Chaotic Characteristics Research Articles

Optical Transmitter Fingerprint Authentication Based on Chaotic Complexity Features for Secure Optical Transmission

Optical Transmitter Fingerprint Authentication Based on Chaotic Complexity Features for Secure Optical Transmission

Analysis of key brake pad parameters on brake system stability

PurposeThis study aims to explore the impact of key parameters of brake pads on the dynamic characteristics of the braking system.Design/methodology/approachThis study conducted experimental research based on a friction testing machine with a slider-disc structure. The experiment studied the impact of key parameters of brake pads (rotation speed, pressure, mass, braking radius, etc.) and the braking environment (dry friction, wetness, sand, etc.) on the stability of the braking system. At the same time, a dynamic model of the brake pad braking system was established and compared with experimental results using the mathematical tool of autocorrelation coefficient.FindingsThe key parameters of brake pads have a significant impact on the dynamic characteristics of the braking system; under different conditions of brake pad mass, tribological parameters, brake pad radius and braking environment, the chaotic characteristics of the braking friction force signal show a trend of expansion or contraction, which can be suppressed by adjusting the key parameters of brake pads.Originality/valueThis study can provide a reference for optimizing the braking strategy and reducing noise and vibration in brake pad systems.

Research on the dynamic characteristics of rotor-bearing-seal system with breathing cracks under steam flow excited force

In order to reveal the influence of breathing-type straight cracks on the motion characteristics and stability of a turbine rotor-bearing-seal system. The study examines the motion characteristic of a cracked rotor system under different loads with different crack depths, the coupled thermal and dynamic loads. The research derives motion differential equations for the cracked system, considering nonlinear dynamic properties such as nonlinear stiffness and damping. The equations incorporate the nonlinear steam flow excited force obtained from numerical simulations. The resulting motion equations are solved using the Runge-Kutta method, and the accuracy of the investigation of nonlinear dynamic characteristics is validated through experimental comparisons. Based on these findings, the motion characteristics and stability of the cracked rotor system under different conditions are analyzed. The results indicate that the motion characteristics of the rotor are progressively altered by the crack depth, and at a certain depth, the motion deviates from the established pattern. The influence of cracks on rotor stability is predominantly notable in the 34–58% THA region and under high load conditions. Moreover, the chaotic characteristics of rotor motion is more significant after applied the coupled thermal and dynamic loads, and the stability of the 34–58% THA region is significantly worse.

O1B7-04 Analysis of Dialogue Speech by Chaotic Characteristics Defined in Human Uttered Voice

O1B7-04 Analysis of Dialogue Speech by Chaotic Characteristics Defined in Human Uttered Voice

Complexity analysis of multiscale flow field of dynamic impinging stream based on chaos theory and Stockwell transform

The complexity analysis of the flow field is of great significance for studying the flow mechanism and strengthening the mixing. The multiscale flow field of dynamic impinging stream reactors is studied by experimental and theoretical methods. The chaotic characteristic parameters (correlation dimension, K entropy, the largest Lyapunov exponent) are investigated under different nozzle spacing L, different outlet velocity difference vd, and different outlet average velocity vp. The flow mechanism of dynamic impinging stream mesoscale flow field is revealed. The results show that the flow field of dynamic impinging stream reactor presents chaotic characteristics, and the chaotic characteristic parameters firstly increases and then decreases with the increase of nozzle spacing L. With the increase of outlet velocity difference and average outlet velocity, the chaotic characteristic parameters show an increasing trend. The time-frequency analysis of the microscale flow field in the dynamic impinging stream reactor is carried by Stockwell transform to obtain the energy evolution, the flow mechanism is further revealed. With the increase of outlet velocity difference and average outlet velocity, the energy of flow field gradually accumulates to the low frequency region, and the flow field energy enlarges. The radial flow field of dynamic impinging stream impact center is divided into radial jet region and radial vortex region by the energy characteristic. According to the analysis of chaotic characteristics and energy evolution of dynamic impinging stream, the optimal working condition is that nozzle spacing L=4d, outlet velocity difference is 1m/s, and outlet average velocity is 1.7m/s.

