Abstract
In the manufacturing process, process monitoring is very important. Real-time contrast (RTC) control chart outperforms existing monitoring methods. However, the performance of RTC control chart depends on the classifier. The existing RTC charts use random forest (RF), support vector machine (SVM), or kernel linear discriminant analysis (KLDA) as a classifier. RF classifier can find cause of faults but the performance is lower than others. Therefore, we suggest the data representation method to improve the RF based RTC control chart. Symbolic aggregate approximation (SAX) is famous method to improve the performance of classification and clustering. We convert the input data by using SAX. We change the parameters of SAX such as alphabet size and breakpoints to improve the performance. Experiment shows that represented data is efficient method to improve the performance of RTC control chart.
Original language | English |
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Title of host publication | 33rd AAAI Conference on Artificial Intelligence, AAAI 2019, 31st Innovative Applications of Artificial Intelligence Conference, IAAI 2019 and the 9th AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2019 |
Publisher | AAAI press |
Pages | 9959-9960 |
Number of pages | 2 |
ISBN (Electronic) | 9781577358091 |
Publication status | Published - 2019 |
Event | 33rd AAAI Conference on Artificial Intelligence, AAAI 2019, 31st Annual Conference on Innovative Applications of Artificial Intelligence, IAAI 2019 and the 9th AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2019 - Honolulu, United States Duration: 2019 Jan 27 → 2019 Feb 1 |
Publication series
Name | 33rd AAAI Conference on Artificial Intelligence, AAAI 2019, 31st Innovative Applications of Artificial Intelligence Conference, IAAI 2019 and the 9th AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2019 |
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Conference
Conference | 33rd AAAI Conference on Artificial Intelligence, AAAI 2019, 31st Annual Conference on Innovative Applications of Artificial Intelligence, IAAI 2019 and the 9th AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2019 |
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Country/Territory | United States |
City | Honolulu |
Period | 19/1/27 → 19/2/1 |
Bibliographical note
Funding Information:This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (NRF-2016R1A2B4013678). This work was also supported by the BK21 Plus (Big Data in Manufacturing and Logistics Systems, Korea University) and by the Samsung Electronics Co., Ltd.
Publisher Copyright:
© 2019, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
ASJC Scopus subject areas
- Artificial Intelligence