scholarly journals Linear Prolate Functions for Signal Extrapolation with Time Shift

2017 ◽  
Vol 08 (04) ◽  
pp. 417-427 ◽  
Author(s):  
Daniela Valente ◽  
Michael Cada ◽  
Jacek Ilow
Keyword(s):  
Time Shift ◽  
Signal Extrapolation

Integration of Atomistic Simulation with Experiment Using Time−Temperature Superposition for a Cross-Linked Epoxy Network

2019 ◽  
Author(s):  
Ketan Khare ◽  
Frederick R. Phelan Jr.
Keyword(s):  
Master Curve ◽  
Glassy State ◽  
Superposition Principle ◽  
Quantitative Comparison ◽  
Time Shift ◽  
Data Sets ◽  
Epoxy Network ◽  
Temperature Superposition ◽  
Time Temperature Superposition ◽  
Time Temperature Superposition Principle

<a></a><a>Quantitative comparison of atomistic simulations with experiment for glass-forming materials is made difficult by the vast mismatch between computationally and experimentally accessible timescales. Recently, we presented results for an epoxy network showing that the computation of specific volume vs. temperature as a function of cooling rate in conjunction with the time–temperature superposition principle (TTSP) enables direct quantitative comparison of simulation with experiment. Here, we follow-up and present results for the translational dynamics of the same material over a temperature range from the rubbery to the glassy state. Using TTSP, we obtain results for translational dynamics out to 10<sup>9</sup> s in TTSP reduced time – a macroscopic timescale. Further, we show that the mean squared displacement (MSD) trends of the network atoms can be collapsed onto a master curve at a reference temperature. The computational master curve is compared with the experimental master curve of the creep compliance for the same network using literature data. We find that the temporal features of the two data sets can be quantitatively compared providing an integrated view relating molecular level dynamics to the macroscopic thermophysical measurement. The time-shift factors needed for the superposition also show excellent agreement with experiment further establishing the veracity of the approach</a>.

scholarly journals Assessment of Spatiotemporal Peak Shift of Intra-Urban Transportation Taking a Case in Bangkok, Thailand

2021 ◽  
Vol 13 (12) ◽  
pp. 6777
Author(s):  
Masanobu Kii ◽  
Yuki Goda ◽  
Varameth Vichiensan ◽  
Hiroyuki Miyazaki ◽  
Rolf Moeckel
Keyword(s):  
Developing Countries ◽  
Travel Time ◽  
Demand Management ◽  
Likelihood Estimation ◽  
Road Transport ◽  
Peak Shift ◽  
Time Shift ◽  
Peak Time ◽  
Management Scenarios ◽  
The Impact

Reducing congestion has been one of the critical targets of transportation policies, particularly in cities in developing countries suffering severe and chronic traffic congestions. Several traditional measures have been in place but seem not very successful. This paper applies the agent-based transportation model MATSim for a transportation analysis in Bangkok to assess the impact of spatiotemporal transportation demand management measures. We collect required data for the simulation from various data sources and apply maximum likelihood estimation with the limited data available. We investigate two demand management scenarios, peak time shift, and decentralization. As a result, we found that these spatiotemporal peak shift measures are effective for road transport to alleviate congestion and reduce travel time. However, the effect of those measures on public transport is not uniform but depends on the users’ circumstances. On average, the simulated results indicate that those measures increase the average travel time and distance. These results suggest that demand management policies require considerations of more detailed conditions to improve usability. The study also confirms that microsimulation can be a tool for transport demand management assessment in developing countries.

scholarly journals Road Roughness Estimation Based on the Vehicle Frequency Response Function

Actuators ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 89
Author(s):  
Qingxia Zhang ◽  
Jilin Hou ◽  
Zhongdong Duan ◽  
Łukasz Jankowski ◽  
Xiaoyang Hu
Keyword(s):  
Frequency Response ◽  
Response Function ◽  
Frequency Response Function ◽  
Gaussian White Noise ◽  
Estimation Method ◽  
Time Shift ◽  
Ride Quality ◽  
The Road ◽  
Road Roughness ◽  
Measured Response

