Non-probabilistic models for in-plane elastic properties of cellular hexagonal honeycomb cores involving imprecise parameters

Inherent imprecisions present in the basic parameters of cellular honeycomb cores, such as the cell angle, the material properties, and the geometric parameters, need to be considered in the analysis and design to meet the high-performance requirements. In this paper, imprecisions associated with the basic parameters of honeycomb cores are considered. Non-probabilistic models for the in-plane elastic properties of hexagonal honeycomb cores are developed in which the imprecisely defined input and response parameters are represented by only their mean values and variations without the requirement of knowing the probability density distributions of the imprecise parameters as is required for probabilistic methods. Thus, the proposed models predict not only the nominal values of the in-plane elastic properties but also their variations from the respective mean values. The applicability of the proposed models is demonstrated by considering the analysis of the in-plane elastic properties of a honeycomb core made of aluminum 5052-H-32 in which the core material properties are defined by their mean values and variations. The results show that realistic variations of the in-plane elastic properties are obtained using the proposed non-probabilistic models. The sensitivity of the in-plane elastic properties to the imprecisions present in each basic parameter is also investigated.

USE OF FUZZY MATHEMATICAL QUADRATIC PROGRAMMING APPROACH IN JOB EVALUATION

Purpose of study: The current paper is the based on mathematical model of the job evolution system. Methodology: The proposed method is the fusion of quadratic programming and fuzzy logic where quadratic programming is used to optimize objective function with related constraints in the form of non-linear formulation. Fuzzy logic is used to control uncertainty related information by estimating imprecise parameters Main Finding: The optimal solution of the job evaluation based on fuzzy environment where goal is imprecise. Application of this study: It is used in the areas where information is not exact. The originality of this study: The novelty of the method is the fusion of quadratic programming and fuzzy logic.

A Mathematical Study of COVID-19 Outbreak with Uncertainties of Controlling Parameters

Rapidly spreading disease, COVID-19 is classified as the human-to-human transmissionable disease and currently it becomes a pandemic in the Globe. In this paper, we propose the conceptual mathematical model and analyze a Susceptible-Exposed-Infected-Quarantined or Isolated-Recovered- Susceptible (SEIRUS) type infectious disease model with imprecise parameters. We have divided the model formulation portion into four subsections. They are namely crisp SEIRUS model, interval SEIRUS model and fuzzy SEIRUS model. The existence condition and boundedness of the solution to our proposed model have been discussed. The asymptotical stability of the system at different equilibrium point is investigated. Also we have explained the global stability at endemic equilibrium point. Application of optimal control of the system is described and solved. Finally, some numerical results have been shown to test the theoretical study of the model. We observed that the population is greatly influenced for the imprecise nature of parameters.

Sustainable Agriculture: Stable Robust Control in Presence of Uncertainties for Multi-Functional Indoor Transportation of Farm Products

Currently, integrated trends play a key role in every aspect of automation applications. In particular, if the mechanization of agriculture becomes a competitive factor among farmers or nations, then the multi-functional transportation of agricultural products is inevitable in global trade. In sustainable transportation, the challenge of overcoming stable control in harsh environments, such as through imprecise parameters or varying loads, should be addressed. In this paper, a novel controller for a nonholonomic mechanical system able to adapt to uncertainties is proposed. Based on the multi-functional autonomous carrier (MAC), the system configuration of the kinematic and dynamic model is launched in order to identify the unstable problems that arise when tracking the trajectory. To solve these troubles, the decoupled formation of a MAC system has been investigated by considering two second-order components, namely a linear speed-based sub-system and angular speed-based sub-system. To stabilize the whole system using the Lyapunov theory, the advanced control techniques are studied. To validate the proposed approach, a series of test scenarios have been carried out. From the superior performance of numerous trials, it is clear that our approach is effective, feasible, and reasonable for the advanced control of agricultural applications.