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Published in 2019 at "IEEE Access"
DOI: 10.1109/access.2019.2939288
Abstract: Collecting sufficient labeled electroencephalography (EEG) data to build an individual classifier for each subject is extremely time-consuming and labor-intensive, especially for the disabled patients. A feasible way is to use labeled EEG data from other…
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Keywords:
eeg signal;
eeg data;
deep domain;
recognition ... See more keywords
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3
Published in 2022 at "IEEE Transactions on Geoscience and Remote Sensing"
DOI: 10.1109/tgrs.2022.3151883
Abstract: Deep neural networks (DNNs) can learn accurately from large quantities of labeled input data but often fail to do so when labeled data are scarce. DNNs sometimes fail to generalize on test data sampled from…
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Keywords:
seismic facies;
domain;
deep domain;
domain adaptation ... See more keywords
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Published in 2018 at "IEEE Transactions on Image Processing"
DOI: 10.1109/tip.2018.2851067
Abstract: Domain adaptation is a promising technique when addressing limited or no labeled target data by borrowing well-labeled knowledge from the auxiliary source data. Recently, researchers have exploited multi-layer structures for discriminative feature learning to reduce…
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Keywords:
semi supervised;
supervised deep;
deep domain;
domain adaptation ... See more keywords
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Published in 2022 at "Micromachines"
DOI: 10.3390/mi13060873
Abstract: Tool condition monitoring (TCM) is of great importance for improving the manufacturing efficiency and surface quality of workpieces. Data-driven machine learning methods are widely used in TCM and have achieved many good results. However, in…
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Keywords:
condition monitoring;
tool condition;
domain;
deep domain ... See more keywords