Road roughness is an important factor in road network maintenance and ride quality. This paper proposes a road-roughness estimation method using the frequency response function (FRF) of a vehicle. First, based on the motion equation of the vehicle and the time shift property of the Fourier transform, the vehicle FRF with respect to the displacements of vehicle–road contact points, which describes the relationship between the measured response and road roughness, is deduced and simplified. The key to road roughness estimation is the vehicle FRF, which can be estimated directly using the measured response and the designed shape of the road based on the least-squares method. To eliminate the singular data in the estimated FRF, the shape function method was employed to improve the local curve of the FRF. Moreover, the road roughness can be estimated online by combining the estimated roughness in the overlapping time periods. Finally, a half-car model was used to numerically validate the proposed methods of road roughness estimation. Driving tests of a vehicle passing over a known-sized hump were designed to estimate the vehicle FRF, and the simulated vehicle accelerations were taken as the measured responses considering a 5% Gaussian white noise. Based on the directly estimated vehicle FRF and updated FRF, the road roughness estimation, which considers the influence of the sensors and quantity of measured data at different vehicle speeds, is discussed and compared. The results show that road roughness can be estimated using the proposed method with acceptable accuracy and robustness.

scholarly journals Deep Unsupervised 4-D Seismic 3-D Time-Shift Estimation With Convolutional Neural Networks

2021 ◽  
pp. 1-16
Author(s):  
Jesper Soren Dramsch ◽  
Anders Nymark Christensen ◽  
Colin MacBeth ◽  
Mikael Luthje
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Time Shift

Intercomparison of Mesoscale Model Simulations of the Daytime Valley Wind System

2011 ◽  
Vol 139 (5) ◽  
pp. 1389-1409 ◽  
Author(s):  
Juerg Schmidli ◽  
Brian Billings ◽  
Fotini K. Chow ◽  
Stephan F. J. de Wekker ◽  
James Doyle ◽  
...  
Keyword(s):  
Land Surface ◽  
Mesoscale Model ◽  
Numerical Models ◽  
Surface Wind ◽  
Sensible Heat Flux ◽  
Surface Wind Speed ◽  
Time Shift ◽  
Wind System ◽  
Valley Wind ◽  
The Mean

Three-dimensional simulations of the daytime thermally induced valley wind system for an idealized valley–plain configuration, obtained from nine nonhydrostatic mesoscale models, are compared with special emphasis on the evolution of the along-valley wind. The models use the same initial and lateral boundary conditions, and standard parameterizations for turbulence, radiation, and land surface processes. The evolution of the mean along-valley wind (averaged over the valley cross section) is similar for all models, except for a time shift between individual models of up to 2 h and slight differences in the speed of the evolution. The analysis suggests that these differences are primarily due to differences in the simulated surface energy balance such as the dependence of the sensible heat flux on surface wind speed. Additional sensitivity experiments indicate that the evolution of the mean along-valley flow is largely independent of the choice of the dynamical core and of the turbulence parameterization scheme. The latter does, however, have a significant influence on the vertical structure of the boundary layer and of the along-valley wind. Thus, this ideal case may be useful for testing and evaluation of mesoscale numerical models with respect to land surface–atmosphere interactions and turbulence parameterizations.

scholarly journals Reduced-Complexity Noncoherently Detected Differential Space-Time Shift Keying

2011 ◽  
Vol 18 (3) ◽  
pp. 153-156 ◽  
Author(s):  
Chao Xu ◽  
Shinya Sugiura ◽  
Soon Xin Ng ◽  
Lajos Hanzo
Keyword(s):  
Space Time ◽  
Time Shift ◽  
Shift Keying ◽  
Reduced Complexity
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