scholarly journals A muographic study of a scoria cone from 11 directions using nuclear emulsion cloud chambers

2021 ◽  
Author(s):  
Seigo Miyamoto ◽  
Shogo Nagahara ◽  
Kunihiro Morishima ◽  
Toshiyuki Nakano ◽  
Masato Koyama ◽  
...  
Keyword(s):  
Data Analysis ◽  
High Speed ◽  
Nuclear Emulsion ◽  
Detection Efficiency ◽  
Forward Direction ◽  
Scoria Cone ◽  
Muon Flux ◽  
Density Structure ◽  
Readout System ◽  
Nuclear Emulsion Detector

Abstract. One of the key challenges for muographic studies is to reveal the detailed 3D density structure of a volcano by increasing the number of observation directions. 3D density imaging by multi-directional muography requires that the individual differences in the performance of the installed muon detectors are small and that the results from each detector can be derived without any bias in the data analysis. Here we describe a pilot muographic study of the Izu–Omuroyama scoria cone in Shizuoka Prefecture, Japan, from 11 directions, using a new nuclear emulsion detector design optimized for quick installation in the field. We describe the details of the data analysis and present a validation of the results. The Izu–Omuroyama scoria cone is an ideal target for the first multi-directional muographic study, given its expected internal density structure and the topography around the cone. We optimized the design of the nuclear emulsion detector for rapid installation at multiple observation sites in the field, and installed these at 11 sites around the volcano. The images in the developed emulsion films were digitized into segmented tracks with a high-speed automated readout system. The muon tracks in each emulsion detector were then reconstructed. After the track selection, including straightness filtering, the detection efficiency of the muons was estimated. Finally, the density distributions in 2D angular space were derived for each observation site by using a muon flux and attenuation models. The observed muon flux was compared with the expected value in the free sky, and is 88 % ± 4 % in the forward direction and 92 % ± 2 % in the backward direction. The density values were validated by comparison with the values obtained from gravity measurements, and are broadly consistent, except for one site. The excess density at this one site may indicate that the density inside the cone is non-axisymmetric, which is consistent with a previous geological study.

Joint High Speed Sealift (JHSS) Segmented Model Test Data Analysis and Validation of Numerical Simulations

2012 ◽  
Author(s):  
Dominic Piro ◽  
Kyle A. Brucker ◽  
Thomas T. O'Shea ◽  
Donald Wyatt ◽  
Douglas Dommermuth ◽  
...  
Keyword(s):  
Data Analysis ◽  
Numerical Simulations ◽  
Test Data ◽  
Model Test ◽  
High Speed ◽  
Segmented Model

Measurement of MKID Performance with High-Speed and Wide-Band Readout System

2014 ◽  
Vol 176 (3-4) ◽  
pp. 459-464 ◽  
Author(s):  
Kenichi Karatsu ◽  
M. Naruse ◽  
T. Nitta ◽  
M. Sekine ◽  
S. Sekiguchi ◽  
...  
Keyword(s):  
High Speed ◽  
Wide Band ◽  
Readout System

Research of High-Speed Railway Running Economic Benefit Based on Gray Model

2012 ◽  
Vol 253-255 ◽  
pp. 1273-1277
Author(s):  
Xue Dong Du ◽  
Na Ren
Keyword(s):  
Data Analysis ◽  
Prediction Model ◽  
Economic Benefit ◽  
High Speed ◽  
Accurate Prediction ◽  
Gray Model ◽  
Practical Application ◽  
High Speed Railway ◽  
Railway Stations ◽  
Train Operation

The research of high-speed railway running economic benefit is important to timely know well the train operation state for the railway administration. A prediction model of high-speed railway running economic benefit is proposed in this article based on Gray model. The Gray model is a good example to make accurate prediction of the development of matters. According to the data analysis of Beijing and Shanghai railway stations, we can know that the result of prediction model is accurate, so the prediction based on Gray model is scientific and reasonable in the practical application.

scholarly journals LARGE AREA HODOSCOPES FOR MUON DIAGNOSTICS OF HELIOSPHERE AND EARTH’S MAGNETOSPHERE

2013 ◽  
Vol 53 (A) ◽  
pp. 807-810
Author(s):  
I. I. Yashin ◽  
N. V. Ampilogov ◽  
I.I. Astapov ◽  
N.S. Barbashina ◽  
V.V. Borog ◽  
...  
Keyword(s):  
Data Analysis ◽  
Real Time ◽  
Remote Monitoring ◽  
Angular Resolution ◽  
Muon Flux ◽  
Large Area ◽  
Real Time Mode ◽  
The Earth ◽  
Muon Hodoscope ◽  
Active Processes

Muon diagnostics is a technique for remote monitoring of active processes in the heliosphere and the magnetosphere of the Earth based on the analysis of angular variations of muon flux simultaneously detected from all directions of the upper hemisphere. To carry out muon diagnostics, special detectors – muon hodoscopes – which can detect muons from any direction with good angular resolution in real-time mode are required. We discuss approaches to data analysis and the results of studies of various extra-terrestrial processes detected by means of the wide aperture URAGAN muon hodoscope.

scholarly journals R2FA-Det: Delving into High-Quality Rotatable Boxes for Ship Detection in SAR Images

2020 ◽  
Vol 12 (12) ◽  
pp. 2031 ◽  
Author(s):  
Shiqi Chen ◽  
Jun Zhang ◽  
Ronghui Zhan
Keyword(s):  
High Speed ◽  
Detection Efficiency ◽  
Feature Representation ◽  
Sar Images ◽  
Shape Information ◽  
Ship Detection ◽  
Accurate Localization ◽  
Aspect Ratios ◽  
Non Local ◽  
Experimental Validations

Recently, convolutional neural network (CNN)-based methods have been extensively explored for ship detection in synthetic aperture radar (SAR) images due to their powerful feature representation abilities. However, there are still several obstacles hindering the development. First, ships appear in various scenarios, which makes it difficult to exclude the disruption of the cluttered background. Second, it becomes more complicated to precisely locate the targets with large aspect ratios, arbitrary orientations and dense distributions. Third, the trade-off between accurate localization and improved detection efficiency needs to be considered. To address these issues, this paper presents a rotate refined feature alignment detector (R 2 FA-Det), which ingeniously balances the quality of bounding box prediction and the high speed of the single-stage framework. Specifically, first, we devise a lightweight non-local attention module and embed it into the stem network. The recalibration of features not only strengthens the object-related features yet adequately suppresses the background interference. In addition, both forms of anchors are integrated into our modified anchor mechanism and thus can enable better representation of densely arranged targets with less computation burden. Furthermore, considering the shortcoming of the feature misalignment existing in the cascaded refinement scheme, a feature-guided alignment module which encodes both the position and shape information of current refined anchors into the feature points is adopted. Extensive experimental validations on two SAR ship datasets are performed and the results demonstrate that our algorithm has higher accuracy with faster speed than some state-of-the-art methods.

Sign in / Sign up

Export Citation Format

Share Document