Comparative statistical aging studies on an oil-pressboard insulation model

Accelerated aging experiments have been conducted on a representative oil-pressboard insulation model to investigate the effect of constant and sequential stresses on the PD behavior using a built-in phase resolved partial discharge analyzer. A cycle of the applied voltage starting from the zero of the positive half cycle was divided into 16 equal phase windows (Φ1 to Φ16) and partial discharge (PD) magnitude distribution in each phase was determined. Based on the experimental results, three stages of aging mechanism were identified. Gumbel's extreme value distribution of the largest element was used to model the first stage of aging process. Second and subsequent stages were modeled using two-parameter Weibull distribution. Spearman's non-parametric rank correlation test statistic and Kolmogrov-Smirnov two sample test were used to relate the aging process of each phase with the corresponding process of the full cycle. To bring out clearly the effect of stress level, its duration and test procedure on the distribution parameters and hence of the aging process, non-parametric ANOVA techniques like Kruskal-Wallis and Fisher's LSD multiple comparison tests were used. Results of the analysis show that two phases (Φ13 and Φ14) near the vicinity of the negative voltage peak were found to contribute significantly to the aging process and their aging mechanism also correlated well with that of the corresponding full cycle mechanism. Attempts have been made to relate these results with the published work of other workers

Statistical properties of entropy-consuming fluctuations in jammed states of laponite suspensions: Fluctuation relations and generalized Gumbel distribution

We report a universal large deviation behavior of spatially averaged global injected power just before the rejuvenation of the jammed state formed by an aging suspension of laponite clay under an applied stress. The probability distribution function (PDF) of these entropy consuming strongly non-Gaussian fluctuations follow an universal large deviation functional form described by the generalized Gumbel (GG) distribution like many other equilibrium and nonequilibrium systems with high degree of correlations but do not obey the Gallavotti-Cohen steady-state fluctuation relation (SSFR). However, far from the unjamming transition (for smaller applied stresses) SSFR is satisfied for both Gaussian as well as non-Gaussian PDF. The observed slow variation of the mean shear rate with system size supports a recent theoretical prediction for observing GG distribution.

Estimation of the shear transformation zone size fin a bulk metallic glass through statistical a analysis of the first pop-in stresses during spherical nanoindentation

The size of the shear transformation zone (STZ) that initiates the elastic to plastic transition in a Zr-based bulk metallic glass was estimated by conducting a statistical analysis of the first pop-in event during spherical nanoindentation. A series of experiments led us to a successful description of the distribution of shear strength for the transition and its dependence on the loading rate. From the activation volume determined by statistical analysis the STZ size was estimated based on a cooperative shearing model. (C) 2012 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.

Theory and Algorithms for Hop-Count-Based Localization with Random Geometric Graph Models of Dense Sensor Networks

Wireless sensor networks can often be viewed in terms of a uniform deployment of a large number of nodes in a region of Euclidean space. Following deployment, the nodes self-organize into a mesh topology with a key aspect being self-localization. Having obtained a mesh topology in a dense, homogeneous deployment, a frequently used approximation is to take the hop distance between nodes to be proportional to the Euclidean distance between them. In this work, we analyze this approximation through two complementary analyses. We assume that the mesh topology is a random geometric graph on the nodes; and that some nodes are designated as anchors with known locations. First, we obtain high probability bounds on the Euclidean distances of all nodes that are h hops away from a fixed anchor node. In the second analysis, we provide a heuristic argument that leads to a direct approximation for the density function of the Euclidean distance between two nodes that are separated by a hop distance h. This approximation is shown, through simulation, to very closely match the true density function. Localization algorithms that draw upon the preceding analyses are then proposed and shown to perform better than some of the well-known algorithms present in the literature. Belief-propagation-based message-passing is then used to further enhance the performance of the proposed localization algorithms. To our knowledge, this is the first usage of message-passing for hop-count-based self-localization.

Suite of tools for statistical N-gram language modeling for pattern mining in whole genome sequences

Genome sequences contain a number of patterns that have biomedical significance. Repetitive sequences of various kinds are a primary component of most of the genomic sequence patterns. We extended the suffix-array based Biological Language Modeling Toolkit to compute n-gram frequencies as well as n-gram language-model based perplexity in windows over the whole genome sequence to find biologically relevant patterns. We present the suite of tools and their application for analysis on whole human genome sequence.

