2017-09-27
100 times larger for the minimal Krylov subspace. 0 5 10 15 20 25 Krylov subspace dimension 10-2 10-1 100 101 102 103 104 jjĜ ¡ 1 jj F SVD...approximation Kn (G;u(0) ) 0 5 10 15 20 25 Krylov subspace dimension 10-2 10-1 100 101 102 103 104 jjx jj fo r m in x jjĜ x ¡ bjj SVD approximation Kn (G;u(0
Directory of Open Access Journals (Sweden)
Huaishuo Xiao
2017-11-01
Full Text Available In order to identify various kinds of combined power quality disturbances, the singular value decomposition (SVD and the improved total least squares-estimation of signal parameters via rotational invariance techniques (TLS-ESPRIT are combined as the basis of disturbance identification in this paper. SVD is applied to identify the catastrophe points of disturbance intervals, based on which the disturbance intervals are segmented. Then the improved TLS-ESPRIT optimized by singular value norm method is used to analyze each data segment, and extract the amplitude, frequency, attenuation coefficient and initial phase of various kinds of disturbances. Multi-group combined disturbance test signals are constructed by MATLAB and the proposed method is also tested by the measured data of IEEE Power and Energy Society (PES Database. The test results show that the new method proposed has a relatively higher accuracy than conventional TLS-ESPRIT, which could be used in the identification of measured data.
International Nuclear Information System (INIS)
Kuo, V.
2016-01-01
Full text: The European Qualifications Framework categorizes learning objectives into three qualifiers “knowledge”, “skills”, and “competences” (KSCs) to help improve the comparability between different fields and disciplines. However, the management of KSCs remains a great challenge given their semantic fuzziness. Similar texts may describe different concepts and different texts may describe similar concepts among different domains. This is difficult for the indexing, searching and matching of semantically similar KSCs within an information system, to facilitate transfer and mobility of KSCs. We present a working example using a semantic inference method known as Latent Semantic Analysis, employing a matrix operation called Singular Value Decomposition, which have been shown to infer semantic associations within unstructured textual data comparable to that of human interpretations. In our example, a few natural language text passages representing KSCs in the nuclear sector are used to demonstrate the capabilities of the system. It can be shown that LSA is able to infer latent semantic associations between texts, and cluster and match separate text passages semantically based on these associations. We propose this methodology for modelling existing natural language KSCs in the nuclear domain so they can be semantically queried, retrieved and filtered upon request. (author
Directory of Open Access Journals (Sweden)
B. B. Zhao
2011-02-01
Full Text Available A singular value decomposition (SVD program on MATLAB platform was effectively used to handle gravity signals for the Tongshi gold field. Firstly, the gravity signals were decomposed into different eigenimages with the help of singular value decomposition method (SVD. Secondly, the thresholds between the eigenvalues reflecting different layers of ore-controlling factors were established by multi-fractal method. Finally images reflecting different layers of ore-controlling factors were rebuilt. This yielded two layers of two-dimensional singular value images that depict regional and local ore-controlling factors, respectively.
., Riwinoto
2013-01-01
Sekarang ini, metode clustering telah diimplementasikan dalam riset DNA. Data dari DNA didapat melalui teknik microarray. Dengan menggunakan metode teknik SVD, dimensi data dikurangi sehingga mempermudah proses komputasi. Dalam paper ini, ditampilkan hasil clustering tanpa pengarahan terhadap gen-gen dari data bakteri ragi dengan menggunakan metode quantum clustering. Sebagai pembanding, dilakukan juga clustering menggunakan metoda Support Vector Clustering. Selain itu juga ditampilkan data h...
Singular Value Decomposition and Ligand Binding Analysis
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André Luiz Galo
2013-01-01
Full Text Available Singular values decomposition (SVD is one of the most important computations in linear algebra because of its vast application for data analysis. It is particularly useful for resolving problems involving least-squares minimization, the determination of matrix rank, and the solution of certain problems involving Euclidean norms. Such problems arise in the spectral analysis of ligand binding to macromolecule. Here, we present a spectral data analysis method using SVD (SVD analysis and nonlinear fitting to determine the binding characteristics of intercalating drugs to DNA. This methodology reduces noise and identifies distinct spectral species similar to traditional principal component analysis as well as fitting nonlinear binding parameters. We applied SVD analysis to investigate the interaction of actinomycin D and daunomycin with native DNA. This methodology does not require prior knowledge of ligand molar extinction coefficients (free and bound, which potentially limits binding analysis. Data are acquired simply by reconstructing the experimental data and by adjusting the product of deconvoluted matrices and the matrix of model coefficients determined by the Scatchard and McGee and von Hippel equation.
Compression of magnetohydrodynamic simulation data using singular value decomposition
International Nuclear Information System (INIS)
Castillo Negrete, D. del; Hirshman, S.P.; Spong, D.A.; D'Azevedo, E.F.
2007-01-01
Numerical calculations of magnetic and flow fields in magnetohydrodynamic (MHD) simulations can result in extensive data sets. Particle-based calculations in these MHD fields, needed to provide closure relations for the MHD equations, will require communication of this data to multiple processors and rapid interpolation at numerous particle orbit positions. To facilitate this analysis it is advantageous to compress the data using singular value decomposition (SVD, or principal orthogonal decomposition, POD) methods. As an example of the compression technique, SVD is applied to magnetic field data arising from a dynamic nonlinear MHD code. The performance of the SVD compression algorithm is analyzed by calculating Poincare plots for electron orbits in a three-dimensional magnetic field and comparing the results with uncompressed data
A Systolic Architecture for Singular Value Decomposition,
1983-01-01
Presented at the 1 st International Colloquium on Vector and Parallel Computing in Scientific Applications, Paris, March 191J Contract N00014-82-K.0703...Gene Golub. Private comunication . given inputs x and n 2 , compute 2 2 2 2 /6/ G. H. Golub and F. T. Luk : "Singular Value I + X1 Decomposition
Singular value decomposition for collaborative filtering on a GPU
Kato, Kimikazu; Hosino, Tikara
2010-06-01
A collaborative filtering predicts customers' unknown preferences from known preferences. In a computation of the collaborative filtering, a singular value decomposition (SVD) is needed to reduce the size of a large scale matrix so that the burden for the next phase computation will be decreased. In this application, SVD means a roughly approximated factorization of a given matrix into smaller sized matrices. Webb (a.k.a. Simon Funk) showed an effective algorithm to compute SVD toward a solution of an open competition called "Netflix Prize". The algorithm utilizes an iterative method so that the error of approximation improves in each step of the iteration. We give a GPU version of Webb's algorithm. Our algorithm is implemented in the CUDA and it is shown to be efficient by an experiment.
Singular value decomposition for collaborative filtering on a GPU
International Nuclear Information System (INIS)
Kato, Kimikazu; Hosino, Tikara
2010-01-01
A collaborative filtering predicts customers' unknown preferences from known preferences. In a computation of the collaborative filtering, a singular value decomposition (SVD) is needed to reduce the size of a large scale matrix so that the burden for the next phase computation will be decreased. In this application, SVD means a roughly approximated factorization of a given matrix into smaller sized matrices. Webb (a.k.a. Simon Funk) showed an effective algorithm to compute SVD toward a solution of an open competition called 'Netflix Prize'. The algorithm utilizes an iterative method so that the error of approximation improves in each step of the iteration. We give a GPU version of Webb's algorithm. Our algorithm is implemented in the CUDA and it is shown to be efficient by an experiment.
Characterization of agricultural land using singular value decomposition
Herries, Graham M.; Danaher, Sean; Selige, Thomas
1995-11-01
A method is defined and tested for the characterization of agricultural land from multi-spectral imagery, based on singular value decomposition (SVD) and key vector analysis. The SVD technique, which bears a close resemblance to multivariate statistic techniques, has previously been successfully applied to problems of signal extraction for marine data and forestry species classification. In this study the SVD technique is used as a classifier for agricultural regions, using airborne Daedalus ATM data, with 1 m resolution. The specific region chosen is an experimental research farm in Bavaria, Germany. This farm has a large number of crops, within a very small region and hence is not amenable to existing techniques. There are a number of other significant factors which render existing techniques such as the maximum likelihood algorithm less suitable for this area. These include a very dynamic terrain and tessellated pattern soil differences, which together cause large variations in the growth characteristics of the crops. The SVD technique is applied to this data set using a multi-stage classification approach, removing unwanted land-cover classes one step at a time. Typical classification accuracy's for SVD are of the order of 85-100%. Preliminary results indicate that it is a fast and efficient classifier with the ability to differentiate between crop types such as wheat, rye, potatoes and clover. The results of characterizing 3 sub-classes of Winter Wheat are also shown.
Advances in audio watermarking based on singular value decomposition
Dhar, Pranab Kumar
2015-01-01
This book introduces audio watermarking methods for copyright protection, which has drawn extensive attention for securing digital data from unauthorized copying. The book is divided into two parts. First, an audio watermarking method in discrete wavelet transform (DWT) and discrete cosine transform (DCT) domains using singular value decomposition (SVD) and quantization is introduced. This method is robust against various attacks and provides good imperceptible watermarked sounds. Then, an audio watermarking method in fast Fourier transform (FFT) domain using SVD and Cartesian-polar transformation (CPT) is presented. This method has high imperceptibility and high data payload and it provides good robustness against various attacks. These techniques allow media owners to protect copyright and to show authenticity and ownership of their material in a variety of applications. · Features new methods of audio watermarking for copyright protection and ownership protection · Outl...
Biclustering via Sparse Singular Value Decomposition
Lee, Mihee
2010-02-16
Sparse singular value decomposition (SSVD) is proposed as a new exploratory analysis tool for biclustering or identifying interpretable row-column associations within high-dimensional data matrices. SSVD seeks a low-rank, checkerboard structured matrix approximation to data matrices. The desired checkerboard structure is achieved by forcing both the left- and right-singular vectors to be sparse, that is, having many zero entries. By interpreting singular vectors as regression coefficient vectors for certain linear regressions, sparsity-inducing regularization penalties are imposed to the least squares regression to produce sparse singular vectors. An efficient iterative algorithm is proposed for computing the sparse singular vectors, along with some discussion of penalty parameter selection. A lung cancer microarray dataset and a food nutrition dataset are used to illustrate SSVD as a biclustering method. SSVD is also compared with some existing biclustering methods using simulated datasets. © 2010, The International Biometric Society.
Note on Symplectic SVD-Like Decomposition
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AGOUJIL Said
2016-02-01
Full Text Available The aim of this study was to introduce a constructive method to compute a symplectic singular value decomposition (SVD-like decomposition of a 2n-by-m rectangular real matrix A, based on symplectic refectors.This approach used a canonical Schur form of skew-symmetric matrix and it allowed us to compute eigenvalues for the structured matrices as Hamiltonian matrix JAA^T.
Cui, Ximing; Wang, Zhe; Kang, Yihua; Pu, Haiming; Deng, Zhiyang
2018-05-01
Singular value decomposition (SVD) has been proven to be an effective de-noising tool for flaw echo signal feature detection in ultrasonic non-destructive evaluation (NDE). However, the uncertainty in the arbitrary manner of the selection of an effective singular value weakens the robustness of this technique. Improper selection of effective singular values will lead to bad performance of SVD de-noising. What is more, the computational complexity of SVD is too large for it to be applied in real-time applications. In this paper, to eliminate the uncertainty in SVD de-noising, a novel flaw indicator, named the maximum singular value indicator (MSI), based on short-time SVD (STSVD), is proposed for flaw feature detection from a measured signal in ultrasonic NDE. In this technique, the measured signal is first truncated into overlapping short-time data segments to put feature information of a transient flaw echo signal in local field, and then the MSI can be obtained from the SVD of each short-time data segment. Research shows that this indicator can clearly indicate the location of ultrasonic flaw signals, and the computational complexity of this STSVD-based indicator is significantly reduced with the algorithm proposed in this paper. Both simulation and experiments show that this technique is very efficient for real-time application in flaw detection from noisy data.
Analysis of local ionospheric time varying characteristics with singular value decomposition
DEFF Research Database (Denmark)
Jakobsen, Jakob Anders; Knudsen, Per; Jensen, Anna B. O.
2010-01-01
In this paper, a time series from 1999 to 2007 of absolute total electron content (TEC) values has been computed and analyzed using singular value decomposition (SVD). The data set has been computed using a Kalman Filter and is based on dual frequency GPS data from three reference stations in Den...
Using many pilot points and singular value decomposition in groundwater model calibration
DEFF Research Database (Denmark)
Christensen, Steen; Doherty, John
2008-01-01
over the model area. Singular value decomposition (SVD) of the normal matrix is used to reduce the large number of pilot point parameters to a smaller number of so-called super parameters that can be estimated by nonlinear regression from the available observations. A number of eigenvectors...
On reliability of singular-value decomposition in attractor reconstruction
International Nuclear Information System (INIS)
Palus, M.; Dvorak, I.
1990-12-01
Applicability of singular-value decomposition for reconstructing the strange attractor from one-dimensional chaotic time series, proposed by Broomhead and King, is extensively tested and discussed. Previously published doubts about its reliability are confirmed: singular-value decomposition, by nature a linear method, is only of a limited power when nonlinear structures are studied. (author). 29 refs, 9 figs
Retrieval of Enterobacteriaceae drug targets using singular value decomposition.
Silvério-Machado, Rita; Couto, Bráulio R G M; Dos Santos, Marcos A
2015-04-15
The identification of potential drug target proteins in bacteria is important in pharmaceutical research for the development of new antibiotics to combat bacterial agents that cause diseases. A new model that combines the singular value decomposition (SVD) technique with biological filters composed of a set of protein properties associated with bacterial drug targets and similarity to protein-coding essential genes of Escherichia coli (strain K12) has been created to predict potential antibiotic drug targets in the Enterobacteriaceae family. This model identified 99 potential drug target proteins in the studied family, which exhibit eight different functions and are protein-coding essential genes or similar to protein-coding essential genes of E.coli (strain K12), indicating that the disruption of the activities of these proteins is critical for cells. Proteins from bacteria with described drug resistance were found among the retrieved candidates. These candidates have no similarity to the human proteome, therefore exhibiting the advantage of causing no adverse effects or at least no known adverse effects on humans. rita_silverio@hotmail.com. Supplementary data are available at Bioinformatics online. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.
