Quantifying Attractor Morphing for High-Sensitivity Detection of Parameter Variations by Point Cloud Averaging

2005 ◽  
Author(s):  
Ali I. Hashmi ◽  
Bogdan I. Epureanu
Keyword(s):  
Atomic Force Microscope ◽  
Point Cloud ◽  
Time Series Data ◽  
Equations Of Motion ◽  
High Sensitivity ◽  
Series Data ◽  
Optimal Basis ◽  
Atomic Force ◽  
Nominal Parameter ◽  
Parameter Values

A novel method of damage detection for systems exhibiting chaotic dynamics is presented. The algorithm reconstructs variations of system parameters without the need for explicit system equations of motion, or knowledge of the nominal parameter values. The concept of a Sensitivity Vector Field (SVF) is developed. This construct captures geometrical deformations of the dynamical attractor of the system in state space. These fields are collected by the means of Point Cloud Averaging (PCA) applied to discrete time series data from the system under healthy (nominal parameter values) and damaged (variations of the parameters) conditions. Test variations are reconstructed from an optimal basis of the SVF snapshots which is generated by means of proper orthogonal decomposition. The method is applied to two system models, a magneto-elastic oscillator and an atomic force microscope. The method is shown to be highly accurate, and capable of identifying multiple simultaneous variations. The success of the method as applied to an atomic force microscope (AFM) and a magneto-elastic oscillator (MEO) indicates a potential for highly accurate sample readings by exploiting recently observed chaotic vibrations.

Oil and Methanol Price volatility

2013 ◽  
Vol 03 (08) ◽  
pp. 01-10
Author(s):  
Majid Delavari ◽  
Nadiya Gandali Ali khani ◽  
Esmaeil Naderi
Keyword(s):  
Crude Oil ◽  
Time Series Data ◽  
Price Volatility ◽  
High Sensitivity ◽  
Oil Price ◽  
Series Data ◽  
Cultural Issues ◽  
Crude Oil Price ◽  
Oil Export ◽  
The One

Crude oil as one of the main sources of energy is also the main source of income for members of OPEC. So, the volatility of crude oil price is one of the main economic variables in the world and analysis of the effect of its changes on key economic factors has been always considered as significant. The reason might be the high sensitivity of oil price to political, economic and cultural issues worldwide and consequently its volatility on the one hand, and the high influence of the volatile prices on macroeconomic variables. On the other hand, for different reasons such as oil price volatilities and income from oil export, economic planners and policy makers in Iran have been mainly focused on the promotion of non-oil exports especially during the last few decades. Therefore, methanol as one of the most commonly used petrochemical products has a high potential for production and export of non-oil products in Iran. For this reason, in the present study there was an attempt to examine the relationship between the prices of Iran’s crude oil and methanol using FIGARCH model and based on the weekly time series data related to the research variables. The results of the study showed that the long memory parameter is equal to 0.32 which is meaning the shocks caused by volatility of methanol market and crude oil price to the methanol price were lasting and meaningful and were revealed in the long term.

scholarly journals Nanopipette/Nanorod-Combined Quartz Tuning Fork–Atomic Force Microscope

Sensors ◽  
2019 ◽  
Vol 19 (8) ◽  
pp. 1794 ◽  
Author(s):  
Sangmin An ◽  
Wonho Jhe
Keyword(s):  
Atomic Force Microscope ◽  
Tuning Fork ◽  
High Sensitivity ◽  
Quartz Tuning Fork ◽  
Force Sensor ◽  
Au Nanoparticle ◽  
Ambient Conditions ◽  
Atomic Force ◽  
In Situ Detection

We introduce a nanopipette/quartz tuning fork (QTF)–atomic force microscope (AFM) for nanolithography and a nanorod/QTF–AFM for nanoscratching with in situ detection of shear dynamics during performance. Capillary-condensed nanoscale water meniscus-mediated and electric field-assisted small-volume liquid ejection and nanolithography in ambient conditions are performed at a low bias voltage (~10 V) via a nanopipette/QTF–AFM. We produce and analyze Au nanoparticle-aggregated nanowire by using nanomeniscus-based particle stacking via a nanopipette/QTF–AFM. In addition, we perform a nanoscratching technique using in situ detection of the mechanical interactions of shear dynamics via a nanorod/QTF–AFM with force sensor capability and high sensitivity.

scholarly journals Mathematical Model for Small Size Time Series Data of Bacterial Secondary Metabolic Pathways