Statistical Methods for Accelerated Life Testing with Particular Reference to Resin Impregnated Paper Bushings

Electrical failure of insulation is known to be an extremal random process wherein nominally identical pro-rated specimens of equipment insulation, at constant stress fail at inordinately different times even under laboratory test conditions. In order to be able to estimate the life of power equipment, it is necessary to run long duration ageing experiments under accelerated stresses, to acquire and analyze insulation specific failure data. In the present work, Resin Impregnated Paper (RIP) a relatively new insulation system of choice used in transformer bushings, is taken as an example. The failure data has been processed using proven statistical methods, both graphical and analytical. The physical model governing insulation failure at constant accelerated stress has been assumed to be based on temperature dependent inverse power law model.

Relative roles of instruction count and cycles per instruction in WCET estimation

Most of the existing WCET estimation methods directly estimate execution time, ET, in cycles. We propose to study ET as a product of two factors, ET = IC * CPI, where IC is instruction count and CPI is cycles per instruction. Considering directly the estimation of ET may lead to a highly pessimistic estimate since implicitly these methods may be using worst case IC and worst case CPI. We hypothesize that there exists a functional relationship between CPI and IC such that CPI=f(IC). This is ascertained by computing the covariance matrix and studying the scatter plots of CPI versus IC. IC and CPI values are obtained by running benchmarks with a large number of inputs using the cycle accurate architectural simulator, Simplescalar on two different architectures. It is shown that the benchmarks can be grouped into different classes based on the CPI versus IC relationship. For some benchmarks like FFT, FIR etc., both IC and CPI are almost a constant irrespective of the input. There are other benchmarks that exhibit a direct or an inverse relationship between CPI and IC. In such a case, one can predict CPI for a given IC as CPI=f(IC). We derive the theoretical worst case IC for a program, denoted as SWIC, using integer linear programming(ILP) and estimate WCET as SWIC*f(SWIC). However, if CPI decreases sharply with IC then measured maximum cycles is observed to be a better estimate. For certain other benchmarks, it is observed that the CPI versus IC relationship is either random or CPI remains constant with varying IC. In such cases, WCET is estimated as the product of SWIC and measured maximum CPI. It is observed that use of the proposed method results in tighter WCET estimates than Chronos, a static WCET analyzer, for most benchmarks for the two architectures considered in this paper.

Device oriented statistical modeling method for process variability in 45nm analog CMOS technology

With the rapid scaling down of the semiconductor process technology, the process variation aware circuit design has become essential today. Several statistical models have been proposed to deal with the process variation. We propose an accurate BSIM model for handling variability in 45nm CMOS technology. The MOSFET is designed to meet the specification of low standby power technology of International Technology Roadmap for Semiconductors (ITRS).The process parameters variation of annealing temperature, oxide thickness, halo dose and title angle of halo implant are considered for the model development. One parameter variation at a time is considered for developing the model. The model validation is done by performance matching with device simulation results and reported error is less than 10%.© (2012) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.

Microcracking and fracture process in cement mortar and concrete: a comparative study using acoustic emission technique

This article reports the acoustic emission (AE) study of precursory micro-cracking activity and fracture behaviour of quasi-brittle materials such as concrete and cement mortar. In the present study, notched three-point bend specimens (TPB) were tested under crack mouth opening displacement (CMOD) control at a rate of 0.0004 mm/sec and the accompanying AE were recorded using a 8 channel AE monitoring system. The various AE statistical parameters including AE event rate , AE energy release rate , amplitude distribution for computing the AE based b-value, cumulative energy (I E) pound and ring down count (RDC) were used for the analysis. The results show that the micro-cracks initiated and grew at an early stage in mortar in the pre peak regime. While in the case of concrete, the micro-crack growth occurred during the peak load regime. However, both concrete and mortar showed three distinct stages of micro-cracking activity, namely initiation, stable growth and nucleation prior to the final failure. The AE statistical behavior of each individual stage is dependent on the number and size distribution of micro-cracks. The results obtained in the laboratory are useful to understand the various stages of micro-cracking activity during the fracture process in quasi-brittle materials such as concrete & mortar and extend them for field applications.

