scholarly journals Path Planning for Localization of Radiation Sources Based on Principal Component Analysis

2021 ◽  
Vol 11 (10) ◽  
pp. 4707
Author(s):  
Takuya Kishimoto ◽  
Hanwool Woo ◽  
Ren Komatsu ◽  
Yusuke Tamura ◽  
Hideki Tomita ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Path Planning ◽  
Gamma Ray ◽  
Principal Component ◽  
Component Analysis ◽  
Field Of View ◽  
Radiation Sources ◽  
Measurement Path ◽  
Time Required ◽  
Gamma Ray Detector

In this paper, we propose a path planning method for the localization of radiation sources using a mobile robot equipped with an imaging gamma-ray detector, which has a field of view in all directions. The ability to detect and localize radiation sources is essential for ensuring nuclear safety, security, and surveillance. To enable the autonomous localization of radiation sources, the robot must have the ability to automatically determine the next location for gamma ray measurement instead of following a predefined path. The number of incident events is approximated to be the squared inverse proportional to the distance between the radiation source and the detector. Therefore, the closer the distance to the source, the shorter the time required to obtain the same radiation counts measured by the detector. Hence, the proposed method is designed to reduce this distance to a position where a sufficient number of gamma-ray events can be obtained; then, a path to surround the radiation sources is generated. The proposed method generates this path by performing principal component analysis based on the results obtained from previous measurements. Both simulations and actual experiments demonstrate that the proposed method can automatically generate a measurement path and accurately localize radiation sources.

An Optimal Filling Path Planning Based on Principal Component Analysis

2020 ◽  
Author(s):  
Yu Zhang ◽  
Tongcai Wang ◽  
Yantong Zhao ◽  
Haochen Li ◽  
Bingshan Liu ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Path Planning ◽  
Principal Component ◽  
Component Analysis ◽  
Filling Path

scholarly journals A Principal Component Analysis of the 3B Gamma‐Ray Burst Data

1998 ◽  
Vol 498 (1) ◽  
pp. 342-348 ◽  
Author(s):  
Z. Bagoly ◽  
A. Meszaros ◽  
I. Horvath ◽  
L. G. Balazs ◽  
P. Meszaros
Keyword(s):  
Principal Component Analysis ◽  
Gamma Ray ◽  
Principal Component ◽  
Component Analysis ◽  
Gamma Ray Burst ◽  
Burst Data

Applying principal component analysis to seismic attributes for interpretation of evaporite facies: Lower Triassic Jialingjiang Formation, Sichuan Basin, China

2017 ◽  
Vol 5 (4) ◽  
pp. T461-T475 ◽  
Author(s):  
Suyun Hu ◽  
Wenzhi Zhao ◽  
Zhaohui Xu ◽  
Hongliu Zeng ◽  
Qilong Fu ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Gamma Ray ◽  
Principal Component ◽  
Lower Triassic ◽  
Component Analysis ◽  
Seismic Attributes ◽  
Hydrocarbon Exploration ◽  
Seismic Survey ◽  
Seismic Attribute ◽  
Siliciclastic Rocks

In China and elsewhere, it is important to predict different lithologies and lithofacies for hydrocarbon exploration in a mixed evaporite-carbonate-siliciclastic system. The lower section of the second member of the Jialingjiang Formation (T1j2L) is mainly composed of anhydrite, dolostone, limestone, and siliciclastic rocks, providing a rare opportunity to reconstruct detailed facies in a [Formula: see text] 3D seismic survey with 31 wells. Wireline logs (sonic, density, and gamma ray) calibrated by core analysis are essential in distinguishing anhydrite, siliciclastics, and carbonates. Although different lithologies are characterized by different acoustic impedance (AI), with certain overlapping, it is still difficult to predict lithology by any single seismic attribute because of the limited seismic resolution in a thinly interbedded formation of multiple lithologies. In our study, principal component analysis (PCA) was applied to extract lithologic information from selected seismic attributes; the first two principal components were used to predict the content of anhydrite, siliciclastics, and carbonates. Content maps of anhydrite, siliciclastics, and carbonates — created by mixing the represented color — were used to reconstruct lithofacies of the T1j2L submember. It is quite difficult, even with the PCA approach, to uniquely resolve the three lithologies due to the overlapped AI and the limited resolution of the seismic data. However, the workflow that we evaluated dramatically improved the prediction accuracy of lithology and lithofacies. Facies transition during the deposition of the T1j2L submember in the study area was inferred from a paleo-uplift in the southwest to a restricted lagoon and then to an open marine setting in the northeast.

Principal Component Analysis as a Strategy for Multispectral Acquisition of Images

1996 ◽  
Vol 54 ◽  
pp. 304-305
Author(s):  
P. Ingram ◽  
D. A. Kopf ◽  
A. LeFurgey
Keyword(s):  
Principal Component Analysis ◽  
Analytical Data ◽  
Computation Time ◽  
Principal Component ◽  
Component Analysis ◽  
Radar Images ◽  
Variance Contribution ◽  
Stem Image ◽  
Satellite Radar ◽  
Time Required

Principal Component Analysis (PCA) has been used by many authors to improve the statistical accuracy and interpretation of multispectral images in a variety of settings from the analysis of satellite radar images to mapping of biological cells and tissues. In this work we describe a strategy to characterize analytical data from biological cryosections by objectively using a scatterplot routine from PCA images to generate masks on the original STEM image; in this way control of the acquisition of spectral data can be performed in a statistically optimal manner.If a set of images are obtained, the corresponding set of Principal Component (PC) images are easily generated. Mathematically, the PC images are the projection of the original images onto a set of orthogonal, complete axes that are the eigenvectors of the symmetric variance-covariance matrix obtained by summing the product of corresponding pixels in each element pair across the entire image (or selected region thereof). The corresponding eigenvalues are the variance contribution of each eigenimage to the total variance of the original set of images. When sorted into order of decreasing variance, the first PC axis represents the greatest covariance that can be found among all the elements; the second represents the largest remaining covariance orthogonal to the first axis and so on. The computation time required for such a calculation is about 10 seconds for eight 128x128 images for a 35 MHz 68040 processor.

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