Relevant bibliographies by topics / Robust functional data analysis

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Robust functional data analysis'

Author: Grafiati
Published: 4 June 2021
Last updated: 18 February 2022

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Journal articles on the topic "Robust functional data analysis"

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Locantore, N., J. S. Marron, D. G. Simpson, N. Tripoli, J. T. Zhang, K. L. Cohen, Graciela Boente, et al. "Robust principal component analysis for functional data." Test 8, no. 1 (June 1999): 1–73. http://dx.doi.org/10.1007/bf02595862.

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Sun, Jian, Haitao Liao, and Belle R. Upadhyaya. "A Robust Functional-Data-Analysis Method for Data Recovery in Multichannel Sensor Systems." IEEE Transactions on Cybernetics 44, no. 8 (August 2014): 1420–31. http://dx.doi.org/10.1109/tcyb.2013.2285876.

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Aaron, Catherine, Alejandro Cholaquidis, Ricardo Fraiman, and Badih Ghattas. "Multivariate and functional robust fusion methods for structured Big Data." Journal of Multivariate Analysis 170 (March 2019): 149–61. http://dx.doi.org/10.1016/j.jmva.2018.06.012.

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Yu, Yunqing. "Functional Principal Component Analysis: A Robust Method for Time-Series Phenotypic Data." Plant Physiology 183, no. 4 (August 2020): 1422–23. http://dx.doi.org/10.1104/pp.20.00797.

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Huang, Su-Yun, Yi-Ren Yeh, and Shinto Eguchi. "Robust Kernel Principal Component Analysis." Neural Computation 21, no. 11 (November 2009): 3179–213. http://dx.doi.org/10.1162/neco.2009.02-08-706.

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This letter discusses the robustness issue of kernel principal component analysis. A class of new robust procedures is proposed based on eigenvalue decomposition of weighted covariance. The proposed procedures will place less weight on deviant patterns and thus be more resistant to data contamination and model deviation. Theoretical influence functions are derived, and numerical examples are presented as well. Both theoretical and numerical results indicate that the proposed robust method outperforms the conventional approach in the sense of being less sensitive to outliers. Our robust method and results also apply to functional principal component analysis.
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De Haas, W. Bas, José Pedro Magalhães, Frans Wiering, and Remco C. Veltkamp. "Automatic Functional Harmonic Analysis." Computer Music Journal 37, no. 4 (December 2013): 37–53. http://dx.doi.org/10.1162/comj_a_00209.

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Music scholars have been studying tonal harmony intensively for centuries, yielding numerous theories and models. Unfortunately, a large number of these theories are formulated in a rather informal fashion and lack mathematical precision. In this article we present HarmTrace, a functional model of Western tonal harmony that builds on well-known theories of tonal harmony. In contrast to other approaches that remain purely theoretical, we present an implemented system that is evaluated empirically. Given a sequence of symbolic chord labels, HarmTrace automatically derives the harmonic relations between chords. For this, we use advanced functional programming techniques that are uniquely available in the Haskell programming language. We show that our system is fast, easy to modify and maintain, robust against noisy data, and that its harmonic analyses comply with Western tonal harmony theory.
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Cristani, M., A. Daducci, P. Farace, P. Marzola, V. Murino, A. Sbarbati, and U. Castellani. "DCE-MRI Data Analysis for Cancer Area Classification." Methods of Information in Medicine 48, no. 03 (2009): 248–53. http://dx.doi.org/10.3414/me9224.

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Summary Objectives: The paper aims at improving the support of medical researchers in the context of in-vivo cancer imaging. Morphological and functional parameters obtained by dynamic contrast-enhanced MRI (DCE-MRI) techniques are analyzed, which aim at investigating the development of tumor microvessels. The main contribution consists in proposing a machine learning methodology to segment automatically these MRI data, by isolating tumor areas with different meaning, in a histological sense. Methods: The proposed approach is based on a three-step procedure: i) robust feature extraction from raw time-intensity curves, ii) voxel segmentation, and iii) voxel classification based on a learning-by-example approach. In the first step, few robust features that compactly represent the response of the tissue to the DCE-MRI analysis are computed. The second step provides a segmentation based on the mean shift (MS) paradigm, which has recently shown to be robust and useful for different and heterogeneous clustering tasks. Finally, in the third step, a support vector machine (SVM) is trained to classify voxels according to the labels obtained by the clustering phase (i.e., each class corresponds to a cluster). Indeed, the SVM is able to classify new unseen subjects with the same kind of tumor. Results: Experiments on different subjects affected by the same kind of tumor evidence that the extracted regions by both the MS clustering and the SVM classifier exhibit a precise medical meaning, as carefully validated by the medical researchers. Moreover, our approach is more stable and robust than methods based on quantification of DCE-MRI data by means of pharmacokinetic models. Conclusions: The proposed method allows to analyze the DCE-MRI data more precisely and faster than previous automated or manual approaches.
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Beaty, Roger E., Yoed N. Kenett, Alexander P. Christensen, Monica D. Rosenberg, Mathias Benedek, Qunlin Chen, Andreas Fink, et al. "Robust prediction of individual creative ability from brain functional connectivity." Proceedings of the National Academy of Sciences 115, no. 5 (January 16, 2018): 1087–92. http://dx.doi.org/10.1073/pnas.1713532115.

