An Adaptive Quality Assessment System-Aspect of Human Factor and Measurement Uncertainty
| Document type: | Conference Papers |
|---|---|
| Peer reviewed: | Yes |
| Author(s): | Jenny Wirandi, Jiandan Chen, Wlodek Kulesza |
| Title: | An Adaptive Quality Assessment System-Aspect of Human Factor and Measurement Uncertainty |
| Journal: | IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT |
| Conference name: | IEEE International Workshop on Advanced Methods for Uncertainty Estimation in Measurement, Sardagna, ITALY, 2007 |
| Year: | 2009 |
| Volume: | 58 |
| Issue: | 1 |
| Pagination: | 68-75 |
| ISSN: | 0018-9456 |
| Publisher: | IEEE |
| URI/DOI: | 10.1109/TIM.2008.2004981 |
| ISI number: | 000261848300010 |
| Organization: | Blekinge Institute of Technology |
| Department: | School of Engineering - Dept. of Signal Processing (Sektionen för teknik – avd. för signalbehandling) School of Engineering S- 372 25 Ronneby +46 455 38 50 00 http://www.tek.bth.se/ |
| Language: | English |
| Abstract: | In this paper, we discuss a model of quality that makes use of the fuzzily defined variable approach to better understand the concept and, thus, enables the further development of this variable. We propose a general method that may estimate a quality index (QI) that handles both qualitative and quantitative issues. The system further uses a neural network since the system learns how to integrate human factors into a quantitative QI. In our case study, we have examined the measurement of image quality and proposed a theoretical model of pulp quality. |
| Subject: | Signal Processing\General |
| Keywords: | Fuzzily defined quantity, image quality, neural network (NN), pulp quality, quality assessment, quality index (QI) |