Directory of Open Access Journals (Sweden)
Khaled Loukhaoukha
2013-01-01
Full Text Available We present a new optimal watermarking scheme based on discrete wavelet transform (DWT and singular value decomposition (SVD using multiobjective ant colony optimization (MOACO. A binary watermark is decomposed using a singular value decomposition. Then, the singular values are embedded in a detailed subband of host image. The trade-off between watermark transparency and robustness is controlled by multiple scaling factors (MSFs instead of a single scaling factor (SSF. Determining the optimal values of the multiple scaling factors (MSFs is a difficult problem. However, a multiobjective ant colony optimization is used to determine these values. Experimental results show much improved performances of the proposed scheme in terms of transparency and robustness compared to other watermarking schemes. Furthermore, it does not suffer from the problem of high probability of false positive detection of the watermarks.
Deconvolutions based on singular value decomposition and the pseudoinverse: a guide for beginners.
Hendler, R W; Shrager, R I
1994-01-01
Singular value decomposition (SVD) is deeply rooted in the theory of linear algebra, and because of this is not readily understood by a large group of researchers who could profit from its application. In this paper, we discuss the subject on a level that should be understandable to scientists who are not well versed in linear algebra. However, because it is necessary that certain key concepts in linear algebra be appreciated in order to comprehend what is accomplished by SVD, we present the section, 'Bare basics of linear algebra'. This is followed by a discussion of the theory of SVD. Next we present step-by-step examples to illustrate how SVD is applied to deconvolute a titration involving a mixture of three pH indicators. One noiseless case is presented as well as two cases where either a fixed or varying noise level is present. Finally, we discuss additional deconvolutions of mixed spectra based on the use of the pseudoinverse.
TRUST MODEL FOR SOCIAL NETWORK USING SINGULAR VALUE DECOMPOSITION
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Davis Bundi Ntwiga
2016-06-01
Full Text Available For effective interactions to take place in a social network, trust is important. We model trust of agents using the peer to peer reputation ratings in the network that forms a real valued matrix. Singular value decomposition discounts the reputation ratings to estimate the trust levels as trust is the subjective probability of future expectations based on current reputation ratings. Reputation and trust are closely related and singular value decomposition can estimate trust using the real valued matrix of the reputation ratings of the agents in the network. Singular value decomposition is an ideal technique in error elimination when estimating trust from reputation ratings. Reputation estimation of trust is optimal at the discounting of 20 %.
Tensor renormalization group with randomized singular value decomposition
Morita, Satoshi; Igarashi, Ryo; Zhao, Hui-Hai; Kawashima, Naoki
2018-03-01
An algorithm of the tensor renormalization group is proposed based on a randomized algorithm for singular value decomposition. Our algorithm is applicable to a broad range of two-dimensional classical models. In the case of a square lattice, its computational complexity and memory usage are proportional to the fifth and the third power of the bond dimension, respectively, whereas those of the conventional implementation are of the sixth and the fourth power. The oversampling parameter larger than the bond dimension is sufficient to reproduce the same result as full singular value decomposition even at the critical point of the two-dimensional Ising model.
Zhang, Shangbin; Lu, Siliang; He, Qingbo; Kong, Fanrang
2016-09-01
For rotating machines, the defective faults of bearings generally are represented as periodic transient impulses in acquired signals. The extraction of transient features from signals has been a key issue for fault diagnosis. However, the background noise reduces identification performance of periodic faults in practice. This paper proposes a time-varying singular value decomposition (TSVD) method to enhance the identification of periodic faults. The proposed method is inspired by the sliding window method. By applying singular value decomposition (SVD) to the signal under a sliding window, we can obtain a time-varying singular value matrix (TSVM). Each column in the TSVM is occupied by the singular values of the corresponding sliding window, and each row represents the intrinsic structure of the raw signal, namely time-singular-value-sequence (TSVS). Theoretical and experimental analyses show that the frequency of TSVS is exactly twice that of the corresponding intrinsic structure. Moreover, the signal-to-noise ratio (SNR) of TSVS is improved significantly in comparison with the raw signal. The proposed method takes advantages of the TSVS in noise suppression and feature extraction to enhance fault frequency for diagnosis. The effectiveness of the TSVD is verified by means of simulation studies and applications to diagnosis of bearing faults. Results indicate that the proposed method is superior to traditional methods for bearing fault diagnosis.
Split-and-Combine Singular Value Decomposition for Large-Scale Matrix
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Jengnan Tzeng
2013-01-01
Full Text Available The singular value decomposition (SVD is a fundamental matrix decomposition in linear algebra. It is widely applied in many modern techniques, for example, high- dimensional data visualization, dimension reduction, data mining, latent semantic analysis, and so forth. Although the SVD plays an essential role in these fields, its apparent weakness is the order three computational cost. This order three computational cost makes many modern applications infeasible, especially when the scale of the data is huge and growing. Therefore, it is imperative to develop a fast SVD method in modern era. If the rank of matrix is much smaller than the matrix size, there are already some fast SVD approaches. In this paper, we focus on this case but with the additional condition that the data is considerably huge to be stored as a matrix form. We will demonstrate that this fast SVD result is sufficiently accurate, and most importantly it can be derived immediately. Using this fast method, many infeasible modern techniques based on the SVD will become viable.
Robust regularized singular value decomposition with application to mortality data
Zhang, Lingsong; Shen, Haipeng; Huang, Jianhua Z.
2013-01-01
We develop a robust regularized singular value decomposition (RobRSVD) method for analyzing two-way functional data. The research is motivated by the application of modeling human mortality as a smooth two-way function of age group and year. The Rob
An ill-conditioning conformal radiotherapy analysis based on singular values decomposition
International Nuclear Information System (INIS)
Lefkopoulos, D.; Grandjean, P.; Bendada, S.; Dominique, C.; Platoni, K.; Schlienger, M.
1995-01-01
Clinical experience in stereotactic radiotherapy of irregular complex lesions had shown that optimization algorithms were necessary to improve the dose distribution. We have developed a general optimization procedure which can be applied to different conformal irradiation techniques. In this presentation this procedure is tested on the stereotactic radiotherapy modality of complex cerebral lesions treated with multi-isocentric technique based on the 'associated targets methodology'. In this inverse procedure we use the singular value decomposition (SVD) analysis which proposes several optimal solutions for the narrow beams weights of each isocentre. The SVD analysis quantifies the ill-conditioning of the dosimetric calculation of the stereotactic irradiation, using the condition number which is the ratio of the bigger to smaller singular values. Our dose distribution optimization approach consists on the study of the irradiation parameters influence on the stereotactic radiotherapy inverse problem. The adjustment of the different irradiation parameters into the 'SVD optimizer' procedure is realized taking into account the ratio of the quality reconstruction to the time calculation. It will permit a more efficient use of the 'SVD optimizer' in clinical applications for real 3D lesions. The evaluation criteria for the choice of satisfactory solutions are based on the dose-volume histograms and clinical considerations. We will present the efficiency of ''SVD optimizer'' to analyze and predict the ill-conditioning in stereotactic radiotherapy and to recognize the topography of the different beams in order to create optimal reconstructed weighting vector. The planification of stereotactic treatments using the ''SVD optimizer'' is examined for mono-isocentrically and complex dual-isocentrically treated lesions. The application of the SVD optimization technique provides conformal dose distribution for complex intracranial lesions. It is a general optimization procedure
Energy Technology Data Exchange (ETDEWEB)
Xing, Zhanqiang; Qu, Jianfeng; Chai, Yi; Tang, Qiu; Zhou, Yuming [Chongqing University, Chongqing (China)
2017-02-15
The gear vibration signal is nonlinear and non-stationary, gear fault diagnosis under variable conditions has always been unsatisfactory. To solve this problem, an intelligent fault diagnosis method based on Intrinsic time-scale decomposition (ITD)-Singular value decomposition (SVD) and Support vector machine (SVM) is proposed in this paper. The ITD method is adopted to decompose the vibration signal of gearbox into several Proper rotation components (PRCs). Subsequently, the singular value decomposition is proposed to obtain the singular value vectors of the proper rotation components and improve the robustness of feature extraction under variable conditions. Finally, the Support vector machine is applied to classify the fault type of gear. According to the experimental results, the performance of ITD-SVD exceeds those of the time-frequency analysis methods with EMD and WPT combined with SVD for feature extraction, and the classifier of SVM outperforms those for K-nearest neighbors (K-NN) and Back propagation (BP). Moreover, the proposed approach can accurately diagnose and identify different fault types of gear under variable conditions.
Directory of Open Access Journals (Sweden)
Koivistoinen Teemu
2007-01-01
Full Text Available As we know, singular value decomposition (SVD is designed for computing singular values (SVs of a matrix. Then, if it is used for finding SVs of an -by-1 or 1-by- array with elements representing samples of a signal, it will return only one singular value that is not enough to express the whole signal. To overcome this problem, we designed a new kind of the feature extraction method which we call ''time-frequency moments singular value decomposition (TFM-SVD.'' In this new method, we use statistical features of time series as well as frequency series (Fourier transform of the signal. This information is then extracted into a certain matrix with a fixed structure and the SVs of that matrix are sought. This transform can be used as a preprocessing stage in pattern clustering methods. The results in using it indicate that the performance of a combined system including this transform and classifiers is comparable with the performance of using other feature extraction methods such as wavelet transforms. To evaluate TFM-SVD, we applied this new method and artificial neural networks (ANNs for ballistocardiogram (BCG data clustering to look for probable heart disease of six test subjects. BCG from the test subjects was recorded using a chair-like ballistocardiograph, developed in our project. This kind of device combined with automated recording and analysis would be suitable for use in many places, such as home, office, and so forth. The results show that the method has high performance and it is almost insensitive to BCG waveform latency or nonlinear disturbance.
Directory of Open Access Journals (Sweden)
Alpo Värri
2007-01-01
Full Text Available As we know, singular value decomposition (SVD is designed for computing singular values (SVs of a matrix. Then, if it is used for finding SVs of an m-by-1 or 1-by-m array with elements representing samples of a signal, it will return only one singular value that is not enough to express the whole signal. To overcome this problem, we designed a new kind of the feature extraction method which we call ‘‘time-frequency moments singular value decomposition (TFM-SVD.’’ In this new method, we use statistical features of time series as well as frequency series (Fourier transform of the signal. This information is then extracted into a certain matrix with a fixed structure and the SVs of that matrix are sought. This transform can be used as a preprocessing stage in pattern clustering methods. The results in using it indicate that the performance of a combined system including this transform and classifiers is comparable with the performance of using other feature extraction methods such as wavelet transforms. To evaluate TFM-SVD, we applied this new method and artificial neural networks (ANNs for ballistocardiogram (BCG data clustering to look for probable heart disease of six test subjects. BCG from the test subjects was recorded using a chair-like ballistocardiograph, developed in our project. This kind of device combined with automated recording and analysis would be suitable for use in many places, such as home, office, and so forth. The results show that the method has high performance and it is almost insensitive to BCG waveform latency or nonlinear disturbance.
Akhbardeh, Alireza; Junnila, Sakari; Koivuluoma, Mikko; Koivistoinen, Teemu; Värri, Alpo
2006-12-01
As we know, singular value decomposition (SVD) is designed for computing singular values (SVs) of a matrix. Then, if it is used for finding SVs of an [InlineEquation not available: see fulltext.]-by-1 or 1-by- [InlineEquation not available: see fulltext.] array with elements representing samples of a signal, it will return only one singular value that is not enough to express the whole signal. To overcome this problem, we designed a new kind of the feature extraction method which we call ''time-frequency moments singular value decomposition (TFM-SVD).'' In this new method, we use statistical features of time series as well as frequency series (Fourier transform of the signal). This information is then extracted into a certain matrix with a fixed structure and the SVs of that matrix are sought. This transform can be used as a preprocessing stage in pattern clustering methods. The results in using it indicate that the performance of a combined system including this transform and classifiers is comparable with the performance of using other feature extraction methods such as wavelet transforms. To evaluate TFM-SVD, we applied this new method and artificial neural networks (ANNs) for ballistocardiogram (BCG) data clustering to look for probable heart disease of six test subjects. BCG from the test subjects was recorded using a chair-like ballistocardiograph, developed in our project. This kind of device combined with automated recording and analysis would be suitable for use in many places, such as home, office, and so forth. The results show that the method has high performance and it is almost insensitive to BCG waveform latency or nonlinear disturbance.
International Nuclear Information System (INIS)
Tsuji, Masashi; Shimazu, Yoichiro; Michishita, Hiroshi
2005-01-01
A new method for evaluating the decay ratios in a boiling water reactor (BWR) using the singular value decomposition (SVD) method had been proposed. In this method, a signal component closely related to the BWR stability can be extracted from independent components of the neutron noise signal decomposed by the SVD method. However, real-time stability monitoring by the SVD method requires an efficient procedure for screening such components. For efficient screening, an artificial neural network (ANN) with three layers was adopted. The trained ANN was actually applied to decomposed components of local power range monitor (LPRM) signals that were measured in stability experiments conducted in the Ringhals-1 BWR. In each LPRM signal, multiple candidates were screened from the decomposed components. However, decay ratios could be estimated by introducing appropriate criterions for selecting the most suitable component among the candidates. The estimated decay ratios are almost identical to those evaluated by visual screening in a previous study. The selected components commonly have the largest singular value, the largest decay ratio and the least squared fitting error among the candidates. By virtue of excellent screening performance of the trained ANN, the real-time stability monitoring by the SVD method can be applied in practice. (author)
Sun, Qianlai; Wang, Yin; Sun, Zhiyi
2018-05-01
For most surface defect detection methods based on image processing, image segmentation is a prerequisite for determining and locating the defect. In our previous work, a method based on singular value decomposition (SVD) was used to determine and approximately locate surface defects on steel strips without image segmentation. For the SVD-based method, the image to be inspected was projected onto its first left and right singular vectors respectively. If there were defects in the image, there would be sharp changes in the projections. Then the defects may be determined and located according sharp changes in the projections of each image to be inspected. This method was simple and practical but the SVD should be performed for each image to be inspected. Owing to the high time complexity of SVD itself, it did not have a significant advantage in terms of time consumption over image segmentation-based methods. Here, we present an improved SVD-based method. In the improved method, a defect-free image is considered as the reference image which is acquired under the same environment as the image to be inspected. The singular vectors of each image to be inspected are replaced by the singular vectors of the reference image, and SVD is performed only once for the reference image off-line before detecting of the defects, thus greatly reducing the time required. The improved method is more conducive to real-time defect detection. Experimental results confirm its validity.
