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This proposed work is presenting the approximation of higher-order (HO) underwater robotic vehicle (URV) controller with the help of multi-point matching technique by incorporating greywolf optimization algorithm (GWOA). The performance of URV system is affected by external and internal dynamics. The proper momentum of URV system is achieved by designing a controller. The URV can be effectively operated by control action of controller. The URV controller is approximated to comparatively lower-order (LO) to propose an efficient, effective and economical controller for HOURV system. The approximation is accomplished with the help of expansion parameters of HOURV controller and its desired LOURV controller. The errors between these expansion parameters of HOURV controller and its desired LOURV controller are minimized using multi-point matching. The multi-point matching is depicted in the form of objective function (OF). The constructed OF is minimized by exploiting GWOA by fulfilling the steady-state matching condition and Hurwitz stability criterion, as constraints. The effectiveness of proposed approach of multi-point matching is verified by comparing the proposed LOURV model with LOURV models obtained with the help of other approximation approaches. The applicability of proposed LOURV controller is evaluated and validated by analyzing responses and tabulated data obtained in the results. Additionally, the statistical data of performance error values (PEVs) are provided in tabulated form along with its bar plot.

The fuzzy min-max (FMM) neural network can be regarded as a typical fuzzy hyperbox classifier that is designed in a sequential way, which leads to an input order drawback and overlap elimination limitation. In this paper, we propose a two-stage-based genetic algorithm (TGA) to construct blue a fuzzy hyperbox classifier (FHC) in a simultaneous way. The simultaneous method is realized by estimating all parameters of hyperboxes at one time rather than by separately determining the parameters of hyperboxes in a sequential way. In this paper, we propose a two-stage-based genetic algorithm to construct the fuzzy hyperbox classifier. The overall TGA consists of two stages, namely, the construction stage and optimization stage. The construction stage is aimed at designing the FHC structure, while the goal of the optimization stage is to further optimize the FHC structure. Using a two-stage genetic algorithm to directly construct a fuzzy hyperbox classifier can overcome the problem of input order and hyperbox overlap. The experimental results show that the proposed FHC yields higher classification accuracy in comparison with the stage-of-the-art FMMs reported in the literature.

The primary objective of this study is to conduct a detailed investigation of the performance of a porous trapezoidal fin subjected to coupled sensible and latent heat transfer at its surface. The comprehensive literature survey reveals that, despite studies on other fin shapes, no previous research has tackled a porous trapezoidal-fin configuration. Assuming linear temperature dependence of thermal conductivity, Darcy’s law is used to describe flow within the porous fin. Employing the differential transformation method, the efficiency of a moisture-absorbing fin has been computed. To model the condensation process, the humidity ratio is approximated as a cubic polynomial function of the porous fin surface temperature, with its relationship obtained through regression-based psychrometric correlations. A comparative investigation has been conducted into the effects of parameters such as relative humidity, trapezoidal expansion ratio, and thermal conductivity coefficient on temperature variation and efficiency of porous fins with specified porosity and permeability. The numerical scheme and resulting outputs were validated through detailed comparisons with available benchmark results. A strong agreement was observed between the DTM-based solutions, the high-precision finite difference method, and existing literature results. The analysis shows that along a dry fin, the maximum temperature difference between the base and tip is approximately 1.5 °C for an expansion ratio of − 0.5 and 2.5 °C for an expansion ratio of + 0.5. When the fin surface is wet, these values increase significantly to 4.76 °C and 7.76 °C, respectively. The results indicate that variations in specific design parameters of porous trapezoidal fins, across different expansion ratios, cause notable changes in fin efficiency. The efficiency of a trapezoidal porous fin is influenced by its geometric expansion ratio, with maximum efficiency occurring at negative expansion ratios. From a comparative perspective, wet porous fins are less efficient than dry fins.

This paper presents the supervised classifier called unbounded fuzzy hypersphere neural network (UFHSNN) model. The basic fuzzy min-max neural network (FMMN), fuzzy hypersphere neural network (FHSNN) and many more its variants use expansion parameter to tune the model. Such tuned model gives good classification accuracy with minimum number of hyperboxes, but always needs to take multiple iterations to find optimal value of expansion parameter and train the model. However, in the proposed model, use of expansion parameter is removed, due to which the model can be trained in a single iteration. The different datasets are applied for verification of the model; also, outcomes are compared with some current FMMN variations. The analysis of outcomes shows that the presented model gives magnificent accuracy with reduced computational complexity.

In the present work, we consider FRW metric and investigate some cosmological quantities in presence of bulk viscosity and particle creation pressure. The obtained results for a viscous cosmological fluid with particle creation show that the Hubble expansion parameter, energy density, bulk viscosity pressure, creation pressure and temperature depend on the particle creation rate and increase with increasing particle creation coefficient. It is found that the bulk viscosity and particle creation pressure seem to play important roles in the evolution of the early Universe.

已有加乘性混合误差模型参数估计方法能达到二阶精度, 但精度评定方法只能达到一阶精度, 若通过传统泰勒级数展开近似函数法来获取参数估值的二阶精度信息, 由于加乘性混合误差模型中参数估值与观测值为一个复杂的非线性关系, 必然需要复杂的求导运算。针对该问题, 本文使用一种无须求导、无须了解非线性函数构成的比例无迹变换(scaled unscented transformation, SUT)法来计算参数估值的二阶精度信息。通过算例分析表明, 利用SUT法求解加乘性混合误差模型能够有效避免复杂的求导运算, 所求得的参数估值及其协方差阵均能达到二阶精度, 从而验证了本文方法的可行性和优势。

内积加密体制作为一种特殊的函数加密, 被广泛应用于云计算领域. 针对现有内积加密方案的公开参数随系统属性个数线性增长的缺陷, 本文利用素数阶群上的双线性映射提出了一个公开参数长度固定且具有适应安全性的内积加密方案. 在设计方案时, 我们通过利用素数阶熵扩张引理给出的公开参数形式, 实现了公开参数长度固定; 方案的密钥生成算法, 通过利用属性向量分量与随机向量结合的技巧, 生成每个私钥分量, 在素数阶熵扩张引理和MDDHk;k+1n 困难假设成立条件下, 利用Game 序列的证明方法, 证明了方案具有适应安全性. 并且与现有内积加密方案相比, 本文方案的公开参数长度仅有16 个群元素, 公开参数的选取不受属性个数影响, 大大降低了公开参数选取量, 使得方案的实用性和操作性更强.

We consider asymptotic coverage properties of one-sided posterior confidence intervals for discrete distributions, with a unidimensional parameter of interest and a nuisance parameter of arbitrary dimension. In this case, no higher order asymptotic expansion of the frequentist coverage for these intervals is established, unless some randomization is added. We study here the existence of such frequentist expansions and propose simple continuity corrections based on a uniform random vector. This helps in determining a family of matching priors for one sided intervals in the discrete case. [PUBLICATION ABSTRACT]