Accurate Onset Time Research Articles

Integrated processing method for microseismic signal based on deep neural network

SUMMARYDenoising and onset time picking of signals are essential before extracting source information from collected seismic/microseismic data. We proposed an advanced deep dual-tasking network (DDTN) that integrates these two procedures sequentially to achieve the optimal performance. Two homo-structured encoder–decoder networks with specially designed structures and parameters are connected in series for handling the denoising and detection of microseismic signals. Based on the similarity of data types, the output of the denoising network will be imported into the detection network to obtain labels for the signal duration. The procedures of denoising and duration detection can be completed in an integrated way, where the denoised signals can improve the accuracy of onset time picking. Results show that the method has a good performance for the denoising of microseismic signals that contain various types and intensities of noise. Compared with existing methods, DDTN removes the noise with a minor waveform distortion. It is ideal for recovering the microseismic signal while maintaining a good capacity for onset time picking when the signal-to-noise ratio is low. Based on that, the second network can detect a more accurate duration of microseismic signals and thus obtain more accurate onset time. The method has great potential to be extended to the study of exploration seismology and earthquakes.

An Integrated Approach to Real-Time Acoustic Emission Damage Source Localization in Piled Raft Foundations

Acoustic emission (AE) has garnered significant interest as a promising way to detect the early-stage development of internal cracks and damage in underground and geotechnical structures, associated with natural disasters. Meanwhile, AE source localization techniques that can identify the damage location in a piled-raft foundation (PRF) are premature because of its complex geometry, although the PRF is a widely used deep foundation type for high-rise buildings. In this study, we propose an integrated approach to localize AE sources in the PRF by using the modified Akaike information criterion (AIC) method and examine its accuracy to mark with pile zones. We performed a series of experiments on a scaled PRF model at a ratio of 1:50, composed of one raft and 25 piles. The results demonstrate that the combined approach with the modified AIC method and the Simplex method can localize the AE source zones with good accuracy, greater than 95% on average. The suggested two-stage AIC picker shows accurate onset time determination, and hence, it significantly improves the accuracy, particularly effective for the signals with low signal-to-noise ratios. The approach exploiting the two-stage AIC picker can be readily used for automated real-time AE monitoring to detect crack generation and its location in buried foundations that cannot be inspected visually.

An Improved Onset Time Picking Method for Low SNR Acoustic Emission Signals

Automatic and accurate onset time picking of the acoustic emission (AE) signals is of great significance for the location of acoustic events. However, the onset times are difficult to be picked accurately due to the presence of noise in signal records. To overcome this problem, an improved method including three consecutive processes is proposed in this study. First, the windowing Lempel-Ziv (WLZ) complexity method is applied to determine an approximate onset time. Then, the time series selected from the start time to the vicinity of the approximate onset time is coarsened at different scales with the application of multi-scale (MS) theory. Finally, Akaike information criterion (AIC) method is utilized to pick up the accurate onset time. Feasibility and performance of the proposed improved method are tested and compared with the other automatic onset time picking methods, such as AIC, short and long time average ratio (STA/LTA), modified energy ratio (MER), AIC with low pass filter and STA/LTA with low pass filter, using the same broken load experiment and 100 rock fracture signals with various signal-to-noise ratios (SNRs). The results show that the accuracy of automatic picks provided by the improved method are far beyond the other methods mentioned. Meanwhile, both the accuracy stability and SNR adaptability of the improved method for rock fracture signals are better than other methods, which proves that the improved method has the excellent performance to pick up onset times of AE signals.