On the use of the singular value decomposition for text retrieval
Energy Technology Data Exchange (ETDEWEB)
Husbands, P.; Simon, H.D.; Ding, C.
2000-12-04
The use of the Singular Value Decomposition (SVD) has been proposed for text retrieval in several recent works. This technique uses the SVD to project very high dimensional document and query vectors into a low dimensional space. In this new space it is hoped that the underlying structure of the collection is revealed thus enhancing retrieval performance. Theoretical results have provided some evidence for this claim and to some extent experiments have confirmed this. However, these studies have mostly used small test collections and simplified document models. In this work we investigate the use of the SVD on large document collections. We show that, if interpreted as a mechanism for representing the terms of the collection, this technique alone is insufficient for dealing with the variability in term occurrence. Section 2 introduces the text retrieval concepts necessary for our work. A short description of our experimental architecture is presented in Section 3. Section 4 describes how term occurrence variability affects the SVD and then shows how the decomposition influences retrieval performance. A possible way of improving SVD-based techniques is presented in Section 5 and concluded in Section 6.
Singular value decomposition methods for wave propagation analysis
Czech Academy of Sciences Publication Activity Database
Santolík, Ondřej; Parrot, M.; Lefeuvre, F.
2003-01-01
Roč. 38, č. 1 (2003), s. 10-1-10-13 ISSN 0048-6604 R&D Projects: GA ČR GA205/01/1064 Grant - others:Barrande(CZ) 98039/98055 Institutional research plan: CEZ:AV0Z3042911; CEZ:MSM 113200004 Keywords : wave propagation * singular value decomposition Subject RIV: DG - Athmosphere Sciences, Meteorology Impact factor: 0.832, year: 2003
Directory of Open Access Journals (Sweden)
Khaled Loukhaoukha
2017-12-01
Full Text Available Among emergent applications of digital watermarking are copyright protection and proof of ownership. Recently, Makbol and Khoo (2013 have proposed for these applications a new robust blind image watermarking scheme based on the redundant discrete wavelet transform (RDWT and the singular value decomposition (SVD. In this paper, we present two ambiguity attacks on this algorithm that have shown that this algorithm fails when used to provide robustness applications like owner identification, proof of ownership, and transaction tracking. Keywords: Ambiguity attack, Image watermarking, Singular value decomposition, Redundant discrete wavelet transform
International Nuclear Information System (INIS)
Emery, L.
1999-01-01
Magnet errors and off-center orbits through sextuples perturb the dispersion and beta functions in a storage ring (SR), which affects machine performance. In a large ring such as the Advanced Photon Source (APS), the magnet errors are difficult to determine with beam-based methods. Also the non-zero orbit through sextuples result from user requests for steering at light source points. For expediency, a singular value decomposition (SVD) matrix method analogous to orbit correction was adopted to make global corrections to these functions using strengths of several quadrupoles as correcting elements. The direct response matrix is calculated from the model of the perfect lattice. The inverse is calculated by SVD with a selected number of singular vectors. Resulting improvement in the lattice functions and machine performance will be presented
Srivastava, Madhur; Freed, Jack H
2017-11-16
Regularization is often utilized to elicit the desired physical results from experimental data. The recent development of a denoising procedure yielding about 2 orders of magnitude in improvement in SNR obviates the need for regularization, which achieves a compromise between canceling effects of noise and obtaining an estimate of the desired physical results. We show how singular value decomposition (SVD) can be employed directly on the denoised data, using pulse dipolar electron spin resonance experiments as an example. Such experiments are useful in measuring distances and their distributions, P(r) between spin labels on proteins. In noise-free model cases exact results are obtained, but even a small amount of noise (e.g., SNR = 850 after denoising) corrupts the solution. We develop criteria that precisely determine an optimum approximate solution, which can readily be automated. This method is applicable to any signal that is currently processed with regularization of its SVD analysis.
A singular-value decomposition approach to X-ray spectral estimation from attenuation data
International Nuclear Information System (INIS)
Tominaga, Shoji
1986-01-01
A singular-value decomposition (SVD) approach is described for estimating the exposure-rate spectral distributions of X-rays from attenuation data measured withvarious filtrations. This estimation problem with noisy measurements is formulated as the problem of solving a system of linear equations with an ill-conditioned nature. The principle of the SVD approach is that a response matrix, representing the X-ray attenuation effect by filtrations at various energies, can be expanded into summation of inherent component matrices, and thereby the spectral distributions can be represented as a linear combination of some component curves. A criterion function is presented for choosing the components needed to form a reliable estimate. The feasibility of the proposed approach is studied in detail in a computer simulation using a hypothetical X-ray spectrum. The application results of the spectral distributions emitted from a therapeutic X-ray generator are shown. Finally some advantages of this approach are pointed out. (orig.)
Combination of Wiener filtering and singular value decomposition filtering for volume imaging PET
International Nuclear Information System (INIS)
Shao, L.; Lewitt, R.M.; Karp, J.S.
1995-01-01
Although the three-dimensional (3D) multi-slice rebinning (MSRB) algorithm in PET is fast and practical, and provides an accurate reconstruction, the MSRB image, in general, suffers from the noise amplified by its singular value decomposition (SVD) filtering operation in the axial direction. Their aim in this study is to combine the use of the Wiener filter (WF) with the SVD to decrease the noise and improve the image quality. The SVD filtering ''deconvolves'' the spatially variant axial response function while the WF suppresses the noise and reduces the blurring not modeled by the axial SVD filter but included in the system modulation transfer function. Therefore, the synthesis of these two techniques combines the advantages of both filters. The authors applied this approach to the volume imaging HEAD PENN-PET brain scanner with an axial extent of 256 mm. This combined filter was evaluated in terms of spatial resolution, image contrast, and signal-to-noise ratio with several phantoms, such as a cold sphere phantom and 3D brain phantom. Specifically, the authors studied both the SVD filter with an axial Wiener filter and the SVD filter with a 3D Wiener filter, and compared the filtered images to those from the 3D reprojection (3DRP) reconstruction algorithm. Their results indicate that the Wiener filter increases the signal-to-noise ratio and also improves the contrast. For the MSRB images of the 3D brain phantom, after 3D WF, both the Gray/White and Gray/Ventricle ratios were improved from 1.8 to 2.8 and 2.1 to 4.1, respectively. In addition, the image quality with the MSRB algorithm is close to that of the 3DRP algorithm with 3D WF applied to both image reconstructions
International Nuclear Information System (INIS)
Devaux, J.Y.; Mazelier, L.; Lefkopoulos, D.
1997-01-01
We have earlier shown that the method of singular value decomposition (SVD) allows the image reconstruction in single-photon-tomography with precision higher than the classical method of filtered back-projections. Actually, the establishing of an elementary response matrix which incorporates both the photon attenuation phenomenon, the scattering, the translation non-invariance principle and the detector response, allows to take into account the totality of physical parameters of acquisition. By an non-consecutive optimized truncation of the singular values we have obtained a significant improvement in the efficiency of the regularization of bad conditioning of this problem. The present study aims at verifying the stability of this truncation under modifications of acquisition conditions. Two series of parameters were tested, first, those modifying the geometry of acquisition: the influence of rotation center, the asymmetric disposition of the elementary-volume sources against the detector and the precision of rotation angle, and secondly, those affecting the correspondence between the matrix and the space to be reconstructed: the effect of partial volume and a noise propagation in the experimental model. For the parameters which introduce a spatial distortion, the alteration of reconstruction has been, as expected, comparable to that observed with the classical reconstruction and proportional with the amplitude of shift from the normal one. In exchange, for the effect of partial volume and of noise, the study of truncation signature revealed a variation in the optimal choice of the conserved singular values but with no effect on the global precision of reconstruction
Electronic diffraction tomography by Green's functions and singular values decompositions
International Nuclear Information System (INIS)
Mayer, A.
2001-01-01
An inverse scattering technique is developed to enable a three-dimensional sample reconstruction from the diffraction figures obtained for different sample orientations by electronic projection microscopy, thus performing a diffraction tomography. In its Green's-functions formulation, this technique takes account of all orders of diffraction by performing an iterative reconstruction of the wave function on the observation screen and in the sample. In a final step, these quantities enable a reconstruction of the potential-energy distribution, which is assumed real valued. The method relies on the use of singular values decomposition techniques, thus providing the best least-squares solutions and enabling a reduction of noise. The technique is applied to the analysis of a three-dimensional nanometric sample that is observed in Fresnel conditions with an electron energy of 40 eV. The algorithm turns out to provide results with a mean relative error around 3% and to be stable against random noise
Inverse electronic scattering by Green's functions and singular values decomposition
International Nuclear Information System (INIS)
Mayer, A.; Vigneron, J.-P.
2000-01-01
An inverse scattering technique is developed to enable a sample reconstruction from the diffraction figures obtained by electronic projection microscopy. In its Green's functions formulation, this technique takes account of all orders of diffraction by performing an iterative reconstruction of the wave function on the observation screen. This scattered wave function is then backpropagated to the sample to determine the potential-energy distribution, which is assumed real valued. The method relies on the use of singular values decomposition techniques, thus providing the best least-squares solutions and enabling a reduction of noise. The technique is applied to the analysis of a two-dimensional nanometric sample that is observed in Fresnel conditions with an electronic energy of 25 eV. The algorithm turns out to provide results with a mean relative error of the order of 5% and to be very stable against random noise
International Nuclear Information System (INIS)
Platoni, K.; Lefkopoulos, D.; Grandjean, P.; Schlienger, M.
1999-01-01
A Linac sterotactic irradiation space is characterized by different angular separations of beams because of the geometry of the stereotactic irradiation. The regions of the stereotactic space characterized by low angular separations are one of the causes of ill-conditioning of the stereotactic irradiation inverse problem. The singular value decomposition (SVD) is a powerful mathematical analysis that permits the measurement of the ill-conditioning of the stereotactic irradiation problem. This study examines the ill-conditioning of the stereotactic irradiation space, provoked by the different angular separations of beams, using the SVD analysis. We subdivided the maximum irradiation space (MIS: (AA) AP x (AA) RL =180 x 180 ) into irradiation subspaces (ISSs), each characterized by its own angular separation. We studied the influence of ISSs on the SVD analysis and the evolution of the reconstruction quality of well defined three-dimensional dose matrices in each configuration. The more the ISS is characterized by low angular separation the more the condition number and the reconstruction inaccuracy are increased. Based on the above results we created two reduced irradiation spaces (RIS: (AA) AP x (AA) RL =180 x 140 and (AA) AP x (AA) RL =180 x 120 ) and compared the reconstruction quality of the RISs with respect to the MIS. The more an irradiation space is free of low angular separations the more the irradiation space contains useful singular components. (orig.)
Use of multiple singular value decompositions to analyze complex intracellular calcium ion signals
Martinez, Josue G.
2009-12-01
We compare calcium ion signaling (Ca(2+)) between two exposures; the data are present as movies, or, more prosaically, time series of images. This paper describes novel uses of singular value decompositions (SVD) and weighted versions of them (WSVD) to extract the signals from such movies, in a way that is semi-automatic and tuned closely to the actual data and their many complexities. These complexities include the following. First, the images themselves are of no interest: all interest focuses on the behavior of individual cells across time, and thus, the cells need to be segmented in an automated manner. Second, the cells themselves have 100+ pixels, so that they form 100+ curves measured over time, so that data compression is required to extract the features of these curves. Third, some of the pixels in some of the cells are subject to image saturation due to bit depth limits, and this saturation needs to be accounted for if one is to normalize the images in a reasonably un-biased manner. Finally, the Ca(2+) signals have oscillations or waves that vary with time and these signals need to be extracted. Thus, our aim is to show how to use multiple weighted and standard singular value decompositions to detect, extract and clarify the Ca(2+) signals. Our signal extraction methods then lead to simple although finely focused statistical methods to compare Ca(2+) signals across experimental conditions.
DEFF Research Database (Denmark)
Christensen, Steen; Doherty, John
2008-01-01
A significant practical problem with the pilot point method is to choose the location of the pilot points. We present a method that is intended to relieve the modeler from much of this responsibility. The basic idea is that a very large number of pilot points are distributed more or less uniformly...... over the model area. Singular value decomposition (SVD) of the (possibly weighted) sensitivity matrix of the pilot point based model produces eigenvectors of which we pick a small number corresponding to significant eigenvalues. Super parameters are defined as factors through which parameter...... combinations corresponding to the chosen eigenvectors are multiplied to obtain the pilot point values. The model can thus be transformed from having many-pilot-point parameters to having a few super parameters that can be estimated by nonlinear regression on the basis of the available observations. (This...
Meuwissen, Theo H E; Indahl, Ulf G; Ødegård, Jørgen
2017-12-27
Non-linear Bayesian genomic prediction models such as BayesA/B/C/R involve iteration and mostly Markov chain Monte Carlo (MCMC) algorithms, which are computationally expensive, especially when whole-genome sequence (WGS) data are analyzed. Singular value decomposition (SVD) of the genotype matrix can facilitate genomic prediction in large datasets, and can be used to estimate marker effects and their prediction error variances (PEV) in a computationally efficient manner. Here, we developed, implemented, and evaluated a direct, non-iterative method for the estimation of marker effects for the BayesC genomic prediction model. The BayesC model assumes a priori that markers have normally distributed effects with probability [Formula: see text] and no effect with probability (1 - [Formula: see text]). Marker effects and their PEV are estimated by using SVD and the posterior probability of the marker having a non-zero effect is calculated. These posterior probabilities are used to obtain marker-specific effect variances, which are subsequently used to approximate BayesC estimates of marker effects in a linear model. A computer simulation study was conducted to compare alternative genomic prediction methods, where a single reference generation was used to estimate marker effects, which were subsequently used for 10 generations of forward prediction, for which accuracies were evaluated. SVD-based posterior probabilities of markers having non-zero effects were generally lower than MCMC-based posterior probabilities, but for some regions the opposite occurred, resulting in clear signals for QTL-rich regions. The accuracies of breeding values estimated using SVD- and MCMC-based BayesC analyses were similar across the 10 generations of forward prediction. For an intermediate number of generations (2 to 5) of forward prediction, accuracies obtained with the BayesC model tended to be slightly higher than accuracies obtained using the best linear unbiased prediction of SNP
Robust regularized singular value decomposition with application to mortality data
Zhang, Lingsong
2013-09-01
We develop a robust regularized singular value decomposition (RobRSVD) method for analyzing two-way functional data. The research is motivated by the application of modeling human mortality as a smooth two-way function of age group and year. The RobRSVD is formulated as a penalized loss minimization problem where a robust loss function is used to measure the reconstruction error of a low-rank matrix approximation of the data, and an appropriately defined two-way roughness penalty function is used to ensure smoothness along each of the two functional domains. By viewing the minimization problem as two conditional regularized robust regressions, we develop a fast iterative reweighted least squares algorithm to implement the method. Our implementation naturally incorporates missing values. Furthermore, our formulation allows rigorous derivation of leaveone- row/column-out cross-validation and generalized cross-validation criteria, which enable computationally efficient data-driven penalty parameter selection. The advantages of the new robust method over nonrobust ones are shown via extensive simulation studies and the mortality rate application. © Institute of Mathematical Statistics, 2013.
