Chaos Mechanism Research Articles

Dynamics modeling of a memristor-based Rucklidge chaotic system: Multistability, offset boosting control and FPGA implementation

The complex dynamics greatly contributes to the application of chaos, especially the memristor chaotic systems that have emerged very recently. In this paper, a novel four-dimensional continuous chaotic system is developed by introducing a generalized memristor model into the classical Rucklidge system, and the rich dynamical traits are confirmed by theories and numerical simulations. System Hamilton energy is deduced and analyzed to expose the underlying energy evolution and chaos mechanism. And then, realizing the offset boosting control of the system promotes its engineering applications. Numerical simulations verify that the offset boosting controller can directly change the polarity of the chaotic signal. Specifically, bifurcation diagram, Lyapunov spectra, system complexity evolution diagram and dynamic evolution map are adopted to expound the rich dynamics. Meanwhile, the extreme multi-stability with various initial values resulting in an infinite number of coexisting attractors is also revealed. Finally, the proposed attractor is implemented using the Field Programmable Gate Array (FPGA) platform and building analog circuit based on Multisim. Among which, the former is potentially used to design the Pseudo-random signal generator in information encryption, while the latter is for verifying the existence of the proposed chaotic system.

Decoding intelligence via symmetry and asymmetry

Humans use pictures to model the world. The structure of a picture maps to mind space to form a concept. When an internal structure matches the corresponding external structure, an observation functions. Whether effective or not, the observation is self-consistent. In epistemology, people often differ from each other in terms of whether a concept is probabilistic or certain. Based on the effect of the presented IG and pull anti algorithm, we attempt to provide a comprehensive answer to this problem. Using the characters of hidden structures, we explain the difference between the macro and micro levels and the same difference between semantics and probability. In addition, the importance of attention is highlighted through the combination of symmetry and asymmetry included and the mechanism of chaos and collapse revealed in the presented model. Because the subject is involved in the expression of the object, representationalism is not complete. However, people undoubtedly reach a consensus based on the objectivity of the representation. Finally, we suggest that emotions could be used to regulate cognition.

Experimental and computational investigation of chaotic advection mixing in laminar rectangular stirred tanks

The phenomenon of chaotic advection mixing in laminar stirred tanks, particularly when handling high-viscosity fluids, represents a common and challenging issue in various chemical and biochemical industrial processes. The effectiveness of these mixing processes is often constrained by the stretching, folding, and squeezing of fluid particles. Previous research efforts have predominantly concentrated on the design and/or optimization of impellers and operation modes to increase the stretching and folding of fluid particles. However, the mechanism of triggering chaos through the squeezing of fluid particles remains comparatively less explored. This study endeavors to address the intricate problem of laminar mixing by proposing the utilization of unbaffled rectangular stirred tanks. Via a combined approach of experimental and computational investigations, we illustrate that the implementation of rectangular stirred tanks yields significant improvements in chaotic mixing within laminar flow regimes. This type of tank not only enhances the convective transport of fluids but also distorts or completely eliminates the segregation regions by squeezing. Remarkably, as the tank L/W ratio increases from 1 to 2.5, the observed asymptotic area coverage from a frontal perspective escalates from approximately 55 % to about 85 %. Concurrently, the dimensionless mixing time decreased significantly from 1950 to 350. Of particular note, macroscopic homogeneous mixing is observed in stirred tanks with L/W ratios of 2 and 2.5 at Re = 29, with dimensionless mixing times of 550 and 350, respectively. This work highlights the potential of rectangular stirred tanks as a simple and cost-effective system for laminar mixing applications. Furthermore, it also hints at the broader applicability of the approach in various industrial mixing contexts, such as the suspension of floating granules, entrainment of air, and others.

