BWR Refill-Reflood Program, Task 4.7 - model development: TRAC-BWR component models

International Nuclear Information System (INIS)

Cheung, Y.K.; Parameswaran, V.; Shaug, J.C.

1983-09-01

TRAC (Transient Reactor Analysis Code) is a computer code for best-estimate analysis for the thermal hydraulic conditions in a reactor system. The development and assessment of the BWR component models developed under the Refill/Reflood Program that are necessary to structure a BWR-version of TRAC are described in this report. These component models are the jet pump, steam separator, steam dryer, two-phase level tracking model, and upper-plenum mixing model. These models have been implemented into TRAC-B02. Also a single-channel option has been developed for individual fuel-channel analysis following a system-response calculation

Fault feature extraction method based on local mean decomposition Shannon entropy and improved kernel principal component analysis model

Directory of Open Access Journals (Sweden)

Jinlu Sheng

2016-07-01

Full Text Available To effectively extract the typical features of the bearing, a new method that related the local mean decomposition Shannon entropy and improved kernel principal component analysis model was proposed. First, the features are extracted by time–frequency domain method, local mean decomposition, and using the Shannon entropy to process the original separated product functions, so as to get the original features. However, the features been extracted still contain superfluous information; the nonlinear multi-features process technique, kernel principal component analysis, is introduced to fuse the characters. The kernel principal component analysis is improved by the weight factor. The extracted characteristic features were inputted in the Morlet wavelet kernel support vector machine to get the bearing running state classification model, bearing running state was thereby identified. Cases of test and actual were analyzed.

3-D inelastic analysis methods for hot section components. Volume 2: Advanced special functions models

Science.gov (United States)

Wilson, R. B.; Banerjee, P. K.

1987-01-01

This Annual Status Report presents the results of work performed during the third year of the 3-D Inelastic Analysis Methods for Hot Sections Components program (NASA Contract NAS3-23697). The objective of the program is to produce a series of computer codes that permit more accurate and efficient three-dimensional analyses of selected hot section components, i.e., combustor liners, turbine blades, and turbine vanes. The computer codes embody a progression of mathematical models and are streamlined to take advantage of geometrical features, loading conditions, and forms of material response that distinguish each group of selected components.

Blind Separation of Acoustic Signals Combining SIMO-Model-Based Independent Component Analysis and Binary Masking

Directory of Open Access Journals (Sweden)

Hiekata Takashi

2006-01-01

Full Text Available A new two-stage blind source separation (BSS method for convolutive mixtures of speech is proposed, in which a single-input multiple-output (SIMO-model-based independent component analysis (ICA and a new SIMO-model-based binary masking are combined. SIMO-model-based ICA enables us to separate the mixed signals, not into monaural source signals but into SIMO-model-based signals from independent sources in their original form at the microphones. Thus, the separated signals of SIMO-model-based ICA can maintain the spatial qualities of each sound source. Owing to this attractive property, our novel SIMO-model-based binary masking can be applied to efficiently remove the residual interference components after SIMO-model-based ICA. The experimental results reveal that the separation performance can be considerably improved by the proposed method compared with that achieved by conventional BSS methods. In addition, the real-time implementation of the proposed BSS is illustrated.

Trajectory modeling of gestational weight: A functional principal component analysis approach.

Directory of Open Access Journals (Sweden)

Menglu Che

Full Text Available Suboptimal gestational weight gain (GWG, which is linked to increased risk of adverse outcomes for a pregnant woman and her infant, is prevalent. In the study of a large cohort of Canadian pregnant women, our goals are to estimate the individual weight growth trajectory using sparsely collected bodyweight data, and to identify the factors affecting the weight change during pregnancy, such as prepregnancy body mass index (BMI, dietary intakes and physical activity. The first goal was achieved through functional principal component analysis (FPCA by conditional expectation. For the second goal, we used linear regression with the total weight gain as the response variable. The trajectory modeling through FPCA had a significantly smaller root mean square error (RMSE and improved adaptability than the classic nonlinear mixed-effect models, demonstrating a novel tool that can be used to facilitate real time monitoring and interventions of GWG. Our regression analysis showed that prepregnancy BMI had a high predictive value for the weight changes during pregnancy, which agrees with the published weight gain guideline.

BANK CAPITAL AND MACROECONOMIC SHOCKS: A PRINCIPAL COMPONENTS ANALYSIS AND VECTOR ERROR CORRECTION MODEL

Directory of Open Access Journals (Sweden)

Christian NZENGUE PEGNET

2011-07-01

Full Text Available The recent financial turmoil has clearly highlighted the potential role of financial factors on amplification of macroeconomic developments and stressed the importance of analyzing the relationship between banks’ balance sheets and economic activity. This paper assesses the impact of the bank capital channel in the transmission of schocks in Europe on the basis of bank's balance sheet data. The empirical analysis is carried out through a Principal Component Analysis and in a Vector Error Correction Model.

New approaches to the modelling of multi-component fuel droplet heating and evaporation

KAUST Repository

Sazhin, Sergei S

2015-02-25

The previously suggested quasi-discrete model for heating and evaporation of complex multi-component hydrocarbon fuel droplets is described. The dependence of density, viscosity, heat capacity and thermal conductivity of liquid components on carbon numbers n and temperatures is taken into account. The effects of temperature gradient and quasi-component diffusion inside droplets are taken into account. The analysis is based on the Effective Thermal Conductivity/Effective Diffusivity (ETC/ED) model. This model is applied to the analysis of Diesel and gasoline fuel droplet heating and evaporation. The components with relatively close n are replaced by quasi-components with properties calculated as average properties of the a priori defined groups of actual components. Thus the analysis of the heating and evaporation of droplets consisting of many components is replaced with the analysis of the heating and evaporation of droplets consisting of relatively few quasi-components. It is demonstrated that for Diesel and gasoline fuel droplets the predictions of the model based on five quasi-components are almost indistinguishable from the predictions of the model based on twenty quasi-components for Diesel fuel droplets and are very close to the predictions of the model based on thirteen quasi-components for gasoline fuel droplets. It is recommended that in the cases of both Diesel and gasoline spray combustion modelling, the analysis of droplet heating and evaporation is based on as little as five quasi-components.

Analysis and Modeling of the Galvanic Skin Response Spontaneous Component in the context of Intelligent Biofeedback Systems Development

Science.gov (United States)

Unakafov, A.

2009-01-01

The paper presents an approach to galvanic skin response (GSR) spontaneous component analysis and modeling. In the study a classification of biofeedback training methods is given, importance of intelligent methods development is shown. The INTENS method, which is perspective for intellectualization, is presented. An important problem of biofeedback training method intellectualization - estimation of the GSR spontaneous component - is solved in the main part of the work. Its main characteristics are described; results of GSR spontaneous component modeling are shown. Results of small research of an optimum material for GSR probes are presented.

Principal components analysis in clinical studies.

Science.gov (United States)

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.

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.

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

Seismic assessment and performance of nonstructural components affected by structural modeling

Energy Technology Data Exchange (ETDEWEB)

Hur, Jieun; Althoff, Eric; Sezen, Halil; Denning, Richard; Aldemir, Tunc [Ohio State University, Columbus (United States)

2017-03-15

Seismic probabilistic risk assessment (SPRA) requires a large number of simulations to evaluate the seismic vulnerability of structural and nonstructural components in nuclear power plants. The effect of structural modeling and analysis assumptions on dynamic analysis of 3D and simplified 2D stick models of auxiliary buildings and the attached nonstructural components is investigated. Dynamic characteristics and seismic performance of building models are also evaluated, as well as the computational accuracy of the models. The presented results provide a better understanding of the dynamic behavior and seismic performance of auxiliary buildings. The results also help to quantify the impact of uncertainties associated with modeling and analysis of simplified numerical models of structural and nonstructural components subjected to seismic shaking on the predicted seismic failure probabilities of these systems.

Estimation of Properties of Pure Components Using Improved Group-Contribution+ (GC+) Based Models and Uncertainty Analysis

DEFF Research Database (Denmark)

Hukkerikar, Amol; Sarup, Bent; Abildskov, Jens

and uncertainty analysis, in general, is developed and used. In total 21 properties of pure components, which include normal boiling point, critical constants, normal melting point among others have been analysed. The statistical analysis of the model performance for these properties is highlighted through...... several illustrative examples. Important issues related to property modeling such as thermodynamic consistency of the predicted properties (relation of normal boiling point versus critical temperature etc.) are analysed. The developed methodology is simple, yet sound and effective and provides not only...

Dynamic Modal Analysis of Vertical Machining Centre Components

OpenAIRE

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...

Determining the number of components in principal components analysis: A comparison of statistical, crossvalidation and approximated methods

NARCIS (Netherlands)

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

NEPR Principle Component Analysis - NOAA TIFF Image

Data.gov (United States)

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...

Modeling accelerator structures and RF components

International Nuclear Information System (INIS)

Ko, K., Ng, C.K.; Herrmannsfeldt, W.B.

1993-03-01

Computer modeling has become an integral part of the design and analysis of accelerator structures RF components. Sophisticated 3D codes, powerful workstations and timely theory support all contributed to this development. We will describe our modeling experience with these resources and discuss their impact on ongoing work at SLAC. Specific examples from R ampersand D on a future linear collide and a proposed e + e - storage ring will be included

Efficient transfer of sensitivity information in multi-component models

International Nuclear Information System (INIS)

Abdel-Khalik, Hany S.; Rabiti, Cristian

2011-01-01

In support of adjoint-based sensitivity analysis, this manuscript presents a new method to efficiently transfer adjoint information between components in a multi-component model, whereas the output of one component is passed as input to the next component. Often, one is interested in evaluating the sensitivities of the responses calculated by the last component to the inputs of the first component in the overall model. The presented method has two advantages over existing methods which may be classified into two broad categories: brute force-type methods and amalgamated-type methods. First, the presented method determines the minimum number of adjoint evaluations for each component as opposed to the brute force-type methods which require full evaluation of all sensitivities for all responses calculated by each component in the overall model, which proves computationally prohibitive for realistic problems. Second, the new method treats each component as a black-box as opposed to amalgamated-type methods which requires explicit knowledge of the system of equations associated with each component in order to reach the minimum number of adjoint evaluations. (author)

Synchrotron-Based Microspectroscopic Analysis of Molecular and Biopolymer Structures Using Multivariate Techniques and Advanced Multi-Components Modeling

International Nuclear Information System (INIS)

Yu, P.

2008-01-01

More recently, advanced synchrotron radiation-based bioanalytical technique (SRFTIRM) has been applied as a novel non-invasive analysis tool to study molecular, functional group and biopolymer chemistry, nutrient make-up and structural conformation in biomaterials. This novel synchrotron technique, taking advantage of bright synchrotron light (which is million times brighter than sunlight), is capable of exploring the biomaterials at molecular and cellular levels. However, with the synchrotron RFTIRM technique, a large number of molecular spectral data are usually collected. The objective of this article was to illustrate how to use two multivariate statistical techniques: (1) agglomerative hierarchical cluster analysis (AHCA) and (2) principal component analysis (PCA) and two advanced multicomponent modeling methods: (1) Gaussian and (2) Lorentzian multi-component peak modeling for molecular spectrum analysis of bio-tissues. The studies indicated that the two multivariate analyses (AHCA, PCA) are able to create molecular spectral corrections by including not just one intensity or frequency point of a molecular spectrum, but by utilizing the entire spectral information. Gaussian and Lorentzian modeling techniques are able to quantify spectral omponent peaks of molecular structure, functional group and biopolymer. By application of these four statistical methods of the multivariate techniques and Gaussian and Lorentzian modeling, inherent molecular structures, functional group and biopolymer onformation between and among biological samples can be quantified, discriminated and classified with great efficiency.

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...

Component Reification in Systems Modelling

DEFF Research Database (Denmark)

Bendisposto, Jens; Hallerstede, Stefan

When modelling concurrent or distributed systems in Event-B, we often obtain models where the structure of the connected components is specified by constants. Their behaviour is specified by the non-deterministic choice of event parameters for events that operate on shared variables. From a certain......? These components may still refer to shared variables. Events of these components should not refer to the constants specifying the structure. The non-deterministic choice between these components should not be via parameters. We say the components are reified. We need to address how the reified components get...... reflected into the original model. This reflection should indicate the constraints on how to connect the components....

Integration of independent component analysis with near-infrared spectroscopy for analysis of bioactive components in the medicinal plant Gentiana scabra Bunge

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.

Principal component analysis and neurocomputing-based models for total ozone concentration over different urban regions of India

Science.gov (United States)

Chattopadhyay, Goutami; Chattopadhyay, Surajit; Chakraborthy, Parthasarathi

2012-07-01

The present study deals with daily total ozone concentration time series over four metro cities of India namely Kolkata, Mumbai, Chennai, and New Delhi in the multivariate environment. Using the Kaiser-Meyer-Olkin measure, it is established that the data set under consideration are suitable for principal component analysis. Subsequently, by introducing rotated component matrix for the principal components, the predictors suitable for generating artificial neural network (ANN) for daily total ozone prediction are identified. The multicollinearity is removed in this way. Models of ANN in the form of multilayer perceptron trained through backpropagation learning are generated for all of the study zones, and the model outcomes are assessed statistically. Measuring various statistics like Pearson correlation coefficients, Willmott's indices, percentage errors of prediction, and mean absolute errors, it is observed that for Mumbai and Kolkata the proposed ANN model generates very good predictions. The results are supported by the linearly distributed coordinates in the scatterplots.

Robustness of Component Models in Energy System Simulators

DEFF Research Database (Denmark)

Elmegaard, Brian

2003-01-01

During the development of the component-based energy system simulator DNA (Dynamic Network Analysis), several obstacles to easy use of the program have been observed. Some of these have to do with the nature of the program being based on a modelling language, not a graphical user interface (GUI......). Others have to do with the interaction between models of the nature of the substances in an energy system (e.g., fuels, air, flue gas), models of the components in a system (e.g., heat exchangers, turbines, pumps), and the solver for the system of equations. This paper proposes that the interaction...

ANOVA-principal component analysis and ANOVA-simultaneous component analysis: a comparison.

NARCIS (Netherlands)

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.

Equivalent water height extracted from GRACE gravity field model with robust independent component analysis

Science.gov (United States)

Guo, Jinyun; Mu, Dapeng; Liu, Xin; Yan, Haoming; Dai, Honglei

2014-08-01

The Level-2 monthly GRACE gravity field models issued by Center for Space Research (CSR), GeoForschungs Zentrum (GFZ), and Jet Propulsion Laboratory (JPL) are treated as observations used to extract the equivalent water height (EWH) with the robust independent component analysis (RICA). The smoothing radii of 300, 400, and 500 km are tested, respectively, in the Gaussian smoothing kernel function to reduce the observation Gaussianity. Three independent components are obtained by RICA in the spatial domain; the first component matches the geophysical signal, and the other two match the north-south strip and the other noises. The first mode is used to estimate EWHs of CSR, JPL, and GFZ, and compared with the classical empirical decorrelation method (EDM). The EWH STDs for 12 months in 2010 extracted by RICA and EDM show the obvious fluctuation. The results indicate that the sharp EWH changes in some areas have an important global effect, like in Amazon, Mekong, and Zambezi basins.

An Efficient Data Compression Model Based on Spatial Clustering and Principal Component Analysis in Wireless Sensor Networks

Directory of Open Access Journals (Sweden)

Yihang Yin

2015-08-01

Full Text Available Wireless sensor networks (WSNs have been widely used to monitor the environment, and sensors in WSNs are usually power constrained. Because inner-node communication consumes most of the power, efficient data compression schemes are needed to reduce the data transmission to prolong the lifetime of WSNs. In this paper, we propose an efficient data compression model to aggregate data, which is based on spatial clustering and principal component analysis (PCA. First, sensors with a strong temporal-spatial correlation are grouped into one cluster for further processing with a novel similarity measure metric. Next, sensor data in one cluster are aggregated in the cluster head sensor node, and an efficient adaptive strategy is proposed for the selection of the cluster head to conserve energy. Finally, the proposed model applies principal component analysis with an error bound guarantee to compress the data and retain the definite variance at the same time. Computer simulations show that the proposed model can greatly reduce communication and obtain a lower mean square error than other PCA-based algorithms.

An Efficient Data Compression Model Based on Spatial Clustering and Principal Component Analysis in Wireless Sensor Networks.

Science.gov (United States)

Yin, Yihang; Liu, Fengzheng; Zhou, Xiang; Li, Quanzhong

2015-08-07

Wireless sensor networks (WSNs) have been widely used to monitor the environment, and sensors in WSNs are usually power constrained. Because inner-node communication consumes most of the power, efficient data compression schemes are needed to reduce the data transmission to prolong the lifetime of WSNs. In this paper, we propose an efficient data compression model to aggregate data, which is based on spatial clustering and principal component analysis (PCA). First, sensors with a strong temporal-spatial correlation are grouped into one cluster for further processing with a novel similarity measure metric. Next, sensor data in one cluster are aggregated in the cluster head sensor node, and an efficient adaptive strategy is proposed for the selection of the cluster head to conserve energy. Finally, the proposed model applies principal component analysis with an error bound guarantee to compress the data and retain the definite variance at the same time. Computer simulations show that the proposed model can greatly reduce communication and obtain a lower mean square error than other PCA-based algorithms.

Modeling and Analysis of Component Faults and Reliability

DEFF Research Database (Denmark)

Le Guilly, Thibaut; Olsen, Petur; Ravn, Anders Peter

2016-01-01

This chapter presents a process to design and validate models of reactive systems in the form of communicating timed automata. The models are extended with faults associated with probabilities of occurrence. This enables a fault tree analysis of the system using minimal cut sets that are automati......This chapter presents a process to design and validate models of reactive systems in the form of communicating timed automata. The models are extended with faults associated with probabilities of occurrence. This enables a fault tree analysis of the system using minimal cut sets...... that are automatically generated. The stochastic information on the faults is used to estimate the reliability of the fault affected system. The reliability is given with respect to properties of the system state space. We illustrate the process on a concrete example using the Uppaal model checker for validating...... the ideal system model and the fault modeling. Then the statistical version of the tool, UppaalSMC, is used to find reliability estimates....

A principal components model of soundscape perception.

Science.gov (United States)

Axelsson, Östen; Nilsson, Mats E; Berglund, Birgitta

2010-11-01

There is a need for a model that identifies underlying dimensions of soundscape perception, and which may guide measurement and improvement of soundscape quality. With the purpose to develop such a model, a listening experiment was conducted. One hundred listeners measured 50 excerpts of binaural recordings of urban outdoor soundscapes on 116 attribute scales. The average attribute scale values were subjected to principal components analysis, resulting in three components: Pleasantness, eventfulness, and familiarity, explaining 50, 18 and 6% of the total variance, respectively. The principal-component scores were correlated with physical soundscape properties, including categories of dominant sounds and acoustic variables. Soundscape excerpts dominated by technological sounds were found to be unpleasant, whereas soundscape excerpts dominated by natural sounds were pleasant, and soundscape excerpts dominated by human sounds were eventful. These relationships remained after controlling for the overall soundscape loudness (Zwicker's N(10)), which shows that 'informational' properties are substantial contributors to the perception of soundscape. The proposed principal components model provides a framework for future soundscape research and practice. In particular, it suggests which basic dimensions are necessary to measure, how to measure them by a defined set of attribute scales, and how to promote high-quality soundscapes.

Cognitive Component Analysis

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...

Integration of Simulink Models with Component-based Software Models

Directory of Open Access Journals (Sweden)

MARIAN, N.

2008-06-01

Full Text Available Model based development aims to facilitate the development of embedded control systems by emphasizing the separation of the design level from the implementation level. Model based design involves the use of multiple models that represent different views of a system, having different semantics of abstract system descriptions. Usually, in mechatronics systems, design proceeds by iterating model construction, model analysis, and model transformation. Constructing a MATLAB/Simulink model, a plant and controller behavior is simulated using graphical blocks to represent mathematical and logical constructs and process flow, then software code is generated. A Simulink model is a representation of the design or implementation of a physical system that satisfies a set of requirements. A software component-based system aims to organize system architecture and behavior as a means of computation, communication and constraints, using computational blocks and aggregates for both discrete and continuous behavior, different interconnection and execution disciplines for event-based and time-based controllers, and so on, to encompass the demands to more functionality, at even lower prices, and with opposite constraints. COMDES (Component-based Design of Software for Distributed Embedded Systems is such a component-based system framework developed by the software engineering group of Mads Clausen Institute for Product Innovation (MCI, University of Southern Denmark. Once specified, the software model has to be analyzed. One way of doing that is to integrate in wrapper files the model back into Simulink S-functions, and use its extensive simulation features, thus allowing an early exploration of the possible design choices over multiple disciplines. The paper describes a safe translation of a restricted set of MATLAB/Simulink blocks to COMDES software components, both for continuous and discrete behavior, and the transformation of the software system into the S

Kernel Principal Component Analysis and its Applications in Face Recognition and Active Shape Models

OpenAIRE

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 ...

Independent component analysis: recent advances

OpenAIRE

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...

Towards the generation of a parametric foot model using principal component analysis: A pilot study.

Science.gov (United States)

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.

Principal component regression analysis with SPSS.

Science.gov (United States)

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.

Component Composition Using Feature Models

DEFF Research Database (Denmark)

Eichberg, Michael; Klose, Karl; Mitschke, Ralf

2010-01-01

interface description languages. If this variability is relevant when selecting a matching component then human interaction is required to decide which components can be bound. We propose to use feature models for making this variability explicit and (re-)enabling automatic component binding. In our...... approach, feature models are one part of service specifications. This enables to declaratively specify which service variant is provided by a component. By referring to a service's variation points, a component that requires a specific service can list the requirements on the desired variant. Using...... these specifications, a component environment can then determine if a binding of the components exists that satisfies all requirements. The prototypical environment Columbus demonstrates the feasibility of the approach....

Implementation of wall film condensation model to two-fluid model in component thermal hydraulic analysis code CUPID - 15237

International Nuclear Information System (INIS)

Lee, J.H.; Park, G.C.; Cho, H.K.

2015-01-01

In the containment of a nuclear reactor, the wall condensation occurs when containment cooling system and structures remove the mass and energy release and this phenomenon is of great importance to ensure containment integrity. If the phenomenon occurs in the presence of non-condensable gases, their accumulation near the condensate film leads to significant reduction in heat transfer during the condensation. This study aims at simulating the wall film condensation in the presence of non-condensable gas using CUPID, a computational multi-fluid dynamics code, which is developed by the Korea Atomic Energy Research Institute (KAERI) for the analysis of transient two-phase flows in nuclear reactor components. In order to simulate the wall film condensation in containment, the code requires a proper wall condensation model and liquid film model applicable to the analysis of the large scale system. In the present study, the liquid film model and wall film condensation model were implemented in the two-fluid model of CUPID. For the condensation simulation, a wall function approach with heat and mass transfer analogy was applied in order to save computational time without considerable refinement for the boundary layer. This paper presents the implemented wall film condensation model and then, introduces the simulation result using CUPID with the model for a conceptual condensation problem in a large system. (authors)

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...

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

SU-E-I-58: Objective Models of Breast Shape Undergoing Mammography and Tomosynthesis Using Principal Component Analysis.

Science.gov (United States)

Feng, Ssj; Sechopoulos, I

2012-06-01

To develop an objective model of the shape of the compressed breast undergoing mammographic or tomosynthesis acquisition. Automated thresholding and edge detection was performed on 984 anonymized digital mammograms (492 craniocaudal (CC) view mammograms and 492 medial lateral oblique (MLO) view mammograms), to extract the edge of each breast. Principal Component Analysis (PCA) was performed on these edge vectors to identify a limited set of parameters and eigenvectors that. These parameters and eigenvectors comprise a model that can be used to describe the breast shapes present in acquired mammograms and to generate realistic models of breasts undergoing acquisition. Sample breast shapes were then generated from this model and evaluated. The mammograms in the database were previously acquired for a separate study and authorized for use in further research. The PCA successfully identified two principal components and their corresponding eigenvectors, forming the basis for the breast shape model. The simulated breast shapes generated from the model are reasonable approximations of clinically acquired mammograms. Using PCA, we have obtained models of the compressed breast undergoing mammographic or tomosynthesis acquisition based on objective analysis of a large image database. Up to now, the breast in the CC view has been approximated as a semi-circular tube, while there has been no objectively-obtained model for the MLO view breast shape. Such models can be used for various breast imaging research applications, such as x-ray scatter estimation and correction, dosimetry estimates, and computer-aided detection and diagnosis. © 2012 American Association of Physicists in Medicine.

Two-component model application for error calculus in the environmental monitoring data analysis

International Nuclear Information System (INIS)

Carvalho, Maria Angelica G.; Hiromoto, Goro

2002-01-01

Analysis and interpretation of results of an environmental monitoring program is often based on the evaluation of the mean value of a particular set of data, which is strongly affected by the analytical errors associated with each measurement. A model proposed by Rocke and Lorenzato assumes two error components, one additive and one multiplicative, to deal with lower and higher concentration values in a single model. In this communication, an application of this method for re-evaluation of the errors reported in a large set of results of total alpha measurements in a environmental sample is presented. The results show that the mean values calculated taking into account the new errors is higher than as obtained with the original errors, being an indicative that the analytical errors reported before were underestimated in the region of lower concentrations. (author)

Use of principal components analysis and three-dimensional atmospheric-transport models for reactor-consequence evaluation

International Nuclear Information System (INIS)

Gudiksen, P.H.; Walton, J.J.; Alpert, D.J.; Johnson, J.D.

1982-01-01

This work explores the use of principal components analysis coupled to three-dimensional atmospheric transport and dispersion models for evaluating the environmental consequences of reactor accidents. This permits the inclusion of meteorological data from multiple sites and the effects of topography in the consequence evaluation; features not normally included in such analyses. The technique identifies prevailing regional wind patterns and their frequencies for use in the transport and dispersion calculations. Analysis of a hypothetical accident scenario involving a release of radioactivity from a reactor situated in a river valley indicated the technique is quite useful whenever recurring wind patterns exist, as is often the case in complex terrain situations. Considerable differences were revealed in a comparison with results obtained from a more conventional Gaussian plume model using only the reactor site meteorology and no topographic effects

A New Model for Birth Weight Prediction Using 2- and 3-Dimensional Ultrasonography by Principal Component Analysis: A Chinese Population Study.

Science.gov (United States)

Liao, Shuxin; Wang, Yunfang; Xiao, Shufang; Deng, Xujie; Fang, Bimei; Yang, Fang

2018-03-30

To establish a new model for birth weight prediction using 2- and 3-dimensional ultrasonography (US) by principal component analysis (PCA). Two- and 3-dimensional US was prospectively performed in women with normal singleton pregnancies within 7 days before delivery (37-41 weeks' gestation). The participants were divided into a development group (n = 600) and a validation group (n = 597). Principal component analysis and stepwise linear regression analysis were used to develop a new prediction model. The new model's accuracy in predicting fetal birth weight was confirmed by the validation group through comparisons with previously published formulas. A total of 1197 cases were recruited in this study. All interclass and intraclass correlation coefficients of US measurements were greater than 0.75. Two principal components (PCs) were considered primary in determining estimated fetal birth weight, which were derived from 9 US measurements. Stepwise linear regression analysis showed a positive association between birth weight and PC1 and PC2. In the development group, our model had a small mean percentage error (mean ± SD, 3.661% ± 3.161%). At least a 47.558% decrease in the mean percentage error and a 57.421% decrease in the standard deviation of the new model compared with previously published formulas were noted. The results were similar to those in the validation group, and the new model covered 100% of birth weights within 10% of actual birth weights. The birth weight prediction model based on 2- and 3-dimensional US by PCA could help improve the precision of estimated fetal birth weight. © 2018 by the American Institute of Ultrasound in Medicine.

Principal Component Clustering Approach to Teaching Quality Discriminant Analysis

Science.gov (United States)

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…

Importance Analysis of In-Service Testing Components for Ulchin Unit 3

International Nuclear Information System (INIS)

Dae-Il Kan; Kil-Yoo Kim; Jae-Joo Ha

2002-01-01

We performed an importance analysis of In-Service Testing (IST) components for Ulchin Unit 3 using the integrated evaluation method for categorizing component safety significance developed in this study. The importance analysis using the developed method is initiated by ranking the component importance using quantitative PSA information. The importance analysis of the IST components not modeled in the PSA is performed through the engineering judgment, based on the expertise of PSA, and the quantitative and qualitative information for the IST components. The PSA scope for importance analysis includes not only Level 1 and 2 internal PSA but also Level 1 external and shutdown/low power operation PSA. The importance analysis results of valves show that 167 (26.55%) of the 629 IST valves are HSSCs and 462 (73.45%) are LSSCs. Those of pumps also show that 28 (70%) of the 40 IST pumps are HSSCs and 12 (30%) are LSSCs. (authors)

Developing a Model Component

Science.gov (United States)

Fields, Christina M.

