Independent component analysis applied to long bunch beams in the Los Alamos Proton Storage Ring
Kolski, Jeffrey S.; Macek, Robert J.; McCrady, Rodney C.; Pang, Xiaoying
2012-11-01
Independent component analysis (ICA) is a powerful blind source separation (BSS) method. Compared to the typical BSS method, principal component analysis, ICA is more robust to noise, coupling, and nonlinearity. The conventional ICA application to turn-by-turn position data from multiple beam position monitors (BPMs) yields information about cross-BPM correlations. With this scheme, multi-BPM ICA has been used to measure the transverse betatron phase and amplitude functions, dispersion function, linear coupling, sextupole strength, and nonlinear beam dynamics. We apply ICA in a new way to slices along the bunch revealing correlations of particle motion within the beam bunch. We digitize beam signals of the long bunch at the Los Alamos Proton Storage Ring with a single device (BPM or fast current monitor) for an entire injection-extraction cycle. ICA of the digitized beam signals results in source signals, which we identify to describe varying betatron motion along the bunch, locations of transverse resonances along the bunch, measurement noise, characteristic frequencies of the digitizing oscilloscopes, and longitudinal beam structure.
Independent component analysis applied to long bunch beams in the Los Alamos Proton Storage Ring
Directory of Open Access Journals (Sweden)
Jeffrey S. Kolski
2012-11-01
Full Text Available Independent component analysis (ICA is a powerful blind source separation (BSS method. Compared to the typical BSS method, principal component analysis, ICA is more robust to noise, coupling, and nonlinearity. The conventional ICA application to turn-by-turn position data from multiple beam position monitors (BPMs yields information about cross-BPM correlations. With this scheme, multi-BPM ICA has been used to measure the transverse betatron phase and amplitude functions, dispersion function, linear coupling, sextupole strength, and nonlinear beam dynamics. We apply ICA in a new way to slices along the bunch revealing correlations of particle motion within the beam bunch. We digitize beam signals of the long bunch at the Los Alamos Proton Storage Ring with a single device (BPM or fast current monitor for an entire injection-extraction cycle. ICA of the digitized beam signals results in source signals, which we identify to describe varying betatron motion along the bunch, locations of transverse resonances along the bunch, measurement noise, characteristic frequencies of the digitizing oscilloscopes, and longitudinal beam structure.
Dai, Wensheng
2014-01-01
Sales forecasting is one of the most important issues in managing information technology (IT) chain store sales since an IT chain store has many branches. Integrating feature extraction method and prediction tool, such as support vector regression (SVR), is a useful method for constructing an effective sales forecasting scheme. Independent component analysis (ICA) is a novel feature extraction technique and has been widely applied to deal with various forecasting problems. But, up to now, only the basic ICA method (i.e., temporal ICA model) was applied to sale forecasting problem. In this paper, we utilize three different ICA methods including spatial ICA (sICA), temporal ICA (tICA), and spatiotemporal ICA (stICA) to extract features from the sales data and compare their performance in sales forecasting of IT chain store. Experimental results from a real sales data show that the sales forecasting scheme by integrating stICA and SVR outperforms the comparison models in terms of forecasting error. The stICA is a promising tool for extracting effective features from branch sales data and the extracted features can improve the prediction performance of SVR for sales forecasting. PMID:25165740
Dai, Wensheng; Wu, Jui-Yu; Lu, Chi-Jie
2014-01-01
Sales forecasting is one of the most important issues in managing information technology (IT) chain store sales since an IT chain store has many branches. Integrating feature extraction method and prediction tool, such as support vector regression (SVR), is a useful method for constructing an effective sales forecasting scheme. Independent component analysis (ICA) is a novel feature extraction technique and has been widely applied to deal with various forecasting problems. But, up to now, only the basic ICA method (i.e., temporal ICA model) was applied to sale forecasting problem. In this paper, we utilize three different ICA methods including spatial ICA (sICA), temporal ICA (tICA), and spatiotemporal ICA (stICA) to extract features from the sales data and compare their performance in sales forecasting of IT chain store. Experimental results from a real sales data show that the sales forecasting scheme by integrating stICA and SVR outperforms the comparison models in terms of forecasting error. The stICA is a promising tool for extracting effective features from branch sales data and the extracted features can improve the prediction performance of SVR for sales forecasting.
Directory of Open Access Journals (Sweden)
Wensheng Dai
2014-01-01
Full Text Available Sales forecasting is one of the most important issues in managing information technology (IT chain store sales since an IT chain store has many branches. Integrating feature extraction method and prediction tool, such as support vector regression (SVR, is a useful method for constructing an effective sales forecasting scheme. Independent component analysis (ICA is a novel feature extraction technique and has been widely applied to deal with various forecasting problems. But, up to now, only the basic ICA method (i.e., temporal ICA model was applied to sale forecasting problem. In this paper, we utilize three different ICA methods including spatial ICA (sICA, temporal ICA (tICA, and spatiotemporal ICA (stICA to extract features from the sales data and compare their performance in sales forecasting of IT chain store. Experimental results from a real sales data show that the sales forecasting scheme by integrating stICA and SVR outperforms the comparison models in terms of forecasting error. The stICA is a promising tool for extracting effective features from branch sales data and the extracted features can improve the prediction performance of SVR for sales forecasting.
Directory of Open Access Journals (Sweden)
Khuat Thanh Tung
2016-11-01
Full Text Available Optical Character Recognition plays an important role in data storage and data mining when the number of documents stored as images is increasing. It is expected to find the ways to convert images of typewritten or printed text into machine-encoded text effectively in order to support for the process of information handling effectively. In this paper, therefore, the techniques which are being used to convert image into editable text in the computer such as principal component analysis, multilayer perceptron network, self-organizing maps, and improved multilayer neural network using principal component analysis are experimented. The obtained results indicated the effectiveness and feasibility of the proposed methods.
Applying independent component analysis to clinical fMRI at 7 T
Directory of Open Access Journals (Sweden)
Simon Daniel Robinson
2013-09-01
Full Text Available Increased BOLD sensitivity at 7 T offers the possibility to increase the reliability of fMRI, but ultra-high field is also associated with an increase in artifacts related to head motion, Nyquist ghosting and parallel imaging reconstruction errors. In this study, the ability of Independent Component Analysis (ICA to separate activation from these artifacts was assessed in a 7 T study of neurological patients performing chin and hand motor tasks. ICA was able to isolate primary motor activation with negligible contamination by motion effects. The results of General Linear Model (GLM analysis of these data were, in contrast, heavily contaminated by motion. Secondary motor areas, basal ganglia and thalamus involvement were apparent in ICA results, but there was low capability to isolate activation in the same brain regions in the GLM analysis, indicating that ICA was more sensitive as well as more specific. A method was developed to simplify the assessment of the large number of independent components. Task-related activation components could be automatically identified via intuitive and effective features. These findings demonstrate that ICA is a practical and sensitive analysis approach in high field fMRI studies, particularly where motion is evoked. Promising applications of ICA in clinical fMRI include presurgical planning and the study of pathologies affecting subcortical brain areas.
Creative design-by-analysis solutions applied to high-temperature components
International Nuclear Information System (INIS)
Dhalla, A.K.
1993-01-01
Elevated temperature design has evolved over the last two decades from design-by-formula philosophy of the ASME Boiler and Pressure Vessel Code, Sections I and VIII (Division 1), to the design-by-analysis philosophy of Section III, Code Case N-47. The benefits of design-by-analysis procedures, which were developed under a US-DOE-sponsored high-temperature structural design (HTSD) program, are illustrated in the paper through five design examples taken from two U.S. liquid metal reactor (LMR) plants. Emphasis in the paper is placed upon the use of a detailed, nonlinear finite element analysis method to understand the structural response and to suggest design optimization so as to comply with Code Case N-47 criteria. A detailed analysis is cost-effective, if selectively used, to qualify an LMR component for service when long-lead-time structural forgings, procured based upon simplified preliminary analysis, do not meet the design criteria, or the operational loads are increased after the components have been fabricated. In the future, the overall costs of a detailed analysis will be reduced even further with the availability of finite element software used on workstations or PCs
International Nuclear Information System (INIS)
Stevenson, J.D.
1989-01-01
This paper presents a review of the current status of simplified methods of seismic design and analysis applicable to nuclear facility structures, systems and components important to public health and safety. In particular, the International Atomic Energy Agency, IAEA TEC DOC 348 procedure for structures and the Bounding Spectra Concept for equipment as being developed by Seismic Qualification Utility Group and the Electric Power Research Institute will be discussed in some detail
Applying independent component analysis to clinical fMRI at 7 T
Simon Daniel Robinson; Veronika eSchöpf; Pedro eCardoso; Alexander eGeissler; Alexander eGeissler; Florian Ph.S Fischmeister; Florian Ph.S Fischmeister; Moritz eWurnig; Moritz eWurnig; Siegfried eTrattnig; Roland eBeisteiner; Roland eBeisteiner
2013-01-01
Increased BOLD sensitivity at 7 T offers the possibility to increase the reliability of fMRI, but ultra-high field is also associated with an increase in artifacts related to head motion, Nyquist ghosting and parallel imaging reconstruction errors. In this study, the ability of Independent Component Analysis (ICA) to separate activation from these artifacts was assessed in a 7 T study of neurological patients performing chin and hand motor tasks. ICA was able to isolate primary motor activati...
Applying independent component analysis to clinical FMRI at 7 t
Robinson, Simon Daniel; Schöpf, Veronika; Cardoso, Pedro; Geissler, Alexander; Fischmeister, Florian P S; Wurnig, Moritz; Trattnig, Siegfried; Beisteiner, Roland
2013-01-01
Increased BOLD sensitivity at 7 T offers the possibility to increase the reliability of fMRI, but ultra-high field is also associated with an increase in artifacts related to head motion, Nyquist ghosting, and parallel imaging reconstruction errors. In this study, the ability of independent component analysis (ICA) to separate activation from these artifacts was assessed in a 7 T study of neurological patients performing chin and hand motor tasks. ICA was able to isolate primary motor activat...
International Nuclear Information System (INIS)
Koch, C.D.; Pirkle, F.L.; Schmidt, J.S.
1981-01-01
A Principal Components Analysis (PCA) has been written to aid in the interpretation of multivariate aerial radiometric data collected by the US Department of Energy (DOE) under the National Uranium Resource Evaluation (NURE) program. The variations exhibited by these data have been reduced and classified into a number of linear combinations by using the PCA program. The PCA program then generates histograms and outlier maps of the individual variates. Black and white plots can be made on a Calcomp plotter by the application of follow-up programs. All programs referred to in this guide were written for a DEC-10. From this analysis a geologist may begin to interpret the data structure. Insight into geological processes underlying the data may be obtained
International Nuclear Information System (INIS)
Yang Hong-Xing; Fu Hong-Bo; Wang Hua-Dong; Jia Jun-Wei; Dong Feng-Zhong; Sigrist, Markus W
2016-01-01
Laser-induced breakdown spectroscopy (LIBS) is a versatile tool for both qualitative and quantitative analysis. In this paper, LIBS combined with principal component analysis (PCA) and support vector machine (SVM) is applied to rock analysis. Fourteen emission lines including Fe, Mg, Ca, Al, Si, and Ti are selected as analysis lines. A good accuracy (91.38% for the real rock) is achieved by using SVM to analyze the spectroscopic peak area data which are processed by PCA. It can not only reduce the noise and dimensionality which contributes to improving the efficiency of the program, but also solve the problem of linear inseparability by combining PCA and SVM. By this method, the ability of LIBS to classify rock is validated. (paper)
González-Vidal, Juan José; Pérez-Pueyo, Rosanna; Soneira, María José; Ruiz-Moreno, Sergio
2015-03-01
A new method has been developed to automatically identify Raman spectra, whether they correspond to single- or multicomponent spectra. The method requires no user input or judgment. There are thus no parameters to be tweaked. Furthermore, it provides a reliability factor on the resulting identification, with the aim of becoming a useful support tool for the analyst in the decision-making process. The method relies on the multivariate techniques of principal component analysis (PCA) and independent component analysis (ICA), and on some metrics. It has been developed for the application of automated spectral analysis, where the analyzed spectrum is provided by a spectrometer that has no previous knowledge of the analyzed sample, meaning that the number of components in the sample is unknown. We describe the details of this method and demonstrate its efficiency by identifying both simulated spectra and real spectra. The method has been applied to artistic pigment identification. The reliable and consistent results that were obtained make the methodology a helpful tool suitable for the identification of pigments in artwork or in paint in general.
International Nuclear Information System (INIS)
Bryan, B.J.; Flanders, H.E. Jr.
1992-01-01
Seismic qualification of Class I nuclear components is accomplished using a variety of analytical methods. This paper compares the results of time history dynamic analyses of a heat exchanger support structure using response spectrum and time history direct integration analysis methods. Dynamic analysis is performed on the detailed component models using the two methods. A nonlinear elastic model is used for both the response spectrum and direct integration methods. A nonlinear model which includes friction and nonlinear springs, is analyzed using time history input by direct integration. The loads from the three cases are compared
Young, Cole; Reinkensmeyer, David J
2014-08-01
Athletes rely on subjective assessment of complex movements from coaches and judges to improve their motor skills. In some sports, such as diving, snowboard half pipe, gymnastics, and figure skating, subjective scoring forms the basis for competition. It is currently unclear whether this scoring process can be mathematically modeled; doing so could provide insight into what motor skill is. Principal components analysis has been proposed as a motion analysis method for identifying fundamental units of coordination. We used PCA to analyze movement quality of dives taken from USA Diving's 2009 World Team Selection Camp, first identifying eigenpostures associated with dives, and then using the eigenpostures and their temporal weighting coefficients, as well as elements commonly assumed to affect scoring - gross body path, splash area, and board tip motion - to identify eigendives. Within this eigendive space we predicted actual judges' scores using linear regression. This technique rated dives with accuracy comparable to the human judges. The temporal weighting of the eigenpostures, body center path, splash area, and board tip motion affected the score, but not the eigenpostures themselves. These results illustrate that (1) subjective scoring in a competitive diving event can be mathematically modeled; (2) the elements commonly assumed to affect dive scoring actually do affect scoring (3) skill in elite diving is more associated with the gross body path and the effect of the movement on the board and water than the units of coordination that PCA extracts, which might reflect the high level of technique these divers had achieved. We also illustrate how eigendives can be used to produce dive animations that an observer can distort continuously from poor to excellent, which is a novel approach to performance visualization. Copyright © 2014 Elsevier B.V. All rights reserved.
International Nuclear Information System (INIS)
Suarez-Pena, B.; Asensio-Lozano, J.
2009-01-01
In this work the study focused on the possibility of use of new aluminium alloys with optimized microstructures that ensure the mechanical properties requested for cast components made by high pressure die casting. The objective was to check the possibility of manufacture of structurally sound eco-steps for escalators with reduced structural integrity. The former arises as a result of a new redesign of the traditional steps aiming at a significant weight reduction. The experimental results show that it is feasible to cut the use of materials during processing and therefore to reduce the impact of the components during its lifetime, whilst the performance and safety standards are kept identical or even improved. (Author) 17 refs
Papageorgiou, Nikolaos S
2009-01-01
Offers an examination of important theoretical methods and procedures in applied analysis. This book details the important theoretical trends in nonlinear analysis and applications to different fields. It is suitable for those working on nonlinear analysis.
Towards Cognitive Component Analysis
DEFF Research Database (Denmark)
Hansen, Lars Kai; Ahrendt, Peter; Larsen, Jan
2005-01-01
Cognitive component analysis (COCA) is here defined as the process of unsupervised grouping of data such that the ensuing group structure is well-aligned with that resulting from human cognitive activity. We have earlier demonstrated that independent components analysis is relevant for representing...
Multiscale principal component analysis
International Nuclear Information System (INIS)
Akinduko, A A; Gorban, A N
2014-01-01
Principal component analysis (PCA) is an important tool in exploring data. The conventional approach to PCA leads to a solution which favours the structures with large variances. This is sensitive to outliers and could obfuscate interesting underlying structures. One of the equivalent definitions of PCA is that it seeks the subspaces that maximize the sum of squared pairwise distances between data projections. This definition opens up more flexibility in the analysis of principal components which is useful in enhancing PCA. In this paper we introduce scales into PCA by maximizing only the sum of pairwise distances between projections for pairs of datapoints with distances within a chosen interval of values [l,u]. The resulting principal component decompositions in Multiscale PCA depend on point (l,u) on the plane and for each point we define projectors onto principal components. Cluster analysis of these projectors reveals the structures in the data at various scales. Each structure is described by the eigenvectors at the medoid point of the cluster which represent the structure. We also use the distortion of projections as a criterion for choosing an appropriate scale especially for data with outliers. This method was tested on both artificial distribution of data and real data. For data with multiscale structures, the method was able to reveal the different structures of the data and also to reduce the effect of outliers in the principal component analysis
Fitzmaurice, Garrett M; Ware, James H
2012-01-01
Praise for the First Edition "". . . [this book] should be on the shelf of everyone interested in . . . longitudinal data analysis.""-Journal of the American Statistical Association Features newly developed topics and applications of the analysis of longitudinal data Applied Longitudinal Analysis, Second Edition presents modern methods for analyzing data from longitudinal studies and now features the latest state-of-the-art techniques. The book emphasizes practical, rather than theoretical, aspects of methods for the analysis of diverse types of lo
Directory of Open Access Journals (Sweden)
Maciej Leśkiewicz
2016-03-01
Full Text Available Quality of two linear methods (PCA and LDA applied to reduce dimensionality of feature analysis is compared and efficiency of their algorithms in classification of the selected biological materials according to their excitation-emission fluorescence matrices is examined. It has been found that LDA method reduces the dimensions (or a number of significant variables more effectively than PCA method. A relatively good discrimination within the examined biological material has been obtained with the use of LDA algorithm.[b]Keywords[/b]: Feature Analysis, Fluorescence Spectroscopy, Biological Material Classification
DEFF Research Database (Denmark)
Feng, Ling
2008-01-01
This dissertation concerns the investigation of the consistency of statistical regularities in a signaling ecology and human cognition, while inferring appropriate actions for a speech-based perceptual task. It is based on unsupervised Independent Component Analysis providing a rich spectrum...... of audio contexts along with pattern recognition methods to map components to known contexts. It also involves looking for the right representations for auditory inputs, i.e. the data analytic processing pipelines invoked by human brains. The main ideas refer to Cognitive Component Analysis, defined...... as the process of unsupervised grouping of generic data such that the ensuing group structure is well-aligned with that resulting from human cognitive activity. Its hypothesis runs ecologically: features which are essentially independent in a context defined ensemble, can be efficiently coded as sparse...
Euler principal component analysis
Liwicki, Stephan; Tzimiropoulos, Georgios; Zafeiriou, Stefanos; Pantic, Maja
Principal Component Analysis (PCA) is perhaps the most prominent learning tool for dimensionality reduction in pattern recognition and computer vision. However, the ℓ 2-norm employed by standard PCA is not robust to outliers. In this paper, we propose a kernel PCA method for fast and robust PCA,
Bayesian Independent Component Analysis
DEFF Research Database (Denmark)
Winther, Ole; Petersen, Kaare Brandt
2007-01-01
In this paper we present an empirical Bayesian framework for independent component analysis. The framework provides estimates of the sources, the mixing matrix and the noise parameters, and is flexible with respect to choice of source prior and the number of sources and sensors. Inside the engine...
Szapacs, Cindy
2006-01-01
Teaching strategies that work for typically developing children often do not work for those diagnosed with an autism spectrum disorder. However, teaching strategies that work for children with autism do work for typically developing children. In this article, the author explains how the principles and concepts of Applied Behavior Analysis can be…
Directory of Open Access Journals (Sweden)
Elfrida Yanti
2011-05-01
Full Text Available Standard cost is generally used by manufacturing business, which direct material, labor, and factory overhead are cleared allocated. On real estate business in this case PT Subur Agung use standard cost based on three costs, raw land, land improvement and interest expense categories instead of direct material, direct labor and overhead. Developer use these cost to predict the project cost and estimate the pre-selling price, in accordance with the cost estimation classification matrix, the variance range is in the expected accuracy rate by testing the variance percentage between standard cost and actual cost. The additional similar projects in PT Subur Agung also follow the same scope. All these evidences have proved the appropriate using standard costing in land cost component of real estate development activities but how it applies this article will analyze in this particular project with using descriptive and exploratory method. The analysis started by knowing the conceptual situation of PT Subur Agung and the data was presented in tables and calculation with detail explanation.
Applied multivariate statistical analysis
Härdle, Wolfgang Karl
2015-01-01
Focusing on high-dimensional applications, this 4th edition presents the tools and concepts used in multivariate data analysis in a style that is also accessible for non-mathematicians and practitioners. It surveys the basic principles and emphasizes both exploratory and inferential statistics; a new chapter on Variable Selection (Lasso, SCAD and Elastic Net) has also been added. All chapters include practical exercises that highlight applications in different multivariate data analysis fields: in quantitative financial studies, where the joint dynamics of assets are observed; in medicine, where recorded observations of subjects in different locations form the basis for reliable diagnoses and medication; and in quantitative marketing, where consumers’ preferences are collected in order to construct models of consumer behavior. All of these examples involve high to ultra-high dimensions and represent a number of major fields in big data analysis. The fourth edition of this book on Applied Multivariate ...
Model reduction by weighted Component Cost Analysis
Kim, Jae H.; Skelton, Robert E.
1990-01-01
Component Cost Analysis considers any given system driven by a white noise process as an interconnection of different components, and assigns a metric called 'component cost' to each component. These component costs measure the contribution of each component to a predefined quadratic cost function. A reduced-order model of the given system may be obtained by deleting those components that have the smallest component costs. The theory of Component Cost Analysis is extended to include finite-bandwidth colored noises. The results also apply when actuators have dynamics of their own. Closed-form analytical expressions of component costs are also derived for a mechanical system described by its modal data. This is very useful to compute the modal costs of very high order systems. A numerical example for MINIMAST system is presented.
DEFF Research Database (Denmark)
Jensen, Thomas; Duun, Sune Bro; Larsen, Jan
2009-01-01
We examine various independent component analysis (ICA) digital signal processing algorithms for estimating the arterial oxygen saturation (SpO2) as measured by a reflective pulse oximeter. The ICA algorithms examined are FastICA, Maximum Likelihood ICA (ICAML), Molgedey and Schuster ICA (ICAMS......), and Mean Field ICA (ICAMF). The signal processing includes pre-processing bandpass filtering to eliminate noise, and post-processing by calculating the SpO2. The algorithms are compared to the commercial state-of-the-art algorithm Discrete Saturation Transform (DST) by Masimo Corporation...
Marami Milani, Mohammad Reza; Hense, Andreas; Rahmani, Elham; Ploeger, Angelika
2016-07-23
This study focuses on multiple linear regression models relating six climate indices (temperature humidity THI, environmental stress ESI, equivalent temperature index ETI, heat load HLI, modified HLI (HLI new ), and respiratory rate predictor RRP) with three main components of cow's milk (yield, fat, and protein) for cows in Iran. The least absolute shrinkage selection operator (LASSO) and the Akaike information criterion (AIC) techniques are applied to select the best model for milk predictands with the smallest number of climate predictors. Uncertainty estimation is employed by applying bootstrapping through resampling. Cross validation is used to avoid over-fitting. Climatic parameters are calculated from the NASA-MERRA global atmospheric reanalysis. Milk data for the months from April to September, 2002 to 2010 are used. The best linear regression models are found in spring between milk yield as the predictand and THI, ESI, ETI, HLI, and RRP as predictors with p -value < 0.001 and R ² (0.50, 0.49) respectively. In summer, milk yield with independent variables of THI, ETI, and ESI show the highest relation ( p -value < 0.001) with R ² (0.69). For fat and protein the results are only marginal. This method is suggested for the impact studies of climate variability/change on agriculture and food science fields when short-time series or data with large uncertainty are available.
Independent component analysis: recent advances
Hyv?rinen, Aapo
2013-01-01
Independent component analysis is a probabilistic method for learning a linear transform of a random vector. The goal is to find components that are maximally independent and non-Gaussian (non-normal). Its fundamental difference to classical multi-variate statistical methods is in the assumption of non-Gaussianity, which enables the identification of original, underlying components, in contrast to classical methods. The basic theory of independent component analysis was mainly developed in th...
Structural analysis of nuclear components
International Nuclear Information System (INIS)
Ikonen, K.; Hyppoenen, P.; Mikkola, T.; Noro, H.; Raiko, H.; Salminen, P.; Talja, H.
1983-05-01
THe report describes the activities accomplished in the project 'Structural Analysis Project of Nuclear Power Plant Components' during the years 1974-1982 in the Nuclear Engineering Laboratory at the Technical Research Centre of Finland. The objective of the project has been to develop Finnish expertise in structural mechanics related to nuclear engineering. The report describes the starting point of the research work, the organization of the project and the research activities on various subareas. Further the work done with computer codes is described and also the problems which the developed expertise has been applied to. Finally, the diploma works, publications and work reports, which are mainly in Finnish, are listed to give a view of the content of the project. (author)
Oden, J Tinsley
2010-01-01
The textbook is designed to drive a crash course for beginning graduate students majoring in something besides mathematics, introducing mathematical foundations that lead to classical results in functional analysis. More specifically, Oden and Demkowicz want to prepare students to learn the variational theory of partial differential equations, distributions, and Sobolev spaces and numerical analysis with an emphasis on finite element methods. The 1996 first edition has been used in a rather intensive two-semester course. -Book News, June 2010
Griffel, DH
2002-01-01
A stimulating introductory text, this volume examines many important applications of functional analysis to mechanics, fluid mechanics, diffusive growth, and approximation. Detailed enough to impart a thorough understanding, the text is also sufficiently straightforward for those unfamiliar with abstract analysis. Its four-part treatment begins with distribution theory and discussions of Green's functions. Essentially independent of the preceding material, the second and third parts deal with Banach spaces, Hilbert space, spectral theory, and variational techniques. The final part outlines the
Shifted Independent Component Analysis
DEFF Research Database (Denmark)
Mørup, Morten; Madsen, Kristoffer Hougaard; Hansen, Lars Kai
2007-01-01
Delayed mixing is a problem of theoretical interest and practical importance, e.g., in speech processing, bio-medical signal analysis and financial data modelling. Most previous analyses have been based on models with integer shifts, i.e., shifts by a number of samples, and have often been carried...
Multiview Bayesian Correlated Component Analysis
DEFF Research Database (Denmark)
Kamronn, Simon Due; Poulsen, Andreas Trier; Hansen, Lars Kai
2015-01-01
are identical. Here we propose a hierarchical probabilistic model that can infer the level of universality in such multiview data, from completely unrelated representations, corresponding to canonical correlation analysis, to identical representations as in correlated component analysis. This new model, which...... we denote Bayesian correlated component analysis, evaluates favorably against three relevant algorithms in simulated data. A well-established benchmark EEG data set is used to further validate the new model and infer the variability of spatial representations across multiple subjects....
International Nuclear Information System (INIS)
2012-12-01
The project of Integrity Assessment of Flawed Components with Structural Discontinuity (IAF) was entrusted to Japan Power Engineering and Inspection Corporation (JAPEIC) from Nuclear and Industrial Safety Agency (NISA) and started from FY 2001. And then, it was taken over to Japan Nuclear Energy Safety Organization (JNES) which was established in October 2003 and carried out until FY 2007. In the IAF project, weld joints between nickel based alloys and low alloy steels around penetrations in reactor vessel, safe-end of nozzles and shroud supports were selected from among components and pipe arrangements in nuclear power plants, where high residual stresses were generated due to welding and complex structure. Residual stresses around of the weld joints were estimated by finite element analysis method (FEM) with a general modeling method, then the reasonability and the conservativeness was evaluated. In addition, for postulated surface crack of stress corrosion cracking (SCC), a simple calculation method of stress intensity factor (K) required to estimate the crack growth was proposed and the effectiveness was confirmed. JNES compiled results of the IAF project into Data Books of Residual Stress Analysis of Weld Joint, and Data Book of Simplified Stress Intensity Factor Calculation for Penetration of Reactor as typical Structure Discontinuity, respectively. Data Books of Residual Stress Analysis in Weld Joint. 1. Butt Weld Joint of Small Diameter Cylinder (4B Sch40) (JNES-RE-2012-0005), 2. Dissimilar Metal Weld Joint in Safe End (One-Side Groove Joint (JNES-RE-2012-0006), 3. Dissimilar Metal Weld Joint in Safe End (Large Diameter Both-Side Groove Joint) (JNES-RE-2012-0007), 4. Weld Joint around Penetrations in Reactor Vessel (Insert Joint) (JNES-RE-2012-0008), 5. Weld Joint in Shroud Support (H8, H9, H10 and H11 Welds) (JNES-RE-2012-0009), 6. Analysis Model of Dissimilar Metal Weld Joint Applied Post Weld Heat Treatment (PWHT) (JNES-RE-2012-0010). Data Book of
Towards intelligent video understanding applied to plasma facing component monitoring
Energy Technology Data Exchange (ETDEWEB)
Martin, V.; Bremond, F. [INRIA, Pulsa team-project, Sophia Antipolis (France); Travere, J.M. [CEA IRFM, Saint Paul-lez-Durance (France); Moncada, V.; Dunand, G. [Sophia Conseil Company, Sophia Antipolis (France)
2011-07-01
Infrared thermography has become a routine diagnostic in many magnetic fusion devices to monitor the heat loads on the plasma facing components (PFCs) for both physics studies and machine protection. The good results of the developed systems obtained so far motivate the use of imaging diagnostics for control, especially during long pulse tokamak operation (e.g. lasting several minutes). In this paper, we promote intelligent monitoring for both real-time purposes (machine protection issues) and post event analysis purposes (PWI understanding). We propose a vision-based system able to automatically detect and classify into different pre-defined categories phenomena as localized hot spots, transient thermal events (e.g. electrical arcing), and unidentified flying objects (UFOs) as dusts from infrared imaging data of PFCs. This original vision system is made intelligent by endowing it with high-level reasoning (i.e. integration of a priori knowledge of thermal event spatial and temporal properties to guide the recognition), self-adaptability to varying conditions (e.g. different plasma scenarios), and learning capabilities (e.g. statistical modelling of thermal event behaviour based on training samples). This approach has been already successfully applied to the recognition of one critical thermal event at Tore Supra. We present here latest results of its extension for the recognition of others thermal events (e.g., B{sub 4}C flakes, impact of fast particles, UFOs) and show how extracted information can be used during plasma operation at Tore Supra to improve the real time control system, and for further analysis of PFC aging. This document is composed of an abstract followed by the slides of the presentation. (authors)
Concept analysis of culture applied to nursing.
Marzilli, Colleen
2014-01-01
Culture is an important concept, especially when applied to nursing. A concept analysis of culture is essential to understanding the meaning of the word. This article applies Rodgers' (2000) concept analysis template and provides a definition of the word culture as it applies to nursing practice. This article supplies examples of the concept of culture to aid the reader in understanding its application to nursing and includes a case study demonstrating components of culture that must be respected and included when providing health care.
Principal Component Analysis as an Efficient Performance ...
African Journals Online (AJOL)
This paper uses the principal component analysis (PCA) to examine the possibility of using few explanatory variables (X's) to explain the variation in Y. It applied PCA to assess the performance of students in Abia State Polytechnic, Aba, Nigeria. This was done by estimating the coefficients of eight explanatory variables in a ...
Applying of component system development in object methodology
Directory of Open Access Journals (Sweden)
Milan Mišovič
2013-01-01
-oriented methodology (Arlo, Neust, 2007, (Kan, Müller, 2005, (Krutch, 2003 for problem domains with double-layer process logic. There is indicated an integration method, based on a certain meta-model (Applying of the Component system Development in object Methodology and leading to the component system formation. The mentioned meta-model is divided into partial workflows that are located in different stages of a classic object process-based methodology. Into account there are taken the consistency of the input and output artifacts in working practices of the meta-model and mentioned object methodology. This paper focuses on static component systems that are starting to explore dynamic and mobile component systems.In addition, in the contribution the component system is understood as a specific system, for its system properties and basic terms notation being used a set and graph and system algebra.
Analysis Method for Integrating Components of Product
Energy Technology Data Exchange (ETDEWEB)
Choi, Jun Ho [Inzest Co. Ltd, Seoul (Korea, Republic of); Lee, Kun Sang [Kookmin Univ., Seoul (Korea, Republic of)
2017-04-15
This paper presents some of the methods used to incorporate the parts constituting a product. A new relation function concept and its structure are introduced to analyze the relationships of component parts. This relation function has three types of information, which can be used to establish a relation function structure. The relation function structure of the analysis criteria was established to analyze and present the data. The priority components determined by the analysis criteria can be integrated. The analysis criteria were divided based on their number and orientation, as well as their direct or indirect characteristic feature. This paper presents a design algorithm for component integration. This algorithm was applied to actual products, and the components inside the product were integrated. Therefore, the proposed algorithm was used to conduct research to improve the brake discs for bicycles. As a result, an improved product similar to the related function structure was actually created.
Analysis Method for Integrating Components of Product
International Nuclear Information System (INIS)
Choi, Jun Ho; Lee, Kun Sang
2017-01-01
This paper presents some of the methods used to incorporate the parts constituting a product. A new relation function concept and its structure are introduced to analyze the relationships of component parts. This relation function has three types of information, which can be used to establish a relation function structure. The relation function structure of the analysis criteria was established to analyze and present the data. The priority components determined by the analysis criteria can be integrated. The analysis criteria were divided based on their number and orientation, as well as their direct or indirect characteristic feature. This paper presents a design algorithm for component integration. This algorithm was applied to actual products, and the components inside the product were integrated. Therefore, the proposed algorithm was used to conduct research to improve the brake discs for bicycles. As a result, an improved product similar to the related function structure was actually created.
DEFF Research Database (Denmark)
Grassi, Silvia; Lyndgaard, Christian Bøge; Rasmussen, Morten Arendt
2017-01-01
This study explores the effect of different settings on beer fermentation process applying an interval-based version of ASCA on FT-IR data. Three main factors (yeast type, temperature, fermentation time) are included in the experimental design, being high sources of variation in brewing...... and strictly interdependent; thus, difficult to be studied through a univariate approach. The three-factor full factorial design leads to a spectral multi-set data, with a total of 12 independent fermentations, which is explored combining ASCA and an interval adaptation of ASCA (interval-ASCA or i......-ASCA). The ASCA models calculated on two separate regions (2900–2250 cm−1 and 1500–980 cm−1) shows differences for average time levels and the interaction between yeast types and time linked to carbon dioxide, maltose consumption and ethanol production, respectively. To better investigate the punctual influence...
Applying of component system development in object methodology, case study
Directory of Open Access Journals (Sweden)
Milan Mišovič
2013-01-01
Full Text Available To create computarization target software as a component system has been a very strong requirement for the last 20 years of software developing. Finally, the architectural components are self-contained units, presenting not only partial and overall system behavior, but also cooperating with each other on the basis of their interfaces. Among others, components have allowed flexible modification of processes the behavior of which is the foundation of components behavior without changing the life of the component system. On the other hand, the component system makes it possible, at design time, to create numerous new connections between components and thus creating modified system behaviors. This all enables the company management to perform, at design time, required behavioral changes of processes in accordance with the requirements of changing production and market.The development of software which is generally referred to as SDP (Software Development Process contains two directions. The first one, called CBD (Component–Based Development, is dedicated to the development of component–based systems CBS (Component–based System, the second target is the development of software under the influence of SOA (Service–Oriented Architecture. Both directions are equipped with their different development methodologies. The subject of this paper is only the first direction and application of development of component–based systems in its object–oriented methodologies. The requirement of today is to carry out the development of component-based systems in the framework of developed object–oriented methodologies precisely in the way of a dominant style. In some of the known methodologies, however, this development is not completely transparent and is not even recognized as dominant. In some cases, it is corrected by the special meta–integration models of component system development into an object methodology.This paper presents a case study
Conversation Analysis in Applied Linguistics
DEFF Research Database (Denmark)
Kasper, Gabriele; Wagner, Johannes
2014-01-01
on applied CA, the application of basic CA's principles, methods, and findings to the study of social domains and practices that are interactionally constituted. We consider three strands—foundational, social problem oriented, and institutional applied CA—before turning to recent developments in CA research...... on learning and development. In conclusion, we address some emerging themes in the relationship of CA and applied linguistics, including the role of multilingualism, standard social science methods as research objects, CA's potential for direct social intervention, and increasing efforts to complement CA......For the last decade, conversation analysis (CA) has increasingly contributed to several established fields in applied linguistics. In this article, we will discuss its methodological contributions. The article distinguishes between basic and applied CA. Basic CA is a sociological endeavor concerned...
Lessons learned in applying function analysis
International Nuclear Information System (INIS)
Mitchel, G.R.; Davey, E.; Basso, R.
2001-01-01
This paper summarizes the lessons learned in undertaking and applying function analysis based on the recent experience of utility, AECL and international design and assessment projects. Function analysis is an analytical technique that can be used to characterize and asses the functions of a system and is widely recognized as an essential component of a 'systematic' approach to design, on that integrated operational and user requirements into the standard design process. (author)
Applied analysis and differential equations
Cârj, Ovidiu
2007-01-01
This volume contains refereed research articles written by experts in the field of applied analysis, differential equations and related topics. Well-known leading mathematicians worldwide and prominent young scientists cover a diverse range of topics, including the most exciting recent developments. A broad range of topics of recent interest are treated: existence, uniqueness, viability, asymptotic stability, viscosity solutions, controllability and numerical analysis for ODE, PDE and stochastic equations. The scope of the book is wide, ranging from pure mathematics to various applied fields such as classical mechanics, biomedicine, and population dynamics.
Cordova-Pozo, Kathya; Hoopes, Andrea J; Cordova, Freddy; Vega, Bernardo; Segura, Zoyla; Hagens, Arnold
2018-02-08
the outcomes, the nature of the outcomes, or cost-effectiveness of interventions. This analysis showed that multi-country projects are complex, entail risks in execution and require robust project management. RBM can be a useful tool to ensure a systematic approach at different phases within a multi-country setting.
Applied survival analysis using R
Moore, Dirk F
2016-01-01
Applied Survival Analysis Using R covers the main principles of survival analysis, gives examples of how it is applied, and teaches how to put those principles to use to analyze data using R as a vehicle. Survival data, where the primary outcome is time to a specific event, arise in many areas of biomedical research, including clinical trials, epidemiological studies, and studies of animals. Many survival methods are extensions of techniques used in linear regression and categorical data, while other aspects of this field are unique to survival data. This text employs numerous actual examples to illustrate survival curve estimation, comparison of survivals of different groups, proper accounting for censoring and truncation, model variable selection, and residual analysis. Because explaining survival analysis requires more advanced mathematics than many other statistical topics, this book is organized with basic concepts and most frequently used procedures covered in earlier chapters, with more advanced topics...
Component evaluation testing and analysis algorithms.
Energy Technology Data Exchange (ETDEWEB)
Hart, Darren M.; Merchant, Bion John
2011-10-01
The Ground-Based Monitoring R&E Component Evaluation project performs testing on the hardware components that make up Seismic and Infrasound monitoring systems. The majority of the testing is focused on the Digital Waveform Recorder (DWR), Seismic Sensor, and Infrasound Sensor. In order to guarantee consistency, traceability, and visibility into the results of the testing process, it is necessary to document the test and analysis procedures that are in place. Other reports document the testing procedures that are in place (Kromer, 2007). This document serves to provide a comprehensive overview of the analysis and the algorithms that are applied to the Component Evaluation testing. A brief summary of each test is included to provide the context for the analysis that is to be performed.
Principal components analysis in clinical studies.
Zhang, Zhongheng; Castelló, Adela
2017-09-01
In multivariate analysis, independent variables are usually correlated to each other which can introduce multicollinearity in the regression models. One approach to solve this problem is to apply principal components analysis (PCA) over these variables. This method uses orthogonal transformation to represent sets of potentially correlated variables with principal components (PC) that are linearly uncorrelated. PCs are ordered so that the first PC has the largest possible variance and only some components are selected to represent the correlated variables. As a result, the dimension of the variable space is reduced. This tutorial illustrates how to perform PCA in R environment, the example is a simulated dataset in which two PCs are responsible for the majority of the variance in the data. Furthermore, the visualization of PCA is highlighted.
Functional Generalized Structured Component Analysis.
Suk, Hye Won; Hwang, Heungsun
2016-12-01
An extension of Generalized Structured Component Analysis (GSCA), called Functional GSCA, is proposed to analyze functional data that are considered to arise from an underlying smooth curve varying over time or other continua. GSCA has been geared for the analysis of multivariate data. Accordingly, it cannot deal with functional data that often involve different measurement occasions across participants and a large number of measurement occasions that exceed the number of participants. Functional GSCA addresses these issues by integrating GSCA with spline basis function expansions that represent infinite-dimensional curves onto a finite-dimensional space. For parameter estimation, functional GSCA minimizes a penalized least squares criterion by using an alternating penalized least squares estimation algorithm. The usefulness of functional GSCA is illustrated with gait data.
Towards intelligent video understanding applied to plasma facing component monitoring
International Nuclear Information System (INIS)
Martin, V.; Travere, J.M.; Moncada, V.; Bremond, F.
2011-01-01
In this paper, we promote intelligent plasma facing component video monitoring for both real-time purposes (machine protection issues) and post event analysis purposes (plasma-wall interaction understanding). We propose a vision-based system able to automatically detect and classify into different pre-defined categories thermal phenomena such as localized hot spots or transient thermal events (e.g. electrical arcing) from infrared imaging data of PFCs. This original computer vision system is made intelligent by endowing it with high level reasoning (i.e. integration of a priori knowledge of thermal event spatio-temporal properties to guide the recognition), self-adaptability to varying conditions (e.g. different thermal scenes and plasma scenarios), and learning capabilities (e.g. statistical modelling of event behaviour based on training samples). (authors)
Interpretable functional principal component analysis.
Lin, Zhenhua; Wang, Liangliang; Cao, Jiguo
2016-09-01
Functional principal component analysis (FPCA) is a popular approach to explore major sources of variation in a sample of random curves. These major sources of variation are represented by functional principal components (FPCs). The intervals where the values of FPCs are significant are interpreted as where sample curves have major variations. However, these intervals are often hard for naïve users to identify, because of the vague definition of "significant values". In this article, we develop a novel penalty-based method to derive FPCs that are only nonzero precisely in the intervals where the values of FPCs are significant, whence the derived FPCs possess better interpretability than the FPCs derived from existing methods. To compute the proposed FPCs, we devise an efficient algorithm based on projection deflation techniques. We show that the proposed interpretable FPCs are strongly consistent and asymptotically normal under mild conditions. Simulation studies confirm that with a competitive performance in explaining variations of sample curves, the proposed FPCs are more interpretable than the traditional counterparts. This advantage is demonstrated by analyzing two real datasets, namely, electroencephalography data and Canadian weather data. © 2015, The International Biometric Society.
Modern problems in applied analysis
Rogosin, Sergei
2018-01-01
This book features a collection of recent findings in Applied Real and Complex Analysis that were presented at the 3rd International Conference “Boundary Value Problems, Functional Equations and Applications” (BAF-3), held in Rzeszow, Poland on 20-23 April 2016. The contributions presented here develop a technique related to the scope of the workshop and touching on the fields of differential and functional equations, complex and real analysis, with a special emphasis on topics related to boundary value problems. Further, the papers discuss various applications of the technique, mainly in solid mechanics (crack propagation, conductivity of composite materials), biomechanics (viscoelastic behavior of the periodontal ligament, modeling of swarms) and fluid dynamics (Stokes and Brinkman type flows, Hele-Shaw type flows). The book is addressed to all readers who are interested in the development and application of innovative research results that can help solve theoretical and real-world problems.
On Bayesian Principal Component Analysis
Czech Academy of Sciences Publication Activity Database
Šmídl, Václav; Quinn, A.
2007-01-01
Roč. 51, č. 9 (2007), s. 4101-4123 ISSN 0167-9473 R&D Projects: GA MŠk(CZ) 1M0572 Institutional research plan: CEZ:AV0Z10750506 Keywords : Principal component analysis ( PCA ) * Variational bayes (VB) * von-Mises–Fisher distribution Subject RIV: BC - Control Systems Theory Impact factor: 1.029, year: 2007 http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6V8V-4MYD60N-6&_user=10&_coverDate=05%2F15%2F2007&_rdoc=1&_fmt=&_orig=search&_sort=d&view=c&_acct=C000050221&_version=1&_urlVersion=0&_userid=10&md5=b8ea629d48df926fe18f9e5724c9003a
Applied systems analysis. No. 22
International Nuclear Information System (INIS)
1980-12-01
Based on a detailed analysis of demands in the area Cologne/Frankfurt, the amount of the system products for this region were ascertained, which under consideration of technical conditions and entrepreneurial aspects seemed to be disposable at cost equality with competative energy supplies. Based on these data, the technical components of the system, location and piping were fixed and first- and operating costs were determined. For a judgement of the economics, the key numbers, cash value, internal rate of interest and cost recovery rate were determined from the difference of costs between the nuclear long distance energy system and alternative facilities. Furthermore specific production cost, associated prices and contribution margin were presented for each product. (orig.) [de
Generalized structured component analysis a component-based approach to structural equation modeling
Hwang, Heungsun
2014-01-01
Winner of the 2015 Sugiyama Meiko Award (Publication Award) of the Behaviormetric Society of Japan Developed by the authors, generalized structured component analysis is an alternative to two longstanding approaches to structural equation modeling: covariance structure analysis and partial least squares path modeling. Generalized structured component analysis allows researchers to evaluate the adequacy of a model as a whole, compare a model to alternative specifications, and conduct complex analyses in a straightforward manner. Generalized Structured Component Analysis: A Component-Based Approach to Structural Equation Modeling provides a detailed account of this novel statistical methodology and its various extensions. The authors present the theoretical underpinnings of generalized structured component analysis and demonstrate how it can be applied to various empirical examples. The book enables quantitative methodologists, applied researchers, and practitioners to grasp the basic concepts behind this new a...
Sghaier, Lilia; Cordella, Christophe B Y; Rutledge, Douglas N; Lefèvre, Fanny; Watiez, Mickaël; Breton, Sylvie; Sassiat, Patrick; Thiebaut, Didier; Vial, Jérôme
2017-06-01
Lipid oxidation leads to the formation of volatile compounds and very often to off-flavors. In the case of the heating of rapeseed oil, unpleasant odors, characterized as a fishy odor, are emitted. In this study, 2 different essential oils (coriander and nutmeg essential oils) were added to refined rapeseed oil as odor masking agents. The aim of this work was to determine a potential antioxidant effect of these essential oils on the thermal stability of rapeseed oil subject to heating cycles between room temperature and 180 °C. For this purpose, normed determinations of different parameters (peroxide value, anisidine value, and the content of total polar compounds, free fatty acids and tocopherols) were carried out to examine the differences between pure and degraded oil. No significant difference was observed between pure rapeseed oil and rapeseed oil with essential oils for each parameter separately. However, a stabilizing effect of the essential oils, with a higher effect for the nutmeg essential oil was highlighted by principal component analysis applied on physicochemical dataset. Moreover, the analysis of the volatile compounds performed by GC × GC showed a substantial loss of the volatile compounds of the essential oils from the first heating cycle. © 2017 Institute of Food Technologists®.
Industrial Design: Applied Arts Component as a Factor in Design ...
African Journals Online (AJOL)
DrNneka
Key Words: Algorithmic/ Heuristic Approaches, Industrial Plasticine, Play dough, .... engineering for instance, is also applicable in the actualization of the applied arts .... It is made of wax which has a chemical make-up that contains sulphur,.
Applying Standard Industrial Components for Active Magnetic Bearings
Directory of Open Access Journals (Sweden)
Bert-Uwe Koehler
2017-02-01
Full Text Available With the increasing number of active magnetic bearing applications, satisfying additional requirements is becoming increasingly more important. As for every technology, moving away from being a niche product and achieving a higher level of maturity, these requirements relate to robustness, reliability, availability, safety, security, traceability, certification, handling, flexibility, reporting, costs, and delivery times. Employing standard industrial components, such as those from flexible modular motion control drive systems, is an approach that allows these requirements to be satisfied while achieving rapid technological innovation. In this article, we discuss technical and non-technical aspects of using standard industrial components in magnetic bearing applications.
Conversation Analysis and Applied Linguistics.
Schegloff, Emanuel A.; Koshik, Irene; Jacoby, Sally; Olsher, David
2002-01-01
Offers biographical guidance on several major areas of conversation-analytic work--turn-taking, repair, and word selection--and indicates past or potential points of contact with applied linguistics. Also discusses areas of applied linguistic work. (Author/VWL)
Principal component regression analysis with SPSS.
Liu, R X; Kuang, J; Gong, Q; Hou, X L
2003-06-01
The paper introduces all indices of multicollinearity diagnoses, the basic principle of principal component regression and determination of 'best' equation method. The paper uses an example to describe how to do principal component regression analysis with SPSS 10.0: including all calculating processes of the principal component regression and all operations of linear regression, factor analysis, descriptives, compute variable and bivariate correlations procedures in SPSS 10.0. The principal component regression analysis can be used to overcome disturbance of the multicollinearity. The simplified, speeded up and accurate statistical effect is reached through the principal component regression analysis with SPSS.
Fried frailty phenotype assessment components as applied to geriatric inpatients
Directory of Open Access Journals (Sweden)
Bieniek J
2016-04-01
Full Text Available Joanna Bieniek, Krzysztof Wilczynski, Jan Szewieczek Department of Geriatrics, School of Health Sciences in Katowice, Medical University of Silesia, Katowice, Poland Background: Management of geriatric patients would be simplified if a universally accepted definition of frailty for clinical use was defined. Among definitions of frailty, Fried frailty phenotype criteria constitute a common reference frame for many geriatric studies. However, this reference frame has been tested primarily in elderly patients presenting with relatively good health status. Objective: The aim of this article was to assess the usefulness and limitations of Fried frailty phenotype criteria in geriatric inpatients, characterized by comorbidity and functional impairments, and to estimate the frailty phenotype prevalence in this group. Patients and methods: Five hundred consecutive patients of the university hospital subacute geriatric ward, aged 79.0±8.4 years (67% women and 33% men, participated in this cross-sectional study. Comprehensive geriatric assessment and Fried frailty phenotype component evaluation were performed in all patients. Results: Multimorbidity (6.0±2.8 diseases characterized our study group, with a wide range of clinical conditions and functional states (Barthel Index of Activities of Daily Living 72.2±28.2 and Mini-Mental State Examination 23.6±7.1 scores. All five Fried frailty components were assessed in 65% of patients (95% confidence interval [CI] =60.8–69.2 (diagnostic group. One or more components were not feasible to be assessed in 35% of the remaining patients (nondiagnostic group because of lack of past patient’s body mass control and/or cognitive or physical impairment. Patients from the nondiagnostic group, as compared to patients from the diagnostic group, presented with more advanced age, higher prevalence of dementia, lower prevalence of hypertension, lower systolic and diastolic blood pressure, body mass index, Mini
Fusion-component lifetime analysis
International Nuclear Information System (INIS)
Mattas, R.F.
1982-09-01
A one-dimensional computer code has been developed to examine the lifetime of first-wall and impurity-control components. The code incorporates the operating and design parameters, the material characteristics, and the appropriate failure criteria for the individual components. The major emphasis of the modeling effort has been to calculate the temperature-stress-strain-radiation effects history of a component so that the synergystic effects between sputtering erosion, swelling, creep, fatigue, and crack growth can be examined. The general forms of the property equations are the same for all materials in order to provide the greatest flexibility for materials selection in the code. The individual coefficients within the equations are different for each material. The code is capable of determining the behavior of a plate, composed of either a single or dual material structure, that is either totally constrained or constrained from bending but not from expansion. The code has been utilized to analyze the first walls for FED/INTOR and DEMO and to analyze the limiter for FED/INTOR
On combined gravity gradient components modelling for applied geophysics
International Nuclear Information System (INIS)
Veryaskin, Alexey; McRae, Wayne
2008-01-01
Gravity gradiometry research and development has intensified in recent years to the extent that technologies providing a resolution of about 1 eotvos per 1 second average shall likely soon be available for multiple critical applications such as natural resources exploration, oil reservoir monitoring and defence establishment. Much of the content of this paper was composed a decade ago, and only minor modifications were required for the conclusions to be just as applicable today. In this paper we demonstrate how gravity gradient data can be modelled, and show some examples of how gravity gradient data can be combined in order to extract valuable information. In particular, this study demonstrates the importance of two gravity gradient components, Txz and Tyz, which, when processed together, can provide more information on subsurface density contrasts than that derived solely from the vertical gravity gradient (Tzz)
Component of the risk analysis
International Nuclear Information System (INIS)
Martinez, I.; Campon, G.
2013-01-01
The power point presentation reviews issues like analysis of risk (Codex), management risk, preliminary activities manager, relationship between government and industries, microbiological danger and communication of risk
Principal Component Analysis In Radar Polarimetry
Directory of Open Access Journals (Sweden)
A. Danklmayer
2005-01-01
Full Text Available Second order moments of multivariate (often Gaussian joint probability density functions can be described by the covariance or normalised correlation matrices or by the Kennaugh matrix (Kronecker matrix. In Radar Polarimetry the application of the covariance matrix is known as target decomposition theory, which is a special application of the extremely versatile Principle Component Analysis (PCA. The basic idea of PCA is to convert a data set, consisting of correlated random variables into a new set of uncorrelated variables and order the new variables according to the value of their variances. It is important to stress that uncorrelatedness does not necessarily mean independent which is used in the much stronger concept of Independent Component Analysis (ICA. Both concepts agree for multivariate Gaussian distribution functions, representing the most random and least structured distribution. In this contribution, we propose a new approach in applying the concept of PCA to Radar Polarimetry. Therefore, new uncorrelated random variables will be introduced by means of linear transformations with well determined loading coefficients. This in turn, will allow the decomposition of the original random backscattering target variables into three point targets with new random uncorrelated variables whose variances agree with the eigenvalues of the covariance matrix. This allows a new interpretation of existing decomposition theorems.
Integrating Data Transformation in Principal Components Analysis
Maadooliat, Mehdi
2015-01-02
Principal component analysis (PCA) is a popular dimension reduction method to reduce the complexity and obtain the informative aspects of high-dimensional datasets. When the data distribution is skewed, data transformation is commonly used prior to applying PCA. Such transformation is usually obtained from previous studies, prior knowledge, or trial-and-error. In this work, we develop a model-based method that integrates data transformation in PCA and finds an appropriate data transformation using the maximum profile likelihood. Extensions of the method to handle functional data and missing values are also developed. Several numerical algorithms are provided for efficient computation. The proposed method is illustrated using simulated and real-world data examples.
Applying critical analysis - main methods
Directory of Open Access Journals (Sweden)
Miguel Araujo Alonso
2012-02-01
Full Text Available What is the usefulness of critical appraisal of literature? Critical analysis is a fundamental condition for the correct interpretation of any study that is subject to review. In epidemiology, in order to learn how to read a publication, we must be able to analyze it critically. Critical analysis allows us to check whether a study fulfills certain previously established methodological inclusion and exclusion criteria. This is frequently used in conducting systematic reviews although eligibility criteria are generally limited to the study design. Critical analysis of literature and be done implicitly while reading an article, as in reading for personal interest, or can be conducted in a structured manner, using explicit and previously established criteria. The latter is done when formally reviewing a topic.
Essentials of applied dynamic analysis
Jia, Junbo
2014-01-01
This book presents up-to-date knowledge of dynamic analysis in engineering world. To facilitate the understanding of the topics by readers with various backgrounds, general principles are linked to their applications from different angles. Special interesting topics such as statistics of motions and loading, damping modeling and measurement, nonlinear dynamics, fatigue assessment, vibration and buckling under axial loading, structural health monitoring, human body vibrations, and vehicle-structure interactions etc., are also presented. The target readers include industry professionals in civil, marine and mechanical engineering, as well as researchers and students in this area.
