Time Window Length Research Articles

On the Systematic Long-Period Noise Reduction on Ocean Floor Broadband Seismic Sensors Collocated with Differential Pressure Gauges

Vertical-component broadband seismic data collected on the ocean floor at regional distances from the shore and water depths on the order of 1-2 km are polluted by noise due to ocean infragravity (IG) waves in a period range (20-200 s) that is critical for regional structure studies and the determination of moment tensors for regional earthquakes. Using broadband seismic and differential pressure gauge data collected at the Monterey Ocean Bottom Broadband (MOBB) observatory, which has been operational since 2002, we identify the optimal length of time window to obtain the transfer function between the vertical seismic and pressure records. The maximum reduction in IG-wave-induced noise is obtained by using a one-day stack of transfer function inferred from data segment collected in the last 24 hours prior to data in which the noise removal method is applied. We show that a one-year stack of transfer function effectively reduces the noise level in the period band 50-100 s by 20 dB and by up to 15 dB at shorter periods. This resultant 20 dB noise reduction is comparable to that obtained from the one-day stack of transfer function. Our result suggests that the removal of noise induced by IG waves on MOBB vertical-component data can be done routinely without continuously calibrating the transfer function, also in near-real time, and is therefore also useful for routine regional moment tensor de- termination. This noise removal method has been implemented in the moment tensor determination system of the Northern California Seismic System.

L1-Regularized Multiway Canonical Correlation Analysis for SSVEP-Based BCI

Canonical correlation analysis (CCA) between recorded electroencephalogram (EEG) and designed reference signals of sine-cosine waves usually works well for steady-state visual evoked potential (SSVEP) recognition in brain-computer interface (BCI) application. However, using the reference signals of sine- cosine waves without subject-specific and inter-trial information can hardly give the optimal recognition accuracy, due to possible overfitting, especially within a short time window length. This paper introduces an L1-regularized multiway canonical correlation analysis (L1-MCCA) for reference signal optimization to improve the SSVEP recognition performance further. A multiway extension of the CCA, called MCCA, is first presented, in which collaborative CCAs are exploited to optimize the reference signals in correlation analysis for SSVEP recognition alternatingly from the channel-way and trial-way arrays of constructed EEG tensor. L1-regularization is subsequently imposed on the trial-way array optimization in the MCCA, and hence results in the more powerful L1-MCCA with function of effective trial selection. Both the proposed MCCA and L1-MCCA methods are validated for SSVEP recognition with EEG data from 10 healthy subjects, and compared to the ordinary CCA without reference signal optimization. Experimental results show that the MCCA significantly outperforms the CCA for SSVEP recognition. The L1-MCCA further improves the recognition accuracy which is significantly higher than that of the MCCA.

Elementary models of dynamic networks

Inspecting the dynamics of networks opens a new dimension in understanding the interactions among the components of complex systems. Our goal is to understand the baseline properties expected from elementary random changes over time, in order to be able to assess the various effects found in longitudinal data. We created elementary dynamic models from classic random and preferential networks. Focusing on edge dynamics, we defined several processes for changing networks of a fixed size. We applied simple rules, including random, preferential and assortative modifications of existing edges – or a combination of these. Starting from initial Erdos-Renyi networks, we examined various basic network properties (e.g., density, clustering, average path length, number of components, degree distribution, etc.) of both snapshot and cumulative networks (for various lengths of aggregation time windows). Our results provide a baseline for changes to be expected in dynamic networks. We found universalities in the dynamic behavior of most network statistics. Furthermore, our findings suggest that certain network properties have a strong, non-trivial dependence on the length of the sampling window.

