Journal Articles
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/19884
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Item Epileptic EEG detection using neural networks and post-classification(2008) Patnaik, L.M.; Manyam, O.K.Electroencephalogram (EEG) has established itself as an important means of identifying and analyzing epileptic seizure activity in humans. In most cases, identification of the epileptic EEG signal is done manually by skilled professionals, who are small in number. In this paper, we try to automate the detection process. We use wavelet transform for feature extraction and obtain statistical parameters from the decomposed wavelet co-efficients. A feed-forward backpropagating artificial neural network (ANN) is used for the classification. We use genetic algorithm for choosing the training set and also implement a post-classification stage using harmonic weights to increase the accuracy. Average specificity of 99.19%, sensitivity of 91.29% and selectivity of 91.14% are obtained. © 2008 Elsevier Ireland Ltd. All rights reserved.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 Slim – Gal for shape optimization of structures(CAFET INNOVA Technical Society 1-2-18/103, Mohini Mansion, Gagan Mahal Road, Domalguda, Hyderabad 500029, 2011) Babu Narayan, K.S.; Devraj, M.; Arun Prabha, K.S.Structural Optimization has been & continues to be an active area of research offering scope and need to handle a wide & varied range of problems. Genetic Algorithms (GA) recently have been, with great success employed to solve structural engineering problems either in conjunction with traditional methods or as alternatives. Sizing, shape and topology design of trusses is an interesting exercise that has attracted the attention of researchers. However design problems have not been kept free of conceptual designs, defeating the possibility of evolution of more efficient & innovative designs, the reason being the complexity of the problem on hand. This paper presents GA based methodology of arriving at the best configuration & member sizing employing simultaneous mode of failure approach for problem formulation of the multi-objective type to yield a structure that satisfies functional & structural requirements optimally. © 2011 CAFET-INNOVA TECHNICAL SOCIETY. All rights reserved.Item Nonlinear system identification using memetic differential evolution trained neural networks(2011) Subudhi, B.; Jena, D.Several gradient-based approaches such as back propagation (BP) and Levenberg Marquardt (LM) methods have been developed for training the neural network (NN) based systems. But, for multimodal cost functions these procedures may lead to local minima, therefore, the evolutionary algorithms (EAs) based procedures are considered as promising alternatives. In this paper we focus on a memetic algorithm based approach for training the multilayer perceptron NN applied to nonlinear system identification. The proposed memetic algorithm is an alternative to gradient search methods, such as back-propagation and back-propagation with momentum which has inherent limitations of many local optima. Here we have proposed the identification of a nonlinear system using memetic differential evolution (DE) algorithm and compared the results with other six algorithms such as Back-propagation (BP), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Differential Evolution (DE), Genetic Algorithm Back-propagation (GABP), Particle Swarm Optimization combined with Back-propagation (PSOBP). In the proposed system identification scheme, we have exploited DE to be hybridized with the back propagation algorithm, i.e. differential evolution back-propagation (DEBP) where the local search BP algorithm is used as an operator to DE. These algorithms have been tested on a standard benchmark problem for nonlinear system identification to prove their efficacy. First examples shows the comparison of different algorithms which proves that the proposed DEBP is having better identification capability in comparison to other. In example 2 good behavior of the identification method is tested on an one degree of freedom (1DOF) experimental aerodynamic test rig, a twin rotor multi-input-multi-output system (TRMS), finally it is applied to Box and Jenkins Gas furnace benchmark identification problem and its efficacy has been tested through correlation analysis. © 2011 Elsevier B.V.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 Pickup and delivery problem using metaheuristics techniques(2012) D'Souza, C.; Omkar, S.N.; Senthilnath, J.Dial-a-ride problem (DARP) is an optimization problem which deals with the minimization of the cost of the provided service where the customers are provided a door-to-door service based on their requests. This optimization model presented in earlier studies, is considered in this study. Due to the non-linear nature of the objective function the traditional optimization methods are plagued with the problem of converging to a local minima. To overcome this pitfall we use metaheuristics namely Simulated Annealing (SA), Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and Artificial Immune System (AIS). From the results obtained, we conclude that Artificial Immune System method effectively tackles this optimization problem by providing us with optimal solutions. © 2011 Published by Elsevier Ltd. All rights reserved.Item Modeling and genetic algorithm-based multi-objective optimization of the MED-TVC desalination system(2012) Janghorban Esfahani, I.; Ataei, A.; Shetty K, K.V.; Oh, T.; Park, J.H.; Yoo, C.This