A procedure for design of hydrogen networks with multiple contaminants

It is necessary to reduce hydrogen consumption to meet increasingly strict environmental and product-quality regulations for refinery plants. In this paper, the concentration potential concepts proposed for design of water-using networks are extended to synthesis of hydrogen networks with multiple contaminants. In the design procedure, the precedence of processes is determined by the values of concentration potential of demands.The usage of complementary source pair(s) to reduce utility consumption is investigated. Three case studies are presented to illustrate the effectiveness of the method. It is shown that the design procedure has clear engineering meaning.

Dual Power Allocation Optimization Based on Stackelberg Game in Heterogeneous Network with Hybrid Energy Supplies

In heterogeneous network with hybrid energy supplies including green energy and on-grid energy, it is imperative to increase the utilization of green energy as well as to improve the utilities of users and networks. As the difference of hybrid energy source in stability and economy, thus, this paper focuses on the network with hybrid energy source, and design the utility of each user in the hybrid energy source system from the perspective of stability, economy and environment pollution. A dual power allocation algorithm based on Stackelberg game to maximize the utilities of users and networks is proposed. In addition, an iteration method is proposed which enables all players to reach the Stackelberg equilibrium(SE). Simulation results validate that players can reach the SE and the utilities of users and networks can be maximization, and the green energy can be efficiently used.

Network Sorting Algorithm of Multi-Frequency Signal with Adaptive SNR

An signal noise ratio（ SNR） adaptive sorting algorithm using the time-frequency（ TF）sparsity of frequency-hopping（ FH） signal is proposed in this paper. Firstly,the Gabor transformation is used as TF transformation in the system and a sorting model is established under undetermined condition; then the SNR adaptive pivot threshold setting method is used to find the TF single source. The mixed matrix is estimated according to the TF matrix of single source. Lastly,signal sorting is realized through improved subspace projection combined with relative power deviation of source. Theoretical analysis and simulation results showthat this algorithm has good effectiveness and performance.

A Novel Routing Strategy to Provide Source Location Privacy in Wireless Sensor Networks

In order to secure the source location privacy when information is sent back to the base station in wireless sensor network, we propose a novel routing strategy which routes the packets to the base station through three stages： directional random routing, h-hop routing in the annular region and the shortest path routing. These stages provide two fold protections to prevent the source location from being tracked down by the adversary. The analysis and simulation results show that proposed scheme, besides providing longer safety period, can significantly reduce energy consumption compared with two baseline schemes.

Supercontinuum Lightwave Sources for Photonic Networks

WDM optical sources based on supercontinuum (SC) generation are reviewed. The design of an SC lightwave source is described that uses a scaling rule among the parameters in an SC generating fiber and a seed optical pulse. Finally, recent efforts to apply SC lightwave source to optical network systems are mentioned.

Truth Discovery with Memory Network

Truth discovery aims to resolve conflicts among multiple sources and find the truth. Conventional methods for truth discovery mainly investigate the mutual effect between the reliability of sources and the credibility of statements. These methods use real numbers, which have a lower representation capability than vectors to represent the reliability. In addition, neural networks have not been used for truth discovery. In this work, we propose memory-network-based models to address truth discovery. Our proposed models use feedforward and feedback memory networks to learn the representation of the credibility of statements. Specifically, our models adopt a memory mechanism to learn the reliability of sources for truth prediction. The proposed models use categorical and continuous data during model learning by automatically assigning different weights to the loss function on the basis of their own effects. Experimental results show that our proposed models outperform state-of-the-art methods for truth discovery.