High pressure clamp for electrical measurements up to 8 GPa and temperature down to 77 K

A compact clamp-type high pressure cell for carrying out electrical conductivity measurements on small solid samples of size 1 mm or less at pressures upto 8 GPa (i.e., 80 kbar) and for use down to 77 K has been designed and fabricated. The pressure generated in the sample region has been calibrated at room temperature against the polymorphic phase transitions of Bismuth and Ytterbium. The pressure relaxation of the clamp at low temperatures has been estimated by monitoring the electrical conductivity behavior of lead. Review of Scientific Instruments is copyrighted by The American Institute of Physics.

Electrical Percolation studies of Polystyrene-multi wall carbon nanotubes Composites

Composites of Polystyrene-multi wall carbon nanotubes (PS-MWNTs) were prepared with loading up to 7 wt% of MWNTs by simple solvent mixing and drying technique. MWNTs with high aspect ratio similar to 4000 were used to make the polymer composites. A very high degree of dispersion of MWNTs was achieved by ultrasonication technique. As a result of high dispersion and high aspect ratio of the MWNTs electrical percolation was observed at rather low weight fraction similar to 0.0021. Characterization of the as prepared PS-MWNTs composites was done by Electron microscopy (EM), X-ray diffraction technique (XRD) and Thermogravimetery analysis (TGA).

Efficient probabilistic contingency analysis through a stability measure considering wind perturbation

The integration of stochastic wind power has accentuated a challenge for power system stability assessment. Since the power system is a time-variant system under wind generation fluctuations, pure time-domain simulations are difficult to provide real-time stability assessment. As a result, the worst-case scenario is simulated to give a very conservative assessment of system transient stability. In this study, a probabilistic contingency analysis through a stability measure method is proposed to provide a less conservative contingency analysis which covers 5-min wind fluctuations and a successive fault. This probabilistic approach would estimate the transfer limit of a critical line for a given fault with stochastic wind generation and active control devices in a multi-machine system. This approach achieves a lower computation cost and improved accuracy using a new stability measure and polynomial interpolation, and is feasible for online contingency analysis.

Negative sequence compensation within fundamental positive sequence reference frame for a stiff micro-grid generation in a wind power system using slip ring induction machine

An isolated wind power generation scheme using slip ring induction machine (SRIM) is proposed. The proposed scheme maintains constant load voltage and frequency irrespective of the wind speed or load variation. The power circuit consists of two back-to-back connected inverters with a common dc link, where one inverter is directly connected to the rotor side of SRIM and the other inverter is connected to the stator side of the SRIM through LC filter. Developing a negative sequence compensation method to ensure that, even under the presence of unbalanced load, the generator experiences almost balanced three-phase current and most of the unbalanced current is directed through the stator side converter is the focus here. The SRIM controller varies the speed of the generator with variation in the wind speed to extract maximum power. The difference of the generated power and the load power is either stored in or extracted from a battery bank, which is interfaced to the common dc link through a multiphase bidirectional fly-back dc-dc converter. The SRIM control scheme, maximum power point extraction algorithm and the fly-back converter topology are incorporated from available literature. The proposed scheme is both simulated and experimentally verified.

A Team of Continuous-Action Learning Automata for Noise-Tolerant Learning of Half-Spaces

Learning automata are adaptive decision making devices that are found useful in a variety of machine learning and pattern recognition applications. Although most learning automata methods deal with the case of finitely many actions for the automaton, there are also models of continuous-action-set learning automata (CALA). A team of such CALA can be useful in stochastic optimization problems where one has access only to noise-corrupted values of the objective function. In this paper, we present a novel formulation for noise-tolerant learning of linear classifiers using a CALA team. We consider the general case of nonuniform noise, where the probability that the class label of an example is wrong may be a function of the feature vector of the example. The objective is to learn the underlying separating hyperplane given only such noisy examples. We present an algorithm employing a team of CALA and prove, under some conditions on the class conditional densities, that the algorithm achieves noise-tolerant learning as long as the probability of wrong label for any example is less than 0.5. We also present some empirical results to illustrate the effectiveness of the algorithm.

R1STM: One-class support tensor machine with randomised kernel

Identifying unusual or anomalous patterns in an underlying dataset is an important but challenging task in many applications. The focus of the unsupervised anomaly detection literature has mostly been on vectorised data. However, many applications are more naturally described using higher-order tensor representations. Approaches that vectorise tensorial data can destroy the structural information encoded in the high-dimensional space, and lead to the problem of the curse of dimensionality. In this paper we present the first unsupervised tensorial anomaly detection method, along with a randomised version of our method. Our anomaly detection method, the One-class Support Tensor Machine (1STM), is a generalisation of conventional one-class Support Vector Machines to higher-order spaces. 1STM preserves the multiway structure of tensor data, while achieving significant improvement in accuracy and efficiency over conventional vectorised methods. We then leverage the theory of nonlinear random projections to propose the Randomised 1STM (R1STM). Our empirical analysis on several real and synthetic datasets shows that our R1STM algorithm delivers comparable or better accuracy to a state-of-the-art deep learning method and traditional kernelised approaches for anomaly detection, while being approximately 100 times faster in training and testing.