An efficient task scheduling in fog computing using improved artificial hummingbird algorithm

IoT edge devices have become more popular due to the rapid growth in IoT applications in recent years. Task scheduling reduces latency and application computation times, while improving quality of service. In this paper, we introduce a new version of Artificial Hummingbird Algorithm (AHA) with Opposition-Based Learning (OBL), chaos mechanism, and Differential Evolution (DE) algorithm, called CODA. AHA is improved by using DE algorithms to determine the optimal configuration of chaotic maps and OBL for determining the optimal initial population. As a result of CODA's high ability, local optima can be avoided and exploration of a region of interest can be improved. CODA is then utilized to schedule tasks in fog computing systems. Analytic Hierarchy Process (AHP) is used to determine the priority of tasks. Task scheduling is primarily intended to reduce energy consumption, duration, and costs. To compare CODA's performance with that of other well-known meta-heuristics, 50 basic functions were used as benchmarks. Additionally, the proposed scheduling scheme is evaluated through different simulations. Energy consumption, makespan, and cost are better as a result of the implemented algorithm. When compared to the existing algorithms that include Artificial Hummingbird Algorithm (AHA), Gravitational Search Algorithm (GSA), Moth-Flame Optimization (MFO), Seagull Optimization Algorithm (SOA), Salp Swarm Algorithm (SSA), Whale Optimization Algorithm (WOA), Sine Cosine Algorithm (SCA), Particle swarm optimization (PSO), Multi-Verse Optimizer (MVO), and Differential evolution (DE), the proposed CODA shows better output in satisfying the task scheduling process. On average, the CODA-based task scheduling model outperforms other research studies in terms of makespan by 46%, cost by 8%, and energy consumption by 41%.

A Quantum Particle Swarm Optimization Algorithm Based on Aggregation Perturbation

A quantum particle swarm hybrid optimization algorithm based on aggregation disturbance is proposed for inventory cost control. This algorithm integrates the K-means algorithm on the basis of traditional particle swarm optimization, recalculates the clustering center, initializes stagnant particles, and solves the problem of particle aggregation. Introducing chaos mechanism into the algorithm, changing the position of particles, enhancing their activity, and improving the algorithm's global optimization ability. At the same time, define the aggregation disturbance factor, determine the current state of particles, optimize speed and position to accelerate escape, and solve the problem of particles falling into local optima. Experiments show that M-IKPSO algorithm has strong stability, fast Rate of convergence and high accuracy compared with other algorithms, and the improvement effect is significant.

Research on operation optimization problem of energy storage station in microgrid based on improved particle swarm optimization

In many microgrids with a lot of uncontrollable DGs, a well-planned operation of the energy storage station is an important guarantee for the stability and economy of the microgrid. To solve the operation optimization problem, the Chaos mechanism, differential evolution, and random inertia weight were introduced to propose the traditional PSO algorithm. A mathematical model for minimizing the running cost of Microgrid is established, and through MATLAB simulation, both algorithms are used to solve the same problem to observe the improvement of the algorithm. The experimental results show that the IPSO algorithm achieves performance improvement.

Dynamic modelling and chaos control for a thin plate oscillator using Bubnov–Galerkin integral method

The utilization of thin plate systems based on acoustic vibration holds significant importance in micro-nano manipulation and the exploration of nonlinear science. This paper focuses on the analysis of an actual thin plate system driven by acoustic wave signals. By combining the mechanical analysis of thin plate microelements with the Bubnov–Galerkin integral method, the governing equation for the forced vibration of a square thin plate is derived. Notably, the reaction force of the thin plate vibration system is defined as f = α|w|, resembling Hooke’s law. The energy function and energy level curve of the system are also analyzed. Subsequently, the amplitude–frequency response function of the thin plate oscillator is solved using the harmonic balance method. Through numerical simulations, the amplitude–frequency curves are analyzed for different vibration modes under the influence of various parameters. Furthermore, the paper demonstrates the occurrence of conservative chaotic motions in the thin plate oscillator using theoretical and numerical methods. Dynamics maps illustrating the system’s states are presented to reveal the evolution laws of the system. By exploring the effects of force fields and system energy, the underlying mechanism of chaos is interpreted. Additionally, the phenomenon of chaos in the oscillator can be controlled through the method of velocity and displacement states feedback, which holds significance for engineering applications.