Acoustic emission Bayesian source location: Onset time challenge

Robust identification of the most accurate observed input data among a pool of observations is key in modeling and decision making. A statistically biased observed measurement deteriorates the predictive power of a model and affects decision-making ability based on the prediction of the model. When two competing methods of measurement are available, such as methods which identify arrival times in acoustic emission (AE) signals, a principal question is whether one of the two obtained datasets, or a combination of the two, should be used later on, for example, to localize an AE source. This question becomes more important when collecting not repeatable data such as AE signals created by a propagating crack. This paper considers an inverse source location problem in a concrete block to address the mentioned issue, a proposed methodology which also has wider application in competitive data selection. Elastic energy released by an AE event, such as a propagating crack, is recorded by acoustic emission data acquisition system. The onset time of AE signals is often used to locate the source of the event, and its accuracy directly affects the precision of source identification. This research proposes an innovative approach to select the most probable onset time obtained from two automatic picker methods. The proposed method selects the most probable onset times, which are observed by each picker for each sensor, in a probabilistic fashion. To validate the proposed method, the most accurate onset time observed by each picker is identified by visual inspection and is compared with the one is selected by the proposed method. Finally, the dataset is used for source location identification. Results show that picked onset times determined by the proposed method generate more accurate source identification when compared with coordinates obtained using each dataset individually.

A Magnetic Resonance Imaging Protocol for Stroke Onset Time Estimation in Permanent Cerebral Ischemia

MRI provides a sensitive and specific imaging tool to detect acute ischemic stroke by means of a reduced diffusion coefficient of brain water. In a rat model of ischemic stroke, differences in quantitative T1 and T2 MRI relaxation times (qT1 and qT2) between the ischemic lesion (delineated by low diffusion) and the contralateral non-ischemic hemisphere increase with time from stroke onset. The time dependency of MRI relaxation time differences is heuristically described by a linear function and thus provides a simple estimate of stroke onset time. Additionally, the volumes of abnormal qT1 and qT2 within the ischemic lesion increase linearly with time providing a complementary method for stroke timing. A (semi)automated computer routine based on the quantified diffusion coefficient is presented to delineate acute ischemic stroke tissue in rat ischemia. This routine also determines hemispheric differences in qT1 and qT2 relaxation times and the location and volume of abnormal qT1 and qT2 voxels within the lesion. Uncertainties associated with onset time estimates of qT1 and qT2 MRI data vary from ± 25 min to ± 47 min for the first 5 hours of stroke. The most accurate onset time estimates can be obtained by quantifying the volume of overlapping abnormal qT1 and qT2 lesion volumes, termed 'Voverlap' (± 25 min) or by quantifying hemispheric differences in qT2 relaxation times only (± 28 min). Overall, qT2 derived parameters outperform those from qT1. The current MRI protocol is tested in the hyperacute phase of a permanent focal ischemia model, which may not be applicable to transient focal brain ischemia.

A Magnetic Resonance Imaging Protocol for Stroke Onset Time Estimation in Permanent Cerebral Ischemia.

MRI provides a sensitive and specific imaging tool to detect acute ischemic stroke by means of a reduced diffusion coefficient of brain water. In a rat model of ischemic stroke, differences in quantitative T1 and T2 MRI relaxation times (qT1 and qT2) between the ischemic lesion (delineated by low diffusion) and the contralateral non-ischemic hemisphere increase with time from stroke onset. The time dependency of MRI relaxation time differences is heuristically described by a linear function and thus provides a simple estimate of stroke onset time. Additionally, the volumes of abnormal qT1 and qT2 within the ischemic lesion increase linearly with time providing a complementary method for stroke timing. A (semi)automated computer routine based on the quantified diffusion coefficient is presented to delineate acute ischemic stroke tissue in rat ischemia. This routine also determines hemispheric differences in qT1 and qT2 relaxation times and the location and volume of abnormal qT1 and qT2 voxels within the lesion. Uncertainties associated with onset time estimates of qT1 and qT2 MRI data vary from ± 25 min to ± 47 min for the first 5 hours of stroke. The most accurate onset time estimates can be obtained by quantifying the volume of overlapping abnormal qT1 and qT2 lesion volumes, termed 'Voverlap' (± 25 min) or by quantifying hemispheric differences in qT2 relaxation times only (± 28 min). Overall, qT2 derived parameters outperform those from qT1. The current MRI protocol is tested in the hyperacute phase of a permanent focal ischemia model, which may not be applicable to transient focal brain ischemia.