2013-01-01

The Spaceport Command and Control System (SCCS) Simulation Computer Software Configuration Item (CSCI) is responsible for providing simulations to support test and verification of SCCS hardware and software. The Universal Coolant Transporter System (UCTS) was a Space Shuttle Orbiter support piece of the Ground Servicing Equipment (GSE). The initial purpose of the UCTS was to provide two support services to the Space Shuttle Orbiter immediately after landing at the Shuttle Landing Facility. The UCTS is designed with the capability of servicing future space vehicles; including all Space Station Requirements necessary for the MPLM Modules. The Simulation uses GSE Models to stand in for the actual systems to support testing of SCCS systems during their development. As an intern at Kennedy Space Center (KSC), my assignment was to develop a model component for the UCTS. I was given a fluid component (dryer) to model in Simulink. I completed training for UNIX and Simulink. The dryer is a Catch All replaceable core type filter-dryer. The filter-dryer provides maximum protection for the thermostatic expansion valve and solenoid valve from dirt that may be in the system. The filter-dryer also protects the valves from freezing up. I researched fluid dynamics to understand the function of my component. The filter-dryer was modeled by determining affects it has on the pressure and velocity of the system. I used Bernoulli's Equation to calculate the pressure and velocity differential through the dryer. I created my filter-dryer model in Simulink and wrote the test script to test the component. I completed component testing and captured test data. The finalized model was sent for peer review for any improvements. I participated in Simulation meetings and was involved in the subsystem design process and team collaborations. I gained valuable work experience and insight into a career path as an engineer.

Sensitivity analysis on the component cooling system of the Angra 1 NPP

International Nuclear Information System (INIS)

Castro Silva, Luiz Euripedes Massiere de

1995-01-01

The component cooling system has been studied within the scope of the Probabilistic Safety Analysis of the Angra I NPP in order to assure that the proposed modelling suits as close as possible the functioning system and its availability aspects. In such a way a sensitivity analysis was performed on the equivalence between the operating modes of the component cooling system and its results show the fitness of the model. (author). 4 refs, 3 figs, 3 tabs

A Fault Prognosis Strategy Based on Time-Delayed Digraph Model and Principal Component Analysis

Directory of Open Access Journals (Sweden)

Ningyun Lu

2012-01-01

Full Text Available Because of the interlinking of process equipments in process industry, event information may propagate through the plant and affect a lot of downstream process variables. Specifying the causality and estimating the time delays among process variables are critically important for data-driven fault prognosis. They are not only helpful to find the root cause when a plant-wide disturbance occurs, but to reveal the evolution of an abnormal event propagating through the plant. This paper concerns with the information flow directionality and time-delay estimation problems in process industry and presents an information synchronization technique to assist fault prognosis. Time-delayed mutual information (TDMI is used for both causality analysis and time-delay estimation. To represent causality structure of high-dimensional process variables, a time-delayed signed digraph (TD-SDG model is developed. Then, a general fault prognosis strategy is developed based on the TD-SDG model and principle component analysis (PCA. The proposed method is applied to an air separation unit and has achieved satisfying results in predicting the frequently occurred “nitrogen-block” fault.

Reliability Evaluation of Machine Center Components Based on Cascading Failure Analysis

Science.gov (United States)

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.

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

Reliability prediction system based on the failure rate model for electronic components

International Nuclear Information System (INIS)

Lee, Seung Woo; Lee, Hwa Ki

2008-01-01

Although many methodologies for predicting the reliability of electronic components have been developed, their reliability might be subjective according to a particular set of circumstances, and therefore it is not easy to quantify their reliability. Among the reliability prediction methods are the statistical analysis based method, the similarity analysis method based on an external failure rate database, and the method based on the physics-of-failure model. In this study, we developed a system by which the reliability of electronic components can be predicted by creating a system for the statistical analysis method of predicting reliability most easily. The failure rate models that were applied are MILHDBK- 217F N2, PRISM, and Telcordia (Bellcore), and these were compared with the general purpose system in order to validate the effectiveness of the developed system. Being able to predict the reliability of electronic components from the stage of design, the system that we have developed is expected to contribute to enhancing the reliability of electronic components

Day-Ahead Crude Oil Price Forecasting Using a Novel Morphological Component Analysis Based Model

Directory of Open Access Journals (Sweden)

Qing Zhu

2014-01-01

Full Text Available As a typical nonlinear and dynamic system, the crude oil price movement is difficult to predict and its accurate forecasting remains the subject of intense research activity. Recent empirical evidence suggests that the multiscale data characteristics in the price movement are another important stylized fact. The incorporation of mixture of data characteristics in the time scale domain during the modelling process can lead to significant performance improvement. This paper proposes a novel morphological component analysis based hybrid methodology for modeling the multiscale heterogeneous characteristics of the price movement in the crude oil markets. Empirical studies in two representative benchmark crude oil markets reveal the existence of multiscale heterogeneous microdata structure. The significant performance improvement of the proposed algorithm incorporating the heterogeneous data characteristics, against benchmark random walk, ARMA, and SVR models, is also attributed to the innovative methodology proposed to incorporate this important stylized fact during the modelling process. Meanwhile, work in this paper offers additional insights into the heterogeneous market microstructure with economic viable interpretations.

Multi-Trait analysis of growth traits: fitting reduced rank models using principal components for Simmental beef cattle

Directory of Open Access Journals (Sweden)

Rodrigo Reis Mota

2016-09-01

Full Text Available ABSTRACT: The aim of this research was to evaluate the dimensional reduction of additive direct genetic covariance matrices in genetic evaluations of growth traits (range 100-730 days in Simmental cattle using principal components, as well as to estimate (covariance components and genetic parameters. Principal component analyses were conducted for five different models-one full and four reduced-rank models. Models were compared using Akaike information (AIC and Bayesian information (BIC criteria. Variance components and genetic parameters were estimated by restricted maximum likelihood (REML. The AIC and BIC values were similar among models. This indicated that parsimonious models could be used in genetic evaluations in Simmental cattle. The first principal component explained more than 96% of total variance in both models. Heritability estimates were higher for advanced ages and varied from 0.05 (100 days to 0.30 (730 days. Genetic correlation estimates were similar in both models regardless of magnitude and number of principal components. The first principal component was sufficient to explain almost all genetic variance. Furthermore, genetic parameter similarities and lower computational requirements allowed for parsimonious models in genetic evaluations of growth traits in Simmental cattle.

An elementary components of variance analysis for multi-center quality control

International Nuclear Information System (INIS)

Munson, P.J.; Rodbard, D.

1977-01-01

The serious variability of RIA results from different laboratories indicates the need for multi-laboratory collaborative quality control (QC) studies. Statistical analysis methods for such studies using an 'analysis of variance with components of variance estimation' are discussed. This technique allocates the total variance into components corresponding to between-laboratory, between-assay, and residual or within-assay variability. Components of variance analysis also provides an intelligent way to combine the results of several QC samples run at different evels, from which we may decide if any component varies systematically with dose level; if not, pooling of estimates becomes possible. We consider several possible relationships of standard deviation to the laboratory mean. Each relationship corresponds to an underlying statistical model, and an appropriate analysis technique. Tests for homogeneity of variance may be used to determine if an appropriate model has been chosen, although the exact functional relationship of standard deviation to lab mean may be difficult to establish. Appropriate graphical display of the data aids in visual understanding of the data. A plot of the ranked standard deviation vs. ranked laboratory mean is a convenient way to summarize a QC study. This plot also allows determination of the rank correlation, which indicates a net relationship of variance to laboratory mean. (orig.) [de

Multi-spectrometer calibration transfer based on independent component analysis.

Science.gov (United States)

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.

Stochastic Modeling Of Wind Turbine Drivetrain Components

DEFF Research Database (Denmark)

Rafsanjani, Hesam Mirzaei; Sørensen, John Dalsgaard

2014-01-01

reliable components are needed for wind turbine. In this paper focus is on reliability of critical components in drivetrain such as bearings and shafts. High failure rates of these components imply a need for more reliable components. To estimate the reliability of these components, stochastic models...... are needed for initial defects and damage accumulation. In this paper, stochastic models are formulated considering some of the failure modes observed in these components. The models are based on theoretical considerations, manufacturing uncertainties, size effects of different scales. It is illustrated how...

Components in models of learning: Different operationalisations and relations between components

Directory of Open Access Journals (Sweden)

Mirkov Snežana

2013-01-01

Full Text Available This paper provides the presentation of different operationalisations of components in different models of learning. Special emphasis is on the empirical verifications of relations between components. Starting from the research of congruence between learning motives and strategies, underlying the general model of school learning that comprises different approaches to learning, we have analyzed the empirical verifications of factor structure of instruments containing the scales of motives and learning strategies corresponding to these motives. Considering the problems in the conceptualization of the achievement approach to learning, we have discussed the ways of operational sing the goal orientations and exploring their role in using learning strategies, especially within the model of the regulation of constructive learning processes. This model has served as the basis for researching learning styles that are the combination of a large number of components. Complex relations between the components point to the need for further investigation of the constructs involved in various models. We have discussed the findings and implications of the studies of relations between the components involved in different models, especially between learning motives/goals and learning strategies. We have analyzed the role of regulation in the learning process, whose elaboration, as indicated by empirical findings, can contribute to a more precise operationalisation of certain learning components. [Projekat Ministarstva nauke Republike Srbije, br. 47008: Unapređivanje kvaliteta i dostupnosti obrazovanja u procesima modernizacije Srbije i br. 179034: Od podsticanja inicijative, saradnje i stvaralaštva u obrazovanju do novih uloga i identiteta u društvu

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.

Modeling the degradation of nuclear components

International Nuclear Information System (INIS)

Stock, D.; Samanta, P.; Vesely, W.

1993-01-01

This paper describes component level reliability models that use information on degradation to predict component reliability, and which have been used to evaluate different maintenance and testing policies. The models are based on continuous time Markov processes, and are a generalization of reliability models currently used in Probabilistic Risk Assessment. An explanation of the models, the model parameters, and an example of how these models can be used to evaluate maintenance policies are discussed

Root cause analysis in support of reliability enhancement of engineering components

International Nuclear Information System (INIS)

Kumar, Sachin; Mishra, Vivek; Joshi, N.S.; Varde, P.V.

2014-01-01

Reliability based methods have been widely used for the safety assessment of plant system, structures and components. These methods provide a quantitative estimation of system reliability but do not give insight into the failure mechanism. Understanding the failure mechanism is a must to avoid the recurrence of the events and enhancement of the system reliability. Root cause analysis provides a tool for gaining detailed insights into the causes of failure of component with particular attention to the identification of fault in component design, operation, surveillance, maintenance, training, procedures and policies which must be improved to prevent repetition of incidents. Root cause analysis also helps in developing Probabilistic Safety Analysis models. A probabilistic precursor study provides a complement to the root cause analysis approach in event analysis by focusing on how an event might have developed adversely. This paper discusses the root cause analysis methodologies and their application in the specific case studies for enhancement of system reliability. (author)

Models for describing the thermal characteristics of building components

DEFF Research Database (Denmark)

Jimenez, M.J.; Madsen, Henrik

2008-01-01

, for example. For the analysis of these tests, dynamic analysis models and methods are required. However, a wide variety of models and methods exists, and the problem of choosing the most appropriate approach for each particular case is a non-trivial and interdisciplinary task. Knowledge of a large family....... The characteristics of each type of model are highlighted. Some available software tools for each of the methods described will be mentioned. A case study also demonstrating the difference between linear and nonlinear models is considered....... of these approaches may therefore be very useful for selecting a suitable approach for each particular case. This paper presents an overview of models that can be applied for modelling the thermal characteristics of buildings and building components using data from outdoor testing. The choice of approach depends...

Space-time latent component Modeling of Geo-referenced health data

OpenAIRE

Lawson, Andrew B.; Song, Hae-Ryoung; Cai, Bo; Hossain, Md Monir; Huang, Kun

2010-01-01

Latent structure models have been proposed in many applications. For space time health data it is often important to be able to find underlying trends in time which are supported by subsets of small areas. Latent structure modeling is one approach to this analysis. This paper presents a mixture-based approach that can be appied to component selction. The analysis of a Georgia ambulatory asthma county level data set is presented and a simulation-based evaluation is made.

Modeling the variability of solar radiation data among weather stations by means of principal components analysis

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

Comparison of common components analysis with principal components analysis and independent components analysis: Application to SPME-GC-MS volatolomic signatures.

Science.gov (United States)

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

Model Reduction via Principe Component Analysis and Markov Chain Monte Carlo (MCMC) Methods

Science.gov (United States)

Gong, R.; Chen, J.; Hoversten, M. G.; Luo, J.

2011-12-01

Geophysical and hydrogeological inverse problems often include a large number of unknown parameters, ranging from hundreds to millions, depending on parameterization and problems undertaking. This makes inverse estimation and uncertainty quantification very challenging, especially for those problems in two- or three-dimensional spatial domains. Model reduction technique has the potential of mitigating the curse of dimensionality by reducing total numbers of unknowns while describing the complex subsurface systems adequately. In this study, we explore the use of principal component analysis (PCA) and Markov chain Monte Carlo (MCMC) sampling methods for model reduction through the use of synthetic datasets. We compare the performances of three different but closely related model reduction approaches: (1) PCA methods with geometric sampling (referred to as 'Method 1'), (2) PCA methods with MCMC sampling (referred to as 'Method 2'), and (3) PCA methods with MCMC sampling and inclusion of random effects (referred to as 'Method 3'). We consider a simple convolution model with five unknown parameters as our goal is to understand and visualize the advantages and disadvantages of each method by comparing their inversion results with the corresponding analytical solutions. We generated synthetic data with noise added and invert them under two different situations: (1) the noised data and the covariance matrix for PCA analysis are consistent (referred to as the unbiased case), and (2) the noise data and the covariance matrix are inconsistent (referred to as biased case). In the unbiased case, comparison between the analytical solutions and the inversion results show that all three methods provide good estimates of the true values and Method 1 is computationally more efficient. In terms of uncertainty quantification, Method 1 performs poorly because of relatively small number of samples obtained, Method 2 performs best, and Method 3 overestimates uncertainty due to inclusion

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.

Reliability Analysis of Fatigue Failure of Cast Components for Wind Turbines

Directory of Open Access Journals (Sweden)

Hesam Mirzaei Rafsanjani

2015-04-01

Full Text Available Fatigue failure is one of the main failure modes for wind turbine drivetrain components made of cast iron. The wind turbine drivetrain consists of a variety of heavily loaded components, like the main shaft, the main bearings, the gearbox and the generator. The failure of each component will lead to substantial economic losses such as cost of lost energy production and cost of repairs. During the design lifetime, the drivetrain components are exposed to variable loads from winds and waves and other sources of loads that are uncertain and have to be modeled as stochastic variables. The types of loads are different for offshore and onshore wind turbines. Moreover, uncertainties about the fatigue strength play an important role in modeling and assessment of the reliability of the components. In this paper, a generic stochastic model for fatigue failure of cast iron components based on fatigue test data and a limit state equation for fatigue failure based on the SN-curve approach and Miner’s rule is presented. The statistical analysis of the fatigue data is performed using the Maximum Likelihood Method which also gives an estimate of the statistical uncertainties. Finally, illustrative examples are presented with reliability analyses depending on various stochastic models and partial safety factors.

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

Principal component and spatial correlation analysis of spectroscopic-imaging data in scanning probe microscopy

International Nuclear Information System (INIS)

Jesse, Stephen; Kalinin, Sergei V

2009-01-01

An approach for the analysis of multi-dimensional, spectroscopic-imaging data based on principal component analysis (PCA) is explored. PCA selects and ranks relevant response components based on variance within the data. It is shown that for examples with small relative variations between spectra, the first few PCA components closely coincide with results obtained using model fitting, and this is achieved at rates approximately four orders of magnitude faster. For cases with strong response variations, PCA allows an effective approach to rapidly process, de-noise, and compress data. The prospects for PCA combined with correlation function analysis of component maps as a universal tool for data analysis and representation in microscopy are discussed.

Human reliability in non-destructive inspections of nuclear power plant components: modeling and analysis

Energy Technology Data Exchange (ETDEWEB)

Vasconcelos, Vanderley de; Soares, Wellington Antonio; Marques, Raíssa Oliveira; Silva Júnior, Silvério Ferreira da; Raso, Amanda Laureano, E-mail: vasconv@cdtn.br, E-mail: soaresw@cdtn.br, E-mail: raissaomarques@gmail.com, E-mail: silvasf@cdtn.br, E-mail: amandaraso@hotmail.com [Centro de Desenvolvimento da Tecnologia Nuclear (CDTN/CNEN-MG), Belo Horizonte, MG (Brazil)

2017-07-01

Non-destructive inspection (NDI) is one of the key elements in ensuring quality of engineering systems and their safe use. NDI is a very complex task, during which the inspectors have to rely on their sensory, perceptual, cognitive, and motor skills. It requires high vigilance once it is often carried out on large components, over a long period of time, and in hostile environments and restriction of workplace. A successful NDI requires careful planning, choice of appropriate NDI methods and inspection procedures, as well as qualified and trained inspection personnel. A failure of NDI to detect critical defects in safety-related components of nuclear power plants, for instance, may lead to catastrophic consequences for workers, public and environment. Therefore, ensuring that NDI methods are reliable and capable of detecting all critical defects is of utmost importance. Despite increased use of automation in NDI, human inspectors, and thus human factors, still play an important role in NDI reliability. Human reliability is the probability of humans conducting specific tasks with satisfactory performance. Many techniques are suitable for modeling and analyzing human reliability in NDI of nuclear power plant components. Among these can be highlighted Failure Modes and Effects Analysis (FMEA) and THERP (Technique for Human Error Rate Prediction). The application of these techniques is illustrated in an example of qualitative and quantitative studies to improve typical NDI of pipe segments of a core cooling system of a nuclear power plant, through acting on human factors issues. (author)

Human reliability in non-destructive inspections of nuclear power plant components: modeling and analysis

International Nuclear Information System (INIS)

Vasconcelos, Vanderley de; Soares, Wellington Antonio; Marques, Raíssa Oliveira; Silva Júnior, Silvério Ferreira da; Raso, Amanda Laureano

2017-01-01

Non-destructive inspection (NDI) is one of the key elements in ensuring quality of engineering systems and their safe use. NDI is a very complex task, during which the inspectors have to rely on their sensory, perceptual, cognitive, and motor skills. It requires high vigilance once it is often carried out on large components, over a long period of time, and in hostile environments and restriction of workplace. A successful NDI requires careful planning, choice of appropriate NDI methods and inspection procedures, as well as qualified and trained inspection personnel. A failure of NDI to detect critical defects in safety-related components of nuclear power plants, for instance, may lead to catastrophic consequences for workers, public and environment. Therefore, ensuring that NDI methods are reliable and capable of detecting all critical defects is of utmost importance. Despite increased use of automation in NDI, human inspectors, and thus human factors, still play an important role in NDI reliability. Human reliability is the probability of humans conducting specific tasks with satisfactory performance. Many techniques are suitable for modeling and analyzing human reliability in NDI of nuclear power plant components. Among these can be highlighted Failure Modes and Effects Analysis (FMEA) and THERP (Technique for Human Error Rate Prediction). The application of these techniques is illustrated in an example of qualitative and quantitative studies to improve typical NDI of pipe segments of a core cooling system of a nuclear power plant, through acting on human factors issues. (author)

Detailed Structural Analysis of Critical Wendelstein 7-X Magnet System Components

International Nuclear Information System (INIS)

Egorov, K.

2006-01-01

The Wendelstein 7-X (W7-X) stellarator experiment is presently under construction and assembly in Greifswald, Germany. The goal of the experiment is to verify that the stellarator magnetic confinement concept is a viable option for a fusion reactor. The complex W7-X magnet system requires a multi-level approach to structural analysis for which two types of finite element models are used: Firstly, global models having reasonably coarse meshes with a number of simplifications and assumptions, and secondly, local models with detailed meshes of critical regions and elements. Widely known sub-modelling technique with boundary conditions extracted from the global models is one of the approaches for local analysis with high assessment efficiency. In particular, the winding pack (WP) of the magnet coils is simulated in the global model as a homogeneous orthotropic material with effective mechanical characteristic representing its real composite structure. This assumption allows assessing the whole magnet system in terms of general structural factors like forces and moments on the support elements, displacements of the main components, deformation and stress in the coil casings, etc. In a second step local models with a detailed description of more critical WP zones are considered in order to analyze their internal components like conductor jackets, turn insulation, etc. This paper provides an overview of local analyses of several critical W7-X magnet system components with particular attention on the coil winding packs. (author)

Space-time latent component modeling of geo-referenced health data.

Science.gov (United States)

Lawson, Andrew B; Song, Hae-Ryoung; Cai, Bo; Hossain, Md Monir; Huang, Kun

2010-08-30

Latent structure models have been proposed in many applications. For space-time health data it is often important to be able to find the underlying trends in time, which are supported by subsets of small areas. Latent structure modeling is one such approach to this analysis. This paper presents a mixture-based approach that can be applied to component selection. The analysis of a Georgia ambulatory asthma county-level data set is presented and a simulation-based evaluation is made. Copyright (c) 2010 John Wiley & Sons, Ltd.

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...

Extracting functional components of neural dynamics with Independent Component Analysis and inverse Current Source Density.

Science.gov (United States)

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.

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.

A Bayesian analysis of the PPP puzzle using an unobserved components model

NARCIS (Netherlands)

R.H. Kleijn (Richard); H.K. van Dijk (Herman)

2001-01-01

textabstractThe failure to describe the time series behaviour of most real exchange rates as temporary deviations from fixed long-term means may be due to time variation of the equilibria themselves, see Engel (2000). We implement this idea using an unobserved components model and decompose the

Bridge Diagnosis by Using Nonlinear Independent Component Analysis and Displacement Analysis

Science.gov (United States)

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.

Variational Bayesian Learning for Wavelet Independent Component Analysis

Science.gov (United States)

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.

Tweaking the Four-Component Model

Science.gov (United States)

Curzer, Howard J.

2014-01-01

By maintaining that moral functioning depends upon four components (sensitivity, judgment, motivation, and character), the Neo-Kohlbergian account of moral functioning allows for uneven moral development within individuals. However, I argue that the four-component model does not go far enough. I offer a more accurate account of moral functioning…

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....

Robust LOD scores for variance component-based linkage analysis.

Science.gov (United States)

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.

Reliability Analysis of Load-Sharing K-out-of-N System Considering Component Degradation

Directory of Open Access Journals (Sweden)

Chunbo Yang

2015-01-01

Full Text Available The K-out-of-N configuration is a typical form of redundancy techniques to improve system reliability, where at least K-out-of-N components must work for successful operation of system. When the components are degraded, more components are needed to meet the system requirement, which means that the value of K has to increase. The current reliability analysis methods overestimate the reliability, because using constant K ignores the degradation effect. In a load-sharing system with degrading components, the workload shared on each surviving component will increase after a random component failure, resulting in higher failure rate and increased performance degradation rate. This paper proposes a method combining a tampered failure rate model with a performance degradation model to analyze the reliability of load-sharing K-out-of-N system with degrading components. The proposed method considers the value of K as a variable which is derived by the performance degradation model. Also, the load-sharing effect is evaluated by the tampered failure rate model. Monte-Carlo simulation procedure is used to estimate the discrete probability distribution of K. The case of a solar panel is studied in this paper, and the result shows that the reliability considering component degradation is less than that ignoring component degradation.

Probabilistic physics-of-failure models for component reliabilities using Monte Carlo simulation and Weibull analysis: a parametric study

International Nuclear Information System (INIS)

Hall, P.L.; Strutt, J.E.

2003-01-01

dispersion of the uncertain parameters. If there is no uncertainty, a single, sharp value of the component lifetime is predicted, corresponding to the limit β=∞. In contrast, the shock-loading model is inherently random, and its predictions correspond closely to those of a constant hazard rate model, characterized by a value of β close to 1 for all finite degrees of parameter uncertainty. The results are discussed in the context of traditional methods for reliability analysis and conventional views on the nature of early-life failures

Failure analysis a practical guide for manufacturers of electronic components and systems

CERN Document Server

Bâzu, Marius

2011-01-01

Failure analysis is the preferred method to investigate product or process reliability and to ensure optimum performance of electrical components and systems. The physics-of-failure approach is the only internationally accepted solution for continuously improving the reliability of materials, devices and processes. The models have been developed from the physical and chemical phenomena that are responsible for degradation or failure of electronic components and materials and now replace popular distribution models for failure mechanisms such as Weibull or lognormal. Reliability engineers nee

Analysis of Moisture Content in Beetroot using Fourier Transform Infrared Spectroscopy and by Principal Component Analysis.

Science.gov (United States)

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.

Improvement of Binary Analysis Components in Automated Malware Analysis Framework

Science.gov (United States)

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

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.

A sensitivity analysis of the WIPP disposal room model: Phase 1

Energy Technology Data Exchange (ETDEWEB)

Labreche, D.A.; Beikmann, M.A. [RE/SPEC, Inc., Albuquerque, NM (United States); Osnes, J.D. [RE/SPEC, Inc., Rapid City, SD (United States); Butcher, B.M. [Sandia National Labs., Albuquerque, NM (United States)

1995-07-01

The WIPP Disposal Room Model (DRM) is a numerical model with three major components constitutive models of TRU waste, crushed salt backfill, and intact halite -- and several secondary components, including air gap elements, slidelines, and assumptions on symmetry and geometry. A sensitivity analysis of the Disposal Room Model was initiated on two of the three major components (waste and backfill models) and on several secondary components as a group. The immediate goal of this component sensitivity analysis (Phase I) was to sort (rank) model parameters in terms of their relative importance to model response so that a Monte Carlo analysis on a reduced set of DRM parameters could be performed under Phase II. The goal of the Phase II analysis will be to develop a probabilistic definition of a disposal room porosity surface (porosity, gas volume, time) that could be used in WIPP Performance Assessment analyses. This report documents a literature survey which quantifies the relative importance of the secondary room components to room closure, a differential analysis of the creep consolidation model and definition of a follow-up Monte Carlo analysis of the model, and an analysis and refitting of the waste component data on which a volumetric plasticity model of TRU drum waste is based. A summary, evaluation of progress, and recommendations for future work conclude the report.

Nonlinear seismic analysis of a reactor structure with impact between core components

International Nuclear Information System (INIS)

Hill, R.G.

1975-01-01

The seismic analysis of the FFTF-PIOTA (Fast Flux Test Facility-Postirradiation Open Test Assembly), subjected to a horizontal DBE (Design Base Earthquake) is presented. The PIOTA is the first in a set of open test assemblies to be designed for the FFTF. Employing the direct method of transient analysis, the governing differential equations describing the motion of the system are set up directly and are implicitly integrated numerically in time. A simple lumped-mass beam model of the FFTF which includes small clearances between core components is used as a ''driver'' for a fine mesh model of the PIOTA. The nonlinear forces due to the impact of the core components and their effect on the PIOTA are computed. 6 references

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...

Structural assessment of aerospace components using image processing algorithms and Finite Element models

DEFF Research Database (Denmark)

Stamatelos, Dimtrios; Kappatos, Vassilios

2017-01-01

Purpose – This paper presents the development of an advanced structural assessment approach for aerospace components (metallic and composites). This work focuses on developing an automatic image processing methodology based on Non Destructive Testing (NDT) data and numerical models, for predicting...... the residual strength of these components. Design/methodology/approach – An image processing algorithm, based on the threshold method, has been developed to process and quantify the geometric characteristics of damages. Then, a parametric Finite Element (FE) model of the damaged component is developed based...... on the inputs acquired from the image processing algorithm. The analysis of the metallic structures is employing the Extended FE Method (XFEM), while for the composite structures the Cohesive Zone Model (CZM) technique with Progressive Damage Modelling (PDM) is used. Findings – The numerical analyses...

Key components of financial-analysis education for clinical nurses.

Science.gov (United States)

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.

Personality disorders in substance abusers: Validation of the DIP-Q through principal components factor analysis and canonical correlation analysis

Directory of Open Access Journals (Sweden)

Hesse Morten

2005-05-01

Full Text Available Abstract Background Personality disorders are common in substance abusers. Self-report questionnaires that can aid in the assessment of personality disorders are commonly used in assessment, but are rarely validated. Methods The Danish DIP-Q as a measure of co-morbid personality disorders in substance abusers was validated through principal components factor analysis and canonical correlation analysis. A 4 components structure was constructed based on 238 protocols, representing antagonism, neuroticism, introversion and conscientiousness. The structure was compared with (a a 4-factor solution from the DIP-Q in a sample of Swedish drug and alcohol abusers (N = 133, and (b a consensus 4-components solution based on a meta-analysis of published correlation matrices of dimensional personality disorder scales. Results It was found that the 4-factor model of personality was congruent across the Danish and Swedish samples, and showed good congruence with the consensus model. A canonical correlation analysis was conducted on a subset of the Danish sample with staff ratings of pathology. Three factors that correlated highly between the two variable sets were found. These variables were highly similar to the three first factors from the principal components analysis, antagonism, neuroticism and introversion. Conclusion The findings support the validity of the DIP-Q as a measure of DSM-IV personality disorders in substance abusers.

Euler principal component analysis

NARCIS (Netherlands)

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,

Identifying the Component Structure of Satisfaction Scales by Nonlinear Principal Components Analysis

NARCIS (Netherlands)

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

Constrained principal component analysis and related techniques

CERN Document Server

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

Two-component mixture cure rate model with spline estimated nonparametric components.

Science.gov (United States)

Wang, Lu; Du, Pang; Liang, Hua

2012-09-01

In some survival analysis of medical studies, there are often long-term survivors who can be considered as permanently cured. The goals in these studies are to estimate the noncured probability of the whole population and the hazard rate of the susceptible subpopulation. When covariates are present as often happens in practice, to understand covariate effects on the noncured probability and hazard rate is of equal importance. The existing methods are limited to parametric and semiparametric models. We propose a two-component mixture cure rate model with nonparametric forms for both the cure probability and the hazard rate function. Identifiability of the model is guaranteed by an additive assumption that allows no time-covariate interactions in the logarithm of hazard rate. Estimation is carried out by an expectation-maximization algorithm on maximizing a penalized likelihood. For inferential purpose, we apply the Louis formula to obtain point-wise confidence intervals for noncured probability and hazard rate. Asymptotic convergence rates of our function estimates are established. We then evaluate the proposed method by extensive simulations. We analyze the survival data from a melanoma study and find interesting patterns for this study. © 2011, The International Biometric Society.