Directory of Open Access Journals (Sweden)
Jiaguang Li
2018-05-01
Full Text Available In some internally-draining dryland basins, ephemeral river systems terminate at the margins of playas. Extreme floods can exert significant geomorphological impacts on the lower reaches of these river systems and the playas, including causing changes to flood extent, channel-floodplain morphology, and sediment dispersal. However, the characterization of these impacts using remote sensing approaches has been challenging owing to variable vegetation and cloud cover, as well as the commonly limited spatial and temporal resolution of data. Here, we use Sentinel-2 Multispectral Instrument (MSI data to investigate the flood extent, flood patterns and channel-floodplain morphodynamics resulting from an extreme flood near the non-vegetated terminus of the Río Colorado, located at the margins of the world’s largest playa (Salar de Uyuni, Bolivia. Daily maximum precipitation frequency analysis based on a 42-year record of daily precipitation data (1976 through 2017 indicates that an approximately 40-year precipitation event (40.7 mm occurred on 6 January 2017, and this was associated with an extreme flood. Sentinel-2 data acquired after this extreme flood were used to separate water bodies and land, first by using modified normalized difference water index (MNDWI, and then by subsequently applying independent component analysis (ICA on the land section of the combined pre- and post-flood images to extract flooding areas. The area around the Río Colorado terminus system was classified into three categories: water bodies, wet land, and dry land. The results are in agreement with visual assessment, with an overall accuracy of 96% and Kappa of 0.9 for water-land classification and an overall accuracy of 83% and Kappa of 0.65 for dry land-wet land classification. The flood extent mapping revealed preferential overbank flow paths on the floodplain, which were closely related to geomorphological changes. Changes included the formation and enlargement of
COPD phenotype description using principal components analysis
DEFF Research Database (Denmark)
Roy, Kay; Smith, Jacky; Kolsum, Umme
2009-01-01
BACKGROUND: Airway inflammation in COPD can be measured using biomarkers such as induced sputum and Fe(NO). This study set out to explore the heterogeneity of COPD using biomarkers of airway and systemic inflammation and pulmonary function by principal components analysis (PCA). SUBJECTS...... AND METHODS: In 127 COPD patients (mean FEV1 61%), pulmonary function, Fe(NO), plasma CRP and TNF-alpha, sputum differential cell counts and sputum IL8 (pg/ml) were measured. Principal components analysis as well as multivariate analysis was performed. RESULTS: PCA identified four main components (% variance...... associations between the variables within components 1 and 2. CONCLUSION: COPD is a multi dimensional disease. Unrelated components of disease were identified, including neutrophilic airway inflammation which was associated with systemic inflammation, and sputum eosinophils which were related to increased Fe...
Integrating Data Transformation in Principal Components Analysis
Maadooliat, Mehdi; Huang, Jianhua Z.; Hu, Jianhua
2015-01-01
Principal component analysis (PCA) is a popular dimension reduction method to reduce the complexity and obtain the informative aspects of high-dimensional datasets. When the data distribution is skewed, data transformation is commonly used prior
NEPR Principle Component Analysis - NOAA TIFF Image
National Oceanic and Atmospheric Administration, Department of Commerce — This GeoTiff is a representation of seafloor topography in Northeast Puerto Rico derived from a bathymetry model with a principle component analysis (PCA). The area...
Structured Performance Analysis for Component Based Systems
Salmi , N.; Moreaux , Patrice; Ioualalen , M.
2012-01-01
International audience; The Component Based System (CBS) paradigm is now largely used to design software systems. In addition, performance and behavioural analysis remains a required step for the design and the construction of efficient systems. This is especially the case of CBS, which involve interconnected components running concurrent processes. % This paper proposes a compositional method for modeling and structured performance analysis of CBS. Modeling is based on Stochastic Well-formed...
Constrained principal component analysis and related techniques
Takane, Yoshio
2013-01-01
In multivariate data analysis, regression techniques predict one set of variables from another while principal component analysis (PCA) finds a subspace of minimal dimensionality that captures the largest variability in the data. How can regression analysis and PCA be combined in a beneficial way? Why and when is it a good idea to combine them? What kind of benefits are we getting from them? Addressing these questions, Constrained Principal Component Analysis and Related Techniques shows how constrained PCA (CPCA) offers a unified framework for these approaches.The book begins with four concre
Directory of Open Access Journals (Sweden)
Godfrey D Pearlson
2015-09-01
Full Text Available Complex inherited phenotypes, including those for many common medical and psychiatric diseases, are most likely underpinned by multiple genes contributing to interlocking molecular biological processes, along with environmental factors (Owen et al., 2010. Despite this, genotyping strategies for complex, inherited, disease-related phenotypes mostly employ univariate analyses, e.g. genome wide association (GWA. Such procedures most often identify isolated risk-related SNPs or loci, not the underlying biological pathways necessary to help guide the development of novel treatment approaches. This article focuses on the multivariate analysis strategy of parallel (i.e. simultaneous combination of SNP and neuroimage information independent component analysis (p-ICA, which typically yields large clusters of functionally related SNPs statistically correlated with phenotype components, whose overall molecular biologic relevance is inferred subsequently using annotation software suites. Because this is a novel approach, whose details are relatively new to the field we summarize its underlying principles and address conceptual questions regarding interpretation of resulting data and provide practical illustrations of the method.
National Research Council Canada - National Science Library
Qi, Yuan
2000-01-01
In this thesis, we propose two new machine learning schemes, a subband-based Independent Component Analysis scheme and a hybrid Independent Component Analysis/Support Vector Machine scheme, and apply...
Independent component analysis based filtering for penumbral imaging
International Nuclear Information System (INIS)
Chen Yenwei; Han Xianhua; Nozaki, Shinya
2004-01-01
We propose a filtering based on independent component analysis (ICA) for Poisson noise reduction. In the proposed filtering, the image is first transformed to ICA domain and then the noise components are removed by a soft thresholding (shrinkage). The proposed filter, which is used as a preprocessing of the reconstruction, has been successfully applied to penumbral imaging. Both simulation results and experimental results show that the reconstructed image is dramatically improved in comparison to that without the noise-removing filters
Applied Behavior Analysis and Statistical Process Control?
Hopkins, B. L.
1995-01-01
Incorporating statistical process control (SPC) methods into applied behavior analysis is discussed. It is claimed that SPC methods would likely reduce applied behavior analysts' intimate contacts with problems and would likely yield poor treatment and research decisions. Cases and data presented by Pfadt and Wheeler (1995) are cited as examples.…
Computer compensation for NMR quantitative analysis of trace components
International Nuclear Information System (INIS)
Nakayama, T.; Fujiwara, Y.
1981-01-01
A computer program has been written that determines trace components and separates overlapping components in multicomponent NMR spectra. This program uses the Lorentzian curve as a theoretical curve of NMR spectra. The coefficients of the Lorentzian are determined by the method of least squares. Systematic errors such as baseline/phase distortion are compensated and random errors are smoothed by taking moving averages, so that there processes contribute substantially to decreasing the accumulation time of spectral data. The accuracy of quantitative analysis of trace components has been improved by two significant figures. This program was applied to determining the abundance of 13C and the saponification degree of PVA
Caldwell University's Department of Applied Behavior Analysis.
Reeve, Kenneth F; Reeve, Sharon A
2016-05-01
Since 2004, faculty members at Caldwell University have developed three successful graduate programs in Applied Behavior Analysis (i.e., PhD, MA, non-degree programs), increased program faculty from two to six members, developed and operated an on-campus autism center, and begun a stand-alone Applied Behavior Analysis Department. This paper outlines a number of strategies used to advance these initiatives, including those associated with an extensive public relations campaign. We also outline challenges that have limited our programs' growth. These strategies, along with a consideration of potential challenges, might prove useful in guiding academicians who are interested in starting their own programs in behavior analysis.
Applied regression analysis a research tool
Pantula, Sastry; Dickey, David
1998-01-01
Least squares estimation, when used appropriately, is a powerful research tool. A deeper understanding of the regression concepts is essential for achieving optimal benefits from a least squares analysis. This book builds on the fundamentals of statistical methods and provides appropriate concepts that will allow a scientist to use least squares as an effective research tool. Applied Regression Analysis is aimed at the scientist who wishes to gain a working knowledge of regression analysis. The basic purpose of this book is to develop an understanding of least squares and related statistical methods without becoming excessively mathematical. It is the outgrowth of more than 30 years of consulting experience with scientists and many years of teaching an applied regression course to graduate students. Applied Regression Analysis serves as an excellent text for a service course on regression for non-statisticians and as a reference for researchers. It also provides a bridge between a two-semester introduction to...
Experimental and principal component analysis of waste ...
African Journals Online (AJOL)
The present study is aimed at determining through principal component analysis the most important variables affecting bacterial degradation in ponds. Data were collected from literature. In addition, samples were also collected from the waste stabilization ponds at the University of Nigeria, Nsukka and analyzed to ...
Independent component analysis for understanding multimedia content
DEFF Research Database (Denmark)
Kolenda, Thomas; Hansen, Lars Kai; Larsen, Jan
2002-01-01
Independent component analysis of combined text and image data from Web pages has potential for search and retrieval applications by providing more meaningful and context dependent content. It is demonstrated that ICA of combined text and image features has a synergistic effect, i.e., the retrieval...
Building an applied activation analysis centre
International Nuclear Information System (INIS)
Bartosek, J.; Kasparec, I.; Masek, J.
1972-01-01
Requirements are defined and all available background material is reported and discussed for the building up of a centre of applied activation analysis in Czechoslovakia. A detailed analysis of potential users and the centre's envisaged availability is also presented as part of the submitted study. A brief economic analysis is annexed. The study covers the situation up to the end of 1972. (J.K.)
Probabilistic Principal Component Analysis for Metabolomic Data.
LENUS (Irish Health Repository)
Nyamundanda, Gift
2010-11-23
Abstract Background Data from metabolomic studies are typically complex and high-dimensional. Principal component analysis (PCA) is currently the most widely used statistical technique for analyzing metabolomic data. However, PCA is limited by the fact that it is not based on a statistical model. Results Here, probabilistic principal component analysis (PPCA) which addresses some of the limitations of PCA, is reviewed and extended. A novel extension of PPCA, called probabilistic principal component and covariates analysis (PPCCA), is introduced which provides a flexible approach to jointly model metabolomic data and additional covariate information. The use of a mixture of PPCA models for discovering the number of inherent groups in metabolomic data is demonstrated. The jackknife technique is employed to construct confidence intervals for estimated model parameters throughout. The optimal number of principal components is determined through the use of the Bayesian Information Criterion model selection tool, which is modified to address the high dimensionality of the data. Conclusions The methods presented are illustrated through an application to metabolomic data sets. Jointly modeling metabolomic data and covariates was successfully achieved and has the potential to provide deeper insight to the underlying data structure. Examination of confidence intervals for the model parameters, such as loadings, allows for principled and clear interpretation of the underlying data structure. A software package called MetabolAnalyze, freely available through the R statistical software, has been developed to facilitate implementation of the presented methods in the metabolomics field.
Nieuwoudt, Helene H; Prior, Bernard A; Pretorius, Isak S; Manley, Marena; Bauer, Florian F
2004-06-16
Principal component analysis (PCA) was used to identify the main sources of variation in the Fourier transform infrared (FT-IR) spectra of 329 wines of various styles. The FT-IR spectra were gathered using a specialized WineScan instrument. The main sources of variation included the reducing sugar and alcohol content of the samples, as well as the stage of fermentation and the maturation period of the wines. The implications of the variation between the different wine styles for the design of calibration models with accurate predictive abilities were investigated using glycerol calibration in wine as a model system. PCA enabled the identification and interpretation of samples that were poorly predicted by the calibration models, as well as the detection of individual samples in the sample set that had atypical spectra (i.e., outlier samples). The Soft Independent Modeling of Class Analogy (SIMCA) approach was used to establish a model for the classification of the outlier samples. A glycerol calibration for wine was developed (reducing sugar content 8% v/v) with satisfactory predictive ability (SEP = 0.40 g/L). The RPD value (ratio of the standard deviation of the data to the standard error of prediction) was 5.6, indicating that the calibration is suitable for quantification purposes. A calibration for glycerol in special late harvest and noble late harvest wines (RS 31-147 g/L, alcohol > 11.6% v/v) with a prediction error SECV = 0.65 g/L, was also established. This study yielded an analytical strategy that combined the careful design of calibration sets with measures that facilitated the early detection and interpretation of poorly predicted samples and outlier samples in a sample set. The strategy provided a powerful means of quality control, which is necessary for the generation of accurate prediction data and therefore for the successful implementation of FT-IR in the routine analytical laboratory.
PCA: Principal Component Analysis for spectra modeling
Hurley, Peter D.; Oliver, Seb; Farrah, Duncan; Wang, Lingyu; Efstathiou, Andreas
2012-07-01
The mid-infrared spectra of ultraluminous infrared galaxies (ULIRGs) contain a variety of spectral features that can be used as diagnostics to characterize the spectra. However, such diagnostics are biased by our prior prejudices on the origin of the features. Moreover, by using only part of the spectrum they do not utilize the full information content of the spectra. Blind statistical techniques such as principal component analysis (PCA) consider the whole spectrum, find correlated features and separate them out into distinct components. This code, written in IDL, classifies principal components of IRS spectra to define a new classification scheme using 5D Gaussian mixtures modelling. The five PCs and average spectra for the four classifications to classify objects are made available with the code.
BUSINESS PROCESS MANAGEMENT SYSTEMS TECHNOLOGY COMPONENTS ANALYSIS
Directory of Open Access Journals (Sweden)
Andrea Giovanni Spelta
2007-05-01
Full Text Available The information technology that supports the implementation of the business process management appproach is called Business Process Management System (BPMS. The main components of the BPMS solution framework are process definition repository, process instances repository, transaction manager, conectors framework, process engine and middleware. In this paper we define and characterize the role and importance of the components of BPMS's framework. The research method adopted was the case study, through the analysis of the implementation of the BPMS solution in an insurance company called Chubb do Brasil. In the case study, the process "Manage Coinsured Events"" is described and characterized, as well as the components of the BPMS solution adopted and implemented by Chubb do Brasil for managing this process.
Bouhlel, Jihéne; Jouan-Rimbaud Bouveresse, Delphine; Abouelkaram, Said; Baéza, Elisabeth; Jondreville, Catherine; Travel, Angélique; Ratel, Jérémy; Engel, Erwan; Rutledge, Douglas N
2018-02-01
The aim of this work is to compare a novel exploratory chemometrics method, Common Components Analysis (CCA), with Principal Components Analysis (PCA) and Independent Components Analysis (ICA). CCA consists in adapting the multi-block statistical method known as Common Components and Specific Weights Analysis (CCSWA or ComDim) by applying it to a single data matrix, with one variable per block. As an application, the three methods were applied to SPME-GC-MS volatolomic signatures of livers in an attempt to reveal volatile organic compounds (VOCs) markers of chicken exposure to different types of micropollutants. An application of CCA to the initial SPME-GC-MS data revealed a drift in the sample Scores along CC2, as a function of injection order, probably resulting from time-related evolution in the instrument. This drift was eliminated by orthogonalization of the data set with respect to CC2, and the resulting data are used as the orthogonalized data input into each of the three methods. Since the first step in CCA is to norm-scale all the variables, preliminary data scaling has no effect on the results, so that CCA was applied only to orthogonalized SPME-GC-MS data, while, PCA and ICA were applied to the "orthogonalized", "orthogonalized and Pareto-scaled", and "orthogonalized and autoscaled" data. The comparison showed that PCA results were highly dependent on the scaling of variables, contrary to ICA where the data scaling did not have a strong influence. Nevertheless, for both PCA and ICA the clearest separations of exposed groups were obtained after autoscaling of variables. The main part of this work was to compare the CCA results using the orthogonalized data with those obtained with PCA and ICA applied to orthogonalized and autoscaled variables. The clearest separations of exposed chicken groups were obtained by CCA. CCA Loadings also clearly identified the variables contributing most to the Common Components giving separations. The PCA Loadings did not
Positive Behavior Support and Applied Behavior Analysis
Johnston, J. M.; Foxx, R. M.; Jacobson, J. W.; Green, G.; Mulick, J. A.
2006-01-01
This article reviews the origins and characteristics of the positive behavior support (PBS) movement and examines those features in the context of the field of applied behavior analysis (ABA). We raise a number of concerns about PBS as an approach to delivery of behavioral services and its impact on how ABA is viewed by those in human services. We…
Applied Behavior Analysis: Beyond Discrete Trial Teaching
Steege, Mark W.; Mace, F. Charles; Perry, Lora; Longenecker, Harold
2007-01-01
We discuss the problem of autism-specific special education programs representing themselves as Applied Behavior Analysis (ABA) programs when the only ABA intervention employed is Discrete Trial Teaching (DTT), and often for limited portions of the school day. Although DTT has many advantages to recommend its use, it is not well suited to teach…
Independent component analysis for automatic note extraction from musical trills
Brown, Judith C.; Smaragdis, Paris
2004-05-01
The method of principal component analysis, which is based on second-order statistics (or linear independence), has long been used for redundancy reduction of audio data. The more recent technique of independent component analysis, enforcing much stricter statistical criteria based on higher-order statistical independence, is introduced and shown to be far superior in separating independent musical sources. This theory has been applied to piano trills and a database of trill rates was assembled from experiments with a computer-driven piano, recordings of a professional pianist, and commercially available compact disks. The method of independent component analysis has thus been shown to be an outstanding, effective means of automatically extracting interesting musical information from a sea of redundant data.
ANOVA-principal component analysis and ANOVA-simultaneous component analysis: a comparison.
Zwanenburg, G.; Hoefsloot, H.C.J.; Westerhuis, J.A.; Jansen, J.J.; Smilde, A.K.
2011-01-01
ANOVA-simultaneous component analysis (ASCA) is a recently developed tool to analyze multivariate data. In this paper, we enhance the explorative capability of ASCA by introducing a projection of the observations on the principal component subspace to visualize the variation among the measurements.
Improvement of Binary Analysis Components in Automated Malware Analysis Framework
2017-02-21
AFRL-AFOSR-JP-TR-2017-0018 Improvement of Binary Analysis Components in Automated Malware Analysis Framework Keiji Takeda KEIO UNIVERSITY Final...TYPE Final 3. DATES COVERED (From - To) 26 May 2015 to 25 Nov 2016 4. TITLE AND SUBTITLE Improvement of Binary Analysis Components in Automated Malware ...analyze malicious software ( malware ) with minimum human interaction. The system autonomously analyze malware samples by analyzing malware binary program
Fault tree analysis with multistate components
International Nuclear Information System (INIS)
Caldarola, L.
1979-02-01
A general analytical theory has been developed which allows one to calculate the occurence probability of the top event of a fault tree with multistate (more than states) components. It is shown that, in order to correctly describe a system with multistate components, a special type of Boolean algebra is required. This is called 'Boolean algebra with restrictions on varibales' and its basic rules are the same as those of the traditional Boolean algebra with some additional restrictions on the variables. These restrictions are extensively discussed in the paper. Important features of the method are the identification of the complete base and of the smallest irredundant base of a Boolean function which does not necessarily need to be coherent. It is shown that the identification of the complete base of a Boolean function requires the application of some algorithms which are not used in today's computer programmes for fault tree analysis. The problem of statistical dependence among primary components is discussed. The paper includes a small demonstrative example to illustrate the method. The example includes also statistical dependent components. (orig.) [de
Effects of Applied Nitrogen Amounts on the Functional Components of Mulberry (Morus alba L.) Leaves.
Sugiyama, Mari; Takahashi, Makoto; Katsube, Takuya; Koyama, Akio; Itamura, Hiroyuki
2016-09-21
This study investigated the effects of applied nitrogen amounts on specific functional components in mulberry (Morus alba L.) leaves. The relationships between mineral elements and the functional components in mulberry leaves were examined using mulberry trees cultivated in different soil conditions in four cultured fields. Then, the relationships between the nitrogen levels and the leaf functional components were studied by culturing mulberry in plastic pots and experimental fields. In the common cultured fields, total nitrogen was negatively correlated with the chlorogenic acid content (R(2) = -0.48) and positively correlated with the 1-deoxynojirimycin content (R(2) = 0.60). Additionally, differences in nitrogen fertilizer application levels affected each functional component in mulberry leaves. For instance, with increased nitrogen levels, the chlorogenic acid and flavonol contents significantly decreased, but the 1-deoxynojirimycin content significantly increased. Selection of the optimal nitrogen application level is necessary to obtain the desired functional components from mulberry leaves.
Multilevel sparse functional principal component analysis.
Di, Chongzhi; Crainiceanu, Ciprian M; Jank, Wolfgang S
2014-01-29
We consider analysis of sparsely sampled multilevel functional data, where the basic observational unit is a function and data have a natural hierarchy of basic units. An example is when functions are recorded at multiple visits for each subject. Multilevel functional principal component analysis (MFPCA; Di et al. 2009) was proposed for such data when functions are densely recorded. Here we consider the case when functions are sparsely sampled and may contain only a few observations per function. We exploit the multilevel structure of covariance operators and achieve data reduction by principal component decompositions at both between and within subject levels. We address inherent methodological differences in the sparse sampling context to: 1) estimate the covariance operators; 2) estimate the functional principal component scores; 3) predict the underlying curves. Through simulations the proposed method is able to discover dominating modes of variations and reconstruct underlying curves well even in sparse settings. Our approach is illustrated by two applications, the Sleep Heart Health Study and eBay auctions.
Fatigue Reliability Analysis of Wind Turbine Cast Components
DEFF Research Database (Denmark)
Rafsanjani, Hesam Mirzaei; Sørensen, John Dalsgaard; Fæster, Søren
2017-01-01
.) and to quantify the relevant uncertainties using available fatigue tests. Illustrative results are presented as obtained by statistical analysis of a large set of fatigue data for casted test components typically used for wind turbines. Furthermore, the SN curves (fatigue life curves based on applied stress......The fatigue life of wind turbine cast components, such as the main shaft in a drivetrain, is generally determined by defects from the casting process. These defects may reduce the fatigue life and they are generally distributed randomly in components. The foundries, cutting facilities and test...... facilities can affect the verification of properties by testing. Hence, it is important to have a tool to identify which foundry, cutting and/or test facility produces components which, based on the relevant uncertainties, have the largest expected fatigue life or, alternatively, have the largest reliability...
Multi-component separation and analysis of bat echolocation calls.
DiCecco, John; Gaudette, Jason E; Simmons, James A
2013-01-01
The vast majority of animal vocalizations contain multiple frequency modulated (FM) components with varying amounts of non-linear modulation and harmonic instability. This is especially true of biosonar sounds where precise time-frequency templates are essential for neural information processing of echoes. Understanding the dynamic waveform design by bats and other echolocating animals may help to improve the efficacy of man-made sonar through biomimetic design. Bats are known to adapt their call structure based on the echolocation task, proximity to nearby objects, and density of acoustic clutter. To interpret the significance of these changes, a method was developed for component separation and analysis of biosonar waveforms. Techniques for imaging in the time-frequency plane are typically limited due to the uncertainty principle and interference cross terms. This problem is addressed by extending the use of the fractional Fourier transform to isolate each non-linear component for separate analysis. Once separated, empirical mode decomposition can be used to further examine each component. The Hilbert transform may then successfully extract detailed time-frequency information from each isolated component. This multi-component analysis method is applied to the sonar signals of four species of bats recorded in-flight by radiotelemetry along with a comparison of other common time-frequency representations.
A Genealogical Interpretation of Principal Components Analysis
McVean, Gil
2009-01-01
Principal components analysis, PCA, is a statistical method commonly used in population genetics to identify structure in the distribution of genetic variation across geographical location and ethnic background. However, while the method is often used to inform about historical demographic processes, little is known about the relationship between fundamental demographic parameters and the projection of samples onto the primary axes. Here I show that for SNP data the projection of samples onto the principal components can be obtained directly from considering the average coalescent times between pairs of haploid genomes. The result provides a framework for interpreting PCA projections in terms of underlying processes, including migration, geographical isolation, and admixture. I also demonstrate a link between PCA and Wright's fst and show that SNP ascertainment has a largely simple and predictable effect on the projection of samples. Using examples from human genetics, I discuss the application of these results to empirical data and the implications for inference. PMID:19834557
Energy Technology Data Exchange (ETDEWEB)
Suarez-Pena, B.; Asensio-Lozano, J.
2009-07-01
In this work the study focused on the possibility of use of new aluminium alloys with optimized microstructures that ensure the mechanical properties requested for cast components made by high pressure die casting. The objective was to check the possibility of manufacture of structurally sound eco-steps for escalators with reduced structural integrity. The former arises as a result of a new redesign of the traditional steps aiming at a significant weight reduction. The experimental results show that it is feasible to cut the use of materials during processing and therefore to reduce the impact of the components during its lifetime, whilst the performance and safety standards are kept identical or even improved. (Author) 17 refs.
APPLICATION OF PRINCIPAL COMPONENT ANALYSIS TO RELAXOGRAPHIC IMAGES
International Nuclear Information System (INIS)
STOYANOVA, R.S.; OCHS, M.F.; BROWN, T.R.; ROONEY, W.D.; LI, X.; LEE, J.H.; SPRINGER, C.S.
1999-01-01
Standard analysis methods for processing inversion recovery MR images traditionally have used single pixel techniques. In these techniques each pixel is independently fit to an exponential recovery, and spatial correlations in the data set are ignored. By analyzing the image as a complete dataset, improved error analysis and automatic segmentation can be achieved. Here, the authors apply principal component analysis (PCA) to a series of relaxographic images. This procedure decomposes the 3-dimensional data set into three separate images and corresponding recovery times. They attribute the 3 images to be spatial representations of gray matter (GM), white matter (WM) and cerebrospinal fluid (CSF) content
Signal-dependent independent component analysis by tunable mother wavelets
International Nuclear Information System (INIS)
Seo, Kyung Ho
2006-02-01
The objective of this study is to improve the standard independent component analysis when applied to real-world signals. Independent component analysis starts from the assumption that signals from different physical sources are statistically independent. But real-world signals such as EEG, ECG, MEG, and fMRI signals are not statistically independent perfectly. By definition, standard independent component analysis algorithms are not able to estimate statistically dependent sources, that is, when the assumption of independence does not hold. Therefore before independent component analysis, some preprocessing stage is needed. This paper started from simple intuition that wavelet transformed source signals by 'well-tuned' mother wavelet will be simplified sufficiently, and then the source separation will show better results. By the correlation coefficient method, the tuning process between source signal and tunable mother wavelet was executed. Gamma component of raw EEG signal was set to target signal, and wavelet transform was executed by tuned mother wavelet and standard mother wavelets. Simulation results by these wavelets was shown
Dekkers, G.J.M.; Nelissen, J.H.M.
2001-01-01
We look at the contribution of various income components on income inequality and the changes in this in Belgium.Starting from the Shorrocks decomposition, we apply bootstrapping to construct confidence intervals for both the annual decomposition and the changes over time.It appears that the
2010-04-01
... 21 Food and Drugs 2 2010-04-01 2010-04-01 false What requirements apply to holding components, dietary supplements, packaging, and labels? 111.455 Section 111.455 Food and Drugs FOOD AND DRUG ADMINISTRATION, DEPARTMENT OF HEALTH AND HUMAN SERVICES (CONTINUED) FOOD FOR HUMAN CONSUMPTION CURRENT GOOD...
Applied decision analysis and risk evaluation
International Nuclear Information System (INIS)
Ferse, W.; Kruber, S.
1995-01-01
During 1994 the workgroup 'Applied Decision Analysis and Risk Evaluation; continued the work on the knowledge based decision support system XUMA-GEFA for the evaluation of the hazard potential of contaminated sites. Additionally a new research direction was started which aims at the support of a later stage of the treatment of contaminated sites: The clean-up decision. For the support of decisions arising at this stage, the methods of decision analysis will be used. Computational aids for evaluation and decision support were implemented and a case study at a waste disposal site in Saxony which turns out to be a danger for the surrounding groundwater ressource was initiated. (orig.)
Radar fall detection using principal component analysis
Jokanovic, Branka; Amin, Moeness; Ahmad, Fauzia; Boashash, Boualem
2016-05-01
Falls are a major cause of fatal and nonfatal injuries in people aged 65 years and older. Radar has the potential to become one of the leading technologies for fall detection, thereby enabling the elderly to live independently. Existing techniques for fall detection using radar are based on manual feature extraction and require significant parameter tuning in order to provide successful detections. In this paper, we employ principal component analysis for fall detection, wherein eigen images of observed motions are employed for classification. Using real data, we demonstrate that the PCA based technique provides performance improvement over the conventional feature extraction methods.
Independent Component Analysis in Multimedia Modeling
DEFF Research Database (Denmark)
Larsen, Jan
2003-01-01
largely refers to text, images/video, audio and combinations of such data. We review a number of applications within single and combined media with the hope that this might provide inspiration for further research in this area. Finally, we provide a detailed presentation of our own recent work on modeling......Modeling of multimedia and multimodal data becomes increasingly important with the digitalization of the world. The objective of this paper is to demonstrate the potential of independent component analysis and blind sources separation methods for modeling and understanding of multimedia data, which...
Mapping ash properties using principal components analysis
Pereira, Paulo; Brevik, Eric; Cerda, Artemi; Ubeda, Xavier; Novara, Agata; Francos, Marcos; Rodrigo-Comino, Jesus; Bogunovic, Igor; Khaledian, Yones
2017-04-01
In post-fire environments ash has important benefits for soils, such as protection and source of nutrients, crucial for vegetation recuperation (Jordan et al., 2016; Pereira et al., 2015a; 2016a,b). The thickness and distribution of ash are fundamental aspects for soil protection (Cerdà and Doerr, 2008; Pereira et al., 2015b) and the severity at which was produced is important for the type and amount of elements that is released in soil solution (Bodi et al., 2014). Ash is very mobile material, and it is important were it will be deposited. Until the first rainfalls are is very mobile. After it, bind in the soil surface and is harder to erode. Mapping ash properties in the immediate period after fire is complex, since it is constantly moving (Pereira et al., 2015b). However, is an important task, since according the amount and type of ash produced we can identify the degree of soil protection and the nutrients that will be dissolved. The objective of this work is to apply to map ash properties (CaCO3, pH, and select extractable elements) using a principal component analysis (PCA) in the immediate period after the fire. Four days after the fire we established a grid in a 9x27 m area and took ash samples every 3 meters for a total of 40 sampling points (Pereira et al., 2017). The PCA identified 5 different factors. Factor 1 identified high loadings in electrical conductivity, calcium, and magnesium and negative with aluminum and iron, while Factor 3 had high positive loadings in total phosphorous and silica. Factor 3 showed high positive loadings in sodium and potassium, factor 4 high negative loadings in CaCO3 and pH, and factor 5 high loadings in sodium and potassium. The experimental variograms of the extracted factors showed that the Gaussian model was the most precise to model factor 1, the linear to model factor 2 and the wave hole effect to model factor 3, 4 and 5. The maps produced confirm the patternd observed in the experimental variograms. Factor 1 and 2
Analysis of spiral components in 16 galaxies
International Nuclear Information System (INIS)
Considere, S.; Athanassoula, E.
1988-01-01
A Fourier analysis of the intensity distributions in the plane of 16 spiral galaxies of morphological types from 1 to 7 is performed. The galaxies processed are NGC 300,598,628,2403,2841,3031,3198,3344,5033,5055,5194,5247,6946,7096,7217, and 7331. The method, mathematically based upon a decomposition of a distribution into a superposition of individual logarithmic spiral components, is first used to determine for each galaxy the position angle PA and the inclination ω of the galaxy plane onto the sky plane. Our results, in good agreement with those issued from different usual methods in the literature, are discussed. The decomposition of the deprojected galaxies into individual spiral components reveals that the two-armed component is everywhere dominant. Our pitch angles are then compared to the previously published ones and their quality is checked by drawing each individual logarithmic spiral on the actual deprojected galaxy images. Finally, the surface intensities for angular periodicities of interest are calculated. A choice of a few of the most important ones is used to elaborate a composite image well representing the main spiral features observed in the deprojected galaxies
Structural analysis of NPP components and structures
International Nuclear Information System (INIS)
Saarenheimo, A.; Keinaenen, H.; Talja, H.
1998-01-01
Capabilities for effective structural integrity assessment have been created and extended in several important cases. In the paper presented applications deal with pressurised thermal shock loading, PTS, and severe dynamic loading cases of containment, reinforced concrete structures and piping components. Hydrogen combustion within the containment is considered in some severe accident scenarios. Can a steel containment withstand the postulated hydrogen detonation loads and still maintain its integrity? This is the topic of Chapter 2. The following Chapter 3 deals with a reinforced concrete floor subjected to jet impingement caused by a postulated rupture of a near-by high-energy pipe and Chapter 4 deals with dynamic loading resistance of the pipe lines under postulated pressure transients due to water hammer. The reliability of the structural integrity analysing methods and capabilities which have been developed for application in NPP component assessment, shall be evaluated and verified. The resources available within the RATU2 programme alone cannot allow performing of the large scale experiments needed for that purpose. Thus, the verification of the PTS analysis capabilities has been conducted by participation in international co-operative programmes. Participation to the European Network for Evaluating Steel Components (NESC) is the topic of a parallel paper in this symposium. The results obtained in two other international programmes are summarised in Chapters 5 and 6 of this paper, where PTS tests with a model vessel and benchmark assessment of a RPV nozzle integrity are described. (author)
Reformulating Component Identification as Document Analysis Problem
Gross, H.G.; Lormans, M.; Zhou, J.
2007-01-01
One of the first steps of component procurement is the identification of required component features in large repositories of existing components. On the highest level of abstraction, component requirements as well as component descriptions are usually written in natural language. Therefore, we can
Nonlinear principal component analysis and its applications
Mori, Yuichi; Makino, Naomichi
2016-01-01
This book expounds the principle and related applications of nonlinear principal component analysis (PCA), which is useful method to analyze mixed measurement levels data. In the part dealing with the principle, after a brief introduction of ordinary PCA, a PCA for categorical data (nominal and ordinal) is introduced as nonlinear PCA, in which an optimal scaling technique is used to quantify the categorical variables. The alternating least squares (ALS) is the main algorithm in the method. Multiple correspondence analysis (MCA), a special case of nonlinear PCA, is also introduced. All formulations in these methods are integrated in the same manner as matrix operations. Because any measurement levels data can be treated consistently as numerical data and ALS is a very powerful tool for estimations, the methods can be utilized in a variety of fields such as biometrics, econometrics, psychometrics, and sociology. In the applications part of the book, four applications are introduced: variable selection for mixed...
Applied research in uncertainty modeling and analysis
Ayyub, Bilal
2005-01-01
Uncertainty has been a concern to engineers, managers, and scientists for many years. For a long time uncertainty has been considered synonymous with random, stochastic, statistic, or probabilistic. Since the early sixties views on uncertainty have become more heterogeneous. In the past forty years numerous tools that model uncertainty, above and beyond statistics, have been proposed by several engineers and scientists. The tool/method to model uncertainty in a specific context should really be chosen by considering the features of the phenomenon under consideration, not independent of what is known about the system and what causes uncertainty. In this fascinating overview of the field, the authors provide broad coverage of uncertainty analysis/modeling and its application. Applied Research in Uncertainty Modeling and Analysis presents the perspectives of various researchers and practitioners on uncertainty analysis and modeling outside their own fields and domain expertise. Rather than focusing explicitly on...
Principal Component Analysis - A Powerful Tool in Computing Marketing Information
Directory of Open Access Journals (Sweden)
Constantin C.
2014-12-01
Full Text Available This paper is about an instrumental research regarding a powerful multivariate data analysis method which can be used by the researchers in order to obtain valuable information for decision makers that need to solve the marketing problem a company face with. The literature stresses the need to avoid the multicollinearity phenomenon in multivariate analysis and the features of Principal Component Analysis (PCA in reducing the number of variables that could be correlated with each other to a small number of principal components that are uncorrelated. In this respect, the paper presents step-by-step the process of applying the PCA in marketing research when we use a large number of variables that naturally are collinear.
Strategic decision analysis applied to borehole seismology
International Nuclear Information System (INIS)
Menke, M.M.; Paulsson, B.N.P.
1994-01-01
Strategic Decision Analysis (SDA) is the evolving body of knowledge on how to achieve high quality in the decision that shapes an organization's future. SDA comprises philosophy, process concepts, methodology, and tools for making good decisions. It specifically incorporates many concepts and tools from economic evaluation and risk analysis. Chevron Petroleum Technology Company (CPTC) has applied SDA to evaluate and prioritize a number of its most important and most uncertain R and D projects, including borehole seismology. Before SDA, there were significant issues and concerns about the value to CPTC of continuing to work on borehole seismology. The SDA process created a cross-functional team of experts to structure and evaluate this project. A credible economic model was developed, discrete risks and continuous uncertainties were assessed, and an extensive sensitivity analysis was performed. The results, even applied to a very restricted drilling program for a few years, were good enough to demonstrate the value of continuing the project. This paper explains the SDA philosophy concepts, and process and demonstrates the methodology and tools using the borehole seismology project example. SDA is useful in the upstream industry not just in the R and D/technology decisions, but also in major exploration and production decisions. Since a major challenge for upstream companies today is to create and realize value, the SDA approach should have a very broad applicability
Review on characterization methods applied to HTR-fuel element components
International Nuclear Information System (INIS)
Koizlik, K.
1976-02-01
One of the difficulties which on the whole are of no special scientific interest, but which bear a lot of technical problems for the development and production of HTR fuel elements is the proper characterization of the element and its components. Consequently a lot of work has been done during the past years to develop characterization procedures for the fuel, the fuel kernel, the pyrocarbon for the coatings, the matrix and graphite and their components binder and filler. This paper tries to give a status report on characterization procedures which are applied to HTR fuel in KFA and cooperating institutions. (orig.) [de
Functional Data Analysis Applied in Chemometrics
DEFF Research Database (Denmark)
Muller, Martha
nutritional status and metabolic phenotype. We want to understand how metabolomic spectra can be analysed using functional data analysis to detect the in uence of dierent factors on specic metabolites. These factors can include, for example, gender, diet culture or dietary intervention. In Paper I we apply...... representation of each spectrum. Subset selection of wavelet coecients generates the input to mixed models. Mixed-model methodology enables us to take the study design into account while modelling covariates. Bootstrap-based inference preserves the correlation structure between curves and enables the estimation...
Principal component analysis networks and algorithms
Kong, Xiangyu; Duan, Zhansheng
2017-01-01
This book not only provides a comprehensive introduction to neural-based PCA methods in control science, but also presents many novel PCA algorithms and their extensions and generalizations, e.g., dual purpose, coupled PCA, GED, neural based SVD algorithms, etc. It also discusses in detail various analysis methods for the convergence, stabilizing, self-stabilizing property of algorithms, and introduces the deterministic discrete-time systems method to analyze the convergence of PCA/MCA algorithms. Readers should be familiar with numerical analysis and the fundamentals of statistics, such as the basics of least squares and stochastic algorithms. Although it focuses on neural networks, the book only presents their learning law, which is simply an iterative algorithm. Therefore, no a priori knowledge of neural networks is required. This book will be of interest and serve as a reference source to researchers and students in applied mathematics, statistics, engineering, and other related fields.
Component fragilities - data collection, analysis and interpretation
International Nuclear Information System (INIS)
Bandyopadhyay, K.K.; Hofmayer, C.H.
1986-01-01
As part of the component fragility research program sponsored by the US Nuclear Regulatory Commission, BNL is involved in establishing seismic fragility levels for various nuclear power plant equipment with emphasis on electrical equipment, by identifying, collecting and analyzing existing test data from various sources. BNL has reviewed approximately seventy test reports to collect fragility or high level test data for switchgears, motor control centers and similar electrical cabinets, valve actuators and numerous electrical and control devices of various manufacturers and models. Through a cooperative agreement, BNL has also obtained test data from EPRI/ANCO. An analysis of the collected data reveals that fragility levels can best be described by a group of curves corresponding to various failure modes. The lower bound curve indicates the initiation of malfunctioning or structural damage, whereas the upper bound curve corresponds to overall failure of the equipment based on known failure modes occurring separately or interactively. For some components, the upper and lower bound fragility levels are observed to vary appreciably depending upon the manufacturers and models. An extensive amount of additional fragility or high level test data exists. If completely collected and properly analyzed, the entire data bank is expected to greatly reduce the need for additional testing to establish fragility levels for most equipment
Component fragilities. Data collection, analysis and interpretation
International Nuclear Information System (INIS)
Bandyopadhyay, K.K.; Hofmayer, C.H.
1985-01-01
As part of the component fragility research program sponsored by the US NRC, BNL is involved in establishing seismic fragility levels for various nuclear power plant equipment with emphasis on electrical equipment. To date, BNL has reviewed approximately seventy test reports to collect fragility or high level test data for switchgears, motor control centers and similar electrical cabinets, valve actuators and numerous electrical and control devices, e.g., switches, transmitters, potentiometers, indicators, relays, etc., of various manufacturers and models. BNL has also obtained test data from EPRI/ANCO. Analysis of the collected data reveals that fragility levels can best be described by a group of curves corresponding to various failure modes. The lower bound curve indicates the initiation of malfunctioning or structural damage, whereas the upper bound curve corresponds to overall failure of the equipment based on known failure modes occurring separately or interactively. For some components, the upper and lower bound fragility levels are observed to vary appreciably depending upon the manufacturers and models. For some devices, testing even at the shake table vibration limit does not exhibit any failure. Failure of a relay is observed to be a frequent cause of failure of an electrical panel or a system. An extensive amount of additional fregility or high level test data exists
Applied spectrophotometry: analysis of a biochemical mixture.
Trumbo, Toni A; Schultz, Emeric; Borland, Michael G; Pugh, Michael Eugene
2013-01-01
Spectrophotometric analysis is essential for determining biomolecule concentration of a solution and is employed ubiquitously in biochemistry and molecular biology. The application of the Beer-Lambert-Bouguer Lawis routinely used to determine the concentration of DNA, RNA or protein. There is however a significant difference in determining the concentration of a given species (RNA, DNA, protein) in isolation (a contrived circumstance) as opposed to determining that concentration in the presence of other species (a more realistic situation). To present the student with a more realistic laboratory experience and also to fill a hole that we believe exists in student experience prior to reaching a biochemistry course, we have devised a three week laboratory experience designed so that students learn to: connect laboratory practice with theory, apply the Beer-Lambert-Bougert Law to biochemical analyses, demonstrate the utility and limitations of example quantitative colorimetric assays, demonstrate the utility and limitations of UV analyses for biomolecules, develop strategies for analysis of a solution of unknown biomolecular composition, use digital micropipettors to make accurate and precise measurements, and apply graphing software. Copyright © 2013 Wiley Periodicals, Inc.
The RAGE Game Software Components Repository for Supporting Applied Game Development
Directory of Open Access Journals (Sweden)
Krassen Stefanov
2017-09-01
Full Text Available This paper presents the architecture of the RAGE repository, which is a unique and dedicated infrastructure that provides access to a wide variety of advanced technology components for applied game development. The RAGE project, which is the principal Horizon2020 research and innovation project on applied gaming, develops up to three dozens of software components (RAGE software assets that are reusable across a wide diversity of game engines, game platforms and programming languages. The RAGE repository provides storage space for assets and their artefacts and is designed as an asset life-cycle management system for defining, publishing, updating, searching and packaging for distribution of these assets. It will be embedded in a social platform for asset developers and other users. A dedicated Asset Repository Manager provides the main functionality of the repository and its integration with other systems. Tools supporting the Asset Manager are presented and discussed. When the RAGE repository is in full operation, applied game developers will be able to easily enhance the quality of their games by including selected advanced game software assets. Making available the RAGE repository system and its variety of software assets aims to enhance the coherence and decisiveness of the applied game industry.
Tissue Microarray Analysis Applied to Bone Diagenesis.
Mello, Rafael Barrios; Silva, Maria Regina Regis; Alves, Maria Teresa Seixas; Evison, Martin Paul; Guimarães, Marco Aurelio; Francisco, Rafaella Arrabaca; Astolphi, Rafael Dias; Iwamura, Edna Sadayo Miazato
2017-01-04
Taphonomic processes affecting bone post mortem are important in forensic, archaeological and palaeontological investigations. In this study, the application of tissue microarray (TMA) analysis to a sample of femoral bone specimens from 20 exhumed individuals of known period of burial and age at death is described. TMA allows multiplexing of subsamples, permitting standardized comparative analysis of adjacent sections in 3-D and of representative cross-sections of a large number of specimens. Standard hematoxylin and eosin, periodic acid-Schiff and silver methenamine, and picrosirius red staining, and CD31 and CD34 immunohistochemistry were applied to TMA sections. Osteocyte and osteocyte lacuna counts, percent bone matrix loss, and fungal spheroid element counts could be measured and collagen fibre bundles observed in all specimens. Decalcification with 7% nitric acid proceeded more rapidly than with 0.5 M EDTA and may offer better preservation of histological and cellular structure. No endothelial cells could be detected using CD31 and CD34 immunohistochemistry. Correlation between osteocytes per lacuna and age at death may reflect reported age-related responses to microdamage. Methodological limitations and caveats, and results of the TMA analysis of post mortem diagenesis in bone are discussed, and implications for DNA survival and recovery considered.
Applied linear algebra and matrix analysis
Shores, Thomas S
2018-01-01
In its second edition, this textbook offers a fresh approach to matrix and linear algebra. Its blend of theory, computational exercises, and analytical writing projects is designed to highlight the interplay between these aspects of an application. This approach places special emphasis on linear algebra as an experimental science that provides tools for solving concrete problems. The second edition’s revised text discusses applications of linear algebra like graph theory and network modeling methods used in Google’s PageRank algorithm. Other new materials include modeling examples of diffusive processes, linear programming, image processing, digital signal processing, and Fourier analysis. These topics are woven into the core material of Gaussian elimination and other matrix operations; eigenvalues, eigenvectors, and discrete dynamical systems; and the geometrical aspects of vector spaces. Intended for a one-semester undergraduate course without a strict calculus prerequisite, Applied Linear Algebra and M...
Epithermal neutron activation analysis in applied microbiology
International Nuclear Information System (INIS)
Marina Frontasyeva
2012-01-01
Some results from applying epithermal neutron activation analysis at FLNP JINR, Dubna, Russia, in medical biotechnology, environmental biotechnology and industrial biotechnology are reviewed. In the biomedical experiments biomass from the blue-green alga Spirulina platensis (S. platensis) has been used as a matrix for the development of pharmaceutical substances containing such essential trace elements as selenium, chromium and iodine. The feasibility of target-oriented introduction of these elements into S. platensis biocomplexes retaining its protein composition and natural beneficial properties was shown. The absorption of mercury on growth dynamics of S. platensis and other bacterial strains was observed. Detoxification of Cr and Hg by Arthrobacter globiformis 151B was demonstrated. Microbial synthesis of technologically important silver nanoparticles by the novel actinomycete strain Streptomyces glaucus 71 MD and blue-green alga S. platensis were characterized by a combined use of transmission electron microscopy, scanning electron microscopy and energy-dispersive analysis of X-rays. It was established that the tested actinomycete S. glaucus 71 MD produces silver nanoparticles extracellularly when acted upon by the silver nitrate solution, which offers a great advantage over an intracellular process of synthesis from the point of view of applications. The synthesis of silver nanoparticles by S. platensis proceeded differently under the short-term and long-term silver action. (author)
Group-wise Principal Component Analysis for Exploratory Data Analysis
Camacho, J.; Rodriquez-Gomez, Rafael A.; Saccenti, E.
2017-01-01
In this paper, we propose a new framework for matrix factorization based on Principal Component Analysis (PCA) where sparsity is imposed. The structure to impose sparsity is defined in terms of groups of correlated variables found in correlation matrices or maps. The framework is based on three new
Gene set analysis using variance component tests.
Huang, Yen-Tsung; Lin, Xihong
2013-06-28
Gene set analyses have become increasingly important in genomic research, as many complex diseases are contributed jointly by alterations of numerous genes. Genes often coordinate together as a functional repertoire, e.g., a biological pathway/network and are highly correlated. However, most of the existing gene set analysis methods do not fully account for the correlation among the genes. Here we propose to tackle this important feature of a gene set to improve statistical power in gene set analyses. We propose to model the effects of an independent variable, e.g., exposure/biological status (yes/no), on multiple gene expression values in a gene set using a multivariate linear regression model, where the correlation among the genes is explicitly modeled using a working covariance matrix. We develop TEGS (Test for the Effect of a Gene Set), a variance component test for the gene set effects by assuming a common distribution for regression coefficients in multivariate linear regression models, and calculate the p-values using permutation and a scaled chi-square approximation. We show using simulations that type I error is protected under different choices of working covariance matrices and power is improved as the working covariance approaches the true covariance. The global test is a special case of TEGS when correlation among genes in a gene set is ignored. Using both simulation data and a published diabetes dataset, we show that our test outperforms the commonly used approaches, the global test and gene set enrichment analysis (GSEA). We develop a gene set analyses method (TEGS) under the multivariate regression framework, which directly models the interdependence of the expression values in a gene set using a working covariance. TEGS outperforms two widely used methods, GSEA and global test in both simulation and a diabetes microarray data.
Thermogravimetric analysis of combustible waste components
DEFF Research Database (Denmark)
Munther, Anette; Wu, Hao; Glarborg, Peter
In order to gain fundamental knowledge about the co-combustion of coal and waste derived fuels, the pyrolytic behaviors of coal, four typical waste components and their mixtures have been studied by a simultaneous thermal analyzer (STA). The investigated waste components were wood, paper, polypro......In order to gain fundamental knowledge about the co-combustion of coal and waste derived fuels, the pyrolytic behaviors of coal, four typical waste components and their mixtures have been studied by a simultaneous thermal analyzer (STA). The investigated waste components were wood, paper...
Fast and accurate methods of independent component analysis: A survey
Czech Academy of Sciences Publication Activity Database
Tichavský, Petr; Koldovský, Zbyněk
2011-01-01
Roč. 47, č. 3 (2011), s. 426-438 ISSN 0023-5954 R&D Projects: GA MŠk 1M0572; GA ČR GA102/09/1278 Institutional research plan: CEZ:AV0Z10750506 Keywords : Blind source separation * artifact removal * electroencephalogram * audio signal processing Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 0.454, year: 2011 http://library.utia.cas.cz/separaty/2011/SI/tichavsky-fast and accurate methods of independent component analysis a survey.pdf
PRINCIPAL COMPONENT ANALYSIS (PCA DAN APLIKASINYA DENGAN SPSS
Directory of Open Access Journals (Sweden)
Hermita Bus Umar
2009-03-01
Full Text Available PCA (Principal Component Analysis are statistical techniques applied to a single set of variables when the researcher is interested in discovering which variables in the setform coherent subset that are relativity independent of one another.Variables that are correlated with one another but largely independent of other subset of variables are combined into factors. The Coals of PCA to which each variables is explained by each dimension. Step in PCA include selecting and mean measuring a set of variables, preparing the correlation matrix, extracting a set offactors from the correlation matrixs. Rotating the factor to increase interpretabilitv and interpreting the result.
Analysis of the interaction between experimental and applied behavior analysis.
Virues-Ortega, Javier; Hurtado-Parrado, Camilo; Cox, Alison D; Pear, Joseph J
2014-01-01
To study the influences between basic and applied research in behavior analysis, we analyzed the coauthorship interactions of authors who published in JABA and JEAB from 1980 to 2010. We paid particular attention to authors who published in both JABA and JEAB (dual authors) as potential agents of cross-field interactions. We present a comprehensive analysis of dual authors' coauthorship interactions using social networks methodology and key word analysis. The number of dual authors more than doubled (26 to 67) and their productivity tripled (7% to 26% of JABA and JEAB articles) between 1980 and 2010. Dual authors stood out in terms of number of collaborators, number of publications, and ability to interact with multiple groups within the field. The steady increase in JEAB and JABA interactions through coauthors and the increasing range of topics covered by dual authors provide a basis for optimism regarding the progressive integration of basic and applied behavior analysis. © Society for the Experimental Analysis of Behavior.
Analysis of failed nuclear plant components
Diercks, D. R.
1993-12-01
Argonne National Laboratory has conducted analyses of failed components from nuclear power- gener-ating stations since 1974. The considerations involved in working with and analyzing radioactive compo-nents are reviewed here, and the decontamination of these components is discussed. Analyses of four failed components from nuclear plants are then described to illustrate the kinds of failures seen in serv-ice. The failures discussed are (1) intergranular stress- corrosion cracking of core spray injection piping in a boiling water reactor, (2) failure of canopy seal welds in adapter tube assemblies in the control rod drive head of a pressurized water reactor, (3) thermal fatigue of a recirculation pump shaft in a boiling water reactor, and (4) failure of pump seal wear rings by nickel leaching in a boiling water reactor.
Analysis of failed nuclear plant components
International Nuclear Information System (INIS)
Diercks, D.R.
1993-01-01
Argonne National Laboratory has conducted analyses of failed components from nuclear power-generating stations since 1974. The considerations involved in working with an analyzing radioactive components are reviewed here, and the decontamination of these components is discussed. Analyses of four failed components from nuclear plants are then described to illustrate the kinds of failures seen in service. The failures discussed are (1) intergranular stress-corrosion cracking of core spray injection piping in a boiling water reactor, (2) failure of canopy seal welds in adapter tube assemblies in the control rod drive head of a pressurized water reactor, (3) thermal fatigue of a recirculation pump shaft in a boiling water reactor, and (4) failure of pump seal wear rings by nickel leaching in a boiling water reactor
Analysis of failed nuclear plant components
International Nuclear Information System (INIS)
Diercks, D.R.
1992-07-01
Argonne National Laboratory has conducted analyses of failed components from nuclear power generating stations since 1974. The considerations involved in working with and analyzing radioactive components are reviewed here, and the decontamination of these components is discussed. Analyses of four failed components from nuclear plants are then described to illustrate the kinds of failures seen in service. The failures discussed are (a) intergranular stress corrosion cracking of core spray injection piping in a boiling water reactor, (b) failure of canopy seal welds in adapter tube assemblies in the control rod drive head of a pressure water reactor, (c) thermal fatigue of a recirculation pump shaft in a boiling water reactor, and (d) failure of pump seal wear rings by nickel leaching in a boiling water reactor
Social network analysis applied to team sports analysis
Clemente, Filipe Manuel; Mendes, Rui Sousa
2016-01-01
Explaining how graph theory and social network analysis can be applied to team sports analysis, This book presents useful approaches, models and methods that can be used to characterise the overall properties of team networks and identify the prominence of each team player. Exploring the different possible network metrics that can be utilised in sports analysis, their possible applications and variances from situation to situation, the respective chapters present an array of illustrative case studies. Identifying the general concepts of social network analysis and network centrality metrics, readers are shown how to generate a methodological protocol for data collection. As such, the book provides a valuable resource for students of the sport sciences, sports engineering, applied computation and the social sciences.
A radiographic analysis of implant component misfit.
LENUS (Irish Health Repository)
Sharkey, Seamus
2011-07-01
Radiographs are commonly used to assess the fit of implant components, but there is no clear agreement on the amount of misfit that can be detected by this method. This study investigated the effect of gap size and the relative angle at which a radiograph was taken on the detection of component misfit. Different types of implant connections (internal or external) and radiographic modalities (film or digital) were assessed.
