scholarly journals Generating Uncertainty Distributions for Seismic Signal Onset Times

2020 ◽  
Author(s):  
Matt Peterson ◽  
Charlie Vollmer ◽  
Ronald Brogan ◽  
David J. Stracuzzi ◽  
Chistopher J. Young
Keyword(s):  
Arrival Time ◽  
Seismic Analysis ◽  
Critical Role ◽  
Time Estimation ◽  
Seismic Signal ◽  
Location Estimation ◽  
Source Characterization ◽  
Arrival Times ◽  
Signal Arrival ◽  
Uncertainty Estimates

ABSTRACT Signal arrival-time estimation plays a critical role in a variety of downstream seismic analyses, including location estimation and source characterization. Any arrival-time errors propagate through subsequent data-processing results. In this article, we detail a general framework for refining estimated seismic signal arrival times along with full estimation of their associated uncertainty. Using the standard short-term average/long-term average threshold algorithm to identify a search window, we demonstrate how to refine the pick estimate through two different approaches. In both cases, new waveform realizations are generated through bootstrap algorithms to produce full a posteriori estimates of uncertainty of onset arrival time of the seismic signal. The onset arrival uncertainty estimates provide additional data-derived information from the signal and have the potential to influence seismic analysis along several fronts.

Supervised Learning for Arrival Time Estimations in Restaurant Meal Delivery

2021 ◽  
Author(s):  
Florentin D. Hildebrandt ◽  
Marlin W. Ulmer
Keyword(s):  
Supervised Learning ◽  
Arrival Time ◽  
Computational Study ◽  
Time Estimation ◽  
Computational Time ◽  
Arrival Times ◽  
Meal Preparation ◽  
Online Simulation ◽  
Online Simulations ◽  
Meal Delivery

Restaurant meal delivery companies have begun to provide customers with meal arrival time estimations to inform the customers’ selection. Accurate estimations increase customer experience, whereas inaccurate estimations may lead to dissatisfaction. Estimating arrival times is a challenging prediction problem because of uncertainty in both delivery and meal preparation process. To account for both processes, we present an offline and online-offline estimation approaches. Our offline method uses supervised learning to map state features directly to expected arrival times. Our online-offline method pairs online simulations with an offline approximation of the delivery vehicles’ routing policy, again achieved via supervised learning. Our computational study shows that both methods perform comparably to a full near-optimal online simulation at a fraction of the computational time. We present an extensive analysis on how arrival time estimation changes the experience for customers, restaurants, and the platform. Our results indicate that accurate arrival times not only raise service perception but also improve the overall delivery system by guiding customer selections, effectively resulting in faster delivery and fresher food.

scholarly journals Short-Term Scheduling Model of Cluster Tool in Wafer Fabrication

Mathematics ◽  
2021 ◽  
Vol 9 (9) ◽  
pp. 1029
Author(s):  
Ying-Mei Tu
Keyword(s):  
Arrival Time ◽  
Time Estimation ◽  
Advanced Technology ◽  
Shop Floor ◽  
Process Time ◽  
Short Term ◽  
Work In Process ◽  
Scheduling Model ◽  
Cluster Tools ◽  
Cluster Tool

Since last decade, the cluster tool has been mainstream in modern semiconductor manufacturing factories. In general, the cluster tool occupies 60% to 70% of production machines for advanced technology factories. The most characteristic feature of this kind of equipment is to integrate the relevant processes into one single machine to reduce wafer transportation time and prevent wafer contaminations as well. Nevertheless, cluster tools also increase the difficulty of production planning significantly, particularly for shop floor control due to complicated machine configurations. The main objective of this study is to propose a short-term scheduling model. The noteworthy goal of scheduling is to maximize the throughput within time constraints. There are two modules included in this scheduling model—arrival time estimation and short-term scheduling. The concept of the dynamic cycle time of the product’s step is applied to estimate the arrival time of the work in process (WIP) in front of machine. Furthermore, in order to avoid violating the time constraint of the WIP, an algorithm to calculate the latest time of the WIP to process on the machine is developed. Based on the latest process time of the WIP and the combination efficiency table, the production schedule of the cluster tools can be re-arranged to fulfill the production goal. The scheduling process will be renewed every three hours to make sure of the effectiveness and good performance of the schedule.