Relationship between Short Term Traffic Flow Chaos Fractal Properties and the Necessary Data for Prediction

Analyze the traffic flow in multi-scale time window of a freeway by using the nonlinear analysis method such as Correlation dimension , recursive map and so on, we find that chaos and fractal still exist in wide observation scales. Traffic flow correlation dimension reduces when the length of time window increases, in the observation scale of minutes. However, traffic flow correlation dimension reduces when the length of time window reduces, in the observation scale of seconds, instead of fractal property disappearance as predicted before. We present that, from the view of prediction, the recording point which is 10 times of the correlation dimension is an essential length of the data to predict. The simple model we present, which includes speed difference between vehicles and observation scales of traffic flow, can explain some of the reasons of the traffic flow chaos.

Rapid on-site peak ground acceleration estimation based on support vector regression and P-wave features in Taiwan

This study extracted some P-wave features from the first few seconds of vertical ground acceleration of a single station. These features include the predominant period, peak acceleration amplitude, peak velocity amplitude, peak displacement amplitude, cumulative absolute velocity and integral of the squared velocity. The support vector regression was employed to establish a regression model which can predict the peak ground acceleration according to these features. Some representative earthquake records of the Taiwan Strong Motion Instrumentation Program from 1992 to 2006 were used to train and validate the support vector regression model. Then the constructed model was tested using the whole earthquake records of the same period as well as the 2010 Kaohsiung earthquake with 6.4 ML. The effects on the performance of the regression models using different P-wave features and different length of time window to extract these features are studied. The results illustrated that, if the first 3s of the vertical ground acceleration was used, the standard deviation of the predicted peak ground acceleration error of the whole tested 15-years earthquake records is 20.89gal.The length of time window could be shortened, e.g. 1s, and the prediction error is slightly sacrificed, in order to prolong the lead-time before destructive S-waves reaches.

Temporal changes in attenuation associated with the 2004 M6.0 Parkfield earthquake

Elevated seismic attenuation is often observed in fault zones due to the high degree of fracturing and fluid content. However, temporal changes in attenuation at the time of an earthquake are poorly constrained but can give indications of fracture damage and healing. In this study, spectral ratios between earthquakes within repeating clusters are calculated in an attempt to resolve temporal variations in attenuation at the time of the 2004 M6.0 Parkfield earthquake. A sharp increase in attenuation is observed immediately after the earthquake, which then decays over the next 2 years. Influences of intercluster magnitude variations, time window length and previously reported postseismic velocity changes are investigated. The postseismic decay is fit by a logarithmic function. The timescale of the decay is found to be similar to that in GPS data and ambient seismic noise velocities following the 2004 M6.0 Parkfield earthquake. The amplitude of the attenuation change corresponds to a decrease of approximately 10% in Qp at the time of the earthquake. The greatest changes are recorded on the northeast of the fault trace, consistent with preferential damage in the extensional quadrant behind a north‐westerly propagating rupture tip. Our analysis suggests that significant changes in seismic attenuation and hence fracture dilatancy during coseismic rupture are limited to depths of less than about 5 km.

Computer assessment of indirect insight during an airflow interrupter maneuver of breathing

The paper answers the questions if it is possible to conclude in objective way on more (than one –Rint – in a classical IT) number of parameters from the time domain post-interrupter signals during the occlusional measurement of respiratory mechanics and also verifies what accuracy can be achieved in such attempt. To obtain reported results, the time-domain enhanced interrupter technique (TD-EIT) was developed in this paper using computer simulations. Three-stage scheme of work was assumed in the project. First, the quality of the model identification was assessed for various combinations of pressure and flow signals recorded during the interruption. Then, the correlation between the working characteristics of the interrupter valve and the precision of the parameter estimation were assessed for the TD-EIT algorithm. Finally, a verification experiment by forward-inverse modeling was organized, in which the mechanical characteristics of a complex model were mapped with reduced analogs and with the use of neural networks for three typical modes: ‘Normal state’, ‘Airway constriction’ and ‘Cheeks supported’. Obtained results show that to became effective in time-domain post-interrupter data exploration, both pressure and flow signals should be used in assessment of respiratory mechanics, taken in a range of at least 100ms and when both slopes (valve closing and opening) of quasi-step excitation are included. What is more, the faster the valve the smaller error of parameter estimation in proposed TD-EIT was observed, and this uncertainty importantly falls down for the length of time window exceeding the limit of 100ms. The pioneering use of neural network for mapping the mechanical properties of lungs with the use of interrupter experiment methodology proves that it is possible to conclude about more (than one) number of parameters characterizing the complex system and that this insight is biased with the error not exceeding of 10%; only peripheral properties are estimated worse. Such observation has a potential to change the experimental protocol, which was used in interrupter measurements up to date and to make this technique more attractive in comparison to other method, i.e. forced oscillation technique or impulse oscillometry. As regards the practical meaning of reported results for engineers and end-users (physicians and patients), proposed solution can be applied in simple portable devices with a feature of easy operation (important for e-monitoring).