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People’s ability to think creatively is a primary means of technological and cultural progress, yet the neural architecture of the highly creative brain remains largely undefined. Here, we employed a recently developed method in functional brain imaging analysis—connectome-based predictive modeling—to identify a brain network associated with high-creative ability, using functional magnetic resonance imaging (fMRI) data acquired from 163 participants engaged in a classic divergent thinking task. At the behavioral level, we found a strong correlation between creative thinking ability and self-reported creative behavior and accomplishment in the arts and sciences (r = 0.54). At the neural level, we found a pattern of functional brain connectivity related to high-creative thinking ability consisting of frontal and parietal regions within default, salience, and executive brain systems. In a leave-one-out cross-validation analysis, we show that this neural model can reliably predict the creative quality of ideas generated by novel participants within the sample. Furthermore, in a series of external validation analyses using data from two independent task fMRI samples and a large task-free resting-state fMRI sample, we demonstrate robust prediction of individual creative thinking ability from the same pattern of brain connectivity. The findings thus reveal a whole-brain network associated with high-creative ability comprised of cortical hubs within default, salience, and executive systems—intrinsic functional networks that tend to work in opposition—suggesting that highly creative people are characterized by the ability to simultaneously engage these large-scale brain networks.
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Wang, Shigang, Yongli Bi, and Yingsong Li. "Improvements on Robust Stability of Sampled-Data System with Long Time Delay." Mathematical Problems in Engineering 2014 (2014): 1–7. http://dx.doi.org/10.1155/2014/580768.

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This paper mainly studies the problem of the robust stability analysis for sampled-data system with long time delay. By constructing an improved Lyapunov-Krasovskii functional and employing some free weighting matrices, some new robust stability criteria can be established in terms of linear matrix inequalities. Furthermore, the proposed equivalent criterion eliminates the effect of free weighing matrices such that numbers of decision variables and computational burden are less than some existing results. A numerical example is also presented and compared with previously proposed algorithm to illustrate the feasibility and effectiveness of the developed results.
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Beckmann, Christian F., Marilena DeLuca, Joseph T. Devlin, and Stephen M. Smith. "Investigations into resting-state connectivity using independent component analysis." Philosophical Transactions of the Royal Society B: Biological Sciences 360, no. 1457 (May 29, 2005): 1001–13. http://dx.doi.org/10.1098/rstb.2005.1634.

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Inferring resting-state connectivity patterns from functional magnetic resonance imaging (fMRI) data is a challenging task for any analytical technique. In this paper, we review a probabilistic independent component analysis (PICA) approach, optimized for the analysis of fMRI data, and discuss the role which this exploratory technique can take in scientific investigations into the structure of these effects. We apply PICA to fMRI data acquired at rest, in order to characterize the spatio-temporal structure of such data, and demonstrate that this is an effective and robust tool for the identification of low-frequency resting-state patterns from data acquired at various different spatial and temporal resolutions. We show that these networks exhibit high spatial consistency across subjects and closely resemble discrete cortical functional networks such as visual cortical areas or sensory–motor cortex.
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Dissertations / Theses on the topic "Robust functional data analysis"

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Willersjö, Nyfelt Emil. "Comparison of the 1st and 2nd order Lee–Carter methods with the robust Hyndman–Ullah method for fitting and forecasting mortality rates." Thesis, Mälardalens högskola, Akademin för utbildning, kultur och kommunikation, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-48383.

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The 1st and 2nd order Lee–Carter methods were compared with the Hyndman–Ullah method in regards to goodness of fit and forecasting ability of mortality rates. Swedish population data was used from the Human Mortality Database. The robust estimation property of the Hyndman–Ullah method was also tested with inclusion of the Spanish flu and a hypothetical scenario of the COVID-19 pandemic. After having presented the three methods and making several comparisons between the methods, it is concluded that the Hyndman–Ullah method is overall superior among the three methods with the implementation of the chosen dataset. Its robust estimation of mortality shocks could also be confirmed.
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Yao, Fang. "Functional data analysis for longitudinal data /." For electronic version search Digital dissertations database. Restricted to UC campuses. Access is free to UC campus dissertations, 2003. http://uclibs.org/PID/11984.

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Hadjipantelis, Pantelis-Zenon. "Functional data analysis in phonetics." Thesis, University of Warwick, 2013. http://wrap.warwick.ac.uk/62527/.

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The study of speech sounds has established itself as a distinct area of research, namely Phonetics. This is because speech production is a complex phenomenon mediated by the interaction of multiple components of a linguistic and non-linguistic nature. To investigate such phenomena, this thesis employs a Functional Data Analysis framework where speech segments are viewed as functions. FDA treats functions as its fundamental unit of analysis; the thesis takes advantage of this, both in conceptual as well as practical terms, achieving theoretical coherence as well as statistical robustness in its insights. The main techniques employed in this work are: Functional principal components analysis, Functional mixed-effects regression models and phylogenetic Gaussian process regression for functional data. As it will be shown, these techniques allow for complementary analyses of linguistic data. The thesis presents a series of novel applications of functional data analysis in Phonetics. Firstly, it investigates the influence linguistic information carries on the speech intonation patterns. It provides these insights through an analysis combining FPCA with a series of mixed effect models, through which meaningful categorical prototypes are built. Secondly, the interplay of phase and amplitude variation in functional phonetic data is investigated. A multivariate mixed effects framework is developed for jointly analysing phase and amplitude information contained in phonetic data. Lastly, the phylogenetic associations between languages within a multi-language phonetic corpus are analysed. Utilizing a small subset of related Romance languages, a phylogenetic investigation of the words' spectrograms (functional objects defined over two continua simultaneously) is conducted to showcase a proof-of-concept experiment allowing the interconnection between FDA and Evolutionary Linguistics.
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Lee, Ho-Jin. "Functional data analysis: classification and regression." Texas A&M University, 2004. http://hdl.handle.net/1969.1/2805.

