WorldWideScience

Sample records for components analysis based

  1. Structured Performance Analysis for Component Based Systems

    OpenAIRE

    Salmi , N.; Moreaux , Patrice; Ioualalen , M.

    2012-01-01

    International audience; The Component Based System (CBS) paradigm is now largely used to design software systems. In addition, performance and behavioural analysis remains a required step for the design and the construction of efficient systems. This is especially the case of CBS, which involve interconnected components running concurrent processes. % This paper proposes a compositional method for modeling and structured performance analysis of CBS. Modeling is based on Stochastic Well-formed...

  2. 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...

  3. Generalized structured component analysis a component-based approach to structural equation modeling

    CERN Document Server

    Hwang, Heungsun

    2014-01-01

    Winner of the 2015 Sugiyama Meiko Award (Publication Award) of the Behaviormetric Society of Japan Developed by the authors, generalized structured component analysis is an alternative to two longstanding approaches to structural equation modeling: covariance structure analysis and partial least squares path modeling. Generalized structured component analysis allows researchers to evaluate the adequacy of a model as a whole, compare a model to alternative specifications, and conduct complex analyses in a straightforward manner. Generalized Structured Component Analysis: A Component-Based Approach to Structural Equation Modeling provides a detailed account of this novel statistical methodology and its various extensions. The authors present the theoretical underpinnings of generalized structured component analysis and demonstrate how it can be applied to various empirical examples. The book enables quantitative methodologists, applied researchers, and practitioners to grasp the basic concepts behind this new a...

  4. 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

  5. Research on Air Quality Evaluation based on Principal Component Analysis

    Science.gov (United States)

    Wang, Xing; Wang, Zilin; Guo, Min; Chen, Wei; Zhang, Huan

    2018-01-01

    Economic growth has led to environmental capacity decline and the deterioration of air quality. Air quality evaluation as a fundamental of environmental monitoring and air pollution control has become increasingly important. Based on the principal component analysis (PCA), this paper evaluates the air quality of a large city in Beijing-Tianjin-Hebei Area in recent 10 years and identifies influencing factors, in order to provide reference to air quality management and air pollution control.

  6. 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.

  7. 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.

  8. 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.

  9. Principal Component Analysis Based Measure of Structural Holes

    Science.gov (United States)

    Deng, Shiguo; Zhang, Wenqing; Yang, Huijie

    2013-02-01

    Based upon principal component analysis, a new measure called compressibility coefficient is proposed to evaluate structural holes in networks. This measure incorporates a new effect from identical patterns in networks. It is found that compressibility coefficient for Watts-Strogatz small-world networks increases monotonically with the rewiring probability and saturates to that for the corresponding shuffled networks. While compressibility coefficient for extended Barabasi-Albert scale-free networks decreases monotonically with the preferential effect and is significantly large compared with that for corresponding shuffled networks. This measure is helpful in diverse research fields to evaluate global efficiency of networks.

  10. 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.

  11. Iris recognition based on robust principal component analysis

    Science.gov (United States)

    Karn, Pradeep; He, Xiao Hai; Yang, Shuai; Wu, Xiao Hong

    2014-11-01

    Iris images acquired under different conditions often suffer from blur, occlusion due to eyelids and eyelashes, specular reflection, and other artifacts. Existing iris recognition systems do not perform well on these types of images. To overcome these problems, we propose an iris recognition method based on robust principal component analysis. The proposed method decomposes all training images into a low-rank matrix and a sparse error matrix, where the low-rank matrix is used for feature extraction. The sparsity concentration index approach is then applied to validate the recognition result. Experimental results using CASIA V4 and IIT Delhi V1iris image databases showed that the proposed method achieved competitive performances in both recognition accuracy and computational efficiency.

  12. 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)

  13. Cnn Based Retinal Image Upscaling Using Zero Component Analysis

    Science.gov (United States)

    Nasonov, A.; Chesnakov, K.; Krylov, A.

    2017-05-01

    The aim of the paper is to obtain high quality of image upscaling for noisy images that are typical in medical image processing. A new training scenario for convolutional neural network based image upscaling method is proposed. Its main idea is a novel dataset preparation method for deep learning. The dataset contains pairs of noisy low-resolution images and corresponding noiseless highresolution images. To achieve better results at edges and textured areas, Zero Component Analysis is applied to these images. The upscaling results are compared with other state-of-the-art methods like DCCI, SI-3 and SRCNN on noisy medical ophthalmological images. Objective evaluation of the results confirms high quality of the proposed method. Visual analysis shows that fine details and structures like blood vessels are preserved, noise level is reduced and no artifacts or non-existing details are added. These properties are essential in retinal diagnosis establishment, so the proposed algorithm is recommended to be used in real medical applications.

  14. Learning Algorithms for Audio and Video Processing: Independent Component Analysis and Support Vector Machine Based Approaches

    National Research Council Canada - National Science Library

    Qi, Yuan

    2000-01-01

    In this thesis, we propose two new machine learning schemes, a subband-based Independent Component Analysis scheme and a hybrid Independent Component Analysis/Support Vector Machine scheme, and apply...

  15. Power Transformer Differential Protection Based on Neural Network Principal Component Analysis, Harmonic Restraint and Park's Plots

    OpenAIRE

    Tripathy, Manoj

    2012-01-01

    This paper describes a new approach for power transformer differential protection which is based on the wave-shape recognition technique. An algorithm based on neural network principal component analysis (NNPCA) with back-propagation learning is proposed for digital differential protection of power transformer. The principal component analysis is used to preprocess the data from power system in order to eliminate redundant information and enhance hidden pattern of differential current to disc...

  16. 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.

  17. 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.

  18. Association test based on SNP set: logistic kernel machine based test vs. principal component analysis.

    Directory of Open Access Journals (Sweden)

    Yang Zhao

    Full Text Available GWAS has facilitated greatly the discovery of risk SNPs associated with complex diseases. Traditional methods analyze SNP individually and are limited by low power and reproducibility since correction for multiple comparisons is necessary. Several methods have been proposed based on grouping SNPs into SNP sets using biological knowledge and/or genomic features. In this article, we compare the linear kernel machine based test (LKM and principal components analysis based approach (PCA using simulated datasets under the scenarios of 0 to 3 causal SNPs, as well as simple and complex linkage disequilibrium (LD structures of the simulated regions. Our simulation study demonstrates that both LKM and PCA can control the type I error at the significance level of 0.05. If the causal SNP is in strong LD with the genotyped SNPs, both the PCA with a small number of principal components (PCs and the LKM with kernel of linear or identical-by-state function are valid tests. However, if the LD structure is complex, such as several LD blocks in the SNP set, or when the causal SNP is not in the LD block in which most of the genotyped SNPs reside, more PCs should be included to capture the information of the causal SNP. Simulation studies also demonstrate the ability of LKM and PCA to combine information from multiple causal SNPs and to provide increased power over individual SNP analysis. We also apply LKM and PCA to analyze two SNP sets extracted from an actual GWAS dataset on non-small cell lung cancer.

  19. 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...

  20. 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.

  1. Principal component analysis of tomato genotypes based on some morphological and biochemical quality indicators

    Directory of Open Access Journals (Sweden)

    Glogovac Svetlana

    2012-01-01

    Full Text Available This study investigates variability of tomato genotypes based on morphological and biochemical fruit traits. Experimental material is a part of tomato genetic collection from Institute of Filed and Vegetable Crops in Novi Sad, Serbia. Genotypes were analyzed for fruit mass, locule number, index of fruit shape, fruit colour, dry matter content, total sugars, total acidity, lycopene and vitamin C. Minimum, maximum and average values and main indicators of variability (CV and σ were calculated. Principal component analysis was performed to determinate variability source structure. Four principal components, which contribute 93.75% of the total variability, were selected for analysis. The first principal component is defined by vitamin C, locule number and index of fruit shape. The second component is determined by dry matter content, and total acidity, the third by lycopene, fruit mass and fruit colour. Total sugars had the greatest part in the fourth component.

  2. Priority of VHS Development Based in Potential Area using Principal Component Analysis

    Science.gov (United States)

    Meirawan, D.; Ana, A.; Saripudin, S.

    2018-02-01

    The current condition of VHS is still inadequate in quality, quantity and relevance. The purpose of this research is to analyse the development of VHS based on the development of regional potential by using principal component analysis (PCA) in Bandung, Indonesia. This study used descriptive qualitative data analysis using the principle of secondary data reduction component. The method used is Principal Component Analysis (PCA) analysis with Minitab Statistics Software tool. The results of this study indicate the value of the lowest requirement is a priority of the construction of development VHS with a program of majors in accordance with the development of regional potential. Based on the PCA score found that the main priority in the development of VHS in Bandung is in Saguling, which has the lowest PCA value of 416.92 in area 1, Cihampelas with the lowest PCA value in region 2 and Padalarang with the lowest PCA value.

  3. Combined approach based on principal component analysis and canonical discriminant analysis for investigating hyperspectral plant response

    Directory of Open Access Journals (Sweden)

    Anna Maria Stellacci

    2012-07-01

    Full Text Available Hyperspectral (HS data represents an extremely powerful means for rapidly detecting crop stress and then aiding in the rational management of natural resources in agriculture. However, large volume of data poses a challenge for data processing and extracting crucial information. Multivariate statistical techniques can play a key role in the analysis of HS data, as they may allow to both eliminate redundant information and identify synthetic indices which maximize differences among levels of stress. In this paper we propose an integrated approach, based on the combined use of Principal Component Analysis (PCA and Canonical Discriminant Analysis (CDA, to investigate HS plant response and discriminate plant status. The approach was preliminary evaluated on a data set collected on durum wheat plants grown under different nitrogen (N stress levels. Hyperspectral measurements were performed at anthesis through a high resolution field spectroradiometer, ASD FieldSpec HandHeld, covering the 325-1075 nm region. Reflectance data were first restricted to the interval 510-1000 nm and then divided into five bands of the electromagnetic spectrum [green: 510-580 nm; yellow: 581-630 nm; red: 631-690 nm; red-edge: 705-770 nm; near-infrared (NIR: 771-1000 nm]. PCA was applied to each spectral interval. CDA was performed on the extracted components to identify the factors maximizing the differences among plants fertilised with increasing N rates. Within the intervals of green, yellow and red only the first principal component (PC had an eigenvalue greater than 1 and explained more than 95% of total variance; within the ranges of red-edge and NIR, the first two PCs had an eigenvalue higher than 1. Two canonical variables explained cumulatively more than 81% of total variance and the first was able to discriminate wheat plants differently fertilised, as confirmed also by the significant correlation with aboveground biomass and grain yield parameters. The combined

  4. 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.

  5. Efficient real time OD matrix estimation based on principal component analysis

    NARCIS (Netherlands)

    Djukic, T.; Flötteröd, G.; Van Lint, H.; Hoogendoorn, S.P.

    2012-01-01

    In this paper we explore the idea of dimensionality reduction and approximation of OD demand based on principal component analysis (PCA). First, we show how we can apply PCA to linearly transform the high dimensional OD matrices into the lower dimensional space without significant loss of accuracy.

  6. Independent component analysis based digital signal processing in coherent optical fiber communication systems

    Science.gov (United States)

    Li, Xiang; Luo, Ming; Qiu, Ying; Alphones, Arokiaswami; Zhong, Wen-De; Yu, Changyuan; Yang, Qi

    2018-02-01

    In this paper, channel equalization techniques for coherent optical fiber transmission systems based on independent component analysis (ICA) are reviewed. The principle of ICA for blind source separation is introduced. The ICA based channel equalization after both single-mode fiber and few-mode fiber transmission for single-carrier and orthogonal frequency division multiplexing (OFDM) modulation formats are investigated, respectively. The performance comparisons with conventional channel equalization techniques are discussed.

  7. A stable systemic risk ranking in China's banking sector: Based on principal component analysis

    Science.gov (United States)

    Fang, Libing; Xiao, Binqing; Yu, Honghai; You, Qixing

    2018-02-01

    In this paper, we compare five popular systemic risk rankings, and apply principal component analysis (PCA) model to provide a stable systemic risk ranking for the Chinese banking sector. Our empirical results indicate that five methods suggest vastly different systemic risk rankings for the same bank, while the combined systemic risk measure based on PCA provides a reliable ranking. Furthermore, according to factor loadings of the first component, PCA combined ranking is mainly based on fundamentals instead of market price data. We clearly find that price-based rankings are not as practical a method as fundamentals-based ones. This PCA combined ranking directly shows systemic risk contributions of each bank for banking supervision purpose and reminds banks to prevent and cope with the financial crisis in advance.

  8. Principal components analysis of an evaluation of the hemiplegic subject based on the Bobath approach.

    Science.gov (United States)

    Corriveau, H; Arsenault, A B; Dutil, E; Lepage, Y

    1992-01-01

    An evaluation based on the Bobath approach to treatment has previously been developed and partially validated. The purpose of the present study was to verify the content validity of this evaluation with the use of a statistical approach known as principal components analysis. Thirty-eight hemiplegic subjects participated in the study. Analysis of the scores on each of six parameters (sensorium, active movements, muscle tone, reflex activity, postural reactions, and pain) was evaluated on three occasions across a 2-month period. Each time this produced three factors that contained 70% of the variation in the data set. The first component mainly reflected variations in mobility, the second mainly variations in muscle tone, and the third mainly variations in sensorium and pain. The results of such exploratory analysis highlight the fact that some of the parameters are not only important but also interrelated. These results seem to partially support the conceptual framework substantiating the Bobath approach to treatment.

  9. 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...

  10. Pixel-level multisensor image fusion based on matrix completion and robust principal component analysis

    Science.gov (United States)

    Wang, Zhuozheng; Deller, J. R.; Fleet, Blair D.

    2016-01-01

    Acquired digital images are often corrupted by a lack of camera focus, faulty illumination, or missing data. An algorithm is presented for fusion of multiple corrupted images of a scene using the lifting wavelet transform. The method employs adaptive fusion arithmetic based on matrix completion and self-adaptive regional variance estimation. Characteristics of the wavelet coefficients are used to adaptively select fusion rules. Robust principal component analysis is applied to low-frequency image components, and regional variance estimation is applied to high-frequency components. Experiments reveal that the method is effective for multifocus, visible-light, and infrared image fusion. Compared with traditional algorithms, the new algorithm not only increases the amount of preserved information and clarity but also improves robustness.

  11. Fault detection in nonlinear chemical processes based on kernel entropy component analysis and angular structure

    Energy Technology Data Exchange (ETDEWEB)

    Jiang, Qingchao; Yan, Xuefeng; Lv, Zhaomin; Guo, Meijin [East China University of Science and Technology, Shanghai (China)

    2013-06-15

    Considering that kernel entropy component analysis (KECA) is a promising new method of nonlinear data transformation and dimensionality reduction, a KECA based method is proposed for nonlinear chemical process monitoring. In this method, an angle-based statistic is designed because KECA reveals structure related to the Renyi entropy of input space data set, and the transformed data sets are produced with a distinct angle-based structure. Based on the angle difference between normal status and current sample data, the current status can be monitored effectively. And, the confidence limit of the angle-based statistics is determined by kernel density estimation based on sample data of the normal status. The effectiveness of the proposed method is demonstrated by case studies on both a numerical process and a simulated continuous stirred tank reactor (CSTR) process. The KECA based method can be an effective method for nonlinear chemical process monitoring.

  12. Fault detection in nonlinear chemical processes based on kernel entropy component analysis and angular structure

    International Nuclear Information System (INIS)

    Jiang, Qingchao; Yan, Xuefeng; Lv, Zhaomin; Guo, Meijin

    2013-01-01

    Considering that kernel entropy component analysis (KECA) is a promising new method of nonlinear data transformation and dimensionality reduction, a KECA based method is proposed for nonlinear chemical process monitoring. In this method, an angle-based statistic is designed because KECA reveals structure related to the Renyi entropy of input space data set, and the transformed data sets are produced with a distinct angle-based structure. Based on the angle difference between normal status and current sample data, the current status can be monitored effectively. And, the confidence limit of the angle-based statistics is determined by kernel density estimation based on sample data of the normal status. The effectiveness of the proposed method is demonstrated by case studies on both a numerical process and a simulated continuous stirred tank reactor (CSTR) process. The KECA based method can be an effective method for nonlinear chemical process monitoring

  13. Fluvial facies reservoir productivity prediction method based on principal component analysis and artificial neural network

    Directory of Open Access Journals (Sweden)

    Pengyu Gao

    2016-03-01

    Full Text Available It is difficult to forecast the well productivity because of the complexity of vertical and horizontal developments in fluvial facies reservoir. This paper proposes a method based on Principal Component Analysis and Artificial Neural Network to predict well productivity of fluvial facies reservoir. The method summarizes the statistical reservoir factors and engineering factors that affect the well productivity, extracts information by applying the principal component analysis method and approximates arbitrary functions of the neural network to realize an accurate and efficient prediction on the fluvial facies reservoir well productivity. This method provides an effective way for forecasting the productivity of fluvial facies reservoir which is affected by multi-factors and complex mechanism. The study result shows that this method is a practical, effective, accurate and indirect productivity forecast method and is suitable for field application.

  14. INCREMENTAL PRINCIPAL COMPONENT ANALYSIS BASED OUTLIER DETECTION METHODS FOR SPATIOTEMPORAL DATA STREAMS

    Directory of Open Access Journals (Sweden)

    A. Bhushan

    2015-07-01

    Full Text Available In this paper, we address outliers in spatiotemporal data streams obtained from sensors placed across geographically distributed locations. Outliers may appear in such sensor data due to various reasons such as instrumental error and environmental change. Real-time detection of these outliers is essential to prevent propagation of errors in subsequent analyses and results. Incremental Principal Component Analysis (IPCA is one possible approach for detecting outliers in such type of spatiotemporal data streams. IPCA has been widely used in many real-time applications such as credit card fraud detection, pattern recognition, and image analysis. However, the suitability of applying IPCA for outlier detection in spatiotemporal data streams is unknown and needs to be investigated. To fill this research gap, this paper contributes by presenting two new IPCA-based outlier detection methods and performing a comparative analysis with the existing IPCA-based outlier detection methods to assess their suitability for spatiotemporal sensor data streams.

  15. 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.

  16. A new methodology based on functional principal component analysis to study postural stability post-stroke.

    Science.gov (United States)

    Sánchez-Sánchez, M Luz; Belda-Lois, Juan-Manuel; Mena-Del Horno, Silvia; Viosca-Herrero, Enrique; Igual-Camacho, Celedonia; Gisbert-Morant, Beatriz

    2018-05-05

    A major goal in stroke rehabilitation is the establishment of more effective physical therapy techniques to recover postural stability. Functional Principal Component Analysis provides greater insight into recovery trends. However, when missing values exist, obtaining functional data presents some difficulties. The purpose of this study was to reveal an alternative technique for obtaining the Functional Principal Components without requiring the conversion to functional data beforehand and to investigate this methodology to determine the effect of specific physical therapy techniques in balance recovery trends in elderly subjects with hemiplegia post-stroke. A randomized controlled pilot trial was developed. Thirty inpatients post-stroke were included. Control and target groups were treated with the same conventional physical therapy protocol based on functional criteria, but specific techniques were added to the target group depending on the subjects' functional level. Postural stability during standing was quantified by posturography. The assessments were performed once a month from the moment the participants were able to stand up to six months post-stroke. The target group showed a significant improvement in postural control recovery trend six months after stroke that was not present in the control group. Some of the assessed parameters revealed significant differences between treatment groups (P Functional Principal Component Analysis to be performed when data is scarce. Moreover, it allowed the dynamics of recovery of two different treatment groups to be determined, showing that the techniques added in the target group increased postural stability compared to the base protocol. Copyright © 2018 Elsevier Ltd. All rights reserved.

  17. Analysis of Active Components in Salvia Miltiorrhiza Injection Based on Vascular Endothelial Cell Protection

    Directory of Open Access Journals (Sweden)

    Shen Jie

    2014-09-01

    Full Text Available Correlation analysis based on chromatograms and pharmacological activities is essential for understanding the effective components in complex herbal medicines. In this report, HPLC and measurement of antioxidant properties were used to describe the active ingredients of Salvia miltiorrhiza injection (SMI. HPLC results showed that tanshinol, protocatechuic aldehyde, rosmarinic acid, salvianolic acid B, protocatechuic acid and their metabolites in rat serum may contribute to the efficacy of SMI. Assessment of antioxidant properties indicated that differences in the composition of serum powder of SMI caused differences in vascular endothelial cell protection. When bivariate correlation was carried out it was found that salvianolic acid B, tanshinol and protocatechuic aldehyde were active components of SMI because they were correlated to antioxidant properties.

  18. Facilitating in vivo tumor localization by principal component analysis based on dynamic fluorescence molecular imaging

    Science.gov (United States)

    Gao, Yang; Chen, Maomao; Wu, Junyu; Zhou, Yuan; Cai, Chuangjian; Wang, Daliang; Luo, Jianwen

    2017-09-01

    Fluorescence molecular imaging has been used to target tumors in mice with xenograft tumors. However, tumor imaging is largely distorted by the aggregation of fluorescent probes in the liver. A principal component analysis (PCA)-based strategy was applied on the in vivo dynamic fluorescence imaging results of three mice with xenograft tumors to facilitate tumor imaging, with the help of a tumor-specific fluorescent probe. Tumor-relevant features were extracted from the original images by PCA and represented by the principal component (PC) maps. The second principal component (PC2) map represented the tumor-related features, and the first principal component (PC1) map retained the original pharmacokinetic profiles, especially of the liver. The distribution patterns of the PC2 map of the tumor-bearing mice were in good agreement with the actual tumor location. The tumor-to-liver ratio and contrast-to-noise ratio were significantly higher on the PC2 map than on the original images, thus distinguishing the tumor from its nearby fluorescence noise of liver. The results suggest that the PC2 map could serve as a bioimaging marker to facilitate in vivo tumor localization, and dynamic fluorescence molecular imaging with PCA could be a valuable tool for future studies of in vivo tumor metabolism and progression.

  19. A Novel Method for Surface Defect Detection of Photovoltaic Module Based on Independent Component Analysis

    Directory of Open Access Journals (Sweden)

    Xuewu Zhang

    2013-01-01

    Full Text Available This paper proposed a new method for surface defect detection of photovoltaic module based on independent component analysis (ICA reconstruction algorithm. Firstly, a faultless image is used as the training image. The demixing matrix and corresponding ICs are obtained by applying the ICA in the training image. Then we reorder the ICs according to the range values and reform the de-mixing matrix. Then the reformed de-mixing matrix is used to reconstruct the defect image. The resulting image can remove the background structures and enhance the local anomalies. Experimental results have shown that the proposed method can effectively detect the presence of defects in periodically patterned surfaces.

  20. [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.

  1. Learning representative features for facial images based on a modified principal component analysis

    Science.gov (United States)

    Averkin, Anton; Potapov, Alexey

    2013-05-01

    The paper is devoted to facial image analysis and particularly deals with the problem of automatic evaluation of the attractiveness of human faces. We propose a new approach for automatic construction of feature space based on a modified principal component analysis. Input data sets for the algorithm are the learning data sets of facial images, which are rated by one person. The proposed approach allows one to extract features of the individual subjective face beauty perception and to predict attractiveness values for new facial images, which were not included into a learning data set. The Pearson correlation coefficient between values predicted by our method for new facial images and personal attractiveness estimation values equals to 0.89. This means that the new approach proposed is promising and can be used for predicting subjective face attractiveness values in real systems of the facial images analysis.

  2. Airborne electromagnetic data levelling using principal component analysis based on flight line difference

    Science.gov (United States)

    Zhang, Qiong; Peng, Cong; Lu, Yiming; Wang, Hao; Zhu, Kaiguang

    2018-04-01

    A novel technique is developed to level airborne geophysical data using principal component analysis based on flight line difference. In the paper, flight line difference is introduced to enhance the features of levelling error for airborne electromagnetic (AEM) data and improve the correlation between pseudo tie lines. Thus we conduct levelling to the flight line difference data instead of to the original AEM data directly. Pseudo tie lines are selected distributively cross profile direction, avoiding the anomalous regions. Since the levelling errors of selective pseudo tie lines show high correlations, principal component analysis is applied to extract the local levelling errors by low-order principal components reconstruction. Furthermore, we can obtain the levelling errors of original AEM data through inverse difference after spatial interpolation. This levelling method does not need to fly tie lines and design the levelling fitting function. The effectiveness of this method is demonstrated by the levelling results of survey data, comparing with the results from tie-line levelling and flight-line correlation levelling.

  3. A Biometric Face Recognition System Using an Algorithm Based on the Principal Component Analysis Technique

    Directory of Open Access Journals (Sweden)

    Gheorghe Gîlcă

    2015-06-01

    Full Text Available This article deals with a recognition system using an algorithm based on the Principal Component Analysis (PCA technique. The recognition system consists only of a PC and an integrated video camera. The algorithm is developed in MATLAB language and calculates the eigenfaces considered as features of the face. The PCA technique is based on the matching between the facial test image and the training prototype vectors. The mathcing score between the facial test image and the training prototype vectors is calculated between their coefficient vectors. If the matching is high, we have the best recognition. The results of the algorithm based on the PCA technique are very good, even if the person looks from one side at the video camera.

  4. 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.

  5. An analytics of electricity consumption characteristics based on principal component analysis

    Science.gov (United States)

    Feng, Junshu

    2018-02-01

    Abstract . More detailed analysis of the electricity consumption characteristics can make demand side management (DSM) much more targeted. In this paper, an analytics of electricity consumption characteristics based on principal component analysis (PCA) is given, which the PCA method can be used in to extract the main typical characteristics of electricity consumers. Then, electricity consumption characteristics matrix is designed, which can make a comparison of different typical electricity consumption characteristics between different types of consumers, such as industrial consumers, commercial consumers and residents. In our case study, the electricity consumption has been mainly divided into four characteristics: extreme peak using, peak using, peak-shifting using and others. Moreover, it has been found that industrial consumers shift their peak load often, meanwhile commercial and residential consumers have more peak-time consumption. The conclusions can provide decision support of DSM for the government and power providers.

  6. 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

  7. Analysis of appraisal tool of system security engineering capability maturity based on component

    International Nuclear Information System (INIS)

    Liu Zhenghai; Yang Xiaohua; Zou Shuliang; Liu Yachun; Xiao Jiantian; Liu Zhiming

    2012-01-01

    Spent Fuel Reprocessing is a part of nuclear fuel cycle and is the inevitably choice of nuclear power sustainable development. Reprocessing needs to face with radiological, criticality, chemical hazards. Besides using the tradition appraisal methods based on the security goals, it is a beneficial supplement that using the appraisal method of system security engineering capability maturity model based on the process. Experts should check and approve large numbers of documents during the appraisal based on system security engineering capability maturity model, so it is necessary that developing a tool to assist the expert to complete the appraisal. The method of developing software based on component is highly effective, nimble and reliable. Component technology is analyzed, the methods of extraction model domain components and general components is introduced, and the appraisal system is developed based on component technology. (authors)

  8. Image edge detection based tool condition monitoring with morphological component analysis.

    Science.gov (United States)

    Yu, Xiaolong; Lin, Xin; Dai, Yiquan; Zhu, Kunpeng

    2017-07-01

    The measurement and monitoring of tool condition are keys to the product precision in the automated manufacturing. To meet the need, this study proposes a novel tool wear monitoring approach based on the monitored image edge detection. Image edge detection has been a fundamental tool to obtain features of images. This approach extracts the tool edge with morphological component analysis. Through the decomposition of original tool wear image, the approach reduces the influence of texture and noise for edge measurement. Based on the target image sparse representation and edge detection, the approach could accurately extract the tool wear edge with continuous and complete contour, and is convenient in charactering tool conditions. Compared to the celebrated algorithms developed in the literature, this approach improves the integrity and connectivity of edges, and the results have shown that it achieves better geometry accuracy and lower error rate in the estimation of tool conditions. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

  9. Infrared and visible image fusion based on robust principal component analysis and compressed sensing

    Science.gov (United States)

    Li, Jun; Song, Minghui; Peng, Yuanxi

    2018-03-01

    Current infrared and visible image fusion methods do not achieve adequate information extraction, i.e., they cannot extract the target information from infrared images while retaining the background information from visible images. Moreover, most of them have high complexity and are time-consuming. This paper proposes an efficient image fusion framework for infrared and visible images on the basis of robust principal component analysis (RPCA) and compressed sensing (CS). The novel framework consists of three phases. First, RPCA decomposition is applied to the infrared and visible images to obtain their sparse and low-rank components, which represent the salient features and background information of the images, respectively. Second, the sparse and low-rank coefficients are fused by different strategies. On the one hand, the measurements of the sparse coefficients are obtained by the random Gaussian matrix, and they are then fused by the standard deviation (SD) based fusion rule. Next, the fused sparse component is obtained by reconstructing the result of the fused measurement using the fast continuous linearized augmented Lagrangian algorithm (FCLALM). On the other hand, the low-rank coefficients are fused using the max-absolute rule. Subsequently, the fused image is superposed by the fused sparse and low-rank components. For comparison, several popular fusion algorithms are tested experimentally. By comparing the fused results subjectively and objectively, we find that the proposed framework can extract the infrared targets while retaining the background information in the visible images. Thus, it exhibits state-of-the-art performance in terms of both fusion effects and timeliness.

  10. 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,

  11. 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...

  12. A Novel Double Cluster and Principal Component Analysis-Based Optimization Method for the Orbit Design of Earth Observation Satellites

    Directory of Open Access Journals (Sweden)

    Yunfeng Dong

    2017-01-01

    Full Text Available The weighted sum and genetic algorithm-based hybrid method (WSGA-based HM, which has been applied to multiobjective orbit optimizations, is negatively influenced by human factors through the artificial choice of the weight coefficients in weighted sum method and the slow convergence of GA. To address these two problems, a cluster and principal component analysis-based optimization method (CPC-based OM is proposed, in which many candidate orbits are gradually randomly generated until the optimal orbit is obtained using a data mining method, that is, cluster analysis based on principal components. Then, the second cluster analysis of the orbital elements is introduced into CPC-based OM to improve the convergence, developing a novel double cluster and principal component analysis-based optimization method (DCPC-based OM. In DCPC-based OM, the cluster analysis based on principal components has the advantage of reducing the human influences, and the cluster analysis based on six orbital elements can reduce the search space to effectively accelerate convergence. The test results from a multiobjective numerical benchmark function and the orbit design results of an Earth observation satellite show that DCPC-based OM converges more efficiently than WSGA-based HM. And DCPC-based OM, to some degree, reduces the influence of human factors presented in WSGA-based HM.

  13. A Parcellation Based Nonparametric Algorithm for Independent Component Analysis with Application to fMRI Data

    Directory of Open Access Journals (Sweden)

    Shanshan eLi

    2016-01-01

    Full Text Available Independent Component analysis (ICA is a widely used technique for separating signals that have been mixed together. In this manuscript, we propose a novel ICA algorithm using density estimation and maximum likelihood, where the densities of the signals are estimated via p-spline based histogram smoothing and the mixing matrix is simultaneously estimated using an optimization algorithm. The algorithm is exceedingly simple, easy to implement and blind to the underlying distributions of the source signals. To relax the identically distributed assumption in the density function, a modified algorithm is proposed to allow for different density functions on different regions. The performance of the proposed algorithm is evaluated in different simulation settings. For illustration, the algorithm is applied to a research investigation with a large collection of resting state fMRI datasets. The results show that the algorithm successfully recovers the established brain networks.

  14. An improved principal component analysis based region matching method for fringe direction estimation

    Science.gov (United States)

    He, A.; Quan, C.

    2018-04-01

    The principal component analysis (PCA) and region matching combined method is effective for fringe direction estimation. However, its mask construction algorithm for region matching fails in some circumstances, and the algorithm for conversion of orientation to direction in mask areas is computationally-heavy and non-optimized. We propose an improved PCA based region matching method for the fringe direction estimation, which includes an improved and robust mask construction scheme, and a fast and optimized orientation-direction conversion algorithm for the mask areas. Along with the estimated fringe direction map, filtered fringe pattern by automatic selective reconstruction modification and enhanced fast empirical mode decomposition (ASRm-EFEMD) is used for Hilbert spiral transform (HST) to demodulate the phase. Subsequently, windowed Fourier ridge (WFR) method is used for the refinement of the phase. The robustness and effectiveness of proposed method are demonstrated by both simulated and experimental fringe patterns.

  15. Fast noise level estimation algorithm based on principal component analysis transform and nonlinear rectification

    Science.gov (United States)

    Xu, Shaoping; Zeng, Xiaoxia; Jiang, Yinnan; Tang, Yiling

    2018-01-01

    We proposed a noniterative principal component analysis (PCA)-based noise level estimation (NLE) algorithm that addresses the problem of estimating the noise level with a two-step scheme. First, we randomly extracted a number of raw patches from a given noisy image and took the smallest eigenvalue of the covariance matrix of the raw patches as the preliminary estimation of the noise level. Next, the final estimation was directly obtained with a nonlinear mapping (rectification) function that was trained on some representative noisy images corrupted with different known noise levels. Compared with the state-of-art NLE algorithms, the experiment results show that the proposed NLE algorithm can reliably infer the noise level and has robust performance over a wide range of image contents and noise levels, showing a good compromise between speed and accuracy in general.

  16. Ancestry inference using principal component analysis and spatial analysis: a distance-based analysis to account for population substructure.

    Science.gov (United States)

    Byun, Jinyoung; Han, Younghun; Gorlov, Ivan P; Busam, Jonathan A; Seldin, Michael F; Amos, Christopher I

    2017-10-16

    Accurate inference of genetic ancestry is of fundamental interest to many biomedical, forensic, and anthropological research areas. Genetic ancestry memberships may relate to genetic disease risks. In a genome association study, failing to account for differences in genetic ancestry between cases and controls may also lead to false-positive results. Although a number of strategies for inferring and taking into account the confounding effects of genetic ancestry are available, applying them to large studies (tens thousands samples) is challenging. The goal of this study is to develop an approach for inferring genetic ancestry of samples with unknown ancestry among closely related populations and to provide accurate estimates of ancestry for application to large-scale studies. In this study we developed a novel distance-based approach, Ancestry Inference using Principal component analysis and Spatial analysis (AIPS) that incorporates an Inverse Distance Weighted (IDW) interpolation method from spatial analysis to assign individuals to population memberships. We demonstrate the benefits of AIPS in analyzing population substructure, specifically related to the four most commonly used tools EIGENSTRAT, STRUCTURE, fastSTRUCTURE, and ADMIXTURE using genotype data from various intra-European panels and European-Americans. While the aforementioned commonly used tools performed poorly in inferring ancestry from a large number of subpopulations, AIPS accurately distinguished variations between and within subpopulations. Our results show that AIPS can be applied to large-scale data sets to discriminate the modest variability among intra-continental populations as well as for characterizing inter-continental variation. The method we developed will protect against spurious associations when mapping the genetic basis of a disease. Our approach is more accurate and computationally efficient method for inferring genetic ancestry in the large-scale genetic studies.

  17. Principal components analysis based control of a multi-dof underactuated prosthetic hand

    Directory of Open Access Journals (Sweden)

    Magenes Giovanni

    2010-04-01

    Full Text Available Abstract Background Functionality, controllability and cosmetics are the key issues to be addressed in order to accomplish a successful functional substitution of the human hand by means of a prosthesis. Not only the prosthesis should duplicate the human hand in shape, functionality, sensorization, perception and sense of body-belonging, but it should also be controlled as the natural one, in the most intuitive and undemanding way. At present, prosthetic hands are controlled by means of non-invasive interfaces based on electromyography (EMG. Driving a multi degrees of freedom (DoF hand for achieving hand dexterity implies to selectively modulate many different EMG signals in order to make each joint move independently, and this could require significant cognitive effort to the user. Methods A Principal Components Analysis (PCA based algorithm is used to drive a 16 DoFs underactuated prosthetic hand prototype (called CyberHand with a two dimensional control input, in order to perform the three prehensile forms mostly used in Activities of Daily Living (ADLs. Such Principal Components set has been derived directly from the artificial hand by collecting its sensory data while performing 50 different grasps, and subsequently used for control. Results Trials have shown that two independent input signals can be successfully used to control the posture of a real robotic hand and that correct grasps (in terms of involved fingers, stability and posture may be achieved. Conclusions This work demonstrates the effectiveness of a bio-inspired system successfully conjugating the advantages of an underactuated, anthropomorphic hand with a PCA-based control strategy, and opens up promising possibilities for the development of an intuitively controllable hand prosthesis.

  18. Contact- and distance-based principal component analysis of protein dynamics

    Energy Technology Data Exchange (ETDEWEB)

    Ernst, Matthias; Sittel, Florian; Stock, Gerhard, E-mail: stock@physik.uni-freiburg.de [Biomolecular Dynamics, Institute of Physics, Albert Ludwigs University, 79104 Freiburg (Germany)

    2015-12-28

    To interpret molecular dynamics simulations of complex systems, systematic dimensionality reduction methods such as principal component analysis (PCA) represent a well-established and popular approach. Apart from Cartesian coordinates, internal coordinates, e.g., backbone dihedral angles or various kinds of distances, may be used as input data in a PCA. Adopting two well-known model problems, folding of villin headpiece and the functional dynamics of BPTI, a systematic study of PCA using distance-based measures is presented which employs distances between C{sub α}-atoms as well as distances between inter-residue contacts including side chains. While this approach seems prohibitive for larger systems due to the quadratic scaling of the number of distances with the size of the molecule, it is shown that it is sufficient (and sometimes even better) to include only relatively few selected distances in the analysis. The quality of the PCA is assessed by considering the resolution of the resulting free energy landscape (to identify metastable conformational states and barriers) and the decay behavior of the corresponding autocorrelation functions (to test the time scale separation of the PCA). By comparing results obtained with distance-based, dihedral angle, and Cartesian coordinates, the study shows that the choice of input variables may drastically influence the outcome of a PCA.

  19. Aerodynamic multi-objective integrated optimization based on principal component analysis

    Directory of Open Access Journals (Sweden)

    Jiangtao HUANG

    2017-08-01

    Full Text Available Based on improved multi-objective particle swarm optimization (MOPSO algorithm with principal component analysis (PCA methodology, an efficient high-dimension multi-objective optimization method is proposed, which, as the purpose of this paper, aims to improve the convergence of Pareto front in multi-objective optimization design. The mathematical efficiency, the physical reasonableness and the reliability in dealing with redundant objectives of PCA are verified by typical DTLZ5 test function and multi-objective correlation analysis of supercritical airfoil, and the proposed method is integrated into aircraft multi-disciplinary design (AMDEsign platform, which contains aerodynamics, stealth and structure weight analysis and optimization module. Then the proposed method is used for the multi-point integrated aerodynamic optimization of a wide-body passenger aircraft, in which the redundant objectives identified by PCA are transformed to optimization constraints, and several design methods are compared. The design results illustrate that the strategy used in this paper is sufficient and multi-point design requirements of the passenger aircraft are reached. The visualization level of non-dominant Pareto set is improved by effectively reducing the dimension without losing the primary feature of the problem.

  20. The Research of Multiple Attenuation Based on Feedback Iteration and Independent Component Analysis

    Science.gov (United States)

    Xu, X.; Tong, S.; Wang, L.

    2017-12-01

    How to solve the problem of multiple suppression is a difficult problem in seismic data processing. The traditional technology for multiple attenuation is based on the principle of the minimum output energy of the seismic signal, this criterion is based on the second order statistics, and it can't achieve the multiple attenuation when the primaries and multiples are non-orthogonal. In order to solve the above problems, we combine the feedback iteration method based on the wave equation and the improved independent component analysis (ICA) based on high order statistics to suppress the multiple waves. We first use iterative feedback method to predict the free surface multiples of each order. Then, in order to predict multiples from real multiple in amplitude and phase, we design an expanded pseudo multi-channel matching filtering method to get a more accurate matching multiple result. Finally, we present the improved fast ICA algorithm which is based on the maximum non-Gauss criterion of output signal to the matching multiples and get better separation results of the primaries and the multiples. The advantage of our method is that we don't need any priori information to the prediction of the multiples, and can have a better separation result. The method has been applied to several synthetic data generated by finite-difference model technique and the Sigsbee2B model multiple data, the primaries and multiples are non-orthogonal in these models. The experiments show that after three to four iterations, we can get the perfect multiple results. Using our matching method and Fast ICA adaptive multiple subtraction, we can not only effectively preserve the effective wave energy in seismic records, but also can effectively suppress the free surface multiples, especially the multiples related to the middle and deep areas.

  1. 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.

  2. Neutronics analysis of the conceptual design of a component test facility based on the spherical tokamak

    International Nuclear Information System (INIS)

    Zheng, S.; Voss, G.M.; Pampin, R.

    2010-01-01

    One of the crucial aspects of fusion research is the optimisation and qualification of suitable materials and components. To enable the design and construction of DEMO in the future, ITER is taken to demonstrate the scientific and technological feasibility and IFMIF will provide rigorous testing of small material samples. Meanwhile, a dedicated, small-scale components testing facility (CTF) is proposed to complement and extend the functions of ITER and IFMIF and operate in association with DEMO so as to reduce the risk of delays during this phase of fusion power development. The design of a spherical tokamak (ST)-based CTF is being developed which offers many advantages over conventional machines, including lower tritium consumption, easier maintenance, and a compact assembly. The neutronics analysis of this system is presented here. Based on a three-dimensional neutronics model generated by the interface programme MCAM from CAD models, a series of nuclear and radiation protection analyses were carried out using the MCNP code and FENDL2.1 nuclear data library to assess the current design and guide its development if needed. The nuclear analyses addresses key neutronics issues such as the neutron wall loading (NWL) profile, nuclear heat loads, and radiation damage to the coil insulation and to structural components, particularly the stainless steel vessel wall close to the NBI ports where shielding is limited. The shielding of the divertor coil and the internal Poloidal Field (PF) coil, which is introduced in the expanded divertor design, are optimised to reduce their radiation damage. The preliminary results show that the peak radiation damage to the structure of martensitic/ferritic steel is about 29 dpa at the mid-plane assuming a life of 12 years at a duty factor 33%, which is much lower than its ∼150 dpa limit. In addition, TBMs installed in 8 mid-plane ports and 6 lower ports, and 60% 6 Li enrichment in the Li 4 SiO 4 breeder, the total tritium generation is

  3. Process parameter optimization based on principal components analysis during machining of hardened steel

    Directory of Open Access Journals (Sweden)

    Suryakant B. Chandgude

    2015-09-01

    Full Text Available The optimum selection of process parameters has played an important role for improving the surface finish, minimizing tool wear, increasing material removal rate and reducing machining time of any machining process. In this paper, optimum parameters while machining AISI D2 hardened steel using solid carbide TiAlN coated end mill has been investigated. For optimization of process parameters along with multiple quality characteristics, principal components analysis method has been adopted in this work. The confirmation experiments have revealed that to improve performance of cutting; principal components analysis method would be a useful tool.

  4. Influencing Factors of Catering and Food Service Industry Based on Principal Component Analysis

    OpenAIRE

    Zi Tang

    2014-01-01

    Scientific analysis of influencing factors is of great importance for the healthy development of catering and food service industry. This study attempts to present a set of critical indicators for evaluating the contribution of influencing factors to catering and food service industry in the particular context of Harbin City, Northeast China. Ten indicators that correlate closely with catering and food service industry were identified and performed by the principal component analysis method u...

  5. Extracting intrinsic functional networks with feature-based group independent component analysis.

    Science.gov (United States)

    Calhoun, Vince D; Allen, Elena

    2013-04-01

    There is increasing use of functional imaging data to understand the macro-connectome of the human brain. Of particular interest is the structure and function of intrinsic networks (regions exhibiting temporally coherent activity both at rest and while a task is being performed), which account for a significant portion of the variance in functional MRI data. While networks are typically estimated based on the temporal similarity between regions (based on temporal correlation, clustering methods, or independent component analysis [ICA]), some recent work has suggested that these intrinsic networks can be extracted from the inter-subject covariation among highly distilled features, such as amplitude maps reflecting regions modulated by a task or even coordinates extracted from large meta analytic studies. In this paper our goal was to explicitly compare the networks obtained from a first-level ICA (ICA on the spatio-temporal functional magnetic resonance imaging (fMRI) data) to those from a second-level ICA (i.e., ICA on computed features rather than on the first-level fMRI data). Convergent results from simulations, task-fMRI data, and rest-fMRI data show that the second-level analysis is slightly noisier than the first-level analysis but yields strikingly similar patterns of intrinsic networks (spatial correlations as high as 0.85 for task data and 0.65 for rest data, well above the empirical null) and also preserves the relationship of these networks with other variables such as age (for example, default mode network regions tended to show decreased low frequency power for first-level analyses and decreased loading parameters for second-level analyses). In addition, the best-estimated second-level results are those which are the most strongly reflected in the input feature. In summary, the use of feature-based ICA appears to be a valid tool for extracting intrinsic networks. We believe it will become a useful and important approach in the study of the macro

  6. 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.

  7. 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.

  8. Optimal pattern synthesis for speech recognition based on principal component analysis

    Science.gov (United States)

    Korsun, O. N.; Poliyev, A. V.

    2018-02-01

    The algorithm for building an optimal pattern for the purpose of automatic speech recognition, which increases the probability of correct recognition, is developed and presented in this work. The optimal pattern forming is based on the decomposition of an initial pattern to principal components, which enables to reduce the dimension of multi-parameter optimization problem. At the next step the training samples are introduced and the optimal estimates for principal components decomposition coefficients are obtained by a numeric parameter optimization algorithm. Finally, we consider the experiment results that show the improvement in speech recognition introduced by the proposed optimization algorithm.

  9. Text Analysis: Critical Component of Planning for Text-Based Discussion Focused on Comprehension of Informational Texts

    Science.gov (United States)

    Kucan, Linda; Palincsar, Annemarie Sullivan

    2018-01-01

    This investigation focuses on a tool used in a reading methods course to introduce reading specialist candidates to text analysis as a critical component of planning for text-based discussions. Unlike planning that focuses mainly on important text content or information, a text analysis approach focuses both on content and how that content is…

  10. 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...