Northeast Puerto Rico and Culebra Island Principle Component Analysis - NOAA TIFF Image

Data.gov (United States)

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...

Modeling cellular networks in fading environments with dominant specular components

KAUST Repository

AlAmmouri, Ahmad

2016-07-26

Stochastic geometry (SG) has been widely accepted as a fundamental tool for modeling and analyzing cellular networks. However, the fading models used with SG analysis are mainly confined to the simplistic Rayleigh fading, which is extended to the Nakagami-m fading in some special cases. However, neither the Rayleigh nor the Nakagami-m accounts for dominant specular components (DSCs) which may appear in realistic fading channels. In this paper, we present a tractable model for cellular networks with generalized two-ray (GTR) fading channel. The GTR fading explicitly accounts for two DSCs in addition to the diffuse components and offers high flexibility to capture diverse fading channels that appear in realistic outdoor/indoor wireless communication scenarios. It also encompasses the famous Rayleigh and Rician fading as special cases. To this end, the prominent effect of DSCs is highlighted in terms of average spectral efficiency. © 2016 IEEE.

An ontology for component-based models of water resource systems

Science.gov (United States)

Elag, Mostafa; Goodall, Jonathan L.

2013-08-01

Component-based modeling is an approach for simulating water resource systems where a model is composed of a set of components, each with a defined modeling objective, interlinked through data exchanges. Component-based modeling frameworks are used within the hydrologic, atmospheric, and earth surface dynamics modeling communities. While these efforts have been advancing, it has become clear that the water resources modeling community in particular, and arguably the larger earth science modeling community as well, faces a challenge of fully and precisely defining the metadata for model components. The lack of a unified framework for model component metadata limits interoperability between modeling communities and the reuse of models across modeling frameworks due to ambiguity about the model and its capabilities. To address this need, we propose an ontology for water resources model components that describes core concepts and relationships using the Web Ontology Language (OWL). The ontology that we present, which is termed the Water Resources Component (WRC) ontology, is meant to serve as a starting point that can be refined over time through engagement by the larger community until a robust knowledge framework for water resource model components is achieved. This paper presents the methodology used to arrive at the WRC ontology, the WRC ontology itself, and examples of how the ontology can aid in component-based water resources modeling by (i) assisting in identifying relevant models, (ii) encouraging proper model coupling, and (iii) facilitating interoperability across earth science modeling frameworks.

Layout Optimization Model for the Production Planning of Precast Concrete Building Components

Directory of Open Access Journals (Sweden)

Dong Wang

2018-05-01

Full Text Available Precast concrete comprises the basic components of modular buildings. The efficiency of precast concrete building component production directly impacts the construction time and cost. In the processes of precast component production, mold setting has a significant influence on the production efficiency and cost, as well as reducing the resource consumption. However, the development of mold setting plans is left to the experience of production staff, with outcomes dependent on the quality of human skill and experience available. This can result in sub-optimal production efficiencies and resource wastage. Accordingly, in order to improve the efficiency of precast component production, this paper proposes an optimization model able to maximize the average utilization rate of pallets used during the molding process. The constraints considered were the order demand, the size of the pallet, layout methods, and the positional relationship of components. A heuristic algorithm was used to identify optimization solutions provided by the model. Through empirical analysis, and as exemplified in the case study, this research is significant in offering a prefabrication production planning model which improves pallet utilization rates, shortens component production time, reduces production costs, and improves the resource utilization. The results clearly demonstrate that the proposed method can facilitate the precast production plan providing strong practical implications for production planners.

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.

Longitudinal functional principal component modelling via Stochastic Approximation Monte Carlo

KAUST Repository

Martinez, Josue G.

2010-06-01

The authors consider the analysis of hierarchical longitudinal functional data based upon a functional principal components approach. In contrast to standard frequentist approaches to selecting the number of principal components, the authors do model averaging using a Bayesian formulation. A relatively straightforward reversible jump Markov Chain Monte Carlo formulation has poor mixing properties and in simulated data often becomes trapped at the wrong number of principal components. In order to overcome this, the authors show how to apply Stochastic Approximation Monte Carlo (SAMC) to this problem, a method that has the potential to explore the entire space and does not become trapped in local extrema. The combination of reversible jump methods and SAMC in hierarchical longitudinal functional data is simplified by a polar coordinate representation of the principal components. The approach is easy to implement and does well in simulated data in determining the distribution of the number of principal components, and in terms of its frequentist estimation properties. Empirical applications are also presented.

Physicochemical properties of different corn varieties by principal components analysis and cluster analysis

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)

Modelling Creativity: Identifying Key Components through a Corpus-Based Approach.

Science.gov (United States)

Jordanous, Anna; Keller, Bill

2016-01-01

Creativity is a complex, multi-faceted concept encompassing a variety of related aspects, abilities, properties and behaviours. If we wish to study creativity scientifically, then a tractable and well-articulated model of creativity is required. Such a model would be of great value to researchers investigating the nature of creativity and in particular, those concerned with the evaluation of creative practice. This paper describes a unique approach to developing a suitable model of how creative behaviour emerges that is based on the words people use to describe the concept. Using techniques from the field of statistical natural language processing, we identify a collection of fourteen key components of creativity through an analysis of a corpus of academic papers on the topic. Words are identified which appear significantly often in connection with discussions of the concept. Using a measure of lexical similarity to help cluster these words, a number of distinct themes emerge, which collectively contribute to a comprehensive and multi-perspective model of creativity. The components provide an ontology of creativity: a set of building blocks which can be used to model creative practice in a variety of domains. The components have been employed in two case studies to evaluate the creativity of computational systems and have proven useful in articulating achievements of this work and directions for further research.

Investigation of low-latitude hydrogen emission in terms of a two-component interstellar gas model

International Nuclear Information System (INIS)

Baker, P.L.; Burton, W.B.

1975-01-01

The high-resolution 21-cm hydrogen line observations at low galactic latitude of Burton and Verschuur have been analyzed to determine the large-scale distribution of galactic hydrogen. The distribution parameters are found by model fitting. Optical depth affects have been computed using a two-component gas model. Analysis shows that a multiphase description of the medium is essential to the interpretation of low-latitude emission observations. Where possible, the number of free parameters in the gas model has been reduced. Calculations were performed for a one-component, uniform spin temperature, gas model in order to show the systematic departures between this model and the data caused by the incorrect treatment of the optical depth effect. In the two-component gas, radiative transfer is treated by a Monte Carlo calculation since the opacity of the gas arises in a randomly distributed, cold, optically thick, low velocity-dispersion, cloud medium. The emission arises in both the cloud medium and a smoothly distributed, optically thin, high velocity-dispersion, intercloud medium. The synthetic profiles computed from the two-component model reproduce both the large-scale trends of the observed emission profiles and the magnitude of the small-scale emission irregularities. The analysis permits the determination of values for []he thickness of the galactic disk between half density points, the total observed neutral hydrogen mass of the Galaxy, and the central number density of the intercloud atoms. In addition, the analysis is sensitive to the size of clouds contributing to the observations. Computations also show that synthetic emission profiles based on the two-component model display both the zero-velocity and high-velocity ridges, indicative of optical thinness on a large scale, in spite of the presence of optically thick gas

Damage Detection of Refractory Based on Principle Component Analysis and Gaussian Mixture Model

Directory of Open Access Journals (Sweden)

Changming Liu

2018-01-01

Full Text Available Acoustic emission (AE technique is a common approach to identify the damage of the refractories; however, there is a complex problem since there are as many as fifteen involved parameters, which calls for effective data processing and classification algorithms to reduce the level of complexity. In this paper, experiments involving three-point bending tests of refractories were conducted and AE signals were collected. A new data processing method of merging the similar parameters in the description of the damage and reducing the dimension was developed. By means of the principle component analysis (PCA for dimension reduction, the fifteen related parameters can be reduced to two parameters. The parameters were the linear combinations of the fifteen original parameters and taken as the indexes for damage classification. Based on the proposed approach, the Gaussian mixture model was integrated with the Bayesian information criterion to group the AE signals into two damage categories, which accounted for 99% of all damage. Electronic microscope scanning of the refractories verified the two types of damage.

Symmetrical components and power analysis for a two-phase microgrid system

DEFF Research Database (Denmark)

Alibeik, M.; Santos Jr., E. C. dos; Blaabjerg, Frede

2014-01-01

This paper presents a mathematical model for the symmetrical components and power analysis of a new microgrid system consisting of three wires and two voltages in quadrature, which is designated as a two-phase microgrid. The two-phase microgrid presents the following advantages: 1) constant power...

Exploring a minimal two-component p53 model

International Nuclear Information System (INIS)

Sun, Tingzhe; Zhu, Feng; Shen, Pingping; Yuan, Ruoshi; Xu, Wei

2010-01-01

The tumor suppressor p53 coordinates many attributes of cellular processes via interlocked feedback loops. To understand the biological implications of feedback loops in a p53 system, a two-component model which encompasses essential feedback loops was constructed and further explored. Diverse bifurcation properties, such as bistability and oscillation, emerge by manipulating the feedback strength. The p53-mediated MDM2 induction dictates the bifurcation patterns. We first identified irradiation dichotomy in p53 models and further proposed that bistability and oscillation can behave in a coordinated manner. Further sensitivity analysis revealed that p53 basal production and MDM2-mediated p53 degradation, which are central to cellular control, are most sensitive processes. Also, we identified that the much more significant variations in amplitude of p53 pulses observed in experiments can be derived from overall amplitude parameter sensitivity. The combined approach with bifurcation analysis, stochastic simulation and sampling-based sensitivity analysis not only gives crucial insights into the dynamics of the p53 system, but also creates a fertile ground for understanding the regulatory patterns of other biological networks

COMPONENT SUPPLY MODEL FOR REPAIR ACTIVITIES NETWORK UNDER CONDITIONS OF PROBABILISTIC INDEFINITENESS.

Directory of Open Access Journals (Sweden)

Victor Yurievich Stroganov

2017-02-01

Full Text Available This article contains the systematization of the major production functions of repair activities network and the list of planning and control functions, which are described in the form of business processes (BP. Simulation model for analysis of the delivery effectiveness of components under conditions of probabilistic uncertainty was proposed. It has been shown that a significant portion of the total number of business processes is represented by the management and planning of the parts and components movement. Questions of construction of experimental design techniques on the simulation model in the conditions of non-stationarity were considered.

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

Modelling safety of multistate systems with ageing components

Energy Technology Data Exchange (ETDEWEB)

Kołowrocki, Krzysztof; Soszyńska-Budny, Joanna [Gdynia Maritime University, Department of Mathematics ul. Morska 81-87, Gdynia 81-225 Poland (Poland)

2016-06-08

An innovative approach to safety analysis of multistate ageing systems is presented. Basic notions of the ageing multistate systems safety analysis are introduced. The system components and the system multistate safety functions are defined. The mean values and variances of the multistate systems lifetimes in the safety state subsets and the mean values of their lifetimes in the particular safety states are defined. The multi-state system risk function and the moment of exceeding by the system the critical safety state are introduced. Applications of the proposed multistate system safety models to the evaluation and prediction of the safty characteristics of the consecutive “m out of n: F” is presented as well.

Modelling safety of multistate systems with ageing components

International Nuclear Information System (INIS)

Kołowrocki, Krzysztof; Soszyńska-Budny, Joanna

2016-01-01

An innovative approach to safety analysis of multistate ageing systems is presented. Basic notions of the ageing multistate systems safety analysis are introduced. The system components and the system multistate safety functions are defined. The mean values and variances of the multistate systems lifetimes in the safety state subsets and the mean values of their lifetimes in the particular safety states are defined. The multi-state system risk function and the moment of exceeding by the system the critical safety state are introduced. Applications of the proposed multistate system safety models to the evaluation and prediction of the safty characteristics of the consecutive “m out of n: F” is presented as well.

Global sensitivity analysis of bogie dynamics with respect to suspension components

Energy Technology Data Exchange (ETDEWEB)

Mousavi Bideleh, Seyed Milad, E-mail: milad.mousavi@chalmers.se; Berbyuk, Viktor, E-mail: viktor.berbyuk@chalmers.se [Chalmers University of Technology, Department of Applied Mechanics (Sweden)

2016-06-15

The effects of bogie primary and secondary suspension stiffness and damping components on the dynamics behavior of a high speed train are scrutinized based on the multiplicative dimensional reduction method (M-DRM). A one-car railway vehicle model is chosen for the analysis at two levels of the bogie suspension system: symmetric and asymmetric configurations. Several operational scenarios including straight and circular curved tracks are considered, and measurement data are used as the track irregularities in different directions. Ride comfort, safety, and wear objective functions are specified to evaluate the vehicle’s dynamics performance on the prescribed operational scenarios. In order to have an appropriate cut center for the sensitivity analysis, the genetic algorithm optimization routine is employed to optimize the primary and secondary suspension components in terms of wear and comfort, respectively. The global sensitivity indices are introduced and the Gaussian quadrature integrals are employed to evaluate the simplified sensitivity indices correlated to the objective functions. In each scenario, the most influential suspension components on bogie dynamics are recognized and a thorough analysis of the results is given. The outcomes of the current research provide informative data that can be beneficial in design and optimization of passive and active suspension components for high speed train bogies.

Global sensitivity analysis of bogie dynamics with respect to suspension components

International Nuclear Information System (INIS)

Mousavi Bideleh, Seyed Milad; Berbyuk, Viktor

2016-01-01

The effects of bogie primary and secondary suspension stiffness and damping components on the dynamics behavior of a high speed train are scrutinized based on the multiplicative dimensional reduction method (M-DRM). A one-car railway vehicle model is chosen for the analysis at two levels of the bogie suspension system: symmetric and asymmetric configurations. Several operational scenarios including straight and circular curved tracks are considered, and measurement data are used as the track irregularities in different directions. Ride comfort, safety, and wear objective functions are specified to evaluate the vehicle’s dynamics performance on the prescribed operational scenarios. In order to have an appropriate cut center for the sensitivity analysis, the genetic algorithm optimization routine is employed to optimize the primary and secondary suspension components in terms of wear and comfort, respectively. The global sensitivity indices are introduced and the Gaussian quadrature integrals are employed to evaluate the simplified sensitivity indices correlated to the objective functions. In each scenario, the most influential suspension components on bogie dynamics are recognized and a thorough analysis of the results is given. The outcomes of the current research provide informative data that can be beneficial in design and optimization of passive and active suspension components for high speed train bogies.

A Bayesian Analysis of Unobserved Component Models Using Ox

Directory of Open Access Journals (Sweden)

Charles S. Bos

2011-05-01

Full Text Available This article details a Bayesian analysis of the Nile river flow data, using a similar state space model as other articles in this volume. For this data set, Metropolis-Hastings and Gibbs sampling algorithms are implemented in the programming language Ox. These Markov chain Monte Carlo methods only provide output conditioned upon the full data set. For filtered output, conditioning only on past observations, the particle filter is introduced. The sampling methods are flexible, and this advantage is used to extend the model to incorporate a stochastic volatility process. The volatility changes both in the Nile data and also in daily S&P 500 return data are investigated. The posterior density of parameters and states is found to provide information on which elements of the model are easily identifiable, and which elements are estimated with less precision.

Assessing prescription drug abuse using functional principal component analysis (FPCA) of wastewater data.

Science.gov (United States)

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.

Isotropic vs. anisotropic components of BAO data: a tool for model selection

Science.gov (United States)

Haridasu, Balakrishna S.; Luković, Vladimir V.; Vittorio, Nicola

2018-05-01

We conduct a selective analysis of the isotropic (DV) and anisotropic (AP) components of the most recent Baryon Acoustic Oscillations (BAO) data. We find that these components provide significantly different constraints and could provide strong diagnostics for model selection, also in view of more precise data to arrive. For instance, in the ΛCDM model we find a mild tension of ~ 2 σ for the Ωm estimates obtained using DV and AP separately. Considering both Ωk and w as free parameters, we find that the concordance model is in tension with the best-fit values provided by the BAO data alone at 2.2σ. We complemented the BAO data with the Supernovae Ia (SNIa) and Observational Hubble datasets to perform a joint analysis on the ΛCDM model and its standard extensions. By assuming ΛCDM scenario, we find that these data provide H0 = 69.4 ± 1.7 km/s Mpc‑1 as the best-fit value for the present expansion rate. In the kΛCDM scenario we find that the evidence for acceleration using the BAO data alone is more than ~ 5.8σ, which increases to 8.4 σ in our joint analysis.

THE STUDY OF THE CHARACTERIZATION INDICES OF FABRICS BY PRINCIPAL COMPONENT ANALYSIS METHOD

Directory of Open Access Journals (Sweden)

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.

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.

Fault Diagnosis Method Based on Information Entropy and Relative Principal Component Analysis

Directory of Open Access Journals (Sweden)

Xiaoming Xu

2017-01-01

Full Text Available In traditional principle component analysis (PCA, because of the neglect of the dimensions influence between different variables in the system, the selected principal components (PCs often fail to be representative. While the relative transformation PCA is able to solve the above problem, it is not easy to calculate the weight for each characteristic variable. In order to solve it, this paper proposes a kind of fault diagnosis method based on information entropy and Relative Principle Component Analysis. Firstly, the algorithm calculates the information entropy for each characteristic variable in the original dataset based on the information gain algorithm. Secondly, it standardizes every variable’s dimension in the dataset. And, then, according to the information entropy, it allocates the weight for each standardized characteristic variable. Finally, it utilizes the relative-principal-components model established for fault diagnosis. Furthermore, the simulation experiments based on Tennessee Eastman process and Wine datasets demonstrate the feasibility and effectiveness of the new method.

Fault Detection of Reciprocating Compressors using a Model from Principles Component Analysis of Vibrations

International Nuclear Information System (INIS)

Ahmed, M; Gu, F; Ball, A D

2012-01-01

Traditional vibration monitoring techniques have found it difficult to determine a set of effective diagnostic features due to the high complexity of the vibration signals originating from the many different impact sources and wide ranges of practical operating conditions. In this paper Principal Component Analysis (PCA) is used for selecting vibration feature and detecting different faults in a reciprocating compressor. Vibration datasets were collected from the compressor under baseline condition and five common faults: valve leakage, inter-cooler leakage, suction valve leakage, loose drive belt combined with intercooler leakage and belt loose drive belt combined with suction valve leakage. A model using five PCs has been developed using the baseline data sets and the presence of faults can be detected by comparing the T 2 and Q values from the features of fault vibration signals with corresponding thresholds developed from baseline data. However, the Q -statistic procedure produces a better detection as it can separate the five faults completely.

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.

Principal component analysis acceleration of rovibrational coarse-grain models for internal energy excitation and dissociation

Science.gov (United States)

Bellemans, Aurélie; Parente, Alessandro; Magin, Thierry

2018-04-01

The present work introduces a novel approach for obtaining reduced chemistry representations of large kinetic mechanisms in strong non-equilibrium conditions. The need for accurate reduced-order models arises from compression of large ab initio quantum chemistry databases for their use in fluid codes. The method presented in this paper builds on existing physics-based strategies and proposes a new approach based on the combination of a simple coarse grain model with Principal Component Analysis (PCA). The internal energy levels of the chemical species are regrouped in distinct energy groups with a uniform lumping technique. Following the philosophy of machine learning, PCA is applied on the training data provided by the coarse grain model to find an optimally reduced representation of the full kinetic mechanism. Compared to recently published complex lumping strategies, no expert judgment is required before the application of PCA. In this work, we will demonstrate the benefits of the combined approach, stressing its simplicity, reliability, and accuracy. The technique is demonstrated by reducing the complex quantum N2(g+1Σ) -N(S4u ) database for studying molecular dissociation and excitation in strong non-equilibrium. Starting from detailed kinetics, an accurate reduced model is developed and used to study non-equilibrium properties of the N2(g+1Σ) -N(S4u ) system in shock relaxation simulations.

Failure trend analysis for safety related components of Korean standard NPPs

International Nuclear Information System (INIS)

Choi, Sun Yeong; Han, Sang Hoon

2005-01-01

The component reliability data of Korean NPP that reflects the plant specific characteristics is required necessarily for PSA of Korean nuclear power plants. We have performed a project to develop the component reliability database (KIND, Korea Integrated Nuclear Reliability Database) and S/W for database management and component reliability analysis. Based on the system, we have collected the component operation data and failure/repair data during from plant operation date to 2002 for YGN 3, 4 and UCN 3, 4 plants. Recently, we provided the component failure rate data for UCN 3, 4 standard PSA model from the KIND. We evaluated the components that have high-ranking failure rates with the component reliability data from plant operation date to 1998 and 2000 for YGN 3,4 and UCN 3, 4 respectively. We also identified their failure mode that occurred frequently. In this study, we analyze the component failure trend and perform site comparison based on the generic data by using the component reliability data which is extended to 2002 for UCN 3, 4 and YGN 3, 4 respectively. We focus on the major safety related rotating components such as pump, EDG etc

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.)

Fluoride in the Serra Geral Aquifer System: Source Evaluation Using Stable Isotopes and Principal Component Analysis

OpenAIRE

Nanni, Arthur Schmidt; Roisenberg, Ari; de Hollanda, Maria Helena Bezerra Maia; Marimon, Maria Paula Casagrande; Viero, Antonio Pedro; Scheibe, Luiz Fernando

2013-01-01

Groundwater with anomalous fluoride content and water mixture patterns were studied in the fractured Serra Geral Aquifer System, a basaltic to rhyolitic geological unit, using a principal component analysis interpretation of groundwater chemical data from 309 deep wells distributed in the Rio Grande do Sul State, Southern Brazil. A four-component model that explains 81% of the total variance in the Principal Component Analysis is suggested. Six hydrochemical groups were identified. δ18O and δ...

Feature-based component model for design of embedded systems

Science.gov (United States)

Zha, Xuan Fang; Sriram, Ram D.

2004-11-01

An embedded system is a hybrid of hardware and software, which combines software's flexibility and hardware real-time performance. Embedded systems can be considered as assemblies of hardware and software components. An Open Embedded System Model (OESM) is currently being developed at NIST to provide a standard representation and exchange protocol for embedded systems and system-level design, simulation, and testing information. This paper proposes an approach to representing an embedded system feature-based model in OESM, i.e., Open Embedded System Feature Model (OESFM), addressing models of embedded system artifacts, embedded system components, embedded system features, and embedded system configuration/assembly. The approach provides an object-oriented UML (Unified Modeling Language) representation for the embedded system feature model and defines an extension to the NIST Core Product Model. The model provides a feature-based component framework allowing the designer to develop a virtual embedded system prototype through assembling virtual components. The framework not only provides a formal precise model of the embedded system prototype but also offers the possibility of designing variation of prototypes whose members are derived by changing certain virtual components with different features. A case study example is discussed to illustrate the embedded system model.

Crude oil price analysis and forecasting based on variational mode decomposition and independent component analysis

Science.gov (United States)

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.

Feedback loops and temporal misalignment in component-based hydrologic modeling

Science.gov (United States)

Elag, Mostafa M.; Goodall, Jonathan L.; Castronova, Anthony M.

2011-12-01

In component-based modeling, a complex system is represented as a series of loosely integrated components with defined interfaces and data exchanges that allow the components to be coupled together through shared boundary conditions. Although the component-based paradigm is commonly used in software engineering, it has only recently been applied for modeling hydrologic and earth systems. As a result, research is needed to test and verify the applicability of the approach for modeling hydrologic systems. The objective of this work was therefore to investigate two aspects of using component-based software architecture for hydrologic modeling: (1) simulation of feedback loops between components that share a boundary condition and (2) data transfers between temporally misaligned model components. We investigated these topics using a simple case study where diffusion of mass is modeled across a water-sediment interface. We simulated the multimedia system using two model components, one for the water and one for the sediment, coupled using the Open Modeling Interface (OpenMI) standard. The results were compared with a more conventional numerical approach for solving the system where the domain is represented by a single multidimensional array. Results showed that the component-based approach was able to produce the same results obtained with the more conventional numerical approach. When the two components were temporally misaligned, we explored the use of different interpolation schemes to minimize mass balance error within the coupled system. The outcome of this work provides evidence that component-based modeling can be used to simulate complicated feedback loops between systems and guidance as to how different interpolation schemes minimize mass balance error introduced when components are temporally misaligned.

Advances in independent component analysis and learning machines

CERN Document Server

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

Evaluation of chemical transport model predictions of primary organic aerosol for air masses classified by particle-component-based factor analysis

OpenAIRE

C. A. Stroud; M. D. Moran; P. A. Makar; S. Gong; W. Gong; J. Zhang; J. G. Slowik; J. P. D. Abbatt; G. Lu; J. R. Brook; C. Mihele; Q. Li; D. Sills; K. B. Strawbridge; M. L. McGuire

2012-01-01

Observations from the 2007 Border Air Quality and Meteorology Study (BAQS-Met 2007) in Southern Ontario, Canada, were used to evaluate predictions of primary organic aerosol (POA) and two other carbonaceous species, black carbon (BC) and carbon monoxide (CO), made for this summertime period by Environment Canada's AURAMS regional chemical transport model. Particle component-based factor analysis was applied to aerosol mass spectrometer measurements made at one urban site (Windsor, ON) and two...

Model-integrating software components engineering flexible software systems

CERN Document Server

Derakhshanmanesh, Mahdi

2015-01-01

In his study, Mahdi Derakhshanmanesh builds on the state of the art in modeling by proposing to integrate models into running software on the component-level without translating them to code. Such so-called model-integrating software exploits all advantages of models: models implicitly support a good separation of concerns, they are self-documenting and thus improve understandability and maintainability and in contrast to model-driven approaches there is no synchronization problem anymore between the models and the code generated from them. Using model-integrating components, software will be

Determination of the optimal number of components in independent components analysis.

Science.gov (United States)

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.

Multivariate analysis of remote LIBS spectra using partial least squares, principal component analysis, and related techniques

Energy Technology Data Exchange (ETDEWEB)

Clegg, Samuel M [Los Alamos National Laboratory; Barefield, James E [Los Alamos National Laboratory; Wiens, Roger C [Los Alamos National Laboratory; Sklute, Elizabeth [MT HOLYOKE COLLEGE; Dyare, Melinda D [MT HOLYOKE COLLEGE

2008-01-01

Quantitative analysis with LIBS traditionally employs calibration curves that are complicated by the chemical matrix effects. These chemical matrix effects influence the LIBS plasma and the ratio of elemental composition to elemental emission line intensity. Consequently, LIBS calibration typically requires a priori knowledge of the unknown, in order for a series of calibration standards similar to the unknown to be employed. In this paper, three new Multivariate Analysis (MV A) techniques are employed to analyze the LIBS spectra of 18 disparate igneous and highly-metamorphosed rock samples. Partial Least Squares (PLS) analysis is used to generate a calibration model from which unknown samples can be analyzed. Principal Components Analysis (PCA) and Soft Independent Modeling of Class Analogy (SIMCA) are employed to generate a model and predict the rock type of the samples. These MV A techniques appear to exploit the matrix effects associated with the chemistries of these 18 samples.

Flexible Multibody Systems Models Using Composite Materials Components

International Nuclear Information System (INIS)

Neto, Maria Augusta; Ambr'osio, Jorge A. C.; Leal, Rog'erio Pereira

2004-01-01

The use of a multibody methodology to describe the large motion of complex systems that experience structural deformations enables to represent the complete system motion, the relative kinematics between the components involved, the deformation of the structural members and the inertia coupling between the large rigid body motion and the system elastodynamics. In this work, the flexible multibody dynamics formulations of complex models are extended to include elastic components made of composite materials, which may be laminated and anisotropic. The deformation of any structural member must be elastic and linear, when described in a coordinate frame fixed to one or more material points of its domain, regardless of the complexity of its geometry. To achieve the proposed flexible multibody formulation, a finite element model for each flexible body is used. For the beam composite material elements, the sections properties are found using an asymptotic procedure that involves a two-dimensional finite element analysis of their cross-section. The equations of motion of the flexible multibody system are solved using an augmented Lagrangian formulation and the accelerations and velocities are integrated in time using a multi-step multi-order integration algorithm based on the Gear method

Principal component analysis in an experimental cold flow model of a fluid catalytic cracking unit by gammametry

International Nuclear Information System (INIS)

Araujo, Janeo Severino C. de; Dantas, Carlos Costa; Santos, Valdemir A. dos; Souza, Jose Edson G. de; Luna-Finkler, Christine L.

2009-01-01

The fluid dynamic behavior of riser of a cold flow model of a Fluid Catalytic Cracking Unit (FCCU) was investigated. The experimental data were obtained by the nuclear technique of gamma transmission. A gamma source was placed diametrically opposite to a detector in any straight section of the riser. The gas-solid flow through riser was monitored with a source of Americium-241 what allowed obtaining information of the axial solid concentration without flow disturbance and also identifying the dependence of this concentration profile with several independent variables. The MatLab R and Statistica R software were used. Statistica tool employed was the Principal Components Analysis (PCA), that consisted of the job of the data organization, through two-dimensional head offices to allow extract relevant information about the importance of the independent variables on axial solid concentration in a cold flow riser. The variables investigated were mass flow rate of solid, mass flow rate of gas, pressure in the riser base and the relative height in the riser. The first two components reached about 98 % of accumulated percentage of explained variance. (author)

Pump Component Model in SPACE Code

International Nuclear Information System (INIS)

Kim, Byoung Jae; Kim, Kyoung Doo

2010-08-01

This technical report describes the pump component model in SPACE code. A literature survey was made on pump models in existing system codes. The models embedded in SPACE code were examined to check the confliction with intellectual proprietary rights. Design specifications, computer coding implementation, and test results are included in this report

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

Modeling and numerical simulation of multi-component flow in porous media

International Nuclear Information System (INIS)

Saad, B.