2018 ◽  
Vol 12 ◽  
pp. 117793221877507 ◽  
Author(s):  
Daisuke Tominaga ◽  
Hideo Kawaguchi ◽  
Yoshimi Hori ◽  
Tomohisa Hasunuma ◽  
Chiaki Ogino ◽  
...  
Keyword(s):  
Metabolic Pathways ◽  
Time Series Data ◽  
Reaction Pathway ◽  
Lactate Production ◽  
Reaction Rates ◽  
Small Sample ◽  
Dynamical Analysis ◽  
Series Data ◽  
Sufficient Information ◽  
Parameter Values

Measuring the concentrations of metabolites and estimating the reaction rates of each reaction step consisting of metabolic pathways are significant for an improvement in microorganisms used in maximizing the production of materials. Although the reaction pathway must be identified for such an improvement, doing so is not easy. Numerous reaction steps have been reported; however, the actual reaction steps activated vary or change according to the conditions. Furthermore, to build mathematical models for a dynamical analysis, the reaction mechanisms and parameter values must be known; however, to date, sufficient information has yet to be published for many cases. In addition, experimental observations are expensive. A new mathematical approach that is applicable to small sample data, and that requires no detailed reaction information, is strongly needed. S-system is one such model that can use smaller samples than other ordinary differential equation models. We propose a simplified S-system to apply minimal quantities of samples for a dynamic analysis of the metabolic pathways. We applied the model to the phenyl lactate production pathway of Escherichia coli. The model obtained suggests that actually activated reaction steps and feedback are inhibitions within the pathway.

scholarly journals Sample Entropy of Human Gait Center of Pressure Displacement: A Systematic Methodological Analysis

Entropy ◽  
2018 ◽  
Vol 20 (8) ◽  
pp. 579 ◽  
Author(s):  
Samira Ahmadi ◽  
Nariman Sepehri ◽  
Christine Wu ◽  
Tony Szturm
Keyword(s):  
Time Series Data ◽  
Inertial Sensors ◽  
Center Of Pressure ◽  
Sampling Rate ◽  
Sample Entropy ◽  
Human Gait ◽  
Series Data ◽  
Low Pass ◽  
Data Points ◽  
Parameter Values

Sample entropy (SampEn) has been used to quantify the regularity or predictability of human gait signals. There are studies on the appropriate use of this measure for inter-stride spatio-temporal gait variables. However, the sensitivity of this measure to preprocessing of the signal and to variant values of template size (m), tolerance size (r), and sampling rate has not been studied when applied to “whole” gait signals. Whole gait signals are the entire time series data obtained from force or inertial sensors. This study systematically investigates the sensitivity of SampEn of the center of pressure displacement in the mediolateral direction (ML COP-D) to variant parameter values and two pre-processing methods. These two methods are filtering the high-frequency components and resampling the signals to have the same average number of data points per stride. The discriminatory ability of SampEn is studied by comparing treadmill walk only (WO) to dual-task (DT) condition. The results suggest that SampEn maintains the directional difference between two walking conditions across variant parameter values, showing a significant increase from WO to DT condition, especially when signals are low-pass filtered. Moreover, when gait speed is different between test conditions, signals should be low-pass filtered and resampled to have the same average number of data points per stride.

Dynamic Responses of an Atomic Force Microscope Interacting with Samples

2004 ◽  
Vol 127 (4) ◽  
pp. 705-709 ◽  
Author(s):  
Jih-Lian Ha ◽  
Rong-Fong Fung ◽  
Yi-Chan Chen
Keyword(s):  
Atomic Force Microscope ◽  
Cantilever Beam ◽  
Contact Region ◽  
Equations Of Motion ◽  
Dynamic Responses ◽  
Governing Equations ◽  
External Voltage ◽  
Atomic Force ◽  
Repulsive Forces ◽  
Tip Mass

The objective of this paper is to formulate the equations of motion and to analyze the vibrations of an atomic force microscope (AFM), which contains a piezoelectric rod coupling with a cantilever beam, and the tip mass interacting with samples. The governing equations of the AFM system are formulated completely by Hamilton’s principle. The piezoelectric rod is treated as an actuator to excite the cantilever beam via an external voltage. The repulsive forces between the tip and samples are modeled by the Hertzian, the Derjaguin-Müller-Toporov, and Johnson-Kendall-Roberts models in the contact region. Finally, numerical results are provided to illustrate the coupling effects between the piezoelectric actuator and the cantilever beam and the interaction effects between the tip and samples on the dynamic responses.