Representation for dialect recognition using topographic independent component analysis
Wei, Qu
2004-10-01
In dialect speech recognition, the feature of tone in one dialect is subject to changes in pitch frequency as well as the length of tone. It is beneficial for the recognition if a representation can be derived to account for the frequency and length changes of tone in an effective and meaningful way. In this paper, we propose a method for learning such a representation from a set of unlabeled speech sentences containing the features of the dialect changed from various pitch frequencies and time length. Topographic independent component analysis (TICA) is applied for the unsupervised learning to produce an emergent result that is a topographic matrix made up of basis components. The dialect speech is topographic in the following sense: the basis components as the units of the speech are ordered in the feature matrix such that components of one dialect are grouped in one axis and changes in time windows are accounted for in the other axis. This provides a meaningful set of basis vectors that may be used to construct dialect subspaces for dialect speech recognition.
Lifetime analysis of fusion-reactor components
International Nuclear Information System (INIS)
Mattas, R.F.
1983-01-01
A one-dimensional computer code has been developed to examine the lifetime of first-wall and impurity-control components. The code incorporates the operating and design parameters, the material characteristics, and the appropriate failure criteria for the individual components. The major emphasis of the modelling effort has been to calculate the temperature-stress-strain-radiation effects history of a component so that the synergystic effects between sputtering erosion, swelling, creep, fatigue, and crack growth can be examined. The general forms of the property equations are the same for all materials in order to provide the greatest flexibility for materials selection in the code. The code is capable of determining the behavior of a plate, composed of either a single or dual material structure, that is either totally constrained or constrained from bending but not from expansion. The code has been utilized to analyze the first walls for FED/INTOR and DEMO
Dynamic analysis of the radiolysis of binary component system
International Nuclear Information System (INIS)
Katayama, M.; Trumbore, C.N.
1975-01-01
Dynamic analysis was performed on a variety of combinations of components in the radiolysis of binary system, taking the hydrogen-producing reaction with hydrocarbon RH 2 as an example. A definite rule was able to be established from this analysis, which is useful for revealing the reaction mechanism. The combinations were as follows: 1) both components A and B do not interact but serve only as diluents, 2) A is a diluent, and B is a radical captor, 3) both A and B are radical captors, 4-1) A is a diluent, and B decomposes after the reception of the exciting energy of A, 4-2) A is a diluent, and B does not participate in decomposition after the reception of the exciting energy of A, 5-1) A is a radical captor, and B decomposes after the reception of the exciting energy of A, 5-2) A is a radical captor, and B does not participate in decomposition after the reception of the exciting energy of A, 6-1) both A and B decompose after the reception of the exciting energy of the partner component; and 6-2) both A and B do not decompose after the reception of the exciting energy of the partner component. According to the dynamical analysis of the above nine combinations, it can be pointed out that if excitation transfer participates, the similar phenomena to radical capture are presented apparently. It is desirable to measure the yield of radicals experimentally with the system which need not much consideration to the excitation transfer. Isotope substitution mixture system is conceived as one of such system. This analytical method was applied to the system containing cyclopentanone, such as cyclopentanone-cyclohexane system. (Iwakiri, K.)
Principal component analysis of psoriasis lesions images
DEFF Research Database (Denmark)
Maletti, Gabriela Mariel; Ersbøll, Bjarne Kjær
2003-01-01
A set of RGB images of psoriasis lesions is used. By visual examination of these images, there seem to be no common pattern that could be used to find and align the lesions within and between sessions. It is expected that the principal components of the original images could be useful during future...
Colilert® applied to food analysis
Directory of Open Access Journals (Sweden)
Maria José Rodrigues
2014-06-01
Full Text Available Colilert® (IDEXX was originally developed for the simultaneous enumeration of coliforms and E. coli in water samples and has been used for the quality control routine of drinking, swimming pools, fresh, coastal and waste waters (Grossi et al., 2013. The Colilert® culture medium contains the indicator nutrient 4-Methylumbelliferyl-β-D-Glucuronide (MUG. MUG acts as a substrate for the E. coli enzyme β-glucuronidase, from which a fluorescent compound is produced. A positive MUG result produces fluorescence when viewed under an ultraviolet lamp. If the test fluorescence is equal to or greater than that of the control, the presence of E. coli has been confirmed (Lopez-Roldan et al., 2013. The present work aimed to apply Colilert® to the enumeration of E. coli in different foods, through the comparison of results against the reference method (ISO 16649-2, 2001 for E. coli food analysis. The study was divided in two stages. During the first stage ten different types of foods were analyzed with Colilert®, these included pastry, raw meat, ready to eat meals, yogurt, raw seabream and salmon, and cooked shrimp. From these it were approved the following: pastry with custard; raw minced pork; soup "caldo-verde"; raw vegetable salad (lettuce and carrots and solid yogurt. The approved foods presented a better insertion in the tray, the colour of the wells was lighter and the UV reading was easier. In the second stage the foods were artificially contaminated with 2 log/g of E. coli (ATCC 25922 and analyzed. Colilert® proved to be an accurate method and the counts were similar to the ones obtained with the reference method. In the present study, the Colilert® method did not reveal neither false-positive or false-negative results, however sometimes the results were difficult to read due to the presence of green fluorescence in some wells. Generally Colilert® was an easy and rapid method, but less objective and more expensive than the reference method.
Technical Note: Introduction of variance component analysis to setup error analysis in radiotherapy
Energy Technology Data Exchange (ETDEWEB)
Matsuo, Yukinori, E-mail: ymatsuo@kuhp.kyoto-u.ac.jp; Nakamura, Mitsuhiro; Mizowaki, Takashi; Hiraoka, Masahiro [Department of Radiation Oncology and Image-applied Therapy, Kyoto University, 54 Shogoin-Kawaharacho, Sakyo, Kyoto 606-8507 (Japan)
2016-09-15
Purpose: The purpose of this technical note is to introduce variance component analysis to the estimation of systematic and random components in setup error of radiotherapy. Methods: Balanced data according to the one-factor random effect model were assumed. Results: Analysis-of-variance (ANOVA)-based computation was applied to estimate the values and their confidence intervals (CIs) for systematic and random errors and the population mean of setup errors. The conventional method overestimates systematic error, especially in hypofractionated settings. The CI for systematic error becomes much wider than that for random error. The ANOVA-based estimation can be extended to a multifactor model considering multiple causes of setup errors (e.g., interpatient, interfraction, and intrafraction). Conclusions: Variance component analysis may lead to novel applications to setup error analysis in radiotherapy.
Technical Note: Introduction of variance component analysis to setup error analysis in radiotherapy
International Nuclear Information System (INIS)
Matsuo, Yukinori; Nakamura, Mitsuhiro; Mizowaki, Takashi; Hiraoka, Masahiro
2016-01-01
Purpose: The purpose of this technical note is to introduce variance component analysis to the estimation of systematic and random components in setup error of radiotherapy. Methods: Balanced data according to the one-factor random effect model were assumed. Results: Analysis-of-variance (ANOVA)-based computation was applied to estimate the values and their confidence intervals (CIs) for systematic and random errors and the population mean of setup errors. The conventional method overestimates systematic error, especially in hypofractionated settings. The CI for systematic error becomes much wider than that for random error. The ANOVA-based estimation can be extended to a multifactor model considering multiple causes of setup errors (e.g., interpatient, interfraction, and intrafraction). Conclusions: Variance component analysis may lead to novel applications to setup error analysis in radiotherapy.
EXAFS and principal component analysis : a new shell game
International Nuclear Information System (INIS)
Wasserman, S.
1998-01-01
The use of principal component (factor) analysis in the analysis EXAFS spectra is described. The components derived from EXAFS spectra share mathematical properties with the original spectra. As a result, the abstract components can be analyzed using standard EXAFS methodology to yield the bond distances and other coordination parameters. The number of components that must be analyzed is usually less than the number of original spectra. The method is demonstrated using a series of spectra from aqueous solutions of uranyl ions
Effect of applied voltage on phase components of composite coatings prepared by micro-arc oxidation
Energy Technology Data Exchange (ETDEWEB)
Zhu, Wenjun [Department of Prosthodontics, Guanghua School of Stomatology, Sun Yat-sen University, Guangzhou 510055 (China); Fang, Yu-Jing [Department of Colorectal Surgery, State Key Laboratory of Oncology in South China, Sun Yat-sen University Cancer Center, Guangzhou 510060 (China); Zheng, Huade [College of Materials Science and Engineering, South China University of Technology, Guangzhou 510641 (China); Tan, Guoxin [Guangdong University of Technology, Guangdong Province 510006 (China); Cheng, Haimei [College of Materials Science and Engineering, South China University of Technology, Guangzhou 510641 (China); Ning, Chengyun, E-mail: imcyning@scut.edu.cn [College of Materials Science and Engineering, South China University of Technology, Guangzhou 510641 (China)
2013-10-01
In this report, we present results from our experiments on composite coatings formed on biomedical titanium substrates by micro-arc oxidation (MAO) in constant-voltage mode. The coatings were prepared on the substrates in an aqueous electrolyte containing calcium acetate and β-glycerol phosphate disodium salt pentahydrate (β-GP). We analyzed the element distribution and phase components of the coatings prepared at different voltages by X-ray diffraction, thin-coating X-ray diffraction, electron-probe microanalysis, and Fourier-transform infrared spectroscopy. The results show that the composite coatings formed at 500 V consist of titania (TiO{sub 2}), hydroxylapatite (HA), and calcium carbonate (CaCO{sub 3}). Furthermore, the concentration of Ca, P, and Ti gradually changes with increasing applied voltage, and the phase components of the composite coatings gradually change from the bottom of the coating to the top: the bottom layer consists of TiO{sub 2}, the middle layer consists of TiO{sub 2} and HA, and the top layer consists of HA and a small amount of CaCO{sub 3}. The formation of HA directly on the coating surface by MAO technique can greatly enhance the surface bioactivity. - Highlights: • Coatings prepared on biomedical titanium substrate by micro-arc oxidation • Coatings composed of titania, hydroxyapatite and calcium carbonate • Hydroxyapatite on the coating surface can enhance the surface bioactivity.
Moving Forward: Positive Behavior Support and Applied Behavior Analysis
Tincani, Matt
2007-01-01
A controversy has emerged about the relationship between positive behavior support and applied behavior analysis. Some behavior analysts suggest that positive behavior support and applied behavior analysis are the same (e.g., Carr & Sidener, 2002). Others argue that positive behavior support is harmful to applied behavior analysis (e.g., Johnston,…
Introduction: Conversation Analysis in Applied Linguistics
Sert, Olcay; Seedhouse, Paul
2011-01-01
This short, introductory paper presents an up-to-date account of works within the field of Applied Linguistics which have been influenced by a Conversation Analytic paradigm. The article reviews recent studies in classroom interaction, materials development, proficiency assessment and language teacher education. We believe that the publication of…
International Nuclear Information System (INIS)
Aumeunier, M-H.; Corre, Y.; Firdaouss, M.; Gauthier, E.; Loarer, T.; Travere, J-M.; Gardarein, J-L.; EFDA JET Contributor
2013-06-01
In nuclear fusion experiments, the protection system of the Plasma Facing Components (PFCs) is commonly ensured by infrared (IR) thermography. Nevertheless, the surface monitoring of new metallic plasma facing component, as in JET and ITER is being challenging. Indeed, the analysis of infrared signals is made more complicated in such a metallic environment since the signals will be perturbed by the reflected photons coming from high temperature regions. To address and anticipate this new measurement environment, predictive photonic models, based on Monte-Carlo ray tracing (SPEOS R CAA V5 Based), have been performed to assess the contribution of the reflective part in the total flux collected by the camera and the resulting temperature error. This paper deals with the effects of metals features, as the emissivity and reflectivity models, on the accuracy of the surface temperature estimation. The reliability of the features models is discussed by comparing the simulation with experimental data obtained with the wide angle IR thermography system of JET ITER like wall. The impact of the temperature distribution is studied by considering two different typical plasma scenarios, in limiter (ITER start-up scenario) and in X-point configurations (standard divertor scenario). The achievable measurement performances of IR system and risks analysis on its functionalities are discussed. (authors)
Incremental Tensor Principal Component Analysis for Handwritten Digit Recognition
Directory of Open Access Journals (Sweden)
Chang Liu
2014-01-01
Full Text Available To overcome the shortcomings of traditional dimensionality reduction algorithms, incremental tensor principal component analysis (ITPCA based on updated-SVD technique algorithm is proposed in this paper. This paper proves the relationship between PCA, 2DPCA, MPCA, and the graph embedding framework theoretically and derives the incremental learning procedure to add single sample and multiple samples in detail. The experiments on handwritten digit recognition have demonstrated that ITPCA has achieved better recognition performance than that of vector-based principal component analysis (PCA, incremental principal component analysis (IPCA, and multilinear principal component analysis (MPCA algorithms. At the same time, ITPCA also has lower time and space complexity.
Manisera, M.; Kooij, A.J. van der; Dusseldorp, E.
2010-01-01
The component structure of 14 Likert-type items measuring different aspects of job satisfaction was investigated using nonlinear Principal Components Analysis (NLPCA). NLPCA allows for analyzing these items at an ordinal or interval level. The participants were 2066 workers from five types of social
Columbia River Component Data Gap Analysis
Energy Technology Data Exchange (ETDEWEB)
L. C. Hulstrom
2007-10-23
This Data Gap Analysis report documents the results of a study conducted by Washington Closure Hanford (WCH) to compile and reivew the currently available surface water and sediment data for the Columbia River near and downstream of the Hanford Site. This Data Gap Analysis study was conducted to review the adequacy of the existing surface water and sediment data set from the Columbia River, with specific reference to the use of the data in future site characterization and screening level risk assessments.
Nonlinear Process Fault Diagnosis Based on Serial Principal Component Analysis.
Deng, Xiaogang; Tian, Xuemin; Chen, Sheng; Harris, Chris J
2018-03-01
Many industrial processes contain both linear and nonlinear parts, and kernel principal component analysis (KPCA), widely used in nonlinear process monitoring, may not offer the most effective means for dealing with these nonlinear processes. This paper proposes a new hybrid linear-nonlinear statistical modeling approach for nonlinear process monitoring by closely integrating linear principal component analysis (PCA) and nonlinear KPCA using a serial model structure, which we refer to as serial PCA (SPCA). Specifically, PCA is first applied to extract PCs as linear features, and to decompose the data into the PC subspace and residual subspace (RS). Then, KPCA is performed in the RS to extract the nonlinear PCs as nonlinear features. Two monitoring statistics are constructed for fault detection, based on both the linear and nonlinear features extracted by the proposed SPCA. To effectively perform fault identification after a fault is detected, an SPCA similarity factor method is built for fault recognition, which fuses both the linear and nonlinear features. Unlike PCA and KPCA, the proposed method takes into account both linear and nonlinear PCs simultaneously, and therefore, it can better exploit the underlying process's structure to enhance fault diagnosis performance. Two case studies involving a simulated nonlinear process and the benchmark Tennessee Eastman process demonstrate that the proposed SPCA approach is more effective than the existing state-of-the-art approach based on KPCA alone, in terms of nonlinear process fault detection and identification.
Use of Sparse Principal Component Analysis (SPCA) for Fault Detection
DEFF Research Database (Denmark)
Gajjar, Shriram; Kulahci, Murat; Palazoglu, Ahmet
2016-01-01
Principal component analysis (PCA) has been widely used for data dimension reduction and process fault detection. However, interpreting the principal components and the outcomes of PCA-based monitoring techniques is a challenging task since each principal component is a linear combination of the ...
Mellard, Daryl F.; Anthony, Jason L.; Woods, Kari L.
2012-01-01
This study extends the literature on the component skills involved in oral reading fluency. Dominance analysis was applied to assess the relative importance of seven reading-related component skills in the prediction of the oral reading fluency of 272 adult literacy learners. The best predictors of oral reading fluency when text difficulty was…
Applied time series analysis and innovative computing
Ao, Sio-Iong
2010-01-01
This text is a systematic, state-of-the-art introduction to the use of innovative computing paradigms as an investigative tool for applications in time series analysis. It includes frontier case studies based on recent research.
Preliminary study of soil permeability properties using principal component analysis
Yulianti, M.; Sudriani, Y.; Rustini, H. A.
2018-02-01
Soil permeability measurement is undoubtedly important in carrying out soil-water research such as rainfall-runoff modelling, irrigation water distribution systems, etc. It is also known that acquiring reliable soil permeability data is rather laborious, time-consuming, and costly. Therefore, it is desirable to develop the prediction model. Several studies of empirical equations for predicting permeability have been undertaken by many researchers. These studies derived the models from areas which soil characteristics are different from Indonesian soil, which suggest a possibility that these permeability models are site-specific. The purpose of this study is to identify which soil parameters correspond strongly to soil permeability and propose a preliminary model for permeability prediction. Principal component analysis (PCA) was applied to 16 parameters analysed from 37 sites consist of 91 samples obtained from Batanghari Watershed. Findings indicated five variables that have strong correlation with soil permeability, and we recommend a preliminary permeability model, which is potential for further development.
Principal Component Analysis for Normal-Distribution-Valued Symbolic Data.
Wang, Huiwen; Chen, Meiling; Shi, Xiaojun; Li, Nan
2016-02-01
This paper puts forward a new approach to principal component analysis (PCA) for normal-distribution-valued symbolic data, which has a vast potential of applications in the economic and management field. We derive a full set of numerical characteristics and variance-covariance structure for such data, which forms the foundation for our analytical PCA approach. Our approach is able to use all of the variance information in the original data than the prevailing representative-type approach in the literature which only uses centers, vertices, etc. The paper also provides an accurate approach to constructing the observations in a PC space based on the linear additivity property of normal distribution. The effectiveness of the proposed method is illustrated by simulated numerical experiments. At last, our method is applied to explain the puzzle of risk-return tradeoff in China's stock market.
Iris recognition based on robust principal component analysis
Karn, Pradeep; He, Xiao Hai; Yang, Shuai; Wu, Xiao Hong
2014-11-01
Iris images acquired under different conditions often suffer from blur, occlusion due to eyelids and eyelashes, specular reflection, and other artifacts. Existing iris recognition systems do not perform well on these types of images. To overcome these problems, we propose an iris recognition method based on robust principal component analysis. The proposed method decomposes all training images into a low-rank matrix and a sparse error matrix, where the low-rank matrix is used for feature extraction. The sparsity concentration index approach is then applied to validate the recognition result. Experimental results using CASIA V4 and IIT Delhi V1iris image databases showed that the proposed method achieved competitive performances in both recognition accuracy and computational efficiency.
Tissue Microarray Analysis Applied to Bone Diagenesis
Barrios Mello, Rafael; Regis Silva, Maria Regina; Seixas Alves, Maria Teresa; Evison, Martin; Guimarães, Marco Aurélio; Francisco, Rafaella Arrabaça; Dias Astolphi, Rafael; Miazato Iwamura, Edna Sadayo
2017-01-01
Taphonomic processes affecting bone post mortem are important in forensic, archaeological and palaeontological investigations. In this study, the application of tissue microarray (TMA) analysis to a sample of femoral bone specimens from 20 exhumed individuals of known period of burial and age at death is described. TMA allows multiplexing of subsamples, permitting standardized comparative analysis of adjacent sections in 3-D and of representative cross-sections of a large number of specimens....
Meta-analysis in applied ecology.
Stewart, Gavin
2010-02-23
This overview examines research synthesis in applied ecology and conservation. Vote counting and pooling unweighted averages are widespread despite the superiority of syntheses based on weighted combination of effects. Such analyses allow exploration of methodological uncertainty in addition to consistency of effects across species, space and time, but exploring heterogeneity remains controversial. Meta-analyses are required to generalize in ecology, and to inform evidence-based decision-making, but the more sophisticated statistical techniques and registers of research used in other disciplines must be employed in ecology to fully realize their benefits.
Projection and analysis of nuclear components
International Nuclear Information System (INIS)
Heeschen, U.
1980-01-01
The classification and the types of analysis carried out in pipings for quality control and safety of nuclear power plants, are presented. The operation and emergency conditions with emphasis of possible simplifications of calculations are described. (author/M.C.K.) [pt
Applied surface analysis of metal materials
International Nuclear Information System (INIS)
Weiss, Z.
1987-01-01
The applications of surface analytical techniques in the solution of technological problems in metalurgy and engineering are reviewed. Some important application areas such as corrosion, grain boundary segregation and metallurgical coatings are presented together with specific requirements for the type of information which is necessary for solving particular problems. The techniques discussed include: electron spectroscopies (Auger Electron Spectroscopy, Electron Spectroscopy for Chemical Analysis), ion spectroscopies (Secondary Ion Mass Spectrometry, Ion Scattering Spectroscopy), Rutherford Back-Scattering, nuclear reaction analysis, optical methods (Glow Discharge Optical Emission Spectrometry), ellipsometry, infrared and Raman spectroscopy, the Moessbauer spectroscopy and methods of consumptive depth profile analysis. Principles and analytical features of these methods are demonstrated and examples of their applications to metallurgy are taken from recent literature. (author). 4 figs., 2 tabs., 112 refs
Applied modal analysis of wind turbine blades
DEFF Research Database (Denmark)
Pedersen, H.B.; Kristensen, O.J.D.
2003-01-01
In this project modal analysis has been used to determine the natural frequencies, damping and the mode shapes for wind turbine blades. Different methods to measure the position and adjust the direction of the measuring points are discussed. Differentequipment for mounting the accelerometers...... is investigated by repeated measurement on the same wind turbine blade. Furthermore the flexibility of the test set-up is investigated, by use ofaccelerometers mounted on the flexible adapter plate during the measurement campaign. One experimental campaign investigated the results obtained from a loaded...... and unloaded wind turbine blade. During this campaign the modal analysis are performed on ablade mounted in a horizontal and a vertical position respectively. Finally the results obtained from modal analysis carried out on a wind turbine blade are compared with results obtained from the Stig Øyes blade_EV1...
How Many Separable Sources? Model Selection In Independent Components Analysis
DEFF Research Database (Denmark)
Woods, Roger P.; Hansen, Lars Kai; Strother, Stephen
2015-01-01
among potential model categories with differing numbers of Gaussian components. Based on simulation studies, the assumptions and approximations underlying the Akaike Information Criterion do not hold in this setting, even with a very large number of observations. Cross-validation is a suitable, though....../Principal Components Analysis (mixed ICA/PCA) model described here accommodates one or more Gaussian components in the independent components analysis model and uses principal components analysis to characterize contributions from this inseparable Gaussian subspace. Information theory can then be used to select from...... might otherwise be questionable. Failure of the Akaike Information Criterion in model selection also has relevance in traditional independent components analysis where all sources are assumed non-Gaussian....
Applied quantitative analysis in the social sciences
Petscher, Yaacov; Compton, Donald L
2013-01-01
To say that complex data analyses are ubiquitous in the education and social sciences might be an understatement. Funding agencies and peer-review journals alike require that researchers use the most appropriate models and methods for explaining phenomena. Univariate and multivariate data structures often require the application of more rigorous methods than basic correlational or analysis of variance models. Additionally, though a vast set of resources may exist on how to run analysis, difficulties may be encountered when explicit direction is not provided as to how one should run a model
Nonparametric inference in nonlinear principal components analysis : exploration and beyond
Linting, Mariëlle
2007-01-01
In the social and behavioral sciences, data sets often do not meet the assumptions of traditional analysis methods. Therefore, nonlinear alternatives to traditional methods have been developed. This thesis starts with a didactic discussion of nonlinear principal components analysis (NLPCA),
Applied Spectrophotometry: Analysis of a Biochemical Mixture
Trumbo, Toni A.; Schultz, Emeric; Borland, Michael G.; Pugh, Michael Eugene
2013-01-01
Spectrophotometric analysis is essential for determining biomolecule concentration of a solution and is employed ubiquitously in biochemistry and molecular biology. The application of the Beer-Lambert-Bouguer Lawis routinely used to determine the concentration of DNA, RNA or protein. There is however a significant difference in determining the…
Blind source separation dependent component analysis
Xiang, Yong; Yang, Zuyuan
2015-01-01
This book provides readers a complete and self-contained set of knowledge about dependent source separation, including the latest development in this field. The book gives an overview on blind source separation where three promising blind separation techniques that can tackle mutually correlated sources are presented. The book further focuses on the non-negativity based methods, the time-frequency analysis based methods, and the pre-coding based methods, respectively.
Kernel principal component analysis for change detection
DEFF Research Database (Denmark)
Nielsen, Allan Aasbjerg; Morton, J.C.
2008-01-01
region acquired at two different time points. If change over time does not dominate the scene, the projection of the original two bands onto the second eigenvector will show change over time. In this paper a kernel version of PCA is used to carry out the analysis. Unlike ordinary PCA, kernel PCA...... with a Gaussian kernel successfully finds the change observations in a case where nonlinearities are introduced artificially....
Real Time Engineering Analysis Based on a Generative Component Implementation
DEFF Research Database (Denmark)
Kirkegaard, Poul Henning; Klitgaard, Jens
2007-01-01
The present paper outlines the idea of a conceptual design tool with real time engineering analysis which can be used in the early conceptual design phase. The tool is based on a parametric approach using Generative Components with embedded structural analysis. Each of these components uses the g...
How Many Separable Sources? Model Selection In Independent Components Analysis
Woods, Roger P.; Hansen, Lars Kai; Strother, Stephen
2015-01-01
Unlike mixtures consisting solely of non-Gaussian sources, mixtures including two or more Gaussian components cannot be separated using standard independent components analysis methods that are based on higher order statistics and independent observations. The mixed Independent Components Analysis/Principal Components Analysis (mixed ICA/PCA) model described here accommodates one or more Gaussian components in the independent components analysis model and uses principal components analysis to characterize contributions from this inseparable Gaussian subspace. Information theory can then be used to select from among potential model categories with differing numbers of Gaussian components. Based on simulation studies, the assumptions and approximations underlying the Akaike Information Criterion do not hold in this setting, even with a very large number of observations. Cross-validation is a suitable, though computationally intensive alternative for model selection. Application of the algorithm is illustrated using Fisher's iris data set and Howells' craniometric data set. Mixed ICA/PCA is of potential interest in any field of scientific investigation where the authenticity of blindly separated non-Gaussian sources might otherwise be questionable. Failure of the Akaike Information Criterion in model selection also has relevance in traditional independent components analysis where all sources are assumed non-Gaussian. PMID:25811988
Thermal analysis applied to irradiated propolis
Energy Technology Data Exchange (ETDEWEB)
Matsuda, Andrea Harumi; Machado, Luci Brocardo; Mastro, N.L. del E-mail: nelida@usp.br
2002-03-01
Propolis is a resinous hive product, collected by bees. Raw propolis requires a decontamination procedure and irradiation appears as a promising technique for this purpose. The valuable properties of propolis for food and pharmaceutical industries have led to increasing interest in its technological behavior. Thermal analysis is a chemical analysis that gives information about changes on heating of great importance for technological applications. Ground propolis samples were {sup 60}Co gamma irradiated with 0 and 10 kGy. Thermogravimetry curves shown a similar multi-stage decomposition pattern for both irradiated and unirradiated samples up to 600 deg. C. Similarly, through differential scanning calorimetry , a coincidence of melting point of irradiated and unirradiated samples was found. The results suggest that the irradiation process do not interfere on the thermal properties of propolis when irradiated up to 10 kGy.
Applied modal analysis of wind turbine blades
Energy Technology Data Exchange (ETDEWEB)
Broen Pedersen, H.; Dahl Kristensen, O.J.
2003-02-01
In this project modal analysis has been used to determine the natural frequencies, damping and the mode shapes for wind turbine blades. Different methods to measure the position and adjust the direction of the measuring points are discussed. Different equipment for mounting the accelerometers are investigated and the most suitable are chosen. Different excitation techniques are tried during experimental campaigns. After a discussion the pendulum hammer were chosen, and a new improved hammer was manufactured. Some measurement errors are investigated. The ability to repeat the measured results is investigated by repeated measurement on the same wind turbine blade. Furthermore the flexibility of the test set-up is investigated, by use of accelerometers mounted on the flexible adapter plate during the measurement campaign. One experimental campaign investigated the results obtained from a loaded and unloaded wind turbine blade. During this campaign the modal analysis are performed on a blade mounted in a horizontal and a vertical position respectively. Finally the results obtained from modal analysis carried out on a wind turbine blade are compared with results obtained from the Stig Oeyes blade{sub E}V1 program. (au)
Independent component analysis of dynamic contrast-enhanced computed tomography images
Energy Technology Data Exchange (ETDEWEB)
Koh, T S [School of Electrical and Electronic Engineering, Nanyang Technological University, 50 Nanyang Ave, Singapore 639798 (Singapore); Yang, X [School of Electrical and Electronic Engineering, Nanyang Technological University, 50 Nanyang Ave, Singapore 639798 (Singapore); Bisdas, S [Department of Diagnostic and Interventional Radiology, Johann Wolfgang Goethe University Hospital, Theodor-Stern-Kai 7, D-60590 Frankfurt (Germany); Lim, C C T [Department of Neuroradiology, National Neuroscience Institute, 11 Jalan Tan Tock Seng, Singapore 308433 (Singapore)
2006-10-07
Independent component analysis (ICA) was applied on dynamic contrast-enhanced computed tomography images of cerebral tumours to extract spatial component maps of the underlying vascular structures, which correspond to different haemodynamic phases as depicted by the passage of the contrast medium. The locations of arteries, veins and tumours can be separately identified on these spatial component maps. As the contrast enhancement behaviour of the cerebral tumour differs from the normal tissues, ICA yields a tumour component map that reveals the location and extent of the tumour. Tumour outlines can be generated using the tumour component maps, with relatively simple segmentation methods. (note)
Reliability analysis applied to structural tests
Diamond, P.; Payne, A. O.
1972-01-01
The application of reliability theory to predict, from structural fatigue test data, the risk of failure of a structure under service conditions because its load-carrying capability is progressively reduced by the extension of a fatigue crack, is considered. The procedure is applicable to both safe-life and fail-safe structures and, for a prescribed safety level, it will enable an inspection procedure to be planned or, if inspection is not feasible, it will evaluate the life to replacement. The theory has been further developed to cope with the case of structures with initial cracks, such as can occur in modern high-strength materials which are susceptible to the formation of small flaws during the production process. The method has been applied to a structure of high-strength steel and the results are compared with those obtained by the current life estimation procedures. This has shown that the conventional methods can be unconservative in certain cases, depending on the characteristics of the structure and the design operating conditions. The suitability of the probabilistic approach to the interpretation of the results from full-scale fatigue testing of aircraft structures is discussed and the assumptions involved are examined.
Artificial intelligence applied to process signal analysis
Corsberg, Dan
1988-01-01
Many space station processes are highly complex systems subject to sudden, major transients. In any complex process control system, a critical aspect of the human/machine interface is the analysis and display of process information. Human operators can be overwhelmed by large clusters of alarms that inhibit their ability to diagnose and respond to a disturbance. Using artificial intelligence techniques and a knowledge base approach to this problem, the power of the computer can be used to filter and analyze plant sensor data. This will provide operators with a better description of the process state. Once a process state is recognized, automatic action could be initiated and proper system response monitored.
Constrained Null Space Component Analysis for Semiblind Source Separation Problem.
Hwang, Wen-Liang; Lu, Keng-Shih; Ho, Jinn
2018-02-01
The blind source separation (BSS) problem extracts unknown sources from observations of their unknown mixtures. A current trend in BSS is the semiblind approach, which incorporates prior information on sources or how the sources are mixed. The constrained independent component analysis (ICA) approach has been studied to impose constraints on the famous ICA framework. We introduced an alternative approach based on the null space component (NCA) framework and referred to the approach as the c-NCA approach. We also presented the c-NCA algorithm that uses signal-dependent semidefinite operators, which is a bilinear mapping, as signatures for operator design in the c-NCA approach. Theoretically, we showed that the source estimation of the c-NCA algorithm converges with a convergence rate dependent on the decay of the sequence, obtained by applying the estimated operators on corresponding sources. The c-NCA can be formulated as a deterministic constrained optimization method, and thus, it can take advantage of solvers developed in optimization society for solving the BSS problem. As examples, we demonstrated electroencephalogram interference rejection problems can be solved by the c-NCA with proximal splitting algorithms by incorporating a sparsity-enforcing separation model and considering the case when reference signals are available.
Improvement of retinal blood vessel detection using morphological component analysis.
Imani, Elaheh; Javidi, Malihe; Pourreza, Hamid-Reza
2015-03-01
Detection and quantitative measurement of variations in the retinal blood vessels can help diagnose several diseases including diabetic retinopathy. Intrinsic characteristics of abnormal retinal images make blood vessel detection difficult. The major problem with traditional vessel segmentation algorithms is producing false positive vessels in the presence of diabetic retinopathy lesions. To overcome this problem, a novel scheme for extracting retinal blood vessels based on morphological component analysis (MCA) algorithm is presented in this paper. MCA was developed based on sparse representation of signals. This algorithm assumes that each signal is a linear combination of several morphologically distinct components. In the proposed method, the MCA algorithm with appropriate transforms is adopted to separate vessels and lesions from each other. Afterwards, the Morlet Wavelet Transform is applied to enhance the retinal vessels. The final vessel map is obtained by adaptive thresholding. The performance of the proposed method is measured on the publicly available DRIVE and STARE datasets and compared with several state-of-the-art methods. An accuracy of 0.9523 and 0.9590 has been respectively achieved on the DRIVE and STARE datasets, which are not only greater than most methods, but are also superior to the second human observer's performance. The results show that the proposed method can achieve improved detection in abnormal retinal images and decrease false positive vessels in pathological regions compared to other methods. Also, the robustness of the method in the presence of noise is shown via experimental result. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.
2010-04-01
... maintenance. Each laboratory must have and follow written procedures to ensure that equipment is routinely... 21 Food and Drugs 4 2010-04-01 2010-04-01 false What requirements apply to the laboratories where...) Laboratory Controls § 212.60 What requirements apply to the laboratories where I test components, in-process...
Thermal transient analysis applied to horizontal wells
Energy Technology Data Exchange (ETDEWEB)
Duong, A.N. [Society of Petroleum Engineers, Canadian Section, Calgary, AB (Canada)]|[ConocoPhillips Canada Resources Corp., Calgary, AB (Canada)
2008-10-15
Steam assisted gravity drainage (SAGD) is a thermal recovery process used to recover bitumen and heavy oil. This paper presented a newly developed model to estimate cooling time and formation thermal diffusivity by using a thermal transient analysis along the horizontal wellbore under a steam heating process. This radial conduction heating model provides information on the heat influx distribution along a horizontal wellbore or elongated steam chamber, and is therefore important for determining the effectiveness of the heating process in the start-up phase in SAGD. Net heat flux estimation in the target formation during start-up can be difficult to measure because of uncertainties regarding heat loss in the vertical section; steam quality along the horizontal segment; distribution of steam along the wellbore; operational conditions; and additional effects of convection heating. The newly presented model can be considered analogous to pressure transient analysis of a buildup after a constant pressure drawdown. The model is based on an assumption of an infinite-acting system. This paper also proposed a new concept of a heating ring to measure the heat storage in the heated bitumen at the time of testing. Field observations were used to demonstrate how the model can be used to save heat energy, conserve steam and enhance bitumen recovery. 18 refs., 14 figs., 2 appendices.
Photometric analysis applied in determining facial type
Directory of Open Access Journals (Sweden)
Luciana Flaquer Martins
2012-10-01
Full Text Available INTRODUCTION: In orthodontics, determining the facial type is a key element in the prescription of a correct diagnosis. In the early days of our specialty, observation and measurement of craniofacial structures were done directly on the face, in photographs or plaster casts. With the development of radiographic methods, cephalometric analysis replaced the direct facial analysis. Seeking to validate the analysis of facial soft tissues, this work compares two different methods used to determining the facial types, the anthropometric and the cephalometric methods. METHODS: The sample consisted of sixty-four Brazilian individuals, adults, Caucasian, of both genders, who agreed to participate in this research. All individuals had lateral cephalograms and facial frontal photographs. The facial types were determined by the Vert Index (cephalometric and the Facial Index (photographs. RESULTS: The agreement analysis (Kappa, made for both types of analysis, found an agreement of 76.5%. CONCLUSIONS: We concluded that the Facial Index can be used as an adjunct to orthodontic diagnosis, or as an alternative method for pre-selection of a sample, avoiding that research subjects have to undergo unnecessary tests.INTRODUÇÃO: em Ortodontia, a determinação do tipo facial é um elemento-chave na prescrição de um diagnóstico correto. Nos primórdios de nossa especialidade, a observação e a medição das estruturas craniofaciais eram feitas diretamente na face, em fotografias ou em modelos de gesso. Com o desenvolvimento dos métodos radiográficos, a análise cefalométrica foi substituindo a análise facial direta. Visando legitimar o estudo dos tecidos moles faciais, esse trabalho comparou a determinação do tipo facial pelos métodos antropométrico e cefalométrico. MÉTODOS: a amostra constou de sessenta e quatro indivíduos brasileiros, adultos, leucodermas, de ambos os sexos, que aceitaram participar da pesquisa. De todos os indivíduos da amostra
Problems of stress analysis of fuelling machine head components
International Nuclear Information System (INIS)
Mathur, D.D.
1975-01-01
The problem of stress analysis of fuelling machine head components are discussed. To fulfil the functional requirements, the components are required to have certain shapes where stress problems cannot be matched to a catalogue of pre-determined solutions. The areas where complex systems of loading due to hydrostatic pressure, weight, moments and temperature gradients coupled with the intricate shapes of the components make it difficult to arrive at satisfactory solutions. Particularly, the analysis requirements of the magazine housing, end cover, gravloc clamps and centre support are highlighted. An experimental stress analysis programme together with a theoretical finite element analysis is perhaps the answer. (author)
Gradient pattern analysis applied to galaxy morphology
Rosa, R. R.; de Carvalho, R. R.; Sautter, R. A.; Barchi, P. H.; Stalder, D. H.; Moura, T. C.; Rembold, S. B.; Morell, D. R. F.; Ferreira, N. C.
2018-06-01
Gradient pattern analysis (GPA) is a well-established technique for measuring gradient bilateral asymmetries of a square numerical lattice. This paper introduces an improved version of GPA designed for galaxy morphometry. We show the performance of the new method on a selected sample of 54 896 objects from the SDSS-DR7 in common with Galaxy Zoo 1 catalogue. The results suggest that the second gradient moment, G2, has the potential to dramatically improve over more conventional morphometric parameters. It separates early- from late-type galaxies better (˜ 90 per cent) than the CAS system (C˜ 79 per cent, A˜ 50 per cent, S˜ 43 per cent) and a benchmark test shows that it is applicable to hundreds of thousands of galaxies using typical processing systems.
Energy analysis applied to uranium resource estimation
International Nuclear Information System (INIS)
Mortimer, N.D.
1980-01-01
It is pointed out that fuel prices and ore costs are interdependent, and that in estimating ore costs (involving the cost of fuels used to mine and process the uranium) it is necessary to take into account the total use of energy by the entire fuel system, through the technique of energy analysis. The subject is discussed, and illustrated with diagrams, under the following heads: estimate of how total workable resources would depend on production costs; sensitivity of nuclear electricity prices to ore costs; variation of net energy requirement with ore grade for a typical PWR reactor design; variation of average fundamental cost of nuclear electricity with ore grade; variation of cumulative uranium resources with current maximum ore costs. (U.K.)
Toward applied behavior analysis of life aloft
Brady, J. V.
1990-01-01
This article deals with systems at multiple levels, at least from cell to organization. It also deals with learning, decision making, and other behavior at multiple levels. Technological development of a human behavioral ecosystem appropriate to space environments requires an analytic and synthetic orientation, explicitly experimental in nature, dictated by scientific and pragmatic considerations, and closely approximating procedures of established effectiveness in other areas of natural science. The conceptual basis of such an approach has its roots in environmentalism which has two main features: (1) knowledge comes from experience rather than from innate ideas, divine revelation, or other obscure sources; and (2) action is governed by consequences rather than by instinct, reason, will, beliefs, attitudes or even the currently fashionable cognitions. Without an experimentally derived data base founded upon such a functional analysis of human behavior, the overgenerality of "ecological systems" approaches render them incapable of ensuring the successful establishment of enduring space habitats. Without an experimentally derived function account of individual behavioral variability, a natural science of behavior cannot exist. And without a natural science of behavior, the social sciences will necessarily remain in their current status as disciplines of less than optimal precision or utility. Such a functional analysis of human performance should provide an operational account of behavior change in a manner similar to the way in which Darwin's approach to natural selection accounted for the evolution of phylogenetic lines (i.e., in descriptive, nonteleological terms). Similarly, as Darwin's account has subsequently been shown to be consonant with information obtained at the cellular level, so too should behavior principles ultimately prove to be in accord with an account of ontogenetic adaptation at a biochemical level. It would thus seem obvious that the most
Probabilistic safety analysis applied to RBMK reactors
International Nuclear Information System (INIS)
Gerez Martin, L.; Fernandez Ramos, P.
1995-01-01
The project financed by the European Union ''Revision of RBMK Reactor Safety was divided into nine Topic Groups dealing with different aspects of safety. The area covered by Topic Group 9 was Probabilistic Safety Analysis. TG9 will have touched on some of the problems discussed by other groups, although in terms of the systematic quantification of the impact of design characteristics and RBMK reactor operating practices on the risk of core damage. On account of the reduced time scale and the resources available for the project, the analysis was made using a simplified method based on the results of PSAs conducted in Western countries and on the judgement of the group members. The simplifies method is based on the concepts of Qualification, Redundancy and Automatic Actuation of the systems considered. PSA experience shows that systems complying with the above-mentioned concepts have a failure probability of 1.0E-3 when redundancy is simple, ie two similar equipment items capable of carrying out the same function. In general terms, this value can be considered to be dominated by potential common cause failures. The value considered above changes according to factors that have a positive effect upon it, such as an additional redundancy with a different equipment item (eg a turbo pumps and a motor pump), individual trains with good separations, etc, or a negative effect, such as the absence of suitable periodical tests, the need for operators to perform manual operations, etc. Similarly, possible actions required by the operator during accident sequences are assigned failure probability values between 1 and 1.0E-4, according to the complexity of the action (including local actions to be performed outside the control room) and the time available
A meta-analysis of executive components of working memory.
Nee, Derek Evan; Brown, Joshua W; Askren, Mary K; Berman, Marc G; Demiralp, Emre; Krawitz, Adam; Jonides, John
2013-02-01
Working memory (WM) enables the online maintenance and manipulation of information and is central to intelligent cognitive functioning. Much research has investigated executive processes of WM in order to understand the operations that make WM "work." However, there is yet little consensus regarding how executive processes of WM are organized. Here, we used quantitative meta-analysis to summarize data from 36 experiments that examined executive processes of WM. Experiments were categorized into 4 component functions central to WM: protecting WM from external distraction (distractor resistance), preventing irrelevant memories from intruding into WM (intrusion resistance), shifting attention within WM (shifting), and updating the contents of WM (updating). Data were also sorted by content (verbal, spatial, object). Meta-analytic results suggested that rather than dissociating into distinct functions, 2 separate frontal regions were recruited across diverse executive demands. One region was located dorsally in the caudal superior frontal sulcus and was especially sensitive to spatial content. The other was located laterally in the midlateral prefrontal cortex and showed sensitivity to nonspatial content. We propose that dorsal-"where"/ventral-"what" frameworks that have been applied to WM maintenance also apply to executive processes of WM. Hence, WM can largely be simplified to a dual selection model.
Digital photoelastic analysis applied to implant dentistry
Ramesh, K.; Hariprasad, M. P.; Bhuvanewari, S.
2016-12-01
Development of improved designs of implant systems in dentistry have necessitated the study of stress fields in the implant regions of the mandible/maxilla for better understanding of the biomechanics involved. Photoelasticity has been used for various studies related to dental implants in view of whole field visualization of maximum shear stress in the form of isochromatic contours. The potential of digital photoelasticity has not been fully exploited in the field of implant dentistry. In this paper, the fringe field in the vicinity of the connected implants (All-On-Four® concept) is analyzed using recent advances in digital photoelasticity. Initially, a novel 3-D photoelastic model making procedure, to closely mimic all the anatomical features of the human mandible is proposed. By choosing appropriate orientation of the model with respect to the light path, the essential region of interest were sought to be analysed while keeping the model under live loading conditions. Need for a sophisticated software module to carefully identify the model domain has been brought out. For data extraction, five-step method is used and isochromatics are evaluated by twelve fringe photoelasticity. In addition to the isochromatic fringe field, whole field isoclinic data is also obtained for the first time in implant dentistry, which could throw important information in improving the structural stability of the implant systems. Analysis is carried out for the implant in the molar as well as the incisor region. In addition, the interaction effects of loaded molar implant on the incisor area are also studied.
Component reliability analysis for development of component reliability DB of Korean standard NPPs
International Nuclear Information System (INIS)
Choi, S. Y.; Han, S. H.; Kim, S. H.
2002-01-01
The reliability data of Korean NPP that reflects the plant specific characteristics is necessary for PSA and Risk Informed Application. We have performed a project to develop the component reliability DB and calculate the component reliability such as failure rate and unavailability. We have collected the component operation data and failure/repair data of Korean standard NPPs. We have analyzed failure data by developing a data analysis method which incorporates the domestic data situation. And then we have compared the reliability results with the generic data for the foreign NPPs
Structural reliability analysis applied to pipeline risk analysis
Energy Technology Data Exchange (ETDEWEB)
Gardiner, M. [GL Industrial Services, Loughborough (United Kingdom); Mendes, Renato F.; Donato, Guilherme V.P. [PETROBRAS S.A., Rio de Janeiro, RJ (Brazil)
2009-07-01
Quantitative Risk Assessment (QRA) of pipelines requires two main components to be provided. These are models of the consequences that follow from some loss of containment incident, and models for the likelihood of such incidents occurring. This paper describes how PETROBRAS have used Structural Reliability Analysis for the second of these, to provide pipeline- and location-specific predictions of failure frequency for a number of pipeline assets. This paper presents an approach to estimating failure rates for liquid and gas pipelines, using Structural Reliability Analysis (SRA) to analyze the credible basic mechanisms of failure such as corrosion and mechanical damage. SRA is a probabilistic limit state method: for a given failure mechanism it quantifies the uncertainty in parameters to mathematical models of the load-resistance state of a structure and then evaluates the probability of load exceeding resistance. SRA can be used to benefit the pipeline risk management process by optimizing in-line inspection schedules, and as part of the design process for new construction in pipeline rights of way that already contain multiple lines. A case study is presented to show how the SRA approach has recently been used on PETROBRAS pipelines and the benefits obtained from it. (author)
Variational Bayesian Learning for Wavelet Independent Component Analysis
Roussos, E.; Roberts, S.; Daubechies, I.
2005-11-01
In an exploratory approach to data analysis, it is often useful to consider the observations as generated from a set of latent generators or "sources" via a generally unknown mapping. For the noisy overcomplete case, where we have more sources than observations, the problem becomes extremely ill-posed. Solutions to such inverse problems can, in many cases, be achieved by incorporating prior knowledge about the problem, captured in the form of constraints. This setting is a natural candidate for the application of the Bayesian methodology, allowing us to incorporate "soft" constraints in a natural manner. The work described in this paper is mainly driven by problems in functional magnetic resonance imaging of the brain, for the neuro-scientific goal of extracting relevant "maps" from the data. This can be stated as a `blind' source separation problem. Recent experiments in the field of neuroscience show that these maps are sparse, in some appropriate sense. The separation problem can be solved by independent component analysis (ICA), viewed as a technique for seeking sparse components, assuming appropriate distributions for the sources. We derive a hybrid wavelet-ICA model, transforming the signals into a domain where the modeling assumption of sparsity of the coefficients with respect to a dictionary is natural. We follow a graphical modeling formalism, viewing ICA as a probabilistic generative model. We use hierarchical source and mixing models and apply Bayesian inference to the problem. This allows us to perform model selection in order to infer the complexity of the representation, as well as automatic denoising. Since exact inference and learning in such a model is intractable, we follow a variational Bayesian mean-field approach in the conjugate-exponential family of distributions, for efficient unsupervised learning in multi-dimensional settings. The performance of the proposed algorithm is demonstrated on some representative experiments.
DEFF Research Database (Denmark)
Tian, Fang; Rades, Thomas; Sandler, Niklas
2008-01-01
The purpose of this research is to gain a greater insight into the hydrate formation processes of different carbamazepine (CBZ) anhydrate forms in aqueous suspension, where principal component analysis (PCA) was applied for data analysis. The capability of PCA to visualize and to reveal simplified...
Cnn Based Retinal Image Upscaling Using Zero Component Analysis
Nasonov, A.; Chesnakov, K.; Krylov, A.
2017-05-01
The aim of the paper is to obtain high quality of image upscaling for noisy images that are typical in medical image processing. A new training scenario for convolutional neural network based image upscaling method is proposed. Its main idea is a novel dataset preparation method for deep learning. The dataset contains pairs of noisy low-resolution images and corresponding noiseless highresolution images. To achieve better results at edges and textured areas, Zero Component Analysis is applied to these images. The upscaling results are compared with other state-of-the-art methods like DCCI, SI-3 and SRCNN on noisy medical ophthalmological images. Objective evaluation of the results confirms high quality of the proposed method. Visual analysis shows that fine details and structures like blood vessels are preserved, noise level is reduced and no artifacts or non-existing details are added. These properties are essential in retinal diagnosis establishment, so the proposed algorithm is recommended to be used in real medical applications.
A survival analysis on critical components of nuclear power plants
International Nuclear Information System (INIS)
Durbec, V.; Pitner, P.; Riffard, T.
1995-06-01
Some tubes of heat exchangers of nuclear power plants may be affected by Primary Water Stress Corrosion Cracking (PWSCC) in highly stressed areas. These defects can shorten the lifetime of the component and lead to its replacement. In order to reduce the risk of cracking, a preventive remedial operation called shot peening was applied on the French reactors between 1985 and 1988. To assess and investigate the effects of shot peening, a statistical analysis was carried on the tube degradation results obtained from in service inspection that are regularly conducted using non destructive tests. The statistical method used is based on the Cox proportional hazards model, a powerful tool in the analysis of survival data, implemented in PROC PHRED recently available in SAS/STAT. This technique has a number of major advantages including the ability to deal with censored failure times data and with the complication of time-dependant co-variables. The paper focus on the modelling and a presentation of the results given by SAS. They provide estimate of how the relative risk of degradation changes after peening and indicate for which values of the prognostic factors analyzed the treatment is likely to be most beneficial. (authors). 2 refs., 3 figs., 6 tabs
Principal Component Analysis of Body Measurements In Three ...
African Journals Online (AJOL)
This study was conducted to explore the relationship among body measurements in 3 strains of broilers chicken (Arbor Acre, Marshal and Ross) using principal component analysis with the view of identifying those components that define body conformation in broilers. A total of 180 birds were used, 60 per strain.
Tomato sorting using independent component analysis on spectral images
Polder, G.; Heijden, van der G.W.A.M.; Young, I.T.
2003-01-01
Independent Component Analysis is one of the most widely used methods for blind source separation. In this paper we use this technique to estimate the most important compounds which play a role in the ripening of tomatoes. Spectral images of tomatoes were analyzed. Two main independent components
[Spectroscopic methods applied to component determination and species identification for coffee].
Chen, Hua-zhou; Xu, Li-li; Qin, Qiang
2014-06-01
Spectroscopic analysis was applied to the determination of the nutrient quality of ground, instant and chicory coffees. By using inductively coupled plasma atomic emission spectrometry (ICP-ES), nine mineral elements were determined in solid coffee samples. Caffeine was determined by ultraviolet (UV) spectrometry and organic matter was investigated by Fourier transform infrared (FTIR) spectroscopy. Oxidation-reduction titration was utilized for measuring the oxalate. The differences between ground coffee and instant coffee was identified on the basis of the contents of caffeine, oxalate and mineral elements. Experimental evidence showed that, caffeine in instant coffee was 2-3 times higher than in ground coffee. Oxalate in instant coffee was significantly higher in ground coffee. Mineral elements of Mg, P and Zn in ground coffee is lower than in instant coffee, while Cu is several times higher. The mineral content in chicory coffee is overall lower than the instant coffee. In addition, we determined the content of Ti for different types of coffees, and simultaneously detected the elements of Cu, Ti and Zn in chicory coffee. As a fast detection technique, FTIR spectroscopy has the potential of detecting the differences between ground coffee and instant coffee, and is able to verify the presence of caffeine and oxalate.
Analysis of Minor Component Segregation in Ternary Powder Mixtures
Directory of Open Access Journals (Sweden)
Asachi Maryam
2017-01-01
Full Text Available In many powder handling operations, inhomogeneity in powder mixtures caused by segregation could have significant adverse impact on the quality as well as economics of the production. Segregation of a minor component of a highly active substance could have serious deleterious effects, an example is the segregation of enzyme granules in detergent powders. In this study, the effects of particle properties and bulk cohesion on the segregation tendency of minor component are analysed. The minor component is made sticky while not adversely affecting the flowability of samples. The segregation extent is evaluated using image processing of the photographic records taken from the front face of the heap after the pouring process. The optimum average sieve cut size of components for which segregation could be reduced is reported. It is also shown that the extent of segregation is significantly reduced by applying a thin layer of liquid to the surfaces of minor component, promoting an ordered mixture.
Applied research of environmental monitoring using instrumental neutron activation analysis
Energy Technology Data Exchange (ETDEWEB)
Chung, Young Sam; Moon, Jong Hwa; Chung, Young Ju
1997-08-01
This technical report is written as a guide book for applied research of environmental monitoring using Instrumental Neutron Activation Analysis. The contents are as followings; sampling and sample preparation as a airborne particulate matter, analytical methodologies, data evaluation and interpretation, basic statistical methods of data analysis applied in environmental pollution studies. (author). 23 refs., 7 tabs., 9 figs.
Key components of financial-analysis education for clinical nurses.
Lim, Ji Young; Noh, Wonjung
2015-09-01
In this study, we identified key components of financial-analysis education for clinical nurses. We used a literature review, focus group discussions, and a content validity index survey to develop key components of financial-analysis education. First, a wide range of references were reviewed, and 55 financial-analysis education components were gathered. Second, two focus group discussions were performed; the participants were 11 nurses who had worked for more than 3 years in a hospital, and nine components were agreed upon. Third, 12 professionals, including professors, nurse executive, nurse managers, and an accountant, participated in the content validity index. Finally, six key components of financial-analysis education were selected. These key components were as follows: understanding the need for financial analysis, introduction to financial analysis, reading and implementing balance sheets, reading and implementing income statements, understanding the concepts of financial ratios, and interpretation and practice of financial ratio analysis. The results of this study will be used to develop an education program to increase financial-management competency among clinical nurses. © 2015 Wiley Publishing Asia Pty Ltd.
Dynamic Modal Analysis of Vertical Machining Centre Components
Anayet U. Patwari; Waleed F. Faris; A. K. M. Nurul Amin; S. K. Loh
2009-01-01
The paper presents a systematic procedure and details of the use of experimental and analytical modal analysis technique for structural dynamic evaluation processes of a vertical machining centre. The main results deal with assessment of the mode shape of the different components of the vertical machining centre. The simplified experimental modal analysis of different components of milling machine was carried out. This model of the different machine tool's structure is made by design software...
Applying homotopy analysis method for solving differential-difference equation
International Nuclear Information System (INIS)
Wang Zhen; Zou Li; Zhang Hongqing
2007-01-01
In this Letter, we apply the homotopy analysis method to solving the differential-difference equations. A simple but typical example is applied to illustrate the validity and the great potential of the generalized homotopy analysis method in solving differential-difference equation. Comparisons are made between the results of the proposed method and exact solutions. The results show that the homotopy analysis method is an attractive method in solving the differential-difference equations
Lęski, Szymon; Kublik, Ewa; Swiejkowski, Daniel A; Wróbel, Andrzej; Wójcik, Daniel K
2010-12-01
Local field potentials have good temporal resolution but are blurred due to the slow spatial decay of the electric field. For simultaneous recordings on regular grids one can reconstruct efficiently the current sources (CSD) using the inverse Current Source Density method (iCSD). It is possible to decompose the resultant spatiotemporal information about the current dynamics into functional components using Independent Component Analysis (ICA). We show on test data modeling recordings of evoked potentials on a grid of 4 × 5 × 7 points that meaningful results are obtained with spatial ICA decomposition of reconstructed CSD. The components obtained through decomposition of CSD are better defined and allow easier physiological interpretation than the results of similar analysis of corresponding evoked potentials in the thalamus. We show that spatiotemporal ICA decompositions can perform better for certain types of sources but it does not seem to be the case for the experimental data studied. Having found the appropriate approach to decomposing neural dynamics into functional components we use the technique to study the somatosensory evoked potentials recorded on a grid spanning a large part of the forebrain. We discuss two example components associated with the first waves of activation of the somatosensory thalamus. We show that the proposed method brings up new, more detailed information on the time and spatial location of specific activity conveyed through various parts of the somatosensory thalamus in the rat.
