scholarly journals Dynamic Analysis of Blast Furnace Sensor Data using Cross-recurrence Quantification Strategies

2021 ◽  
Vol 2132 (1) ◽  
pp. 012024
Author(s):  
X C Sun ◽  
B Wei ◽  
J h Gao ◽  
J C Fu ◽  
Z G Li
Keyword(s):  
Time Series ◽  
Blast Furnace ◽  
Dimensional Space ◽  
Recurrence Plot ◽  
Analytical Framework ◽  
Gas Production ◽  
Sensor Data ◽  
Blast Furnace Gas ◽  
Recurrence Quantification ◽  
Cross Recurrence Quantification

Abstract This paper investigates impact degree of blast furnace related elements towards blast furnace gas (BFG) production. BFG is a by-product in the steel industry, which is one of the enterprise’s most essential energy resources. While because multiple factors affect BFG production it has characteristics of large fluctuations. Most works focus on finding a satisfactory method or improving the accuracy of existing methods to predict BFG production. There are no special studies on the factors that affect the production of BFG. Finding the elements that affect BFG production is benefit to production of BFG, which has a significance in economy. We propose a novel framework, combining cross recurrence plot (CRP) and cross recurrence quantification analysis (CRQA). Moreover, it supplies a general method to convert time series of BFG related data into high-dimensional space. This is the first analytical framework that attempts to reveal the inherent dynamic similarities of blast furnace gas-related elements. The experimental results demonstrate that this framework can realize the visualization of the time series. In addition, the results also identify the factor that has the greatest impact on blast furnace gas production by quantitative analysis.

Forecasting blast furnace gas production and demand through echo state neural network-based models: Pave the way to off-gas optimized management

2019 ◽  
Vol 253 ◽  
pp. 113578 ◽  
Author(s):  
Ismael Matino ◽  
Stefano Dettori ◽  
Valentina Colla ◽  
Valentine Weber ◽  
Sahar Salame
Keyword(s):  
Neural Network ◽  
Blast Furnace ◽  
Gas Production ◽  
Blast Furnace Gas ◽  
The Way

scholarly journals Detection of Dynamical Regime Transitions with Lacunarity as a Multiscale Recurrence Quantification Measure

2020 ◽  
Author(s):  
Tobias Braun ◽  
Norbert Marwan ◽  
Vishnu R. Unni ◽  
Raman I. Sujith ◽  
Juergen Kurths
Keyword(s):  
Time Series ◽  
Recurrence Plot ◽  
Dynamical Regime ◽  
Series Length ◽  
Dynamical Complexity ◽  
Recurrence Quantification ◽  
The Rich ◽  
Short Time ◽  
Recurrent Patterns ◽  
Regime Transitions

<p>We propose Lacunarity as a novel recurrence quantification measure and apply it in the context of dynamical regime transitions. Many complex real-world systems exhibit abrupt regime shifts. We carry out a recurrence plot based analysis for different paradigmatic systems and thermoacoustic combustion time series in order to demonstrate the ability of our method to detect dynamical transitions on variable temporal scales. Lacunarity is usually interpreted as a measure of ‘gappiness’ of an arbitrary spatial pattern. In application to recurrence plots, it quantifies the degree of heterogenity in the temporal recurrent patterns. Our method succeeds to distinguish states of varying dynamical complexity in presence of noise and short time series length. In contrast to traditional recurrence quantifiers, no specification of minimal line lengths is required and features beyond the scope of line structures can be accounted for. Applied to acoustic pressure fluctuation time series, it captures both the rich variability in dynamical complexity and detects shifts of characteristic time scales.</p>

Multiscale Cross-Recurrence Plot and Recurrence Quantification Analysis Based on Coarse-Grained

2021 ◽  
pp. 2150037
Author(s):  
A. Jingjing Huang ◽  
B. Danlei Gu ◽  
C. Qian He
Keyword(s):  
Time Series ◽  
Time Scales ◽  
Real World ◽  
Recurrence Plot ◽  
Recurrence Quantification Analysis ◽  
Coarse Grained ◽  
Internal Dynamic ◽  
Recurrence Quantification ◽  
Quantification Analysis ◽  
Different Time Scales

In this paper, we proposed multiscale cross-recurrence quantification analysis (MSCRQA) method to analyze the dynamic states of two time series at different time scales. We apply this method to model system (two coupled van der Pol oscillators) and real-world system (SSEC and SZSE). It demonstrates that the MSCRQA can show richer and more recognizable information compared with single time scale. The state of dynamics is different under different time scales. MSCRQA method shows another multiscale perspective to fully mine more hidden internal dynamic information of a time series. This method may provide another method reference for practical application to better explore the laws of the real world.

scholarly journals Categorical Cross-Recurrence Quantification Analysis Applied to Communicative Interaction during Ainsworth’s Strange Situation

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-15
Author(s):  
Danitza Lira-Palma ◽  
Karolyn González-Rosales ◽  
Ramón D. Castillo ◽  
Rosario Spencer ◽  
Andrés Fresno
Keyword(s):  
Time Series ◽  
Recurrence Quantification Analysis ◽  
Communicative Interaction ◽  
Strange Situation ◽  
Complex Dynamic Systems ◽  
Attachment Quality ◽  
Recurrence Quantification ◽  
Quantification Analysis ◽  
Cross Recurrence Quantification ◽  
Cross Recurrence Quantification Analysis

