The editors introduce the feature issue on "Energy, Light and the Environment (LEE) 2017", which is based on the topics presented at a congress of the same name held in Boulder, CO, US, from November 6 to November 9. This feature issue presents 13 papers selected from the voluntary submissions by attendees who presented at the progress and have extended their work into complete research articles. The feature issue highlights contributions from authors who presented their research at this congress.
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| http://dx.doi.org/10.1364/OE.26.00A636 | DOI Listing |
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A stacked CNN and random forest ensemble architecture for complex nursing activity recognition and nurse identification.
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College of Mechanical and Electrical Engineering, Wenzhou University, Wenzhou, 325035, China.
Addressing the issues of a single-feature input channel structure, scarcity of training fault data, and insufficient feature learning capabilities in noisy environments for intelligent diagnostic models of mechanical equipment, we propose a method based on a one-dimensional and two-dimensional dual-channel feature information fusion convolutional neural network (1D_2DIFCNN). By constructing a one-dimensional and two-dimensiona dual-channel feature information fusion convolutional network and introducing a Convolutional Block Attention Mechanism, we utilize Random Overlapping Sampling Technique to process raw vibration signals. The model takes as inputs both one-dimensional data and two-dimensional Continuous Wavelet Transform images.
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