  11. SU-F-BRA-13: Knowledge-Based Treatment Planning for Prostate LDR Brachytherapy Based On Principle Component Analysis

    Energy Technology Data Exchange (ETDEWEB)

    Roper, J; Bradshaw, B; Godette, K; Schreibmann, E [Winship Cancer Institute of Emory University, Atlanta, GA (United States); Chanyavanich, V [Rocky Mountain Cancer Centers, Denver, CO (United States)

    2015-06-15

    Purpose: To create a knowledge-based algorithm for prostate LDR brachytherapy treatment planning that standardizes plan quality using seed arrangements tailored to individual physician preferences while being fast enough for real-time planning. Methods: A dataset of 130 prior cases was compiled for a physician with an active prostate seed implant practice. Ten cases were randomly selected to test the algorithm. Contours from the 120 library cases were registered to a common reference frame. Contour variations were characterized on a point by point basis using principle component analysis (PCA). A test case was converted to PCA vectors using the same process and then compared with each library case using a Mahalanobis distance to evaluate similarity. Rank order PCA scores were used to select the best-matched library case. The seed arrangement was extracted from the best-matched case and used as a starting point for planning the test case. Computational time was recorded. Any subsequent modifications were recorded that required input from a treatment planner to achieve an acceptable plan. Results: The computational time required to register contours from a test case and evaluate PCA similarity across the library was approximately 10s. Five of the ten test cases did not require any seed additions, deletions, or moves to obtain an acceptable plan. The remaining five test cases required on average 4.2 seed modifications. The time to complete manual plan modifications was less than 30s in all cases. Conclusion: A knowledge-based treatment planning algorithm was developed for prostate LDR brachytherapy based on principle component analysis. Initial results suggest that this approach can be used to quickly create treatment plans that require few if any modifications by the treatment planner. In general, test case plans have seed arrangements which are very similar to prior cases, and thus are inherently tailored to physician preferences.

  12. Principal Component Analysis Based Two-Dimensional (PCA-2D) Correlation Spectroscopy: PCA Denoising for 2D Correlation Spectroscopy

    International Nuclear Information System (INIS)

    Jung, Young Mee

    2003-01-01

    Principal component analysis based two-dimensional (PCA-2D) correlation analysis is applied to FTIR spectra of polystyrene/methyl ethyl ketone/toluene solution mixture during the solvent evaporation. Substantial amount of artificial noise were added to the experimental data to demonstrate the practical noise-suppressing benefit of PCA-2D technique. 2D correlation analysis of the reconstructed data matrix from PCA loading vectors and scores successfully extracted only the most important features of synchronicity and asynchronicity without interference from noise or insignificant minor components. 2D correlation spectra constructed with only one principal component yield strictly synchronous response with no discernible a asynchronous features, while those involving at least two or more principal components generated meaningful asynchronous 2D correlation spectra. Deliberate manipulation of the rank of the reconstructed data matrix, by choosing the appropriate number and type of PCs, yields potentially more refined 2D correlation spectra

  13. New fatigue damage analysis of complex engineering components based on FEM

    International Nuclear Information System (INIS)

    Ott, W.

    1987-05-01

    A new type of fatigue damage analysis for multiaxial elastoplastic conditions based on a three-dimensional finite element analysis has been developed. The analysis includes the material model after Mroz. The fatigue life evaluation in the critical areas is based on plastic work at these locations. The proposed damage concept can be applied to arbitrary multiaxial stress-strain paths. For the evaluation of the damage cycles in terms of closed stress-strain hysteresis loops are not required. The damage is determined on the basis of uniaxial material data (stress-strain curve, life to crack iniation curve). (orig./HP) [de

  14. Music video shot segmentation using independent component analysis and keyframe extraction based on image complexity

    Science.gov (United States)

    Li, Wei; Chen, Ting; Zhang, Wenjun; Shi, Yunyu; Li, Jun

    2012-04-01

    In recent years, Music video data is increasing at an astonishing speed. Shot segmentation and keyframe extraction constitute a fundamental unit in organizing, indexing, retrieving video content. In this paper a unified framework is proposed to detect the shot boundaries and extract the keyframe of a shot. Music video is first segmented to shots by illumination-invariant chromaticity histogram in independent component (IC) analysis feature space .Then we presents a new metric, image complexity, to extract keyframe in a shot which is computed by ICs. Experimental results show the framework is effective and has a good performance.

  15. Improving Cross-Day EEG-Based Emotion Classification Using Robust Principal Component Analysis

    Directory of Open Access Journals (Sweden)

    Yuan-Pin Lin

    2017-07-01

    Full Text Available Constructing a robust emotion-aware analytical framework using non-invasively recorded electroencephalogram (EEG signals has gained intensive attentions nowadays. However, as deploying a laboratory-oriented proof-of-concept study toward real-world applications, researchers are now facing an ecological challenge that the EEG patterns recorded in real life substantially change across days (i.e., day-to-day variability, arguably making the pre-defined predictive model vulnerable to the given EEG signals of a separate day. The present work addressed how to mitigate the inter-day EEG variability of emotional responses with an attempt to facilitate cross-day emotion classification, which was less concerned in the literature. This study proposed a robust principal component analysis (RPCA-based signal filtering strategy and validated its neurophysiological validity and machine-learning practicability on a binary emotion classification task (happiness vs. sadness using a five-day EEG dataset of 12 subjects when participated in a music-listening task. The empirical results showed that the RPCA-decomposed sparse signals (RPCA-S enabled filtering off the background EEG activity that contributed more to the inter-day variability, and predominately captured the EEG oscillations of emotional responses that behaved relatively consistent along days. Through applying a realistic add-day-in classification validation scheme, the RPCA-S progressively exploited more informative features (from 12.67 ± 5.99 to 20.83 ± 7.18 and improved the cross-day binary emotion-classification accuracy (from 58.31 ± 12.33% to 64.03 ± 8.40% as trained the EEG signals from one to four recording days and tested against one unseen subsequent day. The original EEG features (prior to RPCA processing neither achieved the cross-day classification (the accuracy was around chance level nor replicated the encouraging improvement due to the inter-day EEG variability. This result

  16. 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.

  17. Fault diagnosis of main coolant pump in the nuclear power station based on the principal component analysis

    International Nuclear Information System (INIS)

    Feng Junting; Xu Mi; Wang Guizeng

    2003-01-01

    The fault diagnosis method based on principal component analysis is studied. The fault character direction storeroom of fifteen parameters abnormity is built in the simulation for the main coolant pump of nuclear power station. The measuring data are analyzed, and the results show that it is feasible for the fault diagnosis system of main coolant pump in the nuclear power station

  18. The Purification Method of Matching Points Based on Principal Component Analysis

    Directory of Open Access Journals (Sweden)

    DONG Yang

    2017-02-01

    Full Text Available The traditional purification method of matching points usually uses a small number of the points as initial input. Though it can meet most of the requirements of point constraints, the iterative purification solution is easy to fall into local extreme, which results in the missing of correct matching points. To solve this problem, we introduce the principal component analysis method to use the whole point set as initial input. And thorough mismatching points step eliminating and robust solving, more accurate global optimal solution, which intends to reduce the omission rate of correct matching points and thus reaches better purification effect, can be obtained. Experimental results show that this method can obtain the global optimal solution under a certain original false matching rate, and can decrease or avoid the omission of correct matching points.

  19. QIM blind video watermarking scheme based on Wavelet transform and principal component analysis

    Directory of Open Access Journals (Sweden)

    Nisreen I. Yassin

    2014-12-01

    Full Text Available In this paper, a blind scheme for digital video watermarking is proposed. The security of the scheme is established by using one secret key in the retrieval of the watermark. Discrete Wavelet Transform (DWT is applied on each video frame decomposing it into a number of sub-bands. Maximum entropy blocks are selected and transformed using Principal Component Analysis (PCA. Quantization Index Modulation (QIM is used to quantize the maximum coefficient of the PCA blocks of each sub-band. Then, the watermark is embedded into the selected suitable quantizer values. The proposed scheme is tested using a number of video sequences. Experimental results show high imperceptibility. The computed average PSNR exceeds 45 dB. Finally, the scheme is applied on two medical videos. The proposed scheme shows high robustness against several attacks such as JPEG coding, Gaussian noise addition, histogram equalization, gamma correction, and contrast adjustment in both cases of regular videos and medical videos.

  20. Nuclear reactor component populations, reliability data bases, and their relationship to failure rate estimation and uncertainty analysis

    International Nuclear Information System (INIS)

    Martz, H.F.; Beckman, R.J.

    1981-12-01

    Probabilistic risk analyses are used to assess the risks inherent in the operation of existing and proposed nuclear power reactors. In performing such risk analyses the failure rates of various components which are used in a variety of reactor systems must be estimated. These failure rate estimates serve as input to fault trees and event trees used in the analyses. Component failure rate estimation is often based on relevant field failure data from different reliability data sources such as LERs, NPRDS, and the In-Plant Data Program. Various statistical data analysis and estimation methods have been proposed over the years to provide the required estimates of the component failure rates. This report discusses the basis and extent to which statistical methods can be used to obtain component failure rate estimates. The report is expository in nature and focuses on the general philosophical basis for such statistical methods. Various terms and concepts are defined and illustrated by means of numerous simple examples

  1. METHODS OF ANALYSIS AND CLASSIFICATION OF THE COMPONENTS OF GRAIN MIXTURES BASED ON MEASURING THE REFLECTION AND TRANSMISSION SPECTRA

    Directory of Open Access Journals (Sweden)

    Artem O. Donskikh*

    2017-10-01

    Full Text Available The paper considers methods of classification of grain mixture components based on spectral analysis in visible and near-infrared wavelength ranges using various measurement approaches - reflection, transmission and combined spectrum methods. It also describes the experimental measuring units used and suggests the prototype of a multispectral grain mixture analyzer. The results of the spectral measurement were processed using neural network based classification algorithms. The probabilities of incorrect recognition for various numbers of spectral parts and combinations of spectral methods were estimated. The paper demonstrates that combined usage of two spectral analysis methods leads to higher classification accuracy and allows for reducing the number of the analyzed spectral parts. A detailed description of the proposed measurement device for high-performance real-time multispectral analysis of the components of grain mixtures is given.

  2. 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...

  3. Gas Bubbles Investigation in Contaminated Water Using Optical Tomography Based on Independent Component Analysis Method

    Directory of Open Access Journals (Sweden)

    Mohd Taufiq Mohd Khairi

    2016-01-01

    Full Text Available This paper presents the results of concentration profiles for gas bubble flow in a vertical pipeline containing contaminated water using an optical tomography system. The concentration profiles for the bubble flow quantities are investigated under five different flows conditions, a single bubble, double bubbles, 25% of air opening, 50% of air opening, and 100% of air opening flow rates where a valve is used to control the gas flow in the vertical pipeline. The system is aided by the independent component analysis (ICA algorithm to reconstruct the concentration profiles of the liquid-gas flow. The behaviour of the gas bubbles was investigated in contaminated water in which the water sample was prepared by adding 25 mL of colour ingredients to 3 liters of pure water. The result shows that the application of ICA has enabled the system to detect the presence of gas bubbles in contaminated water. This information provides vital information on the flow inside the pipe and hence could be very significant in increasing the efficiency of the process industries.

  4. The analysis of normative requirements to materials of VVER components, basing on LBB concepts

    Energy Technology Data Exchange (ETDEWEB)

    Anikovsky, V.V.; Karzov, G.P.; Timofeev, B.T. [CRISM Prometey, St. Petersburg (Russian Federation)

    1997-04-01

    The paper demonstrates an insufficiency of some requirements native Norms (when comparing them with the foreign requirements for the consideration of calculating situations): (1) leak before break (LBB); (2) short cracks; (3) preliminary loading (warm prestressing). In particular, the paper presents (1) Comparison of native and foreign normative requirements (PNAE G-7-002-86, Code ASME, BS 1515, KTA) on permissible stress levels and specifically on the estimation of crack initiation and propagation; (2) comparison of RF and USA Norms of pressure vessel material acceptance and also data of pressure vessel hydrotests; (3) comparison of Norms on the presence of defects (RF and USA) in NPP vessels, developments of defect schematization rules; foundation of a calculated defect (semi-axis correlation a/b) for pressure vessel and piping components: (4) sequence of defect estimation (growth of initial defects and critical crack sizes) proceeding from the concept LBB; (5) analysis of crack initiation and propagation conditions according to the acting Norms (including crack jumps); (6) necessity to correct estimation methods of ultimate states of brittle an ductile fracture and elastic-plastic region as applied to calculating situation: (a) LBB and (b) short cracks; (7) necessity to correct estimation methods of ultimate states with the consideration of static and cyclic loading (warm prestressing effect) of pressure vessel; estimation of the effect stability; (8) proposals on PNAE G-7-002-86 Norm corrections.

  5. Radiosurgical treatment planning for intracranial AVM based on images generated by principal component analysis. A simulation study

    International Nuclear Information System (INIS)

    Kawaguchi, Osamu; Kunieda, Etsuo; Nyui, Yoshiyuki

    2009-01-01

    One of the most important factors in stereotactic radiosurgery (SRS) for intracranial arteriovenous malformation (AVM) is to determine accurate target delineation of the nidus. However, since intracranial AVMs are complicated in structure, it is often difficult to clearly determine the target delineation. The purpose of this study was to investigate the usefulness of principal component analysis (PCA) on intra-arterial contrast enhanced dynamic CT (IADCT) images as a tool for delineating accurate target volumes for stereotactic radiosurgery of AVMs. IADCT and intravenous contrast-enhanced CT (IVCT) were used to examine 4 randomly selected cases of AVM. PCA images were generated from the IADCT data. The first component images were considered feeding artery predominant, the second component images were considered draining vein predominant, and the third component images were considered background. Target delineations were first carried out from IVCT, and then again while referring to the first and second components of the PCA images. Dose calculation simulations for radiosurgical treatment plans with IVCT and PCA images were performed. Dose volume histograms of the vein areas as well as the target volumes were compared. In all cases, the calculated target volumes based on IVCT images were larger than those based on PCA images, and the irradiation doses for the vein areas were reduced. In this study, we simulated radiosurgical treatment planning for intracranial AVM based on PCA images. By using PCA images, the irradiation doses for the vein areas were substantially reduced. (author)

  6. [Determination of the Plant Origin of Licorice Oil Extract, a Natural Food Additive, by Principal Component Analysis Based on Chemical Components].

    Science.gov (United States)

    Tada, Atsuko; Ishizuki, Kyoko; Sugimoto, Naoki; Yoshimatsu, Kayo; Kawahara, Nobuo; Suematsu, Takako; Arifuku, Kazunori; Fukai, Toshio; Tamura, Yukiyoshi; Ohtsuki, Takashi; Tahara, Maiko; Yamazaki, Takeshi; Akiyama, Hiroshi

    2015-01-01

    "Licorice oil extract" (LOE) (antioxidant agent) is described in the notice of Japanese food additive regulations as a material obtained from the roots and/or rhizomes of Glycyrrhiza uralensis, G. inflata or G. glabra. In this study, we aimed to identify the original Glycyrrhiza species of eight food additive products using LC/MS. Glabridin, a characteristic compound in G. glabra, was specifically detected in seven products, and licochalcone A, a characteristic compound in G. inflata, was detected in one product. In addition, Principal Component Analysis (PCA) (a kind of multivariate analysis) using the data of LC/MS or (1)H-NMR analysis was performed. The data of thirty-one samples, including LOE products used as food additives, ethanol extracts of various Glycyrrhiza species and commercially available Glycyrrhiza species-derived products were assessed. Based on the PCA results, the majority of LOE products was confirmed to be derived from G. glabra. This study suggests that PCA using (1)H-NMR analysis data is a simple and useful method to identify the plant species of origin of natural food additive products.

  7. Generic component failure data base

    International Nuclear Information System (INIS)

    Eide, S.A.; Calley, M.B.

    1992-01-01

    This report discusses comprehensive component generic failure data base which has been developed for light water reactor probabilistic risk assessments. The Nuclear Computerized Library for Assessing Reactor Reliability (NUCLARR) was used to generate component failure rates. Using this approach, most of the failure rates are based on actual plant data rather then existing estimates

  8. Health status monitoring for ICU patients based on locally weighted principal component analysis.

    Science.gov (United States)

    Ding, Yangyang; Ma, Xin; Wang, Youqing

    2018-03-01

    Intelligent status monitoring for critically ill patients can help medical stuff quickly discover and assess the changes of disease and then make appropriate treatment strategy. However, general-type monitoring model now widely used is difficult to adapt the changes of intensive care unit (ICU) patients' status due to its fixed pattern, and a more robust, efficient and fast monitoring model should be developed to the individual. A data-driven learning approach combining locally weighted projection regression (LWPR) and principal component analysis (PCA) is firstly proposed and applied to monitor the nonlinear process of patients' health status in ICU. LWPR is used to approximate the complex nonlinear process with local linear models, in which PCA could be further applied to status monitoring, and finally a global weighted statistic will be acquired for detecting the possible abnormalities. Moreover, some improved versions are developed, such as LWPR-MPCA and LWPR-JPCA, which also have superior performance. Eighteen subjects were selected from the Physiobank's Multi-parameter Intelligent Monitoring for Intensive Care II (MIMIC II) database, and two vital signs of each subject were chosen for online monitoring. The proposed method was compared with several existing methods including traditional PCA, Partial least squares (PLS), just in time learning combined with modified PCA (L-PCA), and Kernel PCA (KPCA). The experimental results demonstrated that the mean fault detection rate (FDR) of PCA can be improved by 41.7% after adding LWPR. The mean FDR of LWPR-MPCA was increased by 8.3%, compared with the latest reported method L-PCA. Meanwhile, LWPR spent less training time than others, especially KPCA. LWPR is first introduced into ICU patients monitoring and achieves the best monitoring performance including adaptability to changes in patient status, sensitivity for abnormality detection as well as its fast learning speed and low computational complexity. The algorithm

  9. Kernel Principal Component Analysis for dimensionality reduction in fMRI-based diagnosis of ADHD

    Directory of Open Access Journals (Sweden)

    Gagan S Sidhu

    2012-11-01

    Full Text Available This article explores various preprocessing tools that select/create features to help a learner produce a classifier that can use fMRI data to effectively discriminate Attention-Deficit Hyperactivity Disorder (ADHD patients from healthy controls. We consider four different learning tasks: predicting either two (ADHD vs control or three classes (ADHD-1 vs ADHD-3 vs control, where each use either the imaging data only, or the phenotypic and imaging data. After averaging, BOLD-signal normalization, and masking of the fMRI images, we considered applying Fast Fourier Transform (FFT, possibly followed by some Principal Component Analysis (PCA variant (over time: PCA-t; over space and time: PCA-st or the kernelized variant, kPCA-st, to produce inputs to a learner, to determine which learned classifier performs the best – or at least better than the baseline of 64.2%, which is the proportion of the majority class (here, controls.In the two-class setting, PCA-t and PCA-st did not perform statistically better than baseline, whereas FFT and kPCA-st did (FFT, 68.4%; kPCA-st, 70.3%; when combined with the phenotypic data, which by itself produces 72.9% accuracy, all methods performed statistically better than the baseline, but none did better than using the phenotypic data. In the three-class setting, neither the PCA variants, or the phenotypic data classifiers, performed statistically better than the baseline.We next used the FFT output as input to the PCA variants. In the two-class setting, the PCA variants performed statistically better than the baseline using either the FFTed waveforms only (FFT+PCA-t, 69.6%,; FFT+PCA-st, 69.3% ; FFT+kPCA-st, 68.7%, or combining them with the phenotypic data (FFT+PCA-t, 70.6%; FFT+PCA-st, 70.6%; kPCA-st, 76%. In both settings, combining FFT+kPCA-st’s features with the phenotypic data was better than using only the phenotypic data, with the result in the two-class setting being statistically better.

  10. Spatiotemporal analysis of single-trial EEG of emotional pictures based on independent component analysis and source location

    Science.gov (United States)

    Liu, Jiangang; Tian, Jie

    2007-03-01

    The present study combined the Independent Component Analysis (ICA) and low-resolution brain electromagnetic tomography (LORETA) algorithms to identify the spatial distribution and time course of single-trial EEG record differences between neural responses to emotional stimuli vs. the neutral. Single-trial multichannel (129-sensor) EEG records were collected from 21 healthy, right-handed subjects viewing the emotion emotional (pleasant/unpleasant) and neutral pictures selected from International Affective Picture System (IAPS). For each subject, the single-trial EEG records of each emotional pictures were concatenated with the neutral, and a three-step analysis was applied to each of them in the same way. First, the ICA was performed to decompose each concatenated single-trial EEG records into temporally independent and spatially fixed components, namely independent components (ICs). The IC associated with artifacts were isolated. Second, the clustering analysis classified, across subjects, the temporally and spatially similar ICs into the same clusters, in which nonparametric permutation test for Global Field Power (GFP) of IC projection scalp maps identified significantly different temporal segments of each emotional condition vs. neutral. Third, the brain regions accounted for those significant segments were localized spatially with LORETA analysis. In each cluster, a voxel-by-voxel randomization test identified significantly different brain regions between each emotional condition vs. the neutral. Compared to the neutral, both emotional pictures elicited activation in the visual, temporal, ventromedial and dorsomedial prefrontal cortex and anterior cingulated gyrus. In addition, the pleasant pictures activated the left middle prefrontal cortex and the posterior precuneus, while the unpleasant pictures activated the right orbitofrontal cortex, posterior cingulated gyrus and somatosensory region. Our results were well consistent with other functional imaging

  11. Instrument for analysis of electric motors based on slip-poles component

    Science.gov (United States)

    Haynes, Howard D.; Ayers, Curtis W.; Casada, Donald A.

    1996-01-01

    A new instrument for monitoring the condition and speed of an operating electric motor from a remote location. The slip-poles component is derived from a motor current signal. The magnitude of the slip-poles component provides the basis for a motor condition monitor, while the frequency of the slip-poles component provides the basis for a motor speed monitor. The result is a simple-to-understand motor health monitor in an easy-to-use package. Straightforward indications of motor speed, motor running current, motor condition (e.g., rotor bar condition) and synthesized motor sound (audible indication of motor condition) are provided. With the device, a relatively untrained worker can diagnose electric motors in the field without requiring the presence of a trained engineer or technician.

  12. Multiview Bayesian Correlated Component Analysis

    DEFF Research Database (Denmark)

    Kamronn, Simon Due; Poulsen, Andreas Trier; Hansen, Lars Kai

    2015-01-01

    are identical. Here we propose a hierarchical probabilistic model that can infer the level of universality in such multiview data, from completely unrelated representations, corresponding to canonical correlation analysis, to identical representations as in correlated component analysis. This new model, which...... we denote Bayesian correlated component analysis, evaluates favorably against three relevant algorithms in simulated data. A well-established benchmark EEG data set is used to further validate the new model and infer the variability of spatial representations across multiple subjects....

  13. Thermal Analysis of the Driving Component Based on the Thermal Network Method in a Lunar Drilling System and Experimental Verification

    Directory of Open Access Journals (Sweden)

    Dewei Tang

    2017-03-01

    Full Text Available The main task of the third Chinese lunar exploration project is to obtain soil samples that are greater than two meters in length and to acquire bedding information from the surface of the moon. The driving component is the power output unit of the drilling system in the lander; it provides drilling power for core drilling tools. High temperatures can cause the sensors, permanent magnet, gears, and bearings to suffer irreversible damage. In this paper, a thermal analysis model for this driving component, based on the thermal network method (TNM was established and the model was solved using the quasi-Newton method. A vacuum test platform was built and an experimental verification method (EVM was applied to measure the surface temperature of the driving component. Then, the TNM was optimized, based on the principle of heat distribution. Through comparative analyses, the reasonableness of the TNM is validated. Finally, the static temperature field of the driving component was predicted and the “safe working times” of every mode are given.

  14. Comparative analysis of countries in the peer-group based on economic potential and components of sustainable development

    Directory of Open Access Journals (Sweden)

    Sergii VOITKO

    2017-10-01

    Full Text Available The authors study levels of sustainable development potential and determine the positions of Ukraine and other countries in the peer-groups [4], based on individual macroeconomic indicators. The research includes a comparative analysis of absolute and relative terms of GDP, industrial production and the index of competitiveness for the countries included to the peer-groups. The authors analyse the position of countries based on the GDP per capita and components of sustainable development (Quality of Life Index and Security of Life Index. In the article, the authors suggest the methodical approach of performing the comparative analysis of peer-group countries based on their indicators values. This approach gives the possibility to investigate the country’s potential in the limits of the chosen peer-group and propose the recommendations for increase of economic potential in purpose of sustainable development achievement.

  15. 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.

  16. A Blind Adaptive Color Image Watermarking Scheme Based on Principal Component Analysis, Singular Value Decomposition and Human Visual System

    Directory of Open Access Journals (Sweden)

    M. Imran

    2017-09-01

    Full Text Available A blind adaptive color image watermarking scheme based on principal component analysis, singular value decomposition, and human visual system is proposed. The use of principal component analysis to decorrelate the three color channels of host image, improves the perceptual quality of watermarked image. Whereas, human visual system and fuzzy inference system helped to improve both imperceptibility and robustness by selecting adaptive scaling factor, so that, areas more prone to noise can be added with more information as compared to less prone areas. To achieve security, location of watermark embedding is kept secret and used as key at the time of watermark extraction, whereas, for capacity both singular values and vectors are involved in watermark embedding process. As a result, four contradictory requirements; imperceptibility, robustness, security and capacity are achieved as suggested by results. Both subjective and objective methods are acquired to examine the performance of proposed schemes. For subjective analysis the watermarked images and watermarks extracted from attacked watermarked images are shown. For objective analysis of proposed scheme in terms of imperceptibility, peak signal to noise ratio, structural similarity index, visual information fidelity and normalized color difference are used. Whereas, for objective analysis in terms of robustness, normalized correlation, bit error rate, normalized hamming distance and global authentication rate are used. Security is checked by using different keys to extract the watermark. The proposed schemes are compared with state-of-the-art watermarking techniques and found better performance as suggested by results.

  17. 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

  18. Design and validation of a morphing myoelectric hand posture controller based on principal component analysis of human grasping.

    Science.gov (United States)

    Segil, Jacob L; Weir, Richard F ff

    2014-03-01

    An ideal myoelectric prosthetic hand should have the ability to continuously morph between any posture like an anatomical hand. This paper describes the design and validation of a morphing myoelectric hand controller based on principal component analysis of human grasping. The controller commands continuously morphing hand postures including functional grasps using between two and four surface electromyography (EMG) electrodes pairs. Four unique maps were developed to transform the EMG control signals in the principal component domain. A preliminary validation experiment was performed by 10 nonamputee subjects to determine the map with highest performance. The subjects used the myoelectric controller to morph a virtual hand between functional grasps in a series of randomized trials. The number of joints controlled accurately was evaluated to characterize the performance of each map. Additional metrics were studied including completion rate, time to completion, and path efficiency. The highest performing map controlled over 13 out of 15 joints accurately.

  19. 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.

  20. Internal flood analysis at Fermi 2 using a component-based frequency calculation approach

    International Nuclear Information System (INIS)

    Lin, J.C.; Hou, Y.M.; Ramirez, J.V.; Page, E.M.

    2004-01-01

    An analysis to identify potential accident sequences involving internal floods at Fermi Unit 2 was completed to fulfill the individual plant examination requirements. Floods can be significant core damage scenarios if they cause an initiating event and a common mode failure of critical systems. (author)

  1. Adherence to combined Lamivudine + Zidovudine versus individual components: a community-based retrospective medicaid claims analysis.

    Science.gov (United States)

    Legorreta, A; Yu, A; Chernicoff, H; Gilmore, A; Jordan, J; Rosenzweig, J C

    2005-11-01

    Adherence to a fixed dose combination of dual nucleoside antiretroviral therapy was compared between human immunodeficiency virus (HIV)-infected patients newly started on a fixed dosed combination of lamivudine (3TC) 150 mg/zidovudine (ZDV) 300 mg versus its components taken as separate pills. Medicaid pharmacy claims data were used for analyses. To examine the association between treatment group and medication adherence, three types of multivariate regressions were employed. In addition, all regressions were conducted for the whole population using data from 1995 to 2001 as well as a subpopulation, which excluded data prior to September 1997. Model covariates included patient characteristics, healthcare utilization, and non-study antiretroviral therapy use. The likelihood of > or =95% adherence among patients on combination therapy was three times greater than patients taking 3TC and ZDV in separate pills. Also, combination therapy patients had on average 1.4 fewer adherence failures per year of follow-up and nearly double the time to adherence failure compared to the separate pills group. Consistency among study results suggests that fixed dose combination therapies such as lamivudine (3TC) 150 mg/ zidovudine (ZDV) 300 mg should be considered when prescribing HIV treatment that includes an appropriate dual nucleoside.

  2. 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.

  3. 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.

  4. Blood hyperviscosity identification with reflective spectroscopy of tongue tip based on principal component analysis combining artificial neural network.

    Science.gov (United States)

    Liu, Ming; Zhao, Jing; Lu, XiaoZuo; Li, Gang; Wu, Taixia; Zhang, LiFu

    2018-05-10

    With spectral methods, noninvasive determination of blood hyperviscosity in vivo is very potential and meaningful in clinical diagnosis. In this study, 67 male subjects (41 health, and 26 hyperviscosity according to blood sample analysis results) participate. Reflectance spectra of subjects' tongue tips is measured, and a classification method bases on principal component analysis combined with artificial neural network model is built to identify hyperviscosity. Hold-out and Leave-one-out methods are used to avoid significant bias and lessen overfitting problem, which are widely accepted in the model validation. To measure the performance of the classification, sensitivity, specificity, accuracy and F-measure are calculated, respectively. The accuracies with 100 times Hold-out method and 67 times Leave-one-out method are 88.05% and 97.01%, respectively. Experimental results indicate that the built classification model has certain practical value and proves the feasibility of using spectroscopy to identify hyperviscosity by noninvasive determination.

  5. Full and part load exergetic analysis of a hybrid micro gas turbine fuel cell system based on existing components

    International Nuclear Information System (INIS)

    Bakalis, Diamantis P.; Stamatis, Anastassios G.

    2012-01-01

    Highlights: ► Hybrid SOFC/GT system based on existing components. ► Exergy analysis using AspenPlus™ software. ► Greenhouse gases emission is significantly affected by SOFC stack temperature. ► Comparison with a conventional GT of similar power. ► SOFC/GT is almost twice efficient in terms of second low efficiency and CO 2 emission. - Abstract: The paper deals with the examination of a hybrid system consisting of a pre-commercially available high temperature solid oxide fuel cell and an existing recuperated microturbine. The irreversibilities and thermodynamic inefficiencies of the system are evaluated after examining the full and partial load exergetic performance and estimating the amount of exergy destruction and the efficiency of each hybrid system component. At full load operation the system achieves an exergetic efficiency of 59.8%, which increases during the partial load operation, as a variable speed control method is utilized. Furthermore, the effects of the various performance parameters such as fuel cell stack temperature and fuel utilization factor are assessed. The results showed that the components in which chemical reactions occur have the higher exergy destruction rates. The exergetic performance of the system is affected significantly by the stack temperature. Based on the exergetic analysis, suggestions are given for reducing the overall system irreversibility. Finally, the environmental impact of the operation of the hybrid system is evaluated and compared with a similarly rated conventional gas turbine plant. From the comparison it is apparent that the hybrid system obtains nearly double exergetic efficiency and about half the amount of greenhouse gas emissions compared with the conventional plant.

  6. Robust spinal cord resting-state fMRI using independent component analysis-based nuisance regression noise reduction.

    Science.gov (United States)

    Hu, Yong; Jin, Richu; Li, Guangsheng; Luk, Keith Dk; Wu, Ed X

    2018-04-16

    Physiological noise reduction plays a critical role in spinal cord (SC) resting-state fMRI (rsfMRI). To reduce physiological noise and increase the robustness of SC rsfMRI by using an independent component analysis (ICA)-based nuisance regression (ICANR) method. Retrospective. Ten healthy subjects (female/male = 4/6, age = 27 ± 3 years, range 24-34 years). 3T/gradient-echo echo planar imaging (EPI). We used three alternative methods (no regression [Nil], conventional region of interest [ROI]-based noise reduction method without ICA [ROI-based], and correction of structured noise using spatial independent component analysis [CORSICA]) to compare with the performance of ICANR. Reduction of the influence of physiological noise on the SC and the reproducibility of rsfMRI analysis after noise reduction were examined. The correlation coefficient (CC) was calculated to assess the influence of physiological noise. Reproducibility was calculated by intraclass correlation (ICC). Results from different methods were compared by one-way analysis of variance (ANOVA) with post-hoc analysis. No significant difference in cerebrospinal fluid (CSF) pulsation influence or tissue motion influence were found (P = 0.223 in CSF, P = 0.2461 in tissue motion) in the ROI-based (CSF: 0.122 ± 0.020; tissue motion: 0.112 ± 0.015), and Nil (CSF: 0.134 ± 0.026; tissue motion: 0.124 ± 0.019). CORSICA showed a significantly stronger influence of CSF pulsation and tissue motion (CSF: 0.166 ± 0.045, P = 0.048; tissue motion: 0.160 ± 0.032, P = 0.048) than Nil. ICANR showed a significantly weaker influence of CSF pulsation and tissue motion (CSF: 0.076 ± 0.007, P = 0.0003; tissue motion: 0.081 ± 0.014, P = 0.0182) than Nil. The ICC values in the Nil, ROI-based, CORSICA, and ICANR were 0.669, 0.645, 0.561, and 0.766, respectively. ICANR more effectively reduced physiological noise from both tissue motion and CSF pulsation than three alternative methods. ICANR increases the robustness of SC rsf

  7. Quantification and recognition of parkinsonian gait from monocular video imaging using kernel-based principal component analysis

    Directory of Open Access Journals (Sweden)

    Chen Shih-Wei

    2011-11-01

    Full Text Available Abstract Background The computer-aided identification of specific gait patterns is an important issue in the assessment of Parkinson's disease (PD. In this study, a computer vision-based gait analysis approach is developed to assist the clinical assessments of PD with kernel-based principal component analysis (KPCA. Method Twelve PD patients and twelve healthy adults with no neurological history or motor disorders within the past six months were recruited and separated according to their "Non-PD", "Drug-On", and "Drug-Off" states. The participants were asked to wear light-colored clothing and perform three walking trials through a corridor decorated with a navy curtain at their natural pace. The participants' gait performance during the steady-state walking period was captured by a digital camera for gait analysis. The collected walking image frames were then transformed into binary silhouettes for noise reduction and compression. Using the developed KPCA-based method, the features within the binary silhouettes can be extracted to quantitatively determine the gait cycle time, stride length, walking velocity, and cadence. Results and Discussion The KPCA-based method uses a feature-extraction approach, which was verified to be more effective than traditional image area and principal component analysis (PCA approaches in classifying "Non-PD" controls and "Drug-Off/On" PD patients. Encouragingly, this method has a high accuracy rate, 80.51%, for recognizing different gaits. Quantitative gait parameters are obtained, and the power spectrums of the patients' gaits are analyzed. We show that that the slow and irregular actions of PD patients during walking tend to transfer some of the power from the main lobe frequency to a lower frequency band. Our results indicate the feasibility of using gait performance to evaluate the motor function of patients with PD. Conclusion This KPCA-based method requires only a digital camera and a decorated corridor setup

  8. Fusion of LIDAR Data and Multispectral Imagery for Effective Building Detection Based on Graph and Connected Component Analysis

    Science.gov (United States)

    Gilani, S. A. N.; Awrangjeb, M.; Lu, G.

    2015-03-01

    Building detection in complex scenes is a non-trivial exercise due to building shape variability, irregular terrain, shadows, and occlusion by highly dense vegetation. In this research, we present a graph based algorithm, which combines multispectral imagery and airborne LiDAR information to completely delineate the building boundaries in urban and densely vegetated area. In the first phase, LiDAR data is divided into two groups: ground and non-ground data, using ground height from a bare-earth DEM. A mask, known as the primary building mask, is generated from the non-ground LiDAR points where the black region represents the elevated area (buildings and trees), while the white region describes the ground (earth). The second phase begins with the process of Connected Component Analysis (CCA) where the number of objects present in the test scene are identified followed by initial boundary detection and labelling. Additionally, a graph from the connected components is generated, where each black pixel corresponds to a node. An edge of a unit distance is defined between a black pixel and a neighbouring black pixel, if any. An edge does not exist from a black pixel to a neighbouring white pixel, if any. This phenomenon produces a disconnected components graph, where each component represents a prospective building or a dense vegetation (a contiguous block of black pixels from the primary mask). In the third phase, a clustering process clusters the segmented lines, extracted from multispectral imagery, around the graph components, if possible. In the fourth step, NDVI, image entropy, and LiDAR data are utilised to discriminate between vegetation, buildings, and isolated building's occluded parts. Finally, the initially extracted building boundary is extended pixel-wise using NDVI, entropy, and LiDAR data to completely delineate the building and to maximise the boundary reach towards building edges. The proposed technique is evaluated using two Australian data sets

  9. Structural analysis and incipient failure detection of primary circuit components based on correlation-analysis and finite-element models

    International Nuclear Information System (INIS)

    Olma, B.J.

    1977-01-01

    A method is presented to compute vibrational power spectral densities (VPSD's) of primary circuit components based on a finite-element representation of the primary circuit. First this method has been applied to the sodium cooled reactor KNK, Karlsruhe. Now a further application is being developed for a BWR-nuclear power plant. The experimentally determined VPSD's can be considered as the output of a multiple input-output system. They have to be explained as the frequency response of a multidimensional mechanical system, which is excited by stochastic and deterministic mechanical driving forces. The stochastic mechanical forces are generated by the dynamic pressure fluctuations of the fluid. The deterministic mechanical forces are caused by the pressure fluctuations, which are induced by the main coolant pumps or by standing waves. The excitation matrix can be obtained from measured pressure fluctuations. The vibration transfer function matrix can be computed from the mass matrix, damping matrix and stiffness matrix of a theoretical finite-element model or mass-spring model. Based on this theory the computer code 'STAMPO' has been established. This program has been applied to the KNK reactor. The excitation matrix was created from measured jet-noise pressure fluctuations. The mass-, stiffness- and damping matrix has been extracted from a SAP-IV-model of the primary system. Sequentially for each frequency point the complete VPSD matrix has been computed. The diagonal elements of this matrix represent the vibrational auto-power spectral densities, the off-diagonal elements represent the vibrational cross-power spectral densities. The calculations give good agreement with measured VPSD's. The comparison shows that the measured jet-noise pressure fluctuations act nearly uncorrelated on the structure, whereas the output VPSD's are well correlated

  10. Diagnosis of Connective Tissue Disorders based on Independent Component Analysis of Aortic Shape and Motion from 4D MR Images

    DEFF Research Database (Denmark)

    Hansen, Michael Sass; Zhao, Fei; Zhang, Honghai

    2006-01-01

    Independent component analysis (ICA) is employed for com\\$\\backslash\\$-puter-aided diagnosis (CAD) allowing objective identification of subjects with connective tissue disorder from 4D aortic MR images. Stationary independent components assist in the disease detection, which is the first...

  11. A comparison between plaque-based and vessel-based measurement for plaque component using volumetric intravascular ultrasound radiofrequency data analysis.

    Science.gov (United States)

    Shin, Eun-Seok; Garcia-Garcia, Hector M; Garg, Scot; Serruys, Patrick W

    2011-04-01

    Although percent plaque components on plaque-based measurement have been used traditionally in previous studies, the impact of vessel-based measurement for percent plaque components have yet to be studied. The purpose of this study was therefore to correlate percent plaque components derived by plaque- and vessel-based measurement using intravascular ultrasound virtual histology (IVUS-VH). The patient cohort comprised of 206 patients with de novo coronary artery lesions who were imaged with IVUS-VH. Age ranged from 35 to 88 years old, and 124 patients were male. Whole pullback analysis was used to calculate plaque volume, vessel volume, and absolute and percent volumes of fibrous, fibrofatty, necrotic core, and dense calcium. The plaque and vessel volumes were well correlated (r = 0.893, P measurement was also highly correlated with vessel-based measurement. Therefore, the percent plaque component volume calculated by vessel volume could be used instead of the conventional percent plaque component volume calculated by plaque volume.

  12. Parametric Analysis to Study the Influence of Aerogel-Based Renders' Components on Thermal and Mechanical Performance.

    Science.gov (United States)

    Ximenes, Sofia; Silva, Ana; Soares, António; Flores-Colen, Inês; de Brito, Jorge

    2016-05-04

    Statistical models using multiple linear regression are some of the most widely used methods to study the influence of independent variables in a given phenomenon. This study's objective is to understand the influence of the various components of aerogel-based renders on their thermal and mechanical performance, namely cement (three types), fly ash, aerial lime, silica sand, expanded clay, type of aerogel, expanded cork granules, expanded perlite, air entrainers, resins (two types), and rheological agent. The statistical analysis was performed using SPSS (Statistical Package for Social Sciences), based on 85 mortar mixes produced in the laboratory and on their values of thermal conductivity and compressive strength obtained using tests in small-scale samples. The results showed that aerial lime assumes the main role in improving the thermal conductivity of the mortars. Aerogel type, fly ash, expanded perlite and air entrainers are also relevant components for a good thermal conductivity. Expanded clay can improve the mechanical behavior and aerogel has the opposite effect.

  13. Parametric Analysis to Study the Influence of Aerogel-Based Renders’ Components on Thermal and Mechanical Performance

    Directory of Open Access Journals (Sweden)

    Sofia Ximenes

    2016-05-01

    Full Text Available Statistical models using multiple linear regression are some of the most widely used methods to study the influence of independent variables in a given phenomenon. This study’s objective is to understand the influence of the various components of aerogel-based renders on their thermal and mechanical performance, namely cement (three types, fly ash, aerial lime, silica sand, expanded clay, type of aerogel, expanded cork granules, expanded perlite, air entrainers, resins (two types, and rheological agent. The statistical analysis was performed using SPSS (Statistical Package for Social Sciences, based on 85 mortar mixes produced in the laboratory and on their values of thermal conductivity and compressive strength obtained using tests in small-scale samples. The results showed that aerial lime assumes the main role in improving the thermal conductivity of the mortars. Aerogel type, fly ash, expanded perlite and air entrainers are also relevant components for a good thermal conductivity. Expanded clay can improve the mechanical behavior and aerogel has the opposite effect.

  14. Development of a noise reduction program of a prompt gamma spectrum based on principal component analysis for an explosive detection

    International Nuclear Information System (INIS)

    Lee, Yun Hee; Im, Hee Jung; Song, Byung ChoI; Park, Yong Joon; Kim, Won Ho; Cho, Jung Hwan

    2005-01-01

    This work demonstrates a developed program to reduce noises of a prompt gamma-ray spectrum measured by irradiating neutrons into baggage. The noises refer to random variations mainly caused by electrical fluctuations and also by a measurement time. Especially, since the short measurement time yields such a noisy spectrum in which its special peak can not be observed, it is necessary to extract its characteristic signals from the spectrum to identify an explosive hidden in luggage. Principal component analysis(PCA) that is a multivariate statistical technique is closely related to singular value decomposition(SVD). The SVD-based PCA decreases the noise by reconstructing the spectrum after determining the number of principal components corresponding important signals based on the history data that sufficiently describe its population. In this study, we present a visualized program of the above procedure using the MATLAB 7.04 programming language. When our program is started, it requires an arbitrary measured spectrum to be reduced and history spectra as input files. If user selects the files with menu, our program automatically carries out the PCA procedure and provides its noise-reduced spectrum plot as well as the original spectrum plot into an output window. In addition, user can obtain signal-to-noise ratio of an interesting peak by defining the peak and noise ranges with menu

  15. Residual lifetime prediction for lithium-ion battery based on functional principal component analysis and Bayesian approach

    International Nuclear Information System (INIS)

    Cheng, Yujie; Lu, Chen; Li, Tieying; Tao, Laifa

    2015-01-01

    Existing methods for predicting lithium-ion (Li-ion) battery residual lifetime mostly depend on a priori knowledge on aging mechanism, the use of chemical or physical formulation and analytical battery models. This dependence is usually difficult to determine in practice, which restricts the application of these methods. In this study, we propose a new prediction method for Li-ion battery residual lifetime evaluation based on FPCA (functional principal component analysis) and Bayesian approach. The proposed method utilizes FPCA to construct a nonparametric degradation model for Li-ion battery, based on which the residual lifetime and the corresponding confidence interval can be evaluated. Furthermore, an empirical Bayes approach is utilized to achieve real-time updating of the degradation model and concurrently determine residual lifetime distribution. Based on Bayesian updating, a more accurate prediction result and a more precise confidence interval are obtained. Experiments are implemented based on data provided by the NASA Ames Prognostics Center of Excellence. Results confirm that the proposed prediction method performs well in real-time battery residual lifetime prediction. - Highlights: • Capacity is considered functional and FPCA is utilized to extract more information. • No features required which avoids drawbacks induced by feature extraction. • A good combination of both population and individual information. • Avoiding complex aging mechanism and accurate analytical models of batteries. • Easily applicable to different batteries for life prediction and RLD calculation.

  16. Equity in health care in Namibia: developing a needs-based resource allocation formula using principal components analysis

    Directory of Open Access Journals (Sweden)

    Mutirua Kauto

    2007-03-01

    Full Text Available Abstract Background The pace of redressing inequities in the distribution of scarce health care resources in Namibia has been slow. This is due primarily to adherence to the historical incrementalist type of budgeting that has been used to allocate resources. Those regions with high levels of deprivation and relatively greater need for health care resources have been getting less than their fair share. To rectify this situation, which was inherited from the apartheid system, there is a need to develop a needs-based resource allocation mechanism. Methods Principal components analysis was employed to compute asset indices from asset based and health-related variables, using data from the Namibia demographic and health survey of 2000. The asset indices then formed the basis of proposals for regional weights for establishing a needs-based resource allocation formula. Results Comparing the current allocations of public sector health car resources with estimates using a needs based formula showed that regions with higher levels of need currently receive fewer resources than do regions with lower need. Conclusion To address the prevailing inequities in resource allocation, the Ministry of Health and Social Services should abandon the historical incrementalist method of budgeting/resource allocation and adopt a more appropriate allocation mechanism that incorporates measures of need for health care.

  17. Equity in health care in Namibia: developing a needs-based resource allocation formula using principal components analysis.