Relative electrical distance concept for evaluation of network reactive power and loss contributions in a deregulated system

With the liberalisation of electricity market it has become very important to determine the participants making use of the transmission network.Transmission line usage computation requires information of generator to load contributions and the path used by various generators to meet loads and losses. In this study relative electrical distance (RED) concept is used to compute reactive power contributions from various sources like generators, switchable volt-amperes reactive(VAR) sources and line charging susceptances that are scattered throughout the network, to meet the system demands. The transmission line charge susceptances contribution to the system reactive flows and its aid extended in reducing the reactive generation at the generator buses are discussed in this paper. Reactive power transmission cost evaluation is carried out in this study. The proposed approach is also compared with other approaches viz.,proportional sharing and modified Y-bus.Detailed case studies with base case and optimised results are carried out on a sample 8-bus system. IEEE 39-bus system and a practical 72-bus system, an equivalent of Indian Southern grid are also considered for illustration and results are discussed.

The composition dependence of electrical switching behavior of Ge7Se93-xSbx glasses

Bulk Ge7Se93-xSbx (21 <= x <= 32) glasses are prepared by melt quenching method and electrical switching studies have been undertaken on these samples to elucidate the type of switching and the composition and thickness dependence of switching voltages. On the basis of the compressibility and atomic radii, it has been previously observed that Se-based glasses exhibit memory switching behavior. However, the present results indicate that Ge7Se93-xSbx glasses exhibit threshold type electrical switching with high switching voltages. Further, these samples are found to show fluctuations in the current-voltage (I-V) characteristics. The observed threshold behavior of Ge7Se93-xSbx glasses has been understood on the basis of larger atomic radii and lesser compressibilities of Sb and Ge. Further. the high switching voltages and fluctuations in the I-V characteristics of Ge-Se-Sb samples can be attributed to the high resistance of the samples and the difference in thermal conductivities of different structural units constituting the local structure of these glasses. The switching voltages of Ge7Se93-xSbx glasses have been found to decrease with the increase in the Sb concentration. The observed composition dependence of switching voltages has been understood on the basis of higher metallicity of the Sb additive and also in the light of the Chemically Ordered Network (CON) model. Further, the thickness dependence of switching voltages has been studied to reassert the mechanism of switching.

Prediction of Candidate Primary Immunodeficiency Disease Genes Using a Support Vector Machine Learning Approach

Screening and early identification of primary immunodeficiency disease (PID) genes is a major challenge for physicians. Many resources have catalogued molecular alterations in known PID genes along with their associated clinical and immunological phenotypes. However, these resources do not assist in identifying candidate PID genes. We have recently developed a platform designated Resource of Asian PDIs, which hosts information pertaining to molecular alterations, protein-protein interaction networks, mouse studies and microarray gene expression profiling of all known PID genes. Using this resource as a discovery tool, we describe the development of an algorithm for prediction of candidate PID genes. Using a support vector machine learning approach, we have predicted 1442 candidate PID genes using 69 binary features of 148 known PID genes and 3162 non-PID genes as a training data set. The power of this approach is illustrated by the fact that six of the predicted genes have recently been experimentally confirmed to be PID genes. The remaining genes in this predicted data set represent attractive candidates for testing in patients where the etiology cannot be ascribed to any of the known PID genes.

Two temperature viscous accretion flows around rotating black holes: Description of under-fed systems to ultra-luminous X-ray sources

We discuss two temperature accretion disk flows around rotating black holes. As we know that to explain observed hard X-rays the choice of Keplerian angular momentum profile is not unique, we consider the sub-Keplerian regime of the disk. Without any strict knowledge of the magnetic field structure, we assume the cooling mechanism is dominated by bremsstrahlung process. We show that in a range of Shakura-Sunyaev viscosity parameter 0.2 greater than or similar to alpha greater than or similar to 0.0005, flow behavior varies widely, particularly by means of the size of disk, efficiency of cooling and corresponding temperatures of ions and electrons. We also show that the disk around a rotating black hole is hotter compared to that around a Schwarzschild black hole, rendering a larger difference between ion and electron temperatures in the former case. With all the theoretical solutions in hand, finally we reproduce the observed luminosities (L) of two extreme cases-the under-fed AGNs and quasars (e.g. Sgr A') with L greater than or similar to 10(33) erg/s to ultra-luminous X-ray sources with L similar to 10(41) erg/s, at different combinations of mass accretion rate, ratio of specific heats, Shakura-Sunyaev viscosity parameter and Kerr parameter, and conclude that Sgr A' may be an intermediate spinning black hole.