2011-01-01

This work deals with the modelization and numerical simulation of two phase multi-component flow in porous media. The study is divided into two parts. First we study and prove the mathematical existence in a weak sense of two degenerate parabolic systems modeling two phase (liquid and gas) two component (water and hydrogen) flow in porous media. In the first model, we assume that there is a local thermodynamic equilibrium between both phases of hydrogen by using the Henry's law. The second model consists of a relaxation of the previous model: the kinetic of the mass exchange between dissolved hydrogen and hydrogen in the gas phase is no longer instantaneous. The second part is devoted to the numerical analysis of those models. Firstly, we propose a numerical scheme to compare numerical solutions obtained with the first model and numerical solutions obtained with the second model where the characteristic time to recover the thermodynamic equilibrium goes to zero. Secondly, we present a finite volume scheme with a phase-by-phase upstream weighting scheme without simplified assumptions on the state law of gas densities. We also validate this scheme on a 2D test cases. (author)

Multilevel sparse functional principal component analysis.

Science.gov (United States)

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.

SASSYS-1 balance-of-plant component models for an integrated plant response

International Nuclear Information System (INIS)

Ku, J.-Y.

1989-01-01

Models of power plant heat transfer components and rotating machinery have been added to the balance-of-plant model in the SASSYS-1 liquid metal reactor systems analysis code. This work is part of a continuing effort in plant network simulation based on the general mathematical models developed. The models described in this paper extend the scope of the balance-of-plant model to handle non-adiabatic conditions along flow paths. While the mass and momentum equations remain the same, the energy equation now contains a heat source term due to energy transfer across the flow boundary or to work done through a shaft. The heat source term is treated fully explicitly. In addition, the equation of state is rewritten in terms of the quality and separate parameters for each phase. The models are simple enough to run quickly, yet include sufficient detail of dominant plant component characteristics to provide accurate results. 5 refs., 16 figs., 2 tabs

Research on criticality analysis method of CNC machine tools components under fault rate correlation

Science.gov (United States)

Gui-xiang, Shen; Xian-zhuo, Zhao; Zhang, Ying-zhi; Chen-yu, Han

2018-02-01

In order to determine the key components of CNC machine tools under fault rate correlation, a system component criticality analysis method is proposed. Based on the fault mechanism analysis, the component fault relation is determined, and the adjacency matrix is introduced to describe it. Then, the fault structure relation is hierarchical by using the interpretive structure model (ISM). Assuming that the impact of the fault obeys the Markov process, the fault association matrix is described and transformed, and the Pagerank algorithm is used to determine the relative influence values, combined component fault rate under time correlation can obtain comprehensive fault rate. Based on the fault mode frequency and fault influence, the criticality of the components under the fault rate correlation is determined, and the key components are determined to provide the correct basis for equationting the reliability assurance measures. Finally, taking machining centers as an example, the effectiveness of the method is verified.

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

Formal Model-Driven Engineering: Generating Data and Behavioural Components

Directory of Open Access Journals (Sweden)

Chen-Wei Wang

2012-12-01

Full Text Available Model-driven engineering is the automatic production of software artefacts from abstract models of structure and functionality. By targeting a specific class of system, it is possible to automate aspects of the development process, using model transformations and code generators that encode domain knowledge and implementation strategies. Using this approach, questions of correctness for a complex, software system may be answered through analysis of abstract models of lower complexity, under the assumption that the transformations and generators employed are themselves correct. This paper shows how formal techniques can be used to establish the correctness of model transformations used in the generation of software components from precise object models. The source language is based upon existing, formal techniques; the target language is the widely-used SQL notation for database programming. Correctness is established by giving comparable, relational semantics to both languages, and checking that the transformations are semantics-preserving.

Markov and semi-Markov switching linear mixed models used to identify forest tree growth components.

Science.gov (United States)

Chaubert-Pereira, Florence; Guédon, Yann; Lavergne, Christian; Trottier, Catherine

2010-09-01

Tree growth is assumed to be mainly the result of three components: (i) an endogenous component assumed to be structured as a succession of roughly stationary phases separated by marked change points that are asynchronous among individuals, (ii) a time-varying environmental component assumed to take the form of synchronous fluctuations among individuals, and (iii) an individual component corresponding mainly to the local environment of each tree. To identify and characterize these three components, we propose to use semi-Markov switching linear mixed models, i.e., models that combine linear mixed models in a semi-Markovian manner. The underlying semi-Markov chain represents the succession of growth phases and their lengths (endogenous component) whereas the linear mixed models attached to each state of the underlying semi-Markov chain represent-in the corresponding growth phase-both the influence of time-varying climatic covariates (environmental component) as fixed effects, and interindividual heterogeneity (individual component) as random effects. In this article, we address the estimation of Markov and semi-Markov switching linear mixed models in a general framework. We propose a Monte Carlo expectation-maximization like algorithm whose iterations decompose into three steps: (i) sampling of state sequences given random effects, (ii) prediction of random effects given state sequences, and (iii) maximization. The proposed statistical modeling approach is illustrated by the analysis of successive annual shoots along Corsican pine trunks influenced by climatic covariates. © 2009, The International Biometric Society.

PASCAL, Probabilistic Fracture Mechanics Analysis of Structural Components in Aging LWR

International Nuclear Information System (INIS)

Shibata, Katsuyuki; Onizawa, Kunio; Li, Yinsheng; Kato, Daisuke

2005-01-01

A - Description of program or function: PASCAL (PFM analysis of Structural Components in Aging LWR) is a PFM (Probabilistic Fracture Mechanics) code for evaluating the failure probability of aged pressure components. PASCAL has been developed as a part of the JAERI's research program on aging and structural integrity of LWR components, in order to respond to the increasing need of the probabilistic methodology in the regulation and inspection of nuclear components with the objective to provide a rational tool for the evaluation of the reliability and integrity of structural components. In order to improve the accuracy and reliability of the analysis code, some new fracture mechanics models or computational techniques are introduced considering the recent progress in the state of the art and performance of PC. Thus some new analysis models and original methodologies were introduced in PASCAL such as the elastic-plastic fracture criterion based on R6 method, a new crack extension model of semi-elliptical crack evaluation and so on. Moreover a function to evaluate the effect of embrittlement recovery by annealing of irradiated RPV is also introduced in the code based on the USNRC R.G. 1.162(1996). The code has been verified through various failure analysis results and international PTS round robin analysis ICAS which had been organized by the Principal Working Group 3 of OECD/NEA/CSNI. In order to attain a high usability, PASCAL Ver.1 with GUI provides an exclusive FEM pre-processor Pre-PASCAL for generating the input load transient data, a GUI system for generating the input data for PASCAL main processor of main solver and post-processor for output data. - Pre-PASCAL: Pre-PASCAL is an exclusive 3-D FEM pre-processor for generating the input transient data provided with 3 RPV mesh models and two simple specimen mesh models, i.e. CT and CCP. Almost the same input data format with that of PASCAL main processor is used. Output data of temperature and stress distribution

Crude Oil Price Forecasting Based on Hybridizing Wavelet Multiple Linear Regression Model, Particle Swarm Optimization Techniques, and Principal Component Analysis

Science.gov (United States)

Shabri, Ani; Samsudin, Ruhaidah

2014-01-01

Crude oil prices do play significant role in the global economy and are a key input into option pricing formulas, portfolio allocation, and risk measurement. In this paper, a hybrid model integrating wavelet and multiple linear regressions (MLR) is proposed for crude oil price forecasting. In this model, Mallat wavelet transform is first selected to decompose an original time series into several subseries with different scale. Then, the principal component analysis (PCA) is used in processing subseries data in MLR for crude oil price forecasting. The particle swarm optimization (PSO) is used to adopt the optimal parameters of the MLR model. To assess the effectiveness of this model, daily crude oil market, West Texas Intermediate (WTI), has been used as the case study. Time series prediction capability performance of the WMLR model is compared with the MLR, ARIMA, and GARCH models using various statistics measures. The experimental results show that the proposed model outperforms the individual models in forecasting of the crude oil prices series. PMID:24895666

Crude oil price forecasting based on hybridizing wavelet multiple linear regression model, particle swarm optimization techniques, and principal component analysis.

Science.gov (United States)

Shabri, Ani; Samsudin, Ruhaidah

2014-01-01

Crude oil prices do play significant role in the global economy and are a key input into option pricing formulas, portfolio allocation, and risk measurement. In this paper, a hybrid model integrating wavelet and multiple linear regressions (MLR) is proposed for crude oil price forecasting. In this model, Mallat wavelet transform is first selected to decompose an original time series into several subseries with different scale. Then, the principal component analysis (PCA) is used in processing subseries data in MLR for crude oil price forecasting. The particle swarm optimization (PSO) is used to adopt the optimal parameters of the MLR model. To assess the effectiveness of this model, daily crude oil market, West Texas Intermediate (WTI), has been used as the case study. Time series prediction capability performance of the WMLR model is compared with the MLR, ARIMA, and GARCH models using various statistics measures. The experimental results show that the proposed model outperforms the individual models in forecasting of the crude oil prices series.

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

New approaches to the modelling of multi-component fuel droplet heating and evaporation

KAUST Repository

Sazhin, Sergei S; Elwardany, Ahmed E; Heikal, Morgan R

2015-01-01

numbers n and temperatures is taken into account. The effects of temperature gradient and quasi-component diffusion inside droplets are taken into account. The analysis is based on the Effective Thermal Conductivity/Effective Diffusivity (ETC/ED) model

Integrating Data Transformation in Principal Components Analysis

KAUST Repository

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.

Analysis of detached recombining plasmas by collisonal-radiative model with energetic electron component

International Nuclear Information System (INIS)

Ohno, N.; Motoyama, M.; Takamura, S.

2001-01-01

using CR model for a helium plasma (Goto-Fujimoto code), in which the energetic electron component (electron beam) is taken into account in addition to the bulk electron Maxwellian distribution function. It is found that the evaluated bulk electron temperature with the method of Boltzmann plot tends to decrease with an increase in the electron beam density and/or energy because the population densities in relatively lower excited states become large, comparing with those in higher excited state. This result agrees with the experimental observations. We have also analyzed transition of recombining plasma to ionizing one and vice versa in detail. This analysis can reproduce the inverse ELM phenomena observed in JET and ASDEX-U. (orig.)

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.

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

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)

A comparison of response spectrum and direct integration analysis methods as applied to a nuclear component support structure

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

Dynamic thermal modelling and analysis of press-pack IGBTs both at component-level and chip-level

DEFF Research Database (Denmark)

Busca, Cristian; Teodorescu, Remus; Blaabjerg, Frede

2013-01-01

curves under various mechanical clamping conditions are derived. Moreover, the deformation of the internal components of the PP IGBT under operating-like conditions is investigated with the help of the thermal models and the coefficient of thermal expansion (CTE) information.......Thermal models are needed when designing power converters for Wind Turbines (WTs) in order to carry out thermal and reliability assessment of certain designs. Usually the thermal models of Insulated Gate Bipolar Transistors (IGBTs) are given in the datasheet in various forms at component......-level, not taking into account the thermal distribution among the chips. This is especially relevant in the case of Press-Pack (PP) IGBTs because any non-uniformity of the clamping pressure can affect the chip-level thermal impedances. This happens because the contact thermal resistances in the thermal impedance...

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.

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.

A Principle Component Analysis of Galaxy Properties from a Large, Gas-Selected Sample

Directory of Open Access Journals (Sweden)

Yu-Yen Chang

2012-01-01

concluded that this is in conflict with the CDM model. Considering the importance of the issue, we reinvestigate the problem using the principal component analysis on a fivefold larger sample and additional near-infrared data. We use databases from the Arecibo Legacy Fast Arecibo L-band Feed Array Survey for the gas properties, the Sloan Digital Sky Survey for the optical properties, and the Two Micron All Sky Survey for the near-infrared properties. We confirm that the parameters are indeed correlated where a single physical parameter can explain 83% of the variations. When color (g-i is included, the first component still dominates but it develops a second principal component. In addition, the near-infrared color (i-J shows an obvious second principal component that might provide evidence of the complex old star formation. Based on our data, we suggest that it is premature to pronounce the failure of the CDM model and it motivates more theoretical work.

A probabilistic model for component-based shape synthesis

KAUST Repository

Kalogerakis, Evangelos

2012-07-01

We present an approach to synthesizing shapes from complex domains, by identifying new plausible combinations of components from existing shapes. Our primary contribution is a new generative model of component-based shape structure. The model represents probabilistic relationships between properties of shape components, and relates them to learned underlying causes of structural variability within the domain. These causes are treated as latent variables, leading to a compact representation that can be effectively learned without supervision from a set of compatibly segmented shapes. We evaluate the model on a number of shape datasets with complex structural variability and demonstrate its application to amplification of shape databases and to interactive shape synthesis. © 2012 ACM 0730-0301/2012/08-ART55.

Improved separability of dipole sources by tripolar versus conventional disk electrodes: a modeling study using independent component analysis.

Science.gov (United States)

Cao, H; Besio, W; Jones, S; Medvedev, A

2009-01-01

Tripolar electrodes have been shown to have less mutual information and higher spatial resolution than disc electrodes. In this work, a four-layer anisotropic concentric spherical head computer model was programmed, then four configurations of time-varying dipole signals were used to generate the scalp surface signals that would be obtained with tripolar and disc electrodes, and four important EEG artifacts were tested: eye blinking, cheek movements, jaw movements, and talking. Finally, a fast fixed-point algorithm was used for signal independent component analysis (ICA). The results show that signals from tripolar electrodes generated better ICA separation results than from disc electrodes for EEG signals with these four types of artifacts.

Wavelet decomposition based principal component analysis for face recognition using MATLAB

Science.gov (United States)

Sharma, Mahesh Kumar; Sharma, Shashikant; Leeprechanon, Nopbhorn; Ranjan, Aashish

2016-03-01

For the realization of face recognition systems in the static as well as in the real time frame, algorithms such as principal component analysis, independent component analysis, linear discriminate analysis, neural networks and genetic algorithms are used for decades. This paper discusses an approach which is a wavelet decomposition based principal component analysis for face recognition. Principal component analysis is chosen over other algorithms due to its relative simplicity, efficiency, and robustness features. The term face recognition stands for identifying a person from his facial gestures and having resemblance with factor analysis in some sense, i.e. extraction of the principal component of an image. Principal component analysis is subjected to some drawbacks, mainly the poor discriminatory power and the large computational load in finding eigenvectors, in particular. These drawbacks can be greatly reduced by combining both wavelet transform decomposition for feature extraction and principal component analysis for pattern representation and classification together, by analyzing the facial gestures into space and time domain, where, frequency and time are used interchangeably. From the experimental results, it is envisaged that this face recognition method has made a significant percentage improvement in recognition rate as well as having a better computational efficiency.

Improving the robustness of a partial least squares (PLS) model based on pure component selectivity analysis and range optimization: Case study for the analysis of an etching solution containing hydrogen peroxide

Energy Technology Data Exchange (ETDEWEB)

Lee, Youngbok [Department of Chemistry, College of Natural Sciences, Hanyang University Haengdang-Dong, Seoul 133-791 (Korea, Republic of); Chung, Hoeil [Department of Chemistry, College of Natural Sciences, Hanyang University Haengdang-Dong, Seoul 133-791 (Korea, Republic of)]. E-mail: hoeil@hanyang.ac.kr; Arnold, Mark A. [Optical Science and Technology Center and Department of Chemistry, University of Iowa, Iowa City, IA 52242 (United States)

2006-07-14

Pure component selectivity analysis (PCSA) was successfully utilized to enhance the robustness of a partial least squares (PLS) model by examining the selectivity of a given component to other components. The samples used in this study were composed of NH{sub 4}OH, H{sub 2}O{sub 2} and H{sub 2}O, a popular etchant solution in the electronic industry. Corresponding near-infrared (NIR) spectra (9000-7500 cm{sup -1}) were used to build PLS models. The selective determination of H{sub 2}O{sub 2} without influences from NH{sub 4}OH and H{sub 2}O was a key issue since its molecular structure is similar to that of H{sub 2}O and NH{sub 4}OH also has a hydroxyl functional group. The best spectral ranges for the determination of NH{sub 4}OH and H{sub 2}O{sub 2} were found with the use of moving window PLS (MW-PLS) and corresponding selectivity was examined by pure component selectivity analysis. The PLS calibration for NH{sub 4}OH was free from interferences from the other components due to the presence of its unique NH absorption bands. Since the spectral variation from H{sub 2}O{sub 2} was broadly overlapping and much less distinct than that from NH{sub 4}OH, the selectivity and prediction performance for the H{sub 2}O{sub 2} calibration were sensitively varied depending on the spectral ranges and number of factors used. PCSA, based on the comparison between regression vectors from PLS and the net analyte signal (NAS), was an effective method to prevent over-fitting of the H{sub 2}O{sub 2} calibration. A robust H{sub 2}O{sub 2} calibration model with minimal interferences from other components was developed. PCSA should be included as a standard method in PLS calibrations where prediction error only is the usual measure of performance.

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

On the structure of dynamic principal component analysis used in statistical process monitoring

DEFF Research Database (Denmark)

Vanhatalo, Erik; Kulahci, Murat; Bergquist, Bjarne

2017-01-01

When principal component analysis (PCA) is used for statistical process monitoring it relies on the assumption that data are time independent. However, industrial data will often exhibit serial correlation. Dynamic PCA (DPCA) has been suggested as a remedy for high-dimensional and time...... for determining the number of principal components to retain. The number of retained principal components is determined by visual inspection of the serial correlation in the squared prediction error statistic, Q (SPE), together with the cumulative explained variance of the model. The methods are illustrated using...... driven method to determine the maximum number of lags in DPCA with a foundation in multivariate time series analysis. The method is based on the behavior of the eigenvalues of the lagged autocorrelation and partial autocorrelation matrices. Given a specific lag structure we also propose a method...

Modeling and analysis of the disk MHD generator component of a gas core reactor/MHD Rankine cycle space power system

International Nuclear Information System (INIS)

Welch, G.E.; Dugan, E.T.; Lear, W.E. Jr.; Appelbaum, J.G.

1990-01-01

A gas core nuclear reactor (GCR)/disk magnetohydrodynamic (MHD) generator direct closed Rankine space power system concept is described. The GCR/disk MHD generator marriage facilitates efficient high electric power density system performance at relatively high operating temperatures. The system concept promises high specific power levels, on the order of 1 kW e /kg. An overview of the disk MHD generator component magnetofluiddynamic and plasma physics theoretical modeling is provided. Results from a parametric design analysis of the disk MHD generator are presented and discussed

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

General model for Pc-based simulation of PWR and BWR plant components

Energy Technology Data Exchange (ETDEWEB)

Ratemi, W M; Abomustafa, A M [Faculty of enginnering, alfateh univerity Tripoli, (Libyan Arab Jamahiriya)

1995-10-01

In this paper, we present a basic mathematical model derived from physical principles to suit the simulation of PWR-components such as pressurizer, intact steam generator, ruptured steam generator, and the reactor component of a BWR-plant. In our development, we produced an NMMS-package for nuclear modular modelling simulation. Such package is installed on a personal computer and it is designed to be user friendly through color graphics windows interfacing. The package works under three environments, namely, pre-processor, simulation, and post-processor. Our analysis of results using cross graphing technique for steam generator tube rupture (SGTR) accident, yielded a new proposal for on-line monitoring of control strategy of SGTR-accident for nuclear or conventional power plant. 4 figs.

Component simulation in problems of calculated model formation of automatic machine mechanisms

OpenAIRE

Telegin Igor; Kozlov Alexander; Zhirkov Alexander

2017-01-01

The paper deals with the problems of the component simulation method application in the problems of the automation of the mechanical system model formation with the further possibility of their CAD-realization. The purpose of the investigations mentioned consists in the automation of the CAD-model formation of high-speed mechanisms in automatic machines and in the analysis of dynamic processes occurred in their units taking into account their elasto-inertial properties, power dissipation, gap...

A multi-dimensional functional principal components analysis of EEG data.

Science.gov (United States)

Hasenstab, Kyle; Scheffler, Aaron; Telesca, Donatello; Sugar, Catherine A; Jeste, Shafali; DiStefano, Charlotte; Şentürk, Damla

2017-09-01

The electroencephalography (EEG) data created in event-related potential (ERP) experiments have a complex high-dimensional structure. Each stimulus presentation, or trial, generates an ERP waveform which is an instance of functional data. The experiments are made up of sequences of multiple trials, resulting in longitudinal functional data and moreover, responses are recorded at multiple electrodes on the scalp, adding an electrode dimension. Traditional EEG analyses involve multiple simplifications of this structure to increase the signal-to-noise ratio, effectively collapsing the functional and longitudinal components by identifying key features of the ERPs and averaging them across trials. Motivated by an implicit learning paradigm used in autism research in which the functional, longitudinal, and electrode components all have critical interpretations, we propose a multidimensional functional principal components analysis (MD-FPCA) technique which does not collapse any of the dimensions of the ERP data. The proposed decomposition is based on separation of the total variation into subject and subunit level variation which are further decomposed in a two-stage functional principal components analysis. The proposed methodology is shown to be useful for modeling longitudinal trends in the ERP functions, leading to novel insights into the learning patterns of children with Autism Spectrum Disorder (ASD) and their typically developing peers as well as comparisons between the two groups. Finite sample properties of MD-FPCA are further studied via extensive simulations. © 2017, The International Biometric Society.

Component-based modeling of systems for automated fault tree generation

International Nuclear Information System (INIS)

Majdara, Aref; Wakabayashi, Toshio

2009-01-01

One of the challenges in the field of automated fault tree construction is to find an efficient modeling approach that can support modeling of different types of systems without ignoring any necessary details. In this paper, we are going to represent a new system of modeling approach for computer-aided fault tree generation. In this method, every system model is composed of some components and different types of flows propagating through them. Each component has a function table that describes its input-output relations. For the components having different operational states, there is also a state transition table. Each component can communicate with other components in the system only through its inputs and outputs. A trace-back algorithm is proposed that can be applied to the system model to generate the required fault trees. The system modeling approach and the fault tree construction algorithm are applied to a fire sprinkler system and the results are presented

An elementary components of variance analysis for multi-centre quality control

International Nuclear Information System (INIS)

Munson, P.J.; Rodbard, D.

1978-01-01

The serious variability of RIA results from different laboratories indicates the need for multi-laboratory collaborative quality-control (QC) studies. Simple graphical display of data in the form of histograms is useful but insufficient. The paper discusses statistical analysis methods for such studies using an ''analysis of variance with components of variance estimation''. This technique allocates the total variance into components corresponding to between-laboratory, between-assay, and residual or within-assay variability. Problems with RIA data, e.g. severe non-uniformity of variance and/or departure from a normal distribution violate some of the usual assumptions underlying analysis of variance. In order to correct these problems, it is often necessary to transform the data before analysis by using a logarithmic, square-root, percentile, ranking, RIDIT, ''Studentizing'' or other transformation. Ametric transformations such as ranks or percentiles protect against the undue influence of outlying observations, but discard much intrinsic information. Several possible relationships of standard deviation to the laboratory mean are considered. Each relationship corresponds to an underlying statistical model and an appropriate analysis technique. Tests for homogeneity of variance may be used to determine whether an appropriate model has been chosen, although the exact functional relationship of standard deviation to laboratory mean may be difficult to establish. Appropriate graphical display aids visual understanding of the data. A plot of the ranked standard deviation versus ranked laboratory mean is a convenient way to summarize a QC study. This plot also allows determination of the rank correlation, which indicates a net relationship of variance to laboratory mean

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)

Algorithmic fault tree construction by component-based system modeling

International Nuclear Information System (INIS)

Majdara, Aref; Wakabayashi, Toshio

2008-01-01

Computer-aided fault tree generation can be easier, faster and less vulnerable to errors than the conventional manual fault tree construction. In this paper, a new approach for algorithmic fault tree generation is presented. The method mainly consists of a component-based system modeling procedure an a trace-back algorithm for fault tree synthesis. Components, as the building blocks of systems, are modeled using function tables and state transition tables. The proposed method can be used for a wide range of systems with various kinds of components, if an inclusive component database is developed. (author)

Crude Oil Price Forecasting Based on Hybridizing Wavelet Multiple Linear Regression Model, Particle Swarm Optimization Techniques, and Principal Component Analysis

Directory of Open Access Journals (Sweden)

Ani Shabri

2014-01-01

Full Text Available Crude oil prices do play significant role in the global economy and are a key input into option pricing formulas, portfolio allocation, and risk measurement. In this paper, a hybrid model integrating wavelet and multiple linear regressions (MLR is proposed for crude oil price forecasting. In this model, Mallat wavelet transform is first selected to decompose an original time series into several subseries with different scale. Then, the principal component analysis (PCA is used in processing subseries data in MLR for crude oil price forecasting. The particle swarm optimization (PSO is used to adopt the optimal parameters of the MLR model. To assess the effectiveness of this model, daily crude oil market, West Texas Intermediate (WTI, has been used as the case study. Time series prediction capability performance of the WMLR model is compared with the MLR, ARIMA, and GARCH models using various statistics measures. The experimental results show that the proposed model outperforms the individual models in forecasting of the crude oil prices series.

Determining the optimal number of independent components for reproducible transcriptomic data analysis.

Science.gov (United States)

Kairov, Ulykbek; Cantini, Laura; Greco, Alessandro; Molkenov, Askhat; Czerwinska, Urszula; Barillot, Emmanuel; Zinovyev, Andrei

2017-09-11

Independent Component Analysis (ICA) is a method that models gene expression data as an action of a set of statistically independent hidden factors. The output of ICA depends on a fundamental parameter: the number of components (factors) to compute. The optimal choice of this parameter, related to determining the effective data dimension, remains an open question in the application of blind source separation techniques to transcriptomic data. Here we address the question of optimizing the number of statistically independent components in the analysis of transcriptomic data for reproducibility of the components in multiple runs of ICA (within the same or within varying effective dimensions) and in multiple independent datasets. To this end, we introduce ranking of independent components based on their stability in multiple ICA computation runs and define a distinguished number of components (Most Stable Transcriptome Dimension, MSTD) corresponding to the point of the qualitative change of the stability profile. Based on a large body of data, we demonstrate that a sufficient number of dimensions is required for biological interpretability of the ICA decomposition and that the most stable components with ranks below MSTD have more chances to be reproduced in independent studies compared to the less stable ones. At the same time, we show that a transcriptomics dataset can be reduced to a relatively high number of dimensions without losing the interpretability of ICA, even though higher dimensions give rise to components driven by small gene sets. We suggest a protocol of ICA application to transcriptomics data with a possibility of prioritizing components with respect to their reproducibility that strengthens the biological interpretation. Computing too few components (much less than MSTD) is not optimal for interpretability of the results. The components ranked within MSTD range have more chances to be reproduced in independent studies.

Traceable components of terrestrial carbon storage capacity in biogeochemical models.

Science.gov (United States)

Xia, Jianyang; Luo, Yiqi; Wang, Ying-Ping; Hararuk, Oleksandra

2013-07-01

cycle models. Nevertheless, more research is needed to develop tools to decompose NPP and transient dynamics of the modeled carbon cycle into traceable components for structural analysis of land models. © 2013 Blackwell Publishing Ltd.

Techniques to extract physical modes in model-independent analysis of rings

International Nuclear Information System (INIS)

Wang, C.-X.

2004-01-01

A basic goal of Model-Independent Analysis is to extract the physical modes underlying the beam histories collected at a large number of beam position monitors so that beam dynamics and machine properties can be deduced independent of specific machine models. Here we discuss techniques to achieve this goal, especially the Principal Component Analysis and the Independent Component Analysis.

Overview of the model component in ECOCLIM

DEFF Research Database (Denmark)

Geels, Camilla; Boegh, Eva; Bendtsen, J

and atmospheric models. We will use the model system to 1) quantify the potential effects of climate change on ecosystem exchange of GHG and 2) estimate the impacts of changes in management practices including land use change and nitrogen (N) loads. Here the various model components will be introduced...

Gene set analysis using variance component tests.

Science.gov (United States)

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.

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.

Probabilistic Modeling of Wind Turbine Drivetrain Components

DEFF Research Database (Denmark)

Rafsanjani, Hesam Mirzaei

Wind energy is one of several energy sources in the world and a rapidly growing industry in the energy sector. When placed in offshore or onshore locations, wind turbines are exposed to wave excitations, highly dynamic wind loads and/or the wakes from other wind turbines. Therefore, most components...... in a wind turbine experience highly dynamic and time-varying loads. These components may fail due to wear or fatigue, and this can lead to unplanned shutdown repairs that are very costly. The design by deterministic methods using safety factors is generally unable to account for the many uncertainties. Thus......, a reliability assessment should be based on probabilistic methods where stochastic modeling of failures is performed. This thesis focuses on probabilistic models and the stochastic modeling of the fatigue life of the wind turbine drivetrain. Hence, two approaches are considered for stochastic modeling...

Multidisciplinary Product Decomposition and Analysis Based on Design Structure Matrix Modeling

DEFF Research Database (Denmark)

Habib, Tufail

2014-01-01

Design structure matrix (DSM) modeling in complex system design supports to define physical and logical configuration of subsystems, components, and their relationships. This modeling includes product decomposition, identification of interfaces, and structure analysis to increase the architectural...... interactions across subsystems and components. For this purpose, Cambridge advanced modeler (CAM) software tool is used to develop the system matrix. The analysis of the product (printer) architecture includes clustering, partitioning as well as structure analysis of the system. The DSM analysis is helpful...... understanding of the system. Since product architecture has broad implications in relation to product life cycle issues, in this paper, mechatronic product is decomposed into subsystems and components, and then, DSM model is developed to examine the extent of modularity in the system and to manage multiple...