Forecasting solar irradiance with hybrid classical–quantum models: A comprehensive evaluation of deep learning and quantum-enhanced techniques

Predicting solar irradiance has proven to be a challenging task due to its inherently unpredictable and chaotic characteristics. Although machine learning and deep learning models have demonstrated considerable success in this field, the emergence of quantum computing brings a new layer of potential to this complex problem. This paper delves into these emerging methodologies, focusing specifically on their application in the domain of solar irradiance forecasting. This study investigates the integration of quantum layers into a conventional deep feedforward neural network (FFN) and the development of a fully connected quantum neural network (QNN). A bidirectional long short-term memory (BiLSTM) model is employed as a performance benchmark. Among the various models developed within this study, a few have demonstrated notable performance. For example, an FFN composed of five regular layers and two quantum layers (FFN5L2Q), having a total parameter count of 339, generated an error rate of 6.737%. Conversely, another FFN model, featuring eight regular layers and one quantum layer (FFN8L1Q) and having a total parameter count of 5551, exhibited superior performance with an error rate of 4.254%. When compared to the BiLSTM model, which has 49,666 parameters and an error rate of 3.875%, these quantum-enhanced FFNs display robust and competitive performance. This study, therefore, emphasizes the promising prospects of quantum-integrated techniques in augmenting the precision of solar irradiance prediction models.

Chaotic dynamics of an extended Duffing-van der Pol system with a non-smooth perturbation and parametric excitation

Abstract Chaotic dynamics of a fifth-order extended Duffing-van der Pol system with a non-smooth periodic perturbation and parametric excitation are investigated both analytically and numerically in this paper. With the Fourier series, the system is expanded to the equivalent smooth system. The Melnikov perturbation method is used to derive the horseshoe chaos condition in the cases of homoclinic and heteroclinic intersections. The chaotic features for different system parameters are investigated in detail. The monotonic variation of the coefficients of parametric excitation and non-smooth periodic disturbance is found. With numerical methods, we validate the analytical results obtained by Melnikov’s method. The impact of initial conditions is carefully analyzed by basins of attraction and the effect of non-smooth periodic disturbance on the basin of attraction is also investigated. Besides, the effect of different parameters on the bifurcation pathway into chaotic attractors is examined.

Design of a new multi-wing chaotic system and its application in color image encryption

Due to its chaotic carrier function and pseudorandom nature, chaotic systems are frequently applied in the area of secure communication. To render chaotic signals more complicated, a novel multi-wing chaotic system is developed in this study. Initially, a double-wing attractor generated by a novel 3D Lorenz-like chaotic system that comprises two quadratic terms and four linear terms. By inserting a nonlinear feedback controller, the complex system gets transformed into a multi-wing chaotic system. It is noteworthy that the topological shape of the attractor and the number of equilibrium points can both be altered in the particular multi-wing chaotic system. Secondly, the dynamic features of the multi-wing chaotic system are discussed via employing the bifurcation diagram and the Lyapunov exponential spectrum. According to results from simulation, the system displays widely dispersed chaotic characteristics over a large range of parameters. In addition, the complexity analysis findings of chaotic sequences testify that the chaotic sequences have strong complexity. Therefore, the multi-wing chaotic system will have an excellent effect in image encryption. Finally, a scrambling-diffusion encryption scheme is built using the rich chaotic sequences discussed above. The scheme includes two times scrambling of pixel position and once transform of pixel value by the virtue of Galois field (GF). The proposed algorithm has superior efficiency and excellent security according to the results of the feasibility and security analysis. Moreover, the investigation of the multi-wing chaotic system property and its usage in image encryption additionally broadens the applicability of chaos systems and offers some standards for confidential communication.