scholarly journals Choosing the Appropriate Forecasting Model for Predictive Parameter Control

2014 ◽  
Vol 22 (2) ◽  
pp. 319-349 ◽  
Author(s):  
Aldeida Aleti ◽  
Irene Moser ◽  
Indika Meedeniya ◽  
Lars Grunske
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Optimal Algorithm ◽  
Time Series Prediction ◽  
Prediction Method ◽  
Performance Data ◽  
Series Data ◽  
Prediction Methods ◽  
Parameter Control ◽  
Parameter Values

All commonly used stochastic optimisation algorithms have to be parameterised to perform effectively. Adaptive parameter control (APC) is an effective method used for this purpose. APC repeatedly adjusts parameter values during the optimisation process for optimal algorithm performance. The assignment of parameter values for a given iteration is based on previously measured performance. In recent research, time series prediction has been proposed as a method of projecting the probabilities to use for parameter value selection. In this work, we examine the suitability of a variety of prediction methods for the projection of future parameter performance based on previous data. All considered prediction methods have assumptions the time series data has to conform to for the prediction method to provide accurate projections. Looking specifically at parameters of evolutionary algorithms (EAs), we find that all standard EA parameters with the exception of population size conform largely to the assumptions made by the considered prediction methods. Evaluating the performance of these prediction methods, we find that linear regression provides the best results by a very small and statistically insignificant margin. Regardless of the prediction method, predictive parameter control outperforms state of the art parameter control methods when the performance data adheres to the assumptions made by the prediction method. When a parameter's performance data does not adhere to the assumptions made by the forecasting method, the use of prediction does not have a notable adverse impact on the algorithm's performance.

Teaching Visual Analysis of Time Series Data

1996 ◽  
Vol 24 (3) ◽  
pp. 247-261 ◽  
Author(s):  
Ian A. James ◽  
Paul S. Smith ◽  
Derek Milne
Keyword(s):  
Time Series ◽  
Visual Analysis ◽  
Time Series Data ◽  
Mean Shift ◽  
High Sensitivity ◽  
Training Programme ◽  
High Rate ◽  
Series Data ◽  
False Alarms ◽  
Single Subject

Visual analysis, or “eyeballing”, of single subject (N=l) data is the commonest technique for analysing time series data. The present study examined firstly, psychologists' abilities to determine significant change between baseline (A) and therapeutic (B) phases, and secondly, the decision making process in relation to the visual components of such graphs. Thirdly, it looked at the effect that a training programme had on psychologists' abilities to identify significant A−B change. The results revealed that the participants were poor at identifying significant effects from non-significant changes. In particular, the study found a high rate of false alarms (Type 1 errors), and a low rate of misses (Type 2 errors), i.e. high sensitivity but poor specificity. The only visual components to significantly alter decisions were the degree of serial dependency and the mean shift component. The teaching influenced the participants' judgements. In general, participants became more conservative, but there was limited evidence of a significant improvement in their judgements following the teaching.

scholarly journals MULTIVARIATE AR SYSTEMS AND MIXED FREQUENCY DATA: G-IDENTIFIABILITY AND ESTIMATION

2015 ◽  
Vol 32 (4) ◽  
pp. 793-826 ◽  
Author(s):  
Brian D.O. Anderson ◽  
Manfred Deistler ◽  
Elisabeth Felsenstein ◽  
Bernd Funovits ◽  
Lukas Koelbl ◽  
...  
Keyword(s):  
High Frequency ◽  
Time Series Data ◽  
Series Data ◽  
Frequency Data ◽  
Linear Transformations ◽  
Mixed Frequency ◽  
Second Moments ◽  
Flow Variables ◽  
Parameter Values ◽  
Mixed Frequency Data

This paper is concerned with the problem of identifiability of the parameters of a high frequency multivariate autoregressive model from mixed frequency time series data. We demonstrate identifiability for generic parameter values using the population second moments of the observations. In addition we display a constructive algorithm for the parameter values and establish the continuity of the mapping attaching the high frequency parameters to these population second moments. These structural results are obtained using two alternative tools viz. extended Yule Walker equations and blocking of the output process. The cases of stock and flow variables, as well as of general linear transformations of high frequency data, are treated. Finally, we briefly discuss how our constructive identifiability results can be used for parameter estimation based on the sample second moments.

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