Presenting a Novel Evolutionary Method for Reserve Constrained Multi-Area Economic/Emission Dispatch Problem

Economic dispatch (ED) attempts to find the most cost-effective combination of power generation units while meeting operational constraints. Another problem that can’t be resolved by standard economic dispatch problems is figuring out the method of generating dispatch that would be most cost-effective in meeting the local demand without exceeding the tie-line capacity. Making a trade-off between fuel costs and environmental concerns, a contentious problem in industrialized countries, seems essential. As a result, this study introduces a multi-objective approach for different ED problems, such as multi-area emission economic dispatch (MAEED) and reserve constrained multi-area emission economic dispatch (RCMAEED), when there are real-world restrictions present, like the valve point effect (VPE), prohibited operating zones (POZs), multi-fuel operation (MFO), and ramp-rate (RR) restrictions. In this study, the generation cost and emissions are taken into consideration as objective functions. Since the MAED problem in the power system is inherently nonlinear, adding the aforementioned restrictions makes the problem even more challenging. To address the complexity of the multi-objective optimization problem, the modified grasshopper optimization (MGO) algorithm, based on the chaos mechanism, is proposed in this paper. The proposed method has been tested on a four-area power system with sixteen electrical generators, and the results are contrasted with those of previous evolutionary techniques. Based on the results, it can be concluded that using the proposed MGO method to solve the MAED and RCMAED problems will result in generation costs that are around $300 and $600 less than using the MPSO and PSO methods, respectively. Also, the proposed MGO method has reduced emission levels by roughly 30% as compared to the GO method in order to solve the RCMAEED problem.

O-107 Sperm chromosome chaos induced by MEIKIN gene mutations leads to blastocyst formation failure

Abstract Study question What are the effect and corresponding mechanisms of chromosome chaos of sperm caused by mutation of the MEIKIN gene on preimplantation embryo development? Summary answer Sperm chromosome chaos induced by MEIKIN gene mutations directly causes blastocyst formation failure by affecting the activation of the embryonic genome and lineage differentiation. What is known already In mice, Meikin is a meiosis-specific kinetochore factor that plays a crucial role in mono-orientation and protection of centromeric cohesion in meiosis I and the regulation of chromosome alignment during meiosis II. Both male and female Meikin gene knockout mice are completely infertile. Infertility in male Meikin-/- mice originates from defects in meiotic chromosome segregation and subsequent spermatogenesis. To our knowledge, no mutation of the MEIKIN gene has been reported in humans. Study design, size, duration In this study, semen from three patients with MEIKIN mutations and arrested embryos from one of them were analyzed to determine the effect of MEIKIN mutations on sperm and early embryonic development. Participants/materials, setting, methods Three male patients with MEIKIN mutations with a specific clinical phenotype were recruited. We assessed mutant sperm morphology using Papanicolaou staining. Sperm FISH and single sperm chromosome aneuploidy analysis（CNV-seq） were applied to identify mutant sperm Chromosomal abnormality. We also investigated the chromosome constitution and gene expression of the arrested embryos from one patient by parallel single-cell genome and transcriptome sequencing. Main results and the role of chance Novel mutations in the MEIKIN gene were identified in three infertility males with a specific clinical phenotype（They had good-quality embryos on day 3, but these embryos repeatedly failed to implant or develop to the blastocyst stage）. Routine semen assessments were conducted based on the WHO’s guidelines, and all these patients were diagnosed with oligoasthenoteratozoospermia. Sperm FISH showed that the frequencies of aneuploidies in spermatozoa were significantly higher than the normal control（89.05%,70.9%,93.24% vs. 5.03%）, and the CNV-seq analysis of single spermatozoa confirmed mutant sperm had severe chromosome chaos. Each cell of the arrested embryos from one patient had complicated chromosomal abnormalities, and the abnormal chromosomes were mainly derived from sperm. At the gene expression level, these embryos were arrested at the 8-cell to morula stage transition, and defects activation of a large ZGA and lineage specification genes led to blastocyst formation failure. Limitations, reasons for caution In this study, we discovered that mutations in MEIKIN caused male infertility manifesting as sperm chromosome chaos and blastocyst formation failure. However, the molecular mechanism of the sperm chromosome chaos involved in blastocyst formation needs to be further illuminated by using Meikin knockout mouse models. Wider implications of the findings We first reported that male with MEIKIN mutations is related to severe sperm chromosome abnormalities and blastocyst formation failure. Therefore, In the process of ICSI, if there are good-quality cleavage embryos that repeatedly fail to implant or develop to the blastocyst stage, it is recommended to examine male factors concurrently Trial registration number not applicable

