Faculty Publications
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Publications by NITK Faculty
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Item Fuzzy logic controlled microturbine generation system for distributed generation(Elsevier Ltd, 2012) Nayak, S.K.; Gaonkar, D.N.; Shivarudraswamy, R.The microturbine based Distributed Generation (DG) system are becoming the popular source of power industries due to their fuel flexibility, reliability and power quality. The microturbine generation (MTG) system is a complicated thermodynamic electromechanical system with a high speed of rotation, frequency conversion and its control strategy. In spite of several techniques to control high speed of microturbine is not accurate and reliable due to their anti-interference problem. To resolve the anti-interfacing problem, this paper investigates the fuzzy logic based speed governor for a MTG system as an alternative to nominal PI or lead-lag based controller. The development of fuzzy logic based speed governor includes input and output membership function with their respective members. The load variation on MTG system is performed using conventional and fuzzy logic controller, implemented in Matlab/simulink and results are compared with each other. The simulation result shows that, the performance improvement of fuzzy logic governor over a nominal governor based MTG system. © 2011 Published by Elsevier Ltd.Item A fuzzy sectional real-time scheduling algorithm based on system load(Springer Verlag service@springer.de, 2013) Annappa, B.Earliest Deadline First (EDF) Algorithm is one of the most widely known dynamic real-time task scheduling algorithms. However, when a real-time system is overloaded, experiments and analysis have proved that EDF algorithm is ineffective. Considering the algorithm's instability during the practical task executing environment in an overloaded state, it is necessary to apply a few decision making techniques to ensure a good overall performance. In this paper, we propose a dynamic sectional real-time scheduling algorithm called Fuzzy Sectional Scheduling (FSS), which identifies the system load and employs suitable scheduling techniques to improve overall performance. The simulation results show that the Fuzzy Sectional Scheduling Algorithm could improve the real-time system performance to a considerably greater extent compared to the classical algorithms such as EDF, HVF (Highest Value First) and HDF (Highest Density First) algorithms; under all workload conditions. © 2013 Springer.Item Fuzzy integrated sliding mode controller for vector controlled PMSM(Institute of Electrical and Electronics Engineers Inc., 2014) Hussain, S.; Abid Bazaz, M.A.A vector controlled drive based on hybrid fuzzy-sliding mode controller (FSMC) is presented in this paper for improving the dynamic performance of the PMSM drive. Sliding mode controller is a non-linear controller and hence is effective for parameter variations and disturbances. Different types of membership functions are studied for speed control to compensate for the chattering effect otherwise present with SMC. MATLAB Simulation of a 1.7Nm, 300Vdc, 3750rpm PMSM is presented in this paper and speed tracking using FSMC is achieved under different operating conditions. © 2014 IEEE.Item Enhancement of load voltage compensation using positive sinusoidal sequence regulator in fuzzy logic controlled three phase series active filter(Institute of Electrical and Electronics Engineers Inc., 2018) Jayasankar, V.N.; Kumar, N.B.; Vinatha Urundady, U.This study proposes a controller for series active Alter for enhancement in load voltage compensation. The controller consists of a positive sinusoidal sequence regulator based fundamental voltage calculator, a closed loop fuzzy logic based voltage controller and a pulse width modulation controller. Positive sequence sinusoidal signal regulator effectively eliminates the phase delay introduced while calculating fundamental voltage. Numerical simulations are done for different cases to verify the effectiveness of controller under different system conditions. © 2017 IEEE.Item A basic review of fuzzy logic applications in hydrology and water resources(Springer Science and Business Media Deutschland GmbH, 2020) Kambalimath S, S.; Deka, P.C.In recent years, fuzzy logic has emerged as a powerful technique in the analysis of hydrologic components and decision making in water resources. Problems related to hydrology often deal with imprecision and vagueness, which can be very well handled by fuzzy logic-based models. This paper reviews a variety of applications of fuzzy logic in the domain of hydrology and water resources in brief. So far in the literature, fuzzy logic-based hybrid models have been significantly applied in hydrologic studies. Furthermore, in this paper, the literature is reviewed on the basis of applications using pure fuzzy logic models and applications using hybrid-fuzzy modeling approach. This review suggests that hybrid-fuzzy modeling approach works well in many applications of hydrology when compared with pure fuzzy logic modeling. © 2020, The Author(s).Item A novel optimal fuzzy system for color image enhancement using bacterial foraging(Institute of Electrical and Electronics Engineers Inc., 2009) Hanmandlu, M.; Verma, O.P.; Kumar, N.K.; Kulkarni, M.A new approach is presented for the enhancement of color images using the fuzzy logic technique. An objective measure called exposure has been defined to provide an estimate of the underexposed and overexposed regions in the image. This measure serves as the dividing line between the underexposed and overexposed regions of the image. The hue, saturation, and intensity (HSV) color space is employed for the process of enhancement, where the hue component is preserved to keep the original color composition intact. A parametric sigmoid function is used for the enhancement of the luminance component of the underexposed image. A power-law operator is used to improve the overexposed region of the image, and the saturation component of HSV is changed through another power-law operator to recover the lost information in the overexposed region. Objective measures like fuzzy contrast and contrast and visual factors are defined to make the operators adaptive to the image characteristics. Entropy and the visual factors are involved in the objective function, which is optimized using the bacterial foraging algorithm to learn the parameters. Gaussian and triangular membership