Palaniappan Ramu

Bio: Palaniappan Ramu is an academic researcher from Indian Institute of Technology Madras. The author has contributed to research in topics: Computer science & Steganography. The author has an hindex of 13, co-authored 41 publications receiving 847 citations. Previous affiliations of Palaniappan Ramu include University of Florida.

Robust Design of Savonius Wind Turbine

Abstract: The Savonius wind turbine, a class of vertical axis wind turbine (VAWT), is simple and provides a better cost-benefit ratio. It works on the principle of differential drag and is effective in rooftop and ground mounting. Despite the advantages of Savonius wind rotors, they are not preferred due to their low aerodynamic performance levels. In order to address this, several experimental and numerical studies have been carried out in recent years. The primary aim of this work is to provide a simple methodology for the robust optimal design of the Savonius wind turbine. In the parameter design stage, the performance of the turbine is maximized using the traditional Taguchi method. An L27 orthogonal array is used considering five factors of three levels each, which affect C p . Wind speed is considered to be the noise factor. Signal-noise ratio (SNR) metric is used to find the optimal settings for robust design. The aerodynamic performance of the turbine is investigated through dynamic computational fluid dynamics (CFD) models of the design sets. The numerical models used for the simulations are also discussed.

A robust ECG steganography method

Abstract: ECG steganography hides patient's confidential data into their Electrocardiogram during transmission/storage of medical records, in order to ensure protection of patient privacy. Efficiency of a steganography method can be estimated using imperceptibility and robustness of hidden data which are estimated using Peak Signal to Noise Ratio and Bit Error Rate metrics, respectively. In this research work, an attempt has been made to perform ECG steganography using Discrete Wavelet Transform — Singular Value Decomposition method with additive quantization scheme. A vital challenge in steganography is that affects the imperceptibility and robustness of watermark. The novelty of proposed work is that improves the robustness of hidden data using BCH error-correcting codes. MIT-BIH database is used to evaluate the performance of the proposed ECG steganography method. It is observed that the robustness of hidden data is improved owing to BCH codes.

L-moments-based uncertainty quantification for scarce samples including extremes

Abstract: Sampling-based uncertainty quantification demands large data. Hence, when the available sample is scarce, it is customary to assume a distribution and estimate its moments from scarce data, to characterize the uncertainties. Nonetheless, inaccurate assumption about the distribution leads to flawed decisions. In addition, extremes, if present in the scarce data, are prone to be classified as outliers and neglected which leads to wrong estimation of the moments. Therefore, it is desirable to develop a method that is (i) distribution independent or allows distribution identification with scarce samples and (ii) accounts for the extremes in data and yet be insensitive or less sensitive to moments estimation. We propose using L-moments to develop a distribution-independent, robust moment estimation approach to characterize the uncertainty and propagate it through the system model. L-moment ratio diagram that uses higher order L-moments is adopted to choose the appropriate distribution, for uncertainty quantification. This allows for better characterization of the output distribution and the probabilistic estimates obtained using L-moments are found to be less sensitive to the extremes in the data, compared to the results obtained from the conventional moments approach. The efficacy of the proposed approach is demonstrated on conventional distributions covering all types of tails and several engineering examples. Engineering examples include a sheet metal manufacturing process, 7 variable speed reducer, and probabilistic fatigue life estimation.

Treating Uncertainties to Generate a Robust Design of Gas Turbine Disk Using L Moments and Scarce Samples Including Outliers

Abstract: Uncertainties in the input variables are inevitable in any design process. As a consequence, the output responses are also uncertain. Robust design is one of the sought after approach to treat such uncertainties for controlling the variation in the output responses, while maximizing the mean performance. Variation is modeled by a measure of data spread. Often, the details of the uncertainties in the input space are not available readily and they are usually characterized from scarce sample realizations. In addition, there could also be outliers in the realizations. These will increase the error in the measure of spread of the output response. Hence, it is desirable that an approach that is insensitive to outliers but can characterize the spread of data is developed for robust design. In this work we propose using L moments to model the spread of data. The classical robust design formulation is reformulated using the second L moment (l2). The proposed approach is demonstrated on a turbine disk design with 17 design and random variables. The details of the uncertainties are not known. A DoE of 200 samples is used and at each DoE point, we propagate the uncertainties using scarce samples, which include outliers. Robust design is performed and it is shown that the proposed approach works better than the classical robust design formulation.