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Xiaokang Wang
Researcher at St. Francis Xavier University
Publications - 31
Citations - 1917
Xiaokang Wang is an academic researcher from St. Francis Xavier University. The author has contributed to research in topics: Big data & Cloud computing. The author has an hindex of 18, co-authored 27 publications receiving 1205 citations. Previous affiliations of Xiaokang Wang include University of Electronic Science and Technology of China & Huazhong University of Science and Technology.
Papers
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Journal ArticleDOI
A Cloud-Edge Computing Framework for Cyber-Physical-Social Services
TL;DR: A tensor-based cloud-edge computing framework that mainly includes the cloud and edge planes is presented that is used to process large-scale, long-term, global data, which can be used to obtain decision making information such as the feature, law, or rule sets.
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A Data-Driven Auto-CNN-LSTM Prediction Model for Lithium-Ion Battery Remaining Useful Life
TL;DR: A new LIB RUL prediction method based on improved convolution neural network (CNN) and long short-term memory (L STM), namely Auto-CNN-LSTM, is proposed in this article, developed based on deep CNN and LSTM to mine deeper information in finite data.
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A Tensor-Based Big-Data-Driven Routing Recommendation Approach for Heterogeneous Networks
TL;DR: In this article, a tensor-based, holistic, hierarchical approach is introduced to generate efficient routing paths using tensor decomposition methods to implement routing recommendations for big data networks.
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A Big Data-as-a-Service Framework: State-of-the-Art and Perspectives
TL;DR: A tensor-based multiple clustering on bicycle renting and returning data is illustrated, which can provide several suggestions for rebalancing of the bicycle-sharing system and some challenges about the proposed framework are discussed.
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Multi-scale Dense Gate Recurrent Unit Networks for bearing remaining useful life prediction
TL;DR: A novel deep learning network, namely Multi-scale Dense Gate Recurrent Unit Network (MDGRU) is proposed in this paper, which is composed of the feature layers initialized by pre-trained Restricted Boltzmann Machine (RBM) network, multi-scale layers, skip gate recurrent unit layers, dense layers.