Madhav Institute of Technology and Science

Gwalior, India

Madhav Institute of Technology and Science

Gwalior, India
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Kasdekar D.K.,Madhav Institute of Technology and Science | Parashar V.,Madhav Institute of Technology and Science
Materials Today: Proceedings | Year: 2017

Electrochemical machining (ECM) is one of the finest method to machine hard materials of complex geometry. In this paper, box behnken (CCRD) is employed to find out the optimal combinations of process parameters of ANMMC for maximum material removal rate (MRR). Four process parameters viz.voltage, feed rate, electrolyte concentration and Sic% are considered in this study. Experiments are carried out to establish an empirical relationship between process parameters and responses. This composite is widely used in automotive industries due to high wear resistance, high strength to low weight ratio, elevated temperature toughness and high stiffness. In this regard, a versatile prediction model is required to determine the maximum MRR of the composite considering the effect of machining parameters. The box behnken (CCRD) and Artificial neural network (ANN) based prediction model is developed to determine the MRR of AA6061/cu/Sic+Grp and the performance of the Box-behnken and ANN models are compared with experimental results for their effectiveness. © 2017 Elsevier Ltd. All rights reserved.


Dwivedi S.,Madhav Institute of Technology and Science
Journal of Chemical and Pharmaceutical Research | Year: 2013

Textile effluents are the basic problem to pull down the level of nutrient source of water and that source is spirulina, it is well known as it also create a film on water surface so that due to lack of sunlight aquatic life suffers. Here effect of many dyes have been observed by using proper methods, and it is concluded that on increasing the concentration of dyes in water then it resist the growth of spirulina platensis and decrease its nutrient level as well. In this paper three textile dyes have been taken congo red, metanil yellow and mordant green, results have been noticed as, on 14th day in control 0.435 absorbance has been found while at 100mg concentration, 0.397 absorbance has been found in congo red, 0.210 has been found in metanil yellow and 0.210 in mordant green. These results depict that the above statement is true. This paper also shows the effect of these dyes on chlorophyll content, carbohydrate content and protein content, in control on 14th day the chlorophyll content noticed as 0.369 on the other hand congo red at 100mg concentration read 0.242, metanil yellow as 0.136 and mordant green is taken as 0.216.


Sharma P.,Madhav Institute of Technology and Science | Sharma A.K.,Madhav Institute of Technology and Science
Materials Today: Proceedings | Year: 2017

The aim of the work is to study the harmonic analysis of metal and ceramic functionally graded plates. The plates are assumed to have isotropic and the material properties of it are assumed to vary continuously through their thickness according to a power-law distribution of the volume fractions of the plate constituents. Two functionally graded plates of -Al/Al2O3 and Ti-6Al-4V/Aluminum oxide -are considered in the study, and their results were compared so that the right choice can be made in applications like high temperature environment and in reducing the vibration amplitudes. Numerical results for the deformation, stresses, and frequency response amplitude are presented. © 2017 Published by Elsevier B.V.


Parsediya D.K.,Madhav Institute of Technology and Science
International Conference on Electrical Power and Energy Systems, ICEPES 2016 | Year: 2017

Cantilever sensitivity under low mass loading is crucial issue for various sensing applications. Recent advancements promise and focus on the development of high performance cantilevers for effective low mass sensing. MEMS cantilevers are commonly used for low level bio-molecular detection due to their most flexible hinging type structure. The micro-cantilever design used for bio-sensing can also be used for RF switching under the actuation potential. For low mass loading rectangular cantilever does not offer better deflection due to its uniform volume. Also in RF switching application these beams take large actuation voltage for switching. This paper study five different micro-cantilever beam designs. Some proposed designs exhibit better deflection for low mass loading. Hence these designs can be used for low level sensing application. © 2016 IEEE.


Gupta R.A.,Indian National Institute of Engineering | Wadhwani A.K.,Madhav Institute of Technology and Science | Kapoor S.R.,Rajasthan Technical University Kota
IEEE Transactions on Energy Conversion | Year: 2011

Even though induction motors are frequently used electromagnetic devices in industries owing to their high reliability, high efficiency, and low maintenance requirements, they are prone to various faults and failures. Most of these faults occurring in the induction motors are perceptible in nascent stages. This averts the inopportune machine failures and helps to adeptly plan the maintenance schedules. Most of the methods used for preprediction of faults in induction motors are based on complicated techniques involving tortuous mathematical analysis. Although the importance and accuracy of these methods cannot be overruled, but a simple method is required as a first stage necessary condition test, which can classify the motor health condition into one of the three broad categories, namely, healthy, fault prone, and critical. This paper discusses a simple method based on symbolic dynamic analysis of stator current samples to detect faults in the induction motors. The experimentation has been performed on a 3Φ, 1.5 kW, 4P, 1440 RPM squirrel cage motor to validate the proposed scheme. The data captured through the laboratory setup have been used to corroborate the proposed symbolic dynamic-based scheme. © 2010 IEEE.


