A Durable Graphitic-Carbon Support for Pt and Pt3Co Cathode Catalysts in Polymer Electrolyte Fuel Cells

The high efficiency of fuel-cell-powered electric vehicles makes them a potentially viable option for future transportation. Polymer Electrolyte Fuel Cells (PEFCs) are most promising among various fuel cells for electric traction due to their quick start-up and low-temperature operation. In recent years, the performance of PEFCs has reached the acceptable level both for automotive and stationary applications and efforts are now being expended in increasing their durability, which remains a major concern in their commercialization. To make PEFCs meet automotive targets an understanding of the factors affecting the stability of carbon support and platinum catalyst is critical. Alloying platinum (Pt) with first-row transition metals such as cobalt (Co) is reported to facilitate both higher degree of crystallinity and enhanced activity in relation to pristine Pt. But a major challenge for the application of Pt-transition metal alloys in PEFCs is to improve the stability of these binary catalysts. Dissolution of the non-precious metal in the acidic environment could alleviate the activity of the catalysts and hence cell performance. The use of graphitic carbon as cathode-catalyst support enhances the long-term stability of Pt and its alloys in relation to non-graphitic carbon as the former exhibits higher resistance to carbon corrosion in relation to the latter in PEFC cathodes during accelerated-stress test (AST). Changes in electrochemical surface area (ESA), cell performance and charge-transfer resistance are monitored during AST through cyclic voltammetry, cell polarization and impedance measurements, respectively. Studies on catalytic electrodes with X-ray diffraction, Raman spectroscopy and transmission electron microscopy reflect that graphitic carbon-support resists carbon corrosion and helps mitigating aggregation of Pt and Pt3Co catalyst particles. (C) 2012 The Electrochemical Society. DOI: 10.1149/2.051301jes] All rights reserved.

Infinite push: a new support vector ranking algorithm that directly optimizes accuracy at the absolute top of the list

Ranking problems have become increasingly important in machine learning and data mining in recent years, with applications ranging from information retrieval and recommender systems to computational biology and drug discovery. In this paper, we describe a new ranking algorithm that directly maximizes the number of relevant objects retrieved at the absolute top of the list. The algorithm is a support vector style algorithm, but due to the different objective, it no longer leads to a quadratic programming problem. Instead, the dual optimization problem involves l1, ∞ constraints; we solve this dual problem using the recent l1, ∞ projection method of Quattoni et al (2009). Our algorithm can be viewed as an l∞-norm extreme of the lp-norm based algorithm of Rudin (2009) (albeit in a support vector setting rather than a boosting setting); thus we refer to the algorithm as the ‘Infinite Push’. Experiments on real-world data sets confirm the algorithm’s focus on accuracy at the absolute top of the list.

Influence of nature of support on the catalytic activity of supported molybdenum-oxo species in benzyl alcohol conversion

Supported catalysts containing 15 wt.% of molybdenum have been prepared by the incipient wetness impregnation method. CaO, MgO, Al2O3, Zr(OH)4 and Al(OH)3 have been used as supports for the preparation of supported Mo catalysts. Characterisation of all the materials prepared has been carried out through BET surface area measurement, X-ray diffractometry and FT-IR spectroscopy. Catalytic activity measurements have been carried out with reference to structure-sensitive benzyl alcohol conversion in the liquid phase. The percentage conversion of benzyl alcohol to benzaldehyde and toluene varied over a large range depending on the support used for the preparation of catalysts, indicating the importance of the support on catalytic activity of Mo catalysts. Al(OH)3 has been found to be the best support for molybdenum among all the supports used. Support–metal interaction (SMI) has been found to play an important role in determining the catalytic activity of supported catalysts.

Identification of coherent synchronous generators in a multi- machine power system using support vector clustering

This paper illustrates the application of a new technique, based on Support Vector Clustering (SVC) for the direct identification of coherent synchronous generators in a large interconnected Multi-Machine Power Systems. The clustering is based on coherency measures, obtained from the time domain responses of the generators following system disturbances. The proposed clustering algorithm could be integrated into a wide-area measurement system that enables fast identification of coherent clusters of generators for the construction of dynamic equivalent models. An application of the proposed method is demonstrated on a practical 15 generators 72-bus system, an equivalent of Indian Southern grid in an attempt to show the effectiveness of this clustering approach. The effects of short circuit fault locations on coherency are also investigated.