The goal of this study was to characterize the degree of structuring of verbal and motor behaviours, unfolded during the application of an procedure called the Strange Situation. This procedure is used for assessing children’s attachment quality during early stages of their development. Many studies have demonstrated that communicative interactions share features with complex dynamic systems. In such studies, estimations of degree of structure have been used to characterize the system’s synchronization. Thus, assuming that processes of communicative interaction occur in the Strange Situation procedure, it was expected to find traces of synchronization. The metrics were estimated through a Categorical Cross-Recurrence Quantification Analysis applied to the behaviours of individuals and dyads. Two applications of the Strange Situation were implemented and recorded. Verbal and motor interactions among children, caregivers, and strangers were transcribed, categorized, and organized as time series. From each time series of original behaviours, randomized time series were created. Measures of recurrence extracted from Recurrent Plots, such as determinism, entropy, maximum line, laminarity, and trapping time, were calculated. Original and randomized time series were compared in terms of these measures. Results indicated that communicative interaction during the Strange Situation had a structure that mimics properties observed in social interactions where synchronization emerges. In our case, verbal behaviours were more prone to synchronization than motor behaviours, in both individuals and dyads, even though this pattern was more salient among caregivers and strangers than children. The relevance of having measures that can capture synchronization during the administration of the Strange Situation is discussed. Our preliminary findings allow us to point out that the application of RQA and C-RQA to the Strange Situation could not only contribute to methodology, but also contribute to emphasizing the role of coupling in communicative interaction generated by the application of this procedure to measure attachment patterns.

Recurrence quantification analysis for detecting dynamical changes in earthquake magnitude time series

2015 ◽  
Vol 26 (07) ◽  
pp. 1550077 ◽  
Author(s):  
Min Lin ◽  
Gang Zhao ◽  
Gang Wang
Keyword(s):  
Time Series ◽  
Main Shock ◽  
Recurrence Plot ◽  
Recurrence Quantification Analysis ◽  
Confidence Levels ◽  
Aftershock Sequences ◽  
Recurrence Quantification ◽  
Before And After ◽  
Quantification Analysis ◽  
Simultaneous Change

In this study, recurrence plot (RP) and recurrence quantification analysis (RQA) techniques are applied to a magnitude time series composed of seismic events occurred in California region. Using bootstrapping techniques, we give the statistical test of the RQA for detecting dynamical transitions. From our results, we find the different patterns of RPs for magnitude time series before and after the M6.1 Joshua Tree Earthquake. RQA measurements of determinism (DET) and laminarity (LAM) quantifying the order with confidence levels also show peculiar behaviors. It is found that DET and LAM values of the recurrence-based complexity measure significantly increase to a large value at the main shock, and then gradually recovers to a small values after it. The main shock and its aftershock sequences trigger a temporary growth in order and complexity of the deterministic structure in the RP of seismic activity. It implies that the onset of the strong earthquake event is reflected in a sharp and great simultaneous change in RQA measures.

Short-Term Forecast of Blast Furnace Gas Production Amount Based on Grey RBF Neural Network

2015 ◽  
Vol 713-715 ◽  
pp. 1907-1913 ◽  
Author(s):  
Zhi Min Lv ◽  
Zhao Wang ◽  
Zi Yang Wang
Keyword(s):  
Neural Network ◽  
Blast Furnace ◽  
Dynamic Scheduling ◽  
Rbf Neural Network ◽  
Gas Production ◽  
Grey Theory ◽  
Short Term ◽  
Short Term Forecast ◽  
Blast Furnace Gas ◽  
Term Forecast

Dynamic optimization scheduling of the gas in iron and steel enterprises has great significance to reduce gas emission and the short-term forecast is the premise to realize the energy dynamic scheduling. Based on the characteristics that the influencing factors of blast furnace gas amount are complex and difficult to collect, a grey radial basis function (RBF) neural network forecast model is proposed to predict the gas amount for blast furnace in this paper. Combining grey theory, which is used to preprocess the historical data and obtain abundant information, with RBF neural network makes the effective trend forecast in the next 30 minutes come true. The model proposed in this paper is proved to be more accurate according to control experiments against the grey BP neural network.

scholarly journals Detection of dynamical regime transitions with lacunarity as a multiscale recurrence quantification measure

2021 ◽  
Author(s):  
Tobias Braun ◽  
Vishnu R. Unni ◽  
R. I. Sujith ◽  
Juergen Kurths ◽  
Norbert Marwan
Keyword(s):  
Time Series ◽  
Empirical Data ◽  
Acoustic Pressure ◽  
Pressure Fluctuations ◽  
Recurrence Plot ◽  
Dynamical Regime ◽  
Recurrence Plots ◽  
Dynamical Complexity ◽  
Recurrence Quantification ◽  
Regime Transitions

AbstractWe propose lacunarity as a novel recurrence quantification measure and illustrate its efficacy to detect dynamical regime transitions which are exhibited by many complex real-world systems. We carry out a recurrence plot-based analysis for different paradigmatic systems and nonlinear empirical data in order to demonstrate the ability of our method to detect dynamical transitions ranging across different temporal scales. It succeeds to distinguish states of varying dynamical complexity in the presence of noise and non-stationarity, even when the time series is of short length. In contrast to traditional recurrence quantifiers, no specification of minimal line lengths is required and geometric features beyond linear structures in the recurrence plot can be accounted for. This makes lacunarity more broadly applicable as a recurrence quantification measure. Lacunarity is usually interpreted as a measure of heterogeneity or translational invariance of an arbitrary spatial pattern. In application to recurrence plots, it quantifies the degree of heterogeneity in the temporal recurrence patterns at all relevant time scales. We demonstrate the potential of the proposed method when applied to empirical data, namely time series of acoustic pressure fluctuations from a turbulent combustor. Recurrence lacunarity captures both the rich variability in dynamical complexity of acoustic pressure fluctuations and shifting time scales encoded in the recurrence plots. Furthermore, it contributes to a better distinction between stable operation and near blowout states of combustors.

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