    Science.gov (United States)

    Zere, Eyob; Mandlhate, Custodia; Mbeeli, Thomas; Shangula, Kalumbi; Mutirua, Kauto; Kapenambili, William

    2007-03-29

    The pace of redressing inequities in the distribution of scarce health care resources in Namibia has been slow. This is due primarily to adherence to the historical incrementalist type of budgeting that has been used to allocate resources. Those regions with high levels of deprivation and relatively greater need for health care resources have been getting less than their fair share. To rectify this situation, which was inherited from the apartheid system, there is a need to develop a needs-based resource allocation mechanism. Principal components analysis was employed to compute asset indices from asset based and health-related variables, using data from the Namibia demographic and health survey of 2000. The asset indices then formed the basis of proposals for regional weights for establishing a needs-based resource allocation formula. Comparing the current allocations of public sector health car resources with estimates using a needs based formula showed that regions with higher levels of need currently receive fewer resources than do regions with lower need. To address the prevailing inequities in resource allocation, the Ministry of Health and Social Services should abandon the historical incrementalist method of budgeting/resource allocation and adopt a more appropriate allocation mechanism that incorporates measures of need for health care.

  18. Speckle noise reduction technique for Lidar echo signal based on self-adaptive pulse-matching independent component analysis

    Science.gov (United States)

    Xu, Fan; Wang, Jiaxing; Zhu, Daiyin; Tu, Qi

    2018-04-01

    Speckle noise has always been a particularly tricky problem in improving the ranging capability and accuracy of Lidar system especially in harsh environment. Currently, effective speckle de-noising techniques are extremely scarce and should be further developed. In this study, a speckle noise reduction technique has been proposed based on independent component analysis (ICA). Since normally few changes happen in the shape of laser pulse itself, the authors employed the laser source as a reference pulse and executed the ICA decomposition to find the optimal matching position. In order to achieve the self-adaptability of algorithm, local Mean Square Error (MSE) has been defined as an appropriate criterion for investigating the iteration results. The obtained experimental results demonstrated that the self-adaptive pulse-matching ICA (PM-ICA) method could effectively decrease the speckle noise and recover the useful Lidar echo signal component with high quality. Especially, the proposed method achieves 4 dB more improvement of signal-to-noise ratio (SNR) than a traditional homomorphic wavelet method.

  19. [Analysis on "component-target-pathway" of Paeonia lactiflora in treating cardiac diseases based on data mining].

    Science.gov (United States)

    Liu, Yang; Zhang, Fang-Bo; Tang, Shi-Huan; Wang, Ping; Li, Sen; Su, Jin; Zhou, Rong-Rong; Zhang, Jia-Qi; Sun, Hui-Feng

    2018-04-01

    Based on the literature review and modern application of Paeonia lactiflora in heart diseases, this article would predict the target of drug and disease by intergrative pharmacology platform of traditional Chinese medicine (TCMIP, http://www.tcmip.cn), and then explore the molecular mechanism of P. lactiflora in treatment of heart disease, providing theoretical basis and method for further studies on P. lactiflora. According to the ancient books, P. lactiflora with functions of "removing the vascular obstruction, removing the lumps, relieving pain, diuretic, nutrient qi" and other effects, have been used for many times to treat heart disease. Some prescriptions are also favored by the modern physicians nowadays. With the development of science, the chemical components that play a role in heart disease and the interrelation between these components and the body become the research hotspot. In order to further reveal the pharmacological substance base and molecular mechanism of P. lactiflora for the treatment of such diseases, TCM-IP was used to obtain multiple molecular targets and signaling pathways in treatment of heart disease. ATP1A1, a common target of drug and disease, was related to energy, and HDAC2 mainly regulated cardiomyocyte hypertrophy gene and cardiomyocyte expression. Other main drug targets such as GCK, CHUK and PRKAA2 indirectly regulated heart disease through many pathways; multiple disease-associated signaling pathways interfered with various heart diseases including coronary heart disease, myocardial ischemia and myocardial hypertrophy through influencing energy metabolism, enzyme activity and gene expression. In conclusion, P. lactiflora plays a role in protecting heart function by regulating the gene expression of cardiomyocytes directly. Meanwhile, it can indirectly intervene in other pathways of heart function, and thus participate in the treatment of heart disease. In this paper, the molecular mechanism of P. lactiflora for treatment of

  20. 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.

  1. Quantitative profiling of polar metabolites in herbal medicine injections for multivariate statistical evaluation based on independence principal component analysis.

    Directory of Open Access Journals (Sweden)

    Miaomiao Jiang

    Full Text Available Botanical primary metabolites extensively exist in herbal medicine injections (HMIs, but often were ignored to control. With the limitation of bias towards hydrophilic substances, the primary metabolites with strong polarity, such as saccharides, amino acids and organic acids, are usually difficult to detect by the routinely applied reversed-phase chromatographic fingerprint technology. In this study, a proton nuclear magnetic resonance (1H NMR profiling method was developed for efficient identification and quantification of small polar molecules, mostly primary metabolites in HMIs. A commonly used medicine, Danhong injection (DHI, was employed as a model. With the developed method, 23 primary metabolites together with 7 polyphenolic acids were simultaneously identified, of which 13 metabolites with fully separated proton signals were quantified and employed for further multivariate quality control assay. The quantitative 1H NMR method was validated with good linearity, precision, repeatability, stability and accuracy. Based on independence principal component analysis (IPCA, the contents of 13 metabolites were characterized and dimensionally reduced into the first two independence principal components (IPCs. IPC1 and IPC2 were then used to calculate the upper control limits (with 99% confidence ellipsoids of χ2 and Hotelling T2 control charts. Through the constructed upper control limits, the proposed method was successfully applied to 36 batches of DHI to examine the out-of control sample with the perturbed levels of succinate, malonate, glucose, fructose, salvianic acid and protocatechuic aldehyde. The integrated strategy has provided a reliable approach to identify and quantify multiple polar metabolites of DHI in one fingerprinting spectrum, and it has also assisted in the establishment of IPCA models for the multivariate statistical evaluation of HMIs.

  2. 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.

  3. 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.

  4. Identification and analysis of labor productivity components based on ACHIEVE model (case study: staff of Kermanshah University of Medical Sciences).

    Science.gov (United States)

    Ziapour, Arash; Khatony, Alireza; Kianipour, Neda; Jafary, Faranak

    2014-12-15

    Identification and analysis of the components of labor productivity based on ACHIEVE model was performed among employees in different parts of Kermanshah University of Medical Sciences in 2014. This was a descriptive correlational study in which the population consisted of 270 working personnel in different administrative groups (contractual, fixed- term and regular) at Kermanshah University of Medical Sciences (872 people) that were selected among 872 people through stratified random sampling method based on Krejcie and Morgan sampling table. The survey tool included labor productivity questionnaire of ACHIEVE. Questionnaires were confirmed in terms of content and face validity, and their reliability was calculated using Cronbach's alpha coefficient. The data were analyzed by SPSS-18 software using descriptive and inferential statistics. The mean scores for labor productivity dimensions of the employees, including environment (environmental fit), evaluation (training and performance feedback), validity (valid and legal exercise of personnel), incentive (motivation or desire), help (organizational support), clarity (role perception or understanding), ability (knowledge and skills) variables and total labor productivity were 4.10±0.630, 3.99±0.568, 3.97±0.607, 3.76±0.701, 3.63±0.746, 3.59±0.777, 3.49±0.882 and 26.54±4.347, respectively. Also, the results indicated that the seven factors of environment, performance assessment, validity, motivation, organizational support, clarity, and ability were effective in increasing labor productivity. The analysis of the current status of university staff in the employees' viewpoint suggested that the two factors of environment and evaluation, which had the greatest impact on labor productivity in the viewpoint of the staff, were in a favorable condition and needed to be further taken into consideration by authorities.

  5. Identification and Analysis of Labor Productivity Components Based on ACHIEVE Model (Case Study: Staff of Kermanshah University of Medical Sciences)

    Science.gov (United States)

    Ziapour, Arash; Khatony, Alireza; Kianipour, Neda; Jafary, Faranak

    2015-01-01

    Identification and analysis of the components of labor productivity based on ACHIEVE model was performed among employees in different parts of Kermanshah University of Medical Sciences in 2014. This was a descriptive correlational study in which the population consisted of 270 working personnel in different administrative groups (contractual, fixed- term and regular) at Kermanshah University of Medical Sciences (872 people) that were selected among 872 people through stratified random sampling method based on Krejcie and Morgan sampling table. The survey tool included labor productivity questionnaire of ACHIEVE. Questionnaires were confirmed in terms of content and face validity, and their reliability was calculated using Cronbach’s alpha coefficient. The data were analyzed by SPSS-18 software using descriptive and inferential statistics. The mean scores for labor productivity dimensions of the employees, including environment (environmental fit), evaluation (training and performance feedback), validity (valid and legal exercise of personnel), incentive (motivation or desire), help (organizational support), clarity (role perception or understanding), ability (knowledge and skills) variables and total labor productivity were 4.10±0.630, 3.99±0.568, 3.97±0.607, 3.76±0.701, 3.63±0.746, 3.59±0.777, 3.49±0.882 and 26.54±4.347, respectively. Also, the results indicated that the seven factors of environment, performance assessment, validity, motivation, organizational support, clarity, and ability were effective in increasing labor productivity. The analysis of the current status of university staff in the employees’ viewpoint suggested that the two factors of environment and evaluation, which had the greatest impact on labor productivity in the viewpoint of the staff, were in a favorable condition and needed to be further taken into consideration by authorities. PMID:25560364

  6. Statistically based uncertainty analysis for ranking of component importance in the thermal-hydraulic safety analysis of the Advanced Neutron Source Reactor

    International Nuclear Information System (INIS)

    Wilson, G.E.

    1992-01-01

    The Analytic Hierarchy Process (AHP) has been used to help determine the importance of components and phenomena in thermal-hydraulic safety analyses of nuclear reactors. The AHP results are based, in part on expert opinion. Therefore, it is prudent to evaluate the uncertainty of the AHP ranks of importance. Prior applications have addressed uncertainty with experimental data comparisons and bounding sensitivity calculations. These methods work well when a sufficient experimental data base exists to justify the comparisons. However, in the case of limited or no experimental data the size of the uncertainty is normally made conservatively large. Accordingly, the author has taken another approach, that of performing a statistically based uncertainty analysis. The new work is based on prior evaluations of the importance of components and phenomena in the thermal-hydraulic safety analysis of the Advanced Neutron Source Reactor (ANSR), a new facility now in the design phase. The uncertainty during large break loss of coolant, and decay heat removal scenarios is estimated by assigning a probability distribution function (pdf) to the potential error in the initial expert estimates of pair-wise importance between the components. Using a Monte Carlo sampling technique, the error pdfs are propagated through the AHP software solutions to determine a pdf of uncertainty in the system wide importance of each component. To enhance the generality of the results, study of one other problem having different number of elements is reported, as are the effects of a larger assumed pdf error in the expert ranks. Validation of the Monte Carlo sample size and repeatability are also documented

  7. Component-based development process and component lifecycle

    NARCIS (Netherlands)

    Crnkovic, I.; Chaudron, M.R.V.; Larsson, S.

    2006-01-01

    The process of component- and component-based system development differs in many significant ways from the "classical" development process of software systems. The main difference is in the separation of the development process of components from the development process of systems. This fact has a

  8. A Combined Methodology to Eliminate Artifacts in Multichannel Electrogastrogram Based on Independent Component Analysis and Ensemble Empirical Mode Decomposition.

    Science.gov (United States)

    Sengottuvel, S; Khan, Pathan Fayaz; Mariyappa, N; Patel, Rajesh; Saipriya, S; Gireesan, K

    2018-06-01

    Cutaneous measurements of electrogastrogram (EGG) signals are heavily contaminated by artifacts due to cardiac activity, breathing, motion artifacts, and electrode drifts whose effective elimination remains an open problem. A common methodology is proposed by combining independent component analysis (ICA) and ensemble empirical mode decomposition (EEMD) to denoise gastric slow-wave signals in multichannel EGG data. Sixteen electrodes are fixed over the upper abdomen to measure the EGG signals under three gastric conditions, namely, preprandial, postprandial immediately, and postprandial 2 h after food for three healthy subjects and a subject with a gastric disorder. Instantaneous frequencies of intrinsic mode functions that are obtained by applying the EEMD technique are analyzed to individually identify and remove each of the artifacts. A critical investigation on the proposed ICA-EEMD method reveals its ability to provide a higher attenuation of artifacts and lower distortion than those obtained by the ICA-EMD method and conventional techniques, like bandpass and adaptive filtering. Characteristic changes in the slow-wave frequencies across the three gastric conditions could be determined from the denoised signals for all the cases. The results therefore encourage the use of the EEMD-based technique for denoising gastric signals to be used in clinical practice.

  9. Independent component analysis-based artefact reduction: application to the electrocardiogram for improved magnetic resonance imaging triggering

    International Nuclear Information System (INIS)

    Oster, Julien; Pietquin, Olivier; Felblinger, Jacques; Abächerli, Roger; Kraemer, Michel

    2009-01-01

    Electrocardiogram (ECG) is required during magnetic resonance (MR) examination for monitoring patients under anaesthesia or with heart diseases and for synchronizing image acquisition with heart activity (triggering). Accurate and fast QRS detection is therefore desirable, but this task is complicated by artefacts related to the complex MR environment (high magnetic field, radio-frequency pulses and fast switching magnetic gradients). Specific signal processing has been proposed, whether using specific MR QRS detectors or ECG denoising methods. Most state-of-the-art techniques use a connection to the MR system for achieving their task, which is a major drawback since access to the MR system is often restricted. This paper introduces a new method for on-line ECG signal enhancement, called ICARE, which takes advantage of using multi-lead ECG and does not require any connection to the MR system. It is based on independent component analysis (ICA) and applied in real time. This algorithm yields accurate QRS detection for efficient triggering

  10. [Discrimination of varieties of borneol using terahertz spectra based on principal component analysis and support vector machine].

    Science.gov (United States)

    Li, Wu; Hu, Bing; Wang, Ming-wei

    2014-12-01

    In the present paper, the terahertz time-domain spectroscopy (THz-TDS) identification model of borneol based on principal component analysis (PCA) and support vector machine (SVM) was established. As one Chinese common agent, borneol needs a rapid, simple and accurate detection and identification method for its different source and being easily confused in the pharmaceutical and trade links. In order to assure the quality of borneol product and guard the consumer's right, quickly, efficiently and correctly identifying borneol has significant meaning to the production and transaction of borneol. Terahertz time-domain spectroscopy is a new spectroscopy approach to characterize material using terahertz pulse. The absorption terahertz spectra of blumea camphor, borneol camphor and synthetic borneol were measured in the range of 0.2 to 2 THz with the transmission THz-TDS. The PCA scores of 2D plots (PC1 X PC2) and 3D plots (PC1 X PC2 X PC3) of three kinds of borneol samples were obtained through PCA analysis, and both of them have good clustering effect on the 3 different kinds of borneol. The value matrix of the first 10 principal components (PCs) was used to replace the original spectrum data, and the 60 samples of the three kinds of borneol were trained and then the unknown 60 samples were identified. Four kinds of support vector machine model of different kernel functions were set up in this way. Results show that the accuracy of identification and classification of SVM RBF kernel function for three kinds of borneol is 100%, and we selected the SVM with the radial basis kernel function to establish the borneol identification model, in addition, in the noisy case, the classification accuracy rates of four SVM kernel function are above 85%, and this indicates that SVM has strong generalization ability. This study shows that PCA with SVM method of borneol terahertz spectroscopy has good classification and identification effects, and provides a new method for species

  11. 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.

  12. Shaking table test and dynamic response analysis of 3-D component base isolation system using multi-layer rubber bearings and coil springs

    Energy Technology Data Exchange (ETDEWEB)

    Tsutsumi, Hideaki; Yamada, Hiroyuki; Ebisawa, Katsumi; Shibata, Katsuyuki [Japan Atomic Energy Research Inst., Tokai, Ibaraki (Japan). Tokai Research Establishment; Fujimoto, Shigeru [Toshiba Corp., Tokyo (Japan)

    2001-06-01

    Introduction of the base isolation technique into the seismic design of nuclear power plant components as well as buildings has been expected as one of the effective countermeasure to reduce the seismic force applied to components. A research program on the base isolation of nuclear components has been carried out at the Japan Atomic Energy Research Institute (JAERI) since 1991. A methodology and a computer code (EBISA: Equipment Base Isolation System Analysis) for evaluating the failure frequency of the nuclear component with the base isolation were developed. In addition, a test program, which is concerned with the above development, aiming at improvement of failure frequency analysis models in the code has been conducted since 1996 to investigate the dynamic behavior and to verify the effectiveness of component base isolation systems. Two base isolation test systems with different characteristics were fabricated and static and dynamic characteristics were measured by static loading and free vibration tests. One which consists of ball bearings and air springs was installed on the test bed to observe the dynamic response under natural earthquake motion. The effect of base isolation system has been observed under several earthquakes. Three-dimensional response and effect of base isolation of another system using multi-layer-rubber-bearings and coil springs has been investigated under various large earthquake motions by shaking table test. This report describes the results of the shaking table tests and dynamic response analysis. (author)

  13. 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.

  14. Dynamic analysis of large structures with uncertain parameters based on coupling component mode synthesis and perturbation method

    Directory of Open Access Journals (Sweden)

    D. Sarsri

    2016-03-01

    Full Text Available This paper presents a methodological approach to compute the stochastic eigenmodes of large FE models with parameter uncertainties based on coupling of second order perturbation method and component mode synthesis methods. Various component mode synthesis methods are used to optimally reduce the size of the model. The statistical first two moments of dynamic response of the reduced system are obtained by the second order perturbation method. Numerical results illustrating the accuracy and efficiency of the proposed coupled methodological procedures for large FE models with uncertain parameters are presented.

  15. Independent component analysis-based algorithm for automatic identification of Raman spectra applied to artistic pigments and pigment mixtures.

    Science.gov (United States)

    González-Vidal, Juan José; Pérez-Pueyo, Rosanna; Soneira, María José; Ruiz-Moreno, Sergio

    2015-03-01

    A new method has been developed to automatically identify Raman spectra, whether they correspond to single- or multicomponent spectra. The method requires no user input or judgment. There are thus no parameters to be tweaked. Furthermore, it provides a reliability factor on the resulting identification, with the aim of becoming a useful support tool for the analyst in the decision-making process. The method relies on the multivariate techniques of principal component analysis (PCA) and independent component analysis (ICA), and on some metrics. It has been developed for the application of automated spectral analysis, where the analyzed spectrum is provided by a spectrometer that has no previous knowledge of the analyzed sample, meaning that the number of components in the sample is unknown. We describe the details of this method and demonstrate its efficiency by identifying both simulated spectra and real spectra. The method has been applied to artistic pigment identification. The reliable and consistent results that were obtained make the methodology a helpful tool suitable for the identification of pigments in artwork or in paint in general.

  16. 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.

  17. 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...

  18. On Bayesian Principal Component Analysis

    Czech Academy of Sciences Publication Activity Database

    Šmídl, Václav; Quinn, A.

    2007-01-01

    Roč. 51, č. 9 (2007), s. 4101-4123 ISSN 0167-9473 R&D Projects: GA MŠk(CZ) 1M0572 Institutional research plan: CEZ:AV0Z10750506 Keywords : Principal component analysis ( PCA ) * Variational bayes (VB) * von-Mises–Fisher distribution Subject RIV: BC - Control Systems Theory Impact factor: 1.029, year: 2007 http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6V8V-4MYD60N-6&_user=10&_coverDate=05%2F15%2F2007&_rdoc=1&_fmt=&_orig=search&_sort=d&view=c&_acct=C000050221&_version=1&_urlVersion=0&_userid=10&md5=b8ea629d48df926fe18f9e5724c9003a

  19. Structural analysis of nuclear components

    International Nuclear Information System (INIS)

    Ikonen, K.; Hyppoenen, P.; Mikkola, T.; Noro, H.; Raiko, H.; Salminen, P.; Talja, H.

    1983-05-01

    THe report describes the activities accomplished in the project 'Structural Analysis Project of Nuclear Power Plant Components' during the years 1974-1982 in the Nuclear Engineering Laboratory at the Technical Research Centre of Finland. The objective of the project has been to develop Finnish expertise in structural mechanics related to nuclear engineering. The report describes the starting point of the research work, the organization of the project and the research activities on various subareas. Further the work done with computer codes is described and also the problems which the developed expertise has been applied to. Finally, the diploma works, publications and work reports, which are mainly in Finnish, are listed to give a view of the content of the project. (author)

  20. Involvement of the anterior cingulate cortex in time-based prospective memory task monitoring: An EEG analysis of brain sources using Independent Component and Measure Projection Analysis.

    Directory of Open Access Journals (Sweden)

    Gabriela Cruz

    Full Text Available Time-based prospective memory (PM, remembering to do something at a particular moment in the future, is considered to depend upon self-initiated strategic monitoring, involving a retrieval mode (sustained maintenance of the intention plus target checking (intermittent time checks. The present experiment was designed to explore what brain regions and brain activity are associated with these components of strategic monitoring in time-based PM tasks.24 participants were asked to reset a clock every four minutes, while performing a foreground ongoing word categorisation task. EEG activity was recorded and data were decomposed into source-resolved activity using Independent Component Analysis. Common brain regions across participants, associated with retrieval mode and target checking, were found using Measure Projection Analysis.Participants decreased their performance on the ongoing task when concurrently performed with the time-based PM task, reflecting an active retrieval mode that relied on withdrawal of limited resources from the ongoing task. Brain activity, with its source in or near the anterior cingulate cortex (ACC, showed changes associated with an active retrieval mode including greater negative ERP deflections, decreased theta synchronization, and increased alpha suppression for events locked to the ongoing task while maintaining a time-based intention. Activity in the ACC was also associated with time-checks and found consistently across participants; however, we did not find an association with time perception processing per se.The involvement of the ACC in both aspects of time-based PM monitoring may be related to different functions that have been attributed to it: strategic control of attention during the retrieval mode (distributing attentional resources between the ongoing task and the time-based task and anticipatory/decision making processing associated with clock-checks.

  1. Cluster and principal component analysis based on SSR markers of Amomum tsao-ko in Jinping County of Yunnan Province

    Science.gov (United States)

    Ma, Mengli; Lei, En; Meng, Hengling; Wang, Tiantao; Xie, Linyan; Shen, Dong; Xianwang, Zhou; Lu, Bingyue

    2017-08-01

    Amomum tsao-ko is a commercial plant that used for various purposes in medicinal and food industries. For the present investigation, 44 germplasm samples were collected from Jinping County of Yunnan Province. Clusters analysis and 2-dimensional principal component analysis (PCA) was used to represent the genetic relations among Amomum tsao-ko by using simple sequence repeat (SSR) markers. Clustering analysis clearly distinguished the samples groups. Two major clusters were formed; first (Cluster I) consisted of 34 individuals, the second (Cluster II) consisted of 10 individuals, Cluster I as the main group contained multiple sub-clusters. PCA also showed 2 groups: PCA Group 1 included 29 individuals, PCA Group 2 included 12 individuals, consistent with the results of cluster analysis. The purpose of the present investigation was to provide information on genetic relationship of Amomum tsao-ko germplasm resources in main producing areas, also provide a theoretical basis for the protection and utilization of Amomum tsao-ko resources.

  2. INDIA’S ELECTRICITY DEMAND FORECAST USING REGRESSION ANALYSIS AND ARTIFICIAL NEURAL NETWORKS BASED ON PRINCIPAL COMPONENTS

    Directory of Open Access Journals (Sweden)

    S. Saravanan

    2012-07-01

    Full Text Available Power System planning starts with Electric load (demand forecasting. Accurate electricity load forecasting is one of the most important challenges in managing supply and demand of the electricity, since the electricity demand is volatile in nature; it cannot be stored and has to be consumed instantly. The aim of this study deals with electricity consumption in India, to forecast future projection of demand for a period of 19 years from 2012 to 2030. The eleven input variables used are Amount of CO2 emission, Population, Per capita GDP, Per capita gross national income, Gross Domestic savings, Industry, Consumer price index, Wholesale price index, Imports, Exports and Per capita power consumption. A new methodology based on Artificial Neural Networks (ANNs using principal components is also used. Data of 29 years used for training and data of 10 years used for testing the ANNs. Comparison made with multiple linear regression (based on original data and the principal components and ANNs with original data as input variables. The results show that the use of ANNs with principal components (PC is more effective.

  3. Formalization in Component Based Development

    DEFF Research Database (Denmark)

    Holmegaard, Jens Peter; Knudsen, John; Makowski, Piotr

    2006-01-01

    We present a unifying conceptual framework for components, component interfaces, contracts and composition of components by focusing on the collection of properties or qualities that they must share. A specific property, such as signature, functionality behaviour or timing is an aspect. Each aspect...... may be specified in a formal language convenient for its purpose and, in principle, unrelated to languages for other aspects. Each aspect forms its own semantic domain, although a semantic domain may be parameterized by values derived from other aspects. The proposed conceptual framework is introduced...

  4. Ecological Safety Evaluation of Land Use in Ji’an City Based on the Principal Component Analysis

    Institute of Scientific and Technical Information of China (English)

    2010-01-01

    According to the ecological safety evaluation index data of land-use change in Ji’an City from 1999 to 2008,positive treatment on selected reverse indices is conducted by Reciprocal Method.Meanwhile,Index Method is used to standardize the selected indices,and Principal Component Analysis is applied by using year as a unit.FB is obtained,which is related with the ecological safety of land-use change from 1999 to 2008.According to the scientific,integrative,hierarchical,practical and dynamic principles,ecological safety evaluation index system of land-use change in Ji’an City is established.Principal Component Analysis and evaluation model are used to calculate four parameters,including the natural resources safety index of land use,the socio-economic safety indicators of land use,the eco-environmental safety index of land use,and the ecological safety degree of land use in Ji’an City.Result indicates that the ecological safety degree of land use in Ji’an City shows a slow upward trend as a whole.At the same time,ecological safety degree of land-use change is relatively low in Ji’an City with the safety value of 0.645,which is at a weak safety zone and needs further monitoring and maintenance.

  5. Variability Analysis of the Horizontal Geomagnetic Component: A Case Study Based on Records from Vassouras Observatory (Brazil)

    Science.gov (United States)

    Klausner, Virginia; Papa, Andres; Mendes, Odim; Oliveira Domingues, Margarete

    It is well known that any of the components of the magnetic field measured on the Earth's surface presents characteristic frequencies with 24, 12, 8 and 6-hour period. Those typical kinds of oscillations of the geomagnetic field are known as solar quiet variation and are primary due to the global thermotidal wind systems which conduct currents flowing in the "dynamo region" of the ionosphere, the E-region. In this study, the horizontal component amplitude observed by ground-based observatories belonged to the INTERMAGNET network have been used to analyze the global pattern variance of the Sq variation. In particular we focused our attention on Vassouras Observatory (VSS), Rio de Janeiro, Brazil, which has been active since 1915. In the next years, a brazilian network of magnetometers will be implemented and VSS can be used as reference. This work aims mainly to highlight and interpret these quiet daily variations over the Brazilian sector compared to the features from other magnetic stations reasonably distributed over the whole Earth's surface. The methodological approach is based on wavelet cross-correlation technique. This technique is useful to isolate the period of the spectral components of geomagnetic field in each station and to correlate them as function of scale (period) between VSS and the other stations. The wavelet cross-correlation coefficient strongly depends on the scale. We study the geomagnetically quiet days at equinox and solstice months during low and high solar activity. As preliminary remarks, the results show that the records in the magnetic stations have primary a latitudinal dependence affected by the time of year and level of solar activity. On the other hand, records of magnetic stations located at the same dip latitude but at different longitude presented some peculiarities. These results indicated that the winds driven the dynamo are very sensitive of the location of the geomagnetic station, i. e., its effects depend upon the direction

  6. Component protection based automatic control

    International Nuclear Information System (INIS)

    Otaduy, P.J.

    1992-01-01

    Control and safety systems as well as operation procedures are designed on the basis of critical process parameters limits. The expectation is that short and long term mechanical damage and process failures will be avoided by operating the plant within the specified constraints envelopes. In this paper, one of the Advanced Liquid Metal Reactor (ALMR) design duty cycles events is discussed to corroborate that the time has come to explicitly make component protection part of the control system. Component stress assessment and aging data should be an integral part of the control system. Then transient trajectory planning and operating limits could be aimed at minimizing component specific and overall plant component damage cost functions. The impact of transients on critical components could then be managed according to plant lifetime design goals. The need for developing methodologies for online transient trajectory planning and assessment of operating limits in order to facilitate the explicit incorporation of damage assessment capabilities to the plant control and protection systems is discussed. 12 refs

  7. UPLC-MS/MS analysis for antioxidant components of Lycii Fructus based on spectrum-effect relationship.

    Science.gov (United States)

    Zhang, Xian-Fei; Chen, Juan; Yang, Jun-Li; Shi, Yan-Ping

    2018-04-01

    Lycii Fructus is widely cultivated in the Northwest China. It is well-known for its antiaging effect in traditional Chinese medicines (TCMs), but the effective components are not clear. In this work, the ultra-performance liquid chromatography tandem mass spectrometry (UPLC-MS/MS) was used to study the antioxidant components of Lycii Fructus through analyzing the spectrum-effect relationship, and the positive correlation components with antioxidant activity were partially identified. The extractums of Lycii Fructus were adsorbed with macroporous resin, and then eluted with water and 30%, 60%, 90% ethanol in turn. The extract fraction eluted with 60% ethanol was determined as the best, and was taken for subsequent experiments. With the above separation method, UPLC fingerprints of thirty batches of Lycii Fructus (from different areas) were obtained, and thirty common peaks were selected through similarity analysis (SA). Combined with the data of the 2,2-diphenyl-1-picrylhydrazyl (DPPH) and 2,2'-azino-bis (3-ethylbenzothiazoline-6-sulfonic acid) diammonium salt (ABTS) assays, the spectrum-effect relationship was studied. The results showed that the main peaks with antioxidant activity were P14, P26, P8, and P21 for DPPH, and P26, P14, P21, and P19 for ABTS. Using the UPLC-MS/MS data, peaks P14, P19, P21, and P30 were respectively identified as chlorogenic acid, quercetin, kaempferol, and isorhamnetin, and then the results were confirmed through comparison with the standards and other references. Finally, their strong antioxidant activities were validated experimentally. Copyright © 2017 Elsevier B.V. All rights reserved.

  8. Application of independent component analysis to ac dipole based optics measurement and correction at the Relativistic Heavy Ion Collider

    Directory of Open Access Journals (Sweden)

    X. Shen

    2013-11-01

    Full Text Available Correction of beta-beat is of great importance for performance improvement of high energy accelerators, like the Relativistic Hadron Ion Collider (RHIC. At RHIC, using the independent component analysis method, linear optical functions are extracted from the turn by turn beam position data of the ac dipole driven betatron oscillation. Despite the constraint of a limited number of available quadrupole correctors at RHIC, a global beta-beat correction scheme using a beta-beat response matrix method was developed and experimentally demonstrated. In both rings, a factor of 2 or better reduction of beta-beat was achieved within available beam time. At the same time, a new scheme of using horizontal closed orbit bump at sextupoles to correct beta-beat in the arcs was demonstrated in the Yellow ring of RHIC at beam energy of 255 GeV, and a peak beta-beat of approximately 7% was achieved.

  9. Probabilistic Principal Component Analysis for Metabolomic Data.

    LENUS (Irish Health Repository)

    Nyamundanda, Gift

    2010-11-23

    Abstract Background Data from metabolomic studies are typically complex and high-dimensional. Principal component analysis (PCA) is currently the most widely used statistical technique for analyzing metabolomic data. However, PCA is limited by the fact that it is not based on a statistical model. Results Here, probabilistic principal component analysis (PPCA) which addresses some of the limitations of PCA, is reviewed and extended. A novel extension of PPCA, called probabilistic principal component and covariates analysis (PPCCA), is introduced which provides a flexible approach to jointly model metabolomic data and additional covariate information. The use of a mixture of PPCA models for discovering the number of inherent groups in metabolomic data is demonstrated. The jackknife technique is employed to construct confidence intervals for estimated model parameters throughout. The optimal number of principal components is determined through the use of the Bayesian Information Criterion model selection tool, which is modified to address the high dimensionality of the data. Conclusions The methods presented are illustrated through an application to metabolomic data sets. Jointly modeling metabolomic data and covariates was successfully achieved and has the potential to provide deeper insight to the underlying data structure. Examination of confidence intervals for the model parameters, such as loadings, allows for principled and clear interpretation of the underlying data structure. A software package called MetabolAnalyze, freely available through the R statistical software, has been developed to facilitate implementation of the presented methods in the metabolomics field.

  10. Functional connectivity analysis of the neural bases of emotion regulation: A comparison of independent component method with density-based k-means clustering method.

    Science.gov (United States)

    Zou, Ling; Guo, Qian; Xu, Yi; Yang, Biao; Jiao, Zhuqing; Xiang, Jianbo

    2016-04-29

    Functional magnetic resonance imaging (fMRI) is an important tool in neuroscience for assessing connectivity and interactions between distant areas of the brain. To find and characterize the coherent patterns of brain activity as a means of identifying brain systems for the cognitive reappraisal of the emotion task, both density-based k-means clustering and independent component analysis (ICA) methods can be applied to characterize the interactions between brain regions involved in cognitive reappraisal of emotion. Our results reveal that compared with the ICA method, the density-based k-means clustering method provides a higher sensitivity of polymerization. In addition, it is more sensitive to those relatively weak functional connection regions. Thus, the study concludes that in the process of receiving emotional stimuli, the relatively obvious activation areas are mainly distributed in the frontal lobe, cingulum and near the hypothalamus. Furthermore, density-based k-means clustering method creates a more reliable method for follow-up studies of brain functional connectivity.

  11. 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

  12. 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

  13. 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.

  14. Using robust principal component analysis to alleviate day-to-day variability in EEG based emotion classification.

    Science.gov (United States)

    Ping-Keng Jao; Yuan-Pin Lin; Yi-Hsuan Yang; Tzyy-Ping Jung

    2015-08-01

    An emerging challenge for emotion classification using electroencephalography (EEG) is how to effectively alleviate day-to-day variability in raw data. This study employed the robust principal component analysis (RPCA) to address the problem with a posed hypothesis that background or emotion-irrelevant EEG perturbations lead to certain variability across days and somehow submerge emotion-related EEG dynamics. The empirical results of this study evidently validated our hypothesis and demonstrated the RPCA's feasibility through the analysis of a five-day dataset of 12 subjects. The RPCA allowed tackling the sparse emotion-relevant EEG dynamics from the accompanied background perturbations across days. Sequentially, leveraging the RPCA-purified EEG trials from more days appeared to improve the emotion-classification performance steadily, which was not found in the case using the raw EEG features. Therefore, incorporating the RPCA with existing emotion-aware machine-learning frameworks on a longitudinal dataset of each individual may shed light on the development of a robust affective brain-computer interface (ABCI) that can alleviate ecological inter-day variability.

  15. Model reduction by weighted Component Cost Analysis

    Science.gov (United States)

    Kim, Jae H.; Skelton, Robert E.

    1990-01-01

    Component Cost Analysis considers any given system driven by a white noise process as an interconnection of different components, and assigns a metric called 'component cost' to each component. These component costs measure the contribution of each component to a predefined quadratic cost function. A reduced-order model of the given system may be obtained by deleting those components that have the smallest component costs. The theory of Component Cost Analysis is extended to include finite-bandwidth colored noises. The results also apply when actuators have dynamics of their own. Closed-form analytical expressions of component costs are also derived for a mechanical system described by its modal data. This is very useful to compute the modal costs of very high order systems. A numerical example for MINIMAST system is presented.

  16. Radar fall detection using principal component analysis

    Science.gov (United States)

    Jokanovic, Branka; Amin, Moeness; Ahmad, Fauzia; Boashash, Boualem

    2016-05-01

    Falls are a major cause of fatal and nonfatal injuries in people aged 65 years and older. Radar has the potential to become one of the leading technologies for fall detection, thereby enabling the elderly to live independently. Existing techniques for fall detection using radar are based on manual feature extraction and require significant parameter tuning in order to provide successful detections. In this paper, we employ principal component analysis for fall detection, wherein eigen images of observed motions are employed for classification. Using real data, we demonstrate that the PCA based technique provides performance improvement over the conventional feature extraction methods.

  17. A component-based groupware development methodology

    NARCIS (Netherlands)

    Guareis de farias, Cléver; Ferreira Pires, Luis; van Sinderen, Marten J.

    2000-01-01

    Software development in general and groupware applications in particular can greatly benefit from the reusability and interoperability aspects associated with software components. Component-based software development enables the construction of software artefacts by assembling prefabricated,

  18. 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

  19. Validation of the academic management evaluation instru-ment based on principal component analysis for engineering and technological courses

    Directory of Open Access Journals (Sweden)

    Albano Oliveira Nunes

    2015-05-01

    Full Text Available In recent years, the expansion of higher education in Brazil has led to a series of demands related to aspects concerning training at the college level. These processes relate to: academics, professionals, entering in the labor market, among others. In this context, an important aspect is the quality of the courses. Thus, the evaluation becomes a critical diagnostic process of reality and starting point for possible interventions to be put in practice by the coordinators of the programs. This article presents the results of a questionnaire administered at the Federal University of Ceará (UFC, especially to  Systems & Digital Media and Engineering Programs professors. This research aims to identify how the professors from each department see the administrative procedures developed by the departments and also investigate the possibility of using Principal Components Analysis (PCA as a support for management of the higher education training. The methodology included the implementation of Likert scale questionnaire and subsequent mathematical treatment with PCA. The results indicate the potential application of PCA to support the management of higher education; it was possible to extract preliminaries inferences related to management methods and their characteristics. This suggests the possibility of developing the Educametrics field.

  20. 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

  1. 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.

  2. 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.

  3. Judging complex movement performances for excellence: a principal components analysis-based technique applied to competitive diving.

    Science.gov (United States)

    Young, Cole; Reinkensmeyer, David J

    2014-08-01

    Athletes rely on subjective assessment of complex movements from coaches and judges to improve their motor skills. In some sports, such as diving, snowboard half pipe, gymnastics, and figure skating, subjective scoring forms the basis for competition. It is currently unclear whether this scoring process can be mathematically modeled; doing so could provide insight into what motor skill is. Principal components analysis has been proposed as a motion analysis method for identifying fundamental units of coordination. We used PCA to analyze movement quality of dives taken from USA Diving's 2009 World Team Selection Camp, first identifying eigenpostures associated with dives, and then using the eigenpostures and their temporal weighting coefficients, as well as elements commonly assumed to affect scoring - gross body path, splash area, and board tip motion - to identify eigendives. Within this eigendive space we predicted actual judges' scores using linear regression. This technique rated dives with accuracy comparable to the human judges. The temporal weighting of the eigenpostures, body center path, splash area, and board tip motion affected the score, but not the eigenpostures themselves. These results illustrate that (1) subjective scoring in a competitive diving event can be mathematically modeled; (2) the elements commonly assumed to affect dive scoring actually do affect scoring (3) skill in elite diving is more associated with the gross body path and the effect of the movement on the board and water than the units of coordination that PCA extracts, which might reflect the high level of technique these divers had achieved. We also illustrate how eigendives can be used to produce dive animations that an observer can distort continuously from poor to excellent, which is a novel approach to performance visualization. Copyright © 2014 Elsevier B.V. All rights reserved.

  4. Component of the risk analysis

    International Nuclear Information System (INIS)

    Martinez, I.; Campon, G.

    2013-01-01

    The power point presentation reviews issues like analysis of risk (Codex), management risk, preliminary activities manager, relationship between government and industries, microbiological danger and communication of risk

  5. In vivo quantitative evaluation of vascular parameters for angiogenesis based on sparse principal component analysis and aggregated boosted trees

    International Nuclear Information System (INIS)

    Zhao, Fengjun; Liu, Junting; Qu, Xiaochao; Xu, Xianhui; Chen, Xueli; Yang, Xiang; Liang, Jimin; Tian, Jie; Cao, Feng

    2014-01-01

    To solve the multicollinearity issue and unequal contribution of vascular parameters for the quantification of angiogenesis, we developed a quantification evaluation method of vascular parameters for angiogenesis based on in vivo micro-CT imaging of hindlimb ischemic model mice. Taking vascular volume as the ground truth parameter, nine vascular parameters were first assembled into sparse principal components (PCs) to reduce the multicolinearity issue. Aggregated boosted trees (ABTs) were then employed to analyze the importance of vascular parameters for the quantification of angiogenesis via the loadings of sparse PCs. The results demonstrated that vascular volume was mainly characterized by vascular area, vascular junction, connectivity density, segment number and vascular length, which indicated they were the key vascular parameters for the quantification of angiogenesis. The proposed quantitative evaluation method was compared with both the ABTs directly using the nine vascular parameters and Pearson correlation, which were consistent. In contrast to the ABTs directly using the vascular parameters, the proposed method can select all the key vascular parameters simultaneously, because all the key vascular parameters were assembled into the sparse PCs with the highest relative importance. (paper)

  6. Empirical research on financial capability evaluation of A-share listed companies in the securities industry based on principal component analysis

    Directory of Open Access Journals (Sweden)

    Xiuping Wang

    2017-11-01

    Full Text Available Based on the relevant financial data indicators of A-share markets of Shanghai and Shenzhen in 2009, with all of 29 listed companies in the securities industry as the research objects, this paper selects 10variables that can fully reflect the financial capability indicators and uses the principal component analysis to carry out the empirical research on the financial capability. The research results show that the comprehensive financial capability of listed companies in A-share securities industry must be focused on the following four capabilities, investment and income, profit, capital composition and debt repayment and cash flow indicators. In addition, the principal component analysis can effectively evaluate the financial capability of listed companies in A-share securities industry, and solve the problems in the previous analysis methods, such as excessive indicators, information overlapping and so on.

  7. 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...

  8. Design of Smart Ion-Selective Electrode Arrays Based on Source Separation through Nonlinear Independent Component Analysis

    Directory of Open Access Journals (Sweden)

    Duarte L.T.

    2014-03-01

    Full Text Available The development of chemical sensor arrays based on Blind Source Separation (BSS provides a promising solution to overcome the interference problem associated with Ion-Selective Electrodes (ISE. The main motivation behind this new approach is to ease the time-demanding calibration stage. While the first works on this problem only considered the case in which the ions under analysis have equal valences, the present work aims at developing a BSS technique that works when the ions have different charges. In this situation, the resulting mixing model belongs to a particular class of nonlinear systems that have never been studied in the BSS literature. In order to tackle this sort of mixing process, we adopted a recurrent network as separating system. Moreover, concerning the BSS learning strategy, we develop a mutual information minimization approach based on the notion of the differential of the mutual information. The method works requires a batch operation, and, thus, can be used to perform off-line analysis. The validity of our approach is supported by experiments where the mixing model parameters were extracted from actual data.

  9. The Component And System Reliability Analysis Of Multipurpose Reactor G.A. Subway's Based On The Failure Rate Curve

    International Nuclear Information System (INIS)

    Sriyono; Ismu Wahyono, Puradwi; Mulyanto, Dwijo; Kusmono, Siamet

    2001-01-01

    The main component of Multipurpose G.A.Siwabessy had been analyzed by its failure rate curve. The main component ha'..e been analyzed namely, the pump of ''Fuel Storage Pool Purification System'' (AK-AP), ''Primary Cooling System'' (JE01-AP), ''Primary Pool Purification System'' (KBE01-AP), ''Warm Layer System'' (KBE02-AP), ''Cooling Tower'' (PA/D-AH), ''Secondary Cooling System'', and Diesel (BRV). The Failure Rate Curve is made by component database that was taken from 'log book' operation of RSG GAS. The total operation of that curve is 2500 hours. From that curve it concluded that the failure rate of components form of bathtub curve. The maintenance processing causes the curve anomaly

  10. Graphene-based spintronic components

    OpenAIRE

    Zeng, Minggang; Shen, Lei; Su, Haibin; Zhou, Miao; Zhang, Chun; Feng, Yuanping

    2010-01-01

    A major challenge of spintronics is in generating, controlling and detecting spin-polarized current. Manipulation of spin-polarized current, in particular, is difficult. We demonstrate here, based on calculated transport properties of graphene nanoribbons, that nearly +-100% spin-polarized current can be generated in zigzag graphene nanoribbons (ZGNRs) and tuned by a source-drain voltage in the bipolar spin diode, in addition to magnetic configurations of the electrodes. This unusual transpor...

  11. The artifacts of component-based development

    International Nuclear Information System (INIS)

    Rizwan, M.; Qureshi, J.; Hayat, S.A.

    2007-01-01

    Component based development idea was floated in a conference name Mass Produced Software Components in 1968 (1). Since then engineering and scientific libraries are developed to reuse the previously developed functions. This concept is now widely used in SW development as component based development (CBD). Component-based software engineering (CBSE) is used to develop/ assemble software from existing components (2). Software developed using components is called component where (3). This paper presents different architectures of CBD such as Active X, common object request broker architecture (CORBA), remote method invocation (RMI) and simple object access protocol (SOAP). The overall objective of this paper is to support the practice of CBD by comparing its advantages and disadvantages. This paper also evaluates object oriented process model to adapt it for CBD. (author)

  12. 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.

  13. A novel normalization method based on principal component analysis to reduce the effect of peak overlaps in two-dimensional correlation spectroscopy

    Science.gov (United States)

    Wang, Yanwei; Gao, Wenying; Wang, Xiaogong; Yu, Zhiwu

    2008-07-01

    Two-dimensional correlation spectroscopy (2D-COS) has been widely used to separate overlapped spectroscopic bands. However, band overlap may sometimes cause misleading results in the 2D-COS spectra, especially if one peak is embedded within another peak by the overlap. In this work, we propose a new normalization method, based on principal component analysis (PCA). For each spectrum under discussion, the first principal component of PCA is simply taken as the normalization factor of the spectrum. It is demonstrated that the method works well with simulated dynamic spectra. Successful result has also been obtained from the analysis of an overlapped band in the wavenumber range 1440-1486 cm -1 for the evaporation process of a solution containing behenic acid, methanol, and chloroform.

