Classification Model of Control Charts Based on ML-SVM
Author:
Affiliation:
1. Quality and Safety Engineering, China Jiliang University, China
2. Inspection and Testing Center, China
Funder
Ningbo Science and Technology Innovation 2025 Major Special Project
Zhejiang Provincial Public welfare Technology Application Research Project
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3603273.3630506
Reference9 articles.
1. Abbas Tahir Shabbir Ahmad Muhammad Riaz and Zhengming Qian. 2017. "A Bayesian way of monitoring the linear profiles using CUSUM control charts." Communications in Statistics - Simulation and Computation 48 (1): 126-149. https://doi.org/10.1080/03610918.2017.1375520.
2. Synthetic control chart with curtailment for monitoring shifts in fraction non-conforming
3. Miao, Zhihong, and Mingshun Yang. 2019. "Control Chart Pattern Recognition Based on Convolution Neural Network." In Smart Innovations in Communication and Computational Sciences, In Advances in Intelligent Systems and Computing, 97-104.
4. Control chart pattern recognition using the convolutional neural network
5. A cost-sensitive convolution neural network learning for control chart pattern recognition
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