Optimization of Schwarz waveform relaxation over short time windows

Schwarz waveform relaxation algorithms (SWR) are naturally parallel solvers for evolution partial differential equations. They are based on a decomposition of the spatial domain into subdomains, and a partition of the time interval of interest into time windows. On each time window, an iteration, during which subproblems are solved in space-time subdomains, is then used to obtain better and better approximations of the overall solution. The information exchange between subdomains in space-time is performed through classical or optimized transmission conditions (TCs). We analyze in this paper the optimization problem when the time windows are short. We use as our model problem the optimized SWR algorithm with Robin TCs applied to the heat equation. After a general convergence analysis using energy estimates, we prove that in one spatial dimension, the optimized Robin parameter scales like the inverse of the length of the time window, which is fundamentally different from the known scaling on general bounded time windows, which is like the inverse of the square root of the time window length. We illustrate our analysis with a numerical experiment.

Lapse time and frequency-dependent attenuation characteristics of Kumaun Himalaya

We have analyzed local earthquake data set consists of 84 well located events, recorded by a digital seismic network in the Kumaun Himalaya region during 2004–2007 to study seismic attenuation characteristics of the region. Single back-scattering assumption is used to estimate coda Q (Qc) values of the region at frequency range of 1.5, 3, 6, 8, 12 and 18Hz for different lapse time window length. The values of Qc show a ubiquitous observation of frequency dependence and follow a substantially similar trend as observed in other tectonically active parts of the Himalaya. The lapse time dependencies of coda waves are investigated which reveal that by increasing lapse time window from 20 to 50s, Q0 (Qc at 1Hz) increases from 64±2 to 230±19 while frequency dependent parameter n decreases from 1.08 to 0.81. Increase in Qc with lapse time indicates decrease in heterogeneity with depth. The observed values of Q0 and n infer that the medium beneath the study area is highly heterogeneous and tectonically very active. The variation of Qc with lapse time at 1.5Hz follow a similar pattern with the theoretically predicted values of Gusev (1995) although the frequency parameter n does not match with the model. It may suggest that the turbidity decays rapidly with depth for the Kumaun Himalaya. Our results are comparable with those obtained from tectonically active regions in the world.

Quasiperiodic climate variability with a period of 50–80 years: Fourier analysis of measurements and Earth System Model simulations

We examine an oscillation of global mean temperature with a period of about two thirds of a century. We find evidence for the oscillation both in the instrumental temperature record and in an Earth System Model millennium simulation without external forcing. There is also evidence for the oscillation in the Central England Temperature record, the longest instrumental record available. Our method is based on a discrete Fourier transform with varying starting point and length of time window. This method allows us to make a quantitative estimate of the contribution of an oscillation to global mean temperature, to track the phase evolution of the oscillation and to compare measurement and model results. The multidecadal oscillation could provide part of the explanation both for near-constant global mean temperatures in recent years despite warming by rising concentrations of greenhouse gases and for declining global mean temperature in the 1950s and 1960s alongside with the explanation of aerosol cooling. Quantitative estimates of the contribution of the oscillation to global mean temperature vary between ±(0.03–0.17) K. For the instrumental temperature record, our results indicate an amplitude of 0.03 K presently if the IPCC model average represents the effect of external forcings well, and (0.08–0.17) K when using simple linear and quadratic fits for detrending. For the millennium simulation, the amplitude of the oscillation is (0.05–0.06) K, but could be underestimated as compared to reality if external forcing acts to globally synchronize multidecadal variability. The role of the Atlantic Multidecadal Oscillation (AMO) in the model is discussed. The AMO has a spatial temperature distribution similar to earlier literature results and is more correlated with the global oscillation when external forcing is included.

