Z
Zhenghua Chen
Researcher at Institute for Infocomm Research Singapore
Publications - 105
Citations - 5397
Zhenghua Chen is an academic researcher from Institute for Infocomm Research Singapore. The author has contributed to research in topics: Computer science & Deep learning. The author has an hindex of 22, co-authored 70 publications receiving 2830 citations. Previous affiliations of Zhenghua Chen include Agency for Science, Technology and Research & Nanyang Technological University.
Papers
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Deep learning and its applications to machine health monitoring
TL;DR: The applications of deep learning in machine health monitoring systems are reviewed mainly from the following aspects: Auto-encoder and its variants, Restricted Boltzmann Machines, Convolutional Neural Networks, and Recurrent Neural Networks.
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Fusion of WiFi, Smartphone Sensors and Landmarks Using the Kalman Filter for Indoor Localization
TL;DR: This work proposes a sensor fusion framework for combining WiFi, PDR and landmarks, and can provide an average localization accuracy of 1 m, which shows significant improvement using the proposed framework.
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WiFi CSI Based Passive Human Activity Recognition Using Attention Based BLSTM
TL;DR: This paper proposes a new deep learning based approach, i.e., attention based bi-directional long short-term memory (ABLSTM) for passive human activity recognition using WiFi CSI signals, employed to learn representative features in two directions from raw sequential CSI measurements.
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Machine Remaining Useful Life Prediction via an Attention-Based Deep Learning Approach
TL;DR: An attention-based deep learning framework is proposed for machine's RUL prediction that is able to learn the importance of features and time steps, and assign larger weights to more important ones, and the proposed approach outperforms the state-of-the-arts.
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A review on swarm intelligence and evolutionary algorithms for solving flexible job shop scheduling problems
TL;DR: The mathematical model of FJSP is presented, the constraints in applications are summarized, and the encoding and decoding strategies for connecting the problem and algorithms are reviewed to give insight into future research directions.