Faculty Publications
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Publications by NITK Faculty
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Item Spectral-spatial MODIS image analysis using swarm intelligence algorithms and region based segmentation for flood assessment(Springer Verlag service@springer.de, 2013) Senthilnath, J.; Vikram Shenoy, H.; Omkar, S.N.; Mani, V.This paper discusses an approach for river mapping and flood evaluation based on multi-temporal time-series analysis of satellite images utilizing pixel spectral information for image clustering and region based segmentation for extracting water covered regions. MODIS satellite images are analyzed at two stages: before flood and during flood. Multi-temporal MODIS images are processed in two steps. In the first step, clustering algorithms such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) are used to distinguish the water regions from the non-water based on spectral information. These algorithms are chosen since they are quite efficient in solving multi-modal optimization problems. These classified images are then segmented using spatial features of the water region to extract the river. From the results obtained, we evaluate the performance of the methods and conclude that incorporating region based image segmentation along with clustering algorithms provides accurate and reliable approach for the extraction of water covered region. © 2013 Springer.Item Clustering using levy flight cuckoo search(Springer Verlag service@springer.de, 2013) Senthilnath, J.; Das, V.; Omkar, S.N.; Mani, V.In this paper, a comparative study is carried using three nature-inspired algorithms namely Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Cuckoo Search (CS) on clustering problem. Cuckoo search is used with levy flight. The heavy-tail property of levy flight is exploited here. These algorithms are used on three standard benchmark datasets and one real-time multi-spectral satellite dataset. The results are tabulated and analysed using various techniques. Finally we conclude that under the given set of parameters, cuckoo search works efficiently for majority of the dataset and levy flight plays an important role. © 2013 Springer.Item Optimization of soil parameters in multiple layers of ground structure(IEEE Computer Society, 2017) Senthilkumar, R.T.This paper proposes an optimization methodology to estimate the parameters of multilayer earth structure by using the hybrid genetic algorithm and particle swarm optimization. By using four wire Wenner method on the ground is to acquire the experimental apparent resistivity curve. With the measured experimental apparent resistivity, can compute the theoretical apparent resistivity curve and estimate the soil parameters such as a number of layers, thickness of each layer (Nth layer thickness is infinity) and its resistivity. The representation of unknown soil is determined by comparing the closeness of experimental apparent resistivity curve with the theoretical optimized apparent resistivity. © 2017 IEEE.Item Optimization of adaptive resonance theory neural network using particle swarm optimization technique(Springer Verlag service@springer.de, 2018) Satpute, K.; Kumar, R.With the advancement of computers and its computational enhancement over several decades of use, but with the growth in the dependencies and use of these systems, more and more concerns over the risk and security issues in networks have raised. In this paper, we have proposed approach using particle swarm optimization to optimize ART. Adaptive resonance theory is one of the most well-known machine-learning-based unsupervised neural networks, which can efficiently handle high-dimensional dataset. PSO on the other hand is a swarm intelligence-based algorithm, efficient in nonlinear optimization problem and easy to implement. The method is based on anomaly detection as it can also detect unknown attack types. PSO is used to optimize vigilance parameter of ART-1 and to classify network data into attack or normal. KDD ’99 (knowledge discovery and data mining) dataset has been used for this purpose. © Springer Nature Singapore Pte Ltd. 2018.Item Comparative Analysis on Diverse Heuristic-Based Joint Probabilistic Data Association for Multi-target Tracking in a Cluttered Environment(Springer Science and Business Media Deutschland GmbH, 2022) Lingadevaru, P.; Srihari, P.The target tracking using the passive multi-static radar system produces various detections via distinct signal propagation paths. Trackers solve the uncertainties that arise from the measurement path as well as the measurement origin. The existing multi-target tracking algorithms suffer from high computational loads, because they require the entire probable joint measurement-to-track assignments. This paper proposes to develop a comparative analysis on diverse heuristic algorithms for implementing the optimized JDPA model for tracking multiple targets using multi-static passive radar system in the presence of clutter. Here, the nature-inspired algorithms like Particle Swarm Optimization (PSO) and Grey Wolf Optimization (GWO) are used for analyzing the optimized JDPA model for tracking multiple targets in multi-static passive scenario. This paper further, aims to tune the position and velocity of the tracker towards the target using two heuristic algorithms, and intends to analyze the effect of those algorithms on improving the performance of multiple target tracking. The key objective of the proposed model is to minimize the Mean Absolute Error (MAE) between the estimated trajectory of the track and the true target state. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.Item Current approaches of artificial intelligence in breakwaters - A review(Techno Press technop2@chollian.net, 2017) Kundapura, S.; Hegde, A.V.A breakwater has always been an ideal option to prevent shoreline erosion due to wave action as well as to maintain the tranquility in the lagoon area. The effects of the impinging wave on the structure could be analyzed and evaluated by several physical and numerical methods. An alternate approach to the numerical methods in the prediction of performance of a breakwater is Artificial Intelligence (AI) tools. In the recent decade many researchers have implemented several Artificial Intelligence (AI) tools in the prediction of performance, stability number and scour of breakwaters. This paper is a comprehensive review which serves as a guide to the current state of the art knowledge in application of soft computing techniques in breakwaters. This study aims to provide a detailed review of different soft computing techniques used in the prediction of performance of different breakwaters considering various combinations of input and response variables. © 2017 Techno-Press, Ltd.Item Particle swarm optimized fuzzy control of structure with tuned liquid column damper(Research India Publications subscription@ripublication.com, 2016) Abubaker, S.; Nagan, S.; Nasar, T.Modern civil engineering structures are long and slender and they have less damping. Therefore they are subjected to large vibrations when earthquake or wind acts. These excitations may cause structural collapse of the structure. Therefore to control these vibrations supplementary control devices are used. Tuned liquid column damper (TLCD) is one of a passive control device to reduce the excitations. TLCD will transfer the energy from the structure to TLCD by the motion of water in a U-shape tube like devices fitted with an orifice opening. Due to this motion the excitations will reduced. Also a fuzzy controller is designed to control the output of the TLCD. In this paper, particle swarm optimized (PSO) fuzzy controller was introduced in to the TLCD-structure system. The PSO will optimize the IF-THEN rules of fuzzy controller. The optimized results are compared with structure without TLCD, and fuzzy controlled TLCD-structure system. From this paper, the vibrations can be effectively suppressed with the proposed fuzzy controller. © Research India Publications.Item An Efficient Approach to Optimize Wear Behavior of Cryogenic Milling Process of SS316 Using Regression Analysis and Particle Swarm Techniques(Springer, 2019) Karthik, M.C.; Malghan, R.L.; Shettigar, S.; Rao, S.S.; Herbert, M.A.The present work is an endeavor to carry out a machining using LN2 in face milling operations and to produce the milling samples with excellent wear resistance property. The output response (wear rate) depends on appropriate choice of speed, feed, and depth of cut. The experimental data are conducted (collected) for SS316 as per central composite design. The present work exemplifies an employment of conventional and nonconventional strategies for optimizing the milling factors of cryogenically treated samples in face milling to achieve the desired wear (response). The results of nonlinear regression (desirability strategy) and nonconventional [particle swarm optimization, (PSO)] optimization techniques are compared, and PSO is found to outperform the desirability function approach. The present work even highlights the effect and results of LN2 on wear in contrast to wet condition. © 2018, The Indian Institute of Metals - IIM.Item Compressive strength prediction of SCC containing fly ash using SVM and PSO-SVM models(Structural Engineering Research Centre, 2021) Rajeshwari, R.; Mandal, S.; C, C.Self-Compacting Concrete (SCC), is a highly workable material, compacted by its self weight without observable segregation and bleeding. In this study, Support Vector Machine (SVM) and particle swarm optimization based SVM models are employed to predict the 28 days compressive strength of individual SCC mix. A database of 62 no’s of SCC compressive strength from literature with cement partially replaced by fly ash is used for training the models. The test data consists of two groups, an individual study consisting of 9 datasets and other combination of three studies with 19 datasets tested separately. Similar input parameters from the train data is extended for testing the models prediction accuracy. Statistical parameters such as correlation coefficient, root mean square error and scatter index are used to evaluate the models’ prediction results. The particle swarm optimization based SVM model is capable of selecting appropriate SVM parameters to increase the prediction accuracy. From the results, it is seen that both SVM and particle swarm optimized SVM models have good capability in predicting the SCC compressive strength. © 2021, Structural Engineering Research Centre. All rights reserved.Item Comparative Analysis of Maximum Power Point Tracking Algorithms for Standalone PV System Under Variable Weather Conditions(River Publishers, 2022) Ghatak, A.; Pandit, T.; Kishan, D.; Raushan, R.Renewable energy systems are becoming increasingly predominant in the current scenario, and Photovoltaic (PV) arrays are one of the most widely used renewable energy generation sources. The current-voltage characteristics of PV arrays are non-linear, necessitating the need for supervisory techniques in order to ensure that the array functions at maximum efficiency, which is performed by Maximum Power Point Tracking (MPPT) techniques. These techniques are categorized into classical, intelligent and optimization algorithms. This paper performs a comparative analysis between five different MPPT techniques belonging to these categories – Perturb and Observe (P&O), Incremental Conductance (IC), Fuzzy Logic Control (FLC), Particle Swarm Optimization (PSO) and Cuckoo Search Algorithm (CSA). A standalone PV system interfaced with a Boost converter is simulated on MATLAB Simulink for the performance evaluation of the MPPT techniques. Solar energy is extremely susceptible to changes in local weather conditions, mainly variations in solar insolation levels. The designed system is tested against a varying insolation profile in order to examine the robustness of the MPPT techniques, with their operation efficiencies showcased. © 2022 River Publishers.