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Citizen Science: A Study of People, Expertise and Sustainable Development

International Joint Conference on Neural Networks

Computational methods in optimization considering uncertainties – An overview

Review of uncertainty-based multidisciplinary design optimization methods for aerospace vehicles

Treatment of uncertainties in structural design involves identifying the boundaries of the failure domain to estimate reliability. When the structural responses are discontinuous or highly nonlinear, the failure regions tend to be an island in the design space. The boundaries of these islands are to be approximated to estimate reliability and perform optimization. This work proposes Alpha (?) shapes, a computational geometry technique to approximate such boundaries. The ? shapes are simple to construct and only require Delaunay Tessellation. Once the boundaries are approximated based on responses sampled in a design space, a computationally efficient ray shooting algorithm is used to estimate the reliability without any additional simulations. The proposed approach is successfully used to decompose the design space and perform Reliability based Design Optimization of a tube impacting a rigid wall and a tuned mass damper.

https://ed.iitm.ac.in/~raman/MAO2012_Ahull_V3.pdf

Alpha shape based design space decomposition for island failure regions in reliability based design

While planning resource management systems in rural areas, it is important to consider criteria that are specific to the local social conditions. Such criteria might change from one region to another and are hence best identified using a participatory approach. In this work, we propose a participatory framework to identify such criteria and derive their weights. These identified criteria and their weights are used as parameters to develop a quantitative model for evaluating efficiency of each system. Such a model can serve as a support tool for stakeholders to simulate and analyze “what-if” scenarios, evaluate alternatives, and select one which best satisfies their requirements. We use existing systems to test the model by comparing efficiencies evaluated by the model to efficiencies perceived by the stakeholders. The model is calibrated by repeating the process until statistically significant correlation is achieved between evaluated and perceived efficiencies. The novelty of the proposed framework lies in treating efficiencies perceived by the stakeholders as the ground truth since they know these systems well and are their ultimate users. The framework is successfully demonstrated using case study of rainwater harvesting (RWH) systems in an Indian village. The resulting calibrated model can be used to plan new RWH systems in this region and similar regions elsewhere. The framework can be used to plan other resource management systems in various regions.

Participatory Model Calibration for Improving Resource Management Systems: Case Study of Rainwater Harvesting in an Indian Village

Estimating probabilistic fatigue of Nitinol with scarce samples

• Differentiation of Drug Sensitive and Resistant Tuberculosis using chest X-rays. • Both lungs and mediastinum are precisely segmented using active contour models. • Significant lung and mediastinum shape features are extracted for characterization. • Multilayer perceptron and support vector machine classifiers perform better. • Combination of mediastinum and lung features improves the diagnostic accuracy. The rise of Drug Resistant Tuberculosis (DR TB), particularly Multi DR (MDR), and Extensively DR (XDR) has reduced the rate of control of the disease. Computer aided diagnosis using Chest X-rays (CXRs) can help in mass screening and timely diagnosis of DR TB, which is essential to administer proper treatment regimens. In CXRs, lungs and mediastinum are two significant regions which contain the information about the likelihood of DR TB. The objective of this work is to analyze the shape characteristics of lungs and mediastinum to improve the diagnostics accuracy for differentiation of Drug Sensitive (DS), MDR and XDR TB using computer aided diagnostics system. The CXR images of DS and DR TB patients are obtained from a public database. The lung fields are segmented from the CXRs using Reaction Diffusion Level Set Evolution. Mediastinum is segmented from the delineated lung masks using Chan Vese model. The shape features from each lung and mediastinum masks are extracted and analysed. The discriminative power of individual and combination of both lung and mediastinum features are evaluated using machine learning techniques for classification of DS vs MDR, MDR vs XDR and DS vs XDR TB images. The performances of classifiers are compared using standard metrics. The proposed segmentation methods are able to delineate lungs and mediastinum from the CXR images. The extracted lung and mediastinum features are found to be statistically significant (p < 0.05) for differentiation of DS and DR TB conditions. Using the combination of both lung and mediastinum features, Multi-Layer Perceptron classifier achieves maximum F-measure of 82.4%, 81.0% and 87.0% for differentiation of DS vs MDR, MDR vs XDR and DS vs XDR, respectively. Analysis of mediastinum along with the lungs in chest X-rays could improve the diagnostic performance for differentiation of drug sensitive and resistant TB conditions. The proposed methodology is able to differentiate DS, MDR and XDR TB, and found to be clinically relevant. Hence, this work is useful for computer-based early detection of DS and DR TB conditions. 

Evaluation of Diagnostic Value of Mediastinum for Differentiation of Drug Sensitive, Multi and Extensively Drug Resistant Tuberculosis using Chest X-rays

Palaniappan Ramu

Papers

Alpha shape based design space decomposition for island failure regions in reliability based design

Participatory Model Calibration for Improving Resource Management Systems: Case Study of Rainwater Harvesting in an Indian Village

Estimating probabilistic fatigue of Nitinol with scarce samples

Evaluation of Diagnostic Value of Mediastinum for Differentiation of Drug Sensitive, Multi and Extensively Drug Resistant Tuberculosis using Chest X-rays

A survey of machine learning techniques in structural and multidisciplinary optimization