Distributed memory systems for simulating artificial neural networks

In executing tasks involving intelligent information processing, the human brain performs better than the digital computer. The human brain derives its power from a large number [O(10¹¹)] of neurons which are interconnected by a dense interconnection network [O(10⁵) connections per neuron]. Artificial neural network (ANN) paradigms adopt the structure of the brain to try to emulate the intelligent information processing methods of the brain. ANN techniques are being employed to solve problems in areas such as pattern recognition, and robotic processing. Simulation of ANNs involves implementation of large number of neurons and a massive interconnection network. In this paper, we discuss various simulation models of ANNs and their implementation on distributed memory systems. Our investigations reveal that communication-efficient networks of distributed memory systems perform better than other topologies in implementing ANNs.     

Graft copolymerization of acrylonitrile onto acetylated jute fibers

Graft copolymerization of acrylonitrile (AN) onto acetylated chemically modified jute was carried out in the temperature range 40–60°C using V⁵⁺-cyclohexanone redox initiator system. The effects of temperature, time, concentrations of metal ion (V⁵⁺), monomer (AN), cyclohexanone, some inorganic salts, and organic solvents on percent grafting have been studied. IR spectra of acetylated chemically modified jute and grafted jute have been taken, and their characteristic bands have been identified. Grafting has improved the thermal stability and also the lightfastness rating of jute fibers dyed with basic dyes.     

Limits of compensation in a failed antenna array

In large antenna arrays, the possibility of occurrence of faults in some of the radiating elements cannot be precluded at all times. In such situations, the radiation pattern of the array gets distorted, mostly with an increase in sidelobe level and decrease in gain. Although it is not possible to restore the pattern fully by rearranging the excitations of the functioning elements, compensation methods have been reported in the literature for restoring one performance parameter of the array and making a trade‐off on some other parameter. In this article, we have made a study on the tolerance level of this compensation process. One part of the study deals with the thinning in the failed array, that is, to find a limit on the minimum number of functioning elements of the array that can restore the digital beamforming of the failed array. The second part of study deals with finding the maximum number of element failures that can be compensated. The study was carried out by optimizing the amplitude excitations of the failed array. Instead of classical optimization techniques, particle swarm optimization was used for the compensation process. © 2014 Wiley Periodicals, Inc. Int J RF and Microwave CAE 24:635–645, 2014.     

Mechanical behavior of electrophoretically modified CFRP composites at elevated temperatures: An assessment of the influence of graphene carboxyl bath concentration

The study aims at investigating the mechanical behavior of carbon fiber reinforced polymer (CFRP) composites modified with graphene carboxyl at elevated temperature (ET-110°C) and understanding the effect of electrophoretic deposition bath concentration (0.5 g/L, 1.0 g/L, and 1.5 g/L) on their mechanical behavior at ET. The 1.5 g/L composite has revealed a maximum improvement in energy absorbed before failure of 33.25% at RT and 22.54% at ET for flexural testing and ∼35% at RT for short beam shear testing, over neat CFRP composite. The modified composites have shown an improved flexural strain to failure at both RT and ET, with 1.5 g/L composite exhibiting maximum enhancement of 12.41% at RT and 26.52% at ET over neat composite. However, at ET, modified composites exhibited lower flexural strength and interlaminar shear strength values in comparison to that of neat. Viscoelastic behavior of all composites was studied to understand bath concentration's effect on thermal behavior via dynamic mechanical thermal analysis. Differential scanning calorimetry was employed for governing the glass transition temperature of composites. Fractography of tested samples (both ET and RT) was performed utilizing a scanning electron microscope to determine the prominent failure mode.     

Sulfidation properties of Fe-Al alloys at 1173 K in H2S-H2 atmospheres

The sulfidation kinetics and morphological development of reaction products are reported for Fe-9 and 18 at.% Al alloys exposed at 1173 K to H₂S-H₂ atmospheres at sulfur pressures in the range 10^–1–10³ Pa. The Fe-9 Al alloy sulfidized parabolically at Pa giving rise to a duplex scale composed of an outer Al-doped FeS layer and an inner FeS + FeAl₂S₄ lamellar layer and to an internal sulfidation zone containing Al₂S₃ precipitates. The Fe-18 Al alloy which was sulfidized at .     

Bacteria foraging optimization in antenna engineering: An application to array fault finding

Finding fault elements in linear antenna arrays using bacteria foraging optimization (BFO) is presented. One of the better options of array diagnosis is to perform it by measuring the radiated field, because in this case, removal of the array from its working site is not required and thereby not interrupting its normal operation. This task of fault finding from far‐field data is designed as an optimization problem where the difference between the far‐field power pattern obtained for a given configuration of failed element(s) and the measured one is minimized w. r. t. the excitations of the array elements. This set of excitations on comparison with the excitations of the original array gives the idea of the fault position and their type, such as either complete fault or partial fault. BFO being relatively new to microwave community when compared with other soft‐computing techniques, its performance was observed w. r. t. time of computation and convergence of the iterative process. Possibility of finding the faults from random sample points and use of minimum number of sample points for array fault finding are the novelties of the present work. © 2012 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2013.     

Vibration and damping characteristics of CNTR viscoelastic skewed shell structures under the influence of hygrothermal conditions

Engineering with Computers - The vibration and damping characteristics of carbon nanotubes reinforced (CNTR) skewed shell structure under a hygrothermal environment have been investigated using the...     

A simple decision tree-based disturbance monitoring system for VSC-based HVDC transmission link integrating a DFIG wind farm

Fault detection and classification is a key challenge for the protection of High Voltage DC (HVDC) transmission lines. In this paper, the Teager–Kaiser Energy Operator (TKEO) algorithm associated with a decision tree-based fault classi f ier is proposed to detect and classify various DC faults. The Change Identification Filter is applied to the average and differential current components, to detect the first instant of fault occurrence (above threshold) and register a Change Identified Point (CIP). Further, if a CIP is registered for a positive or negative line, only three samples of currents (i.e., CIP and each side of CIP) are sent to the proposed TKEO algorithm, which produces their respective 8 indices through which the, fault can be detected along with its classification. The new approach enables quicker detection allowing utility grids to be restored as soon as possible. This novel approach also reduces computing complexity and the time required to identify faults with classification. The importance and accuracy of the proposed scheme are also thor oughly tested and compared with other methods for various faults on HVDC transmission lines.     

Discovering excitatory relationships using dynamic Bayesian networks

Mining temporal network models from discrete event streams is an important problem with applications in computational neuroscience, physical plant diagnostics, and human–computer interaction modeling. In this paper, we introduce the notion of excitatory networks which are essentially temporal models where all connections are stimulative, rather than inhibitive. The emphasis on excitatory connections facilitates learning of network models by creating bridges to frequent episode mining. Specifically, we show that frequent episodes help identify nodes with high mutual information relationships and that such relationships can be summarized into a dynamic Bayesian network (DBN). This leads to an algorithm that is significantly faster than state-of-the-art methods for inferring DBNs, while simultaneously providing theoretical guarantees on network optimality. We demonstrate the advantages of our approach through an application in neuroscience, where we show how strong excitatory networks can be efficiently inferred from both mathematical models of spiking neurons and several real neuroscience datasets.