  14. 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 ...

  15. Classification Technique for Ultrasonic Weld Inspection Signals using a Neural Network based on 2-dimensional fourier Transform and Principle Component Analysis

    International Nuclear Information System (INIS)

    Kim, Jae Joon

    2004-01-01

    Neural network-based signal classification systems are increasingly used in the analysis of large volumes of data obtained in NDE applications. Ultrasonic inspection methods on the other hand are commonly used in the nondestructive evaluation of welds to detect flaws. An important characteristic of ultrasonic inspection is the ability to identify the type of discontinuity that gives rise to a peculiar signal. Standard techniques rely on differences in individual A-scans to classify the signals. This paper proposes an ultrasonic signal classification technique based on the information tying in the neighboring signals. The approach is based on a 2-dimensional Fourier transform and the principal component analysis to generate a reduced dimensional feature vector for classification. Results of applying the technique to data obtained from the inspection of actual steel welds are presented

  16. Analysis of spiral components in 16 galaxies

    International Nuclear Information System (INIS)

    Considere, S.; Athanassoula, E.

    1988-01-01

    A Fourier analysis of the intensity distributions in the plane of 16 spiral galaxies of morphological types from 1 to 7 is performed. The galaxies processed are NGC 300,598,628,2403,2841,3031,3198,3344,5033,5055,5194,5247,6946,7096,7217, and 7331. The method, mathematically based upon a decomposition of a distribution into a superposition of individual logarithmic spiral components, is first used to determine for each galaxy the position angle PA and the inclination ω of the galaxy plane onto the sky plane. Our results, in good agreement with those issued from different usual methods in the literature, are discussed. The decomposition of the deprojected galaxies into individual spiral components reveals that the two-armed component is everywhere dominant. Our pitch angles are then compared to the previously published ones and their quality is checked by drawing each individual logarithmic spiral on the actual deprojected galaxy images. Finally, the surface intensities for angular periodicities of interest are calculated. A choice of a few of the most important ones is used to elaborate a composite image well representing the main spiral features observed in the deprojected galaxies

  17. 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...

  18. Horizontal integration in markets for complementary components and vertical product differentiation: A case-based analysis in the semiconductor industry

    OpenAIRE

    Westbrock, B.

    2005-01-01

    Observations of recent mergers and acquisitions (M&A) in the semiconductor and computer industry indicate that activities concentrate on the technology leaders in this market. The author examines the influence of players’ heterogeneous product technologies on their involvement in M&A. He provides a rationale for the influence with the help of a case study and a two-stage non cooperative game. The case is about an acquisition wave between suppliers in two semiconductor component markets. Exe...

  19. Components of Task-Based Needs Analysis of the ESP Learners with the Specialization of Business and Tourism

    Science.gov (United States)

    Poghosyan, Naira

    2016-01-01

    In the following paper we shall thoroughly analyze the target learning needs of the learners within an ESP (English for Specific Purposes) context. The main concerns of ESP have always been and remain with the needs analysis, text analysis and preparing learners to communicate effectively in the tasks prescribed by their study or work situation.…

  20. 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

  1. 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...

  2. Factors affecting medication adherence in community-managed patients with hypertension based on the principal component analysis: evidence from Xinjiang, China

    Directory of Open Access Journals (Sweden)

    Zhang YJ

    2018-05-01

    Full Text Available Yuji Zhang,* Xiaoju Li,* Lu Mao, Mei Zhang, Ke Li, Yinxia Zheng, Wangfei Cui, Hongpo Yin, Yanli He, Mingxia Jing Department of Public Health, Shihezi University School of Medicine, Shihezi, Xinjiang, China *These authors contributed equally to this work Purpose: The analysis of factors affecting the nonadherence to antihypertensive medications is important in the control of blood pressure among patients with hypertension. The purpose of this study was to assess the relationship between factors and medication adherence in Xinjiang community-managed patients with hypertension based on the principal component analysis.Patients and methods: A total of 1,916 community-managed patients with hypertension, selected randomly through a multi-stage sampling, participated in the survey. Self-designed questionnaires were used to classify the participants as either adherent or nonadherent to their medication regimen. A principal component analysis was used in order to eliminate the correlation between factors. Factors related to nonadherence were analyzed by using a χ2-test and a binary logistic regression model.Results: This study extracted nine common factors, with a cumulative variance contribution rate of 63.6%. Further analysis revealed that the following variables were significantly related to nonadherence: severity of disease, community management, diabetes, and taking traditional medications.Conclusion: Community management plays an important role in improving the patients’ medication-taking behavior. Regular medication regimen instruction and better community management services through community-level have the potential to reduce nonadherence. Mild hypertensive patients should be monitored by community health care providers. Keywords: hypertension, medication adherence, factors, principal component analysis, community management, China

  3. Factors affecting medication adherence in community-managed patients with hypertension based on the principal component analysis: evidence from Xinjiang, China.

    Science.gov (United States)

    Zhang, Yuji; Li, Xiaoju; Mao, Lu; Zhang, Mei; Li, Ke; Zheng, Yinxia; Cui, Wangfei; Yin, Hongpo; He, Yanli; Jing, Mingxia

    2018-01-01

    The analysis of factors affecting the nonadherence to antihypertensive medications is important in the control of blood pressure among patients with hypertension. The purpose of this study was to assess the relationship between factors and medication adherence in Xinjiang community-managed patients with hypertension based on the principal component analysis. A total of 1,916 community-managed patients with hypertension, selected randomly through a multi-stage sampling, participated in the survey. Self-designed questionnaires were used to classify the participants as either adherent or nonadherent to their medication regimen. A principal component analysis was used in order to eliminate the correlation between factors. Factors related to nonadherence were analyzed by using a χ 2 -test and a binary logistic regression model. This study extracted nine common factors, with a cumulative variance contribution rate of 63.6%. Further analysis revealed that the following variables were significantly related to nonadherence: severity of disease, community management, diabetes, and taking traditional medications. Community management plays an important role in improving the patients' medication-taking behavior. Regular medication regimen instruction and better community management services through community-level have the potential to reduce nonadherence. Mild hypertensive patients should be monitored by community health care providers.

  4. Laser based refurbishment of steel mill components

    CSIR Research Space (South Africa)

    Kazadi, P

    2006-03-01

    Full Text Available Laser refurbishment capabilities were demonstrated and promising results were obtained for repair of distance sleeves, foot rolls, descaler cassette, idler rolls. Based on the cost projections and the results of the in-situ testing, components which...

  5. 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

  6. 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.

  7. Assessment of water quality in the elbe river at flood water conditions based on cluster analysis, principle components analysis, and source apportionment

    Energy Technology Data Exchange (ETDEWEB)

    Baborowski, Martina [Department of River Ecology, UFZ-Helmholtz Centre for Environmental Research, Magdeburg (Germany); Simeonov, Vasil [Faculty of Chemistry, University of Sofia, Sofia (Bulgaria); Einax, Juergen W. [Institute of Inorganic and Analytical Chemistry, Friedrich Schiller University of Jena, Jena (Germany)

    2012-04-15

    An assessment of water quality measurements during a spring flood in the Elbe River is presented. Daily samples were taken at a site in the middle Elbe, which is part of the network of the International Commission for the Protection of the Elbe River (IKSE/MKOL). Cluster analysis (CA), principal components analysis (PCA), and source apportionment (APCS apportioning) were used to assess the flood-dependent matter transport. As a result, three main components could be extracted as important to the matter transport in the Elbe River basin during flood events: (i) re-suspended contaminated sediments, which led to temporarily increased concentrations of suspended matter and of most of the investigated heavy metals; (ii) water discharge related concentrations of pedogenic dissolved organic matter (DOM) as well as preliminary diluted concentrations of uranium and chloride, parameters with stable pollution background in the river basin; and (iii) abandoned mines, i.e., their dewatering systems, with particular influence on nickel, manganese, and zinc concentrations. (Copyright copyright 2012 WILEY-VCH Verlag GmbH and Co. KGaA, Weinheim)

  8. Assessment of water quality in the elbe river at flood water conditions based on cluster analysis, principle components analysis, and source apportionment

    International Nuclear Information System (INIS)

    Baborowski, Martina; Simeonov, Vasil; Einax, Juergen W.

    2012-01-01

    An assessment of water quality measurements during a spring flood in the Elbe River is presented. Daily samples were taken at a site in the middle Elbe, which is part of the network of the International Commission for the Protection of the Elbe River (IKSE/MKOL). Cluster analysis (CA), principal components analysis (PCA), and source apportionment (APCS apportioning) were used to assess the flood-dependent matter transport. As a result, three main components could be extracted as important to the matter transport in the Elbe River basin during flood events: (i) re-suspended contaminated sediments, which led to temporarily increased concentrations of suspended matter and of most of the investigated heavy metals; (ii) water discharge related concentrations of pedogenic dissolved organic matter (DOM) as well as preliminary diluted concentrations of uranium and chloride, parameters with stable pollution background in the river basin; and (iii) abandoned mines, i.e., their dewatering systems, with particular influence on nickel, manganese, and zinc concentrations. (Copyright copyright 2012 WILEY-VCH Verlag GmbH and Co. KGaA, Weinheim)

  9. Designing simulator-based training: An approach integrating cognitive task analysis and four-component instructional design

    NARCIS (Netherlands)

    Tjiam, I.M.; Schout, B.M.; Hendrikx, A.J.M.; Scherpbier, A.J.J.A.; Witjes, J.A.; Van Merrienboer, J.J.

    2012-01-01

    Most studies of simulator-based surgical skills training have focused on the acquisition of psychomotor skills, but surgical procedures are complex tasks requiring both psychomotor and cognitive skills. As skills training is modelled on expert performance consisting partly of unconscious automatic

  10. 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

  11. 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

  12. Comparative analysis of components incorporated in conservation priority assessments: a case study based on South African species of terrestrial mammals

    CSIR Research Space (South Africa)

    Keith, M

    2007-04-01

    Full Text Available priority assessments of South African terrestrial mammals 99 Estimates of conservation value a) Relative Endemism (RE) – (modified from Freitag & van Jaarsveld (1997)). The extent of occurrence, obtained from various sources (Halte- north & Diller 1980... of threat a) Relative Body Mass (RBM) – Based on average body weights (in grams) for each taxon obtained from Dorst & Dandelot (1972), Haltenorth & Diller (1980), Skinner & Smithers (1990), and Skinner & Chimimba (2005) and was computed as: RBM = log...

  13. How Many Separable Sources? Model Selection In Independent Components Analysis

    DEFF Research Database (Denmark)

    Woods, Roger P.; Hansen, Lars Kai; Strother, Stephen

    2015-01-01

    among potential model categories with differing numbers of Gaussian components. Based on simulation studies, the assumptions and approximations underlying the Akaike Information Criterion do not hold in this setting, even with a very large number of observations. Cross-validation is a suitable, though....../Principal Components Analysis (mixed ICA/PCA) model described here accommodates one or more Gaussian components in the independent components analysis model and uses principal components analysis to characterize contributions from this inseparable Gaussian subspace. Information theory can then be used to select from...... might otherwise be questionable. Failure of the Akaike Information Criterion in model selection also has relevance in traditional independent components analysis where all sources are assumed non-Gaussian....

  14. How Many Separable Sources? Model Selection In Independent Components Analysis

    Science.gov (United States)

    Woods, Roger P.; Hansen, Lars Kai; Strother, Stephen

    2015-01-01

    Unlike mixtures consisting solely of non-Gaussian sources, mixtures including two or more Gaussian components cannot be separated using standard independent components analysis methods that are based on higher order statistics and independent observations. The mixed Independent Components Analysis/Principal Components Analysis (mixed ICA/PCA) model described here accommodates one or more Gaussian components in the independent components analysis model and uses principal components analysis to characterize contributions from this inseparable Gaussian subspace. Information theory can then be used to select from among potential model categories with differing numbers of Gaussian components. Based on simulation studies, the assumptions and approximations underlying the Akaike Information Criterion do not hold in this setting, even with a very large number of observations. Cross-validation is a suitable, though computationally intensive alternative for model selection. Application of the algorithm is illustrated using Fisher's iris data set and Howells' craniometric data set. Mixed ICA/PCA is of potential interest in any field of scientific investigation where the authenticity of blindly separated non-Gaussian sources might otherwise be questionable. Failure of the Akaike Information Criterion in model selection also has relevance in traditional independent components analysis where all sources are assumed non-Gaussian. PMID:25811988

  15. Development of GPU Based Parallel Computing Module for Solving Pressure Equation in the CUPID Component Thermo-Fluid Analysis Code

    International Nuclear Information System (INIS)

    Lee, Jin Pyo; Joo, Han Gyu

    2010-01-01

    In the thermo-fluid analysis code named CUPID, the linear system of pressure equations must be solved in each iteration step. The time for repeatedly solving the linear system can be quite significant because large sparse matrices of Rank more than 50,000 are involved and the diagonal dominance of the system is hardly hold. Therefore parallelization of the linear system solver is essential to reduce the computing time. Meanwhile, Graphics Processing Units (GPU) have been developed as highly parallel, multi-core processors for the global demand of high quality 3D graphics. If a suitable interface is provided, parallelization using GPU can be available to engineering computing. NVIDIA provides a Software Development Kit(SDK) named CUDA(Compute Unified Device Architecture) to code developers so that they can manage GPUs for parallelization using the C language. In this research, we implement parallel routines for the linear system solver using CUDA, and examine the performance of the parallelization. In the next section, we will describe the method of CUDA parallelization for the CUPID code, and then the performance of the CUDA parallelization will be discussed

  16. Principal component analysis for the forensic discrimination of black inkjet inks based on the Vis-NIR fibre optics reflection spectra.

    Science.gov (United States)

    Gál, Lukáš; Oravec, Michal; Gemeiner, Pavol; Čeppan, Michal

    2015-12-01

    Nineteen black inkjet inks of six different brands were examined by fibre optics reflection spectroscopy in Visible and Near Infrared Region (Vis-NIR FORS) directly on paper with a view to achieving good resolution between them. These different inks were tested on nineteen different inkjet printers from three brands. Samples were obtained from prints by reflection probe. Processed reflection spectra in the range 500-1000 nm were used as samples in principal component analysis. Variability between spectra of the same ink obtained from different prints, as well as between spectra of square areas and lines was examined. For both spectra obtained from square areas and lines reference, Principal Component Analysis (PCA) models were created. According to these models, the inkjet inks were divided into clusters. PCA method is able to separate inks containing carbon black as main colorant from the other inks using other colorants. Some spectra were recorded from another piece of printer and used as validation samples. Spectra of validation samples were projected onto reference PCA models. According to position of validation samples in score plots it can be concluded that PCA based on Vis-NIR FORS can reliably differentiate inkjet inks which are included in the reference database. The presented method appears to be a suitable tool for forensic examination of questioned documents containing inkjet inks. Inkjet inks spectra were obtained without extraction or cutting sample with possibility to measure out of the laboratory. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

  17. Simultaneous analysis of 11 main active components in Cirsium setosum based on HPLC-ESI-MS/MS and combined with statistical methods.

    Science.gov (United States)

    Sun, Qian; Chang, Lu; Ren, Yanping; Cao, Liang; Sun, Yingguang; Du, Yingfeng; Shi, Xiaowei; Wang, Qiao; Zhang, Lantong

    2012-11-01

    A novel method based on high-performance liquid chromatography coupled with electrospray ionization tandem mass spectrometry was developed for simultaneous determination of the 11 major active components including ten flavonoids and one phenolic acid in Cirsium setosum. Separation was performed on a reversed-phase C(18) column with gradient elution of methanol and 0.1‰ acetic acid (v/v). The identification and quantification of the analytes were achieved on a hybrid quadrupole linear ion trap mass spectrometer. Multiple-reaction monitoring scanning was employed for quantification with switching electrospray ion source polarity between positive and negative modes in a single run. Full validation of the assay was carried out including linearity, precision, accuracy, stability, limits of detection and quantification. The results demonstrated that the method developed was reliable, rapid, and specific. The 25 batches of C. setosum samples from different sources were first determined using the developed method and the total contents of 11 analytes ranged from 1717.460 to 23028.258 μg/g. Among them, the content of linarin was highest, and its mean value was 7340.967 μg/g. Principal component analysis and hierarchical clustering analysis were performed to differentiate and classify the samples, which is helpful for comprehensive evaluation of the quality of C. setosum. © 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  18. The effectiveness of community-based coordinating interventions in dementia care: a meta-analysis and subgroup analysis of intervention components.

    Science.gov (United States)

    Backhouse, Amy; Ukoumunne, Obioha C; Richards, David A; McCabe, Rose; Watkins, Ross; Dickens, Chris

    2017-11-13

    Interventions aiming to coordinate services for the community-based dementia population vary in components, organisation and implementation. In this review we aimed to evaluate the effectiveness of community-based care coordinating interventions on health outcomes and investigate whether specific components of interventions influence their effects. We searched four databases from inception to April 2017: Medline, The Cochrane Library, EMBASE and PsycINFO. This was aided by a search of four grey literature databases, and backward and forward citation tracking of included papers. Title and abstract screening was followed by a full text screen by two independent reviewers, and quality was assessed using the CASP appraisal tool. We then conducted meta-analyses and subgroup analyses. A total of 14 randomised controlled trials (RCTs) involving 10,372 participants were included in the review. Altogether we carried out 12 meta-analyses and 19 subgroup analyses. Meta-analyses found coordinating interventions showed a statistically significant improvement in both patient behaviour measured using the Neuropsychiatric Inventory (NPI) (mean difference (MD) = -9.5; 95% confidence interval (CI): -18.1 to -1.0; p = 0.03; number of studies (n) = 4; I 2  = 88%) and caregiver burden (standardised mean difference (SMD) = -0.54; 95% CI: -1.01 to -0.07; p = 0.02; n = 5, I 2  = 92%) compared to the control group. Subgroup analyses found interventions using a case manager with a nursing background showed a greater positive effect on caregiver quality of life than those that used case managers from other professional backgrounds (SMD = 0.94 versus 0.03, respectively; p < 0.001). Interventions that did not provide supervision for the case managers showed greater effectiveness for reducing the percentage of patients that are institutionalised compared to those that provided supervision (odds ratio (OR) = 0.27 versus 0.96 respectively; p = 0.02). There was little

  19. The effectiveness of community-based coordinating interventions in dementia care: a meta-analysis and subgroup analysis of intervention components

    Directory of Open Access Journals (Sweden)

    Amy Backhouse

    2017-11-01

    Full Text Available Abstract Background Interventions aiming to coordinate services for the community-based dementia population vary in components, organisation and implementation. In this review we aimed to evaluate the effectiveness of community-based care coordinating interventions on health outcomes and investigate whether specific components of interventions influence their effects. Methods We searched four databases from inception to April 2017: Medline, The Cochrane Library, EMBASE and PsycINFO. This was aided by a search of four grey literature databases, and backward and forward citation tracking of included papers. Title and abstract screening was followed by a full text screen by two independent reviewers, and quality was assessed using the CASP appraisal tool. We then conducted meta-analyses and subgroup analyses. Results A total of 14 randomised controlled trials (RCTs involving 10,372 participants were included in the review. Altogether we carried out 12 meta-analyses and 19 subgroup analyses. Meta-analyses found coordinating interventions showed a statistically significant improvement in both patient behaviour measured using the Neuropsychiatric Inventory (NPI (mean difference (MD = −9.5; 95% confidence interval (CI: −18.1 to −1.0; p = 0.03; number of studies (n = 4; I2 = 88% and caregiver burden (standardised mean difference (SMD = −0.54; 95% CI: -1.01 to −0.07; p = 0.02; n = 5, I2 = 92% compared to the control group. Subgroup analyses found interventions using a case manager with a nursing background showed a greater positive effect on caregiver quality of life than those that used case managers from other professional backgrounds (SMD = 0.94 versus 0.03, respectively; p < 0.001. Interventions that did not provide supervision for the case managers showed greater effectiveness for reducing the percentage of patients that are institutionalised compared to those that provided supervision (odds ratio (OR = 0.27 versus 0

  20. IAEA's experience in compiling a generic component reliability data base

    International Nuclear Information System (INIS)

    Tomic, B.; Lederman, L.

    1991-01-01

    Reliability data are essential in probabilistic safety assessment, with component reliability parameters being particularly important. Component failure data which is plant specific would be most appropriate but this is rather limited. However, similar components are used in different designs. Generic data, that is all data that is not plant specific to the plant being analyzed but which relates to components more generally, is important. The International Atomic Energy Agency has compiled the Generic Component Reliability Data Base from data available in the open literature. It is part of the IAEA computer code package for fault/event tree analysis. The Data Base contains 1010 different records including most of the components used in probabilistic safety analyses of nuclear power plants. The data base input was quality controlled and data sources noted. The data compilation procedure and problems associated with using generic data are explained. (UK)

  1. Adaptive operational modal identification for slow linear time-varying structures based on frozen-in coefficient method and limited memory recursive principal component analysis

    Science.gov (United States)

    Wang, Cheng; Guan, Wei; Wang, J. Y.; Zhong, Bineng; Lai, Xiongming; Chen, Yewang; Xiang, Liang

    2018-02-01

    To adaptively identify the transient modal parameters for linear weakly damped structures with slow time-varying characteristics under unmeasured stationary random ambient loads, this paper proposes a novel operational modal analysis (OMA) method based on the frozen-in coefficient method and limited memory recursive principal component analysis (LMRPCA). In the modal coordinate, the random vibration response signals of mechanical weakly damped structures can be decomposed into the inner product of modal shapes and modal responses, from which the natural frequencies and damping ratios can be well acquired by single-degree-of-freedom (SDOF) identification approach such as FFT. Hence, for the OMA method based on principal component analysis (PCA), it becomes very crucial to examine the relation between the transformational matrix and the modal shapes matrix, to find the association between the principal components (PCs) matrix and the modal responses matrix, and to turn the operational modal parameter identification problem into PCA of the stationary random vibration response signals of weakly damped mechanical structures. Based on the theory of "time-freezing", the method of frozen-in coefficient, and the assumption of "short time invariant" and "quasistationary", the non-stationary random response signals of the weakly damped and slow linear time-varying structures (LTV) can approximately be seen as the stationary random response time series of weakly damped and linear time invariant structures (LTI) in a short interval. Thus, the adaptive identification of time-varying operational modal parameters is turned into decompositing the PCs of stationary random vibration response signals subsection of weakly damped mechanical structures after choosing an appropriate limited memory window. Finally, a three-degree-of-freedom (DOF) structure with weakly damped and slow time-varying mass is presented to illustrate this method of identification. Results show that the LMRPCA

  2. The Component-Based Application for GAMESS

    Energy Technology Data Exchange (ETDEWEB)

    Peng, Fang [Iowa State Univ., Ames, IA (United States)

    2007-01-01

    GAMESS, a quantum chetnistry program for electronic structure calculations, has been freely shared by high-performance application scientists for over twenty years. It provides a rich set of functionalities and can be run on a variety of parallel platforms through a distributed data interface. While a chemistry computation is sophisticated and hard to develop, the resource sharing among different chemistry packages will accelerate the development of new computations and encourage the cooperation of scientists from universities and laboratories. Common Component Architecture (CCA) offers an enviromnent that allows scientific packages to dynamically interact with each other through components, which enable dynamic coupling of GAMESS with other chetnistry packages, such as MPQC and NWChem. Conceptually, a cotnputation can be constructed with "plug-and-play" components from scientific packages and require more than componentizing functions/subroutines of interest, especially for large-scale scientific packages with a long development history. In this research, we present our efforts to construct cotnponents for GAMESS that conform to the CCA specification. The goal is to enable the fine-grained interoperability between three quantum chemistry programs, GAMESS, MPQC and NWChem, via components. We focus on one of the three packages, GAMESS; delineate the structure of GAMESS computations, followed by our approaches to its component development. Then we use GAMESS as the driver to interoperate integral components from the other tw"o packages, arid show the solutions for interoperability problems along with preliminary results. To justify the versatility of the design, the Tuning and Analysis Utility (TAU) components have been coupled with GAMESS and its components, so that the performance of GAMESS and its components may be analyzed for a wide range of systetn parameters.

  3. The Utility of Job Dimensions Based on Form B of the Position Analysis Questionnaire (PAQ) in a Job Component Validation Model. Report No. 5.

    Science.gov (United States)

    Marquardt, Lloyd D.; McCormick, Ernest J.

    The study involved the use of a structured job analysis instrument called the Position Analysis Questionnaire (PAQ) as the direct basis for the establishment of the job component validity of aptitude tests (that is, a procedure for estimating the aptitude requirements for jobs strictly on the basis of job analysis data). The sample of jobs used…

  4. 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.

  5. Efficient three-dimensional resist profile-driven source mask optimization optical proximity correction based on Abbe-principal component analysis and Sylvester equation

    Science.gov (United States)

    Lin, Pei-Chun; Yu, Chun-Chang; Chen, Charlie Chung-Ping

    2015-01-01

    As one of the critical stages of a very large scale integration fabrication process, postexposure bake (PEB) plays a crucial role in determining the final three-dimensional (3-D) profiles and lessening the standing wave effects. However, the full 3-D chemically amplified resist simulation is not widely adopted during the postlayout optimization due to the long run-time and huge memory usage. An efficient simulation method is proposed to simulate the PEB while considering standing wave effects and resolution enhancement techniques, such as source mask optimization and subresolution assist features based on the Sylvester equation and Abbe-principal component analysis method. Simulation results show that our algorithm is 20× faster than the conventional Gaussian convolution method.

  6. 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 ...

  7. 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 ...

  8. 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...

  9. Multichannel Signals Reconstruction Based on Tunable Q-Factor Wavelet Transform-Morphological Component Analysis and Sparse Bayesian Iteration for Rotating Machines

    Directory of Open Access Journals (Sweden)

    Qing Li

    2018-04-01

    Full Text Available High-speed remote transmission and large-capacity data storage are difficult issues in signals acquisition of rotating machines condition monitoring. To address these concerns, a novel multichannel signals reconstruction approach based on tunable Q-factor wavelet transform-morphological component analysis (TQWT-MCA and sparse Bayesian iteration algorithm combined with step-impulse dictionary is proposed under the frame of compressed sensing (CS. To begin with, to prevent the periodical impulses loss and effectively separate periodical impulses from the external noise and additive interference components, the TQWT-MCA method is introduced to divide the raw vibration signal into low-resonance component (LRC, i.e., periodical impulses and high-resonance component (HRC, thus, the periodical impulses are preserved effectively. Then, according to the amplitude range of generated LRC, the step-impulse dictionary atom is designed to match the physical structure of periodical impulses. Furthermore, the periodical impulses and HRC are reconstructed by the sparse Bayesian iteration combined with step-impulse dictionary, respectively, finally, the final reconstructed raw signals are obtained by adding the LRC and HRC, meanwhile, the fidelity of the final reconstructed signals is tested by the envelop spectrum and error analysis, respectively. In this work, the proposed algorithm is applied to simulated signal and engineering multichannel signals of a gearbox with multiple faults. Experimental results demonstrate that the proposed approach significantly improves the reconstructive accuracy compared with the state-of-the-art methods such as non-convex Lq (q = 0.5 regularization, spatiotemporal sparse Bayesian learning (SSBL and L1-norm, etc. Additionally, the processing time, i.e., speed of storage and transmission has increased dramatically, more importantly, the fault characteristics of the gearbox with multiple faults are detected and saved, i.e., the

  10. Comparison of thermal behavior of natural and hot-washed sisal fibers based on their main components: Cellulose, xylan and lignin. TG-FTIR analysis of volatile products

    Energy Technology Data Exchange (ETDEWEB)

    Benítez-Guerrero, Mónica, E-mail: monica_benitez_guerrero@yahoo.es [Departamento de Ingeniería Civil, Materiales y Fabricación, Universidad de Málaga, Escuela de Ingenierías, C/ Dr. Ortiz Ramos s/n, Campus Teatinos, 29071 Málaga (Spain); López-Beceiro, Jorge [Departamento de Ingeniería Industrial II, Escola Politécnica Superior, Universidade da Coruña, Avda. Mendizábal, 15403 Ferrol (Spain); Sánchez-Jiménez, Pedro E. [Instituto de Ciencia de Materiales de Sevilla, CSIC-Universidad de Sevilla, C/ Américo Vespucio 49, 41092 Sevilla (Spain); Pascual-Cosp, José [Departamento de Ingeniería Civil, Materiales y Fabricación, Universidad de Málaga, Escuela de Ingenierías, C/ Dr. Ortiz Ramos s/n, Campus Teatinos, 29071 Málaga (Spain)

    2014-04-01

    Highlights: • Thermal decomposition of sisal fibers has been discussed. • Decompositions of lignocellulosic components and sisal are compared by TXRD and TG-FTIR. • Hot washing reduces the temperature range in which sisal decomposition occurs. • Sisal cellulose decomposition goes by an alternative route to levoglucosan generation. - Abstract: This paper presents in a comprehensive way the thermal behavior of natural and hot-washed sisal fibers, based on the fundamental components of lignocellulosic materials: cellulose, xylan and lignin. The research highlights the influence exerted on the thermal stability of sisal fibers by other constituents such as non-cellulosic polysaccharides (NCP) and mineral matter. Thermal changes were investigated by thermal X-ray diffraction (TXRD), analyzing the crystallinity index (%Ic) of cellulosic samples, and by simultaneous thermogravimetric and differential thermal analysis coupled with Fourier-transformed infrared spectrometry (TG/DTA-FTIR), which allowed to examine the evolution of the main volatile compounds evolved during the degradation under inert and oxidizing atmospheres. The work demonstrates the potential of this technique to elucidate different steps during the thermal decomposition of sisal, providing extensible results to other lignocellulosic fibers, through the analysis of the evolution of CO{sub 2}, CO, H{sub 2}O, CH{sub 4}, acetic acid, formic acid, methanol, formaldehyde and 2-butanone, and comparing it with the volatile products from pyrolysis of the biomass components. The hydroxyacetaldehyde detected during pyrolysis of sisal is indicative of an alternative route to that of levoglucosan, generated during cellulose pyrolysis. Hot-washing at 75 °C mostly extracts non-cellulosic components of low decomposition temperature, and reduces the range of temperature in which sisal decomposition occurs, causing a retard in the pyrolysis stage and increasing Tb{sub NCP} and Tb{sub CEL}, temperatures at the

  11. Cloud Based Big Data Infrastructure: Architectural Components and Automated Provisioning

    OpenAIRE

    Demchenko, Yuri; Turkmen, Fatih; Blanchet, Christophe; Loomis, Charles; Laat, Caees de

    2016-01-01

    This paper describes the general architecture and functional components of the cloud based Big Data Infrastructure (BDI). The proposed BDI architecture is based on the analysis of the emerging Big Data and data intensive technologies and supported by the definition of the Big Data Architecture Framework (BDAF) that defines the following components of the Big Data technologies: Big Data definition, Data Management including data lifecycle and data structures, Big Data Infrastructure (generical...

  12. PCA: Principal Component Analysis for spectra modeling

    Science.gov (United States)

    Hurley, Peter D.; Oliver, Seb; Farrah, Duncan; Wang, Lingyu; Efstathiou, Andreas

    2012-07-01

    The mid-infrared spectra of ultraluminous infrared galaxies (ULIRGs) contain a variety of spectral features that can be used as diagnostics to characterize the spectra. However, such diagnostics are biased by our prior prejudices on the origin of the features. Moreover, by using only part of the spectrum they do not utilize the full information content of the spectra. Blind statistical techniques such as principal component analysis (PCA) consider the whole spectrum, find correlated features and separate them out into distinct components. This code, written in IDL, classifies principal components of IRS spectra to define a new classification scheme using 5D Gaussian mixtures modelling. The five PCs and average spectra for the four classifications to classify objects are made available with the code.

  13. 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.

  14. Component-Based Cartoon Face Generation

    Directory of Open Access Journals (Sweden)

    Saman Sepehri Nejad

    2016-11-01

    Full Text Available In this paper, we present a cartoon face generation method that stands on a component-based facial feature extraction approach. Given a frontal face image as an input, our proposed system has the following stages. First, face features are extracted using an extended Active Shape Model. Outlines of the components are locally modified using edge detection, template matching and Hermit interpolation. This modification enhances the diversity of output and accuracy of the component matching required for cartoon generation. Second, to bring cartoon-specific features such as shadows, highlights and, especially, stylish drawing, an array of various face photographs and corresponding hand-drawn cartoon faces are collected. These cartoon templates are automatically decomposed into cartoon components using our proposed method for parameterizing cartoon samples, which is fast and simple. Then, using shape matching methods, the appropriate cartoon component is selected and deformed to fit the input face. Finally, a cartoon face is rendered in a vector format using the rendering rules of the selected template. Experimental results demonstrate effectiveness of our approach in generating life-like cartoon faces.

  15. Selection of components based on their importance

    International Nuclear Information System (INIS)

    Stvan, F.

    2004-12-01

    A proposal is presented for sorting components of the Dukovany nuclear power plant with respect to their importance. The classification scheme includes property priority, property criticality and property structure. Each area has its criteria with weight coefficients to calculate the importance of each component by the Risk Priority Number method. The aim of the process is to generate a list of components in order of operating and safety importance, which will help spend funds to ensure operation and safety in an optimal manner. This proposal is linked to a proposal for a simple database which should serve to enter information and perform assessments. The present stage focused on a safety assessment of components categorized in safety classes BT1, BT2 and BT3 pursuant to Decree No. 76. Assessment was performed based ona PSE study for Level 1. The database includes inputs for entering financial data, which are represented by a potential damage resulting from the given failure and by the loss of MWh in financial terms. In a next input, the failure incidence intensity and time of correction can be entered. Information regarding the property structure, represented by the degree of backup and reparability of the component, is the last input available

  16. 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.

  17. Scientific data base for safeguards components

    International Nuclear Information System (INIS)

    Hall, R.C.; Jones, R.D.

    1978-01-01

    The need to store and maintain vast amounts of data and the desire to avoid nonfunctional redundancy have provided an impetus for modern data base technology. Large-scale data base management systems (DBMS) have emerged during the past two decades evolving from earlier generalized file processing systems. This evolution has primarily involved certain business applications (e.g., production control, payroll, order entry) because of their high volume data processing characterization. Current data base technology, however, is becoming increasingly concerned with generality. Many diverse applications, including scientific ones, are benefiting from the generalized data base management software which has resulted. The concept of a data base management system is examined. The three common models which have been proposed for organizing data and relationships are identified: the network model, the hierarchical model, and the relational model. A specific implementation using a hierarchical data base management system is described. This is the data base for safeguards components which has been developed at Sandia Laboratories using the System 2000 developed by MRI Systems Corporation. Its organization, components, and functions are presented. The various interfaces it permits to user programs (e.g., safeguards automated facility evaluation software) and interactive terminal users are described

  18. 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.

  19. 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…

  20. Efficient mining of myxobacterial metabolite profiles enabled by liquid chromatography-electrospray ionisation-time-of-flight mass spectrometry and compound-based principal component analysis

    International Nuclear Information System (INIS)

    Krug, Daniel; Zurek, Gabriela; Schneider, Birgit; Garcia, Ronald; Mueller, Rolf

    2008-01-01

    Bacteria producing secondary metabolites are an important source of natural products with highly diverse structures and biological activities. Developing methods to efficiently mine procaryotic secondary metabolomes for the presence of potentially novel natural products is therefore of considerable interest. Modern mass spectrometry-coupled liquid chromatography can effectively capture microbial metabolic diversity with ever improving sensitivity and accuracy. In addition, computational and statistical tools increasingly enable the targeted analysis and exploration of information-rich LC-MS datasets. In this article, we describe the use of such techniques for the characterization of myxobacterial secondary metabolomes. Using accurate mass data from high-resolution ESI-TOF measurements, target screening has facilitated the rapid identification of known myxobacterial metabolites in extracts from nine Myxococcus species. Furthermore, principal component analysis (PCA), implementing an advanced compound-based bucketing approach, readily revealed the presence of further compounds which contribute to variation among the metabolite profiles under investigation. The generation of molecular formulae for putative novel compounds with high confidence due to evaluation of both exact mass position and isotopic pattern, is exemplified as an important key for de-replication and prioritization of candidates for further characterization

  1. 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

  2. Online Monitoring of Water-Quality Anomaly in Water Distribution Systems Based on Probabilistic Principal Component Analysis by UV-Vis Absorption Spectroscopy

    Directory of Open Access Journals (Sweden)

    Dibo Hou

    2014-01-01

    Full Text Available This study proposes a probabilistic principal component analysis- (PPCA- based method for online monitoring of water-quality contaminant events by UV-Vis (ultraviolet-visible spectroscopy. The purpose of this method is to achieve fast and sound protection against accidental and intentional contaminate injection into the water distribution system. The method is achieved first by properly imposing a sliding window onto simultaneously updated online monitoring data collected by the automated spectrometer. The PPCA algorithm is then executed to simplify the large amount of spectrum data while maintaining the necessary spectral information to the largest extent. Finally, a monitoring chart extensively employed in fault diagnosis field methods is used here to search for potential anomaly events and to determine whether the current water-quality is normal or abnormal. A small-scale water-pipe distribution network is tested to detect water contamination events. The tests demonstrate that the PPCA-based online monitoring model can achieve satisfactory results under the ROC curve, which denotes a low false alarm rate and high probability of detecting water contamination events.

  3. 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.

  4. 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

  5. WE-DE-201-04: Cross Validation of Knowledge-Based Treatment Planning for Prostate LDR Brachytherapy Using Principle Component Analysis

    Energy Technology Data Exchange (ETDEWEB)

    Roper, J; Ghavidel, B; Godette, K; Schreibmann, E [Winship Cancer Institute of Emory University, GA (United States); Chanyavanich, V [Rocky Mountain Cancer Centers, CO (United States)

    2016-06-15

    Purpose: To validate a knowledge-based algorithm for prostate LDR brachytherapy treatment planning. Methods: A dataset of 100 cases was compiled from an active prostate seed implant service. Cases were randomized into 10 subsets. For each subset, the 90 remaining library cases were registered to a common reference frame and then characterized on a point by point basis using principle component analysis (PCA). Each test case was converted to PCA vectors using the same process and compared with each library case using a Mahalanobis distance to evaluate similarity. Rank order PCA scores were used to select the best-matched library case. The seed arrangement was extracted from the best-matched case and used as a starting point for planning the test case. Any subsequent modifications were recorded that required input from a treatment planner to achieve V100>95%, V150<60%, V200<20%. To simulate operating-room planning constraints, seed activity was held constant, and the seed count could not increase. Results: The computational time required to register test-case contours and evaluate PCA similarity across the library was 10s. Preliminary analysis of 2 subsets shows that 9 of 20 test cases did not require any seed modifications to obtain an acceptable plan. Five test cases required fewer than 10 seed modifications or a grid shift. Another 5 test cases required approximately 20 seed modifications. An acceptable plan was not achieved for 1 outlier, which was substantially larger than its best match. Modifications took between 5s and 6min. Conclusion: A knowledge-based treatment planning algorithm for prostate LDR brachytherapy is being cross validated using 100 prior cases. Preliminary results suggest that for this size library, acceptable plans can be achieved without planner input in about half of the cases while varying amounts of planner input are needed in remaining cases. Computational time and planning time are compatible with clinical practice.

  6. Base isolation strategies for structures and components

    International Nuclear Information System (INIS)

    Varma, Veto; Reddy, G.R.; Vaze, K.K.; Kushwaha, H.S.

    2003-08-01

    In the present report the effect of laminated rubber bearing (LRB) system on the dynamic response of the structure was studied. A LRB system was designed and tested in the laboratory for its dynamic characteristics. Finite element analysis was also performed and based on this analysis, isolator for PHWR nuclear power plant was designed. Analysis of the building was performed with and without isolator. Comparison of responses was made in terms of frequencies, accelerations and displacements and floor response spectra. (author)

  7. Improving the use of principal component analysis to reduce physiological noise and motion artifacts to increase the sensitivity of task-based fMRI.

    Science.gov (United States)

    Soltysik, David A; Thomasson, David; Rajan, Sunder; Biassou, Nadia

    2015-02-15

    Functional magnetic resonance imaging (fMRI) time series are subject to corruption by many noise sources, especially physiological noise and motion. Researchers have developed many methods to reduce physiological noise, including RETROICOR, which retroactively removes cardiac and respiratory waveforms collected during the scan, and CompCor, which applies principal components analysis (PCA) to remove physiological noise components without any physiological monitoring during the scan. We developed four variants of the CompCor method. The optimized CompCor method applies PCA to time series in a noise mask, but orthogonalizes each component to the BOLD response waveform and uses an algorithm to determine a favorable number of components to use as "nuisance regressors." Whole brain component correction (WCompCor) is similar, except that it applies PCA to time-series throughout the whole brain. Low-pass component correction (LCompCor) identifies low-pass filtered components throughout the brain, while high-pass component correction (HCompCor) identifies high-pass filtered components. We compared the new methods with the original CompCor method by examining the resulting functional contrast-to-noise ratio (CNR), sensitivity, and specificity. (1) The optimized CompCor method increased the CNR and sensitivity compared to the original CompCor method and (2) the application of WCompCor yielded the best improvement in the CNR and sensitivity. The sensitivity of the optimized CompCor, WCompCor, and LCompCor methods exceeded that of the original CompCor method. However, regressing noise signals showed a paradoxical consequence of reducing specificity for all noise reduction methods attempted. Published by Elsevier B.V.

  8. 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.

  9. Principal component analysis-based imaging angle determination for 3D motion monitoring using single-slice on-board imaging.

    Science.gov (United States)

    Chen, Ting; Zhang, Miao; Jabbour, Salma; Wang, Hesheng; Barbee, David; Das, Indra J; Yue, Ning

    2018-04-10

    Through-plane motion introduces uncertainty in three-dimensional (3D) motion monitoring when using single-slice on-board imaging (OBI) modalities such as cine MRI. We propose a principal component analysis (PCA)-based framework to determine the optimal imaging plane to minimize the through-plane motion for single-slice imaging-based motion monitoring. Four-dimensional computed tomography (4DCT) images of eight thoracic cancer patients were retrospectively analyzed. The target volumes were manually delineated at different respiratory phases of 4DCT. We performed automated image registration to establish the 4D respiratory target motion trajectories for all patients. PCA was conducted using the motion information to define the three principal components of the respiratory motion trajectories. Two imaging planes were determined perpendicular to the second and third principal component, respectively, to avoid imaging with the primary principal component of the through-plane motion. Single-slice images were reconstructed from 4DCT in the PCA-derived orthogonal imaging planes and were compared against the traditional AP/Lateral image pairs on through-plane motion, residual error in motion monitoring, absolute motion amplitude error and the similarity between target segmentations at different phases. We evaluated the significance of the proposed motion monitoring improvement using paired t test analysis. The PCA-determined imaging planes had overall less through-plane motion compared against the AP/Lateral image pairs. For all patients, the average through-plane motion was 3.6 mm (range: 1.6-5.6 mm) for the AP view and 1.7 mm (range: 0.6-2.7 mm) for the Lateral view. With PCA optimization, the average through-plane motion was 2.5 mm (range: 1.3-3.9 mm) and 0.6 mm (range: 0.2-1.5 mm) for the two imaging planes, respectively. The absolute residual error of the reconstructed max-exhale-to-inhale motion averaged 0.7 mm (range: 0.4-1.3 mm, 95% CI: 0.4-1.1 mm) using

  10. A Genealogical Interpretation of Principal Components Analysis

    Science.gov (United States)

    McVean, Gil

    2009-01-01

    Principal components analysis, PCA, is a statistical method commonly used in population genetics to identify structure in the distribution of genetic variation across geographical location and ethnic background. However, while the method is often used to inform about historical demographic processes, little is known about the relationship between fundamental demographic parameters and the projection of samples onto the primary axes. Here I show that for SNP data the projection of samples onto the principal components can be obtained directly from considering the average coalescent times between pairs of haploid genomes. The result provides a framework for interpreting PCA projections in terms of underlying processes, including migration, geographical isolation, and admixture. I also demonstrate a link between PCA and Wright's fst and show that SNP ascertainment has a largely simple and predictable effect on the projection of samples. Using examples from human genetics, I discuss the application of these results to empirical data and the implications for inference. PMID:19834557

  11. Imprecise system reliability and component importance based on survival signature

    International Nuclear Information System (INIS)

    Feng, Geng; Patelli, Edoardo; Beer, Michael; Coolen, Frank P.A.

    2016-01-01

    The concept of the survival signature has recently attracted increasing attention for performing reliability analysis on systems with multiple types of components. It opens a new pathway for a structured approach with high computational efficiency based on a complete probabilistic description of the system. In practical applications, however, some of the parameters of the system might not be defined completely due to limited data, which implies the need to take imprecisions of component specifications into account. This paper presents a methodology to include explicitly the imprecision, which leads to upper and lower bounds of the survival function of the system. In addition, the approach introduces novel and efficient component importance measures. By implementing relative importance index of each component without or with imprecision, the most critical component in the system can be identified depending on the service time of the system. Simulation method based on survival signature is introduced to deal with imprecision within components, which is precise and efficient. Numerical example is presented to show the applicability of the approach for systems. - Highlights: • Survival signature is a novel way for system reliability and component importance • High computational efficiency based on a complete description of system. • Include explicitly the imprecision, which leads to bounds of the survival function. • A novel relative importance index is proposed as importance measure. • Allows to identify critical components depending on the service time of the system.

