A Study of the Connectivity Over Time Behavior of On-Demand Ad Hoc Path Selection Schemes

Selecting the most appropriate path between each pair of nodes in an ad hoc network is an issue with major impact on network performance. Many schemes were proposed and compared in the literature, using various criteria. However, the connectivity over time behavior of these schemes, which is important to some practical applications, was not well studied, especially with regards to the terrain type. In this work we use simulation to study this aspect of network performance. We demonstrate that a different connectivity requirement and a different time horizon may dictate a different scheme to use. We also demonstrate that path selection schemes are not equally sensitive to the terrain.     

Optical Communications Research Activities at COM RD1 Siemens S.A.

Abstract

This article summarizes the research activities carried out by the R&D Optical Networks Communications group (COM RD1), at Siemens S.A., in collaboration with both universities and other research institutes. These activities cover various aspects of advanced modulation formats, all optical wavelength conversion, optical quality of signal monitoring in transparent WDM networks, routing, and wavelength assignment in optical networks.     

Aliphatic Polyester/polyhedral Oligomeric Silsesquioxanes Hybrid Networks via Copper‐free 1,3‐dipolar Cycloaddition Click Reaction

In this study, we describe the preparation and characterization of a new class of thermoset hybrid networks containing aliphatic polyester and polyhedral oligomeric silsesquioxanes (POSS). The copper‐free 1,3‐dipolar cycloaddition click reaction of internal alkyne functionalized aliphatic polyester and multifunctional azido POSS with different concentrations led to highly crosslinked thermoset networks. The click reactions performed under ambient conditions (i.e., in tetrahydrofuran at room temperature for 1 day) in the absence of any catalyst. The chemical composition of hybrid networks and homogenous distribution of POSS molecules were confirmed by Fourier transform infrared spectroscopy and scanning electron microscopy with energy dispersive spectroscopy. The swelling ratios of hybrid networks were commonly decreased by increasing POSS‐N₃ content and by changing polar solvents to apolar solvents. Thermogravimetric analysis results demonstrated that the thermal stability of hybrid networks increased with higher POSS feeding ratio. Tensile tests were applied to evaluate the mechanical properties of hybrid networks. Compared to neat aliphatic polyester, the mechanical properties of hybrid networks significantly improved. For instance, the tensile strength were enhanced from 5 MPa to 19 MPa by increasing the concentration of azido functionalized POSS from 10 to 40. © 2019 Wiley Periodicals, Inc. J. Polym. Sci., Part A: Polym. Chem. 2019 , 57, 2222–2227     

Morphological study of an intrinsically stretchable photovoltaic bulk heterojunction

Thin‐film polymer solar cell consisting of [6,6]‐phenyl‐C71‐butyric acid methyl ester (PC₇₁BM) and poly[[4,8‐bis[(2‐ethylhexyl)oxy]benzo[1,2‐b:4,5‐b′]dithiophene‐2,6‐diyl][3‐fluoro‐2‐[(2‐ethylhexyl)carbonyl]thieno[3,4‐b]thiophenediyl]] (PTB7) demonstrates elastic stretchability with the aid of a high boiling point additive, 1,8‐diiodooctane (DIO). The usage of DIO not only helps to form uniformly distributed nanocrystalline grains, but may also create free volumes between the nano‐grains that allow for relative sliding between the nano‐grains. The relative sliding can accommodate large external deformation. Large dichroic ratios of the optical absorption of both PC₇₁BM and PTB7 were observed under large‐strain deformation, indicating reorientation of the nanocrystalline PC₇₁BM and PTB7 polymer chains along stretching direction. The dichroic ratio decreases to nearly 1.0 as the blend was relaxed to 0% strain. Therefore, the nanometer‐size grain blending morphology provides an approach to impart stretchability to organic semiconductors that are otherwise un‐stretchable. © 2018 Wiley Periodicals, Inc. J. Polym. Sci., Part B: Polym. Phys. 2018 , 56, 814–820     

Predictive control of uncertain nonlinear parabolic PDE systems using a Galerkin/neural-network-based model

In this paper, a model predictive control (MPC) scheme for a class of parabolic partial differential equation (PDE) systems with unknown nonlinearities, arising in the context of transport-reaction processes, is proposed. A spatial operator of a parabolic PDE system is characterized by a spectrum that can be partitioned into a finite slow and an infinite fast complement. In this view, first, Galerkin method is used to derive a set of finite dimensional slow ordinary differential equation (ODE) system that captures the dominant dynamics of the initial PDE system. Then, a Multilayer Neural Network (MNN) is employed to parameterize the unknown nonlinearities in the resulting finite dimensional ODE model. Finally, a Galerkin/neural-network-based ODE model is used to predict future states in the MPC algorithm. The proposed controller is applied to stabilize an unstable steady-state of the temperature profile of a catalytic rod subject to input and state constraints.     

BP neural network recognition algorithm for scour monitoring of subsea pipelines based on active thermometry

Scour monitoring is an important concern for subsea pipeline systems. The active-thermometry-based scour monitoring is based on the difference of heat transfer properties between sediment and sand, recognizes the surrounding media though temperature changing patterns during heating and cooling processes, and hence detects the free spans. Based on the scour monitoring system, a two-layer BP neural network was employed to process the monitoring data and achieved media recognition. The network's inputs were normalized temperature time histories. The network's outputs denoted different media: sediment and water. To validate the method, three experiments were conducted; one was used for training the network and the other two for testing. Also, the effect of noise on the network's performance was studied through simulation. The results demonstrated the feasibility and robustness of the neural network.     

Dynamic stability of a class of fractional-order nonlinear systems via fixed point theory

In this paper, the problem of the uniform stability for a class of fuzzy fractional-order genetic regulatory networks with random discrete delays, distributed delays, and parameter uncertainties is studied. Although there is a portion of literature on using fixed point theorems to study the stability of fractional neural networks, most of them required the fractional order to be in $\left(\frac{1}{2},1\right)$. However, the case of the fractional-order belonging to $(0,\frac{1}{2})$ has not been discussed. To solve it, this work proposes a novel idea of using fixed point theory to study the stability of fuzzy (0,1) order neural networks, the problem of the uniqueness of the solution of the considered genetic regulatory networks is resolved, and a novel sufficient condition to guarantee the uniform stability of above genetic regulatory networks is also derived. Eventually, an example is given to demonstrate that the obtained result is effective.     

Computational-Based Approach for Predicting Porosity of Electrospun Nanofiber Mats Using Response Surface Methodology and Artificial Neural Network Methods

Comparative studies between response surface methodology (RSM) and artificial neural network (ANN) methods to find the effects of electrospinning parameters on the porosity of nanofiber mats is described. The four important electrospinning parameters studied included solution concentration (wt.%), applied voltage (kV), spinning distance (cm) and volume flow rate (mL/h). It was found that the applied voltage and solution concentration are the two critical parameters affecting the porosity of the nanofiber mats. The two approaches were compared for their modeling and optimization capabilities with the modeling capability of RSM showing superiority over ANN, having comparatively lower values of errors. The mean relative error for the RSM and ANN models were 1.97% and 2.62% and the root mean square errors (RMSE) were 1.50 and 1.95, respectively. The superiority of the RSM-based approach is due to its high prediction accuracy and the ability to compute the combined effects of the electrospinning factors on the porosity of the nanofiber mats.