Nonlinear Time Series Analysis and Prediction of General Aviation Accidents Based on Multi-Timescales

General aviation accidents have complex interactions and influences within them that cannot be simply explained and predicted by linear models. This study is based on chaos theory and uses general aviation accident data to conduct research on different timescales (HM-scale, ET-scale, and EF-scale). First, time series are constructed by excluding seasonal patterns from the statistics of general aviation accidents. Secondly, the chaotic properties of multi-timescale series are determined by the 0–1 test and Lyapunov exponent. Finally, by introducing the sparrow search algorithm and tent chaotic mapping, a CSSA-LSSVM prediction model is proposed. The accident data of the National Transportation Safety Board (NTSB) of the United States in the past 15 years is selected for case analysis. The results show that the phase diagram of the 0–1 test presents Brownian motion characteristics, and the maximum Lyapunov exponents of the three scales are all positive, proving the chaotic characteristics of multi-timescale series. The CSSA-LSSVM prediction model’s testing results illustrate its superiority in time series predicting, and when the timescale declines, the prediction error reduces gradually while the fitting effect strengthens and then decreases. This study uncovers the nonlinear chaotic features of general aviation accidents and demonstrates the significance of multi-timescale research in time series analysis and prediction.

A Mechanical Defect Localization and Identification Method for High-Voltage Circuit Breakers Based on the Segmentation of Vibration Signals and Extraction of Chaotic Features.

To address the problem of mechanical defect identification in a high-voltage circuit breaker (HVCB), this paper studies the circuit breaker vibration signal and proposes a method of feature extraction based on phase-space reconstruction of the vibration substages. To locate mechanical defects in circuit breakers, vibration signals are divided into different substages according to the time sequence of the parts of the circuit breakers. The largest Lyapunov exponent (LLE) of the vibration signals' substages is calculated, and then the substages are reconstructed in high-dimensional phase space. The geometric features of the phase trajectory mean center distance (MCD) and vector diameter offset (VDO) are calculated, and the LLE, MCD, and VDO are selected as the three fault identification features of the vibration substages. The eigenvalue anomaly rate of each substage of the vibration signal under defect state are calculated and analyzed to locate the vibration substage of the mechanical defect. Finally, a fault diagnosis model is constructed by a support vector machine (SVM), and the common mechanical defects of circuit breakers simulated in the laboratory are effectively identified.

A Class of Discrete Memristor Chaotic Maps Based on the Internal Perturbation

Further exploration into the influence of a memristor on the behavior of chaotic systems deserves attention. When constructing memristor chaotic systems, it is commonly believed that increasing the number of memristors will lead to better system performance. This paper proposes a class of chaotic maps with different discrete memristors, achieved through internal perturbation based on the Sine map. The I-V curve of the discrete memristor has a symmetrical structure. The dynamic characteristics of the designed system are analyzed using the chaotic attractor phase diagram, Lyapunov exponent (LE) spectrum, and bifurcation diagram. Numerical simulations demonstrate that internal perturbations of discrete memristors enhance the Sine map’s chaotic characteristics, expand the chaos range, and improve the ergodicity and LE value. Moreover, the type of discrete memristors has a significant impact on the dynamic characteristics of the system, while the number of discrete memristors has little influence. Therefore, in this paper, a direction for the design of a discrete memristor chaotic system is provided. Finally, a discrete memristor chaotic map with a simple structure and better performance is selected. Based on this, a pseudo-random sequence generator is designed, and the generated sequence passes the National Institute of Standards and Technology (NIST) test.

Technique for Enhancing the Chaotic Characteristics of Chaotic Maps Using Delayed Coupling and Its Application in Image Encryption

Chaotic systems are widely used in many scientific fields for their dynamic characteristics. This study proposes a new delayed coupling method, which not only disturbs the control coefficient in chaotic maps but also affects their function structure, such that using this improved method will produce chaotic maps with better effect. The numerical simulation results prove that the delayed coupling method can greatly improve the chaotic characteristics of chaotic maps. Furthermore, an image encryption algorithm based on the delayed coupling Logistic map is proposed. Several numerical simulations indicate that the image encryption algorithm has a high level of security, and can compete with other encryption algorithms.