Rapid Estimation of Earthquake Source and Ground-Motion Parameters for Earthquake Early Warning Using Data from a Single Three-Component Broadband or Strong-Motion Sensor

We propose a new algorithm to rapidly determine earthquake source and ground-motion parameters for earthquake early warning (EEW). This algorithm uses the acceleration, velocity, and displacement waveforms of a single three-component broadband (BB) or strong-motion (SM) sensor to perform real-time earthquake/noise discrimination and near/far source classification. When an earthquake is detected, the algorithm estimates the moment magnitude M, epicentral distance Δ, and peak ground velocity (PGV) at the site of observation. The algorithm was constructed by using an artificial neural network (ANN) approach. Our training and test datasets consist of 2431 three-component SM and BB records of 161 crustal earthquakes in California, Japan, and Taiwan with 3.1 ≤ M ≤ 7.6 at Δ ≤ 115 km. First estimates become available at t_0 = 0.25 s after the P pick and are regularly updated. We find that displacement and velocity waveforms are most relevant for the estimation of M and PGV, while acceleration is important for earthquake/noise discrimination. Including site corrections reduces the errors up to 10%. The estimates improve by an additional 10% if we use both the vertical and horizontal components of recorded ground motions. The uncertainties of the predicted parameters decrease with increasing time window length t_0; larger magnitude events show a slower decay of these uncertainties than small earthquakes. We compare our approach with the τ_c algorithm and find that our prediction errors are around 60% smaller. However, in general there is a limitation to the prediction accuracy an EEW system can provide if based on single-sensor observations.

Performance of different processing schemes in seismic noise cross-correlations

SUMMARY The estimation of the Green's function between two points on the Earth's surface by the cross-correlation of seismic noise time-series became a widely used method in seismology. In general, very long time-series (months to years) as well as massive normalization and/or data selection are necessary to obtain useful cross-correlation functions. One task of this study is to evaluate the influence of different established normalization methods on the obtained cross-correlation functions. Furthermore, we evaluate two waveform preserving time domain normalizations as well as a new fully automated data selection approach. The cross-correlation functions analysed in this study are obtained from 12 months of seismic noise recorded in 2004 at five seismic stations in the United States with station distances on a continental scale. For practical reasons, the cross-correlation functions of such long time-series are calculated by stacking the cross-correlation functions obtained from shorter time windows. We use this stacking process for the implementation of the waveform preserving time domain normalizations. The time window length is in general an important parameter of the cross-correlation processing, as it influences the normalization and data selection. Therefore, we evaluate the cross-correlation functions obtained with 47 different time window lengths between one hr and 24 hr. The time domain normalizations intend to suppress the influence of transient signals like earthquake waves as well as long-term (e.g. seasonal) amplitude variations. We compare the proposed waveform preserving time domain normalizations with the established running absolute mean normalization and the one-bit normalization. We demonstrate that a waveform preserving time domain normalization can replace a non-linear time domain normalization, if a time window length similar to the duration of the typically occurring transient signals is used. Next to the time domain normalizations also the spectral whitening in the frequency domain is evaluated. Spectral whitening is a powerful normalization to improve the emergence of broad-band signals in seismic noise cross-correlations. Nevertheless, we observe spectral whitening to depend strongly on the time window length. An unwanted amplification of a persistent microseism signal is observed on the continental scale with time windows shorter than 12 hr. Our approach of automated data selection is based on a statistical time-series classification and reliably excludes time windows with transient signals occurring contemporaneously at both sites (e.g. earthquake waves). This data selection approach is capable to replace a non-linear time domain normalization, but no improvement of the waveform symmetry or the signal-to-noise ratio of the cross-correlation functions is observed in general.

