None

Fujita, Reiko*; *; Kondo, Naruhito*; Utsunomiya, Kazuhiro*

JNC TJ8420 2000-004, 41 Pages, 2000/03

JNC-TJ8420-2000-004.pdf:5.08MB

no abstracts in English

None

PNC TN1410 92-006, 17 Pages, 1991/12

PNC-TN1410-92-006.pdf:0.86MB

no abstracts in English

None

; Koakutsu, Masayuki; *; Yoshida, Michihiro; ; *;

PNC TN8450 91-005, 103 Pages, 1991/02

PNC-TN8450-91-005.pdf:2.7MB

None

Study on inspection of drum-filled coment monolithic solids of radioactive wastes (III); Nondestructive ultrasonic test

Hihakai Kensa, 29(11), p.783 - 789, 1980/00

http://ci.nii.ac.jp/naid/40003264091

no abstracts in English

Recovery and investigation of radioactive wastes disposed of in deepsea by USEPA

Hoken Butsuri, 14(3), p.197 - 201, 1979/00

https://doi.org/10.5453/jhps.14.197

no abstracts in English

Leaching tests of full size monolithic cement products of LWR wastes under high hydrostatic pressure for sea disposal

; ; ;

Nihon Genshiryoku Gakkai-Shi, 20(12), p.887 - 896, 1978/00

https://doi.org/10.3327/jaesj.20.887

no abstracts in English

Classification of radioactive waste drums using -ray spectra

Uechi, Yasufumi; Tanaka, Yoshio; Hata, Haruhi*; Yokoyama, Kaoru*

no journal, , 

We investigated the feasibility of using support vector machine (SVM), a computer learning method, to classify uranium waste drums as natural uranium or reprocessed uranium based on their origins. The method was trained using 12 training datasets and tested on 955 datasets of -ray spectra obtained with NaI(Tl) scintillation detectors. The results showed that only 4 out of 955 test datasets were different from the original labels-one of them was mislabeled and the other three were misclassified by SVM. These findings suggest that SVM is an effective method to classify a large quantity of data within a short period of time. Consequently, SVM is a feasible method as a supplemental tool to check original labels.