System diagnostics using qualitative analysis and component functional classification
International Nuclear Information System (INIS)
Reifman, J.; Wei, T.Y.C.
1993-01-01
A method for detecting and identifying faulty component candidates during off-normal operations of nuclear power plants involves the qualitative analysis of macroscopic imbalances in the conservation equations of mass, energy and momentum in thermal-hydraulic control volumes associated with one or more plant components and the functional classification of components. The qualitative analysis of mass and energy is performed through the associated equations of state, while imbalances in momentum are obtained by tracking mass flow rates which are incorporated into a first knowledge base. The plant components are functionally classified, according to their type, as sources or sinks of mass, energy and momentum, depending upon which of the three balance equations is most strongly affected by a faulty component which is incorporated into a second knowledge base. Information describing the connections among the components of the system forms a third knowledge base. The method is particularly adapted for use in a diagnostic expert system to detect and identify faulty component candidates in the presence of component failures and is not limited to use in a nuclear power plant, but may be used with virtually any type of thermal-hydraulic operating system. 5 figures
Principal Component Analysis of Process Datasets with Missing Values
Directory of Open Access Journals (Sweden)
Kristen A. Severson
2017-07-01
Full Text Available Datasets with missing values arising from causes such as sensor failure, inconsistent sampling rates, and merging data from different systems are common in the process industry. Methods for handling missing data typically operate during data pre-processing, but can also occur during model building. This article considers missing data within the context of principal component analysis (PCA, which is a method originally developed for complete data that has widespread industrial application in multivariate statistical process control. Due to the prevalence of missing data and the success of PCA for handling complete data, several PCA algorithms that can act on incomplete data have been proposed. Here, algorithms for applying PCA to datasets with missing values are reviewed. A case study is presented to demonstrate the performance of the algorithms and suggestions are made with respect to choosing which algorithm is most appropriate for particular settings. An alternating algorithm based on the singular value decomposition achieved the best results in the majority of test cases involving process datasets.
Multistage principal component analysis based method for abdominal ECG decomposition
International Nuclear Information System (INIS)
Petrolis, Robertas; Krisciukaitis, Algimantas; Gintautas, Vladas
2015-01-01
Reflection of fetal heart electrical activity is present in registered abdominal ECG signals. However this signal component has noticeably less energy than concurrent signals, especially maternal ECG. Therefore traditionally recommended independent component analysis, fails to separate these two ECG signals. Multistage principal component analysis (PCA) is proposed for step-by-step extraction of abdominal ECG signal components. Truncated representation and subsequent subtraction of cardio cycles of maternal ECG are the first steps. The energy of fetal ECG component then becomes comparable or even exceeds energy of other components in the remaining signal. Second stage PCA concentrates energy of the sought signal in one principal component assuring its maximal amplitude regardless to the orientation of the fetus in multilead recordings. Third stage PCA is performed on signal excerpts representing detected fetal heart beats in aim to perform their truncated representation reconstructing their shape for further analysis. The algorithm was tested with PhysioNet Challenge 2013 signals and signals recorded in the Department of Obstetrics and Gynecology, Lithuanian University of Health Sciences. Results of our method in PhysioNet Challenge 2013 on open data set were: average score: 341.503 bpm 2 and 32.81 ms. (paper)
Sensitivity analysis approaches applied to systems biology models.
Zi, Z
2011-11-01
With the rising application of systems biology, sensitivity analysis methods have been widely applied to study the biological systems, including metabolic networks, signalling pathways and genetic circuits. Sensitivity analysis can provide valuable insights about how robust the biological responses are with respect to the changes of biological parameters and which model inputs are the key factors that affect the model outputs. In addition, sensitivity analysis is valuable for guiding experimental analysis, model reduction and parameter estimation. Local and global sensitivity analysis approaches are the two types of sensitivity analysis that are commonly applied in systems biology. Local sensitivity analysis is a classic method that studies the impact of small perturbations on the model outputs. On the other hand, global sensitivity analysis approaches have been applied to understand how the model outputs are affected by large variations of the model input parameters. In this review, the author introduces the basic concepts of sensitivity analysis approaches applied to systems biology models. Moreover, the author discusses the advantages and disadvantages of different sensitivity analysis methods, how to choose a proper sensitivity analysis approach, the available sensitivity analysis tools for systems biology models and the caveats in the interpretation of sensitivity analysis results.
Functional Principal Components Analysis of Shanghai Stock Exchange 50 Index
Directory of Open Access Journals (Sweden)
Zhiliang Wang
2014-01-01
Full Text Available The main purpose of this paper is to explore the principle components of Shanghai stock exchange 50 index by means of functional principal component analysis (FPCA. Functional data analysis (FDA deals with random variables (or process with realizations in the smooth functional space. One of the most popular FDA techniques is functional principal component analysis, which was introduced for the statistical analysis of a set of financial time series from an explorative point of view. FPCA is the functional analogue of the well-known dimension reduction technique in the multivariate statistical analysis, searching for linear transformations of the random vector with the maximal variance. In this paper, we studied the monthly return volatility of Shanghai stock exchange 50 index (SSE50. Using FPCA to reduce dimension to a finite level, we extracted the most significant components of the data and some relevant statistical features of such related datasets. The calculated results show that regarding the samples as random functions is rational. Compared with the ordinary principle component analysis, FPCA can solve the problem of different dimensions in the samples. And FPCA is a convenient approach to extract the main variance factors.
International Nuclear Information System (INIS)
Pashkov, V.A.; Kurganova, F.I.; Grishchuk, M.Kh.
1987-01-01
The method to calculate the combination scattering power of the components of the dissociating N 2 O 4 ↔ 2NO 2 → 2NO+O 2 gas subjected to the laser radiation effect is given. The combination scattering power has been calculated for temperatures 400-600 K, pressures 1-3 MPa, with the neodymium laser (λ=1.06 μm) as a source and the possibility of measuring the local temperatures and concentration of the given gas components with the help of the combination scattering has been analysed. It follows from the calculated data that combination scattering power of N 2 O 4 ↔ 2NO 2 ↔ 2NO+O 2 gas in excitation with the neodymium laser as a source is sufficient for detection. Gas temperature is likely to be measured with the minimum error relative to stokes and anti-stokes bands of the combination scattering, produced by nitrogen tetroxide. From calculated data it also follows that measurement of NO 2 concentration in the range 400-600 K is possible. At the same time combination scattering power, produced by NO and O 2 components is sufficient for measurement merely with the concentration of the components of the order of 10 18 molecules/cm 3 guaranteed in static conditions only at N 2 O 4 ↔ 2NO 2 ↔ 2NO+O 2 gas temperature 500 K and higher
How doctors apply semantic components to specify search in work-related information retrieval
DEFF Research Database (Denmark)
Lykke, Marianne; Price, Susan L.; Delcambre, Lois L. M.
2012-01-01
Workplace searching is often context-specific and targets a “right answer” within some domain-specific aspect of the search topic. We have developed the semantic component (SC) model that allows searchers to specify a search within context-specific aspects of the main topic of documents. The goal...
Lin, Nan; Jiang, Junhai; Guo, Shicheng; Xiong, Momiao
2015-01-01
Due to the advancement in sensor technology, the growing large medical image data have the ability to visualize the anatomical changes in biological tissues. As a consequence, the medical images have the potential to enhance the diagnosis of disease, the prediction of clinical outcomes and the characterization of disease progression. But in the meantime, the growing data dimensions pose great methodological and computational challenges for the representation and selection of features in image cluster analysis. To address these challenges, we first extend the functional principal component analysis (FPCA) from one dimension to two dimensions to fully capture the space variation of image the signals. The image signals contain a large number of redundant features which provide no additional information for clustering analysis. The widely used methods for removing the irrelevant features are sparse clustering algorithms using a lasso-type penalty to select the features. However, the accuracy of clustering using a lasso-type penalty depends on the selection of the penalty parameters and the threshold value. In practice, they are difficult to determine. Recently, randomized algorithms have received a great deal of attentions in big data analysis. This paper presents a randomized algorithm for accurate feature selection in image clustering analysis. The proposed method is applied to both the liver and kidney cancer histology image data from the TCGA database. The results demonstrate that the randomized feature selection method coupled with functional principal component analysis substantially outperforms the current sparse clustering algorithms in image cluster analysis. PMID:26196383
Sparse Principal Component Analysis in Medical Shape Modeling
DEFF Research Database (Denmark)
Sjöstrand, Karl; Stegmann, Mikkel Bille; Larsen, Rasmus
2006-01-01
Principal component analysis (PCA) is a widely used tool in medical image analysis for data reduction, model building, and data understanding and exploration. While PCA is a holistic approach where each new variable is a linear combination of all original variables, sparse PCA (SPCA) aims...... analysis in medicine. Results for three different data sets are given in relation to standard PCA and sparse PCA by simple thresholding of sufficiently small loadings. Focus is on a recent algorithm for computing sparse principal components, but a review of other approaches is supplied as well. The SPCA...
Efficacy of the Principal Components Analysis Techniques Using ...
African Journals Online (AJOL)
Second, the paper reports results of principal components analysis after the artificial data were submitted to three commonly used procedures; scree plot, Kaiser rule, and modified Horn's parallel analysis, and demonstrate the pedagogical utility of using artificial data in teaching advanced quantitative concepts. The results ...
Principal Component Clustering Approach to Teaching Quality Discriminant Analysis
Xian, Sidong; Xia, Haibo; Yin, Yubo; Zhai, Zhansheng; Shang, Yan
2016-01-01
Teaching quality is the lifeline of the higher education. Many universities have made some effective achievement about evaluating the teaching quality. In this paper, we establish the Students' evaluation of teaching (SET) discriminant analysis model and algorithm based on principal component clustering analysis. Additionally, we classify the SET…
2010-04-01
... 21 Food and Drugs 2 2010-04-01 2010-04-01 false What requirements apply to rejected components... supplement? 111.170 Section 111.170 Food and Drugs FOOD AND DRUG ADMINISTRATION, DEPARTMENT OF HEALTH AND HUMAN SERVICES (CONTINUED) FOOD FOR HUMAN CONSUMPTION CURRENT GOOD MANUFACTURING PRACTICE IN...
Fault Localization for Synchrophasor Data using Kernel Principal Component Analysis
Directory of Open Access Journals (Sweden)
CHEN, R.
2017-11-01
Full Text Available In this paper, based on Kernel Principal Component Analysis (KPCA of Phasor Measurement Units (PMU data, a nonlinear method is proposed for fault location in complex power systems. Resorting to the scaling factor, the derivative for a polynomial kernel is obtained. Then, the contribution of each variable to the T2 statistic is derived to determine whether a bus is the fault component. Compared to the previous Principal Component Analysis (PCA based methods, the novel version can combat the characteristic of strong nonlinearity, and provide the precise identification of fault location. Computer simulations are conducted to demonstrate the improved performance in recognizing the fault component and evaluating its propagation across the system based on the proposed method.
Clinical usefulness of physiological components obtained by factor analysis
International Nuclear Information System (INIS)
Ohtake, Eiji; Murata, Hajime; Matsuda, Hirofumi; Yokoyama, Masao; Toyama, Hinako; Satoh, Tomohiko.
1989-01-01
The clinical usefulness of physiological components obtained by factor analysis was assessed in 99m Tc-DTPA renography. Using definite physiological components, another dynamic data could be analyzed. In this paper, the dynamic renal function after ESWL (Extracorporeal Shock Wave Lithotripsy) treatment was examined using physiological components in the kidney before ESWL and/or a normal kidney. We could easily evaluate the change of renal functions by this method. The usefulness of a new analysis using physiological components was summarized as follows: 1) The change of a dynamic function could be assessed in quantity as that of the contribution ratio. 2) The change of a sick condition could be morphologically evaluated as that of the functional image. (author)
Animal Research in the "Journal of Applied Behavior Analysis"
Edwards, Timothy L.; Poling, Alan
2011-01-01
This review summarizes the 6 studies with nonhuman animal subjects that have appeared in the "Journal of Applied Behavior Analysis" and offers suggestions for future research in this area. Two of the reviewed articles described translational research in which pigeons were used to illustrate and examine behavioral phenomena of applied significance…
Dimensional Analysis with space discrimination applied to Fickian difussion phenomena
International Nuclear Information System (INIS)
Diaz Sanchidrian, C.; Castans, M.
1989-01-01
Dimensional Analysis with space discrimination is applied to Fickian difussion phenomena in order to transform its partial differen-tial equations into ordinary ones, and also to obtain in a dimensionl-ess fom the Ficks second law. (Author)
Numerical analysis of magnetoelastic coupled buckling of fusion reactor components
International Nuclear Information System (INIS)
Demachi, K.; Yoshida, Y.; Miya, K.
1994-01-01
For a tokamak fusion reactor, it is one of the most important subjects to establish the structural design in which its components can stand for strong magnetic force induced by plasma disruption. A number of magnetostructural analysis of the fusion reactor components were done recently. However, in these researches the structural behavior was calculated based on the small deformation theory where the nonlinearity was neglected. But it is known that some kinds of structures easily exceed the geometrical nonlinearity. In this paper, the deflection and the magnetoelastic buckling load of fusion reactor components during plasma disruption were calculated
Jones, Susan R.; Wijeyesinghe, Charmaine L.
2011-01-01
This chapter explores how the framework of intersectionality can be used by faculty in course development and classroom teaching. An overview of intersectionality, highlighting core assumptions and tenets of the framework, is presented first. These assumptions and tenets are then applied to classroom dynamics and the practice of teaching in…
Condition monitoring with Mean field independent components analysis
DEFF Research Database (Denmark)
Pontoppidan, Niels Henrik; Sigurdsson, Sigurdur; Larsen, Jan
2005-01-01
We discuss condition monitoring based on mean field independent components analysis of acoustic emission energy signals. Within this framework it is possible to formulate a generative model that explains the sources, their mixing and also the noise statistics of the observed signals. By using...... a novelty approach we may detect unseen faulty signals as indeed faulty with high precision, even though the model learns only from normal signals. This is done by evaluating the likelihood that the model generated the signals and adapting a simple threshold for decision. Acoustic emission energy signals...... from a large diesel engine is used to demonstrate this approach. The results show that mean field independent components analysis gives a better detection of fault compared to principal components analysis, while at the same time selecting a more compact model...
Van Mechelen, Iven; Kiers, Henk A.L.
1999-01-01
The three-mode component analysis model is discussed as a tool for a contextualized study of personality. When applied to person x situation x response data, the model includes sets of latent dimensions for persons, situations, and responses as well as a so-called core array, which may be considered
Efficient real time OD matrix estimation based on principal component analysis
Djukic, T.; Flötteröd, G.; Van Lint, H.; Hoogendoorn, S.P.
2012-01-01
In this paper we explore the idea of dimensionality reduction and approximation of OD demand based on principal component analysis (PCA). First, we show how we can apply PCA to linearly transform the high dimensional OD matrices into the lower dimensional space without significant loss of accuracy.
Application of principal component analysis to time series of daily air pollution and mortality
Quant C; Fischer P; Buringh E; Ameling C; Houthuijs D; Cassee F; MGO
2004-01-01
We investigated whether cause-specific daily mortality can be attributed to specific sources of air pollution. To construct indicators of source-specific air pollution, we applied a principal component analysis (PCA) on routinely collected air pollution data in the Netherlands during the period
Principal Component Analysis: Resources for an Essential Application of Linear Algebra
Pankavich, Stephen; Swanson, Rebecca
2015-01-01
Principal Component Analysis (PCA) is a highly useful topic within an introductory Linear Algebra course, especially since it can be used to incorporate a number of applied projects. This method represents an essential application and extension of the Spectral Theorem and is commonly used within a variety of fields, including statistics,…
Application of independent component analysis to H-1 MR spectroscopic imaging exams of brain tumours
Szabo de Edelenyi, F.; Simonetti, A.W.; Postma, G.; Huo, R.; Buydens, L.M.C.
2005-01-01
The low spatial resolution of clinical H-1 MRSI leads to partial volume effects. To overcome this problem, we applied independent component analysis (ICA) on a set of H-1 MRSI exams of brain turnours. With this method, tissue types that yield statistically independent spectra can be separated. Up to
Components installation. Scheduling techniques applied at Framatome for the French nuclear program
International Nuclear Information System (INIS)
Cremese, G.
1982-09-01
The first scheduling objective is a timely delivery of heavy components manufactured by FRAMATOME factories: reactor vessel, steam generators and pressurizer. The second scheduling function is the preparation and updating of overall and detailed schedules for the coordination and follow-up of: design activities, progress at equipment suppliers, construction and erection activities of sub-contractors, test and start-up tasks by FRAMATOME and customer teams, and maintenance operations by FRAMATOME teams. I shall first describe the schedules for the contract first unit then show how the following units of the contract are scheduled in their turn
Automatic ECG analysis using principal component analysis and wavelet transformation
Khawaja, Antoun
2007-01-01
The main objective of this book is to analyse and detect small changes in ECG waves and complexes that indicate cardiac diseases and disorders. Detecting predisposition to Torsade de Points (TDP) by analysing the beat-to-beat variability in T wave morphology is the main core of this work. The second main topic is detecting small changes in QRS complex and predicting future QRS complexes of patients. Moreover, the last main topic is clustering similar ECG components in different groups.
Comparison of control systems applied to the handling of radioactive reactor components
International Nuclear Information System (INIS)
Robinson, C.; Harris, E.G.; Dyer, P.C.; Williams, J.G.B.
1985-01-01
The first generation of nuclear power stations have individual reactors each incorporating complete facilities for servicing components and refuelling. In the later designs, each power station has two reactors which are connected by a central block. This central block contains one set of facilities to service both reactors, but to improve the station capability, some of these are to be replicated. The central block incorporates a hoist well which was used during construction for the accessing of complete components. On completion of this work, the physical size of the hoist well is such as to permit the incorporation of additional facilities if these are shown to be operationally and economically desirable. Since a number of years of power operation has elapsed, the advantages of back-fitting to existing fuel-handling facilities has been illustrated. Since the mechanical arrangements and operating procedures are substantially similar for both the original and new handling facilities, the paper will illustrate the control systems provided for each. The configuration of the system is arranged to have two channels of control which complies with the current standard requirements in the United Kingdom. These requirements are more stringent than when the existing facility was designed and constructed, as described in the relevant sections of the paper. The new system has been designed and is being manufactured to comply with the Central Electricity Generating Board standard for nuclear fuel route interlock and control systems. (author)
Liu, Xiao-Fang; Xue, Chang-Hu; Wang, Yu-Ming; Li, Zhao-Jie; Xue, Yong; Xu, Jie
2011-11-01
The present study is to investigate the feasibility of multi-elements analysis in determination of the geographical origin of sea cucumber Apostichopus japonicus, and to make choice of the effective tracers in sea cucumber Apostichopus japonicus geographical origin assessment. The content of the elements such as Al, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, As, Se, Mo, Cd, Hg and Pb in sea cucumber Apostichopus japonicus samples from seven places of geographical origin were determined by means of ICP-MS. The results were used for the development of elements database. Cluster analysis(CA) and principal component analysis (PCA) were applied to differentiate the sea cucumber Apostichopus japonicus geographical origin. Three principal components which accounted for over 89% of the total variance were extracted from the standardized data. The results of Q-type cluster analysis showed that the 26 samples could be clustered reasonably into five groups, the classification results were significantly associated with the marine distribution of the sea cucumber Apostichopus japonicus samples. The CA and PCA were the effective methods for elements analysis of sea cucumber Apostichopus japonicus samples. The content of the mineral elements in sea cucumber Apostichopus japonicus samples was good chemical descriptors for differentiating their geographical origins.
The Significance of Regional Analysis in Applied Geography.
Sommers, Lawrence M.
Regional analysis is central to applied geographic research, contributing to better planning and policy development for a variety of societal problems facing the United States. The development of energy policy serves as an illustration of the capabilities of this type of analysis. The United States has had little success in formulating a national…
Independent component analysis in non-hypothesis driven metabolomics
DEFF Research Database (Denmark)
Li, Xiang; Hansen, Jakob; Zhao, Xinjie
2012-01-01
In a non-hypothesis driven metabolomics approach plasma samples collected at six different time points (before, during and after an exercise bout) were analyzed by gas chromatography-time of flight mass spectrometry (GC-TOF MS). Since independent component analysis (ICA) does not need a priori...... information on the investigated process and moreover can separate statistically independent source signals with non-Gaussian distribution, we aimed to elucidate the analytical power of ICA for the metabolic pattern analysis and the identification of key metabolites in this exercise study. A novel approach...... based on descriptive statistics was established to optimize ICA model. In the GC-TOF MS data set the number of principal components after whitening and the number of independent components of ICA were optimized and systematically selected by descriptive statistics. The elucidated dominating independent...
Analysis of the frequency components of X-ray images
International Nuclear Information System (INIS)
Matsuo, Satoru; Komizu, Mitsuru; Kida, Tetsuo; Noma, Kazuo; Hashimoto, Keiji; Onishi, Hideo; Masuda, Kazutaka
1997-01-01
We examined the relation between the frequency components of x-ray images of the chest and phalanges and their read sizes for digitizing. Images of the chest and phalanges were radiographed using three types of screens and films, and the noise images in background density were digitized with a drum scanner, changing the read sizes. The frequency components for these images were evaluated by converting them to the secondary Fourier to obtain the power spectrum and signal to noise ratio (SNR). After changing the cut-off frequency on the power spectrum to process a low pass filter, we also examined the frequency components of the images in relation to the normalized mean square error (NMSE) for the image converted to reverse Fourier and the original image. Results showed that the frequency components were 2.0 cycles/mm for the chest image and 6.0 cycles/mm for the phalanges. Therefore, it is necessary to collect data applying the read sizes of 200 μm and 50 μm for the chest and phalangeal images, respectively, in order to digitize these images without loss of their frequency components. (author)
Analysis methods for structure reliability of piping components
International Nuclear Information System (INIS)
Schimpfke, T.; Grebner, H.; Sievers, J.
2004-01-01
In the frame of the German reactor safety research program of the Federal Ministry of Economics and Labour (BMWA) GRS has started to develop an analysis code named PROST (PRObabilistic STructure analysis) for estimating the leak and break probabilities of piping systems in nuclear power plants. The long-term objective of this development is to provide failure probabilities of passive components for probabilistic safety analysis of nuclear power plants. Up to now the code can be used for calculating fatigue problems. The paper mentions the main capabilities and theoretical background of the present PROST development and presents some of the results of a benchmark analysis in the frame of the European project NURBIM (Nuclear Risk Based Inspection Methodologies for Passive Components). (orig.)
Roberts, Brian; Holmes, Stephen D
2006-12-01
Mistuning a harmonic produces an exaggerated change in its pitch. This occurs because the component becomes inconsistent with the regular pattern that causes the other harmonics (constituting the spectral frame) to integrate perceptually. These pitch shifts were measured when the fundamental (F0) component of a complex tone (nominal F0 frequency = 200 Hz) was mistuned by +8% and -8%. The pitch-shift gradient was defined as the difference between these values and its magnitude was used as a measure of frame integration. An independent and random perturbation (spectral jitter) was applied simultaneously to most or all of the frame components. The gradient magnitude declined gradually as the degree of jitter increased from 0% to +/-40% of F0. The component adjacent to the mistuned target made the largest contribution to the gradient, but more distant components also contributed. The stimuli were passed through an auditory model, and the exponential height of the F0-period peak in the averaged summary autocorrelation function correlated well with the gradient magnitude. The fit improved when the weighting on more distant channels was attenuated by a factor of three per octave. The results are consistent with a grouping mechanism that computes a weighted average of periodicity strength across several components.
The analysis of multivariate group differences using common principal components
Bechger, T.M.; Blanca, M.J.; Maris, G.
2014-01-01
Although it is simple to determine whether multivariate group differences are statistically significant or not, such differences are often difficult to interpret. This article is about common principal components analysis as a tool for the exploratory investigation of multivariate group differences
Principal Component Analysis: Most Favourite Tool in Chemometrics
Indian Academy of Sciences (India)
Abstract. Principal component analysis (PCA) is the most commonlyused chemometric technique. It is an unsupervised patternrecognition technique. PCA has found applications in chemistry,biology, medicine and economics. The present work attemptsto understand how PCA work and how can we interpretits results.
Scalable Robust Principal Component Analysis Using Grassmann Averages
DEFF Research Database (Denmark)
Hauberg, Søren; Feragen, Aasa; Enficiaud, Raffi
2016-01-01
In large datasets, manual data verification is impossible, and we must expect the number of outliers to increase with data size. While principal component analysis (PCA) can reduce data size, and scalable solutions exist, it is well-known that outliers can arbitrarily corrupt the results. Unfortu...
Reliability Analysis of Fatigue Fracture of Wind Turbine Drivetrain Components
DEFF Research Database (Denmark)
Berzonskis, Arvydas; Sørensen, John Dalsgaard
2016-01-01
in the volume of the casted ductile iron main shaft, on the reliability of the component. The probabilistic reliability analysis conducted is based on fracture mechanics models. Additionally, the utilization of the probabilistic reliability for operation and maintenance planning and quality control is discussed....
Principal component analysis of image gradient orientations for face recognition
Tzimiropoulos, Georgios; Zafeiriou, Stefanos; Pantic, Maja
We introduce the notion of Principal Component Analysis (PCA) of image gradient orientations. As image data is typically noisy, but noise is substantially different from Gaussian, traditional PCA of pixel intensities very often fails to estimate reliably the low-dimensional subspace of a given data
Adaptive tools in virtual environments: Independent component analysis for multimedia
DEFF Research Database (Denmark)
Kolenda, Thomas
2002-01-01
The thesis investigates the role of independent component analysis in the setting of virtual environments, with the purpose of finding properties that reflect human context. A general framework for performing unsupervised classification with ICA is presented in extension to the latent semantic in...... were compared to investigate computational differences and separation results. The ICA properties were finally implemented in a chat room analysis tool and briefly investigated for visualization of search engines results....
Development of computational methods of design by analysis for pressure vessel components
International Nuclear Information System (INIS)
Bao Shiyi; Zhou Yu; He Shuyan; Wu Honglin
2005-01-01
Stress classification is not only one of key steps when pressure vessel component is designed by analysis, but also a difficulty which puzzles engineers and designers at all times. At present, for calculating and categorizing the stress field of pressure vessel components, there are several computation methods of design by analysis such as Stress Equivalent Linearization, Two-Step Approach, Primary Structure method, Elastic Compensation method, GLOSS R-Node method and so on, that are developed and applied. Moreover, ASME code also gives an inelastic method of design by analysis for limiting gross plastic deformation only. When pressure vessel components design by analysis, sometimes there are huge differences between the calculating results for using different calculating and analysis methods mentioned above. As consequence, this is the main reason that affects wide application of design by analysis approach. Recently, a new approach, presented in the new proposal of a European Standard, CEN's unfired pressure vessel standard EN 13445-3, tries to avoid problems of stress classification by analyzing pressure vessel structure's various failure mechanisms directly based on elastic-plastic theory. In this paper, some stress classification methods mentioned above, are described briefly. And the computational methods cited in the European pressure vessel standard, such as Deviatoric Map, and nonlinear analysis methods (plastic analysis and limit analysis), are depicted compendiously. Furthermore, the characteristics of computational methods of design by analysis are summarized for selecting the proper computational method when design pressure vessel component by analysis. (authors)
Directory of Open Access Journals (Sweden)
Janusa Goelzer Sabin
2004-09-01
Full Text Available Neste trabalho a identificação e a discriminação de dois diferentes fármacos utilizados como antidepressivos foi estudada, empregando os espectros de reflexão difusa no infravermelho médio com transformada de Fourier (DRIFTS, juntamente com a análise de componentes principais (PCA e o método de classificação SIMCA. Os espectros no infravermelho de amostras contendo diferentes concentrações dos princípios ativos cloridrato de amitriptilina e cloridrato de imipramina, foram coletados em um espectrofotômetro NICOLET Magna 550, sendo realizadas 2 réplicas para cada amostra, com resolução de 4 cm-1 e 32 varreduras. A análise de componentes principais confirmou a existência de dois grupos distintos, correspondendo aos dois diferentes princípios ativos utilizados, além de evidenciar a presença de amostras anômalas no conjunto de dados que, possivelmente, iriam interferir na modelagem. Já o método de classificação SIMCA possibilitou o reconhecimento de amostras dos princípios ativos cloridrato de imipramina e cloridrato de amitriptilina com resultados indicando 100% de classificação correta das classes modeladas.In this work the certification of two different drugs used as antidepressants was studied, using diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS, together with the analysis of principal components (PCA and the method of soft independent modeling of class analogies (SIMCA. The DRIFT spectra of samples with different concentrations of the active principles amitriptiline and imipramine hydrochlorides had been collected in Magna 550 spectrofotometer, two spectra for each sample, with resolution of 4 cm-1 and 32 scans. The PCA confirmed the existence of two distinct groups, corresponding to the two different active principles used. Otherwise the method of classification SIMCA made possible the recognition of samples of the principles amitriptyline and imipramine hydrochlorides with results indicating
Applied data analysis and modeling for energy engineers and scientists
Reddy, T Agami
2011-01-01
""Applied Data Analysis and Modeling for Energy Engineers and Scientists"" discusses mathematical models, data analysis, and decision analysis in modeling. The approach taken in this volume focuses on the modeling and analysis of thermal systems in an engineering environment, while also covering a number of other critical areas. Other material covered includes the tools that researchers and engineering professionals will need in order to explore different analysis methods, use critical assessment skills and reach sound engineering conclusions. The book also covers process and system design and
PEMBUATAN PERANGKAT LUNAK PENGENALAN WAJAH MENGGUNAKAN PRINCIPAL COMPONENTS ANALYSIS
Directory of Open Access Journals (Sweden)
Kartika Gunadi
2001-01-01
Full Text Available Face recognition is one of many important researches, and today, many applications have implemented it. Through development of techniques like Principal Components Analysis (PCA, computers can now outperform human in many face recognition tasks, particularly those in which large database of faces must be searched. Principal Components Analysis was used to reduce facial image dimension into fewer variables, which are easier to observe and handle. Those variables then fed into artificial neural networks using backpropagation method to recognise the given facial image. The test results show that PCA can provide high face recognition accuracy. For the training faces, a correct identification of 100% could be obtained. From some of network combinations that have been tested, a best average correct identification of 91,11% could be obtained for the test faces while the worst average result is 46,67 % correct identification Abstract in Bahasa Indonesia : Pengenalan wajah manusia merupakan salah satu bidang penelitian yang penting, dan dewasa ini banyak aplikasi yang dapat menerapkannya. Melalui pengembangan suatu teknik seperti Principal Components Analysis (PCA, komputer sekarang dapat melebihi kemampuan otak manusia dalam berbagai tugas pengenalan wajah, terutama tugas-tugas yang membutuhkan pencarian pada database wajah yang besar. Principal Components Analysis digunakan untuk mereduksi dimensi gambar wajah sehingga menghasilkan variabel yang lebih sedikit yang lebih mudah untuk diobsevasi dan ditangani. Hasil yang diperoleh kemudian akan dimasukkan ke suatu jaringan saraf tiruan dengan metode Backpropagation untuk mengenali gambar wajah yang telah diinputkan ke dalam sistem. Hasil pengujian sistem menunjukkan bahwa penggunaan PCA untuk pengenalan wajah dapat memberikan tingkat akurasi yang cukup tinggi. Untuk gambar wajah yang diikutsertakankan dalam latihan, dapat diperoleh 100% identifikasi yang benar. Dari beberapa kombinasi jaringan yang
International Nuclear Information System (INIS)
Suzuki, Kazuhiko
2007-09-01
Concepts of assuring structural integrity of plant components have been developed under limited conditions of either highly ductile or brittle materials. There are some cases where operation in more and more severe conditions causes a significant reduction in ductility for materials with a high ductility before service. Use of high strength steels with relatively reduced ductility is increasing as industry applications. Current concepts of structural integrity assurance under the limited conditions of material properties or on the requirement of no significant changes in material properties even after long service will fail to incorporate expected technological innovations. A systematic concept of assuring the structural integrity should be developed for applying to highly ductile materials through brittle materials. Objectives of the on-going research are to propose a detail of the systematic concept by considering how we can develop the concept without restricting materials and for systematic considerations on a broad range of material properties from highly ductile materials through brittle materials. First, background of concepts of existing structural codes for components of highly ductile materials or for structural parts of brittle materials are discussed. Next, issues of existing code for parts of brittle materials are identified, and then resolutions to the issues are proposed. Based on the above-mentioned discussions and proposals, a systematic concept is proposed for application to components with reduced ductility materials and for applying to components of materials with significantly changing material properties due to long service. (author)
Energy Technology Data Exchange (ETDEWEB)
Zerbst, U.; Beeck, F.; Scheider, I.; Brocks, W. [GKSS-Forschungszentrum Geesthacht GmbH (Germany). Inst. fuer Werkstofforschung
1998-11-01
Under the roof of SINTAP (Structural Integrity Assessment Procedures for European Industry), a European BRITE-EURAM project, a study is being carried out into the possibility of establishing on the basis of existing models a standard European flaw assessment method. The R6 Routine and the ETM are important, existing examples in this context. The paper presents the two methods, explaining their advantages and shortcomes as well as common features. Their applicability is shown by experiments with two pressure vessels subject to internal pressure and flawed by a surface crack or a through-wall crack, respectively. Both the R6 Routine and the ETM results have been compared with results of component tests carried out in the 1980s at TWI and are found to yield acceptable conservative, i.e. sufficiently safe, lifetime predictions, as they do not give lifetime assessments which unduly underestimate the effects of flaws under operational loads. (orig./CB) [Deutsch] Gegenwaertig wird im Rahmen von SINTAP (Structural Integrity Assessment Procedures for European Industries), einem europaeischen BRITE-EURAM-Projekt geprueft, inwieweit auf der Grundlage vorhandener Modelle eine einheitliche europaeische Fehlerbewertungsmethode erstellt werden kann. Eine zentrale Stellung kommt dabei Verfahren wie der R6-Routine und dem ETM zu. In der vorliegenden Arbeit wurden beide Methoden vorgestellt, wobei ihre Vor- und Nachteile, aber auch ihre Gemeinsamkeiten herausgearbeitet wurden. Die Anwendung wurde an zwei innendruckbelasteten Behaeltern mit Oberflaechen- bzw. wanddurchdringendem Riss demonstriert. Sowohl R6-Routine als auch ETM ergaben im Vergleich mit am TWI zu Beginn der 80er Jahre durchgefuehrten Bauteilexperimenten eine vertretbare konservative Vorhersage, d.h. eine nicht allzu grosse Unterschaetzung der ertragbaren Last der Bauteile. (orig.)
Fourier convergence analysis applied to neutron diffusion Eigenvalue problem
International Nuclear Information System (INIS)
Lee, Hyun Chul; Noh, Jae Man; Joo, Hyung Kook
2004-01-01
Fourier error analysis has been a standard technique for the stability and convergence analysis of linear and nonlinear iterative methods. Though the methods can be applied to Eigenvalue problems too, all the Fourier convergence analyses have been performed only for fixed source problems and a Fourier convergence analysis for Eigenvalue problem has never been reported. Lee et al proposed new 2-D/1-D coupling methods and they showed that the new ones are unconditionally stable while one of the two existing ones is unstable at a small mesh size and that the new ones are better than the existing ones in terms of the convergence rate. In this paper the convergence of method A in reference 4 for the diffusion Eigenvalue problem was analyzed by the Fourier analysis. The Fourier convergence analysis presented in this paper is the first one applied to a neutronics eigenvalue problem to the best of our knowledge
Experimental modal analysis of components of the LHC experiments
Guinchard, M; Catinaccio, A; Kershaw, K; Onnela, A
2007-01-01
Experimental modal analysis of components of the LHC experiments is performed with the purpose of determining their fundamental frequencies, their damping and the mode shapes of light and fragile detector components. This process permits to confirm or replace Finite Element analysis in the case of complex structures (with cables and substructure coupling). It helps solving structural mechanical problems to improve the operational stability and determine the acceleration specifications for transport operations. This paper describes the hardware and software equipment used to perform a modal analysis on particular structures such as a particle detector and the method of curve fitting to extract the results of the measurements. This paper exposes also the main results obtained for the LHC Experiments.
Path and correlation analysis of perennial ryegrass (Lolium perenne L.) seed yield components
DEFF Research Database (Denmark)
Abel, Simon; Gislum, René; Boelt, Birte
2017-01-01
Maximum perennial ryegrass seed production potential is substantially greater than harvested yields with harvested yields representing only 20% of calculated potential. Similar to wheat, maize and other agriculturally important crops, seed yield is highly dependent on a number of interacting seed...... yield components. This research was performed to apply and describe path analysis of perennial ryegrass seed yield components in relation to harvested seed yields. Utilising extensive yield components which included subdividing reproductive inflorescences into five size categories, path analysis...... was undertaken assuming a unidirectional causal-admissible relationship between seed yield components and harvested seed yield in six commercial seed production fields. Both spikelets per inflorescence and florets per spikelet had a significant (p seed yield; however, total...
Sparse logistic principal components analysis for binary data
Lee, Seokho
2010-09-01
We develop a new principal components analysis (PCA) type dimension reduction method for binary data. Different from the standard PCA which is defined on the observed data, the proposed PCA is defined on the logit transform of the success probabilities of the binary observations. Sparsity is introduced to the principal component (PC) loading vectors for enhanced interpretability and more stable extraction of the principal components. Our sparse PCA is formulated as solving an optimization problem with a criterion function motivated from a penalized Bernoulli likelihood. A Majorization-Minimization algorithm is developed to efficiently solve the optimization problem. The effectiveness of the proposed sparse logistic PCA method is illustrated by application to a single nucleotide polymorphism data set and a simulation study. © Institute ol Mathematical Statistics, 2010.
Components of Program for Analysis of Spectra and Their Testing
Directory of Open Access Journals (Sweden)
Ivan Taufer
2013-11-01
Full Text Available The spectral analysis of aqueous solutions of multi-component mixtures is used for identification and distinguishing of individual componentsin the mixture and subsequent determination of protonation constants and absorptivities of differently protonated particles in the solution in steadystate (Meloun and Havel 1985, (Leggett 1985. Apart from that also determined are the distribution diagrams, i.e. concentration proportions ofthe individual components at different pH values. The spectra are measured with various concentrations of the basic components (one or severalpolyvalent weak acids or bases and various pH values within the chosen range of wavelengths. The obtained absorbance response area has to beanalyzed by non-linear regression using specialized algorithms. These algorithms have to meet certain requirements concerning the possibility ofcalculations and the level of outputs. A typical example is the SQUAD(84 program, which was gradually modified and extended, see, e.g., (Melounet al. 1986, (Meloun et al. 2012.
Compressive Online Robust Principal Component Analysis with Multiple Prior Information
DEFF Research Database (Denmark)
Van Luong, Huynh; Deligiannis, Nikos; Seiler, Jürgen
-rank components. Unlike conventional batch RPCA, which processes all the data directly, our method considers a small set of measurements taken per data vector (frame). Moreover, our method incorporates multiple prior information signals, namely previous reconstructed frames, to improve these paration...... and thereafter, update the prior information for the next frame. Using experiments on synthetic data, we evaluate the separation performance of the proposed algorithm. In addition, we apply the proposed algorithm to online video foreground and background separation from compressive measurements. The results show...
Optimization benefits analysis in production process of fabrication components
Prasetyani, R.; Rafsanjani, A. Y.; Rimantho, D.
2017-12-01
The determination of an optimal number of product combinations is important. The main problem at part and service department in PT. United Tractors Pandu Engineering (shortened to PT.UTPE) Is the optimization of the combination of fabrication component products (known as Liner Plate) which influence to the profit that will be obtained by the company. Liner Plate is a fabrication component that serves as a protector of core structure for heavy duty attachment, such as HD Vessel, HD Bucket, HD Shovel, and HD Blade. The graph of liner plate sales from January to December 2016 has fluctuated and there is no direct conclusion about the optimization of production of such fabrication components. The optimal product combination can be achieved by calculating and plotting the amount of production output and input appropriately. The method that used in this study is linear programming methods with primal, dual, and sensitivity analysis using QM software for Windows to obtain optimal fabrication components. In the optimal combination of components, PT. UTPE provide the profit increase of Rp. 105,285,000.00 for a total of Rp. 3,046,525,000.00 per month and the production of a total combination of 71 units per unit variance per month.
Spectral analysis and filter theory in applied geophysics
Buttkus, Burkhard
2000-01-01
This book is intended to be an introduction to the fundamentals and methods of spectral analysis and filter theory and their appli cations in geophysics. The principles and theoretical basis of the various methods are described, their efficiency and effectiveness eval uated, and instructions provided for their practical application. Be sides the conventional methods, newer methods arediscussed, such as the spectral analysis ofrandom processes by fitting models to the ob served data, maximum-entropy spectral analysis and maximum-like lihood spectral analysis, the Wiener and Kalman filtering methods, homomorphic deconvolution, and adaptive methods for nonstation ary processes. Multidimensional spectral analysis and filtering, as well as multichannel filters, are given extensive treatment. The book provides a survey of the state-of-the-art of spectral analysis and fil ter theory. The importance and possibilities ofspectral analysis and filter theory in geophysics for data acquisition, processing an...
Cloud Masking for Remotely Sensed Data Using Spectral and Principal Components Analysis
Directory of Open Access Journals (Sweden)
A. Ahmad
2012-06-01
Full Text Available Two methods of cloud masking tuned to tropical conditions have been developed, based on spectral analysis and Principal Components Analysis (PCA of Moderate Resolution Imaging Spectroradiometer (MODIS data. In the spectral approach, thresholds were applied to four reflective bands (1, 2, 3, and 4, three thermal bands (29, 31 and 32, the band 2/band 1 ratio, and the difference between band 29 and 31 in order to detect clouds. The PCA approach applied a threshold to the first principal component derived from the seven quantities used for spectral analysis. Cloud detections were compared with the standard MODIS cloud mask, and their accuracy was assessed using reference images and geographical information on the study area.
Multi-spectrometer calibration transfer based on independent component analysis.
Liu, Yan; Xu, Hao; Xia, Zhenzhen; Gong, Zhiyong
2018-02-26
Calibration transfer is indispensable for practical applications of near infrared (NIR) spectroscopy due to the need for precise and consistent measurements across different spectrometers. In this work, a method for multi-spectrometer calibration transfer is described based on independent component analysis (ICA). A spectral matrix is first obtained by aligning the spectra measured on different spectrometers. Then, by using independent component analysis, the aligned spectral matrix is decomposed into the mixing matrix and the independent components of different spectrometers. These differing measurements between spectrometers can then be standardized by correcting the coefficients within the independent components. Two NIR datasets of corn and edible oil samples measured with three and four spectrometers, respectively, were used to test the reliability of this method. The results of both datasets reveal that spectra measurements across different spectrometers can be transferred simultaneously and that the partial least squares (PLS) models built with the measurements on one spectrometer can predict that the spectra can be transferred correctly on another.
Animal research in the Journal of Applied Behavior Analysis.
Edwards, Timothy L; Poling, Alan
2011-01-01
This review summarizes the 6 studies with nonhuman animal subjects that have appeared in the Journal of Applied Behavior Analysis and offers suggestions for future research in this area. Two of the reviewed articles described translational research in which pigeons were used to illustrate and examine behavioral phenomena of applied significance (say-do correspondence and fluency), 3 described interventions that changed animals' behavior (self-injury by a baboon, feces throwing and spitting by a chimpanzee, and unsafe trailer entry by horses) in ways that benefited the animals and the people in charge of them, and 1 described the use of trained rats that performed a service to humans (land-mine detection). We suggest that each of these general research areas merits further attention and that the Journal of Applied Behavior Analysis is an appropriate outlet for some of these publications.
Applied Behavior Analysis Is a Science And, Therefore, Progressive
Leaf, Justin B.; Leaf, Ronald; McEachin, John; Taubman, Mitchell; Ala'i-Rosales, Shahla; Ross, Robert K.; Smith, Tristram; Weiss, Mary Jane
2016-01-01
Applied behavior analysis (ABA) is a science and, therefore, involves progressive approaches and outcomes. In this commentary we argue that the spirit and the method of science should be maintained in order to avoid reductionist procedures, stifled innovation, and rote, unresponsive protocols that become increasingly removed from meaningful…
X-ray fluorescence spectrometry applied to soil analysis
International Nuclear Information System (INIS)
Salvador, Vera Lucia Ribeiro; Sato, Ivone Mulako; Scapin Junior, Wilson Santo; Scapin, Marcos Antonio; Imakima, Kengo
1997-01-01
This paper studies the X-ray fluorescence spectrometry applied to the soil analysis. A comparative study of the WD-XRFS and ED-XRFS techniques was carried out by using the following soil samples: SL-1, SOIL-7 and marine sediment SD-M-2/TM, from IAEA, and clay, JG-1a from Geological Survey of Japan (GSJ)
Progressive-Ratio Schedules and Applied Behavior Analysis
Poling, Alan
2010-01-01
Establishing appropriate relations between the basic and applied areas of behavior analysis has been of long and persistent interest to the author. In this article, the author illustrates that there is a direct relation between how hard an organism will work for access to an object or activity, as indexed by the largest ratio completed under a…
B. F. Skinner's Contributions to Applied Behavior Analysis
Morris, Edward K.; Smith, Nathaniel G.; Altus, Deborah E.
2005-01-01
Our paper reviews and analyzes B. F. Skinner's contributions to applied behavior analysis in order to assess his role as the field's originator and founder. We found, first, that his contributions fall into five categorizes: the style and content of his science, his interpretations of typical and atypical human behavior, the implications he drew…
Applied Behavior Analysis: Current Myths in Public Education
Fielding, Cheryl; Lowdermilk, John; Lanier, Lauren L.; Fannin, Abigail G.; Schkade, Jennifer L.; Rose, Chad A.; Simpson, Cynthia G.
2013-01-01
The effective use of behavior management strategies and related policies continues to be a debated issue in public education. Despite overwhelming evidence espousing the benefits of the implementation of procedures derived from principles based on the science of applied behavior analysis (ABA), educators often indicate many common misconceptions…
Positive Behavior Support and Applied Behavior Analysis: A Familial Alliance
Dunlap, Glen; Carr, Edward G.; Horner, Robert H.; Zarcone, Jennifer R.; Schwartz, Ilene
2008-01-01
Positive behavior support (PBS) emerged in the mid-1980s as an approach for understanding and addressing problem behaviors. PBS was derived primarily from applied behavior analysis (ABA). Over time, however, PBS research and practice has incorporated evaluative methods, assessment and intervention procedures, and conceptual perspectives associated…
Neutron activation analysis applied to energy and environment
International Nuclear Information System (INIS)
Lyon, W.S.
1975-01-01
Neutron activation analysis was applied to a number of problems concerned with energy production and the environment. Burning of fossil fuel, the search for new sources of uranium, possible presence of toxic elements in food and water, and the relationship of trace elements to cardiovascular disease are some of the problems in which neutron activation was used. (auth)
Probabilistic structural analysis methods for select space propulsion system components
Millwater, H. R.; Cruse, T. A.
1989-01-01
The Probabilistic Structural Analysis Methods (PSAM) project developed at the Southwest Research Institute integrates state-of-the-art structural analysis techniques with probability theory for the design and analysis of complex large-scale engineering structures. An advanced efficient software system (NESSUS) capable of performing complex probabilistic analysis has been developed. NESSUS contains a number of software components to perform probabilistic analysis of structures. These components include: an expert system, a probabilistic finite element code, a probabilistic boundary element code and a fast probability integrator. The NESSUS software system is shown. An expert system is included to capture and utilize PSAM knowledge and experience. NESSUS/EXPERT is an interactive menu-driven expert system that provides information to assist in the use of the probabilistic finite element code NESSUS/FEM and the fast probability integrator (FPI). The expert system menu structure is summarized. The NESSUS system contains a state-of-the-art nonlinear probabilistic finite element code, NESSUS/FEM, to determine the structural response and sensitivities. A broad range of analysis capabilities and an extensive element library is present.
Fetal source extraction from magnetocardiographic recordings by dependent component analysis
Energy Technology Data Exchange (ETDEWEB)
Araujo, Draulio B de [Department of Physics and Mathematics, FFCLRP, University of Sao Paulo, Ribeirao Preto, SP (Brazil); Barros, Allan Kardec [Department of Electrical Engineering, Federal University of Maranhao, Sao Luis, Maranhao (Brazil); Estombelo-Montesco, Carlos [Department of Physics and Mathematics, FFCLRP, University of Sao Paulo, Ribeirao Preto, SP (Brazil); Zhao, Hui [Department of Medical Physics, University of Wisconsin, Madison, WI (United States); Filho, A C Roque da Silva [Department of Physics and Mathematics, FFCLRP, University of Sao Paulo, Ribeirao Preto, SP (Brazil); Baffa, Oswaldo [Department of Physics and Mathematics, FFCLRP, University of Sao Paulo, Ribeirao Preto, SP (Brazil); Wakai, Ronald [Department of Medical Physics, University of Wisconsin, Madison, WI (United States); Ohnishi, Noboru [Department of Information Engineering, Nagoya University (Japan)
2005-10-07
Fetal magnetocardiography (fMCG) has been extensively reported in the literature as a non-invasive, prenatal technique that can be used to monitor various functions of the fetal heart. However, fMCG signals often have low signal-to-noise ratio (SNR) and are contaminated by strong interference from the mother's magnetocardiogram signal. A promising, efficient tool for extracting signals, even under low SNR conditions, is blind source separation (BSS), or independent component analysis (ICA). Herein we propose an algorithm based on a variation of ICA, where the signal of interest is extracted using a time delay obtained from an autocorrelation analysis. We model the system using autoregression, and identify the signal component of interest from the poles of the autocorrelation function. We show that the method is effective in removing the maternal signal, and is computationally efficient. We also compare our results to more established ICA methods, such as FastICA.
Robust LOD scores for variance component-based linkage analysis.
Blangero, J; Williams, J T; Almasy, L
2000-01-01
The variance component method is now widely used for linkage analysis of quantitative traits. Although this approach offers many advantages, the importance of the underlying assumption of multivariate normality of the trait distribution within pedigrees has not been studied extensively. Simulation studies have shown that traits with leptokurtic distributions yield linkage test statistics that exhibit excessive Type I error when analyzed naively. We derive analytical formulae relating the deviation from the expected asymptotic distribution of the lod score to the kurtosis and total heritability of the quantitative trait. A simple correction constant yields a robust lod score for any deviation from normality and for any pedigree structure, and effectively eliminates the problem of inflated Type I error due to misspecification of the underlying probability model in variance component-based linkage analysis.
Constrained independent component analysis approach to nonobtrusive pulse rate measurements
Tsouri, Gill R.; Kyal, Survi; Dianat, Sohail; Mestha, Lalit K.
2012-07-01
Nonobtrusive pulse rate measurement using a webcam is considered. We demonstrate how state-of-the-art algorithms based on independent component analysis suffer from a sorting problem which hinders their performance, and propose a novel algorithm based on constrained independent component analysis to improve performance. We present how the proposed algorithm extracts a photoplethysmography signal and resolves the sorting problem. In addition, we perform a comparative study between the proposed algorithm and state-of-the-art algorithms over 45 video streams using a finger probe oxymeter for reference measurements. The proposed algorithm provides improved accuracy: the root mean square error is decreased from 20.6 and 9.5 beats per minute (bpm) for existing algorithms to 3.5 bpm for the proposed algorithm. An error of 3.5 bpm is within the inaccuracy expected from the reference measurements. This implies that the proposed algorithm provided performance of equal accuracy to the finger probe oximeter.
Saccenti, E.; Camacho, J.
2015-01-01
Principal component analysis is one of the most commonly used multivariate tools to describe and summarize data. Determining the optimal number of components in a principal component model is a fundamental problem in many fields of application. In this paper we compare the performance of several
Progress Towards Improved Analysis of TES X-ray Data Using Principal Component Analysis
Busch, S. E.; Adams, J. S.; Bandler, S. R.; Chervenak, J. A.; Eckart, M. E.; Finkbeiner, F. M.; Fixsen, D. J.; Kelley, R. L.; Kilbourne, C. A.; Lee, S.-J.;
2015-01-01
The traditional method of applying a digital optimal filter to measure X-ray pulses from transition-edge sensor (TES) devices does not achieve the best energy resolution when the signals have a highly non-linear response to energy, or the noise is non-stationary during the pulse. We present an implementation of a method to analyze X-ray data from TESs, which is based upon principal component analysis (PCA). Our method separates the X-ray signal pulse into orthogonal components that have the largest variance. We typically recover pulse height, arrival time, differences in pulse shape, and the variation of pulse height with detector temperature. These components can then be combined to form a representation of pulse energy. An added value of this method is that by reporting information on more descriptive parameters (as opposed to a single number representing energy), we generate a much more complete picture of the pulse received. Here we report on progress in developing this technique for future implementation on X-ray telescopes. We used an 55Fe source to characterize Mo/Au TESs. On the same dataset, the PCA method recovers a spectral resolution that is better by a factor of two than achievable with digital optimal filters.
Crude oil price analysis and forecasting based on variational mode decomposition and independent component analysis
E, Jianwei; Bao, Yanling; Ye, Jimin
2017-10-01
As one of the most vital energy resources in the world, crude oil plays a significant role in international economic market. The fluctuation of crude oil price has attracted academic and commercial attention. There exist many methods in forecasting the trend of crude oil price. However, traditional models failed in predicting accurately. Based on this, a hybrid method will be proposed in this paper, which combines variational mode decomposition (VMD), independent component analysis (ICA) and autoregressive integrated moving average (ARIMA), called VMD-ICA-ARIMA. The purpose of this study is to analyze the influence factors of crude oil price and predict the future crude oil price. Major steps can be concluded as follows: Firstly, applying the VMD model on the original signal (crude oil price), the modes function can be decomposed adaptively. Secondly, independent components are separated by the ICA, and how the independent components affect the crude oil price is analyzed. Finally, forecasting the price of crude oil price by the ARIMA model, the forecasting trend demonstrates that crude oil price declines periodically. Comparing with benchmark ARIMA and EEMD-ICA-ARIMA, VMD-ICA-ARIMA can forecast the crude oil price more accurately.
Research on Air Quality Evaluation based on Principal Component Analysis
Wang, Xing; Wang, Zilin; Guo, Min; Chen, Wei; Zhang, Huan
2018-01-01
Economic growth has led to environmental capacity decline and the deterioration of air quality. Air quality evaluation as a fundamental of environmental monitoring and air pollution control has become increasingly important. Based on the principal component analysis (PCA), this paper evaluates the air quality of a large city in Beijing-Tianjin-Hebei Area in recent 10 years and identifies influencing factors, in order to provide reference to air quality management and air pollution control.