Novel Method for Detecting Weak AE Signals Based on the Similarity of Time-Frequency Spectra

Geophysics ◽  
2021 ◽  
pp. 1-62
Author(s):  
Wencheng Yang ◽  
Xiao Li ◽  
Yibo Wang ◽  
Yue Zheng ◽  
Peng Guo
Keyword(s):  
Mathematical Morphology ◽  
Signal To Noise Ratio ◽  
Critical Role ◽  
Real Data ◽  
Monitoring Method ◽  
Frequency Spectra ◽  
Arrival Times ◽  
Time Frequency ◽  
Fracturing Process ◽  
Ae Signals

As a key monitoring method, the acoustic emission (AE) technique has played a critical role in characterizing the fracturing process of laboratory rock mechanics experiments. However, this method is limited by low signal-to-noise ratio (SNR) because of a large amount of noise in the measurement and environment and inaccurate AE location. Furthermore, it is difficult to distinguish two or more hits because their arrival times are very close when AE signals are mixed with the strong background noise. Thus, we propose a new method for detecting weak AE signals using the mathematical morphology character correlation of the time-frequency spectrum. The character in all hits of an AE event can be extracted from time-frequency spectra based on the theory of mathematical morphology. Through synthetic and real data experiments, we determined that this method accurately identifies weak AE signals. Compared with conventional methods, the proposed approach can detect AE signals with a lower SNR.

Short-Term Vehicle Velocity Forecasting via Cycle Segmentation Model Selection

2021 ◽  
Author(s):  
Yuanzhi Liu ◽  
Jie Zhang
Keyword(s):  
Model Selection ◽  
Waiting Time ◽  
Short Term Memory ◽  
Critical Role ◽  
Time Estimation ◽  
Forecasting Model ◽  
Short Term ◽  
Forecasting Accuracy ◽  
Vehicle Velocity ◽  
Selection Framework

Abstract Vehicle velocity forecasting plays a critical role in scheduling the operations of varying systems and devices in a passenger vehicle. This paper first generates a repeated urban driving cycle dataset at a fixed route in the Dallas area, aiming to contribute to the improvement of vehicle energy efficiency for commuting routes. The generated driving cycles are divided into cycle segments based on intersection/stop identification, deceleration and reacceleration identification, and waiting time estimation, which could be used for better evaluating the effectiveness of model localization. Then, a segment-based vehicle velocity forecasting model is developed, where a machine learning model is trained/developed at each segment, using the hidden Markov chain (HMM) model and long short-term memory network (LSTM). To further improve the forecasting accuracy, a localized model selection framework is developed, which can dynamically choose a forecasting model (i.e., HMM or LSTM) for each segment. Results show that (i) the segment-based forecast could improve the forecasting accuracy by up to 24%, compared the whole cycle-based forecast; and (ii) the localized model selection framework could further improve the forecasting accuracy by 6.8%, compared to the segment-based LSTM model. Moreover, the potential of leveraging the stopping location at an intersection to estimate the waiting time is also evaluated in this study.

scholarly journals Seismic detection of transient changes beneath Black Rapids Glacier, Alaska, U.S.A.: II. Basal morphology and processes

1999 ◽  
Vol 45 (149) ◽  
pp. 132-146 ◽  
Author(s):  
Matt Nolan ◽  
Keith Echelmeyer
Keyword(s):  
Hydraulic System ◽  
Seismic Analysis ◽  
Pore Water Pressure ◽  
Water Pressure ◽  
Basal Layer ◽  
Thick Layer ◽  
Reflection Coefficients ◽  
Overburden Pressure ◽  
Arrival Times ◽  
Black Rapids Glacier

AbstractUsing changes observed in daily seismic reflections, we have investigated the basal morphology of Black Rapids Glacier, Alaska, U.S.A. The englacial drainage of ice-marginal lakes caused significant changes in the daily reflections, as well as dramatic increases in basal motion. Changes in reflection arrival times and amplitudes indicate that there is a basal till layer at least 5 m thick at some locations beneath this surge-type glacier. Rapid changes in the observed reflection coefficients during the drainage events indicate that changes in till properties must occur throughout the entire 5 m thick layer, they must last for several days following the lake drainages and they must be completely reversible over as little as 36 min. Our seismic analysis shows that changes in effective pressure of the till are unlikely to cause the required changes in the reflection coefficients, but that a decrease in till saturation is likely. We therefore interpret the cause of the seismic anomalies as being a temporary decrease in saturation as water is input to the subglacial hydraulic system, and propose that such a change may occur quickly and reversibly by a redistribution of overburden pressure. Higher water pressures within the hydraulic system cause that region to support more of the glacier’s weight, leaving the remaining areas to support less. Any till within these areas of decreased normal stress would experience a consequent decrease in pore-water pressure, causing gas to exolve, thus decreasing saturation. This decrease in saturation would cause a change in the strength of the basal layer and may affect basal dynamics.

scholarly journals Detection of specific areas and densities for ultrasound tomography

