Inverse optimality in the class of Hopfield neural networks with input nonlinearity

This paper presents the chaos suppression problem in the class of Hopfield neural networks (HNNs) with input nonlinearity using inverse optimality approach. Using the inverse optimality technique and based on Lyapunov stability theory, a stabilizing control law, which is optimal with respect to meaningful cost functional, is determined to achieve global asymptotically stability in the closed-loop system. Numerical simulation is performed on a four-dimensional hyper-chaotic HNN to demonstrate the effectiveness of the proposed method.     

Development of a robust canopy height model derived from ALS point clouds for predicting individual crown attributes at the species level

ABSTRACT

Tree crown attributes are important parameters during the assessment and monitoring of forest ecosystems. Canopy height models (CHMs) derived from airborne laser scanning (ALS) data have proved to be a reliable source for extracting different biophysical characteristics of single trees and at stand level. However, ALS-derived tree measurements (e.g., mean crown diameter) can be negatively affected by pits that appear in the CHMs. Thus, we propose a novel method for generating pit-free CHMs from ALS point clouds for estimating crown attributes (i.e., area and mean diameter) at the species level. The method automatically calculates a threshold for a pixel based on the range of height values within neighbouring pixels; if the pixel falls below the threshold then it is recognized as a pitted pixel. The pit is then filled with the median of the values of the neighbouring pixels. Manually delineated individual tree crowns (ITC) of four deciduous and two coniferous species on Colour Infrared (CIR) stereo images were used as a reference in the analysis. In addition, a variety of different algorithms for constructing CHMs were compared to investigate the performance of different CHMs in similar forest conditions. Comparisons between the estimated and observed crown area (R² = 0.95, RMSE% = 19.12% for all individuals) and mean diameter (R² = 0.92, RMSE% = 12.16% for all individuals) revealed that ITC attributes were correctly estimated by segmentation of the pit-free CHM proposed in this study. The goodness of matching and geometry revealed that the delineated crowns correctly matched up to the reference data and had identical geometry in approximately 70% of cases. The results showed that the proposed method produced a CHM that estimates crown attributes more accurately than the other investigated CHMs. Furthermore, the findings suggest that the proposed algorithm used to fill pits with the median of height observed in surrounding pixels significantly improve the accuracy of the results the species level due to a higher correlation between the estimated and observed crown attributes. Based on these results, we concluded that the proposed pit filling method is capable of providing an automatic and objective solution for constructing pit-free CHMs for assessing individual crown attributes of mixed forest stands.     

Regenerable MgO-based sorbents for high-temperature CO2 removal from syngas: 1. Sorbent development, evaluation, and reaction modeling

Highly reactive and mechanically strong low-cost regenerable MgO-based sorbents were prepared by modification of dolomite which involved partial calcinations followed by impregnation with a potassium-based salt. The sorbents are capable of removing CO₂ from gasification-based processes such as Integrated Gasification Combined Cycle (IGCC). The sorbents have high reactivity and good capacity toward CO₂ absorption in the temperature range of 300-450 °C at 20 atm. and can be easily regenerated at 500 °C. The reaction appears to be first order with respect to CO₂ concentration with an activation energy of 44 kJ/mol. The reactivity and the absorption capacity of the sorbents increase with increasing temperature, as long as the partial pressure of CO₂ is above the equilibrium value for sorbent carbonation. The reactivity of the sorbents appears to improve in the presence of steam, which is likely due to the increase in the BET surface area and the porosity of the sorbent. A two-zone expanding grain model, consisting of a high-reactivity outer shell and a low-reactivity inner core is shown to provide an excellent fit to the TGA experimental data on sorbent carbonation at various operating conditions.     