Kumar, Ravi; Bhaduri, Basanta; Nishchal, Naveen K.
2018-01-01
In this study, we propose a quick response (QR) code based nonlinear optical image encryption technique using spiral phase transform (SPT), equal modulus decomposition (EMD) and singular value decomposition (SVD). First, the primary image is converted into a QR code and then multiplied with a spiral phase mask (SPM). Next, the product is spiral phase transformed with particular spiral phase function, and further, the EMD is performed on the output of SPT, which results into two complex images, Z 1 and Z 2. Among these, Z 1 is further Fresnel propagated with distance d, and Z 2 is reserved as a decryption key. Afterwards, SVD is performed on Fresnel propagated output to get three decomposed matrices i.e. one diagonal matrix and two unitary matrices. The two unitary matrices are modulated with two different SPMs and then, the inverse SVD is performed using the diagonal matrix and modulated unitary matrices to get the final encrypted image. Numerical simulation results confirm the validity and effectiveness of the proposed technique. The proposed technique is robust against noise attack, specific attack, and brutal force attack. Simulation results are presented in support of the proposed idea.
Singular value decomposition based feature extraction technique for physiological signal analysis.
Chang, Cheng-Ding; Wang, Chien-Chih; Jiang, Bernard C
2012-06-01
Multiscale entropy (MSE) is one of the popular techniques to calculate and describe the complexity of the physiological signal. Many studies use this approach to detect changes in the physiological conditions in the human body. However, MSE results are easily affected by noise and trends, leading to incorrect estimation of MSE values. In this paper, singular value decomposition (SVD) is adopted to replace MSE to extract the features of physiological signals, and adopt the support vector machine (SVM) to classify the different physiological states. A test data set based on the PhysioNet website was used, and the classification results showed that using SVD to extract features of the physiological signal could attain a classification accuracy rate of 89.157%, which is higher than that using the MSE value (71.084%). The results show the proposed analysis procedure is effective and appropriate for distinguishing different physiological states. This promising result could be used as a reference for doctors in diagnosis of congestive heart failure (CHF) disease.
Multi-Label Classification by Semi-Supervised Singular Value Decomposition.
Jing, Liping; Shen, Chenyang; Yang, Liu; Yu, Jian; Ng, Michael K
2017-10-01
Multi-label problems arise in various domains, including automatic multimedia data categorization, and have generated significant interest in computer vision and machine learning community. However, existing methods do not adequately address two key challenges: exploiting correlations between labels and making up for the lack of labelled data or even missing labelled data. In this paper, we proposed to use a semi-supervised singular value decomposition (SVD) to handle these two challenges. The proposed model takes advantage of the nuclear norm regularization on the SVD to effectively capture the label correlations. Meanwhile, it introduces manifold regularization on mapping to capture the intrinsic structure among data, which provides a good way to reduce the required labelled data with improving the classification performance. Furthermore, we designed an efficient algorithm to solve the proposed model based on the alternating direction method of multipliers, and thus, it can efficiently deal with large-scale data sets. Experimental results for synthetic and real-world multimedia data sets demonstrate that the proposed method can exploit the label correlations and obtain promising and better label prediction results than the state-of-the-art methods.
International Nuclear Information System (INIS)
Stamatopoulos, V G; Karras, D A; Mertzios, B G
2009-01-01
An efficient modification of singular value decomposition (SVD) is proposed in this paper aiming at denoising and more importantly at quantifying more accurately the statistically independent spectra of metabolite sources in magnetic resonance spectroscopy (MRS). Although SVD is known in MRS applications and several efficient algorithms exist for estimating SVD summation terms in which the raw MRS data are analyzed, however, it would be more beneficial for such an analysis if techniques with the ability to estimate statistically independent spectra could be employed. SVD is known to separate signal and noise subspaces but it assumes orthogonal properties for the components comprising signal subspace, which is not always the case, and might impose heavy constraints for the MRS case. A much more relaxing constraint would be to assume statistically independent components. Therefore, a modification of the main methodology incorporating techniques for calculating the assumed statistically independent spectra is proposed by applying SVD on the MRS spectrogram through application of the short time Fourier transform (STFT). This approach is based on combining SVD on STFT spectrogram followed by an iterative application of independent component analysis (ICA). Moreover, it is shown that the proposed methodology combined with a regression analysis would lead to improved quantification of the MRS signals. An experimental study based on synthetic MRS signals has been conducted to evaluate the herein proposed methodologies. The results obtained have been discussed and it is shown to be quite promising
Liang, Bin; Li, Yongbao; Wei, Ran; Guo, Bin; Xu, Xuang; Liu, Bo; Li, Jiafeng; Wu, Qiuwen; Zhou, Fugen
2018-01-01
With robot-controlled linac positioning, robotic radiotherapy systems such as CyberKnife significantly increase freedom of radiation beam placement, but also impose more challenges on treatment plan optimization. The resampling mechanism in the vendor-supplied treatment planning system (MultiPlan) cannot fully explore the increased beam direction search space. Besides, a sparse treatment plan (using fewer beams) is desired to improve treatment efficiency. This study proposes a singular value decomposition linear programming (SVDLP) optimization technique for circular collimator based robotic radiotherapy. The SVDLP approach initializes the input beams by simulating the process of covering the entire target volume with equivalent beam tapers. The requirements on dosimetry distribution are modeled as hard and soft constraints, and the sparsity of the treatment plan is achieved by compressive sensing. The proposed linear programming (LP) model optimizes beam weights by minimizing the deviation of soft constraints subject to hard constraints, with a constraint on the l 1 norm of the beam weight. A singular value decomposition (SVD) based acceleration technique was developed for the LP model. Based on the degeneracy of the influence matrix, the model is first compressed into lower dimension for optimization, and then back-projected to reconstruct the beam weight. After beam weight optimization, the number of beams is reduced by removing the beams with low weight, and optimizing the weights of the remaining beams using the same model. This beam reduction technique is further validated by a mixed integer programming (MIP) model. The SVDLP approach was tested on a lung case. The results demonstrate that the SVD acceleration technique speeds up the optimization by a factor of 4.8. Furthermore, the beam reduction achieves a similar plan quality to the globally optimal plan obtained by the MIP model, but is one to two orders of magnitude faster. Furthermore, the SVDLP
Liang, Bin; Li, Yongbao; Wei, Ran; Guo, Bin; Xu, Xuang; Liu, Bo; Li, Jiafeng; Wu, Qiuwen; Zhou, Fugen
2018-01-05
With robot-controlled linac positioning, robotic radiotherapy systems such as CyberKnife significantly increase freedom of radiation beam placement, but also impose more challenges on treatment plan optimization. The resampling mechanism in the vendor-supplied treatment planning system (MultiPlan) cannot fully explore the increased beam direction search space. Besides, a sparse treatment plan (using fewer beams) is desired to improve treatment efficiency. This study proposes a singular value decomposition linear programming (SVDLP) optimization technique for circular collimator based robotic radiotherapy. The SVDLP approach initializes the input beams by simulating the process of covering the entire target volume with equivalent beam tapers. The requirements on dosimetry distribution are modeled as hard and soft constraints, and the sparsity of the treatment plan is achieved by compressive sensing. The proposed linear programming (LP) model optimizes beam weights by minimizing the deviation of soft constraints subject to hard constraints, with a constraint on the l 1 norm of the beam weight. A singular value decomposition (SVD) based acceleration technique was developed for the LP model. Based on the degeneracy of the influence matrix, the model is first compressed into lower dimension for optimization, and then back-projected to reconstruct the beam weight. After beam weight optimization, the number of beams is reduced by removing the beams with low weight, and optimizing the weights of the remaining beams using the same model. This beam reduction technique is further validated by a mixed integer programming (MIP) model. The SVDLP approach was tested on a lung case. The results demonstrate that the SVD acceleration technique speeds up the optimization by a factor of 4.8. Furthermore, the beam reduction achieves a similar plan quality to the globally optimal plan obtained by the MIP model, but is one to two orders of magnitude faster. Furthermore, the SVDLP
Real-time Automatic Detectors of P and S Waves Using Singular Values Decomposition
Kurzon, I.; Vernon, F.; Rosenberger, A.; Ben-Zion, Y.
2013-12-01
We implement a new method for the automatic detection of the primary P and S phases using Singular Value Decomposition (SVD) analysis. The method is based on a real-time iteration algorithm of Rosenberger (2010) for the SVD of three component seismograms. Rosenberger's algorithm identifies the incidence angle by applying SVD and separates the waveforms into their P and S components. We have been using the same algorithm with the modification that we filter the waveforms prior to the SVD, and then apply SNR (Signal-to-Noise Ratio) detectors for picking the P and S arrivals, on the new filtered+SVD-separated channels. A recent deployment in San Jacinto Fault Zone area provides a very dense seismic network that allows us to test the detection algorithm in diverse setting, such as: events with different source mechanisms, stations with different site characteristics, and ray paths that diverge from the SVD approximation used in the algorithm, (e.g., rays propagating within the fault and recorded on linear arrays, crossing the fault). We have found that a Butterworth band-pass filter of 2-30Hz, with four poles at each of the corner frequencies, shows the best performance in a large variety of events and stations within the SJFZ. Using the SVD detectors we obtain a similar number of P and S picks, which is a rare thing to see in ordinary SNR detectors. Also for the actual real-time operation of the ANZA and SJFZ real-time seismic networks, the above filter (2-30Hz) shows a very impressive performance, tested on many events and several aftershock sequences in the region from the MW 5.2 of June 2005, through the MW 5.4 of July 2010, to MW 4.7 of March 2013. Here we show the results of testing the detectors on the most complex and intense aftershock sequence, the MW 5.2 of June 2005, in which in the very first hour there were ~4 events a minute. This aftershock sequence was thoroughly reviewed by several analysts, identifying 294 events in the first hour, located in a
Local sensitivity analysis for inverse problems solved by singular value decomposition
Hill, M.C.; Nolan, B.T.
2010-01-01
Local sensitivity analysis provides computationally frugal ways to evaluate models commonly used for resource management, risk assessment, and so on. This includes diagnosing inverse model convergence problems caused by parameter insensitivity and(or) parameter interdependence (correlation), understanding what aspects of the model and data contribute to measures of uncertainty, and identifying new data likely to reduce model uncertainty. Here, we consider sensitivity statistics relevant to models in which the process model parameters are transformed using singular value decomposition (SVD) to create SVD parameters for model calibration. The statistics considered include the PEST identifiability statistic, and combined use of the process-model parameter statistics composite scaled sensitivities and parameter correlation coefficients (CSS and PCC). The statistics are complimentary in that the identifiability statistic integrates the effects of parameter sensitivity and interdependence, while CSS and PCC provide individual measures of sensitivity and interdependence. PCC quantifies correlations between pairs or larger sets of parameters; when a set of parameters is intercorrelated, the absolute value of PCC is close to 1.00 for all pairs in the set. The number of singular vectors to include in the calculation of the identifiability statistic is somewhat subjective and influences the statistic. To demonstrate the statistics, we use the USDA’s Root Zone Water Quality Model to simulate nitrogen fate and transport in the unsaturated zone of the Merced River Basin, CA. There are 16 log-transformed process-model parameters, including water content at field capacity (WFC) and bulk density (BD) for each of five soil layers. Calibration data consisted of 1,670 observations comprising soil moisture, soil water tension, aqueous nitrate and bromide concentrations, soil nitrate concentration, and organic matter content. All 16 of the SVD parameters could be estimated by
Yang, Honggang; Lin, Huibin; Ding, Kang
2018-05-01
The performance of sparse features extraction by commonly used K-Singular Value Decomposition (K-SVD) method depends largely on the signal segment selected in rolling bearing diagnosis, furthermore, the calculating speed is relatively slow and the dictionary becomes so redundant when the fault signal is relatively long. A new sliding window denoising K-SVD (SWD-KSVD) method is proposed, which uses only one small segment of time domain signal containing impacts to perform sliding window dictionary learning and select an optimal pattern with oscillating information of the rolling bearing fault according to a maximum variance principle. An inner product operation between the optimal pattern and the whole fault signal is performed to enhance the characteristic of the impacts' occurrence moments. Lastly, the signal is reconstructed at peak points of the inner product to realize the extraction of the rolling bearing fault features. Both simulation and experiments verify that the method could extract the fault features effectively.
The Analysis of Two-Way Functional Data Using Two-Way Regularized Singular Value Decompositions
Huang, Jianhua Z.
2009-12-01
Two-way functional data consist of a data matrix whose row and column domains are both structured, for example, temporally or spatially, as when the data are time series collected at different locations in space. We extend one-way functional principal component analysis (PCA) to two-way functional data by introducing regularization of both left and right singular vectors in the singular value decomposition (SVD) of the data matrix. We focus on a penalization approach and solve the nontrivial problem of constructing proper two-way penalties from oneway regression penalties. We introduce conditional cross-validated smoothing parameter selection whereby left-singular vectors are cross- validated conditional on right-singular vectors, and vice versa. The concept can be realized as part of an alternating optimization algorithm. In addition to the penalization approach, we briefly consider two-way regularization with basis expansion. The proposed methods are illustrated with one simulated and two real data examples. Supplemental materials available online show that several "natural" approaches to penalized SVDs are flawed and explain why so. © 2009 American Statistical Association.