Varshney S.,Madhav Institute of Technology and Science | Srivastava L.,Madhav Institute of Technology and Science | Pandit M.,Madhav Institute of Technology and Science
International Journal of Electrical Power and Energy Systems | Year: 2012

This paper presents the application of cascade neural network (CANN) based approach for integrated security (voltage and line flow security) assessment. The developed cascade neural network is a combination of one screening module and two ranking modules, which are Levenberg-Marquardt Algorithm based neural networks (LMANNs). All the single line outage contingency cases are applied to the screening module, which is 3-layered feed-forward ANN having two outputs. The screening module is trained to classify them either in critical contingency class or in non-critical contingency class from the viewpoint of voltage/line loading. The screened critical contingencies are passed to the corresponding ranking modules, which are developed simultaneously by using parallel computing. Parallel computing deals with the development of programs where multiple concurrent processes cooperate in the fulfillment of a common task. For contingency screening and ranking, two performance indices: one based on voltage security of power system (VPI) and other based on line flow (MWPI) are used. Effectiveness of the proposed cascade neural network based approach has been demonstrated by applying it for contingency selection and ranking at different loading conditions for IEEE 30-bus and a practical 75-bus Indian system. The results obtained clearly indicate the superiority of the proposed approach in terms of speedup in training time of neural networks as compared to the case when the two ranking neural networks were developed sequentially to estimate VPI and MWPI. © 2012 Elsevier Ltd. All rights reserved.


Sharma P.,Madhav Institute of Technology and Science | Arya K.V.,ABV Indian Institute of Information Technology and Management | Yadav R.N.,Maulana Azad National Institute of Technology
Signal Processing | Year: 2013

This paper presents an efficient face recognition method where enhanced local Gabor binary pattern histogram sequence has been used for efficient face feature extraction and generalized neural network with wavelet as activation function is being used for classification. In this method the face is first decomposed into multiresolution Gabor wavelets the magnitude responses of which are applied to enhanced local binary patterns. The efficiency has been significantly improved by combining two efficient local appearance descriptors named Gabor wavelet and enhanced local binary pattern with generalized neural network. Generalized neural network is a proven technique for pattern recognition and is insensitive to small changes in input data. The proposed method is robust-to-slight variation of imaging conditions and pose variations. Performance comparison with other existing techniques shows that the proposed technique provides better results in terms of false acceptance rate, false rejection rate, equal error rate and time complexity. © 2012 Elsevier B.V.


Khaliq A.,Jamia Millia Islamia University | Agrawal B.K.,Jamia Millia Islamia University | Kumar R.,Madhav Institute of Technology and Science
International Journal of Refrigeration | Year: 2012

In the proposed cogeneration cycle, a LiBr-H2O absorption refrigeration system is employed to the combined power and ejector refrigeration system which uses R141b as a working fluid. Estimates for irreversibilities of individual components of the cycle lead to possible measures for performance improvement. Results of exergy distribution of waste heat in the cycle show that around 53.6% of the total input exergy is destroyed due to irreversibilities in the components, 22.7% is available as a useful exergy output, and 23.7% is exhaust exergy lost to the environment, whereas energy distribution shows 44% is exhaust energy and 19.7% is useful energy output. Results also show that proposed cogeneration cycle yields much better thermal and exergy efficiencies than the previously investigated combined power and ejector cooling cycle. Current investigation clearly show that the second law analysis is quantitatively visualizes losses within a cycle and gives clear trends for optimization. © 2011 Elsevier Ltd and IIR. All rights reserved.


Saxena A.R.,Madhav Institute of Technology and Science
Journal of Electrical Systems | Year: 2014

In this paper, the comparative analysis of two maximum power point tracking (MPPT) algorithms namely Perturb and Observe (P&O) and Incremental conductance (InC) is presented for the Photo-Voltaic (PV) power generation system. The mathematical model of the PV array is developed and transformed into MATLAB/Simulink environment. This model is used throughout the paper to simulate the PV source characteristics identical to that of a 20 Wp PV panel. The MPPT algorithms generate proper duty ratio for interfacing dc-dc boost converter driving resistive load. The performances of these algorithms are evaluated at gradual and rapidly changing weather conditions where it is observed that InC method tracks the rapidly changing insolation level at a faster rate as compared to P&O. Depending upon the prevailing environmental conditions the MPPT algorithms finds a unique operating point to track the maximum available power. The algorithms find a fixed duty ratio by comparing the previous power, voltage and current thereby optimizing the power output of the panel. The main objective is to compare the tracking capability and stability of the algorithms under different environmental situations on par with other real world tests. © JES 2014.


Singh H.,Madhav Institute of Technology and Science | Srivastava L.,Madhav Institute of Technology and Science
International Journal of Electrical Power and Energy Systems | Year: 2014

Reactive power or VAR management is one of the most crucial tasks required for proper operation and control of a power system. Reactive power management is carried out to reduce losses and to improve voltage profile in a power system, by adjusting the reactive power control variables such as generator voltages, transformer tap-settings and other sources of reactive power such as capacitor banks or FACTS devices. VAR management provides better system security, improved power transfer capability and overall system operation. VAR management is a complex combinatorial optimization problem involving nonlinear functions having multiple local minima and nonlinear and discontinuous constraints. In this paper, the VAR management problem is formulated as a nonlinear constrained multi-objective optimization problem with equality and inequality constraints for minimization of real power losses and voltage deviation simultaneously. This multi-objective problem is solved using Differential Evolution (DE), which is a population based search algorithm. For avoiding the time and the effort in tuning the parameters of DE algorithm, a modified DE algorithm with time varying chaotic mutation and crossover is proposed for solving the multi-objective VAR management problem. Weighing factor method has been employed for finding Pareto optimal set for VAR management problem. Fuzzy membership function is used to obtain the best compromise solution out of the available Pareto-optimal solutions. Effectiveness of the proposed modified DE algorithm based approach has been demonstrated on IEEE 30-bus system and is found to be superior to classical DE and its variants Self-adaptive Differential Evolution (SaDE) and Ensemble of Mutation and Crossover Strategies and Parameters in Differential Evolution (EPSDE) in terms of convergence behavior and solution quality. © 2013 Elsevier Ltd. All rights reserved.

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