Khalifa University

About: Khalifa University is a education organization based out in Abu Dhabi, United Arab Emirates. It is known for research contribution in the topics: Computer science & Adsorption. The organization has 3752 authors who have published 10909 publications receiving 141629 citations.

A Review on the Corrosion Behaviour of Nanocoatings on Metallic Substrates

Abstract: Growth in nanocoatings technology is moving towards implementing nanocoatings in many sectors of the industry due to their excellent abilities. Nanocoatings offer numerous advantages, including surface hardness, adhesive strength, long-term and/or high-temperature corrosion resistance, the enhancement of tribological properties, etc. In addition, nanocoatings can be applied in thinner and smoother thickness, which allows flexibility in equipment design, improved efficiency, lower fuel economy, lower carbon footprints, and lower maintenance and operating costs. Nanocoatings are utilised efficiently to reduce the effect of a corrosive environment. A nanocoating is a coating that either has constituents in the nanoscale, or is composed of layers that are less than 100 nm. The fine sizes of nanomaterials and the high density of their ground boundaries enable good adhesion and an excellent physical coverage of the coated surface. Yet, such fine properties might form active sites for corrosion attack. This paper reviews the corrosion behaviour of metallic, ceramic, and nanocomposite coatings on the surface of metallic substrates. It summarises the factors affecting the corrosion of these substrates, as well as the conditions where such coatings provided required protection.

Memristor-Based Echo State Network With Online Least Mean Square

Abstract: In this paper, we propose a novel computational architecture of memristor-based echo state network (MESN) with the online least mean square (LMS) algorithm. Newman and Watts small-world network is adopted for the topological structure of MESN network with memristive neural synapses. In the MESN network, the state matrix of the reservoir layer, which is obtained by raising the dimension of input data, is utilized as an input of the LMS algorithm to train the output weight matrix on chip. After certain iterations, the resistance value of memristor is adjusted to a constant. Thus, the final weight output matrix is obtained. To verify the effectiveness of the proposed MESN network, car evaluation and short-term power load forecasting are employed with the effect evaluation of the node number and the connectivity degree of the reservoir layer. The research provides a novel way to design neuromorphic computing systems.