Behavior Of Complicated Systems Research Articles

Mittag–Leffler kernel operator on prey-predator model interfusing intra-specific competition and prey fear factor

In the present research, a prey-predator system that classifies predator species into premature and veteran species was proposed. Mathematical models of the system of species that include the impact of panic and a variety of functional reactions to the species have been recently reviewed. The consequence of fear is a reduction factor in the fundamental reproduction of the prey species. Relying on the prior research, we propose a mathematical predator-prey model employing the concept of fractional order system that contains two crucial components: intra-specific competition under mature predator density and fear, which affects the progeny of the prey community. Incorporating self-similarity characteristics is the significance of adopting a fractional order system. Following that, using memory kernel, we have interpret this model into the perspective of Caputo, that is, Atangana_Baleanu_Caputo (A_B_C) kind of fractional order derivatives. Singularity and non-locality are two of the main properties of fractional operators that make it simpler to figure out the natural behaviour of complicated systems. Details regarding the unique solution to the established model's derivation are provided. The dichotomy operator investigates the stability of the system under Hyper-Ulam`s stability method. The numerical interpolation to the suggested model generates the best precise approximate result. Through computer simulations, a thorough investigation on the predator species exhibiting intra-specific factor and prey species exhibiting fear factor is investigated. Using Adam's semi-analytical technique, we were able to derive the numerical estimates and determine the solution. The purpose to employ the Adams-Moulton approach is that it will provide more implicitly accurate results. Extensive numerical experiments are carried out using MATLAB software in order to portray the dynamical behaviour of the system. These observations lead us to the conclusion that total population expansion in the environment would be managed by the successful adoption of the fear factor in prey and the intra-species rivalry in predators.

Top‐down functional composition

AbstractThe functional decomposition methodology forms the basis for how to design system behavior in the systems engineering literature. Unfortunately, this design approach has inherent weaknesses that do not allow for requirement sets to be both complete and design agnostic, both of which are considered necessary characteristics for good requirement sets. In practice, it is also difficult to design the behavior of complicated systems using functional decomposition since a large portion of the design challenge must be handled up‐front.This paper introduces a top‐down functional composition methodology approach to behavioral design. The paper argues that the presented methodology does not have the shortcomings associated with functional decomposition and that it also enables early and continuous‐, integration of design, test of design and validation of design. Therefore, this approach is more beneficial for engineering design of complicated systems, especially when model based systems engineering (MBSE) is applied.

Quantifying the Nonlinear Interaction in the Nervous System Based on Phase-Locked Amplitude Relationship.

This paper introduces the Cross-frequency Amplitude Transfer Function (CATF), a model-free method for quantifying nonlinear stimulus-response interaction based on phase-locked amplitude relationship. The CATF estimates the amplitude transfer from input frequencies at stimulation signal to their harmonics/intermodulation at the response signal. We first verified the performance of CATF in simulation tests with systems containing a static nonlinear function and a linear dynamic, i.e., Hammerstein and Wiener systems. We then applied the CATF to investigate the second-order nonlinear amplitude transfer in the human proprioceptive system from the periphery to the cortex. The simulation demonstrated that the CATF is a general method, which can well quantify nonlinear stimulus-response amplitude transfer for different orders of nonlinearity in Wiener or Hammerstein system configurations. Applied to the human proprioceptive system, we found a complicated nonlinear system behavior with substantial amplitude transfer from the periphery stimulation to cortical response signals in the alpha band. This complicated system behavior may be associated with the nonlinear behavior of the muscle spindle and the dynamic interaction in the thalamocortical radiation. This paper provides a new tool to identify nonlinear interaction in the nervous system. The results provide novel insight of nonlinear dynamics in the human proprioceptive system.

Integration of Dimension Reduction and Uncertainty Quantification in Designing Stretchable Strain Gauge Sensor

Interests in strain gauge sensors employing stretchable patch antenna have escalated in the area of structural health monitoring, because the malleable sensor is sensitive to capturing strain variation in any shape of structure. However, owing to the narrow frequency bandwidth of the patch antenna, the operation quality of the strain sensor is not often assured under structural deformation, which creates unpredictable frequency shifts. Geometric properties of the stretchable antenna also severely regulate the performance of the sensor. Especially rugged substrate created by printing procedure and manual fabrication derives multivariate design variables. Such design variables intensify the computational burden and uncertainties that impede reliable analysis of the strain sensor. In this research, therefore, a framework is proposed not only to comprehensively capture the sensor’s geometric design variables, but also to effectively reduce the multivariate dimensions. The geometric uncertainties are characterized based on the measurements from real specimens and a Gaussian copula is used to represent them with the correlations. A dimension reduction process with a clear decision criterion by entropy-based correlation coefficient dwindles uncertainties that inhibit precise system reliability assessment. After handling the uncertainties, an artificial neural network-based surrogate model predicts the system responses, and a probabilistic neural network derives a precise estimation of the variability of complicated system behavior. To elicit better performance of the stretchable antenna-based strain sensor, a shape optimization process is then executed by developing an optimal design of the strain sensor, which can resolve the issue of the frequency shift in the narrow bandwidth. Compared with the conventional rigid antenna-based strain sensors, the proposed design brings flexible shape adjustment that enables the resonance frequency to be maintained in reliable frequency bandwidth and antenna performance to be maximized under deformation. Hence, the efficacy of the proposed design framework that employs uncertainty characterization, dimension reduction, and machine learning-based behavior prediction is epitomized by the stretchable antenna-based strain sensor.

