Selecting primary sensor types considering nonlinear error characteristics

by

Y.E. Shishkin, A.V. Skatkov

Institute of Natural and Technical Systems, RF, Sevastopol, Lenin St., 28

E-mail: iurii.e.shishkin@gmail.com

DOI: 10.33075/2220-5861-2023-4-112-123 

UDC 681.3      

EDN: https://elibrary.ru/speyxa

Abstract:

The paper addresses the application of simulation modeling and mathematical methods to assist decision-making in selecting the type of primary sensor, considering its non-linear error characteristics. The approach integrates algorithmic solutions based on simulation modeling and a mathematical model of integer-type Boolean multi-criteria optimization problems. The solution aims to formalize the procedures for creating optimal switching models and measurement scales that minimize measurement error as a non-linear function across the scale, under conditions of both high and low intensities of random noise, while ensuring minimal loss of accuracy.

In modern environmental monitoring systems, a systemic approach to the selection of the primary sensor type to achieve the required accuracy is crucial. The paper examines the issue of measurement errors as a non-linear function relative to the chosen scale, which significantly depends on the type of primary sensor and the upper limit of this scale. A dual methodological approach is proposed, combining algorithmic solutions based on simulation modeling and a mathematical model based on methods for solving integer-type Boolean multi-criteria optimization problems.

Experimental confirmation suggests that simple quality measurement criterion comparisons become insufficiently effective in noisy conditions, prompting the use of penalty functions for more accurate consideration of non-linear error. The results are applicable across various domains where measurement precision and reliability are crucial, such as technology, engineering, medicine, and natural-technical systems.

Keywords: measurement scale selection, simulation modeling, algorithmic solution, mathematical optimization model, multi-criteria optimization problem.

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