Cheng Cheng

TL;DR: Overall, this Article suggests a simple and operable decision rule to quickly predict patients at the highest risk, allowing them to be prioritized and potentially reducing the mortality rate.

TL;DR: An integrated algorithm which combines adaptive unscented kalman filter (AUKF) and genetic algorithm optimized support vector regression (GA-SVR) achieves better prediction accuracy than existed methods.

TL;DR: A hybrid neural network that combines the advantages of a convolutional neural network with those of long short-term memory is designed for model training and prediction and demonstrates wide generality and reduced errors when compared with the other state-of-the-art methods.

TL;DR: In this paper, a novel Wasserstein distance-based deep transfer learning (WD-DTL) network was proposed for both supervised and unsupervised fault diagnosis tasks. But, the proposed network is not suitable for the task of automatic fault diagnosis.

TL;DR: A novel DTL approach to intelligent fault diagnosis, namely Wasserstein Distance based Deep Transfer Learning (WD-DTL), to learn domain feature representations (generated by a CNN based feature extractor) and to minimize the distributions between the source and target domains through adversarial training.