Initialising ...
Initialising ...
Initialising ...
Initialising ...
Initialising ...
Initialising ...
Initialising ...
Kuno, Yusuke; Aso, Ryoji
Nihon Genshiryoku Gakkai-Shi ATOMO
, 50(5), P. 324, 2008/05
no abstracts in English
Suzuki, Mitsutoshi; Hori, Masato; Aso, Ryoji; Usuda, Shigekazu
Journal of Nuclear Science and Technology, 43(10), p.1270 - 1279, 2006/10
Times Cited Count：3 Percentile：24.08(Nuclear Science & Technology)The multiscale statistical process control (MSSPC) method is applied to explain material unaccounted for (MUF) in large scale reprocessing plants using numerical calculations. Continuous wavelet functions are used to decompose the process data, which simulate batch operation superimposed by various types of disturbance, and the disturbance components included in the data are divided into time and frequency spaces. The diagnosis of MSSPC is applied to distinguish abnormal events from the process data and shows how to detect abrupt and protracted diversions using principle component analysis. Quantitative performance of MSSPC for the time series data is shown with average run lengths given by Monte-Carlo simulation to compare to the non-detection probability B. Recent discussion about bias corrections in material balances is introduced and another approach is presented to evaluate MUF without assuming the measurement error model.
Suzuki, Mitsutoshi; Hori, Masato; Aso, Ryoji; Usuda, Shigekazu
Proceedings of INMM 47th Annual Meeting (CD-ROM), 8 Pages, 2006/00
The multiscale statistical process control (MSSPC) method is applied to explain material unaccounted for (MUF) in a large scale reprocessing plant using numerical calculations. Increasing the amount of nuclear material throughput per year, which is more than 5000kgPu in commercial reprocessing plant, the accumulated annual measurement errors will exceed 1SQ (=8kgPu) and frequent Near Real Time Accountancy (NRTA) and process monitoring measures are required to satisfy the IAEA safeguard criteria. In this study, continuous wavelet functions are used to decompose the process data, which is simulated batch operation mode superimposed by various types of disturbance, and the disturbance components constituting the data are divided into both time and frequency region. Because MSSPC based on wavelet decomposition provides efficient performance over a wide range of abnormal events, the protracted or abrupt diversion loss, not known a priori, can be detected for nuclear safeguards purpose. The diagnosis for MSSPC is applied to distinguish an abnormal event from the normal data and shows how to detect both types of diversion loss using principle component analysis (PCA). MUF data is generally supposed to be autocorrelated time series data. Quantitative performance of MSSPC for the time series data is shown with the average run lengths simulated by Monte-Calro calculation to compare the nondetection probability B. Recent discussion about bias corrections (BC) in material balances is introduced and other approach is presented to evaluate MUF in an explanatory manner without an adoption of BC.
Inoue, Naoko; Hori, Keiichiro; Dosho, Hisaharu; Ota, Kiyokazu*; Otsuka, Naoto; Aso, Ryoji; Senzaki, Masao
Proceedings of INMM 47th Annual Meeting (CD-ROM), 6 Pages, 2006/00
no abstracts in English
Sasao, Nobuyuki*; ; *; ; Kato, Masato; Funasaka, Hideyuki; *
PNC TN8410 88-027, 52 Pages, 1988/06
None
Suzuki, Mitsutoshi; Hori, Masato; Aso, Ryoji; Usuda, Shigekazu
no journal, ,
no abstracts in English
Aso, Ryoji
no journal, ,
no abstracts in English
