Oil-impregnated insulation paper has been widely used in transformers because of its low cost and desirable physical and electrical properties. However, research to improve the insulation properties of oil-impregnated insulation paper is rarely found. In this paper, nano-TiO2was used to stick to the surface of cellulose which was used to make insulation paper. After oil-impregnated insulation paper reinforced by nano-TiO2was prepared, the tensile strength, breakdown strength, and dielectric properties of the oil-impregnated insulation paper were investigated to determine whether the modified paper had a better insulation performance. The results show that there were no major changes in tensile strength, and the value of the breakdown strength was greatly improved from 51.13 kV/mm to 61.78 kV/mm. Also, the values of the relative dielectric constant, the dielectric loss, and conductivity declined. The discussion reveals that nano-TiO2plays a major role in the phenomenon. Because of the existence of nano-TiO2, the contact interface of cellulose and oil was changed, and a large number of shallow traps were produced. These shallow traps changed the insulation properties of oil-impregnated insulation paper. The results show that the proposed solution offers a new method to improve the properties of oil-impregnated insulation paper.
Graph representation learning is a fundamental task of various applications, aiming to learn low-dimensional embeddings for nodes which can preserve graph topology information. However, many existing methods focus on static graphs while ignoring graph evolving patterns. Inspired by the success of graph convolutional networks(GCNs) in static graph embedding, we propose a novel k-core based temporal graph convolutional network, namely CTGCN, to learn node representations for dynamic graphs. In contrast to previous dynamic graph embedding methods, CTGCN can preserve both local connective proximity and global structural similarity in a unified framework while simultaneously capturing graph dynamics. In the proposed framework, the traditional graph convolution operation is generalized into two parts: feature transformation and feature aggregation, which gives CTGCN more flexibility and enables CTGCN to learn connective and structural information under the same framework. Experimental results on 7 real-world graphs demonstrate CTGCN outperforms existing state-of-the-art graph embedding methods in several tasks, such as link prediction and structural role classification. The source code of this work can be obtained from https://github.com/jhljx/CTGCN.
SUMMARYEnergy conservation is an important issue in mobile ad hoc networks (MANET), where the terminals are always supplied with limited energy. A new routing protocol is presented according to the study on the influence of low-energy nodes in ad hoc networks. The novel routing protocol (energy sensing routing protocol, ESRP) is based on the energy sensing strategy. Multiple strategy routing and substitute routing are both adopted in this paper. Referring to the level of the residual energy and the situation of energy consumption, different routes are chosen for packets transmission. The local maintenance is adopted, which can reduce packets retransmission effectively when the link breaks. We focus on the network lifetime most in all performances. The evaluation is done in comparison with other routing protocols on NS2 platform, and the simulation results show that this routing protocol can prolong the network lifetime and balance energy consumption effectively. key words: ad hoc, energy conservation, routing discovery, local maintenance
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