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Search Results: 1 - 10 of 201613 matches for " Srinivasa Pai P. "
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Radial-Basis-Function-Network-Based Prediction of Performance and Emission Characteristics in a Bio Diesel Engine Run on WCO Ester
Shiva Kumar,P. Srinivasa Pai,B. R. Shrinivasa Rao
Advances in Artificial Intelligence , 2012, DOI: 10.1155/2012/610487
Abstract: Radial basis function neural networks (RBFNNs), which is a relatively new class of neural networks, have been investigated for their applicability for prediction of performance and emission characteristics of a diesel engine fuelled with waste cooking oil (WCO). The RBF networks were trained using the experimental data, where in load percentage, compression ratio, blend percentage, injection timing, and injection pressure were taken as the input parameters, and brake thermal efficiency (BTE), brake specific energy consumption (BSEC), exhaust gas temperature ( ), and engine emissions were used as the output parameters. The number of RBF centers was selected randomly. The network was initially trained using variable width values for the RBF units using a heuristic and then was trained by using fixed width values. Studies showed that RBFNN predicted results matched well with the experimental results over a wide range of operating conditions. Prediction accuracy for all the output parameters was above 90% in case of performance parameters and above 70% in case of emission parameters. 1. Introduction The world is presently confronted with a twin crisis of fossil fuel depletion and environmental degradation. Indiscriminate extraction and lavish consumption of fossil fuels have led to a reduction in underground-based carbon resources. The search for an alternative fuel which promises a harmonious correlation with the sustainable development, energy conservation, and management has become highly pronounced in the present context. The fuels of bio-origin like vegetable oils can provide a feasible solution to this crisis. The energy density, cetane number, and heat of vaporization of vegetable oils are comparable to diesel values. It is renewable, available everywhere, and has proved to be a cleaner fuel and more environment friendly than the fossil fuels [1–3]. Also from the literature, it is revealed that the emissions from the biodiesel engines are comparatively lesser from the engines with the petroleum-based fuels [4–6]. But the higher viscosity of vegetable oils affects the flow properties of fuel such as spray, atomization, and consequent vaporization and air fuel mixing. Heating and blending of vegetable oils may reduce the viscosity and improve the volatility of the vegetable oils, but its molecular structure remains unchanged. Literature survey revealed that converting vegetable oils into methyl esters will overcome all problems related with vegetable oils [7, 8]. However, high cost of biodiesel is the major obstacle for its commercialization. The
Neural Network Modeling and Prediction of Surface Roughness in Machining Aluminum Alloys  [PDF]
N. Fang, N. Fang, P. Srinivasa Pai, N. Edwards
Journal of Computer and Communications (JCC) , 2016, DOI: 10.4236/jcc.2016.45001

Artificial neural network is a powerful technique of computational intelligence and has been applied in a variety of fields such as engineering and computer science. This paper deals with the neural network modeling and prediction of surface roughness in machining aluminum alloys using data collected from both force and vibration sensors. Two neural network models, including a Multi-Layer Perceptron (MLP) model and a Radial Basis Function (RBF) model, were developed in the present study. Each model includes eight inputs and five outputs. The eight inputs include the cutting speed, the ratio of the feed rate to the tool-edge radius, cutting forces in three directions, and cutting vibrations in three directions. The five outputs are five surface roughness parameters. Described in detail is how training and test data were generated from real-world machining experiments that covered a wide range of cutting conditions. The results show that the MLP model provides significantly higher accuracy of prediction for surface roughness than does the RBF model.

Significance of Tribocorrosion in Biomedical Applications: Overview and Current Status
M. T. Mathew,P. Srinivasa Pai,R. Pourzal,A. Fischer,M. A. Wimmer
Advances in Tribology , 2009, DOI: 10.1155/2009/250986
Abstract: Recently, “tribocorrosion,” a research area combining the science of tribology and corrosion, has drawn attention from scientists and engineers belonging to a wide spectrum of research domains. This is due to its practical impact on daily life and also the accompanying economical burdens. It encompasses numerous applications including the offshore, space, and biomedical industry, for instance, in the case of artificial joints (Total Hip Replacement, THR) in orthopedic surgery, where implant metals are constantly exposed to tribological events (joint articulations) in the presence of corrosive solutions, that is, body fluids. Keeping the importance of this upcoming area of research in biomedical applications in mind, it was thought to consolidate the work in this area with some fundamental aspects so that a comprehensive picture of the current state of knowledge can be depicted. Complexity of tribocorrosion processes has been highlighted, as it is influenced by several parameters (mechanical and corrosion) and also due to the lack of an integrated/efficient test system. Finally a review of the recent work in the area of biotribocorrosion is provided, by focusing on orthopedic surgery and dentistry.