Abstract P059: Telemedicine Data Indicate Short-term Associations Between Clinical Cardiovascular Events and Ambient Particulate Air Pollution

Introduction: Particulate matter (PM) air pollution has been linked to cardiovascular morbidity and mortality with large public health burdens. Although associations between PM and subclinical autonomic and vascular changes have been shown to occur within minutes to hours, studies have been limited in their ability to investigate such short time scales for clinical cardiac events. Accurate onset time is essential for risk estimation of such short exposures, but publically available data lack this information. This study utilized unique telemedicine data to more accurately estimate the short-term effects of PM exposure in triggering acute cardiac events. Hypothesis: We assessed the hypothesis that elevated short-term (1-24 hours) concentrations of coarse PM (2.5 to 10 μm in aerodynamic diameter, PM 10-2.5 ) and fine PM ( < 2.5 μm, PM 2.5 ) in two metropolitan areas in Israel are associated with an increased risk of clinical cardiac events. Methods: We conducted a time-stratified case-crossover study in which cardiac events were identified using time-resolved telemedicine data from the Tel Aviv and Haifa areas, Israel, between 2002 and 2013. Calls to the telemedicine service for cardiac-related symptoms that resulted in an in-home examination by a mobile intensive care and transfer to a hospital were classified into ischemic, arrhythmia, or non-specific events. Onset time was determined by the time of a medical phone call. Ambient pollutant concentrations measured at central monitoring stations were compared between case and control periods using multivariate conditional logistic regression after adjusting for meteorology and other risk factors. We further explored sensitivity for certainty of diagnosis and effect modification by personal characteristics. Results: A total of 7,617 telemedicine subscribers reported 12,661 (20% ischemic, 31% arrhythmic and 49% non-specific) cardiac events that resulted in an evacuation to a hospital over our 12 year period. After adjustment for confounders, a 10 μg/m 3 higher PM 10-2.5 concentration was associated with a greater odds of a clinical cardiac event, with the strongest associations for events due to arrhythmia (OR 1.006, 95% CI: 1.001-1.011) within 12 hours. Similar associations were found at other time points, for ischemic and non-specific cardiac events, as well as for PM 2.5 but these associations often had wider confidence intervals. Risks tended to be higher for subjects who had diabetes, previous ischemic heart disease or were obese, although these differences did not achieve statistical significance. Conclusions: Exposure to PM air pollution over the course of hours may increase the risk of acute clinical cardiac events. Telemedicine allows for more accurate information on event onset and thus can be a powerful tool for epidemiologic studies focusing on short-term exposures to environmental or other stressors.

A Statistical Study on Characteristics of Disappearing Prominences

AbstractReal-time monitoring of filaments is essential for the prediction of their eruption and the ensuing coronal mass ejection (CME). We apply an automated algorithm for the detection and tracking of filaments in full-disc Hα images to obtain their physical attributes. This provides an accurate onset time of the eruption, and also allows us to study the physical characteristics of the erupting filaments in an objective manner.

Human movement onset detection from isometric force and torque measurements: A supervised pattern recognition approach

Recent research has successfully introduced the application of robotics and mechatronics to functional assessment and motor therapy. Measurements of movement initiation in isometric conditions are widely used in clinical rehabilitation and their importance in functional assessment has been demonstrated for specific parts of the human body. The determination of the voluntary movement initiation time, also referred to as onset time, represents a challenging issue since the time window characterizing the movement onset is of particular relevance for the understanding of recovery mechanisms after a neurological damage. Establishing it manually as well as a troublesome task may also introduce oversight errors and loss of information. The most commonly used methods for automatic onset time detection compare the raw signal, or some extracted measures such as its derivatives (i.e., velocity and acceleration) with a chosen threshold. However, they suffer from high variability and systematic errors because of the weakness of the signal, the abnormality of response profiles as well as the variability of movement initiation times among patients. In this paper, we introduce a technique to optimise onset detection according to each input signal. It is based on a classification system that enables us to establish which deterministic method provides the most accurate onset time on the basis of information directly derived from the raw signal. The approach was tested on annotated force and torque datasets. Each dataset is constituted by 768 signals acquired from eight anatomical districts in 96 patients who carried out six tasks related to common daily activities. The results show that the proposed technique improves not only on the performance achieved by each of the deterministic methods, but also on that attained by a group of clinical experts. The paper describes a classification system detecting the voluntary movement initiation time and adaptable to different signals. By using a set of features directly derived from raw data, we obtained promising results. Furthermore, although the technique has been developed within the scope of isometric force and torque signal analysis, it can be applied to other detection problems where several simple detectors are available.