Behavior of FRP-confined concrete after high temperature exposure

This paper presents the results of an experimental program to investigate the effect of high temperature on the performance of concrete externally confined with FRP sheets. For this purpose, a two-phase experimental program was conducted. In the first phase, 42 standard 100 × 200 mm concrete cylinders were prepared. Out of these specimens, 14 cylinders were left unwrapped; 14 specimens were wrapped with one layer of CFRP sheet; and the remaining 14 specimens were wrapped with one layer of GFRP sheet. Some of the unconfined and FRP-confined specimens were exposed to room temperature; whereas, other cylinders were exposed to heating regime of 100 °C and 200 °C for a period of 1, 2 or 3 h. After high temperature exposure, specimens were tested under uniaxial compression till failure. The test results demonstrated that at a temperature of 100 °C (a little more than the glass transition temperature (T_g) of the epoxy resin), both CFRP- and GFRP-wrapped specimens experienced small loss in strength resulting from melting of epoxy. This loss of strength was more pronounced when the temperature reached 200 °C. In the second phase of the experimental program, three 100 × 100 × 650 mm concrete prisms were prepared and then overlaid by one layer of CFRP and GFRP laminates for conducting pull-off strength tests as per ASTM D4541 – 09. The objective of this testing was to evaluate the degradation in bond strength between FRP and concrete substrate when exposed to elevated temperature environments. One prism was exposed to room temperature whereas the other two specimens were exposed to heating regime of 100 °C and 200 °C for a period of 3 h. It was concluded that a significant degradation in the bond strength occurred at a temperature of 200 °C especially for CFRP-overlaid specimens.     

Characterization of stabilized porous magnetite core–shell nanogel composites based on crosslinked acrylamide/sodium acrylate copolymers

Stabilized and dispersed superparamagnetic porous nanogels based on sodium acrylate (AA‐Na) and acrylamide (AM) in a surfactant‐free aqueous system were synthesized via solution polymerization at room temperature. The formation of magnetite nanoparticles was confirmed and their properties characterized using Fourier transform infrared spectroscopy. Extensive characterization of the magnetic polymer particles using transmission electron microscopy (TEM), dynamic light scattering and zeta potential measurements revealed that Fe₃O₄ nanoparticles were incorporated into the shells of poly(AM/AA‐Na). The average particle size was 5–8 nm as determined from TEM. AM/AA‐Na nanoparticles with a diameter of about 11 nm were effectively assembled onto the negatively charged surface of the as‐synthesized Fe₃O₄ nanoparticles via electrostatic interaction. Crosslinked magnetite nanocomposites were prepared by in situ development of surface‐modified magnetite nanoparticles in an AM/AA‐Na hydrogel. Scanning electron microscopy was used to study the surface morphology of the prepared composites. The morphology, phase composition and crystallinity of the prepared nanocomposites were characterized. Atomic force microscopy and argon adsorption–desorption measurements of Fe₃O₄.AM/AA indicated that the architecture of the polymer network can be a hollow porous sphere or a solid phase, depending on the AA‐Na content. © 2013 Society of Chemical Industry     

Creating probabilistic databases from duplicated data

A major source of uncertainty in databases is the presence of duplicate items, i.e., records that refer to the same real-world entity. However, accurate deduplication is a difficult task and imperfect data cleaning may result in loss of valuable information. A reasonable alternative approach is to keep duplicates when the correct cleaning strategy is not certain, and utilize an efficient probabilistic query-answering technique to return query results along with probabilities of each answer being correct. In this paper, we present a flexible modular framework for scalably creating a probabilistic database out of a dirty relation of duplicated data and overview the challenges raised in utilizing this framework for large relations of string data. We study the problem of associating probabilities with duplicates that are detected using state-of-the-art scalable approximate join methods. We argue that standard thresholding techniques are not sufficiently robust for this task, and propose new clustering algorithms suitable for inferring duplicates and their associated probabilities. We show that the inferred probabilities accurately reflect the error in duplicate records.     

