Some Modified Inhibition Models for Response Time series

1989 ◽  
pp. 115-132
Author(s):  
Gerard J. P. Van Breukelen
Keyword(s):  
Time Series ◽  
Response Time

scholarly journals A Model and Diagnostic Measures for Response-Time Series on Tests of Concentration - Historical Background, Conceptual-Framework, and Some Applications

1995 ◽  
Vol 27 (2) ◽  
pp. 147-179 ◽  
Author(s):  
G.J.P. Vanbreukelen ◽  
E.E.C.I. Roskam ◽  
P.A.T.M. Eling ◽  
R.W.T.L. Jansen ◽  
D.A.P.B. Souren ◽  
...  
Keyword(s):  
Time Series ◽  
Response Time ◽  
Conceptual Framework ◽  
Historical Background ◽  
Diagnostic Measures

Minimizing response time in time series classification

2015 ◽  
Vol 46 (2) ◽  
pp. 449-476 ◽  
Author(s):  
Shin Ando ◽  
Einoshin Suzuki
Keyword(s):  
Time Series ◽  
Response Time ◽  
Time Series Classification

scholarly journals Spectral Analysis on Sub-Tidal Variability of Salinity in Modaomen Waterway of Pearl River Estuary, China

2012 ◽  
Vol 518-523 ◽  
pp. 1982-1985
Author(s):  
Yuan Ying Chen ◽  
Xiao Ling Yin ◽  
Dong Lin Bai ◽  
Li Cheng Li
Keyword(s):  
Time Series ◽  
Spectral Analysis ◽  
Response Time ◽  
River Estuary ◽  
Pearl River Estuary ◽  
Tidal Range ◽  
Pearl River ◽  
Estuary Mouth ◽  
Power Spectrum Method ◽  
Tidal Variability

Salinity and tidal range time series observed in Modaomen waterway was analyzed in power spectrum method, and both showed the period of half month (14.22d). Moreover, the salinity and tidal range time series were coherent at that period through cross spectrum analysis. Besides, the phase analysis at the period of 14.22d showed that, within the estuary, the salinity time series upstream lagged that of the downstream, and the response time of salinity time series to the tidal range time series was about 9-10d, increasing upstream. But the phase of salinity time series and response time to the tidal range at the estuary mouth did not correspond with the laws within the estuary. The response time of salinity to the tidal range was about 12d there.

Comparison of Spectral and Wavelet Estimators of Transfer Function for Linear Systems

2012 ◽  
Vol 2 (3) ◽  
pp. 214-237 ◽  
Author(s):  
M. A. A. Bakar ◽  
D. A. Green ◽  
A. V. Metcalfe
Keyword(s):  
Time Series ◽  
Linear System ◽  
Response Time ◽  
Wave Energy ◽  
Series Data ◽  
Wavelet Estimator ◽  
Cross Spectrum ◽  
The Cross ◽  
Wavelet Estimators ◽  
Spectral Estimator

AbstractWe compare spectral and wavelet estimators of the response amplitude operator (RAO) of a linear system, with various input signals and added noise scenarios. The comparison is based on a model of a heaving buoy wave energy device (HBWED), which oscillates vertically as a single mode of vibration linear system. HBWEDs and other single degree of freedom wave energy devices such as oscillating wave surge convertors (OWSC) are currently deployed in the ocean, making such devices important systems to both model and analyse in some detail. The results of the comparison relate to any linear system. It was found that the wavelet estimator of the RAO offers no advantage over the spectral estimators if both input and response time series data are noise free and long time series are available. If there is noise on only the response time series, only the wavelet estimator or the spectral estimator that uses the cross-spectrum of the input and response signals in the numerator should be used. For the case of noise on only the input time series, only the spectral estimator that uses the cross-spectrum in the denominator gives a sensible estimate of the RAO. If both the input and response signals are corrupted with noise, a modification to both the input and response spectrum estimates can provide a good estimator of the RAO. A combination of wavelet and spectral methods is introduced as an alternative RAO estimator. The conclusions apply for autoregressive emulators of sea surface elevation, impulse, and pseudorandom binary sequences (PRBS) inputs. However, a wavelet estimator is needed in the special case of a chirp input where the signal has a continuously varying frequency.

scholarly journals On the Investigation of State Space Reconstruction of Nonlinear Aeroelastic Response Time Series