Impact of heavy metals on bacterial communities from mangrove soils of the Mahanadi Delta (India)

This study aimed to assess soil nutrient status and heavy metal content and their impact on the predominant soil bacterial communities of mangroves of the Mahanadi Delta. Mangrove soil of the Mahanadi Delta is slightly acidic and the levels of soil nutrients such as carbon, nitrogen, phosphorous and potash vary with season and site. The seasonal average concentrations (g/g) of various heavy metals were in the range: 14810-63370 (Fe), 2.8-32.6 (Cu), 13.4-55.7 (Ni), 1.8-7.9 (Cd), 16.6-54.7 (Pb), 24.4-132.5 (Zn) and 13.3-48.2 (Co). Among the different heavy metals analysed, Co, Cu and Cd were above their permissible limits, as prescribed by Indian Standards (Co=17g/g, Cu=30 g/g, Cd=3-6 g/g), indicating pollution in the mangrove soil. A viable plate count revealed the presence of different groups of bacteria in the mangrove soil, i.e. heterotrophs, free-living N-2 fixers, nitrifyers, denitrifyers, phosphate solubilisers, cellulose degraders and sulfur oxidisers. Principal component analysis performed using multivariate statistical methods showed a positive relationship between soil nutrients and microbial load. Whereas metal content such as Cu, Co and Ni showed a negative impact on some of the studied soil bacteria.

A Reduced Device-Count Hybrid Multilevel Inverter Topology with single DC Source and Improved Fault Tolerance

A new hybrid multilevel power converter topology is presented in this paper. The proposed power converter topology uses only one DC source and floating capacitors charged to asymmetrical voltage levels, are used for generating different voltage levels. The SVPWM based control strategy used in this converter maintains the capacitor voltages at the required levels in the entire modulation range including the over-modulation region. For the voltage levels: nine and above, the number of components required in the proposed topology is significantly lower, compared to the conventional multilevel inverter topologies. The number of capacitors required in this topology reduces drastically compared to the conventional flying capacitor topology, when the number of levels in the inverter output increases. This topology has better fault tolerance, as it is capable of operating with reduced number of levels, in the entire modulation range, in the event of any failure in the H-bridges. The transient as well as the steady state performance of the nine-level version of the proposed topology is experimentally verified in the entire modulation range including the over-modulation region.

Photocurrent spectroscopic studies of diketopyrrolopyrrole-based statistical copolymers

Diketopyrrolopyrrole (DPP) containing copolymers have gained a lot of interest in organic optoelectronics with great potential in organic photovoltaics. In this work, DPP based statistical copolymers, with slightly different bandgap energies and a varying fraction of donor-acceptor ratio are investigated using monochromatic photocurrent spectroscopy and Fourier-transform photocurrent spectroscopy (FTPS). The statistical copolymer with a lower DPP fraction, when blended with a fullerene derivative, shows the signature of an inter charge transfer complex state in photocurrent spectroscopy. Furthermore, the absorption spectrum of the blended sample with a lower DPP fraction is seen to change as a function of an external bias, qualitatively similar to the quantum confined Stark effect, from where we estimate the exciton binding energy. The statistical copolymer with a higher DPP fraction shows no signal of the inter charge transfer states and yields a higher external quantum efficiency in a photovoltaic structure. In order to gain insight into the origin of the observed charge transfer transitions, we present theoretical studies using density-functional theory and time-dependent density-functional theory for the two pristine DPP based statistical monomers.

Photocurrent spectroscopic studies of diketopyrrolopyrrole-based statistical copolymers

Diketopyrrolopyrrole (DPP) containing copolymers have gained a lot of interest in organic optoelectronics with great potential in organic photovoltaics. In this work, DPP based statistical copolymers, with slightly different bandgap energies and a varying fraction of donor-acceptor ratio are investigated using monochromatic photocurrent spectroscopy and Fourier-transform photocurrent spectroscopy (FTPS). The statistical copolymer with a lower DPP fraction, when blended with a fullerene derivative, shows the signature of an inter charge transfer complex state in photocurrent spectroscopy. Furthermore, the absorption spectrum of the blended sample with a lower DPP fraction is seen to change as a function of an external bias, qualitatively similar to the quantum confined Stark effect, from where we estimate the exciton binding energy. The statistical copolymer with a higher DPP fraction shows no signal of the inter charge transfer states and yields a higher external quantum efficiency in a photovoltaic structure. In order to gain insight into the origin of the observed charge transfer transitions, we present theoretical studies using density-functional theory and time-dependent density-functional theory for the two pristine DPP based statistical monomers.