Failure Predictions for VHTR Core Components using a Probabilistic Contiuum Damage Mechanics Model

Energy Technology Data Exchange (ETDEWEB)

Fok, Alex

2013-10-30

The proposed work addresses the key research need for the development of constitutive models and overall failure models for graphite and high temperature structural materials, with the long-term goal being to maximize the design life of the Next Generation Nuclear Plant (NGNP). To this end, the capability of a Continuum Damage Mechanics (CDM) model, which has been used successfully for modeling fracture of virgin graphite, will be extended as a predictive and design tool for the core components of the very high- temperature reactor (VHTR). Specifically, irradiation and environmental effects pertinent to the VHTR will be incorporated into the model to allow fracture of graphite and ceramic components under in-reactor conditions to be modeled explicitly using the finite element method. The model uses a combined stress-based and fracture mechanics-based failure criterion, so it can simulate both the initiation and propagation of cracks. Modern imaging techniques, such as x-ray computed tomography and digital image correlation, will be used during material testing to help define the baseline material damage parameters. Monte Carlo analysis will be performed to address inherent variations in material properties, the aim being to reduce the arbitrariness and uncertainties associated with the current statistical approach. The results can potentially contribute to the current development of American Society of Mechanical Engineers (ASME) codes for the design and construction of VHTR core components.

Intercity Travel Demand Analysis Model

OpenAIRE

Ming Lu; Hai Zhu; Xia Luo; Lei Lei

2014-01-01

It is well known that intercity travel is an important component of travel demand which belongs to short distance corridor travel. The conventional four-step method is no longer suitable for short distance corridor travel demand analysis for the time spent on urban traffic has a great impact on traveler's main mode choice. To solve this problem, the author studied the existing intercity travel demand analysis model, then improved it based on the study, and finally established a combined model...

Autonomous learning in gesture recognition by using lobe component analysis

Science.gov (United States)

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.

Application of principal component analysis (PCA) as a sensory assessment tool for fermented food products.

Science.gov (United States)

Ghosh, Debasree; Chattopadhyay, Parimal

2012-06-01

The objective of the work was to use the method of quantitative descriptive analysis (QDA) to describe the sensory attributes of the fermented food products prepared with the incorporation of lactic cultures. Panellists were selected and trained to evaluate various attributes specially color and appearance, body texture, flavor, overall acceptability and acidity of the fermented food products like cow milk curd and soymilk curd, idli, sauerkraut and probiotic ice cream. Principal component analysis (PCA) identified the six significant principal components that accounted for more than 90% of the variance in the sensory attribute data. Overall product quality was modelled as a function of principal components using multiple least squares regression (R (2) = 0.8). The result from PCA was statistically analyzed by analysis of variance (ANOVA). These findings demonstrate the utility of quantitative descriptive analysis for identifying and measuring the fermented food product attributes that are important for consumer acceptability.

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.)

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)

Multiobjective Optimization of ELID Grinding Process Using Grey Relational Analysis Coupled with Principal Component Analysis

Directory of Open Access Journals (Sweden)

S. Prabhu

2014-06-01

Full Text Available Carbon nanotube (CNT mixed grinding wheel has been used in the electrolytic in-process dressing (ELID grinding process to analyze the surface characteristics of AISI D2 Tool steel material. CNT grinding wheel is having an excellent thermal conductivity and good mechanical property which is used to improve the surface finish of the work piece. The multiobjective optimization of grey relational analysis coupled with principal component analysis has been used to optimize the process parameters of ELID grinding process. Based on the Taguchi design of experiments, an L9 orthogonal array table was chosen for the experiments. The confirmation experiment verifies the proposed that grey-based Taguchi method has the ability to find out the optimal process parameters with multiple quality characteristics of surface roughness and metal removal rate. Analysis of variance (ANOVA has been used to verify and validate the model. Empirical model for the prediction of output parameters has been developed using regression analysis and the results were compared for with and without using CNT grinding wheel in ELID grinding process.

Multi-component separation and analysis of bat echolocation calls.

Science.gov (United States)

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.

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.)

COMPARING INDEPENDENT COMPONENT ANALYSIS WITH PRINCIPLE COMPONENT ANALYSIS IN DETECTING ALTERATIONS OF PORPHYRY COPPER DEPOSIT (CASE STUDY: ARDESTAN AREA, CENTRAL IRAN

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.

Comparing Independent Component Analysis with Principle Component Analysis in Detecting Alterations of Porphyry Copper Deposit (case Study: Ardestan Area, Central Iran)

Science.gov (United States)

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.

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

A comparative study of the proposed models for the components of the national health information system.

Science.gov (United States)

Ahmadi, Maryam; Damanabi, Shahla; Sadoughi, Farahnaz

2014-04-01

National Health Information System plays an important role in ensuring timely and reliable access to Health information, which is essential for strategic and operational decisions that improve health, quality and effectiveness of health care. In other words, using the National Health information system you can improve the quality of health data, information and knowledge used to support decision making at all levels and areas of the health sector. Since full identification of the components of this system - for better planning and management influential factors of performanceseems necessary, therefore, in this study different attitudes towards components of this system are explored comparatively. This is a descriptive and comparative kind of study. The society includes printed and electronic documents containing components of the national health information system in three parts: input, process and output. In this context, search for information using library resources and internet search were conducted, and data analysis was expressed using comparative tables and qualitative data. The findings showed that there are three different perspectives presenting the components of national health information system Lippeveld and Sauerborn and Bodart model in 2000, Health Metrics Network (HMN) model from World Health Organization in 2008, and Gattini's 2009 model. All three models outlined above in the input (resources and structure) require components of management and leadership, planning and design programs, supply of staff, software and hardware facilities and equipment. Plus, in the "process" section from three models, we pointed up the actions ensuring the quality of health information system, and in output section, except for Lippeveld Model, two other models consider information products and use and distribution of information as components of the national health information system. the results showed that all the three models have had a brief discussion about the

Preliminary study of soil permeability properties using principal component analysis

Science.gov (United States)

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.

The modelling and analysis of the mechanics of ropes

CERN Document Server

Leech, C M

2014-01-01

This book considers the modelling and analysis of the many types of ropes, linear fibre assemblies. The construction of these structures is very diverse and in the work these are considered from the modelling point of view. As well as the conventional twisted structures, braid and plaited structures and parallel assemblies are modelled and analysed, first for their assembly and secondly for their mechanical behaviour. Also since the components are assemblies of components, fibres into yarns, into strands, and into ropes the hierarchical nature of the construction is considered. The focus of the modelling is essentially toward load extension behaviour but there is reference to bending of ropes, encompassed by the two extremes, no slip between the components and zero friction resistance to component slip. Friction in ropes is considered both between the rope components, sliding, sawing and scissoring, and within the components, dilation and distortion, these latter modes being used to model component set, the p...

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.

The SPAtial EFficiency metric (SPAEF): multiple-component evaluation of spatial patterns for optimization of hydrological models

Science.gov (United States)

Koch, Julian; Cüneyd Demirel, Mehmet; Stisen, Simon

2018-05-01

The process of model evaluation is not only an integral part of model development and calibration but also of paramount importance when communicating modelling results to the scientific community and stakeholders. The modelling community has a large and well-tested toolbox of metrics to evaluate temporal model performance. In contrast, spatial performance evaluation does not correspond to the grand availability of spatial observations readily available and to the sophisticate model codes simulating the spatial variability of complex hydrological processes. This study makes a contribution towards advancing spatial-pattern-oriented model calibration by rigorously testing a multiple-component performance metric. The promoted SPAtial EFficiency (SPAEF) metric reflects three equally weighted components: correlation, coefficient of variation and histogram overlap. This multiple-component approach is found to be advantageous in order to achieve the complex task of comparing spatial patterns. SPAEF, its three components individually and two alternative spatial performance metrics, i.e. connectivity analysis and fractions skill score, are applied in a spatial-pattern-oriented model calibration of a catchment model in Denmark. Results suggest the importance of multiple-component metrics because stand-alone metrics tend to fail to provide holistic pattern information. The three SPAEF components are found to be independent, which allows them to complement each other in a meaningful way. In order to optimally exploit spatial observations made available by remote sensing platforms, this study suggests applying bias insensitive metrics which further allow for a comparison of variables which are related but may differ in unit. This study applies SPAEF in the hydrological context using the mesoscale Hydrologic Model (mHM; version 5.8), but we see great potential across disciplines related to spatially distributed earth system modelling.

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

Empirical validation of the triple-code model of numerical processing for complex math operations using functional MRI and group Independent Component Analysis of the mental addition and subtraction of fractions.

Science.gov (United States)

Schmithorst, Vincent J; Brown, Rhonda Douglas

2004-07-01

The suitability of a previously hypothesized triple-code model of numerical processing, involving analog magnitude, auditory verbal, and visual Arabic codes of representation, was investigated for the complex mathematical task of the mental addition and subtraction of fractions. Functional magnetic resonance imaging (fMRI) data from 15 normal adult subjects were processed using exploratory group Independent Component Analysis (ICA). Separate task-related components were found with activation in bilateral inferior parietal, left perisylvian, and ventral occipitotemporal areas. These results support the hypothesized triple-code model corresponding to the activated regions found in the individual components and indicate that the triple-code model may be a suitable framework for analyzing the neuropsychological bases of the performance of complex mathematical tasks. Copyright 2004 Elsevier Inc.

Maximum likelihood estimation of semiparametric mixture component models for competing risks data.

Science.gov (United States)

Choi, Sangbum; Huang, Xuelin

2014-09-01

In the analysis of competing risks data, the cumulative incidence function is a useful quantity to characterize the crude risk of failure from a specific event type. In this article, we consider an efficient semiparametric analysis of mixture component models on cumulative incidence functions. Under the proposed mixture model, latency survival regressions given the event type are performed through a class of semiparametric models that encompasses the proportional hazards model and the proportional odds model, allowing for time-dependent covariates. The marginal proportions of the occurrences of cause-specific events are assessed by a multinomial logistic model. Our mixture modeling approach is advantageous in that it makes a joint estimation of model parameters associated with all competing risks under consideration, satisfying the constraint that the cumulative probability of failing from any cause adds up to one given any covariates. We develop a novel maximum likelihood scheme based on semiparametric regression analysis that facilitates efficient and reliable estimation. Statistical inferences can be conveniently made from the inverse of the observed information matrix. We establish the consistency and asymptotic normality of the proposed estimators. We validate small sample properties with simulations and demonstrate the methodology with a data set from a study of follicular lymphoma. © 2014, The International Biometric Society.

Component based modelling of piezoelectric ultrasonic actuators for machining applications

International Nuclear Information System (INIS)

Saleem, A; Ahmed, N; Salah, M; Silberschmidt, V V

2013-01-01

Ultrasonically Assisted Machining (UAM) is an emerging technology that has been utilized to improve the surface finishing in machining processes such as turning, milling, and drilling. In this context, piezoelectric ultrasonic transducers are being used to vibrate the cutting tip while machining at predetermined amplitude and frequency. However, modelling and simulation of these transducers is a tedious and difficult task. This is due to the inherent nonlinearities associated with smart materials. Therefore, this paper presents a component-based model of ultrasonic transducers that mimics the nonlinear behaviour of such a system. The system is decomposed into components, a mathematical model of each component is created, and the whole system model is accomplished by aggregating the basic components' model. System parameters are identified using Finite Element technique which then has been used to simulate the system in Matlab/SIMULINK. Various operation conditions are tested and performed to demonstrate the system performance

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)

ROCK PROPERTIES MODEL ANALYSIS MODEL REPORT

International Nuclear Information System (INIS)

Clinton Lum

2002-01-01

) Generation of derivative property models via linear coregionalization with porosity; (5) Post-processing of the simulated models to impart desired secondary geologic attributes and to create summary and uncertainty models; and (6) Conversion of the models into real-world coordinates. The conversion to real world coordinates is performed as part of the integration of the RPM into the Integrated Site Model (ISM) 3.1; this activity is not part of the current analysis. The ISM provides a consistent volumetric portrayal of the rock layers, rock properties, and mineralogy of the Yucca Mountain site and consists of three components: (1) Geologic Framework Model (GFM); (2) RPM, which is the subject of this AMR; and (3) Mineralogic Model. The interrelationship of the three components of the ISM and their interface with downstream uses are illustrated in Figure 1. Figure 2 shows the geographic boundaries of the RPM and other component models of the ISM

Robustness analysis of bogie suspension components Pareto optimised values

Science.gov (United States)

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.

Design and Application of an Ontology for Component-Based Modeling of Water Systems

Science.gov (United States)

Elag, M.; Goodall, J. L.

2012-12-01

Many Earth system modeling frameworks have adopted an approach of componentizing models so that a large model can be assembled by linking a set of smaller model components. These model components can then be more easily reused, extended, and maintained by a large group of model developers and end users. While there has been a notable increase in component-based model frameworks in the Earth sciences in recent years, there has been less work on creating framework-agnostic metadata and ontologies for model components. Well defined model component metadata is needed, however, to facilitate sharing, reuse, and interoperability both within and across Earth system modeling frameworks. To address this need, we have designed an ontology for the water resources community named the Water Resources Component (WRC) ontology in order to advance the application of component-based modeling frameworks across water related disciplines. Here we present the design of the WRC ontology and demonstrate its application for integration of model components used in watershed management. First we show how the watershed modeling system Soil and Water Assessment Tool (SWAT) can be decomposed into a set of hydrological and ecological components that adopt the Open Modeling Interface (OpenMI) standard. Then we show how the components can be used to estimate nitrogen losses from land to surface water for the Baltimore Ecosystem study area. Results of this work are (i) a demonstration of how the WRC ontology advances the conceptual integration between components of water related disciplines by handling the semantic and syntactic heterogeneity present when describing components from different disciplines and (ii) an investigation of a methodology by which large models can be decomposed into a set of model components that can be well described by populating metadata according to the WRC ontology.

Fault detection of flywheel system based on clustering and principal component analysis

Directory of Open Access Journals (Sweden)

Wang Rixin

2015-12-01

Full Text Available Considering the nonlinear, multifunctional properties of double-flywheel with closed-loop control, a two-step method including clustering and principal component analysis is proposed to detect the two faults in the multifunctional flywheels. At the first step of the proposed algorithm, clustering is taken as feature recognition to check the instructions of “integrated power and attitude control” system, such as attitude control, energy storage or energy discharge. These commands will ask the flywheel system to work in different operation modes. Therefore, the relationship of parameters in different operations can define the cluster structure of training data. Ordering points to identify the clustering structure (OPTICS can automatically identify these clusters by the reachability-plot. K-means algorithm can divide the training data into the corresponding operations according to the reachability-plot. Finally, the last step of proposed model is used to define the relationship of parameters in each operation through the principal component analysis (PCA method. Compared with the PCA model, the proposed approach is capable of identifying the new clusters and learning the new behavior of incoming data. The simulation results show that it can effectively detect the faults in the multifunctional flywheels system.

Experiment planning using high-level component models at W7-X

International Nuclear Information System (INIS)

Lewerentz, Marc; Spring, Anett; Bluhm, Torsten; Heimann, Peter; Hennig, Christine; Kühner, Georg; Kroiss, Hugo; Krom, Johannes G.; Laqua, Heike; Maier, Josef; Riemann, Heike; Schacht, Jörg; Werner, Andreas; Zilker, Manfred

2012-01-01

Highlights: ► Introduction of models for an abstract description of fusion experiments. ► Component models support creating feasible experiment programs at planning time. ► Component models contain knowledge about physical and technical constraints. ► Generated views on models allow to present crucial information. - Abstract: The superconducting stellarator Wendelstein 7-X (W7-X) is a fusion device, which is capable of steady state operation. Furthermore W7-X is a very complex technical system. To cope with these requirements a modular and strongly hierarchical component-based control and data acquisition system has been designed. The behavior of W7-X is characterized by thousands of technical parameters of the participating components. The intended sequential change of those parameters during an experiment is defined in an experiment program. Planning such an experiment program is a crucial and complex task. To reduce the complexity an abstract, more physics-oriented high-level layer has been introduced earlier. The so-called high-level (physics) parameters are used to encapsulate technical details. This contribution will focus on the extension of this layer to a high-level component model. It completely describes the behavior of a component for a certain period of time. It allows not only defining simple value ranges but also complex dependencies between physics parameters. This can be: dependencies within components, dependencies between components or temporal dependencies. Component models can now be analyzed to generate various views of an experiment. A first implementation of such an analyze process is already finished. A graphical preview of a planned discharge can be generated from a chronological sequence of component models. This allows physicists to survey complex planned experiment programs at a glance.

Use of regularized principal component analysis to model anatomical changes during head and neck radiation therapy for treatment adaptation and response assessment

International Nuclear Information System (INIS)

Chetvertkov, Mikhail A.; Siddiqui, Farzan; Chetty, Indrin; Kumarasiri, Akila; Liu, Chang; Gordon, J. James; Kim, Jinkoo

2016-01-01

Purpose: To develop standard (SPCA) and regularized (RPCA) principal component analysis models of anatomical changes from daily cone beam CTs (CBCTs) of head and neck (H&N) patients and assess their potential use in adaptive radiation therapy, and for extracting quantitative information for treatment response assessment. Methods: Planning CT images of ten H&N patients were artificially deformed to create “digital phantom” images, which modeled systematic anatomical changes during radiation therapy. Artificial deformations closely mirrored patients’ actual deformations and were interpolated to generate 35 synthetic CBCTs, representing evolving anatomy over 35 fractions. Deformation vector fields (DVFs) were acquired between pCT and synthetic CBCTs (i.e., digital phantoms) and between pCT and clinical CBCTs. Patient-specific SPCA and RPCA models were built from these synthetic and clinical DVF sets. EigenDVFs (EDVFs) having the largest eigenvalues were hypothesized to capture the major anatomical deformations during treatment. Results: Principal component analysis (PCA) models achieve variable results, depending on the size and location of anatomical change. Random changes prevent or degrade PCA’s ability to detect underlying systematic change. RPCA is able to detect smaller systematic changes against the background of random fraction-to-fraction changes and is therefore more successful than SPCA at capturing systematic changes early in treatment. SPCA models were less successful at modeling systematic changes in clinical patient images, which contain a wider range of random motion than synthetic CBCTs, while the regularized approach was able to extract major modes of motion. Conclusions: Leading EDVFs from the both PCA approaches have the potential to capture systematic anatomical change during H&N radiotherapy when systematic changes are large enough with respect to random fraction-to-fraction changes. In all cases the RPCA approach appears to be more

Use of regularized principal component analysis to model anatomical changes during head and neck radiation therapy for treatment adaptation and response assessment

Energy Technology Data Exchange (ETDEWEB)

Chetvertkov, Mikhail A., E-mail: chetvertkov@wayne.edu [Department of Radiation Oncology, Wayne State University School of Medicine, Detroit, Michigan 48201 and Department of Radiation Oncology, Henry Ford Health System, Detroit, Michigan 48202 (United States); Siddiqui, Farzan; Chetty, Indrin; Kumarasiri, Akila; Liu, Chang; Gordon, J. James [Department of Radiation Oncology, Henry Ford Health System, Detroit, Michigan 48202 (United States); Kim, Jinkoo [Department of Radiation Oncology, Stony Brook University Hospital, Stony Brook, New York 11794 (United States)

2016-10-15

Purpose: To develop standard (SPCA) and regularized (RPCA) principal component analysis models of anatomical changes from daily cone beam CTs (CBCTs) of head and neck (H&N) patients and assess their potential use in adaptive radiation therapy, and for extracting quantitative information for treatment response assessment. Methods: Planning CT images of ten H&N patients were artificially deformed to create “digital phantom” images, which modeled systematic anatomical changes during radiation therapy. Artificial deformations closely mirrored patients’ actual deformations and were interpolated to generate 35 synthetic CBCTs, representing evolving anatomy over 35 fractions. Deformation vector fields (DVFs) were acquired between pCT and synthetic CBCTs (i.e., digital phantoms) and between pCT and clinical CBCTs. Patient-specific SPCA and RPCA models were built from these synthetic and clinical DVF sets. EigenDVFs (EDVFs) having the largest eigenvalues were hypothesized to capture the major anatomical deformations during treatment. Results: Principal component analysis (PCA) models achieve variable results, depending on the size and location of anatomical change. Random changes prevent or degrade PCA’s ability to detect underlying systematic change. RPCA is able to detect smaller systematic changes against the background of random fraction-to-fraction changes and is therefore more successful than SPCA at capturing systematic changes early in treatment. SPCA models were less successful at modeling systematic changes in clinical patient images, which contain a wider range of random motion than synthetic CBCTs, while the regularized approach was able to extract major modes of motion. Conclusions: Leading EDVFs from the both PCA approaches have the potential to capture systematic anatomical change during H&N radiotherapy when systematic changes are large enough with respect to random fraction-to-fraction changes. In all cases the RPCA approach appears to be more

Building Block Approach' for Structural Analysis of Thermoplastic Composite Components for Automotive Applications

Science.gov (United States)

Carello, M.; Amirth, N.; Airale, A. G.; Monti, M.; Romeo, A.

2017-12-01

Advanced thermoplastic prepreg composite materials stand out with regard to their ability to allow complex designs with high specific strength and stiffness. This makes them an excellent choice for lightweight automotive components to reduce mass and increase fuel efficiency, while maintaining the functionality of traditional thermosetting prepreg (and mechanical characteristics) and with a production cycle time and recyclability suited to mass production manufacturing. Currently, the aerospace and automotive sectors struggle to carry out accurate Finite Elements (FE) component analyses and in some cases are unable to validate the obtained results. In this study, structural Finite Elements Analysis (FEA) has been done on a thermoplastic fiber reinforced component designed and manufactured through an integrated injection molding process, which consists in thermoforming the prepreg laminate and overmolding the other parts. This process is usually referred to as hybrid molding, and has the provision to reinforce the zones subjected to additional stresses with thermoformed themoplastic prepreg as required and overmolded with a shortfiber thermoplastic resin in single process. This paper aims to establish an accurate predictive model on a rational basis and an innovative methodology for the structural analysis of thermoplastic composite components by comparison with the experimental tests results.

Nonlinear Process Fault Diagnosis Based on Serial Principal Component Analysis.

Science.gov (United States)

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.

Independent component and pathway-based analysis of miRNA-regulated gene expression in a model of type 1 diabetes

Directory of Open Access Journals (Sweden)

Hagedorn Peter H

2011-02-01

Full Text Available Abstract Background Several approaches have been developed for miRNA target prediction, including methods that incorporate expression profiling. However the methods are still in need of improvements due to a high false discovery rate. So far, none of the methods have used independent component analysis (ICA. Here, we developed a novel target prediction method based on ICA that incorporates both seed matching and expression profiling of miRNA and mRNA expressions. The method was applied on a cellular model of type 1 diabetes. Results Microrray profiling identified eight miRNAs (miR-124/128/192/194/204/375/672/708 with differential expression. Applying ICA on the mRNA profiling data revealed five significant independent components (ICs correlating to the experimental conditions. The five ICs also captured the miRNA expressions by explaining >97% of their variance. By using ICA, seven of the eight miRNAs showed significant enrichment of sequence predicted targets, compared to only four miRNAs when using simple negative correlation. The ICs were enriched for miRNA targets that function in diabetes-relevant pathways e.g. type 1 and type 2 diabetes and maturity onset diabetes of the young (MODY. Conclusions In this study, ICA was applied as an attempt to separate the various factors that influence the mRNA expression in order to identify miRNA targets. The results suggest that ICA is better at identifying miRNA targets than negative correlation. Additionally, combining ICA and pathway analysis constitutes a means for prioritizing between the predicted miRNA targets. Applying the method on a model of type 1 diabetes resulted in identification of eight miRNAs that appear to affect pathways of relevance to disease mechanisms in diabetes.

Nonlinear fitness-space-structure adaptation and principal component analysis in genetic algorithms: an application to x-ray reflectivity analysis

International Nuclear Information System (INIS)

Tiilikainen, J; Tilli, J-M; Bosund, V; Mattila, M; Hakkarainen, T; Airaksinen, V-M; Lipsanen, H

2007-01-01

Two novel genetic algorithms implementing principal component analysis and an adaptive nonlinear fitness-space-structure technique are presented and compared with conventional algorithms in x-ray reflectivity analysis. Principal component analysis based on Hessian or interparameter covariance matrices is used to rotate a coordinate frame. The nonlinear adaptation applies nonlinear estimates to reshape the probability distribution of the trial parameters. The simulated x-ray reflectivity of a realistic model of a periodic nanolaminate structure was used as a test case for the fitting algorithms. The novel methods had significantly faster convergence and less stagnation than conventional non-adaptive genetic algorithms. The covariance approach needs no additional curve calculations compared with conventional methods, and it had better convergence properties than the computationally expensive Hessian approach. These new algorithms can also be applied to other fitting problems where tight interparameter dependence is present

Detailed finite element method modeling of evaporating multi-component droplets

Energy Technology Data Exchange (ETDEWEB)

Diddens, Christian, E-mail: C.Diddens@tue.nl

2017-07-01

The evaporation of sessile multi-component droplets is modeled with an axisymmetic finite element method. The model comprises the coupled processes of mixture evaporation, multi-component flow with composition-dependent fluid properties and thermal effects. Based on representative examples of water–glycerol and water–ethanol droplets, regular and chaotic examples of solutal Marangoni flows are discussed. Furthermore, the relevance of the substrate thickness for the evaporative cooling of volatile binary mixture droplets is pointed out. It is shown how the evaporation of the more volatile component can drastically decrease the interface temperature, so that ambient vapor of the less volatile component condenses on the droplet. Finally, results of this model are compared with corresponding results of a lubrication theory model, showing that the application of lubrication theory can cause considerable errors even for moderate contact angles of 40°. - Graphical abstract:.

State of the art seismic analysis for CANDU reactor structure components using condensation method

Energy Technology Data Exchange (ETDEWEB)

Soliman, S A; Ibraham, A M; Hodgson, S [Atomic Energy of Canada Ltd., Saskatoon, SK (Canada)

1996-12-31

The reactor structure assembly seismic analysis is a relatively complex process because of the intricate geometry with many different discontinuities, and due to the hydraulic attached mass which follows the structure during its vibration. In order to simulate reasonably accurate behaviour of the reactor structure assembly, detailed finite element models are generated and used for both modal and stress analysis. Guyan reduction condensation method was used in the analysis. The attached mass, which includes the fluid mass contained in the components plus the added mass which accounts for the inertia of the surrounding fluid entrained by the accelerating structure immersed in the fluid, was calculated and attached to the vibrating structures. The masses of the attached components, supported partly or totally by the assembly which includes piping, reactivity control units, end fittings, etc. are also considered in the analysis. (author). 4 refs., 6 tabs., 4 figs.

Group-wise Principal Component Analysis for Exploratory Data Analysis

NARCIS (Netherlands)

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

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...

Towards a Component Based Model for Database Systems

Directory of Open Access Journals (Sweden)

Octavian Paul ROTARU

2004-02-01

Full Text Available Due to their effectiveness in the design and development of software applications and due to their recognized advantages in terms of reusability, Component-Based Software Engineering (CBSE concepts have been arousing a great deal of interest in recent years. This paper presents and extends a component-based approach to object-oriented database systems (OODB introduced by us in [1] and [2]. Components are proposed as a new abstraction level for database system, logical partitions of the schema. In this context, the scope is introduced as an escalated property for transactions. Components are studied from the integrity, consistency, and concurrency control perspective. The main benefits of our proposed component model for OODB are the reusability of the database design, including the access statistics required for a proper query optimization, and a smooth information exchange. The integration of crosscutting concerns into the component database model using aspect-oriented techniques is also discussed. One of the main goals is to define a method for the assessment of component composition capabilities. These capabilities are restricted by the component’s interface and measured in terms of adaptability, degree of compose-ability and acceptability level. The above-mentioned metrics are extended from database components to generic software components. This paper extends and consolidates into one common view the ideas previously presented by us in [1, 2, 3].[1] Octavian Paul Rotaru, Marian Dobre, Component Aspects in Object Oriented Databases, Proceedings of the International Conference on Software Engineering Research and Practice (SERP’04, Volume II, ISBN 1-932415-29-7, pages 719-725, Las Vegas, NV, USA, June 2004.[2] Octavian Paul Rotaru, Marian Dobre, Mircea Petrescu, Integrity and Consistency Aspects in Component-Oriented Databases, Proceedings of the International Symposium on Innovation in Information and Communication Technology (ISIICT�

Development of an Automated LIBS Analytical Test System Integrated with Component Control and Spectrum Analysis Capabilities

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)

Particle Markov Chain Monte Carlo Techniques of Unobserved Component Time Series Models Using Ox

DEFF Research Database (Denmark)

Nonejad, Nima

This paper details Particle Markov chain Monte Carlo techniques for analysis of unobserved component time series models using several economic data sets. PMCMC combines the particle filter with the Metropolis-Hastings algorithm. Overall PMCMC provides a very compelling, computationally fast...... and efficient framework for estimation. These advantages are used to for instance estimate stochastic volatility models with leverage effect or with Student-t distributed errors. We also model changing time series characteristics of the US inflation rate by considering a heteroskedastic ARFIMA model where...

Abstract Interfaces for Data Analysis Component Architecture for Data Analysis Tools

CERN Document Server

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...

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

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

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.

