Two-frequency excitation of single-mode Faraday waves

2015 ◽  
Vol 764 ◽  
pp. 538-571 ◽  
Author(s):  
W. Batson ◽  
F. Zoueshtiagh ◽  
R. Narayanan
Keyword(s):  
Degrees Of Freedom ◽  
Time Series Data ◽  
Single Frequency ◽  
Series Data ◽  
Nonlinear Phenomena ◽  
Nonlinear Behaviour ◽  
Faraday Waves ◽  
Low Frequencies ◽  
Axisymmetric Mode ◽  
Frequency Excitation

AbstractThe purpose of this work is to investigate, for the first time, excitation of Faraday waves in small containers using two commensurate frequencies. This spatial restriction, which is encountered at low frequencies, leads to a wave composed primarily of one spatial eigenmode of the container. When two frequencies are used, the mode resonates primarily with one frequency, while the role of the second is to alter the instability threshold and the resulting nonlinear dynamics. As the parameter space expands greatly as a result of the introduction of three new degrees of freedom, viz. the frequency, amplitude and phase of the new component, the linear theory is first used as a guide to highlight basic two-frequency phenomena. These predictions and nonlinear phenomena are then studied experimentally with the system of Batson, Zoueshtiagh & Narayanan (J. Fluid Mech., vol. 729, 2013, pp. 496–523), who studied single-frequency excitation of different modes in a cylindrical cell. The two-frequency experiments of this work focus on excitation of the fundamental axisymmetric mode, and are quantitatively compared to the model via a posteriori Fourier decomposition of the parametric input. In doing so, experimental dependence of the instability on the new degrees of freedom is demonstrated, in accordance with the model predictions. This is done for a variety of frequency ratios, and overall agreement between the observed and predicted onset conditions is identical to that already reported for the single-frequency experiment. For each frequency ratio, the nonlinear behaviour is experimentally characterized by bifurcation and time series data, which is shown to differ significantly from comparable single-frequency excitations. Finally, we present and discuss a wave in which both temporal frequencies are used to simultaneously excite different spatial modes.

Short-Term Prediction of Ship Motion Based on EMD-SVM

2014 ◽  
Vol 571-572 ◽  
pp. 252-257
Author(s):  
Sun Bo Liu ◽  
Ping An Shi ◽  
Lei Wu
Keyword(s):  
Degrees Of Freedom ◽  
Time Series Data ◽  
Ship Motion ◽  
Series Data ◽  
Support Vector ◽  
Intrinsic Mode Functions ◽  
Model Decomposition ◽  
Aircraft Landing ◽  
Non Linear ◽  
Short Term Prediction

Ship sailing at sea is affected by many factors, such as winds, waves and so on, which makes six degrees of freedom motions and thus influences the shipboard arms control, aircraft landing and other operations. In view of the non-linear and non-stationary features of ship motion in waves, a new method based on EMD (Empirical Model Decomposition) and SVM (Support Vector Machine) is presented to predict the ship motion. The EMD is used to decompose the ship motion time series data into several IMFs (intrinsic mode functions) and a residual trend term, which decreases the difficulty of prediction. As the IMF is relatively stationary, but also non-linear, these features are fit to be processed by using SVM. Then the decompositions are used as inputs into SVM to forecast ship motion. The simulation and comparison analysis show that the EMD-SVM prediction model can effectively forecast the ship motion in waves.

scholarly journals Time Stand Still: Effects of Temporal Window Selection on Eye Tracking Analysis

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Jonathan E. Peelle ◽  
Kristin J. Van Engen
Keyword(s):  
Eye Tracking ◽  
Degrees Of Freedom ◽  
Time Series Data ◽  
Time Window ◽  
Time Windows ◽  
Basis Set ◽  
Series Data ◽  
Parameter Estimates ◽  
Start Time ◽  
Window Selection

The number of possible approaches to conducting and analyzing a research study—often referred to as researcher degrees of freedom—has been increasingly under scrutiny as a challenge to the reproducibility of experimental results. Here we focus on the specific instance of time window selection for time series data. As an example, we use data from a visual world eye tracking paradigm in which participants heard a word and were instructed to click on one of four pictures corresponding to the target (e.g., “Click on the hat”). We examined statistical models for a range of start times following the beginning of the carrier phrase, and for each start time a range of window lengths, resulting in 8281 unique time windows. For each time window we ran the same logistic linear mixed effects model, including effects of time, age, noise, and word frequency on an orthogonalized polynomial basis set. Comparing results across these time ranges shows substantial changes in both parameter estimates and p values, even within intuitively “reasonable” boundaries. In some cases varying the window selection in the range of 100–200 ms caused parameter estimates to change from positive to negative. Rather than rush to provide specific recommendations for time window selection (which differs across studies), we advocate for transparency regarding time window selection and awareness of the effects this choice may have on results. Preregistration and multiverse model exploration are two complementary strategies to help mitigate bias introduced by any particular time window choice.

