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ランダムフォレスト法による$$gamma$$線スペクトルを用いた放射性廃棄物ドラム缶の分類

Classification of radioactive waste drums using random forests for their $$gamma$$-ray spectra

秦 はるひ ; 石森 有

Hata, Haruhi; Ishimori, Yuu

放射性廃棄物ドラム缶の分類に対し、機械学習法の一つであるランダムフォレスト法が適用できるか検討した。ウランの起源が天然または回収燃料かで分類された954点のドラム缶の$$gamma$$線スペクトルデータを利用した。300点を訓練データ用にとりわけ、残りの654点のスペクトルデータを用いて、ランダムフォレストの分類の正答率を評価した。カウント数の対数の差分値をとる前処理を行う場合、ランダムフォレスト法で654点を正確に分類できた。

The feasibility of Random Forests, one of machine learning methods was examined for the classification of radioactive waste drums. It was carried out using 954 $$gamma$$-ray spectra of drums which were already classified to natural or reprocessed uranium. After 300 spectra were selected at random to reassemble training datasets, the percentages of correct classification by Random Forests were evaluated with another 654 spectra. When the counts of spectra were reprocessed as the difference of their logarithm, Random Forests accurately classified 654 drums.

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