Component Analysis of Long-Lag, Wide-Pulse Gamma-Ray Burst ...

Indian Academy of Sciences (India)

Principal Component Analysis of Long-Lag, Wide-Pulse Gamma-Ray. Burst Data. Zhao-Yang Peng. ∗. & Wen-Shuai Liu. Department of Physics, Yunnan Normal University, Kunming 650500, China. ∗ e-mail: pzy@ynao.ac.cn. Abstract. We have carried out a Principal Component Analysis (PCA) of the temporal and spectral ...

Hapten design and indirect competitive immunoassay for parathion determination: Correlation with molecular modeling and principal component analysis

Energy Technology Data Exchange (ETDEWEB)

Liu Yihua [Institute of Pesticide and Environmental Toxicology, Zhejiang University, Hangzhou 310029 (China); Jin Maojun [Institute of Pesticide and Environmental Toxicology, Zhejiang University, Hangzhou 310029 (China); Gui Wenjun [Institute of Pesticide and Environmental Toxicology, Zhejiang University, Hangzhou 310029 (China); Cheng Jingli [Institute of Pesticide and Environmental Toxicology, Zhejiang University, Hangzhou 310029 (China); Guo Yirong [Institute of Pesticide and Environmental Toxicology, Zhejiang University, Hangzhou 310029 (China); Zhu Guonian [Institute of Pesticide and Environmental Toxicology, Zhejiang University, Hangzhou 310029 (China)]. E-mail: zhugn@zju.edu.cn

2007-05-22

A novel procedure for parathion hapten design is described. The optimal antigen for parathion was selected after molecular modeling studies of six types of potentially immunizing haptens with the aim to identify the best mimicking target analyte. Heterologous competitive indirect enzyme-linked immunosorbent assay (ELISA) was developed after screening a battery of competitors as coating antigens. The relationship between the heterology degree of the competitor and the resulting immunoassay detectability was investigated according to the electronic similarities of the competitor haptens and the target analyte. Molecular modeling and principal component analysis were performed to understand the electronic distribution and steric parameters of the haptens at their minimum energetic levels. The results suggested that the competitors should have a high heterology to produce assays with good detectability values. An indirect competitive ELISA was finally selected for further investigation. The immunoassay had an IC{sub 50} value of 4.79 ng mL{sup -1} and a limit of detection of 0.31 ng mL{sup -1}. There was little or no cross-reactivity to similar compounds tested except for the insecticide parathion-methyl, which showed a cross-reactivity of 7.8%.

Model validation and calibration based on component functions of model output

International Nuclear Information System (INIS)

Wu, Danqing; Lu, Zhenzhou; Wang, Yanping; Cheng, Lei

2015-01-01

The target in this work is to validate the component functions of model output between physical observation and computational model with the area metric. Based on the theory of high dimensional model representations (HDMR) of independent input variables, conditional expectations are component functions of model output, and the conditional expectations reflect partial information of model output. Therefore, the model validation of conditional expectations tells the discrepancy between the partial information of the computational model output and that of the observations. Then a calibration of the conditional expectations is carried out to reduce the value of model validation metric. After that, a recalculation of the model validation metric of model output is taken with the calibrated model parameters, and the result shows that a reduction of the discrepancy in the conditional expectations can help decrease the difference in model output. At last, several examples are employed to demonstrate the rationality and necessity of the methodology in case of both single validation site and multiple validation sites. - Highlights: • A validation metric of conditional expectations of model output is proposed. • HDRM explains the relationship of conditional expectations and model output. • An improved approach of parameter calibration updates the computational models. • Validation and calibration process are applied at single site and multiple sites. • Validation and calibration process show a superiority than existing methods

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

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

A Filtering of Incomplete GNSS Position Time Series with Probabilistic Principal Component Analysis

Science.gov (United States)

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.

Dual-energy x-ray image decomposition by independent component analysis

Science.gov (United States)

Jiang, Yifeng; Jiang, Dazong; Zhang, Feng; Zhang, Dengfu; Lin, Gang

2001-09-01

The spatial distributions of bone and soft tissue in human body are separated by independent component analysis (ICA) of dual-energy x-ray images. It is because of the dual energy imaging modelí-s conformity to the ICA model that we can apply this method: (1) the absorption in body is mainly caused by photoelectric absorption and Compton scattering; (2) they take place simultaneously but are mutually independent; and (3) for monochromatic x-ray sources the total attenuation is achieved by linear combination of these two absorption. Compared with the conventional method, the proposed one needs no priori information about the accurate x-ray energy magnitude for imaging, while the results of the separation agree well with the conventional one.

Using containment analysis to improve component cooling water heat exchanger limits

International Nuclear Information System (INIS)

Da Silva, H.C.; Tajbakhsh, A.

1995-01-01

The Comanche Peak Steam Electric Station design requires that exit temperatures from the Component Cooling Water Heat Exchanger remain below 330.37 K during the Emergency Core Cooling System recirculation stage, following a hypothetical Loss of Coolant Accident (LOCA). Due to measurements indicating a higher than expected combination of: (a) high fouling factor in the Component Cooling Water Heat Exchanger with (b) high ultimate heat sink temperatures, that might lead to temperatures in excess of the 330.37 K limit, if a LOCA were to occur, TUElectric adjusted key flow rates in the Component Cooling Water network. This solution could only be implemented with improvements to the containment analysis methodology of record. The new method builds upon the CONTEMPT-LT/028 code by: (a) coupling the long term post-LOCA thermohydraulics with a more detailed analytical model for the complex Component Cooling Water Heat Exchanger network and (b) changing the way mass and energy releases are calculated after core reflood and steam generator energy is dumped to the containment. In addition, a simple code to calculate normal cooldowns was developed to confirm RHR design bases were met with the improved limits

Task-related component analysis for functional neuroimaging and application to near-infrared spectroscopy data.

Science.gov (United States)

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.

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 ...

Integrating Data Transformation in Principal Components Analysis

KAUST Repository

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

Model reduction using a posteriori analysis

KAUST Repository

Whiteley, Jonathan P.

2010-05-01

Mathematical models in biology and physiology are often represented by large systems of non-linear ordinary differential equations. In many cases, an observed behaviour may be written as a linear functional of the solution of this system of equations. A technique is presented in this study for automatically identifying key terms in the system of equations that are responsible for a given linear functional of the solution. This technique is underpinned by ideas drawn from a posteriori error analysis. This concept has been used in finite element analysis to identify regions of the computational domain and components of the solution where a fine computational mesh should be used to ensure accuracy of the numerical solution. We use this concept to identify regions of the computational domain and components of the solution where accurate representation of the mathematical model is required for accuracy of the functional of interest. The technique presented is demonstrated by application to a model problem, and then to automatically deduce known results from a cell-level cardiac electrophysiology model. © 2010 Elsevier Inc.

Model reduction using a posteriori analysis

KAUST Repository

Whiteley, Jonathan P.

2010-01-01

Mathematical models in biology and physiology are often represented by large systems of non-linear ordinary differential equations. In many cases, an observed behaviour may be written as a linear functional of the solution of this system of equations. A technique is presented in this study for automatically identifying key terms in the system of equations that are responsible for a given linear functional of the solution. This technique is underpinned by ideas drawn from a posteriori error analysis. This concept has been used in finite element analysis to identify regions of the computational domain and components of the solution where a fine computational mesh should be used to ensure accuracy of the numerical solution. We use this concept to identify regions of the computational domain and components of the solution where accurate representation of the mathematical model is required for accuracy of the functional of interest. The technique presented is demonstrated by application to a model problem, and then to automatically deduce known results from a cell-level cardiac electrophysiology model. © 2010 Elsevier Inc.

Independent component analysis for automatic note extraction from musical trills

Science.gov (United States)

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.

Probabilistic risk analysis of aging components which fail on demand. A Bayesian model. Application to maintenance optimization of Diesel engine linings

International Nuclear Information System (INIS)

Procaccia, H.; Lannoy, A.; Clarotti, C.A.

1997-01-01

The optimization of preventive maintenance of aging materials can be well modelled through the application of the two-parameters Weibull law. Unfortunately there is no theoretical model describing the aging of components implied in emergency interventions which may fail in very moment of demand. These components are relevant to the safety of Nuclear Power Plants and the main problem with them is the optimization of the maintenance and the test schedule. Indeed the effect of any test is twofold, namely: the test reveals failures which otherwise would occur when the Power Plant would need urgent intervention but at the same time it causes the aging process to step up. The aging process is suspected to affect certain safeguard equipment, but it does not exist a theoretical model of component aging in function of demands. Sections 2.1 and 2.2 of the paper are devoted to the description of a Bayesian model of discrete aging, deriving the likelihood function of the model (data modelling) and to selecting 'the best prior' (Analyst knowledge) that can be associated to the likelihood, respectively. The aspects of numerical integration relative to assessing the predictive reliability of the aging components are discussed in Section 2.3. Section 3 reports a Reliability Centered Maintenance application of the model to cylinders of emergency electrical generators of the NPP nuclear sectors. These components are subjected to an annual endoscopic checking and to a systematical replacement at every 5 years. The statistical Bayesian decision theory was applied to determine an optimal period of cylinder replacement based on the aging model presented in this paper. It is shown that the optimal value is between 10 to 12 years. (author)

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.

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...

Refinement and verification in component-based model-driven design

DEFF Research Database (Denmark)

Chen, Zhenbang; Liu, Zhiming; Ravn, Anders Peter

2009-01-01

Modern software development is complex as it has to deal with many different and yet related aspects of applications. In practical software engineering this is now handled by a UML-like modelling approach in which different aspects are modelled by different notations. Component-based and object-o...... be integrated in computer-aided software engineering (CASE) tools for adding formally supported checking, transformation and generation facilities.......Modern software development is complex as it has to deal with many different and yet related aspects of applications. In practical software engineering this is now handled by a UML-like modelling approach in which different aspects are modelled by different notations. Component-based and object...

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)

Proportional and scale change models to project failures of mechanical components with applications to space station

Science.gov (United States)

Taneja, Vidya S.

1996-01-01

In this paper we develop the mathematical theory of proportional and scale change models to perform reliability analysis. The results obtained will be applied for the Reaction Control System (RCS) thruster valves on an orbiter. With the advent of extended EVA's associated with PROX OPS (ISSA & MIR), and docking, the loss of a thruster valve now takes on an expanded safety significance. Previous studies assume a homogeneous population of components with each component having the same failure rate. However, as various components experience different stresses and are exposed to different environments, their failure rates change with time. In this paper we model the reliability of a thruster valves by treating these valves as a censored repairable system. The model for each valve will take the form of a nonhomogeneous process with the intensity function that is either treated as a proportional hazard model, or a scale change random effects hazard model. Each component has an associated z, an independent realization of the random variable Z from a distribution G(z). This unobserved quantity z can be used to describe heterogeneity systematically. For various models methods for estimating the model parameters using censored data will be developed. Available field data (from previously flown flights) is from non-renewable systems. The estimated failure rate using such data will need to be modified for renewable systems such as thruster valve.

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 ...

A two-component dark matter model with real singlet scalars ...

Indian Academy of Sciences (India)

2016-01-05

Jan 5, 2016 ... We propose a two-component dark matter (DM) model, each component of which is a real singlet scalar, to explain results from both direct and indirect detection experiments. We put the constraints on the model parameters from theoretical bounds, PLANCK relic density results and direct DM experiments.

Functional Generalized Structured Component Analysis.

Science.gov (United States)

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 automatic analysis of dynamic radionuclide studies using principal-components factor analysis

International Nuclear Information System (INIS)

Nigran, K.S.; Barber, D.C.

1985-01-01

A method is proposed for automatic analysis of dynamic radionuclide studies using the mathematical technique of principal-components factor analysis. This method is considered as a possible alternative to the conventional manual regions-of-interest method widely used. The method emphasises the importance of introducing a priori information into the analysis about the physiology of at least one of the functional structures in a study. Information is added by using suitable mathematical models to describe the underlying physiological processes. A single physiological factor is extracted representing the particular dynamic structure of interest. Two spaces 'study space, S' and 'theory space, T' are defined in the formation of the concept of intersection of spaces. A one-dimensional intersection space is computed. An example from a dynamic 99 Tcsup(m) DTPA kidney study is used to demonstrate the principle inherent in the method proposed. The method requires no correction for the blood background activity, necessary when processing by the manual method. The careful isolation of the kidney by means of region of interest is not required. The method is therefore less prone to operator influence and can be automated. (author)

Exploring functional data analysis and wavelet principal component analysis on ecstasy (MDMA wastewater data

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.

Development of the interactive model between Component Cooling Water System and Containment Cooling System using GOTHIC

International Nuclear Information System (INIS)

Byun, Choong Sup; Song, Dong Soo; Jun, Hwang Yong

2006-01-01

In a design point of view, component cooling water (CCW) system is not full-interactively designed with its heat loads. Heat loads are calculated from the CCW design flow and temperature condition which is determined with conservatism. Then the CCW heat exchanger is sized by using total maximized heat loads from above calculation. This approach does not give the optimized performance results and the exact trends of CCW system and the loads during transient. Therefore a combined model for performance analysis of containment and the component cooling water(CCW) system is developed by using GOTHIC software code. The model is verified by using the design parameters of component cooling water heat exchanger and the heat loads during the recirculation mode of loss of coolant accident scenario. This model may be used for calculating the realistic containment response and CCW performance, and increasing the ultimate heat sink temperature limits

Three-component model of solar wind--interstellar medium interaction: some numerical results

International Nuclear Information System (INIS)

Baranov, V.; Ermakov, M.; Lebedev, M.

1981-01-01

A three-component (electrons, protons, H atoms) model for the interaction between the local interstellar medium and the solar wind is considered. A numerical analysis has been performed to determine how resonance charge exchange in interstellar H atoms that have penetrated the solar wind would affect the two-shock model developed previously by Baranov et al. In particular, if n/sub Hinfinity//n/sub e/infinity>10 (n/sub Hinfinity/, n/sub e/infinity denote the number density of H atoms and electrons in the local ISM) the inner shock may approach the sun as closely as the outer planetary orbits

Tomato sorting using independent component analysis on spectral images

NARCIS (Netherlands)

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

Using principal component analysis and annual seasonal trend analysis to assess karst rocky desertification in southwestern China.

Science.gov (United States)

Zhang, Zhiming; Ouyang, Zhiyun; Xiao, Yi; Xiao, Yang; Xu, Weihua

2017-06-01

Increasing exploitation of karst resources is causing severe environmental degradation because of the fragility and vulnerability of karst areas. By integrating principal component analysis (PCA) with annual seasonal trend analysis (ASTA), this study assessed karst rocky desertification (KRD) within a spatial context. We first produced fractional vegetation cover (FVC) data from a moderate-resolution imaging spectroradiometer normalized difference vegetation index using a dimidiate pixel model. Then, we generated three main components of the annual FVC data using PCA. Subsequently, we generated the slope image of the annual seasonal trends of FVC using median trend analysis. Finally, we combined the three PCA components and annual seasonal trends of FVC with the incidence of KRD for each type of carbonate rock to classify KRD into one of four categories based on K-means cluster analysis: high, moderate, low, and none. The results of accuracy assessments indicated that this combination approach produced greater accuracy and more reasonable KRD mapping than the average FVC based on the vegetation coverage standard. The KRD map for 2010 indicated that the total area of KRD was 78.76 × 10 3  km 2 , which constitutes about 4.06% of the eight southwest provinces of China. The largest KRD areas were found in Yunnan province. The combined PCA and ASTA approach was demonstrated to be an easily implemented, robust, and flexible method for the mapping and assessment of KRD, which can be used to enhance regional KRD management schemes or to address assessment of other environmental issues.

IMPROVED SEARCH OF PRINCIPAL COMPONENT ANALYSIS DATABASES FOR SPECTRO-POLARIMETRIC INVERSION

International Nuclear Information System (INIS)

Casini, R.; Lites, B. W.; Ramos, A. Asensio; Ariste, A. López

2013-01-01

We describe a simple technique for the acceleration of spectro-polarimetric inversions based on principal component analysis (PCA) of Stokes profiles. This technique involves the indexing of the database models based on the sign of the projections (PCA coefficients) of the first few relevant orders of principal components of the four Stokes parameters. In this way, each model in the database can be attributed a distinctive binary number of 2 4n bits, where n is the number of PCA orders used for the indexing. Each of these binary numbers (indices) identifies a group of ''compatible'' models for the inversion of a given set of observed Stokes profiles sharing the same index. The complete set of the binary numbers so constructed evidently determines a partition of the database. The search of the database for the PCA inversion of spectro-polarimetric data can profit greatly from this indexing. In practical cases it becomes possible to approach the ideal acceleration factor of 2 4n as compared to the systematic search of a non-indexed database for a traditional PCA inversion. This indexing method relies on the existence of a physical meaning in the sign of the PCA coefficients of a model. For this reason, the presence of model ambiguities and of spectro-polarimetric noise in the observations limits in practice the number n of relevant PCA orders that can be used for the indexing

[Quality evaluation of rhubarb dispensing granules based on multi-component simultaneous quantitative analysis and bioassay].

Science.gov (United States)

Tan, Peng; Zhang, Hai-Zhu; Zhang, Ding-Kun; Wu, Shan-Na; Niu, Ming; Wang, Jia-Bo; Xiao, Xiao-He

2017-07-01

This study attempts to evaluate the quality of Chinese formula granules by combined use of multi-component simultaneous quantitative analysis and bioassay. The rhubarb dispensing granules were used as the model drug for demonstrative study. The ultra-high performance liquid chromatography (UPLC) method was adopted for simultaneously quantitative determination of the 10 anthraquinone derivatives (such as aloe emodin-8-O-β-D-glucoside) in rhubarb dispensing granules; purgative biopotency of different batches of rhubarb dispensing granules was determined based on compound diphenoxylate tablets-induced mouse constipation model; blood activating biopotency of different batches of rhubarb dispensing granules was determined based on in vitro rat antiplatelet aggregation model; SPSS 22.0 statistical software was used for correlation analysis between 10 anthraquinone derivatives and purgative biopotency, blood activating biopotency. The results of multi-components simultaneous quantitative analysisshowed that there was a great difference in chemical characterizationand certain differences inpurgative biopotency and blood activating biopotency among 10 batches of rhubarb dispensing granules. The correlation analysis showed that the intensity of purgative biopotency was significantly correlated with the content of conjugated anthraquinone glycosides (Panalysis and bioassay can achieve objective quantification and more comprehensive reflection on overall quality difference among different batches of rhubarb dispensing granules. Copyright© by the Chinese Pharmaceutical Association.

Principle of maximum entropy for reliability analysis in the design of machine components

Science.gov (United States)

Zhang, Yimin

2018-03-01

We studied the reliability of machine components with parameters that follow an arbitrary statistical distribution using the principle of maximum entropy (PME). We used PME to select the statistical distribution that best fits the available information. We also established a probability density function (PDF) and a failure probability model for the parameters of mechanical components using the concept of entropy and the PME. We obtained the first four moments of the state function for reliability analysis and design. Furthermore, we attained an estimate of the PDF with the fewest human bias factors using the PME. This function was used to calculate the reliability of the machine components, including a connecting rod, a vehicle half-shaft, a front axle, a rear axle housing, and a leaf spring, which have parameters that typically follow a non-normal distribution. Simulations were conducted for comparison. This study provides a design methodology for the reliability of mechanical components for practical engineering projects.

Option valuation with the simplified component GARCH model

DEFF Research Database (Denmark)

Dziubinski, Matt P.

We introduce the Simplified Component GARCH (SC-GARCH) option pricing model, show and discuss sufficient conditions for non-negativity of the conditional variance, apply it to low-frequency and high-frequency financial data, and consider the option valuation, comparing the model performance...

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.

Evaluation of RCAS Inflow Models for Wind Turbine Analysis

Energy Technology Data Exchange (ETDEWEB)

Tangler, J.; Bir, G.

2004-02-01

The finite element structural modeling in the Rotorcraft Comprehensive Analysis System (RCAS) provides a state-of-the-art approach to aeroelastic analysis. This, coupled with its ability to model all turbine components, results in a methodology that can simulate complex system interactions characteristic of large wind. In addition, RCAS is uniquely capable of modeling advanced control algorithms and the resulting dynamic responses.

Variability of indoor and outdoor VOC measurements: An analysis using variance components

International Nuclear Information System (INIS)

Jia, Chunrong; Batterman, Stuart A.; Relyea, George E.

2012-01-01

This study examines concentrations of volatile organic compounds (VOCs) measured inside and outside of 162 residences in southeast Michigan, U.S.A. Nested analyses apportioned four sources of variation: city, residence, season, and measurement uncertainty. Indoor measurements were dominated by seasonal and residence effects, accounting for 50 and 31%, respectively, of the total variance. Contributions from measurement uncertainty (<20%) and city effects (<10%) were small. For outdoor measurements, season, city and measurement variation accounted for 43, 29 and 27% of variance, respectively, while residence location had negligible impact (<2%). These results show that, to obtain representative estimates of indoor concentrations, measurements in multiple seasons are required. In contrast, outdoor VOC concentrations can use multi-seasonal measurements at centralized locations. Error models showed that uncertainties at low concentrations might obscure effects of other factors. Variance component analyses can be used to interpret existing measurements, design effective exposure studies, and determine whether the instrumentation and protocols are satisfactory. - Highlights: ► The variability of VOC measurements was partitioned using nested analysis. ► Indoor VOCs were primarily controlled by seasonal and residence effects. ► Outdoor VOC levels were homogeneous within neighborhoods. ► Measurement uncertainty was high for many outdoor VOCs. ► Variance component analysis is useful for designing effective sampling programs. - Indoor VOC concentrations were primarily controlled by seasonal and residence effects; and outdoor concentrations were homogeneous within neighborhoods. Variance component analysis is a useful tool for designing effective sampling programs.

Detailed measurements and modelling of thermo active components using a room size test facility

DEFF Research Database (Denmark)

Weitzmann, Peter; Svendsen, Svend

2005-01-01

measurements in an office sized test facility with thermo active ceiling and floor as well as modelling of similar conditions in a computer program designed for analysis of building integrated heating and cooling systems. A method for characterizing the cooling capacity of thermo active components is described...... typically within 1-2K of the measured results. The simulation model, whose room model splits up the radiative and convective heat transfer between room and surfaces, can also be used to predict the dynamical conditions, where especially the temperature rise during the day is important for designing...

Scale modeling flow-induced vibrations of reactor components

International Nuclear Information System (INIS)

Mulcahy, T.M.

1982-06-01

Similitude relationships currently employed in the design of flow-induced vibration scale-model tests of nuclear reactor components are reviewed. Emphasis is given to understanding the origins of the similitude parameters as a basis for discussion of the inevitable distortions which occur in design verification testing of entire reactor systems and in feature testing of individual component designs for the existence of detrimental flow-induced vibration mechanisms. Distortions of similitude parameters made in current test practice are enumerated and selected example tests are described. Also, limitations in the use of specific distortions in model designs are evaluated based on the current understanding of flow-induced vibration mechanisms and structural response

Models for integrated components coupled with their EM environment

NARCIS (Netherlands)

Ioan, D.; Schilders, W.H.A.; Ciuprina, G.; Meijs, van der N.P.; Schoenmaker, W.

2008-01-01

Abstract: Purpose – The main aim of this study is the modelling of the interaction of on-chip components with their electromagnetic environment. Design/methodology/approach – The integrated circuit is decomposed in passive and active components interconnected by means of terminals and connectors

Experimental modal analysis of components of the LHC experiments

CERN Document Server

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.

Pool scrubbing models for iodine components

Energy Technology Data Exchange (ETDEWEB)

Fischer, K [Battelle Ingenieurtechnik GmbH, Eschborn (Germany)

1996-12-01

Pool scrubbing is an important mechanism to retain radioactive fission products from being carried into the containment atmosphere or into the secondary piping system. A number of models and computer codes has been developed to predict the retention of aerosols and fission product vapours that are released from the core and injected into water pools of BWR and PWR type reactors during severe accidents. Important codes in this field are BUSCA, SPARC and SUPRA. The present paper summarizes the models for scrubbing of gaseous Iodine components in these codes, discusses the experimental validation, and gives an assessment of the state of knowledge reached and the open questions which persist. The retention of gaseous Iodine components is modelled by the various codes in a very heterogeneous manner. Differences show up in the chemical species considered, the treatment of mass transfer boundary layers on the gaseous and liquid sides, the gas-liquid interface geometry, calculation of equilibrium concentrations and numerical procedures. Especially important is the determination of the pool water pH value. This value is affected by basic aerosols deposited in the water, e.g. Cesium and Rubidium compounds. A consistent model requires a mass balance of these compounds in the pool, thus effectively coupling the pool scrubbing phenomena of aerosols and gaseous Iodine species. Since the water pool conditions are also affected by drainage flow of condensate water from different regions in the containment, and desorption of dissolved gases on the pool surface is determined by the gas concentrations above the pool, some basic limitations of specialized pool scrubbing codes are given. The paper draws conclusions about the necessity of coupling between containment thermal-hydraulics and pool scrubbing models, and proposes ways of further simulation model development in order to improve source term predictions. (author) 2 tabs., refs.

Pool scrubbing models for iodine components

International Nuclear Information System (INIS)

Fischer, K.

1996-01-01

Pool scrubbing is an important mechanism to retain radioactive fission products from being carried into the containment atmosphere or into the secondary piping system. A number of models and computer codes has been developed to predict the retention of aerosols and fission product vapours that are released from the core and injected into water pools of BWR and PWR type reactors during severe accidents. Important codes in this field are BUSCA, SPARC and SUPRA. The present paper summarizes the models for scrubbing of gaseous Iodine components in these codes, discusses the experimental validation, and gives an assessment of the state of knowledge reached and the open questions which persist. The retention of gaseous Iodine components is modelled by the various codes in a very heterogeneous manner. Differences show up in the chemical species considered, the treatment of mass transfer boundary layers on the gaseous and liquid sides, the gas-liquid interface geometry, calculation of equilibrium concentrations and numerical procedures. Especially important is the determination of the pool water pH value. This value is affected by basic aerosols deposited in the water, e.g. Cesium and Rubidium compounds. A consistent model requires a mass balance of these compounds in the pool, thus effectively coupling the pool scrubbing phenomena of aerosols and gaseous Iodine species. Since the water pool conditions are also affected by drainage flow of condensate water from different regions in the containment, and desorption of dissolved gases on the pool surface is determined by the gas concentrations above the pool, some basic limitations of specialized pool scrubbing codes are given. The paper draws conclusions about the necessity of coupling between containment thermal-hydraulics and pool scrubbing models, and proposes ways of further simulation model development in order to improve source term predictions. (author) 2 tabs., refs

Principal Component Analysis-Based Pattern Analysis of Dose-Volume Histograms and Influence on Rectal Toxicity

International Nuclear Information System (INIS)

Soehn, Matthias; Alber, Markus; Yan Di

2007-01-01

Purpose: The variability of dose-volume histogram (DVH) shapes in a patient population can be quantified using principal component analysis (PCA). We applied this to rectal DVHs of prostate cancer patients and investigated the correlation of the PCA parameters with late bleeding. Methods and Materials: PCA was applied to the rectal wall DVHs of 262 patients, who had been treated with a four-field box, conformal adaptive radiotherapy technique. The correlated changes in the DVH pattern were revealed as 'eigenmodes,' which were ordered by their importance to represent data set variability. Each DVH is uniquely characterized by its principal components (PCs). The correlation of the first three PCs and chronic rectal bleeding of Grade 2 or greater was investigated with uni- and multivariate logistic regression analyses. Results: Rectal wall DVHs in four-field conformal RT can primarily be represented by the first two or three PCs, which describe ∼94% or 96% of the DVH shape variability, respectively. The first eigenmode models the total irradiated rectal volume; thus, PC1 correlates to the mean dose. Mode 2 describes the interpatient differences of the relative rectal volume in the two- or four-field overlap region. Mode 3 reveals correlations of volumes with intermediate doses (∼40-45 Gy) and volumes with doses >70 Gy; thus, PC3 is associated with the maximal dose. According to univariate logistic regression analysis, only PC2 correlated significantly with toxicity. However, multivariate logistic regression analysis with the first two or three PCs revealed an increased probability of bleeding for DVHs with more than one large PC. Conclusions: PCA can reveal the correlation structure of DVHs for a patient population as imposed by the treatment technique and provide information about its relationship to toxicity. It proves useful for augmenting normal tissue complication probability modeling approaches

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

Modeling fabrication of nuclear components: An integrative approach

Energy Technology Data Exchange (ETDEWEB)

Hench, K.W.

1996-08-01

Reduction of the nuclear weapons stockpile and the general downsizing of the nuclear weapons complex has presented challenges for Los Alamos. One is to design an optimized fabrication facility to manufacture nuclear weapon primary components in an environment of intense regulation and shrinking budgets. This dissertation presents an integrative two-stage approach to modeling the casting operation for fabrication of nuclear weapon primary components. The first stage optimizes personnel radiation exposure for the casting operation layout by modeling the operation as a facility layout problem formulated as a quadratic assignment problem. The solution procedure uses an evolutionary heuristic technique. The best solutions to the layout problem are used as input to the second stage - a simulation model that assesses the impact of competing layouts on operational performance. The focus of the simulation model is to determine the layout that minimizes personnel radiation exposures and nuclear material movement, and maximizes the utilization of capacity for finished units.