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Functional data refer to data which consist of observed functions or curves evaluated at a finite subset of some interval. In this dissertation, we discuss statistical analysis, especially classification and regression when data are available in function forms. Due to the nature of functional data, one considers function spaces in presenting such type of data, and each functional observation is viewed as a realization generated by a random mechanism in the spaces. The classification procedure in this dissertation is based on dimension reduction techniques of the spaces. One commonly used method is Functional Principal Component Analysis (Functional PCA) in which eigen decomposition of the covariance function is employed to find the highest variability along which the data have in the function space. The reduced space of functions spanned by a few eigenfunctions are thought of as a space where most of the features of the functional data are contained. We also propose a functional regression model for scalar responses. Infinite dimensionality of the spaces for a predictor causes many problems, and one such problem is that there are infinitely many solutions. The space of the parameter function is restricted to Sobolev-Hilbert spaces and the loss function, so called, e-insensitive loss function is utilized. As a robust technique of function estimation, we present a way to find a function that has at most e deviation from the observed values and at the same time is as smooth as possible.
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Friman, Ola. "Adaptive analysis of functional MRI data /." Linköping : Univ, 2003. http://www.bibl.liu.se/liupubl/disp/disp2003/tek836s.pdf.

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Zoglat, Abdelhak. "Analysis of variance for functional data." Thesis, University of Ottawa (Canada), 1994. http://hdl.handle.net/10393/10136.

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In this dissertation we present an extension to the well known theory of multivariate analysis of variance. In various situations data are continuous stochastic functions of time or space. The speed of pollutants diffusing through a river, the real amplitude of a signal received from a broadcasting satellite, or the hydraulic conductivity rates at a given region are examples of such processes. After the mathematical background we develop tools for analyzing such data. Namely, we develop estimators, tests, and confidence sets for the parameters of interest. We extend these results, obtained under the normality assumption, and show that they are still valid if this assumption is relaxed. Some examples of applications of our techniques are given. We also outline how the latter can apply to random and mixed models for continuous data. In the appendix, we give some programs which we use to compute the distributions of some of our tests statistics.
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Martinenko, Evgeny. "Functional Data Analysis and its application to cancer data." Doctoral diss., University of Central Florida, 2014. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/6323.

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The objective of the current work is to develop novel procedures for the analysis of functional data and apply them for investigation of gender disparity in survival of lung cancer patients. In particular, we use the time-dependent Cox proportional hazards model where the clinical information is incorporated via time-independent covariates, and the current age is modeled using its expansion over wavelet basis functions. We developed computer algorithms and applied them to the data set which is derived from Florida Cancer Data depository data set (all personal information which allows to identify patients was eliminated). We also studied the problem of estimation of a continuous matrix-variate function of low rank. We have constructed an estimator of such function using its basis expansion and subsequent solution of an optimization problem with the Schattennorm penalty. We derive an oracle inequality for the constructed estimator, study its properties via simulations and apply the procedure to analysis of Dynamic Contrast medical imaging data.
Ph.D.
Doctorate
Mathematics
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Mathematics
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Kröger, Viktor. "Classification in Functional Data Analysis : Applications on Motion Data." Thesis, Umeå universitet, Institutionen för matematik och matematisk statistik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-184963.

