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Stochastic model and optimum sampling interval of variables for environmental measurement systems

机译:环境测量系统变量的随机模型及最优采样间隔

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It is common in engineering to model time-dependent variables as diffusion process represented by stochastic differential equations. This is usually helpful when empirical datasets describing time evolution of variables are available. This helps in accurate estimation of parameters of the stochastic differential equation which describes the dynamic system. Additionally, it helps in characterization and determination of optimal performance of the system. The above have been conducted in this study using real environmental field data. Linear stochastic model was fitted to longitudinal datasets and optimum sampling interval investigated. A new method has been proposed for determination of optimum sampling interval. Results obtained differ from those of hypothetical optimum which do not take energy consumption into consideration.
机译:它在工程方面是模型时间依赖变量,作为由随机微分方程表示的扩散过程。当描述变量时间演化的经验数据集可用时,这通常是有用的。这有助于准确地估计描述动态系统的随机微分方程的参数。此外,它有助于表征和确定系统的最佳性能。上面已经在本研究中进行了使用真实环境现场数据进行的。线性随机模型适用于纵向数据集和最佳采样间隔。已经提出了一种确定最佳采样间隔的新方法。获得的结果与假设最佳的结果不同,不考虑能量消耗。

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