Nonlinear Data Analysis Method Research Articles

The influence of water hardness perturbations on bubble departure dynamics

The influence of small changes to water hardness on the nonlinear behaviour of liquid penetration into a capillary and the resulting air pressure fluctuations during air bubble formation are examined in this paper. Experiments were undertaken in which bubbles were generated both in water having a surface tensile force of σ = 72.2 mN/m and in an aqueous solution of calcium carbonate having a surface tensile force of σ = 75.4 mN/m, each contained in a glass capillary with an internal diameter of 1 mm. It is shown that both the maximum value of liquid penetration into the capillary and bubble growth time are affected by perturbations to the water hardness. The time it takes for the bubble to depart the capillary was estimated using the following nonlinear data analysis methods: time delay (τ), attractor reconstructions, correlation dimension (D), and largest Lyapunov exponent (λ). All estimates demonstrate that the pressure fluctuations in the c–c aqueous solutions and extent of liquid solution penetration into the capillary during the time between subsequent bubble departures behave chaotically. Furthermore, this work demonstrates that the dynamics of bubble formation along with the bubble waiting time are very sensitive to small perturbation in the physical properties of the liquid, and this sensitivity has a significant effect on the observed chaotic behaviour.

Study of freshness monitoring on small larimichthys polyactis based on multiple sensor array system and non-linear data analysis method

ABSTRACT In this paper, freshness monitoring model on small larimichthys polyactis based on multiple sensor array system (MSAS) combined with total volatile basic nitrogen (TVB-N) examination was studied. Larimichthys polyactis was obtained and prepared as detecting samples. MSAS was developed based on volatile gas content of the fish samples. Meanwhile, TVB-N was examined to provide quality reference for MSAS measurement. MSAS responding data were processed by principal component analysis (PCA) and non-linear signal analysis model. Larimichthys polyactis freshness deciding model was developed by non-linear fitting between signal processing features and TVB-N parameters. Results indicated that the method studied in this work presented good freshness deciding accuracy for larimichthys polyactis. This method also presented some advantages including rapid analysis, easy operation, and low cost. It is promising in aquatic products quality raped determination.

An inquiry into the structure and dynamics of crude oil price using the fast iterative filtering algorithm

Abstract The identification of the temporal scales related to market activities is crucial for understanding the dynamics of international crude oil prices. Standard analysis techniques fail in producing consistently good results due to the non-linear behaviour of the oil market. In this paper we propose an innovative approach based on the concurring application of a new non-linear data analysis method, Fast Iterative Filtering (FIF), and a multi-scale statistical analysis (Standardized Mean Test). This approach proves to be able to separate automatically crude oil price data into three components: a long term trend, an intermediate or middle period behaviour, and a transitory or short-run behaviour. The economic meaning of each component is clearly identified as: high frequency variations, caused by normal supply-demand disequilibrium; medium term fluctuations, driven by geopolitical, financial, and technological shocks; a low frequency trend reflecting the global business cycle. All these results make the proposed approach a more performing tool for analysing oil price data structure and dynamics. Such a method, if coupled with different prediction techniques (e.g., ARIMA, ARCH, etc., or ANN, SVM, etc.), can potentially show higher performance than existing hybrid models in forecasting crude oil prices.

Recurrence Quantification Analysis of Postural Stability with Pendant-Based IMU under Varying Balance Conditions

Recurrence quantification analysis (RQA) has been widely implemented as a nonlinear data analysis method in multiple research areas and has shown good potential for the study of human postural stability. Dynamic features identified by RQA may be useful for characterizing balance disorders using inertial measurement units (IMUs) placed on the lower back near the body’s center of mass. However, wearing IMU sensors may be challenging for some specific populations at risk for falling such as pregnant women due to discomfort and lack of fit. A novel pendant-based IMU sensor was proposed and shown to have good sensitivity to postural instability in different standing tasks, when compared with the sensor worn on the lower back with a waist belt. This study’s objective is to evaluate the sensitivity of the pendant sensor to balance perturbations using RQA-the nonstationary method. Six RQA measurements (%recurrence, %deterministic, linemax, entropy, %laminarity, trapping time) were extracted for data analysis. The objective of this research is to validate whether the pendant-based sensor can distinguish different balance conditions as well as or better than a sensor on a waist belt using RQA measurements.

A continuous spectrophotometric assay and nonlinear kinetic analysis of methionine γ-lyase catalysis

In this article, we present a new, easy-to-implement assay for methionine γ-lyase (MGL)-catalyzed γ-elimination reactions of l-methionine and its analogues that produce α-ketobutyrate (α-KB) as product. The assay employs ultraviolet–visible (UV–Vis) spectrophotometry to continuously monitor the rate of formation of α-KB by its absorbance at 315 nm. We also employ a nonlinear data analysis method that obviates the need for an “initial slope” determination, which can introduce errors when the progress curves are nonlinear. The spectrophotometric assay is validated through product analysis by 1H NMR (nuclear magnetic resonance), which showed that under the conditions of study l-methionine (l-met) and l-methionine sulfone (l-met sulfone) substrates were converted to α-KB product with greater than 99% yield. Using this assay method, we determined for the first time the Michaelis–Menten parameters for a recombinant form of MGL from Porphyromonas gingivalis, obtaining respective kcat and Km values of 328 ± 8 min−1 and 1.2 ± 0.1 mM for l-met γ-elimination and 2048 ± 59 min−1 and 38 ± 2 mM for l-met sulfone γ-elimination reactions. We envisage that this assay method will be useful for determining the activity of MGL γ-elimination reactions that produce α-KB as the end product.

