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- W4224296020 abstract "The precise control of a complex system requires a good numerical model with elegant consideration of the individual uniqueness. A data-driven online machine learning (OML) framework is proposed in this paper to achieve this goal. A set of UVLED reliability testing data is applied to verify the feasibility of the OML concept. Moreover, the design guidelines are then deduced.The OML model is built based on neural network (NN) architecture. The genetic algorithm (GA) and the principal component analysis (PCA) are applied to obtain the optimal NN initial weighting parameters. After hundreds of iterations, this model is defined as the based model of ML. Through careful investigation of the evolution of the NN parameters at each iteration, the feasibility of OML for engineering applications is validated. Moreover, the basic strategy for OML has been established, including the continuous monitoring of the weighting change and the prediction error. The result shows that only 1,000-hour data is required to predict the individual LED performance of 3,000 hours with acceptable accuracy." @default.
- W4224296020 created "2022-04-26" @default.
- W4224296020 creator A5068931208 @default.
- W4224296020 date "2022-04-25" @default.
- W4224296020 modified "2023-09-23" @default.
- W4224296020 title "Continous AI-aided learning to establish the digital twin models for predicting the individual reliability characteristics" @default.
- W4224296020 doi "https://doi.org/10.1109/eurosime54907.2022.9758861" @default.
- W4224296020 hasPublicationYear "2022" @default.
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