2006 ◽  
Vol 13 (4-5) ◽  
pp. 393-407 ◽  
Author(s):  
Flávio D. Marques ◽  
Eduardo M. Belo ◽  
Vilma A. Oliveira ◽  
José R. Rosolen ◽  
Andréia R. Simoni
Keyword(s):  
Experimental Data ◽  
Time Series ◽  
Response Time ◽  
State Space ◽  
The State ◽  
Aeroelastic Response ◽  
State Space Reconstruction ◽  
Wing Model ◽  
Linear Behavior ◽  
Non Linear

Stall-induced aeroelastic motion may present severe non-linear behavior. Mathematical models for predicting such phenomena are still not available for practical applications and they are not enough reliable to capture physical effects. Experimental data can provide suitable information to help the understanding of typical non-linear aeroelastic phenomena. Dynamic systems techniques based on time series analysis can be adequately applied to non-linear aeroelasticity. When experimental data are available, the methods of state space reconstruction have been widely considered. This paper presents the state space reconstruction approach for the characterization of the stall-induced aeroelastic non-linear behavior. A wind tunnel scaled wing model has been tested. The wing model is subjected to different airspeeds and dynamic incidence angle variations. The method of delays is used to identify an embedded attractor in the state space from experimentally acquired aeroelastic response time series. To obtain an estimate of the time delay used in the state space reconstruction from time series, the autocorrelation function analyis is used. For the calculation of the embedding dimension the correlation integral approach is considered. The reconstructed attractors can reveal typical non-linear structures associated, for instance, to chaos or limit cycles.

scholarly journals Analyzing Spatio-Temporal Factors to Estimate the Response Time between SMOS and In-Situ Soil Moisture at Different Depths

2020 ◽  
Vol 12 (16) ◽  
pp. 2614
Author(s):  
Christoph Herbert ◽  
Miriam Pablos ◽  
Mercè Vall-llossera ◽  
Adriano Camps ◽  
José Martínez-Fernández
Keyword(s):  
Time Series ◽  
Soil Moisture ◽  
Response Time ◽  
Temporal Variability ◽  
Characteristic Time ◽  
Time Lag ◽  
Near Surface ◽  
Time Length ◽  
Spatio Temporal

A comprehensive understanding of temporal variability of subsurface soil moisture (SM) is paramount in hydrological and agricultural applications such as rainfed farming and irrigation. Since the SMOS (Soil Moisture and Ocean Salinity) mission was launched in 2009, globally available satellite SM retrievals have been used to investigate SM dynamics, based on the fact that useful information about subsurface SM is contained in their time series. SM along the depth profile is influenced by atmospheric forcing and local SM properties. Until now, subsurface SM was estimated by weighting preceding information of remotely sensed surface SM time series according to an optimized depth-specific characteristic time length. However, especially in regions with extreme SM conditions, the response time is supposed to be seasonally variable and depends on related processes occurring at different timescales. Aim of this study was to quantify the response time by means of the time lag between the trend series of satellite and in-situ SM observations using a Dynamic Time Warping (DTW) technique. DTW was applied to the SMOS satellite SM L4 product at 1 km resolution developed by the Barcelona Expert Center (BEC), and in-situ near-surface and root-zone SM of four representative stations at multiple depths, located in the Soil Moisture Measurements Station Network of the University of Salamanca (REMEDHUS) in Western Spain. DTW was customized to control the rate of accumulation and reduction of time lag during wetting and drying conditions and to consider the onset dates of pronounced precipitation events to increase sensitivity to prominent features of the input series. The temporal variability of climate factors in combination with crop growing seasons were used to indicate prevailing SM-related processes. Hereby, a comparison of long-term precipitation recordings and estimations of potential evapotranspiration (PET) allowed us to estimate SM seasons. The spatial heterogeneity of land use was analyzed by means of high-resolution images of Normalized Difference Vegetation Index (NDVI) from Sentinel-2 to provide information about the level of spatial representativeness of SMOS observations to each in-situ station. Results of the spatio-temporal analysis of the study were then evaluated to understand seasonally and spatially changing patterns in time lag. The time lag evolution describes a variable characteristic time length by considering the relevant processes which link SMOS and in-situ SM observation, which is an important step to accurately infer subsurface SM from satellite time series. At a further stage, the approach needs to be applied to different SM networks to understand the seasonal, climate- and site-specific characteristic behaviour of time lag and to decide, whether general conclusions can be drawn.

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