Multi-source adaptive thresholding adaboost with application to virtual metrology

References

• Chao, Qun, HaoHan Gao, JianFeng Tao, YuanHang Wang, Jian Zhou, and ChengLiang Liu. 2022. “Adaptive Decision-Level Fusion Strategy for the Fault Diagnosis of Axial Piston Pumps Using Multiple Channels of Vibration Signals.” Science China Technological Sciences 65 (2): 470–480. https://doi.org/10.1007/s11431-021-1904-7.
   Web of Science ®Google Scholar
• Chen, Tianqi, and Carlos Guestrin. 2016, August 13–17. “Xgboost: A Scalable Tree Boosting System.” In Proceedings of the 22nd ACM Sigkdd International Conference on Knowledge Discovery and Data Mining, 785–794. San Francisco, CA, USA.
   Google Scholar
• Chiarini, Andrea, and Maneesh Kumar. 2022. “What is Quality 4.0? An Exploratory Sequential Mixed Methods Study of Italian Manufacturing Companies.” International Journal of Production Research 60 (16): 4890–4910. https://doi.org/10.1080/00207543.2021.1942285.
   Web of Science ®Google Scholar
• Choi, Jeongsub, and Myong Kee Jeong. 2018. “Deep Autoencoder with Clipping Fusion Regularization on Multistep Process Signals for Virtual Metrology.” IEEE Sensors Letters 3 (1): 1–4. https://doi.org/10.1109/LSENS.2018.2884735.
   Google Scholar
• Dreyfus, Paul-Arthur, Foivos Psarommatis, Gokan May, and Dimitris Kiritsis. 2022. “Virtual Metrology As An Approach for Product Quality Estimation in Industry 4.0: A Systematic Review and Integrative Conceptual Framework.” International Journal of Production Research 60 (2): 742–765. https://doi.org/10.1080/00207543.2021.1976433.
   Web of Science ®Google Scholar
• Duan, Tony, Avati Anand, Daisy Yi Ding, Khanh K. Thai, Sanjay Basu, Andrew Ng, and Alejandro Schuler. 2020. “Ngboost: Natural Gradient Boosting for Probabilistic Prediction.” In International Conference on Machine Learning, 2690–2700. PMLR.
   Google Scholar
• Freund, Yoav, and Robert E. Schapire. 1997. “A Decision-Theoretic Generalization of on-line Learning and An Application to Boosting.” Journal of Computer and System Sciences 55 (1): 119–139. https://doi.org/10.1006/jcss.1997.1504.
   Web of Science ®Google Scholar
• Gumaei, Abdu, Walaa N. Ismail, Md Rafiul Hassan, Mohammad Mehedi Hassan, Ebtsam Mohamed, Abdullah Alelaiwi, and Giancarlo Fortino. 2022. “A Decision-Level Fusion Method for COVID-19 Patient Health Prediction.” Big Data Research 27:100287. https://doi.org/10.1016/j.bdr.2021.100287.
   Web of Science ®Google Scholar
• He, Hengtao, Chao-Kai Wen, Shi Jin, and Geoffrey Ye Li. 2020. “Model-Driven Deep Learning for MIMO Detection.” IEEE Transactions on Signal Processing 68:1702–1715. https://doi.org/10.1109/TSP.2020.2976585.
   Web of Science ®Google Scholar
• Hwang, Sangheum, Myong K. Jeong, and Bong-Jin Yum. 2013. “Robust Relevance Vector Machine with Variational Inference for Improving Virtual Metrology Accuracy.” IEEE Transactions on Semiconductor Manufacturing 27 (1): 83–94. https://doi.org/10.1109/TSM.2013.2286498.
   Web of Science ®Google Scholar
• Jing, Luyang, Taiyong Wang, Ming Zhao, and Peng Wang. 2017. “An Adaptive Multi-Sensor Data Fusion Method Based on Deep Convolutional Neural Networks for Fault Diagnosis of Planetary Gearbox.” Sensors 17 (2): 414. https://doi.org/10.3390/s17020414.
   PubMed Web of Science ®Google Scholar
• Kang, Pilsung, Dongil Kim, Hyoung-joo Lee, Seungyong Doh, and Sungzoon Cho. 2011. “Virtual Metrology for Run-to-Run Control in Semiconductor Manufacturing.” Expert Systems with Applications 38 (3): 2508–2522. https://doi.org/10.1016/j.eswa.2010.08.040.