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Anterior cruciate knee ligament injuries are common and well known, especially amongst athletes.These injuries often require surgeries and long rehabilitation programs, and can lead to functionloss and re-injuries (Marshall et al., 1977). This work aims to explore the possibility of applyingsupervised classification on knee functionality, using different types of models, and testing differentdivisions of classes. The data used is gathered through a performance test, where individualsperform one-leg hops with motion sensors attached to their bodies. The obtained data representsthe position over time, and is considered functional data.With functional data analysis (FDA), a process can be analysed as a continuous function of time,instead of being reduced to finite data points. FDA includes many useful tools, but also somechallenges. A functional observation can for example be differentiated, a handy tool not found inthe multivariate tool-box. The speed, and acceleration, can then be calculated from the obtaineddata. How to define "similarity" is, on the other hand, not as obvious as with points. In this work,an FDA-approach is taken on classifying knee kinematic data, from a long-term follow-up studyon knee ligament injuries.This work studies kernel functional classifiers, and k-nearest neighbours models, and performssignificance tests on the model accuracy, using re-sampling methods. Additionally, depending onhow similarity is defined, the models can distinguish different features of the data. Attempts atutilising more information through incorporation of ensemble-methods, does not exceed the singlemodels it is created from. Further, it is shown that classification on optimised sub-domains, canbe superior to classifiers using the full domain, in terms of predictive power.
Främre korsbandsskador är vanliga och välkända skador, speciellt bland idrottsutövare. Skadornakräver ofta operationer och långa rehabiliteringsprogram, och kan leda till funktionell nedsättningoch återskador (Marshall et al., 1977). Målet med det här arbetet är att utforska möjligheten attklassificera knän utifrån funktionalitet, där utfallet är känt. Detta genom att använda olika typerav modeller, och genom att testa olika indelningar av grupper. Datat som används är insamlatunder ett prestandatest, där personer hoppat på ett ben med rörelsesensorer på kroppen. Deninsamlade datan representerar position över tid, och betraktas som funktionell data.Med funktionell dataanalys (FDA) kan en process analyseras som en kontinuerlig funktion av tid,istället för att reduceras till ett ändligt antal datapunkter. FDA innehåller många användbaraverktyg, men även utmaningar. En funktionell observation kan till exempel deriveras, ett händigtverktyg som inte återfinns i den multivariata verktygslådan. Hastigheten och accelerationen kandå beräknas utifrån den insamlade datan. Hur "likhet" är definierat, å andra sidan, är inte likauppenbart som med punkt-data. I det här arbetet används FDA för att klassificera knärörelsedatafrån en långtidsuppföljningsstudie av främre korsbandsskador.I detta arbete studeras både funktionella kärnklassificerare och k-närmsta grannar-metoder, och ut-för signifikanstest av modellträffsäkerheten genom omprovtagning. Vidare kan modellerna urskiljaolika egenskaper i datat, beroende på hur närhet definieras. Ensemblemetoder används i ett försökatt nyttja mer av informationen, men lyckas inte överträffa någon av de enskilda modellerna somutgör ensemblen. Vidare så visas också att klassificering på optimerade deldefinitionsmängder kange en högre förklaringskraft än klassificerare som använder hela definitionsmängden.
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Anderson, Joseph T. "Geometric Methods for Robust Data Analysis in High Dimension." The Ohio State University, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=osu1488372786126891.

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Alshabani, Ali Khair Saber. "Statistical analysis of human movement functional data." Thesis, University of Nottingham, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.421478.

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Books on the topic "Robust functional data analysis"

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Kosiorowski, Daniel. Statystyczne funkcje głębi w odpornej analizie ekonomicznej: The statistical functions of depth in robust economic analysis. Kraków: Wydawnictwo Uniwersytetu Ekonomicznego w Krakowie, 2012.

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1952-, Silverman B. W., ed. Functional data analysis. New York: Springer, 1997.

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Ramsay, J. O., and B. W. Silverman. Functional Data Analysis. New York, NY: Springer New York, 2005. http://dx.doi.org/10.1007/b98888.

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Ramsay, J. O., and B. W. Silverman. Functional Data Analysis. New York, NY: Springer New York, 1997. http://dx.doi.org/10.1007/978-1-4757-7107-7.

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Srivastava, Anuj, and Eric P. Klassen. Functional and Shape Data Analysis. New York, NY: Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4939-4020-2.

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Morettin, Pedro A., Aluísio Pinheiro, and Brani Vidakovic. Wavelets in Functional Data Analysis. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-59623-5.

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Analysis of variance for functional data. Boca Raon: CRC Press, Taylor & Francis Group, 2014.

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Poldrack, Russell A. Handbook of functional MRI data analysis. Cambridge: Cambridge University Press, 2011.

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Rieder, Helmut, ed. Robust Statistics, Data Analysis, and Computer Intensive Methods. New York, NY: Springer New York, 1996. http://dx.doi.org/10.1007/978-1-4612-2380-1.

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Lind, Rick. Robust flutter margin analysis that incorporates flight data. Edwards, Calif: National Aeronautics and Space Administration, Dryden Flight Research Center, 1998.

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Book chapters on the topic "Robust functional data analysis"

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Fahrmeir, L., C. Gössl, and A. Hennerfeind. "Spatial Smoothing with Robust Priors in Functional MRI." In Studies in Classification, Data Analysis, and Knowledge Organization, 50–57. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-642-55721-7_6.

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Hullait, Harjit, David S. Leslie, Nicos G. Pavlidis, and Steve King. "Robust Functional Regression for Outlier Detection." In Advanced Analytics and Learning on Temporal Data, 3–13. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-39098-3_1.

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Skrepth, Champskud J., and Andreas Uhl. "Robust Hash Functions for Visual Data: An Experimental Comparison." In Pattern Recognition and Image Analysis, 986–93. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-44871-6_114.

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Rivera-García, Diego, Luis Angel García-Escudero, Agustín Mayo-Iscar, and Joaquín Ortega. "Robust Clustering for Time Series Using Spectral Densities and Functional Data Analysis." In Advances in Computational Intelligence, 142–53. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-59147-6_13.

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Pillati, Marilena, and Daniela G. Calò. "Robust Centre Location in Radial Basis Function Networks." In Studies in Classification, Data Analysis, and Knowledge Organization, 121–32. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-642-17111-6_10.

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Gasser, Theo. "Advances in Nonparametric Function Estimation." In Robust Statistics, Data Analysis, and Computer Intensive Methods, 173–84. New York, NY: Springer New York, 1996. http://dx.doi.org/10.1007/978-1-4612-2380-1_11.