On the degrees of freedom in MCMC-based Wishart models for time series data

2015 ◽  
Vol 98 ◽  
pp. 59-64 ◽  
Author(s):  
Yuewen Xiao ◽  
Yu-Cheng Ku ◽  
Peter Bloomfield ◽  
Sujit K. Ghosh
Keyword(s):  
Time Series ◽  
Degrees Of Freedom ◽  
Time Series Data ◽  
Series Data

Thermodynamic Effects on Cavitation in Water and Cryogenic Fluids

2010 ◽  
Author(s):  
Maria Grazia De Giorgi ◽  
Maria Giovanna Rodio ◽  
Antonio Ficarella
Keyword(s):  
High Speed ◽  
Time Series Data ◽  
Cold Water ◽  
Hot Water ◽  
Series Data ◽  
Cryogenic Fluid ◽  
Low Frequencies ◽  
Pixel Intensity ◽  
Cavitation Phenomenon ◽  
Optical Visualization

The present study focuses on the formation of cavitation in cold and hot water and in cryogenic fluid, characterized by strong variations in fluid properties caused by a change in temperature. Cavitation phenomenon is investigated in water and nitrogen flows in a convergent-divergent nozzle through pressure measurements and the optical visualization method. High-speed photographic recordings have been made, the cavitation phenomena evolution and the related frequency content are investigated by means of pixel intensity time series data. The results obtained concur with those obtained with the spectral analysis of the pressure signals. In the case of cryogenic fluid frequency peaks are shifted towards lower frequencies, with respect to cold water and the magnitude of the signal rises, in particular at low frequencies, for nitrogen and hot water. This can be due to thermal effects that contribute also to the low frequencies in the case of cryogenic fluid. To verify the validity of this assumption, a simple model based on the resolution of Rayleigh equation is used.

scholarly journals The Impact of Audit Committee Characteristics on the Creative Accounting Practices Reduction in Jordanian Commercial Banks

2019 ◽  
Vol 13 (6) ◽  
pp. 113
Author(s):  
Khalil Suleiman Abu Saleem
Keyword(s):  
Commercial Banks ◽  
Audit Committee ◽  
Degrees Of Freedom ◽  
Time Series Data ◽  
Stock Exchange ◽  
Hypothesis Test ◽  
Annual Reports ◽  
Series Data ◽  
Creative Accounting ◽  
The Impact

This study aimed at determining the impact of the characteristics of the Audit Committee (The effect of Activity of the Audit Committee, the size of the Audit Committee, and Independence of the Audit Committee) in reducing creative accounting practicesin Jordanian commercial banks.The study population is composed of all Jordanian banks listed on the Amman Stock Exchange (16), during the period from 2011 to 2017. The study sample is represented by all Jordanian commercial banks. The current study is based on panel data since the data combine one-time and cross-section data for a period of time. The data was composed of a set of indicators for 13 Jordanian commercial banks for the period from 2011 to 2017, and data have been collected from the banks' annual reports. The adoption of the study on the analysis of time-series data comes from the increase in degrees of freedom. The results of the hypothesis test indicate that there is a significant effect of Audit Committee characteristics on the reduction of creative accounting practices in Jordanian banks at a level of significance of 0.05 except for variable (size of the Audit Committee).

scholarly journals Characterizing the structure of nonlinear systems using gradual wavelet reconstruction

2010 ◽  
Vol 17 (6) ◽  
pp. 615-632 ◽  
Author(s):  
C. J. Keylock
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Threshold Level ◽  
Surrogate Data ◽  
Differential Sensitivity ◽  
Series Data ◽  
Lorenz Attractor ◽  
Nonlinear Behaviour ◽  
Original Signal ◽  
Multifractional Brownian Motion

Abstract. In this paper, classical surrogate data methods for testing hypotheses concerning nonlinearity in time-series data are extended using a wavelet-based scheme. This gives a method for systematically exploring the properties of a signal relative to some metric or set of metrics. A signal continuum is defined from a linear variant of the original signal (same histogram and approximately the same Fourier spectrum) to the exact replication of the original signal. Surrogate data are generated along this continuum with the wavelet transform fixing in place an increasing proportion of the properties of the original signal. Eventually, chaotic or nonlinear behaviour will be preserved in the surrogates. The technique permits various research questions to be answered and examples covered in the paper include identifying a threshold level at which signals or models for those signals may be considered similar on some metric, analysing the complexity of the Lorenz attractor, characterising the differential sensitivity of metrics to the presence of multifractality for a turbulence time-series, and determining the amplitude of variability of the Hölder exponents in a multifractional Brownian motion that is detectable by a calculation method. Thus, a wide class of analyses of relevance to geophysics can be undertaken within this framework.

