Fuzzy Classification of Time Series Data

The problem of classification of time series data is an interesting problem in the field of data mining. Even though several algorithms have been proposed for the problem of time series classification we have developed an innovative algorithm which is computationally fast and accurate in several cases when compared with 1NN classifier. In our method we are calculating the fuzzy membership of each test pattern to be classified to each class. We have experimented with 6 benchmark datasets and compared our method with 1NN classifier.

Role of Vertex Index in Substructure Identification and Activity Prediction: A Study on Antitubercular Activity of a Series of Acid Alkyl Ester Derivatives

Tuberculosis (TB) is a life threatening disease caused due to infection from Mycobacterium tuberculosis (Mtb). That most of the TB strains have become resistant to various existing drugs, development of effective novel drug candidates to combat this disease is a need of the day. In spite of intensive research world-wide, the success rate of discovering a new anti-TB drug is very poor. Therefore, novel drug discovery methods have to be tried. We have used a rule based computational method that utilizes a vertex index, named `distance exponent index (D-x)' (taken x = -4 here) for predicting anti-TB activity of a series of acid alkyl ester derivatives. The method is meant to identify activity related substructures from a series a compounds and predict activity of a compound on that basis. The high degree of successful prediction in the present study suggests that the said method may be useful in discovering effective anti-TB compound. It is also apparent that substructural approaches may be leveraged for wide purposes in computer-aided drug design.

Time-Domain Approach to Estimate Series Capacitance of an Isolated Phase Winding of a Transformer

Information available in frequency response data is equivalently available in the time domain as a response due to an impulse excitation. The idea to pursue this equivalence to estimate series capacitance is linked to the well-known fact that under impulse excitation, the line/neutral current in a transformer has three distinct components, of which, the initial capacitive component is the first to manifest, followed by the oscillatory and inductive components. Of these, the capacitive component is temporally well separated from the rest-a crucial feature permitting its direct access and analysis. Further, the winding initially behaves as a pure capacitive network, so the initial component must obviously originate from only the (series and shunt) capacitances. With this logic, it should therefore be possible to estimate series capacitance, just by measuring the initial capacitive component of line current and the total shunt capacitance. The principle of the method and details of its implementation on two actual isolated transformerwindings (uniformly wound) are presented. For implementation, a low-voltage recurrent surge generator, a current probe, and a digital oscilloscope are all that is needed. The method is simple and requires no programming and needs least user intervention, thus paving the way for its widespread use.

A Comparison of Machine Learning Techniques for Modeling River Flow Time Series: The Case of Upper Cauvery River Basin

Models of river flow time series are essential in efficient management of a river basin. It helps policy makers in developing efficient water utilization strategies to maximize the utility of scarce water resource. Time series analysis has been used extensively for modeling river flow data. The use of machine learning techniques such as support-vector regression and neural network models is gaining increasing popularity. In this paper we compare the performance of these techniques by applying it to a long-term time-series data of the inflows into the Krishnaraja Sagar reservoir (KRS) from three tributaries of the river Cauvery. In this study flow data over a period of 30 years from three different observation points established in upper Cauvery river sub-basin is analyzed to estimate their contribution to KRS. Specifically, ANN model uses a multi-layer feed forward network trained with a back-propagation algorithm and support vector regression with epsilon intensive-loss function is used. Auto-regressive moving average models are also applied to the same data. The performance of different techniques is compared using performance metrics such as root mean squared error (RMSE), correlation, normalized root mean squared error (NRMSE) and Nash-Sutcliffe Efficiency (NSE).

Using long time series of agricultural-derived nitrates for estimating catchment transit times

The estimation of water and solute transit times in catchments is crucial for predicting the response of hydrosystems to external forcings (climatic or anthropogenic). The hydrogeochemical signatures of tracers (either natural or anthropogenic) in streams have been widely used to estimate transit times in catchments as they integrate the various processes at stake. However, most of these tracers are well suited for catchments with mean transit times lower than about 4-5 years. Since the second half of the 20th century, the intensification of agriculture led to a general increase of the nitrogen load in rivers. As nitrate is mainly transported by groundwater in agricultural catchments, this signal can be used to estimate transit times greater than several years, even if nitrate is not a conservative tracer. Conceptual hydrological models can be used to estimate catchment transit times provided their consistency is demonstrated, based on their ability to simulate the stream chemical signatures at various time scales and catchment internal processes such as N storage in groundwater. The objective of this study was to assess if a conceptual lumped model was able to simulate the observed patterns of nitrogen concentration, at various time scales, from seasonal to pluriannual and thus if it was relevant to estimate the nitrogen transit times in headwater catchments. A conceptual lumped model, representing shallow groundwater flow as two parallel linear stores with double porosity, and riparian processes by a constant nitrogen removal function, was applied on two paired agricultural catchments which belong to the Research Observatory ORE AgrHys. The Global Likelihood Uncertainty Estimation (GLUE) approach was used to estimate parameter values and uncertainties. The model performance was assessed on (i) its ability to simulate the contrasted patterns of stream flow and stream nitrate concentrations at seasonal and inter-annual time scales, (ii) its ability to simulate the patterns observed in groundwater at the same temporal scales, and (iii) the consistency of long-term simulations using the calibrated model and the general pattern of the nitrate concentration increase in the region since the beginning of the intensification of agriculture in the 1960s. The simulated nitrate transit times were found more sensitive to climate variability than to parameter uncertainty, and average values were found to be consistent with results from others studies in the same region involving modeling and groundwater dating. This study shows that a simple model can be used to simulate the main dynamics of nitrogen in an intensively polluted catchment and then be used to estimate the transit times of these pollutants in the system which is crucial to guide mitigation plans design and assessment. (C) 2015 Elsevier B.V. All rights reserved.