Recurrence quantification analysis across sleep stages

Abstract In this work we employ a nonlinear data analysis method called recurrence quantification analysis (RQA) to analyze differences between sleep stages and wake using cardio-respiratory signals, only. The data were recorded during full-night polysomnographies of 313 healthy subjects in nine different sleep laboratories. The raw signals are first normalized to common time bases and ranges. Thirteen different RQA and cross-RQA features derived from ECG, respiratory effort, heart rate and their combinations are additionally reconditioned with windowed standard deviation filters and ZSCORE normalization procedures leading to a total feature count of 195. The discriminative power between Wake, NREM and REM of each feature is evaluated using the Cohen's kappa coefficient. Besides kappa performance, sensitivity, specificity, accuracy and inter-correlations of the best 20 features with high discriminative power is also analyzed. The best kappa values for each class versus the other classes are 0.24, 0.12 and 0.31 for NREM, REM and Wake, respectively. Significance is tested with ANOVA F-test (mostly p

Implementation of Chaotic Analysis on River Discharge Time Series

The gauged river data play an important role in modeling, planning and management of the river basins. Among the hydrological data, the daily discharge data seem to be more significant for determining the amount of energy production and the control the risks of floods and drought. Hence, the data need correct measurement, analysis, and reliable estimates. The purpose of the paper is to investigate the question whether all the stations in a river basin exhibit chaotic behavior. For this purpose, the daily discharge data of four gauge stations are examined by using three nonlinear data analysis methods: 1) phase space reconstruction; 2) correlation dimension; and 3) local approximation where all those methods provide identification of chaotic behaviors. The results show that all stations exhibit chaotic character. Taking into account the proven chaotic characteristic of the stations, local approximation method is applied to observe the prediction accuracy. Considering the fact that global warming is a serious threat on natural resources, the prediction accuracy is becoming a key factor to ensure sustainability. Hence, this study is a good example on the implementation of chaotic analysis by means of the obtained results from the methods.

Evaluation of Data Analysis Methods for the CRS Consolidation Test

AbstractThe constant rate-of-strain (CRS) laboratory consolidation test is used to measure consolidation properties of fine-grained soils. Although the CRS test offers many advantages over the conventional incremental-loading consolidation test, uncertainties associated with the method of data analysis have presented an obstacle to more widespread use of the CRS test in practice. This paper presents results from a numerical investigation of the accuracy of linear and nonlinear data analysis methods for the CRS consolidation test. Numerical simulations were conducted using a validated large strain consolidation model for CRS loading conditions, published material properties for two reconstituted clay soils, and three applied strain rates. Based on the numerical results, recommendations are provided for analysis of CRS consolidation data, including changes to the ASTM D4186 equations for nonlinear data analysis. The most appropriate analysis method for soils with linear compressibility is the current ASTM D...

Nonlinear data processing method for the signal enhancement of GPR data

Abstract An alternative data processing procedure is proposed in this paper for the purpose of enhancing the signal/noise (S/N) ratio of ground penetrating radar (GPR) data. The processing methodology is achieved by performing the logarithmic transform in conjunction with the ensemble empirical mode decomposition (EEMD), a new nonlinear data analysis method in signal processing. The synthetic model study and field example indicate that the logarithmic transform is effective in alleviating the attenuation problem. Additionally, the spectrogram obtained from Hilbert–Huang transform (HHT) shows that the decomposition sensitivity of the EEMD method is greatly improved with the aid of the logarithmic transform. This new method allows us to extract the signal components from noisy GPR data efficiently. The success of this study suggests a possible nonlinear analysis application in future GPR investigation, particularly in the filter design and gain correction.

Brain signal analysis based on recurrences

The EEG is one of the most commonly used tools in brain research. Though of high relevance in research, the data obtained is very noisy and nonstationary. In the present article we investigate the applicability of a nonlinear data analysis method, the recurrence quantification analysis (RQA), to such data. The method solely rests on the natural property of recurrence which is a phenomenon inherent to complex systems, such as the brain. We show that this method is indeed suitable for the analysis of EEG data and that it might improve contemporary EEG analysis.

Fracture Mechanics Analysis for Various Fiber/Matrix Interface Loadings

Fiber/matrix (F/M) cracking was analyzed to provide better understanding and guidance in developing F/M interface fracture toughness tests. Two configurations, corresponding to F/M cracking at a broken fiber and at the free edge, were investigated. The effects of mechanical loading, thermal cooldown, and friction were investigated. Each configuration was analyzed for two loadings: longitudinal and normal to the fiber. A nonlinear finite element analysis was performed to model friction and slip at the F/M interface. A new procedure for fitting a square-root singularity to calculated stresses was developed to determine stress intensity factors (K sub I and K sub II) for a bimaterial interface crack. For the case of F/M cracking at a broken fiber with longitudinal loading, crack tip conditions were strongly influenced by interface friction. As a result, an F/M interface toughness test based on this case was not recommended because nonlinear data analysis methods would be required. For the free edge crack configuration, both mechanical and thermal loading caused crack opening, theory avoiding fractional effects. A F/M interface toughness test based on this configuration would provide data for K(sub I/K(sub II) ratios of about 0.7 and 1.6 for fiber and radial normal loading, respectively. However, thermal effects must be accounted for in the data analysis.