   Web of Science ®Google Scholar
• Kang, Pilsung, Hyoung-Joo Lee, Sungzoon Cho, Dongil Kim, Jinwoo Park, Chan-Kyoo Park, and Seungyong Doh. 2009. “A Virtual Metrology System for Semiconductor Manufacturing.” Expert Systems with Applications 36 (10): 12554–12561. https://doi.org/10.1016/j.eswa.2009.05.053.
   Web of Science ®Google Scholar
• Khaleghi, Bahador, Alaa Khamis, Fakhreddine O. Karray, and Saiedeh N. Razavi. 2013. “Multisensor Data Fusion: A Review of the State-of-the-Art.” Information Fusion 14 (1): 28–44. https://doi.org/10.1016/j.inffus.2011.08.001.
   Web of Science ®Google Scholar
• Kim, Eunji, Sungzoon Cho, Byeongeon Lee, and Myoungsu Cho. 2019. “Fault Detection and Diagnosis Using Self-Attentive Convolutional Neural Networks for Variable-length Sensor Data in Semiconductor Manufacturing.” IEEE Transactions on Semiconductor Manufacturing 32 (3): 302–309. https://doi.org/10.1109/TSM.66.
   Web of Science ®Google Scholar
• Kim, Youngju, Hoyeop Lee, and Chang Ouk Kim. 2023. “A Variational Autoencoder for a Semiconductor Fault Detection Model Robust to Process Drift Due to Incomplete Maintenance.” Journal of Intelligent Manufacturing 34: 529–540. https://doi.org/10.1007/s10845-021-01810-2.
   Web of Science ®Google Scholar
• Krishna, Sajja Tulasi, and Hemantha Kumar Kalluri. 2019. “Deep Learning and Transfer Learning Approaches for Image Classification.” International Journal of Recent Technology and Engineering (IJRTE) 7 (5S4): 427–432.
   Google Scholar
• Kummer, Nikolai, and Homayoun Najjaran. 2014. “Adaboost. MRT: Boosting Regression for Multivariate Estimation.” Artificial Intelligence Research 3 (4): 64–76. https://doi.org/10.5430/air.v3n4p64.
   Google Scholar
• Lee, Ki Bum, and Chang Ouk Kim. 2020. “Recurrent Feature-Incorporated Convolutional Neural Network for Virtual Metrology of the Chemical Mechanical Planarization Process.” Journal of Intelligent Manufacturing 31 (1): 73–86. https://doi.org/10.1007/s10845-018-1437-4.
   Web of Science ®Google Scholar
• Lewis, Roger J. 2000. “An Introduction to Classification and Regression Tree (CART) Analysis.” In Annual Meeting of the Society for Academic Emergency Medicine in San Francisco, California, Vol. 14. Citeseer.
   Google Scholar
• Liu, Fen, Yuxuan Liu, and Hongqiang Sang. 2020. “Multi-Classifier Decision-Level Fusion Classification of Workpiece Surface Defects Based on a Convolutional Neural Network.” Symmetry 12 (5): 867. https://doi.org/10.3390/sym12050867.
   Google Scholar
• Long, Zhuo, Xiaofei Zhang, Li Zhang, Guojun Qin, Shoudao Huang, Dianyi Song, Haidong Shao, and Gongping Wu. 2021. “Motor Fault Diagnosis Using Attention Mechanism and Improved Adaboost Driven by Multi-Sensor Information.” Measurement 170:108718. https://doi.org/10.1016/j.measurement.2020.108718.
   Web of Science ®Google Scholar
• Luo, Ren C, Chin-Chun Yih, and Kuo-Long Su. 2002. “Multisensor Fusion and Integration: Approaches, Applications, and Future Research Directions.” IEEE Sensors Journal 2 (2): 107–119. https://doi.org/10.1109/JSEN.2002.1000251.
   Web of Science ®Google Scholar
• Munir, Mohsin, Shoaib Ahmed Siddiqui, Andreas Dengel, and Sheraz Ahmed. 2018. “DeepAnT: A Deep Learning Approach for Unsupervised Anomaly Detection in Time Series.” IEEE Access 7:1991–2005. https://doi.org/10.1109/ACCESS.2018.2886457.
   Google Scholar
• Oh, Sang-Il, and Hang-Bong Kang. 2017. “Object Detection and Classification by Decision-Level Fusion for Intelligent Vehicle Systems.” Sensors 17 (1): 207. https://doi.org/10.3390/s17010207.
