Name
Abstract | ||
|---|---|---|
It has previously been shown that smoothing algorithms can provide the basis for methods to detect nuclear material losses and moreover can also provide a general approach to industrial statistical process control. The present paper extends this result by showing that a set of robust smoothers also produces methods that can be used in statistical process control. Further, it is shown that these smoothers are somewhat more sensitive to out of control points than those methods previously studied. The methods are successfully illustrated on chemical process data. |
Year | DOI | Venue |
|---|---|---|
1997 | 10.1021/ci9600317 | JOURNAL OF CHEMICAL INFORMATION AND COMPUTER SCIENCES |
Keywords | Field | DocType |
statistical process control | Mathematical optimization,Computer science,Smoothing,Statistical process control | Journal |
Volume | Issue | ISSN |
37 | 2 | 0095-2338 |
Citations | PageRank | References |
1 | 0.39 | 1 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| John Grznar | 1 | 8 | 1.76 |
| David E. Booth | 2 | 109 | 21.34 |
| Paul R. Sebastian | 3 | 9 | 3.48 |