  12. Damage detection methodology under variable load conditions based on strain field pattern recognition using FBGs, nonlinear principal component analysis, and clustering techniques

    Science.gov (United States)

    Sierra-Pérez, Julián; Torres-Arredondo, M.-A.; Alvarez-Montoya, Joham

    2018-01-01

    Structural health monitoring consists of using sensors integrated within structures together with algorithms to perform load monitoring, damage detection, damage location, damage size and severity, and prognosis. One possibility is to use strain sensors to infer structural integrity by comparing patterns in the strain field between the pristine and damaged conditions. In previous works, the authors have demonstrated that it is possible to detect small defects based on strain field pattern recognition by using robust machine learning techniques. They have focused on methodologies based on principal component analysis (PCA) and on the development of several unfolding and standardization techniques, which allow dealing with multiple load conditions. However, before a real implementation of this approach in engineering structures, changes in the strain field due to conditions different from damage occurrence need to be isolated. Since load conditions may vary in most engineering structures and promote significant changes in the strain field, it is necessary to implement novel techniques for uncoupling such changes from those produced by damage occurrence. A damage detection methodology based on optimal baseline selection (OBS) by means of clustering techniques is presented. The methodology includes the use of hierarchical nonlinear PCA as a nonlinear modeling technique in conjunction with Q and nonlinear-T 2 damage indices. The methodology is experimentally validated using strain measurements obtained by 32 fiber Bragg grating sensors bonded to an aluminum beam under dynamic bending loads and simultaneously submitted to variations in its pitch angle. The results demonstrated the capability of the methodology for clustering data according to 13 different load conditions (pitch angles), performing the OBS and detecting six different damages induced in a cumulative way. The proposed methodology showed a true positive rate of 100% and a false positive rate of 1.28% for a

  13. Comprehensive Quality Assessment Based Specific Chemical Profiles for Geographic and Tissue Variation in Gentiana rigescens Using HPLC and FTIR Method Combined with Principal Component Analysis

    Directory of Open Access Journals (Sweden)

    Jie Li

    2017-12-01

    Full Text Available Roots, stems, leaves, and flowers of Longdan (Gentiana rigescens Franch. ex Hemsl were collected from six geographic origins of Yunnan Province (n = 240 to implement the quality assessment based on contents of gentiopicroside, loganic acid, sweroside and swertiamarin and chemical profile using HPLC-DAD and FTIR method combined with principal component analysis (PCA. The content of gentiopicroside (major iridoid glycoside was the highest in G. rigescens, regardless of tissue and geographic origin. The level of swertiamarin was the lowest, even unable to be detected in samples from Kunming and Qujing. Significant correlations (p < 0.05 between gentiopicroside, loganic acid, sweroside, and swertiamarin were found at inter- or intra-tissues, which were highly depended on geographic origins, indicating the influence of environmental conditions on the conversion and transport of secondary metabolites in G. rigescens. Furthermore, samples were reasonably classified as three clusters along large producing areas where have similar climate conditions, characterized by carbohydrates, phenols, benzoates, terpenoids, aliphatic alcohols, aromatic hydrocarbons, and so forth. The present work provided global information on the chemical profile and contents of major iridoid glycosides in G. rigescens originated from six different origins, which is helpful for controlling quality of herbal medicines systematically.

  14. Impact of parameter fluctuations on the performance of ethanol precipitation in production of Re Du Ning Injections, based on HPLC fingerprints and principal component analysis.

    Science.gov (United States)

    Sun, Li-Qiong; Wang, Shu-Yao; Li, Yan-Jing; Wang, Yong-Xiang; Wang, Zhen-Zhong; Huang, Wen-Zhe; Wang, Yue-Sheng; Bi, Yu-An; Ding, Gang; Xiao, Wei

    2016-01-01

    The present study was designed to determine the relationships between the performance of ethanol precipitation and seven process parameters in the ethanol precipitation process of Re Du Ning Injections, including concentrate density, concentrate temperature, ethanol content, flow rate and stir rate in the addition of ethanol, precipitation time, and precipitation temperature. Under the experimental and simulated production conditions, a series of precipitated resultants were prepared by changing these variables one by one, and then examined by HPLC fingerprint analyses. Different from the traditional evaluation model based on single or a few constituents, the fingerprint data of every parameter fluctuation test was processed with Principal Component Analysis (PCA) to comprehensively assess the performance of ethanol precipitation. Our results showed that concentrate density, ethanol content, and precipitation time were the most important parameters that influence the recovery of active compounds in precipitation resultants. The present study would provide some reference for pharmaceutical scientists engaged in research on pharmaceutical process optimization and help pharmaceutical enterprises adapt a scientific and reasonable cost-effective approach to ensure the batch-to-batch quality consistency of the final products. Copyright © 2016 China Pharmaceutical University. Published by Elsevier B.V. All rights reserved.

  15. Comprehensive quality assessment based specific chemical profiles for geographic and tissue variation in Gentiana rigescens using HPLC and FTIR method combined with principal component analysis

    Science.gov (United States)

    Li, Jie; Zhang, Ji; Zhao, Yan-Li; Huang, Heng-Yu; Wang, Yuan-Zhong

    2017-12-01

    Roots, stems, leaves and flowers of Longdan (Gentiana rigescens Franch. ex Hemsl) were collected from six geographic origins of Yunnan Province (n = 240) to implement the quality assessment based on contents of gentiopicroside, loganic acid, sweroside and swertiamarin and chemical profile using HPLC-DAD and FTIR method combined with principal component analysis (PCA). The content of gentiopicroside (major iridoid glycoside) was the highest in G. rigescens, regardless of tissue and geographic origin. The level of swertiamarin was the lowest, even unable to be detected in samples from Kunming and Qujing. Significant correlations (p < 0.05) between gentiopicroside, loganic acid, sweroside and swertiamarin were found at inter- or intra-tissues, which were highly depended on geographic origins, indicating the influence of environmental conditions on the conversion and transport of secondary metabolites in G. rigescens. Furthermore, samples were reasonably classified as three clusters along large producing areas where have similar climate conditions, characterized by carbohydrates, phenols, benzoates, terpenoids, aliphatic alcohols, aromatic hydrocarbons, and so forth. The present work provided global information on the chemical profile and contents of major iridoid glycosides in G. rigescens originated from six different origins, which is helpful for controlling quality of herbal medicines systematically.

  16. Collagen-based proteinaceous binder-pigment interaction study under UV ageing conditions by MALDI-TOF-MS and principal component analysis.

    Science.gov (United States)

    Romero-Pastor, Julia; Navas, Natalia; Kuckova, Stepanka; Rodríguez-Navarro, Alejandro; Cardell, Carolina

    2012-03-01

    This study focuses on acquiring information on the degradation process of proteinaceous binders due to ultra violet (UV) radiation and possible interactions owing to the presence of historical mineral pigments. With this aim, three different paint model samples were prepared according to medieval recipes, using rabbit glue as proteinaceus binders. One of these model samples contained only the binder, and the other two were prepared by mixing each of the pigments (cinnabar or azurite) with the binder (glue tempera model samples). The model samples were studied by applying Principal Component Analysis (PCA) to their mass spectra obtained with Matrix-Assisted Laser Desorption/Ionization-Time of Flight Mass Spectrometry (MALDI-TOF-MS). The complementary use of Fourier Transform Infrared Spectroscopy to study conformational changes of secondary structure of the proteinaceous binder is also proposed. Ageing effects on the model samples after up to 3000 h of UV irradiation were periodically analyzed by the proposed approach. PCA on MS data proved capable of identifying significant changes in the model samples, and the results suggested different aging behavior based on the pigment present. This research represents the first attempt to use this approach (PCA on MALDI-TOF-MS data) in the field of Cultural Heritage and demonstrates the potential benefits in the study of proteinaceous artistic materials for purposes of conservation and restoration. Copyright © 2012 John Wiley & Sons, Ltd.

  17. Nuclear component design ontology building based on ASME codes

    International Nuclear Information System (INIS)

    Bao Shiyi; Zhou Yu; He Shuyan

    2005-01-01

    The adoption of ontology analysis in the study of concept knowledge acquisition and representation for the nuclear component design process based on computer-supported cooperative work (CSCW) makes it possible to share and reuse numerous concept knowledge of multi-disciplinary domains. A practical ontology building method is accordingly proposed based on Protege knowledge model in combination with both top-down and bottom-up approaches together with Formal Concept Analysis (FCA). FCA exhibits its advantages in the way it helps establish and improve taxonomic hierarchy of concepts and resolve concept conflict occurred in modeling multi-disciplinary domains. With Protege-3.0 as the ontology building tool, a nuclear component design ontology based ASME codes is developed by utilizing the ontology building method. The ontology serves as the basis to realize concept knowledge sharing and reusing of nuclear component design. (authors)

  18. Future technological and economic performance of IGCC and FT production facilities with and without CO2 capture: Combining component based learning curve and bottom-up analysis

    NARCIS (Netherlands)

    Knoope, M.M.J.; Meerman, J.C.; Ramirez, C.A.; Faaij, A.P.C.

    2013-01-01

    This study aims to investigate the technological and economic prospects of integrated gasification facilities for power (IGCC) and Fischer–Tropsch (FT) liquid production with and without CCS over time. For this purpose, a component based experience curve was constructed and applied to identify the

  19. Independent Component Analysis in Multimedia Modeling

    DEFF Research Database (Denmark)

    Larsen, Jan

    2003-01-01

    largely refers to text, images/video, audio and combinations of such data. We review a number of applications within single and combined media with the hope that this might provide inspiration for further research in this area. Finally, we provide a detailed presentation of our own recent work on modeling......Modeling of multimedia and multimodal data becomes increasingly important with the digitalization of the world. The objective of this paper is to demonstrate the potential of independent component analysis and blind sources separation methods for modeling and understanding of multimedia data, which...

  20. Blind source separation dependent component analysis

    CERN Document Server

    Xiang, Yong; Yang, Zuyuan

    2015-01-01

    This book provides readers a complete and self-contained set of knowledge about dependent source separation, including the latest development in this field. The book gives an overview on blind source separation where three promising blind separation techniques that can tackle mutually correlated sources are presented. The book further focuses on the non-negativity based methods, the time-frequency analysis based methods, and the pre-coding based methods, respectively.

  1. 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)

  2. 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.

  3. Reformulating Component Identification as Document Analysis Problem

    NARCIS (Netherlands)

    Gross, H.G.; Lormans, M.; Zhou, J.

    2007-01-01

    One of the first steps of component procurement is the identification of required component features in large repositories of existing components. On the highest level of abstraction, component requirements as well as component descriptions are usually written in natural language. Therefore, we can

  4. Prediction of sensitivity to gefitinib/erlotinib for EGFR mutations in NSCLC based on structural interaction fingerprints and multilinear principal component analysis.

    Science.gov (United States)

    Zou, Bin; Lee, Victor H F; Yan, Hong

    2018-03-07

    Non-small cell lung cancer (NSCLC) with activating EGFR mutations, especially exon 19 deletions and the L858R point mutation, is particularly responsive to gefitinib and erlotinib. However, the sensitivity varies for less common and rare EGFR mutations. There are various explanations for the low sensitivity of EGFR exon 20 insertions and the exon 20 T790 M point mutation to gefitinib/erlotinib. However, few studies discuss, from a structural perspective, why less common mutations, like G719X and L861Q, have moderate sensitivity to gefitinib/erlotinib. To decode the drug sensitivity/selectivity of EGFR mutants, it is important to analyze the interaction between EGFR mutants and EGFR inhibitors. In this paper, the 30 most common EGFR mutants were selected and the technique of protein-ligand interaction fingerprint (IFP) was applied to analyze and compare the binding modes of EGFR mutant-gefitinib/erlotinib complexes. Molecular dynamics simulations were employed to obtain the dynamic trajectory and a matrix of IFPs for each EGFR mutant-inhibitor complex. Multilinear Principal Component Analysis (MPCA) was applied for dimensionality reduction and feature selection. The selected features were further analyzed for use as a drug sensitivity predictor. The results showed that the accuracy of prediction of drug sensitivity was very high for both gefitinib and erlotinib. Targeted Projection Pursuit (TPP) was used to show that the data points can be easily separated based on their sensitivities to gefetinib/erlotinib. We can conclude that the IFP features of EGFR mutant-TKI complexes and the MPCA-based tensor object feature extraction are useful to predict the drug sensitivity of EGFR mutants. The findings provide new insights for studying and predicting drug resistance/sensitivity of EGFR mutations in NSCLC and can be beneficial to the design of future targeted therapies and innovative drug discovery.

  5. [Classification of Children with Attention-Deficit/Hyperactivity Disorder and Typically Developing Children Based on Electroencephalogram Principal Component Analysis and k-Nearest Neighbor].

    Science.gov (United States)

    Yang, Jiaojiao; Guo, Qian; Li, Wenjie; Wang, Suhong; Zou, Ling

    2016-04-01

    This paper aims to assist the individual clinical diagnosis of children with attention-deficit/hyperactivity disorder using electroencephalogram signal detection method.Firstly,in our experiments,we obtained and studied the electroencephalogram signals from fourteen attention-deficit/hyperactivity disorder children and sixteen typically developing children during the classic interference control task of Simon-spatial Stroop,and we completed electroencephalogram data preprocessing including filtering,segmentation,removal of artifacts and so on.Secondly,we selected the subset electroencephalogram electrodes using principal component analysis(PCA)method,and we collected the common channels of the optimal electrodes which occurrence rates were more than 90%in each kind of stimulation.We then extracted the latency(200~450ms)mean amplitude features of the common electrodes.Finally,we used the k-nearest neighbor(KNN)classifier based on Euclidean distance and the support vector machine(SVM)classifier based on radial basis kernel function to classify.From the experiment,at the same kind of interference control task,the attention-deficit/hyperactivity disorder children showed lower correct response rates and longer reaction time.The N2 emerged in prefrontal cortex while P2 presented in the inferior parietal area when all kinds of stimuli demonstrated.Meanwhile,the children with attention-deficit/hyperactivity disorder exhibited markedly reduced N2 and P2amplitude compared to typically developing children.KNN resulted in better classification accuracy than SVM classifier,and the best classification rate was 89.29%in StI task.The results showed that the electroencephalogram signals were different in the brain regions of prefrontal cortex and inferior parietal cortex between attention-deficit/hyperactivity disorder and typically developing children during the interference control task,which provided a scientific basis for the clinical diagnosis of attention

  6. Numerical analysis of magnetoelastic coupled buckling of fusion reactor components

    International Nuclear Information System (INIS)

    Demachi, K.; Yoshida, Y.; Miya, K.

    1994-01-01

    For a tokamak fusion reactor, it is one of the most important subjects to establish the structural design in which its components can stand for strong magnetic force induced by plasma disruption. A number of magnetostructural analysis of the fusion reactor components were done recently. However, in these researches the structural behavior was calculated based on the small deformation theory where the nonlinearity was neglected. But it is known that some kinds of structures easily exceed the geometrical nonlinearity. In this paper, the deflection and the magnetoelastic buckling load of fusion reactor components during plasma disruption were calculated

  7. Multivariate analysis of the volatile components in tobacco based on infrared-assisted extraction coupled to headspace solid-phase microextraction and gas chromatography-mass spectrometry.

    Science.gov (United States)

    Yang, Yanqin; Pan, Yuanjiang; Zhou, Guojun; Chu, Guohai; Jiang, Jian; Yuan, Kailong; Xia, Qian; Cheng, Changhe

    2016-11-01

    A novel infrared-assisted extraction coupled to headspace solid-phase microextraction followed by gas chromatography with mass spectrometry method has been developed for the rapid determination of the volatile components in tobacco. The optimal extraction conditions for maximizing the extraction efficiency were as follows: 65 μm polydimethylsiloxane-divinylbenzene fiber, extraction time of 20 min, infrared power of 175 W, and distance between the infrared lamp and the headspace vial of 2 cm. Under the optimum conditions, 50 components were found to exist in all ten tobacco samples from different geographical origins. Compared with conventional water-bath heating and nonheating extraction methods, the extraction efficiency of infrared-assisted extraction was greatly improved. Furthermore, multivariate analysis including principal component analysis, hierarchical cluster analysis, and similarity analysis were performed to evaluate the chemical information of these samples and divided them into three classifications, including rich, moderate, and fresh flavors. The above-mentioned classification results were consistent with the sensory evaluation, which was pivotal and meaningful for tobacco discrimination. As a simple, fast, cost-effective, and highly efficient method, the infrared-assisted extraction coupled to headspace solid-phase microextraction technique is powerful and promising for distinguishing the geographical origins of the tobacco samples coupled to suitable chemometrics. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  8. Nonlinear principal component analysis and its applications

    CERN Document Server

    Mori, Yuichi; Makino, Naomichi

    2016-01-01

    This book expounds the principle and related applications of nonlinear principal component analysis (PCA), which is useful method to analyze mixed measurement levels data. In the part dealing with the principle, after a brief introduction of ordinary PCA, a PCA for categorical data (nominal and ordinal) is introduced as nonlinear PCA, in which an optimal scaling technique is used to quantify the categorical variables. The alternating least squares (ALS) is the main algorithm in the method. Multiple correspondence analysis (MCA), a special case of nonlinear PCA, is also introduced. All formulations in these methods are integrated in the same manner as matrix operations. Because any measurement levels data can be treated consistently as numerical data and ALS is a very powerful tool for estimations, the methods can be utilized in a variety of fields such as biometrics, econometrics, psychometrics, and sociology. In the applications part of the book, four applications are introduced: variable selection for mixed...

  9. 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.

  10. A systematic approach for component-based software development

    NARCIS (Netherlands)

    Guareis de farias, Cléver; van Sinderen, Marten J.; Ferreira Pires, Luis

    2000-01-01

    Component-based software development enables the construction of software artefacts by assembling prefabricated, configurable and independently evolving building blocks, called software components. This paper presents an approach for the development of component-based software artefacts. This

  11. A Combined Approach for Component-based Software Design

    NARCIS (Netherlands)

    Guareis de farias, Cléver; van Sinderen, Marten J.; Ferreira Pires, Luis; Quartel, Dick; Baldoni, R.

    2001-01-01

    Component-based software development enables the construction of software artefacts by assembling binary units of production, distribution and deployment, the so-called software components. Several approaches addressing component-based development have been proposed recently. Most of these

  12. 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...

  13. 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....

  14. Component analysis of a school-based substance use prevention program in Spain: contributions of problem solving and social skills training content.

    Science.gov (United States)

    Espada, José P; Griffin, Kenneth W; Pereira, Juan R; Orgilés, Mireia; García-Fernández, José M

    2012-02-01

    The objective of the present research was to examine the contribution of two intervention components, social skills training and problem solving training, to alcohol- and drug-related outcomes in a school-based substance use prevention program. Participants included 341 Spanish students from age 12 to 15 who received the prevention program Saluda in one of four experimental conditions: full program, social skills condition, problem solving condition, and a wait-list control group. Students completed self-report surveys at the pretest, posttest and 12-month follow-up assessments. Compared to the wait-list control group, the three intervention conditions produced reductions in alcohol use and intentions to use other substances. The intervention effect size for alcohol use was greatest in magnitude for the full program with all components. Problem-solving skills measured at the follow-up were strongest in the condition that received the full program with all components. We discuss the implications of these findings, including the advantages and disadvantages of implementing tailored interventions to students by selecting intervention components after a skills-based needs assessment.

  15. New approach based on fuzzy logic and principal component analysis for the classification of two-dimensional maps in health and disease. Application to lymphomas.

    Science.gov (United States)

    Marengo, Emilio; Robotti, Elisa; Righetti, Pier Giorgio; Antonucci, Francesca

    2003-07-04

    Two-dimensional (2D) electrophoresis is the most wide spread technique for the separation of proteins in biological systems. This technique produces 2D maps of high complexity, which creates difficulties in the comparison of different samples. The method proposed in this paper for the comparison of different 2D maps can be summarised in four steps: (a) digitalisation of the image; (b) fuzzyfication of the digitalised map in order to consider the variability of the two-dimensional electrophoretic separation; (c) decoding by principal component analysis of the previously obtained fuzzy maps, in order to reduce the system dimensionality; (d) classification analysis (linear discriminant analysis), in order to separate the samples contained in the dataset according to the classes present in said dataset. This method was applied to a dataset constituted by eight samples: four belonging to healthy human lymph-nodes and four deriving from non-Hodgkin lymphomas. The amount of fuzzyfication of the original map is governed by the sigma parameter. The larger the value, the more fuzzy theresulting transformed map. The effect of the fuzzyfication parameter was investigated, the optimal results being obtained for sigma = 1.75 and 2.25. Principal component analysis and linear discriminant analysis allowed the separation of the two classes of samples without any misclassification.

  16. Condition monitoring with Mean field independent components analysis

    DEFF Research Database (Denmark)

    Pontoppidan, Niels Henrik; Sigurdsson, Sigurdur; Larsen, Jan

    2005-01-01

    We discuss condition monitoring based on mean field independent components analysis of acoustic emission energy signals. Within this framework it is possible to formulate a generative model that explains the sources, their mixing and also the noise statistics of the observed signals. By using...... a novelty approach we may detect unseen faulty signals as indeed faulty with high precision, even though the model learns only from normal signals. This is done by evaluating the likelihood that the model generated the signals and adapting a simple threshold for decision. Acoustic emission energy signals...... from a large diesel engine is used to demonstrate this approach. The results show that mean field independent components analysis gives a better detection of fault compared to principal components analysis, while at the same time selecting a more compact model...

  17. Component failure data base of TRIGA reactors

    International Nuclear Information System (INIS)

    Djuricic, M.

    2004-10-01

    This compilation provides failure data such as first criticality, component type description (reactor component, population, cumulative calendar time, cumulative operating time, demands, failure mode, failures, failure rate, failure probability) and specific information on each type of component of TRIGA Mark-II reactors in Austria, Bangladesh, Germany, Finland, Indonesia, Italy, Indonesia, Slovenia and Romania. (nevyjel)

  18. 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.

  19. IAEA's experience in compiling a generic component reliability data base

    International Nuclear Information System (INIS)

    Tomic, B.; Lederman, L.

    1988-01-01

    Reliability data are an essential part of probabilistic safety assessment. The quality of data can determine the quality of the study as a whole. It is obvious that component failure data originated from the plant being analyzed would be most appropriate. However, in few cases complete reliance on plant experience is possible, mainly because of the rather limited operating experience. Nuclear plants, although of different design, often use fairly similar components, so some of the experience could be combined and transferred from one plant to another. In addition information about component failures is available also from experts with knowledge on component design, manufacturing and operation. That bring us to the importance of assessing generic data. (Generic is meant to be everything that is not plant specific regarding the plant being analyzed). The generic data available in the open literature, can be divided in three broad categories. The first one includes data base used in previous analysis. These can be plant specific or updated from generic with plant specific information (latter case deserve special attention). The second one is based on compilation of plants' operating experience usually based on some kind of event reporting system. The third category includes data sources based on expert opinions (single or aggregate) or combination of expert opinions and other nuclear and non-nuclear experience. This paper reflects insights gained in compiling data from generic data sources and highlights advantages and pitfalls of using generic component reliability data in PSAs

  20. Principal component analysis networks and algorithms

    CERN Document Server

    Kong, Xiangyu; Duan, Zhansheng

    2017-01-01

    This book not only provides a comprehensive introduction to neural-based PCA methods in control science, but also presents many novel PCA algorithms and their extensions and generalizations, e.g., dual purpose, coupled PCA, GED, neural based SVD algorithms, etc. It also discusses in detail various analysis methods for the convergence, stabilizing, self-stabilizing property of algorithms, and introduces the deterministic discrete-time systems method to analyze the convergence of PCA/MCA algorithms. Readers should be familiar with numerical analysis and the fundamentals of statistics, such as the basics of least squares and stochastic algorithms. Although it focuses on neural networks, the book only presents their learning law, which is simply an iterative algorithm. Therefore, no a priori knowledge of neural networks is required. This book will be of interest and serve as a reference source to researchers and students in applied mathematics, statistics, engineering, and other related fields.

  1. Lifestyles Based on Health Components in Iran

    Directory of Open Access Journals (Sweden)

    Babaei

    2016-07-01

    Full Text Available Context Lifestyle is a way employed by people, groups and nations and is formed in specific geographical, economic, political, cultural and religious texts. Health depends on lifestyle and is essential to preserve and promote health and improve lifestyle. Objectives The present study aimed to investigate lifestyle based on health-oriented components in Iran. Data Sources The research was conducted through E-banks including scientific information database (SID, Iran medical science databank (Iran Medex, Iran journal databank (Magiran and other databases such as Elsevier, PubMed and google scholar meta search engine regarding the subject from 2000 to 2014. Moreover, Official Iranian statistics and information were applied. The search terms used included lifestyle, health, health promoting behaviors, health-oriented lifestyle and lifestyle in Iran. Study Selection In the primary research, many papers were observed out of which 157 (120 in Farsi and 37 in English were selected. Data Extraction Following the careful study of these papers and excluding the unqualified papers, 19 papers with thorough information and higher relevance with the research purpose were selected. Results After examining articles based on the selected keywords and search strategies, 215 articles (134 in Farsi and 81 in English were obtained. Components of lifestyle and health are increasing in recent years; therefore, 8 (42% and 11 (58% articles were published during 2005 - 2010 and 2011 - 2014, respectively. Among them, there were 3 (16%, 8 (42%, 2 (10.5%, 2 (10.5% and 0 articles on the review of literature, descriptive-analytic, qualitative, analytic and descriptive articles, respectively. Conclusions Due to positive effect of healthy lifestyle on health promotion of individuals, it would be better for the government to provide comprehensive programs and policies in the society to enhance awareness of people about positive effects of health-oriented lifestyle on life and

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

    Directory of Open Access Journals (Sweden)

    C. A. Stroud

    2012-09-01

    Full Text Available 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 rural sites (Harrow and Bear Creek, ON to derive hydrocarbon-like organic aerosol (HOA factors. A novel diagnostic model evaluation was performed by investigating model POA bias as a function of HOA mass concentration and indicator ratios (e.g. BC/HOA. Eight case studies were selected based on factor analysis and back trajectories to help classify model bias for certain POA source types. By considering model POA bias in relation to co-located BC and CO biases, a plausible story is developed that explains the model biases for all three species.

    At the rural sites, daytime mean PM1 POA mass concentrations were under-predicted compared to observed HOA concentrations. POA under-predictions were accentuated when the transport arriving at the rural sites was from the Detroit/Windsor urban complex and for short-term periods of biomass burning influence. Interestingly, the daytime CO concentrations were only slightly under-predicted at both rural sites, whereas CO was over-predicted at the urban Windsor site with a normalized mean bias of 134%, while good agreement was observed at Windsor for the comparison of daytime PM1 POA and HOA mean values, 1.1 μg m−3 and 1.2 μg m−3, respectively. Biases in model POA predictions also trended from positive to negative with increasing HOA values. Periods of POA over-prediction were most evident at the urban site on calm nights due to an overly-stable model surface layer

  3. 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...

  4. Seismic component fragility data base for IPEEE

    International Nuclear Information System (INIS)

    Bandyopadhyay, K.; Hofmayer, C.

    1990-01-01

    Seismic probabilistic risk assessment or a seismic margin study will require a reliable data base of seismic fragility of various equipment classes. Brookhaven National Laboratory (BNL) has selected a group of equipment and generically evaluated the seismic fragility of each equipment class by use of existing test data. This paper briefly discusses the evaluation methodology and the fragility results. The fragility analysis results when used in the Individual Plant Examination for External Events (IPEEE) Program for nuclear power plants are expected to provide insights into seismic vulnerabilities of equipment for earthquakes beyond the design basis. 3 refs., 1 fig., 1 tab

  5. 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.)

  6. Integration of Simulink Models with Component-based Software Models

    DEFF Research Database (Denmark)

    Marian, Nicolae

    2008-01-01

    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...... 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...

  7. 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.

  8. Thermogravimetric analysis of combustible waste components

    DEFF Research Database (Denmark)

    Munther, Anette; Wu, Hao; Glarborg, Peter

    In order to gain fundamental knowledge about the co-combustion of coal and waste derived fuels, the pyrolytic behaviors of coal, four typical waste components and their mixtures have been studied by a simultaneous thermal analyzer (STA). The investigated waste components were wood, paper, polypro......In order to gain fundamental knowledge about the co-combustion of coal and waste derived fuels, the pyrolytic behaviors of coal, four typical waste components and their mixtures have been studied by a simultaneous thermal analyzer (STA). The investigated waste components were wood, paper...

  9. 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.

  10. Constrained independent component analysis approach to nonobtrusive pulse rate measurements

    Science.gov (United States)

    Tsouri, Gill R.; Kyal, Survi; Dianat, Sohail; Mestha, Lalit K.

    2012-07-01

    Nonobtrusive pulse rate measurement using a webcam is considered. We demonstrate how state-of-the-art algorithms based on independent component analysis suffer from a sorting problem which hinders their performance, and propose a novel algorithm based on constrained independent component analysis to improve performance. We present how the proposed algorithm extracts a photoplethysmography signal and resolves the sorting problem. In addition, we perform a comparative study between the proposed algorithm and state-of-the-art algorithms over 45 video streams using a finger probe oxymeter for reference measurements. The proposed algorithm provides improved accuracy: the root mean square error is decreased from 20.6 and 9.5 beats per minute (bpm) for existing algorithms to 3.5 bpm for the proposed algorithm. An error of 3.5 bpm is within the inaccuracy expected from the reference measurements. This implies that the proposed algorithm provided performance of equal accuracy to the finger probe oximeter.

  11. A Fast and Sensitive New Satellite SO2 Retrieval Algorithm based on Principal Component Analysis: Application to the Ozone Monitoring Instrument

    Science.gov (United States)

    Li, Can; Joiner, Joanna; Krotkov, A.; Bhartia, Pawan K.

    2013-01-01

    We describe a new algorithm to retrieve SO2 from satellite-measured hyperspectral radiances. We employ the principal component analysis technique in regions with no significant SO2 to capture radiance variability caused by both physical processes (e.g., Rayleigh and Raman scattering and ozone absorption) and measurement artifacts. We use the resulting principal components and SO2 Jacobians calculated with a radiative transfer model to directly estimate SO2 vertical column density in one step. Application to the Ozone Monitoring Instrument (OMI) radiance spectra in 310.5-340 nm demonstrates that this approach can greatly reduce biases in the operational OMI product and decrease the noise by a factor of 2, providing greater sensitivity to anthropogenic emissions. The new algorithm is fast, eliminates the need for instrument-specific radiance correction schemes, and can be easily adapted to other sensors. These attributes make it a promising technique for producing longterm, consistent SO2 records for air quality and climate research.

  12. The development of component-based information systems

    CERN Document Server

    Cesare, Sergio de; Macredie, Robert

    2015-01-01

    This work provides a comprehensive overview of research and practical issues relating to component-based development information systems (CBIS). Spanning the organizational, developmental, and technical aspects of the subject, the original research included here provides fresh insights into successful CBIS technology and application. Part I covers component-based development methodologies and system architectures. Part II analyzes different aspects of managing component-based development. Part III investigates component-based development versus commercial off-the-shelf products (COTS), includi

  13. Analysis of failed nuclear plant components

    Science.gov (United States)

    Diercks, D. R.

    1993-12-01

    Argonne National Laboratory has conducted analyses of failed components from nuclear power- gener-ating stations since 1974. The considerations involved in working with and analyzing radioactive compo-nents are reviewed here, and the decontamination of these components is discussed. Analyses of four failed components from nuclear plants are then described to illustrate the kinds of failures seen in serv-ice. The failures discussed are (1) intergranular stress- corrosion cracking of core spray injection piping in a boiling water reactor, (2) failure of canopy seal welds in adapter tube assemblies in the control rod drive head of a pressurized water reactor, (3) thermal fatigue of a recirculation pump shaft in a boiling water reactor, and (4) failure of pump seal wear rings by nickel leaching in a boiling water reactor.

  14. Analysis of failed nuclear plant components

    International Nuclear Information System (INIS)

    Diercks, D.R.

    1993-01-01

    Argonne National Laboratory has conducted analyses of failed components from nuclear power-generating stations since 1974. The considerations involved in working with an analyzing radioactive components are reviewed here, and the decontamination of these components is discussed. Analyses of four failed components from nuclear plants are then described to illustrate the kinds of failures seen in service. The failures discussed are (1) intergranular stress-corrosion cracking of core spray injection piping in a boiling water reactor, (2) failure of canopy seal welds in adapter tube assemblies in the control rod drive head of a pressurized water reactor, (3) thermal fatigue of a recirculation pump shaft in a boiling water reactor, and (4) failure of pump seal wear rings by nickel leaching in a boiling water reactor

  15. Analysis of failed nuclear plant components

    International Nuclear Information System (INIS)

    Diercks, D.R.

    1992-07-01

    Argonne National Laboratory has conducted analyses of failed components from nuclear power generating stations since 1974. The considerations involved in working with and analyzing radioactive components are reviewed here, and the decontamination of these components is discussed. Analyses of four failed components from nuclear plants are then described to illustrate the kinds of failures seen in service. The failures discussed are (a) intergranular stress corrosion cracking of core spray injection piping in a boiling water reactor, (b) failure of canopy seal welds in adapter tube assemblies in the control rod drive head of a pressure water reactor, (c) thermal fatigue of a recirculation pump shaft in a boiling water reactor, and (d) failure of pump seal wear rings by nickel leaching in a boiling water reactor

  16. A radiographic analysis of implant component misfit.

    LENUS (Irish Health Repository)

    Sharkey, Seamus

    2011-07-01

    Radiographs are commonly used to assess the fit of implant components, but there is no clear agreement on the amount of misfit that can be detected by this method. This study investigated the effect of gap size and the relative angle at which a radiograph was taken on the detection of component misfit. Different types of implant connections (internal or external) and radiographic modalities (film or digital) were assessed.

  17. Components of Program for Analysis of Spectra and Their Testing

    Directory of Open Access Journals (Sweden)

    Ivan Taufer

    2013-11-01

    Full Text Available The spectral analysis of aqueous solutions of multi-component mixtures is used for identification and distinguishing of individual componentsin the mixture and subsequent determination of protonation constants and absorptivities of differently protonated particles in the solution in steadystate (Meloun and Havel 1985, (Leggett 1985. Apart from that also determined are the distribution diagrams, i.e. concentration proportions ofthe individual components at different pH values. The spectra are measured with various concentrations of the basic components (one or severalpolyvalent weak acids or bases and various pH values within the chosen range of wavelengths. The obtained absorbance response area has to beanalyzed by non-linear regression using specialized algorithms. These algorithms have to meet certain requirements concerning the possibility ofcalculations and the level of outputs. A typical example is the SQUAD(84 program, which was gradually modified and extended, see, e.g., (Melounet al. 1986, (Meloun et al. 2012.

  18. A refinement driven component-based design

    DEFF Research Database (Denmark)

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

    2007-01-01

    the work on the Common Component Modelling Example (CoCoME). This gives evidence that the formal techniques developed in rCOS can be integrated into a model-driven development process and shows where it may be integrated in computer-aided software engineering (CASE) tools for adding formally supported...

  19. 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

  20. 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

  1. 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

  2. Mapping ash properties using principal components analysis

    Science.gov (United States)

    Pereira, Paulo; Brevik, Eric; Cerda, Artemi; Ubeda, Xavier; Novara, Agata; Francos, Marcos; Rodrigo-Comino, Jesus; Bogunovic, Igor; Khaledian, Yones

    2017-04-01

    In post-fire environments ash has important benefits for soils, such as protection and source of nutrients, crucial for vegetation recuperation (Jordan et al., 2016; Pereira et al., 2015a; 2016a,b). The thickness and distribution of ash are fundamental aspects for soil protection (Cerdà and Doerr, 2008; Pereira et al., 2015b) and the severity at which was produced is important for the type and amount of elements that is released in soil solution (Bodi et al., 2014). Ash is very mobile material, and it is important were it will be deposited. Until the first rainfalls are is very mobile. After it, bind in the soil surface and is harder to erode. Mapping ash properties in the immediate period after fire is complex, since it is constantly moving (Pereira et al., 2015b). However, is an important task, since according the amount and type of ash produced we can identify the degree of soil protection and the nutrients that will be dissolved. The objective of this work is to apply to map ash properties (CaCO3, pH, and select extractable elements) using a principal component analysis (PCA) in the immediate period after the fire. Four days after the fire we established a grid in a 9x27 m area and took ash samples every 3 meters for a total of 40 sampling points (Pereira et al., 2017). The PCA identified 5 different factors. Factor 1 identified high loadings in electrical conductivity, calcium, and magnesium and negative with aluminum and iron, while Factor 3 had high positive loadings in total phosphorous and silica. Factor 3 showed high positive loadings in sodium and potassium, factor 4 high negative loadings in CaCO3 and pH, and factor 5 high loadings in sodium and potassium. The experimental variograms of the extracted factors showed that the Gaussian model was the most precise to model factor 1, the linear to model factor 2 and the wave hole effect to model factor 3, 4 and 5. The maps produced confirm the patternd observed in the experimental variograms. Factor 1 and 2

  3. Investigation of the nanoscale two-component ZnS-ZnO heterostructures by means of HR-TEM and X-ray based analysis

    Science.gov (United States)

    Pankin, I. A.; Polozhentsev, O. E.; Soldatov, M. A.; Bugaev, A. L.; Tsaturyan, A.; Lomachenko, K. A.; Guda, A. A.; Budnyk, A. P.; Lamberti, C.; Soldatov, A. V.

    2018-06-01

    This article is devoted to the spectroscopic characterization of ZnS-ZnO nanoscale heterostructures synthesized by the microwave-assisted solvothermal method. The synthesized samples were investigated by means of X-ray powder diffraction (XRPD), high energy resolution fluorescence detected X-ray absorption near-edge-structure (HERFD-XANES) spectroscopy, valence-to-core X-ray emission spectroscopy (VtC-XES) and high resolution transmission electron microscopy (HR-TEM) as well as energy dispersive X-ray spectroscopy (EDX). The average crystallite size estimated by the broadening of XRPD peaks increases from 2.7 nm to 3.7 nm in the temperature range from 100 °C to 150 °C. HR-TEM images show that nanoparticles are arranged in aggregates with the 60-200 nm size. Theoretical estimation shows that the systems synthesized at higher temperatures more prone to the agglomeration. The full profile Reitveld analysis of XRPD data reveals the formation of hexagonal zinc sulfide structure, whereas electron diffraction data reveal also the formation of cubic zinc sulfide and claim the polymorphous character of the system. High energy resolution Zn K-edge XANES data unambiguously demonstrate the presence of a certain amount of the zinc oxide which is likely to have an amorphous structure and could not be detected by XRPD. Qualitative analysis of XANES data allows deriving ZnS/ZnO ratio as a function of synthesis temperature. EDX analysis depicts homogeneous distribution of ZnS and amorphous ZnO phases across the conglomerates. A complementary element-selective valence to core X-ray emission spectroscopy evidences formation of two-component system and confirms estimations of ZnS/ZnO fractions obtained by linear combination fit of XANES data.

  4. Educational Technologies Based on Software Components

    Directory of Open Access Journals (Sweden)

    Marian DARDALA

    2006-01-01

    Full Text Available Informatics technologies allow to easily develop and adapt e-learning systems. In order to be used by any users, the system must be developed to permit the lessons construction in a dynamic way. The component technology is a solution to this problem and offers the possibility to define the basic objects that will be connected at the run time to develop the personalized e-lessons.

  5. 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.

  6. Principal component analysis of psoriasis lesions images

    DEFF Research Database (Denmark)

    Maletti, Gabriela Mariel; Ersbøll, Bjarne Kjær

    2003-01-01

    A set of RGB images of psoriasis lesions is used. By visual examination of these images, there seem to be no common pattern that could be used to find and align the lesions within and between sessions. It is expected that the principal components of the original images could be useful during future...

  7. Nonlinear Principal Component Analysis Using Strong Tracking Filter

    Institute of Scientific and Technical Information of China (English)

    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.

  8. 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

  9. Investigation of True High Frequency Electrical Substrates of fMRI-Based Resting State Networks Using Parallel Independent Component Analysis of Simultaneous EEG/fMRI Data.

    Science.gov (United States)

    Kyathanahally, Sreenath P; Wang, Yun; Calhoun, Vince D; Deshpande, Gopikrishna

    2017-01-01

    Previous work using simultaneously acquired electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) data has shown that the slow temporal dynamics of resting state brain networks (RSNs), e.g., default mode network (DMN), visual network (VN), obtained from fMRI are correlated with smoothed and down sampled versions of various EEG features such as microstates and band-limited power envelopes. Therefore, even though the down sampled and smoothed envelope of EEG gamma band power is correlated with fMRI fluctuations in the RSNs, it does not mean that the electrical substrates of the RSNs fluctuate with periods state fMRI fluctuations in the RSNs, researchers have speculated that truly high frequency electrical substrates may exist for the RSNs, which would make resting fluctuations obtained from fMRI more meaningful to typically occurring fast neuronal processes in the sub-100 ms time scale. In this study, we test this critical hypothesis using an integrated framework involving simultaneous EEG/fMRI acquisition, fast fMRI sampling ( TR = 200 ms) using multiband EPI (MB EPI), and EEG/fMRI fusion using parallel independent component analysis (pICA) which does not require the down sampling of EEG to fMRI temporal resolution . Our results demonstrate that with faster sampling, high frequency electrical substrates (fluctuating with periods <100 ms time scale) of the RSNs can be observed. This provides a sounder neurophysiological basis for the RSNs.

  10. Anti-inflammatory and anti-angiogenic activities in vitro of eight diterpenes from Daphne genkwa based on hierarchical cluster and principal component analysis.

    Science.gov (United States)

    Wang, Ling; Lan, Xin-Yi; Ji, Jun; Zhang, Chun-Feng; Li, Fei; Wang, Chong-Zhi; Yuan, Chun-Su

    2018-06-01

    Rheumatoid arthritis (RA) is one of the most prevalent chronic inflammatory and angiogenic diseases. The aim of this study was to evaluate the anti-inflammatory and anti-angiogenic activities in vitro of eight diterpenoids isolated from Daphne genkwa. LC-MS was used to identify diterpenes isolated from D. genkwa. The anti-inflammatory and anti-angiogenic activities of eight diterpenoids were evaluated on LPS-induced macrophage RAW264.7 cells and TNF-α-stimulated human umbilical vein endothelial cells (HUVECs) using hierarchical cluster analysis (HCA) and principal component analysis (PCA). The eight diterpenes isolated from D. genkwa were identified as yuanhuaphnin, isoyuanhuacine, 12-O-(2'E,4'E-decadienoyl)-4-hydroxyphorbol-13-acetyl, yuanhuagine, isoyuanhuadine, yuanhuadine, yuanhuaoate C and yuanhuacine. All the eight diterpenes significantly down-regulated the excessive secretion of TNF-α, IL-6, IL-1β and NO in LPS-induced RAW264.7 macrophages. However, only 12-O-(2'E,4'E-decadienoyl)-4-hydroxyphorbol-13-acetyl markedly reduced production of VEGF, MMP-3, ICAM and VCAM in TNF-α-stimulated HUVECs. HCA obtained 4 clusters, containing 12-O-(2'E,4'E-decadienoyl)-4-hydroxyphorbol-13-acetyl, isoyuanhuacine, isoyuanhuadine and five other compounds. PCA showed that the ranking of diterpenes sorted by efficacy from highest to lowest was 12-O-(2'E,4'E-decadienoyl)-4-hydroxyphorbol-13-acetyl, yuanhuaphnin, isoyuanhuacine, yuanhuacine, yuanhuaoate C, yuanhuagine, isoyuanhuadine, yuanhuadine. In conclusion, eight diterpenes isolated from D. genkwa showed different levels of activity in LPS-induced RAW264.7 cells and TNF-α-stimulated HUVECs. The comprehensive evaluation of activity by HCA and PCA indicated that of the eight diterpenes, 12-O-(2'E,4'E-decadienoyl)-4-hydroxyphorbol-13-acetyl was the best, and can be developed as a new drug for RA therapy.

  11. 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.

  12. 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.

  13. 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

  14. Component-based framework for subsurface simulations

    International Nuclear Information System (INIS)

    Palmer, B J; Fang, Yilin; Hammond, Glenn; Gurumoorthi, Vidhya

    2007-01-01

    Simulations in the subsurface environment represent a broad range of phenomena covering an equally broad range of scales. Developing modelling capabilities that can integrate models representing different phenomena acting at different scales present formidable challenges both from the algorithmic and computer science perspective. This paper will describe the development of an integrated framework that will be used to combine different models into a single simulation. Initial work has focused on creating two frameworks, one for performing smooth particle hydrodynamics (SPH) simulations of fluid systems, the other for performing grid-based continuum simulations of reactive subsurface flow. The SPH framework is based on a parallel code developed for doing pore scale simulations, the continuum grid-based framework is based on the STOMP (Subsurface Transport Over Multiple Phases) code developed at PNNL Future work will focus on combining the frameworks together to perform multiscale, multiphysics simulations of reactive subsurface flow

  15. 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

  16. Agile Objects: Component-Based Inherent Survivability

    Science.gov (United States)

    2003-12-01

    pages 37-55, February 1990. [3] Clar, David D., Van Jacobson, John Romkey, and Howard Salwen. “An analysis of TCP processing overhead”. IEEE...www.microsoft.com/technet/treeview/default.asp?url=/technet/prodtech nol/acs/Default.asp 34. Chaum , D.L., Untraceable Electronic Mail, Return

  17. Columbia River Component Data Gap Analysis

    Energy Technology Data Exchange (ETDEWEB)

    L. C. Hulstrom

    2007-10-23

    This Data Gap Analysis report documents the results of a study conducted by Washington Closure Hanford (WCH) to compile and reivew the currently available surface water and sediment data for the Columbia River near and downstream of the Hanford Site. This Data Gap Analysis study was conducted to review the adequacy of the existing surface water and sediment data set from the Columbia River, with specific reference to the use of the data in future site characterization and screening level risk assessments.

  18. Simulation based analysis and an application to an offshore oil and gas production system of the Natvig measures of component importance in repairable systems

    International Nuclear Information System (INIS)

    Natvig, Bent; Eide, Kristina A.; Gasemyr, Jorund; Huseby, Arne B.; Isaksen, Stefan L.

    2009-01-01

    In the present paper the Natvig measures of component importance for repairable systems, and its extended version are analyzed for two three-component systems and a bridge system. The measures are also applied to an offshore oil and gas production system. According to the extended version of the Natvig measure a component is important if both by failing it strongly reduces the expected system uptime and by being repaired it strongly reduces the expected system downtime. The results include a study of how different distributions affect the ranking of the components. All numerical results are computed using discrete event simulation. In a companion paper [Huseby AB, Eide KA, Isaksen SL, Natvig B, Gasemyr, J. Advanced discrete event simulation methods with application to importance measure estimation. 2009, submitted for publication] the advanced simulation methods needed in these calculations are described.