International Nuclear Information System (INIS)
Kulisek, Jonathan A.; Anderson, Kevin K.; Casella, Andrew M.; Gesh, Christopher J.; Warren, Glen A.
2013-01-01
This study investigates the use of a Lead Slowing-Down Spectrometer (LSDS) for the direct and independent measurement of fissile isotopes in light-water nuclear reactor fuel assemblies. The current study applies MCNPX, a Monte Carlo radiation transport code, to simulate the measurement of the assay of the used nuclear fuel assemblies in the LSDS. An empirical model has been developed based on the calibration of the LSDS to responses generated from the simulated assay of six well-characterized fuel assemblies. The effects of self-shielding are taken into account by using empirical basis vectors calculated from the singular value decomposition (SVD) of a matrix containing the self-shielding functions from the assay of assemblies in the calibration set. The performance of the empirical algorithm was tested on version 1 of the Next-Generation Safeguards Initiative (NGSI) used fuel library consisting of 64 assemblies, as well as a set of 27 diversion assemblies, both of which were developed by Los Alamos National Laboratory. The potential for direct and independent assay of the sum of the masses of Pu-239 and Pu-241 to within 2%, on average, has been demonstrated
Biton, Yaacov; Rabinovitch, Avinoam; Braunstein, Doron; Aviram, Ira; Campbell, Katherine; Mironov, Sergey; Herron, Todd; Jalife, José; Berenfeld, Omer
2018-01-01
Cardiac fibrillation is a major clinical and societal burden. Rotors may drive fibrillation in many cases, but their role and patterns are often masked by complex propagation. We used Singular Value Decomposition (SVD), which ranks patterns of activation hierarchically, together with Wiener-Granger causality analysis (WGCA), which analyses direction of information among observations, to investigate the role of rotors in cardiac fibrillation. We hypothesized that combining SVD analysis with WGCA should reveal whether rotor activity is the dominant driving force of fibrillation even in cases of high complexity. Optical mapping experiments were conducted in neonatal rat cardiomyocyte monolayers (diameter, 35 mm), which were genetically modified to overexpress the delayed rectifier K+ channel IKr only in one half of the monolayer. Such monolayers have been shown previously to sustain fast rotors confined to the IKr overexpressing half and driving fibrillatory-like activity in the other half. SVD analysis of the optical mapping movies revealed a hierarchical pattern in which the primary modes corresponded to rotor activity in the IKr overexpressing region and the secondary modes corresponded to fibrillatory activity elsewhere. We then applied WGCA to evaluate the directionality of influence between modes in the entire monolayer using clear and noisy movies of activity. We demonstrated that the rotor modes influence the secondary fibrillatory modes, but influence was detected also in the opposite direction. To more specifically delineate the role of the rotor in fibrillation, we decomposed separately the respective SVD modes of the rotor and fibrillatory domains. In this case, WGCA yielded more information from the rotor to the fibrillatory domains than in the opposite direction. In conclusion, SVD analysis reveals that rotors can be the dominant modes of an experimental model of fibrillation. Wiener-Granger causality on modes of the rotor domains confirms their
Efficiently enclosing the compact binary parameter space by singular-value decomposition
International Nuclear Information System (INIS)
Cannon, Kipp; Hanna, Chad; Keppel, Drew
2011-01-01
Gravitational-wave searches for the merger of compact binaries use matched filtering as the method of detecting signals and estimating parameters. Such searches construct a fine mesh of filters covering a signal parameter space at high density. Previously it has been shown that singular-value decomposition can reduce the effective number of filters required to search the data. Here we study how the basis provided by the singular-value decomposition changes dimension as a function of template-bank density. We will demonstrate that it is sufficient to use the basis provided by the singular-value decomposition of a low-density bank to accurately reconstruct arbitrary points within the boundaries of the template bank. Since this technique is purely numerical, it may have applications to interpolating the space of numerical relativity waveforms.
Mapping of Natural Radionuclides using Noise Adjusted Singular Value Decomposition, NASVD
DEFF Research Database (Denmark)
Aage, Helle Karina
2006-01-01
Mapping of natural radionuclides from airborne gamma spectrometry suffer from random ”noise” in the spectra due to short measurement times. This is partly compensated for by using large volume detectors to improve the counting statistics. One method of further improving the quality of the measured...... spectra is to remove from the spectra a large fraction of this random noise using a special variant of Singular Value Decomposition: Noise Adjusted Singular Value Decomposition. In 1997-1999 the natural radionuclides on the Danish Island of Bornholm were mapped using a combination of the standard 3...
Jia, Zhongxiao; Yang, Yanfei
2018-05-01
In this paper, we propose new randomization based algorithms for large scale linear discrete ill-posed problems with general-form regularization: subject to , where L is a regularization matrix. Our algorithms are inspired by the modified truncated singular value decomposition (MTSVD) method, which suits only for small to medium scale problems, and randomized SVD (RSVD) algorithms that generate good low rank approximations to A. We use rank-k truncated randomized SVD (TRSVD) approximations to A by truncating the rank- RSVD approximations to A, where q is an oversampling parameter. The resulting algorithms are called modified TRSVD (MTRSVD) methods. At every step, we use the LSQR algorithm to solve the resulting inner least squares problem, which is proved to become better conditioned as k increases so that LSQR converges faster. We present sharp bounds for the approximation accuracy of the RSVDs and TRSVDs for severely, moderately and mildly ill-posed problems, and substantially improve a known basic bound for TRSVD approximations. We prove how to choose the stopping tolerance for LSQR in order to guarantee that the computed and exact best regularized solutions have the same accuracy. Numerical experiments illustrate that the best regularized solutions by MTRSVD are as accurate as the ones by the truncated generalized singular value decomposition (TGSVD) algorithm, and at least as accurate as those by some existing truncated randomized generalized singular value decomposition (TRGSVD) algorithms. This work was supported in part by the National Science Foundation of China (Nos. 11771249 and 11371219).
Devi, B Pushpa; Singh, Kh Manglem; Roy, Sudipta
2016-01-01
This paper proposes a new watermarking algorithm based on the shuffled singular value decomposition and the visual cryptography for copyright protection of digital images. It generates the ownership and identification shares of the image based on visual cryptography. It decomposes the image into low and high frequency sub-bands. The low frequency sub-band is further divided into blocks of same size after shuffling it and then the singular value decomposition is applied to each randomly selected block. Shares are generated by comparing one of the elements in the first column of the left orthogonal matrix with its corresponding element in the right orthogonal matrix of the singular value decomposition of the block of the low frequency sub-band. The experimental results show that the proposed scheme clearly verifies the copyright of the digital images, and is robust to withstand several image processing attacks. Comparison with the other related visual cryptography-based algorithms reveals that the proposed method gives better performance. The proposed method is especially resilient against the rotation attack.
International Nuclear Information System (INIS)
Sun Bin; Zhou Yunlong; Zhao Peng; Guan Yuebo
2007-01-01
Aiming at the non-stationary characteristics of differential pressure fluctuation signals of gas-liquid two-phase flow, and the slow convergence of learning and liability of dropping into local minima for BP neural networks, flow regime identification method based on Singular Value Decomposition (SVD) and Least Square Support Vector Machine (LS-SVM) is presented. First of all, the Empirical Mode Decomposition (EMD) method is used to decompose the differential pressure fluctuation signals of gas-liquid two-phase flow into a number of stationary Intrinsic Mode Functions (IMFs) components from which the initial feature vector matrix is formed. By applying the singular vale decomposition technique to the initial feature vector matrixes, the singular values are obtained. Finally, the singular values serve as the flow regime characteristic vector to be LS-SVM classifier and flow regimes are identified by the output of the classifier. The identification result of four typical flow regimes of air-water two-phase flow in horizontal pipe has shown that this method achieves a higher identification rate. (authors)
Analytical singular-value decomposition of three-dimensional, proximity-based SPECT systems
Energy Technology Data Exchange (ETDEWEB)
Barrett, Harrison H. [Arizona Univ., Tucson, AZ (United States). College of Optical Sciences; Arizona Univ., Tucson, AZ (United States). Center for Gamma-Ray Imaging; Holen, Roel van [Ghent Univ. (Belgium). Medical Image and Signal Processing (MEDISIP); Arizona Univ., Tucson, AZ (United States). Center for Gamma-Ray Imaging
2011-07-01
An operator formalism is introduced for the description of SPECT imaging systems that use solid-angle effects rather than pinholes or collimators, as in recent work by Mitchell and Cherry. The object is treated as a 3D function, without discretization, and the data are 2D functions on the detectors. An analytic singular-value decomposition of the resulting integral operator is performed and used to compute the measurement and null components of the objects. The results of the theory are confirmed with a Landweber algorithm that does not require a system matrix. (orig.)
International Nuclear Information System (INIS)
Erba, M.; Mattioli, M.; Segui, J.L.
1997-10-01
This paper addresses the problem of removing sawtooth oscillations from multichannel plasma data in a self-consistent way, thereby preserving transients that have a different physical origin. The technique which does this is called the Generalized Singular Value Decomposition (GSVD), and its properties are discussed. Using the GSVD, we analyze spatially resolved electron temperature measurements from the Tore Supra tokamak, made in transient regimes that are perturbed either by the laser blow-off injection of impurities or by pellet injection. Non-local transport issues are briefly discussed. (author)
Singular value decomposition and artificial neutral network for analyzing bonner sphere data
International Nuclear Information System (INIS)
Zhu, Qingjun; Song, Gang; Song, Fengquan; Guo, Qian; Wu, Yican
2012-01-01
The objective of this study was to build an effective and reliable method based on the artificial neural network (ANN) model for unfolding neutron spectrum. The number of counts measured by 15 Bonner spheres and 281 neutron spectra were selected as the database. After singular value decomposition was used to determine the relationship between Bonner spheres, 11 Bonner spheres were chosen as input descriptors. The three-layer feedforward neural networks (11-5-1) were employed to predict the spectrum in each energy bin. Using information entropy theory and the results of the ANN calculations, the sensitivity of each sphere to the entropy of the spectrum was quantitatively analyzed. The spectra results were compared with the results obtained using the maximum entropy method (MEM). The averaged root mean-square-error (MSE) of the MEM output and the desired spectra was 0.012; the averaged MSE of the ANN calculations was 0.006. The MSE results indicate that the 11-5-1 ANN models are able to accurately and reliably predict neutron spectra. The ANN model developed in this study to unfold neutron spectra from the counts measured by 11 Bonner spheres provides an alternative method for unfolding spectrum. The singular value decomposition is an effective method for the analysis of data obtained from Bonner spheres and the neutron spectra.
On low-rank updates to the singular value and Tucker decompositions
Energy Technology Data Exchange (ETDEWEB)
O' Hara, M J
2009-10-06
The singular value decomposition is widely used in signal processing and data mining. Since the data often arrives in a stream, the problem of updating matrix decompositions under low-rank modification has been widely studied. Brand developed a technique in 2006 that has many advantages. However, the technique does not directly approximate the updated matrix, but rather its previous low-rank approximation added to the new update, which needs justification. Further, the technique is still too slow for large information processing problems. We show that the technique minimizes the change in error per update, so if the error is small initially it remains small. We show that an updating algorithm for large sparse matrices should be sub-linear in the matrix dimension in order to be practical for large problems, and demonstrate a simple modification to the original technique that meets the requirements.
Jha, Abhinav K.; Barrett, Harrison H.; Frey, Eric C.; Clarkson, Eric; Caucci, Luca; Kupinski, Matthew A.
2015-09-01
Recent advances in technology are enabling a new class of nuclear imaging systems consisting of detectors that use real-time maximum-likelihood (ML) methods to estimate the interaction position, deposited energy, and other attributes of each photon-interaction event and store these attributes in a list format. This class of systems, which we refer to as photon-processing (PP) nuclear imaging systems, can be described by a fundamentally different mathematical imaging operator that allows processing of the continuous-valued photon attributes on a per-photon basis. Unlike conventional photon-counting (PC) systems that bin the data into images, PP systems do not have any binning-related information loss. Mathematically, while PC systems have an infinite-dimensional null space due to dimensionality considerations, PP systems do not necessarily suffer from this issue. Therefore, PP systems have the potential to provide improved performance in comparison to PC systems. To study these advantages, we propose a framework to perform the singular-value decomposition (SVD) of the PP imaging operator. We use this framework to perform the SVD of operators that describe a general two-dimensional (2D) planar linear shift-invariant (LSIV) PP system and a hypothetical continuously rotating 2D single-photon emission computed tomography (SPECT) PP system. We then discuss two applications of the SVD framework. The first application is to decompose the object being imaged by the PP imaging system into measurement and null components. We compare these components to the measurement and null components obtained with PC systems. In the process, we also present a procedure to compute the null functions for a PC system. The second application is designing analytical reconstruction algorithms for PP systems. The proposed analytical approach exploits the fact that PP systems acquire data in a continuous domain to estimate a continuous object function. The approach is parallelizable and
Jha, Abhinav K; Barrett, Harrison H; Frey, Eric C; Clarkson, Eric; Caucci, Luca; Kupinski, Matthew A
2015-09-21
Recent advances in technology are enabling a new class of nuclear imaging systems consisting of detectors that use real-time maximum-likelihood (ML) methods to estimate the interaction position, deposited energy, and other attributes of each photon-interaction event and store these attributes in a list format. This class of systems, which we refer to as photon-processing (PP) nuclear imaging systems, can be described by a fundamentally different mathematical imaging operator that allows processing of the continuous-valued photon attributes on a per-photon basis. Unlike conventional photon-counting (PC) systems that bin the data into images, PP systems do not have any binning-related information loss. Mathematically, while PC systems have an infinite-dimensional null space due to dimensionality considerations, PP systems do not necessarily suffer from this issue. Therefore, PP systems have the potential to provide improved performance in comparison to PC systems. To study these advantages, we propose a framework to perform the singular-value decomposition (SVD) of the PP imaging operator. We use this framework to perform the SVD of operators that describe a general two-dimensional (2D) planar linear shift-invariant (LSIV) PP system and a hypothetical continuously rotating 2D single-photon emission computed tomography (SPECT) PP system. We then discuss two applications of the SVD framework. The first application is to decompose the object being imaged by the PP imaging system into measurement and null components. We compare these components to the measurement and null components obtained with PC systems. In the process, we also present a procedure to compute the null functions for a PC system. The second application is designing analytical reconstruction algorithms for PP systems. The proposed analytical approach exploits the fact that PP systems acquire data in a continuous domain to estimate a continuous object function. The approach is parallelizable and
International Nuclear Information System (INIS)
Mandal, Shyamapada; Santhi, B.; Sridhar, S.; Vinolia, K.; Swaminathan, P.
