Open-loop training of recurrent neural networks for nonlinear dynamical system identification

Liu, D.

doi:10.1109/ijcnn.2001.939534

Cited by 2 publications

(1 citation statement)

References 14 publications

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“…Recurrent Neural Network (RNN) in general and Long short-term memory (LSTM) networks in specific are known for various applications including nonlinear system identification [146], time-series prediction [147,148], and speech recognition [149]. Furthermore, RNN nodes form a directed graph along a temporal sequence which makes them able to capture temporal and statistical complexities of time-series.…”

Section: Discussionmentioning

confidence: 99%

Wireless Sensing for Ground Engaging Tools

Ibrahim

Hassan

Hassanein

et al. 2020

GLOBECOM 2020 - 2020 IEEE Global Communications Conference

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A long-standing industrial problem is tackled with an industrial internet of things (IIoT) application. This thesis presents the hardware design and development of a wireless sensor network for monitoring ground engaging tools of earth processing equipment. This work addresses several challenges in the hardware design. The challenges included establishing wireless communications from within a metallic enclosure, Investigating the effect of antenna tuning on the link budget, designing a protective package for the sensor node (SN) to withstand vibrations and mechanical shocks, and creating a low-power design that allowed for a minimum battery life of one year.Furthermore, this work puts forward the design of novel application-specific sensors including a low-power ferrous-selective proximity sensor and a low-cost capacitive wear-level sensor.Hardware designs were evaluated with laboratory and field tests. The conducted tests demonstrated the SN's functionality and performance in harsh operating conditions. The journey from problem definition and to successful field testing of the complete wireless sensing solution is covered in this thesis.In addition, a novel energy-efficient approach to wireless real-time sensing is presented. For an SN transmitting samples of a discrete time-series in real-time, its DisclaimerThis work was made possible through the software developed by Galal Hassan. His work on the shovel project covered firmware development for the sensor node, software development for the sink, and setting up the back-end cloud server including the dashboard. Furthermore, Galal has contributed significantly to the success of the shovel project by developing an energy-efficient MAC protocol [1] which allowed for satisfying the life-time requirement of the sensor nodes.

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Section: Discussionmentioning

confidence: 99%