60 years of absolute standardization of radionuclides by coincidence counting methods in the Romanian metrology laboratory

2021 ◽  
pp. 109707
Author(s):  
Maria Sahagia ◽  
Enric Leon Grigorescu ◽  
Aurelian Luca ◽  
Anamaria Cristina Wätjen ◽  
Constantin Ivan ◽  
...  

Cosmic ray measurements on mountains are limited in general to altitudes below about 4000 meters. Above this height Regener has made successful use of small balloons carrying self-recording apparatus, and occasional flights have been made with manned balloons by Piccard, Cosyns, and by American workers. Balloon experiments are, however, hardly practicable in this country, so we decided to investigate cosmic rays, and in particular the production of showers, using an aeroplane. Facilities for flying to a height of about 10 km. Were generously provided by the Air Ministry. Apparatus Two independent sets of three tube counters were used in conjunction with the usual coincidence counting circuits. The counters could be arranged in a vertical line to record vertical penetrating particles, or in a triangle to record showers. The triple coincidences were recorded by telephone counters which were photographed at intervals together with a clock and aneroid barometer. The detailed design of the apparatus required some consideration since the aeroplane available (the Vickers Vespa machine used for high altitude experiments at the Royal Aircraft Establishment) had an open observer’s cockpit in which the counting set had to be installed.


2021 ◽  
Vol 268 ◽  
pp. 354-362
Author(s):  
Lynn M Orfahli ◽  
Majid Rezaei ◽  
Brian A Figueroa ◽  
Audrey V Crawford ◽  
Michael J Annunziata ◽  
...  

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Siqi Tang ◽  
Zhisong Pan ◽  
Xingyu Zhou

This paper proposes an accurate crowd counting method based on convolutional neural network and low-rank and sparse structure. To this end, we firstly propose an effective deep-fusion convolutional neural network to promote the density map regression accuracy. Furthermore, we figure out that most of the existing CNN based crowd counting methods obtain overall counting by direct integral of estimated density map, which limits the accuracy of counting. Instead of direct integral, we adopt a regression method based on low-rank and sparse penalty to promote accuracy of the projection from density map to global counting. Experiments demonstrate the importance of such regression process on promoting the crowd counting performance. The proposed low-rank and sparse based deep-fusion convolutional neural network (LFCNN) outperforms existing crowd counting methods and achieves the state-of-the-art performance.


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