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Palaniappan Ramu

Other affiliations: University of Florida
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.


Papers
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Journal ArticleDOI
TL;DR: In this paper, a survey of inverse reliability measures and their advantages compared with the direct measures of safety such as probability of failure and reliability index is presented. But the authors focus on different advantages of inverse measures, such as improved computational efficiency of Reliability-Based Design Optimisation (RBDO), accuracy in Response Surface Approximation (RSAs), and easy estimates of resources needed for achieving target safety levels.
Abstract: Several inverse reliability measures (e.g. Probabilistic Performance Measure (PPM) and Probabilistic Sufficiency Factor (PSF)) that are essentially equivalent have been introduced in recent years as measures of safety. The different names for essentially the same measure reflect the fact that different researchers focused on different advantages of inverse measures. These advantages include improved computational efficiency of Reliability-Based Design Optimisation (RBDO), accuracy in Response Surface Approximations (RSAs) and easy estimates of resources needed for achieving target safety levels. This paper surveys these inverse measures and describes their advantages compared with the direct measures of safety such as probability of failure and reliability index. Methods to compute the inverse measures are also described. RBDO with inverse measure is demonstrated with a beam design example.

49 citations

Journal ArticleDOI
TL;DR: A novel technique which uses curvelet transforms to hide patient information into their ECG signal is presented and the observations validate that its performance is superior compared with the random locations approach.
Abstract: Biomedical signals transmitted over the internet are usually tagged with patient information. Data hiding techniques such as steganography ensures the security of such data by hiding the data into signals. However, data hiding results in signal deterioration that might affect diagnosability. A novel technique which uses curvelet transforms to hide patient information into their ECG signal is presented. Curvelet transform decomposes the ECG signal into frequency sub-bands. A quantisation approach is used to embed patient data into coefficients whose values are around zero, in the high-frequency sub-band. Performance metrics provide the measure of watermark imperceptibility of the proposed approach. BER is used to measure the ability to extract patient data. The proposed approach is demonstrated on the MIT-BIH database and the observations validate that its performance is superior compared with the random locations approach. Although the performance of the proposed approach decreases as patient information size increases, the peak signal-to-noise ratio values are high. Therefore, the proposed approach can be used for the safe transfer of patient data.

41 citations

Journal ArticleDOI
Abstract: The effect of shallow cryogenic treatment (SCT) on the microstructure and mechanical properties of Al7075-T6 is investigated in the present work. The alloy was subjected to shallow CT at −80 °C for 72 h. Mechanical tests such as Vickers hardness test, tensile, and fatigue tests were performed on both native and treated samples. It was observed that the mechanical properties such as hardness, yield strength, and ultimate tensile strength increased by about 30, 17, and 7%, respectively, for the treated sample. The treated alloy was characterized by using the techniques such as optical microscopy, electron back scattered diffraction (EBSD), energy-dispersive x-ray spectroscopy (EDS), and transmission electron microscopy (TEM) to observe the changes in the microstructural features. EBSD results show precipitation, better distribution of second-phase particles, and higher dislocation density in the treated alloy as compared to the untreated alloy. The treatment imparts improved hardness and strength to the alloy due to precipitation hardening and high dislocation density. Fracture morphologies of the treated and the native samples were characterized by using scanning electron microscopy and it was observed that the striations were denser in the treated sample justifying the higher fatigue strength.

41 citations

Journal ArticleDOI
TL;DR: A review of the uncertainty treatment practices in design optimization of structural and multidisciplinary systems under uncertainties can be found in this paper, where uncertainties of a structural or multi-disciplinary system are taken into account by using safety factors specified in the regulations or design codes.
Abstract: Design optimization of structural and multidisciplinary systems under uncertainty has been an active area of research due to its evident advantages over deterministic design optimization. In deterministic design optimization, the uncertainties of a structural or multidisciplinary system are taken into account by using safety factors specified in the regulations or design codes. This uncertainty treatment is a subjective and indirect way of dealing with uncertainty. On the other hand, design under uncertainty approaches provide an objective and direct way of dealing with uncertainty. This paper provides a review of the uncertainty treatment practices in design optimization of structural and multidisciplinary systems under uncertainties. To this end, the activities in uncertainty modeling are first reviewed, where theories and methods on uncertainty categorization (or classification), uncertainty handling (or management), and uncertainty characterization are discussed. Second, the tools and techniques developed and used for uncertainty modeling and propagation are discussed under the broad two classes of probabilistic and non-probabilistic approaches. Third, various design optimization methods under uncertainty which incorporate all the techniques covered in uncertainty modeling and analysis are reviewed. In addition to these in-depth reviews on uncertainty modeling, uncertainty analysis, and design optimization under uncertainty, some real-life engineering applications and benchmark test examples are provided in this paper so that readers can develop an appreciation on where and how the discussed techniques can be applied and how to compare them. Finally, concluding remarks are provided, and areas for future research are suggested.

30 citations

Proceedings ArticleDOI
01 May 2006
TL;DR: In this paper, a tail-modeling technique is used to approximate the performance measure in inverse reliability analysis, and the accuracy and convergence properties of the proposed approach are demonstrated using benchmark problems in structural design under uncertainty.
Abstract: This paper presents an approach for the reliability–based design optimization of highly safe structural systems where a tail–model is used for computing the reliability constraint during design optimization. It is generally accepted that using central models (e.g., moment– based method or stochastic response surfaces) for estimating large percentiles such as those required in reliability constraint calculations can lead to significant inaccuracies in the result. The tail–model is an adaptation of a powerful result from extreme value theory in statistics related to the distribution of exceedances. The conditional excess distribution above a certain threshold is approximated using the generalized Pareto distribution (GPD). The shape and scale parameters in the GPD are estimated using the least–square method. The tail–modeling technique is utilized to approximate the performance measure in inverse reliability analysis. The accuracy and convergence properties are studied using an analytical function. The effectiveness and efficiency of the proposed approach are demonstrated using benchmark problems in structural design under uncertainty.

26 citations


Cited by
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Book ChapterDOI
17 Jul 2002

1,123 citations

Journal ArticleDOI
TL;DR: In this article, the authors present a brief survey on some of the most relevant developments in the field of optimization under uncertainty, including reliability-based optimization, robust design optimization and model updating.

487 citations

Journal ArticleDOI
TL;DR: A comprehensive review of Uncertainty-Based Multidisciplinary Design Optimization (UMDO) theory and the state of the art in UMDO methods for aerospace vehicles is presented.

426 citations