  19. Does the component processes task assess text-based inferences important for reading comprehension? A path analysis in primary school children

    NARCIS (Netherlands)

    S.I. Wassenburg (Stephanie); B.B. de Koning (Björn); de Vries, M.H. (Meinou H.); M. van der Schoot (Menno)

    2016-01-01

    textabstractUsing a component processes task (CPT) that differentiates between higher-level cognitive processes of reading comprehension provides important advantages over commonly used general reading comprehension assessments. The present study contributes to further development of the CPT by

  20. Does the component processes task assess text-based inferences important for reading comprehension? A path analysis in primary school children

    NARCIS (Netherlands)

    Wassenburg, Stephanie I.; de Koning, Björn B.; de Vries, Meinou H.; van der Schoot, Menno

    2016-01-01

    Using a component processes task (CPT) that differentiates between higher-level cognitive processes of reading comprehension provides important advantages over commonly used general reading comprehension assessments. The present study contributes to further development of the CPT by evaluating the

  1. Fast principal component analysis for stacking seismic data

    Science.gov (United States)

    Wu, Juan; Bai, Min

    2018-04-01

    Stacking seismic data plays an indispensable role in many steps of the seismic data processing and imaging workflow. Optimal stacking of seismic data can help mitigate seismic noise and enhance the principal components to a great extent. Traditional average-based seismic stacking methods cannot obtain optimal performance when the ambient noise is extremely strong. We propose a principal component analysis (PCA) algorithm for stacking seismic data without being sensitive to noise level. Considering the computational bottleneck of the classic PCA algorithm in processing massive seismic data, we propose an efficient PCA algorithm to make the proposed method readily applicable for industrial applications. Two numerically designed examples and one real seismic data are used to demonstrate the performance of the presented method.

  2. Component-based development of software language engineering tools

    NARCIS (Netherlands)

    Ssanyu, J.; Hemerik, C.

    2011-01-01

    In this paper we outline how Software Language Engineering (SLE) could benefit from Component-based Software Development (CBSD) techniques and present an architecture aimed at developing a coherent set of lightweight SLE components, fitting into a general-purpose component framework. In order to

  3. 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

  4. Fetal ECG extraction using independent component analysis by Jade approach

    Science.gov (United States)

    Giraldo-Guzmán, Jader; Contreras-Ortiz, Sonia H.; Lasprilla, Gloria Isabel Bautista; Kotas, Marian

    2017-11-01

    Fetal ECG monitoring is a useful method to assess the fetus health and detect abnormal conditions. In this paper we propose an approach to extract fetal ECG from abdomen and chest signals using independent component analysis based on the joint approximate diagonalization of eigenmatrices approach. The JADE approach avoids redundancy, what reduces matrix dimension and computational costs. Signals were filtered with a high pass filter to eliminate low frequency noise. Several levels of decomposition were tested until the fetal ECG was recognized in one of the separated sources output. The proposed method shows fast and good performance.

  5. 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.

  6. 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.

  7. Projection and analysis of nuclear components

    International Nuclear Information System (INIS)

    Heeschen, U.

    1980-01-01

    The classification and the types of analysis carried out in pipings for quality control and safety of nuclear power plants, are presented. The operation and emergency conditions with emphasis of possible simplifications of calculations are described. (author/M.C.K.) [pt

  8. 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),

  9. Application of fuzzy-MOORA method: Ranking of components for reliability estimation of component-based software systems

    Directory of Open Access Journals (Sweden)

    Zeeshan Ali Siddiqui

    2016-01-01

    Full Text Available Component-based software system (CBSS development technique is an emerging discipline that promises to take software development into a new era. As hardware systems are presently being constructed from kits of parts, software systems may also be assembled from components. It is more reliable to reuse software than to create. It is the glue code and individual components reliability that contribute to the reliability of the overall system. Every component contributes to overall system reliability according to the number of times it is being used, some components are of critical usage, known as usage frequency of component. The usage frequency decides the weight of each component. According to their weights, each component contributes to the overall reliability of the system. Therefore, ranking of components may be obtained by analyzing their reliability impacts on overall application. In this paper, we propose the application of fuzzy multi-objective optimization on the basis of ratio analysis, Fuzzy-MOORA. The method helps us find the best suitable alternative, software component, from a set of available feasible alternatives named software components. It is an accurate and easy to understand tool for solving multi-criteria decision making problems that have imprecise and vague evaluation data. By the use of ratio analysis, the proposed method determines the most suitable alternative among all possible alternatives, and dimensionless measurement will realize the job of ranking of components for estimating CBSS reliability in a non-subjective way. Finally, three case studies are shown to illustrate the use of the proposed technique.

  10. Kernel principal component analysis for change detection

    DEFF Research Database (Denmark)

    Nielsen, Allan Aasbjerg; Morton, J.C.

    2008-01-01

    region acquired at two different time points. If change over time does not dominate the scene, the projection of the original two bands onto the second eigenvector will show change over time. In this paper a kernel version of PCA is used to carry out the analysis. Unlike ordinary PCA, kernel PCA...... with a Gaussian kernel successfully finds the change observations in a case where nonlinearities are introduced artificially....

  11. Applying the results based management framework to the CERCA multi-component project in adolescent sexual and reproductive health: a retrospective analysis.

    Science.gov (United States)

    Cordova-Pozo, Kathya; Hoopes, Andrea J; Cordova, Freddy; Vega, Bernardo; Segura, Zoyla; Hagens, Arnold

    2018-02-08

    Adolescent sexual and reproductive health (SRH), problems such as unplanned pregnancies are complex and multifactorial, thus requiring multifaceted prevention interventions. Evaluating the impact of such interventions is important to ensure efficiency, effectiveness and accountability for project funders and community members. In this study, we propose Results Based Management (RBM) as a framework for project management, using the Community Embedded Reproductive Health Care for Adolescents (CERCA) as a case study for RBM. The CERCA Project (2010-2014) tested interventions to reduce adolescent pregnancy in three Latin American countries, Bolivia, Ecuador and Nicaragua. Activities were designed to increase adolescent SRH behaviors in four domains: communication with parents, partners and peers; access to SRH information; access to SRH services; and use of contraception. When the project ended, the outcome evaluation showed limited impact with concerns about accuracy of monitoring and attrition of participants. We reviewed and analyzed a series of CERCA documents and related data sources. Key findings from these documents were organized within an RBM framework (planning, monitoring, and impact evaluation) to understand how CERCA methodology and performance might have reaped improved results. Strengths and weaknesses were identified in all three elements of the RBM framework. In Planning, the proposed Theory of Change (ToC) differed from that which was carried out in the intervention package. Each country implemented a different intervention package without articulated assumptions on how the activities of intervention would bring about change. In Monitoring, the project oversight was mainly based on administrative and financial requirements rather than monitoring fidelity and quality of intervention activities. In Impact Evaluation, the original CERCA evaluation assessed intervention effects among adolescents, without identifying success and failure factors related to

  12. Component-Based Approach in Learning Management System Development

    Science.gov (United States)

    Zaitseva, Larisa; Bule, Jekaterina; Makarov, Sergey

    2013-01-01

    The paper describes component-based approach (CBA) for learning management system development. Learning object as components of e-learning courses and their metadata is considered. The architecture of learning management system based on CBA being developed in Riga Technical University, namely its architecture, elements and possibilities are…

  13. 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)

  14. 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.

  15. 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

  16. Protein structure similarity from principle component correlation analysis

    Directory of Open Access Journals (Sweden)

    Chou James

    2006-01-01

    Full Text Available Abstract Background Owing to rapid expansion of protein structure databases in recent years, methods of structure comparison are becoming increasingly effective and important in revealing novel information on functional properties of proteins and their roles in the grand scheme of evolutionary biology. Currently, the structural similarity between two proteins is measured by the root-mean-square-deviation (RMSD in their best-superimposed atomic coordinates. RMSD is the golden rule of measuring structural similarity when the structures are nearly identical; it, however, fails to detect the higher order topological similarities in proteins evolved into different shapes. We propose new algorithms for extracting geometrical invariants of proteins that can be effectively used to identify homologous protein structures or topologies in order to quantify both close and remote structural similarities. Results We measure structural similarity between proteins by correlating the principle components of their secondary structure interaction matrix. In our approach, the Principle Component Correlation (PCC analysis, a symmetric interaction matrix for a protein structure is constructed with relationship parameters between secondary elements that can take the form of distance, orientation, or other relevant structural invariants. When using a distance-based construction in the presence or absence of encoded N to C terminal sense, there are strong correlations between the principle components of interaction matrices of structurally or topologically similar proteins. Conclusion The PCC method is extensively tested for protein structures that belong to the same topological class but are significantly different by RMSD measure. The PCC analysis can also differentiate proteins having similar shapes but different topological arrangements. Additionally, we demonstrate that when using two independently defined interaction matrices, comparison of their maximum

  17. Recursive Principal Components Analysis Using Eigenvector Matrix Perturbation

    Directory of Open Access Journals (Sweden)

    Deniz Erdogmus

    2004-10-01

    Full Text Available Principal components analysis is an important and well-studied subject in statistics and signal processing. The literature has an abundance of algorithms for solving this problem, where most of these algorithms could be grouped into one of the following three approaches: adaptation based on Hebbian updates and deflation, optimization of a second-order statistical criterion (like reconstruction error or output variance, and fixed point update rules with deflation. In this paper, we take a completely different approach that avoids deflation and the optimization of a cost function using gradients. The proposed method updates the eigenvector and eigenvalue matrices simultaneously with every new sample such that the estimates approximately track their true values as would be calculated from the current sample estimate of the data covariance matrix. The performance of this algorithm is compared with that of traditional methods like Sanger's rule and APEX, as well as a structurally similar matrix perturbation-based method.

  18. 基于主成分-聚类分析法的建筑节能气候区划%Further climatic zoning of building energy efficiency based on principal component analysis and cluster analysis

    Institute of Scientific and Technical Information of China (English)

    张慧玲; 付祥钊

    2012-01-01

    Taking HDD18, CDD26, average temperature of the coldest month, average temperature of the hottest month, solar radiation in winter, solar radiation in summer, humidity ratio in winter and humidity ratio in summer as division indexes based on the analysis of climate indexes impacting the building cooling and heating energy consumptions, applies the principal component analysis and cluster analysis to building energy efficiency climatic zoning of the 270 cities in China, which are divided into the severe-cold dry heat high-solar-radiation zone, severe-cold cool high-solar-radiation zone, severe-cold summer free zone, cold slight-hot zone, temperate-hot-humid zone, temperate-cool zone and raw hot-wet zone. Describes the main climatic characteristics and geographic distribution of the seven climate zones, and clarifies the key point of building energy efficiency and the appropriate technical strategies respectively.%通过分析影响建筑冷热耗量的气候指标,提出了以采暖度日数HDD18、空调度日数CDD26、最冷月平均温度、最热月平均温度、冬季太阳辐射热量、夏季太阳辐射热量、冬季含湿量和夏季含湿量8个气候指标作为建筑节能气候区划指标,采用主成分分析与聚类分析相结合的区划方法对我国270个城市进行了建筑节能气候区划,划分为严寒干热高辐区、严寒凉爽高辐区、严寒无夏区、寒冷微热区、温和炎热湿润区、温和凉爽区和阴冷湿热区,介绍了这7个气候区的主要气候特征和主要的地理范围,明确了各区的建筑节能重点和适宜的技术策略.

  19. Fast grasping of unknown objects using principal component analysis

    Science.gov (United States)

    Lei, Qujiang; Chen, Guangming; Wisse, Martijn

    2017-09-01

    Fast grasping of unknown objects has crucial impact on the efficiency of robot manipulation especially subjected to unfamiliar environments. In order to accelerate grasping speed of unknown objects, principal component analysis is utilized to direct the grasping process. In particular, a single-view partial point cloud is constructed and grasp candidates are allocated along the principal axis. Force balance optimization is employed to analyze possible graspable areas. The obtained graspable area with the minimal resultant force is the best zone for the final grasping execution. It is shown that an unknown object can be more quickly grasped provided that the component analysis principle axis is determined using single-view partial point cloud. To cope with the grasp uncertainty, robot motion is assisted to obtain a new viewpoint. Virtual exploration and experimental tests are carried out to verify this fast gasping algorithm. Both simulation and experimental tests demonstrated excellent performances based on the results of grasping a series of unknown objects. To minimize the grasping uncertainty, the merits of the robot hardware with two 3D cameras can be utilized to suffice the partial point cloud. As a result of utilizing the robot hardware, the grasping reliance is highly enhanced. Therefore, this research demonstrates practical significance for increasing grasping speed and thus increasing robot efficiency under unpredictable environments.

  20. 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.

  1. 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

  2. Improvement of retinal blood vessel detection using morphological component analysis.

    Science.gov (United States)

    Imani, Elaheh; Javidi, Malihe; Pourreza, Hamid-Reza

    2015-03-01

    Detection and quantitative measurement of variations in the retinal blood vessels can help diagnose several diseases including diabetic retinopathy. Intrinsic characteristics of abnormal retinal images make blood vessel detection difficult. The major problem with traditional vessel segmentation algorithms is producing false positive vessels in the presence of diabetic retinopathy lesions. To overcome this problem, a novel scheme for extracting retinal blood vessels based on morphological component analysis (MCA) algorithm is presented in this paper. MCA was developed based on sparse representation of signals. This algorithm assumes that each signal is a linear combination of several morphologically distinct components. In the proposed method, the MCA algorithm with appropriate transforms is adopted to separate vessels and lesions from each other. Afterwards, the Morlet Wavelet Transform is applied to enhance the retinal vessels. The final vessel map is obtained by adaptive thresholding. The performance of the proposed method is measured on the publicly available DRIVE and STARE datasets and compared with several state-of-the-art methods. An accuracy of 0.9523 and 0.9590 has been respectively achieved on the DRIVE and STARE datasets, which are not only greater than most methods, but are also superior to the second human observer's performance. The results show that the proposed method can achieve improved detection in abnormal retinal images and decrease false positive vessels in pathological regions compared to other methods. Also, the robustness of the method in the presence of noise is shown via experimental result. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

  3. 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.

  4. Selection of independent components based on cortical mapping of electromagnetic activity

    Science.gov (United States)

    Chan, Hui-Ling; Chen, Yong-Sheng; Chen, Li-Fen

    2012-10-01

    Independent component analysis (ICA) has been widely used to attenuate interference caused by noise components from the electromagnetic recordings of brain activity. However, the scalp topographies and associated temporal waveforms provided by ICA may be insufficient to distinguish functional components from artifactual ones. In this work, we proposed two component selection methods, both of which first estimate the cortical distribution of the brain activity for each component, and then determine the functional components based on the parcellation of brain activity mapped onto the cortical surface. Among all independent components, the first method can identify the dominant components, which have strong activity in the selected dominant brain regions, whereas the second method can identify those inter-regional associating components, which have similar component spectra between a pair of regions. For a targeted region, its component spectrum enumerates the amplitudes of its parceled brain activity across all components. The selected functional components can be remixed to reconstruct the focused electromagnetic signals for further analysis, such as source estimation. Moreover, the inter-regional associating components can be used to estimate the functional brain network. The accuracy of the cortical activation estimation was evaluated on the data from simulation studies, whereas the usefulness and feasibility of the component selection methods were demonstrated on the magnetoencephalography data recorded from a gender discrimination study.

  5. Analysis of European Union Economy in Terms of GDP Components

    Directory of Open Access Journals (Sweden)

    Simona VINEREAN

    2013-12-01

    Full Text Available The impact of the crises on national economies represented a subject of analysis and interest for a wide variety of research studies. Thus, starting from the GDP composition, the present research exhibits an analysis of the impact of European economies, at an EU level, of the events that followed the crisis of 2007 – 2008. Firstly, the research highlighted the existence of two groups of countries in 2012 in European Union, namely segments that were compiled in relation to the structure of the GDP’s components. In the second stage of the research, a factor analysis was performed on the resulted segments, that showed that the economies of cluster A are based more on personal consumption compared to the economies of cluster B, and in terms of government consumption, the situation is reversed. Thus, between the two groups of countries, a different approach regarding the role of fiscal policy in the economy can be noted, with a greater emphasis on savings in cluster B. Moreover, besides the two groups of countries resulted, Ireland and Luxembourg stood out because these two countries did not fit in either of the resulted segments and their economies are based, to a large extent, on the positive balance of the external balance.

  6. 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.

  7. 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)

  8. Verifying Embedded Systems using Component-based Runtime Observers

    DEFF Research Database (Denmark)

    Guan, Wei; Marian, Nicolae; Angelov, Christo K.

    against formally specified properties. This paper presents a component-based design method for runtime observers, which are configured from instances of prefabricated reusable components---Predicate Evaluator (PE) and Temporal Evaluator (TE). The PE computes atomic propositions for the TE; the latter...... is a reconfigurable component processing a data structure, representing the state transition diagram of a non-deterministic state machine, i.e. a Buchi automaton derived from a system property specified in Linear Temporal Logic (LTL). Observer components have been implemented using design models and design patterns...

  9. 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...

  10. A flexible framework for sparse simultaneous component based data integration

    Directory of Open Access Journals (Sweden)

    Van Deun Katrijn

    2011-11-01

    Full Text Available Abstract 1 Background High throughput data are complex and methods that reveal structure underlying the data are most useful. Principal component analysis, frequently implemented as a singular value decomposition, is a popular technique in this respect. Nowadays often the challenge is to reveal structure in several sources of information (e.g., transcriptomics, proteomics that are available for the same biological entities under study. Simultaneous component methods are most promising in this respect. However, the interpretation of the principal and simultaneous components is often daunting because contributions of each of the biomolecules (transcripts, proteins have to be taken into account. 2 Results We propose a sparse simultaneous component method that makes many of the parameters redundant by shrinking them to zero. It includes principal component analysis, sparse principal component analysis, and ordinary simultaneous component analysis as special cases. Several penalties can be tuned that account in different ways for the block structure present in the integrated data. This yields known sparse approaches as the lasso, the ridge penalty, the elastic net, the group lasso, sparse group lasso, and elitist lasso. In addition, the algorithmic results can be easily transposed to the context of regression. Metabolomics data obtained with two measurement platforms for the same set of Escherichia coli samples are used to illustrate the proposed methodology and the properties of different penalties with respect to sparseness across and within data blocks. 3 Conclusion Sparse simultaneous component analysis is a useful method for data integration: First, simultaneous analyses of multiple blocks offer advantages over sequential and separate analyses and second, interpretation of the results is highly facilitated by their sparseness. The approach offered is flexible and allows to take the block structure in different ways into account. As such

  11. A flexible framework for sparse simultaneous component based data integration.

    Science.gov (United States)

    Van Deun, Katrijn; Wilderjans, Tom F; van den Berg, Robert A; Antoniadis, Anestis; Van Mechelen, Iven

    2011-11-15

    High throughput data are complex and methods that reveal structure underlying the data are most useful. Principal component analysis, frequently implemented as a singular value decomposition, is a popular technique in this respect. Nowadays often the challenge is to reveal structure in several sources of information (e.g., transcriptomics, proteomics) that are available for the same biological entities under study. Simultaneous component methods are most promising in this respect. However, the interpretation of the principal and simultaneous components is often daunting because contributions of each of the biomolecules (transcripts, proteins) have to be taken into account. We propose a sparse simultaneous component method that makes many of the parameters redundant by shrinking them to zero. It includes principal component analysis, sparse principal component analysis, and ordinary simultaneous component analysis as special cases. Several penalties can be tuned that account in different ways for the block structure present in the integrated data. This yields known sparse approaches as the lasso, the ridge penalty, the elastic net, the group lasso, sparse group lasso, and elitist lasso. In addition, the algorithmic results can be easily transposed to the context of regression. Metabolomics data obtained with two measurement platforms for the same set of Escherichia coli samples are used to illustrate the proposed methodology and the properties of different penalties with respect to sparseness across and within data blocks. Sparse simultaneous component analysis is a useful method for data integration: First, simultaneous analyses of multiple blocks offer advantages over sequential and separate analyses and second, interpretation of the results is highly facilitated by their sparseness. The approach offered is flexible and allows to take the block structure in different ways into account. As such, structures can be found that are exclusively tied to one data platform

  12. PRINCIPAL COMPONENT ANALYSIS STUDIES OF TURBULENCE IN OPTICALLY THICK GAS

    Energy Technology Data Exchange (ETDEWEB)

    Correia, C.; Medeiros, J. R. De [Departamento de Física Teórica e Experimental, Universidade Federal do Rio Grande do Norte, 59072-970, Natal (Brazil); Lazarian, A. [Astronomy Department, University of Wisconsin, Madison, 475 N. Charter St., WI 53711 (United States); Burkhart, B. [Harvard-Smithsonian Center for Astrophysics, 60 Garden St, MS-20, Cambridge, MA 02138 (United States); Pogosyan, D., E-mail: caioftc@dfte.ufrn.br [Canadian Institute for Theoretical Astrophysics, University of Toronto, Toronto, ON (Canada)

    2016-02-20

    In this work we investigate the sensitivity of principal component analysis (PCA) to the velocity power spectrum in high-opacity regimes of the interstellar medium (ISM). For our analysis we use synthetic position–position–velocity (PPV) cubes of fractional Brownian motion and magnetohydrodynamics (MHD) simulations, post-processed to include radiative transfer effects from CO. We find that PCA analysis is very different from the tools based on the traditional power spectrum of PPV data cubes. Our major finding is that PCA is also sensitive to the phase information of PPV cubes and this allows PCA to detect the changes of the underlying velocity and density spectra at high opacities, where the spectral analysis of the maps provides the universal −3 spectrum in accordance with the predictions of the Lazarian and Pogosyan theory. This makes PCA a potentially valuable tool for studies of turbulence at high opacities, provided that proper gauging of the PCA index is made. However, we found the latter to not be easy, as the PCA results change in an irregular way for data with high sonic Mach numbers. This is in contrast to synthetic Brownian noise data used for velocity and density fields that show monotonic PCA behavior. We attribute this difference to the PCA's sensitivity to Fourier phase information.

  13. PRINCIPAL COMPONENT ANALYSIS STUDIES OF TURBULENCE IN OPTICALLY THICK GAS

    International Nuclear Information System (INIS)

    Correia, C.; Medeiros, J. R. De; Lazarian, A.; Burkhart, B.; Pogosyan, D.

    2016-01-01

    In this work we investigate the sensitivity of principal component analysis (PCA) to the velocity power spectrum in high-opacity regimes of the interstellar medium (ISM). For our analysis we use synthetic position–position–velocity (PPV) cubes of fractional Brownian motion and magnetohydrodynamics (MHD) simulations, post-processed to include radiative transfer effects from CO. We find that PCA analysis is very different from the tools based on the traditional power spectrum of PPV data cubes. Our major finding is that PCA is also sensitive to the phase information of PPV cubes and this allows PCA to detect the changes of the underlying velocity and density spectra at high opacities, where the spectral analysis of the maps provides the universal −3 spectrum in accordance with the predictions of the Lazarian and Pogosyan theory. This makes PCA a potentially valuable tool for studies of turbulence at high opacities, provided that proper gauging of the PCA index is made. However, we found the latter to not be easy, as the PCA results change in an irregular way for data with high sonic Mach numbers. This is in contrast to synthetic Brownian noise data used for velocity and density fields that show monotonic PCA behavior. We attribute this difference to the PCA's sensitivity to Fourier phase information

  14. Flexible optical network components based on densely integrated microring resonators

    NARCIS (Netherlands)

    Geuzebroek, D.H.

    2005-01-01

    This thesis addresses the design, realization and characterization of reconfigurable optical network components based on multiple microring resonators. Since thermally tunable microring resonators can be used as wavelength selective space switches, very compact devices with high complexity and

  15. Component-Based Software Engineering and Runtime Type Definition

    OpenAIRE

    A. R. Shakurov

    2011-01-01

    The component-based approach to software engineering, its current implementations and their limitations are discussed. A new extended architecture for such systems is presented. Its main architectural concepts and principles are considered.

  16. Parallel PDE-Based Simulations Using the Common Component Architecture

    International Nuclear Information System (INIS)

    McInnes, Lois C.; Allan, Benjamin A.; Armstrong, Robert; Benson, Steven J.; Bernholdt, David E.; Dahlgren, Tamara L.; Diachin, Lori; Krishnan, Manoj Kumar; Kohl, James A.; Larson, J. Walter; Lefantzi, Sophia; Nieplocha, Jarek; Norris, Boyana; Parker, Steven G.; Ray, Jaideep; Zhou, Shujia

    2006-01-01

    The complexity of parallel PDE-based simulations continues to increase as multimodel, multiphysics, and multi-institutional projects become widespread. A goal of component based software engineering in such large-scale simulations is to help manage this complexity by enabling better interoperability among various codes that have been independently developed by different groups. The Common Component Architecture (CCA) Forum is defining a component architecture specification to address the challenges of high-performance scientific computing. In addition, several execution frameworks, supporting infrastructure, and general purpose components are being developed. Furthermore, this group is collaborating with others in the high-performance computing community to design suites of domain-specific component interface specifications and underlying implementations. This chapter discusses recent work on leveraging these CCA efforts in parallel PDE-based simulations involving accelerator design, climate modeling, combustion, and accidental fires and explosions. We explain how component technology helps to address the different challenges posed by each of these applications, and we highlight how component interfaces built on existing parallel toolkits facilitate the reuse of software for parallel mesh manipulation, discretization, linear algebra, integration, optimization, and parallel data redistribution. We also present performance data to demonstrate the suitability of this approach, and we discuss strategies for applying component technologies to both new and existing applications

  17. Principal Component Analysis for Normal-Distribution-Valued Symbolic Data.

    Science.gov (United States)

    Wang, Huiwen; Chen, Meiling; Shi, Xiaojun; Li, Nan

    2016-02-01

    This paper puts forward a new approach to principal component analysis (PCA) for normal-distribution-valued symbolic data, which has a vast potential of applications in the economic and management field. We derive a full set of numerical characteristics and variance-covariance structure for such data, which forms the foundation for our analytical PCA approach. Our approach is able to use all of the variance information in the original data than the prevailing representative-type approach in the literature which only uses centers, vertices, etc. The paper also provides an accurate approach to constructing the observations in a PC space based on the linear additivity property of normal distribution. The effectiveness of the proposed method is illustrated by simulated numerical experiments. At last, our method is applied to explain the puzzle of risk-return tradeoff in China's stock market.

  18. 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.

  19. Constrained Null Space Component Analysis for Semiblind Source Separation Problem.

    Science.gov (United States)

    Hwang, Wen-Liang; Lu, Keng-Shih; Ho, Jinn

    2018-02-01

    The blind source separation (BSS) problem extracts unknown sources from observations of their unknown mixtures. A current trend in BSS is the semiblind approach, which incorporates prior information on sources or how the sources are mixed. The constrained independent component analysis (ICA) approach has been studied to impose constraints on the famous ICA framework. We introduced an alternative approach based on the null space component (NCA) framework and referred to the approach as the c-NCA approach. We also presented the c-NCA algorithm that uses signal-dependent semidefinite operators, which is a bilinear mapping, as signatures for operator design in the c-NCA approach. Theoretically, we showed that the source estimation of the c-NCA algorithm converges with a convergence rate dependent on the decay of the sequence, obtained by applying the estimated operators on corresponding sources. The c-NCA can be formulated as a deterministic constrained optimization method, and thus, it can take advantage of solvers developed in optimization society for solving the BSS problem. As examples, we demonstrated electroencephalogram interference rejection problems can be solved by the c-NCA with proximal splitting algorithms by incorporating a sparsity-enforcing separation model and considering the case when reference signals are available.

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

    DEFF Research Database (Denmark)

    Bang-Berthelsen, Claus Heiner; Pedersen, Lykke; Fløyel, Tina

    2011-01-01

    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...... (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...... 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....

  1. Surface composition of biomedical components by ion beam analysis

    International Nuclear Information System (INIS)

    Kenny, M.J.; Wielunski, L.S.; Baxter, G.R.

    1991-01-01

    Materials used for replacement body parts must satisfy a number of requirements such as biocompatibility and mechanical ability to handle the task with regard to strength, wear and durability. When using a CVD coated carbon fibre reinforced carbon ball, the surface must be ion implanted with uniform dose of nitrogen ions in order to make it wear resistant. The mechanism by which the wear resistance is improved is one of radiation damage and the required dose of about 10 16 cm -2 can have a tolerance of about 20%. To implant a spherical surface requires manipulation of the sample within the beam and control system (either computer or manually operated) to enable uniform dose all the way from polar to equatorial regions on the surface. A manipulator has been designed and built for this purpose. In order to establish whether the dose is uniform, nuclear reaction analysis using the reaction 14 N(d,α) 12 C is an ideal method of profiling. By taking measurements at a number of points on the surface, the uniformity of nitrogen dose can be ascertained. It is concluded that both Rutherford Backscattering and Nuclear Reaction Analysis can be used for rapid analysis of surface composition of carbon based materials used for replacement body components. 2 refs., 2 figs

  2. 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.

  3. Assessment of oil weathering by gas chromatography-mass spectrometry, time warping and principal component analysis

    DEFF Research Database (Denmark)

    Malmquist, Linus M.V.; Olsen, Rasmus R.; Hansen, Asger B.

    2007-01-01

    weathering state and to distinguish between various weathering processes is investigated and discussed. The method is based on comprehensive and objective chromatographic data processing followed by principal component analysis (PCA) of concatenated sections of gas chromatography–mass spectrometry...

  4. Sparse Principal Component Analysis in Medical Shape Modeling

    DEFF Research Database (Denmark)

    Sjöstrand, Karl; Stegmann, Mikkel Bille; Larsen, Rasmus

    2006-01-01

    Principal component analysis (PCA) is a widely used tool in medical image analysis for data reduction, model building, and data understanding and exploration. While PCA is a holistic approach where each new variable is a linear combination of all original variables, sparse PCA (SPCA) aims...... analysis in medicine. Results for three different data sets are given in relation to standard PCA and sparse PCA by simple thresholding of sufficiently small loadings. Focus is on a recent algorithm for computing sparse principal components, but a review of other approaches is supplied as well. The SPCA...

  5. 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 ...

  6. 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...

  7. 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)

  8. Service oriented architecture assessment based on software components

    Directory of Open Access Journals (Sweden)

    Mahnaz Amirpour

    2016-01-01

    Full Text Available Enterprise architecture, with detailed descriptions of the functions of information technology in the organization, tries to reduce the complexity of technology applications resulting in tools with greater efficiency in achieving the objectives of the organization. Enterprise architecture consists of a set of models describing this technology in different components performance as well as various aspects of the applications in any organization. Therefore, information technology development and maintenance management can perform well within organizations. This study aims to suggest a method to identify different types of services in service-oriented architecture analysis step that applies some previous approaches in an integrated form and, based on the principles of software engineering, to provide a simpler and more transparent approach through the expression of analysis details. Advantages and disadvantages of proposals should be evaluated before the implementation and costs allocation. Evaluation methods can better identify strengths and weaknesses of the current situation apart from selecting appropriate model out of several suggestions, and clarify this technology development solution for organizations in the future. We will be able to simulate data and processes flow within the organization by converting the output of the model to colored Petri nets and evaluate and test it by examining various inputs to enterprise architecture before implemented in terms of reliability and response time. A model of application has been studied for the proposed model and the results can describe and design architecture for data.

  9. 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

  10. Isocyanide based multi component reactions in combinatorial chemistry.

    NARCIS (Netherlands)

    Dömling, A.

    1998-01-01

    Although usually regarded as a recent development, the combinatorial approach to the synthesis of libraries of new drug candidates was first described as early as 1961 using the isocyanide-based one-pot multicomponent Ugi reaction. Isocyanide-based multi component reactions (MCR's) markedly differ

  11. A Study on Components of Internal Control-Based Administrative System in Secondary Schools

    Science.gov (United States)

    Montri, Paitoon; Sirisuth, Chaiyuth; Lammana, Preeda

    2015-01-01

    The aim of this study was to study the components of the internal control-based administrative system in secondary schools, and make a Confirmatory Factor Analysis (CFA) to confirm the goodness of fit of empirical data and component model that resulted from the CFA. The study consisted of three steps: 1) studying of principles, ideas, and theories…

  12. 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)

  13. Size distribution measurements and chemical analysis of aerosol components

    Energy Technology Data Exchange (ETDEWEB)

    Pakkanen, T.A.

    1995-12-31

    The principal aims of this work were to improve the existing methods for size distribution measurements and to draw conclusions about atmospheric and in-stack aerosol chemistry and physics by utilizing size distributions of various aerosol components measured. A sample dissolution with dilute nitric acid in an ultrasonic bath and subsequent graphite furnace atomic absorption spectrometric analysis was found to result in low blank values and good recoveries for several elements in atmospheric fine particle size fractions below 2 {mu}m of equivalent aerodynamic particle diameter (EAD). Furthermore, it turned out that a substantial amount of analyses associated with insoluble material could be recovered since suspensions were formed. The size distribution measurements of in-stack combustion aerosols indicated two modal size distributions for most components measured. The existence of the fine particle mode suggests that a substantial fraction of such elements with two modal size distributions may vaporize and nucleate during the combustion process. In southern Norway, size distributions of atmospheric aerosol components usually exhibited one or two fine particle modes and one or two coarse particle modes. Atmospheric relative humidity values higher than 80% resulted in significant increase of the mass median diameters of the droplet mode. Important local and/or regional sources of As, Br, I, K, Mn, Pb, Sb, Si and Zn were found to exist in southern Norway. The existence of these sources was reflected in the corresponding size distributions determined, and was utilized in the development of a source identification method based on size distribution data. On the Finnish south coast, atmospheric coarse particle nitrate was found to be formed mostly through an atmospheric reaction of nitric acid with existing coarse particle sea salt but reactions and/or adsorption of nitric acid with soil derived particles also occurred. Chloride was depleted when acidic species reacted

  14. 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.

  15. 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

  16. 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

  17. Reliability-based sensitivity of mechanical components with arbitrary distribution parameters

    International Nuclear Information System (INIS)

    Zhang, Yi Min; Yang, Zhou; Wen, Bang Chun; He, Xiang Dong; Liu, Qiaoling

    2010-01-01

    This paper presents a reliability-based sensitivity method for mechanical components with arbitrary distribution parameters. Techniques from the perturbation method, the Edgeworth series, the reliability-based design theory, and the sensitivity analysis approach were employed directly to calculate the reliability-based sensitivity of mechanical components on the condition that the first four moments of the original random variables are known. The reliability-based sensitivity information of the mechanical components can be accurately and quickly obtained using a practical computer program. The effects of the design parameters on the reliability of mechanical components were studied. The method presented in this paper provides the theoretic basis for the reliability-based design of mechanical components

  18. Automatic ECG analysis using principal component analysis and wavelet transformation

    OpenAIRE

    Khawaja, Antoun

    2007-01-01

    The main objective of this book is to analyse and detect small changes in ECG waves and complexes that indicate cardiac diseases and disorders. Detecting predisposition to Torsade de Points (TDP) by analysing the beat-to-beat variability in T wave morphology is the main core of this work. The second main topic is detecting small changes in QRS complex and predicting future QRS complexes of patients. Moreover, the last main topic is clustering similar ECG components in different groups.

  19. 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

  20. 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.

  1. 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

  2. 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

  3. Context sensitivity and ambiguity in component-based systems design

    Energy Technology Data Exchange (ETDEWEB)

    Bespalko, S.J.; Sindt, A.

    1997-10-01

    Designers of components-based, real-time systems need to guarantee to correctness of soft-ware and its output. Complexity of a system, and thus the propensity for error, is best characterized by the number of states a component can encounter. In many cases, large numbers of states arise where the processing is highly dependent on context. In these cases, states are often missed, leading to errors. The following are proposals for compactly specifying system states which allow the factoring of complex components into a control module and a semantic processing module. Further, the need for methods that allow for the explicit representation of ambiguity and uncertainty in the design of components is discussed. Presented herein are examples of real-world problems which are highly context-sensitive or are inherently ambiguous.

  4. Summary of component reliability data for probabilistic safety analysis of Korean standard nuclear power plant

    International Nuclear Information System (INIS)

    Choi, S. Y.; Han, S. H.

    2004-01-01

    The reliability data of Korean NPP that reflects the plant specific characteristics is necessary for PSA of Korean nuclear power plants. We have performed a study to develop the component reliability DB and S/W for component reliability analysis. Based on the system, we had have collected the component operation data and failure/repair data during plant operation data to 1998/2000 for YGN 3,4/UCN 3,4 respectively. Recently, we have upgraded the database by collecting additional data by 2002 for Korean standard nuclear power plants and performed component reliability analysis and Bayesian analysis again. In this paper, we supply the summary of component reliability data for probabilistic safety analysis of Korean standard nuclear power plant and describe the plant specific characteristics compared to the generic data

  5. The analysis of multivariate group differences using common principal components

    NARCIS (Netherlands)

    Bechger, T.M.; Blanca, M.J.; Maris, G.

    2014-01-01

    Although it is simple to determine whether multivariate group differences are statistically significant or not, such differences are often difficult to interpret. This article is about common principal components analysis as a tool for the exploratory investigation of multivariate group differences

  6. 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.

  7. 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...

  8. Principal component analysis of image gradient orientations for face recognition

    NARCIS (Netherlands)

    Tzimiropoulos, Georgios; Zafeiriou, Stefanos; Pantic, Maja

    We introduce the notion of Principal Component Analysis (PCA) of image gradient orientations. As image data is typically noisy, but noise is substantially different from Gaussian, traditional PCA of pixel intensities very often fails to estimate reliably the low-dimensional subspace of a given data

  9. Adaptive tools in virtual environments: Independent component analysis for multimedia

    DEFF Research Database (Denmark)

    Kolenda, Thomas

    2002-01-01

    The thesis investigates the role of independent component analysis in the setting of virtual environments, with the purpose of finding properties that reflect human context. A general framework for performing unsupervised classification with ICA is presented in extension to the latent semantic in...... were compared to investigate computational differences and separation results. The ICA properties were finally implemented in a chat room analysis tool and briefly investigated for visualization of search engines results....

  10. 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.

  11. Management of Globally Distributed Component-Based Software Development Projects

    NARCIS (Netherlands)

    J. Kotlarsky (Julia)

    2005-01-01

    textabstractGlobally Distributed Component-Based Development (GD CBD) is expected to become a promising area, as increasing numbers of companies are setting up software development in a globally distributed environment and at the same time are adopting CBD methodologies. Being an emerging area, the

  12. Monitoring extensions for component-based distributed software

    NARCIS (Netherlands)

    Diakov, N.K.; Papir, Z.; van Sinderen, Marten J.; Quartel, Dick

    2000-01-01

    This paper defines a generic class of monitoring extensions to component-based distributed enterprise software. Introducing a monitoring extension to a legacy application system can be very costly. In this paper, we identify the minimum support for application monitoring within the generic

  13. 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

  14. 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.

  15. Major component analysis of dynamic networks of physiologic organ interactions

    International Nuclear Information System (INIS)

    Liu, Kang K L; Ma, Qianli D Y; Ivanov, Plamen Ch; Bartsch, Ronny P

    2015-01-01

    The human organism is a complex network of interconnected organ systems, where the behavior of one system affects the dynamics of other systems. Identifying and quantifying dynamical networks of diverse physiologic systems under varied conditions is a challenge due to the complexity in the output dynamics of the individual systems and the transient and nonlinear characteristics of their coupling. We introduce a novel computational method based on the concept of time delay stability and major component analysis to investigate how organ systems interact as a network to coordinate their functions. We analyze a large database of continuously recorded multi-channel physiologic signals from healthy young subjects during night-time sleep. We identify a network of dynamic interactions between key physiologic systems in the human organism. Further, we find that each physiologic state is characterized by a distinct network structure with different relative contribution from individual organ systems to the global network dynamics. Specifically, we observe a gradual decrease in the strength of coupling of heart and respiration to the rest of the network with transition from wake to deep sleep, and in contrast, an increased relative contribution to network dynamics from chin and leg muscle tone and eye movement, demonstrating a robust association between network topology and physiologic function. (paper)

  16. Principal Component Analysis of Process Datasets with Missing Values

    Directory of Open Access Journals (Sweden)

    Kristen A. Severson

    2017-07-01

    Full Text Available Datasets with missing values arising from causes such as sensor failure, inconsistent sampling rates, and merging data from different systems are common in the process industry. Methods for handling missing data typically operate during data pre-processing, but can also occur during model building. This article considers missing data within the context of principal component analysis (PCA, which is a method originally developed for complete data that has widespread industrial application in multivariate statistical process control. Due to the prevalence of missing data and the success of PCA for handling complete data, several PCA algorithms that can act on incomplete data have been proposed. Here, algorithms for applying PCA to datasets with missing values are reviewed. A case study is presented to demonstrate the performance of the algorithms and suggestions are made with respect to choosing which algorithm is most appropriate for particular settings. An alternating algorithm based on the singular value decomposition achieved the best results in the majority of test cases involving process datasets.

  17. Predicting Insolvency : A comparison between discriminant analysis and logistic regression using principal components

    OpenAIRE

    Geroukis, Asterios; Brorson, Erik

    2014-01-01

    In this study, we compare the two statistical techniques logistic regression and discriminant analysis to see how well they classify companies based on clusters – made from the solvency ratio ­– using principal components as independent variables. The principal components are made with different financial ratios. We use cluster analysis to find groups with low, medium and high solvency ratio of 1200 different companies found on the NASDAQ stock market and use this as an apriori definition of ...

  18. 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.

  19. 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.

  20. Component-based software for high-performance scientific computing

    Energy Technology Data Exchange (ETDEWEB)

    Alexeev, Yuri; Allan, Benjamin A; Armstrong, Robert C; Bernholdt, David E; Dahlgren, Tamara L; Gannon, Dennis; Janssen, Curtis L; Kenny, Joseph P; Krishnan, Manojkumar; Kohl, James A; Kumfert, Gary; McInnes, Lois Curfman; Nieplocha, Jarek; Parker, Steven G; Rasmussen, Craig; Windus, Theresa L

    2005-01-01

    Recent advances in both computational hardware and multidisciplinary science have given rise to an unprecedented level of complexity in scientific simulation software. This paper describes an ongoing grass roots effort aimed at addressing complexity in high-performance computing through the use of Component-Based Software Engineering (CBSE). Highlights of the benefits and accomplishments of the Common Component Architecture (CCA) Forum and SciDAC ISIC are given, followed by an illustrative example of how the CCA has been applied to drive scientific discovery in quantum chemistry. Thrusts for future research are also described briefly.

  1. Component-based software for high-performance scientific computing

    International Nuclear Information System (INIS)

    Alexeev, Yuri; Allan, Benjamin A; Armstrong, Robert C; Bernholdt, David E; Dahlgren, Tamara L; Gannon, Dennis; Janssen, Curtis L; Kenny, Joseph P; Krishnan, Manojkumar; Kohl, James A; Kumfert, Gary; McInnes, Lois Curfman; Nieplocha, Jarek; Parker, Steven G; Rasmussen, Craig; Windus, Theresa L

    2005-01-01

    Recent advances in both computational hardware and multidisciplinary science have given rise to an unprecedented level of complexity in scientific simulation software. This paper describes an ongoing grass roots effort aimed at addressing complexity in high-performance computing through the use of Component-Based Software Engineering (CBSE). Highlights of the benefits and accomplishments of the Common Component Architecture (CCA) Forum and SciDAC ISIC are given, followed by an illustrative example of how the CCA has been applied to drive scientific discovery in quantum chemistry. Thrusts for future research are also described briefly

  2. APPLICATION OF PRINCIPAL COMPONENT ANALYSIS TO RELAXOGRAPHIC IMAGES

    International Nuclear Information System (INIS)

    STOYANOVA, R.S.; OCHS, M.F.; BROWN, T.R.; ROONEY, W.D.; LI, X.; LEE, J.H.; SPRINGER, C.S.

    1999-01-01

    Standard analysis methods for processing inversion recovery MR images traditionally have used single pixel techniques. In these techniques each pixel is independently fit to an exponential recovery, and spatial correlations in the data set are ignored. By analyzing the image as a complete dataset, improved error analysis and automatic segmentation can be achieved. Here, the authors apply principal component analysis (PCA) to a series of relaxographic images. This procedure decomposes the 3-dimensional data set into three separate images and corresponding recovery times. They attribute the 3 images to be spatial representations of gray matter (GM), white matter (WM) and cerebrospinal fluid (CSF) content

  3. A CORBA BASED ARCHITECTURE FOR ACCESSING REUSABLE SOFTWARE COMPONENTS ON THE WEB.

    Directory of Open Access Journals (Sweden)

    R. Cenk ERDUR

    2003-01-01

    Full Text Available In a very near future, as a result of the continious growth of Internet and advances in networking technologies, Internet will become the common software repository for people and organizations who employ component based reuse approach in their software development life cycles. In order to use the reusable components such as source codes, analysis, designs, design patterns during new software development processes, environments that support the identification of the components over Internet are needed. Basic elements of such an environment are the coordinator programs which deliver user requests to appropriate component libraries, user interfaces for querying, and programs that wrap the component libraries. First, a CORBA based architecture is proposed for such an environment. Then, an alternative architecture that is based on the Java 2 platform technologies is given for the same environment. Finally, the two architectures are compared.

  4. 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.

  5. A method for the detection of alcohol vapours based on optical sensing of magnesium 5,10,15,20-tetraphenyl porphyrin thin film by an optical spectrometer and principal component analysis

    Energy Technology Data Exchange (ETDEWEB)

    Kladsomboon, Sumana [Department of Physics and Center of Nanoscience and Nanotechnology, Faculty of Science, Mahidol University, Bangkok 10400 (Thailand); Kerdcharoen, Teerakiat, E-mail: teerakiat.ker@mahidol.ac.th [Department of Physics and Center of Nanoscience and Nanotechnology, Faculty of Science, Mahidol University, Bangkok 10400 (Thailand); NANOTEC Center of Excellence at Mahidol University, National Nanotechnology Center (Thailand)

    2012-12-13

    Highlights: Black-Right-Pointing-Pointer We prepared magnesium porphyrin thin film as optical sensing materials. Black-Right-Pointing-Pointer UV-vis spectrometer was modified to perform as optical artificial nose. Black-Right-Pointing-Pointer Change in optical absorption at various spectral regions is used as a sensor array. Black-Right-Pointing-Pointer Principal component analysis was employed to discriminate alcohol vapours. - Abstract: In this work we have proposed a method for the detection of alcohol vapours, i.e. methanol, ethanol and isopropanol, based on the optical sensing response of magnesium 5,10,15,20-tetraphenyl porphyrin (MgTPP) thin films, as measured by optical spectrometry with the assistance of chemometric analysis. We have implemented a scheme which allows a laboratory UV-vis spectrometer to act as a so-called 'electronic nose' with very little modification. MgTPP thin films were prepared by a spin coating technique, using chloroform as the solvent, and then subjected to thermal annealing at 280 Degree-Sign C in an argon atmosphere. These MgTPP optical gas sensors presented significant responses with methanol compared to ethanol and isopropanol, based on the dynamic flow of alcohol vapours at the same mol% of alcohol concentration. Density functional theory (DFT) calculations were performed to model the underlying mechanism of this selectivity. The performance of the optical gas sensors was optimised by varying the fabrication parameters. It is hoped that the MgTPP thin film together with an off-the-shelf optical spectrometer and a simple chemometrics algorithm can be a valuable tool for the analysis of alcoholic content in the beverage industry.