Evaluating the Impact of Conservatism in Industrial Fatigue Analysis of Life-Limited Components
Directory of Open Access Journals (Sweden)
Hoole Joshua
2018-01-01
Full Text Available This paper presents a review of the conservatism approaches applied by different industrial sectors to the stress-life (S-N analysis of ‘life-limited’ or ‘safe-life’ components. A comparison of the fatigue design standards for 6 industrial sectors identified that the conservatism approaches are highly inconsistent when comparing the areas of variability and uncertainty accounted for along with the conservatism magnitude and method of application. Through the use of a case-study based on the SAE keyhole benchmark and 4340 steel S-N data, the industrial sector which introduces the greatest reduction of a component life-limit was identified as the nuclear sector. The results of the case-study also highlighted that conservatism applied to account for scatter in S-N data currently provides the greatest contribution to the reduction of component life-limits.
Wang, Zhuozheng; Deller, J. R.; Fleet, Blair D.
2016-01-01
Acquired digital images are often corrupted by a lack of camera focus, faulty illumination, or missing data. An algorithm is presented for fusion of multiple corrupted images of a scene using the lifting wavelet transform. The method employs adaptive fusion arithmetic based on matrix completion and self-adaptive regional variance estimation. Characteristics of the wavelet coefficients are used to adaptively select fusion rules. Robust principal component analysis is applied to low-frequency image components, and regional variance estimation is applied to high-frequency components. Experiments reveal that the method is effective for multifocus, visible-light, and infrared image fusion. Compared with traditional algorithms, the new algorithm not only increases the amount of preserved information and clarity but also improves robustness.
Tanaka, Hirokazu; Katura, Takusige; Sato, Hiroki
2013-01-01
Reproducibility of experimental results lies at the heart of scientific disciplines. Here we propose a signal processing method that extracts task-related components by maximizing the reproducibility during task periods from neuroimaging data. Unlike hypothesis-driven methods such as general linear models, no specific time courses are presumed, and unlike data-driven approaches such as independent component analysis, no arbitrary interpretation of components is needed. Task-related components are constructed by a linear, weighted sum of multiple time courses, and its weights are optimized so as to maximize inter-block correlations (CorrMax) or covariances (CovMax). Our analysis method is referred to as task-related component analysis (TRCA). The covariance maximization is formulated as a Rayleigh-Ritz eigenvalue problem, and corresponding eigenvectors give candidates of task-related components. In addition, a systematic statistical test based on eigenvalues is proposed, so task-related and -unrelated components are classified objectively and automatically. The proposed test of statistical significance is found to be independent of the degree of autocorrelation in data if the task duration is sufficiently longer than the temporal scale of autocorrelation, so TRCA can be applied to data with autocorrelation without any modification. We demonstrate that simple extensions of TRCA can provide most distinctive signals for two tasks and can integrate multiple modalities of information to remove task-unrelated artifacts. TRCA was successfully applied to synthetic data as well as near-infrared spectroscopy (NIRS) data of finger tapping. There were two statistically significant task-related components; one was a hemodynamic response, and another was a piece-wise linear time course. In summary, we conclude that TRCA has a wide range of applications in multi-channel biophysical and behavioral measurements. Copyright © 2012 Elsevier Inc. All rights reserved.
Efficient training of multilayer perceptrons using principal component analysis
International Nuclear Information System (INIS)
Bunzmann, Christoph; Urbanczik, Robert; Biehl, Michael
2005-01-01
A training algorithm for multilayer perceptrons is discussed and studied in detail, which relates to the technique of principal component analysis. The latter is performed with respect to a correlation matrix computed from the example inputs and their target outputs. Typical properties of the training procedure are investigated by means of a statistical physics analysis in models of learning regression and classification tasks. We demonstrate that the procedure requires by far fewer examples for good generalization than traditional online training. For networks with a large number of hidden units we derive the training prescription which achieves, within our model, the optimal generalization behavior
International Nuclear Information System (INIS)
Maciga, G.; Papponetti, M.; Crutzen, S.; Jehenson, P.
1990-01-01
Performance demonstration for NDT has been an active topic for several years. Interest in it came to the fore in the early 1980's when several institutions started to propose to use of realistic training assemblies and the formal approach of Validation Centers. These steps were justified for example by the results of the PISC exercises which concluded that there was a need for performance demonstration starting with capability assessment of techniques and procedure as they were routinely applied. If the PISC programme is put under the general ''Nuclear Motivation'', the PISC Methodology could be extended to problems to structural components in general, such as on conventional power plants, chemical, aerospace and offshore industries, where integrity and safety have regarded as being of great importance. Some themes of NDT inspections of fossil power plant and offshore components that could be objects of validation studies will be illustrated. (author)
Directory of Open Access Journals (Sweden)
S. Mahmoudishadi
2017-09-01
Full Text Available The image processing techniques in transform domain are employed as analysis tools for enhancing the detection of mineral deposits. The process of decomposing the image into important components increases the probability of mineral extraction. In this study, the performance of Principal Component Analysis (PCA and Independent Component Analysis (ICA has been evaluated for the visible and near-infrared (VNIR and Shortwave infrared (SWIR subsystems of ASTER data. Ardestan is located in part of Central Iranian Volcanic Belt that hosts many well-known porphyry copper deposits. This research investigated the propylitic and argillic alteration zones and outer mineralogy zone in part of Ardestan region. The two mentioned approaches were applied to discriminate alteration zones from igneous bedrock using the major absorption of indicator minerals from alteration and mineralogy zones in spectral rang of ASTER bands. Specialized PC components (PC2, PC3 and PC6 were used to identify pyrite and argillic and propylitic zones that distinguish from igneous bedrock in RGB color composite image. Due to the eigenvalues, the components 2, 3 and 6 account for 4.26% ,0.9% and 0.09% of the total variance of the data for Ardestan scene, respectively. For the purpose of discriminating the alteration and mineralogy zones of porphyry copper deposit from bedrocks, those mentioned percentages of data in ICA independent components of IC2, IC3 and IC6 are more accurately separated than noisy bands of PCA. The results of ICA method conform to location of lithological units of Ardestan region, as well.
Mahmoudishadi, S.; Malian, A.; Hosseinali, F.
2017-09-01
The image processing techniques in transform domain are employed as analysis tools for enhancing the detection of mineral deposits. The process of decomposing the image into important components increases the probability of mineral extraction. In this study, the performance of Principal Component Analysis (PCA) and Independent Component Analysis (ICA) has been evaluated for the visible and near-infrared (VNIR) and Shortwave infrared (SWIR) subsystems of ASTER data. Ardestan is located in part of Central Iranian Volcanic Belt that hosts many well-known porphyry copper deposits. This research investigated the propylitic and argillic alteration zones and outer mineralogy zone in part of Ardestan region. The two mentioned approaches were applied to discriminate alteration zones from igneous bedrock using the major absorption of indicator minerals from alteration and mineralogy zones in spectral rang of ASTER bands. Specialized PC components (PC2, PC3 and PC6) were used to identify pyrite and argillic and propylitic zones that distinguish from igneous bedrock in RGB color composite image. Due to the eigenvalues, the components 2, 3 and 6 account for 4.26% ,0.9% and 0.09% of the total variance of the data for Ardestan scene, respectively. For the purpose of discriminating the alteration and mineralogy zones of porphyry copper deposit from bedrocks, those mentioned percentages of data in ICA independent components of IC2, IC3 and IC6 are more accurately separated than noisy bands of PCA. The results of ICA method conform to location of lithological units of Ardestan region, as well.
Research in applied mathematics, numerical analysis, and computer science
1984-01-01
Research conducted at the Institute for Computer Applications in Science and Engineering (ICASE) in applied mathematics, numerical analysis, and computer science is summarized and abstracts of published reports are presented. The major categories of the ICASE research program are: (1) numerical methods, with particular emphasis on the development and analysis of basic numerical algorithms; (2) control and parameter identification; (3) computational problems in engineering and the physical sciences, particularly fluid dynamics, acoustics, and structural analysis; and (4) computer systems and software, especially vector and parallel computers.
Principal component analysis of the nonlinear coupling of harmonic modes in heavy-ion collisions
BoŻek, Piotr
2018-03-01
The principal component analysis of flow correlations in heavy-ion collisions is studied. The correlation matrix of harmonic flow is generalized to correlations involving several different flow vectors. The method can be applied to study the nonlinear coupling between different harmonic modes in a double differential way in transverse momentum or pseudorapidity. The procedure is illustrated with results from the hydrodynamic model applied to Pb + Pb collisions at √{sN N}=2760 GeV. Three examples of generalized correlations matrices in transverse momentum are constructed corresponding to the coupling of v22 and v4, of v2v3 and v5, or of v23,v33 , and v6. The principal component decomposition is applied to the correlation matrices and the dominant modes are calculated.
A component analysis of positive behaviour support plans.
McClean, Brian; Grey, Ian
2012-09-01
Positive behaviour support (PBS) emphasises multi-component interventions by natural intervention agents to help people overcome challenging behaviours. This paper investigates which components are most effective and which factors might mediate effectiveness. Sixty-one staff working with individuals with intellectual disability and challenging behaviours completed longitudinal competency-based training in PBS. Each staff participant conducted a functional assessment and developed and implemented a PBS plan for one prioritised individual. A total of 1,272 interventions were available for analysis. Measures of challenging behaviour were taken at baseline, after 6 months, and at an average of 26 months follow-up. There was a significant reduction in the frequency, management difficulty, and episodic severity of challenging behaviour over the duration of the study. Escape was identified by staff as the most common function, accounting for 77% of challenging behaviours. The most commonly implemented components of intervention were setting event changes and quality-of-life-based interventions. Only treatment acceptability was found to be related to decreases in behavioural frequency. No single intervention component was found to have a greater association with reductions in challenging behaviour.
Probabilistic methods in nuclear power plant component ageing analysis
International Nuclear Information System (INIS)
Simola, K.
1992-03-01
The nuclear power plant ageing research is aimed to ensure that the plant safety and reliability are maintained at a desired level through the designed, and possibly extended lifetime. In ageing studies, the reliability of components, systems and structures is evaluated taking into account the possible time- dependent decrease in reliability. The results of analyses can be used in the evaluation of the remaining lifetime of components and in the development of preventive maintenance, testing and replacement programmes. The report discusses the use of probabilistic models in the evaluations of the ageing of nuclear power plant components. The principles of nuclear power plant ageing studies are described and examples of ageing management programmes in foreign countries are given. The use of time-dependent probabilistic models to evaluate the ageing of various components and structures is described and the application of models is demonstrated with two case studies. In the case study of motor- operated closing valves the analysis are based on failure data obtained from a power plant. In the second example, the environmentally assisted crack growth is modelled with a computer code developed in United States, and the applicability of the model is evaluated on the basis of operating experience
Development of component failure data for seismic risk analysis
International Nuclear Information System (INIS)
Fray, R.R.; Moulia, T.A.
1981-01-01
This paper describes the quantification and utilization of seismic failure data used in the Diablo Canyon Seismic Risk Study. A single variable representation of earthquake severity that uses peak horizontal ground acceleration to characterize earthquake severity was employed. The use of a multiple variable representation would allow direct consideration of vertical accelerations and the spectral nature of earthquakes but would have added such complexity that the study would not have been feasible. Vertical accelerations and spectral nature were indirectly considered because component failure data were derived from design analyses, qualification tests and engineering judgment that did include such considerations. Two types of functions were used to describe component failure probabilities. Ramp functions were used for components, such as piping and structures, qualified by stress analysis. 'Anchor points' for ramp functions were selected by assuming a zero probability of failure at code allowable stress levels and unity probability of failure at ultimate stress levels. The accelerations corresponding to allowable and ultimate stress levels were determined by conservatively assuming a linear relationship between seismic stress and ground acceleration. Step functions were used for components, such as mechanical and electrical equipment, qualified by testing. Anchor points for step functions were selected by assuming a unity probability of failure above the qualification acceleration. (orig./HP)
Reliability Evaluation of Machine Center Components Based on Cascading Failure Analysis
Zhang, Ying-Zhi; Liu, Jin-Tong; Shen, Gui-Xiang; Long, Zhe; Sun, Shu-Guang
2017-07-01
In order to rectify the problems that the component reliability model exhibits deviation, and the evaluation result is low due to the overlook of failure propagation in traditional reliability evaluation of machine center components, a new reliability evaluation method based on cascading failure analysis and the failure influenced degree assessment is proposed. A direct graph model of cascading failure among components is established according to cascading failure mechanism analysis and graph theory. The failure influenced degrees of the system components are assessed by the adjacency matrix and its transposition, combined with the Pagerank algorithm. Based on the comprehensive failure probability function and total probability formula, the inherent failure probability function is determined to realize the reliability evaluation of the system components. Finally, the method is applied to a machine center, it shows the following: 1) The reliability evaluation values of the proposed method are at least 2.5% higher than those of the traditional method; 2) The difference between the comprehensive and inherent reliability of the system component presents a positive correlation with the failure influenced degree of the system component, which provides a theoretical basis for reliability allocation of machine center system.
Integrative sparse principal component analysis of gene expression data.
Liu, Mengque; Fan, Xinyan; Fang, Kuangnan; Zhang, Qingzhao; Ma, Shuangge
2017-12-01
In the analysis of gene expression data, dimension reduction techniques have been extensively adopted. The most popular one is perhaps the PCA (principal component analysis). To generate more reliable and more interpretable results, the SPCA (sparse PCA) technique has been developed. With the "small sample size, high dimensionality" characteristic of gene expression data, the analysis results generated from a single dataset are often unsatisfactory. Under contexts other than dimension reduction, integrative analysis techniques, which jointly analyze the raw data of multiple independent datasets, have been developed and shown to outperform "classic" meta-analysis and other multidatasets techniques and single-dataset analysis. In this study, we conduct integrative analysis by developing the iSPCA (integrative SPCA) method. iSPCA achieves the selection and estimation of sparse loadings using a group penalty. To take advantage of the similarity across datasets and generate more accurate results, we further impose contrasted penalties. Different penalties are proposed to accommodate different data conditions. Extensive simulations show that iSPCA outperforms the alternatives under a wide spectrum of settings. The analysis of breast cancer and pancreatic cancer data further shows iSPCA's satisfactory performance. © 2017 WILEY PERIODICALS, INC.
Applied Fourier analysis from signal processing to medical imaging
Olson, Tim
2017-01-01
The first of its kind, this focused textbook serves as a self-contained resource for teaching from scratch the fundamental mathematics of Fourier analysis and illustrating some of its most current, interesting applications, including medical imaging and radar processing. Developed by the author from extensive classroom teaching experience, it provides a breadth of theory that allows students to appreciate the utility of the subject, but at as accessible a depth as possible. With myriad applications included, this book can be adapted to a one or two semester course in Fourier Analysis or serve as the basis for independent study. Applied Fourier Analysis assumes no prior knowledge of analysis from its readers, and begins by making the transition from linear algebra to functional analysis. It goes on to cover basic Fourier series and Fourier transforms before delving into applications in sampling and interpolation theory, digital communications, radar processing, medical i maging, and heat and wave equations. Fo...
Source Signals Separation and Reconstruction Following Principal Component Analysis
Directory of Open Access Journals (Sweden)
WANG Cheng
2014-02-01
Full Text Available For separation and reconstruction of source signals from observed signals problem, the physical significance of blind source separation modal and independent component analysis is not very clear, and its solution is not unique. Aiming at these disadvantages, a new linear and instantaneous mixing model and a novel source signals separation reconstruction solving method from observed signals based on principal component analysis (PCA are put forward. Assumption of this new model is statistically unrelated rather than independent of source signals, which is different from the traditional blind source separation model. A one-to-one relationship between linear and instantaneous mixing matrix of new model and linear compound matrix of PCA, and a one-to-one relationship between unrelated source signals and principal components are demonstrated using the concept of linear separation matrix and unrelated of source signals. Based on this theoretical link, source signals separation and reconstruction problem is changed into PCA of observed signals then. The theoretical derivation and numerical simulation results show that, in despite of Gauss measurement noise, wave form and amplitude information of unrelated source signal can be separated and reconstructed by PCA when linear mixing matrix is column orthogonal and normalized; only wave form information of unrelated source signal can be separated and reconstructed by PCA when linear mixing matrix is column orthogonal but not normalized, unrelated source signal cannot be separated and reconstructed by PCA when mixing matrix is not column orthogonal or linear.
Prestudy - Development of trend analysis of component failure
International Nuclear Information System (INIS)
Poern, K.
1995-04-01
The Bayesian trend analysis model that has been used for the computation of initiating event intensities (I-book) is based on the number of events that have occurred during consecutive time intervals. The model itself is a Poisson process with time-dependent intensity. For the analysis of aging it is often more relevant to use times between failures for a given component as input, where by 'time' is meant a quantity that best characterizes the age of the component (calendar time, operating time, number of activations etc). Therefore, it has been considered necessary to extend the model and the computer code to allow trend analysis of times between events, and also of several sequences of times between events. This report describes this model extension as well as an application on an introductory ageing analysis of centrifugal pumps defined in Table 5 of the T-book. The application in turn directs the attention to the need for further development of both the trend model and the data base. Figs
Aeromagnetic Compensation Algorithm Based on Principal Component Analysis
Directory of Open Access Journals (Sweden)
Peilin Wu
2018-01-01
Full Text Available Aeromagnetic exploration is an important exploration method in geophysics. The data is typically measured by optically pumped magnetometer mounted on an aircraft. But any aircraft produces significant levels of magnetic interference. Therefore, aeromagnetic compensation is important in aeromagnetic exploration. However, multicollinearity of the aeromagnetic compensation model degrades the performance of the compensation. To address this issue, a novel aeromagnetic compensation method based on principal component analysis is proposed. Using the algorithm, the correlation in the feature matrix is eliminated and the principal components are using to construct the hyperplane to compensate the platform-generated magnetic fields. The algorithm was tested using a helicopter, and the obtained improvement ratio is 9.86. The compensated quality is almost the same or slightly better than the ridge regression. The validity of the proposed method was experimentally demonstrated.
Fast principal component analysis for stacking seismic data
Wu, Juan; Bai, Min
2018-04-01
Stacking seismic data plays an indispensable role in many steps of the seismic data processing and imaging workflow. Optimal stacking of seismic data can help mitigate seismic noise and enhance the principal components to a great extent. Traditional average-based seismic stacking methods cannot obtain optimal performance when the ambient noise is extremely strong. We propose a principal component analysis (PCA) algorithm for stacking seismic data without being sensitive to noise level. Considering the computational bottleneck of the classic PCA algorithm in processing massive seismic data, we propose an efficient PCA algorithm to make the proposed method readily applicable for industrial applications. Two numerically designed examples and one real seismic data are used to demonstrate the performance of the presented method.
Demixed principal component analysis of neural population data.
Kobak, Dmitry; Brendel, Wieland; Constantinidis, Christos; Feierstein, Claudia E; Kepecs, Adam; Mainen, Zachary F; Qi, Xue-Lian; Romo, Ranulfo; Uchida, Naoshige; Machens, Christian K
2016-04-12
Neurons in higher cortical areas, such as the prefrontal cortex, are often tuned to a variety of sensory and motor variables, and are therefore said to display mixed selectivity. This complexity of single neuron responses can obscure what information these areas represent and how it is represented. Here we demonstrate the advantages of a new dimensionality reduction technique, demixed principal component analysis (dPCA), that decomposes population activity into a few components. In addition to systematically capturing the majority of the variance of the data, dPCA also exposes the dependence of the neural representation on task parameters such as stimuli, decisions, or rewards. To illustrate our method we reanalyze population data from four datasets comprising different species, different cortical areas and different experimental tasks. In each case, dPCA provides a concise way of visualizing the data that summarizes the task-dependent features of the population response in a single figure.
Energy Technology Data Exchange (ETDEWEB)
Voutay, O.
2003-02-01
Seismic data contain further geological information than well, due to their good spatial extent. But the seismic measure is band pass limited and the contrasts in acoustic or elastic properties derived from seismic are not directly linked to the reservoir properties. Thus, it is difficult to give a geological interpretation to seismic data. Basically, relevant seismic attributes are extracted at the reservoir level, and then are calibrated with information available at wells by using pattern recognition and statistical estimation techniques. These methods are successfully used in the post-stack domain. But, for multi-cube seismic information such as pre-stack or 4D data, the number of attributes can considerably increase and statistical methods are not often used. It is necessary to find a parameterization allowing an optimal description the seismic variability in the time window of interest. We propose to extract new attributes from seismic multi-cube data with Generalised Principal Analysis and to use them for reservoir interpretation with statistical techniques. The new attributes can be clearly related to the initial data set, and then be physically interpreted, while optimally summarizing the initial seismic information. By applying the Generalised Principal Analysis to 3D pre-stack surveys, the contribution of the pre-stack seismic information to reservoir characterisation is compared to the post-stack seismic one, in both synthetic and real cases. By applying the Generalised Principal Analysis to real 4D surveys, the seismic repeatability is quantified and the seismic changes in the reservoir with calendar time are highlighted and interpreted. A coherency cube has also been defined, based on the Generalised Principal Analysis. This attribute is a coherence measurement in three dimensions representing the local similarity between 4D or AVO surveys. (author)
Multigroup Moderation Test in Generalized Structured Component Analysis
Directory of Open Access Journals (Sweden)
Angga Dwi Mulyanto
2016-05-01
Full Text Available Generalized Structured Component Analysis (GSCA is an alternative method in structural modeling using alternating least squares. GSCA can be used for the complex analysis including multigroup. GSCA can be run with a free software called GeSCA, but in GeSCA there is no multigroup moderation test to compare the effect between groups. In this research we propose to use the T test in PLS for testing moderation Multigroup on GSCA. T test only requires sample size, estimate path coefficient, and standard error of each group that are already available on the output of GeSCA and the formula is simple so the user does not need a long time for analysis.
Determination of the optimal number of components in independent components analysis.
Kassouf, Amine; Jouan-Rimbaud Bouveresse, Delphine; Rutledge, Douglas N
2018-03-01
Independent components analysis (ICA) may be considered as one of the most established blind source separation techniques for the treatment of complex data sets in analytical chemistry. Like other similar methods, the determination of the optimal number of latent variables, in this case, independent components (ICs), is a crucial step before any modeling. Therefore, validation methods are required in order to decide about the optimal number of ICs to be used in the computation of the final model. In this paper, three new validation methods are formally presented. The first one, called Random_ICA, is a generalization of the ICA_by_blocks method. Its specificity resides in the random way of splitting the initial data matrix into two blocks, and then repeating this procedure several times, giving a broader perspective for the selection of the optimal number of ICs. The second method, called KMO_ICA_Residuals is based on the computation of the Kaiser-Meyer-Olkin (KMO) index of the transposed residual matrices obtained after progressive extraction of ICs. The third method, called ICA_corr_y, helps to select the optimal number of ICs by computing the correlations between calculated proportions and known physico-chemical information about samples, generally concentrations, or between a source signal known to be present in the mixture and the signals extracted by ICA. These three methods were tested using varied simulated and experimental data sets and compared, when necessary, to ICA_by_blocks. Results were relevant and in line with expected ones, proving the reliability of the three proposed methods. Copyright © 2017 Elsevier B.V. All rights reserved.
Chen, Shuming; Wang, Dengfeng; Liu, Bo
This paper investigates optimization design of the thickness of the sound package performed on a passenger automobile. The major characteristics indexes for performance selected to evaluate the processes are the SPL of the exterior noise and the weight of the sound package, and the corresponding parameters of the sound package are the thickness of the glass wool with aluminum foil for the first layer, the thickness of the glass fiber for the second layer, and the thickness of the PE foam for the third layer. In this paper, the process is fundamentally with multiple performances, thus, the grey relational analysis that utilizes grey relational grade as performance index is especially employed to determine the optimal combination of the thickness of the different layers for the designed sound package. Additionally, in order to evaluate the weighting values corresponding to various performance characteristics, the principal component analysis is used to show their relative importance properly and objectively. The results of the confirmation experiments uncover that grey relational analysis coupled with principal analysis methods can successfully be applied to find the optimal combination of the thickness for each layer of the sound package material. Therefore, the presented method can be an effective tool to improve the vehicle exterior noise and lower the weight of the sound package. In addition, it will also be helpful for other applications in the automotive industry, such as the First Automobile Works in China, Changan Automobile in China, etc.
Bridge Diagnosis by Using Nonlinear Independent Component Analysis and Displacement Analysis
Zheng, Juanqing; Yeh, Yichun; Ogai, Harutoshi
A daily diagnosis system for bridge monitoring and maintenance is developed based on wireless sensors, signal processing, structure analysis, and displacement analysis. The vibration acceleration data of a bridge are firstly collected through the wireless sensor network by exerting. Nonlinear independent component analysis (ICA) and spectral analysis are used to extract the vibration frequencies of the bridge. After that, through a band pass filter and Simpson's rule the vibration displacement is calculated and the vibration model is obtained to diagnose the bridge. Since linear ICA algorithms work efficiently only in linear mixing environments, a nonlinear ICA model, which is more complicated, is more practical for bridge diagnosis systems. In this paper, we firstly use the post nonlinear method to change the signal data, after that perform linear separation by FastICA, and calculate the vibration displacement of the bridge. The processed data can be used to understand phenomena like corrosion and crack, and evaluate the health condition of the bridge. We apply this system to Nakajima Bridge in Yahata, Kitakyushu, Japan.
Duval, J; Coyette, F; Seron, X
2008-08-01
This paper describes and evaluates a programme of neuropsychological rehabilitation which aims to improve three sub-components of the working memory central executive: processing load, updating and dual-task monitoring, by the acquisition of three re-organisation strategies (double coding, serial processing and speed reduction). Our programme has two stages: cognitive rehabilitation (graduated exercises subdivided into three sub-programmes each corresponding to a sub-component) which enables the patient to acquire the three specific strategies; and an ecological rehabilitation, including analyses of scenarios and simulations of real-life situations, which aims to transfer the strategies learned to everyday life. The programme also includes information meetings. It was applied to a single case who had working memory deficits after a surgical operation for a cerebral tumour on his left internal temporal ganglioglioma. Multiple baseline tests were used to measure the effectiveness of the rehabilitation. The programme proved to be effective for all three working memory components; a generalisation of its effects to everyday life was observed, and the effects were undiminished three months later.
Energy Technology Data Exchange (ETDEWEB)
Guerin, P.; Baudron, A. M.; Lautard, J. J. [Commissariat a l' Energie Atomique, DEN/DANS/DM2S/SERMA/LENR, CEA Saclay, 91191 Gif sur Yvette (France)
2006-07-01
This paper describes a new technique for determining the pin power in heterogeneous core calculations. It is based on a domain decomposition with overlapping sub-domains and a component mode synthesis technique for the global flux determination. Local basis functions are used to span a discrete space that allows fundamental global mode approximation through a Galerkin technique. Two approaches are given to obtain these local basis functions: in the first one (Component Mode Synthesis method), the first few spatial eigenfunctions are computed on each sub-domain, using periodic boundary conditions. In the second one (Factorized Component Mode Synthesis method), only the fundamental mode is computed, and we use a factorization principle for the flux in order to replace the higher order Eigenmodes. These different local spatial functions are extended to the global domain by defining them as zero outside the sub-domain. These methods are well-fitted for heterogeneous core calculations because the spatial interface modes are taken into account in the domain decomposition. Although these methods could be applied to higher order angular approximations - particularly easily to a SPN approximation - the numerical results we provide are obtained using a diffusion model. We show the methods' accuracy for reactor cores loaded with UOX and MOX assemblies, for which standard reconstruction techniques are known to perform poorly. Furthermore, we show that our methods are highly and easily parallelizable. (authors)
International Nuclear Information System (INIS)
Guerin, P.; Baudron, A. M.; Lautard, J. J.
2006-01-01
This paper describes a new technique for determining the pin power in heterogeneous core calculations. It is based on a domain decomposition with overlapping sub-domains and a component mode synthesis technique for the global flux determination. Local basis functions are used to span a discrete space that allows fundamental global mode approximation through a Galerkin technique. Two approaches are given to obtain these local basis functions: in the first one (Component Mode Synthesis method), the first few spatial eigenfunctions are computed on each sub-domain, using periodic boundary conditions. In the second one (Factorized Component Mode Synthesis method), only the fundamental mode is computed, and we use a factorization principle for the flux in order to replace the higher order Eigenmodes. These different local spatial functions are extended to the global domain by defining them as zero outside the sub-domain. These methods are well-fitted for heterogeneous core calculations because the spatial interface modes are taken into account in the domain decomposition. Although these methods could be applied to higher order angular approximations - particularly easily to a SPN approximation - the numerical results we provide are obtained using a diffusion model. We show the methods' accuracy for reactor cores loaded with UOX and MOX assemblies, for which standard reconstruction techniques are known to perform poorly. Furthermore, we show that our methods are highly and easily parallelizable. (authors)
Development of guidelines for inelastic analysis in design of fast reactor components
International Nuclear Information System (INIS)
Nakamura, Kyotada; Kasahara, Naoto; Morishita, Masaki; Shibamoto, Hiroshi; Inoue, Kazuhiko; Nakayama, Yasunari
2008-01-01
The interim guidelines for the application of inelastic analysis to design of fast reactor components were developed. These guidelines are referred from 'Elevated Temperature Structural Design Guide for Commercialized Fast Reactor (FDS)'. The basic policies of the guidelines are more rational predictions compared with elastic analysis approach and a guarantee of conservative results for design conditions. The guidelines recommend two kinds of constitutive equations to estimate strains conservatively. They also provide the methods for modeling load histories and estimating fatigue and creep damage based on the results of inelastic analysis. The guidelines were applied to typical design examples and their results were summarized as exemplars to support users
Applying DEA sensitivity analysis to efficiency measurement of Vietnamese universities
Directory of Open Access Journals (Sweden)
Thi Thanh Huyen Nguyen
2015-11-01
Full Text Available The primary purpose of this study is to measure the technical efficiency of 30 doctorate-granting universities, the universities or the higher education institutes with PhD training programs, in Vietnam, applying the sensitivity analysis of data envelopment analysis (DEA. The study uses eight sets of input-output specifications using the replacement as well as aggregation/disaggregation of variables. The measurement results allow us to examine the sensitivity of the efficiency of these universities with the sets of variables. The findings also show the impact of variables on their efficiency and its “sustainability”.
Robustness analysis of bogie suspension components Pareto optimised values
Mousavi Bideleh, Seyed Milad
2017-08-01
Bogie suspension system of high speed trains can significantly affect vehicle performance. Multiobjective optimisation problems are often formulated and solved to find the Pareto optimised values of the suspension components and improve cost efficiency in railway operations from different perspectives. Uncertainties in the design parameters of suspension system can negatively influence the dynamics behaviour of railway vehicles. In this regard, robustness analysis of a bogie dynamics response with respect to uncertainties in the suspension design parameters is considered. A one-car railway vehicle model with 50 degrees of freedom and wear/comfort Pareto optimised values of bogie suspension components is chosen for the analysis. Longitudinal and lateral primary stiffnesses, longitudinal and vertical secondary stiffnesses, as well as yaw damping are considered as five design parameters. The effects of parameter uncertainties on wear, ride comfort, track shift force, stability, and risk of derailment are studied by varying the design parameters around their respective Pareto optimised values according to a lognormal distribution with different coefficient of variations (COVs). The robustness analysis is carried out based on the maximum entropy concept. The multiplicative dimensional reduction method is utilised to simplify the calculation of fractional moments and improve the computational efficiency. The results showed that the dynamics response of the vehicle with wear/comfort Pareto optimised values of bogie suspension is robust against uncertainties in the design parameters and the probability of failure is small for parameter uncertainties with COV up to 0.1.
Sparse principal component analysis in medical shape modeling
Sjöstrand, Karl; Stegmann, Mikkel B.; Larsen, Rasmus
2006-03-01
Principal component analysis (PCA) is a widely used tool in medical image analysis for data reduction, model building, and data understanding and exploration. While PCA is a holistic approach where each new variable is a linear combination of all original variables, sparse PCA (SPCA) aims at producing easily interpreted models through sparse loadings, i.e. each new variable is a linear combination of a subset of the original variables. One of the aims of using SPCA is the possible separation of the results into isolated and easily identifiable effects. This article introduces SPCA for shape analysis in medicine. Results for three different data sets are given in relation to standard PCA and sparse PCA by simple thresholding of small loadings. Focus is on a recent algorithm for computing sparse principal components, but a review of other approaches is supplied as well. The SPCA algorithm has been implemented using Matlab and is available for download. The general behavior of the algorithm is investigated, and strengths and weaknesses are discussed. The original report on the SPCA algorithm argues that the ordering of modes is not an issue. We disagree on this point and propose several approaches to establish sensible orderings. A method that orders modes by decreasing variance and maximizes the sum of variances for all modes is presented and investigated in detail.
Protein structure similarity from principle component correlation analysis
Directory of Open Access Journals (Sweden)
Chou James
2006-01-01
Full Text Available Abstract Background Owing to rapid expansion of protein structure databases in recent years, methods of structure comparison are becoming increasingly effective and important in revealing novel information on functional properties of proteins and their roles in the grand scheme of evolutionary biology. Currently, the structural similarity between two proteins is measured by the root-mean-square-deviation (RMSD in their best-superimposed atomic coordinates. RMSD is the golden rule of measuring structural similarity when the structures are nearly identical; it, however, fails to detect the higher order topological similarities in proteins evolved into different shapes. We propose new algorithms for extracting geometrical invariants of proteins that can be effectively used to identify homologous protein structures or topologies in order to quantify both close and remote structural similarities. Results We measure structural similarity between proteins by correlating the principle components of their secondary structure interaction matrix. In our approach, the Principle Component Correlation (PCC analysis, a symmetric interaction matrix for a protein structure is constructed with relationship parameters between secondary elements that can take the form of distance, orientation, or other relevant structural invariants. When using a distance-based construction in the presence or absence of encoded N to C terminal sense, there are strong correlations between the principle components of interaction matrices of structurally or topologically similar proteins. Conclusion The PCC method is extensively tested for protein structures that belong to the same topological class but are significantly different by RMSD measure. The PCC analysis can also differentiate proteins having similar shapes but different topological arrangements. Additionally, we demonstrate that when using two independently defined interaction matrices, comparison of their maximum
Principal component analysis of FDG PET in amnestic MCI
International Nuclear Information System (INIS)
Nobili, Flavio; Girtler, Nicola; Brugnolo, Andrea; Dessi, Barbara; Rodriguez, Guido; Salmaso, Dario; Morbelli, Silvia; Piccardo, Arnoldo; Larsson, Stig A.; Pagani, Marco
2008-01-01
The purpose of the study is to evaluate the combined accuracy of episodic memory performance and 18 F-FDG PET in identifying patients with amnestic mild cognitive impairment (aMCI) converting to Alzheimer's disease (AD), aMCI non-converters, and controls. Thirty-three patients with aMCI and 15 controls (CTR) were followed up for a mean of 21 months. Eleven patients developed AD (MCI/AD) and 22 remained with aMCI (MCI/MCI). 18 F-FDG PET volumetric regions of interest underwent principal component analysis (PCA) that identified 12 principal components (PC), expressed by coarse component scores (CCS). Discriminant analysis was performed using the significant PCs and episodic memory scores. PCA highlighted relative hypometabolism in PC5, including bilateral posterior cingulate and left temporal pole, and in PC7, including the bilateral orbitofrontal cortex, both in MCI/MCI and MCI/AD vs CTR. PC5 itself plus PC12, including the left lateral frontal cortex (LFC: BAs 44, 45, 46, 47), were significantly different between MCI/AD and MCI/MCI. By a three-group discriminant analysis, CTR were more accurately identified by PET-CCS + delayed recall score (100%), MCI/MCI by PET-CCS + either immediate or delayed recall scores (91%), while MCI/AD was identified by PET-CCS alone (82%). PET increased by 25% the correct allocations achieved by memory scores, while memory scores increased by 15% the correct allocations achieved by PET. Combining memory performance and 18 F-FDG PET yielded a higher accuracy than each single tool in identifying CTR and MCI/MCI. The PC containing bilateral posterior cingulate and left temporal pole was the hallmark of MCI/MCI patients, while the PC including the left LFC was the hallmark of conversion to AD. (orig.)
Principal component analysis of FDG PET in amnestic MCI
Energy Technology Data Exchange (ETDEWEB)
Nobili, Flavio; Girtler, Nicola; Brugnolo, Andrea; Dessi, Barbara; Rodriguez, Guido [University of Genoa, Clinical Neurophysiology, Department of Endocrinological and Medical Sciences, Genoa (Italy); S. Martino Hospital, Alzheimer Evaluation Unit, Genoa (Italy); S. Martino Hospital, Head-Neck Department, Genoa (Italy); Salmaso, Dario [CNR, Institute of Cognitive Sciences and Technologies, Rome (Italy); CNR, Institute of Cognitive Sciences and Technologies, Padua (Italy); Morbelli, Silvia [University of Genoa, Nuclear Medicine Unit, Department of Internal Medicine, Genoa (Italy); Piccardo, Arnoldo [Galliera Hospital, Nuclear Medicine Unit, Department of Imaging Diagnostics, Genoa (Italy); Larsson, Stig A. [Karolinska Hospital, Department of Nuclear Medicine, Stockholm (Sweden); Pagani, Marco [CNR, Institute of Cognitive Sciences and Technologies, Rome (Italy); CNR, Institute of Cognitive Sciences and Technologies, Padua (Italy); Karolinska Hospital, Department of Nuclear Medicine, Stockholm (Sweden)
2008-12-15
The purpose of the study is to evaluate the combined accuracy of episodic memory performance and {sup 18}F-FDG PET in identifying patients with amnestic mild cognitive impairment (aMCI) converting to Alzheimer's disease (AD), aMCI non-converters, and controls. Thirty-three patients with aMCI and 15 controls (CTR) were followed up for a mean of 21 months. Eleven patients developed AD (MCI/AD) and 22 remained with aMCI (MCI/MCI). {sup 18}F-FDG PET volumetric regions of interest underwent principal component analysis (PCA) that identified 12 principal components (PC), expressed by coarse component scores (CCS). Discriminant analysis was performed using the significant PCs and episodic memory scores. PCA highlighted relative hypometabolism in PC5, including bilateral posterior cingulate and left temporal pole, and in PC7, including the bilateral orbitofrontal cortex, both in MCI/MCI and MCI/AD vs CTR. PC5 itself plus PC12, including the left lateral frontal cortex (LFC: BAs 44, 45, 46, 47), were significantly different between MCI/AD and MCI/MCI. By a three-group discriminant analysis, CTR were more accurately identified by PET-CCS + delayed recall score (100%), MCI/MCI by PET-CCS + either immediate or delayed recall scores (91%), while MCI/AD was identified by PET-CCS alone (82%). PET increased by 25% the correct allocations achieved by memory scores, while memory scores increased by 15% the correct allocations achieved by PET. Combining memory performance and {sup 18}F-FDG PET yielded a higher accuracy than each single tool in identifying CTR and MCI/MCI. The PC containing bilateral posterior cingulate and left temporal pole was the hallmark of MCI/MCI patients, while the PC including the left LFC was the hallmark of conversion to AD. (orig.)
Nuclear analysis techniques as a component of thermoluminescence dating
Energy Technology Data Exchange (ETDEWEB)
Prescott, J.R.; Hutton, J.T.; Habermehl, M.A. [Adelaide Univ., SA (Australia); Van Moort, J. [Tasmania Univ., Sandy Bay, TAS (Australia)
1996-12-31
In luminescence dating, an age is found by first measuring dose accumulated since the event being dated, then dividing by the annual dose rate. Analyses of minor and trace elements performed by nuclear techniques have long formed an essential component of dating. Results from some Australian sites are reported to illustrate the application of nuclear techniques of analysis in this context. In particular, a variety of methods for finding dose rates are compared, an example of a site where radioactive disequilibrium is significant and a brief summary is given of a problem which was not resolved by nuclear techniques. 5 refs., 2 tabs.
Principal Component Analysis Based Measure of Structural Holes
Deng, Shiguo; Zhang, Wenqing; Yang, Huijie
2013-02-01
Based upon principal component analysis, a new measure called compressibility coefficient is proposed to evaluate structural holes in networks. This measure incorporates a new effect from identical patterns in networks. It is found that compressibility coefficient for Watts-Strogatz small-world networks increases monotonically with the rewiring probability and saturates to that for the corresponding shuffled networks. While compressibility coefficient for extended Barabasi-Albert scale-free networks decreases monotonically with the preferential effect and is significantly large compared with that for corresponding shuffled networks. This measure is helpful in diverse research fields to evaluate global efficiency of networks.
Fetal ECG extraction using independent component analysis by Jade approach
Giraldo-Guzmán, Jader; Contreras-Ortiz, Sonia H.; Lasprilla, Gloria Isabel Bautista; Kotas, Marian
2017-11-01
Fetal ECG monitoring is a useful method to assess the fetus health and detect abnormal conditions. In this paper we propose an approach to extract fetal ECG from abdomen and chest signals using independent component analysis based on the joint approximate diagonalization of eigenmatrices approach. The JADE approach avoids redundancy, what reduces matrix dimension and computational costs. Signals were filtered with a high pass filter to eliminate low frequency noise. Several levels of decomposition were tested until the fetal ECG was recognized in one of the separated sources output. The proposed method shows fast and good performance.
Nuclear analysis techniques as a component of thermoluminescence dating
Energy Technology Data Exchange (ETDEWEB)
Prescott, J R; Hutton, J T; Habermehl, M A [Adelaide Univ., SA (Australia); Van Moort, J [Tasmania Univ., Sandy Bay, TAS (Australia)
1997-12-31
In luminescence dating, an age is found by first measuring dose accumulated since the event being dated, then dividing by the annual dose rate. Analyses of minor and trace elements performed by nuclear techniques have long formed an essential component of dating. Results from some Australian sites are reported to illustrate the application of nuclear techniques of analysis in this context. In particular, a variety of methods for finding dose rates are compared, an example of a site where radioactive disequilibrium is significant and a brief summary is given of a problem which was not resolved by nuclear techniques. 5 refs., 2 tabs.
Nonlinear Principal Component Analysis Using Strong Tracking Filter
Institute of Scientific and Technical Information of China (English)
无
2007-01-01
The paper analyzes the problem of blind source separation (BSS) based on the nonlinear principal component analysis (NPCA) criterion. An adaptive strong tracking filter (STF) based algorithm was developed, which is immune to system model mismatches. Simulations demonstrate that the algorithm converges quickly and has satisfactory steady-state accuracy. The Kalman filtering algorithm and the recursive leastsquares type algorithm are shown to be special cases of the STF algorithm. Since the forgetting factor is adaptively updated by adjustment of the Kalman gain, the STF scheme provides more powerful tracking capability than the Kalman filtering algorithm and recursive least-squares algorithm.
Structure analysis of active components of traditional Chinese medicines
DEFF Research Database (Denmark)
Zhang, Wei; Sun, Qinglei; Liu, Jianhua
2013-01-01
Traditional Chinese Medicines (TCMs) have been widely used for healing of different health problems for thousands of years. They have been used as therapeutic, complementary and alternative medicines. TCMs usually consist of dozens to hundreds of various compounds, which are extracted from raw...... herbal sources by aqueous or alcoholic solvents. Therefore, it is difficult to correlate the pharmaceutical effect to a specific lead compound in the TCMs. A detailed analysis of various components in TCMs has been a great challenge for modern analytical techniques in recent decades. In this chapter...
Advances in independent component analysis and learning machines
Bingham, Ella; Laaksonen, Jorma; Lampinen, Jouko
2015-01-01
In honour of Professor Erkki Oja, one of the pioneers of Independent Component Analysis (ICA), this book reviews key advances in the theory and application of ICA, as well as its influence on signal processing, pattern recognition, machine learning, and data mining. Examples of topics which have developed from the advances of ICA, which are covered in the book are: A unifying probabilistic model for PCA and ICA Optimization methods for matrix decompositions Insights into the FastICA algorithmUnsupervised deep learning Machine vision and image retrieval A review of developments in the t
International Nuclear Information System (INIS)
Carvajal Escobar, Yesid; Marco Segura, Juan B
2005-01-01
An EOF analysis or principal component analysis (PC) was made for monthly precipitation (1972-1998) using 50 stations, and for monthly rate of flow (1951-2000) at 8 stations in the Valle del Cauca state, Colombia. Previously, we had applied 5 measures in order to verify the convenience of the analysis. These measures were: i) evaluation of significance level of correlation between variables; II) the kaiser-Meyer-Oikin (KMO) test; III) the Bartlett sphericity test; (IV) the measurement of sample adequacy (MSA), and v) the percentage of non-redundant residues with absolute values>0.05. For the selection of the significant PCS in every set of variables we applied seven criteria: the graphical method, the explained variance percentage, the mean root, the tests of Velicer, Bartlett, Broken Stich and the cross validation test. We chose the latter as the best one. It is robust and quantitative. Precipitation stations were divided in three homogeneous groups, applying a hierarchical cluster analysis, which was verified through the geographic method and the discriminate analysis for the first four EOFs of precipitation. There are many advantages to the EOF method: reduction of the dimensionality of multivariate data, calculation of missing data, evaluation and reduction of multi-co linearity, building of homogeneous groups, and detection of outliers. With the first four principal components we can explain 60.34% of the total variance of monthly precipitation for the Valle del Cauca state, and 94% of the total variance for the selected records of rates of flow
Development of motion image prediction method using principal component analysis
International Nuclear Information System (INIS)
Chhatkuli, Ritu Bhusal; Demachi, Kazuyuki; Kawai, Masaki; Sakakibara, Hiroshi; Kamiaka, Kazuma
2012-01-01
Respiratory motion can induce the limit in the accuracy of area irradiated during lung cancer radiation therapy. Many methods have been introduced to minimize the impact of healthy tissue irradiation due to the lung tumor motion. The purpose of this research is to develop an algorithm for the improvement of image guided radiation therapy by the prediction of motion images. We predict the motion images by using principal component analysis (PCA) and multi-channel singular spectral analysis (MSSA) method. The images/movies were successfully predicted and verified using the developed algorithm. With the proposed prediction method it is possible to forecast the tumor images over the next breathing period. The implementation of this method in real time is believed to be significant for higher level of tumor tracking including the detection of sudden abdominal changes during radiation therapy. (author)
A novel approach to analyzing fMRI and SNP data via parallel independent component analysis
Liu, Jingyu; Pearlson, Godfrey; Calhoun, Vince; Windemuth, Andreas
2007-03-01
There is current interest in understanding genetic influences on brain function in both the healthy and the disordered brain. Parallel independent component analysis, a new method for analyzing multimodal data, is proposed in this paper and applied to functional magnetic resonance imaging (fMRI) and a single nucleotide polymorphism (SNP) array. The method aims to identify the independent components of each modality and the relationship between the two modalities. We analyzed 92 participants, including 29 schizophrenia (SZ) patients, 13 unaffected SZ relatives, and 50 healthy controls. We found a correlation of 0.79 between one fMRI component and one SNP component. The fMRI component consists of activations in cingulate gyrus, multiple frontal gyri, and superior temporal gyrus. The related SNP component is contributed to significantly by 9 SNPs located in sets of genes, including those coding for apolipoprotein A-I, and C-III, malate dehydrogenase 1 and the gamma-aminobutyric acid alpha-2 receptor. A significant difference in the presences of this SNP component is found between the SZ group (SZ patients and their relatives) and the control group. In summary, we constructed a framework to identify the interactions between brain functional and genetic information; our findings provide new insight into understanding genetic influences on brain function in a common mental disorder.
Fast grasping of unknown objects using principal component analysis
Lei, Qujiang; Chen, Guangming; Wisse, Martijn
2017-09-01
Fast grasping of unknown objects has crucial impact on the efficiency of robot manipulation especially subjected to unfamiliar environments. In order to accelerate grasping speed of unknown objects, principal component analysis is utilized to direct the grasping process. In particular, a single-view partial point cloud is constructed and grasp candidates are allocated along the principal axis. Force balance optimization is employed to analyze possible graspable areas. The obtained graspable area with the minimal resultant force is the best zone for the final grasping execution. It is shown that an unknown object can be more quickly grasped provided that the component analysis principle axis is determined using single-view partial point cloud. To cope with the grasp uncertainty, robot motion is assisted to obtain a new viewpoint. Virtual exploration and experimental tests are carried out to verify this fast gasping algorithm. Both simulation and experimental tests demonstrated excellent performances based on the results of grasping a series of unknown objects. To minimize the grasping uncertainty, the merits of the robot hardware with two 3D cameras can be utilized to suffice the partial point cloud. As a result of utilizing the robot hardware, the grasping reliance is highly enhanced. Therefore, this research demonstrates practical significance for increasing grasping speed and thus increasing robot efficiency under unpredictable environments.
Independent component analysis classification of laser induced breakdown spectroscopy spectra
International Nuclear Information System (INIS)
Forni, Olivier; Maurice, Sylvestre; Gasnault, Olivier; Wiens, Roger C.; Cousin, Agnès; Clegg, Samuel M.; Sirven, Jean-Baptiste; Lasue, Jérémie
2013-01-01
The ChemCam instrument on board Mars Science Laboratory (MSL) rover uses the laser-induced breakdown spectroscopy (LIBS) technique to remotely analyze Martian rocks. It retrieves spectra up to a distance of seven meters to quantify and to quantitatively analyze the sampled rocks. Like any field application, on-site measurements by LIBS are altered by diverse matrix effects which induce signal variations that are specific to the nature of the sample. Qualitative aspects remain to be studied, particularly LIBS sample identification to determine which samples are of interest for further analysis by ChemCam and other rover instruments. This can be performed with the help of different chemometric methods that model the spectra variance in order to identify a the rock from its spectrum. In this paper we test independent components analysis (ICA) rock classification by remote LIBS. We show that using measures of distance in ICA space, namely the Manhattan and the Mahalanobis distance, we can efficiently classify spectra of an unknown rock. The Mahalanobis distance gives overall better performances and is easier to manage than the Manhattan distance for which the determination of the cut-off distance is not easy. However these two techniques are complementary and their analytical performances will improve with time during MSL operations as the quantity of available Martian spectra will grow. The analysis accuracy and performances will benefit from a combination of the two approaches. - Highlights: • We use a novel independent component analysis method to classify LIBS spectra. • We demonstrate the usefulness of ICA. • We report the performances of the ICA classification. • We compare it to other classical classification schemes
Analysis of concrete beams using applied element method
Lincy Christy, D.; Madhavan Pillai, T. M.; Nagarajan, Praveen
2018-03-01
The Applied Element Method (AEM) is a displacement based method of structural analysis. Some of its features are similar to that of Finite Element Method (FEM). In AEM, the structure is analysed by dividing it into several elements similar to FEM. But, in AEM, elements are connected by springs instead of nodes as in the case of FEM. In this paper, background to AEM is discussed and necessary equations are derived. For illustrating the application of AEM, it has been used to analyse plain concrete beam of fixed support condition. The analysis is limited to the analysis of 2-dimensional structures. It was found that the number of springs has no much influence on the results. AEM could predict deflection and reactions with reasonable degree of accuracy.
Apply Functional Modelling to Consequence Analysis in Supervision Systems
DEFF Research Database (Denmark)
Zhang, Xinxin; Lind, Morten; Gola, Giulio
2013-01-01
This paper will first present the purpose and goals of applying functional modelling approach to consequence analysis by adopting Multilevel Flow Modelling (MFM). MFM Models describe a complex system in multiple abstraction levels in both means-end dimension and whole-part dimension. It contains...... consequence analysis to practical or online applications in supervision systems. It will also suggest a multiagent solution as the integration architecture for developing tools to facilitate the utilization results of functional consequence analysis. Finally a prototype of the multiagent reasoning system...... causal relations between functions and goals. A rule base system can be developed to trace the causal relations and perform consequence propagations. This paper will illustrate how to use MFM for consequence reasoning by using rule base technology and describe the challenges for integrating functional...
Analysis of Brick Masonry Wall using Applied Element Method
Lincy Christy, D.; Madhavan Pillai, T. M.; Nagarajan, Praveen
2018-03-01
The Applied Element Method (AEM) is a versatile tool for structural analysis. Analysis is done by discretising the structure as in the case of Finite Element Method (FEM). In AEM, elements are connected by a set of normal and shear springs instead of nodes. AEM is extensively used for the analysis of brittle materials. Brick masonry wall can be effectively analyzed in the frame of AEM. The composite nature of masonry wall can be easily modelled using springs. The brick springs and mortar springs are assumed to be connected in series. The brick masonry wall is analyzed and failure load is determined for different loading cases. The results were used to find the best aspect ratio of brick to strengthen brick masonry wall.
International Nuclear Information System (INIS)
Zeng, J.; Li, G.; Sun, J.
2013-01-01
Principal components analysis and cluster analysis were used to investigate the properties of different corn varieties. The chemical compositions and some properties of corn flour which processed by drying milling were determined. The results showed that the chemical compositions and physicochemical properties were significantly different among twenty six corn varieties. The quality of corn flour was concerned with five principal components from principal component analysis and the contribution rate of starch pasting properties was important, which could account for 48.90%. Twenty six corn varieties could be classified into four groups by cluster analysis. The consistency between principal components analysis and cluster analysis indicated that multivariate analyses were feasible in the study of corn variety properties. (author)
International Nuclear Information System (INIS)
Arndt, T.E.
1994-06-01
This paper presents an example of a component replacement (electric heater) when installed in an older ventilation system that was constructed before the issuance of ASME N509 and N510. Many of the existing ventilation systems at the Hanford Site were designed, fabricated, and installed before the issuance of ASME N509 and N510. Requiring the application of these codes to existing ventilation systems presents challenges to the engineer when design changes are needed. Although it may seem that the application of ASME N509 or N510 may be a hindrance at times, this does not need to occur. Proper preparation at the start of project or design modifications can minimize frustration to the engineer when it is judged that portions of ASME N509 and N510 do not apply in a particular application
Energy Technology Data Exchange (ETDEWEB)
Arndt, T.E. [Westinghouse Hanford Company, Richland, WA (United States)
1995-02-01
This paper presents an example of a component replacement (electric heater) when installed in an older ventilation system that was constructed before the issuance of ASME N509{sup 1} and N510{sup 2}. Many of the existing ventilation systems at the Hanford Site were designed, fabricated, and installed before the issuance of ASME N509{sup 1} and N510{sup 2}. Requiring the application of these codes to existing ventilation systems presents challenges to the engineer when design changes are needed. Although it may seem that the application of ASME N509{sup 1} or N510{sup 2} may be a hindrance at times, this does not need to occur. Proper preparation at the start of project or design modifications can minimize frustration to the engineer when it is judged that portions of ASME N509{sup 1} and N510{sup 2} do not apply in a particular application.
International Nuclear Information System (INIS)
Waheed, S.; Rahman, S.; Siddique, N.
2013-01-01
Different types of Ca supplements are available in the local markets of Pakistan. It is sometimes difficult to classify these with respect to their composition. In the present work principal component analysis (PCA) technique was applied to classify different Ca supplements on the basis of their elemental data obtained using instrumental neutron activation analysis (INAA) and atomic absorption spectrometry (AAS) techniques. The graphical representation of principal component analysis (PCA) scores utilizing intricate analytical data successfully generated four different types of Ca supplements with compatible samples grouped together. These included Ca supplements with CaCO/sub 3/as Ca source along with vitamin C, the supplements with CaCO/sub 3/ as Ca source along with vitamin D, Supplements with Ca from bone meal and supplements with chelated calcium. (author)
PRINCIPAL COMPONENT ANALYSIS STUDIES OF TURBULENCE IN OPTICALLY THICK GAS
Energy Technology Data Exchange (ETDEWEB)
Correia, C.; Medeiros, J. R. De [Departamento de Física Teórica e Experimental, Universidade Federal do Rio Grande do Norte, 59072-970, Natal (Brazil); Lazarian, A. [Astronomy Department, University of Wisconsin, Madison, 475 N. Charter St., WI 53711 (United States); Burkhart, B. [Harvard-Smithsonian Center for Astrophysics, 60 Garden St, MS-20, Cambridge, MA 02138 (United States); Pogosyan, D., E-mail: caioftc@dfte.ufrn.br [Canadian Institute for Theoretical Astrophysics, University of Toronto, Toronto, ON (Canada)
2016-02-20
In this work we investigate the sensitivity of principal component analysis (PCA) to the velocity power spectrum in high-opacity regimes of the interstellar medium (ISM). For our analysis we use synthetic position–position–velocity (PPV) cubes of fractional Brownian motion and magnetohydrodynamics (MHD) simulations, post-processed to include radiative transfer effects from CO. We find that PCA analysis is very different from the tools based on the traditional power spectrum of PPV data cubes. Our major finding is that PCA is also sensitive to the phase information of PPV cubes and this allows PCA to detect the changes of the underlying velocity and density spectra at high opacities, where the spectral analysis of the maps provides the universal −3 spectrum in accordance with the predictions of the Lazarian and Pogosyan theory. This makes PCA a potentially valuable tool for studies of turbulence at high opacities, provided that proper gauging of the PCA index is made. However, we found the latter to not be easy, as the PCA results change in an irregular way for data with high sonic Mach numbers. This is in contrast to synthetic Brownian noise data used for velocity and density fields that show monotonic PCA behavior. We attribute this difference to the PCA's sensitivity to Fourier phase information.