Oil classification using X-ray scattering and principal component analysis

Energy Technology Data Exchange (ETDEWEB)

Almeida, Danielle S.; Souza, Amanda S.; Lopes, Ricardo T., E-mail: dani.almeida84@gmail.com, E-mail: ricardo@lin.ufrj.br, E-mail: amandass@bioqmed.ufrj.br [Universidade Federal do Rio de Janeiro (UFRJ), Rio de Janeiro, RJ (Brazil); Oliveira, Davi F.; Anjos, Marcelino J., E-mail: davi.oliveira@uerj.br, E-mail: marcelin@uerj.br [Universidade do Estado do Rio de Janeiro (UERJ), Rio de Janeiro, RJ (Brazil). Inst. de Fisica Armando Dias Tavares

2015-07-01

X-ray scattering techniques have been considered promising for the classification and characterization of many types of samples. This study employed this technique combined with chemical analysis and multivariate analysis to characterize 54 vegetable oil samples (being 25 olive oils)with different properties obtained in commercial establishments in Rio de Janeiro city. The samples were chemically analyzed using the following indexes: iodine, acidity, saponification and peroxide. In order to obtain the X-ray scattering spectrum, an X-ray tube with a silver anode operating at 40kV and 50 μA was used. The results showed that oils cab ne divided in tow large groups: olive oils and non-olive oils. Additionally, in a multivariate analysis (Principal Component Analysis - PCA), two components were obtained and accounted for more than 80% of the variance. One component was associated with chemical parameters and the other with scattering profiles of each sample. Results showed that use of X-ray scattering spectra combined with chemical analysis and PCA can be a fast, cheap and efficient method for vegetable oil characterization. (author)

Oil classification using X-ray scattering and principal component analysis

International Nuclear Information System (INIS)

Almeida, Danielle S.; Souza, Amanda S.; Lopes, Ricardo T.; Oliveira, Davi F.; Anjos, Marcelino J.

2015-01-01

X-ray scattering techniques have been considered promising for the classification and characterization of many types of samples. This study employed this technique combined with chemical analysis and multivariate analysis to characterize 54 vegetable oil samples (being 25 olive oils)with different properties obtained in commercial establishments in Rio de Janeiro city. The samples were chemically analyzed using the following indexes: iodine, acidity, saponification and peroxide. In order to obtain the X-ray scattering spectrum, an X-ray tube with a silver anode operating at 40kV and 50 μA was used. The results showed that oils cab ne divided in tow large groups: olive oils and non-olive oils. Additionally, in a multivariate analysis (Principal Component Analysis - PCA), two components were obtained and accounted for more than 80% of the variance. One component was associated with chemical parameters and the other with scattering profiles of each sample. Results showed that use of X-ray scattering spectra combined with chemical analysis and PCA can be a fast, cheap and efficient method for vegetable oil characterization. (author)

Thermochemical modelling of multi-component systems

International Nuclear Information System (INIS)

Sundman, B.; Gueneau, C.

2015-01-01

Computational thermodynamic, also known as the Calphad method, is a standard tool in industry for the development of materials and improving processes and there is an intense scientific development of new models and databases. The calculations are based on thermodynamic models of the Gibbs energy for each phase as a function of temperature, pressure and constitution. Model parameters are stored in databases that are developed in an international scientific collaboration. In this way, consistent and reliable data for many properties like heat capacity, chemical potentials, solubilities etc. can be obtained for multi-component systems. A brief introduction to this technique is given here and references to more extensive documentation are provided. (authors)

Level shift two-components autoregressive conditional heteroscedasticity modelling for WTI crude oil market

Science.gov (United States)

Sin, Kuek Jia; Cheong, Chin Wen; Hooi, Tan Siow

2017-04-01

This study aims to investigate the crude oil volatility using a two components autoregressive conditional heteroscedasticity (ARCH) model with the inclusion of abrupt jump feature. The model is able to capture abrupt jumps, news impact, clustering volatility, long persistence volatility and heavy-tailed distributed error which are commonly observed in the crude oil time series. For the empirical study, we have selected the WTI crude oil index from year 2000 to 2016. The results found that by including the multiple-abrupt jumps in ARCH model, there are significant improvements of estimation evaluations as compared with the standard ARCH models. The outcomes of this study can provide useful information for risk management and portfolio analysis in the crude oil markets.

Intercity Travel Demand Analysis Model

Directory of Open Access Journals (Sweden)

Ming Lu

2014-01-01

Full Text Available It is well known that intercity travel is an important component of travel demand which belongs to short distance corridor travel. The conventional four-step method is no longer suitable for short distance corridor travel demand analysis for the time spent on urban traffic has a great impact on traveler's main mode choice. To solve this problem, the author studied the existing intercity travel demand analysis model, then improved it based on the study, and finally established a combined model of main mode choice and access mode choice. At last, an integrated multilevel nested logit model structure system was built. The model system includes trip generation, destination choice, and mode-route choice based on multinomial logit model, and it achieved linkage and feedback of each part through logsum variable. This model was applied in Shenzhen intercity railway passenger demand forecast in 2010 as a case study. As a result, the forecast results were consistent with the actuality. The model's correctness and feasibility were verified.

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

Component simulation in problems of calculated model formation of automatic machine mechanisms

Directory of Open Access Journals (Sweden)

Telegin Igor

2017-01-01

Full Text Available The paper deals with the problems of the component simulation method application in the problems of the automation of the mechanical system model formation with the further possibility of their CAD-realization. The purpose of the investigations mentioned consists in the automation of the CAD-model formation of high-speed mechanisms in automatic machines and in the analysis of dynamic processes occurred in their units taking into account their elasto-inertial properties, power dissipation, gaps in kinematic pairs, friction forces, design and technological loads. As an example in the paper there are considered a formalization of stages in the computer model formation of the cutting mechanism in cold stamping automatic machine AV1818 and methods of for the computation of their parameters on the basis of its solid-state model.

Dual-use tools and systematics-aware analysis workflows in the ATLAS Run-II analysis model

CERN Document Server

FARRELL, Steven; The ATLAS collaboration

2015-01-01

The ATLAS analysis model has been overhauled for the upcoming run of data collection in 2015 at 13 TeV. One key component of this upgrade was the Event Data Model (EDM), which now allows for greater flexibility in the choice of analysis software framework and provides powerful new features that can be exploited by analysis software tools. A second key component of the upgrade is the introduction of a dual-use tool technology, which provides abstract interfaces for analysis software tools to run in either the Athena framework or a ROOT-based framework. The tool interfaces, including a new interface for handling systematic uncertainties, have been standardized for the development of improved analysis workflows and consolidation of high-level analysis tools. This presentation will cover the details of the dual-use tool functionality, the systematics interface, and how these features fit into a centrally supported analysis environment.

Dual-use tools and systematics-aware analysis workflows in the ATLAS Run-2 analysis model

CERN Document Server

FARRELL, Steven; The ATLAS collaboration; Calafiura, Paolo; Delsart, Pierre-Antoine; Elsing, Markus; Koeneke, Karsten; Krasznahorkay, Attila; Krumnack, Nils; Lancon, Eric; Lavrijsen, Wim; Laycock, Paul; Lei, Xiaowen; Strandberg, Sara Kristina; Verkerke, Wouter; Vivarelli, Iacopo; Woudstra, Martin

2015-01-01

The ATLAS analysis model has been overhauled for the upcoming run of data collection in 2015 at 13 TeV. One key component of this upgrade was the Event Data Model (EDM), which now allows for greater flexibility in the choice of analysis software framework and provides powerful new features that can be exploited by analysis software tools. A second key component of the upgrade is the introduction of a dual-use tool technology, which provides abstract interfaces for analysis software tools to run in either the Athena framework or a ROOT-based framework. The tool interfaces, including a new interface for handling systematic uncertainties, have been standardized for the development of improved analysis workflows and consolidation of high-level analysis tools. This paper will cover the details of the dual-use tool functionality, the systematics interface, and how these features fit into a centrally supported analysis environment.

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...

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 ...

Bayesian Sensitivity Analysis of a Nonlinear Dynamic Factor Analysis Model with Nonparametric Prior and Possible Nonignorable Missingness.

Science.gov (United States)

Tang, Niansheng; Chow, Sy-Miin; Ibrahim, Joseph G; Zhu, Hongtu

2017-12-01

Many psychological concepts are unobserved and usually represented as latent factors apprehended through multiple observed indicators. When multiple-subject multivariate time series data are available, dynamic factor analysis models with random effects offer one way of modeling patterns of within- and between-person variations by combining factor analysis and time series analysis at the factor level. Using the Dirichlet process (DP) as a nonparametric prior for individual-specific time series parameters further allows the distributional forms of these parameters to deviate from commonly imposed (e.g., normal or other symmetric) functional forms, arising as a result of these parameters' restricted ranges. Given the complexity of such models, a thorough sensitivity analysis is critical but computationally prohibitive. We propose a Bayesian local influence method that allows for simultaneous sensitivity analysis of multiple modeling components within a single fitting of the model of choice. Five illustrations and an empirical example are provided to demonstrate the utility of the proposed approach in facilitating the detection of outlying cases and common sources of misspecification in dynamic factor analysis models, as well as identification of modeling components that are sensitive to changes in the DP prior specification.

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

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...

Thermal Analysis of Fermilab Mu2e Beamstop and Structural Analysis of Beamline Components

Energy Technology Data Exchange (ETDEWEB)

Narug, Colin S. [Northern Illinois U.

2018-01-01

The Mu2e project at Fermilab National Accelerator Laboratory aims to observe the unique conversion of muons to electrons. The success or failure of the experiment to observe this conversion will further the understanding of the standard model of physics. Using the particle accelerator, protons will be accelerated and sent to the Mu2e experiment, which will separate the muons from the beam. The muons will then be observed to determine their momentum and the particle interactions occur. At the end of the Detector Solenoid, the internal components will need to absorb the remaining particles of the experiment using polymer absorbers. Because the internal structure of the beamline is in a vacuum, the heat transfer mechanisms that can disperse the energy generated by the particle absorption is limited to conduction and radiation. To determine the extent that the absorbers will heat up over one year of operation, a transient thermal finite element analysis has been performed on the Muon Beam Stop. The levels of energy absorption were adjusted to determine the thermal limit for the current design. Structural finite element analysis has also been performed to determine the safety factors of the Axial Coupler, which connect and move segments of the beamline. The safety factor of the trunnion of the Instrument Feed Through Bulk Head has also been determined for when it is supporting the Muon Beam Stop. The results of the analysis further refine the design of the beamline components prior to testing, fabrication, and installation.

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.

Development of hydrogen combustion analysis model

Energy Technology Data Exchange (ETDEWEB)

Lim, Tae Jin; Lee, K. D.; Kim, S. N. [Soongsil University, Seoul (Korea, Republic of); Hong, J. S.; Kwon, H. Y. [Seoul National Polytechnic University, Seoul (Korea, Republic of); Kim, Y. B.; Kim, J. S. [Seoul National University, Seoul (Korea, Republic of)

1997-07-01

The objectives of this project is to construct a credible DB for component reliability by developing methodologies and computer codes for assessing component independent failure and common cause failure probability, incorporating applicability and dependency of the data. In addition to this, the ultimate goal is to systematize all the analysis procedures so as to provide plans for preventing component failures by employing flexible tools for the change of specific plant or data sources. For the first subject, we construct a DB for similarity index and dependence matrix and propose a systematic procedure for data analysis by investigating the similarity and redundancy of the generic data sources. Next, we develop a computer code for this procedure and construct reliability data base for major components. The second subject is focused on developing CCF procedure for assessing the plant specific defense ability, rather than developing another CCF model. We propose a procedure and computer code for estimating CCF event probability by incorporating plant specific defensive measure. 116 refs., 25 tabs., 24 figs. (author)

Dynamic sensitivity analysis of long running landslide models through basis set expansion and meta-modelling

Science.gov (United States)

Rohmer, Jeremy

2016-04-01

Predicting the temporal evolution of landslides is typically supported by numerical modelling. Dynamic sensitivity analysis aims at assessing the influence of the landslide properties on the time-dependent predictions (e.g., time series of landslide displacements). Yet two major difficulties arise: 1. Global sensitivity analysis require running the landslide model a high number of times (> 1000), which may become impracticable when the landslide model has a high computation time cost (> several hours); 2. Landslide model outputs are not scalar, but function of time, i.e. they are n-dimensional vectors with n usually ranging from 100 to 1000. In this article, I explore the use of a basis set expansion, such as principal component analysis, to reduce the output dimensionality to a few components, each of them being interpreted as a dominant mode of variation in the overall structure of the temporal evolution. The computationally intensive calculation of the Sobol' indices for each of these components are then achieved through meta-modelling, i.e. by replacing the landslide model by a "costless-to-evaluate" approximation (e.g., a projection pursuit regression model). The methodology combining "basis set expansion - meta-model - Sobol' indices" is then applied to the La Frasse landslide to investigate the dynamic sensitivity analysis of the surface horizontal displacements to the slip surface properties during the pore pressure changes. I show how to extract information on the sensitivity of each main modes of temporal behaviour using a limited number (a few tens) of long running simulations. In particular, I identify the parameters, which trigger the occurrence of a turning point marking a shift between a regime of low values of landslide displacements and one of high values.

A Component-Based Modeling and Validation Method for PLC Systems

Directory of Open Access Journals (Sweden)

Rui Wang

2014-05-01

Full Text Available Programmable logic controllers (PLCs are complex embedded systems that are widely used in industry. This paper presents a component-based modeling and validation method for PLC systems using the behavior-interaction-priority (BIP framework. We designed a general system architecture and a component library for a type of device control system. The control software and hardware of the environment were all modeled as BIP components. System requirements were formalized as monitors. Simulation was carried out to validate the system model. A realistic example from industry of the gates control system was employed to illustrate our strategies. We found a couple of design errors during the simulation, which helped us to improve the dependability of the original systems. The results of experiment demonstrated the effectiveness of our approach.

Model selection for convolutive ICA with an application to spatiotemporal analysis of EEG

DEFF Research Database (Denmark)

Dyrholm, Mads; Makeig, S.; Hansen, Lars Kai

2007-01-01

We present a new algorithm for maximum likelihood convolutive independent component analysis (ICA) in which components are unmixed using stable autoregressive filters determined implicitly by estimating a convolutive model of the mixing process. By introducing a convolutive mixing model...... for the components, we show how the order of the filters in the model can be correctly detected using Bayesian model selection. We demonstrate a framework for deconvolving a subspace of independent components in electroencephalography (EEG). Initial results suggest that in some cases, convolutive mixing may...

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)

Distribution system modeling and analysis

CERN Document Server

Kersting, William H

2001-01-01

For decades, distribution engineers did not have the sophisticated tools developed for analyzing transmission systems-often they had only their instincts. Things have changed, and we now have computer programs that allow engineers to simulate, analyze, and optimize distribution systems. Powerful as these programs are, however, without a real understanding of the operating characteristics of a distribution system, engineers using the programs can easily make serious errors in their designs and operating procedures. Distribution System Modeling and Analysis helps prevent those errors. It gives readers a basic understanding of the modeling and operating characteristics of the major components of a distribution system. One by one, the author develops and analyzes each component as a stand-alone element, then puts them all together to analyze a distribution system comprising the various shunt and series devices for power-flow and short-circuit studies. He includes the derivation of all models and includes many num...

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

Data and information needs for WPP testing and component modeling

International Nuclear Information System (INIS)

Kuhn, W.L.

1987-01-01

The modeling task of the Waste Package Program (WPP) is to develop conceptual models that describe the interactions of waste package components with their environment and the interactions among waste package components. The task includes development and maintenance of a database of experimental data, and statistical analyses to fit model coefficients, test the significance of the fits, and propose experimental designs. The modeling task collaborates with experimentalists to apply physicochemical principles to develop the conceptual models, with emphasis on the subsequent mathematical development. The reason for including the modeling task in the predominantly experimental WPP is to keep the modeling of component behavior closely associated with the experimentation. Whenever possible, waste package degradation processes are described in terms of chemical reactions or transport processes. The integration of equations for assumed or calculated repository conditions predicts variations with time in the repository. Within the context of the waste package program, the composition and rate of arrival of brine to the waste package are environmental variables. These define the environment to be simulated or explored during waste package component and interactions testing. The containment period is characterized by rapid changes in temperature, pressure, oxygen fugacity, and salt porosity. Brine migration is expected to be most rapid during this period. The release period is characterized by modest and slowly changing temperatures, high pressure, low oxygen fugacity, and low porosity. The need is to define the scenario within which waste package degradation calculations are to be made and to quantify the rate of arrival and composition of the brine. Appendix contains 4 vugraphs

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.

Usage of Parameterized Fatigue Spectra and Physics-Based Systems Engineering Models for Wind Turbine Component Sizing: Preprint

Energy Technology Data Exchange (ETDEWEB)

Parsons, Taylor; Guo, Yi; Veers, Paul; Dykes, Katherine; Damiani, Rick

2016-01-26

Software models that use design-level input variables and physics-based engineering analysis for estimating the mass and geometrical properties of components in large-scale machinery can be very useful for analyzing design trade-offs in complex systems. This study uses DriveSE, an OpenMDAO-based drivetrain model that uses stress and deflection criteria to size drivetrain components within a geared, upwind wind turbine. Because a full lifetime fatigue load spectrum can only be defined using computationally-expensive simulations in programs such as FAST, a parameterized fatigue loads spectrum that depends on wind conditions, rotor diameter, and turbine design life has been implemented. The parameterized fatigue spectrum is only used in this paper to demonstrate the proposed fatigue analysis approach. This paper details a three-part investigation of the parameterized approach and a comparison of the DriveSE model with and without fatigue analysis on the main shaft system. It compares loads from three turbines of varying size and determines if and when fatigue governs drivetrain sizing compared to extreme load-driven design. It also investigates the model's sensitivity to shaft material parameters. The intent of this paper is to demonstrate how fatigue considerations in addition to extreme loads can be brought into a system engineering optimization.

Ignalina NPP Safety Analysis: Models and Results

International Nuclear Information System (INIS)

Uspuras, E.

1999-01-01

Research directions, linked to safety assessment of the Ignalina NPP, of the scientific safety analysis group are presented: Thermal-hydraulic analysis of accidents and operational transients; Thermal-hydraulic assessment of Ignalina NPP Accident Localization System and other compartments; Structural analysis of plant components, piping and other parts of Main Circulation Circuit; Assessment of RBMK-1500 reactor core and other. Models and main works carried out last year are described. (author)

A proposed centralised distribution model for the South African automotive component industry

Directory of Open Access Journals (Sweden)

Micheline J. Naude

2009-12-01

Full Text Available Purpose: This article explores the possibility of developing a distribution model, similar to the model developed and implemented by the South African pharmaceutical industry, which could be implemented by automotive component manufacturers for supply to independent retailers. Problem Investigated: The South African automotive components distribution chain is extensive with a number of players of varying sizes, from the larger spares distribution groups to a number of independent retailers. Distributing to the smaller independent retailers is costly for the automotive component manufacturers. Methodology: This study is based on a preliminary study of an explorative nature. Interviews were conducted with a senior staff member from a leading automotive component manufacturer in KwaZulu Natal and nine participants at a senior management level at five of their main customers (aftermarket retailers. Findings: The findings from the empirical study suggest that the aftermarket component industry is mature with the role players well established. The distribution chain to the independent retailer is expensive in terms of transaction and distribution costs for the automotive component manufacturer. A proposed centralised distribution model for supply to independent retailers has been developed which should reduce distribution costs for the automotive component manufacturer in terms of (1 the lowest possible freight rate; (2 timely and controlled delivery; and (3 reduced congestion at the customer's receiving dock. Originality: This research is original in that it explores the possibility of implementing a centralised distribution model for independent retailers in the automotive component industry. Furthermore, there is a dearth of published research on the South African automotive component industry particularly addressing distribution issues. Conclusion: The distribution model as suggested is a practical one and should deliver added value to automotive

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 ...

Penalising Model Component Complexity: A Principled, Practical Approach to Constructing Priors

KAUST Repository

Simpson, Daniel

2017-04-06

In this paper, we introduce a new concept for constructing prior distributions. We exploit the natural nested structure inherent to many model components, which defines the model component to be a flexible extension of a base model. Proper priors are defined to penalise the complexity induced by deviating from the simpler base model and are formulated after the input of a user-defined scaling parameter for that model component, both in the univariate and the multivariate case. These priors are invariant to repa-rameterisations, have a natural connection to Jeffreys\\' priors, are designed to support Occam\\'s razor and seem to have excellent robustness properties, all which are highly desirable and allow us to use this approach to define default prior distributions. Through examples and theoretical results, we demonstrate the appropriateness of this approach and how it can be applied in various situations.

Penalising Model Component Complexity: A Principled, Practical Approach to Constructing Priors

KAUST Repository

Simpson, Daniel; Rue, Haavard; Riebler, Andrea; Martins, Thiago G.; Sø rbye, Sigrunn H.

2017-01-01

In this paper, we introduce a new concept for constructing prior distributions. We exploit the natural nested structure inherent to many model components, which defines the model component to be a flexible extension of a base model. Proper priors are defined to penalise the complexity induced by deviating from the simpler base model and are formulated after the input of a user-defined scaling parameter for that model component, both in the univariate and the multivariate case. These priors are invariant to repa-rameterisations, have a natural connection to Jeffreys' priors, are designed to support Occam's razor and seem to have excellent robustness properties, all which are highly desirable and allow us to use this approach to define default prior distributions. Through examples and theoretical results, we demonstrate the appropriateness of this approach and how it can be applied in various situations.

30 seismic analysis of FBR vessels: Coupling between components and vessels, fluid communications, imperfections

International Nuclear Information System (INIS)

Gantenbein, F.; Gibert, R.J.; Aita, S.; Durandet, E.

1988-01-01

The internal structures of a loop type breeder reactors such as SUPERPHENIX are composed of axisymmetrical shells separated by fluid volumes. Seismic analysis is usually performed by axisymmetric finite element model taking into account fluid structure interaction but the geometry is in fact 3D due to components, small communications between fluid volumes and imperfections in the vessels. The methods to take this 3D behaviour into account are based on Fourier decomposition of the motion and substructuration. They are briefly described in the following chapters. The influence of components and of small communications on a block reactor similar to SPX1 will also be presented. 15 refs, 20 figs

An application of principal component analysis to the clavicle and clavicle fixation devices.

LENUS (Irish Health Repository)

Daruwalla, Zubin J

2010-01-01

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.

A Recurrent Probabilistic Neural Network with Dimensionality Reduction Based on Time-series Discriminant Component Analysis.

Science.gov (United States)

Hayashi, Hideaki; Shibanoki, Taro; Shima, Keisuke; Kurita, Yuichi; Tsuji, Toshio

2015-12-01

This paper proposes a probabilistic neural network (NN) developed on the basis of time-series discriminant component analysis (TSDCA) that can be used to classify high-dimensional time-series patterns. TSDCA involves the compression of high-dimensional time series into a lower dimensional space using a set of orthogonal transformations and the calculation of posterior probabilities based on a continuous-density hidden Markov model with a Gaussian mixture model expressed in the reduced-dimensional space. The analysis can be incorporated into an NN, which is named a time-series discriminant component network (TSDCN), so that parameters of dimensionality reduction and classification can be obtained simultaneously as network coefficients according to a backpropagation through time-based learning algorithm with the Lagrange multiplier method. The TSDCN is considered to enable high-accuracy classification of high-dimensional time-series patterns and to reduce the computation time taken for network training. The validity of the TSDCN is demonstrated for high-dimensional artificial data and electroencephalogram signals in the experiments conducted during the study.

Statistical analysis tolerance using jacobian torsor model based on uncertainty propagation method

Directory of Open Access Journals (Sweden)

W Ghie

2016-04-01

Full Text Available One risk inherent in the use of assembly components is that the behaviourof these components is discovered only at the moment an assembly isbeing carried out. The objective of our work is to enable designers to useknown component tolerances as parameters in models that can be usedto predict properties at the assembly level. In this paper we present astatistical approach to assemblability evaluation, based on tolerance andclearance propagations. This new statistical analysis method for toleranceis based on the Jacobian-Torsor model and the uncertainty measurementapproach. We show how this can be accomplished by modeling thedistribution of manufactured dimensions through applying a probabilitydensity function. By presenting an example we show how statisticaltolerance analysis should be used in the Jacobian-Torsor model. This workis supported by previous efforts aimed at developing a new generation ofcomputational tools for tolerance analysis and synthesis, using theJacobian-Torsor approach. This approach is illustrated on a simple threepartassembly, demonstrating the method’s capability in handling threedimensionalgeometry.

Sparse logistic principal components analysis for binary data

KAUST Repository

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.

Creep fatigue design of FBR components

International Nuclear Information System (INIS)

Bhoje, S.B.; Chellapandi, P.

1997-01-01

This paper deals with the characteristic features of Fast Breeder Reactor (FBR) with reference to creep fatigue, current creep fatigue design approach in compliance with RCCMR (1987) design code, material data, effects of weldments and neutron irradiation, material constitutive models employed, structural analysis and further R and D required for achieving maturity in creep fatigue design of FBR components. For the analysis reported in this paper, material constitutive models developed based on ORNIb (Oak Ridge National Laboratory) and Chaboche viscoplastic theories are employed to demonstrate the potential of FBR components for higher plant temperatures and/or longer life. The results are presented for the studies carried out towards life prediction of Prototype Fast Breeder Reactor (PFBR) components. (author). 24 refs, 8 figs, 5 tabs

Reliability for systems of degrading components with distinct component shock sets

International Nuclear Information System (INIS)

Song, Sanling; Coit, David W.; Feng, Qianmei

2014-01-01

This paper studies reliability for multi-component systems subject to dependent competing risks of degradation wear and random shocks, with distinct shock sets. In practice, many systems are exposed to distinct and different types of shocks that can be categorized according to their sizes, function, affected components, etc. Previous research primarily focuses on simple systems with independent failure processes, systems with independent component time-to-failure, or components that share the same shock set or type of shocks. In our new model, we classify random shocks into different sets based on their sizes or function. Shocks with specific sizes or function can selectively affect one or more components in the system but not necessarily all components. Additionally the shocks from the different shock sets can arrive at different rates and have different relative magnitudes. Preventive maintenance (PM) optimization is conducted for the system with different component shock sets. Decision variables for two different maintenance scheduling problems, the PM replacement time interval, and the PM inspection time interval, are determined by minimizing a defined system cost rate. Sensitivity analysis is performed to provide insight into the behavior of the proposed maintenance policies. These models can be applied directly or customized for many complex systems that experience dependent competing failure processes with different component shock sets. A MEMS (Micro-electro mechanical systems) oscillator is a typical system subject to dependent and competing failure processes, and it is used as a numerical example to illustrate our new reliability and maintenance models

Evaluating fugacity models for trace components in landfill gas

Energy Technology Data Exchange (ETDEWEB)

Shafi, Sophie [Integrated Waste Management Centre, Sustainable Systems Department, Building 61, School of Industrial and Manufacturing Science, Cranfield University, Cranfield, Bedfordshire MK43 0AL (United Kingdom); Sweetman, Andrew [Department of Environmental Science, Lancaster University, Lancaster LA1 4YQ (United Kingdom); Hough, Rupert L. [Integrated Waste Management Centre, Sustainable Systems Department, Building 61, School of Industrial and Manufacturing Science, Cranfield University, Cranfield, Bedfordshire MK43 0AL (United Kingdom); Smith, Richard [Integrated Waste Management Centre, Sustainable Systems Department, Building 61, School of Industrial and Manufacturing Science, Cranfield University, Cranfield, Bedfordshire MK43 0AL (United Kingdom); Rosevear, Alan [Science Group - Waste and Remediation, Environment Agency, Reading RG1 8DQ (United Kingdom); Pollard, Simon J.T. [Integrated Waste Management Centre, Sustainable Systems Department, Building 61, School of Industrial and Manufacturing Science, Cranfield University, Cranfield, Bedfordshire MK43 0AL (United Kingdom)]. E-mail: s.pollard@cranfield.ac.uk

2006-12-15

A fugacity approach was evaluated to reconcile loadings of vinyl chloride (chloroethene), benzene, 1,3-butadiene and trichloroethylene in waste with concentrations observed in landfill gas monitoring studies. An evaluative environment derived from fictitious but realistic properties such as volume, composition, and temperature, constructed with data from the Brogborough landfill (UK) test cells was used to test a fugacity approach to generating the source term for use in landfill gas risk assessment models (e.g. GasSim). SOILVE, a dynamic Level II model adapted here for landfills, showed greatest utility for benzene and 1,3-butadiene, modelled under anaerobic conditions over a 10 year simulation. Modelled concentrations of these components (95 300 {mu}g m{sup -3}; 43 {mu}g m{sup -3}) fell within measured ranges observed in gas from landfills (24 300-180 000 {mu}g m{sup -3}; 20-70 {mu}g m{sup -3}). This study highlights the need (i) for representative and time-referenced biotransformation data; (ii) to evaluate the partitioning characteristics of organic matter within waste systems and (iii) for a better understanding of the role that gas extraction rate (flux) plays in producing trace component concentrations in landfill gas. - Fugacity for trace component in landfill gas.

