Investigation of true remnant polarization response in heterostructured artificial biferroics

Epitaxial bilayered thin films composed of ferromagnetic La0.6Sr0.4MnO3 and ferroelectric 0.7Pb (Mg1/3Nb2/3)O3-0.3(PbTiO3) were fabricated on LaAlO3 (100) substrates by pulsed laser ablation. Ferroelectric, ferromagnetic and magneto-dielectric characterizations performed earlier indicated the possible existence of strain-mediated magneto-electric coupling in these biferroic heterostructures. In order to investigate their true remnant polarization characteristics, usable in devices, room-temperature polarization versus electric field, positive-up negative-down (PUND) pulse polarization studies and remnant hysteresis measurements were carried out. The PUND and remnant hysteresis measurements revealed the significant contribution of the non-remnant component in the observed polarization hysteresis response of these heterostructures. (C) 2010 Published by Elsevier Ltd

Prediction of swelling pressure of soil using artificial intelligence techniques

The swelling pressure of soil depends upon various soil parameters such as mineralogy, clay content, Atterberg's limits, dry density, moisture content, initial degree of saturation, etc. along with structural and environmental factors. It is very difficult to model and analyze swelling pressure effectively taking all the above aspects into consideration. Various statistical/empirical methods have been attempted to predict the swelling pressure based on index properties of soil. In this paper, the computational intelligence techniques artificial neural network and support vector machine have been used to develop models based on the set of available experimental results to predict swelling pressure from the inputs; natural moisture content, dry density, liquid limit, plasticity index, and clay fraction. The generalization of the model to new set of data other than the training set of data is discussed which is required for successful application of a model. A detailed study of the relative performance of the computational intelligence techniques has been carried out based on different statistical performance criteria.

Prediction of swelling pressure of soil using artificial intelligence techniques

The swelling pressure of soil depends upon various soil parameters such as mineralogy, clay content, Atterberg's limits, dry density, moisture content, initial degree of saturation, etc. along with structural and environmental factors. It is very difficult to model and analyze swelling pressure effectively taking all the above aspects into consideration. Various statistical/empirical methods have been attempted to predict the swelling pressure based on index properties of soil. In this paper, the computational intelligence techniques artificial neural network and support vector machine have been used to develop models based on the set of available experimental results to predict swelling pressure from the inputs; natural moisture content, dry density, liquid limit, plasticity index, and clay fraction. The generalization of the model to new set of data other than the training set of data is discussed which is required for successful application of a model. A detailed study of the relative performance of the computational intelligence techniques has been carried out based on different statistical performance criteria.

Multiple contour extraction from graylevel images using an artificial neural network

For active contour modeling (ACM), we propose a novel self-organizing map (SOM)-based approach, called the batch-SOM (BSOM), that attempts to integrate the advantages of SOM- and snake-based ACMs in order to extract the desired contours from images. We employ feature points, in the form of ail edge-map (as obtained from a standard edge-detection operation), to guide the contour (as in the case of SOM-based ACMs) along with the gradient and intensity variations in a local region to ensure that the contour does not "leak" into the object boundary in case of faulty feature points (weak or broken edges). In contrast with the snake-based ACMs, however, we do not use an explicit energy functional (based on gradient or intensity) for controlling the contour movement. We extend the BSOM to handle extraction of contours of multiple objects, by splitting a single contour into as many subcontours as the objects in the image. The BSOM and its extended version are tested on synthetic binary and gray-level images with both single and multiple objects. We also demonstrate the efficacy of the BSOM on images of objects having both convex and nonconvex boundaries. The results demonstrate the superiority of the BSOM over others. Finally, we analyze the limitations of the BSOM.

Characterization of mitochondrial neutral protease activity and the response of lysosomal enzymes to clofibrate feeding in rat liver

The phosphate-inhibitable neutral protease activity of the heavy mitochondrial fraction of rat liver is of lysosomal origin. The activity is essentially due to the thiol proteinases of the lysosomes. Digitonin treatment of the mitochondrial fraction results in the release of about 85 per cent of the neutral protease activity and the residual activity has an alkaline pH optimum and is not inhibited by phosphate. Clofibrate feeding at 0.5 per cent level in the diet results in enhanced levels of lysosomal enzymes. The increase is however restricted to the lysosome-rich fraction such that the activities associated with the heavy mitochondrial fraction show a significant decrease. It is suggested that clofibrate inhibits engulfment of mitochondria by lysosomes and this results in enhanced mitochondrial protein content.