Chaotic characteristics and mixing performance of pseudoplastic fluids in a stirred tank

Abstract The key for improving the mixing efficiency of pseudoplastic fluids is to enhance the chaotic degree in the flow field. The xanthan gum solution was used to study the chaotic characteristics and mixing performance in a stirred tank with the impeller of perturbed six-bent-bladed turbine. Based on the velocity time series collected by the experiment of particle image velocimetry (PIV), the distributions of the largest Lyapunov exponent (LLE) and Kolmogorov entropy (K entropy) of the system were obtained through the programming calculation using the software MATLAB (R2016a) for characterizing the chaotic degree. The mixing performance of the fluid was numerically investigated using the Computational Fluid Dynamics package, and the velocity distributions were compared with the results obtained by the experiment of PIV. The relevance between the chaotic degree and the mixing performance was clarified. Results showed that the numerical results of velocity distributions agreed well with the experimental data which validated the Computational Fluid Dynamics model established. When the speed reached 600 rpm, the LLE and K entropy climbed the maximal values at the same time, which meant the greatest degree of chaos, and the mixing energy per unit volume was minimal at that moment, which was corresponding to the highest mixing efficiency. As the speed increased further, the LLE and K entropy decreased instead, which meant the chaos reduction, and the corresponding mixing energy per unit volume increased with the low mixing efficiency.

Fixed-Time Adaptive Chaotic Control for Permanent Magnet Synchronous Motor Subject to Unknown Parameters and Perturbations

It is well known that the permanent magnet synchronous motor (PMSM) exhibits chaotic characteristics when its parameters fall within a certain range, which can lead to system instability. This article proposes an adaptive control strategy for achieving the fixed-time chaotic stabilization of PMSM, even in the presence of unknown parameters and perturbations. The developed controller is synthesized by combining a parametric adaptive mechanism with a fixed-time control technique. The stability analysis demonstrates that the system states under the developed controller can converge to small neighborhoods around the equilibrium point within a fixed time. Thanks to the adoption of the parametric adaptive mechanism, the developed controller is not only insensitive to unknown parameters but also robust against perturbations. Finally, simulated studies are conducted to verify and emphasize the effectiveness of the developed control strategy.

Maximizing the throughput of a rotating Seru with nonpreemptive discrete stations

AbstractSeru production systems are an effective way to respond to ever‐changing market demand. This article focuses on maximizing the throughput of rotating serus with nonpreemptive stations, where a worker's operations cannot be disrupted. We analyze the effects of unbalanced worker velocities on non‐value‐added idle times. Through the use of dynamical system theories, we explicate the mechanism and dynamics of rotating serus, and identify the rules used to coordinate workers and distribute work content among stations to achieve the highest throughput. These findings provide practical guidelines for managers in floor shops for optimizing rotating seru design and maximizing throughput. Additionally, we explore the chaotic characteristics of rotating serus and simulate the effect of various factors on throughput. Finally, our comparative analysis demonstrates that the rotating seru offers a viable alternative to existing production systems to adapt to fluctuating demand.

Analysis of the fractal characteristics for combustion instability in a premixed natural gas engine

To investigate the influence of gas injection timing (GIT) on the combustion instability of a premixed natural gas engine, experiments were conducted under low load conditions using various GITs. Multifractal and multiscale entropy analyses were employed to examine the fractal characteristics and complexity of the experimental time series for indicated mean effective pressure (IMEP) and heat release (Q) at different scales. Statistical analysis and return maps of the IMEP and Q time series were utilized to verify the results. The findings revealed that the combustion process of the natural gas engine demonstrates clear fractal characteristics at different scales. A strong correlation is found between the combustion instability and the fractal characteristics. Furthermore, the probability densities of the IMEP and Q time series exhibit super-Gaussian distributions. Retarding the GIT results in an initial increase, followed by a decrease in the difference value of the Hurst index and singular spectrum width. The mapping point distributions of the IMEP and Q time series initially disperse and subsequently concentrate. The fractal complexity and chaotic characteristics of combustion instability initially strengthen and then gradually diminish. Moreover, under lower load conditions, the anti-persistent correlation becomes more pronounced, and the intermittence and complexity of the fractal characteristics also intensify, signifying a more significant impact of GIT on the combustion instability of the natural gas engine. Notably, when the GIT is approximately 60°CA after top dead center, the combustion process exhibits stronger fractal characteristics, accompanied by a greater dispersion degree of the mapping points. This study provides a theoretical basis for enhancing the lean-burn stability of natural gas engines.