On linear series and a conjecture of D. C. Butler

Let C be a smooth irreducible projective curve of genus g and L a line bundle of degree d generated by a linear subspace V of H-0 (L) of dimension n+1. We prove a conjecture of D. C. Butler on the semistability of the kernel of the evaluation map V circle times O-C -> L and obtain new results on the stability of this kernel. The natural context for this problem is the theory of coherent systems on curves and our techniques involve wall crossing formulae in this theory.

Series-connected substrate-integrated lead-carbon hybrid ultracapacitors with voltage-management circuit

Cell voltage for a fully charged-substrate-integrated lead-carbon hybrid ultracapacitor is about 2.3 V. Therefore, for applications requiring higher DC voltage, several of these ultracapacitors need to be connected in series. However, voltage distribution across each series-connected ultracapacitor tends to be uneven due to tolerance in capacitance and parasitic parallel-resistance values. Accordingly, voltage-management circuit is required to protect constituent ultracapacitors from exceeding their rated voltage. In this study, the design and characterization of the substrate-integrated lead-carbon hybrid ultracapacitor with co-located terminals is discussed. Voltage-management circuit for the ultracapacitor is presented, and its effectiveness is validated experimentally.

Maximum likelihood analysis of multi-stress accelerated life test data of series systems with competing log-normal causes of failure

This article presents frequentist inference of accelerated life test data of series systems with independent log-normal component lifetimes. The means of the component log-lifetimes are assumed to depend on the stress variables through a linear stress translation function that can accommodate the standard stress translation functions in the literature. An expectation-maximization algorithm is developed to obtain the maximum likelihood estimates of model parameters. The maximum likelihood estimates are then further refined by bootstrap, which is also used to infer about the component and system reliability metrics at usage stresses. The developed methodology is illustrated by analyzing a real as well as a simulated dataset. A simulation study is also carried out to judge the effectiveness of the bootstrap. It is found that in this model, application of bootstrap results in significant improvement over the simple maximum likelihood estimates.

Nonvanishing of Koecher-Maass series attached to Siegel cusp forms

We prove a nonvanishing result for Koecher-Maass series attached to Siegel cusp forms of weight k and degree n in certain strips on the complex plane. When n = 2, we prove such a result for forms orthogonal to the space of the Saito-Kurokawa lifts `up to finitely many exceptions', in bounded regions. (C) 2015 Elsevier Inc. All rights reserved.

La erodabilidad de cuatro series de suelos determinada por el mini-simulador de lluvia y el nomograma de Wischmeier

Se realizó un estudio con el propósito de obtener información acerca de le Erodabilidad del suelo, así como distinguir un método confiable y sencillo pera la determinación de ésta. Se seleccionaron cuatro Series de suelos (San Ignacio, Nejapa, Esquipulas y Zambrano), ubicadas en le Cuenca Sur del Lago de Managua, en base a una recopilación de información existente (topografía, reconocimiento y caracterización del terreno), se procedió a obtener les pérdidas de suelo, escurrimiento superficial, concentración de sedimentos y el Índice de Erodabilidad de cada Serie de suelos, por medio de un Mini-simulador de lluvia de Erodabilidad por Kamphorst (1987). El factor de erodabilidad (K) se obtuvo a través de cuatro propiedades del suelo (textura, materia orgánica, estructura y permeabilidad), cuyos valores son introducidos en el Nomograma de Wischmeier (1971). Una vez obtenidos los Índices de erodabilidad (I.K.) y Factor de erodabilidad (K) se determinó que las cuatro Series muestran diferentes grados de susceptibilidad e le erosión. Además, las pérdidas de suelo, escurrimiento superficial, concentración de sedimentos, el índice de erodabilidad y el Factor de erodabilidad son influenciadas por la materia orgánica. Así también se comprobó que el comparar el Índice y el Factor de erodabilidad, tienen un alto valor de correlación. Se pudo observar que le textura no tiene una influencia directa sobre les pérdidas de suelo, escurrimiento superficial, Índice de erodabilidad y Factor de erodabilidad.

Reversible jump sampler for autoregressive time series

We use reversible jump Markov chain Monte Carlo (MCMC) methods to address the problem of model order uncertainty in autoregressive (AR) time series within a Bayesian framework. Efficient model jumping is achieved by proposing model space moves from the full conditional density for the AR parameters, which is obtained analytically. This is compared with an alternative method, for which the moves are cheaper to compute, in which proposals are made only for new parameters in each move. Results are presented for both synthetic and audio time series.

Bayesian model selection for time series using Markov chain Monte Carlo

We present a stochastic simulation technique for subset selection in time series models, based on the use of indicator variables with the Gibbs sampler within a hierarchical Bayesian framework. As an example, the method is applied to the selection of subset linear AR models, in which only significant lags are included. Joint sampling of the indicators and parameters is found to speed convergence. We discuss the possibility of model mixing where the model is not well determined by the data, and the extension of the approach to include non-linear model terms.

Microstructural modelling in friction stir welding of 2000 series aluminium alloys

Simple process models are applied to predict microstructural changes due to the thermal cycle imposed in friction stir welding. A softening model developed for heat-treatable aluminium alloys of the 6000 series is applied to the aerospace alloy 2014 in the peak-aged (T6) condition. It is found that the model is not readily applicable to alloy 2024 in the naturally aged (T3) temper, but the softening behaviour can still be described semi-empirically. Both analytical and numerical (finite element) thermal models are used to predict the thermal histories in trial welds. These are coupled to the microstructural model to investigate: (a) the hardness profile across the welded plate; (b) alloy softening ahead of the approaching welding tool. By incorporating the softening model applied to 6082-T6 alloy, the hardness profile of friction stir welds in dissimilar alloys is also predicted. © AFM, EDP Sciences 2005.