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Data Analysis Methods for Assessing Eutrophication
Published in Michael Karydis, Dimitra Kitsiou, Marine Eutrophication A Global Perspective, 2019
Michael Karydis, Dimitra Kitsiou
Within the field of descriptive statistics is also a non-linear time series analysis method known as Recurrence Quantification Analysis (RQA) which has been proposed by Zardivar et al. (2008b) to identify regime shifts in environmental time series; this method is based on the Recurrence Plots (RP) and is characterized by some advantages regarding the assumptions of the method. It is independent of the data set size, data stationarity and the statistical distribution of the data. It can also be useful for a fast screen of the data sets but usually a combination with other techniques is advisable (Andersen et al., 2006b). Zardivar et al. (2008b) studied regime shifts in the Mediterranean coastal lagoon, Sacca di Goro, in Italy. The results indicated that for the identification of a single threshold in a time series RPs are robust even against high noise levels (up to 100%).
Vibration Investigation on Drilling GFRP Through Recurrence Quantification Analysis
Published in P. C. Thomas, Vishal John Mathai, Geevarghese Titus, Emerging Technologies for Sustainability, 2020
Recurrence quantification analysis (RQA) is a non-linear method for analysis of dynamical systems. RQA can provide useful information even for short and non-stationary data where other techniques found unsuitable.
Nonlinear Dynamic Analysis of the Transition from MILD Regime to Thermoacoustic Instability in a Reverse Flow Combustor
Published in Combustion Science and Technology, 2022
Atanu Dolai, Santanu Pramanik, Pabitra Badhuk, Ravikrishna RV
Even though the recurrence plot is useful for visualization of the structure of the phase space, quantitative information regarding the system behavior is not readily obtained. Recurrence quantification analysis (RQA) is a method to extract quantitative information regarding the distribution of recurring points in the phase space. The purpose of RQA is to identify specific measures that can reliably predict the onset of combustion instability. Among several measures, in the present study, the recurrence rate (RR), determinism (DET), and laminarity (LAM) are used for quantifying the RP. RR represents the ratio of the number of recurring points to the total number of points in the RP. RR is calculated using the following equation,
Image analysis using recurrence quantification plots, surface modeling and fused deposition modeling tools to design 3 dimensional models
Published in Computer-Aided Design and Applications, 2018
Many ‘time-series’ data analysis strategies are available to extract information from a data set. Various strategies can be applied to filter out noise in order to estimate underlying trends; or transformations such as analysing the data in the frequency domain or phase space [6] could be performed to provide insights into a dynamic system. Recurrence quantification analysis (RQA) is a non-linear data analysis tool and was developed to visualise recurrence behaviors [14, 15]. Recurrence plots (RPs) are N x N matrix based representations formulated by: where i, j = 1 to N is the point in phase space at which the system is situated at time i,
Which subject-related variables contribute to movement variability during a simulated repetitive and standardised occupational task? Recurrence quantification analysis of surface electromyographic signals
Published in Ergonomics, 2021
Clarisse Gaudez, Marc Mouzé-Amady
Recently, based on concepts from dynamic systems theory, non-linear tools to analyse physiological signals were developed to quantify the signal’s complexity by analysing its temporal structure in order to extract information relating to movement variability (Tallon et al. 2013). Among these tools, recurrence quantification analysis (RQA) has a number of advantages. Indeed, it requires no mathematical assumptions (Kamath 2013; Ramdani et al. 2013; Tallon et al. 2013). It can be applied to short or long chronological data series, and the data do not need to be stationary. In addition, no data pre-treatment steps are necessary (such as filtering or data transformation to get a particular statistical distribution). Recurrence is a basic feature of many deterministic systems, and RQA quantifies the structural variability of a time series by counting recurrent patterns in the underlying dynamical system (Marwan et al. 2002; Webber and Zbilut 1994; Zbilut and Webber 1992). RQA has been applied to various physiological signals such as mechanomyographic (Madeleine, Hansen, and Samani 2014), cardiac (Singh et al. 2019; Acharya et al. 2015; Gonzalez et al. 2013), electroencephalic (Gruszczyńska et al. 2019; Heunis et al. 2018) signals, and to various physical signals recorded from human subjects, e.g. displacement of the centre of pressure (Cao et al. 2015; Decker et al. 2015), stride times (Hollman et al. 2016), or vibroarthrography (Andersen, Arendt-Nielsen, and Madeleine 2018). Studies considering dependent variables derived from linear analyses, which provide information on the overall magnitude or frequency of the signal, and those derived from RQA often reveal complementary information (Hollman et al. 2016; Javorka et al. 2009). However, RQA-derived variables appear more sensitive to the differing conditions in which a task was performed or to population differences than linear variables (Decker et al. 2015; Haddad et al. 2008).