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Sarkar A.,MCKV Institute of Engineering | Sengupta S.,University of Calcutta
IEEE Transactions on Instrumentation and Measurement | Year: 2011

A novel digital signal processing algorithm for online estimation of the fundamental frequency of the distorted power system signals is presented. The basic algorithm relies on the development of an efficient variance reduction algorithm; and design of a new stable bandpass infinite impulse response (IIR) second-degree digital integrator (SDDI) with reduced approximation error. Compared with the well-established technique such as the enhanced-phase-locked- loop (EPLL) system, the proposed algorithm provides the following: 1) higher degree of immunity and insensitivity to harmonics and noise and 2) faster response during step frequency change. Structural simplicity, wide range of application, controls over speed and accuracy, and parameter robustness are other salient features of the method. The only limitation as compared with the EPLL system is its slower transient response during step change in signal magnitude. Based on simulation studies, performances of the proposed algorithm at different operating conditions have been presented, and its accuracy and response time have been compared with the EPLL systems. © 2006 IEEE.

Ghosal S.,Jadavpur University | Chaki S.,MCKV Institute of Engineering
International Journal of Advanced Manufacturing Technology | Year: 2010

The paper presents an artificial neural networkoptimization hybrid model to predict and optimize penetration depth of CO2 LASER-MIG hybrid welding used for 5005 Al-Mg alloy. The input welding parameters are power, focal distance from the work piece surface, torch angle, and the distance between the laser and the welding torch. The model combines single hidden layer back propagation artificial neural networks (ANN) with Bayesian regularization for prediction and quasi-Newton search algorithm for optimization. In this method, training and prediction performance of different ANN architectures are initially tested, and the architecture with the best performance is further used for optimization. Finally, the best ANN architecture is found to show much better prediction capability compared to a regression model developed from the experimental data. © Springer-Verlag London Limited 2009.

Chatterjee P.,MCKV Institute of Engineering | Chakraborty S.,Jadavpur University
International Journal of Advanced Manufacturing Technology | Year: 2013

Traditional edged cutting tool-based machining processes are now being continuously replaced by nontraditional machining (NTM) processes so as to generate complex and intricate shapes on advanced and harder materials, like titanium, stainless steel, high-strength temperature-resistant alloys, fiber-reinforced composites, and engineering ceramics. These NTM processes, while using energy in its direct form for removing materials from the workpiece surfaces, have the capabilities of meeting some higher level requirements, such as low tolerance, high surface finish, higher production rate, automated data transmission, miniaturization, etc., and are also quite suitable in the areas of micro- and nano-machining. Selection of the most appropriate NTM process to generate a desired shape feature on a given work material is often a challenging task as it involves consideration of diverse machining characteristics and performance of the NTM processes. This paper explores in details the applicability, suitability, and potentiality of evaluation of mixed data method for solving the NTM process selection problems. Three illustrative examples are presented, which validate the usefulness of this method. The observed results exactly corroborate with those obtained by the past researchers. © 2013 Springer-Verlag London.

Chaki S.,MCKV Institute of Engineering | Ghosal S.,Jadavpur University
Production Engineering | Year: 2011

Laser assisted oxygen cutting (LASOX) process is an efficient method for cutting thick mild steel plates compared to conventional laser cutting process. However, scanty information is available as to modeling of the process. The paper presents an optimized SA-ANN model of artificial neural network (ANN) and simulated annealing (SA) to predict and optimize cutting quality of LASOX cutting process of mild steel plates. Optimization of SA-ANN parameters is carried out first where the ANN architecture and initial temperature for SA are optimized. The optimized ANN architecture is further trained using single hidden layer back propagation neural network (BPNN) with Bayesian regularization (BR). The trained ANN is then used to evaluate the objective function during optimization with SA. Experimental dataset employed for the purpose consists of input cutting parameters comprising laser power, cutting speed, gas pressure and stand-off distance while the resulting cutting quality is represented by heat affected zone (HAZ) width, kerf width and surface roughness. Results indicate that the SA-ANN model can predict the optimized output with reasonably good accuracy (around 3%). The proposed approach can be extended for prediction and optimization of operational parameters with reasonable accuracy for any experimental dataset. © 2011 German Academic Society for Production Engineering (WGP).

Chatterjee P.,MCKV Institute of Engineering | Chakraborty S.,Jadavpur University
Materials and Design | Year: 2012

The role of materials in the engineering design process has already been well recognized. Choice of an appropriate material for a particular product is one of the critical tasks for the designers. Designers need to identify materials with specific functionalities in order to find feasible design concepts and fulfill the product's end requirements. There is a vast array of materials with diverse properties available to the designers to satisfy different design requirements. The large number of available materials together with the complex relationships between various selection criteria, often make the material selection process a difficult and time consuming task. A systematic and efficient approach towards material selection is necessary in order to select the best alternative for a given engineering application. This paper focuses on the application of four preference ranking-based multi-criteria decision-making (MCDM) methods for solving a gear material selection problem. These are extended PROMETHEE II (EXPROM2), complex proportional assessment of alternatives with gray relations (COPRAS-G), ORESTE (Organization, Rangement Et Synthese De Donnes Relationnelles) and operational competitiveness rating analysis (OCRA) methods. Using these four methods, a list of all the possible choices from the best to the worst suitable materials is obtained taking into account different material selection criteria. The ranking performance of these methods is also compared with that of the past researchers. © 2011 Elsevier Ltd.

