Copper nanowires and Graphene nano-composite based electrodes with high transparency and conductivity

Paper presented to the 3rd Southern African Solar Energy Conference, South Africa, 11-13 May, 2015.In this paper, we present ITO-free hybrid transparent conducting electrodes that are based on bilayers of reduced Graphene Oxide (rGO) and Copper Nanowires (CuNWs). The unique structural features of these in-house fabricated electrodes allowed for superior performance with an optical transmittance of 84% and sheet resistance of 21.7 Ω/sq obtained, showing their potential of becoming a replacement for ITO based electrodes. The fabricated hybrid electrodes exhibited extreme stability when subjected to various environmental and durability tests. Adoption of such high performance TCEs, will lead to simple, versatile and cost effective solutions for next generation optoelectronics industry.cf201

A comparison of the classification capabilities of the 1-dimensional kohonen neural network with two pratitioning and three hierarchical cluster analysis algorithms

neural networks, Kohonen models, cluster analysis,

Multi-machine earliness and tardiness scheduling problem: an interconnected neural network approach

This paper addresses the problem of scheduling a set of independent jobs with sequence-dependent setups and distinct due dates on non-uniform multi-machines to minimize the total weighted earliness and tardiness, and explores the use of artificial neural networks as a valid alternative to the traditional scheduling approaches. The objective is to propose a dynamical gradient neural network, which employs a penalty function approach with time varying coefficients for the solution of the problem which is known to be NP-hard. After the appropriate energy function was constructed, the dynamics are defined by steepest gradient descent on the energy function. The proposed neural network system is composed of two maximum neural networks, three piecewise linear and one log-sigmoid network all of which interact with each other. The motivation for using maximum networks is to reduce the network complexity and to obtain a simplified energy function. To overcome the tradeoff problem encountered in using the penalty function approach, a time varying penalty coefficient methodology is proposed to be used during simulation experiments. Simulation results of the proposed approach on a scheduling problem indicate that the proposed coupled network yields an optimal solution which makes it attractive for applications of larger sized problems