Large‐scale fading behavior for a cellular network with uniform spatial distribution

Large‐scale fading (LSF) between interacting nodes is a fundamental element in radio communications, responsible for weakening the propagation, and thus worsening the service quality. Given the importance of channel‐losses in general, and the inevitability of random spatial geometry in real‐life wireless networks, it was then natural to merge these two paradigms together in order to obtain an improved stochastical model for the LSF indicator. Therefore, in exact closed‐form notation, we generically derived the LSF distribution between a prepositioned reference base‐station and an arbitrary node for a multi‐cellular random network model. In fact, we provided an explicit and definitive formulation that considered at once: the lattice profile, the users' random geometry, the effect of the far‐field phenomenon, the path‐loss behavior, and the stochastic impact of channel scatters. The veracity and accuracy of the theoretical analysis were also confirmed through Monte Carlo simulations.     

Low end‐to‐end delay fuzzy networking protocol for mobile wireless sensing

Because of the practical limitations of the energy and processing capabilities, the deployment of many Wireless Sensor Networks (WSN) is facing two main challenges of increasing network lifetime and reducing End to End Delay (EED) which become critical when the nodes are mobile and use non‐rechargeable energy sources. One way to help to extend network lifetime is using fuzzy logic in a form of artificial intelligence. To this end we propose a new routing protocol for using mobile WSNs, which holds the nodes in an equal level of energy and decreases energy dissipation of the network. An optimum path is selected based on the cost of each node to increase network lifetime. In order to lessen EED, we also attempt to design a novel zoning‐scheme for the network area. In this scheme, zonation is dynamic and works based on the Data Link (DL) position. The simulation result shows a significant improvement in lifetime and EED by proposed protocol compared with existing protocols. Copyright © 2016 John Wiley & Sons, Ltd.     

Supertough (Polyamide 6)/(acrylonitrile butadiene rubber) nano alloy through in situ polymerization of caprolactam in the presence of acrylonitrile butadiene rubber nanophase

In this work, polymerization of caprolactam (CL) was carried out in the presence of acrylonitrile butadiene rubber (NBR) during the reactive melt‐mixing process. During shear mixing, NBR particles swelled and dissolved in the molten CL, which led to separation and distribution of rubber particles to nanoscale in the dissolution stage. Then, in an internal mixer, supertough Polyamide 6 was prepared via melt polymerization of CL/NBR mixture, sodium caprolactam as a catalyst, and hexamethylene diisocyanate as an activator. The effects of various concentrations of catalyst and activator on the initiation time of the reaction were determined. Physical and mechanical properties of different formulations prepared via reactive melt blending were determined by tensile and impact measurements, differential scanning calorimetry, Fourier‐transform infrared spectroscopy, X‐ray scattering techniques, transmission electron microscopy, and dynamic mechanical thermal analysis. Experimental results showed that a recipe with 3% nitrile rubber in a CL/NBR mixture enhances the physical and mechanical properties the best, compared with other formulations. This condition led to the formation of NBR nanospheres during melt polymerization of Polyamide 6 as well. J. VINYL ADDIT. TECHNOL., 21:116–121, 2015. © 2014 Society of Plastics Engineers     

Modelling and parameter estimation of ultra-dispersed in situ catalytic upgrading experiments in a batch reactor

In situ catalytic upgrading of heavy oil and bitumen has been suggested and tested in the laboratory for utilization of heavy oil resources. Experimental observations have demonstrated potential, so this innovative recovery technique may have a role in the development of large resources of heavy oil and bitumen. Accurate analytical and numerical modelling is necessary in order to correctly interpret experimental measurements of the in situ upgrading, leading to a better understanding and design of field-scale processes. In this paper, we present modelling and parameter estimation for ultra-dispersed catalytic upgrading experiments conducted in a batch reactor. The Monte Carlo simulation technique was used to estimate the most appropriate reaction parameters. The combination of an analytical batch reactor model and the Monte Carlo simulation technique allows for the fast generation of a large number of upgrading experiment realizations. Comparisons of analytical modelling results with the experimental measurements of the upgrading experiments at different temperatures are in close agreement. Results reveal that ultra-dispersed catalytic upgrading in a batch reactor results in a fairly high residue conversion and can potentially increase the API gravity of the produced oil.