Name
Abstract | ||
|---|---|---|
A series of repeated nuclear material balances forms a time series of often autocorrelated observations. Outliers, deviations from an in-control production process or time series pattern, indicate an out-of-control situation relative to the process norm. In this paper various methods, especially neural networks, will be examined with respect to their use to detect nuclear material diversions or losses more rapidly and accurately than currently used methods. The neural network technique will be enhanced with the use of a simulation computer program for creating the training data set. This simulation approach provides the opportunity of including outliers of various types in a data set for training the neural network because an actual process data set used for training possibly may not have outliers. In this paper, the methods will be compared on their ability to identify outliers and reduce false alarms. These methods were tested on data sets of nuclear material balances with known removals, and the results are tabulated and described, Based on these results, we believe the algorithms used will assist the nuclear industry in process control provide a new approach to nuclear material safeguards, and also provide a new approach to training neural networks for process control applications. |
Year | DOI | Venue |
|---|---|---|
1996 | 10.1021/ci950146v | JOURNAL OF CHEMICAL INFORMATION AND COMPUTER SCIENCES |
Keywords | Field | DocType |
neural network | Training set,Data mining,Data set,Outlier,Nuclear material,Scheduling (production processes),Artificial intelligence,Computer program,Artificial neural network,Mathematics,Machine learning,Autocorrelation | Journal |
Volume | Issue | ISSN |
36 | 3 | 0095-2338 |
Citations | PageRank | References |
1 | 0.54 | 6 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| James H. Hamburg | 1 | 1 | 0.54 |
| David E. Booth | 2 | 109 | 21.34 |
| G. Jay Weinroth | 3 | 4 | 1.08 |