Maity S.R.,Haldia Institute of Technology | Chatterjee P.,MCKV Institute of Engineering | Chakraborty S.,Jadavpur University
Materials and Design | Year: 2012

In today's metalworking industry, many types of materials, ranging from high carbon steel to ceramics and diamonds, are used as cutting tools. Because of the wide range of conditions and requirements, no single cutting tool material meets all the needs of machining applications. Each tool material has its own properties and characteristics that make it best for a specific machining application. While evaluating a cutting tool material for a machining operation, the applicability is dependant on having the correct combination of its physical properties. Thus, it is extensively important to select the most appropriate cutting tool material with the desired properties for enhanced machining performance. This paper considers an exhaustive list of 19 cutting tool materials whose performance are evaluated based on ten selection criteria. The grey complex proportional assessment (COPRAS-G) method is then applied to solve this cutting tool material selection problem considering grey data in the decision matrix. Synthetic single crystal and polycrystal diamonds emerge out as the best two choices. Oil quenched tool steel (AISI O2) and powder metal tool steel (AISI A11) may also be used as the suitable cutting tool materials. Sialon and sintered reaction bonded silicon nitride are the worst chosen cutting tool materials. © 2011 Elsevier Ltd.

De I.,MCKV Institute of Engineering | Sil J.,IIEST
Applied Soft Computing Journal | Year: 2015

Image quality assessment of distorted or decompressed images without any reference to the original image is challenging from computational point of view. Quality of an image is best judged by human observers without any reference image, and evaluated using subjective measures. The paper aims at designing a generic no-reference image quality assessment (NR-IQA) method by incorporating human visual perception in assigning quality class labels to the images. Using fuzzy logic approach, we consider information theoretic entropies of visually salient regions of images as features and assess quality of the images using linguistic values. The features are transformed into fuzzy feature space by designing an algorithm based on interval type-2 (IT2) fuzzy sets. The algorithm measures uncertainty present in the input-output feature space to predict image quality accurately as close to human observations. We have taken a set of training images belonging to five different pre-assigned quality class labels for calculating foot print of uncertainty (FOU) corresponding to each class. To assess the quality class label of the test images, maximum of T-conorm applied on the lower and upper membership functions of the test images belonging to different classes is calculated. Our proposed image quality metric is compared with other no-reference quality metrics demonstrating more accurate results and compatible with subjective mean opinion score metric. © 2015 Elsevier B.V. All rights reserved.

Chakraborty K.,MCKV Institute of Engineering | Chakraborty M.,Bengal Engineering and Science University | Kar T.K.,Bengal Engineering and Science University
Nonlinear Analysis: Hybrid Systems | Year: 2011

In this paper, we analyze the dynamical behaviour of a bioeconomic model system using differential algebraic equations. The system describes a prey-predator fishery with prey dispersal in a two-patch environment, one of which is a free fishing zone and other is a protected zone. It is observed that a singularity-induced bifurcation phenomenon appears when a variation of the economic interest of harvesting is taken into account. We have incorporated a state feedback controller to stabilize the model system in the case of positive economic interest. A discrete-type gestational delay of predators is incorporated, and its effect on the dynamical behaviour of the model is analyzed. The occurrence of Hopf bifurcation of the proposed model with positive economic profit is shown in the neighbourhood of the coexisting equilibrium point through considering the delay as a bifurcation parameter. Finally, some numerical simulations are given to verify the analytical results, and the system is analyzed through graphical illustrations. © 2011 Elsevier Ltd.

Chakraborty K.,MCKV Institute of Engineering | Das S.,Bengal Engineering and Science University | Kar T.K.,Bengal Engineering and Science University
Nonlinear Analysis: Real World Applications | Year: 2011

This paper describes a preypredator fishery model with stage structure for prey. The adult prey and predator populations are harvested in the proposed system. The dynamic behavior of the model system is discussed. It is observed that singularity induced bifurcation phenomenon is appeared when variation of the economic interest of harvesting is taken into account. We have incorporated state feedback controller to stabilize the model system in the case of positive economic interest. Fishing effort used to harvest the adult prey and predator populations is used as a control to develop a dynamic framework to investigate the optimal utilization of the resource, sustainability properties of the stock and the resource rent earned from the resource. Pontryagin's maximum principle is used to characterize the optimal control. The optimal system is derived and then solved numerically using an iterative method with RungeKutta fourth-order scheme. Simulation results show that the optimal control scheme can achieve sustainable ecosystem. © 2011 Elsevier Ltd. All rights reserved.

Ghosh S.,MCKV Institute of Engineering
2016 IEEE 1st International Conference on Control, Measurement and Instrumentation, CMI 2016 | Year: 2016

The paper proposes a new recursive approach for the analysis of singular system using Taylor series. Firstly, theoretical foundation of the recursive algorithm using both first and second orders Taylor series has been established in a lucid manner. Numerical examples have been treated along with relevant tables and curves in support of effectiveness of the method. Further, mean integral square error (MISE) has been estimated for both first and second order Taylor series approaches and found to be within tolerable limits. Thus, the method proves to be attractive and efficient as well. © 2016 IEEE.

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