Artificial Bee Colony (ABC) for multi-objective design optimization of composite structures

In this paper, we present a generic method/model for multi-objective design optimization of laminated composite components, based on Vector Evaluated Artificial Bee Colony (VEABC) algorithm. VEABC is a parallel vector evaluated type, swarm intelligence multi-objective variant of the Artificial Bee Colony algorithm (ABC). In the current work a modified version of VEABC algorithm for discrete variables has been developed and implemented successfully for the multi-objective design optimization of composites. The problem is formulated with multiple objectives of minimizing weight and the total cost of the composite component to achieve a specified strength. The primary optimization variables are the number of layers, its stacking sequence (the orientation of the layers) and thickness of each layer. The classical lamination theory is utilized to determine the stresses in the component and the design is evaluated based on three failure criteria: failure mechanism based failure criteria, maximum stress failure criteria and the tsai-wu failure criteria. The optimization method is validated for a number of different loading configurations-uniaxial, biaxial and bending loads. The design optimization has been carried for both variable stacking sequences, as well fixed standard stacking schemes and a comparative study of the different design configurations evolved has been presented. Finally the performance is evaluated in comparison with other nature inspired techniques which includes Particle Swarm Optimization (PSO), Artificial Immune System (AIS) and Genetic Algorithm (GA). The performance of ABC is at par with that of PSO, AIS and GA for all the loading configurations. (C) 2009 Elsevier B.V. All rights reserved.

Artificial neural networks and multicriterion analysis for sustainable irrigation planning

The objective of the present paper is to select the best compromise irrigation planning strategy for the case study of Jayakwadi irrigation project, Maharashtra, India. Four-phase methodology is employed. In phase 1, separate linear programming (LP) models are formulated for the three objectives, namely. net economic benefits, agricultural production and labour employment. In phase 2, nondominated (compromise) irrigation planning strategies are generated using the constraint method of multiobjective optimisation. In phase 3, Kohonen neural networks (KNN) based classification algorithm is employed to sort nondominated irrigation planning strategies into smaller groups. In phase 4, multicriterion analysis (MCA) technique, namely, Compromise Programming is applied to rank strategies obtained from phase 3. It is concluded that the above integrated methodology is effective for modeling multiobjective irrigation planning problems and the present approach can be extended to situations where number of irrigation planning strategies are even large in number. (c) 2004 Elsevier Ltd. All rights reserved.

A Five-Level Inverter Scheme for a Four-Pole Induction Motor Drive by Feeding the Identical Voltage-Profile Windings From Both Sides

This paper presents a five-level inverter scheme with four two-level inverters for a four-pole induction motor (IM) drive. In a conventional three-phase four-pole IM, there exists two identical voltage-profile winding coil groups per phase around the armature, which are connected in series and spatially apart by two pole pitches. In this paper, these two identical voltage-profile pole-pair winding coils in each phase of the IM are disconnected and fed from four two-level inverters from four sides of the windings with one-fourth dc-link voltage as compared to a conventional five-level neutral-point-clamped inverter. The scheme presented in this paper does not require any special design modification for the induction machine. For this paper, a four-pole IM drive is used, and the scheme can be easily extended to IMs with more than four poles. The proposed scheme is experimentally verified on a four-pole 5-hp IM drive.

Voltage and Temperature Aware Statistical Leakage Analysis Framework Using Artificial Neural Networks

Artificial neural networks (ANNs) have shown great promise in modeling circuit parameters for computer aided design applications. Leakage currents, which depend on process parameters, supply voltage and temperature can be modeled accurately with ANNs. However, the complex nature of the ANN model, with the standard sigmoidal activation functions, does not allow analytical expressions for its mean and variance. We propose the use of a new activation function that allows us to derive an analytical expression for the mean and a semi-analytical expression for the variance of the ANN-based leakage model. To the best of our knowledge this is the first result in this direction. Our neural network model also includes the voltage and temperature as input parameters, thereby enabling voltage and temperature aware statistical leakage analysis (SLA). All existing SLA frameworks are closely tied to the exponential polynomial leakage model and hence fail to work with sophisticated ANN models. In this paper, we also set up an SLA framework that can efficiently work with these ANN models. Results show that the cumulative distribution function of leakage current of ISCAS'85 circuits can be predicted accurately with the error in mean and standard deviation, compared to Monte Carlo-based simulations, being less than 1% and 2% respectively across a range of voltage and temperature values.