study proposes a systematic approach of analysis and optimization of the multi-effect distillation-thermal vapor compression (MED-TVC) desalination system. The effect of input variables, such as temperature difference, motive steam mass flow rate, and preheated feed water temperature was investigated using response surface methodology (RSM) and partial least squares (PLS) technique. Mathematical and economical models with exergy analysis were used for total annual cost (TAC), gain output ratio (GOR) and fresh water flow rate (Q). Multi-objective optimization (MOO) to minimize TAC and maximize GOR and Q was performed using a genetic algorithm (GA) based on an artificial neural network (ANN) model. Best Pareto optimal solution selected from the Pareto sets showed that the MED-TVC system with 6 effects is the best system among the systems with 3, 4, 5 and 6 effects, which has a minimum value of unit product cost (UPC) and maximum values of GOR and Q. The system with 6 effects under the optimum operation conditions can save 14%, 12.5%, 2% in cost and reduces the amount of steam used for the production of 1m 3 of fresh water by 50%, 34% and 18% as compared to systems with 3, 4 and 5 effects, respectively. © 2012 Elsevier B.V..Item Coordinated voltage regulation of distribution network with distributed generators and multiple voltage-control devices(2012) Shivarudraswamy, R.; Gaonkar, D.N.In recent years, there has been a considerable increase in the number of generators connected to distribution networks. While offering a number of benefits and opportunities, increasing penetration of distributed generation systems can cause several technical concerns. One major concern is the rise in steady-state voltage level of a distribution system. This is very important, as distribution networks are traditionally designed to maintain customer voltage constant, within tolerance limit as dictated by statute. The present practice of limiting generation capacity cannot be a solution, as it leads to under-utilization of distributed generation sources. In this article, coordinated voltage regulation of distribution system with distributed generators is presented. The developed method uses the genetic algorithm to determine the optimal operating point for multiple voltage-control devices. The simulated results using the developed method are presented in this article, considering the time-varying load profile. The fuzzy-clustering technique is also employed to obtain the load pattern for the simulation. The reported results show that the method presented is capable of providing the voltage profile within the statute limits. © 2012 Taylor and Francis Group, LLC.Item MPI-based parallel synchronous vector evaluated particle swarm optimization for multi-objective design optimization of composite structures(2012) Omkar, S.N.; Venkatesh, A.; Mudigere, M.This paper presents a decentralized/peer-to-peer architecture-based parallel version of the vector evaluated particle swarm optimization (VEPSO) algorithm for multi-objective design optimization of laminated composite plates using message passing interface (MPI). The design optimization of laminated composite plates being a combinatorially explosive constrained non-linear optimization problem (CNOP), with many design variables and a vast solution space, warrants the use of non-parametric and heuristic optimization algorithms like PSO. Optimization requires minimizing both the weight and cost of these composite plates, simultaneously, which renders the problem multi-objective. Hence VEPSO, a multi-objective variant of the PSO algorithm, is used. Despite the use of such a heuristic, the application problem, being computationally intensive, suffers from long execution times due to sequential computation. Hence, a parallel version of the PSO algorithm for the problem has been developed to run on several nodes of an IBM P720 cluster. The proposed parallel algorithm, using MPI's collective communication directives, establishes a peer-to-peer relationship between the constituent parallel processes, deviating from the more common master-slave approach, in achieving reduction of computation time by factor of up to 10. Finally we show the effectiveness of the proposed parallel algorithm by comparing it with a serial implementation of VEPSO and a parallel implementation of the vector evaluated genetic algorithm (VEGA) for the same design problem. © 2012 Elsevier Ltd. All rights reserved.Item Use of genetic algorithm to determine lightning channel-base current-function parameters(Institute of Electrical and Electronics Engineers Inc., 2014) Chandrasekaran, K.; Punekar, G.S.A genetic algorithm (GA) is applied to calculate the lightning current parameters of the channel-base current function. Heidler's function parameters for subsequent return stroke are tuned for a typical value of I-m (\approx 12 kA) for different (di/dt)-{{\rm max}} and also for a typical value of (di/dt)-{{\rm max}} ( \approx 40 kA/\mus) for different values of I-m, using a GA as the tool. This is a first of its kind attempt to show that a GA can be used for identifying Heidler's function parameters (as adopted by IEC Standard 62305-1, 2006) to easily obtain the required lightning current wave shape. The data are thus generated and reported (including the extreme cases) in this paper that are thought to be useful in modeling lightning channel-base currents. Further, this approach will be useful in research related to the radiated lightning electromagnetic pulse and its coupling with nearby objects. © 1964-2012 IEEE.