PRINCIPAL COMPONENT ANALYSIS STUDIES OF TURBULENCE IN OPTICALLY THICK GAS
International Nuclear Information System (INIS)
Correia, C.; Medeiros, J. R. De; Lazarian, A.; Burkhart, B.; Pogosyan, D.
2016-01-01
In this work we investigate the sensitivity of principal component analysis (PCA) to the velocity power spectrum in high-opacity regimes of the interstellar medium (ISM). For our analysis we use synthetic position–position–velocity (PPV) cubes of fractional Brownian motion and magnetohydrodynamics (MHD) simulations, post-processed to include radiative transfer effects from CO. We find that PCA analysis is very different from the tools based on the traditional power spectrum of PPV data cubes. Our major finding is that PCA is also sensitive to the phase information of PPV cubes and this allows PCA to detect the changes of the underlying velocity and density spectra at high opacities, where the spectral analysis of the maps provides the universal −3 spectrum in accordance with the predictions of the Lazarian and Pogosyan theory. This makes PCA a potentially valuable tool for studies of turbulence at high opacities, provided that proper gauging of the PCA index is made. However, we found the latter to not be easy, as the PCA results change in an irregular way for data with high sonic Mach numbers. This is in contrast to synthetic Brownian noise data used for velocity and density fields that show monotonic PCA behavior. We attribute this difference to the PCA's sensitivity to Fourier phase information
Directory of Open Access Journals (Sweden)
Yung-Kun Chuang
2014-09-01
Full Text Available Independent component (IC analysis was applied to near-infrared spectroscopy for analysis of gentiopicroside and swertiamarin; the two bioactive components of Gentiana scabra Bunge. ICs that are highly correlated with the two bioactive components were selected for the analysis of tissue cultures, shoots and roots, which were found to distribute in three different positions within the domain [two-dimensional (2D and 3D] constructed by the ICs. This setup could be used for quantitative determination of respective contents of gentiopicroside and swertiamarin within the plants. For gentiopicroside, the spectral calibration model based on the second derivative spectra produced the best effect in the wavelength ranges of 600–700 nm, 1600–1700 nm, and 2000–2300 nm (correlation coefficient of calibration = 0.847, standard error of calibration = 0.865%, and standard error of validation = 0.909%. For swertiamarin, a spectral calibration model based on the first derivative spectra produced the best effect in the wavelength ranges of 600–800 nm and 2200–2300 nm (correlation coefficient of calibration = 0.948, standard error of calibration = 0.168%, and standard error of validation = 0.216%. Both models showed a satisfactory predictability. This study successfully established qualitative and quantitative correlations for gentiopicroside and swertiamarin with near-infrared spectra, enabling rapid and accurate inspection on the bioactive components of G. scabra Bunge at different growth stages.
Failure cause analysis and improvement for magnetic component cabinet
International Nuclear Information System (INIS)
Ge Bing
1999-01-01
The magnetic component cabinet is an important thermal control device fitted on the nuclear power. Because it used a self-saturation amplifier as a primary component, the magnetic component cabinet has some boundness. For increasing the operation safety on the nuclear power, the author describes a new scheme. In order that the magnetic component cabinet can be replaced, the new type component cabinet is developed. Integrate circuit will replace the magnetic components of every function parts. The author has analyzed overall failure cause for magnetic component cabinet and adopted some measures
Salvatore, Stefania; Røislien, Jo; Baz-Lomba, Jose A; Bramness, Jørgen G
2017-03-01
Wastewater-based epidemiology is an alternative method for estimating the collective drug use in a community. We applied functional data analysis, a statistical framework developed for analysing curve data, to investigate weekly temporal patterns in wastewater measurements of three prescription drugs with known abuse potential: methadone, oxazepam and methylphenidate, comparing them to positive and negative control drugs. Sewage samples were collected in February 2014 from a wastewater treatment plant in Oslo, Norway. The weekly pattern of each drug was extracted by fitting of generalized additive models, using trigonometric functions to model the cyclic behaviour. From the weekly component, the main temporal features were then extracted using functional principal component analysis. Results are presented through the functional principal components (FPCs) and corresponding FPC scores. Clinically, the most important weekly feature of the wastewater-based epidemiology data was the second FPC, representing the difference between average midweek level and a peak during the weekend, representing possible recreational use of a drug in the weekend. Estimated scores on this FPC indicated recreational use of methylphenidate, with a high weekend peak, but not for methadone and oxazepam. The functional principal component analysis uncovered clinically important temporal features of the weekly patterns of the use of prescription drugs detected from wastewater analysis. This may be used as a post-marketing surveillance method to monitor prescription drugs with abuse potential. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.
Quality Aware Compression of Electrocardiogram Using Principal Component Analysis.
Gupta, Rajarshi
2016-05-01
Electrocardiogram (ECG) compression finds wide application in various patient monitoring purposes. Quality control in ECG compression ensures reconstruction quality and its clinical acceptance for diagnostic decision making. In this paper, a quality aware compression method of single lead ECG is described using principal component analysis (PCA). After pre-processing, beat extraction and PCA decomposition, two independent quality criteria, namely, bit rate control (BRC) or error control (EC) criteria were set to select optimal principal components, eigenvectors and their quantization level to achieve desired bit rate or error measure. The selected principal components and eigenvectors were finally compressed using a modified delta and Huffman encoder. The algorithms were validated with 32 sets of MIT Arrhythmia data and 60 normal and 30 sets of diagnostic ECG data from PTB Diagnostic ECG data ptbdb, all at 1 kHz sampling. For BRC with a CR threshold of 40, an average Compression Ratio (CR), percentage root mean squared difference normalized (PRDN) and maximum absolute error (MAE) of 50.74, 16.22 and 0.243 mV respectively were obtained. For EC with an upper limit of 5 % PRDN and 0.1 mV MAE, the average CR, PRDN and MAE of 9.48, 4.13 and 0.049 mV respectively were obtained. For mitdb data 117, the reconstruction quality could be preserved up to CR of 68.96 by extending the BRC threshold. The proposed method yields better results than recently published works on quality controlled ECG compression.
ARP1400 DVI break analysis using the MARS 3.1 multi-D component
International Nuclear Information System (INIS)
Hwang, Moon-Kyu; Lim, Hong-Sik; Lee, Seung-Wook; Bae, Sung-Won; Chung, Bub-Dong
2006-01-01
The current version of MARS 3.1 has a multi-D component intended to simulate an asymmetric multidimensional fluid behavior in a reactor core, downcomer or in a steam generator, in a more realistic manner. The feature is implemented in the 1-D module of the code. As opposed to the cross flow junction modeling, the multi-D component allows for a lateral momentum transfer as well as a sheer stress. Thus, a full three-Dimensional analysis capability is available as in the case of RELAP5-3D or CATHARE. In this study the multi-D component is applied to the hypothetical accident of a DVI (Direct Vessel Injection) break in the APR1400 plant, and the results are analyzed
Applied research and development of neutron activation analysis
International Nuclear Information System (INIS)
Chung, Yong Sam; Moon, Jong Hwa; Kim, Sun Ha; Baek, Sung Ryel; Kim, Young Gi; Jung, Hwan Sung; Park, Kwang Won; Kang, Sang Hun; Lim, Jong Myoung
2003-05-01
The aims of this project are to establish the quality control system of Neutron Activation Analysis(NAA) due to increase of industrial needs for standard analytical method and to prepare and identify the standard operation procedure of NAA through practical testing for different analytical items. R and D implementations of analytical quality system using neutron irradiation facility and gamma-ray measurement system and automation of NAA facility in HANARO research reactor are as following ; 1) Establishment of NAA quality control system for the maintenance of best measurement capability and the promotion of utilization of HANARO research reactor 2) Improvement of analytical sensitivity for industrial applied technologies and establishment of certified standard procedures 3) Standardization and development of Prompt Gamma-ray Activation Analysis (PGAA) technology
Multi-component controllers in reactor physics optimality analysis
International Nuclear Information System (INIS)
Aldemir, T.
1978-01-01
An algorithm is developed for the optimality analysis of thermal reactor assemblies with multi-component control vectors. The neutronics of the system under consideration is assumed to be described by the two-group diffusion equations and constraints are imposed upon the state and control variables. It is shown that if the problem is such that the differential and algebraic equations describing the system can be cast into a linear form via a change of variables, the optimal control components are piecewise constant functions and the global optimal controller can be determined by investigating the properties of the influence functions. Two specific problems are solved utilizing this approach. A thermal reactor consisting of fuel, burnable poison and moderator is found to yield maximal power when the assembly consists of two poison zones and the power density is constant throughout the assembly. It is shown that certain variational relations have to be considered to maintain the activeness of the system equations as differential constraints. The problem of determining the maximum initial breeding ratio for a thermal reactor is solved by treating the fertile and fissile material absorption densities as controllers. The optimal core configurations are found to consist of three fuel zones for a bare assembly and two fuel zones for a reflected assembly. The optimum fissile material density is determined to be inversely proportional to the thermal flux
Autonomous learning in gesture recognition by using lobe component analysis
Lu, Jian; Weng, Juyang
2007-02-01
Gesture recognition is a new human-machine interface method implemented by pattern recognition(PR).In order to assure robot safety when gesture is used in robot control, it is required to implement the interface reliably and accurately. Similar with other PR applications, 1) feature selection (or model establishment) and 2) training from samples, affect the performance of gesture recognition largely. For 1), a simple model with 6 feature points at shoulders, elbows, and hands, is established. The gestures to be recognized are restricted to still arm gestures, and the movement of arms is not considered. These restrictions are to reduce the misrecognition, but are not so unreasonable. For 2), a new biological network method, called lobe component analysis(LCA), is used in unsupervised learning. Lobe components, corresponding to high-concentrations in probability of the neuronal input, are orientation selective cells follow Hebbian rule and lateral inhibition. Due to the advantage of LCA method for balanced learning between global and local features, large amount of samples can be used in learning efficiently.
Analysis of tangible and intangible hotel service quality components
Directory of Open Access Journals (Sweden)
Marić Dražen
2016-01-01
Full Text Available The issue of service quality is one of the essential areas of marketing theory and practice, as high quality can lead to customer satisfaction and loyalty, i.e. successful business results. It is vital for any company, especially in services sector, to understand and grasp the consumers' expectations and perceptions pertaining to the broad range of factors affecting consumers' evaluation of services, their satisfaction and loyalty. Hospitality is a service sector where the significance of these elements grows exponentially. The aim of this study is to identify the significance of individual quality components in hospitality industry. The questionnaire used for gathering data comprised 19 tangible and 14 intangible attributes of service quality, which the respondents rated on a five-degree scale. The analysis also identified the factorial structure of the tangible and intangible elements of hotel service. The paper aims to contribute to the existing literature by pointing to the significance of tangible and intangible components of service quality. A very small number of studies conducted in hospitality and hotel management identify the sub-factors within these two dimensions of service quality. The paper also provides useful managerial implications. The obtained results help managers in hospitality to establish the service offers that consumers find the most important when choosing a given hotel.
Directory of Open Access Journals (Sweden)
Anna Maria Stellacci
2012-07-01
Full Text Available Hyperspectral (HS data represents an extremely powerful means for rapidly detecting crop stress and then aiding in the rational management of natural resources in agriculture. However, large volume of data poses a challenge for data processing and extracting crucial information. Multivariate statistical techniques can play a key role in the analysis of HS data, as they may allow to both eliminate redundant information and identify synthetic indices which maximize differences among levels of stress. In this paper we propose an integrated approach, based on the combined use of Principal Component Analysis (PCA and Canonical Discriminant Analysis (CDA, to investigate HS plant response and discriminate plant status. The approach was preliminary evaluated on a data set collected on durum wheat plants grown under different nitrogen (N stress levels. Hyperspectral measurements were performed at anthesis through a high resolution field spectroradiometer, ASD FieldSpec HandHeld, covering the 325-1075 nm region. Reflectance data were first restricted to the interval 510-1000 nm and then divided into five bands of the electromagnetic spectrum [green: 510-580 nm; yellow: 581-630 nm; red: 631-690 nm; red-edge: 705-770 nm; near-infrared (NIR: 771-1000 nm]. PCA was applied to each spectral interval. CDA was performed on the extracted components to identify the factors maximizing the differences among plants fertilised with increasing N rates. Within the intervals of green, yellow and red only the first principal component (PC had an eigenvalue greater than 1 and explained more than 95% of total variance; within the ranges of red-edge and NIR, the first two PCs had an eigenvalue higher than 1. Two canonical variables explained cumulatively more than 81% of total variance and the first was able to discriminate wheat plants differently fertilised, as confirmed also by the significant correlation with aboveground biomass and grain yield parameters. The combined
Choi, Ji Yeh; Hwang, Heungsun; Yamamoto, Michio; Jung, Kwanghee; Woodward, Todd S
2017-06-01
Functional principal component analysis (FPCA) and functional multiple-set canonical correlation analysis (FMCCA) are data reduction techniques for functional data that are collected in the form of smooth curves or functions over a continuum such as time or space. In FPCA, low-dimensional components are extracted from a single functional dataset such that they explain the most variance of the dataset, whereas in FMCCA, low-dimensional components are obtained from each of multiple functional datasets in such a way that the associations among the components are maximized across the different sets. In this paper, we propose a unified approach to FPCA and FMCCA. The proposed approach subsumes both techniques as special cases. Furthermore, it permits a compromise between the techniques, such that components are obtained from each set of functional data to maximize their associations across different datasets, while accounting for the variance of the data well. We propose a single optimization criterion for the proposed approach, and develop an alternating regularized least squares algorithm to minimize the criterion in combination with basis function approximations to functions. We conduct a simulation study to investigate the performance of the proposed approach based on synthetic data. We also apply the approach for the analysis of multiple-subject functional magnetic resonance imaging data to obtain low-dimensional components of blood-oxygen level-dependent signal changes of the brain over time, which are highly correlated across the subjects as well as representative of the data. The extracted components are used to identify networks of neural activity that are commonly activated across the subjects while carrying out a working memory task.
Analysis of European Union Economy in Terms of GDP Components
Directory of Open Access Journals (Sweden)
Simona VINEREAN
2013-12-01
Full Text Available The impact of the crises on national economies represented a subject of analysis and interest for a wide variety of research studies. Thus, starting from the GDP composition, the present research exhibits an analysis of the impact of European economies, at an EU level, of the events that followed the crisis of 2007 – 2008. Firstly, the research highlighted the existence of two groups of countries in 2012 in European Union, namely segments that were compiled in relation to the structure of the GDP’s components. In the second stage of the research, a factor analysis was performed on the resulted segments, that showed that the economies of cluster A are based more on personal consumption compared to the economies of cluster B, and in terms of government consumption, the situation is reversed. Thus, between the two groups of countries, a different approach regarding the role of fiscal policy in the economy can be noted, with a greater emphasis on savings in cluster B. Moreover, besides the two groups of countries resulted, Ireland and Luxembourg stood out because these two countries did not fit in either of the resulted segments and their economies are based, to a large extent, on the positive balance of the external balance.
Principal component analysis of 1/fα noise
International Nuclear Information System (INIS)
Gao, J.B.; Cao Yinhe; Lee, J.-M.
2003-01-01
Principal component analysis (PCA) is a popular data analysis method. One of the motivations for using PCA in practice is to reduce the dimension of the original data by projecting the raw data onto a few dominant eigenvectors with large variance (energy). Due to the ubiquity of 1/f α noise in science and engineering, in this Letter we study the prototypical stochastic model for 1/f α processes--the fractional Brownian motion (fBm) processes using PCA, and find that the eigenvalues from PCA of fBm processes follow a power-law, with the exponent being the key parameter defining the fBm processes. We also study random-walk-type processes constructed from DNA sequences, and find that the eigenvalue spectrum from PCA of those random-walk processes also follow power-law relations, with the exponent characterizing the correlation structures of the DNA sequence. In fact, it is observed that PCA can automatically remove linear trends induced by patchiness in the DNA sequence, hence, PCA has a similar capability to the detrended fluctuation analysis. Implications of the power-law distributed eigenvalue spectrum are discussed
Surface composition of biomedical components by ion beam analysis
International Nuclear Information System (INIS)
Kenny, M.J.; Wielunski, L.S.; Baxter, G.R.
1991-01-01
Materials used for replacement body parts must satisfy a number of requirements such as biocompatibility and mechanical ability to handle the task with regard to strength, wear and durability. When using a CVD coated carbon fibre reinforced carbon ball, the surface must be ion implanted with uniform dose of nitrogen ions in order to make it wear resistant. The mechanism by which the wear resistance is improved is one of radiation damage and the required dose of about 10 16 cm -2 can have a tolerance of about 20%. To implant a spherical surface requires manipulation of the sample within the beam and control system (either computer or manually operated) to enable uniform dose all the way from polar to equatorial regions on the surface. A manipulator has been designed and built for this purpose. In order to establish whether the dose is uniform, nuclear reaction analysis using the reaction 14 N(d,α) 12 C is an ideal method of profiling. By taking measurements at a number of points on the surface, the uniformity of nitrogen dose can be ascertained. It is concluded that both Rutherford Backscattering and Nuclear Reaction Analysis can be used for rapid analysis of surface composition of carbon based materials used for replacement body components. 2 refs., 2 figs
Recursive Principal Components Analysis Using Eigenvector Matrix Perturbation
Directory of Open Access Journals (Sweden)
Deniz Erdogmus
2004-10-01
Full Text Available Principal components analysis is an important and well-studied subject in statistics and signal processing. The literature has an abundance of algorithms for solving this problem, where most of these algorithms could be grouped into one of the following three approaches: adaptation based on Hebbian updates and deflation, optimization of a second-order statistical criterion (like reconstruction error or output variance, and fixed point update rules with deflation. In this paper, we take a completely different approach that avoids deflation and the optimization of a cost function using gradients. The proposed method updates the eigenvector and eigenvalue matrices simultaneously with every new sample such that the estimates approximately track their true values as would be calculated from the current sample estimate of the data covariance matrix. The performance of this algorithm is compared with that of traditional methods like Sanger's rule and APEX, as well as a structurally similar matrix perturbation-based method.
International Nuclear Information System (INIS)
Lu, Wei-Zhen; He, Hong-Di; Dong, Li-yun
2011-01-01
This study aims to evaluate the performance of two statistical methods, principal component analysis and cluster analysis, for the management of air quality monitoring network of Hong Kong and the reduction of associated expenses. The specific objectives include: (i) to identify city areas with similar air pollution behavior; and (ii) to locate emission sources. The statistical methods were applied to the mass concentrations of sulphur dioxide (SO 2 ), respirable suspended particulates (RSP) and nitrogen dioxide (NO 2 ), collected in monitoring network of Hong Kong from January 2001 to December 2007. The results demonstrate that, for each pollutant, the monitoring stations are grouped into different classes based on their air pollution behaviors. The monitoring stations located in nearby area are characterized by the same specific air pollution characteristics and suggested with an effective management of air quality monitoring system. The redundant equipments should be transferred to other monitoring stations for allowing further enlargement of the monitored area. Additionally, the existence of different air pollution behaviors in the monitoring network is explained by the variability of wind directions across the region. The results imply that the air quality problem in Hong Kong is not only a local problem mainly from street-level pollutions, but also a region problem from the Pearl River Delta region. (author)
International Nuclear Information System (INIS)
Ding Yu; Tian Di; Chen Feipeng; Chen Pengfei; Qiao Shujun; Yang Guang; Li Chunsheng
2015-01-01
The present paper proposes an automated Laser-Induced Breakdown Spectroscopy (LIBS) analytical test system, which consists of a LIBS measurement and control platform based on a modular design concept, and a LIBS qualitative spectrum analysis software and is developed in C#. The platform provides flexible interfacing and automated control; it is compatible with different manufacturer component models and is constructed in modularized form for easy expandability. During peak identification, a more robust peak identification method with improved stability in peak identification has been achieved by applying additional smoothing on the slope obtained by calculation before peak identification. For the purpose of element identification, an improved main lines analysis method, which detects all elements on the spectral peak to avoid omission of certain elements without strong spectral lines, is applied to element identification in the tested LIBS samples. This method also increases the identification speed. In this paper, actual applications have been carried out. According to tests, the analytical test system is compatible with components of various models made by different manufacturers. It can automatically control components to get experimental data and conduct filtering, peak identification and qualitative analysis, etc. on spectral data. (paper)
Size distribution measurements and chemical analysis of aerosol components
Energy Technology Data Exchange (ETDEWEB)
Pakkanen, T.A.
1995-12-31
The principal aims of this work were to improve the existing methods for size distribution measurements and to draw conclusions about atmospheric and in-stack aerosol chemistry and physics by utilizing size distributions of various aerosol components measured. A sample dissolution with dilute nitric acid in an ultrasonic bath and subsequent graphite furnace atomic absorption spectrometric analysis was found to result in low blank values and good recoveries for several elements in atmospheric fine particle size fractions below 2 {mu}m of equivalent aerodynamic particle diameter (EAD). Furthermore, it turned out that a substantial amount of analyses associated with insoluble material could be recovered since suspensions were formed. The size distribution measurements of in-stack combustion aerosols indicated two modal size distributions for most components measured. The existence of the fine particle mode suggests that a substantial fraction of such elements with two modal size distributions may vaporize and nucleate during the combustion process. In southern Norway, size distributions of atmospheric aerosol components usually exhibited one or two fine particle modes and one or two coarse particle modes. Atmospheric relative humidity values higher than 80% resulted in significant increase of the mass median diameters of the droplet mode. Important local and/or regional sources of As, Br, I, K, Mn, Pb, Sb, Si and Zn were found to exist in southern Norway. The existence of these sources was reflected in the corresponding size distributions determined, and was utilized in the development of a source identification method based on size distribution data. On the Finnish south coast, atmospheric coarse particle nitrate was found to be formed mostly through an atmospheric reaction of nitric acid with existing coarse particle sea salt but reactions and/or adsorption of nitric acid with soil derived particles also occurred. Chloride was depleted when acidic species reacted
International Nuclear Information System (INIS)
Haaland, D.M.; Easterling, R.G.; Vopicka, D.A.
1985-01-01
In an extension of earlier work, weighted multivariate least-squares methods of quantitative FT-IR analysis have been developed. A linear least-squares approximation to nonlinearities in the Beer-Lambert law is made by allowing the reference spectra to be a set of known mixtures, The incorporation of nonzero intercepts in the relation between absorbance and concentration further improves the approximation of nonlinearities while simultaneously accounting for nonzero spectra baselines. Pathlength variations are also accommodated in the analysis, and under certain conditions, unknown sample pathlengths can be determined. All spectral data are used to improve the precision and accuracy of the estimated concentrations. During the calibration phase of the analysis, pure component spectra are estimated from the standard mixture spectra. These can be compared with the measured pure component spectra to determine which vibrations experience nonlinear behavior. In the predictive phase of the analysis, the calculated spectra are used in our previous least-squares analysis to estimate sample component concentrations. These methods were applied to the analysis of the IR spectra of binary mixtures of esters. Even with severely overlapping spectral bands and nonlinearities in the Beer-Lambert law, the average relative error in the estimated concentration was <1%
A first application of independent component analysis to extracting structure from stock returns.
Back, A D; Weigend, A S
1997-08-01
This paper explores the application of a signal processing technique known as independent component analysis (ICA) or blind source separation to multivariate financial time series such as a portfolio of stocks. The key idea of ICA is to linearly map the observed multivariate time series into a new space of statistically independent components (ICs). We apply ICA to three years of daily returns of the 28 largest Japanese stocks and compare the results with those obtained using principal component analysis. The results indicate that the estimated ICs fall into two categories, (i) infrequent large shocks (responsible for the major changes in the stock prices), and (ii) frequent smaller fluctuations (contributing little to the overall level of the stocks). We show that the overall stock price can be reconstructed surprisingly well by using a small number of thresholded weighted ICs. In contrast, when using shocks derived from principal components instead of independent components, the reconstructed price is less similar to the original one. ICA is shown to be a potentially powerful method of analyzing and understanding driving mechanisms in financial time series. The application to portfolio optimization is described in Chin and Weigend (1998).
F4E studies for the electromagnetic analysis of ITER components
Energy Technology Data Exchange (ETDEWEB)
Testoni, P., E-mail: pietro.testoni@f4e.europa.eu [Fusion for Energy, Torres Diagonal Litoral B3, c/ Josep Plá n.2, Barcelona (Spain); Cau, F.; Portone, A. [Fusion for Energy, Torres Diagonal Litoral B3, c/ Josep Plá n.2, Barcelona (Spain); Albanese, R. [Associazione EURATOM/ENEA/CREATE, DIETI, Università Federico II di Napoli, Napoli (Italy); Juirao, J. [Numerical Analysis TEChnologies S.L. (NATEC), c/ Marqués de San Esteban, 52 Entlo D Gijón (Spain)
2014-10-15
Highlights: • Several ITER components have been analyzed from the electromagnetic point of view. • Categorization of DINA load cases is described. • VDEs, MDs and MFD have been studied. • Integral values of forces and moments components versus time have been computed for all the ITER components under study. - Abstract: Fusion for Energy (F4E) is involved in a relevant number of activities in the area of electromagnetic analysis in support of ITER general design and EU in-kind procurement. In particular several ITER components (vacuum vessel, blanket shield modules and first wall panels, test blanket modules, ICRH antenna) are being analyzed from the electromagnetic point of view. In this paper we give an updated description of our main activities, highlighting the main assumptions, objectives, results and conclusions. The plasma instabilities we consider, typically disruptions and VDEs, can be both toroidally symmetric and asymmetric. This implies that, depending on the specific component and loading conditions, FE models we use span from a sector of 10 up to 360° of the ITER machine. The techniques for simulating the electromagnetic phenomena involved in a disruption and the postprocessing of the results to obtain the loads acting on the structures are described. Finally we summarize the typical loads applied to different components and give a critical view of the results.
Automated SEM Modal Analysis Applied to the Diogenites
Bowman, L. E.; Spilde, M. N.; Papike, James J.
1996-01-01
Analysis of volume proportions of minerals, or modal analysis, is routinely accomplished by point counting on an optical microscope, but the process, particularly on brecciated samples such as the diogenite meteorites, is tedious and prone to error by misidentification of very small fragments, which may make up a significant volume of the sample. Precise volume percentage data can be gathered on a scanning electron microscope (SEM) utilizing digital imaging and an energy dispersive spectrometer (EDS). This form of automated phase analysis reduces error, and at the same time provides more information than could be gathered using simple point counting alone, such as particle morphology statistics and chemical analyses. We have previously studied major, minor, and trace-element chemistry of orthopyroxene from a suite of diogenites. This abstract describes the method applied to determine the modes on this same suite of meteorites and the results of that research. The modal abundances thus determined add additional information on the petrogenesis of the diogenites. In addition, low-abundance phases such as spinels were located for further analysis by this method.
Directory of Open Access Journals (Sweden)
Pengyu Gao
2016-03-01
Full Text Available It is difficult to forecast the well productivity because of the complexity of vertical and horizontal developments in fluvial facies reservoir. This paper proposes a method based on Principal Component Analysis and Artificial Neural Network to predict well productivity of fluvial facies reservoir. The method summarizes the statistical reservoir factors and engineering factors that affect the well productivity, extracts information by applying the principal component analysis method and approximates arbitrary functions of the neural network to realize an accurate and efficient prediction on the fluvial facies reservoir well productivity. This method provides an effective way for forecasting the productivity of fluvial facies reservoir which is affected by multi-factors and complex mechanism. The study result shows that this method is a practical, effective, accurate and indirect productivity forecast method and is suitable for field application.
Thermal analysis of the first canted-undulator front-end components at SSRF
Energy Technology Data Exchange (ETDEWEB)
Xu, Zhongmin, E-mail: xuzhongmin@sinap.ac.cn; Feng, Xinkang; Wang, Naxiu; Wu, Guanyuan; Zhang, Min; Wang, Jie
2015-02-21
The performance of three kinds of masks: pre-mask, splitter mask and fixed mask-photon shutter, used for the first canted-undulator front end under heat loads at SSRF, is studied. Because these components are shared with two beamlines, the X-rays from both dual undulators and bending magnets can strike on them. Under these complicated conditions, they will absorb much more thermal power than when they operate in usual beamline. So thermal and stress analysis is indispensable for their mechanical design. The method of applying the non-uniform power density using Ansys is presented. During thermal stress analysis, the normal operation or the worst possible case is considered. The finite element analyses results, such as the maximum temperature of the body and the cooling wall and the maximum stress of these components, show the design of them is reasonable and safe.
Authenticity analysis of citrus essential oils by HPLC-UV-MS on oxygenated heterocyclic components
Directory of Open Access Journals (Sweden)
Hao Fan
2015-03-01
Full Text Available Citrus essential oils are widely applied in food industry as the backbone of citrus flavors. Unfortunately, due to relatively simple chemical composition and tremendous price differences among citrus species, adulteration has been plaguing the industry since its inception. Skilled blenders are capable of making blends that are almost indistinguishable from authentic oils through conventional gas chromatography analysis. A reversed-phase high performance liquid chromatography (HPLC method was developed for compositional study of nonvolatile constituents in essential oils from major citrus species. The nonvolatile oxygenated heterocyclic components identified in citrus oils were proved to be more effective as markers in adulteration detection than the volatile components. Authors are hoping such an analysis procedure can be served as a routine quality control test for authenticity evaluation in citrus essential oils.
THE STUDY OF THE CHARACTERIZATION INDICES OF FABRICS BY PRINCIPAL COMPONENT ANALYSIS METHOD
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HRISTIAN Liliana
2017-05-01
Full Text Available The paper was pursued to prioritize the worsted fabrics type, for the manufacture of outerwear products by characterization indeces of fabrics, using the mathematical model of Principal Component Analysis (PCA. There are a number of variables with a certain influence on the quality of fabrics, but some of these variables are more important than others, so it is useful to identify those variables to a better understanding the factors which can lead the improving of the fabrics quality. A solution to this problem can be the application of a method of factorial analysis, the so-called Principal Component Analysis, with the final goal of establishing and analyzing those variables which influence in a significant manner the internal structure of combed wool fabrics according to armire type. By applying PCA it is obtained a small number of the linear combinations (principal components from a set of variables, describing the internal structure of the fabrics, which can hold as much information as possible from the original variables. Data analysis is an important initial step in decision making, allowing identification of the causes that lead to a decision- making situations. Thus it is the action of transforming the initial data in order to extract useful information and to facilitate reaching the conclusions. The process of data analysis can be defined as a sequence of steps aimed at formulating hypotheses, collecting primary information and validation, the construction of the mathematical model describing this phenomenon and reaching these conclusions about the behavior of this model.
Application of Principal Component Analysis in Prompt Gamma Spectra for Material Sorting
Energy Technology Data Exchange (ETDEWEB)
Im, Hee Jung; Lee, Yun Hee; Song, Byoung Chul; Park, Yong Joon; Kim, Won Ho
2006-11-15
For the detection of illicit materials in a very short time by comparing unknown samples' gamma spectra to pre-programmed material signatures, we at first, selected a method to reduce the noise of the obtained gamma spectra. After a noise reduction, a pattern recognition technique was applied to discriminate the illicit materials from the innocuous materials in the noise reduced data. Principal component analysis was applied for a noise reduction and pattern recognition in prompt gamma spectra. A computer program for the detection of illicit materials based on PCA method was developed in our lab and can be applied to the PGNAA system for the baggage checking at all ports of entry at a very short time.
A Principal Component Analysis of 39 Scientific Impact Measures
Bollen, Johan; Van de Sompel, Herbert
2009-01-01
Background The impact of scientific publications has traditionally been expressed in terms of citation counts. However, scientific activity has moved online over the past decade. To better capture scientific impact in the digital era, a variety of new impact measures has been proposed on the basis of social network analysis and usage log data. Here we investigate how these new measures relate to each other, and how accurately and completely they express scientific impact. Methodology We performed a principal component analysis of the rankings produced by 39 existing and proposed measures of scholarly impact that were calculated on the basis of both citation and usage log data. Conclusions Our results indicate that the notion of scientific impact is a multi-dimensional construct that can not be adequately measured by any single indicator, although some measures are more suitable than others. The commonly used citation Impact Factor is not positioned at the core of this construct, but at its periphery, and should thus be used with caution. PMID:19562078
Analysis of contaminants on electronic components by reflectance FTIR spectroscopy
International Nuclear Information System (INIS)
Griffith, G.W.
1982-09-01
The analysis of electronic component contaminants by infrared spectroscopy is often a difficult process. Most of the contaminants are very small, which necessitates the use of microsampling techniques. Beam condensers will provide the required sensitivity but most require that the sample be removed from the substrate before analysis. Since it can be difficult and time consuming, it is usually an undesirable approach. Micro ATR work can also be exasperating, due to the difficulty of positioning the sample at the correct place under the ATR plate in order to record a spectrum. This paper describes a modified reflection beam condensor which has been adapted to a Nicolet 7199 FTIR. The sample beam is directed onto the sample surface and reflected from the substrate back to the detector. A micropositioning XYZ stage and a close-focusing telescope are used to position the contaminant directly under the infrared beam. It is possible to analyze contaminants on 1 mm wide leads surrounded by an epoxy matrix using this device. Typical spectra of contaminants found on small circuit boards are included
A principal component analysis of 39 scientific impact measures.
Directory of Open Access Journals (Sweden)
Johan Bollen
Full Text Available BACKGROUND: The impact of scientific publications has traditionally been expressed in terms of citation counts. However, scientific activity has moved online over the past decade. To better capture scientific impact in the digital era, a variety of new impact measures has been proposed on the basis of social network analysis and usage log data. Here we investigate how these new measures relate to each other, and how accurately and completely they express scientific impact. METHODOLOGY: We performed a principal component analysis of the rankings produced by 39 existing and proposed measures of scholarly impact that were calculated on the basis of both citation and usage log data. CONCLUSIONS: Our results indicate that the notion of scientific impact is a multi-dimensional construct that can not be adequately measured by any single indicator, although some measures are more suitable than others. The commonly used citation Impact Factor is not positioned at the core of this construct, but at its periphery, and should thus be used with caution.
Sensitivity Analysis on Elbow Piping Components in Seismically Isolated NPP under Seismic Loading
Energy Technology Data Exchange (ETDEWEB)
Ju, Hee Kun; Hahm, Dae Gi; Kim, Min Kyu [KAERI, Daejeon (Korea, Republic of); Jeon, Bub Gyu; Kim, Nam Sik [Pusan National University, Busan (Korea, Republic of)
2016-05-15
In this study, the FE model is verified using specimen test results and simulation with parameter variations are conducted. Effective parameters will randomly sampled and used as input values for simulations to be applied to the fragility analysis. pipelines are representative of them because they could undergo larger displacements when they are supported on both isolated and non-isolated structures simultaneously. Especially elbows are critical components of pipes under severed loading conditions such as earthquake action because strain is accumulated on them during the repeated bending of the pipe. Therefore, seismic performance of pipe elbow components should be examined thoroughly based on the fragility analysis. Fragility assessment of interface pipe should take different sources of uncertainty into account. However, selection of important sources and repeated tests with many random input values are very time consuming and expensive, so numerical analysis is commonly used. In the present study, finite element (FE) model of elbow component will be validated using the dynamic test results of elbow components. Using the verified model, sensitivity analysis will be implemented as a preliminary process of seismic fragility of piping system. Several important input parameters are selected and how the uncertainty of them are apportioned to the uncertainty of the elbow response is to be studied. Piping elbows are critical components under cyclic loading conditions as they are subjected large displacement. In a seismically isolated NPP, seismic capacity of piping system should be evaluated with caution. Seismic fragility assessment preliminarily needs parameter sensitivity analysis about the output of interest with different input parameter values.
Applying Authentic Data Analysis in Learning Earth Atmosphere
Johan, H.; Suhandi, A.; Samsudin, A.; Wulan, A. R.
2017-09-01
The aim of this research was to develop earth science learning material especially earth atmosphere supported by science research with authentic data analysis to enhance reasoning through. Various earth and space science phenomenon require reasoning. This research used experimental research with one group pre test-post test design. 23 pre-service physics teacher participated in this research. Essay test was conducted to get data about reason ability. Essay test was analyzed quantitatively. Observation sheet was used to capture phenomena during learning process. The results showed that student’s reasoning ability improved from unidentified and no reasoning to evidence based reasoning and inductive/deductive rule-based reasoning. Authentic data was considered using Grid Analysis Display System (GrADS). Visualization from GrADS facilitated students to correlate the concepts and bring out real condition of nature in classroom activity. It also helped student to reason the phenomena related to earth and space science concept. It can be concluded that applying authentic data analysis in learning process can help to enhance students reasoning. This study is expected to help lecture to bring out result of geoscience research in learning process and facilitate student understand concepts.
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Osmar Abílio de Carvalho Júnior
2014-04-01
Full Text Available Speckle noise (salt and pepper is inherent to synthetic aperture radar (SAR, which causes a usual noise-like granular aspect and complicates the image classification. In SAR image analysis, the spatial information might be a particular benefit for denoising and mapping classes characterized by a statistical distribution of the pixel intensities from a complex and heterogeneous spectral response. This paper proposes the Probability Density Components Analysis (PDCA, a new alternative that combines filtering and frequency histogram to improve the classification procedure for the single-channel synthetic aperture radar (SAR images. This method was tested on L-band SAR data from the Advanced Land Observation System (ALOS Phased-Array Synthetic-Aperture Radar (PALSAR sensor. The study area is localized in the Brazilian Amazon rainforest, northern Rondônia State (municipality of Candeias do Jamari, containing forest and land use patterns. The proposed algorithm uses a moving window over the image, estimating the probability density curve in different image components. Therefore, a single input image generates an output with multi-components. Initially the multi-components should be treated by noise-reduction methods, such as maximum noise fraction (MNF or noise-adjusted principal components (NAPCs. Both methods enable reducing noise as well as the ordering of multi-component data in terms of the image quality. In this paper, the NAPC applied to multi-components provided large reductions in the noise levels, and the color composites considering the first NAPC enhance the classification of different surface features. In the spectral classification, the Spectral Correlation Mapper and Minimum Distance were used. The results obtained presented as similar to the visual interpretation of optical images from TM-Landsat and Google Maps.
Multivariate calibration applied to the quantitative analysis of infrared spectra
Energy Technology Data Exchange (ETDEWEB)
Haaland, D.M.
1991-01-01
Multivariate calibration methods are very useful for improving the precision, accuracy, and reliability of quantitative spectral analyses. Spectroscopists can more effectively use these sophisticated statistical tools if they have a qualitative understanding of the techniques involved. A qualitative picture of the factor analysis multivariate calibration methods of partial least squares (PLS) and principal component regression (PCR) is presented using infrared calibrations based upon spectra of phosphosilicate glass thin films on silicon wafers. Comparisons of the relative prediction abilities of four different multivariate calibration methods are given based on Monte Carlo simulations of spectral calibration and prediction data. The success of multivariate spectral calibrations is demonstrated for several quantitative infrared studies. The infrared absorption and emission spectra of thin-film dielectrics used in the manufacture of microelectronic devices demonstrate rapid, nondestructive at-line and in-situ analyses using PLS calibrations. Finally, the application of multivariate spectral calibrations to reagentless analysis of blood is presented. We have found that the determination of glucose in whole blood taken from diabetics can be precisely monitored from the PLS calibration of either mind- or near-infrared spectra of the blood. Progress toward the non-invasive determination of glucose levels in diabetics is an ultimate goal of this research. 13 refs., 4 figs.
Applied research and development of neutron activation analysis
Energy Technology Data Exchange (ETDEWEB)
Chung, Yong Sam; Moon, Jong Hwa; Kim, Sun Ha; Bak, Sung Ryel; Park, Yong Chul; Kim, Young Ki; Chung, Hwan Sung; Park, Kwang Won; Kang, Sang Hun
2000-05-01
This report is written for results of research and development as follows : improvement of neutron irradiation facilities, counting system and development of automation system and capsules for NAA in HANARO ; improvement of analytical procedures and establishment of analytical quality control and assurance system; applied research and development of environment, industry and human health and its standardization. For identification and standardization of analytical method, environmental biological samples and polymer are analyzed and uncertainity of measurement are estimated. Also data intercomparison and proficency test were performed. Using airborne particulate matter chosen as a environmental indicators, trace elemental concentrations of sample collected at urban and rural site are determined and then the calculation of statistics and the factor analysis are carried out for investigation of emission source. International cooperation research project was carried out for utilization of nuclear techniques.
Applied research and development of neutron activation analysis
International Nuclear Information System (INIS)
Chung, Yong Sam; Moon, Jong Hwa; Kim, Sun Ha; Bak, Sung Ryel; Park, Yong Chul; Kim, Young Ki; Chung, Hwan Sung; Park, Kwang Won; Kang, Sang Hun
2000-05-01
This report is written for results of research and development as follows : improvement of neutron irradiation facilities, counting system and development of automation system and capsules for NAA in HANARO ; improvement of analytical procedures and establishment of analytical quality control and assurance system; applied research and development of environment, industry and human health and its standardization. For identification and standardization of analytical method, environmental biological samples and polymer are analyzed and uncertainity of measurement are estimated. Also data intercomparison and proficency test were performed. Using airborne particulate matter chosen as a environmental indicators, trace elemental concentrations of sample collected at urban and rural site are determined and then the calculation of statistics and the factor analysis are carried out for investigation of emission source. International cooperation research project was carried out for utilization of nuclear techniques
Failure characteristic analysis of a component on standby state
International Nuclear Information System (INIS)
Shin, Sungmin; Kang, Hyungook
2013-01-01
Periodic operations for a specific type of component, however, can accelerate aging effects which increase component unavailability. For the other type of components, the aging effect caused by operation can be ignored. Therefore frequent operations can decrease component unavailability. Thus, to get optimum unavailability proper operation period and method should be studied considering the failure characteristics of each component. The information of component failure is given according to the main causes of failure depending on time flow. However, to get the optimal unavailability, proper interval of operation for inspection should be decided considering the time dependent and independent causes together. According to this study, gradually shorter operation interval for inspection is better to get the optimal component unavailability than that of specific period
Machine learning of frustrated classical spin models. I. Principal component analysis
Wang, Ce; Zhai, Hui
2017-10-01
This work aims at determining whether artificial intelligence can recognize a phase transition without prior human knowledge. If this were successful, it could be applied to, for instance, analyzing data from the quantum simulation of unsolved physical models. Toward this goal, we first need to apply the machine learning algorithm to well-understood models and see whether the outputs are consistent with our prior knowledge, which serves as the benchmark for this approach. In this work, we feed the computer data generated by the classical Monte Carlo simulation for the X Y model in frustrated triangular and union jack lattices, which has two order parameters and exhibits two phase transitions. We show that the outputs of the principal component analysis agree very well with our understanding of different orders in different phases, and the temperature dependences of the major components detect the nature and the locations of the phase transitions. Our work offers promise for using machine learning techniques to study sophisticated statistical models, and our results can be further improved by using principal component analysis with kernel tricks and the neural network method.
Portable XRF and principal component analysis for bill characterization in forensic science
International Nuclear Information System (INIS)
Appoloni, C.R.; Melquiades, F.L.
2014-01-01
Several modern techniques have been applied to prevent counterfeiting of money bills. The objective of this study was to demonstrate the potential of Portable X-ray Fluorescence (PXRF) technique and the multivariate analysis method of Principal Component Analysis (PCA) for classification of bills in order to use it in forensic science. Bills of Dollar, Euro and Real (Brazilian currency) were measured directly at different colored regions, without any previous preparation. Spectra interpretation allowed the identification of Ca, Ti, Fe, Cu, Sr, Y, Zr and Pb. PCA analysis separated the bills in three groups and subgroups among Brazilian currency. In conclusion, the samples were classified according to its origin identifying the elements responsible for differentiation and basic pigment composition. PXRF allied to multivariate discriminate methods is a promising technique for rapid and no destructive identification of false bills in forensic science. - Highlights: • The paper is about a direct method for bills discrimination by EDXRF and principal component analysis. • The bills are analyzed directly, without sample preparation and non destructively. • The results demonstrates that the methodology is feasible and could be applied in forensic science for identification of origin and false banknotes. • The novelty is that portable EDXRF is very fast and efficient for bills characterization
International Nuclear Information System (INIS)
Guerin, P.; Baudron, A.M.; Lautard, J.J.; Van Criekingen, S.
2007-01-01
This paper describes a new technique for determining the pin power in heterogeneous three-dimensional calculations. It is based on a domain decomposition with overlapping sub-domains and a component mode synthesis (CMS) technique for the global flux determination. Local basis functions are used to span a discrete space that allows fundamental global mode approximation through a Galerkin technique. Two approaches are given to obtain these local basis functions. In the first one (the CMS method), the first few spatial eigenfunctions are computed on each sub-domain, using periodic boundary conditions. In the second one (factorized CMS method), only the fundamental mode is computed, and we use a factorization principle for the flux in order to replace the higher-order Eigenmodes. These different local spatial functions are extended to the global domain by defining them as zero outside the sub-domain. These methods are well fitted for heterogeneous core calculations because the spatial interface modes are taken into account in the domain decomposition. Although these methods could be applied to higher-order angular approximations-particularly easily to an SPN approximation-the numerical results we provide are obtained using a diffusion model. We show the methods' accuracy for reactor cores loaded with uranium dioxide and mixed oxide assemblies, for which standard reconstruction techniques are known to perform poorly. Furthermore, we show that our methods are highly and easily parallelizable. (authors)
Directory of Open Access Journals (Sweden)
Le Corre Gwenaëlle
2018-01-01
Full Text Available This study focuses on applications from the automotive industry, on mechanical components submitted to vibration loads. On one hand, the characterization of loading for dimensioning new structures in fatigue is enriched and updated by customer data analysis. On the other hand, the loads characterization also aims to provide robust specifications for simulation or physical tests. These specifications are needed early in the project, in order to perform the first durability verification activities. At this time, detailed information about the geometry and the material is rare. Vibration specifications need to be adapted to a calculation time or physical test durations in accordance with the pace imposed by the projects timeframe. In the trucks industry, the dynamic behaviour can vary significantly from one configuration of truck to another, as the trucks architecture impacts the load environment of the components. The vibration specifications need to be robust by taking care of the diversity of vehicles and markets considered in the scope of the projects. For non-stiff structures, the lifetime depends, among other things, on the frequency content of the loads, as well as the interactions between the components of the multi-input loads. In this context, this paper proposes an approach to compare sets of variable amplitude multi-input loads applied on non-stiff structures. The comparison is done in terms of damage, with limited information on the structure where the loads sets are applied on. The methodology is presented, as well as an application. Activities planned to validate the methodology are also exposed.
Directory of Open Access Journals (Sweden)
Veerla Srinivas
2006-06-01
Full Text Available Abstract Background An alternative to standard approaches to uncover biologically meaningful structures in micro array data is to treat the data as a blind source separation (BSS problem. BSS attempts to separate a mixture of signals into their different sources and refers to the problem of recovering signals from several observed linear mixtures. In the context of micro array data, "sources" may correspond to specific cellular responses or to co-regulated genes. Results We applied independent component analysis (ICA to three different microarray data sets; two tumor data sets and one time series experiment. To obtain reliable components we used iterated ICA to estimate component centrotypes. We found that many of the low ranking components indeed may show a strong biological coherence and hence be of biological significance. Generally ICA achieved a higher resolution when compared with results based on correlated expression and a larger number of gene clusters with significantly enriched for gene ontology (GO categories. In addition, components characteristic for molecular subtypes and for tumors with specific chromosomal translocations were identified. ICA also identified more than one gene clusters significant for the same GO categories and hence disclosed a higher level of biological heterogeneity, even within coherent groups of genes. Conclusion Although the ICA approach primarily detects hidden variables, these surfaced as highly correlated genes in time series data and in one instance in the tumor data. This further strengthens the biological relevance of latent variables detected by ICA.
Applying Conjoint Analysis to Study Attitudes of Thai Government Organisations
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Natee Suriyanon
2012-11-01
Full Text Available This article presents the application of choice-based conjointanalysis to analyse the attitude of Thai government organisationstowards the restriction of the contractor’s right to claimcompensation for unfavourable effects from undesirable events.The analysis reveals that the organisations want to restrict only 6out of 14 types of the claiming rights that were studied. The rightthat they want to restrict most is the right to claim for additionaldirect costs due to force majeure. They are willing to pay between0.087% - 0.210% of the total project direct cost for restricting eachtype of contractor right. The total additional cost for restrictingall six types of rights that the organisations are willing to pay is0.882%. The last section of this article applies the knowledgegained from a choice based conjoint analysis experiment to theanalysis of the standard contract of the Thai government. Theanalysis reveals three types of rights where Thai governmentorganisations are willing to forego restrictions, but the presentstandard contract does not grant such rights.
Major component analysis of dynamic networks of physiologic organ interactions
International Nuclear Information System (INIS)
Liu, Kang K L; Ma, Qianli D Y; Ivanov, Plamen Ch; Bartsch, Ronny P
2015-01-01
The human organism is a complex network of interconnected organ systems, where the behavior of one system affects the dynamics of other systems. Identifying and quantifying dynamical networks of diverse physiologic systems under varied conditions is a challenge due to the complexity in the output dynamics of the individual systems and the transient and nonlinear characteristics of their coupling. We introduce a novel computational method based on the concept of time delay stability and major component analysis to investigate how organ systems interact as a network to coordinate their functions. We analyze a large database of continuously recorded multi-channel physiologic signals from healthy young subjects during night-time sleep. We identify a network of dynamic interactions between key physiologic systems in the human organism. Further, we find that each physiologic state is characterized by a distinct network structure with different relative contribution from individual organ systems to the global network dynamics. Specifically, we observe a gradual decrease in the strength of coupling of heart and respiration to the rest of the network with transition from wake to deep sleep, and in contrast, an increased relative contribution to network dynamics from chin and leg muscle tone and eye movement, demonstrating a robust association between network topology and physiologic function. (paper)
Sensor Failure Detection of FASSIP System using Principal Component Analysis
Sudarno; Juarsa, Mulya; Santosa, Kussigit; Deswandri; Sunaryo, Geni Rina
2018-02-01
In the nuclear reactor accident of Fukushima Daiichi in Japan, the damages of core and pressure vessel were caused by the failure of its active cooling system (diesel generator was inundated by tsunami). Thus researches on passive cooling system for Nuclear Power Plant are performed to improve the safety aspects of nuclear reactors. The FASSIP system (Passive System Simulation Facility) is an installation used to study the characteristics of passive cooling systems at nuclear power plants. The accuracy of sensor measurement of FASSIP system is essential, because as the basis for determining the characteristics of a passive cooling system. In this research, a sensor failure detection method for FASSIP system is developed, so the indication of sensor failures can be detected early. The method used is Principal Component Analysis (PCA) to reduce the dimension of the sensor, with the Squarred Prediction Error (SPE) and statistic Hotteling criteria for detecting sensor failure indication. The results shows that PCA method is capable to detect the occurrence of a failure at any sensor.
Finite element elastic-plastic analysis of LMFBR components
International Nuclear Information System (INIS)
Levy, A.; Pifko, A.; Armen, H. Jr.
1978-01-01
The present effort involves the development of computationally efficient finite element methods for accurately predicting the isothermal elastic-plastic three-dimensional response of thick and thin shell structures subjected to mechanical and thermal loads. This work will be used as the basis for further development of analytical tools to be used to verify the structural integrity of liquid metal fast breeder reactor (LMFBR) components. The methods presented here have been implemented into the three-dimensional solid element module (HEX) of the Grumman PLANS finite element program. These methods include the use of optimal stress points as well as a variable number of stress points within an element. This allows monitoring the stress history at many points within an element and hence provides an accurate representation of the elastic-plastic boundary using a minimum number of degrees of freedom. Also included is an improved thermal stress analysis capability in which the temperature variation and corresponding thermal strain variation are represented by the same functional form as the displacement variation. Various problems are used to demonstrate these improved capabilities. (Auth.)
Energy Technology Data Exchange (ETDEWEB)
Palacio Garcia, L.; Andrzejak, R.; Prchkovska, V.; Rodrigues, P.
2016-07-01
It is commonly thought that our brain is not active when it does not receive any external input. However, during rest, there are still certain distant regions of the brain that are functionally correlated between them: the so-called resting-state networks. This functional connectivity of the brain is disrupted in many neurological diseases. In particular, it has been shown that one of the most studied resting-state networks (the default-mode network) is affected in multiple sclerosis, which is the most common disabling neurological condition affecting the central nervous system of young adults. In this work, I focus on the study of the differences in the resting-state networks between multiple sclerosis patients and healthy volunteers. In order to study the effects of multiple sclerosis on the functional connectivity of the brain, a numerical method known as independent component analysis (ICA) is applied. This technique divides the resting-state fMRI data into independent components. Nonetheless, noise, which could be due to head motion or physiological artifacts, may corrupt the data by indicating a false activation. Therefore, I create a web user interface that allows the user to manually classify all the independent components for a given subject. Eventually, the components classified as noise should be removed from the functional data in order to prevent them from taking part in any further analysis. (Author)
Directory of Open Access Journals (Sweden)
Xuewu Zhang
2013-01-01
Full Text Available This paper proposed a new method for surface defect detection of photovoltaic module based on independent component analysis (ICA reconstruction algorithm. Firstly, a faultless image is used as the training image. The demixing matrix and corresponding ICs are obtained by applying the ICA in the training image. Then we reorder the ICs according to the range values and reform the de-mixing matrix. Then the reformed de-mixing matrix is used to reconstruct the defect image. The resulting image can remove the background structures and enhance the local anomalies. Experimental results have shown that the proposed method can effectively detect the presence of defects in periodically patterned surfaces.
International Nuclear Information System (INIS)
1993-01-01
For an analysis of the safety-related significance of residual stresses, mechanical, magnetic as well as ultrasonic and diffraction methods can be applied as testing methods. The results of an interlaboratory test concerning the experimental determination of residual stresses in a railway track are included. Further, questions are analyzed concerning the in-service inspections of components and systems with regard to their operational safety and life. Measurement methods are explained by examples from power plant engineering, nuclear power plant engineering, construction and traffic engineering as well as aeronautics. (DG) [de
Derivation of design response spectra for analysis and testing of components and systems
International Nuclear Information System (INIS)
Krutzik, N.
