyapunov exponent: the Rosenstein time-series method and the exact variational method. This dual approach provides reliable and reproducible detection of deterministic chaotic regimes and reveals a rich attractor landscape with alternating bands of periodicity, quasi-periodicity, and chaos. The novelty of this work lies in combining high-resolution mapping with rigorous chaos indicators, enabling fine-grained identification of dynamical transitions. The results not only deepen the fundamental understanding of nonlinear laser dynamics but also provide actionable guidelines for exploiting or avoiding chaos in photonic devices, with potential applications in random chaos-based communications, number generation, and optical security systems. Full article

To enhance the speed regulation accuracy and robustness of permanent magnet synchronous motor (PMSM) drives under complex operating conditions, this paper proposes a dual-loop active disturbance rejection control strategy optimized by an opposition-based learning hybrid optimization algorithm (DLADRC-OBLHOA). First, the vector control system and ADRC model of the PMSM are established. Then, a nonlinear function, ifal, is introduced to improve the performance of the speed-loop ADRC. Meanwhile, an active disturbance rejection controller is also introduced into the current loop to suppress current disturbances. To address the challenge of tuning multiple ADRC parameters, an opposition-based learning hybrid optimization algorithm (OBLHOA) is developed. This algorithm integrates chaotic mapping for population initialization and employs opposition-based learning to enhance global search capability. The proposed OBLHOA is utilized to optimize the speed-loop ADRC parameters, thereby achieving high-precision speed control of the PMSM system. Its optimization performance is validated on 12 benchmark functions from the IEEE CEC2022 test suite, demonstrating superior convergence speed and solution accuracy compared to conventional heuristic algorithms. The proposed strategy achieves superior speed regulation accuracy and reliability under complex operating conditions when deployed on high-performance processors, but its effectiveness may diminish on resource-limited hardware. Moreover, simulation results show that the DLADRC-OBLHOA control strategy outperforms PI control, traditional ADRC, and ADRC-$ifal$ in terms of tracking accuracy and disturbance rejection capability. Full article

This paper proposes a novel intelligent optimization algorithm, ICPMSHOA, that effectively balances population diversity and convergence performance by integrating an iterative chaotic map with infinite collapses (ICMIC), centroid opposition-based learning, and periodic mutation strategy. To verify its performance, we adopted benchmark functions from the IEEE CEC 2017 and 2022 standard test suites and compared it with six algorithms, including OOA and BWO. The results show that ICPMSHOA has significant improvements in convergence speed, global search capability, and stability, with statistically significant advantages. Furthermore, the algorithm performs outstandingly in three practical engineering constrained optimization problems: Haverly’s pooling problem, hybrid pooling–preparation problem, and optimization design of industrial refrigeration systems. This study confirms that ICPMSHOA provides efficient and reliable solutions for complex optimization tasks and has strong practical value in engineering scenarios. Full article

This study investigates the dynamic behaviors of the discrete epidemic model influenced by media coverage through integrated analytical and numerical approaches. The primary objective is to quantitatively assess the impact of media coverage on disease outbreak patterns using mathematical modeling. Firstly, the Euler method is used to discretize the model (2), and the periodic solution is strictly analyzed. Secondly, the coefficients and conditions of restricted flip and Neimark–Sacker bifurcation are studied by using the center manifold theorem and bifurcation theory. By calculating the largest Lyapunov exponent near the critical bifurcation point, the occurrence of chaos and limit cycles is proved. On this basis, the chaotic control of the system is carried out by using state feedback and hybrid control. Under certain conditions, the chaos and bifurcation of the system can be stabilized by control strategies. Numerical simulations further reveal bifurcation dynamics, chaotic behaviors, and control technologies. Our results show that media coverage is a key factor in regulating the intensity of disease transmission and chaos. The control technology can effectively prevent the large-scale outbreak of epidemic diseases. Importantly, enhanced media coverage can effectively promote public awareness and defensive behaviors, thereby contributing to the mitigation of disease transmission. Full article