functions (MFs) are chosen for the underexposed and overexposed regions of the image, respectively. Separate MFs and operators for the two regions make the approach universal to all types of contrast degradations. This approach is applicable to a degraded image of mixed type. On comparison, this approach is found to be better than the genetic algorithm (GA)-based and entropy-based approaches. © 2009 IEEE.Item Fuzzy logic modeling for groundwater level forecasting of west coast region in India(2011) Dandagala, D.; Deka, P.C.Forecasting the groundwater table in unconfined aquifer is essential for efficient planning of conjunctive use in a basin. In this study, fuzzy logic (FL) models have been developed for groundwater level forecasting in west coast humid region of Karnataka state, India. The FL modeling was carried out to forecast the groundwater table by one week lead time at three different sites over the study area. Mamdani fuzzy inference system was adopted in the present study and finally centroid of area defuzzification method has been applied to obtain crisp output. The results concluded that the FL model performed quite satisfactorily as assessed by various performance indices such as Root mean square error, Coefficient of correlation, and Mean absolute error. © 2011 CAFET-INNOVA TECHNICAL SOCIETY. All rights reserved.Item Genetic algorithm based support vector machine regression in predicting wave transmission of horizontally interlaced multi-layer moored floating pipe breakwater(Elsevier Ltd, 2012) Patil, S.G.; Mandal, S.; Hegde, A.V.Planning and design of coastal protection works like floating pipe breakwater require information about the performance characteristics of the structure in reducing the wave energy. Several researchers have carried out analytical and numerical studies on floating breakwaters in the past but failed to give a simple mathematical model to predict the wave transmission through floating breakwaters by considering all the boundary conditions. Computational intelligence techniques, such as, Artificial Neural Networks (ANN), fuzzy logic, genetic programming and Support Vector Machine (SVM) are successfully used to solve complex problems. In the present paper, a hybrid Genetic Algorithm Tuned Support Vector Machine Regression (GA-SVMR) model is developed to predict wave transmission of horizontally interlaced multilayer moored floating pipe breakwater (HIMMFPB). Furthermore, optimal SVM and kernel parameters of GA-SVMR models are determined by genetic algorithm. The GA-SVMR model is trained on the data set obtained from experimental wave transmission of HIMMFPB using regular wave flume at Marine Structure Laboratory, National Institute of Technology, Karnataka, Surathkal, Mangalore, India. The results are compared with ANN and Adaptive Neuro-Fuzzy Inference System (ANFIS) models in terms of correlation coefficient, root mean square error and scatter index. Performance of GA-SVMR is found to be reliably superior. b-spline kernel function performs better than other kernel functions for the given set of data. © 2011 Elsevier Ltd. All rights reserved.Item Particle Swarm Optimization based support vector machine for damage level prediction of non-reshaped berm breakwater(Elsevier Ltd, 2015) Narayana, N.; Mandal, S.; Rao, S.; Patil, S.G.The damage analysis of coastal structure is very much essential for better and safe design of the structure. In the past, several researchers have carried out physical model studies on non-reshaped berm breakwaters, but failed to give a simple mathematical model to predict damage level for non-reshaped berm breakwaters by considering all the boundary conditions. This is due to the complexity and non-linearity associated with design parameters and damage level determination of non-reshaped berm breakwater. Soft computing tools like Artificial Neural Network, Fuzzy Logic, Support Vector Machine (SVM), etc, are successfully used to solve complex problems. In the present study, SVM and hybrid of Particle Swarm Optimization (PSO) with SVM (PSO-SVM) are developed to predict damage level of non-reshaped berm breakwaters. Optimal kernel parameters of PSO-SVM are determined by PSO algorithm. Both the models are trained on the data set obtained from experiments carried out in Marine Structures Laboratory, Department of Applied Mechanics and Hydraulics, National Institute of Technology Karnataka, Surathkal, India. Results of both models are compared in terms of statistical measures, such as correlation coefficient, root mean square error and scatter index. The PSO-SVM model with polynomial kernel function outperformed other SVM models. © 2014 Elsevier B.V.Item Prediction of secondary dendrite arm spacing in squeeze casting using fuzzy logic based approaches(De Gruyter Open Ltd, 2015) Gowdru Chandrashekarappa, M.G.C.; Krishna, K.; Parappagoudar, M.B.The quality of the squeeze castings is significantly affected by secondary dendrite arm spacing, which is influenced by squeeze cast input parameters. The relationships of secondary dendrite arm spacing with the input parameters, namely time delay, pressure duration, squeeze pressure, pouring and die temperatures are complex in nature. The present research work focuses on the development of input-output relationships using fuzzy logic approach. In fuzzy logic approach, squeeze cast process variables are expressed as a function of input parameters and secondary dendrite arm spacing is expressed as an output parameter. It is important to note that two fuzzy logic based approaches have been developed for the said problem. The first approach deals with the manually constructed mamdani based fuzzy system and the second approach deals with automatic evolution of the Takagi and Sugeno's fuzzy system. It is important to note that the performance of the developed models is tested for both linear and non-linear type membership functions. In addition the developed models were compared with the ten test cases which are different from those of training data. The developed fuzzy systems eliminates the need of a number of trials in selection of most influential squeeze cast process parameters. This will reduce time and cost of trial experimentations. The results showed that, all the developed models can be effectively used for making prediction. Further, the present research work will help foundrymen to select parameters in squeeze casting to obtain the desired quality casting without much of time and resource consuming. © by M.B. Parappagoudarb 2015.