A new optimum technique for parameter identification of the proton exchange membrane fuel cells based on improved remora optimizer

ABSTRACT The present study proposes a new parameter estimation technique for undefined parameters of proton exchange membrane fuel cell (PEMFC) stack models. To provide an efficient model identifier, an objective function by the Sum of Squared Error (SSE) between the empirical data and the predicted date of the PEM fuel cell stack has been employed, and the aim is to minimize this objective function. Here, a new improved design of Remora Optimizer is designed and used for the minimization purpose. The improved method has been improved by self-adaptive weighting for improving the convergence rate and for providing promising balance searching in both initial and next steps, and chaos mechanism for increasing the optimization process while keeping the exploration term based on pseudorandom values. The technique is used in two test cases and then a comparison of the results with several newest techniques is performed. Simulation results showed that the proposed IROA method with 2.05% and 0.019% error ratio for NedSstack PS6 and Horizon 500W PEMFC stack models, respectively, provides the best results compared with other comparative methods. Simulation achievements indicate that the presented approach has higher efficiency than the other compared methods.

A chaos-based adaptive equilibrium optimizer algorithm for solving global optimization problems.

The equilibrium optimizer (EO) algorithm is a newly developed physics-based optimization algorithm, which inspired by a mixed dynamic mass balance equation on a controlled fixed volume. The EO algorithm has a number of strengths, such as simple structure, easy implementation, few parameters and its effectiveness has been demonstrated on numerical optimization problems. However, the canonical EO still presents some drawbacks, such as poor balance between exploration and exploitation operation, tendency to get stuck in local optima and low convergence accuracy. To tackle these limitations, this paper proposes a new EO-based approach with an adaptive gbest-guided search mechanism and a chaos mechanism (called a chaos-based adaptive equilibrium optimizer algorithm (ACEO)). Firstly, an adaptive gbest-guided mechanism is injected to enrich the population diversity and expand the search range. Next, the chaos mechanism is incorporated to enable the algorithm to escape from the local optima. The effectiveness of the developed ACEO is demonstrated on 23 classical benchmark functions, and compared with the canonical EO, EO variants and other frontier metaheuristic approaches. The experimental results reveal that the developed ACEO method remarkably outperforms the canonical EO and other competitors. In addition, ACEO is implemented to solve a mobile robot path planning (MRPP) task, and compared with other typical metaheuristic techniques. The comparison indicates that ACEO beats its competitors, and the ACEO algorithm can provide high-quality feasible solutions for MRPP.

A Multiobjective Evolutionary Approach for Solving the Multi-Area Dynamic Economic Emission Dispatch Problem Considering Reliability Concerns

Economic dispatch (ED) problems, especially in multi-area power networks, have been challenging concerns for power system operators for several decades. In this paper, we introduce a novel approach for solving the multiobjective multi-area dynamic ED (MADED) problem in the presence of practical constraints such as valve-point effect (VPE), prohibited operating zone (POZ), multi-fuel operation (MFO), and ramp rate (RR) limitations. Different objective functions including energy not supplied (ENS), generation costs, and emissions are investigated. The reliability objective, which has been less studied in economic dispatch area, distinguishes the proposed study from other studies. A compromise has been made from economic and reliability points of view. The MADED problem in the power system is inherently a complex and nonlinear problem, considering the operational constraint increments and the intricacy of the problem. Hence, the modified grasshopper optimization (MGO) algorithm based on a chaos mechanism is presented to prevent being trapped in local optima. The proposed method is tested on two systems including a 10 unit, 3-zone test system and a 40-unit 3-zone test system, and then, the outcomes are compared with those of other evolutionary techniques such as gray wolf optimization (GWO) and modified honey bee mating optimization (MHBMO). The simulation results demonstrate that the suggested strategy is successful in resolving both single-objective and multiobjective MADED problems.