Evaluation of Effectiveness of Wavelet Based Denoising Schemes Using ANN and SVM for Bearing Condition Classification
Vijay G. S.,Kumar H. S.,Srinivasa Pai P.,Sriram N. S.,Raj B. K. N. Rao
Computational Intelligence and Neuroscience , 2012, DOI: 10.1155/2012/582453
Abstract: The wavelet based denoising has proven its ability to denoise the bearing vibration signals by improving the signal-to-noise ratio (SNR) and reducing the root-mean-square error (RMSE). In this paper seven wavelet based denoising schemes have been evaluated based on the performance of the Artificial Neural Network (ANN) and the Support Vector Machine (SVM), for the bearing condition classification. The work consists of two parts, the first part in which a synthetic signal simulating the defective bearing vibration signal with Gaussian noise was subjected to these denoising schemes. The best scheme based on the SNR and the RMSE was identified. In the second part, the vibration signals collected from a customized Rolling Element Bearing (REB) test rig for four bearing conditions were subjected to these denoising schemes. Several time and frequency domain features were extracted from the denoised signals, out of which a few sensitive features were selected using the Fisher’s Criterion (FC). Extracted features were used to train and test the ANN and the SVM. The best denoising scheme identified, based on the classification performances of the ANN and the SVM, was found to be the same as the one obtained using the synthetic signal. 1. Introduction The detection of fault in the machinery, in its incipient stage itself, has gained prime importance as it avoids machine down time, catastrophic failure of the machinery, threat to human life, high maintenance costs, and so forth. The fault diagnostic techniques based on the vibration signal analysis have become popular in recent times [1, 2]. The problem of the strong noise components masking the weak characteristic signals has always posed challenges to the condition monitoring expert. Several wavelet based signal processing techniques aiming at denoising the measured signal so as to increase the Signal-to-Noise Ratio (SNR) and reduce the Root-Mean-Square Error (RMSE) have been proposed and tried by several researchers [3–7]. The details of the techniques used by some of the researchers have been explained in Section 2.2. The wavelet based denoising technique has gained popularity due to its effectiveness and ease of application [8]. It overcomes the difficulty of determining the resonant frequency of the system. Therefore, the wavelet technique has been adopted in this work for denoising the bearing vibration signals. The detail coefficients, obtained from the Discrete Wavelet Transform (DWT), generally include a large proportion of the high-frequency noise components along with some of the characteristic
A Semi Analytic Approach to Coupled Boundary Value Problem  [PDF]
Nityanand P. Pai, Nagaraj N. Katagi
American Journal of Computational Mathematics (AJCM) , 2014, DOI: 10.4236/ajcm.2014.44027
Abstract: The present problem is considered as a coupled boundary value problem and is analyzed using a semi analytic method. A series method is used to obtain the solution and region of validity is extended by suitable techniques. In this case of series solution the results obtained are better than pure numerical findings up to moderately large Reynolds numbers. The variation of physical parameters is discussed in detail.
Factors influencing the result of strabismus surgery
Srinivasa Rao P
Indian Journal of Ophthalmology , 1965,
Haemorrhagic pneumonitis: A rare presentation of leptospirosis.
Pai N,Adhikari P
Journal of Postgraduate Medicine , 2001,
Abstract: Leptospirosis is an uncommon zoonosis. As a systemic disease, it presents itself by multisystem involvement. Pulmonary involvement with leptospirosis often is manifested by respiratory symptoms; but pneumonia commonly is not a prominent clinical manifestation of the illness. We report a case of leptospiral pneumonia in which pulmonary manifestations were primary clinical features of the illness. The prompt resolution of chest x-ray on institution of treatment is noteworthy.
Active systemic lesions in cases of suspected ocular tuberculosis
Srinivasa Rao P,Bhat K
Indian Journal of Ophthalmology , 1967,
Ocular morbidity in school children in rural coastal area of Karnataka
Kuruvilla James,Srinivasa Rao P
Indian Journal of Ophthalmology , 1978,
Unilateral proptosis, its management and rehabilitation-(a case report)
Kuruvilla James,Srinivasa Rao P
Indian Journal of Ophthalmology , 1977,
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