Artificial database for character recognition research

This paper describes a technique for artificial generation of learning and test sample sets suitable for character recognition research. Sample sets of English (Latin), Malayalam, Kannada and Tamil characters are generated easily through their prototype specifications by the endpoint co-ordinates, nature of segments and connectivity.

Generating artificial chromosomes with probability control in genetic algorithm for machine scheduling problems

In this paper, a novel genetic algorithm is developed by generating artificial chromosomes with probability control to solve the machine scheduling problems. Generating artificial chromosomes for Genetic Algorithm (ACGA) is closely related to Evolutionary Algorithms Based on Probabilistic Models (EAPM). The artificial chromosomes are generated by a probability model that extracts the gene information from current population. ACGA is considered as a hybrid algorithm because both the conventional genetic operators and a probability model are integrated. The ACGA proposed in this paper, further employs the ``evaporation concept'' applied in Ant Colony Optimization (ACO) to solve the permutation flowshop problem. The ``evaporation concept'' is used to reduce the effect of past experience and to explore new alternative solutions. In this paper, we propose three different methods for the probability of evaporation. This probability of evaporation is applied as soon as a job is assigned to a position in the permutation flowshop problem. Experimental results show that our ACGA with the evaporation concept gives better performance than some algorithms in the literature.

Effect of colostrum feeding method and presence of dam on the sleep, rest and sucking behaviour of newborn calves

"In rats, sucking milk reduces anxiety and promotes non-rapid eye movement (NREM) sleep, and in calves it induces resting but the effect on sleep is unknown. Here, we investigated how calves' sleep was affected by colostrum feeding methods. Forty-one calves were blocked by birth date and randomly allotted within blocks to the experimental treatments. Calves were housed for four days either with their dam (DAM) or individually with warm colostrum feeding (2 L four times a day) from either a teat bucket (TEAT) or an open bucket (BUCKET). DAM calves suckled their dam freely. Calves' sleeping and sucking behaviour was filmed continuously for 48 h at the ages of two and three days. Behavioural sleep (BS) was defined as calves resting at least 30 s with their head still and raised (non-rapid eye movement) or with their head against their body or the ground (rapid eye movement, REM). Latency from the end of colostrum feeding to the start of BS was recorded. We compared behaviour of TEAT calves with that of DAM and BUCKET calves using mixed models. Milk meal duration was significantly longer for TEAT calves than for BUCKET calves (mean +/- S.E.M.; 8.3 +/- 0.6 min vs. 5.2 +/- 0.6 min), but equal to that of DAM calves. We found no effect of feeding method on the duration of daily BS (12 h 59 min I h 38 min) but we found a tendency for the daily amount of NREM sleep; BUCKET calves had less NREM sleep per day than TEAT calves (6 h 18 min vs. 7 h 48 min, S.E.M. = 45 min) and also longer latencies from milk ingestion to BS (21.9 +/- 2.0 min vs. 16.2 +/- 2.0 min). DAM calves slept longer bouts than TEAT calves (10.8 +/- 1.0 min vs. 8.3 +/- 1.0 min) and less often (78 +/- 4 vs. 92 +/- 4). Sucking colostrum from a teat bucket compared with drinking from an open"

Artificial commutation for inversion into a weak AC system in a multiterminal HVDC system

This paper deals with the application of artificial commutation for a normally rated inverter connecting a weak AC system in a multiterminal HVDC (MTDC) system. Artificial commutation is achieved using series capacitors. A modular digital simulation technique is developed to study the dynamic performance of the system. It is shown that by a proper selection of the value of the capacitor it is possible to limit the valve stresses and the DC harmonics to acceptable levels and achieve an improved performance during severe transient conditions. The determination of the value of the series capacitor is based on a parametric study.