Reduced operating voltage of organic electroluminescent devices by plasma treatment of the indium tin oxide anode Appl. Phys. Lett. 74, 3558 (1999); 10.1063/1.124160 Indium-tin oxide treatments for single-and double-layer polymeric light-emitting diodes: The relation between the anode physical, chemical, and morphological properties and the device performance A thin 3-nm-thick parylene layer is deposited by chemical vapor deposition at room temperature on the indium tin oxide ͑ITO͒ coated glass substrate to form a bilayer anode of an organic light emitting diode. The parylene layer forms a conformal film to cover the spikes present in the ITO film. This parylene film presents a smoother surface to the subsequent organic layers. The parylene film not only reduces the occurrence of dark spots, acting as a barrier for oxygen diffusion from either the ITO or from the atmosphere and stabilizing the migration of the electrodes during electrical stress, but also improves the injection of holes from the anode. By inserting another parylene layer in between the organic and cathode layers, the probability of formation of nonemissive areas is further reduced.
Clustering is a widely used technique of finding interesting patterns residing in the dataset that are not obviously known. The K-Means algorithm is the most commonly used partitioned clustering algorithm because it can be easily implemented and is the most efficient in terms of the execution time. However, due to its sensitiveness to initial partition it can only generate a local optimal solution. Particle Swarm Optimization (PSO) technique offers a globalized search methodology but suffers from slow convergence near optimal solution. In this paper, we present a new Hybrid Sequential clustering approach, which uses PSO in sequence with K-Means algorithm for data clustering. The proposed approach overcomes drawbacks of both algorithms, improves clustering and avoids being trapped in a local optimal solution. Experiments on four kinds of data sets have been conducted. The obtained results are compared with K-Means, PSO, Hybrid, K-Means+Genetic Algorithm and it has been found that the proposed algorithm generates more accurate, robust and better clustering results.
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