Journal Articles

Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/19884

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Subject: search.filters.subject.Fuzzy logic

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    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).
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    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.
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    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.
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    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.
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    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.
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    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.
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    Fuzzy logic approach for reactive power coordination in grid connected wind farms to improve steady state voltage stability
    (Institution of Engineering and Technology journals@theiet.org, 2017) Moger, T.; Dhadbanjan, T.
    This study presents a fuzzy logic approach for reactive power coordination in grid connected wind farms with different types of wind generator units to improve steady state voltage stability of power systems. The load bus voltage deviation is minimised by changing the reactive power controllers according to their sensitivity using fuzzy set theory. The proposed approach uses only few controllers of high sensitivity to achieve the desired objectives. The 297-bus and 417-bus equivalent grid connected wind systems are considered to present the simulation results. To prove the effectiveness of the proposed approach, a comparative analysis is carried out with the conventional linear programming based reactive power optimisation technique. Results demonstrated that the proposed approach is more effective in improving the system performance as compared with the conventional existing technique. © 2016 The Institution of Engineering and Technology.
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    Fuzzy PI controller for bidirectional power flow applications with harmonic current mitigation under unbalanced scenario
    (AIMS Press, 2018) Nagaraj, C.; Manjunatha Sharma, K.
    The depletion of fossil fuels and environmental concern forces the extraction of power from low carbon fuels causes generation problem due to intermittent solar-wind renewable energy sources and power electronic applications. Furthermore, the significant amount of non-linear loads in the system causes power quality problems. Nowadays, the more and more DC loads like LED lights to save energy consumption are connected to the AC distribution system. These DC loads are connected at DC grid side in order to avoid the extra AC/DC power conversion loss. In this paper, the proposed d-q reference current method applied for shunt active power filter based 3-phase 4-leg bidirectional interfacing converter with fuzzy PI controller to achieve the real power transfer between DC grid side and AC grid side with current harmonics compensation at common connecting point simultaneously under balanced and unbalanced distorted grid and non-linear load conditions. The hysteresis current control comparator without PLL is used to compare actual grid current with reference filter current and generate the switching pulses for driving the bidirectional interfacing converter. The DC grid shunt connected intermittent hybrid solar-wind energy sources are integrating with AC grid utility through bidirectional interfacing converter has been into consideration for simulation studies. The MATLAB/SIMULINK tool is used to yield the improved grid current THD with fuzzy logic controller over PI controller. © 2018 the Author(s), licensee AIMS Press.
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    Estimation of saturated hydraulic conductivity using fuzzy neural network in a semi-arid basin scale for murum soils of India
    (Taylor and Francis Ltd. michael.wagreich@univie.ac.at, 2018) More, S.B.; Deka, P.C.
    Saturated hydraulic conductivity, Ks is an important input parameter in modeling flow process in soil. Measurement of Ks in field is time consuming and costly. Also, due to inherent temporal and spatial variability of this parameter, large number of samples are required to characterize the areas of site. In this study, a hybrid approach consists of Fuzzy Neural Network (FNN), has been proposed to estimate Ks from limited number of field measurements using Guelph permeameter. The various soil properties such as bulk density, porosity, specific gravity, sand, clay, silt and organic matter were used as input variables and Ks was kept as output. In this study, 175 field measurements and soil samples were collected in a grid of 40 m × 200 m with uniform spacing along the slope of barren land in the site of Punanaka (Solapur city), India. To quantify the prediction accuracy, this FNN approach is compared with regression, Fuzzy Mamdani approach and artificial neural network with BP algorithm. The various statistical performance indices like root mean square error, coefficient of determination (R2), and Mean relative error were used for evaluation of model performance. It was found that the hybrid FNN approach in comparison with others could more accurately predict saturated hydraulic conductivity. © 2017 Indian Society for Hydraulics.
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    Novel color normalization method for hematoxylin eosin stained histopathology images
    (Institute of Electrical and Electronics Engineers Inc., 2019) Roy, S.; Lal, S.; Kini, J.R.
    With the advent of computer-assisted diagnosis (CAD), the accuracy of cancer detection from histopathology images is significantly increased. However, color variation in the CAD system is inevitable due to the variability of stain concentration and manual tissue sectioning. The small variation in color may lead to the misclassification of cancer cells. Therefore, color normalization is a very much essential step prior to segmentation and classification in order to reduce the inter-variability of background color among a set of source images. In this paper, a novel color normalization method is proposed for Hematoxylin and Eosin stained histopathology images. Conventional Reinhard algorithm is modified in our proposed method by incorporating fuzzy logic. Moreover, mathematically, it is proved that our proposed method satisfies all three hypotheses of color normalization. Furthermore, several quality metrics are estimated locally for evaluating the performance of various color normalization methods. The experimental result reveals that our proposed method has outperformed all other benchmark methods. © 2019 IEEE.