Joint Angle Coordination Strategies During Whole Body Rotations on a Single Lower-Limb Support: An Investigation Through Ballet Pirouettes

2020 ◽  
Vol 36 (2) ◽  
pp. 103-112
Author(s):  
Melanie B. Lott ◽  
Gan Xu
Keyword(s):  
Principal Components ◽  
Degrees Of Freedom ◽  
Joint Angle ◽  
Time Series Data ◽  
Principal Component ◽  
Vertical Axis ◽  
Whole Body ◽  
Series Data ◽  
Coordination Strategies ◽  
Coordination Patterns

Despite the prevalence of turning maneuvers in everyday life, relatively little research has been conducted on joint angle kinematic coordination during whole-body rotations around a vertical axis. Ballet pirouettes provide an opportunity to study dynamically balanced, whole-body rotations on a single support and the potential to scale results to smaller angular displacements executed by general populations. The purpose of this study was to determine the supporting limb’s ankle, knee, hip, and pelvis-trunk joint angle excursions and kinematic coordination strategies utilized during the pirouette’s turn phase. Advanced dancers (n = 6) performed pirouettes while whole-body 3-dimensional kinematics were recorded. Group mean ankle ab/adduction excursion was significantly greater than all other excursions (P < .05). Principal components analysis was applied to joint angle time-series data (4 joints × 3 degrees of freedom = 12 variables). The first 4 principal components explained 92% (2%) of variance, confirming redundancy in joint angle data. Evolution of the data along the first principal component in successful pirouettes oscillated at the pirouette’s rotational frequency. Principal component eigenvector coefficients provided evidence of ankle–hip coordination, although specific coordination patterns varied between individuals and across trials. These results indicate that successful pirouettes are executed with continuous, oscillatory joint angle coordination patterns.

scholarly journals Nonlinear methods to quantify Movement Variability in Human-Humanoid Interaction Activities

2021 ◽  
Author(s):  
Miguel Xochicale ◽  
Chris Baber
Keyword(s):  
Degrees Of Freedom ◽  
Time Series Data ◽  
Inertial Sensors ◽  
Motor Task ◽  
Human Movement ◽  
Series Data ◽  
Diagnostic Tools ◽  
Movement Variability ◽  
Nonlinear Methods ◽  
Skill Performance

Abstract Human movement variability arises from the process of mastering redundant (bio)mechanical degrees of freedom to successfully accomplish any given motor task where flexibility and stability of many possible joint combinations helps to adapt to environment conditions. While the analysis of movement of variability is becoming increasingly popular as a diagnostic tool or skill performance evaluation, there are remain challenges on applying the most appropriate methods. We therefore investigate nonlinear methods such as reconstructed state space (RSSs), uniform time-delay embedding, recurrence plots (RPs) and recurrence quantification analysis (RQAs) with real-world time-series data of wearable inertial sensors. That said, twenty healthy participants imitated vertical and horizontal arm movements in normal and faster velocity from an humanoid robot. We applied nonlinear methods to the collected data to found visual differences in the patterns of RSSs and RPs and statistical differences with RQAs. We conclude that Shannon Entropy with RQA is a robust method that helps to quantify activities, types of sensors, windows lengths and level of smoothness. Hence this work might enhance the development of better diagnostic tools for applications in rehabilitation and sport science for skill performance or new forms of human-humanoid interaction for quantification of movement adaptations and motor pathologies.

scholarly journals Nonlinear Methods to Quantify Movement Variability in Human-Humanoid Interaction Activities

2021 ◽  
Author(s):  
Miguel Xochicale ◽  
Chris Baber
Keyword(s):  
Time Series ◽  
Degrees Of Freedom ◽  
Time Series Data ◽  
Inertial Sensors ◽  
Motor Task ◽  
Human Movement ◽  
Series Data ◽  
Diagnostic Tools ◽  
Movement Variability ◽  
Skill Performance

Abstract Human movement variability arises from the process of mastering redundant (bio)mechanical degrees of freedom to successfully accomplish any given motor task where stability and flexibility of many possible joint combinations helps to adapt to environment conditions. While the analysis of movement of variability is becoming increasingly useful as a diagnostic tool or skill performance evaluation, there are remain challenges on applying the most appropriate methods for their quantification. We therefore investigate reconstructed state space (RSSs), uniform time-delay embedding, recurrence plots (RPs) and recurrence quantification analysis (RQAs) with real-world time-series data of wearable inertial sensors. To which twenty healthy participants imitated vertical and horizontal arm movements in normal and faster velocity from an humanoid robot. We applied RSSs, RPs and RQAs to the time-series data to found visual differences in the patterns of RSSs and RPs and statistical differences with RQAs. We conclude that RQA with Shannon Entropy is a suitable method to quantify activities, types of sensors, windows lengths and level of smoothness. Hence this work might enhance the development of better diagnostic tools for applications in new forms of human-humanoid interaction for quantification of movement adaptations and motor pathologies or rehabilitation and sport science for skill performance.

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