Time filter

Source Type

Anwar S.,Birla Institute of Technology | Rajamohan G.,Indian National Institute of Foundry and Forge Technology
Advances in Intelligent Systems and Computing | Year: 2017

Human action recognition refers to the classification of human action from video clips automatically. Images extracted from the video clips at regular time interval are processed to identify the action contained in them. This is done by comparing these images with images taken from appropriate standard action databases. Thus, human action recognition becomes the task of verifying the similarity between two images. This paper proposes mutual difference score as a measure of similarity between two images. The proposed measure has been validated using the Weizmann and KTH datasets. © Springer Nature Singapore Pte Ltd. 2017.

Yadav S.,Indian National Institute of Foundry and Forge Technology | Srivastava V.,Banaras Hindu University | Banerjee S.,Allahabad University | Gode F.,Suleman Demierl University | Sharma Y.C.,Banaras Hindu University
Environmental Science and Pollution Research | Year: 2013

This paper highlights the utility of riverbed sand (RS) for the treatment of Ni(II) from aqueous solutions. For enhancement of removal efficiency, RS was modified by simple methods. Raw and modified sands were characterized by scanning electron microscope (SEM), Energy Dispersive Spectroscopy (EDS), and Fourier Transform Infrared Spectroscopy (FTIR) to investigate the effect of modifying the surface of RS. For optimization of various important process parameters, batch mode experiments were conducted by choosing specific parameters such as pH (4. 0-8. 0), adsorbent dose (1. 0-2. 0 g), and metal ion concentrations (5-15 mg/L). Removal efficiency decreased from 68. 76 to 54. 09 % by increasing the concentration of Ni(II) in solution from 5 to 15 mg/L. Removal was found to be highly dependent on pH of aqueous solutions and maximum removal was achieved at pH 8. 0. The process of removal follows first-order kinetics, and the value of rate constant was found to be 0. 048 min-1 at 5 mg/L and 25 °C. Value of intraparticle diffusion rate constant (kid) was found to be 0. 021 mg/g min1/2 at 25 °C. Removal of Ni(II) decreased by increasing temperature which confirms exothermic nature of this system. For equilibrium studies, adsorption data was analyzed by Freundlich and Langmuir models. Thermodynamic studies for the present process were performed by determining the values of ΔG°, ΔH°, and ΔS°. Negative value of {increment}H° further confirms the exothermic nature of the removal process. The results of the present investigation indicate that modified riverbed sand (MRS) has high potential for the removal of Ni(II) from aqueous solutions, and resultant data can serve as baseline data for designing treatment plants at industrial scale. © 2012 Springer-Verlag.

Malapati M.,Indian National Institute of Foundry and Forge Technology | Bhattacharyya B.,Jadavpur University
Materials and Manufacturing Processes | Year: 2011

Electrochemical micromachining (EMM) appears to be very promising as a future micromachining process due to higher machining rate, better precision and control, and wide range of materials that can be machined. The present article highlights the experimental study of EMM process parameters, i.e., pulse frequency, machining voltage, duty ratio, electrolyte concentration, and micro-tool feed-rate, and their influences on micromachining criteria such as material removal rate (MRR) and machining accuracy during micro-channel generation. Scanning type strategy is considered for the movement of micro-tool during micro-channel generation Experiments are planned based on response surface methodology (RSM) and conducted on the indigenously developed EMM system setup. Empirical mathematical models of various process parameters on MRR and machining accuracy in EMM process are developed through RSM. The validity of the models is tested through analysis of variance (ANOVA). Optimal values for multiobjective optimization of the process parameters have been found out as pulse frequency of 52.2818kHz, machining voltage of 10.1033V, duty ratio of 68.3890%, electrolyte concentration of 85.1515g/l, and micro-tool feed-rate of 208.5860m/sec for the maximum MRR and improved accuracy. Response surface plots for each response are analyzed. Condition of machined micro-channels is also analyzed through scanning electron microscope (SEM) micrographs. The developed models will be very useful to find out the optimal parametric setting to produce high accuracy micro-channels utilizing scanning movement strategy of micro-tool. © 2011 Taylor & Francis Group, LLC.