Lithium-ion battery state of health estimation using meta-heuristic optimization and Gaussian process regression

Wrapper methods are widely employed in feature selection for status prediction of lithium-ion batteries and Gaussian process regression (GPR) is often adopted for state-of-health (SOH) estimation. However, the number and the source of the features are not considered as constraints in the wrapper methods, most existing optimization algorithms for finding optimal hyperparameters of GPR easily get trapped into the local optimum. In this work, a newly developed meta-heuristic optimization algorithm, Hunger Game Search (HGS) is utilized, because of its robustness and competitive performance to find the solution of both constrained and unconstrained problems. Also to further improve HGS the chaos mechanism is embedded to form Chaos-HGS (CHGS). It is utilized to find global optimal hyperparameters in GPR and its binary variant (CBHGS) is adopted to solve feature selection problems. Effects of chaos maps and constraints upon SOH prediction and hyperparameters optimization are investigated. To gain a deeper understanding of wrapper method results, intrinsic characteristics of features are mined through Gaussian Process Latent variables model (GPLVM). Relationships between features and the capacity of the battery are examined through clustering analysis. The effectiveness of the proposed algorithms and data mining methods is verified on the lithium-ion battery dataset of the NASA Prognostics Center of Excellence. The results show that the features found by CBHGS working with GPR can provide SOH prediction with higher accuracy and lower cost.

Sliding Mode Control of Two-Parameter Fourth-Order Chaos Model of Power System

As a complex nonlinear system, the power system in operation may experience chaotic oscillation which can threaten the stability of the system for unexpected events and uncertain factors. Initially, through considering factors such as the generators, the power system node voltage amplitude and phase angle, this brief establishes a fourth-order power system dynamic model and a two-parameter fourth-order power system mathematical model with load reactive power and mechanical input power as chaotic parameters to explore the mechanism and characteristics of chaos in the power system with the chaos theory. In addition, a sliding mode controller based on exponential reaching law for four state variables is designed, which is used to suppress the chaotic oscillation of the power system and guarantee the system can work stably at the target operating point within the chaotic parameter range. The designed controller can effectively suppress the chaos in power system and has an ideal performance.

Viscous dissipation as a mechanism for spatiotemporal chaos in Rayleigh-Bénard convection between poorly conducting boundaries at infinite Prandtl number

Nonlinear Rayleigh-B \ifmmode \acute{}\else \'{}\fi{}enard convection in an infinite Prandtl number fluid layer between poorly conducting boundaries is considered as a model for convection in the Earth's upper mantle. It is shown that accounting for the generally neglected impact of viscous dissipation may lead to the development of large-scale spatiotemporal chaotic dynamics governed by the familiar Kuramoto-Sivashinsky (KS) equation, ${\mathrm{\ensuremath{\Phi}}}_{\ensuremath{\tau}}+{\ensuremath{\nabla}}^{4}\mathrm{\ensuremath{\Phi}}+2{\ensuremath{\nabla}}^{2}\mathrm{\ensuremath{\Phi}}\ensuremath{-}{(\ensuremath{\nabla}\mathrm{\ensuremath{\Phi}})}^{2}+\ensuremath{\alpha}\phantom{\rule{0}{0ex}}\mathrm{\ensuremath{\Phi}}=0,$ known to occur in various physical systems. The figure shows an irregular pattern of chaotically recombining cells developing at $\ensuremath{\alpha}=0.1$.

Host regulation and seasonality generate population chaos in a fig-wasp mutualism

Mutualistic interactions between species play a key role in ecosystem function. Theoretical models and laboratory experiments suggest that mutualistic systems can display chaotic population dynamics. Evidence of mutualistic chaos in nature, however, is rare and poorly understood. Here, we show the existence of chaotic behaviour in a mutualistic system. We combine a mathematical model with field data to analyse population fluctuations and the stability of a host-mutualist-exploiter system. Our goal is to understand whether the system can evolve to a steady state and under what environmental conditions. We establish a discrete, seasonally explicit host-mutualist-exploiter model and estimate the parameters using data collected in the field. Model simulations show that the host decreases the ratio of extra reward to host sanction to maintain its higher population density when external environment variation (such as variation in seasonal temperature) is small. The model parameterized with field data predicts that host regulation and environmental variation together, but not individually, can lead to population chaos. Our work provides a new mechanism of population chaos in a mutualistic system and may inspire more interest in exploring chaotic dynamics in natural ecological systems.

