Time Mark Generator for an X‐Y Recorder

1968 ◽  
Vol 39 (1) ◽  
pp. 127-127
Author(s):  
R. E. Fassnacht ◽  
J. R. Dillinger
Keyword(s):  
Time Mark

A hybrid time mark program

SIMULATION ◽  
1967 ◽  
Vol 8 (4) ◽  
pp. 190-190
Author(s):  
Robert Vichnevetsky ◽  
Omri Serlin
Keyword(s):  
Time Mark

Application of Improved U-Net and ResU-Net Based Semantic Segmentation Method for Digitization of Analog Seismograms

2020 ◽  
Author(s):  
Jiahua Zhao ◽  
Miaki Ishii ◽  
Hiromi Ishii ◽  
Thomas Lee
Keyword(s):  
Time Series ◽  
Scientific Discovery ◽  
Semantic Segmentation ◽  
Machine Learning Algorithms ◽  
Classification Model ◽  
Segmentation Method ◽  
Data Set ◽  
Time Mark ◽  
Scanned Images ◽  
Digital Time

<p>Analog seismograms contain rich and valuable information over nearly a century. However, these analog seismic records are difficult to analyze quantitatively using modern techniques that require digital time series. At the same time, because these seismograms are deteriorating with age and need substantial storage space, their future has become uncertain. Conversion of the analog seismograms to digital time series will allow more conventional access and storage of the data as well as making them available for exciting scientific discovery. The digitization software, DigitSeis, reads a scanned image of a seismogram and generates digitized and timed traces, but the initial step of recognizing trace and time mark segments, as well as other features such as hand-written notes, within the image poses certain challenges. Armed with manually processed analyses of image classification, we aim to automate this process using machine learning algorithms. The semantic segmentation methods have made breakthroughs in many fields. In order to solve the problem of accurate classification of scanned images for analog seismograms, we develop and test an improved deep convolutional neural network based on U-Net, Improved U-Net, and a deeper network segmentation method that adds the residual blocks, ResU-Net. There are two segmentation objects are the traces and time marks in scanned images, and the goal is to train a binary classification model for each type of segmentation object, i.e., there are two models, one for trace objects and another for time mark objects, for each of the neural networks. The networks are trained on the 300 images of the digitizated results of analog seismograms from Harvard-Adam Dziewoński Observatory from 1939. Application of the algorithms to a test data set results in the pixel accuracy (PA) for the Improved U-Net of 95% for traces and nearly 100% for time marks, with Intersection over Union (IoU) of 79% and 75% for traces and time marks, respectively. The PA of ResU-Net are 97% and nearly 100% for traces and time marks, with IoU of 83% and 74%. These experiments show that Improved U-Net is more effective for semantic segmentation of time marks, while ResU-Net is more suitable for traces. In general, both network models work well in separating and identifying objects, and provide a significant step forward in nearly automating digitizing analog seismograms.</p>

scholarly journals A Real Time Coincidence System for High Count-Rate TOF or Non-TOF PET Cameras Using Hybrid Method Combining AND-Logic and Time-Mark Technology

2010 ◽  
Vol 57 (2) ◽  
pp. 708-714 ◽  
Author(s):  
Chao Wang ◽  
Hongdi Li ◽  
Rocio A. Ramirez ◽  
Yuxuan Zhang ◽  
Hossain Baghaei ◽  
...  
Keyword(s):  
Real Time ◽  
Hybrid Method ◽  
Count Rate ◽  
High Count ◽  
High Count Rate ◽  
Time Mark ◽  
Coincidence System ◽  
Time Coincidence ◽  
Tof Pet

Problems of Framing

2020 ◽  
Vol 13 ◽  
pp. 118-129
Author(s):  
Augustus Wachbrit ◽  
Keyword(s):  
Time Mark

In “Fatalism and Time,” Mark Bernstein argues against the notion that the B-theory of time is fatalistic. However, when he frames the differences between the A-theory of time and the B-theory of time, I argue that Bernstein imports some troublesome conceptual baggage in the form of what he calls “atemporal truths,” which, in the end, dooms the B-theory to fatalism, the consequence he sought to avoid. From my examination of Bernstein’s framing of the B-theory of time, I suggest that, given the proper framing of that theory, it is not doomed to fatalism.

No Future for Bethulia? Judith and Queer Time

2020 ◽  
Vol 28 (4) ◽  
pp. 451-465
Author(s):  
Caryn Tamber-Rosenau
Keyword(s):  
Jewish History ◽  
Main Character ◽  
Theoretical Frameworks ◽  
Time Mark ◽  
Daily Schedule ◽  
The Future ◽  
Queer Temporality ◽  
Book Of Judith

Abstract The Book of Judith and its main character are fascinating for the ways in which they play with time and history. This article argues that theoretical frameworks of queer temporality are instructive for understanding Judith. Judith’s childlessness, her aberrant daily schedule, and her refusal to work on her enemies’ time mark her as someone resisting normative time and a focus on the future. At the same time, however, Judith does ensure a future for Bethulia, and, by extension, for Israel. Consequently, this article also explores how the Book of Judith itself plays with the idea of history, calling into question the very future Judith supposedly ensures. The article also highlights the absence of eschatological thinking in the Book of Judith. Finally, this article discusses the implications of such an erring, queer narrative for thinking about Jewish history and the biblical canon.

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