Autocorrelation analysis of ambient noise in northeastern Japan subduction zone

We obtained ambient seismic noise interferograms as seismic reflection images using autocorrelation functions (ACFs) in the northeastern Japan subduction zone. The ACFs with a time window length of 120s were calculated from the continuous seismic records obtained at each seismic station over an analysis period of 300days. These ACFs show some distinct signals with relatively large amplitude without any significant temporal variations during the analysis period. The signals, which are stable, appear at both small lag times of less than 10s and large lag times of 20–50s during the analysis period. The lag time of 10s corresponds to the travel time of the PP reflection arrival from the continental Mohorovičić discontinuity. The signals with the large lag times between 30 and 50s correspond to the back-scattered signals from the mantle wedge or the plate boundary; these signals are identified clearly at the stations located on the back-arc side. In the ACFs calculated from the records obtained from the fore-arc side stations, weak signals (interpreted as the reflection from the plate boundary) with a lag time range of 20 to 30s are observed. We constructed depth-migrated images using the ACFs to obtain the reflectivity profile by assuming that the ACFs represent Green's functions composed of a random wavefield excited by stochastic sources or scatterers distributed in the vertical or near-vertical direction from the stations. Further, we assumed that the ACFs can be treated as zero-offset seismic traces recorded at each of the stations. The depth-migrated images show a relatively seismically transparent structure within the subducting Pacific slab and a reflective structure within the mantle wedge; this reflective structure is characterized by low-velocity zones corresponding to the wedge flow imaged by 3-D seismic velocity tomography.

Anatomy of a bit: Information in a time series observation

Appealing to several multivariate information measures--some familiar, some new here--we analyze the information embedded in discrete-valued stochastic time series. We dissect the uncertainty of a single observation to demonstrate how the measures' asymptotic behavior sheds structural and semantic light on the generating process's internal information dynamics. The measures scale with the length of time window, which captures both intensive (rates of growth) and subextensive components. We provide interpretations for the components, developing explicit relationships between them. We also identify the informational component shared between the past and the future that is not contained in a single observation. The existence of this component directly motivates the notion of a process's effective (internal) states and indicates why one must build models.

Evaluating CO2 emissions, cost, and service quality trade-offs in an urban delivery system case study

Growing pressure to limit greenhouse gas emissions is changing the way businesses operate. This paper presents the trade-offs between cost, service quality (represented by time window guarantees), and emissions of an urban pickup and delivery system under these changing pressures. A model, developed by the authors in ArcGIS, is used to evaluate these trade-offs for a specific case study involving a real fleet with specific operational characteristics. The problem is modeled as an emissions minimization vehicle routing problem with time windows. Analyses of different external policies and internal operational changes provide insight into the impact of these changes on cost, service quality, and emissions. Specific consideration of the influence of time windows, customer density, and vehicle choice are included.The results show a stable relationship between monetary cost and kilograms of CO2, with each kilogram of CO2 associated with a $3.50 increase in cost, illustrating the influence of fuel use on both cost and emissions. In addition, customer density and time window length are strongly correlated with monetary cost and kilograms of CO2 per order. The addition of 80 customers or extending the time window 100 minutes would save approximately $3.50 and 1 kilogram of CO2 per order. Lastly, the evaluation of four different fleets illustrates significant environmental and monetary gains can be achieved through the use of hybrid vehicles.The results demonstrate there is not a trade-off between CO2 emissions and cost, but that these two metrics trend together. This suggests the most effective way to encourage fleet operators to limit emissions is to increase the cost of fuel or CO2 production, as this is consistent with current incentives that exist to reduce cost, and therefore emissions.

