Locations optimization of multiple cable-drivers for an anti-wind disturbance system in large antenna

Since the structural vibration deformation of a large antenna under wind disturbance leads to the pointing deterioration, an adaptive anti-wind disturbance system was presented by Liang et al. (Int. J. Antennas Propag., Article ID 2015341, 2019). To further improve the vibration suppression performance, this paper presents a locations-optimization algorithm of multiple cable-drivers for the anti-wind disturbance system. First, according to the spatial geometric relationship among the antenna structure, single slide track, four cables and drivers, the feasible domain of the drivers is determined. Next, the separate optimization method, and the joint optimization method for the locations of the four drivers are proposed, respectively. Finally, the simulation implementation of a 7.3 m antenna under various wind conditions is used to compare the joint optimization method and the separate optimization method.

A Detailed Examination of Sphicas (2014), Generalized EOQ Formula Using a New Parameter: Coefficient of Backorder Attractiveness

Researchers have used analytic methods (calculus) to solve inventory models with fixed and linear backorder costs. They have found conditions to partition the feasible domain into two parts. For one part, the system of the first partial derivatives has a solution. For the other part, the inventory model degenerates to the inventory model without shortages. A scholar tried to use the algebraic method to solve this kind of model. The scholar mentioned the partition of the feasible domain. However, other researchers cannot understand why the partition appears, even though the scholar provided two motivations for his derivations. After two other researchers provided their derivations by algebraic methods, the scholar showed a generalized solution to combine inventory models with and without shortages together. In this paper, we will point out that this generalized solution approach not only did not provide explanations for his previous partition but also contained twelve questionable results. Recently, an expert indicated questionable findings from two other researchers. Hence, we can claim that solving inventory models with fixed and linear backorder costs is still an open problem for future researchers.

Preference Attitude-Based Method for Ranking Intuitionistic Fuzzy Numbers and Its Application in Renewable Energy Selection

Many applications of intuitionistic fuzzy sets depend on ranking or comparing intuitionistic fuzzy numbers. This paper presents a novel ranking method for intuitionistic fuzzy numbers based on the preference attitudinal accuracy and score functions. The proposed ranking method considers not only the preference attitude of decision maker, but also all the possible values in feasible domain. Some desirable properties of preference attitudinal accuracy and score functions are verified in detail. A total order on the set of intuitionistic fuzzy numbers is established by using the proposed two functions. The proposed ranking method is also applied to select renewable energy. The advantage and validity of the proposed method are shown by comparing with some representative ranking methods.

The Budyko functions under non-steady state conditions: new approach and comparison with previous formulations

The Budyko functions relate the evaporation ratio E / P (E is evaporation and P precipitation) to the aridity index Φ = Ep / P (Ep is potential evaporation) and are valid on long timescales under steady state conditions. A new formulation physically based (noted ML) is proposed to extend the Budyko framework under non-steady state conditions taking into account the change in soil water storage S. The ML formulation introduces an additional parameter S* = S / Ep and can be applied with all classical Budyko functions. In the standard Budyko space (Ep / P, E / P), and for the particular case where the Fu-Zhang equation is used as a Budyko function, the ML formulation yields similar results to the analytical solution of Greve et al. (2016), and a simple relationship can be established between their respective parameters. Then, the ML formulation is extended to the space [(Ep / (P + S), E / (P + S)] and compared to the formulations of Chen et al. (2013) and Du et al. (2016). We show that the ML and Greve et al. formulations have similar upper feasible domain but their lower feasible domain is different from those of Chen et al. (2103) and Du et al. (2016). Moreover, the domain of variation of Ep / (P + S) differs: it is bounded by an upper limit 1 / S* in the ML formulation, while it is bounded with a lower limit in Chen et al.'s and Du et al.'s formulations.