Support vector machine based high speed protection relay for EHV/UHV transmission line

This paper presents a fast and accurate relaying technique for a long 765kv UHV transmission line based on support vector machine. For a long EHV/UHV transmission line with large distributed capacitance, a traditional distance relay which uses a lumped parameter model of the transmission line can cause malfunction of the relay. With a frequency of 1kHz, 1/4th cycle of instantaneous values of currents and voltages of all phases at the relying end are fed to Support Vector Machine(SVM). The SVM detects fault type accurately using 3 milliseconds of post-fault data and reduces the fault clearing time which improves the system stability and power transfer capability. The performance of relaying scheme has been checked with a typical 765kV Indian transmission System which is simulated using the Electromagnetic Transients Program(EMTP) developed by authors in which the distributed parameter line model is used. More than 15,000 different short circuit fault cases are simulated by varying fault location, fault impedance, fault incidence angle and fault type to train the SVM for high speed accurate relaying. Simulation studies have shown that the proposed relay provides fast and accurate protection irrespective of fault location, fault impedance, incidence time of fault and fault type. And also the proposed scheme can be used as augmentation for the existing relaying, particularly for Zone-2, Zone-3 protection.

A study on the usefulness of Support Vector Machines for the realtime computational simulation of soft biological organs

Realistic and realtime computational simulation of soft biological organs (e.g., liver, kidney) is necessary when one tries to build a quality surgical simulator that can simulate surgical procedures involving these organs. Since the realistic simulation of these soft biological organs should account for both nonlinear material behavior and large deformation, achieving realistic simulations in realtime using continuum mechanics based numerical techniques necessitates the use of a supercomputer or a high end computer cluster which are costly. Hence there is a need to employ soft computing techniques like Support Vector Machines (SVMs) which can do function approximation, and hence could achieve physically realistic simulations in realtime by making use of just a desktop computer. Present work tries to simulate a pig liver in realtime. Liver is assumed to be homogeneous, isotropic, and hyperelastic. Hyperelastic material constants are taken from the literature. An SVM is employed to achieve realistic simulations in realtime, using just a desktop computer. The code for the SVM is obtained from [1]. The SVM is trained using the dataset generated by performing hyperelastic analyses on the liver geometry, using the commercial finite element software package ANSYS. The methodology followed in the present work closely follows the one followed in [2] except that [2] uses Artificial Neural Networks (ANNs) while the present work uses SVMs to achieve realistic simulations in realtime. Results indicate the speed and accuracy that is obtained by employing the SVM for the targeted realistic and realtime simulation of the liver.

Identification of fault location in power distribution system with distributed generation using support vector machines

This paper presents a multi-class support vector machine (SVMs) approach for locating and diagnosing faults in electric power distribution feeders with the penetration of Distributed Generations (DGs). The proposed approach is based on the three phase voltage and current measurements which are available at all the sources i.e. substation and at the connection points of DG. To illustrate the proposed methodology, a practical distribution feeder emanating from 132/11kV-grid substation in India with loads and suitable number of DGs at different locations is considered. To show the effectiveness of the proposed methodology, practical situations in distribution systems (DS) such as all types of faults with a wide range of varying fault locations, source short circuit (SSC) levels and fault impedances are considered for studies. The proposed fault location scheme is capable of accurately identify the fault type, location of faulted feeder section and the fault impedance. The results demonstrate the feasibility of applying the proposed method in practical in smart grid distribution automation (DA) for fault diagnosis.

Dynamic state estimation for identifying earthquake support motions in instrumented structures

The problem of identification of multi-component and (or) spatially varying earthquake support motions based on measured responses in instrumented structures is considered. The governing equations of motion are cast in the state space form and a time domain solution to the input identification problem is developed based on the Kalman and particle filtering methods. The method allows for noise in measured responses, imperfections in mathematical model for the structure, and possible nonlinear behavior of the structure. The unknown support motions are treated as hypothetical additional system states and a prior model for these motions are taken to be given in terms of white noise processes. For linear systems, the solution is developed within the Kalman filtering framework while, for nonlinear systems, the Monte Carlo simulation based particle filtering tools are employed. In the latter case, the question of controlling sampling variance based on the idea of Rao-Blackwellization is also explored. Illustrative examples include identification of multi-component and spatially varying support motions in linear/nonlinear structures.

Support vector clustering-based direct coherency identification of generators in a multi-machine power system

This study investigates the application of support vector clustering (SVC) for the direct identification of coherent synchronous generators in large interconnected multi-machine power systems. The clustering is based on coherency measure, which indicates the degree of coherency between any pair of generators. The proposed SVC algorithm processes the coherency measure matrix that is formulated using the generator rotor measurements to cluster the coherent generators. The proposed approach is demonstrated on IEEE 10 generator 39-bus system and an equivalent 35 generators, 246-bus system of practical Indian southern grid. The effect of number of data samples and fault locations are also examined for determining the accuracy of the proposed approach. An extended comparison with other clustering techniques is also included, to show the effectiveness of the proposed approach in grouping the data into coherent groups of generators. This effectiveness of the coherent clusters obtained with the proposed approach is compared in terms of a set of clustering validity indicators and in terms of statistical assessment that is based on the coherency degree of a generator pair.

