Almalki Saad J, Afifi W A, El-Bagoury Abd Al-Aziz Hosni, Abd-Elmougod Gamal A
Department of Mathematics and Statistics, College of Science, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia.
Department of Mathematics and Statistics, Faculty of Science, Taibah University, Yanbu, Saudi Arabia.
Results Phys. 2021 Jul;26:104455. doi: 10.1016/j.rinp.2021.104455. Epub 2021 Jun 15.
The study of search plans has found considerable interest between searchers due to its interesting applications in our real life like searching for located and moving targets. This paper develops a method for detecting moving targets. We propose a novel strategy based on weight function , , where , are the total probabilities of un-detecting, and total effort respectively, is searching for moving novel coronavirus disease (COVID-19) cells among finite set of different states. The total search effort will be presented in a more flexible way, so it will be presented as a random variable with a given distribution. The objective is searching for COVID-19 which hidden in one of n cells in each fixed number of time intervals m and the detection functions are supposed to be known to the searcher or robot. We look in depth for the optimal distribution of the total effort which minimizes the probability of undetected the target over the set of possible different states. The effectiveness of this model is illustrated by presenting a numerical example.
搜索计划的研究因其在现实生活中的有趣应用（如搜索定位和移动目标）而在搜索者之间引起了相当大的兴趣。本文开发了一种检测移动目标的方法。我们提出了一种基于权重函数(w(p,e))的新颖策略，其中(p)、(e)分别是未检测到的总概率和总努力，是在有限的不同状态集中搜索移动的新型冠状病毒病（COVID-19）细胞。总搜索努力将以更灵活的方式呈现，因此它将作为具有给定分布的随机变量呈现。目标是在每个固定数量的时间间隔(m)内搜索隐藏在(n)个细胞之一中的COVID-19，并且检测函数假定搜索者或机器人已知。我们深入研究总努力的最优分布，该分布使在一组可能的不同状态下未检测到目标的概率最小化。通过给出一个数值例子来说明该模型的有效性。