  6. A method for the detection of alcohol vapours based on optical sensing of magnesium 5,10,15,20-tetraphenyl porphyrin thin film by an optical spectrometer and principal component analysis

    International Nuclear Information System (INIS)

    Kladsomboon, Sumana; Kerdcharoen, Teerakiat

    2012-01-01

    Highlights: ► We prepared magnesium porphyrin thin film as optical sensing materials. ► UV–vis spectrometer was modified to perform as optical artificial nose. ► Change in optical absorption at various spectral regions is used as a sensor array. ► Principal component analysis was employed to discriminate alcohol vapours. - Abstract: In this work we have proposed a method for the detection of alcohol vapours, i.e. methanol, ethanol and isopropanol, based on the optical sensing response of magnesium 5,10,15,20-tetraphenyl porphyrin (MgTPP) thin films, as measured by optical spectrometry with the assistance of chemometric analysis. We have implemented a scheme which allows a laboratory UV–vis spectrometer to act as a so-called “electronic nose” with very little modification. MgTPP thin films were prepared by a spin coating technique, using chloroform as the solvent, and then subjected to thermal annealing at 280 °C in an argon atmosphere. These MgTPP optical gas sensors presented significant responses with methanol compared to ethanol and isopropanol, based on the dynamic flow of alcohol vapours at the same mol% of alcohol concentration. Density functional theory (DFT) calculations were performed to model the underlying mechanism of this selectivity. The performance of the optical gas sensors was optimised by varying the fabrication parameters. It is hoped that the MgTPP thin film together with an off-the-shelf optical spectrometer and a simple chemometrics algorithm can be a valuable tool for the analysis of alcoholic content in the beverage industry.

  7. 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.

  8. Empirical projection-based basis-component decomposition method

    Science.gov (United States)

    Brendel, Bernhard; Roessl, Ewald; Schlomka, Jens-Peter; Proksa, Roland

    2009-02-01

    Advances in the development of semiconductor based, photon-counting x-ray detectors stimulate research in the domain of energy-resolving pre-clinical and clinical computed tomography (CT). For counting detectors acquiring x-ray attenuation in at least three different energy windows, an extended basis component decomposition can be performed in which in addition to the conventional approach of Alvarez and Macovski a third basis component is introduced, e.g., a gadolinium based CT contrast material. After the decomposition of the measured projection data into the basis component projections, conventional filtered-backprojection reconstruction is performed to obtain the basis-component images. In recent work, this basis component decomposition was obtained by maximizing the likelihood-function of the measurements. This procedure is time consuming and often unstable for excessively noisy data or low intrinsic energy resolution of the detector. Therefore, alternative procedures are of interest. Here, we introduce a generalization of the idea of empirical dual-energy processing published by Stenner et al. to multi-energy, photon-counting CT raw data. Instead of working in the image-domain, we use prior spectral knowledge about the acquisition system (tube spectra, bin sensitivities) to parameterize the line-integrals of the basis component decomposition directly in the projection domain. We compare this empirical approach with the maximum-likelihood (ML) approach considering image noise and image bias (artifacts) and see that only moderate noise increase is to be expected for small bias in the empirical approach. Given the drastic reduction of pre-processing time, the empirical approach is considered a viable alternative to the ML approach.

  9. Eliminating the Influence of Harmonic Components in Operational Modal Analysis

    DEFF Research Database (Denmark)

    Jacobsen, Niels-Jørgen; Andersen, Palle; Brincker, Rune

    2007-01-01

    structures, in contrast, are subject inherently to deterministic forces due to the rotating parts in the machinery. These forces are seen as harmonic components in the responses, and their influence should be eliminated before extracting the modes in their vicinity. This paper describes a new method based...... on the well-known Enhanced Frequency Domain Decomposition (EFDD) technique for eliminating these harmonic components in the modal parameter extraction process. For assessing the quality of the method, various experiments were carried out where the results were compared with those obtained with pure stochastic...

  10. Optimization benefits analysis in production process of fabrication components

    Science.gov (United States)

    Prasetyani, R.; Rafsanjani, A. Y.; Rimantho, D.

    2017-12-01

    The determination of an optimal number of product combinations is important. The main problem at part and service department in PT. United Tractors Pandu Engineering (shortened to PT.UTPE) Is the optimization of the combination of fabrication component products (known as Liner Plate) which influence to the profit that will be obtained by the company. Liner Plate is a fabrication component that serves as a protector of core structure for heavy duty attachment, such as HD Vessel, HD Bucket, HD Shovel, and HD Blade. The graph of liner plate sales from January to December 2016 has fluctuated and there is no direct conclusion about the optimization of production of such fabrication components. The optimal product combination can be achieved by calculating and plotting the amount of production output and input appropriately. The method that used in this study is linear programming methods with primal, dual, and sensitivity analysis using QM software for Windows to obtain optimal fabrication components. In the optimal combination of components, PT. UTPE provide the profit increase of Rp. 105,285,000.00 for a total of Rp. 3,046,525,000.00 per month and the production of a total combination of 71 units per unit variance per month.

  11. 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.

  12. 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

  13. Polarized BRDF for coatings based on three-component assumption

    Science.gov (United States)

    Liu, Hong; Zhu, Jingping; Wang, Kai; Xu, Rong

    2017-02-01

    A pBRDF(polarized bidirectional reflection distribution function) model for coatings is given based on three-component reflection assumption in order to improve the polarized scattering simulation capability for space objects. In this model, the specular reflection is given based on microfacet theory, the multiple reflection and volume scattering are given separately according to experimental results. The polarization of specular reflection is considered from Fresnel's law, and both multiple reflection and volume scattering are assumed depolarized. Simulation and measurement results of two satellite coating samples SR107 and S781 are given to validate that the pBRDF modeling accuracy can be significantly improved by the three-component model given in this paper.

  14. Trimming of mammalian transcriptional networks using network component analysis

    Directory of Open Access Journals (Sweden)

    Liao James C

    2010-10-01

    Full Text Available Abstract Background Network Component Analysis (NCA has been used to deduce the activities of transcription factors (TFs from gene expression data and the TF-gene binding relationship. However, the TF-gene interaction varies in different environmental conditions and tissues, but such information is rarely available and cannot be predicted simply by motif analysis. Thus, it is beneficial to identify key TF-gene interactions under the experimental condition based on transcriptome data. Such information would be useful in identifying key regulatory pathways and gene markers of TFs in further studies. Results We developed an algorithm to trim network connectivity such that the important regulatory interactions between the TFs and the genes were retained and the regulatory signals were deduced. Theoretical studies demonstrated that the regulatory signals were accurately reconstructed even in the case where only three independent transcriptome datasets were available. At least 80% of the main target genes were correctly predicted in the extreme condition of high noise level and small number of datasets. Our algorithm was tested with transcriptome data taken from mice under rapamycin treatment. The initial network topology from the literature contains 70 TFs, 778 genes, and 1423 edges between the TFs and genes. Our method retained 1074 edges (i.e. 75% of the original edge number and identified 17 TFs as being significantly perturbed under the experimental condition. Twelve of these TFs are involved in MAPK signaling or myeloid leukemia pathways defined in the KEGG database, or are known to physically interact with each other. Additionally, four of these TFs, which are Hif1a, Cebpb, Nfkb1, and Atf1, are known targets of rapamycin. Furthermore, the trimmed network was able to predict Eno1 as an important target of Hif1a; this key interaction could not be detected without trimming the regulatory network. Conclusions The advantage of our new algorithm

  15. Secure wireless embedded systems via component-based design

    DEFF Research Database (Denmark)

    Hjorth, T.; Torbensen, R.

    2010-01-01

    This paper introduces the method secure-by-design as a way of constructing wireless embedded systems using component-based modeling frameworks. This facilitates design of secure applications through verified, reusable software. Following this method we propose a security framework with a secure c......, with full support for confidentiality, authentication, and integrity using keypairs. The approach has been demonstrated in a multi-platform home automation prototype that can remotely unlock a door using a PDA over the Internet....

  16. Implementing components of the routines-based model

    OpenAIRE

    McWilliam, Robin; Fernández Valero, Rosa

    2015-01-01

    The MBR is comprised of 17 components that can generally be grouped into practices related to (a) functional assessment and intervention planning (for example, Routines-Based Interview), (b) organization of services (including location and staffing), (c) service delivery to children and families (using a consultative approach with families and teachers, integrated therapy), (d) classroom organization (for example, classroom zones), and (e) supervision and training through ch...

  17. 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)

  18. 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

  19. Design Optimization Method for Composite Components Based on Moment Reliability-Sensitivity Criteria

    Science.gov (United States)

    Sun, Zhigang; Wang, Changxi; Niu, Xuming; Song, Yingdong

    2017-08-01

    In this paper, a Reliability-Sensitivity Based Design Optimization (RSBDO) methodology for the design of the ceramic matrix composites (CMCs) components has been proposed. A practical and efficient method for reliability analysis and sensitivity analysis of complex components with arbitrary distribution parameters are investigated by using the perturbation method, the respond surface method, the Edgeworth series and the sensitivity analysis approach. The RSBDO methodology is then established by incorporating sensitivity calculation model into RBDO methodology. Finally, the proposed RSBDO methodology is applied to the design of the CMCs components. By comparing with Monte Carlo simulation, the numerical results demonstrate that the proposed methodology provides an accurate, convergent and computationally efficient method for reliability-analysis based finite element modeling engineering practice.

  20. 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

  1. Empirical usability testing in a component-based environment : improving test efficiency with component-specific usability measures

    NARCIS (Netherlands)

    Brinkman, W.P.; Haakma, R.; Bouwhuis, D.G.; Bastide, R.; Palanque, P.; Roth, J.

    2005-01-01

    This paper addresses the issue of usability testing in a component-based software engineering environment, specifically measuring the usability of different versions of a component in a more powerful manner than other, more holistic, usability methods. Three component-specific usability measures are

  2. EEG-Based Emotion Recognition Using Deep Learning Network with Principal Component Based Covariate Shift Adaptation

    Directory of Open Access Journals (Sweden)

    Suwicha Jirayucharoensak

    2014-01-01

    Full Text Available Automatic emotion recognition is one of the most challenging tasks. To detect emotion from nonstationary EEG signals, a sophisticated learning algorithm that can represent high-level abstraction is required. This study proposes the utilization of a deep learning network (DLN to discover unknown feature correlation between input signals that is crucial for the learning task. The DLN is implemented with a stacked autoencoder (SAE using hierarchical feature learning approach. Input features of the network are power spectral densities of 32-channel EEG signals from 32 subjects. To alleviate overfitting problem, principal component analysis (PCA is applied to extract the most important components of initial input features. Furthermore, covariate shift adaptation of the principal components is implemented to minimize the nonstationary effect of EEG signals. Experimental results show that the DLN is capable of classifying three different levels of valence and arousal with accuracy of 49.52% and 46.03%, respectively. Principal component based covariate shift adaptation enhances the respective classification accuracy by 5.55% and 6.53%. Moreover, DLN provides better performance compared to SVM and naive Bayes classifiers.

  3. Dynamic analysis of the radiolysis of binary component system

    International Nuclear Information System (INIS)

    Katayama, M.; Trumbore, C.N.

    1975-01-01

    Dynamic analysis was performed on a variety of combinations of components in the radiolysis of binary system, taking the hydrogen-producing reaction with hydrocarbon RH 2 as an example. A definite rule was able to be established from this analysis, which is useful for revealing the reaction mechanism. The combinations were as follows: 1) both components A and B do not interact but serve only as diluents, 2) A is a diluent, and B is a radical captor, 3) both A and B are radical captors, 4-1) A is a diluent, and B decomposes after the reception of the exciting energy of A, 4-2) A is a diluent, and B does not participate in decomposition after the reception of the exciting energy of A, 5-1) A is a radical captor, and B decomposes after the reception of the exciting energy of A, 5-2) A is a radical captor, and B does not participate in decomposition after the reception of the exciting energy of A, 6-1) both A and B decompose after the reception of the exciting energy of the partner component; and 6-2) both A and B do not decompose after the reception of the exciting energy of the partner component. According to the dynamical analysis of the above nine combinations, it can be pointed out that if excitation transfer participates, the similar phenomena to radical capture are presented apparently. It is desirable to measure the yield of radicals experimentally with the system which need not much consideration to the excitation transfer. Isotope substitution mixture system is conceived as one of such system. This analytical method was applied to the system containing cyclopentanone, such as cyclopentanone-cyclohexane system. (Iwakiri, K.)

  4. Component Pin Recognition Using Algorithms Based on Machine Learning

    Science.gov (United States)

    Xiao, Yang; Hu, Hong; Liu, Ze; Xu, Jiangchang

    2018-04-01

    The purpose of machine vision for a plug-in machine is to improve the machine’s stability and accuracy, and recognition of the component pin is an important part of the vision. This paper focuses on component pin recognition using three different techniques. The first technique involves traditional image processing using the core algorithm for binary large object (BLOB) analysis. The second technique uses the histogram of oriented gradients (HOG), to experimentally compare the effect of the support vector machine (SVM) and the adaptive boosting machine (AdaBoost) learning meta-algorithm classifiers. The third technique is the use of an in-depth learning method known as convolution neural network (CNN), which involves identifying the pin by comparing a sample to its training. The main purpose of the research presented in this paper is to increase the knowledge of learning methods used in the plug-in machine industry in order to achieve better results.

  5. Analysis of the Components of Economic Potential of Agricultural Enterprises

    OpenAIRE

    Vyacheslav Skobara; Volodymyr Podkopaev

    2014-01-01

    Problems of efficiency of enterprises are increasingly associated with the use of the economic potential of the company. This article addresses the structural components of the economic potential of agricultural enterprise, development and substantiation of the model of economic potential with due account of the peculiarities of agricultural production. Based on the study of various approaches to the potential structure established is the definition of of production, labour, financial and man...

  6. Representation for dialect recognition using topographic independent component analysis

    Science.gov (United States)

    Wei, Qu

    2004-10-01

    In dialect speech recognition, the feature of tone in one dialect is subject to changes in pitch frequency as well as the length of tone. It is beneficial for the recognition if a representation can be derived to account for the frequency and length changes of tone in an effective and meaningful way. In this paper, we propose a method for learning such a representation from a set of unlabeled speech sentences containing the features of the dialect changed from various pitch frequencies and time length. Topographic independent component analysis (TICA) is applied for the unsupervised learning to produce an emergent result that is a topographic matrix made up of basis components. The dialect speech is topographic in the following sense: the basis components as the units of the speech are ordered in the feature matrix such that components of one dialect are grouped in one axis and changes in time windows are accounted for in the other axis. This provides a meaningful set of basis vectors that may be used to construct dialect subspaces for dialect speech recognition.

  7. Development of component failure data for seismic risk analysis

    International Nuclear Information System (INIS)

    Fray, R.R.; Moulia, T.A.

    1981-01-01

    This paper describes the quantification and utilization of seismic failure data used in the Diablo Canyon Seismic Risk Study. A single variable representation of earthquake severity that uses peak horizontal ground acceleration to characterize earthquake severity was employed. The use of a multiple variable representation would allow direct consideration of vertical accelerations and the spectral nature of earthquakes but would have added such complexity that the study would not have been feasible. Vertical accelerations and spectral nature were indirectly considered because component failure data were derived from design analyses, qualification tests and engineering judgment that did include such considerations. Two types of functions were used to describe component failure probabilities. Ramp functions were used for components, such as piping and structures, qualified by stress analysis. 'Anchor points' for ramp functions were selected by assuming a zero probability of failure at code allowable stress levels and unity probability of failure at ultimate stress levels. The accelerations corresponding to allowable and ultimate stress levels were determined by conservatively assuming a linear relationship between seismic stress and ground acceleration. Step functions were used for components, such as mechanical and electrical equipment, qualified by testing. Anchor points for step functions were selected by assuming a unity probability of failure above the qualification acceleration. (orig./HP)

  8. Integrative sparse principal component analysis of gene expression data.

    Science.gov (United States)

    Liu, Mengque; Fan, Xinyan; Fang, Kuangnan; Zhang, Qingzhao; Ma, Shuangge

    2017-12-01

    In the analysis of gene expression data, dimension reduction techniques have been extensively adopted. The most popular one is perhaps the PCA (principal component analysis). To generate more reliable and more interpretable results, the SPCA (sparse PCA) technique has been developed. With the "small sample size, high dimensionality" characteristic of gene expression data, the analysis results generated from a single dataset are often unsatisfactory. Under contexts other than dimension reduction, integrative analysis techniques, which jointly analyze the raw data of multiple independent datasets, have been developed and shown to outperform "classic" meta-analysis and other multidatasets techniques and single-dataset analysis. In this study, we conduct integrative analysis by developing the iSPCA (integrative SPCA) method. iSPCA achieves the selection and estimation of sparse loadings using a group penalty. To take advantage of the similarity across datasets and generate more accurate results, we further impose contrasted penalties. Different penalties are proposed to accommodate different data conditions. Extensive simulations show that iSPCA outperforms the alternatives under a wide spectrum of settings. The analysis of breast cancer and pancreatic cancer data further shows iSPCA's satisfactory performance. © 2017 WILEY PERIODICALS, INC.

  9. Design and analysis of automobile components using industrial procedures

    Science.gov (United States)

    Kedar, B.; Ashok, B.; Rastogi, Nisha; Shetty, Siddhanth

    2017-11-01

    Today’s automobiles depend upon mechanical systems that are crucial for aiding in the movement and safety features of the vehicle. Various safety systems such as Antilock Braking System (ABS) and passenger restraint systems have been developed to ensure that in the event of a collision be it head on or any other type, the safety of the passenger is ensured. On the other side, manufacturers also want their customers to have a good experience while driving and thus aim to improve the handling and the drivability of the vehicle. Electronics systems such as Cruise Control and active suspension systems are designed to ensure passenger comfort. Finally, to ensure optimum and safe driving the various components of a vehicle must be manufactured using the latest state of the art processes and must be tested and inspected with utmost care so that any defective component can be prevented from being sent out right at the beginning of the supply chain. Therefore, processes which can improve the lifetime of their respective components are in high demand and much research and development is done on these processes. With a solid base research conducted, these processes can be used in a much more versatile manner for different components, made up of different materials and under different input conditions. This will help increase the profitability of the process and also upgrade its value to the industry.

  10. Cognitive components underpinning the development of model-based learning.

    Science.gov (United States)

    Potter, Tracey C S; Bryce, Nessa V; Hartley, Catherine A

    2017-06-01

    Reinforcement learning theory distinguishes "model-free" learning, which fosters reflexive repetition of previously rewarded actions, from "model-based" learning, which recruits a mental model of the environment to flexibly select goal-directed actions. Whereas model-free learning is evident across development, recruitment of model-based learning appears to increase with age. However, the cognitive processes underlying the development of model-based learning remain poorly characterized. Here, we examined whether age-related differences in cognitive processes underlying the construction and flexible recruitment of mental models predict developmental increases in model-based choice. In a cohort of participants aged 9-25, we examined whether the abilities to infer sequential regularities in the environment ("statistical learning"), maintain information in an active state ("working memory") and integrate distant concepts to solve problems ("fluid reasoning") predicted age-related improvements in model-based choice. We found that age-related improvements in statistical learning performance did not mediate the relationship between age and model-based choice. Ceiling performance on our working memory assay prevented examination of its contribution to model-based learning. However, age-related improvements in fluid reasoning statistically mediated the developmental increase in the recruitment of a model-based strategy. These findings suggest that gradual development of fluid reasoning may be a critical component process underlying the emergence of model-based learning. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

  11. A Component Based Approach to Scientific Workflow Management

    CERN Document Server

    Le Goff, Jean-Marie; Baker, Nigel; Brooks, Peter; McClatchey, Richard

    2001-01-01

    CRISTAL is a distributed scientific workflow system used in the manufacturing and production phases of HEP experiment construction at CERN. The CRISTAL project has studied the use of a description driven approach, using meta- modelling techniques, to manage the evolving needs of a large physics community. Interest from such diverse communities as bio-informatics and manufacturing has motivated the CRISTAL team to re-engineer the system to customize functionality according to end user requirements but maximize software reuse in the process. The next generation CRISTAL vision is to build a generic component architecture from which a complete software product line can be generated according to the particular needs of the target enterprise. This paper discusses the issues of adopting a component product line based approach and our experiences of software reuse.

  12. A component based approach to scientific workflow management

    International Nuclear Information System (INIS)

    Baker, N.; Brooks, P.; McClatchey, R.; Kovacs, Z.; LeGoff, J.-M.

    2001-01-01

    CRISTAL is a distributed scientific workflow system used in the manufacturing and production phases of HEP experiment construction at CERN. The CRISTAL project has studied the use of a description driven approach, using meta-modelling techniques, to manage the evolving needs of a large physics community. Interest from such diverse communities as bio-informatics and manufacturing has motivated the CRISTAL team to re-engineer the system to customize functionality according to end user requirements but maximize software reuse in the process. The next generation CRISTAL vision is to build a generic component architecture from which a complete software product line can be generated according to the particular needs of the target enterprise. This paper discusses the issues of adopting a component product line based approach and our experiences of software reuse

  13. Analysis of Femtosecond Timing Noise and Stability in Microwave Components

    International Nuclear Information System (INIS)

    2011-01-01

    To probe chemical dynamics, X-ray pump-probe experiments trigger a change in a sample with an optical laser pulse, followed by an X-ray probe. At the Linac Coherent Light Source, LCLS, timing differences between the optical pulse and x-ray probe have been observed with an accuracy as low as 50 femtoseconds. This sets a lower bound on the number of frames one can arrange over a time scale to recreate a 'movie' of the chemical reaction. The timing system is based on phase measurements from signals corresponding to the two laser pulses; these measurements are done by using a double-balanced mixer for detection. To increase the accuracy of the system, this paper studies parameters affecting phase detection systems based on mixers, such as signal input power, noise levels, temperature drift, and the effect these parameters have on components such as the mixers, splitters, amplifiers, and phase shifters. Noise data taken with a spectrum analyzer show that splitters based on ferrite cores perform with less noise than strip-line splitters. The data also shows that noise in specific mixers does not correspond with the changes in sensitivity per input power level. Temperature drift is seen to exist on a scale between 1 and 27 fs/ o C for all of the components tested. Results show that any components using more metallic conductor tend to exhibit more noise as well as more temperature drift. The scale of these effects is large enough that specific care should be given when choosing components and designing the housing of high precision microwave mixing systems for use in detection systems such as the LCLS. With these improvements, the timing accuracy can be improved to lower than currently possible.

  14. Demixed principal component analysis of neural population data.

    Science.gov (United States)

    Kobak, Dmitry; Brendel, Wieland; Constantinidis, Christos; Feierstein, Claudia E; Kepecs, Adam; Mainen, Zachary F; Qi, Xue-Lian; Romo, Ranulfo; Uchida, Naoshige; Machens, Christian K

    2016-04-12

    Neurons in higher cortical areas, such as the prefrontal cortex, are often tuned to a variety of sensory and motor variables, and are therefore said to display mixed selectivity. This complexity of single neuron responses can obscure what information these areas represent and how it is represented. Here we demonstrate the advantages of a new dimensionality reduction technique, demixed principal component analysis (dPCA), that decomposes population activity into a few components. In addition to systematically capturing the majority of the variance of the data, dPCA also exposes the dependence of the neural representation on task parameters such as stimuli, decisions, or rewards. To illustrate our method we reanalyze population data from four datasets comprising different species, different cortical areas and different experimental tasks. In each case, dPCA provides a concise way of visualizing the data that summarizes the task-dependent features of the population response in a single figure.

  15. 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)

  16. 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.

  17. The Blame Game: Performance Analysis of Speaker Diarization System Components

    NARCIS (Netherlands)

    Huijbregts, M.A.H.; Wooters, Chuck

    2007-01-01

    In this paper we discuss the performance analysis of a speaker diarization system similar to the system that was submitted by ICSI at the NIST RT06s evaluation benchmark. The analysis that is based on a series of oracle experiments, provides a good understanding of the performance of each system

  18. Analysis and test of insulated components for rotary engine

    Science.gov (United States)

    Badgley, Patrick R.; Doup, Douglas; Kamo, Roy

    1989-01-01

    The direct-injection stratified-charge (DISC) rotary engine, while attractive for aviation applications due to its light weight, multifuel capability, and potentially low fuel consumption, has until now required a bulky and heavy liquid-cooling system. NASA-Lewis has undertaken the development of a cooling system-obviating, thermodynamically superior adiabatic rotary engine employing state-of-the-art thermal barrier coatings to thermally insulate engine components. The thermal barrier coating material for the cast aluminum, stainless steel, and ductile cast iron components was plasma-sprayed zirconia. DISC engine tests indicate effective thermal barrier-based heat loss reduction, but call for superior coefficient-of-thermal-expansion matching of materials and better tribological properties in the coatings used.

  19. Sparse principal component analysis in medical shape modeling

    Science.gov (United States)

    Sjöstrand, Karl; Stegmann, Mikkel B.; Larsen, Rasmus

    2006-03-01

    Principal component analysis (PCA) is a widely used tool in medical image analysis for data reduction, model building, and data understanding and exploration. While PCA is a holistic approach where each new variable is a linear combination of all original variables, sparse PCA (SPCA) aims at producing easily interpreted models through sparse loadings, i.e. each new variable is a linear combination of a subset of the original variables. One of the aims of using SPCA is the possible separation of the results into isolated and easily identifiable effects. This article introduces SPCA for shape analysis in medicine. Results for three different data sets are given in relation to standard PCA and sparse PCA by simple thresholding of small loadings. Focus is on a recent algorithm for computing sparse principal components, but a review of other approaches is supplied as well. The SPCA algorithm has been implemented using Matlab and is available for download. The general behavior of the algorithm is investigated, and strengths and weaknesses are discussed. The original report on the SPCA algorithm argues that the ordering of modes is not an issue. We disagree on this point and propose several approaches to establish sensible orderings. A method that orders modes by decreasing variance and maximizes the sum of variances for all modes is presented and investigated in detail.

  20. Principal component analysis of FDG PET in amnestic MCI

    International Nuclear Information System (INIS)

    Nobili, Flavio; Girtler, Nicola; Brugnolo, Andrea; Dessi, Barbara; Rodriguez, Guido; Salmaso, Dario; Morbelli, Silvia; Piccardo, Arnoldo; Larsson, Stig A.; Pagani, Marco

    2008-01-01

    The purpose of the study is to evaluate the combined accuracy of episodic memory performance and 18 F-FDG PET in identifying patients with amnestic mild cognitive impairment (aMCI) converting to Alzheimer's disease (AD), aMCI non-converters, and controls. Thirty-three patients with aMCI and 15 controls (CTR) were followed up for a mean of 21 months. Eleven patients developed AD (MCI/AD) and 22 remained with aMCI (MCI/MCI). 18 F-FDG PET volumetric regions of interest underwent principal component analysis (PCA) that identified 12 principal components (PC), expressed by coarse component scores (CCS). Discriminant analysis was performed using the significant PCs and episodic memory scores. PCA highlighted relative hypometabolism in PC5, including bilateral posterior cingulate and left temporal pole, and in PC7, including the bilateral orbitofrontal cortex, both in MCI/MCI and MCI/AD vs CTR. PC5 itself plus PC12, including the left lateral frontal cortex (LFC: BAs 44, 45, 46, 47), were significantly different between MCI/AD and MCI/MCI. By a three-group discriminant analysis, CTR were more accurately identified by PET-CCS + delayed recall score (100%), MCI/MCI by PET-CCS + either immediate or delayed recall scores (91%), while MCI/AD was identified by PET-CCS alone (82%). PET increased by 25% the correct allocations achieved by memory scores, while memory scores increased by 15% the correct allocations achieved by PET. Combining memory performance and 18 F-FDG PET yielded a higher accuracy than each single tool in identifying CTR and MCI/MCI. The PC containing bilateral posterior cingulate and left temporal pole was the hallmark of MCI/MCI patients, while the PC including the left LFC was the hallmark of conversion to AD. (orig.)

  1. Principal component analysis of FDG PET in amnestic MCI

    Energy Technology Data Exchange (ETDEWEB)

    Nobili, Flavio; Girtler, Nicola; Brugnolo, Andrea; Dessi, Barbara; Rodriguez, Guido [University of Genoa, Clinical Neurophysiology, Department of Endocrinological and Medical Sciences, Genoa (Italy); S. Martino Hospital, Alzheimer Evaluation Unit, Genoa (Italy); S. Martino Hospital, Head-Neck Department, Genoa (Italy); Salmaso, Dario [CNR, Institute of Cognitive Sciences and Technologies, Rome (Italy); CNR, Institute of Cognitive Sciences and Technologies, Padua (Italy); Morbelli, Silvia [University of Genoa, Nuclear Medicine Unit, Department of Internal Medicine, Genoa (Italy); Piccardo, Arnoldo [Galliera Hospital, Nuclear Medicine Unit, Department of Imaging Diagnostics, Genoa (Italy); Larsson, Stig A. [Karolinska Hospital, Department of Nuclear Medicine, Stockholm (Sweden); Pagani, Marco [CNR, Institute of Cognitive Sciences and Technologies, Rome (Italy); CNR, Institute of Cognitive Sciences and Technologies, Padua (Italy); Karolinska Hospital, Department of Nuclear Medicine, Stockholm (Sweden)

    2008-12-15

    The purpose of the study is to evaluate the combined accuracy of episodic memory performance and {sup 18}F-FDG PET in identifying patients with amnestic mild cognitive impairment (aMCI) converting to Alzheimer's disease (AD), aMCI non-converters, and controls. Thirty-three patients with aMCI and 15 controls (CTR) were followed up for a mean of 21 months. Eleven patients developed AD (MCI/AD) and 22 remained with aMCI (MCI/MCI). {sup 18}F-FDG PET volumetric regions of interest underwent principal component analysis (PCA) that identified 12 principal components (PC), expressed by coarse component scores (CCS). Discriminant analysis was performed using the significant PCs and episodic memory scores. PCA highlighted relative hypometabolism in PC5, including bilateral posterior cingulate and left temporal pole, and in PC7, including the bilateral orbitofrontal cortex, both in MCI/MCI and MCI/AD vs CTR. PC5 itself plus PC12, including the left lateral frontal cortex (LFC: BAs 44, 45, 46, 47), were significantly different between MCI/AD and MCI/MCI. By a three-group discriminant analysis, CTR were more accurately identified by PET-CCS + delayed recall score (100%), MCI/MCI by PET-CCS + either immediate or delayed recall scores (91%), while MCI/AD was identified by PET-CCS alone (82%). PET increased by 25% the correct allocations achieved by memory scores, while memory scores increased by 15% the correct allocations achieved by PET. Combining memory performance and {sup 18}F-FDG PET yielded a higher accuracy than each single tool in identifying CTR and MCI/MCI. The PC containing bilateral posterior cingulate and left temporal pole was the hallmark of MCI/MCI patients, while the PC including the left LFC was the hallmark of conversion to AD. (orig.)

  2. Laser-Based Maintenance and Repair Technologies for Reactor Components

    International Nuclear Information System (INIS)

    Masaki Yoda; Naruhiko Mukai; Makoto Ochiai; Masataka Tamura; Satoshi Okada; Katsuhiko Sato; Motohiko Kimura; Yuji Sano; Noboru Saito; Seishi Shima; Tetsuo Yamamoto

    2004-01-01

    Toshiba has developed various laser-based maintenance and repair technologies and applied them to existing nuclear power plants. Laser-based technology is considered to be the best tool for remote processing in nuclear power plants, and particularly so for the maintenance and repair of reactor core components. Accessibility could be drastically improved by a simple handling system owing to the absence of reactive force against laser irradiation and the flexible optical fiber. For the preventive maintenance, laser peening (LP) technology was developed and applied to reactor components in operating BWR plants. LP is a novel process to improve residual stress from tensile to compressive on material surface layer by irradiating focused high-power laser pulses in water. We have developed a fiber-delivered LP system as a preventive maintenance measure against stress corrosion cracking (SCC). Laser ultrasonic testing (LUT) has a great potential to be applied to the remote inspection of reactor components. Laser-induced surface acoustic wave (SAW) inspection system was developed using a compact probe with a multi-mode optical fiber and an interferometer. The developed system successfully detected a micro slit of 0.5 mm depth on weld metal and heat-affected zone (HAZ). An artificial SCC was also detected by the system. We are developing a new LP system combined with LUT to treat the inner surface of bottom-mounted instruments (BMI) of PWR plants. Underwater laser seal welding (LSW) technology was also developed to apply surface crack. LSW is expected to isolate the crack tip from corrosive water environment and to stop the propagation of the crack. Rapid heating and cooling of the process minimize the heat effect, which extends the applicability to neutron-irradiated material. This paper describes recent advances in the development and application of such laser-based technologies. (authors)

  3. Optimal test intervals of standby components based on actual plant-specific data

    International Nuclear Information System (INIS)

    Jones, R.B.; Bickel, J.H.

    1987-01-01

    Based on standard reliability analysis techniques, both under testing and over testing affect the availability of standby components. If tests are performed too often, unavailability is increased since the equipment is being used excessively. Conversely if testing is performed too infrequently, the likelihood of component unavailability is also increased due to the formation of rust, heat or radiation damage, dirt infiltration, etc. Thus from a physical perspective, an optimal test interval should exist which minimizes unavailability. This paper illustrates the application of an unavailability model that calculates optimal testing intervals for components with a failure database. (orig./HSCH)

  4. Nuclear analysis techniques as a component of thermoluminescence dating

    Energy Technology Data Exchange (ETDEWEB)

    Prescott, J.R.; Hutton, J.T.; Habermehl, M.A. [Adelaide Univ., SA (Australia); Van Moort, J. [Tasmania Univ., Sandy Bay, TAS (Australia)

    1996-12-31

    In luminescence dating, an age is found by first measuring dose accumulated since the event being dated, then dividing by the annual dose rate. Analyses of minor and trace elements performed by nuclear techniques have long formed an essential component of dating. Results from some Australian sites are reported to illustrate the application of nuclear techniques of analysis in this context. In particular, a variety of methods for finding dose rates are compared, an example of a site where radioactive disequilibrium is significant and a brief summary is given of a problem which was not resolved by nuclear techniques. 5 refs., 2 tabs.

  5. Fast and accurate methods of independent component analysis: A survey

    Czech Academy of Sciences Publication Activity Database

    Tichavský, Petr; Koldovský, Zbyněk

    2011-01-01

    Roč. 47, č. 3 (2011), s. 426-438 ISSN 0023-5954 R&D Projects: GA MŠk 1M0572; GA ČR GA102/09/1278 Institutional research plan: CEZ:AV0Z10750506 Keywords : Blind source separation * artifact removal * electroencephalogram * audio signal processing Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 0.454, year: 2011 http://library.utia.cas.cz/separaty/2011/SI/tichavsky-fast and accurate methods of independent component analysis a survey.pdf

  6. PRINCIPAL COMPONENT ANALYSIS (PCA DAN APLIKASINYA DENGAN SPSS

    Directory of Open Access Journals (Sweden)

    Hermita Bus Umar

    2009-03-01

    Full Text Available PCA (Principal Component Analysis are statistical techniques applied to a single set of variables when the researcher is interested in discovering which variables in the setform coherent subset that are relativity independent of one another.Variables that are correlated with one another but largely independent of other subset of variables are combined into factors. The Coals of PCA to which each variables is explained by each dimension. Step in PCA include selecting and mean measuring a set of variables, preparing the correlation matrix, extracting a set offactors from the correlation matrixs. Rotating the factor to increase interpretabilitv and interpreting the result.

  7. Nuclear analysis techniques as a component of thermoluminescence dating

    Energy Technology Data Exchange (ETDEWEB)

    Prescott, J R; Hutton, J T; Habermehl, M A [Adelaide Univ., SA (Australia); Van Moort, J [Tasmania Univ., Sandy Bay, TAS (Australia)

    1997-12-31

    In luminescence dating, an age is found by first measuring dose accumulated since the event being dated, then dividing by the annual dose rate. Analyses of minor and trace elements performed by nuclear techniques have long formed an essential component of dating. Results from some Australian sites are reported to illustrate the application of nuclear techniques of analysis in this context. In particular, a variety of methods for finding dose rates are compared, an example of a site where radioactive disequilibrium is significant and a brief summary is given of a problem which was not resolved by nuclear techniques. 5 refs., 2 tabs.

  8. Structure analysis of active components of traditional Chinese medicines

    DEFF Research Database (Denmark)

    Zhang, Wei; Sun, Qinglei; Liu, Jianhua

    2013-01-01

    Traditional Chinese Medicines (TCMs) have been widely used for healing of different health problems for thousands of years. They have been used as therapeutic, complementary and alternative medicines. TCMs usually consist of dozens to hundreds of various compounds, which are extracted from raw...... herbal sources by aqueous or alcoholic solvents. Therefore, it is difficult to correlate the pharmaceutical effect to a specific lead compound in the TCMs. A detailed analysis of various components in TCMs has been a great challenge for modern analytical techniques in recent decades. In this chapter...

  9. 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

  10. 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.

  11. Bayou Choctaw Well Integrity Grading Component Based on Geomechanical Simulation

    Energy Technology Data Exchange (ETDEWEB)

    Park, Byoung [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States). Geotechnology & Engineering Dept.

    2016-09-08

    This letter report provides a Bayou Choctaw (BC) Strategic Petroleum Reserve (SPR) well grading system based on the geomechanical simulation. The analyses described in this letter were used to evaluate the caverns’ geomechanical effect on wellbore integrity, which is an important component in the well integrity grading system recently developed by Roberts et al. [2015]. Using these analyses, the wellbores for caverns BC-17 and 20 are expected to be significantly impacted by cavern geomechanics, BC-18 and 19 are expected to be medium impacted; and the other caverns are expected to be less impacted.

  12. Biochemical component identification by plasmonic improved whispering gallery mode optical resonance based sensor

    Science.gov (United States)

    Saetchnikov, Vladimir A.; Tcherniavskaia, Elina A.; Saetchnikov, Anton V.; Schweiger, Gustav; Ostendorf, Andreas

    2014-05-01

    Experimental data on detection and identification of variety of biochemical agents, such as proteins, microelements, antibiotic of different generation etc. in both single and multi component solutions under varied in wide range concentration analyzed on the light scattering parameters of whispering gallery mode optical resonance based sensor are represented. Multiplexing on parameters and components has been realized using developed fluidic sensor cell with fixed in adhesive layer dielectric microspheres and data processing. Biochemical component identification has been performed by developed network analysis techniques. Developed approach is demonstrated to be applicable both for single agent and for multi component biochemical analysis. Novel technique based on optical resonance on microring structures, plasmon resonance and identification tools has been developed. To improve a sensitivity of microring structures microspheres fixed by adhesive had been treated previously by gold nanoparticle solution. Another technique used thin film gold layers deposited on the substrate below adhesive. Both biomolecule and nanoparticle injections caused considerable changes of optical resonance spectra. Plasmonic gold layers under optimized thickness also improve parameters of optical resonance spectra. Biochemical component identification has been also performed by developed network analysis techniques both for single and for multi component solution. So advantages of plasmon enhancing optical microcavity resonance with multiparameter identification tools is used for development of a new platform for ultra sensitive label-free biomedical sensor.

  13. Development of motion image prediction method using principal component analysis

    International Nuclear Information System (INIS)

    Chhatkuli, Ritu Bhusal; Demachi, Kazuyuki; Kawai, Masaki; Sakakibara, Hiroshi; Kamiaka, Kazuma

    2012-01-01

    Respiratory motion can induce the limit in the accuracy of area irradiated during lung cancer radiation therapy. Many methods have been introduced to minimize the impact of healthy tissue irradiation due to the lung tumor motion. The purpose of this research is to develop an algorithm for the improvement of image guided radiation therapy by the prediction of motion images. We predict the motion images by using principal component analysis (PCA) and multi-channel singular spectral analysis (MSSA) method. The images/movies were successfully predicted and verified using the developed algorithm. With the proposed prediction method it is possible to forecast the tumor images over the next breathing period. The implementation of this method in real time is believed to be significant for higher level of tumor tracking including the detection of sudden abdominal changes during radiation therapy. (author)

  14. Seismic fragility analysis of structural components for HFBR facilities

    International Nuclear Information System (INIS)

    Park, Y.J.; Hofmayer, C.H.

    1992-01-01

    The paper presents a summary of recently completed seismic fragility analyses of the HFBR facilities. Based on a detailed review of past PRA studies, various refinements were made regarding the strength and ductility evaluation of structural components. Available laboratory test data were analysed to evaluate the formulations used to predict the ultimate strength and deformation capacities of steel, reinforced concrete and masonry structures. The biasness and uncertainties were evaluated within the framework of the fragility evaluation methods widely accepted in the nuclear industry. A few examples of fragility calculations are also included to illustrate the use of the presented formulations

  15. The ethical component of professional competence in nursing: an analysis.

    Science.gov (United States)

    Paganini, Maria Cristina; Yoshikawa Egry, Emiko

    2011-07-01

    The purpose of this article is to initiate a philosophical discussion about the ethical component of professional competence in nursing from the perspective of Brazilian nurses. Specifically, this article discusses professional competence in nursing practice in the Brazilian health context, based on two different conceptual frameworks. The first framework is derived from the idealistic and traditional approach while the second views professional competence through the lens of historical and dialectical materialism theory. The philosophical analyses show that the idealistic view of professional competence differs greatly from practice. Combining nursing professional competence with philosophical perspectives becomes a challenge when ideals are opposed by the reality and implications of everyday nursing practice.

  16. [Development and application of component-based Chinese medicine theory].

    Science.gov (United States)

    Zhang, Jun-Hua; Fan, Guan-Wei; Zhang, Han; Fan, Xiao-Hui; Wang, Yi; Liu, Li-Mei; Li, Chuan; Gao, Yue; Gao, Xiu-Mei; Zhang, Bo-Li

    2017-11-01

    Traditional Chinese medicine (TCM) prescription is the main therapies for disease prevention and treatment in Chinese medicine. Following the guidance of the theory of TCM and developing drug by composing prescriptions of TCM materials and pieces, it is a traditional application mode of TCM, and still widely used in clinic. TCM prescription has theoretical advantages and rich clinical application experience in dealing with multi-factor complex diseases, but scientific research is relatively weak. The lack of scientific cognition of the effective substances and mechanism of Chinese medicine leads to insufficient understanding of the efficacy regularity, which affects the stability of effect and hinders the improvement of quality of Chinese medicinal products. Component-based Chinese medicine (CCM) is an innovation based on inheritance, which breaks through the tradition of experience-based prescription and realize the transformation of compatibility from herbal pieces to components. CCM is an important achievement during the research process of modernization of Chinese medicine. Under the support of three national "973" projects, in order to reveal the scientific connotation of the prescription compatibility theory and develop innovative Chinese drugs, we have launched theoretical innovation and technological innovation around the "two relatively clear", and opened up the research field of CCM. CCM is an innovation based on inheritance, breaking through the tradition of experience based prescription, and realizing the transformation from compatibility of herbal pieces to component compatibility, which is an important achievement of the modernization of traditional Chinese medicine. In the past more than 10 years, with the deepening of research and the expansion of application, the theory and methods of CCM and efficacy-oriented compatibility have been continuously improved. The value of CCM is not only in developing new drug, more important is to build a

  17. 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

  18. Integration of Simulink Models with Component-based Software Models

    DEFF Research Database (Denmark)

    Marian, Nicolae; Top, Søren

    2008-01-01

    , communication and constraints, using computational blocks and aggregates for both discrete and continuous behaviour, 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...... 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 behaviour, and the transformation of the software system into the S-functions. The general aim of this work is the improvement of multi-disciplinary development of embedded systems with the focus on the relation...

  19. Optimal condition-based maintenance decisions for systems with dependent stochastic degradation of components

    International Nuclear Information System (INIS)

    Hong, H.P.; Zhou, W.; Zhang, S.; Ye, W.

    2014-01-01

    Components in engineered systems are subjected to stochastic deterioration due to the operating environmental conditions, and the uncertainty in material properties. The components need to be inspected and possibly replaced based on preventive or failure replacement criteria to provide the intended and safe operation of the system. In the present study, we investigate the influence of dependent stochastic degradation of multiple components on the optimal maintenance decisions. We use copula to model the dependent stochastic degradation of components, and formulate the optimal decision problem based on the minimum expected cost rule and the stochastic dominance rules. The latter is used to cope with decision maker's risk attitude. We illustrate the developed probabilistic analysis approach and the influence of the dependency of the stochastic degradation on the preferred decisions through numerical examples

  20. Color Independent Components Based SIFT Descriptors for Object/Scene Classification

    Science.gov (United States)

    Ai, Dan-Ni; Han, Xian-Hua; Ruan, Xiang; Chen, Yen-Wei

    In this paper, we present a novel color independent components based SIFT descriptor (termed CIC-SIFT) for object/scene classification. We first learn an efficient color transformation matrix based on independent component analysis (ICA), which is adaptive to each category in a database. The ICA-based color transformation can enhance contrast between the objects and the background in an image. Then we compute CIC-SIFT descriptors over all three transformed color independent components. Since the ICA-based color transformation can boost the objects and suppress the background, the proposed CIC-SIFT can extract more effective and discriminative local features for object/scene classification. The comparison is performed among seven SIFT descriptors, and the experimental classification results show that our proposed CIC-SIFT is superior to other conventional SIFT descriptors.