2017-01-01
Highlights: • A novel approach to classify the fault pattern using data-driven methods. • Application of robust reconstruction method (SVD) to identify the faulty sensor. • Analysing fault pattern for plenty of sensors using SDF with less time complexity. • An efficient data-driven model is designed to the false and missed alarms. - Abstract: A mathematical model with two layers is developed using data-driven methods for thermocouple sensor fault detection and classification in Nuclear Power Plants (NPP). The Singular Value Decomposition (SVD) based method is applied to detect the faulty sensor from a data set of all sensors, at the first layer. In the second layer, the Symbolic Dynamic Filter (SDF) is employed to classify the fault pattern. If SVD detects any false fault, it is also re-evaluated by the SDF, i.e., the model has two layers of checking to balance the false alarms. The proposed fault detection and classification method is compared with the Principal Component Analysis. Two case studies are taken from Fast Breeder Test Reactor (FBTR) to prove the efficiency of the proposed method.
Hiwatashi, Akio; Togao, Osamu; Yamashita, Koji; Kikuchi, Kazufumi; Yoshimoto, Koji; Mizoguchi, Masahiro; Suzuki, Satoshi O; Yoshiura, Takashi; Honda, Hiroshi
2016-07-01
Correction of contrast leakage is recommended when enhancing lesions during perfusion analysis. The purpose of this study was to assess the diagnostic performance of computed tomography perfusion (CTP) with a delay-invariant singular-value decomposition algorithm (SVD+) and a Patlak plot in differentiating glioblastomas from lymphomas. This prospective study included 17 adult patients (12 men and 5 women) with pathologically proven glioblastomas (n=10) and lymphomas (n=7). CTP data were analyzed using SVD+ and a Patlak plot. The relative tumor blood volume and flow compared to contralateral normal-appearing gray matter (rCBV and rCBF derived from SVD+, and rBV and rFlow derived from the Patlak plot) were used to differentiate between glioblastomas and lymphomas. The Mann-Whitney U test and receiver operating characteristic (ROC) analyses were used for statistical analysis. Glioblastomas showed significantly higher rFlow (3.05±0.49, mean±standard deviation) than lymphomas (1.56±0.53; P0.05), rCBF (1.38±0.41 vs. 1.29±0.47; P>0.05), or rCBV (1.78±0.47 vs. 1.87±0.66; P>0.05). ROC analysis showed the best diagnostic performance with rFlow (Az=0.871), followed by rBV (Az=0.771), rCBF (Az=0.614), and rCBV (Az=0.529). CTP analysis with a Patlak plot was helpful in differentiating between glioblastomas and lymphomas, but CTP analysis with SVD+ was not. Copyright © 2016 Elsevier Masson SAS. All rights reserved.
Leblond, Frederic; Tichauer, Kenneth M; Pogue, Brian W
2010-11-29
The spatial resolution and recovered contrast of images reconstructed from diffuse fluorescence tomography data are limited by the high scattering properties of light propagation in biological tissue. As a result, the image reconstruction process can be exceedingly vulnerable to inaccurate prior knowledge of tissue optical properties and stochastic noise. In light of these limitations, the optimal source-detector geometry for a fluorescence tomography system is non-trivial, requiring analytical methods to guide design. Analysis of the singular value decomposition of the matrix to be inverted for image reconstruction is one potential approach, providing key quantitative metrics, such as singular image mode spatial resolution and singular data mode frequency as a function of singular mode. In the present study, these metrics are used to analyze the effects of different sources of noise and model errors as related to image quality in the form of spatial resolution and contrast recovery. The image quality is demonstrated to be inherently noise-limited even when detection geometries were increased in complexity to allow maximal tissue sampling, suggesting that detection noise characteristics outweigh detection geometry for achieving optimal reconstructions.
Robust imaging of localized scatterers using the singular value decomposition and ℓ1 minimization
International Nuclear Information System (INIS)
Chai, A; Moscoso, M; Papanicolaou, G
2013-01-01
We consider narrow band, active array imaging of localized scatterers in a homogeneous medium with and without additive noise. We consider both single and multiple illuminations and study ℓ 1 minimization-based imaging methods. We show that for large arrays, with array diameter comparable to range, and when scatterers are sparse and well separated, ℓ 1 minimization using a single illumination and without additive noise can recover the location and reflectivity of the scatterers exactly. For multiple illuminations, we introduce a hybrid method which combines the singular value decomposition and ℓ 1 minimization. This method can be used when the essential singular vectors of the array response matrix are available. We show that with this hybrid method we can recover the location and reflectivity of the scatterers exactly when there is no noise in the data. Numerical simulations indicate that the hybrid method is, in addition, robust to noise in the data. We also compare the ℓ 1 minimization-based methods with others including Kirchhoff migration, ℓ 2 minimization and multiple signal classification. (paper)
Automatic online spike sorting with singular value decomposition and fuzzy C-mean clustering.
Oliynyk, Andriy; Bonifazzi, Claudio; Montani, Fernando; Fadiga, Luciano
2012-08-08
Understanding how neurons contribute to perception, motor functions and cognition requires the reliable detection of spiking activity of individual neurons during a number of different experimental conditions. An important problem in computational neuroscience is thus to develop algorithms to automatically detect and sort the spiking activity of individual neurons from extracellular recordings. While many algorithms for spike sorting exist, the problem of accurate and fast online sorting still remains a challenging issue. Here we present a novel software tool, called FSPS (Fuzzy SPike Sorting), which is designed to optimize: (i) fast and accurate detection, (ii) offline sorting and (iii) online classification of neuronal spikes with very limited or null human intervention. The method is based on a combination of Singular Value Decomposition for fast and highly accurate pre-processing of spike shapes, unsupervised Fuzzy C-mean, high-resolution alignment of extracted spike waveforms, optimal selection of the number of features to retain, automatic identification the number of clusters, and quantitative quality assessment of resulting clusters independent on their size. After being trained on a short testing data stream, the method can reliably perform supervised online classification and monitoring of single neuron activity. The generalized procedure has been implemented in our FSPS spike sorting software (available free for non-commercial academic applications at the address: http://www.spikesorting.com) using LabVIEW (National Instruments, USA). We evaluated the performance of our algorithm both on benchmark simulated datasets with different levels of background noise and on real extracellular recordings from premotor cortex of Macaque monkeys. The results of these tests showed an excellent accuracy in discriminating low-amplitude and overlapping spikes under strong background noise. The performance of our method is competitive with respect to other robust spike
Automatic online spike sorting with singular value decomposition and fuzzy C-mean clustering
Directory of Open Access Journals (Sweden)
Oliynyk Andriy
2012-08-01
Full Text Available Abstract Background Understanding how neurons contribute to perception, motor functions and cognition requires the reliable detection of spiking activity of individual neurons during a number of different experimental conditions. An important problem in computational neuroscience is thus to develop algorithms to automatically detect and sort the spiking activity of individual neurons from extracellular recordings. While many algorithms for spike sorting exist, the problem of accurate and fast online sorting still remains a challenging issue. Results Here we present a novel software tool, called FSPS (Fuzzy SPike Sorting, which is designed to optimize: (i fast and accurate detection, (ii offline sorting and (iii online classification of neuronal spikes with very limited or null human intervention. The method is based on a combination of Singular Value Decomposition for fast and highly accurate pre-processing of spike shapes, unsupervised Fuzzy C-mean, high-resolution alignment of extracted spike waveforms, optimal selection of the number of features to retain, automatic identification the number of clusters, and quantitative quality assessment of resulting clusters independent on their size. After being trained on a short testing data stream, the method can reliably perform supervised online classification and monitoring of single neuron activity. The generalized procedure has been implemented in our FSPS spike sorting software (available free for non-commercial academic applications at the address: http://www.spikesorting.com using LabVIEW (National Instruments, USA. We evaluated the performance of our algorithm both on benchmark simulated datasets with different levels of background noise and on real extracellular recordings from premotor cortex of Macaque monkeys. The results of these tests showed an excellent accuracy in discriminating low-amplitude and overlapping spikes under strong background noise. The performance of our method is
Modal Analysis Using the Singular Value Decomposition and Rational Fraction Polynomials
2017-04-06
also consistent. In the subsequent analysis, the normal direction at each node is computed using the cross product of the connected element edges...For this mode in particular, the revised RFP mode shape is much cleaner than the original SVD mode shape. The algorithm for computing the revised
Vatankhah, Saeed; Renaut, Rosemary A.; Ardestani, Vahid E.
2018-04-01
We present a fast algorithm for the total variation regularization of the 3-D gravity inverse problem. Through imposition of the total variation regularization, subsurface structures presenting with sharp discontinuities are preserved better than when using a conventional minimum-structure inversion. The associated problem formulation for the regularization is nonlinear but can be solved using an iteratively reweighted least-squares algorithm. For small-scale problems the regularized least-squares problem at each iteration can be solved using the generalized singular value decomposition. This is not feasible for large-scale, or even moderate-scale, problems. Instead we introduce the use of a randomized generalized singular value decomposition in order to reduce the dimensions of the problem and provide an effective and efficient solution technique. For further efficiency an alternating direction algorithm is used to implement the total variation weighting operator within the iteratively reweighted least-squares algorithm. Presented results for synthetic examples demonstrate that the novel randomized decomposition provides good accuracy for reduced computational and memory demands as compared to use of classical approaches.
Directory of Open Access Journals (Sweden)
Jinyu Lu
2014-01-01
Full Text Available A novel iris biometric watermarking scheme is proposed focusing on iris recognition instead of the traditional watermark for increasing the security of the digital products. The preprocess of iris image is to be done firstly, which generates the iris biometric template from person's eye images. And then the templates are to be on discrete cosine transform; the value of the discrete cosine is encoded to BCH error control coding. The host image is divided into four areas equally correspondingly. The BCH codes are embedded in the singular values of each host image's coefficients which are obtained through discrete cosine transform (DCT. Numerical results reveal that proposed method can extract the watermark effectively and illustrate its security and robustness.
Directory of Open Access Journals (Sweden)
Patricia López-Rodríguez
2014-12-01
Full Text Available Radar high resolution range profiles are widely used among the target recognition community for the detection and identification of flying targets. In this paper, singular value decomposition is applied to extract the relevant information and to model each aircraft as a subspace. The identification algorithm is based on angle between subspaces and takes place in a transformed domain. In order to have a wide database of radar signatures and evaluate the performance, simulated range profiles are used as the recognition database while the test samples comprise data of actual range profiles collected in a measurement campaign. Thanks to the modeling of aircraft as subspaces only the valuable information of each target is used in the recognition process. Thus, one of the main advantages of using singular value decomposition, is that it helps to overcome the notable dissimilarities found in the shape and signal-to-noise ratio between actual and simulated profiles due to their difference in nature. Despite these differences, the recognition rates obtained with the algorithm are quite promising.
Directory of Open Access Journals (Sweden)
Haichao Cai
2015-01-01
Full Text Available When detecting the ultrasonic flaw of thick-walled pipe, the flaw echo signals are often interrupted by scanning system frequency and background noise. In particular when the thick-walled pipe defect is small, echo signal amplitude is often drowned in noise signal and affects the extraction of defect signal and the position determination accuracy. This paper presents the modified S-transform domain singular value decomposition method for the analysis of ultrasonic flaw echo signals. By changing the scale rule of Gaussian window functions with S-transform to improve the time-frequency resolution. And the paper tries to decompose the singular value decomposition of time-frequency matrix after the S-transform to determine the singular entropy of effective echo signal and realize the adaptive filter. Experiments show that, using this method can not only remove high frequency noise but also remove the low frequency noise and improve the signal-to-noise ratio of echo signal.
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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.
Yuasa, T.; Akiba, M.; Takeda, T.; Kazama, M.; Hoshino, A.; Watanabe, Y.; Hyodo, K.; Dilmanian, F. A.; Akatsuka, T.; Itai, Y.
1997-02-01
We describe a new attenuation correction method for fluorescent X-ray computed tomography (FXCT) applied to image nonradioactive contrast materials in vivo. The principle of the FXCT imaging is that of computed tomography of the first generation. Using monochromatized synchrotron radiation from the BLNE-5A bending-magnet beam line of Tristan Accumulation Ring in KEK, Japan, we studied phantoms with the FXCT method, and we succeeded in delineating a 4-mm-diameter channel filled with a 500 /spl mu/g I/ml iodine solution in a 20-mm-diameter acrylic cylindrical phantom. However, to detect smaller iodine concentrations, attenuation correction is needed. We present a correction method based on the equation representing the measurement process. The discretized equation system is solved by the least-squares method using the singular value decomposition. The attenuation correction method is applied to the projections by the Monte Carlo simulation and the experiment to confirm its effectiveness.
International Nuclear Information System (INIS)
Yang Jia; Ge Liangquan; Xiong Shengqing
2010-01-01
From the features of spectra shape of Chang'e-1 γ-ray spectrometer(CE1-GRS) data, it is difficult to determine elemental compositions on the lunar surface. Aimed at this problem, this paper proposes using noise adjusted singular value decomposition (NASVD) method to extract orthogonal spectral components from CE1-GRS data. Then the peak signals in the spectra of lower-order layers corresponding to the observed spectrum of each lunar region are respectively analyzed. Elemental compositions of each lunar region can be determined based upon whether the energy corresponding to each peak signal equals to the energy corresponding to the characteristic gamma-ray line emissions of specific elements. The result shows that a number of elements such as U, Th, K, Fe, Ti, Si, O, Al, Mg, Ca and Na are qualitatively determined by this method. (authors)
Sun, Qi; Fu, Shujun
2017-09-20
Fringe orientation is an important feature of fringe patterns and has a wide range of applications such as guiding fringe pattern filtering, phase unwrapping, and abstraction. Estimating fringe orientation is a basic task for subsequent processing of fringe patterns. However, various noise, singular and obscure points, and orientation data degeneration lead to inaccurate calculations of fringe orientation. Thus, to deepen the understanding of orientation estimation and to better guide orientation estimation in fringe pattern processing, some advanced gradient-field-based orientation estimation methods are compared and analyzed. At the same time, following the ideas of smoothing regularization and computing of bigger gradient fields, a regularized singular-value decomposition (RSVD) technique is proposed for fringe orientation estimation. To compare the performance of these gradient-field-based methods, quantitative results and visual effect maps of orientation estimation are given on simulated and real fringe patterns that demonstrate that the RSVD produces the best estimation results at a cost of relatively less time.