Visualization of the power waveforms frequency fluctuations with the use of the constant length time window

Visualization of the power waveforms frequency fluctuations with the use of the constant length time window

Time-window Bias in Case-control Studies

Time-related biases in cohort studies can produce illusory "beneficial" effects of medications due entirely to an artifact of the analytic design. We describe "time-window bias" in the context of a case-control study, reporting that statin use was associated with a 45% reduction in the incidence of lung cancer. This bias results from the use of time-windows of different lengths between cases and controls to define time-dependent exposures. We illustrate the bias using a population of 365,467 patients from the United Kingdom's General Practice Research Database, including 1786 incident cases of lung cancer during 1998-2004. The case-control approach used in the published study yielded a rate ratio of lung cancer incidence of 0.62 with statin use (95% confidence interval = 0.55-0.71). A case-control approach that properly accounts for time produces a rate ratio of 0.99 (0.85-1.16)-suggesting no benefit of statins on lung cancer risk. We show analytically that the magnitude of the bias is proportional to the ratio of the unequal time-window lengths.

Optimal subwork zone operational strategy for short‐term work zone projects in four‐lane two‐way freeways

SUMMARYRoadside trees in Singapore are regularly trimmed for the purpose of traffic safety and roadside tree‐trimming project is one typical type of short‐term work zone projects. To implement such a short‐term work zone project, contractors usually divide an entire work zone into multiple subwork zones with the uniform length. This paper aims to determine an optimal subwork zone strategy for the short‐term work zone projects in four‐lane two‐way freeways with time window and uniform subwork zone length constraints. The deterministic queuing model is employed to estimate total user delay caused by the work zone project by taking into account variable traffic speeds. Based on the user delay estimations, this paper proceeds to build a minimization model subject to time window and uniform length constraints for the optimal subwork zone strategy problem. This paper also presents a variation of the minimization model to examine the impact of unequal subwork zone length constraint. Since these minimization models belong to the mixed‐integer non‐differentiable optimization problems, an iterative algorithm embedding with the genetic simulated annealing method is thus proposed to solve these models. Finally, a numerical example is carried out to investigate the effectiveness of the proposed models. Copyright © 2010 John Wiley & Sons, Ltd.

Fluctuation scaling of quotation activities in the foreign exchange market

We study the scaling behavior of quotation activities for various currency pairs in the foreign exchange market. The components’ centrality is estimated from multiple time series and visualized as a currency pair network. The power-law relationship between a mean of quotation activity and its standard deviation for each currency pair is found. The scaling exponent α and the ratio between common and specific fluctuations η increase with the length of the observation time window Δ t . The result means that although for Δ t = 1 ( min ) , the market dynamics are governed by specific processes, and at a longer time scale Δ t > 100 ( min ) the common information flow becomes more important. We point out that quotation activities are not independently Poissonian for Δ t = 1 ( min ) , and temporally or mutually correlated activities of quotations can happen even at this time scale. A stochastic model for the foreign exchange market based on a bipartite graph representation is proposed.

On the significance of phase in the short term Fourier spectrum for speech intelligibility

This paper investigates the significance of the magnitude or the phase in the short term Fourier spectrum for speech intelligibility as a function of the time-window length. For a wide range of window lengths (1/16-2048 ms), two hybrid signals were obtained by a cross-wise combination of the magnitude and phase spectra of speech and white noise. Speech intelligibility data showed the significance of the phase spectrum for longer windows (>256 ms) and for very short windows (<4 ms), and that of the magnitude spectrum for medium-range window lengths. The hybrid signals used in the intelligibility test were analyzed in terms of the preservation of the original narrow-band speech envelopes. Correlations between the narrow-band envelopes of the original speech and the hybrid signals show a similar pattern as a function of window length. This result illustrates the importance of the preservation of narrow-band envelopes for speech intelligibility. The observed significance of the phase spectrum in recovering the narrow-band envelopes for the long term windows and for the very short term windows is discussed.