Understanding science teacher enhancement programs: Essential components and a model

Science.gov (United States)

Spiegel, Samuel Albert

Researchers and practioners alike recognize that "the national goal that every child in the United States has access to high-quality school education in science and mathematics cannot be realized without the availability of effective professional development of teachers" (Hewson, 1997, p. 16). Further, there is a plethora of reports calling for the improvement of professional development efforts (Guskey & Huberman, 1995; Kyle, 1995; Loucks-Horsley, Hewson, Love, & Stiles, 1997). In this study I analyze a successful 3-year teacher enhancement program, one form of professional development, to: (1) identify essential components of an effective teacher enhancement program; and (2) create a model to identify and articulate the critical issues in designing, implementing, and evaluating teacher enhancement programs. Five primary sources of information were converted into data: (1) exit questionnaires, (2) exit surveys, (3) exit interview transcripts, (4) focus group transcripts, and (5) other artifacts. Additionally, a focus group was used to conduct member checks. Data were analyzed in an iterative process which led to the development of the list of essential components. The Components are categorized by three organizers: Structure (e.g., science research experience, a mediator throughout the program), Context (e.g., intensity, collaboration), and Participant Interpretation (e.g., perceived to be "safe" to examine personal beliefs and practices, actively engaged). The model is based on: (1) a 4-year study of a successful teacher enhancement program; (2) an analysis of professional development efforts reported in the literature; and (3) reflective discussions with implementors, evaluators, and participants of professional development programs. The model consists of three perspectives, cognitive, symbolic interaction, and organizational, representing different viewpoints from which to consider issues relevant to the success of a teacher enhancement program. These

Two-component network model in voice identification technologies

Directory of Open Access Journals (Sweden)

Edita K. Kuular

2018-03-01

Full Text Available Among the most important parameters of biometric systems with voice modalities that determine their effectiveness, along with reliability and noise immunity, a speed of identification and verification of a person has been accentuated. This parameter is especially sensitive while processing large-scale voice databases in real time regime. Many research studies in this area are aimed at developing new and improving existing algorithms for presentation and processing voice records to ensure high performance of voice biometric systems. Here, it seems promising to apply a modern approach, which is based on complex network platform for solving complex massive problems with a large number of elements and taking into account their interrelationships. Thus, there are known some works which while solving problems of analysis and recognition of faces from photographs, transform images into complex networks for their subsequent processing by standard techniques. One of the first applications of complex networks to sound series (musical and speech analysis are description of frequency characteristics by constructing network models - converting the series into networks. On the network ontology platform a previously proposed technique of audio information representation aimed on its automatic analysis and speaker recognition has been developed. This implies converting information into the form of associative semantic (cognitive network structure with amplitude and frequency components both. Two speaker exemplars have been recorded and transformed into pertinent networks with consequent comparison of their topological metrics. The set of topological metrics for each of network models (amplitude and frequency one is a vector, and together  those combine a matrix, as a digital "network" voiceprint. The proposed network approach, with its sensitivity to personal conditions-physiological, psychological, emotional, might be useful not only for person identification

A minimal model for two-component dark matter

International Nuclear Information System (INIS)

Esch, Sonja; Klasen, Michael; Yaguna, Carlos E.

2014-01-01

We propose and study a new minimal model for two-component dark matter. The model contains only three additional fields, one fermion and two scalars, all singlets under the Standard Model gauge group. Two of these fields, one fermion and one scalar, are odd under a Z_2 symmetry that renders them simultaneously stable. Thus, both particles contribute to the observed dark matter density. This model resembles the union of the singlet scalar and the singlet fermionic models but it contains some new features of its own. We analyze in some detail its dark matter phenomenology. Regarding the relic density, the main novelty is the possible annihilation of one dark matter particle into the other, which can affect the predicted relic density in a significant way. Regarding dark matter detection, we identify a new contribution that can lead either to an enhancement or to a suppression of the spin-independent cross section for the scalar dark matter particle. Finally, we define a set of five benchmarks models compatible with all present bounds and examine their direct detection prospects at planned experiments. A generic feature of this model is that both particles give rise to observable signals in 1-ton direct detection experiments. In fact, such experiments will be able to probe even a subdominant dark matter component at the percent level.

Integrating environmental component models. Development of a software framework

NARCIS (Netherlands)

Schmitz, O.

2014-01-01

Integrated models consist of interacting component models that represent various natural and social systems. They are important tools to improve our understanding of environmental systems, to evaluate cause–effect relationships of human–natural interactions, and to forecast the behaviour of

Interpretable functional principal component analysis.

Science.gov (United States)

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.

A discrete-time Bayesian network reliability modeling and analysis framework

International Nuclear Information System (INIS)

Boudali, H.; Dugan, J.B.

2005-01-01

Dependability tools are becoming an indispensable tool for modeling and analyzing (critical) systems. However the growing complexity of such systems calls for increasing sophistication of these tools. Dependability tools need to not only capture the complex dynamic behavior of the system components, but they must be also easy to use, intuitive, and computationally efficient. In general, current tools have a number of shortcomings including lack of modeling power, incapacity to efficiently handle general component failure distributions, and ineffectiveness in solving large models that exhibit complex dependencies between their components. We propose a novel reliability modeling and analysis framework based on the Bayesian network (BN) formalism. The overall approach is to investigate timed Bayesian networks and to find a suitable reliability framework for dynamic systems. We have applied our methodology to two example systems and preliminary results are promising. We have defined a discrete-time BN reliability formalism and demonstrated its capabilities from a modeling and analysis point of view. This research shows that a BN based reliability formalism is a powerful potential solution to modeling and analyzing various kinds of system components behaviors and interactions. Moreover, being based on the BN formalism, the framework is easy to use and intuitive for non-experts, and provides a basis for more advanced and useful analyses such as system diagnosis

Analyses of component degradation to evaluate maintenance effectiveness and aging effects

International Nuclear Information System (INIS)

Samanta, P.K.; Hsu, F.; Subudhi, M.; Vesely, W.E.

1991-01-01

This paper describes degradation modeling, an approach for analyzing degradation and failure of components to understand the aging process of components. As used in our study, degradation modeling is the analysis of information on degradation of components for developing models of the degradation process and its implications. This modeling focuses on the analysis of the times of degradations of components, to model how the rate of degradation changes with the age of the component. With this methodology we also determine the effectiveness of maintenance as applicable to aging evaluations. The specific applications which are performed show quantitative models of degradation rates of components and failure rates of components from plant-specific data. The statistical techniques allow aging trends to be identified in the degradation data and in the failure data. Initial estimates of the effectiveness of maintenance in limiting degradations from becoming failures are developed. These results are important first steps in degradation modeling, and show that degradation can be modeled to identify aging trends. 2 refs., 8 figs., 1 tab

RACLETTE: a model for evaluating the thermal response of plasma facing components to slow high power plasma transients. Part II: Analysis of ITER plasma facing components

Science.gov (United States)

Federici, Gianfranco; Raffray, A. René

1997-04-01

The transient thermal model RACLETTE (acronym of Rate Analysis Code for pLasma Energy Transfer Transient Evaluation) described in part I of this paper is applied here to analyse the heat transfer and erosion effects of various slow (100 ms-10 s) high power energy transients on the actively cooled plasma facing components (PFCs) of the International Thermonuclear Experimental Reactor (ITER). These have a strong bearing on the PFC design and need careful analysis. The relevant parameters affecting the heat transfer during the plasma excursions are established. The temperature variation with time and space is evaluated together with the extent of vaporisation and melting (the latter only for metals) for the different candidate armour materials considered for the design (i.e., Be for the primary first wall, Be and CFCs for the limiter, Be, W, and CFCs for the divertor plates) and including for certain cases low-density vapour shielding effects. The critical heat flux, the change of the coolant parameters and the possible severe degradation of the coolant heat removal capability that could result under certain conditions during these transients, for example for the limiter, are also evaluated. Based on the results, the design implications on the heat removal performance and erosion damage of the variuos ITER PFCs are critically discussed and some recommendations are made for the selection of the most adequate protection materials and optimum armour thickness.

What Time Is Sunrise? Revisiting the Refraction Component of Sunrise/set Prediction Models

Science.gov (United States)

Wilson, Teresa; Bartlett, Jennifer L.; Hilton, James Lindsay

2017-01-01

Algorithms that predict sunrise and sunset times currently have an error of one to four minutes at mid-latitudes (0° - 55° N/S) due to limitations in the atmospheric models they incorporate. At higher latitudes, slight changes in refraction can cause significant discrepancies, even including difficulties determining when the Sun appears to rise or set. While different components of refraction are known, how they affect predictions of sunrise/set has not yet been quantified. A better understanding of the contributions from temperature profile, pressure, humidity, and aerosols could significantly improve the standard prediction. We present a sunrise/set calculator that interchanges the refraction component by varying the refraction model. We then compare these predictions with data sets of observed rise/set times to create a better model. Sunrise/set times and meteorological data from multiple locations will be necessary for a thorough investigation of the problem. While there are a few data sets available, we will also begin collecting this data using smartphones as part of a citizen science project. The mobile application for this project will be available in the Google Play store. Data analysis will lead to more complete models that will provide more accurate rise/set times for the benefit of astronomers, navigators, and outdoorsmen everywhere.

Automatic scatter detection in fluorescence landscapes by means of spherical principal component analysis

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...

Multi-component fiber track modelling of diffusion-weighted magnetic resonance imaging data

Directory of Open Access Journals (Sweden)

Yasser M. Kadah

2010-01-01

Full Text Available In conventional diffusion tensor imaging (DTI based on magnetic resonance data, each voxel is assumed to contain a single component having diffusion properties that can be fully represented by a single tensor. Even though this assumption can be valid in some cases, the general case involves the mixing of components, resulting in significant deviation from the single tensor model. Hence, a strategy that allows the decomposition of data based on a mixture model has the potential of enhancing the diagnostic value of DTI. This project aims to work towards the development and experimental verification of a robust method for solving the problem of multi-component modelling of diffusion tensor imaging data. The new method demonstrates significant error reduction from the single-component model while maintaining practicality for clinical applications, obtaining more accurate Fiber tracking 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)

Analysis of diffusivity of the oscillating reaction components in a microreactor system

Directory of Open Access Journals (Sweden)

Martina Šafranko

2017-01-01

Full Text Available When performing oscillating reactions, periodical changes in the concentrations of reactants, intermediaries, and products take place. Due to the mentioned periodical changes of the concentrations, the information about the diffusivity of the components included into oscillating reactions is very important for the control of the oscillating reactions. Non-linear dynamics makes oscillating reactions very interesting for analysis in different reactor systems. In this paper, the analysis of diffusivity of the oscillating reaction components was performed in a microreactor, with the aim of identifying the limiting component. The geometry of the microreactor microchannel and a well defined flow profile ensure optimal conditions for the diffusion phenomena analysis, because diffusion profiles in a microreactor depend only on the residence time. In this paper, the analysis of diffusivity of the oscillating reaction components was performed in a microreactor equipped with 2 Y-shape inlets and 2 Y-shape outlets, with active volume of V = 4 μL at different residence times.

An application of principal component analysis to the clavicle and clavicle fixation devices

OpenAIRE

Daruwalla, Zubin J; Courtis, Patrick; Fitzpatrick, Clare; Fitzpatrick, David; Mullett, Hannan

2010-01-01

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 method...

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.

Passive Microwave Components and Antennas

DEFF Research Database (Denmark)

State-of-the-art microwave systems always require higher performance and lower cost microwave components. Constantly growing demands and performance requirements of industrial and scientific applications often make employing traditionally designed components impractical. For that reason, the design...... and development process remains a great challenge today. This problem motivated intensive research efforts in microwave design and technology, which is responsible for a great number of recently appeared alternative approaches to analysis and design of microwave components and antennas. This book highlights...... techniques. Modelling and computations in electromagnetics is a quite fast-growing research area. The recent interest in this field is caused by the increased demand for designing complex microwave components, modeling electromagnetic materials, and rapid increase in computational power for calculation...

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.

Low-level profiling and MARTE-compatible modeling of software components for real-time systems

NARCIS (Netherlands)

Triantafyllidis, K.; Bondarev, E.; With, de P.H.N.

2012-01-01

In this paper, we present a method for (a) profiling of individual components at high accuracy level, (b) modeling of the components with the accurate data obtained from profiling, and (c) model conversion to the MARTE profile. The resulting performance models of individual components are used at

Component Degradation Susceptibilities As The Bases For Modeling Reactor Aging Risk

International Nuclear Information System (INIS)

Unwin, Stephen D.; Lowry, Peter P.; Toyooka, Michael Y.

2010-01-01

The extension of nuclear power plant operating licenses beyond 60 years in the United States will be necessary if we are to meet national energy needs while addressing the issues of carbon and climate. Characterizing the operating risks associated with aging reactors is problematic because the principal tool for risk-informed decision-making, Probabilistic Risk Assessment (PRA), is not ideally-suited to addressing aging systems. The components most likely to drive risk in an aging reactor - the passives - receive limited treatment in PRA, and furthermore, standard PRA methods are based on the assumption of stationary failure rates: a condition unlikely to be met in an aging system. A critical barrier to modeling passives aging on the wide scale required for a PRA is that there is seldom sufficient field data to populate parametric failure models, and nor is there the availability of practical physics models to predict out-year component reliability. The methodology described here circumvents some of these data and modeling needs by using materials degradation metrics, integrated with conventional PRA models, to produce risk importance measures for specific aging mechanisms and component types. We suggest that these measures have multiple applications, from the risk-screening of components to the prioritization of materials research.

A Note on McDonald's Generalization of Principal Components Analysis

Science.gov (United States)

Shine, Lester C., II

1972-01-01

It is shown that McDonald's generalization of Classical Principal Components Analysis to groups of variables maximally channels the totalvariance of the original variables through the groups of variables acting as groups. An equation is obtained for determining the vectors of correlations of the L2 components with the original variables.…

Photonic Beamformer Model Based on Analog Fiber-Optic Links’ Components

International Nuclear Information System (INIS)

Volkov, V A; Gordeev, D A; Ivanov, S I; Lavrov, A P; Saenko, I I

2016-01-01

The model of photonic beamformer for wideband microwave phased array antenna is investigated. The main features of the photonic beamformer model based on true-time-delay technique, DWDM technology and fiber chromatic dispersion are briefly analyzed. The performance characteristics of the key components of photonic beamformer for phased array antenna in the receive mode are examined. The beamformer model composed of the components available on the market of fiber-optic analog communication links is designed and tentatively investigated. Experimental demonstration of the designed model beamforming features includes actual measurement of 5-element microwave linear array antenna far-field patterns in 6-16 GHz frequency range for antenna pattern steering up to 40°. The results of experimental testing show good accordance with the calculation estimates. (paper)

Registration of dynamic dopamine D{sub 2}receptor images using principal component analysis

Energy Technology Data Exchange (ETDEWEB)

Acton, P.D.; Ell, P.J. [Institute of Nuclear Medicine, University College London Medical School, London (United Kingdom); Pilowsky, L.S.; Brammer, M.J. [Institute of Psychiatry, De Crespigny Park, London (United Kingdom); Suckling, J. [Clinical Age Research Unit, Kings College School of Medicine and Dentistry, London (United Kingdom)

1997-11-01

This paper describes a novel technique for registering a dynamic sequence of single-photon emission tomography (SPET) dopamine D{sub 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 {sub 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 <0.001) improvements in image registration, leading to alignment errors in x and y of about 25% of the alternative methods, with reductions in autocorrelations over time. It could also be applied to align image sequences which the other methods failed completely to register, particularly {sup 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 {chi}{sup 2}of the fit to the compartmental model, and provided superior quality registration of particularly difficult dynamic sequences. (orig.) With 4 figs., 2 tabs., 26 refs.

Functional principal component analysis of glomerular filtration rate curves after kidney transplant.

Science.gov (United States)

Dong, Jianghu J; Wang, Liangliang; Gill, Jagbir; Cao, Jiguo

2017-01-01

This article is motivated by some longitudinal clinical data of kidney transplant recipients, where kidney function progression is recorded as the estimated glomerular filtration rates at multiple time points post kidney transplantation. We propose to use the functional principal component analysis method to explore the major source of variations of glomerular filtration rate curves. We find that the estimated functional principal component scores can be used to cluster glomerular filtration rate curves. Ordering functional principal component scores can detect abnormal glomerular filtration rate curves. Finally, functional principal component analysis can effectively estimate missing glomerular filtration rate values and predict future glomerular filtration rate values.

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

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

Research on development model of nuclear component based on life cycle management

International Nuclear Information System (INIS)

Bao Shiyi; Zhou Yu; He Shuyan

2005-01-01

At present the development process of nuclear component, even nuclear component itself, is more and more supported by computer technology. This increasing utilization of the computer and software has led to the faster development of nuclear technology on one hand and also brought new problems on the other hand. Especially, the combination of hardware, software and humans has increased nuclear component system complexities to an unprecedented level. To solve this problem, Life Cycle Management technology is adopted in nuclear component system. Hence, an intensive discussion on the development process of a nuclear component is proposed. According to the characteristics of the nuclear component development, such as the complexities and strict safety requirements of the nuclear components, long-term design period, changeable design specifications and requirements, high capital investment, and satisfaction for engineering codes/standards, the development life-cycle model of nuclear component is presented. The development life-cycle model is classified at three levels, namely, component level development life-cycle, sub-component development life-cycle and component level verification/certification life-cycle. The purposes and outcomes of development processes are stated in detailed. A process framework for nuclear component based on system engineering and development environment of nuclear component is discussed for future research work. (authors)

Time-domain ultra-wideband radar, sensor and components theory, analysis and design

CERN Document Server

Nguyen, Cam

2014-01-01

This book presents the theory, analysis, and design of ultra-wideband (UWB) radar and sensor systems (in short, UWB systems) and their components. UWB systems find numerous applications in the military, security, civilian, commercial and medicine fields. This book addresses five main topics of UWB systems: System Analysis, Transmitter Design, Receiver Design, Antenna Design and System Integration and Test. The developments of a practical UWB system and its components using microwave integrated circuits, as well as various measurements, are included in detail to demonstrate the theory, analysis and design technique. Essentially, this book will enable the reader to design their own UWB systems and components. In the System Analysis chapter, the UWB principle of operation as well as the power budget analysis and range resolution analysis are presented. In the UWB Transmitter Design chapter, the design, fabrication and measurement of impulse and monocycle pulse generators are covered. The UWB Receiver Design cha...

Study on determination of durability analysis process and fatigue damage parameter for rubber component

International Nuclear Information System (INIS)

Moon, Seong In; Cho, Il Je; Woo, Chang Su; Kim, Wan Doo

2011-01-01

Rubber components, which have been widely used in the automotive industry as anti-vibration components for many years, are subjected to fluctuating loads, often failing due to the nucleation and growth of defects or cracks. To prevent such failures, it is necessary to understand the fatigue failure mechanism for rubber materials and to evaluate the fatigue life for rubber components. The objective of this study is to develop a durability analysis process for vulcanized rubber components, that can predict fatigue life at the initial product design step. The determination method of nonlinear material constants for FE analysis was proposed. Also, to investigate the applicability of the commonly used damage parameters, fatigue tests and corresponding finite element analyses were carried out and normal and shear strain was proposed as the fatigue damage parameter for rubber components. Fatigue analysis for automotive rubber components was performed and the durability analysis process was reviewed

A component analysis of positive behaviour support plans.

Science.gov (United States)

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.

THE STUDY OF THE CHARACTERIZATION INDICES OF FABRICS BY PRINCIPAL COMPONENT ANALYSIS METHOD

OpenAIRE

HRISTIAN Liliana; OSTAFE Maria Magdalena; BORDEIANU Demetra Lacramioara; APOSTOL Laura Liliana

2017-01-01

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 s...

The n-component cubic model and flows: subgraph break-collapse method

International Nuclear Information System (INIS)

Essam, J.W.; Magalhaes, A.C.N. de.

1988-01-01

We generalise to the n-component cubic model the subgraph break-collapse method which we previously developed for the Potts model. The relations used are based on expressions which we recently derived for the Z(λ) model in terms of mod-λ flows. Our recursive algorithm is similar, for n = 2, to the break-collapse method for the Z(4) model proposed by Mariz and coworkers. It allows the exact calculation for the partition function and correlation functions for n-component cubic clusters with n as a variable, without the need to examine all of the spin configurations. (author) [pt

An application of principal component analysis to the clavicle and clavicle fixation devices.

Science.gov (United States)

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.

ARTS: A System-Level Framework for Modeling MPSoC Components and Analysis of their Causality

DEFF Research Database (Denmark)

Mahadevan, Shankar; Storgaard, Michael; Madsen, Jan

2005-01-01

Designing complex heterogeneousmultiprocessor Systemon- Chip (MPSoC) requires support for modeling and analysis of the different layers i.e. application, operating system (OS) and platform architecture. This paper presents an abstract system-level modeling framework, called ARTS, to support...

Principal Component Analysis of Working Memory Variables during Child and Adolescent Development.

Science.gov (United States)

Barriga-Paulino, Catarina I; Rodríguez-Martínez, Elena I; Rojas-Benjumea, María Ángeles; Gómez, Carlos M

2016-10-03

Correlation and Principal Component Analysis (PCA) of behavioral measures from two experimental tasks (Delayed Match-to-Sample and Oddball), and standard scores from a neuropsychological test battery (Working Memory Test Battery for Children) was performed on data from participants between 6-18 years old. The correlation analysis (p 1), the scores of the first extracted component were significantly correlated (p < .05) to most behavioral measures, suggesting some commonalities of the processes of age-related changes in the measured variables. The results suggest that this first component would be related to age but also to individual differences during the cognitive maturation process across childhood and adolescence stages. The fourth component would represent the speed-accuracy trade-off phenomenon as it presents loading components with different signs for reaction times and errors.

A Four–Component Model of Age–Related Memory Change

Science.gov (United States)

Healey, M. Karl; Kahana, Michael J.

2015-01-01

We develop a novel, computationally explicit, theory of age–related memory change within the framework of the context maintenance and retrieval (CMR2) model of memory search. We introduce a set of benchmark findings from the free recall and recognition tasks that includes aspects of memory performance that show both age-related stability and decline. We test aging theories by lesioning the corresponding mechanisms in a model fit to younger adult free recall data. When effects are considered in isolation, many theories provide an adequate account, but when all effects are considered simultaneously, the existing theories fail. We develop a novel theory by fitting the full model (i.e., allowing all parameters to vary) to individual participants and comparing the distributions of parameter values for older and younger adults. This theory implicates four components: 1) the ability to sustain attention across an encoding episode, 2) the ability to retrieve contextual representations for use as retrieval cues, 3) the ability to monitor retrievals and reject intrusions, and 4) the level of noise in retrieval competitions. We extend CMR2 to simulate a recognition memory task using the same mechanisms the free recall model uses to reject intrusions. Without fitting any additional parameters, the four–component theory that accounts for age differences in free recall predicts the magnitude of age differences in recognition memory accuracy. Confirming a prediction of the model, free recall intrusion rates correlate positively with recognition false alarm rates. Thus we provide a four–component theory of a complex pattern of age differences across two key laboratory tasks. PMID:26501233

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...

Analysis and Classification of Acoustic Emission Signals During Wood Drying Using the Principal Component Analysis

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

Analysis and Classification of Acoustic Emission Signals During Wood Drying Using the Principal Component Analysis

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

Component-Based Modelling for Scalable Smart City Systems Interoperability: A Case Study on Integrating Energy Demand Response Systems.

Science.gov (United States)

Palomar, Esther; Chen, Xiaohong; Liu, Zhiming; Maharjan, Sabita; Bowen, Jonathan

2016-10-28

Smart city systems embrace major challenges associated with climate change, energy efficiency, mobility and future services by embedding the virtual space into a complex cyber-physical system. Those systems are constantly evolving and scaling up, involving a wide range of integration among users, devices, utilities, public services and also policies. Modelling such complex dynamic systems' architectures has always been essential for the development and application of techniques/tools to support design and deployment of integration of new components, as well as for the analysis, verification, simulation and testing to ensure trustworthiness. This article reports on the definition and implementation of a scalable component-based architecture that supports a cooperative energy demand response (DR) system coordinating energy usage between neighbouring households. The proposed architecture, called refinement of Cyber-Physical Component Systems (rCPCS), which extends the refinement calculus for component and object system (rCOS) modelling method, is implemented using Eclipse Extensible Coordination Tools (ECT), i.e., Reo coordination language. With rCPCS implementation in Reo, we specify the communication, synchronisation and co-operation amongst the heterogeneous components of the system assuring, by design scalability and the interoperability, correctness of component cooperation.

Nonparametric inference in nonlinear principal components analysis : exploration and beyond

NARCIS (Netherlands)

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),

RACLETTE: a model for evaluating the thermal response of plasma facing components to slow high power plasma transients. Pt. II. Analysis of ITER plasma facing components

International Nuclear Information System (INIS)

Federici, G.; Raffray, A.R.

1997-01-01

For pt.I see ibid., p.85-100, 1997. The transient thermal model RACLETTE (acronym of Rate Analysis Code for pLasma Energy Transfer Transient Evaluation) described in part I of this paper is applied here to analyse the heat transfer and erosion effects of various slow (100 ms-10 s) high power energy transients on the actively cooled plasma facing components (PFCs) of the International Thermonuclear Experimental Reactor (ITER). These have a strong bearing on the PFC design and need careful analysis. The relevant parameters affecting the heat transfer during the plasma excursions are established. The temperature variation with time and space is evaluated together with the extent of vaporisation and melting (the latter only for metals) for the different candidate armour materials considered for the design (i.e., Be for the primary first wall, Be and CFCs for the limiter, Be, W, and CFCs for the divertor plates) and including for certain cases low-density vapour shielding effects. The critical heat flux, the change of the coolant parameters and the possible severe degradation of the coolant heat removal capability that could result under certain conditions during these transients, for example for the limiter, are also evaluated. Based on the results, the design implications on the heat removal performance and erosion damage of the various ITER PFCs are critically discussed and some recommendations are made for the selection of the most adequate protection materials and optimum armour thickness. (orig.)

Three-Component Forward Modeling for Transient Electromagnetic Method

Directory of Open Access Journals (Sweden)

Bin Xiong

2010-01-01

Full Text Available In general, the time derivative of vertical magnetic field is considered only in the data interpretation of transient electromagnetic (TEM method. However, to survey in the complex geology structures, this conventional technique has begun gradually to be unsatisfied with the demand of field exploration. To improve the integrated interpretation precision of TEM, it is necessary to study the three-component forward modeling and inversion. In this paper, a three-component forward algorithm for 2.5D TEM based on the independent electric and magnetic field has been developed. The main advantage of the new scheme is that it can reduce the size of the global system matrix to the utmost extent, that is to say, the present is only one fourth of the conventional algorithm. In order to illustrate the feasibility and usefulness of the present algorithm, several typical geoelectric models of the TEM responses produced by loop sources at air-earth interface are presented. The results of the numerical experiments show that the computation speed of the present scheme is increased obviously and three-component interpretation can get the most out of the collected data, from which we can easily analyze or interpret the space characteristic of the abnormity object more comprehensively.

A molecular systems approach to modelling human skin pigmentation: identifying underlying pathways and critical components.

Science.gov (United States)

Raghunath, Arathi; Sambarey, Awanti; Sharma, Neha; Mahadevan, Usha; Chandra, Nagasuma

2015-04-29

Ultraviolet radiations (UV) serve as an environmental stress for human skin, and result in melanogenesis, with the pigment melanin having protective effects against UV induced damage. This involves a dynamic and complex regulation of various biological processes that results in the expression of melanin in the outer most layers of the epidermis, where it can exert its protective effect. A comprehensive understanding of the underlying cross talk among different signalling molecules and cell types is only possible through a systems perspective. Increasing incidences of both melanoma and non-melanoma skin cancers necessitate the need to better comprehend UV mediated effects on skin pigmentation at a systems level, so as to ultimately evolve knowledge-based strategies for efficient protection and prevention of skin diseases. A network model for UV-mediated skin pigmentation in the epidermis was constructed and subjected to shortest path analysis. Virtual knock-outs were carried out to identify essential signalling components. We describe a network model for UV-mediated skin pigmentation in the epidermis. The model consists of 265 components (nodes) and 429 directed interactions among them, capturing the manner in which one component influences the other and channels information. Through shortest path analysis, we identify novel signalling pathways relevant to pigmentation. Virtual knock-outs or perturbations of specific nodes in the network have led to the identification of alternate modes of signalling as well as enabled determining essential nodes in the process. The model presented provides a comprehensive picture of UV mediated signalling manifesting in human skin pigmentation. A systems perspective helps provide a holistic purview of interconnections and complexity in the processes leading to pigmentation. The model described here is extensive yet amenable to expansion as new data is gathered. Through this study, we provide a list of important proteins essential

Probabilistic structural analysis methods for select space propulsion system components

Science.gov (United States)

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.

Probabilistic Structural Analysis Methods for select space propulsion system components (PSAM). Volume 2: Literature surveys of critical Space Shuttle main engine components

Science.gov (United States)

Rajagopal, K. R.

1992-01-01

The technical effort and computer code development is summarized. Several formulations for Probabilistic Finite Element Analysis (PFEA) are described with emphasis on the selected formulation. The strategies being implemented in the first-version computer code to perform linear, elastic PFEA is described. The results of a series of select Space Shuttle Main Engine (SSME) component surveys are presented. These results identify the critical components and provide the information necessary for probabilistic structural analysis. Volume 2 is a summary of critical SSME components.

The role of damage analysis in the assessment of service-exposed components

International Nuclear Information System (INIS)

Bendick, W.; Muesch, H.; Weber, H.

1987-01-01

Components in power stations are subjected to service conditions under which creep processes take place limiting the component's lifetime by material exhaustion. To ensure a safe and economic plant operation it is necessary to get information about the exhaustion grade of single components as well as of the whole plant. A comprehensive lifetime assessment requests the complete knowledge of the service parameters, the component's deformtion behavior, and the change in material properties caused by longtime exposure to high service temperatures. A basis of evaluation is given by: 1) determination of material exhaustion by calculation, 2) investigation of the material properties, and 3) damage analysis. The purpose of this report is to show the role which damage analysis can play in the assessment of service-exposed components. As an example the test results of a damaged pipe bend will be discussed. (orig./MM)