Stochastic Feed-forward Attention Mechanism for Reliable Defect Classification and Interpretation

Author:

Lee Jiyoon,Mok Chunghyup,Kim Sanghoon,Moon Seokho,Kim Seo-Yeon,Kim Seoung Bum

Publisher

Springer International Publishing

Reference17 articles.

1. Sahno, J., Shevtshenko, E.: Quality improvement methodologies for continuous improvement of production processes and product quality and their evolution. In: 9th International DAAAM Baltic Conference “Industrial Engineering”, pp. 181-186 (2014)

2. Stojanovic, L., Dinic, M., Stojanovic, N., Stojadinovic, A.: Big-data-driven anomaly detection in industry (4.0): an approach and a case study. In: 2016 IEEE International Conference Big Data, IEEE, pp. 1647–1652 (2016)

3. Cho, Y.S., Kim, S.B.: Quality-discriminative localization of multisensor signals for root cause analysis. IEEE Trans. Syst. Man, Cybern. Syst. 52(7), 4374–4387 (2021)

4. Kang, S.B., Lee, J.H., Song, K.Y., Pahk, H.J.: Automatic defect classification of TFT-LCD panels using machine learning. In: 2009 IEEE International Symposium on Industrial Electronics, pp. 2175-2177. IEEE (2009)

5. Ryu, J.-H., Heo, M.-O., Zhang, B.-T.: CNN-based classification methods for filtering of defect pixel circuit images. J. Comput. Sci. Eng. 907-909 (2017)

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