Das, Siddhartha; Siopsis, George; Weedbrook, Christian
2018-02-01
With the significant advancement in quantum computation during the past couple of decades, the exploration of machine-learning subroutines using quantum strategies has become increasingly popular. Gaussian process regression is a widely used technique in supervised classical machine learning. Here we introduce an algorithm for Gaussian process regression using continuous-variable quantum systems that can be realized with technology based on photonic quantum computers under certain assumptions regarding distribution of data and availability of efficient quantum access. Our algorithm shows that by using a continuous-variable quantum computer a dramatic speedup in computing Gaussian process regression can be achieved, i.e., the possibility of exponentially reducing the time to compute. Furthermore, our results also include a continuous-variable quantum-assisted singular value decomposition method of nonsparse low rank matrices and forms an important subroutine in our Gaussian process regression algorithm.
Digital Repository Service at National Institute of Oceanography (India)
Murty, T.V.R.; Rao, M.M.M.; SuryaPrakash, S.; Chandramouli, P.; Murthy, K.S.R.
An integrated friendly-user interactive multiple Ocean Application Pacakage has been developed utilizing the well known statistical technique called Singular Value Decomposition (SVD) to achieve image and data compression in MATLAB environment...
Koh, T S; Wu, X Y; Cheong, L H; Lim, C C T
2004-12-01
The assessment of tissue perfusion by dynamic contrast-enhanced (DCE) imaging involves a deconvolution process. For analysis of DCE imaging data, we implemented a regression approach to select appropriate regularization parameters for deconvolution using the standard and generalized singular value decomposition methods. Monte Carlo simulation experiments were carried out to study the performance and to compare with other existing methods used for deconvolution analysis of DCE imaging data. The present approach is found to be robust and reliable at the levels of noise commonly encountered in DCE imaging, and for different models of the underlying tissue vasculature. The advantages of the present method, as compared with previous methods, include its efficiency of computation, ability to achieve adequate regularization to reproduce less noisy solutions, and that it does not require prior knowledge of the noise condition. The proposed method is applied on actual patient study cases with brain tumors and ischemic stroke, to illustrate its applicability as a clinical tool for diagnosis and assessment of treatment response.
Image Denoising Using Singular Value Difference in the Wavelet Domain
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Min Wang
2018-01-01
Full Text Available Singular value (SV difference is the difference in the singular values between a noisy image and the original image; it varies regularly with noise intensity. This paper proposes an image denoising method using the singular value difference in the wavelet domain. First, the SV difference model is generated for different noise variances in the three directions of the wavelet transform and the noise variance of a new image is used to make the calculation by the diagonal part. Next, the single-level discrete 2-D wavelet transform is used to decompose each noisy image into its low-frequency and high-frequency parts. Then, singular value decomposition (SVD is used to obtain the SVs of the three high-frequency parts. Finally, the three denoised high-frequency parts are reconstructed by SVD from the SV difference, and the final denoised image is obtained using the inverse wavelet transform. Experiments show the effectiveness of this method compared with relevant existing methods.
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Sri Priya Ponnapalli
Full Text Available The number of high-dimensional datasets recording multiple aspects of a single phenomenon is increasing in many areas of science, accompanied by a need for mathematical frameworks that can compare multiple large-scale matrices with different row dimensions. The only such framework to date, the generalized singular value decomposition (GSVD, is limited to two matrices. We mathematically define a higher-order GSVD (HO GSVD for N≥2 matrices D(i∈R(m(i × n, each with full column rank. Each matrix is exactly factored as D(i=U(iΣ(iV(T, where V, identical in all factorizations, is obtained from the eigensystem SV=VΛ of the arithmetic mean S of all pairwise quotients A(iA(j(-1 of the matrices A(i=D(i(TD(i, i≠j. We prove that this decomposition extends to higher orders almost all of the mathematical properties of the GSVD. The matrix S is nondefective with V and Λ real. Its eigenvalues satisfy λ(k≥1. Equality holds if and only if the corresponding eigenvector v(k is a right basis vector of equal significance in all matrices D(i and D(j, that is σ(i,k/σ(j,k=1 for all i and j, and the corresponding left basis vector u(i,k is orthogonal to all other vectors in U(i for all i. The eigenvalues λ(k=1, therefore, define the "common HO GSVD subspace." We illustrate the HO GSVD with a comparison of genome-scale cell-cycle mRNA expression from S. pombe, S. cerevisiae and human. Unlike existing algorithms, a mapping among the genes of these disparate organisms is not required. We find that the approximately common HO GSVD subspace represents the cell-cycle mRNA expression oscillations, which are similar among the datasets. Simultaneous reconstruction in the common subspace, therefore, removes the experimental artifacts, which are dissimilar, from the datasets. In the simultaneous sequence-independent classification of the genes of the three organisms in this common subspace, genes of highly conserved sequences but significantly different cell
High Performance Polar Decomposition on Distributed Memory Systems
Sukkari, Dalal E.; Ltaief, Hatem; Keyes, David E.
2016-01-01
The polar decomposition of a dense matrix is an important operation in linear algebra. It can be directly calculated through the singular value decomposition (SVD) or iteratively using the QR dynamically-weighted Halley algorithm (QDWH). The former
Classification of subsurface objects using singular values derived from signal frames
Chambers, David H; Paglieroni, David W
2014-05-06
The classification system represents a detected object with a feature vector derived from the return signals acquired by an array of N transceivers operating in multistatic mode. The classification system generates the feature vector by transforming the real-valued return signals into complex-valued spectra, using, for example, a Fast Fourier Transform. The classification system then generates a feature vector of singular values for each user-designated spectral sub-band by applying a singular value decomposition (SVD) to the N.times.N square complex-valued matrix formed from sub-band samples associated with all possible transmitter-receiver pairs. The resulting feature vector of singular values may be transformed into a feature vector of singular value likelihoods and then subjected to a multi-category linear or neural network classifier for object classification.
Yuan, Rui; Lv, Yong; Song, Gangbing
2018-04-16
Rolling bearings are important components in rotary machinery systems. In the field of multi-fault diagnosis of rolling bearings, the vibration signal collected from single channels tends to miss some fault characteristic information. Using multiple sensors to collect signals at different locations on the machine to obtain multivariate signal can remedy this problem. The adverse effect of a power imbalance between the various channels is inevitable, and unfavorable for multivariate signal processing. As a useful, multivariate signal processing method, Adaptive-projection has intrinsically transformed multivariate empirical mode decomposition (APIT-MEMD), and exhibits better performance than MEMD by adopting adaptive projection strategy in order to alleviate power imbalances. The filter bank properties of APIT-MEMD are also adopted to enable more accurate and stable intrinsic mode functions (IMFs), and to ease mode mixing problems in multi-fault frequency extractions. By aligning IMF sets into a third order tensor, high order singular value decomposition (HOSVD) can be employed to estimate the fault number. The fault correlation factor (FCF) analysis is used to conduct correlation analysis, in order to determine effective IMFs; the characteristic frequencies of multi-faults can then be extracted. Numerical simulations and the application of multi-fault situation can demonstrate that the proposed method is promising in multi-fault diagnoses of multivariate rolling bearing signal.
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Morris Brian J
2011-05-01
Full Text Available Abstract Background The quantification of experimentally-induced alterations in biological pathways remains a major challenge in systems biology. One example of this is the quantitative characterization of alterations in defined, established metabolic pathways from complex metabolomic data. At present, the disruption of a given metabolic pathway is inferred from metabolomic data by observing an alteration in the level of one or more individual metabolites present within that pathway. Not only is this approach open to subjectivity, as metabolites participate in multiple pathways, but it also ignores useful information available through the pairwise correlations between metabolites. This extra information may be incorporated using a higher-level approach that looks for alterations between a pair of correlation networks. In this way experimentally-induced alterations in metabolic pathways can be quantitatively defined by characterizing group differences in metabolite clustering. Taking this approach increases the objectivity of interpreting alterations in metabolic pathways from metabolomic data. Results We present and justify a new technique for comparing pairs of networks--in our case these networks are based on the same set of nodes and there are two distinct types of weighted edges. The algorithm is based on the Generalized Singular Value Decomposition (GSVD, which may be regarded as an extension of Principle Components Analysis to the case of two data sets. We show how the GSVD can be interpreted as a technique for reordering the two networks in order to reveal clusters that are exclusive to only one. Here we apply this algorithm to a new set of metabolomic data from the prefrontal cortex (PFC of a translational model relevant to schizophrenia, rats treated subchronically with the N-methyl-D-Aspartic acid (NMDA receptor antagonist phencyclidine (PCP. This provides us with a means to quantify which predefined metabolic pathways (Kyoto
A High Performance QDWH-SVD Solver using Hardware Accelerators
Sukkari, Dalal E.
2015-04-08
This paper describes a new high performance implementation of the QR-based Dynamically Weighted Halley Singular Value Decomposition (QDWH-SVD) solver on multicore architecture enhanced with multiple GPUs. The standard QDWH-SVD algorithm was introduced by Nakatsukasa and Higham (SIAM SISC, 2013) and combines three successive computational stages: (1) the polar decomposition calculation of the original matrix using the QDWH algorithm, (2) the symmetric eigendecomposition of the resulting polar factor to obtain the singular values and the right singular vectors and (3) the matrix-matrix multiplication to get the associated left singular vectors. A comprehensive test suite highlights the numerical robustness of the QDWH-SVD solver. Although it performs up to two times more flops when computing all singular vectors compared to the standard SVD solver algorithm, our new high performance implementation on single GPU results in up to 3.8x improvements for asymptotic matrix sizes, compared to the equivalent routines from existing state-of-the-art open-source and commercial libraries. However, when only singular values are needed, QDWH-SVD is penalized by performing up to 14 times more flops. The singular value only implementation of QDWH-SVD on single GPU can still run up to 18% faster than the best existing equivalent routines. Integrating mixed precision techniques in the solver can additionally provide up to 40% improvement at the price of losing few digits of accuracy, compared to the full double precision floating point arithmetic. We further leverage the single GPU QDWH-SVD implementation by introducing the first multi-GPU SVD solver to study the scalability of the QDWH-SVD framework.
SVD vs PCA: Comparison of Performance in an Imaging Spectrometer
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Wilma Oblefias
2004-12-01
Full Text Available The calculation of basis spectra from a spectral library is an important prerequisite of any compact imaging spectrometer. In this paper, we compare the basis spectra computed by singular-value decomposition (SVD and principal component analysis (PCA in terms of estimation performance with respect to resolution, presence of noise, intensity variation, and quantization error. Results show that SVD is robust in intensity variation while PCA is not. However, PCA performs better with signals of low signal-to-noise ratio. No significant difference is seen between SVD and PCA in terms of resolution and quantization error.
Stable computation of generalized singular values
Energy Technology Data Exchange (ETDEWEB)
Drmac, Z.; Jessup, E.R. [Univ. of Colorado, Boulder, CO (United States)
1996-12-31
We study floating-point computation of the generalized singular value decomposition (GSVD) of a general matrix pair (A, B), where A and B are real matrices with the same numbers of columns. The GSVD is a powerful analytical and computational tool. For instance, the GSVD is an implicit way to solve the generalized symmetric eigenvalue problem Kx = {lambda}Mx, where K = A{sup {tau}}A and M = B{sup {tau}}B. Our goal is to develop stable numerical algorithms for the GSVD that are capable of computing the singular value approximations with the high relative accuracy that the perturbation theory says is possible. We assume that the singular values are well-determined by the data, i.e., that small relative perturbations {delta}A and {delta}B (pointwise rounding errors, for example) cause in each singular value {sigma} of (A, B) only a small relative perturbation {vert_bar}{delta}{sigma}{vert_bar}/{sigma}.
New Collaborative Filtering Algorithms Based on SVD++ and Differential Privacy
Xian, Zhengzheng; Li, Qiliang; Li, Gai; Li, Lei
2017-01-01
Collaborative filtering technology has been widely used in the recommender system, and its implementation is supported by the large amount of real and reliable user data from the big-data era. However, with the increase of the users’ information-security awareness, these data are reduced or the quality of the data becomes worse. Singular Value Decomposition (SVD) is one of the common matrix factorization methods used in collaborative filtering, which introduces the bias information of users a...
Analytic robust stability analysis of SVD orbit feedback
Pfingstner, Jürgen
2012-01-01
Orbit feedback controllers are indispensable for the operation of modern particle accelerators. Many such controllers are based on the decoupling of the inputs and outputs of the system to be controlled with the help of the singular value decomposition (SVD controller). It is crucial to verify the stability of SVD controllers, also in the presence of mismatches between the used accelerator model and the real machine (robust stability problem). In this paper, analytical criteria for guaranteed stability margins of SVD orbit feedback systems for three different types of model mismatches are presented: scaling errors of actuators and BPMs (beam position monitors) and additive errors of the orbit response matrix. For the derivation of these criteria, techniques from robust control theory have been used, e.g the small gain theorem. The obtained criteria can be easily applied directly to other SVD orbit feedback systems. As an example, the criteria were applied to the orbit feedback system of the Compact Linear ...
Unitary embedding for data hiding with the SVD
Bergman, Clifford; Davidson, Jennifer
2005-03-01
Steganography is the study of data hiding for the purpose of covert communication. A secret message is inserted into a cover file so that the very existence of the message is not apparent. Most current steganography algorithms insert data in the spatial or transform domains; common transforms include the discrete cosine transform, the discrete Fourier transform, and discrete wavelet transform. In this paper, we present a data-hiding algorithm that exploits a decomposition representation of the data instead of a frequency-based transformation of the data. The decomposition transform used is the singular value decomposition (SVD). The SVD of a matrix A is a decomposition A= USV' in which S is a nonnegative diagonal matrix and U and V are orthogonal matrices. We show how to use the orthogonal matrices in the SVD as a vessel in which to embed information. Several challenges were presented in order to accomplish this, and we give effective information-hiding using the SVD can be just as effective as using transform-based techniques. Furthermore, different problems arise when using the SVD than using a transform-based technique. We have applied the SVD to image data, but the technique can be formulated for other data types such as audio and video.