Aircraft trajectory clustering techniques using circular statistics

This paper presents a statistical aircraft trajectory clustering approach aimed at discriminating between typical manned and expected unmanned traffic patterns. First, a resampled version of each trajectory is modelled using a mixture of Von Mises distributions (circular statistics). Second, the remodelled trajectories are globally aligned using tools from bioinformatics. Third, the alignment scores are used to cluster the trajectories using an iterative k-medoids approach and an appropriate distance function. The approach is then evaluated using synthetically generated unmanned aircraft flights combined with real air traffic position reports taken over a sector of Northern Queensland, Australia. Results suggest that the technique is useful in distinguishing between expected unmanned and manned aircraft traffic behaviour, as well as identifying some common conventional air traffic patterns.

Synthesis of liquid crystalline benzothiazole base derivatives: A study of their optical and electrical properties

Two new donor-acceptor type liquid crystalline semiconductors based on benzothiazole have been synthesized. Their structural, photophysical and electronic properties were investigated using X-ray diffraction, atomic force microscopy, cyclic voltammetry, UV-Vis, photoluminescence, and Raman spectroscopy. The liquid crystalline behaviour of the molecules was thoroughly examined by differential scanning calorimetry (DSC) and optical polarizing microscope. The DSC and thermogravimetric analysis (TGA) show that these materials posses excellent thermal stability and have decomposition temperatures in excess of 300 degrees C. Beyond 160 degrees C both molecules show a smectic A liquid crystalline phase that exists till about 240 degrees C. Field-effect transistors were fabricated by vacuum evaporating the semiconductor layer using standard bottom gate/top contact geometry. The devices exhibit p-channel behaviour with hole mobilities of 10(-2) cm(2)/Vs. (C) 2009 Elsevier B.V. All rights reserved.

Two-temperature accretion around rotating black holes: a description of the general advective flow paradigm in the presence of various cooling processes to explain low to high luminous sources

We investigate viscous two-temperature accretion disc flows around rotating black holes. We describe the global solution of accretion flows with a sub-Keplerian angular momentum profile, by solving the underlying conservation equations including explicit cooling processes self-consistently. Bremsstrahlung, synchrotron and inverse Comptonization of soft photons are considered as possible cooling mechanisms. We focus on the set of solutions for sub-Eddington, Eddington and super-Eddington mass accretion rates around Schwarzschild and Kerr black holes with a Kerr parameter of 0.998. It is found that the flow, during its infall from the Keplerian to sub-Kepleria transition region to the black hole event horizon, passes through various phases of advection: the general advective paradigm to the radiatively inefficient phase, and vice versa. Hence, the flow governs a much lower electron temperature similar to 10(8)-10(9.5) K, in the range of accretion rate in Eddington units 0.01 less than or similar to (M) over dot less than or similar to 100, compared to the hot protons of temperature similar to 10(10.2)-10(11.8) K. Therefore, the solution may potentially explain the hard X-rays and gamma-rays emitted from active galactic nuclei (AGNs) and X-ray binaries. We then compare the solutions for two different regimes of viscosity. We conclude that a weakly viscous flow is expected to be cooling dominated, particularly at the inner region of the disc, compared to its highly viscous counterpart, which is radiatively inefficient. With all the solutions in hand, we finally reproduce the observed luminosities of the underfed AGNs and quasars (e. g. Sgr A*) to ultraluminous X-ray sources (e. g. SS433), at different combinations of input parameters, such as the mass accretion rate and the ratio of specific heats. The set of solutions also predicts appropriately the luminosity observed in highly luminous AGNs and ultraluminous quasars (e. g. PKS 0743-67).

Time Domain Characteristics of Electrical Measures for a Piezoelectric Thin Film to Identify Defects in the Substrate

A reduced 3D continuum model of dynamic piezoelectricity in a thin-film surface-bonded to the substrate/host is presented in this article. While employing large area flexible thin piezoelectric films for novel applications in device/diagnostics, the feasibility of the proposed model in sensing the surface and/or sub-surface defects is demonstrated through simulations - which involve metallic beams with cracks and composite beam with delaminations of various sizes. We have introduced a set of electrical measures to capture the severity of the damage in the existing structures. Characteristics of these electrical measures in terms of the potential difference and its spatial gradients are illustrated in the time domain. Sensitivity studies of the proposed measures in terms of the defected areas and their region of occurence relative to the sensing film are reported. The simulations' results for electrical measures for damaged hosts/substrates are compared with those due to undamaged hosts/substrates, which show monotonicity with high degree of sensitivity to variations in the damage parameters.

A blind indic script recognizer for multi-script documents

We report a hierarchical blind script identifier for 11 different Indian scripts. An initial grouping of the 11 scripts is accomplished at the first level of this hierarchy. At the subsequent level, we recognize the script in each group. The various nodes of this tree use different feature-classifier combinations. A database of 20,000 words of different font styles and sizes is collected and used for each script. Effectiveness of Gabor and Discrete Cosine Transform features has been independently, evaluated using nearest neighbor linear discriminant and support vector machine classifiers. The minimum and maximum accuracies obtained, using this hierarchical mechanism, are 92.2% and 97.6%, respectively.