A chaos-coupled multi-objective scheduling decision method for liner shipping based on the NSGA-III algorithm

Reducing sailing costs, time and emissions are three important decision-making objectives for ship operators. In this paper, a multi-objective decision model for liner shipping is established to optimize ship route, speed and number of ships deployed based on the three objectives. Moreover, the above model also takes into consideration the three latest ship emission reduction policies, including Emission Control Area, the 2020 Sulfur Limit Order and maritime Carbon Emission Taxation regulations. To solve the multi-objective scheduling problem, an improved non-dominated sorting genetic algorithm-III (NSGA-III) algorithm based on a chaos mechanism is proposed to find the Pareto solutions. And then a Multi-Criteria Decision Making (MCDM) method, the Hyperplane-based ranking method (HYRM) is developed to find the trade-off solution from the Pareto solutions. The proposed method was applied to two liner shipping service routes around southeast Asia and eastern United States, the results show that the proposed method can simultaneously reduce sailing costs, time and ship emission according to the users' preferences, and it can help shipping companies cope with fuel price fluctuations effectively.

Bistable chaotic family and its chaotic mechanism

The emergence of many chaotic families shows that chaos can be generated by deterministic approaches. From the perspective of the mechanism of chaos, we add the opposite time-delayed effects to an overdamped bistable system to obtain chaos. The effect of time delay is determined by the change of the optimal window which origin from logical stochastic resonance. A variety of chaotic systems with different chaotic attractors are obtained by different combinations of opposite effects. Furthermore, we propose the mechanism of chaos in time-delayed bistable systems, that is, the opposite time-delayed effects lead to repeated increasing and decreasing in depth of potential wells. This mechanism is verified by constructing a force-free Duffing equation with an oscillatory double-well potential. Results show that the bistable systems can form bistable chaotic family based on the same chaos generation mechanism.

Mechanism of the low-frequency wide-band within a nonlinear acoustic metamaterial

In this paper, the formation and broadening mechanism of the low-frequency bandgap are revealed by designing a type of nonlinear acoustic metamaterial (NAM) with gaps. The mechanism of the chaotic evolution through the harmonic coupling for the NAM structure is revealed in detail, which can effectively change spectral characteristics in the bifurcation sequence, and finally, lead to a low frequency and chaos. Then, a low-frequency broadband within the NAM structures with gaps is obtained by the finite element method (FEM) due to the mechanism of chaos. Most importantly, the width of the bandgap can be successfully enlarged by adjusting the small gap, because the energy of the elastic wave can be greatly decreased or redistributed with the system going from periodic motion to doubling bifurcation. This mechanism for achieving a low-frequency bandgap overcomes the shortcomings both in the traditional local resonance with too large additional mass and in the inertial amplification structures with narrow bandgaps. Therefore, this idea of achieving a low-frequency bandgap has great theoretical significance for controlling low-frequency sound waves.

Age estimation from facial images based on Gabor feature fusion and the CIASO‐SA algorithm

AbstractAiming at the problem of long time‐consuming and low accuracy of existing age estimation approaches, a new age estimation method using Gabor feature fusion, and an improved atomic search algorithm for feature selection is proposed. Firstly, texture features of five scales and eight directions in the face region are extracted by Gabor wavelet transform. The statistical histogram is introduced to encode and fuse the directional index with the largest feature value on Gabor scales. Secondly, a new hybrid feature selection algorithm chaotic improved atom search optimisation with simulated annealing (CIASO‐SA) is presented, which is based on an improved atomic search algorithm and the simulated annealing algorithm. Besides, the CIASO‐SA algorithm introduces a chaos mechanism during atomic initialisation, significantly improving the convergence speed and accuracy of the algorithm. Finally, a support vector machine (SVM) is used to get classification results of the age group. To verify the performance of the proposed algorithm, face images with three resolutions in the Adience dataset are tested. Using the Gabor real part fusion feature at 48 × 48 resolution, the average accuracy and 1‐off accuracy of age classification exhibit a maximum of 60.4% and 85.9%, respectively. Obtained results prove the superiority of the proposed algorithm over the state‐of‐the‐art methods, which is of great referential value for application to the mobile terminals.