ICACT20240049 Question.1
Questioner: bbaniya@fsmail.bradley.edu 2024-02-07 오전 8:56:08 |
ICACT20240049 Answer.1
Answer by Auhor huxlee@g.skku.edu 2024-02-07 오전 8:56:08
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| Thank you to all the authors for their valuable contributions. I have one question for you: The authors proposed and validated the "Multivariate PCA-based Composite Criteria Evaluation Method," which incorporates a straightforward PCA algorithm and two statistical techniques. Is there any quantitative number/s (detection rate) to determine the frequency of anomaly detection in this method? |
Thank you for your interest in the paper and for your questions.
In response to your question, the detection rate is variable depending on the characteristics of the data, so it is difficult to give a quantitative number.
In the case of the data used in this paper, the data analysis was conducted as a proof of concept, and the characteristics were relatively clear. |
ICACT20240049 Question.2
Questioner: franklinlu888@outlook.com 2024-02-07 오전 8:59:15 |
ICACT20240049 Answer.2
Answer by Auhor huxlee@g.skku.edu 2024-02-07 오전 8:59:15
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| Good afternoon, I am the chair of Session 2B, Bing-Yuh Lu from China, and welcome you to ICACT 2024. Your artwork proposed a nice Multivariate PCA-based Composite Criteria Evaluation Method. That is a wonderful work. However, is there any data to present the accuracy of the method you proposed? Thank you! |
Dear Bing-Yuh Lu,
Thank you for your interest in the paper and for your questions. To answer your question, the data used in the paper is manufacturing data collected through the MES of a factory that is actually producing for the data analysis PoC task. The data used for analysis is not completely raw data, but a dataset with preprocessing such as primary outlier removal. |
ICACT20240049 Question.3
Questioner: franklinlu888@outlook.com 2024-02-07 오전 9:00:05 |
ICACT20240049 Answer.3
Answer by Auhor huxlee@g.skku.edu 2024-02-07 오전 9:00:05
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| Nice job! |
Thank you for your interest in the paper. |