Automated seizure onset detection for accurate onset time determination in intracranial EEG

Objective A novel algorithm for automated seizure onset detection is presented. The method allows for precise identification of electrographic seizure onset times within large databases of electrographic data. Methods The patient-specific algorithm extracts salient spectral and temporal features in five frequency bands within a sliding window of an electrographic recording. Feature windows are classified as containing or not containing a seizure onset via support vector machines. A clustering and regression analysis is utilized to accurately localize seizure onsets in time. User-adjustable parameters allow for tuning of detection sensitivity, false positive rate, and latency. The method was tested on intracranial electrographic data recorded from six patients with a total of 1792 recorded seizure onsets from 8246 total electrographic recordings. Results Testing of algorithm performance via cross-validation resulted in sensitivities between 80% and 98%, false positive rates from 0.002 to 0.046 per minute (0.12–2.8 per hour), and median detection time within 100 ms of the electrographic onset for all patients. In five of the six patients, more than 90% of all detected onsets were less than 3 s from the electrographic onset. Conclusions The detection system was able to detect seizure onset times in a temporally unbiased fashion with low latency while maintaining reasonable sensitivities and false positive rates. The regression algorithm for temporal localization of onsets confers a considerable benefit in terms of detection latency. Significance With the use of our algorithm, large databases of electrographic data can be rapidly processed and seizure onset times accurately marked, facilitating research and analyses of peri-onset events that require precise seizure onset alignment.

Effect of catalysts on thin-film polymerization of thermotropic liquid crystalline copolyester

Using a novel thin-film polymerization technique, we have investigated in situ uncatalyzed and catalyzed polycondensation reaction systems for 73/27 (mol ratio) poly(p-oxybenzoate/2,6-oxynaphthoate) [P(OBA/ONA)] thermotropic liquid crystalline copolyester. We have also determined the effect of catalysts on the kinetics and morphological changes of the reactions. Because the thin-film polymerization is conducted on the heating stage and the morphology is observed in situ by a polarizing microscope, we can directly observe and determine the accurate onset time for LC phase generation. The number-average degree of polymerization (DP) at the onset of this morphological change decreases with decreasing reaction temperature in the range of 230-290 °C. The LC phase may form at a DP as low as 2 at 230 °C. Most importantly and surprisingly, the reaction rate constant obtained from the thin-film polymerization is much greater (20-30 times) than the previous reported value obtained from the bulk polymerization reaction because the release of acetic acid in the former is much easier and quicker than in the latter. Clearly, the thin-film polymerization may be a better and accurate technique to observe the approximately inherent properties of polymerization kinetics than the traditional bulk polymerization reaction. Three kinds of catalysts, namely, sodium acetate, calcium acetate, and antimony oxide, have been studied. Sodium acetate has obvious acceleration effect on the reaction. Reaction rate constant increases almost proportionally to the catalyst content in the low catalyst content range, and activation energy slightly decreases with an increase in sodium acetate percentage. Calcium acetate has a higher catalytic effect than sodium acetate when the catalyst content is high, but the trend reverses when the catalyst content is low. Polymerization with high content of calcium acetate produces LCP with undesirable morphology because it suppresses the coalescence process among LC domains. Antimony oxide is a polymerization inhibitor for this reaction. It slows down the reaction, but does not alter the sequence of the morphological changes during the polymerization.

A simple device detecting onset time of taste stimulation

A simple piece of equipment is described for recording the accurate onset time of chemical stimulation applied to the tongue surface. The equipment consists of an infrared light emitting diode, a phototransistor for monitoring the stimulus onset, and solid state logic circuits for generating electric pulses at the moment of stimulus onset.