Time filter

Source Type

Utica, NY, United States

Bhaduri K.,Critical Technologies Inc | Stefanski M.D.,Stanford University | Srivastava A.N.,NASA
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics | Year: 2011

Consider a scenario in which the data owner has some private or sensitive data and wants a data miner to access them for studying important patterns without revealing the sensitive information. Privacy-preserving data mining aims to solve this problem by randomly transforming the data prior to their release to the data miners. Previous works only considered the case of linear data perturbationsadditive, multiplicative, or a combination of bothfor studying the usefulness of the perturbed output. In this paper, we discuss nonlinear data distortion using potentially nonlinear random data transformation and show how it can be useful for privacy-preserving anomaly detection from sensitive data sets. We develop bounds on the expected accuracy of the nonlinear distortion and also quantify privacy by using standard definitions. The highlight of this approach is to allow a user to control the amount of privacy by varying the degree of nonlinearity. We show how our general transformation can be used for anomaly detection in practice for two specific problem instances: a linear model and a popular nonlinear model using the sigmoid function. We also analyze the proposed nonlinear transformation in full generality and then show that, for specific cases, it is distance preserving. A main contribution of this paper is the discussion between the invertibility of a transformation and privacy preservation and the application of these techniques to outlier detection. The experiments conducted on real-life data sets demonstrate the effectiveness of the approach. © 2010 IEEE. Source

Stepanyan V.,Critical Technologies Inc | Krishnakumar K.,NASA
Proceedings of the IEEE Conference on Decision and Control | Year: 2011

This paper presents design and performance analysis of a modified reference model MRAC (M-MRAC) architecture for a class of multi-input multi-output uncertain nonlinear systems in the presence of bounded disturbances. M-MRAC incorporates an error feedback in the reference model definition, which allows for fast adaptation without generating high frequency oscillations in the control signal, which closely follows the certainty equivalent control signal. The benefits of the method are demonstrated via a simulation example of an aircraft's wing rock motion. © 2011 IEEE. Source

Saha B.,Critical Technologies Inc | Goebel K.,NASA
International Journal of Prognostics and Health Management | Year: 2011

One of the key motivating factors for using particle filters for prognostics is the ability to include model parameters as part of the state vector to be estimated. This performs model adaptation in conjunction with state tracking, and thus, produces a tuned model that can used for long term predictions. This feature of particle filters works in most part due to the fact that they are not subject to the "curse of dimensionality", i.e. the exponential growth of computational complexity with state dimension. However, in practice, this property holds for "well-designed" particle filters only as dimensionality increases. This paper explores the notion of wellness of design in the context of predicting remaining useful life for individual discharge cycles of Li-ion batteries. Prognostic metrics are used to analyze the tradeoff between different model designs and prediction performance. Results demonstrate how sensitivity analysis may be used to arrive at a well-designed prognostic model that can take advantage of the model adaptation properties of a particle filter. Source

Card S.W.,Critical Technologies Inc
Proceedings of the 12th Annual Genetic and Evolutionary Computation Conference, GECCO '10 - Companion Publication | Year: 2010

Commensurate indicators of diversity and fitness with desirable metric properties are derived from information distances based on Shannon entropy and Kolmogorov complexity. These metrics measure various useful distances: from an information theoretic characterization of the phenotypic behavior of a candidate model in the population to that of an ideal model of the target system's input-output relationship (fitness); from behavior of one candidate model to that of another (total information diversity); from the information about the target provided by one model to that provided by another (target relevant information diversity); from the code of one model to that of another (genotypic representation diversity); etc. Algorithms are cited for calculating the Shannon entropy based metrics from discrete data and estimating analogs there of from heuristically binned continuous data; references are cited to methods for estimating the Kolmogorov complexity based metric. Not in the paper, but at the workshop, results will be shown of applying these algorithms to several synthetic and real world data sets: the simplest known deterministic chaotic flow; symbolic regression test functions; industrial process monitoring and control variables; and international political leadership data. Ongoing work is outlined. © 2010 ACM. Source

Morik K.,TU Dortmund | Bhaduri K.,Critical Technologies Inc | Kargupta H.,University of Maryland Baltimore County
Data Mining and Knowledge Discovery | Year: 2012

Data mining techniques are presented to explore and analyze environmental spatio-temporal data or help to design and operate better sustainable systems. The measurement process needs to be understood, managed, and controlled. The data collections of environmental and engineering approaches to sustainability are challenging data mining in various ways. The high-dimensional data sets are organized into spatial and temporal neighborhoods and the relation between these two orderings needs to be taken into account by the mining algorithms. Many tools have been developed that display the data in different views and allow for interactive analysis. Another approach to sustainability is to enhance the management of human consumption of resources. This engineering approach aims at controlling processes such that natural resources are conserved. Source

Discover hidden collaborations