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Chaudhuri, Probal, Kjell Doksum, and Alexander Samarov. "Nonparametric Estimation of Global Functionals of Conditional Quantiles." In Robust Statistics, Data Analysis, and Computer Intensive Methods, 63–78. New York, NY: Springer New York, 1996. http://dx.doi.org/10.1007/978-1-4612-2380-1_5.

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Windham, Michael P. "Robust Clustering." In Data Analysis, 385–92. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/978-3-642-58250-9_31.

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Waiczies, Sonia, Christian Prinz, Ludger Starke, Jason M. Millward, Paula Ramos Delgado, Jens Rosenberg, Marc Nazaré, Helmar Waiczies, Andreas Pohlmann, and Thoralf Niendorf. "Functional Imaging Using Fluorine (19F) MR Methods: Basic Concepts." In Methods in Molecular Biology, 279–99. New York, NY: Springer US, 2021. http://dx.doi.org/10.1007/978-1-0716-0978-1_17.

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AbstractKidney-associated pathologies would greatly benefit from noninvasive and robust methods that can objectively quantify changes in renal function. In the past years there has been a growing incentive to develop new applications for fluorine (19F) MRI in biomedical research to study functional changes during disease states. 19F MRI represents an instrumental tool for the quantification of exogenous 19F substances in vivo. One of the major benefits of 19F MRI is that fluorine in its organic form is absent in eukaryotic cells. Therefore, the introduction of exogenous 19F signals in vivo will yield background-free images, thus providing highly selective detection with absolute specificity in vivo. Here we introduce the concept of 19F MRI, describe existing challenges, especially those pertaining to signal sensitivity, and give an overview of preclinical applications to illustrate the utility and applicability of this technique for measuring renal function in animal models.This chapter is based upon work from the COST Action PARENCHIMA, a community-driven network funded by the European Cooperation in Science and Technology (COST) program of the European Union, which aims to improve the reproducibility and standardization of renal MRI biomarkers. This introduction chapter is complemented by two separate chapters describing the experimental procedure and data analysis.
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Filzmoser, Peter, and Karel Hron. "Robust Statistical Analysis." In Compositional Data Analysis, 59–72. Chichester, UK: John Wiley & Sons, Ltd, 2011. http://dx.doi.org/10.1002/9781119976462.ch5.

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Conference papers on the topic "Robust functional data analysis"

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Kadah, Yasser M. "Robust analysis of event-related functional magnetic resonance imaging data using independent component analysis." In Medical Imaging 2002, edited by Anne V. Clough and Chin-Tu Chen. SPIE, 2002. http://dx.doi.org/10.1117/12.463604.

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Ao, Jingqi, Sunanda Mitra, Zheng Liu, and Brian Nutter. "A robust independent component analysis (ICA) model for functional magnetic resonance imaging (fMRI) data." In SPIE Medical Imaging, edited by Ronald M. Summers and Bram van Ginneken. SPIE, 2011. http://dx.doi.org/10.1117/12.878711.

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Youssef, Tamer, Abou-Bakr M. Youssef, Stephen M. LaConte, Xiaoping P. Hu, and Yasser M. Kadah. "Robust ordering of independent components in functional magnetic resonance imaging time series data using canonical correlation analysis." In Medical Imaging 2003, edited by Anne V. Clough and Amir A. Amini. SPIE, 2003. http://dx.doi.org/10.1117/12.480689.

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Venkataraman, P. "Data Clustering Using the Natural Bézier Functions." In ASME 2018 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/detc2018-85103.

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An unorthodox and effective non-iterative procedure for spherical clusters is demonstrated in this paper. It uses natural Bézier functions to determine initial cluster locations using the content of the data. The natural Bernstein-Bézier functions are very robust in representing data through continuous functions in the application of functional data analysis. This paper demonstrates that they are equally robust at resolving data clusters in classification problems. The original data is scaled and segmented. A natural Bézier function is fitted for each segment and the initial clusters are centered at the function extremums that are distinctly located. A self-selection process based on least distance is used to assign the data to these initial cluster centers. A minimum membership count is imposed and nearby clusters are combined to reduce these initial cluster centers based on visual clues. Centroid recalculation and data reassignment can be used for centroid convergence. The method is demonstrated for two dimensional spherical clusters. This approach requires no iteration. Four examples from clustering benchmark datasets are used to showcase the method. The data include different numbers of clusters, different data density for the clusters, as well as different levels of overlap. This method is new and different from other data clustering methods available in the literature. It is better than the standard k-means clustering method since it does not require information on the number of clusters or cluster membership count. The method is non-iterative and does not require random initialization or distance optimization.
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Zheng, Wei, Xiaofeng Zhu, Yonghua Zhu, and Shichao Zhang. "Robust Feature Selection on Incomplete Data." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/443.

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Feature selection is an indispensable preprocessing procedure for high-dimensional data analysis,but previous feature selection methods usually ignore sample diversity (i.e., every sample has individual contribution for the model construction) andhave limited ability to deal with incomplete datasets where a part of training samples have unobserved data. To address these issues, in this paper, we firstly propose a robust feature selectionframework to relieve the influence of outliers, andthen introduce an indicator matrix to avoid unobserved data to take participation in numerical computation of feature selection so that both our proposed feature selection framework and exiting feature selection frameworks are available to conductfeature selection on incomplete data sets. We further propose a new optimization algorithm to optimize the resulting objective function as well asprove our algorithm to converge fast. Experimental results on both real and artificial incompletedata sets demonstrated that our proposed methodoutperformed the feature selection methods undercomparison in terms of clustering performance.
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Song, Zhengyi, and Young Moon. "Data Modeling and Workflow Analysis of Cyber-Manufacturing Systems." In ASME 2020 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/imece2020-23149.