This paper investigates the complex dynamics and fractal attractors that arise in a 60-dimensional ring lattice system of electrically coupled nonchaotic Rulkov neurons. While networks of chaotic Rulkov neurons have been widely studied, systems of nonchaotic Rulkov neurons have not been extensively explored due to the piecewise complexity of the nonchaotic Rulkov map. Here, we find that rich dynamics emerge from the electrical coupling of regular-spiking Rulkov neurons, including chaotic spiking, synchronized chaotic bursting, and synchronized hyperchaos. By systematically varying the electrical coupling strength between neurons, we also uncover general trends in the maximal Lyapunov exponent across the system’s dynamical regimes. By means of the Kaplan–Yorke conjecture, we examine the fractal geometry of the ring system’s high-dimensional chaotic attractors and find that these attractors can occupy as many as 45 of the 60 dimensions of state space. We further explore how variations in chaotic behavior—quantified by the full Lyapunov spectra—correspond to changes in the attractors’ fractal dimensions. This analysis advances our understanding of how complex collective behavior can emerge from the interaction of multiple simple neuron models and highlights the deep interplay between dynamics and geometry in high-dimensional systems. Full article

This paper presents a novel four-dimensional (4D) memristive system, notable for its simplicity and unique dynamic behaviors. Comprising only seven terms and devoid of equilibrium points, this system is capable of generating hidden attractors. A comprehensive analysis of its dynamic properties is conducted, including Lyapunov exponents, Kaplan–Yorke dimensions, temporal diagrams and phase portraits, multistability, and offset boosting. Numerical simulations over a sufficiently long time interval show that the Lyapunov function remains bounded, thereby confirming the dissipative nature and global stability of the newly proposed 4D hyperchaotic system. The theoretical model is further validated through electronic simulations of the chaotic system using Multisim software. Additionally, the paper explores synchronization between two identical 4D hyperchaotic systems. The proposed system, despite its structural simplicity, exhibits intricate chaotic dynamics, making it suitable for various practical applications. In particular, a method for chaotic encryption and decryption of an information signal was developed and validated through numerical testing. Based on the method of complete synchronization of chaotic systems, the possibility of detecting a weak signal is demonstrated. The proposed system is also implemented on an Arduino UNO to demonstrate its practical applicability for real-time chaotic signal generation and image encryption. Full article

This study experimentally investigates the wake-induced vibration (WIV) behavior of a bio-inspired harbor seal whisker model subjected to upstream propeller-generated unsteady flows. Vibration amplitudes, frequencies, and wake–whisker interactions were systematically evaluated under various flow conditions. The test matrix included propeller rotational speed N_p = 0~5000 r/min, propeller diameter D_p = 60~100 mm, incoming flow velocity U_∞ = 0~0.2 m/s, and separation distance between the whisker model and the propeller L/D = 10~30 (D = 16 mm, diameter of the whisker model). Results show that inline (IL) and crossflow (CF) vibration amplitudes increase significantly with propeller speed and decrease with increasing separation distance. Under combined inflow and wake excitation, non-monotonic trends emerge. Frequency analysis reveals transitions from periodic to subharmonic and broadband responses, depending on wake structure and coherence. A non-dimensional surface fit using L/D and the advance ratio (J = U_∞/(N_pD_p)) yielded predictive equations for RMS responses with good accuracy. Phase trajectory analysis further distinguishes stable oscillations from chaotic-like dynamics, highlighting changes in system stability. These findings offer new insight into WIV mechanisms and provide a foundation for biomimetic flow sensing and underwater tracking applications. Full article

Chaotic systems, characterized by extreme sensitivity to initial conditions and complex dynamical behaviors, exhibit significant potential for applications in various fields. Effective control of chaotic system synchronization is particularly crucial in sensor-related applications. This paper proposes a preset-time fuzzy zeroing neural network (PTCFZNN) model based on Takagi–Sugeno fuzzy control to achieve chaotic synchronization in aperiodic parameter exciting chaotic systems. The designed PTCFZNN model accurately handles the complex dynamic variations inherent in chaotic systems, overcoming the challenges posed by aperiodic parameter excitation to achieve synchronization. Additionally, field-programmable gate array (FPGA) verification experiments successfully implemented the PTCFZNN-based chaotic system synchronization control on hardware platforms, confirming its feasibility for practical engineering applications. Furthermore, experimental studies on chaos-masking communication applications of the PTCFZNN-based chaotic system synchronization further validate its effectiveness in enhancing communication confidentiality and anti-jamming capability, highlighting its important application value for securing sensor data transmission. Full article