Optimization driven cellular automata for traffic flow prediction at signalized intersections

The traffic flow prediction using cellular automata (CA) is a trendy research domain that identified the potential of CA in modelling the traffic flow. CA is a technique, which utilizes the basic units for describing the overall behaviour of complicated systems. The CA model poses a benefit for defining the characteristics of traffic flow. This paper proposes a modified CA model to reveal the prediction of traffic flows at the signalised intersection. Based on the CA model, the traffic density and the average speed are computed for studying the characteristics and spatial evolution of traffic flow in signalised intersection. Moreover, a CA model with a self-organizing traffic signal system is devised by proposing a new optimization model for controlling the traffic rules. The Sunflower Cat Optimization (SCO) algorithm is employed for efficiently predicting traffic. The SCO is designed by integrating the Sunflower optimization algorithm (SFO) and Cat swarm optimization (CSO) algorithm. Also, the fitness function is devised, which helps to guide the control rules evaluated by traffic simulation using the CA model. Thus, the cellular automaton is optimized using the SCO algorithm for predicting the traffic flows. The proposed Sunflower Cat Optimization-based cellular automata (SCO-CA) outperformed other methods with minimal travel time, distance, average traffic density, and maximal average speed.

Integrating Mach Number Prediction with Outlier Detection for Wind Tunnel Systems

AbstractMach number prediction plays a crucial role in wind tunnel systems. Due to the complicated system behavior, many existing predictors cannot obtain the desired level of accuracy. In addition...

Performance evaluation and dynamic optimization of speed scaling on web servers in cloud computing

The energy consumption in large-scale data centers is attracting more and more attention today with the increasing data center energy costs making the enhanced performance very expensive. This is becoming a bottleneck to further developments in terms of both scale and performance of cloud computing. Thus, the reduction of the energy consumption by data centers is becoming a key research topic in green IT and green computing. The web servers providing cloud service computing run at various speeds for different scenarios. By shifting among these states using speed scaling, the energy consumption is proportional to the workload, which is termed energyproportionality. This study uses stochastic service decision nets to investigate energy-efficient speed scaling on web servers. This model combines stochastic Petri nets with Markov decision process models. This enables the model to dynamically optimize the speed scaling strategy and make performance evaluations. The model is graphical and intuitive enough to characterize complicated system behavior and decisions. The model is serviceoriented using the typical service patterns to reduce the complex model to a simple model with a smaller state space. Performance and reward equivalent analyse substantially reduces the system behavior sub-net. The model gives the optimal strategy and evaluates performance and energy metrics more concisely.

A Hybrid Approach for Multi-Agent Learning Systems

Abstract This study proposes a hybrid approach to construct amulti-agent learning system that is applied to RoboCup (Robot world cup tournament) soccer game. RoboCup competitive games involve the complicated system behavior of multiple agents, which makes it a popular research domain in recent years. The goal of RoboCup is to promote AI and robotics, and some researchers even hope that RoboCup robots can eventually defeat human soccer players. A hybrid approach called the Case Based Reasoning-Genetic Algorithm (CBR-GA) was used to provide a better strategy for robots in their planning under all kinds of conditions and to store experiences learned during the game for further use. In addition, the Rule Based Reasoning (RBR) method was integrated to improve the defect of CBR-GA. Using the hybrid approach without pre-defined knowledge and a complicated mathematical basis, robots can learn and accumulate experience to become smarter. The robots not only learn how to score but also figure out how to avoid makin...

Bandwidth allocation for guaranteed versus best effort service categories

Modern communication networks evolve towards integration of guaranteed-performance and best-effort service types. The coexistence of these two service types offers substantial benefits, such as resource sharing between service classes, and the ability of the user to select an appropriate service class according to its individual requirements and preferences. Notwithstanding, such interaction gives rise to more complicated system behavior and related performance issues, which need to be explored and understood in order to allow efficient network operation. In this paper we examine potential congestion phenomena, which arise due to the combined effect of bandwidth sharing and user migration between service classes. We propose a simplified fluid model for session flow, consisting of two coupled queues with state-dependent flows, which captures the essential ingredients of service-class interaction. Our analysis shows that the system might exhibit bistable behavior, in the sense that transient congestion may stir the system from a stable and efficient operating point to an inefficient and congested one. We identify conditions which give rise to bistability, and propose a call admission control scheme which prevents the system from getting trapped in a congested-type equilibrium, while not interfering with normal system operation.