Yadav S.,Indian National Institute of Foundry and Forge Technology | Srivastava V.,Indian Institute of Technology BHU Varanasi | Banerjee S.,Allahabad University | Weng C.-H.,I - Shou University | Sharma Y.C.,Indian Institute of Technology BHU Varanasi
Catena | Year: 2013

The objective of this study is to investigate the removal of Cr(VI) from aqueous solutions by using modified sand as adsorbent. The modified sand was characterized by Scanning Electron Microscopy (SEM), Energy Dispersive X-ray Analysis (EDX) and FTIR. pH zpc of the raw and modified sands was found to be 6.98 and 6.66, respectively. Removal efficiency of the modified sand was investigated by using batch adsorption experiments. The effect of important parameters such as initial concentration, contact time, adsorbent dosage, pH and temperature on removal of Cr(VI) was investigated. It was demonstrated that the removal efficiency increased from 67.24% to 80.40% by the decreasing initial metal concentration from 15 to 5mgL -1. Effect of pH was investigated by varying the solution pH from 2.0 to 8.0. The optimum pH for adsorption of Cr(VI) on modified sand was found to be 2.5 with a maximum removal of ~80.40%. Extent of removal decreases by increasing the temperature from 25°C to 35°C confirming exothermic nature. Kinetics of removal process was studied by applying pseudo-first order and pseudo-second order models. Pseudo-first order rate constant was found to be 0.037min -1 while the rate constant for pseudo-second order reaction was found to be 0.0236gmg -1min -1 at 25°C. Values of thermodynamic parameters viz. {increment}G°, {increment}H° and {increment}S° were calculated and found to be -3.67kJmol -1, -68.74kJmol -1and -0.243kJmol -1K -1, respectively at 25°C. The values of δG° were found to be negative at all temperatures indicating the spontaneity of the removal process. A negative value of {increment}H° further confirms the exothermic nature of removal process. The experimental data were fitted to Langmuir as well as Freundlich adsorption isotherm equations. The results obtained in the present study show the modified sand to be a better adsorbent for removal of Cr(VI). © 2012 Elsevier B.V.

Sood A.K.,Indian National Institute of Foundry and Forge Technology | Ohdar R.K.,Indian National Institute of Foundry and Forge Technology | Mahapatra S.S.,National Institute of Technology Rourkela
Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture | Year: 2010

Dimensional accuracy of a fused deposition modelling (FDM) built part is greatly influenced by many process parameters. In this study, the effect of five process parameters such as layer thickness, part build orientation, raster angle, air gap, and raster width along with their interactions has been studied using Taguchi's L27 orthogonal array. Experimental results indicate that the measured dimension is always more than the desired value along the thickness direction but the length, width, and diameter of hole of test part are less than the desired value. It has been observed that optimal factor settings for each performance characteristic such as percentage change in length, width, thickness, and diameter are different. In order to minimize four responses simultaneously, the grey-Taguchi method is adopted and optimum factor levels have been reported. Finally, overall dimensional accuracy is predicted using artificial neural network (ANN).

Sood A.K.,Indian National Institute of Foundry and Forge Technology | Ohdar R.K.,Indian National Institute of Foundry and Forge Technology | Mahapatra S.S.,National Institute of Technology Rourkela
Materials and Design | Year: 2010

Fused deposition modelling (FDM) is a fast growing rapid prototyping (RP) technology due to its ability to build functional parts having complex geometrical shape in reasonable time period. The quality of built parts depends on many process variables. In this study, five important process parameters such as layer thickness, orientation, raster angle, raster width and air gap are considered. Their influence on three responses such as tensile, flexural and impact strength of test specimen is studied. Experiments are conducted based on central composite design (CCD) in order to reduce experimental runs. Empirical models relating response and process parameters are developed. The validity of the models is tested using analysis of variance (ANOVA). Response surface plots for each response is analysed and optimal parameter setting for each response is determined. The major reason for weak strength may be attributed to distortion within or between the layers. Finally, concept of desirability function is used for maximizing all responses simultaneously. © 2009 Elsevier Ltd. All rights reserved.