Analysis of the configuration characteristics of the chaotic attractor for machine tool dynamic error under the singular spectrum

To explore the chaotic characteristics of the motion error of CNC machine tools, this paper proposes a chaotic attractor feature extraction method for the early motion error of CNC machine tools. Firstly, the chaotic verification of each time series feature of machine tool motion error proves that the change of CNC machine tool motion error is a nonlinear change process with chaotic characteristics. It then establishes the multivariate error phase space and performs a singular spectrum analysis. Because the motion error is strongly disturbed by noise, it is difficult to determine the number of singular values by the traditional singular spectrum analysis method, and this paper proposes to calculate the phase space index of the reconstructed sequence by traversing the number of singular values, to determine the main feature components; Finally, under different noise environments and different signal lengths, three feature extraction methods are introduced for experimental comparison with this paper's method, which shows that the chaotic attractor reconstructed after determining the number of singular values has a more apparent geometric structure. The method proposed in this paper has advantages in machine tool motion error.

Reducing manipulations in a control simulation experiment based on instability vectors with the Lorenz-63 model

Abstract. Controlling weather is an outstanding and pioneering challenge for researchers around the world, due to the chaotic features of the complex atmosphere. A control simulation experiment (CSE) on the Lorenz-63 model, which consists of positive and negative regimes represented by the states of variable x, demonstrated that the variables can be controlled to stay in the target regime by adding perturbations with a constant magnitude to an independent model run (Miyoshi and Sun, 2022). The current study tries to reduce the input manipulation of the CSE, including the total control times and magnitudes of perturbations, by investigating how controls affect the instability of systems. For that purpose, we first explored the instability properties of Lorenz-63 models without and under control. Experiments show that the maximum growth rate of the singular vector (SV) reduces when the variable x was controlled in the target regime. Subsequently, this research proposes to update the magnitude of perturbations adaptively based on the maximum growth rate of SV; consequently, the times to control will also change. The proposed method successfully reduces around 40 % of total control times and around 20 % of total magnitudes of perturbations compared to the case with a constant magnitude. Results of this research suggest that investigating the impacts of control on instability would be beneficial for designing methods to control the complex atmosphere with feasible manipulations.

Short-term wind speed prediction based on fractal dimension-variational mode decomposition and general continued fraction

Accurate wind speed prediction is significant to maintain the stable power system operation and enhance wind power utilization efficiency. However, most of wind speed prediction models cannot well fit the variation pattern of wind series due to its random and chaotic characteristics. This study proposes a novel wind speed prediction model incorporating fractal dimension (FD) and variational mode decomposition (VMD) and general continued fraction (GCF). More specifically, the fractal feature of wind speed series is analyzed, and the parameter determination strategy for VMD based on fractal feature of wind series is developed to overcome the problem of ambiguous mode parameter K for VMD, so that the relatively regular modes can be extracted from the raw wind series. To well capture the variation characteristics of wind series, a novel GCF model is derived on the basis of the inverse difference quotient theory, and the structure parameters of GCF model are determined by the bats algorithm (BA). To verify the accuracy and stability of the proposed model, several experiments involving comparisons with some mainstream models are executed on five wind speed datasets, and the proposed model outperforms comparative models with mean absolute percentage error reduction of 0.48 %–74.44 %. The results of the experiments indicate the proposed model has great capacity of capturing the variation characteristics of wind speed series.

Pseudo random number generator based on vertical-cavity surface-emitting lasers under current modulation embedded in microcontroller

This paper explores the dynamics, microcontroller validation of a vertical-cavity surface-emitting laser (VCSEL) under current modulation (CM) and employs it to model a process of pseudo-random number generator (PRNG). Solitary VCSEL has three equilibrium points whose stabilities depend on the direct current density. VCSEL under CM (VCSELCM) experiences antimonotonicity, chaotic bubble attractor as well as chaotic attractor. A microcontroller implementation of VCSELCM is used to establish the dynamical behaviors spotted in VCSELCM during the numerical simulations. Lastly, the chaotic characteristics depicted by the VCSELCM are used to design a process of PRNG. The generated random bits are validated successfully by a standard statistical tool set by the National Institute of Standards and Technology (NIST-800–22).