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Abstract Cyber-Manufacturing System (CMS) is a vision for the factory of the future, where physical manufacturing resources and processes are integrated with computational workflows to provide on-demand, adaptive, and scalable manufacturing services. In CMS, functional manufacturing components in a factory floor are digitized and encapsulated in production services; and are accessible by users throughout the network. CMS utilizes data-centric technologies to program manufacturing activities in factory floors. Leveraging advanced technologies, CMS can provide robust solutions to achieve better manufacturing agility, flexibility, scalability, and sustainability than from traditional factories. While data is the main driver of the manufacturing activities in CMS, the lack of (i) a generic data model of explicit representation of the entities and stakeholders in CMS and (ii) workflow definition and analysis for service-orientated functionalities and manufacturing intelligence of CMS is still hindering the implementation of a fully executable CMS. To address such problems, this paper (i) formalizes a data modeling of CMS using Entity-Relationship (E-R) diagram, (ii) presents the definition and analysis of workflows along with data pipelines and Extract/Transform/Load (ETL) processes that automate the entire lifecycle activities in CMS and (iii) deploys the proposed data model and workflows in a Web-based application, and (iv) tests the functionality of this application with an industrial case and eventually validates the proposed data model and workflows.
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Karri, Satyaprakash, John Charonko, and Pavlos Vlachos. "Robust Gradient Estimation Schemes Using Radial Basis Functions." In ASME 2008 Fluids Engineering Division Summer Meeting collocated with the Heat Transfer, Energy Sustainability, and 3rd Energy Nanotechnology Conferences. ASMEDC, 2008. http://dx.doi.org/10.1115/fedsm2008-55151.

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Utilization of Radial Basis Functions (RBFs) for gradient estimation is tested over various noisy flow fields. A novel mathematical formulation which minimizes the energy functional associated with the analytical surface fit for Gaussian (GA) and Generalized Multiquadratic (GMQ) RBFs is presented. Error analysis of the wall gradient estimation was performed at various resolutions, interpolation grid sizes, and noise levels in synthetically generated Poiseuille and Womersley flow fields for RBFs along with standard finite difference schemes. To test the effectiveness of the methods with DPIV (Digital Particle Image Velocimetry) data, the methods were compared using the velocities obtained by processing images generated from DNS data of an open turbulent channel. Random, bias and total error were computed in all cases. In the absence of noise all tested methods perform well, with error contained under 10% at all resolutions. In the presence of noise the RBFs perform robustly with a total error that can be contained under 10–15% even with 10% noise using various interpolation grid sizes, For turbulent flow data, although the total error is approximately 5% for finite difference schemes in the absence of noise, the error can go as high as 150% in the presence of as little as 1% noise. With DPIV processed data the error is 25–40% for TPS and MQ methods optimization of the fitting parameters that minimize the energy functional associated with the analytical surface using RBFs results in robust gradient estimators are obtained that are applicable to steady, unsteady and turbulent flow fields.
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O’Halloran, Bryan M., Robert B. Stone, and Irem Y. Tumer. "Link Between Function-Flow Failure Rates and Failure Modes for Early Design Stage Reliability Analysis." In ASME 2011 International Mechanical Engineering Congress and Exposition. ASMEDC, 2011. http://dx.doi.org/10.1115/imece2011-63110.

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This scope of this paper is to provide an extension to the Function Failure Design Method (FFDM). We first implement a more robust knowledge base using Failure Mode/Mechanism Distributions 1997 (FMD-97). Then failure rates from Nonelectric Parts Reliability Data (NPRD-95) are added to more effectively determine the likelihood that a failure mode will occur. The proposed Functional Failure Rate Design Method (FFRDM) uses functional inputs to effectively offer recommendations to mitigate failure modes that have a high likelihood of occurrence. This work uses a past example where FFDM and Failure Modes and Effects Analysis (FMEA) were compared to show that improvements have been made. A four step process is presented to show how the FFRDM is used during conceptual design.
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Wei, Zhigang, and Kamran Nikbin. "Uncertainty Characterization of Engineering Failure Data." In ASME 2014 Pressure Vessels and Piping Conference. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/pvp2014-28877.

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How to quantitatively measure the uncertainty of engineering failure data is an important but still unsolved task in probabilistic risk analysis. This paper aims to fill the gap first by specifying the requirements for a robust uncertainty measure to meet the criteria. Complexity and uncertainty measurements in computational complexity, classical statistical mechanics and information theory are also reviewed for possible inspiration. In this paper, a new groundbreaking parameter, which is related to reliability or survival function, is selected to characterize the uncertainty of engineering failure data with given probabilistic distributions. The uncertainty formulae based on the Shannon entropy and the new uncertainty parameter for various distribution functions are also provided. Finally, several examples are given to demonstrate the applicability of the new uncertainty measure in durability and reliability analyses.
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Dopker, B., P. Murray, and F. N. Choong. "An Object Oriented Data Base and Application Management System for Integrated, Interdisciplinary Mechanical System Simulation." In ASME 1989 Design Technical Conferences. American Society of Mechanical Engineers, 1989. http://dx.doi.org/10.1115/detc1989-0109.

