Uncertainty Analysis in Ship-Model Resistance Test

2015 ◽  
Author(s):  
Sverre Steen ◽  
Ankit ◽  
Sergey Gavrilin
Keyword(s):  
Time Window ◽  
Time Windows ◽  
Low Frequency ◽  
High Amplitude ◽  
Mean Value ◽  
Frequency Noise ◽  
Multiple Time ◽  
Ship Model ◽  
Force Data ◽  
Multiple Time Windows

In a ship model resistance test, the towing force measurements usually contain high amplitude and low frequency noise. The length of the towing tank is finite, which induces an uncertainty in the estimated mean value of the towing force. In practical work, a time-window is manually selected from the complete measurement time history to compute the mean value to be used in the further analysis. Due to the combination of high-amplitude, low frequency noise and limited length of time series, the selection of the time window is found to matter for the resulting mean value. The idea pursued in this paper is that the uncertainty in the estimated mean value of resistance can be improved by considering multiple time windows rather than just one. First, the paper deals with the estimation of mean value of resistance from the given towing force data using 1) Single Time Window (STW) technique 2) Multiple Time Windows (MTW) technique. By applying these techniques to a given time series with a known mean, we can compare reliability of these estimators. Finally, the carriage and ship model are mathematically modeled as moving spring-mass-damper system. Comparison of the simulated towing force data with the experimental data allows us to understand the major sources of noise in towing force data.

Nycthemeral Rhythm of the Frequency and Biomechanical Energy of High Frequency Intraocular Pressure Fluctuations

2013 ◽  
Author(s):  
Vincent Libertiaux ◽  
William P. Seigfreid ◽  
Massimo A. Fazio ◽  
Juan F. Reynaud ◽  
Claude F. Burgoyne ◽  
...  
Keyword(s):  
Intraocular Pressure ◽  
High Frequency ◽  
Pressure Fluctuations ◽  
Low Frequency ◽  
Pulse Amplitude ◽  
Mean Value ◽  
Multiple Time Scales ◽  
Dynamic Contour Tonometry ◽  
Multiple Time ◽  
Ocular Pulse

The optic nerve head (ONH) is the site of insult in glaucoma, the second leading cause of blindness worldwide. Intraocular pressure (IOP) is commonly regarded as a major factor in the onset and progression of the disease1 and lowering IOP is the only clinical treatment that has been shown to retard the onset and progression of glaucoma2. However, many patients continue to progress even at an epidemiologically-determined normal level of IOP3. This suggests that in addition to the mean value of IOP, IOP fluctuations could be a factor in glaucomatous pathophysiology. The importance of low frequency fluctuations of clinically-measured mean IOP remains controversial. These studies all rely on snapshot measurements of mean IOP at each time point, and those measurements are taken at relatively infrequent intervals (hourly at the most frequent, but usually monthly or longer). Recently however, there has been some interest in ocular pulse amplitude, or the fluctuation in IOP associated with the cardiac cycle, which can be measured by Dynamic Contour Tonometry (DCT). DCT provides continuous measurement of IOP, but only for a period of tens of seconds in which a patient can tolerate corneal contact without blinking or eye movement, which ironically are two of the most common sources of large high frequency IOP fluctuations according to our telemetric data collected from monkeys4 and previous human studies. In a recent report, continuous IOP telemetry was used in three nonhuman primates to characterize IOP dynamics at multiple time scales for multiple 24-hour periods5.

scholarly journals Maritime inventory routing problem with multiple time windows

2017 ◽  
Vol 13 (5) ◽  
pp. 1-27
Author(s):  
Nurhadi Siswanto ◽  
◽  
Stefanus Eko Wiratno ◽  
Ahmad Rusdiansyah ◽  
Ruhul Sarker ◽  
...  
Keyword(s):  
Time Windows ◽  
Multiple Time ◽  
Inventory Routing ◽  
Routing Problem ◽  
Inventory Routing Problem ◽  
Multiple Time Windows ◽  
Maritime Inventory Routing

scholarly journals Dissection of Bitcoin’s multiscale bubble history from January 2012 to February 2018

2019 ◽  
Vol 6 (7) ◽  
pp. 180643 ◽  
Author(s):  
J. C. Gerlach ◽  
G. Demos ◽  
D. Sornette
Keyword(s):  
Time Windows ◽  
Crash Risk ◽  
Multiple Time ◽  
Market Growth ◽  
Time Period ◽  
Quantitative Metrics ◽  
Dollar Price ◽  
Market Regime ◽  
Multiple Time Windows

We present a detailed bubble analysis of the Bitcoin to US Dollar price dynamics from January 2012 to February 2018. We introduce a robust automatic peak detection method that classifies price time series into periods of uninterrupted market growth (drawups) and regimes of uninterrupted market decrease (drawdowns). In combination with the Lagrange Regularization Method for detecting the beginning of a new market regime, we identify three major peaks and 10 additional smaller peaks, that have punctuated the dynamics of Bitcoin price during the analysed time period. We explain this classification of long and short bubbles by a number of quantitative metrics and graphs to understand the main socio-economic drivers behind the ascent of Bitcoin over this period. Then, a detailed analysis of the growing risks associated with the three long bubbles using the Log-Periodic Power-Law Singularity (LPPLS) model is based on the LPPLS Confidence Indicators , defined as the fraction of qualified fits of the LPPLS model over multiple time windows. Furthermore, for various fictitious ‘present’ times t 2 before the crashes, we employ a clustering method to group the predicted critical times t c of the LPPLS fits over different time scales, where t c is the most probable time for the ending of the bubble. Each cluster is proposed as a plausible scenario for the subsequent Bitcoin price evolution. We present these predictions for the three long bubbles and the four short bubbles that our time scale of analysis was able to resolve. Overall, our predictive scheme provides useful information to warn of an imminent crash risk.

scholarly journals Study of rogue wave by low-pass filtered noise in a multistable fiber laser

2013 ◽  
Vol 23 ◽  
pp. 23-26
Author(s):  
R. Sevilla Escoboza ◽  
G. Huerta Cuéllar ◽  
J. García López ◽  
D. López Mancilla ◽  
C. Castañeda Hernández ◽  
...  
Keyword(s):  
Fiber Laser ◽  
Rogue Wave ◽  
Low Frequency ◽  
Pump Current ◽  
Rogue Waves ◽  
High Amplitude ◽  
Frequency Noise ◽  
Level Laser ◽  
Low Pass ◽  
Filtered Noise

Clear evidence of rogue waves in a multistable system is revealed with an erbium-doped fiber laser driven by harmonic pump modulation (Pisarchik, Jaimes-Reátegui, Sevilla-Escoboza, Huerta-Cuellar & Taki, 2011). We demonstrate numerically and experimentally that a low-pass noise filtering can control the probability for the appearance of a particular state. The results of numerical simulations with the use of a three-level laser model display good agreement with experimental results. The mechanism for the rogue wave formation lies in the interplay of stochastic processes with multistable deterministic dynamics. Low-frequency noise applied to a diode pump current induces rare jumps to coexisting subharmonic states with high-amplitude pulses perceived as rogue waves. The probability of these events depends on the noise filtered frequency and grows up when the noise amplitude increases. The probability distribution of spike amplitudes confirms the rogue wave character of the observed phenomenon.

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