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Carbon quantum dots (CQDs) reinforced polypyrrole nanowire (PPy-NW) was constructed via electrostatic self-assembly strategy. The as-made CQDs/PPy-NW composite demonstrated superior electrochemical properties benefited from a dotted line structure with large specific area, more active sites and outstanding electronic conductivity. As the results, the CQDs/PPy-NW composite electrode displayed a specific...
Recently, carbon quantum dots (CQDs) as a new zero-dimensional carbon nanomaterial have become a focus in electrochemical energy storage. In this paper, flexible all-solid-state supercapacitors (ASSSs) were electrochemically synthesized by on-step co-deposition of appropriate amounts of pyrrole monomer and CQDs in aqueous solution. The different electrodeposition time plays an important role in controlling...
The random-hidden-node extreme learning machine (ELM) is a much more generalized cluster of single-hidden-layer feed-forward neural networks (SLFNs) which has three parts: random projection, non-linear transformation, and ridge regression (RR) model. Networks with deep architectures have demonstrated state-of-the-art performance in a variety of settings, especially with computer vision tasks. Deep...
This paper is concerned with multi-kernel extreme learning machine (MK-ELM) which adapts the multi-kernel learning (MKL) framework to extreme learning machine (ELM). MK-ELM approach iteratively determines the combination of kernels by gradient descent wrapping a standard optimization method based ELM. Such MKL methods are very useful in information fusion research and applications. MK-ELM's performance...
The optimization method based extreme learning machine (optimization-based ELM) is generalized from single-hidden-layer feed-forward neural networks (SLFNs) by making use of kernels instead of neuron-alike hidden nodes. This approach is known for its high scalability, low computational complexity, and mild optimization constrains. The multi-kernel learning (MKL) framework Simple MKL iteratively determines...
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