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Abstract An object oriented data base and application management system for integrated, interdisciplinary mechanical system simulation is developed and implemented; with emphasis on flexible body dynamic simulation, stress history calculation, and fatigue life prediction. The system consists of (1) functional objects that perform a particular analysis task, (2) data objects that store data in the data base, (3) data and object management tools that manage the communication between the different parts of the system, and (4) network communication tools that communicate between different hardware platforms. Functional objects are developed and implemented for the different analysis needs of the system. Such objects (1) hide implementation details, (2) provide easy extendability, and (3) simplify use of the system. Data are organized in terms of objects, object hierarchies, and object attributes. This provides quasi data inheritance and information hiding, which allows for a data base schema that is both robust and extendable.
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Reports on the topic "Robust functional data analysis"

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Hoaglin, David C., and Frederick Mosteller. Robust/Resistant Techniques of Data Analysis. Fort Belvoir, VA: Defense Technical Information Center, October 1985. http://dx.doi.org/10.21236/ada163972.

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Ravichandran, A., and K. Kant. Analysis and Synthesis of Robust Data Structures. Fort Belvoir, VA: Defense Technical Information Center, August 1990. http://dx.doi.org/10.21236/ada224568.

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Parzen, Emanuel. Multi-Sample Functional Statistical Data Analysis. Fort Belvoir, VA: Defense Technical Information Center, May 1989. http://dx.doi.org/10.21236/ada210992.

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Parzen, Emanuel, Scott D. Grimshaw, and William P. Alexander. Functional Statistical Data Analysis and Modeling. Fort Belvoir, VA: Defense Technical Information Center, February 1990. http://dx.doi.org/10.21236/ada219387.

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Liu, Baiyan, Bing Yan, Hailin Jiang, Xuewei Zhao, Luyao Wang, Tie Li, and Fuchun Wang. The effectiveness of herbal acupoint application for functional diarrhea Protocol for a meta-analysis and data mining. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, July 2021. http://dx.doi.org/10.37766/inplasy2021.7.0094.

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Patel, Reena. Complex network analysis for early detection of failure mechanisms in resilient bio-structures. Engineer Research and Development Center (U.S.), June 2021. http://dx.doi.org/10.21079/11681/41042.

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Bio-structures owe their remarkable mechanical properties to their hierarchical geometrical arrangement as well as heterogeneous material properties. This dissertation presents an integrated, interdisciplinary approach that employs computational mechanics combined with flow network analysis to gain fundamental insights into the failure mechanisms of high performance, light-weight, structured composites by examining the stress flow patterns formed in the nascent stages of loading for the rostrum of the paddlefish. The data required for the flow network analysis was generated from the finite element analysis of the rostrum. The flow network was weighted based on the parameter of interest, which is stress in the current study. The changing kinematics of the structural system was provided as input to the algorithm that computes the minimum-cut of the flow network. The proposed approach was verified using two classical problems three- and four-point bending of a simply-supported concrete beam. The current study also addresses the methodology used to prepare data in an appropriate format for a seamless transition from finite element binary database files to the abstract mathematical domain needed for the network flow analysis. A robust, platform-independent procedure was developed that efficiently handles the large datasets produced by the finite element simulations. Results from computational mechanics using Abaqus and complex network analysis are presented.
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Dutra, Lauren M., Matthew C. Farrelly, Brian Bradfield, Jamie Ridenhour, and Jamie Guillory. Modeling the Probability of Fraud in Social Media in a National Cannabis Survey. RTI Press, September 2021. http://dx.doi.org/10.3768/rtipress.2021.mr.0046.2109.

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Cannabis legalization has spread rapidly in the United States. Although national surveys provide robust information on the prevalence of cannabis use, cannabis disorders, and related outcomes, information on knowledge, attitudes, and beliefs (KABs) about cannabis is lacking. To inform the relationship between cannabis legalization and cannabis-related KABs, RTI International launched the National Cannabis Climate Survey (NCCS) in 2016. The survey sampled US residents 18 years or older via mail (n = 2,102), mail-to-web (n = 1,046), and two social media data collections (n = 11,957). This report outlines two techniques that we used to problem-solve several challenges with the resulting data: (1) developing a model for detecting fraudulent cases in social media completes after standard fraud detection measures were insufficient and (2) designing a weighting scheme to pool multiple probability and nonprobability samples. We also describe our approach for validating the pooled dataset. The fraud prevention and detection processes, predictive model of fraud, and the methods used to weight the probability and nonprobability samples can be applied to current and future complex data collections and analysis of existing datasets.
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Downes, Jane, ed. Chalcolithic and Bronze Age Scotland: ScARF Panel Report. Society for Antiquaries of Scotland, September 2012. http://dx.doi.org/10.9750/scarf.09.2012.184.

