Influence of Measurements Uncertainty on Uncertainty of Gilbert-Huang Transform Modes

Poliarus, Oleksandr; Ianushkevych, Sergii; Koval, Oleksandr; Lebedynskyi, Andrii; Medvedovska, Ya. S.; Poliakov, Ye. A.

doi:10.1109/caol46282.2019.9019512

Cited by 3 publications

(1 citation statement)

References 9 publications

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“…Як показали дослідження авторів, перетворення Гільберта-Хуанга [15] в моди (компоненти) є прийнятними для неоднорідних випадкових полів та нестаціонарних випадкових процесів. Вимірювальні аспекти отримання мод Гільберта-Хуанга наведені в [16]. Головною перевагою такого розкладання є універсальність і адаптивність до будь-якого практично важливого процесу, причому ці моди є ортогональними.…”

Section: сутність методуunclassified

Method, models and information technology of decision making about the state of technical objects under non-stationary input influences

Poliarus¹,

Lebedynskyi²,

Сhepusenko³

2021

Bulletin of KhNAHU

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There is a significant increase in the amount of measuring information at complex and large technical objects, such as bridges. Decision making about these objects states under non-stationary input influences is a difficult task. The article proposes to make the transition from single-channel information processing to multi-channel one. Each channel processes one of the Hilbert-Huang modes, into which each realization of the nonstationary signal decomposes. It is shown that the first three modes of decomposition are often enough, which in most cases create a stationary process. If a mode is non-stationary, it is possible to decompose it into these modes. The final decision according to statistical criteria is made not on realizations as it is traditionally carried out, but on Hilbert-Huang modes.

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Section: сутність методуunclassified

Method, models and information technology of decision making about the state of technical objects under non-stationary input influences

Poliarus¹,

Lebedynskyi²,

Сhepusenko³

2021

Bulletin of KhNAHU

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Detection of Landmarks by Autonomous Mobile Robots Using Camera-Based Sensors in Outdoor Environments

2021

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Simplified Model of Linear Inertial Measurement Systems

Poliarus¹,

Медведовська²,

Поляков³

et al. 2021

Bulletin of KhNAHU

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Problem. To increase the metrological reliability of measuring systems at technical objects, the number of sensors measuring the same process parameter is increased to several units and a model of a multi-channel measuring system is synthesized. This synthesis is usually based on the use of Markov's theory of linear filtering, but the presence of a connection between the input and output signals of the linear inertial system through the convolution integral significantly complicates the process of obtaining the optimal device. Goal. The aim of the article is to develop a method for approximating the integral equation of convolution, which describes a linear inertial system, and to estimate the limits of its application on the example of linear inertial sensors. Methodology. Instead of the output signal in the form of a convolution equation, the output signal, defined as the product of the input signal to an unknown time function is used. This function is represented by the Karhunen-Loev series. The distance in the functional space with a quadratic metric between these output signals is minimized by means of a genetic algorithm and the coefficients of the series and, therefore, the unknown function itself, are determined. Results. In the simulation, the relative difference between the output signals, which were calculated from exact and simplified expressions, was determined. Realizations of stationary signals were used as input signals, and the pulse characteristics of the linear inertial system varied over a wide range. The errors of approximation of the integral convolution equation by a simple model do not exceed a few percent. Origina-lity. The approximation of the convolution equation by a simplified model of the system is original and, although it cannot be applied in a wide range of conditions, it is acceptable for a separate class of stationary signals without restrictions. The accuracy of the approximation of the convolution equation is greatest if the width of the spectrum of the input signals is less than the bandwidth of the measuring system. Practical value. The obtained connection between input and output signals based on a simplified model allows to synthesize multi-channel measuring systems using advanced Markov filtering methods for a separate class of stationary input signals. To expand the application of the method in a wide range of conditions, a set of simplified models that are created in advance can be used.

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Influence of Measurements Uncertainty on Uncertainty of Gilbert-Huang Transform Modes

Cited by 3 publications

References 9 publications

Method, models and information technology of decision making about the state of technical objects under non-stationary input influences

Method, models and information technology of decision making about the state of technical objects under non-stationary input influences

Detection of Landmarks by Autonomous Mobile Robots Using Camera-Based Sensors in Outdoor Environments

Simplified Model of Linear Inertial Measurement Systems

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