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- W4206039180 abstract "In this paper we present the results of our participation in the IEEE BigData 2021 Cup: Soft Sensing at Scale. The goal of the contest is to classify eleven different tasks based on high-dimensional time series soft sensing data from a wafer manufacturing process. To achieve the classification, two different data-driven soft sensing approaches have been implemented. One approach is especially optimized towards cost and runtime, whereas the other achieves better accuracy but comes at a higher runtime and cost. Both methods can be deployed in the manufacturing line, e.g. a wafer manufacturing factory, by using incremental learning, thus making sure that the models are always up to date in an changing environment with different data distributions. Both models can be integrated for different soft sensing scenarios in a manufacturing line, i.e. the cost-optimized model in a timing-critical scenario and the accuracy-optimized model in a scenario requiring exactly that high accuracy. This guarantees low costs and high accuracy where they are needed." @default.
- W4206039180 created "2022-01-26" @default.
- W4206039180 creator A5043444611 @default.
- W4206039180 date "2021-12-15" @default.
- W4206039180 modified "2023-10-14" @default.
- W4206039180 title "Applying self-normalizing neural networks to tackle data-driven soft sensing problems in manufacturing lines" @default.
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- W4206039180 doi "https://doi.org/10.1109/bigdata52589.2021.9671496" @default.
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