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The main recommendations of the panel report can be summarised under five key headings:  Building the Scottish Bronze Age: Narratives should be developed to account for the regional and chronological trends and diversity within Scotland at this time. A chronology Bronze Age Scotland: ScARF Panel Report iv based upon Scottish as well as external evidence, combining absolute dating (and the statistical modelling thereof) with re-examined typologies based on a variety of sources – material cultural, funerary, settlement, and environmental evidence – is required to construct a robust and up to date framework for advancing research.  Bronze Age people: How society was structured and demographic questions need to be imaginatively addressed including the degree of mobility (both short and long-distance communication), hierarchy, and the nature of the ‘family’ and the ‘individual’. A range of data and methodologies need to be employed in answering these questions, including harnessing experimental archaeology systematically to inform archaeologists of the practicalities of daily life, work and craft practices.  Environmental evidence and climate impact: The opportunity to study the effects of climatic and environmental change on past society is an important feature of this period, as both palaeoenvironmental and archaeological data can be of suitable chronological and spatial resolution to be compared. Palaeoenvironmental work should be more effectively integrated within Bronze Age research, and inter-disciplinary approaches promoted at all stages of research and project design. This should be a two-way process, with environmental science contributing to interpretation of prehistoric societies, and in turn, the value of archaeological data to broader palaeoenvironmental debates emphasised. Through effective collaboration questions such as the nature of settlement and land-use and how people coped with environmental and climate change can be addressed.  Artefacts in Context: The Scottish Chalcolithic and Bronze Age provide good evidence for resource exploitation and the use, manufacture and development of technology, with particularly rich evidence for manufacture. Research into these topics requires the application of innovative approaches in combination. This could include biographical approaches to artefacts or places, ethnographic perspectives, and scientific analysis of artefact composition. In order to achieve this there is a need for data collation, robust and sustainable databases and a review of the categories of data.  Wider Worlds: Research into the Scottish Bronze Age has a considerable amount to offer other European pasts, with a rich archaeological data set that includes intact settlement deposits, burials and metalwork of every stage of development that has been the subject of a long history of study. Research should operate over different scales of analysis, tracing connections and developments from the local and regional, to the international context. In this way, Scottish Bronze Age studies can contribute to broader questions relating both to the Bronze Age and to human society in general.
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Jones, Emily, Beatriz Kira, Anna Sands, and Danilo B. Garrido Alves. The UK and Digital Trade: Which way forward? Blavatnik School of Government, February 2021. http://dx.doi.org/10.35489/bsg-wp-2021/038.

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The internet and digital technologies are upending global trade. Industries and supply chains are being transformed, and the movement of data across borders is now central to the operation of the global economy. Provisions in trade agreements address many aspects of the digital economy – from cross-border data flows, to the protection of citizens’ personal data, and the regulation of the internet and new technologies like artificial intelligence and algorithmic decision-making. The UK government has identified digital trade as a priority in its Global Britain strategy and one of the main sources of economic growth to recover from the pandemic. It wants the UK to play a leading role in setting the international standards and regulations that govern the global digital economy. The regulation of digital trade is a fast-evolving and contentious issue, and the US, European Union (EU), and China have adopted different approaches. Now that the UK has left the EU, it will need to navigate across multiple and often conflicting digital realms. The UK needs to decide which policy objectives it will prioritise, how to regulate the digital economy domestically, and how best to achieve its priorities when negotiating international trade agreements. There is an urgent need to develop a robust, evidence-based approach to the UK’s digital trade strategy that takes into account the perspectives of businesses, workers, and citizens, as well as the approaches of other countries in the global economy. This working paper aims to inform UK policy debates by assessing the state of play in digital trade globally. The authors present a detailed analysis of five policy areas that are central to discussions on digital trade for the UK: cross-border data flows and privacy; internet access and content regulation; intellectual property and innovation; e-commerce (including trade facilitation and consumer protection); and taxation (customs duties on e-commerce and digital services taxes). In each of these areas the authors compare and contrast the approaches taken by the US, EU and China, discuss the public policy implications, and examine the choices facing the UK.
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Berkowitz, Jacob, Nathan Beane, Kevin Philley, Nia Hurst, and Jacob Jung. An assessment of long-term, multipurpose ecosystem functions and engineering benefits derived from historical dredged sediment beneficial use projects. Engineer Research and Development Center (U.S.), August 2021. http://dx.doi.org/10.21079/11681/41382.

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The beneficial use of dredged materials improves environmental outcomes while maximizing navigation benefits and minimizing costs, in accordance with the principles of the Engineering With Nature® (EWN) initiative. Yet, few studies document the long-term benefits of innovative dredged material management strategies or conduct comprehensive life-cycle analysis because of a combination of (1) short monitoring time frames and (2) the paucity of constructed projects that have reached ecological maturity. In response, we conducted an ecological functional and engineering benefit assessment of six historic (>40 years old) dredged material–supported habitat improvement projects where initial postconstruction beneficial use monitoring data was available. Conditions at natural reference locations were also documented to facilitate a comparison between natural and engineered landscape features. Results indicate the projects examined provide valuable habitat for a variety of species in addition to yielding a number of engineering (for example, shoreline protection) and other (for example, carbon storage) benefits. Our findings also suggest establishment of ecological success criteria should not overemphasize replicating reference conditions but remain focused on achieving specific ecological functions (that is, habitat and biogeochemical cycling) and engineering benefits (that is, storm surge reduction, navigation channel maintenance) achievable through project design and operational management.
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