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Interval-Valued Reduced RNN for Fault Detection and Diagnosis for Wind Energy Conversion Systems
IEEE Sensors Journal, 2022Recurrent neural network (RNN) is one of the most used deep learning techniques in fault detection and diagnosis (FDD) of industrial systems. However, its implementation suffers from some limitations presented in the hard training step and the high time ...
M. Mansouri +5 more
semanticscholar +1 more source
LION: Linear Group RNN for 3D Object Detection in Point Clouds
Neural Information Processing SystemsThe benefit of transformers in large-scale 3D point cloud perception tasks, such as 3D object detection, is limited by their quadratic computation cost when modeling long-range relationships. In contrast, linear RNNs have low computational complexity and
Zhe Liu +6 more
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DL-RNN: An Accurate Indoor Localization Method via Double RNNs
IEEE Sensors Journal, 2020Wireless fingerprinting localization method learns a mapping function from a fingerprint measurement to the estimated location, which is more suitable for complex indoor environments than the propagation model-based methods. However, most traditional methods only consider the location matching at single time or space points, but ignore the fact that ...
Siqi Bai +5 more
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Advanced RNN Based NARMA Predictors
Journal of VLSI signal processing systems for signal, image and video technology, 2000zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Mandic, Danilo P., Chambers, Jonathon A.
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Proceedings of the 27th ACM International Conference on Information and Knowledge Management, 2018
Failure event prediction is becoming increasingly important in wide applications, such as the planning of proactive maintenance, the active investment management, and disease surveillance. To address the issue, the hazard function in survival analysis has been employed to describe the pattern of failures.
Bin Liang +3 more
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Failure event prediction is becoming increasingly important in wide applications, such as the planning of proactive maintenance, the active investment management, and disease surveillance. To address the issue, the hazard function in survival analysis has been employed to describe the pattern of failures.
Bin Liang +3 more
openaire +1 more source
Expert systems with applications, 2019
Long-term prediction of multivariate time series is still an important but challenging problem. The key to solve this problem is to capture the spatial correlations at the same time, the spatio-temporal relationships at different times and the long-term ...
Yeqi Liu +3 more
semanticscholar +1 more source
Long-term prediction of multivariate time series is still an important but challenging problem. The key to solve this problem is to capture the spatial correlations at the same time, the spatio-temporal relationships at different times and the long-term ...
Yeqi Liu +3 more
semanticscholar +1 more source
IEEE Transactions on Image Processing, 2019
Learning 3D global features by aggregating multiple views has been introduced as a successful strategy for 3D shape analysis. In recent deep learning models with end-to-end training, pooling is a widely adopted procedure for view aggregation.
Zhizhong Han +7 more
semanticscholar +1 more source
Learning 3D global features by aggregating multiple views has been introduced as a successful strategy for 3D shape analysis. In recent deep learning models with end-to-end training, pooling is a widely adopted procedure for view aggregation.
Zhizhong Han +7 more
semanticscholar +1 more source
2020
In this chapter, we will introduce the typical deep neural networks from the viewpoint of CNN family, especially region-based CNN, SSD, and YOLO. Meanwhile, from the viewpoint of time series analysis, we depict the RNN family, namely, LSTM, GRU, FRU, etc.
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In this chapter, we will introduce the typical deep neural networks from the viewpoint of CNN family, especially region-based CNN, SSD, and YOLO. Meanwhile, from the viewpoint of time series analysis, we depict the RNN family, namely, LSTM, GRU, FRU, etc.
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ABCDM: An Attention-based Bidirectional CNN-RNN Deep Model for sentiment analysis
Future generations computer systems, 2021Mohammad Ehsan Basiri +4 more
semanticscholar +1 more source
2018
This chapter will discuss the concepts of recurrent neural networks (RNNs) and their modified version, long short-term memory (LSTM). LSTM is mainly used for sequence prediction. You will learn about the varieties of sequence prediction and then learn how to do time-series forecasting with the help of the LSTM model.
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This chapter will discuss the concepts of recurrent neural networks (RNNs) and their modified version, long short-term memory (LSTM). LSTM is mainly used for sequence prediction. You will learn about the varieties of sequence prediction and then learn how to do time-series forecasting with the help of the LSTM model.
openaire +1 more source