   PubMed Web of Science ®Google Scholar
• Psarommatis, Foivos, Gökan May, Paul-Arthur Dreyfus, and Dimitris Kiritsis. 2020. “Zero Defect Manufacturing: State-of-the-Art Review, Shortcomings and Future Directions in Research.” International Journal of Production Research 58 (1): 1–17. https://doi.org/10.1080/00207543.2019.1605228.
   Web of Science ®Google Scholar
• Psarommatis, Foivos, Sylvain Prouvost, Gökan May, and Dimitris Kiritsis. 2020. “Product Quality Improvement Policies in Industry 4.0: Characteristics, Enabling Factors, Barriers, and Evolution Toward Zero Defect Manufacturing.” Frontiers in Computer Science 2:26. https://doi.org/10.3389/fcomp.2020.00026.
   Web of Science ®Google Scholar
• Psarommatis, Foivos, João Sousa, João Pedro Mendonça, and Dimitris Kiritsis. 2022. “Zero-Defect Manufacturing the Approach for Higher Manufacturing Sustainability in the Era of Industry 4.0: A Position Paper.” International Journal of Production Research 60 (1): 73–91. https://doi.org/10.1080/00207543.2021.1987551.
   Web of Science ®Google Scholar
• Schmitt, Michael, and Xiao Xiang Zhu. 2016. “Data Fusion and Remote Sensing: An Ever-Growing Relationship.” IEEE Geoscience and Remote Sensing Magazine 4 (4): 6–23. https://doi.org/10.1109/MGRS.2016.2561021.
   Web of Science ®Google Scholar
• Sidek, Othman, and S. A. Quadri. 2012. “A Review of Data Fusion Models and Systems.” International Journal of Image and Data Fusion 3 (1): 3–21. https://doi.org/10.1080/19479832.2011.645888.
   Google Scholar
• Solomatine, Dimitri P., and Durga L. Shrestha. 2004. “AdaBoost. RT: A Boosting Algorithm for Regression Problems.” In 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No. 04CH37541). Vol. 2, 1163–1168. IEEE.
   Google Scholar
• Su, An-Jhih, Jyh-Cheng Jeng, Hsiao-Ping Huang, Cheng-Ching Yu, Shih-Yu Hung, and Ching-Kong Chao. 2007. “Control Relevant Issues in Semiconductor Manufacturing: Overview with Some New Results.” Control Engineering Practice 15 (10): 1268–1279. https://doi.org/10.1016/j.conengprac.2006.11.003.
   Web of Science ®Google Scholar
• Tian, Hui-Xin, and Zhi-Zhong Mao. 2009. “An Ensemble ELM Based on Modified AdaBoost.RT Algorithm for Predicting the Temperature of Molten Steel in Ladle Furnace.” IEEE Transactions on Automation Science and Engineering 7 (1): 73–80. https://doi.org/10.1109/TASE.2008.2005640.
   Web of Science ®Google Scholar
• Wang, Zhongyan, and Fuyuan Xiao. 2018. “An Improved Multisensor Data Fusion Method and Its Application in Fault Diagnosis.” IEEE Access 7:3928–3937. https://doi.org/10.1109/ACCESS.2018.2889358.
   Web of Science ®Google Scholar
• Wei, Yupeng, Dazhong Wu, and Janis Terpenny. 2020. “Decision-Level Data Fusion in Quality Control and Predictive Maintenance.” IEEE Transactions on Automation Science and Engineering 18 (1): 184–194. https://doi.org/10.1109/TASE.8856.
   Web of Science ®Google Scholar
• Xie, Yifan, Tianhui Wang, Young-Seon Jeong, Ali Tosyali, and Myong K. Jeong. 2023. “True Sparse PCA for Reducing the Number of Essential Sensors in Virtual Metrology.” International Journal of Production Research 1–16. https://doi.org/10.1080/00207543.2023.2217282.
   Web of Science ®Google Scholar
• Yuan, Huawei, Sinan Li, Siew-Chong Tan, and S. Y. Ron Hui. 2020. “Means of Reducing Number of Sensors in Single-Phase Power Converters with an Active Power Buffer.” In 2020 IEEE Applied Power Electronics Conference and Exposition (APEC), 1273–1278. IEEE.
   Google Scholar
• Zhang, Peng-Bo, and Zhi-Xin Yang. 2016. “A Novel AdaBoost Framework with Robust Threshold and Structural Optimization.” IEEE Transactions on Cybernetics 48 (1): 64–76. https://doi.org/10.1109/TCYB.2016.2623900.
   PubMed Web of Science ®Google Scholar