Mahapatra S.S.,National Institute of Technology Rourkela | Sood A.K.,Indian National Institute of Foundry and Forge Technology
International Journal of Advanced Manufacturing Technology | Year: 2012

Fused deposition modeling has a complex part building mechanism making it difficult to obtain reasonably good functional relationship between responses and process parameters. To solve this problem, present study proposes use of artificial neural network (ANN) model to determine the relationship between five input parameters such as layer thickness, orientation, raster angle, raster width, and air gap with three output responses viz., roughness in top, bottom, and side surface of the built part. Bayesian regularization is adopted for selection of optimum network architecture because of its ability to fix number of network parameters irrespective of network size. ANN model is trained using Levenberg-Marquardt algorithm, and the resulting network has good generalization capability that eliminates the chance of over fitting. Finally, bacterial foraging optimization algorithm which attempts to model the individual and group behavior of Escherichia coli bacteria as a distributed optimization process is used to suggest theoretical combination of parameter settings to improve overall roughness of part. This paper also investigates use of chaotic time series sequence known as logistic function and demonstrates its superiority in terms of convergence and solution quality. © 2011 Springer-Verlag.

Mandal S.K.,Indian National Institute of Foundry and Forge Technology | Chan F.T.S.,Hong Kong Polytechnic University | Tiwari M.K.,Indian Institute of Technology Kharagpur
Expert Systems with Applications | Year: 2012

The generation of leak along the pipeline carrying crude oils and liquid fuels results enormous financial loss to the industry and also affects the public health. Hence, the leak detection and localization problem has always been a major concern for the companies. In spite of the various techniques developed, accuracy and time involved in the prediction is still a matter of concern. In this paper, a novel leak detection scheme based on rough set theory and support vector machine (SVM) is proposed to overcome the problem of false leak detection. In this approach, 'rough set theory' is explored to reduce the length of experimental data as well as generate rules. It is embedded to enhance the decision making process. Further, SVM classifier is employed to inspect the cases that could not be detected by applied rules. For the computational training of SVM, this paper uses swarm intelligence technique: artificial bee colony (ABC) algorithm, which imitates intelligent food searching behavior of honey bees. The results of proposed leak detection scheme with ABC are compared with those obtained by using particle swarm optimization (PSO) and one of its variants, so-called enhanced particle swarm optimization (EPSO). The experimental results advocate the use of propounded method for detecting leaks with maximum accuracy. © 2011 Elsevier Ltd. All rights reserved.

Kumar S.R.,Indian National Institute of Foundry and Forge Technology | Nuthalapati M.,Indian National Institute of Foundry and Forge Technology | Maity J.,National Institute of Technology Durgapur
Scripta Materialia | Year: 2012

A nanocrystalline thin film of ZnSe was successfully electrodeposited on a copper substrate using a non-aqueous solution. X-ray diffraction analysis confirmed the deposition of crystalline ZnSe with a 12 nm crystallite size. Scanning electron microscopy and atomic force microscopy studies revealed a densely packed non-porous granular deposit. The Schottky diode characteristic corroborated a typical semiconducting behaviour of ZnSe deposit. The band gap (2.71 eV) obtained through spectroscopic analysis and the measured resistivity (1.5 Ω cm) were also in good agreement with the reported data of semiconducting ZnSe. © 2012 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.

Shrivastava A.K.,Indian National Institute of Foundry and Forge Technology
Indian Journal of Environmental Protection | Year: 2010

Zinc is one of the toxic metals which is present in air, soil, water and food. It is emitted into the environment from various industries and plants (zinc smelters, zinc plating, galvanizing, in making alloys, etc.). In the present paper, adsorption studies are carried out. Here adsorbent point of use granular activated carbon (POU-GACFC) impregnated with waste tea leaves carbon (WTLC) has been used for sorption of zinc(II) from water/wastewater. A design of treatment system for water containing zinc is shown. © 2010 - Kalpana Corporation.

Loading Indian National Institute of Foundry and Forge Technology collaborators
Loading Indian National Institute of Foundry and Forge Technology collaborators