1996-01-01
Some institutions participating in the Benchmark Project performed parallel calculations for the WWER-1000 Kozloduy NPP. The investigations were based on various mathematical models and procedures for consideration of soil-structure interaction effects, simultaneously applying uniform soil dynamic and seismological input data. The methods, mathematical models and dynamic response results were evaluated and discussed in detail and finally compared by means of different structural models and soil representations with the aim of deriving final enveloped and smoothed dynamic response data (benchmark response spectra). This should be used for requalification by analysis testing of the mechanical and electrical components and systems located in this type of reactor building
Kernel Principal Component Analysis and its Applications in Face Recognition and Active Shape Models
Wang, Quan
2012-01-01
Principal component analysis (PCA) is a popular tool for linear dimensionality reduction and feature extraction. Kernel PCA is the nonlinear form of PCA, which better exploits the complicated spatial structure of high-dimensional features. In this paper, we first review the basic ideas of PCA and kernel PCA. Then we focus on the reconstruction of pre-images for kernel PCA. We also give an introduction on how PCA is used in active shape models (ASMs), and discuss how kernel PCA can be applied ...
International Nuclear Information System (INIS)
Kleinert, K.P.
1992-01-01
The study analyses and compares German, French, British and Italian practices and procedures applied for various LMFBR projects both related to the quality assurance system and related to the particular type of class of component:Class 1: primary reactor vessel; Class 2: Secondary sodium pump; Class 3: Primary cold trap. Various areas of analysis and comparison were selected to identify the underlying concepts of grading of requirements and measures, to identify the similarities and differences, and to give recommendations for further actions concerning quality assurance requirements 60 refs., 21 tabs., 6 figs
The Evidence-Based Practice of Applied Behavior Analysis.
Slocum, Timothy A; Detrich, Ronnie; Wilczynski, Susan M; Spencer, Trina D; Lewis, Teri; Wolfe, Katie
2014-05-01
Evidence-based practice (EBP) is a model of professional decision-making in which practitioners integrate the best available evidence with client values/context and clinical expertise in order to provide services for their clients. This framework provides behavior analysts with a structure for pervasive use of the best available evidence in the complex settings in which they work. This structure recognizes the need for clear and explicit understanding of the strength of evidence supporting intervention options, the important contextual factors including client values that contribute to decision making, and the key role of clinical expertise in the conceptualization, intervention, and evaluation of cases. Opening the discussion of EBP in this journal, Smith (The Behavior Analyst, 36, 7-33, 2013) raised several key issues related to EBP and applied behavior analysis (ABA). The purpose of this paper is to respond to Smith's arguments and extend the discussion of the relevant issues. Although we support many of Smith's (The Behavior Analyst, 36, 7-33, 2013) points, we contend that Smith's definition of EBP is significantly narrower than definitions that are used in professions with long histories of EBP and that this narrowness conflicts with the principles that drive applied behavior analytic practice. We offer a definition and framework for EBP that aligns with the foundations of ABA and is consistent with well-established definitions of EBP in medicine, psychology, and other professions. In addition to supporting the systematic use of research evidence in behavior analytic decision making, this definition can promote clear communication about treatment decisions across disciplines and with important outside institutions such as insurance companies and granting agencies.
Trimming of mammalian transcriptional networks using network component analysis
Directory of Open Access Journals (Sweden)
Liao James C
2010-10-01
Full Text Available Abstract Background Network Component Analysis (NCA has been used to deduce the activities of transcription factors (TFs from gene expression data and the TF-gene binding relationship. However, the TF-gene interaction varies in different environmental conditions and tissues, but such information is rarely available and cannot be predicted simply by motif analysis. Thus, it is beneficial to identify key TF-gene interactions under the experimental condition based on transcriptome data. Such information would be useful in identifying key regulatory pathways and gene markers of TFs in further studies. Results We developed an algorithm to trim network connectivity such that the important regulatory interactions between the TFs and the genes were retained and the regulatory signals were deduced. Theoretical studies demonstrated that the regulatory signals were accurately reconstructed even in the case where only three independent transcriptome datasets were available. At least 80% of the main target genes were correctly predicted in the extreme condition of high noise level and small number of datasets. Our algorithm was tested with transcriptome data taken from mice under rapamycin treatment. The initial network topology from the literature contains 70 TFs, 778 genes, and 1423 edges between the TFs and genes. Our method retained 1074 edges (i.e. 75% of the original edge number and identified 17 TFs as being significantly perturbed under the experimental condition. Twelve of these TFs are involved in MAPK signaling or myeloid leukemia pathways defined in the KEGG database, or are known to physically interact with each other. Additionally, four of these TFs, which are Hif1a, Cebpb, Nfkb1, and Atf1, are known targets of rapamycin. Furthermore, the trimmed network was able to predict Eno1 as an important target of Hif1a; this key interaction could not be detected without trimming the regulatory network. Conclusions The advantage of our new algorithm
Decoding the auditory brain with canonical component analysis.
de Cheveigné, Alain; Wong, Daniel D E; Di Liberto, Giovanni M; Hjortkjær, Jens; Slaney, Malcolm; Lalor, Edmund
2018-05-15
The relation between a stimulus and the evoked brain response can shed light on perceptual processes within the brain. Signals derived from this relation can also be harnessed to control external devices for Brain Computer Interface (BCI) applications. While the classic event-related potential (ERP) is appropriate for isolated stimuli, more sophisticated "decoding" strategies are needed to address continuous stimuli such as speech, music or environmental sounds. Here we describe an approach based on Canonical Correlation Analysis (CCA) that finds the optimal transform to apply to both the stimulus and the response to reveal correlations between the two. Compared to prior methods based on forward or backward models for stimulus-response mapping, CCA finds significantly higher correlation scores, thus providing increased sensitivity to relatively small effects, and supports classifier schemes that yield higher classification scores. CCA strips the brain response of variance unrelated to the stimulus, and the stimulus representation of variance that does not affect the response, and thus improves observations of the relation between stimulus and response. Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.
A Removal of Eye Movement and Blink Artifacts from EEG Data Using Morphological Component Analysis
Directory of Open Access Journals (Sweden)
Balbir Singh
2017-01-01
Full Text Available EEG signals contain a large amount of ocular artifacts with different time-frequency properties mixing together in EEGs of interest. The artifact removal has been substantially dealt with by existing decomposition methods known as PCA and ICA based on the orthogonality of signal vectors or statistical independence of signal components. We focused on the signal morphology and proposed a systematic decomposition method to identify the type of signal components on the basis of sparsity in the time-frequency domain based on Morphological Component Analysis (MCA, which provides a way of reconstruction that guarantees accuracy in reconstruction by using multiple bases in accordance with the concept of “dictionary.” MCA was applied to decompose the real EEG signal and clarified the best combination of dictionaries for this purpose. In our proposed semirealistic biological signal analysis with iEEGs recorded from the brain intracranially, those signals were successfully decomposed into original types by a linear expansion of waveforms, such as redundant transforms: UDWT, DCT, LDCT, DST, and DIRAC. Our result demonstrated that the most suitable combination for EEG data analysis was UDWT, DST, and DIRAC to represent the baseline envelope, multifrequency wave-forms, and spiking activities individually as representative types of EEG morphologies.
Response spectrum analysis of coupled structural response to a three component seismic disturbance
International Nuclear Information System (INIS)
Boulet, J.A.M.; Carley, T.G.
1977-01-01
The work discussed herein is a comparison and evaluation of several response spectrum analysis (RSA) techniques as applied to the same structural model with seismic excitation having three spatial components. Lagrange's equations of motion for the system were written in matrix form and uncoupled with the modal matrix. Numerical integration (fourth order Runge-Kutta) of the resulting model equations produced time histories of system displacements in response to simultaneous application of three orthogonal components of ground motion, and displacement response spectra for each modal coordinate in response to each of the three ground motion components. Five different RSA techniques were used to combine the spectral displacements and the modal matrix to give approximations of maximum system displacements. These approximations were then compared with the maximum system displacements taken from the time histories. The RSA techniques used are the method of absolute sums, the square root of the sum of the squares, the double sum approach, the method of closely spaced modes, and Lin's method. The vectors of maximum system displacements as computed by the time history analysis and the five response spectrum analysis methods are presented. (Auth.)
A Removal of Eye Movement and Blink Artifacts from EEG Data Using Morphological Component Analysis
Wagatsuma, Hiroaki
2017-01-01
EEG signals contain a large amount of ocular artifacts with different time-frequency properties mixing together in EEGs of interest. The artifact removal has been substantially dealt with by existing decomposition methods known as PCA and ICA based on the orthogonality of signal vectors or statistical independence of signal components. We focused on the signal morphology and proposed a systematic decomposition method to identify the type of signal components on the basis of sparsity in the time-frequency domain based on Morphological Component Analysis (MCA), which provides a way of reconstruction that guarantees accuracy in reconstruction by using multiple bases in accordance with the concept of “dictionary.” MCA was applied to decompose the real EEG signal and clarified the best combination of dictionaries for this purpose. In our proposed semirealistic biological signal analysis with iEEGs recorded from the brain intracranially, those signals were successfully decomposed into original types by a linear expansion of waveforms, such as redundant transforms: UDWT, DCT, LDCT, DST, and DIRAC. Our result demonstrated that the most suitable combination for EEG data analysis was UDWT, DST, and DIRAC to represent the baseline envelope, multifrequency wave-forms, and spiking activities individually as representative types of EEG morphologies. PMID:28194221
A stable systemic risk ranking in China's banking sector: Based on principal component analysis
Fang, Libing; Xiao, Binqing; Yu, Honghai; You, Qixing
2018-02-01
In this paper, we compare five popular systemic risk rankings, and apply principal component analysis (PCA) model to provide a stable systemic risk ranking for the Chinese banking sector. Our empirical results indicate that five methods suggest vastly different systemic risk rankings for the same bank, while the combined systemic risk measure based on PCA provides a reliable ranking. Furthermore, according to factor loadings of the first component, PCA combined ranking is mainly based on fundamentals instead of market price data. We clearly find that price-based rankings are not as practical a method as fundamentals-based ones. This PCA combined ranking directly shows systemic risk contributions of each bank for banking supervision purpose and reminds banks to prevent and cope with the financial crisis in advance.
Data analysis of x-ray fluorescence holography by subtracting normal component from inverse hologram
International Nuclear Information System (INIS)
Happo, Naohisa; Hayashi, Kouichi; Hosokawa, Shinya
2010-01-01
X-ray fluorescence holography (XFH) is a powerful technique for determining three-dimensional local atomic arrangements around a specific fluorescing element. However, the raw experimental hologram is predominantly a mixed hologram, i.e., a mixture of hologram generated in both normal and inverse modes, which produces unreliable atomic images. In this paper, we propose a practical subtraction method of the normal component from the inverse XFH data by a Fourier transform for the calculated hologram of a model ZnTe cluster. Many spots originating from the normal components could be properly removed using a mask function, and clear atomic images were reconstructed at adequate positions of the model cluster. This method was successfully applied to the analysis of experimental ZnTe single crystal XFH data. (author)
Variability search in M 31 using principal component analysis and the Hubble Source Catalogue
Moretti, M. I.; Hatzidimitriou, D.; Karampelas, A.; Sokolovsky, K. V.; Bonanos, A. Z.; Gavras, P.; Yang, M.
2018-06-01
Principal component analysis (PCA) is being extensively used in Astronomy but not yet exhaustively exploited for variability search. The aim of this work is to investigate the effectiveness of using the PCA as a method to search for variable stars in large photometric data sets. We apply PCA to variability indices computed for light curves of 18 152 stars in three fields in M 31 extracted from the Hubble Source Catalogue. The projection of the data into the principal components is used as a stellar variability detection and classification tool, capable of distinguishing between RR Lyrae stars, long-period variables (LPVs) and non-variables. This projection recovered more than 90 per cent of the known variables and revealed 38 previously unknown variable stars (about 30 per cent more), all LPVs except for one object of uncertain variability type. We conclude that this methodology can indeed successfully identify candidate variable stars.
Directory of Open Access Journals (Sweden)
Reinhold Orglmeister
2010-01-01
Full Text Available When a number of speakers are simultaneously active, for example in meetings or noisy public places, the sources of interest need to be separated from interfering speakers and from each other in order to be robustly recognized. Independent component analysis (ICA has proven a valuable tool for this purpose. However, ICA outputs can still contain strong residual components of the interfering speakers whenever noise or reverberation is high. In such cases, nonlinear postprocessing can be applied to the ICA outputs, for the purpose of reducing remaining interferences. In order to improve robustness to the artefacts and loss of information caused by this process, recognition can be greatly enhanced by considering the processed speech feature vector as a random variable with time-varying uncertainty, rather than as deterministic. The aim of this paper is to show the potential to improve recognition of multiple overlapping speech signals through nonlinear postprocessing together with uncertainty-based decoding techniques.
Gene Module Identification from Microarray Data Using Nonnegative Independent Component Analysis
Directory of Open Access Journals (Sweden)
Ting Gong
2007-01-01
Full Text Available Genes mostly interact with each other to form transcriptional modules for performing single or multiple functions. It is important to unravel such transcriptional modules and to determine how disturbances in them may lead to disease. Here, we propose a non-negative independent component analysis (nICA approach for transcriptional module discovery. nICA method utilizes the non-negativity constraint to enforce the independence of biological processes within the participated genes. In such, nICA decomposes the observed gene expression into positive independent components, which fi ts better to the reality of corresponding putative biological processes. In conjunction with nICA modeling, visual statistical data analyzer (VISDA is applied to group genes into modules in latent variable space. We demonstrate the usefulness of the approach through the identification of composite modules from yeast data and the discovery of pathway modules in muscle regeneration.
To apply or not to apply: a survey analysis of grant writing costs and benefits.
von Hippel, Ted; von Hippel, Courtney
2015-01-01
We surveyed 113 astronomers and 82 psychologists active in applying for federally funded research on their grant-writing history between January, 2009 and November, 2012. We collected demographic data, effort levels, success rates, and perceived non-financial benefits from writing grant proposals. We find that the average proposal takes 116 PI hours and 55 CI hours to write; although time spent writing was not related to whether the grant was funded. Effort did translate into success, however, as academics who wrote more grants received more funding. Participants indicated modest non-monetary benefits from grant writing, with psychologists reporting a somewhat greater benefit overall than astronomers. These perceptions of non-financial benefits were unrelated to how many grants investigators applied for, the number of grants they received, or the amount of time they devoted to writing their proposals. We also explored the number of years an investigator can afford to apply unsuccessfully for research grants and our analyses suggest that funding rates below approximately 20%, commensurate with current NIH and NSF funding, are likely to drive at least half of the active researchers away from federally funded research. We conclude with recommendations and suggestions for individual investigators and for department heads.
To apply or not to apply: a survey analysis of grant writing costs and benefits.
Directory of Open Access Journals (Sweden)
Ted von Hippel
Full Text Available We surveyed 113 astronomers and 82 psychologists active in applying for federally funded research on their grant-writing history between January, 2009 and November, 2012. We collected demographic data, effort levels, success rates, and perceived non-financial benefits from writing grant proposals. We find that the average proposal takes 116 PI hours and 55 CI hours to write; although time spent writing was not related to whether the grant was funded. Effort did translate into success, however, as academics who wrote more grants received more funding. Participants indicated modest non-monetary benefits from grant writing, with psychologists reporting a somewhat greater benefit overall than astronomers. These perceptions of non-financial benefits were unrelated to how many grants investigators applied for, the number of grants they received, or the amount of time they devoted to writing their proposals. We also explored the number of years an investigator can afford to apply unsuccessfully for research grants and our analyses suggest that funding rates below approximately 20%, commensurate with current NIH and NSF funding, are likely to drive at least half of the active researchers away from federally funded research. We conclude with recommendations and suggestions for individual investigators and for department heads.
Applied genre analysis: a multi-perspective model
Directory of Open Access Journals (Sweden)
Vijay K Bhatia
2002-04-01
Full Text Available Genre analysis can be viewed from two different perspectives: it may be seen as a reflection of the complex realities of the world of institutionalised communication, or it may be seen as a pedagogically effective and convenient tool for the design of language teaching programmes, often situated within simulated contexts of classroom activities. This paper makes an attempt to understand and resolve the tension between these two seemingly contentious perspectives to answer the question: "Is generic description a reflection of reality, or a convenient fiction invented by applied linguists?". The paper also discusses issues related to the nature and use of linguistic description in a genre-based educational enterprise, claiming that instead of using generic descriptions as models for linguistic reproduction of conventional forms to respond to recurring social contexts, as is often the case in many communication based curriculum contexts, they can be used as analytical resource to understand and manipulate complex inter-generic and multicultural realisations of professional discourse, which will enable learners to use generic knowledge to respond to novel social contexts and also to create new forms of discourse to achieve pragmatic success as well as other powerful human agendas.
SUCCESS CONCEPT ANALYSIS APPLIED TO THE INFORMATION TECHNOLOGY PROJECT MANAGEMENT
Directory of Open Access Journals (Sweden)
Cassio C. Montenegro Duarte
2012-05-01
Full Text Available This study evaluates the concept of success in project management that is applicable to the IT universe, from the classical theory associated with the techniques of project management. Therefore, it applies the theoretical analysis associated to the context of information technology in enterprises as well as the classic literature of traditional project management, focusing on its application in business information technology. From the literature developed in the first part of the study, four propositions were prepared for study which formed the basis for the development of the field research with three large companies that develop projects of Information Technology. The methodology used in the study predicted the development of the multiple case study. Empirical evidence suggests that the concept of success found in the classical literature in project management adjusts to the environment management of IT projects. Showed that it is possible to create the model of standard IT projects in order to replicate it in future derivatives projects, which depends on the learning acquired at the end of a long and continuous process and sponsorship of senior management, which ultimately results in its merger into the company culture.
Applying importance-performance analysis to patient safety culture.
Lee, Yii-Ching; Wu, Hsin-Hung; Hsieh, Wan-Lin; Weng, Shao-Jen; Hsieh, Liang-Po; Huang, Chih-Hsuan
2015-01-01
The Sexton et al.'s (2006) safety attitudes questionnaire (SAQ) has been widely used to assess staff's attitudes towards patient safety in healthcare organizations. However, to date there have been few studies that discuss the perceptions of patient safety both from hospital staff and upper management. The purpose of this paper is to improve and to develop better strategies regarding patient safety in healthcare organizations. The Chinese version of SAQ based on the Taiwan Joint Commission on Hospital Accreditation is used to evaluate the perceptions of hospital staff. The current study then lies in applying importance-performance analysis technique to identify the major strengths and weaknesses of the safety culture. The results show that teamwork climate, safety climate, job satisfaction, stress recognition and working conditions are major strengths and should be maintained in order to provide a better patient safety culture. On the contrary, perceptions of management and hospital handoffs and transitions are important weaknesses and should be improved immediately. Research limitations/implications - The research is restricted in generalizability. The assessment of hospital staff in patient safety culture is physicians and registered nurses. It would be interesting to further evaluate other staff's (e.g. technicians, pharmacists and others) opinions regarding patient safety culture in the hospital. Few studies have clearly evaluated the perceptions of healthcare organization management regarding patient safety culture. Healthcare managers enable to take more effective actions to improve the level of patient safety by investigating key characteristics (either strengths or weaknesses) that healthcare organizations should focus on.
Applying multicriteria analysis for choosing the best marination for pork
Directory of Open Access Journals (Sweden)
Nieto VMOS
2015-01-01
Full Text Available Objective. This research aimed to choose a best marination solution using the Analytic Hierarchy Process (AHP. Materials and methods. Pork meat samples were collected in a commercial slaughterhouse, and they were randomly distributed in four treatments with three different salt contents blend. Color, pH, retention of the solution, exudate and cooking loss, shear force and sensory attributes were assessed and evaluated. Multicriteria analysis using AHP was applied to the results in order to choose the best overall marination solution. Criteria used for selection were the physical and sensory characteristics of meat, and based on these criteria were classified solutions marination. Results. Results showed that the combination of the salts was the best alternative (Na2CO3+NaCl+Na5P3O10, followed by the solutions of (Na2CO3 + NaCl, and (Na5P3O10 + NaCl. Conclusions. All tested solutions with the salts used alone or in combination led to better physical and sensory attributes than the meat not marinated.
A seismic analysis of nuclear power plant components subjected to multi-excitations of earthquakes
International Nuclear Information System (INIS)
Ichiki, T.; Matsumoto, T.; Gunyasu, K.
1977-01-01
In this analysis, the modal analysis methods are used to determine the seismic responses of structural systems instead of the direct integration method. These results have been compared with some kinds of other analytical methods, and investigated the accuracy of numerical results of these analysis, applying to such components as Reactor Pressure Vessel and Reactor Internals of an actual plant. The results of this method of analysis are summarized as follows: (1) one of the seismic analysis methods concerning systems subjected to multi-excitations of earthquakes has been presented to the conference of JSME. Although the analytical theory presented to that conference is correct, it has a serious problem about the accuracy of numerical results. This computer program and theory cannot be used practically due to the time necessary to calculate. However, the method described in this paper overcomes those serious problems stated above and has no problem about the computer time and precision. So, it is possible to apply this method to the seismic design of an actual nuclear power plant practically. (2) The feed back effects of the seismic responses of Reactor Internals to Reactor Building are considered so small that we can separate the model of Reactor Internals from Reactor Building. (3) The results of seismic response of Reactor Internals are fairly consistent with those obtained from the model coupled with Reactor Building. (4) This analysis method can be extended to the model of Reactor Internals subjected to more than two random excitations of earthquakes. (5) It is possible that this analysis method is also applied to the seismic analysis of such three-dimensional systems as piping systems subjected to multi-excitations of earthquakes
DEFF Research Database (Denmark)
Morgenstjerne, Axel; Karlsen, Brian; Larsen, Jan
2005-01-01
Independent Component Analysis (ICA) is applied to classify unexploded ordnance (UXO) on laboratory UXO test-field data, acquired by stand-off detection. The data are acquired by an Electromagnetic Induction Spectroscopy (EMIS) metal detector and a ground penetrating radar (GPR) detector. The metal...
Nordemann, D. J. R.; Rigozo, N. R.; de Souza Echer, M. P.; Echer, E.
2008-11-01
We present here an implementation of a least squares iterative regression method applied to the sine functions embedded in the principal components extracted from geophysical time series. This method seems to represent a useful improvement for the non-stationary time series periodicity quantitative analysis. The principal components determination followed by the least squares iterative regression method was implemented in an algorithm written in the Scilab (2006) language. The main result of the method is to obtain the set of sine functions embedded in the series analyzed in decreasing order of significance, from the most important ones, likely to represent the physical processes involved in the generation of the series, to the less important ones that represent noise components. Taking into account the need of a deeper knowledge of the Sun's past history and its implication to global climate change, the method was applied to the Sunspot Number series (1750-2004). With the threshold and parameter values used here, the application of the method leads to a total of 441 explicit sine functions, among which 65 were considered as being significant and were used for a reconstruction that gave a normalized mean squared error of 0.146.
Principal components analysis of protein structure ensembles calculated using NMR data
International Nuclear Information System (INIS)
Howe, Peter W.A.
2001-01-01
One important problem when calculating structures of biomolecules from NMR data is distinguishing converged structures from outlier structures. This paper describes how Principal Components Analysis (PCA) has the potential to classify calculated structures automatically, according to correlated structural variation across the population. PCA analysis has the additional advantage that it highlights regions of proteins which are varying across the population. To apply PCA, protein structures have to be reduced in complexity and this paper describes two different representations of protein structures which achieve this. The calculated structures of a 28 amino acid peptide are used to demonstrate the methods. The two different representations of protein structure are shown to give equivalent results, and correct results are obtained even though the ensemble of structures used as an example contains two different protein conformations. The PCA analysis also correctly identifies the structural differences between the two conformations
Directory of Open Access Journals (Sweden)
A. Bhushan
2015-07-01
Full Text Available In this paper, we address outliers in spatiotemporal data streams obtained from sensors placed across geographically distributed locations. Outliers may appear in such sensor data due to various reasons such as instrumental error and environmental change. Real-time detection of these outliers is essential to prevent propagation of errors in subsequent analyses and results. Incremental Principal Component Analysis (IPCA is one possible approach for detecting outliers in such type of spatiotemporal data streams. IPCA has been widely used in many real-time applications such as credit card fraud detection, pattern recognition, and image analysis. However, the suitability of applying IPCA for outlier detection in spatiotemporal data streams is unknown and needs to be investigated. To fill this research gap, this paper contributes by presenting two new IPCA-based outlier detection methods and performing a comparative analysis with the existing IPCA-based outlier detection methods to assess their suitability for spatiotemporal sensor data streams.
Directory of Open Access Journals (Sweden)
Beatriz Galindo-Prieto
2018-02-01
Full Text Available Independent component analysis combined with various strategies for cross-validation, uncertainty estimates by jack-knifing and critical Hotelling’s T2 limits estimation, proposed in this paper, is used for classification purposes in hyperspectral images. To the best of our knowledge, the combined approach of methods used in this paper has not been previously applied to hyperspectral imaging analysis for interpretation and classification in the literature. The data analysis performed here aims to distinguish between four different types of plastics, some of them containing brominated flame retardants, from their near infrared hyperspectral images. The results showed that the method approach used here can be successfully used for unsupervised classification. A comparison of validation approaches, especially leave-one-out cross-validation and regions of interest scheme validation is also evaluated.
Portable XRF and principal component analysis for bill characterization in forensic science.
Appoloni, C R; Melquiades, F L
2014-02-01
Several modern techniques have been applied to prevent counterfeiting of money bills. The objective of this study was to demonstrate the potential of Portable X-ray Fluorescence (PXRF) technique and the multivariate analysis method of Principal Component Analysis (PCA) for classification of bills in order to use it in forensic science. Bills of Dollar, Euro and Real (Brazilian currency) were measured directly at different colored regions, without any previous preparation. Spectra interpretation allowed the identification of Ca, Ti, Fe, Cu, Sr, Y, Zr and Pb. PCA analysis separated the bills in three groups and subgroups among Brazilian currency. In conclusion, the samples were classified according to its origin identifying the elements responsible for differentiation and basic pigment composition. PXRF allied to multivariate discriminate methods is a promising technique for rapid and no destructive identification of false bills in forensic science. Copyright © 2013 Elsevier Ltd. All rights reserved.
Organizational Design Analysis of Fleet Readiness Center Southwest Components Department
National Research Council Canada - National Science Library
Montes, Jose F
2007-01-01
.... The purpose of this MBA Project is to analyze the proposed organizational design elements of the FRCSW Components Department that resulted from the integration of the Naval Aviation Depot at North Island (NADEP N.I...
Zia, Asif Iqbal
2015-06-01
The surface roughness of thin-film gold electrodes induces instability in impedance spectroscopy measurements of capacitive interdigital printable sensors. Post-fabrication thermodynamic annealing was carried out at temperatures ranging from 30 °C to 210 °C in a vacuum oven and the variation in surface morphology of thin-film gold electrodes was observed by scanning electron microscopy. Impedance spectra obtained at different temperatures were translated into equivalent circuit models by applying complex nonlinear least square curve-fitting algorithm. Principal component analysis was applied to deduce the classification of the parameters affected due to the annealing process and to evaluate the performance stability using mathematical model. Physics of the thermodynamic annealing was discussed based on the surface activation energies. The post anneal testing of the sensors validated the achieved stability in impedance measurement. © 2001-2012 IEEE.
QIM blind video watermarking scheme based on Wavelet transform and principal component analysis
Directory of Open Access Journals (Sweden)
Nisreen I. Yassin
2014-12-01
Full Text Available In this paper, a blind scheme for digital video watermarking is proposed. The security of the scheme is established by using one secret key in the retrieval of the watermark. Discrete Wavelet Transform (DWT is applied on each video frame decomposing it into a number of sub-bands. Maximum entropy blocks are selected and transformed using Principal Component Analysis (PCA. Quantization Index Modulation (QIM is used to quantize the maximum coefficient of the PCA blocks of each sub-band. Then, the watermark is embedded into the selected suitable quantizer values. The proposed scheme is tested using a number of video sequences. Experimental results show high imperceptibility. The computed average PSNR exceeds 45 dB. Finally, the scheme is applied on two medical videos. The proposed scheme shows high robustness against several attacks such as JPEG coding, Gaussian noise addition, histogram equalization, gamma correction, and contrast adjustment in both cases of regular videos and medical videos.
Zia, Asif Iqbal; Mukhopadhyay, Subhas Chandra; Yu, Paklam; Al-Bahadly, Ibrahim H.; Gooneratne, Chinthaka Pasan; Kosel, Jü rgen
2015-01-01
The surface roughness of thin-film gold electrodes induces instability in impedance spectroscopy measurements of capacitive interdigital printable sensors. Post-fabrication thermodynamic annealing was carried out at temperatures ranging from 30 °C to 210 °C in a vacuum oven and the variation in surface morphology of thin-film gold electrodes was observed by scanning electron microscopy. Impedance spectra obtained at different temperatures were translated into equivalent circuit models by applying complex nonlinear least square curve-fitting algorithm. Principal component analysis was applied to deduce the classification of the parameters affected due to the annealing process and to evaluate the performance stability using mathematical model. Physics of the thermodynamic annealing was discussed based on the surface activation energies. The post anneal testing of the sensors validated the achieved stability in impedance measurement. © 2001-2012 IEEE.
International Nuclear Information System (INIS)
Jung, Young Mee
2003-01-01
Principal component analysis based two-dimensional (PCA-2D) correlation analysis is applied to FTIR spectra of polystyrene/methyl ethyl ketone/toluene solution mixture during the solvent evaporation. Substantial amount of artificial noise were added to the experimental data to demonstrate the practical noise-suppressing benefit of PCA-2D technique. 2D correlation analysis of the reconstructed data matrix from PCA loading vectors and scores successfully extracted only the most important features of synchronicity and asynchronicity without interference from noise or insignificant minor components. 2D correlation spectra constructed with only one principal component yield strictly synchronous response with no discernible a asynchronous features, while those involving at least two or more principal components generated meaningful asynchronous 2D correlation spectra. Deliberate manipulation of the rank of the reconstructed data matrix, by choosing the appropriate number and type of PCs, yields potentially more refined 2D correlation spectra
Gao, Yang; Chen, Maomao; Wu, Junyu; Zhou, Yuan; Cai, Chuangjian; Wang, Daliang; Luo, Jianwen
2017-09-01
Fluorescence molecular imaging has been used to target tumors in mice with xenograft tumors. However, tumor imaging is largely distorted by the aggregation of fluorescent probes in the liver. A principal component analysis (PCA)-based strategy was applied on the in vivo dynamic fluorescence imaging results of three mice with xenograft tumors to facilitate tumor imaging, with the help of a tumor-specific fluorescent probe. Tumor-relevant features were extracted from the original images by PCA and represented by the principal component (PC) maps. The second principal component (PC2) map represented the tumor-related features, and the first principal component (PC1) map retained the original pharmacokinetic profiles, especially of the liver. The distribution patterns of the PC2 map of the tumor-bearing mice were in good agreement with the actual tumor location. The tumor-to-liver ratio and contrast-to-noise ratio were significantly higher on the PC2 map than on the original images, thus distinguishing the tumor from its nearby fluorescence noise of liver. The results suggest that the PC2 map could serve as a bioimaging marker to facilitate in vivo tumor localization, and dynamic fluorescence molecular imaging with PCA could be a valuable tool for future studies of in vivo tumor metabolism and progression.
Byun, Jinyoung; Han, Younghun; Gorlov, Ivan P; Busam, Jonathan A; Seldin, Michael F; Amos, Christopher I
2017-10-16
Accurate inference of genetic ancestry is of fundamental interest to many biomedical, forensic, and anthropological research areas. Genetic ancestry memberships may relate to genetic disease risks. In a genome association study, failing to account for differences in genetic ancestry between cases and controls may also lead to false-positive results. Although a number of strategies for inferring and taking into account the confounding effects of genetic ancestry are available, applying them to large studies (tens thousands samples) is challenging. The goal of this study is to develop an approach for inferring genetic ancestry of samples with unknown ancestry among closely related populations and to provide accurate estimates of ancestry for application to large-scale studies. In this study we developed a novel distance-based approach, Ancestry Inference using Principal component analysis and Spatial analysis (AIPS) that incorporates an Inverse Distance Weighted (IDW) interpolation method from spatial analysis to assign individuals to population memberships. We demonstrate the benefits of AIPS in analyzing population substructure, specifically related to the four most commonly used tools EIGENSTRAT, STRUCTURE, fastSTRUCTURE, and ADMIXTURE using genotype data from various intra-European panels and European-Americans. While the aforementioned commonly used tools performed poorly in inferring ancestry from a large number of subpopulations, AIPS accurately distinguished variations between and within subpopulations. Our results show that AIPS can be applied to large-scale data sets to discriminate the modest variability among intra-continental populations as well as for characterizing inter-continental variation. The method we developed will protect against spurious associations when mapping the genetic basis of a disease. Our approach is more accurate and computationally efficient method for inferring genetic ancestry in the large-scale genetic studies.
Bar-On, L.; Desloovere, K.; Molenaers, G.; Harlaar, J.; Kindt, T.; Aertbelien, E.
2014-01-01
Clinical assessment of spasticity is compromised by the difficulty to distinguish neural from non-neural components of increased joint torque. Quantifying the contributions of each of these components is crucial to optimize the selection of anti-spasticity treatments such as botulinum toxin (BTX).
Roopwani, Rahul; Buckner, Ira S
2011-10-14
Principal component analysis (PCA) was applied to pharmaceutical powder compaction. A solid fraction parameter (SF(c/d)) and a mechanical work parameter (W(c/d)) representing irreversible compression behavior were determined as functions of applied load. Multivariate analysis of the compression data was carried out using PCA. The first principal component (PC1) showed loadings for the solid fraction and work values that agreed with changes in the relative significance of plastic deformation to consolidation at different pressures. The PC1 scores showed the same rank order as the relative plasticity ranking derived from the literature for common pharmaceutical materials. The utility of PC1 in understanding deformation was extended to binary mixtures using a subset of the original materials. Combinations of brittle and plastic materials were characterized using the PCA method. The relationships between PC1 scores and the weight fractions of the mixtures were typically linear showing ideal mixing in their deformation behaviors. The mixture consisting of two plastic materials was the only combination to show a consistent positive deviation from ideality. The application of PCA to solid fraction and mechanical work data appears to be an effective means of predicting deformation behavior during compaction of simple powder mixtures. Copyright © 2011 Elsevier B.V. All rights reserved.
Almeida, Mariana R; Correa, Deleon N; Zacca, Jorge J; Logrado, Lucio Paulo Lima; Poppi, Ronei J
2015-02-20
The aim of this study was to develop a methodology using Raman hyperspectral imaging and chemometric methods for identification of pre- and post-blast explosive residues on banknote surfaces. The explosives studied were of military, commercial and propellant uses. After the acquisition of the hyperspectral imaging, independent component analysis (ICA) was applied to extract the pure spectra and the distribution of the corresponding image constituents. The performance of the methodology was evaluated by the explained variance and the lack of fit of the models, by comparing the ICA recovered spectra with the reference spectra using correlation coefficients and by the presence of rotational ambiguity in the ICA solutions. The methodology was applied to forensic samples to solve an automated teller machine explosion case. Independent component analysis proved to be a suitable method of resolving curves, achieving equivalent performance with the multivariate curve resolution with alternating least squares (MCR-ALS) method. At low concentrations, MCR-ALS presents some limitations, as it did not provide the correct solution. The detection limit of the methodology presented in this study was 50 μg cm(-2). Copyright © 2014 Elsevier B.V. All rights reserved.
Fatigue Analysis of Tubesheet/Shell Juncture Applying the Mitigation Factor for Over-conservatism
International Nuclear Information System (INIS)
Kang, Deog Ji; Kim, Kyu Hyoung; Lee, Jae Gon
2009-01-01
If the environmental fatigue requirements are applied to the primary components of a nuclear power plant, to which the present ASME Code fatigue curves are applied, some locations with high level CUF (Cumulative Usage Factor) are anticipated not to meet the code criteria. The application of environmental fatigue damage is still particularly controversial for plants with 60-year design lives. Therefore, it is need to develop a detailed fatigue analysis procedure to identify the conservatisms in the procedure and to lower the cumulative usage factor. Several factors are being considered to mitigate the conservatism such as three-dimensional finite element modeling. In the present analysis, actual pressure transient data instead of conservative maximum and minimum pressure data was applied as one of mitigation factors. Unlike in the general method, individual transient events were considered instead of the grouped transient events. The tubesheet/shell juncture in the steam generator assembly is the one of the weak locations and was, therefore, selected as a target to evaluate the mitigation factor in the present analysis
A Filtering of Incomplete GNSS Position Time Series with Probabilistic Principal Component Analysis
Gruszczynski, Maciej; Klos, Anna; Bogusz, Janusz
2018-04-01
For the first time, we introduced the probabilistic principal component analysis (pPCA) regarding the spatio-temporal filtering of Global Navigation Satellite System (GNSS) position time series to estimate and remove Common Mode Error (CME) without the interpolation of missing values. We used data from the International GNSS Service (IGS) stations which contributed to the latest International Terrestrial Reference Frame (ITRF2014). The efficiency of the proposed algorithm was tested on the simulated incomplete time series, then CME was estimated for a set of 25 stations located in Central Europe. The newly applied pPCA was compared with previously used algorithms, which showed that this method is capable of resolving the problem of proper spatio-temporal filtering of GNSS time series characterized by different observation time span. We showed, that filtering can be carried out with pPCA method when there exist two time series in the dataset having less than 100 common epoch of observations. The 1st Principal Component (PC) explained more than 36% of the total variance represented by time series residuals' (series with deterministic model removed), what compared to the other PCs variances (less than 8%) means that common signals are significant in GNSS residuals. A clear improvement in the spectral indices of the power-law noise was noticed for the Up component, which is reflected by an average shift towards white noise from - 0.98 to - 0.67 (30%). We observed a significant average reduction in the accuracy of stations' velocity estimated for filtered residuals by 35, 28 and 69% for the North, East, and Up components, respectively. CME series were also subjected to analysis in the context of environmental mass loading influences of the filtering results. Subtraction of the environmental loading models from GNSS residuals provides to reduction of the estimated CME variance by 20 and 65% for horizontal and vertical components, respectively.
Zhang, Qiong; Peng, Cong; Lu, Yiming; Wang, Hao; Zhu, Kaiguang
2018-04-01
A novel technique is developed to level airborne geophysical data using principal component analysis based on flight line difference. In the paper, flight line difference is introduced to enhance the features of levelling error for airborne electromagnetic (AEM) data and improve the correlation between pseudo tie lines. Thus we conduct levelling to the flight line difference data instead of to the original AEM data directly. Pseudo tie lines are selected distributively cross profile direction, avoiding the anomalous regions. Since the levelling errors of selective pseudo tie lines show high correlations, principal component analysis is applied to extract the local levelling errors by low-order principal components reconstruction. Furthermore, we can obtain the levelling errors of original AEM data through inverse difference after spatial interpolation. This levelling method does not need to fly tie lines and design the levelling fitting function. The effectiveness of this method is demonstrated by the levelling results of survey data, comparing with the results from tie-line levelling and flight-line correlation levelling.
Dovbeshko, G. I.; Repnytska, O. P.; Pererva, T.; Miruta, A.; Kosenkov, D.
2004-07-01
Conformation analysis of mutated DNA-bacteriophages (PLys-23, P23-2, P47- the numbers have been assigned by T. Pererva) induced by MS2 virus incorporated in Ecoli AB 259 Hfr 3000 has been done. Surface enhanced infrared absorption (SEIRA) spectroscopy and principal component analysis has been applied for solving this problem. The nucleic acids isolated from the mutated phages had a form of double stranded DNA with different modifications. The nucleic acid from phage P47 was undergone the structural rearrangement in the most degree. The shape and position ofthe fine structure of the Phosphate asymmetrical band at 1071cm-1 as well as the stretching OH vibration at 3370-3390 cm-1 has indicated to the appearance ofadditional OH-groups. The Z-form feature has been found in the base vibration region (1694 cm-1) and the sugar region (932 cm-1). A supposition about modification of structure of DNA by Z-fragments for P47 phage has been proposed. The P23-2 and PLys-23 phages have showed the numerous minor structural changes also. On the basis of SEIRA spectra we have determined the characteristic parameters of the marker bands of nucleic acid used for construction of principal components. Contribution of different spectral parameters of nucleic acids to principal components has been estimated.
Dong, Fengxia; Mitchell, Paul D; Colquhoun, Jed
2015-01-01
Measuring farm sustainability performance is a crucial component for improving agricultural sustainability. While extensive assessments and indicators exist that reflect the different facets of agricultural sustainability, because of the relatively large number of measures and interactions among them, a composite indicator that integrates and aggregates over all variables is particularly useful. This paper describes and empirically evaluates a method for constructing a composite sustainability indicator that individually scores and ranks farm sustainability performance. The method first uses non-negative polychoric principal component analysis to reduce the number of variables, to remove correlation among variables and to transform categorical variables to continuous variables. Next the method applies common-weight data envelope analysis to these principal components to individually score each farm. The method solves weights endogenously and allows identifying important practices in sustainability evaluation. An empirical application to Wisconsin cranberry farms finds heterogeneity in sustainability practice adoption, implying that some farms could adopt relevant practices to improve the overall sustainability performance of the industry. Copyright © 2014 Elsevier Ltd. All rights reserved.
Mapping brain activity in gradient-echo functional MRI using principal component analysis
Khosla, Deepak; Singh, Manbir; Don, Manuel
1997-05-01
The detection of sites of brain activation in functional MRI has been a topic of immense research interest and many technique shave been proposed to this end. Recently, principal component analysis (PCA) has been applied to extract the activated regions and their time course of activation. This method is based on the assumption that the activation is orthogonal to other signal variations such as brain motion, physiological oscillations and other uncorrelated noises. A distinct advantage of this method is that it does not require any knowledge of the time course of the true stimulus paradigm. This technique is well suited to EPI image sequences where the sampling rate is high enough to capture the effects of physiological oscillations. In this work, we propose and apply tow methods that are based on PCA to conventional gradient-echo images and investigate their usefulness as tools to extract reliable information on brain activation. The first method is a conventional technique where a single image sequence with alternating on and off stages is subject to a principal component analysis. The second method is a PCA-based approach called the common spatial factor analysis technique (CSF). As the name suggests, this method relies on common spatial factors between the above fMRI image sequence and a background fMRI. We have applied these methods to identify active brain ares during visual stimulation and motor tasks. The results from these methods are compared to those obtained by using the standard cross-correlation technique. We found good agreement in the areas identified as active across all three techniques. The results suggest that PCA and CSF methods have good potential in detecting the true stimulus correlated changes in the presence of other interfering signals.
International Nuclear Information System (INIS)
Kamijo, Tetsuya; Huang, Guangwei
2017-01-01
The purpose of this study is to show the effectiveness of principle component analysis (PCA) as a method of alternatives analysis useful for improving the discussion of alternatives and public involvement. This study examined public consultations by applying quantitative text analysis (QTA) to the minutes of meetings and showed a positive correlation between the discussion of alternatives and the sense of public involvement. The discussion of alternatives may improve public involvement. A table of multiple criteria analysis for alternatives with detailed scores may exclude the public from involvement due to the general public's limited capacity to understand the mathematical algorithm and to process too much information. PCA allowed for the reduction of multiple criteria down to a small number of uncorrelated variables (principle components), a display of the merits and demerits of the alternatives, and potentially made the identification of preferable alternatives by the stakeholders easier. PCA is likely to enhance the discussion of alternatives and as a result, lead to improved public involvement.
Abstract Interfaces for Data Analysis Component Architecture for Data Analysis Tools
Barrand, G; Dönszelmann, M; Johnson, A; Pfeiffer, A
2001-01-01
The fast turnover of software technologies, in particular in the domain of interactivity (covering user interface and visualisation), makes it difficult for a small group of people to produce complete and polished software-tools before the underlying technologies make them obsolete. At the HepVis '99 workshop, a working group has been formed to improve the production of software tools for data analysis in HENP. Beside promoting a distributed development organisation, one goal of the group is to systematically design a set of abstract interfaces based on using modern OO analysis and OO design techniques. An initial domain analysis has come up with several categories (components) found in typical data analysis tools: Histograms, Ntuples, Functions, Vectors, Fitter, Plotter, Analyzer and Controller. Special emphasis was put on reducing the couplings between the categories to a minimum, thus optimising re-use and maintainability of any component individually. The interfaces have been defined in Java and C++ and i...
Cabrelli, Carlos; Jaffard, Stephane; Molter, Ursula
2016-01-01
This volume is a selection of written notes corresponding to courses taught at the CIMPA School: "New Trends in Applied Harmonic Analysis: Sparse Representations, Compressed Sensing and Multifractal Analysis". New interactions between harmonic analysis and signal and image processing have seen striking development in the last 10 years, and several technological deadlocks have been solved through the resolution of deep theoretical problems in harmonic analysis. New Trends in Applied Harmonic Analysis focuses on two particularly active areas that are representative of such advances: multifractal analysis, and sparse representation and compressed sensing. The contributions are written by leaders in these areas, and covers both theoretical aspects and applications. This work should prove useful not only to PhD students and postdocs in mathematics and signal and image processing, but also to researchers working in related topics.
International Nuclear Information System (INIS)
Zarzo, Manuel; Marti, Pau
2011-01-01
Research highlights: →Principal components analysis was applied to R s data recorded at 30 stations. → Four principal components explain 97% of the data variability. → The latent variables can be fitted according to latitude, longitude and altitude. → The PCA approach is more effective for gap infilling than conventional approaches. → The proposed method allows daily R s estimations at locations in the area of study. - Abstract: Measurements of global terrestrial solar radiation (R s ) are commonly recorded in meteorological stations. Daily variability of R s has to be taken into account for the design of photovoltaic systems and energy efficient buildings. Principal components analysis (PCA) was applied to R s data recorded at 30 stations in the Mediterranean coast of Spain. Due to equipment failures and site operation problems, time series of R s often present data gaps or discontinuities. The PCA approach copes with this problem and allows estimation of present and past values by taking advantage of R s records from nearby stations. The gap infilling performance of this methodology is compared with neural networks and alternative conventional approaches. Four principal components explain 66% of the data variability with respect to the average trajectory (97% if non-centered values are considered). A new method based on principal components regression was also developed for R s estimation if previous measurements are not available. By means of multiple linear regression, it was found that the latent variables associated to the four relevant principal components can be fitted according to the latitude, longitude and altitude of the station where data were recorded from. Additional geographical or climatic variables did not increase the predictive goodness-of-fit. The resulting models allow the estimation of daily R s values at any location in the area under study and present higher accuracy than artificial neural networks and some conventional approaches
Directory of Open Access Journals (Sweden)
Julia Chernova
2016-07-01
Full Text Available Abstract Background Within-person variation in dietary records can lead to biased estimates of the distribution of food intake. Quantile estimation is especially relevant in the case of skewed distributions and in the estimation of under- or over-consumption. The analysis of the intake distributions of occasionally-consumed foods presents further challenges due to the high frequency of zero records. Two-part mixed-effects models account for excess-zeros, daily variation and correlation arising from repeated individual dietary records. In practice, the application of the two-part model with random effects involves Monte Carlo (MC simulations. However, these can be time-consuming and the precision of MC estimates depends on the size of the simulated data which can hinder reproducibility of results. Methods We propose a new approach based on numerical integration as an alternative to MC simulations to estimate the distribution of occasionally-consumed foods in sub-populations. The proposed approach and MC methods are compared by analysing the alcohol intake distribution in a sub-population of individuals at risk of developing metabolic syndrome. Results The rate of convergence of the results of MC simulations to the results of our proposed method is model-specific, depends on the number of draws from the target distribution, and is relatively slower at the tails of the distribution. Our data analyses also show that model misspecification can lead to incorrect model parameter estimates. For example, under the wrong model assumption of zero correlation between the components, one of the predictors turned out as non-significant at 5 % significance level (p-value 0.062 but it was estimated as significant in the correctly specified model (p-value 0.016. Conclusions The proposed approach for the analysis of the intake distributions of occasionally-consumed foods provides a quicker and more precise alternative to MC simulation methods, particularly in the
Directory of Open Access Journals (Sweden)
Stefania Salvatore
2016-07-01
Full Text Available Abstract Background Wastewater-based epidemiology (WBE is a novel approach in drug use epidemiology which aims to monitor the extent of use of various drugs in a community. In this study, we investigate functional principal component analysis (FPCA as a tool for analysing WBE data and compare it to traditional principal component analysis (PCA and to wavelet principal component analysis (WPCA which is more flexible temporally. Methods We analysed temporal wastewater data from 42 European cities collected daily over one week in March 2013. The main temporal features of ecstasy (MDMA were extracted using FPCA using both Fourier and B-spline basis functions with three different smoothing parameters, along with PCA and WPCA with different mother wavelets and shrinkage rules. The stability of FPCA was explored through bootstrapping and analysis of sensitivity to missing data. Results The first three principal components (PCs, functional principal components (FPCs and wavelet principal components (WPCs explained 87.5-99.6 % of the temporal variation between cities, depending on the choice of basis and smoothing. The extracted temporal features from PCA, FPCA and WPCA were consistent. FPCA using Fourier basis and common-optimal smoothing was the most stable and least sensitive to missing data. Conclusion FPCA is a flexible and analytically tractable method for analysing temporal changes in wastewater data, and is robust to missing data. WPCA did not reveal any rapid temporal changes in the data not captured by FPCA. Overall the results suggest FPCA with Fourier basis functions and common-optimal smoothing parameter as the most accurate approach when analysing WBE data.
Applying HAZOP analysis in assessing remote handling compatibility of ITER port plugs
International Nuclear Information System (INIS)
Duisings, L.P.M.; Til, S. van; Magielsen, A.J.; Ronden, D.M.S.; Elzendoorn, B.S.Q.; Heemskerk, C.J.M.
2013-01-01
Highlights: ► We applied HAZOP analysis to assess the criticality of remote handling maintenance activities on port plugs in the ITER Hot Cell facility. ► We identified several weak points in the general upper port plug maintenance concept. ► We made clear recommendations on redesign in port plug design, operational sequence and Hot Cell equipment. ► The use of a HAZOP approach for the ECH UL port can also be applied to ITER port plugs in general. -- Abstract: This paper describes the application of a Hazard and Operability Analysis (HAZOP) methodology in assessing the criticality of remote handling maintenance activities on port plugs in the ITER Hot Cell facility. As part of the ECHUL consortium, the remote handling team at the DIFFER Institute is developing maintenance tools and procedures for critical components of the ECH Upper launcher (UL). Based on NRG's experience with nuclear risk analysis and Hot Cell procedures, early versions of these tool concepts and maintenance procedures were subjected to a HAZOP analysis. The analysis identified several weak points in the general upper port plug maintenance concept and led to clear recommendations on redesigns in port plug design, the operational sequence and ITER Hot Cell equipment. The paper describes the HAZOP methodology and illustrates its application with specific procedures: the Steering Mirror Assembly (SMA) replacement and the exchange of the Mid Shield Optics (MSO) in the ECH UPL. A selection of recommended changes to the launcher design associated with the accessibility, maintainability and manageability of replaceable components are presented
Independent component analysis of edge information for face recognition
Karande, Kailash Jagannath
2013-01-01
The book presents research work on face recognition using edge information as features for face recognition with ICA algorithms. The independent components are extracted from edge information. These independent components are used with classifiers to match the facial images for recognition purpose. In their study, authors have explored Canny and LOG edge detectors as standard edge detection methods. Oriented Laplacian of Gaussian (OLOG) method is explored to extract the edge information with different orientations of Laplacian pyramid. Multiscale wavelet model for edge detection is also propos
Eliminating the Influence of Harmonic Components in Operational Modal Analysis
DEFF Research Database (Denmark)
Jacobsen, Niels-Jørgen; Andersen, Palle; Brincker, Rune
2007-01-01
structures, in contrast, are subject inherently to deterministic forces due to the rotating parts in the machinery. These forces are seen as harmonic components in the responses, and their influence should be eliminated before extracting the modes in their vicinity. This paper describes a new method based...... on the well-known Enhanced Frequency Domain Decomposition (EFDD) technique for eliminating these harmonic components in the modal parameter extraction process. For assessing the quality of the method, various experiments were carried out where the results were compared with those obtained with pure stochastic...
Abstract interfaces for data analysis - component architecture for data analysis tools
International Nuclear Information System (INIS)
Barrand, G.; Binko, P.; Doenszelmann, M.; Pfeiffer, A.; Johnson, A.
2001-01-01
The fast turnover of software technologies, in particular in the domain of interactivity (covering user interface and visualisation), makes it difficult for a small group of people to produce complete and polished software-tools before the underlying technologies make them obsolete. At the HepVis'99 workshop, a working group has been formed to improve the production of software tools for data analysis in HENP. Beside promoting a distributed development organisation, one goal of the group is to systematically design a set of abstract interfaces based on using modern OO analysis and OO design techniques. An initial domain analysis has come up with several categories (components) found in typical data analysis tools: Histograms, Ntuples, Functions, Vectors, Fitter, Plotter, analyzer and Controller. Special emphasis was put on reducing the couplings between the categories to a minimum, thus optimising re-use and maintainability of any component individually. The interfaces have been defined in Java and C++ and implementations exist in the form of libraries and tools using C++ (Anaphe/Lizard, OpenScientist) and Java (Java Analysis Studio). A special implementation aims at accessing the Java libraries (through their Abstract Interfaces) from C++. The authors give an overview of the architecture and design of the various components for data analysis as discussed in AIDA
Current status of neutron activation analysis and applied nuclear chemistry
International Nuclear Information System (INIS)
Lyon, W.S.
1990-01-01
A review of recent scientometric studies of citations and publication data shows the present state of NAA and applied nuclear chemistry as compared to other analytical techniques. (author) 9 refs.; 7 tabs
Design and analysis of automobile components using industrial procedures
Kedar, B.; Ashok, B.; Rastogi, Nisha; Shetty, Siddhanth
2017-11-01
Today’s automobiles depend upon mechanical systems that are crucial for aiding in the movement and safety features of the vehicle. Various safety systems such as Antilock Braking System (ABS) and passenger restraint systems have been developed to ensure that in the event of a collision be it head on or any other type, the safety of the passenger is ensured. On the other side, manufacturers also want their customers to have a good experience while driving and thus aim to improve the handling and the drivability of the vehicle. Electronics systems such as Cruise Control and active suspension systems are designed to ensure passenger comfort. Finally, to ensure optimum and safe driving the various components of a vehicle must be manufactured using the latest state of the art processes and must be tested and inspected with utmost care so that any defective component can be prevented from being sent out right at the beginning of the supply chain. Therefore, processes which can improve the lifetime of their respective components are in high demand and much research and development is done on these processes. With a solid base research conducted, these processes can be used in a much more versatile manner for different components, made up of different materials and under different input conditions. This will help increase the profitability of the process and also upgrade its value to the industry.
Analysis of soft rock mineral components and roadway failure mechanism
Institute of Scientific and Technical Information of China (English)
陈杰
2001-01-01
The mineral components and microstructure of soft rock sampled from roadway floor inXiagou pit are determined by X-ray diffraction and scanning electron microscope. Ccmbined withthe test of expansion and water softening property of the soft rock, the roadway failure mechanism is analyzed, and the reasonable repair supporting principle of roadway is put forward.
Analysis Of The Executive Components Of The Farmer Field School ...
African Journals Online (AJOL)
The purpose of this study was to investigate the executive components of the Farmer Field School (FFS) project in Uromieh county of West Azerbaijan Province, Iran. All the members and non-members (as control group) of FFS pilots in Uromieh county (N= 98) were included in the study. Data were collected by use of ...
Principal Components Analysis of Job Burnout and Coping ...
African Journals Online (AJOL)
The key component structure of job burnout were feelings of disgust, insomnia, headaches, weight loss or gain feeling of omniscient, pain of unexplained origin, hopelessness, agitation and workaholics, while the factor structure of coping strategies were development of self realistic picture, retaining hope, asking for help ...
Phenolic components, antioxidant activity, and mineral analysis of ...
African Journals Online (AJOL)
In addition to being consumed as food, caper (Capparis spinosa L.) fruits are also used in folk medicine to treat inflammatory disorders, such as rheumatism. C. spinosa L. is rich in phenolic compounds, making it increasingly popular because of its components' potential benefits to human health. We analyzed a number of ...
