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Search Results: 1 - 10 of 21588 matches for " Kumar Manish "
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Interpretation of water quality parameters for Villages of Sanganer Tehsil, by using Multivariate Statistical analysis  [PDF]
Yashbir Singh, Manish Kumar
Journal of Water Resource and Protection (JWARP) , 2010, DOI: 10.4236/jwarp.2010.210102
Abstract: In this study, the factor analysis techniques is applied to water quality data sets obtained from the Sanganer Tehsil, Jaipur District, Rajasthan (India). The data obtained were standardized and subjected to principal components analysis (PCA) extraction to simplifying its interpretation and to define the parameters responsible for the main variability in water quality for Sanganer Tehsil in Jaipur District. The PCA analysis resulted in two factors explaining more than 94.5% of the total variation in water quality data set. The first factor indicates the variation in water quality is due to anthropogenic sources and second factor shows variation in water quality due to organic sources that are taking place in the system. Finally the results of PCA reflect a good look on the water quality monitoring and interpretation of the surface water.
An Integrated Geometric Modeling Methodology for 2.5D Cylindrical Prismatic Part for Computer Aided Process Planning  [PDF]
Viswa Mohan Pedagopu, Manish Kumar
Intelligent Control and Automation (ICA) , 2014, DOI: 10.4236/ica.2014.54020
Abstract: The field of solid modeling has created numerous techniques for unambiguous computer representations of three-dimensional objects. Its data structures and algorithms have been used in a broad range of applications: Computer-Aided Design and Computer-Aided Manufacturing (CAD/ CAM), robotics, computer vision, computer graphics and visualization, virtual reality, etc. This research paper is used to generate process plan from feature-based modeling, based on an integrated geometric modeling system that supports both feature-based modeling and information storage. Present system is developed only for milling components and limited to selective machining features for prismatic components and further implemented for more machining features to develop algorithms for modeling the components through the input of machining features. As a result, feature information is directly available to downstream activities, and feature extraction is no longer needed. The various systematic steps involved in this approach are study of Design, identification of Features, selection of Processes, Tools and Machines, Machining and Inspection [DFPTMMI]. Machining features generated in the design stage are recognized and stored under the Visual Basic control of CATIA software ActiveX interface. Algorithms are developed for individual features and these algorithms are embedded in Visual Basic forms. This system is discussed and suited for 2.5 Dimensional part approach, however, that can be extended to 3 dimensional prismatic part and complex features machining. Finally a process planning chart has been presented as a model process planning.
A Time-Varying Parameter Vector Autoregression Model for Forecasting Emerging Market Exchange Rates
Manish Kumar
International Journal of Economic Sciences and Applied Research , 2010,
Abstract: In this study, a vector autoregression (VAR) model with time-varying parameters (TVP) to predict the daily Indian rupee (INR)/US dollar (USD) exchange rates for the Indian economy is developed. The method is based on characterization of the TVP as an optimal control problem. The methodology is a blend of the flexible least squares and Kalman filter techniques. The out-of-sample forecasting performance of the TVP-VAR model is evaluated against the simple VAR and ARIMA models, by employing a cross-validation process and metrics such as mean absolute error, root mean square error, and directional accuracy. Outof-sample results in terms of conventional forecast evaluation statistics and directional accuracy show TVP-VAR model consistently outperforms the simple VAR and ARIMA models.
Manish KUMAR
Scientific Annals of the Alexandru Ioan Cuza University of Iasi : Economic Sciences Series , 2009,
Abstract: The present study examines dynamic relation between stock index and exchange rate by using the daily data for India. The empirical evidence suggests that there is no long-run relationship; however, there is bidirectional causality between stock index and exchange rates. The findings of the causality tests strongly support portfolio or macroeconomic approach on the relationship between exchange rates and stock prices. An attempt is also made to forecast daily returns of INR/USD exchange rates by exploiting the information of causal relationship between exchange rates and stock index using Vector autoregression (VAR) model. VAR’s out-of-sample performance is benchmarked against the traditional ARIMA model. The potential of the two models are rigorously evaluated by employing a cross-validation scheme and statistical metrics like mean absolute error, root mean square error and directional accuracy. Out-of-sample performance shows that VAR model is robust, and consistently produces superior predictions than ARIMA model.
Improving the accuracy: volatility modeling and forecasting using high-frequency data and the variational component
Manish Kumar
Journal of Industrial Engineering and Management , 2010, DOI: 10.3926/jiem..v3n1.p199-220
Abstract: In this study, we predict the daily volatility of the S&P CNX NIFTY market index of India using the basic ‘heterogeneous autoregressive’ (HAR) and its variant. In doing so, we estimated several HAR and Log form of HAR models using different regressor. The different regressors were obtained by extracting the jump and continuous component and the threshold jump and continuous component from the realized volatility. We also tried to investigate whether dividing volatility into simple and threshold jumps and continuous variation yields a substantial improvement in volatility forecasting or not. The results provide the evidence that inclusion of realized bipower variance in the HAR models helps in predicting future volatility.
Embedding problems for subgroups of the fundamental group
Manish Kumar
Mathematics , 2009,
Abstract: Let $\pi_1(C)$ be the fundamental group of a smooth irreducible affine curve $C$ over an algebraically closed field of positive characteristic. It is shown that given an embedding problem for $\pi_1(C)$ there exist an open index $p$ normal subgroup of $\pi_1(C)$ so that the embedding problem restricted to this group has a solution. In particular, $\pi_1(C)$ is "almost $\omega$-free".
The fundamental group of affine curves in positive characteristic
Manish Kumar
Mathematics , 2009,
Abstract: It is shown that the commutator subgroup of the fundamental group of a smooth affine curve over an uncountable algebraically closed field $k$ of positive characteristic is a profinite free group of rank equal to the cardinality of $k$.
On the compositum of wildly ramified extensions
Manish Kumar
Mathematics , 2013,
Abstract: We compute the ramification filtration on wildly ramified $p^2$-cyclic extensions of local fields of characteristic $p$. The ramification filtration on the compositum of two $p$-cyclic and $p^2$-cyclic extensions are also computed. As an application, some partial results towards Abhyankar's Inertia conjecture has been proved.
Fundamental Group in nonzero characteristic
Manish Kumar
Mathematics , 2006,
Abstract: A proof of freeness of the commutator subgroup of the fundamental group of a smooth irreducible affine curve over a countable algebraically closed field of nonzero characteristic. A description of the abelianizations of the fundamental groups of smooth affine curves over an algebracially closed field of nonzero characteristic is also given.
Galois closure of henselization
Manish Kumar
Mathematics , 2014,
Abstract: It is shown that the Galois closure of the henselization of a one dimensional local field arising in geometric and arithmetic situation is separably closed.
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