2019 ◽  
pp. 121-127
Author(s):  
Victoria Erofeeva ◽  
Vasilisa Galyamina ◽  
Kseniya Gonta ◽  
Anna Leonova ◽  
Oleg Granichin ◽  
...  
Keyword(s):  
Image Reconstruction ◽  
Ultrasound Imaging ◽  
Arrival Time ◽  
Detection Accuracy ◽  
Problem Solution ◽  
Ultrasound Tomography ◽  
Arrival Times ◽  
Whole Process ◽  
Imaging Reconstruction ◽  
Tomography Data

In this paper we consider the problem of ultrasound tomography. Recently, an increased interest in ultrasound tomography has been caused by non-invasiveness of the method and increased detection accuracy (as compared to radiation tomography), and also ultrasound tomography does not put at risk human health. We study possibilities of detection of specific areas and determining their density using ultrasound tomography data. The process of image reconstruction based on ultrasound data is computationally complex and time consuming. It contains the following parts: calculation of the time-of-flight (TOF) of a signal, detection of specific areas, calculation of density of specific areas. The calculation of the arrival time of a signal is a very important part, because the errors in the calculation of quantities strongly influence the total problem solution. We offer ultrasound imaging reconstruction technology that can be easily parallelized. The whole process is described: from extracting the arrival times of signals raw data feeding from physical receivers to obtaining the desired results.

Seismic analysis of the 7 January 1998 Chemical plant explosion at Kean Canyon, Nevada

1999 ◽  
Vol 89 (4) ◽  
pp. 938-945 ◽  
Author(s):  
Gene A. Ichinose ◽  
Kenneth D. Smith ◽  
John G. Anderson
Keyword(s):  
Seismic Analysis ◽  
Correlation Method ◽  
Chemical Safety ◽  
P Waves ◽  
Arrival Times ◽  
Chemical Company ◽  
Optimal Separation ◽  
The Difference ◽  
Possible Cause

Abstract An accident at the Sierra Chemical Company Kean Canyon plant, 16 km east of Reno, Nevada, resulted in two explosions 3.52 sec apart that devastated the facility. An investigation into a possible cause for the accident required the determination of the chronological order of the explosions. We resolved the high-precision relative locations and chronology of the explosions using a cross-correlation method applied to both seismic and air waves. The difference in relative arrival times of air waves between the explosions indicated that the first explosion occurred at the northern site. We then determined two station centroid separations between explosions, which average about 73 m with uncertainties ranging from ± 17 to 41 m depending on the alignment of station pairs. We estimated a centroid separation of 80 m using P waves with a larger uncertainty of ± 340 m. We performed a grid search for an optimal separation and the azimuth by combining air-wave arrivals from three station pairs. The best solution for the relative location of the second explosion is 73.2 m S35°E from the first explosion. This estimate is well within the uncertainties of the survey by the U.S. Chemical Safety and Hazard Investigation Board (CSB). The CSB reported a separation of approximately 76.2 m S33°E. The spectral amplitudes of P waves are 3 to 4 times higher for the second explosion relative to the first explosion, but the air waves have similar spectral amplitudes. We suggest that this difference is due to the partitioning of energy between the ground and air caused by downward directivity at the southern explosion, and upward directivity at the northern explosion. This is consistent with the absence of a crater for the first explosion and a 1.8-m-deep crater for the second explosion.

Assessment of ambulance services performance by queuing theory, at the Center for Disaster and Emergency Management: A descriptive-analytical study

2021 ◽  
Vol 19 (5) ◽  
pp. 469-478
Author(s):  
Mehdi Nasr Isfahani, MD ◽  
Azar Niknam, PhD Student ◽  
Mahoobeh Doosti-Irani, PhD Student
Keyword(s):  
Call Center ◽  
Queuing Theory ◽  
Analytical Study ◽  
Arrival Time ◽  
Statistical Data ◽  
Average Duration ◽  
Critical Role ◽  
International Standards ◽  
Admission Process ◽  
The City

Background: The emergency departments of the hospitals and emergency medical services (EMSs) centers have a critical role for providing urgent medical care for patients. The statistical data of the present study were provided by the EMS headquarters of the city of Isfahan, from August to November 2017. Results: The findings showed that on average, 210 missions were accomplished each day by the emergency call center, with an average duration of about 53 minutes, for each mission. In addition, the average time for response time (the time between a call and dispatch of the ambulance) was less than 3 minutes, and the average time for arrival time (the time between request of ambulance and the arrival to the scene) was 8.1 minutes. Adequacy of current number of ambulances and staff is evaluated.Conclusion: Considering an average of 8.1 minutes for arrival time, we conclude that the EMS of Isfahan is within an acceptable range, compared to the international standards. In fact, it is shown that the infrastructures of EMS system including ambulance fleets, staff, and equipment are sufficient, and as an effective step for reducing the total time of the mission, the EMS has to operate seamlessly with the patient’s admission process in hospitals. Information such as workload hours, availability of resources and staff, etc. ought to be shared between the EMS and the hospital.

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