DEFF Research Database (Denmark)
Kotwa, Ewelina Katarzyna; Jørgensen, Bo Munk; Brockhoff, Per B.
2013-01-01
In this paper, we introduce a new method, based on spherical principal component analysis (S‐PCA), for the identification of Rayleigh and Raman scatters in fluorescence excitation–emission data. These scatters should be found and eliminated as a prestep before fitting parallel factor analysis...... models to the data, in order to avoid model degeneracies. The work is inspired and based on a previous research, where scatter removal was automatic (based on a robust version of PCA called ROBPCA) and required no visual data inspection but appeared to be computationally intensive. To overcome...... this drawback, we implement the fast S‐PCA in the scatter identification routine. Moreover, an additional pattern interpolation step that complements the method, based on robust regression, will be applied. In this way, substantial time savings are gained, and the user's engagement is restricted to a minimum...
Ferrero, A; Campos, J; Rabal, A M; Pons, A; Hernanz, M L; Corróns, A
2011-09-26
The Bidirectional Reflectance Distribution Function (BRDF) is essential to characterize an object's reflectance properties. This function depends both on the various illumination-observation geometries as well as on the wavelength. As a result, the comprehensive interpretation of the data becomes rather complex. In this work we assess the use of the multivariable analysis technique of Principal Components Analysis (PCA) applied to the experimental BRDF data of a ceramic colour standard. It will be shown that the result may be linked to the various reflection processes occurring on the surface, assuming that the incoming spectral distribution is affected by each one of these processes in a specific manner. Moreover, this procedure facilitates the task of interpolating a series of BRDF measurements obtained for a particular sample. © 2011 Optical Society of America
Applying reliability analysis to design electric power systems for More-electric aircraft
Zhang, Baozhu
The More-Electric Aircraft (MEA) is a type of aircraft that replaces conventional hydraulic and pneumatic systems with electrically powered components. These changes have significantly challenged the aircraft electric power system design. This thesis investigates how reliability analysis can be applied to automatically generate system topologies for the MEA electric power system. We first use a traditional method of reliability block diagrams to analyze the reliability level on different system topologies. We next propose a new methodology in which system topologies, constrained by a set reliability level, are automatically generated. The path-set method is used for analysis. Finally, we interface these sets of system topologies with control synthesis tools to automatically create correct-by-construction control logic for the electric power system.
International Nuclear Information System (INIS)
Pirkle, F.L.; Stablein, N.K.; Howell, J.A.; Wecksung, G.W.; Duran, B.S.
1982-11-01
One objective of the aerial radiometric surveys flown as part of the US Department of Energy's National Uranium Resource Evaluation (NURE) program was to ascertain the regional distribution of near-surface radioelement abundances. Some method for identifying groups of observations with similar radioelement values was therefore required. It is shown in this report that cluster analysis can identify such groups even when no a priori knowledge of the geology of an area exists. A method of convergent k-means cluster analysis coupled with a hierarchical cluster analysis is used to classify 6991 observations (three radiometric variables at each observation location) from the Precambrian rocks of the Copper Mountain, Wyoming, area. Another method, one that combines a principal components analysis with a convergent k-means analysis, is applied to the same data. These two methods are compared with a convergent k-means analysis that utilizes available geologic knowledge. All three methods identify four clusters. Three of the clusters represent background values for the Precambrian rocks of the area, and one represents outliers (anomalously high 214 Bi). A segmentation of the data corresponding to geologic reality as discovered by other methods has been achieved based solely on analysis of aerial radiometric data. The techniques employed are composites of classical clustering methods designed to handle the special problems presented by large data sets. 20 figures, 7 tables
International Nuclear Information System (INIS)
Gentillon, C.D.; Meachum, T.R.; Brady, B.M.
1987-01-01
The goal of the trend and pattern analysis of MFW (main feedwater) component failure data is to identify component attributes that are associated with relatively high incidences of failure. Manufacturer, valve type, and pump rotational speed are examples of component attributes under study; in addition, the pattern of failures among NPP units is studied. A series of statistical methods is applied to identify trends and patterns in failures and trends in occurrences in time with regard to these component attributes or variables. This process is followed by an engineering evaluation of the statistical results. In the remainder of this paper, the characteristics of the NPRDS that facilitate its use in reliability and risk studies are highlighted, the analysis methods are briefly described, and the lessons learned thus far for improving MFW system availability and reliability are summarized (orig./GL)
Directory of Open Access Journals (Sweden)
Yixuan Ku
2007-08-01
Full Text Available Our previous studies on scalp-recorded event-related potentials (ERPs showed that somatosensory N140 evoked by a tactile vibration in working memory tasks was enhanced when human subjects expected a coming visual stimulus that had been paired with the tactile stimulus. The results suggested that such enhancement represented the cortical activities involved in tactile-visual crossmodal association. In the present study, we further hypothesized that the enhancement represented the neural activities in somatosensory and frontal cortices in the crossmodal association. By applying independent component analysis (ICA to the ERP data, we found independent components (ICs located in the medial prefrontal cortex (around the anterior cingulate cortex, ACC and the primary somatosensory cortex (SI. The activity represented by the IC in SI cortex showed enhancement in expectation of the visual stimulus. Such differential activity thus suggested the participation of SI cortex in the task-related crossmodal association. Further, the coherence analysis and the Granger causality spectral analysis of the ICs showed that SI cortex appeared to cooperate with ACC in attention and perception of the tactile stimulus in crossmodal association. The results of our study support with new evidence an important idea in cortical neurophysiology: higher cognitive operations develop from the modality-specific sensory cortices (in the present study, SI cortex that are involved in sensation and perception of various stimuli.
Principal component analysis of solar flares in the soft X-ray flux
International Nuclear Information System (INIS)
Teuber, D.L.; Reichmann, E.J.; Wilson, R.M.; National Aeronautics and Space Administration, Huntsville, AL
1979-01-01
Principal component analysis is a technique for extracting the salient features from a mass of data. It applies, in particular, to the analysis of nonstationary ensembles. Computational schemes for this task require the evaluation of eigenvalues of matrices. We have used EISPACK Matrix Eigen System Routines on an IBM 360-75 to analyze full-disk proportional-counter data from the X-ray event analyzer (X-REA) which was part of the Skylab ATM/S-056 experiment. Empirical orthogonal functions have been derived for events in the soft X-ray spectrum between 2.5 and 20 A during different time frames between June 1973 and January 1974. Results indicate that approximately 90% of the cumulative power of each analyzed flare is contained in the largest eigenvector. The first two largest eigenvectors are sufficient for an empirical curve-fit through the raw data and a characterization of solar flares in the soft X-ray flux. Power spectra of the two largest eigenvectors reveal a previously reported periodicity of approximately 5 min. Similar signatures were also obtained from flares that are synchronized on maximum pulse-height when subjected to a principal component analysis. (orig.)
Analysis and test of insulated components for rotary engine
Badgley, Patrick R.; Doup, Douglas; Kamo, Roy
1989-01-01
The direct-injection stratified-charge (DISC) rotary engine, while attractive for aviation applications due to its light weight, multifuel capability, and potentially low fuel consumption, has until now required a bulky and heavy liquid-cooling system. NASA-Lewis has undertaken the development of a cooling system-obviating, thermodynamically superior adiabatic rotary engine employing state-of-the-art thermal barrier coatings to thermally insulate engine components. The thermal barrier coating material for the cast aluminum, stainless steel, and ductile cast iron components was plasma-sprayed zirconia. DISC engine tests indicate effective thermal barrier-based heat loss reduction, but call for superior coefficient-of-thermal-expansion matching of materials and better tribological properties in the coatings used.
COMPONENTS OF THE UNEMPLOYMENT ANALYSIS IN CONTEMPORARY ECONOMIES
Directory of Open Access Journals (Sweden)
Ion Enea-SMARANDACHE
2010-03-01
Full Text Available The unemployment is a permanent phenomenon in majority countries of the world, either with advanced economies, either in course of developed economies, and the implications and the consequences are more complexes, so that, practically, the fight with unemployment becomes a fundamental objective for the economy politics. In context, the authors proposed to set apart essentially components for unemployment analyse with the scope of identification the measures and the instruments of counteracted.
Analysis of Femtosecond Timing Noise and Stability in Microwave Components
International Nuclear Information System (INIS)
2011-01-01
To probe chemical dynamics, X-ray pump-probe experiments trigger a change in a sample with an optical laser pulse, followed by an X-ray probe. At the Linac Coherent Light Source, LCLS, timing differences between the optical pulse and x-ray probe have been observed with an accuracy as low as 50 femtoseconds. This sets a lower bound on the number of frames one can arrange over a time scale to recreate a 'movie' of the chemical reaction. The timing system is based on phase measurements from signals corresponding to the two laser pulses; these measurements are done by using a double-balanced mixer for detection. To increase the accuracy of the system, this paper studies parameters affecting phase detection systems based on mixers, such as signal input power, noise levels, temperature drift, and the effect these parameters have on components such as the mixers, splitters, amplifiers, and phase shifters. Noise data taken with a spectrum analyzer show that splitters based on ferrite cores perform with less noise than strip-line splitters. The data also shows that noise in specific mixers does not correspond with the changes in sensitivity per input power level. Temperature drift is seen to exist on a scale between 1 and 27 fs/ o C for all of the components tested. Results show that any components using more metallic conductor tend to exhibit more noise as well as more temperature drift. The scale of these effects is large enough that specific care should be given when choosing components and designing the housing of high precision microwave mixing systems for use in detection systems such as the LCLS. With these improvements, the timing accuracy can be improved to lower than currently possible.
Analysis of the Components of Economic Potential of Agricultural Enterprises
Vyacheslav Skobara; Volodymyr Podkopaev
2014-01-01
Problems of efficiency of enterprises are increasingly associated with the use of the economic potential of the company. This article addresses the structural components of the economic potential of agricultural enterprise, development and substantiation of the model of economic potential with due account of the peculiarities of agricultural production. Based on the study of various approaches to the potential structure established is the definition of of production, labour, financial and man...
Energy Technology Data Exchange (ETDEWEB)
Zhang, C. [School of Materials Science and Engineering, Northwestern Polytechnical University, Xi’an 710072 (China); Laboratoire de Mecanique des Contacts et des Structures (LaMCoS), INSA Lyon, 20 Avenue des Sciences, F-69621 Villeurbanne Cedex (France); Li, H. [School of Materials Science and Engineering, Northwestern Polytechnical University, Xi’an 710072 (China); Li, M.Q., E-mail: zc9997242256@126.com [School of Materials Science and Engineering, Northwestern Polytechnical University, Xi’an 710072 (China)
2016-05-15
Graphical abstract: This study focused on the detailed analysis of surface asperity deformation mechanism in diffusion bonding of steel hollow structural component. A special surface with regular patterns was processed to be joined so as to observe the extent of surface asperity deformation under different applied bonding pressures. Fracture surface characteristic combined with surface roughness profiles distinctly revealed the enhanced surface asperity deformation as the applied pressure increases. The influence of surface asperity deformation mechanism on joint formation was analyzed: (a) surface asperity deformation not only directly expanded the interfacial contact areas, but also released deformation heat and caused defects, indirectly accelerating atomic diffusion, then benefits to void shrinkage; (b) surface asperity deformation readily introduced stored energy difference between two opposite sides of interface grain boundary, resulting in strain induced interface grain boundary migration. In addition, the influence of void on interface grain boundary migration was analyzed in detail. - Highlights: • A high quality hollow structural component has been fabricated by diffusion bonding. • Surface asperity deformation not only expands the interfacial contact areas, but also causes deformation heat and defects to improve the atomic diffusion. • Surface asperity deformation introduces the stored energy difference between the two opposite sides of interface grain boundary, leading to strain induced interface grain boundary migration. • The void exerts a dragging force on the interface grain boundary to retard or stop interface grain boundary migration. - Abstract: This study focused on the detailed analysis of surface asperity deformation mechanism in similar diffusion bonding as well as on the fabrication of high quality martensitic stainless steel hollow structural components. A special surface with regular patterns was processed to be joined so as to
RISK ANALYSIS APPLIED IN OIL EXPLORATION AND PRODUCTION
African Journals Online (AJOL)
ES Obe
aDepartment of Civil Engineering, University of Nigeria, Nsukka, Enugu State, Nigeria. ... The analysis in this work is ... risk analysis, oil field, risk management, projects, investment opportunity. 1. .... own merit but since the company has limited.
Empirical modeling and data analysis for engineers and applied scientists
Pardo, Scott A
2016-01-01
This textbook teaches advanced undergraduate and first-year graduate students in Engineering and Applied Sciences to gather and analyze empirical observations (data) in order to aid in making design decisions. While science is about discovery, the primary paradigm of engineering and "applied science" is design. Scientists are in the discovery business and want, in general, to understand the natural world rather than to alter it. In contrast, engineers and applied scientists design products, processes, and solutions to problems. That said, statistics, as a discipline, is mostly oriented toward the discovery paradigm. Young engineers come out of their degree programs having taken courses such as "Statistics for Engineers and Scientists" without any clear idea as to how they can use statistical methods to help them design products or processes. Many seem to think that statistics is only useful for demonstrating that a device or process actually does what it was designed to do. Statistics courses emphasize creati...
Applying thematic analysis theory to practice: a researcher's experience.
Tuckett, Anthony G
2005-01-01
This article describes an experience of thematic analysis. In order to answer the question 'What does analysis look like in practice?' it describes in brief how the methodology of grounded theory, the epistemology of social constructionism, and the theoretical stance of symbolic interactionism inform analysis. Additionally, analysis is examined by evidencing the systematic processes--here termed organising, coding, writing, theorising, and reading--that led the researcher to develop a final thematic schema.
Risk-informed importance analysis of in-service testing components for Ulchin units 3 and 4
International Nuclear Information System (INIS)
Kang, D. I.; Kim, K. Y.; Ha, J. J.
2001-01-01
In this paper, we perform risk-informed importance analysis of in-service tesing (IST) components for Ulchin Units 3 and 4. The importance analysis using PSA is performed through Level 1 internal and external, shutdown/low power operation, and Level 2 internal PSA. The sensitivity analysis is also performed. For the components not modeled in PSA logic, we develop and apply a new integrated importance analysis method. The importance analysis results for IST valves show that 167 (26.55%) of 629 IST valves are HSSCs and 462 (73.45%) are LSSCs. The importance analysis results for IST pumps show that 28 (70%) of 40 IST pumps are HSSCs and 12 (30%) are KSSCs
Directory of Open Access Journals (Sweden)
V. I. Freyman
2015-11-01
Full Text Available Subject of Research.Representation features of education results for competence-based educational programs are analyzed. Solution importance of decoding and proficiency estimation for elements and components of discipline parts of competences is shown. The purpose and objectives of research are formulated. Methods. The paper deals with methods of mathematical logic, Boolean algebra, and parametrical analysis of complex diagnostic test results, that controls proficiency of some discipline competence elements. Results. The method of logical conditions analysis is created. It will give the possibility to formulate logical conditions for proficiency determination of each discipline competence element, controlled by complex diagnostic test. Normalized test result is divided into noncrossing zones; a logical condition about controlled elements proficiency is formulated for each of them. Summarized characteristics for test result zones are imposed. An example of logical conditions forming for diagnostic test with preset features is provided. Practical Relevance. The proposed method of logical conditions analysis is applied in the decoding algorithm of proficiency test diagnosis for discipline competence elements. It will give the possibility to automate the search procedure for elements with insufficient proficiency, and is also usable for estimation of education results of a discipline or a component of competence-based educational program.
Finger crease pattern recognition using Legendre moments and principal component analysis
Luo, Rongfang; Lin, Tusheng
2007-03-01
The finger joint lines defined as finger creases and its distribution can identify a person. In this paper, we propose a new finger crease pattern recognition method based on Legendre moments and principal component analysis (PCA). After obtaining the region of interest (ROI) for each finger image in the pre-processing stage, Legendre moments under Radon transform are applied to construct a moment feature matrix from the ROI, which greatly decreases the dimensionality of ROI and can represent principal components of the finger creases quite well. Then, an approach to finger crease pattern recognition is designed based on Karhunen-Loeve (K-L) transform. The method applies PCA to a moment feature matrix rather than the original image matrix to achieve the feature vector. The proposed method has been tested on a database of 824 images from 103 individuals using the nearest neighbor classifier. The accuracy up to 98.584% has been obtained when using 4 samples per class for training. The experimental results demonstrate that our proposed approach is feasible and effective in biometrics.
International Nuclear Information System (INIS)
Chen, S.; Liu, H.-L.; Yang Yihong; Hsu, Y.-Y.; Chuang, K.-S.
2006-01-01
Quantification of cerebral blood flow (CBF) with dynamic susceptibility contrast (DSC) magnetic resonance imaging (MRI) requires the determination of the arterial input function (AIF). The segmentation of surrounding tissue by manual selection is error-prone due to the partial volume artifacts. Independent component analysis (ICA) has the advantage in automatically decomposing the signals into interpretable components. Recently group ICA technique has been applied to fMRI study and showed reduced variance caused by motion artifact and noise. In this work, we investigated the feasibility and efficacy of the use of group ICA technique to extract the AIF. Both simulated and in vivo data were analyzed in this study. The simulation data of eight phantoms were generated using randomized lesion locations and time activity curves. The clinical data were obtained from spin-echo EPI MR scans performed in seven normal subjects. Group ICA technique was applied to analyze data through concatenating across seven subjects. The AIFs were calculated from the weighted average of the signals in the region selected by ICA. Preliminary results of this study showed that group ICA technique could not extract accurate AIF information from regions around the vessel. The mismatched location of vessels within the group reduced the benefits of group study
Structured Sparse Principal Components Analysis With the TV-Elastic Net Penalty.
de Pierrefeu, Amicie; Lofstedt, Tommy; Hadj-Selem, Fouad; Dubois, Mathieu; Jardri, Renaud; Fovet, Thomas; Ciuciu, Philippe; Frouin, Vincent; Duchesnay, Edouard
2018-02-01
Principal component analysis (PCA) is an exploratory tool widely used in data analysis to uncover the dominant patterns of variability within a population. Despite its ability to represent a data set in a low-dimensional space, PCA's interpretability remains limited. Indeed, the components produced by PCA are often noisy or exhibit no visually meaningful patterns. Furthermore, the fact that the components are usually non-sparse may also impede interpretation, unless arbitrary thresholding is applied. However, in neuroimaging, it is essential to uncover clinically interpretable phenotypic markers that would account for the main variability in the brain images of a population. Recently, some alternatives to the standard PCA approach, such as sparse PCA (SPCA), have been proposed, their aim being to limit the density of the components. Nonetheless, sparsity alone does not entirely solve the interpretability problem in neuroimaging, since it may yield scattered and unstable components. We hypothesized that the incorporation of prior information regarding the structure of the data may lead to improved relevance and interpretability of brain patterns. We therefore present a simple extension of the popular PCA framework that adds structured sparsity penalties on the loading vectors in order to identify the few stable regions in the brain images that capture most of the variability. Such structured sparsity can be obtained by combining, e.g., and total variation (TV) penalties, where the TV regularization encodes information on the underlying structure of the data. This paper presents the structured SPCA (denoted SPCA-TV) optimization framework and its resolution. We demonstrate SPCA-TV's effectiveness and versatility on three different data sets. It can be applied to any kind of structured data, such as, e.g., -dimensional array images or meshes of cortical surfaces. The gains of SPCA-TV over unstructured approaches (such as SPCA and ElasticNet PCA) or structured approach
Measurement uncertainty analysis techniques applied to PV performance measurements
International Nuclear Information System (INIS)
Wells, C.
1992-10-01
The purpose of this presentation is to provide a brief introduction to measurement uncertainty analysis, outline how it is done, and illustrate uncertainty analysis with examples drawn from the PV field, with particular emphasis toward its use in PV performance measurements. The uncertainty information we know and state concerning a PV performance measurement or a module test result determines, to a significant extent, the value and quality of that result. What is measurement uncertainty analysis? It is an outgrowth of what has commonly been called error analysis. But uncertainty analysis, a more recent development, gives greater insight into measurement processes and tests, experiments, or calibration results. Uncertainty analysis gives us an estimate of the I interval about a measured value or an experiment's final result within which we believe the true value of that quantity will lie. Why should we take the time to perform an uncertainty analysis? A rigorous measurement uncertainty analysis: Increases the credibility and value of research results; allows comparisons of results from different labs; helps improve experiment design and identifies where changes are needed to achieve stated objectives (through use of the pre-test analysis); plays a significant role in validating measurements and experimental results, and in demonstrating (through the post-test analysis) that valid data have been acquired; reduces the risk of making erroneous decisions; demonstrates quality assurance and quality control measures have been accomplished; define Valid Data as data having known and documented paths of: Origin, including theory; measurements; traceability to measurement standards; computations; uncertainty analysis of results
Król, Małgorzata; Karoly, Agnes; Kościelniak, Paweł
2014-09-01
Forensic laboratories are increasingly engaged in the examination of fraudulent documents, and what is important, in many cases these are inkjet-printed documents. That is why systematic approaches to inkjet printer inks comparison and identification have been carried out by both non-destructive and destructive methods. In this study, micro-Raman spectroscopy and capillary electrophoresis (CE) were applied to the analysis of colour inkjet printer inks. Micro-Raman spectroscopy was used to study the chemical composition of colour inks in situ on a paper surface. It helps to characterize and differentiate inkjet inks, and can be used to create a spectra database of inks taken from different cartridge brands and cartridge numbers. Capillary electrophoresis in micellar electrophoretic capillary chromatography mode was applied to separate colour and colourless components of inks, enabling group identification of those components which occur in a sufficient concentration (giving intensive peaks). Finally, on the basis of the obtained results, differentiation of the analysed inks was performed. Twenty-three samples of inkjet printer inks were examined and the discriminating power (DP) values for both presented methods were established in the routine work of experts during the result interpretation step. DP was found to be 94.0% (Raman) and 95.6% (CE) when all the analysed ink samples were taken into account, and it was 96.7% (Raman) and 98.4% (CE), when only cartridges with different index numbers were considered. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.
An applied general equilibrium model for Dutch agribusiness policy analysis
Peerlings, J.
1993-01-01
The purpose of this thesis was to develop a basic static applied general equilibrium (AGE) model to analyse the effects of agricultural policy changes on Dutch agribusiness. In particular the effects on inter-industry transactions, factor demand, income, and trade are of
Towards the generation of a parametric foot model using principal component analysis: A pilot study.
Scarton, Alessandra; Sawacha, Zimi; Cobelli, Claudio; Li, Xinshan
2016-06-01
There have been many recent developments in patient-specific models with their potential to provide more information on the human pathophysiology and the increase in computational power. However they are not yet successfully applied in a clinical setting. One of the main challenges is the time required for mesh creation, which is difficult to automate. The development of parametric models by means of the Principle Component Analysis (PCA) represents an appealing solution. In this study PCA has been applied to the feet of a small cohort of diabetic and healthy subjects, in order to evaluate the possibility of developing parametric foot models, and to use them to identify variations and similarities between the two populations. Both the skin and the first metatarsal bones have been examined. Besides the reduced sample of subjects considered in the analysis, results demonstrated that the method adopted herein constitutes a first step towards the realization of a parametric foot models for biomechanical analysis. Furthermore the study showed that the methodology can successfully describe features in the foot, and evaluate differences in the shape of healthy and diabetic subjects. Copyright © 2016 IPEM. Published by Elsevier Ltd. All rights reserved.
Probabilistic structural analysis of aerospace components using NESSUS
Shiao, Michael C.; Nagpal, Vinod K.; Chamis, Christos C.
1988-01-01
Probabilistic structural analysis of a Space Shuttle main engine turbopump blade is conducted using the computer code NESSUS (numerical evaluation of stochastic structures under stress). The goal of the analysis is to derive probabilistic characteristics of blade response given probabilistic descriptions of uncertainties in blade geometry, material properties, and temperature and pressure distributions. Probability densities are derived for critical blade responses. Risk assessment and failure life analysis is conducted assuming different failure models.
Diao, K; Farmani, R; Fu, G; Astaraie-Imani, M; Ward, S; Butler, D
2014-01-01
Large water distribution systems (WDSs) are networks with both topological and behavioural complexity. Thereby, it is usually difficult to identify the key features of the properties of the system, and subsequently all the critical components within the system for a given purpose of design or control. One way is, however, to more explicitly visualize the network structure and interactions between components by dividing a WDS into a number of clusters (subsystems). Accordingly, this paper introduces a clustering strategy that decomposes WDSs into clusters with stronger internal connections than external connections. The detected cluster layout is very similar to the community structure of the served urban area. As WDSs may expand along with urban development in a community-by-community manner, the correspondingly formed distribution clusters may reveal some crucial configurations of WDSs. For verification, the method is applied to identify all the critical links during firefighting for the vulnerability analysis of a real-world WDS. Moreover, both the most critical pipes and clusters are addressed, given the consequences of pipe failure. Compared with the enumeration method, the method used in this study identifies the same group of the most critical components, and provides similar criticality prioritizations of them in a more computationally efficient time.
Dynamic of consumer groups and response of commodity markets by principal component analysis
Nobi, Ashadun; Alam, Shafiqul; Lee, Jae Woo
2017-09-01
This study investigates financial states and group dynamics by applying principal component analysis to the cross-correlation coefficients of the daily returns of commodity futures. The eigenvalues of the cross-correlation matrix in the 6-month timeframe displays similar values during 2010-2011, but decline following 2012. A sharp drop in eigenvalue implies the significant change of the market state. Three commodity sectors, energy, metals and agriculture, are projected into two dimensional spaces consisting of two principal components (PC). We observe that they form three distinct clusters in relation to various sectors. However, commodities with distinct features have intermingled with one another and scattered during severe crises, such as the European sovereign debt crises. We observe the notable change of the position of two dimensional spaces of groups during financial crises. By considering the first principal component (PC1) within the 6-month moving timeframe, we observe that commodities of the same group change states in a similar pattern, and the change of states of one group can be used as a warning for other group.
The baking analysis for vacuum vessel and plasma facing components of the KSTAR tokamak
International Nuclear Information System (INIS)
Lee, K. H.; Woo, H. K.; Im, K. H.; Cho, S. Y.; Kim, J. B.
2000-01-01
The base pressure of vacuum vessel of the KSTAR (Korea Superconducting Tokamak Advanced Research) Tokamak is to be a ultra high vacuum, 10 -6 ∼10 -7 Pa, to produce clean plasma with low impurity containments. For this purpose, the KSTAR vacuum vessel and plasma facing components need to be baked up to at least 250 .deg. C, 350 .deg. C respectively, within 24 hours by hot nitrogen gas from a separate baking/cooling line system to remove impurities from the plasma-material interaction surfaces before plasma operation. Here by applying the implicit numerical method to the heat balance equations of the system, overall temperature distributions of the KSTAR vacuum vessel and plasma facing components are obtained during the whole baking process. The model for 2-dimensional baking analysis are segmented into 9 imaginary sectors corresponding to each plasma facing component and has up-down symmetry. Under the resulting combined loads including dead weight, baking gas pressure, vacuum pressure and thermal loads, thermal stresses in the vacuum vessel during bakeout are calculated by using the ANSYS code. It is found that the vacuum vessel and its supports are structurally rigid based on the thermal stress analyses
The baking analysis for vacuum vessel and plasma facing components of the KSTAR tokamak
Energy Technology Data Exchange (ETDEWEB)
Lee, K.H. [Chungnam National University Graduate School, Taejeon (Korea); Im, K.H.; Cho, S.Y. [Korea Basic Science Institute, Taejeon (Korea); Kim, J.B. [Hyundai Heavy Industries Co., Ltd. (Korea); Woo, H.K. [Chungnam National University, Taejeon (Korea)
2000-11-01
The base pressure of vacuum vessel of the KSTAR (Korea Superconducting Tokamak Advanced Research) Tokamak is to be a ultra high vacuum, 10{sup -6} {approx} 10{sup -7} Pa, to produce clean plasma with low impurity containments. for this purpose, the KSTAR vacuum vessel and plasma facing components need to be baked up to at least 250 deg.C, 350 deg.C respectively, within 24 hours by hot nitrogen gas from a separate baking/cooling line system to remove impurities from the plasma-material interaction surfaces before plasma operation. Here by applying the implicit numerical method to the heat balance equations of the system, overall temperature distributions of the KSTAR vacuum vessel and plasma facing components are obtained during the whole baking process. The model for 2-dimensional baking analysis are segmented into 9 imaginary sectors corresponding to each plasma facing component and has up-down symmetry. Under the resulting combined loads including dead weight, baking gas pressure, vacuum pressure and thermal loads, thermal stresses in the vacuum vessel during bakeout are calculated by using the ANSYS code. It is found that the vacuum vessel and its supports are structurally rigid based on the thermal stress analyses. (author). 9 refs., 11 figs., 1 tab.
The baking analysis for vacuum vessel and plasma facing components of the KSTAR tokamak
Energy Technology Data Exchange (ETDEWEB)
Lee, K. H.; Woo, H. K. [Chungnam National Univ., Taejon (Korea, Republic of); Im, K. H.; Cho, S. Y. [korea Basic Science Institute, Taejon (Korea, Republic of); Kim, J. B. [Hyundai Heavy Industries Co., Ltd., Ulsan (Korea, Republic of)
2000-07-01
The base pressure of vacuum vessel of the KSTAR (Korea Superconducting Tokamak Advanced Research) Tokamak is to be a ultra high vacuum, 10{sup -6}{approx}10{sup -7}Pa, to produce clean plasma with low impurity containments. For this purpose, the KSTAR vacuum vessel and plasma facing components need to be baked up to at least 250 .deg. C, 350 .deg. C respectively, within 24 hours by hot nitrogen gas from a separate baking/cooling line system to remove impurities from the plasma-material interaction surfaces before plasma operation. Here by applying the implicit numerical method to the heat balance equations of the system, overall temperature distributions of the KSTAR vacuum vessel and plasma facing components are obtained during the whole baking process. The model for 2-dimensional baking analysis are segmented into 9 imaginary sectors corresponding to each plasma facing component and has up-down symmetry. Under the resulting combined loads including dead weight, baking gas pressure, vacuum pressure and thermal loads, thermal stresses in the vacuum vessel during bakeout are calculated by using the ANSYS code. It is found that the vacuum vessel and its supports are structurally rigid based on the thermal stress analyses.
International Nuclear Information System (INIS)
Huseby, Arne B.; Natvig, Bent
2013-01-01
Discrete event models are frequently used in simulation studies to model and analyze pure jump processes. A discrete event model can be viewed as a system consisting of a collection of stochastic processes, where the states of the individual processes change as results of various kinds of events occurring at random points of time. We always assume that each event only affects one of the processes. Between these events the states of the processes are considered to be constant. In the present paper we use discrete event simulation in order to analyze a multistate network flow system of repairable components. In order to study how the different components contribute to the system, it is necessary to describe the often complicated interaction between component processes and processes at the system level. While analytical considerations may throw some light on this, a simulation study often allows the analyst to explore more details. By producing stable curve estimates for the development of the various processes, one gets a much better insight in how such systems develop over time. These methods are particulary useful in the study of advanced importancez measures of repairable components. Such measures can be very complicated, and thus impossible to calculate analytically. By using discrete event simulations, however, this can be done in a very natural and intuitive way. In particular significant differences between the Barlow–Proschan measure and the Natvig measure in multistate network flow systems can be explored
Gould, Laura Feagans; Mendelson, Tamar; Dariotis, Jacinda K.; Ancona, Matthew; Smith, Ali S. R.; Gonzalez, Andres A.; Smith, Atman A.; Greenberg, Mark T.
2014-01-01
In the past years, the number of mindfulness-based intervention and prevention programs has increased steadily. In order to achieve the intended program outcomes, program implementers need to understand the essential and indispensable components that define a program's success. This chapter describes the complex process of identifying the core…
IAEA-ASSET's root cause analysis method applied to sodium leakage incident at Monju
International Nuclear Information System (INIS)
Watanabe, Norio; Hirano, Masashi
1997-08-01
The present study applied the ASSET (Analysis and Screening of Safety Events Team) methodology (This method identifies occurrences such as component failures and operator errors, identifies their respective direct/root causes and determines corrective actions.) to the analysis of the sodium leakage incident at Monju, based on the published reports by mainly the Science and Technology Agency, aiming at systematic identification of direct/root causes and corrective actions, and discussed the effectiveness and problems of the ASSET methodology. The results revealed the following seven occurrences and showed the direct/root causes and contributing factors for the individual occurrences: failure of thermometer well tube, delayed reactor manual trip, inadequate continuous monitoring of leakage, misjudgment of leak rate, non-required operator action (turbine trip), retarded emergency sodium drainage, and retarded securing of ventilation system. Most of the occurrences stemmed from deficiencies in emergency operating procedures (EOPs), which were mainly caused by defects in the EOP preparation process and operator training programs. The corrective actions already proposed in the published reports were reviewed, identifying issues to be further studied. Possible corrective actions were discussed for these issues. The present study also demonstrated the effectiveness of the ASSET methodology and pointed out some problems, for example, in delineating causal relations among occurrences, for applying it to the detail and systematic analysis of event direct/root causes and determination of concrete measures. (J.P.N.)
IAEA-ASSET`s root cause analysis method applied to sodium leakage incident at Monju
Energy Technology Data Exchange (ETDEWEB)
Watanabe, Norio; Hirano, Masashi [Japan Atomic Energy Research Inst., Tokai, Ibaraki (Japan). Tokai Research Establishment
1997-08-01
The present study applied the ASSET (Analysis and Screening of Safety Events Team) methodology (This method identifies occurrences such as component failures and operator errors, identifies their respective direct/root causes and determines corrective actions.) to the analysis of the sodium leakage incident at Monju, based on the published reports by mainly the Science and Technology Agency, aiming at systematic identification of direct/root causes and corrective actions, and discussed the effectiveness and problems of the ASSET methodology. The results revealed the following seven occurrences and showed the direct/root causes and contributing factors for the individual occurrences: failure of thermometer well tube, delayed reactor manual trip, inadequate continuous monitoring of leakage, misjudgment of leak rate, non-required operator action (turbine trip), retarded emergency sodium drainage, and retarded securing of ventilation system. Most of the occurrences stemmed from deficiencies in emergency operating procedures (EOPs), which were mainly caused by defects in the EOP preparation process and operator training programs. The corrective actions already proposed in the published reports were reviewed, identifying issues to be further studied. Possible corrective actions were discussed for these issues. The present study also demonstrated the effectiveness of the ASSET methodology and pointed out some problems, for example, in delineating causal relations among occurrences, for applying it to the detail and systematic analysis of event direct/root causes and determination of concrete measures. (J.P.N.)
Seismic fragility analysis of structural components for HFBR facilities
International Nuclear Information System (INIS)
Park, Y.J.; Hofmayer, C.H.
1992-01-01
The paper presents a summary of recently completed seismic fragility analyses of the HFBR facilities. Based on a detailed review of past PRA studies, various refinements were made regarding the strength and ductility evaluation of structural components. Available laboratory test data were analysed to evaluate the formulations used to predict the ultimate strength and deformation capacities of steel, reinforced concrete and masonry structures. The biasness and uncertainties were evaluated within the framework of the fragility evaluation methods widely accepted in the nuclear industry. A few examples of fragility calculations are also included to illustrate the use of the presented formulations
The ethical component of professional competence in nursing: an analysis.
Paganini, Maria Cristina; Yoshikawa Egry, Emiko
2011-07-01
The purpose of this article is to initiate a philosophical discussion about the ethical component of professional competence in nursing from the perspective of Brazilian nurses. Specifically, this article discusses professional competence in nursing practice in the Brazilian health context, based on two different conceptual frameworks. The first framework is derived from the idealistic and traditional approach while the second views professional competence through the lens of historical and dialectical materialism theory. The philosophical analyses show that the idealistic view of professional competence differs greatly from practice. Combining nursing professional competence with philosophical perspectives becomes a challenge when ideals are opposed by the reality and implications of everyday nursing practice.
Energy Technology Data Exchange (ETDEWEB)
Kang, Ho Yang [Korea Research Institute of Standards and Science, Daejeon (Korea, Republic of); Kim, Ki Bok [Chungnam National University, Daejeon (Korea, Republic of)
2003-06-15
In this study, acoustic emission (AE) signals due to surface cracking and moisture movement in the flat-sawn boards of oak (Quercus Variablilis) during drying under the ambient conditions were analyzed and classified using the principal component analysis. The AE signals corresponding to surface cracking showed higher in peak amplitude and peak frequency, and shorter in rise time than those corresponding to moisture movement. To reduce the multicollinearity among AE features and to extract the significant AE parameters, correlation analysis was performed. Over 99% of the variance of AE parameters could be accounted for by the first to the fourth principal components. The classification feasibility and success rate were investigated in terms of two statistical classifiers having six independent variables (AE parameters) and six principal components. As a result, the statistical classifier having AE parameters showed the success rate of 70.0%. The statistical classifier having principal components showed the success rate of 87.5% which was considerably than that of the statistical classifier having AE parameters
International Nuclear Information System (INIS)
Kang, Ho Yang; Kim, Ki Bok
2003-01-01
In this study, acoustic emission (AE) signals due to surface cracking and moisture movement in the flat-sawn boards of oak (Quercus Variablilis) during drying under the ambient conditions were analyzed and classified using the principal component analysis. The AE signals corresponding to surface cracking showed higher in peak amplitude and peak frequency, and shorter in rise time than those corresponding to moisture movement. To reduce the multicollinearity among AE features and to extract the significant AE parameters, correlation analysis was performed. Over 99% of the variance of AE parameters could be accounted for by the first to the fourth principal components. The classification feasibility and success rate were investigated in terms of two statistical classifiers having six independent variables (AE parameters) and six principal components. As a result, the statistical classifier having AE parameters showed the success rate of 70.0%. The statistical classifier having principal components showed the success rate of 87.5% which was considerably than that of the statistical classifier having AE parameters
The Blame Game: Performance Analysis of Speaker Diarization System Components
Huijbregts, M.A.H.; Wooters, Chuck
2007-01-01
In this paper we discuss the performance analysis of a speaker diarization system similar to the system that was submitted by ICSI at the NIST RT06s evaluation benchmark. The analysis that is based on a series of oracle experiments, provides a good understanding of the performance of each system
How Has Applied Behavior Analysis and Behavior Therapy Changed?: An Historical Analysis of Journals
O'Donohue, William; Fryling, Mitch
2007-01-01
Applied behavior analysis and behavior therapy are now nearly a half century old. It is interesting to ask if and how these disciplines have changed over time, particularly regarding some of their key internal controversies (e.g., role of cognitions). We examined the first five years and the 2000-2004 five year period of the "Journal of Applied…
An application of principal component analysis to the clavicle and clavicle fixation devices.
Daruwalla, Zubin J; Courtis, Patrick; Fitzpatrick, Clare; Fitzpatrick, David; Mullett, Hannan
2010-03-26
Principal component analysis (PCA) enables the building of statistical shape models of bones and joints. This has been used in conjunction with computer assisted surgery in the past. However, PCA of the clavicle has not been performed. Using PCA, we present a novel method that examines the major modes of size and three-dimensional shape variation in male and female clavicles and suggests a method of grouping the clavicle into size and shape categories. Twenty-one high-resolution computerized tomography scans of the clavicle were reconstructed and analyzed using a specifically developed statistical software package. After performing statistical shape analysis, PCA was applied to study the factors that account for anatomical variation. The first principal component representing size accounted for 70.5 percent of anatomical variation. The addition of a further three principal components accounted for almost 87 percent. Using statistical shape analysis, clavicles in males have a greater lateral depth and are longer, wider and thicker than in females. However, the sternal angle in females is larger than in males. PCA confirmed these differences between genders but also noted that men exhibit greater variance and classified clavicles into five morphological groups. This unique approach is the first that standardizes a clavicular orientation. It provides information that is useful to both, the biomedical engineer and clinician. Other applications include implant design with regard to modifying current or designing future clavicle fixation devices. Our findings support the need for further development of clavicle fixation devices and the questioning of whether gender-specific devices are necessary.
An application of principal component analysis to the clavicle and clavicle fixation devices
Directory of Open Access Journals (Sweden)
Fitzpatrick David
2010-03-01
Full Text Available Abstract Background Principal component analysis (PCA enables the building of statistical shape models of bones and joints. This has been used in conjunction with computer assisted surgery in the past. However, PCA of the clavicle has not been performed. Using PCA, we present a novel method that examines the major modes of size and three-dimensional shape variation in male and female clavicles and suggests a method of grouping the clavicle into size and shape categories. Materials and methods Twenty-one high-resolution computerized tomography scans of the clavicle were reconstructed and analyzed using a specifically developed statistical software package. After performing statistical shape analysis, PCA was applied to study the factors that account for anatomical variation. Results The first principal component representing size accounted for 70.5 percent of anatomical variation. The addition of a further three principal components accounted for almost 87 percent. Using statistical shape analysis, clavicles in males have a greater lateral depth and are longer, wider and thicker than in females. However, the sternal angle in females is larger than in males. PCA confirmed these differences between genders but also noted that men exhibit greater variance and classified clavicles into five morphological groups. Discussion And Conclusions This unique approach is the first that standardizes a clavicular orientation. It provides information that is useful to both, the biomedical engineer and clinician. Other applications include implant design with regard to modifying current or designing future clavicle fixation devices. Our findings support the need for further development of clavicle fixation devices and the questioning of whether gender-specific devices are necessary.
Measurement uncertainty analysis techniques applied to PV performance measurements
Energy Technology Data Exchange (ETDEWEB)
Wells, C.
1992-10-01
The purpose of this presentation is to provide a brief introduction to measurement uncertainty analysis, outline how it is done, and illustrate uncertainty analysis with examples drawn from the PV field, with particular emphasis toward its use in PV performance measurements. The uncertainty information we know and state concerning a PV performance measurement or a module test result determines, to a significant extent, the value and quality of that result. What is measurement uncertainty analysis It is an outgrowth of what has commonly been called error analysis. But uncertainty analysis, a more recent development, gives greater insight into measurement processes and tests, experiments, or calibration results. Uncertainty analysis gives us an estimate of the I interval about a measured value or an experiment's final result within which we believe the true value of that quantity will lie. Why should we take the time to perform an uncertainty analysis A rigorous measurement uncertainty analysis: Increases the credibility and value of research results; allows comparisons of results from different labs; helps improve experiment design and identifies where changes are needed to achieve stated objectives (through use of the pre-test analysis); plays a significant role in validating measurements and experimental results, and in demonstrating (through the post-test analysis) that valid data have been acquired; reduces the risk of making erroneous decisions; demonstrates quality assurance and quality control measures have been accomplished; define Valid Data as data having known and documented paths of: Origin, including theory; measurements; traceability to measurement standards; computations; uncertainty analysis of results.
Measurement uncertainty analysis techniques applied to PV performance measurements
Energy Technology Data Exchange (ETDEWEB)
Wells, C
1992-10-01
The purpose of this presentation is to provide a brief introduction to measurement uncertainty analysis, outline how it is done, and illustrate uncertainty analysis with examples drawn from the PV field, with particular emphasis toward its use in PV performance measurements. The uncertainty information we know and state concerning a PV performance measurement or a module test result determines, to a significant extent, the value and quality of that result. What is measurement uncertainty analysis? It is an outgrowth of what has commonly been called error analysis. But uncertainty analysis, a more recent development, gives greater insight into measurement processes and tests, experiments, or calibration results. Uncertainty analysis gives us an estimate of the I interval about a measured value or an experiment`s final result within which we believe the true value of that quantity will lie. Why should we take the time to perform an uncertainty analysis? A rigorous measurement uncertainty analysis: Increases the credibility and value of research results; allows comparisons of results from different labs; helps improve experiment design and identifies where changes are needed to achieve stated objectives (through use of the pre-test analysis); plays a significant role in validating measurements and experimental results, and in demonstrating (through the post-test analysis) that valid data have been acquired; reduces the risk of making erroneous decisions; demonstrates quality assurance and quality control measures have been accomplished; define Valid Data as data having known and documented paths of: Origin, including theory; measurements; traceability to measurement standards; computations; uncertainty analysis of results.
Heisenberg principle applied to the analysis of speckle interferometry fringes
Sciammarella, C. A.; Sciammarella, F. M.
2003-11-01
Optical techniques that are used to measure displacements utilize a carrier. When a load is applied the displacement field modulates the carrier. The accuracy of the information that can be recovered from the modulated carrier is limited by a number of factors. In this paper, these factors are analyzed and conclusions concerning the limitations in information recovery are illustrated with examples taken from experimental data.
Concluding Essay: On Applied Linguistics and Discourse Analysis.
Kaplan, Robert B.
1990-01-01
Discusses trends and problems in regarding discourse analysis as a viable paradigm that can govern research, focusing on such issues as the wide diversity and variety of research that can be considered discourse analysis, the predominant focus on English language, research approaches, and undefined variables affecting research outcomes. (seven…
Nesakumar, Noel; Baskar, Chanthini; Kesavan, Srinivasan; Rayappan, John Bosco Balaguru; Alwarappan, Subbiah
2018-05-22
The moisture content of beetroot varies during long-term cold storage. In this work, we propose a strategy to identify the moisture content and age of beetroot using principal component analysis coupled Fourier transform infrared spectroscopy (FTIR). Frequent FTIR measurements were recorded directly from the beetroot sample surface over a period of 34 days for analysing its moisture content employing attenuated total reflectance in the spectral ranges of 2614-4000 and 1465-1853 cm -1 with a spectral resolution of 8 cm -1 . In order to estimate the transmittance peak height (T p ) and area under the transmittance curve [Formula: see text] over the spectral ranges of 2614-4000 and 1465-1853 cm -1 , Gaussian curve fitting algorithm was performed on FTIR data. Principal component and nonlinear regression analyses were utilized for FTIR data analysis. Score plot over the ranges of 2614-4000 and 1465-1853 cm -1 allowed beetroot quality discrimination. Beetroot quality predictive models were developed by employing biphasic dose response function. Validation experiment results confirmed that the accuracy of the beetroot quality predictive model reached 97.5%. This research work proves that FTIR spectroscopy in combination with principal component analysis and beetroot quality predictive models could serve as an effective tool for discriminating moisture content in fresh, half and completely spoiled stages of beetroot samples and for providing status alerts.
Azerbaev, Alexander A.; Abdulkadyrov, Magomed A.; Belousov, Sergey P.; Ignatov, Aleksandr N.; Mukhammedzyanov, Timur R.
2016-10-01
Vacuum system for reflective coatings deposition on large-size optical components up to 4.0 m diameter using the method of magnetron sputtering was built at JSC LZOS. The technological process for deposition of reflective Al coating with protective SiO2 layer was designed and approved. After climatic tests the lifetime of such coating was estimated as 30 years. Uniformity of coating thickness ±5% was achieved on maximum diameter 4.0 m.
Registration of dynamic dopamine D2receptor images using principal component analysis
International Nuclear Information System (INIS)
Acton, P.D.; Ell, P.J.; Pilowsky, L.S.; Brammer, M.J.; Suckling, J.
1997-01-01
This paper describes a novel technique for registering a dynamic sequence of single-photon emission tomography (SPET) dopamine D 2 receptor images, using principal component analysis (PCA). Conventional methods for registering images, such as count difference and correlation coefficient algorithms, fail to take into account the dynamic nature of the data, resulting in large systematic errors when registering time-varying images. However, by using principal component analysis to extract the temporal structure of the image sequence, misregistration can be quantified by examining the distribution of eigenvalues. The registration procedures were tested using a computer-generated dynamic phantom derived from a high-resolution magnetic resonance image of a realistic brain phantom. Each method was also applied to clinical SPET images of dopamine D 2 receptors, using the ligands iodine-123 iodobenzamide and iodine-123 epidepride, to investigate the influence of misregistration on kinetic modelling parameters and the binding potential. The PCA technique gave highly significant (P 123 I-epidepride scans. The PCA method produced data of much greater quality for subsequent kinetic modelling, with an improvement of nearly 50% in the χ 2 of the fit to the compartmental model, and provided superior quality registration of particularly difficult dynamic sequences. (orig.)
Gaudreault, Nathaly; Mezghani, Neila; Turcot, Katia; Hagemeister, Nicola; Boivin, Karine; de Guise, Jacques A
2011-03-01
Interpreting gait data is challenging due to intersubject variability observed in the gait pattern of both normal and pathological populations. The objective of this study was to investigate the impact of using principal component analysis for grouping knee osteoarthritis (OA) patients' gait data in more homogeneous groups when studying the effect of a physiotherapy treatment. Three-dimensional (3D) knee kinematic and kinetic data were recorded during the gait of 29 participants diagnosed with knee OA before and after they received 12 weeks of physiotherapy treatment. Principal component analysis was applied to extract groups of knee flexion/extension, adduction/abduction and internal/external rotation angle and moment data. The treatment's effect on parameters of interest was assessed using paired t-tests performed before and after grouping the knee kinematic data. Increased quadriceps and hamstring strength was observed following treatment (Pphysiotherapy on gait mechanics of knee osteoarthritis patients may be masked or underestimated if kinematic data are not separated into more homogeneous groups when performing pre- and post-treatment comparisons. Copyright © 2010 Elsevier Ltd. All rights reserved.
Dynamic analysis and qualification test of nuclear components
International Nuclear Information System (INIS)
Kim, B.K.; Lee, C.H.; Park, S.H.; Kim, Y.M.; Kim, B.S.; Kim, I.G.; Chung, C.W.; Kim, Y.M.
1981-01-01
This report contains the study on the dynamic characteristics of Wolsung fuel rod and on the dynamic balancing of rotating machinery to evaluate the performance of nuclear reactor components. The study on the dynamic characteristics of Wolsung fuel rod was carried out by both experimental and theoretical methods. Forced vibration testing of actual Wolsung fuel rod using sine sweep and sine dwell excitation was conducted to find the dynamic and nonlinear characteristics of the fuel rod. The data obtained by the test were used to analyze the nonlinear impact characteristics of the fuel rod which has a motion-constraint stop in the center of the rod. The parameters used in the test were the input force level of the exciter, the clearance gap between the fuel rod and the motion constraints, and the frequencies. Test results were in good agreement with the analytical results
Dissolution And Analysis Of Yellowcake Components For Fingerprinting UOC Sources
International Nuclear Information System (INIS)
Hexel, Cole R.; Bostick, Debra A.; Kennedy, Angel K.; Begovich, John M.; Carter, Joel A.
2012-01-01
There are a number of chemical and physical parameters that might be used to help elucidate the ore body from which uranium ore concentrate (UOC) was derived. It is the variation in the concentration and isotopic composition of these components that can provide information as to the identity of the ore body from which the UOC was mined and the type of subsequent processing that has been undertaken. Oak Ridge National Laboratory (ORNL) in collaboration with Lawrence Livermore and Los Alamos National Laboratories is surveying ore characteristics of yellowcake samples from known geologic origin. The data sets are being incorporated into a national database to help in sourcing interdicted material, as well as aid in safeguards and nonproliferation activities. Geologic age and attributes from chemical processing are site-specific. Isotopic abundances of lead, neodymium, and strontium provide insight into the provenance of geologic location of ore material. Variations in lead isotopes are due to the radioactive decay of uranium in the ore. Likewise, neodymium isotopic abundances are skewed due to the radiogenic decay of samarium. Rubidium decay similarly alters the isotopic signature of strontium isotopic composition in ores. This paper will discuss the chemical processing of yellowcake performed at ORNL. Variations in lead, neodymium, and strontium isotopic abundances are being analyzed in UOC from two geologic sources. Chemical separation and instrumental protocols will be summarized. The data will be correlated with chemical signatures (such as elemental composition, uranium, carbon, and nitrogen isotopic content) to demonstrate the utility of principal component and cluster analyses to aid in the determination of UOC provenance.
Liu, Jiangang; Tian, Jie
2007-03-01
The present study combined the Independent Component Analysis (ICA) and low-resolution brain electromagnetic tomography (LORETA) algorithms to identify the spatial distribution and time course of single-trial EEG record differences between neural responses to emotional stimuli vs. the neutral. Single-trial multichannel (129-sensor) EEG records were collected from 21 healthy, right-handed subjects viewing the emotion emotional (pleasant/unpleasant) and neutral pictures selected from International Affective Picture System (IAPS). For each subject, the single-trial EEG records of each emotional pictures were concatenated with the neutral, and a three-step analysis was applied to each of them in the same way. First, the ICA was performed to decompose each concatenated single-trial EEG records into temporally independent and spatially fixed components, namely independent components (ICs). The IC associated with artifacts were isolated. Second, the clustering analysis classified, across subjects, the temporally and spatially similar ICs into the same clusters, in which nonparametric permutation test for Global Field Power (GFP) of IC projection scalp maps identified significantly different temporal segments of each emotional condition vs. neutral. Third, the brain regions accounted for those significant segments were localized spatially with LORETA analysis. In each cluster, a voxel-by-voxel randomization test identified significantly different brain regions between each emotional condition vs. the neutral. Compared to the neutral, both emotional pictures elicited activation in the visual, temporal, ventromedial and dorsomedial prefrontal cortex and anterior cingulated gyrus. In addition, the pleasant pictures activated the left middle prefrontal cortex and the posterior precuneus, while the unpleasant pictures activated the right orbitofrontal cortex, posterior cingulated gyrus and somatosensory region. Our results were well consistent with other functional imaging
Applying causal mediation analysis to personality disorder research.
Walters, Glenn D
2018-01-01
This article is designed to address fundamental issues in the application of causal mediation analysis to research on personality disorders. Causal mediation analysis is used to identify mechanisms of effect by testing variables as putative links between the independent and dependent variables. As such, it would appear to have relevance to personality disorder research. It is argued that proper implementation of causal mediation analysis requires that investigators take several factors into account. These factors are discussed under 5 headings: variable selection, model specification, significance evaluation, effect size estimation, and sensitivity testing. First, care must be taken when selecting the independent, dependent, mediator, and control variables for a mediation analysis. Some variables make better mediators than others and all variables should be based on reasonably reliable indicators. Second, the mediation model needs to be properly specified. This requires that the data for the analysis be prospectively or historically ordered and possess proper causal direction. Third, it is imperative that the significance of the identified pathways be established, preferably with a nonparametric bootstrap resampling approach. Fourth, effect size estimates should be computed or competing pathways compared. Finally, investigators employing the mediation method are advised to perform a sensitivity analysis. Additional topics covered in this article include parallel and serial multiple mediation designs, moderation, and the relationship between mediation and moderation. (PsycINFO Database Record (c) 2018 APA, all rights reserved).
Signed directed social network analysis applied to group conflict
DEFF Research Database (Denmark)
Zheng, Quan; Skillicorn, David; Walther, Olivier
2015-01-01
Real-world social networks contain relationships of multiple different types, but this richness is often ignored in graph-theoretic modelling. We show how two recently developed spectral embedding techniques, for directed graphs (relationships are asymmetric) and for signed graphs (relationships...... are both positive and negative), can be combined. This combination is particularly appropriate for intelligence, terrorism, and law enforcement applications. We illustrate by applying the novel embedding technique to datasets describing conflict in North-West Africa, and show how unusual interactions can...
Applied risk analysis to the future Brazilian electricity generation matrix
Energy Technology Data Exchange (ETDEWEB)
Maues, Jair; Fernandez, Eloi; Correa, Antonio
2010-09-15
This study compares energy conversion systems for the generation of electrical power, with an emphasis on the Brazilian energy matrix. The financial model applied in this comparison is based on the Portfolio Theory, developed by Harry Markowitz. The risk-return ratio related to the electrical generation mix predicted in the National Energy Plan - 2030, published in 2006 by the Brazilian Energy Research Office, is evaluated. The increase of non-traditional renewable energy in this expected electrical generating mix, specifically, residues of sugar cane plantations and wind energy, reduce not only the risk but also the average cost of the kilowatt-hour generated.