  1. 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)

  2. Progress Towards Improved Analysis of TES X-ray Data Using Principal Component Analysis

    Science.gov (United States)

    Busch, S. E.; Adams, J. S.; Bandler, S. R.; Chervenak, J. A.; Eckart, M. E.; Finkbeiner, F. M.; Fixsen, D. J.; Kelley, R. L.; Kilbourne, C. A.; Lee, S.-J.; hide

    2015-01-01

    The traditional method of applying a digital optimal filter to measure X-ray pulses from transition-edge sensor (TES) devices does not achieve the best energy resolution when the signals have a highly non-linear response to energy, or the noise is non-stationary during the pulse. We present an implementation of a method to analyze X-ray data from TESs, which is based upon principal component analysis (PCA). Our method separates the X-ray signal pulse into orthogonal components that have the largest variance. We typically recover pulse height, arrival time, differences in pulse shape, and the variation of pulse height with detector temperature. These components can then be combined to form a representation of pulse energy. An added value of this method is that by reporting information on more descriptive parameters (as opposed to a single number representing energy), we generate a much more complete picture of the pulse received. Here we report on progress in developing this technique for future implementation on X-ray telescopes. We used an 55Fe source to characterize Mo/Au TESs. On the same dataset, the PCA method recovers a spectral resolution that is better by a factor of two than achievable with digital optimal filters.

  3. Harmonic Stability Analysis of Offshore Wind Farm with Component Connection Method

    DEFF Research Database (Denmark)

    Hou, Peng; Ebrahimzadeh, Esmaeil; Wang, Xiongfei

    2017-01-01

    In this paper, an eigenvalue-based harmonic stability analysis method for offshore wind farm is proposed. Considering the internal cable connection layout, a component connection method (CCM) is adopted to divide the system into individual blocks as current controller of converters, LCL filters...

  4. 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.

  5. Feature selection for neural network based defect classification of ceramic components using high frequency ultrasound.

    Science.gov (United States)

    Kesharaju, Manasa; Nagarajah, Romesh

    2015-09-01

    The motivation for this research stems from a need for providing a non-destructive testing method capable of detecting and locating any defects and microstructural variations within armour ceramic components before issuing them to the soldiers who rely on them for their survival. The development of an automated ultrasonic inspection based classification system would make possible the checking of each ceramic component and immediately alert the operator about the presence of defects. Generally, in many classification problems a choice of features or dimensionality reduction is significant and simultaneously very difficult, as a substantial computational effort is required to evaluate possible feature subsets. In this research, a combination of artificial neural networks and genetic algorithms are used to optimize the feature subset used in classification of various defects in reaction-sintered silicon carbide ceramic components. Initially wavelet based feature extraction is implemented from the region of interest. An Artificial Neural Network classifier is employed to evaluate the performance of these features. Genetic Algorithm based feature selection is performed. Principal Component Analysis is a popular technique used for feature selection and is compared with the genetic algorithm based technique in terms of classification accuracy and selection of optimal number of features. The experimental results confirm that features identified by Principal Component Analysis lead to improved performance in terms of classification percentage with 96% than Genetic algorithm with 94%. Copyright © 2015 Elsevier B.V. All rights reserved.

  6. Failure cause analysis and improvement for magnetic component cabinet

    International Nuclear Information System (INIS)

    Ge Bing

    1999-01-01

    The magnetic component cabinet is an important thermal control device fitted on the nuclear power. Because it used a self-saturation amplifier as a primary component, the magnetic component cabinet has some boundness. For increasing the operation safety on the nuclear power, the author describes a new scheme. In order that the magnetic component cabinet can be replaced, the new type component cabinet is developed. Integrate circuit will replace the magnetic components of every function parts. The author has analyzed overall failure cause for magnetic component cabinet and adopted some measures

  7. Quality Aware Compression of Electrocardiogram Using Principal Component Analysis.

    Science.gov (United States)

    Gupta, Rajarshi

    2016-05-01

    Electrocardiogram (ECG) compression finds wide application in various patient monitoring purposes. Quality control in ECG compression ensures reconstruction quality and its clinical acceptance for diagnostic decision making. In this paper, a quality aware compression method of single lead ECG is described using principal component analysis (PCA). After pre-processing, beat extraction and PCA decomposition, two independent quality criteria, namely, bit rate control (BRC) or error control (EC) criteria were set to select optimal principal components, eigenvectors and their quantization level to achieve desired bit rate or error measure. The selected principal components and eigenvectors were finally compressed using a modified delta and Huffman encoder. The algorithms were validated with 32 sets of MIT Arrhythmia data and 60 normal and 30 sets of diagnostic ECG data from PTB Diagnostic ECG data ptbdb, all at 1 kHz sampling. For BRC with a CR threshold of 40, an average Compression Ratio (CR), percentage root mean squared difference normalized (PRDN) and maximum absolute error (MAE) of 50.74, 16.22 and 0.243 mV respectively were obtained. For EC with an upper limit of 5 % PRDN and 0.1 mV MAE, the average CR, PRDN and MAE of 9.48, 4.13 and 0.049 mV respectively were obtained. For mitdb data 117, the reconstruction quality could be preserved up to CR of 68.96 by extending the BRC threshold. The proposed method yields better results than recently published works on quality controlled ECG compression.

  8. Modelling raster-based monthly water balance components for Europe

    Energy Technology Data Exchange (ETDEWEB)

    Ulmen, C.

    2000-11-01

    The terrestrial runoff component is a comparatively small but sensitive and thus significant quantity in the global energy and water cycle at the interface between landmass and atmosphere. As opposed to soil moisture and evapotranspiration which critically determine water vapour fluxes and thus water and energy transport, it can be measured as an integrated quantity over a large area, i.e. the river basin. This peculiarity makes terrestrial runoff ideally suited for the calibration, verification and validation of general circulation models (GCMs). Gauging stations are not homogeneously distributed in space. Moreover, time series are not necessarily continuously measured nor do they in general have overlapping time periods. To overcome this problems with regard to regular grid spacing used in GCMs, different methods can be applied to transform irregular data to regular so called gridded runoff fields. The present work aims to directly compute the gridded components of the monthly water balance (including gridded runoff fields) for Europe by application of the well-established raster-based macro-scale water balance model WABIMON used at the Federal Institute of Hydrology, Germany. Model calibration and validation is performed by separated examination of 29 representative European catchments. Results indicate a general applicability of the model delivering reliable overall patterns and integrated quantities on a monthly basis. For time steps less then too weeks further research and structural improvements of the model are suggested. (orig.)

  9. Multi-component controllers in reactor physics optimality analysis

    International Nuclear Information System (INIS)

    Aldemir, T.

    1978-01-01

    An algorithm is developed for the optimality analysis of thermal reactor assemblies with multi-component control vectors. The neutronics of the system under consideration is assumed to be described by the two-group diffusion equations and constraints are imposed upon the state and control variables. It is shown that if the problem is such that the differential and algebraic equations describing the system can be cast into a linear form via a change of variables, the optimal control components are piecewise constant functions and the global optimal controller can be determined by investigating the properties of the influence functions. Two specific problems are solved utilizing this approach. A thermal reactor consisting of fuel, burnable poison and moderator is found to yield maximal power when the assembly consists of two poison zones and the power density is constant throughout the assembly. It is shown that certain variational relations have to be considered to maintain the activeness of the system equations as differential constraints. The problem of determining the maximum initial breeding ratio for a thermal reactor is solved by treating the fertile and fissile material absorption densities as controllers. The optimal core configurations are found to consist of three fuel zones for a bare assembly and two fuel zones for a reflected assembly. The optimum fissile material density is determined to be inversely proportional to the thermal flux

  10. 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.

  11. Analysis of tangible and intangible hotel service quality components

    Directory of Open Access Journals (Sweden)

    Marić Dražen

    2016-01-01

    Full Text Available The issue of service quality is one of the essential areas of marketing theory and practice, as high quality can lead to customer satisfaction and loyalty, i.e. successful business results. It is vital for any company, especially in services sector, to understand and grasp the consumers' expectations and perceptions pertaining to the broad range of factors affecting consumers' evaluation of services, their satisfaction and loyalty. Hospitality is a service sector where the significance of these elements grows exponentially. The aim of this study is to identify the significance of individual quality components in hospitality industry. The questionnaire used for gathering data comprised 19 tangible and 14 intangible attributes of service quality, which the respondents rated on a five-degree scale. The analysis also identified the factorial structure of the tangible and intangible elements of hotel service. The paper aims to contribute to the existing literature by pointing to the significance of tangible and intangible components of service quality. A very small number of studies conducted in hospitality and hotel management identify the sub-factors within these two dimensions of service quality. The paper also provides useful managerial implications. The obtained results help managers in hospitality to establish the service offers that consumers find the most important when choosing a given hotel.

  12. Multilevel component analysis of time-resolved metabolic fingerprinting data

    NARCIS (Netherlands)

    Jansen, J.J.; Hoefsloot, H.C.J.; Greef, J. van der; Timmerman, M.E.; Smilde, A.K.

    2005-01-01

    Genomics-based technologies in systems biology have gained a lot of popularity in recent years. These technologies generate large amounts of data. To obtain information from this data, multivariate data analysis methods are required. Many of the datasets generated in genomics are multilevel

  13. High Q, Miniaturized LCP-Based Passive Components

    KAUST Repository

    Shamim, Atif

    2014-10-16

    Various methods and systems are provided for high Q, miniaturized LCP-based passive components. In one embodiment, among others, a spiral inductor includes a center connection and a plurality of inductors formed on a liquid crystal polymer (LCP) layer, the plurality of inductors concentrically spiraling out from the center connection. In another embodiment, a vertically intertwined inductor includes first and second inductors including a first section disposed on a side of the LCP layer forming a fraction of a turn and a second section disposed on another side of the LCP layer. At least a portion of the first section of the first inductor is substantially aligned with at least a portion of the second section of the second inductor and at least a portion of the first section of the second inductor is substantially aligned with at least a portion of the second section of the first inductor.

  14. High Q, Miniaturized LCP-Based Passive Components

    KAUST Repository

    Shamim, Atif; Arabi, Eyad A.

    2014-01-01

    Various methods and systems are provided for high Q, miniaturized LCP-based passive components. In one embodiment, among others, a spiral inductor includes a center connection and a plurality of inductors formed on a liquid crystal polymer (LCP) layer, the plurality of inductors concentrically spiraling out from the center connection. In another embodiment, a vertically intertwined inductor includes first and second inductors including a first section disposed on a side of the LCP layer forming a fraction of a turn and a second section disposed on another side of the LCP layer. At least a portion of the first section of the first inductor is substantially aligned with at least a portion of the second section of the second inductor and at least a portion of the first section of the second inductor is substantially aligned with at least a portion of the second section of the first inductor.

  15. 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

  16. 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.

  17. 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.

  18. Performance of nickel base superalloy components in gas turbines

    DEFF Research Database (Denmark)

    Dahl, Kristian Vinter

    2006-01-01

    The topic of this thesis is the microstructural behaviour of hot section components in the industrial gas turbine......The topic of this thesis is the microstructural behaviour of hot section components in the industrial gas turbine...

  19. Principal component analysis of NEXAFS spectra for molybdenum speciation in hydrotreating catalysts

    International Nuclear Information System (INIS)

    Faro Junior, Arnaldo da C.; Rodrigues, Victor de O.; Eon, Jean-G.; Rocha, Angela S.

    2010-01-01

    Bulk and supported molybdenum based catalysts, modified by nickel, phosphorous or tungsten were studied by NEXAFS spectroscopy at the Mo L III and L II edges. The techniques of principal component analysis (PCA) together with a linear combination analysis (LCA) allowed the detection and quantification of molybdenum atoms in two different coordination states in the oxide form of the catalysts, namely tetrahedral and octahedral coordination. (author)

  20. Principal Component and Cluster Analysis for determining diversification of bottom morphology based on bathymetric profiles from Brepollen (Hornsund, Spitsbergen* The project was partly supported by The Polish Ministry of Sciences and Higher Education Grant No. N N525 350038.

    Directory of Open Access Journals (Sweden)

    Mateusz Moskalik

    2014-01-01

    Full Text Available Navigation charts of the post-glacial regions of Arctic fjords tend not to cover regions from which glaciers have retreated. Whilst research vessels can make detailed bathymetric models using multibeam echosounders, they are often too large to enter such areas. To map these regions therefore requires smaller boats carrying single beam echosounders. To obtain morphology models of equivalent quality to those generated using multibeam echosounders, new ways of processing data from single beam echosounders have to be found. The results and comprehensive analysis of such measurements conducted in Brepollen (Hornsund, Spitsbergen are presented in this article. The morphological differentiation of the seafloor was determined by calculating statistical, spectral and wavelet transformation, fractal and median filtration parameters of segments of bathymetric profiles. This set of parameters constituted the input for Principal Component Analysis and then in the form of Principal Components for the Cluster Analysis. As a result of this procedure, three morphological classes are proposed for Brepollen: (i steep slopes (southern Brepollen, (ii flat bottoms (central Brepollen and gentle slopes (the Storebreen glacier valley and the southern part of the Hornbreen glacier valley, (iii the morphologically most diverse region (the central Storebreen valley, the northern part of the Hornbreen glacier valley and the north-eastern part of central Brepollen.

  1. GPR Detection of Buried Symmetrically Shaped Mine-like Objects using Selective Independent Component Analysis

    DEFF Research Database (Denmark)

    Karlsen, Brian; Sørensen, Helge Bjarup Dissing; Larsen, Jan

    2003-01-01

    from small-scale anti-personal (AP) mines to large-scale anti-tank (AT) mines were designed. Large-scale SF-GPR measurements on this series of mine-like objects buried in soil were performed. The SF-GPR data was acquired using a wideband monostatic bow-tie antenna operating in the frequency range 750......This paper addresses the detection of mine-like objects in stepped-frequency ground penetrating radar (SF-GPR) data as a function of object size, object content, and burial depth. The detection approach is based on a Selective Independent Component Analysis (SICA). SICA provides an automatic...... ranking of components, which enables the suppression of clutter, hence extraction of components carrying mine information. The goal of the investigation is to evaluate various time and frequency domain ICA approaches based on SICA. Performance comparison is based on a series of mine-like objects ranging...

  2. Prognostic Health Monitoring System: Component Selection Based on Risk Criteria and Economic Benefit Assessment

    International Nuclear Information System (INIS)

    Pham, Binh T.; Agarwal, Vivek; Lybeck, Nancy J.; Tawfik, Magdy S.

    2012-01-01

    Prognostic health monitoring (PHM) is a proactive approach to monitor the ability of structures, systems, and components (SSCs) to withstand structural, thermal, and chemical loadings over the SSCs planned service lifespan. The current efforts to extend the operational license lifetime of the aging fleet of U.S. nuclear power plants from 40 to 60 years and beyond can benefit from a systematic application of PHM technology. Implementing a PHM system would strengthen the safety of nuclear power plants, reduce plant outage time, and reduce operation and maintenance costs. However, a nuclear power plant has thousands of SSCs, so implementing a PHM system that covers all SSCs requires careful planning and prioritization. This paper therefore focuses on a component selection that is based on the analysis of a component's failure probability, risk, and cost. Ultimately, the decision on component selection depends on the overall economical benefits arising from safety and operational considerations associated with implementing the PHM system. (author)

  3. Evaluating the hydrological consistency of satellite based water cycle components

    KAUST Repository

    Lopez Valencia, Oliver Miguel

    2016-06-15

    Advances in multi-satellite based observations of the earth system have provided the capacity to retrieve information across a wide-range of land surface hydrological components and provided an opportunity to characterize terrestrial processes from a completely new perspective. Given the spatial advantage that space-based observations offer, several regional-to-global scale products have been developed, offering insights into the multi-scale behaviour and variability of hydrological states and fluxes. However, one of the key challenges in the use of satellite-based products is characterizing the degree to which they provide realistic and representative estimates of the underlying retrieval: that is, how accurate are the hydrological components derived from satellite observations? The challenge is intrinsically linked to issues of scale, since the availability of high-quality in-situ data is limited, and even where it does exist, is generally not commensurate to the resolution of the satellite observation. Basin-scale studies have shown considerable variability in achieving water budget closure with any degree of accuracy using satellite estimates of the water cycle. In order to assess the suitability of this type of approach for evaluating hydrological observations, it makes sense to first test it over environments with restricted hydrological inputs, before applying it to more hydrological complex basins. Here we explore the concept of hydrological consistency, i.e. the physical considerations that the water budget impose on the hydrologic fluxes and states to be temporally and spatially linked, to evaluate the reproduction of a set of large-scale evaporation (E) products by using a combination of satellite rainfall (P) and Gravity Recovery and Climate Experiment (GRACE) observations of storage change, focusing on arid and semi-arid environments, where the hydrological flows can be more realistically described. Our results indicate no persistent hydrological

  4. 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.

  5. A Principal Component Analysis of 39 Scientific Impact Measures

    Science.gov (United States)

    Bollen, Johan; Van de Sompel, Herbert

    2009-01-01

    Background The impact of scientific publications has traditionally been expressed in terms of citation counts. However, scientific activity has moved online over the past decade. To better capture scientific impact in the digital era, a variety of new impact measures has been proposed on the basis of social network analysis and usage log data. Here we investigate how these new measures relate to each other, and how accurately and completely they express scientific impact. Methodology We performed a principal component analysis of the rankings produced by 39 existing and proposed measures of scholarly impact that were calculated on the basis of both citation and usage log data. Conclusions Our results indicate that the notion of scientific impact is a multi-dimensional construct that can not be adequately measured by any single indicator, although some measures are more suitable than others. The commonly used citation Impact Factor is not positioned at the core of this construct, but at its periphery, and should thus be used with caution. PMID:19562078

  6. Analysis of contaminants on electronic components by reflectance FTIR spectroscopy

    International Nuclear Information System (INIS)

    Griffith, G.W.

    1982-09-01

    The analysis of electronic component contaminants by infrared spectroscopy is often a difficult process. Most of the contaminants are very small, which necessitates the use of microsampling techniques. Beam condensers will provide the required sensitivity but most require that the sample be removed from the substrate before analysis. Since it can be difficult and time consuming, it is usually an undesirable approach. Micro ATR work can also be exasperating, due to the difficulty of positioning the sample at the correct place under the ATR plate in order to record a spectrum. This paper describes a modified reflection beam condensor which has been adapted to a Nicolet 7199 FTIR. The sample beam is directed onto the sample surface and reflected from the substrate back to the detector. A micropositioning XYZ stage and a close-focusing telescope are used to position the contaminant directly under the infrared beam. It is possible to analyze contaminants on 1 mm wide leads surrounded by an epoxy matrix using this device. Typical spectra of contaminants found on small circuit boards are included

  7. A principal component analysis of 39 scientific impact measures.

    Directory of Open Access Journals (Sweden)

    Johan Bollen

    Full Text Available BACKGROUND: The impact of scientific publications has traditionally been expressed in terms of citation counts. However, scientific activity has moved online over the past decade. To better capture scientific impact in the digital era, a variety of new impact measures has been proposed on the basis of social network analysis and usage log data. Here we investigate how these new measures relate to each other, and how accurately and completely they express scientific impact. METHODOLOGY: We performed a principal component analysis of the rankings produced by 39 existing and proposed measures of scholarly impact that were calculated on the basis of both citation and usage log data. CONCLUSIONS: Our results indicate that the notion of scientific impact is a multi-dimensional construct that can not be adequately measured by any single indicator, although some measures are more suitable than others. The commonly used citation Impact Factor is not positioned at the core of this construct, but at its periphery, and should thus be used with caution.

  8. 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

  9. Blind Component Separation in Wavelet Space: Application to CMB Analysis

    Directory of Open Access Journals (Sweden)

    J. Delabrouille

    2005-09-01

    Full Text Available It is a recurrent issue in astronomical data analysis that observations are incomplete maps with missing patches or intentionally masked parts. In addition, many astrophysical emissions are nonstationary processes over the sky. All these effects impair data processing techniques which work in the Fourier domain. Spectral matching ICA (SMICA is a source separation method based on spectral matching in Fourier space designed for the separation of diffuse astrophysical emissions in cosmic microwave background observations. This paper proposes an extension of SMICA to the wavelet domain and demonstrates the effectiveness of wavelet-based statistics for dealing with gaps in the data.

  10. Component fragility data base for reliability and probability studies

    International Nuclear Information System (INIS)

    Bandyopadhyay, K.; Hofmayer, C.; Kassier, M.; Pepper, S.

    1989-01-01

    Safety-related equipment in a nuclear plant plays a vital role in its proper operation and control, and failure of such equipment due to an earthquake may pose a risk to the safe operation of the plant. Therefore, in order to assess the overall reliability of a plant, the reliability of performance of the equipment should be studied first. The success of a reliability or a probability study depends to a great extent on the data base. To meet this demand, Brookhaven National Laboratory (BNL) has formed a test data base relating the seismic capacity of equipment specimens to the earthquake levels. Subsequently, the test data have been analyzed for use in reliability and probability studies. This paper describes the data base and discusses the analysis methods. The final results that can be directly used in plant reliability and probability studies are also presented in this paper

  11. Innovative high-performance liquid chromatography method development for the screening of 19 antimalarial drugs based on a generic approach, using design of experiments, independent component analysis and design space.

    Science.gov (United States)

    Debrus, B; Lebrun, P; Kindenge, J Mbinze; Lecomte, F; Ceccato, A; Caliaro, G; Mbay, J Mavar Tayey; Boulanger, B; Marini, R D; Rozet, E; Hubert, Ph

    2011-08-05

    An innovative methodology based on design of experiments (DoE), independent component analysis (ICA) and design space (DS) was developed in previous works and was tested out with a mixture of 19 antimalarial drugs. This global LC method development methodology (i.e. DoE-ICA-DS) was used to optimize the separation of 19 antimalarial drugs to obtain a screening method. DoE-ICA-DS methodology is fully compliant with the current trend of quality by design. DoE was used to define the set of experiments to model the retention times at the beginning, the apex and the end of each peak. Furthermore, ICA was used to numerically separate coeluting peaks and estimate their unbiased retention times. Gradient time, temperature and pH were selected as the factors of a full factorial design. These retention times were modelled by stepwise multiple linear regressions. A recently introduced critical quality attribute, namely the separation criterion (S), was also used to assess the quality of separations rather than using the resolution. Furthermore, the resulting mathematical models were also studied from a chromatographic point of view to understand and investigate the chromatographic behaviour of each compound. Good adequacies were found between the mathematical models and the expected chromatographic behaviours predicted by chromatographic theory. Finally, focusing at quality risk management, the DS was computed as the multidimensional subspace where the probability for the separation criterion to lie in acceptance limits was higher than a defined quality level. The DS was computed propagating the prediction error from the modelled responses to the quality criterion using Monte Carlo simulations. DoE-ICA-DS allowed encountering optimal operating conditions to obtain a robust screening method for the 19 considered antimalarial drugs in the framework of the fight against counterfeit medicines. Moreover and only on the basis of the same data set, a dedicated method for the

  12. 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.

  13. Performance analysis of a Principal Component Analysis ensemble classifier for Emotiv headset P300 spellers.

    Science.gov (United States)

    Elsawy, Amr S; Eldawlatly, Seif; Taher, Mohamed; Aly, Gamal M

    2014-01-01

    The current trend to use Brain-Computer Interfaces (BCIs) with mobile devices mandates the development of efficient EEG data processing methods. In this paper, we demonstrate the performance of a Principal Component Analysis (PCA) ensemble classifier for P300-based spellers. We recorded EEG data from multiple subjects using the Emotiv neuroheadset in the context of a classical oddball P300 speller paradigm. We compare the performance of the proposed ensemble classifier to the performance of traditional feature extraction and classifier methods. Our results demonstrate the capability of the PCA ensemble classifier to classify P300 data recorded using the Emotiv neuroheadset with an average accuracy of 86.29% on cross-validation data. In addition, offline testing of the recorded data reveals an average classification accuracy of 73.3% that is significantly higher than that achieved using traditional methods. Finally, we demonstrate the effect of the parameters of the P300 speller paradigm on the performance of the method.

  14. Principal component analysis for neural electron/jet discrimination in highly segmented calorimeters

    International Nuclear Information System (INIS)

    Vassali, M.R.; Seixas, J.M.

    2001-01-01

    A neural electron/jet discriminator based on calorimetry is developed for the second-level trigger system of the ATLAS detector. As preprocessing of the calorimeter information, a principal component analysis is performed on each segment of the two sections (electromagnetic and hadronic) of the calorimeter system, in order to reduce significantly the dimension of the input data space and fully explore the detailed energy deposition profile, which is provided by the highly-segmented calorimeter system. It is shown that projecting calorimeter data onto 33 segmented principal components, the discrimination efficiency of the neural classifier reaches 98.9% for electrons (with only 1% of false alarm probability). Furthermore, restricting data projection onto only 9 components, an electron efficiency of 99.1% is achieved (with 3% of false alarm), which confirms that a fast triggering system may be designed using few components

  15. 78 FR 6344 - Certain Wireless Communications Base Stations and Components Thereof Notice of Receipt of...

    Science.gov (United States)

    2013-01-30

    ... INTERNATIONAL TRADE COMMISSION Certain Wireless Communications Base Stations and Components.... International Trade Commission has received a complaint entitled Certain Wireless Communications Base Stations... communications base stations and components thereof. The complaint names as respondents Telefonaktiebolaget LM...

  16. Entropy-based automated classification of independent components separated from fMCG

    International Nuclear Information System (INIS)

    Comani, S; Srinivasan, V; Alleva, G; Romani, G L

    2007-01-01

    Fetal magnetocardiography (fMCG) is a noninvasive technique suitable for the prenatal diagnosis of the fetal heart function. Reliable fetal cardiac signals can be reconstructed from multi-channel fMCG recordings by means of independent component analysis (ICA). However, the identification of the separated components is usually accomplished by visual inspection. This paper discusses a novel automated system based on entropy estimators, namely approximate entropy (ApEn) and sample entropy (SampEn), for the classification of independent components (ICs). The system was validated on 40 fMCG datasets of normal fetuses with the gestational age ranging from 22 to 37 weeks. Both ApEn and SampEn were able to measure the stability and predictability of the physiological signals separated with ICA, and the entropy values of the three categories were significantly different at p <0.01. The system performances were compared with those of a method based on the analysis of the time and frequency content of the components. The outcomes of this study showed a superior performance of the entropy-based system, in particular for early gestation, with an overall ICs detection rate of 98.75% and 97.92% for ApEn and SampEn respectively, as against a value of 94.50% obtained with the time-frequency-based system. (note)

  17. Identifying constituents in commercial gasoline using Fourier transform-infrared spectroscopy and independent component analysis.

    Science.gov (United States)

    Pasadakis, Nikos; Kardamakis, Andreas A

    2006-09-25

    A new method is proposed that enables the identification of five refinery fractions present in commercial gasoline mixtures using infrared spectroscopic analysis. The data analysis and interpretation was carried out based on independent component analysis (ICA) and spectral similarity techniques. The FT-IR spectra of the gasoline constituents were determined using the ICA method, exclusively based on the spectra of their mixtures as a blind separation procedure, i.e. assuming unknown the spectra of the constituents. The identity of the constituents was subsequently determined using similarity measures commonly employed in spectra library searches against the spectra of the constituent components. The high correlation scores that were obtained in the identification of the constituents indicates that the developed method can be employed as a rapid and effective tool in quality control, fingerprinting or forensic applications, where gasoline constituents are suspected.

  18. Failure characteristic analysis of a component on standby state

    International Nuclear Information System (INIS)

    Shin, Sungmin; Kang, Hyungook

    2013-01-01

    Periodic operations for a specific type of component, however, can accelerate aging effects which increase component unavailability. For the other type of components, the aging effect caused by operation can be ignored. Therefore frequent operations can decrease component unavailability. Thus, to get optimum unavailability proper operation period and method should be studied considering the failure characteristics of each component. The information of component failure is given according to the main causes of failure depending on time flow. However, to get the optimal unavailability, proper interval of operation for inspection should be decided considering the time dependent and independent causes together. According to this study, gradually shorter operation interval for inspection is better to get the optimal component unavailability than that of specific period

  19. 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.

  20. 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.

  1. 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...

  2. Functional Connectivity Parcellation of the Human Thalamus by Independent Component Analysis.

    Science.gov (United States)

    Zhang, Sheng; Li, Chiang-Shan R

    2017-11-01

    As a key structure to relay and integrate information, the thalamus supports multiple cognitive and affective functions through the connectivity between its subnuclei and cortical and subcortical regions. Although extant studies have largely described thalamic regional functions in anatomical terms, evidence accumulates to suggest a more complex picture of subareal activities and connectivities of the thalamus. In this study, we aimed to parcellate the thalamus and examine whole-brain connectivity of its functional clusters. With resting state functional magnetic resonance imaging data from 96 adults, we used independent component analysis (ICA) to parcellate the thalamus into 10 components. On the basis of the independence assumption, ICA helps to identify how subclusters overlap spatially. Whole brain functional connectivity of each subdivision was computed for independent component's time course (ICtc), which is a unique time series to represent an IC. For comparison, we computed seed-region-based functional connectivity using the averaged time course across all voxels within a thalamic subdivision. The results showed that, at p analysis, ICtc analysis revealed patterns of connectivity that were more distinguished between thalamic clusters. ICtc analysis demonstrated thalamic connectivity to the primary motor cortex, which has eluded the analysis as well as previous studies based on averaged time series, and clarified thalamic connectivity to the hippocampus, caudate nucleus, and precuneus. The new findings elucidate functional organization of the thalamus and suggest that ICA clustering in combination with ICtc rather than seed-region analysis better distinguishes whole-brain connectivities among functional clusters of a brain region.

  3. 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.

  4. Internet MEMS design tools based on component technology

    Science.gov (United States)

    Brueck, Rainer; Schumer, Christian

    1999-03-01

    The micro electromechanical systems (MEMS) industry in Europe is characterized by small and medium sized enterprises specialized on products to solve problems in specific domains like medicine, automotive sensor technology, etc. In this field of business the technology driven design approach known from micro electronics is not appropriate. Instead each design problem aims at its own, specific technology to be used for the solution. The variety of technologies at hand, like Si-surface, Si-bulk, LIGA, laser, precision engineering requires a huge set of different design tools to be available. No single SME can afford to hold licenses for all these tools. This calls for a new and flexible way of designing, implementing and distributing design software. The Internet provides a flexible manner of offering software access along with methodologies of flexible licensing e.g. on a pay-per-use basis. New communication technologies like ADSL, TV cable of satellites as carriers promise to offer a bandwidth sufficient even for interactive tools with graphical interfaces in the near future. INTERLIDO is an experimental tool suite for process specification and layout verification for lithography based MEMS technologies to be accessed via the Internet. The first version provides a Java implementation even including a graphical editor for process specification. Currently, a new version is brought into operation that is based on JavaBeans component technology. JavaBeans offers the possibility to realize independent interactive design assistants, like a design rule checking assistants, a process consistency checking assistants, a technology definition assistants, a graphical editor assistants, etc. that may reside distributed over the Internet, communicating via Internet protocols. Each potential user thus is able to configure his own dedicated version of a design tool set dedicated to the requirements of the current problem to be solved.

  5. Using the Cluster Analysis and the Principal Component Analysis in Evaluating the Quality of a Destination

    Directory of Open Access Journals (Sweden)

    Ida Vajčnerová

    2016-01-01

    Full Text Available The objective of the paper is to explore possibilities of evaluating the quality of a tourist destination by means of the principal components analysis (PCA and the cluster analysis. In the paper both types of analysis are compared on the basis of the results they provide. The aim is to identify advantage and limits of both methods and provide methodological suggestion for their further use in the tourism research. The analyses is based on the primary data from the customers’ satisfaction survey with the key quality factors of a destination. As output of the two statistical methods is creation of groups or cluster of quality factors that are similar in terms of respondents’ evaluations, in order to facilitate the evaluation of the quality of tourist destinations. Results shows the possibility to use both tested methods. The paper is elaborated in the frame of wider research project aimed to develop a methodology for the quality evaluation of tourist destinations, especially in the context of customer satisfaction and loyalty.

  6. 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.

  7. Sensor Failure Detection of FASSIP System using Principal Component Analysis

    Science.gov (United States)

    Sudarno; Juarsa, Mulya; Santosa, Kussigit; Deswandri; Sunaryo, Geni Rina

    2018-02-01

    In the nuclear reactor accident of Fukushima Daiichi in Japan, the damages of core and pressure vessel were caused by the failure of its active cooling system (diesel generator was inundated by tsunami). Thus researches on passive cooling system for Nuclear Power Plant are performed to improve the safety aspects of nuclear reactors. The FASSIP system (Passive System Simulation Facility) is an installation used to study the characteristics of passive cooling systems at nuclear power plants. The accuracy of sensor measurement of FASSIP system is essential, because as the basis for determining the characteristics of a passive cooling system. In this research, a sensor failure detection method for FASSIP system is developed, so the indication of sensor failures can be detected early. The method used is Principal Component Analysis (PCA) to reduce the dimension of the sensor, with the Squarred Prediction Error (SPE) and statistic Hotteling criteria for detecting sensor failure indication. The results shows that PCA method is capable to detect the occurrence of a failure at any sensor.

  8. A meta-analysis of executive components of working memory.

    Science.gov (United States)

    Nee, Derek Evan; Brown, Joshua W; Askren, Mary K; Berman, Marc G; Demiralp, Emre; Krawitz, Adam; Jonides, John

    2013-02-01

    Working memory (WM) enables the online maintenance and manipulation of information and is central to intelligent cognitive functioning. Much research has investigated executive processes of WM in order to understand the operations that make WM "work." However, there is yet little consensus regarding how executive processes of WM are organized. Here, we used quantitative meta-analysis to summarize data from 36 experiments that examined executive processes of WM. Experiments were categorized into 4 component functions central to WM: protecting WM from external distraction (distractor resistance), preventing irrelevant memories from intruding into WM (intrusion resistance), shifting attention within WM (shifting), and updating the contents of WM (updating). Data were also sorted by content (verbal, spatial, object). Meta-analytic results suggested that rather than dissociating into distinct functions, 2 separate frontal regions were recruited across diverse executive demands. One region was located dorsally in the caudal superior frontal sulcus and was especially sensitive to spatial content. The other was located laterally in the midlateral prefrontal cortex and showed sensitivity to nonspatial content. We propose that dorsal-"where"/ventral-"what" frameworks that have been applied to WM maintenance also apply to executive processes of WM. Hence, WM can largely be simplified to a dual selection model.

  9. Finite element elastic-plastic analysis of LMFBR components

    International Nuclear Information System (INIS)

    Levy, A.; Pifko, A.; Armen, H. Jr.

    1978-01-01

    The present effort involves the development of computationally efficient finite element methods for accurately predicting the isothermal elastic-plastic three-dimensional response of thick and thin shell structures subjected to mechanical and thermal loads. This work will be used as the basis for further development of analytical tools to be used to verify the structural integrity of liquid metal fast breeder reactor (LMFBR) components. The methods presented here have been implemented into the three-dimensional solid element module (HEX) of the Grumman PLANS finite element program. These methods include the use of optimal stress points as well as a variable number of stress points within an element. This allows monitoring the stress history at many points within an element and hence provides an accurate representation of the elastic-plastic boundary using a minimum number of degrees of freedom. Also included is an improved thermal stress analysis capability in which the temperature variation and corresponding thermal strain variation are represented by the same functional form as the displacement variation. Various problems are used to demonstrate these improved capabilities. (Auth.)

  10. 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

  11. Gas chromatography/mass spectrometry based component profiling and quality prediction for Japanese sake.

    Science.gov (United States)

    Mimura, Natsuki; Isogai, Atsuko; Iwashita, Kazuhiro; Bamba, Takeshi; Fukusaki, Eiichiro

    2014-10-01

    Sake is a Japanese traditional alcoholic beverage, which is produced by simultaneous saccharification and alcohol fermentation of polished and steamed rice by Aspergillus oryzae and Saccharomyces cerevisiae. About 300 compounds have been identified in sake, and the contribution of individual components to the sake flavor has been examined at the same time. However, only a few compounds could explain the characteristics alone and most of the attributes still remain unclear. The purpose of this study was to examine the relationship between the component profile and the attributes of sake. Gas chromatography coupled with mass spectrometry (GC/MS)-based non-targeted analysis was employed to obtain the low molecular weight component profile of Japanese sake including both nonvolatile and volatile compounds. Sake attributes and overall quality were assessed by analytical descriptive sensory test and the prediction model of the sensory score from the component profile was constructed by means of orthogonal projections to latent structures (OPLS) regression analysis. Our results showed that 12 sake attributes [ginjo-ka (aroma of premium ginjo sake), grassy/aldehydic odor, sweet aroma/caramel/burnt odor, sulfury odor, sour taste, umami, bitter taste, body, amakara (dryness), aftertaste, pungent/smoothness and appearance] and overall quality were accurately explained by component profiles. In addition, we were able to select statistically significant components according to variable importance on projection (VIP). Our methodology clarified the correlation between sake attribute and 200 low molecular components and presented the importance of each component thus, providing new insights to the flavor study of sake. Copyright © 2014 The Society for Biotechnology, Japan. Published by Elsevier B.V. All rights reserved.

  12. 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

  13. Analysis of Minor Component Segregation in Ternary Powder Mixtures

    Directory of Open Access Journals (Sweden)

    Asachi Maryam

    2017-01-01

    Full Text Available In many powder handling operations, inhomogeneity in powder mixtures caused by segregation could have significant adverse impact on the quality as well as economics of the production. Segregation of a minor component of a highly active substance could have serious deleterious effects, an example is the segregation of enzyme granules in detergent powders. In this study, the effects of particle properties and bulk cohesion on the segregation tendency of minor component are analysed. The minor component is made sticky while not adversely affecting the flowability of samples. The segregation extent is evaluated using image processing of the photographic records taken from the front face of the heap after the pouring process. The optimum average sieve cut size of components for which segregation could be reduced is reported. It is also shown that the extent of segregation is significantly reduced by applying a thin layer of liquid to the surfaces of minor component, promoting an ordered mixture.

  14. A Unified Approach to Functional Principal Component Analysis and Functional Multiple-Set Canonical Correlation.

    Science.gov (United States)

    Choi, Ji Yeh; Hwang, Heungsun; Yamamoto, Michio; Jung, Kwanghee; Woodward, Todd S

    2017-06-01

    Functional principal component analysis (FPCA) and functional multiple-set canonical correlation analysis (FMCCA) are data reduction techniques for functional data that are collected in the form of smooth curves or functions over a continuum such as time or space. In FPCA, low-dimensional components are extracted from a single functional dataset such that they explain the most variance of the dataset, whereas in FMCCA, low-dimensional components are obtained from each of multiple functional datasets in such a way that the associations among the components are maximized across the different sets. In this paper, we propose a unified approach to FPCA and FMCCA. The proposed approach subsumes both techniques as special cases. Furthermore, it permits a compromise between the techniques, such that components are obtained from each set of functional data to maximize their associations across different datasets, while accounting for the variance of the data well. We propose a single optimization criterion for the proposed approach, and develop an alternating regularized least squares algorithm to minimize the criterion in combination with basis function approximations to functions. We conduct a simulation study to investigate the performance of the proposed approach based on synthetic data. We also apply the approach for the analysis of multiple-subject functional magnetic resonance imaging data to obtain low-dimensional components of blood-oxygen level-dependent signal changes of the brain over time, which are highly correlated across the subjects as well as representative of the data. The extracted components are used to identify networks of neural activity that are commonly activated across the subjects while carrying out a working memory task.

  15. A probabilistic model for component-based shape synthesis

    KAUST Repository

    Kalogerakis, Evangelos; Chaudhuri, Siddhartha; Koller, Daphne; Koltun, Vladlen

    2012-01-01

    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

  16. Development of web-based integrity evaluation system for primary components in a nuclear power plant

    International Nuclear Information System (INIS)

    Lee, S.M.; Kim, J.C.; Choi, J.B.; Kim, Y.J.; Choi, S.N.; Jang, K.S.; Hong, S.Y.

    2004-01-01

    A nuclear power plant is composed of a number of primary components. Maintaining the integrity of these components is one of the most critical issues in nuclear industry. In order to maintain the integrity of these primary components, a complicated procedure is required including periodical in-service inspection, failure assessment, fracture mechanics analysis, etc. Also, experts in different fields have to co-operate to resolve the integrity issues on the basis of inspection results. This integrity evaluation process usually takes long, and thus, is detrimental for the plant productivity. Therefore, an effective safety evaluation system is essential to manage integrity issues on a nuclear power plant. In this paper, a web-based integrity evaluation system for primary components in a nuclear power plant is proposed. The proposed system, which is named as WEBIES (web-based integrity evaluation system), has been developed in the form of 3-tier system architecture. The system consists of three servers; application program server, user interface program server and data warehouse server. The application program server includes the defect acceptance analysis module and the fracture mechanics analysis module which are programmed on the basis of ASME sec. XI, appendix A. The data warehouse server provides data for the integrity evaluation including material properties, geometry information, inspection data and stress data. The user interface program server provides information to all co- workers in the field of integrity evaluation. The developed system provides engineering knowledge-based information and concurrent and collaborative working environment through internet, and thus, is expected to raise the efficiency of integrity evaluation procedures on primary components of a nuclear power plant. (orig.)

  17. Development of web-based integrity evaluation system for primary components in a nuclear power plant

    Energy Technology Data Exchange (ETDEWEB)

    Lee, S.M.; Kim, J.C.; Choi, J.B.; Kim, Y.J. [SAFE Research Center, Sungkyunkwan Univ., Suwon (Korea); Choi, S.N.; Jang, K.S.; Hong, S.Y. [Korea Electronic Power Research Inst., Daejeon (Korea)

    2004-07-01

    A nuclear power plant is composed of a number of primary components. Maintaining the integrity of these components is one of the most critical issues in nuclear industry. In order to maintain the integrity of these primary components, a complicated procedure is required including periodical in-service inspection, failure assessment, fracture mechanics analysis, etc. Also, experts in different fields have to co-operate to resolve the integrity issues on the basis of inspection results. This integrity evaluation process usually takes long, and thus, is detrimental for the plant productivity. Therefore, an effective safety evaluation system is essential to manage integrity issues on a nuclear power plant. In this paper, a web-based integrity evaluation system for primary components in a nuclear power plant is proposed. The proposed system, which is named as WEBIES (web-based integrity evaluation system), has been developed in the form of 3-tier system architecture. The system consists of three servers; application program server, user interface program server and data warehouse server. The application program server includes the defect acceptance analysis module and the fracture mechanics analysis module which are programmed on the basis of ASME sec. XI, appendix A. The data warehouse server provides data for the integrity evaluation including material properties, geometry information, inspection data and stress data. The user interface program server provides information to all co- workers in the field of integrity evaluation. The developed system provides engineering knowledge-based information and concurrent and collaborative working environment through internet, and thus, is expected to raise the efficiency of integrity evaluation procedures on primary components of a nuclear power plant. (orig.)

  18. Denoising of chaotic signal using independent component analysis and empirical mode decomposition with circulate translating

    International Nuclear Information System (INIS)

    Wang Wen-Bo; Zhang Xiao-Dong; Chang Yuchan; Wang Xiang-Li; Wang Zhao; Chen Xi; Zheng Lei

    2016-01-01

    In this paper, a new method to reduce noises within chaotic signals based on ICA (independent component analysis) and EMD (empirical mode decomposition) is proposed. The basic idea is decomposing chaotic signals and constructing multidimensional input vectors, firstly, on the base of EMD and its translation invariance. Secondly, it makes the independent component analysis on the input vectors, which means that a self adapting denoising is carried out for the intrinsic mode functions (IMFs) of chaotic signals. Finally, all IMFs compose the new denoised chaotic signal. Experiments on the Lorenz chaotic signal composed of different Gaussian noises and the monthly observed chaotic sequence on sunspots were put into practice. The results proved that the method proposed in this paper is effective in denoising of chaotic signals. Moreover, it can correct the center point in the phase space effectively, which makes it approach the real track of the chaotic attractor. (paper)

  19. Cloud Masking for Remotely Sensed Data Using Spectral and Principal Components Analysis

    Directory of Open Access Journals (Sweden)

    A. Ahmad

    2012-06-01

    Full Text Available Two methods of cloud masking tuned to tropical conditions have been developed, based on spectral analysis and Principal Components Analysis (PCA of Moderate Resolution Imaging Spectroradiometer (MODIS data. In the spectral approach, thresholds were applied to four reflective bands (1, 2, 3, and 4, three thermal bands (29, 31 and 32, the band 2/band 1 ratio, and the difference between band 29 and 31 in order to detect clouds. The PCA approach applied a threshold to the first principal component derived from the seven quantities used for spectral analysis. Cloud detections were compared with the standard MODIS cloud mask, and their accuracy was assessed using reference images and geographical information on the study area.

  20. Performance assessment of air quality monitoring networks using principal component analysis and cluster analysis

    International Nuclear Information System (INIS)

    Lu, Wei-Zhen; He, Hong-Di; Dong, Li-yun

    2011-01-01

    This study aims to evaluate the performance of two statistical methods, principal component analysis and cluster analysis, for the management of air quality monitoring network of Hong Kong and the reduction of associated expenses. The specific objectives include: (i) to identify city areas with similar air pollution behavior; and (ii) to locate emission sources. The statistical methods were applied to the mass concentrations of sulphur dioxide (SO 2 ), respirable suspended particulates (RSP) and nitrogen dioxide (NO 2 ), collected in monitoring network of Hong Kong from January 2001 to December 2007. The results demonstrate that, for each pollutant, the monitoring stations are grouped into different classes based on their air pollution behaviors. The monitoring stations located in nearby area are characterized by the same specific air pollution characteristics and suggested with an effective management of air quality monitoring system. The redundant equipments should be transferred to other monitoring stations for allowing further enlargement of the monitored area. Additionally, the existence of different air pollution behaviors in the monitoring network is explained by the variability of wind directions across the region. The results imply that the air quality problem in Hong Kong is not only a local problem mainly from street-level pollutions, but also a region problem from the Pearl River Delta region. (author)