Design and construction of geocell foundation to support the embankment on settled red mud

This paper presents the case history of the construction of a 3 m high embankment on the geocell foundation over the soft settled red mud. Red mud is a waste product from the Bayer process of Aluminum industry. Geotechnical problems of the site, the design of the geocell foundation based on experimental investigation and the construction sequences of the geocell foundations in the field are discussed in the paper. Based on the experimental studies, an analytical model was also developed to estimate the load carrying capacity of the soft clay bed reinforced with geocell and combination of geocell and geogrid. The results of the experimental and analytical studies revealed that the use of combination of geocell and the geogrid is always beneficial than using the geocell alone. Hence, the combination of geocell and geogrid was recommended to stabilize the embankment base. The reported embankment is located in Lanjigharh (Orissa) in India. Construction of the embankment on the geocell foundation has already been completed. The constructed embankmenthas already sustained two monsoon rains without any cracks and seepage. (C) 2013 Elsevier Ltd. All rights reserved.

Selective and Efficient Detection of Nitro-Aromatic Explosives in Multiple Media including Water, Micelles, Organogel, and Solid Support

Selective detection of nitro-aromatic compounds (NACs) at nanomolar concentration is achieved for the first time in multiple media including water, micelles or in organogels as well as using test strips. Mechanism of interaction of NACs with highly fluorescent p-phenylenevinylene-based molecules has been described as the electron transfer phenomenon from the electron-rich chromophoric probe to the electron deficient NACs. The selectivity in sensing is guided by the pK(a) of the probes as well as the NACs under consideration. TNP-induced selective gel-to-sol transition in THF medium is also observed through the reorganization of molecular self-assembly and the portable test trips are made successfully for rapid on-site detection purpose.

External support and innovation performance of SMEs in Bangalore: Role of firm level factors

This paper probes the role of internal factors in SMEs in obtaining external support and achieving innovation performance in the context of auto component, electronics and machine tool industries of Bangalore in India. Using step-wise logistic regression analysis, the study found that only if SMEs have internal technical competence in terms of technically qualified entrepreneur, an exclusive design centre, and innovate more frequently, they will be able to obtain external support. Further using step-wise multiple regression the study concluded that SMEs which have come up to implement innovative ideas or exploit market opportunities and which have obtained external support with technically qualified entrepreneurs are able to exhibit better innovation performance.

Multi-site downscaling of maximum and minimum daily temperature using support vector machine

Climate change impact assessment studies involve downscaling large-scale atmospheric predictor variables (LSAPVs) simulated by general circulation models (GCMs) to site-scale meteorological variables. This article presents a least-square support vector machine (LS-SVM)-based methodology for multi-site downscaling of maximum and minimum daily temperature series. The methodology involves (1) delineation of sites in the study area into clusters based on correlation structure of predictands, (2) downscaling LSAPVs to monthly time series of predictands at a representative site identified in each of the clusters, (3) translation of the downscaled information in each cluster from the representative site to that at other sites using LS-SVM inter-site regression relationships, and (4) disaggregation of the information at each site from monthly to daily time scale using k-nearest neighbour disaggregation methodology. Effectiveness of the methodology is demonstrated by application to data pertaining to four sites in the catchment of Beas river basin, India. Simulations of Canadian coupled global climate model (CGCM3.1/T63) for four IPCC SRES scenarios namely A1B, A2, B1 and COMMIT were downscaled to future projections of the predictands in the study area. Comparison of results with those based on recently proposed multivariate multiple linear regression (MMLR) based downscaling method and multi-site multivariate statistical downscaling (MMSD) method indicate that the proposed method is promising and it can be considered as a feasible choice in statistical downscaling studies. The performance of the method in downscaling daily minimum temperature was found to be better when compared with that in downscaling daily maximum temperature. Results indicate an increase in annual average maximum and minimum temperatures at all the sites for A1B, A2 and B1 scenarios. The projected increment is high for A2 scenario, and it is followed by that for A1B, B1 and COMMIT scenarios. Projections, in general, indicated an increase in mean monthly maximum and minimum temperatures during January to February and October to December.

Molecules support morphology: species status of South Indian populations of the widely distributed Hanuman langur

The taxonomy of the Hanuman langur (Semnopithecus spp.), a widely distributed Asian colobine monkey, has been in a flux for a long time due to much disagreement between various classification schemes. However, results from a recent field-based morphological study were consistent with Hill's (Ceylon J Sci 21:277-305, 1939) species level classification scheme. Here we tested the validity of S. hypoleucos and S. priam, the two South Indian species recognized by Hill. To this end, one mitochondrial and four nuclear markers were sequenced from over 72 non-invasive samples of Hanuman langurs and S. johnii collected from across India. The molecular data were subjected to various tree building methods. The nuclear data was also used in a Bayesian structure analysis and to determine the genealogical sorting index of each hypothesized species. Results from nuclear data suggest that the South Indian population of Hanuman langur consists of two units that correspond to the species recognized by Hill. However in the mitochondrial tree S. johnii and S. priam were polyphyletic probably due to retention of ancestral polymorphism and/or low levels of hybridization. Implications of these results on conservation of Hanuman langurs are also discussed.