Xiao, Xiaolin; Moreno-Moral, Aida; Rotival, Maxime; Bottolo, Leonardo; Petretto, Enrico
2014-01-01
Recent high-throughput efforts such as ENCODE have generated a large body of genome-scale transcriptional data in multiple conditions (e.g., cell-types and disease states). Leveraging these data is especially important for network-based approaches to human disease, for instance to identify coherent transcriptional modules (subnetworks) that can inform functional disease mechanisms and pathological pathways. Yet, genome-scale network analysis across conditions is significantly hampered by the paucity of robust and computationally-efficient methods. Building on the Higher-Order Generalized Singular Value Decomposition, we introduce a new algorithmic approach for efficient, parameter-free and reproducible identification of network-modules simultaneously across multiple conditions. Our method can accommodate weighted (and unweighted) networks of any size and can similarly use co-expression or raw gene expression input data, without hinging upon the definition and stability of the correlation used to assess gene co-expression. In simulation studies, we demonstrated distinctive advantages of our method over existing methods, which was able to recover accurately both common and condition-specific network-modules without entailing ad-hoc input parameters as required by other approaches. We applied our method to genome-scale and multi-tissue transcriptomic datasets from rats (microarray-based) and humans (mRNA-sequencing-based) and identified several common and tissue-specific subnetworks with functional significance, which were not detected by other methods. In humans we recapitulated the crosstalk between cell-cycle progression and cell-extracellular matrix interactions processes in ventricular zones during neocortex expansion and further, we uncovered pathways related to development of later cognitive functions in the cortical plate of the developing brain which were previously unappreciated. Analyses of seven rat tissues identified a multi-tissue subnetwork of co
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Xiaolin Xiao
2014-01-01
Full Text Available Recent high-throughput efforts such as ENCODE have generated a large body of genome-scale transcriptional data in multiple conditions (e.g., cell-types and disease states. Leveraging these data is especially important for network-based approaches to human disease, for instance to identify coherent transcriptional modules (subnetworks that can inform functional disease mechanisms and pathological pathways. Yet, genome-scale network analysis across conditions is significantly hampered by the paucity of robust and computationally-efficient methods. Building on the Higher-Order Generalized Singular Value Decomposition, we introduce a new algorithmic approach for efficient, parameter-free and reproducible identification of network-modules simultaneously across multiple conditions. Our method can accommodate weighted (and unweighted networks of any size and can similarly use co-expression or raw gene expression input data, without hinging upon the definition and stability of the correlation used to assess gene co-expression. In simulation studies, we demonstrated distinctive advantages of our method over existing methods, which was able to recover accurately both common and condition-specific network-modules without entailing ad-hoc input parameters as required by other approaches. We applied our method to genome-scale and multi-tissue transcriptomic datasets from rats (microarray-based and humans (mRNA-sequencing-based and identified several common and tissue-specific subnetworks with functional significance, which were not detected by other methods. In humans we recapitulated the crosstalk between cell-cycle progression and cell-extracellular matrix interactions processes in ventricular zones during neocortex expansion and further, we uncovered pathways related to development of later cognitive functions in the cortical plate of the developing brain which were previously unappreciated. Analyses of seven rat tissues identified a multi
Using SVD on Clusters to Improve Precision of Interdocument Similarity Measure
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Wen Zhang
2016-01-01
Full Text Available Recently, LSI (Latent Semantic Indexing based on SVD (Singular Value Decomposition is proposed to overcome the problems of polysemy and homonym in traditional lexical matching. However, it is usually criticized as with low discriminative power for representing documents although it has been validated as with good representative quality. In this paper, SVD on clusters is proposed to improve the discriminative power of LSI. The contribution of this paper is three manifolds. Firstly, we make a survey of existing linear algebra methods for LSI, including both SVD based methods and non-SVD based methods. Secondly, we propose SVD on clusters for LSI and theoretically explain that dimension expansion of document vectors and dimension projection using SVD are the two manipulations involved in SVD on clusters. Moreover, we develop updating processes to fold in new documents and terms in a decomposed matrix by SVD on clusters. Thirdly, two corpora, a Chinese corpus and an English corpus, are used to evaluate the performances of the proposed methods. Experiments demonstrate that, to some extent, SVD on clusters can improve the precision of interdocument similarity measure in comparison with other SVD based LSI methods.
Qualitatively Assessing Randomness in SVD Results
Lamb, K. W.; Miller, W. P.; Kalra, A.; Anderson, S.; Rodriguez, A.
2012-12-01
Singular Value Decomposition (SVD) is a powerful tool for identifying regions of significant co-variability between two spatially distributed datasets. SVD has been widely used in atmospheric research to define relationships between sea surface temperatures, geopotential height, wind, precipitation and streamflow data for myriad regions across the globe. A typical application for SVD is to identify leading climate drivers (as observed in the wind or pressure data) for a particular hydrologic response variable such as precipitation, streamflow, or soil moisture. One can also investigate the lagged relationship between a climate variable and the hydrologic response variable using SVD. When performing these studies it is important to limit the spatial bounds of the climate variable to reduce the chance of random co-variance relationships being identified. On the other hand, a climate region that is too small may ignore climate signals which have more than a statistical relationship to a hydrologic response variable. The proposed research seeks to identify a qualitative method of identifying random co-variability relationships between two data sets. The research identifies the heterogeneous correlation maps from several past results and compares these results with correlation maps produced using purely random and quasi-random climate data. The comparison identifies a methodology to determine if a particular region on a correlation map may be explained by a physical mechanism or is simply statistical chance.
Zhao, Ming; Jia, Xiaodong
2017-09-01
Singular value decomposition (SVD), as an effective signal denoising tool, has been attracting considerable attention in recent years. The basic idea behind SVD denoising is to preserve the singular components (SCs) with significant singular values. However, it is shown that the singular values mainly reflect the energy of decomposed SCs, therefore traditional SVD denoising approaches are essentially energy-based, which tend to highlight the high-energy regular components in the measured signal, while ignoring the weak feature caused by early fault. To overcome this issue, a reweighted singular value decomposition (RSVD) strategy is proposed for signal denoising and weak feature enhancement. In this work, a novel information index called periodic modulation intensity is introduced to quantify the diagnostic information in a mechanical signal. With this index, the decomposed SCs can be evaluated and sorted according to their information levels, rather than energy. Based on that, a truncated linear weighting function is proposed to control the contribution of each SC in the reconstruction of the denoised signal. In this way, some weak but informative SCs could be highlighted effectively. The advantages of RSVD over traditional approaches are demonstrated by both simulated signals and real vibration/acoustic data from a two-stage gearbox as well as train bearings. The results demonstrate that the proposed method can successfully extract the weak fault feature even in the presence of heavy noise and ambient interferences.
Image fusion via nonlocal sparse K-SVD dictionary learning.
Li, Ying; Li, Fangyi; Bai, Bendu; Shen, Qiang
2016-03-01
Image fusion aims to merge two or more images captured via various sensors of the same scene to construct a more informative image by integrating their details. Generally, such integration is achieved through the manipulation of the representations of the images concerned. Sparse representation plays an important role in the effective description of images, offering a great potential in a variety of image processing tasks, including image fusion. Supported by sparse representation, in this paper, an approach for image fusion by the use of a novel dictionary learning scheme is proposed. The nonlocal self-similarity property of the images is exploited, not only at the stage of learning the underlying description dictionary but during the process of image fusion. In particular, the property of nonlocal self-similarity is combined with the traditional sparse dictionary. This results in an improved learned dictionary, hereafter referred to as the nonlocal sparse K-SVD dictionary (where K-SVD stands for the K times singular value decomposition that is commonly used in the literature), and abbreviated to NL_SK_SVD. The performance of the NL_SK_SVD dictionary is applied for image fusion using simultaneous orthogonal matching pursuit. The proposed approach is evaluated with different types of images, and compared with a number of alternative image fusion techniques. The resultant superior fused images using the present approach demonstrates the efficacy of the NL_SK_SVD dictionary in sparse image representation.
A High-Speed and Low-Energy-Consumption Processor for SVD-MIMO-OFDM Systems
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Hiroki Iwaizumi
2013-01-01
Full Text Available A processor design for singular value decomposition (SVD and compression/decompression of feedback matrices, which are mandatory operations for SVD multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM systems, is proposed and evaluated. SVD-MIMO is a transmission method for suppressing multistream interference and improving communication quality by beamforming. An application specific instruction-set processor (ASIP architecture is adopted to achieve flexibility in terms of operations and matrix size. The proposed processor realizes a high-speed/low-power design and real-time processing by the parallelization of floating-point units (FPUs and arithmetic instructions specialized in complex matrix operations.
Biclustering via Sparse Singular Value Decomposition
Lee, Mihee; Shen, Haipeng; Huang, Jianhua Z.; Marron, J. S.
2010-01-01
discussion of penalty parameter selection. A lung cancer microarray dataset and a food nutrition dataset are used to illustrate SSVD as a biclustering method. SSVD is also compared with some existing biclustering methods using simulated datasets. © 2010
New Collaborative Filtering Algorithms Based on SVD++ and Differential Privacy
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Zhengzheng Xian
2017-01-01
Full Text Available Collaborative filtering technology has been widely used in the recommender system, and its implementation is supported by the large amount of real and reliable user data from the big-data era. However, with the increase of the users’ information-security awareness, these data are reduced or the quality of the data becomes worse. Singular Value Decomposition (SVD is one of the common matrix factorization methods used in collaborative filtering, which introduces the bias information of users and items and is realized by using algebraic feature extraction. The derivative model SVD++ of SVD achieves better predictive accuracy due to the addition of implicit feedback information. Differential privacy is defined very strictly and can be proved, which has become an effective measure to solve the problem of attackers indirectly deducing the personal privacy information by using background knowledge. In this paper, differential privacy is applied to the SVD++ model through three approaches: gradient perturbation, objective-function perturbation, and output perturbation. Through theoretical derivation and experimental verification, the new algorithms proposed can better protect the privacy of the original data on the basis of ensuring the predictive accuracy. In addition, an effective scheme is given that can measure the privacy protection strength and predictive accuracy, and a reasonable range for selection of the differential privacy parameter is provided.
A New Adaptive Gamma Correction Based Algorithm Using DWT-SVD for Non-Contrast CT Image Enhancement.
Kallel, Fathi; Ben Hamida, Ahmed
2017-12-01
The performances of medical image processing techniques, in particular CT scans, are usually affected by poor contrast quality introduced by some medical imaging devices. This suggests the use of contrast enhancement methods as a solution to adjust the intensity distribution of the dark image. In this paper, an advanced adaptive and simple algorithm for dark medical image enhancement is proposed. This approach is principally based on adaptive gamma correction using discrete wavelet transform with singular-value decomposition (DWT-SVD). In a first step, the technique decomposes the input medical image into four frequency sub-bands by using DWT and then estimates the singular-value matrix of the low-low (LL) sub-band image. In a second step, an enhanced LL component is generated using an adequate correction factor and inverse singular value decomposition (SVD). In a third step, for an additional improvement of LL component, obtained LL sub-band image from SVD enhancement stage is classified into two main classes (low contrast and moderate contrast classes) based on their statistical information and therefore processed using an adaptive dynamic gamma correction function. In fact, an adaptive gamma correction factor is calculated for each image according to its class. Finally, the obtained LL sub-band image undergoes inverse DWT together with the unprocessed low-high (LH), high-low (HL), and high-high (HH) sub-bands for enhanced image generation. Different types of non-contrast CT medical images are considered for performance evaluation of the proposed contrast enhancement algorithm based on adaptive gamma correction using DWT-SVD (DWT-SVD-AGC). Results show that our proposed algorithm performs better than other state-of-the-art techniques.
Application of EMD-Based SVD and SVM to Coal-Gangue Interface Detection
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Wei Liu
2014-01-01
Full Text Available Coal-gangue interface detection during top-coal caving mining is a challenging problem. This paper proposes a new vibration signal analysis approach to detecting the coal-gangue interface based on singular value decomposition (SVD techniques and support vector machines (SVMs. Due to the nonstationary characteristics in vibration signals of the tail boom support of the longwall mining machine in this complicated environment, the empirical mode decomposition (EMD is used to decompose the raw vibration signals into a number of intrinsic mode functions (IMFs by which the initial feature vector matrices can be formed automatically. By applying the SVD algorithm to the initial feature vector matrices, the singular values of matrices can be obtained and used as the input feature vectors of SVMs classifier. The analysis results of vibration signals from the tail boom support of a longwall mining machine show that the method based on EMD, SVD, and SVM is effective for coal-gangue interface detection even when the number of samples is small.
Fault diagnosis for tilting-pad journal bearing based on SVD and LMD
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Zhang Xiaotao
2016-01-01
Full Text Available Aiming at fault diagnosis for tilting-pad journal bearing with fluid support developed recently, a new method based on singular value decomposition (SVD and local mean decomposition (LMD is proposed. First, the phase space reconstruction of Hankel matrix and SVD method are used as pre-filter process unit to reduce the random noises in the original signal. Then the purified signal is decomposed by LMD into a series of production functions (PFs. Based on PFs, time frequency map and marginal spectrum can be obtained for fault diagnosis. Finally, this method is applied to numerical simulation and practical experiment data. The results show that the proposed method can effectively detect fault features of tilting-pad journal bearing.
A Fast SVD-Hidden-nodes based Extreme Learning Machine for Large-Scale Data Analytics.
Deng, Wan-Yu; Bai, Zuo; Huang, Guang-Bin; Zheng, Qing-Hua
2016-05-01
Big dimensional data is a growing trend that is emerging in many real world contexts, extending from web mining, gene expression analysis, protein-protein interaction to high-frequency financial data. Nowadays, there is a growing consensus that the increasing dimensionality poses impeding effects on the performances of classifiers, which is termed as the "peaking phenomenon" in the field of machine intelligence. To address the issue, dimensionality reduction is commonly employed as a preprocessing step on the Big dimensional data before building the classifiers. In this paper, we propose an Extreme Learning Machine (ELM) approach for large-scale data analytic. In contrast to existing approaches, we embed hidden nodes that are designed using singular value decomposition (SVD) into the classical ELM. These SVD nodes in the hidden layer are shown to capture the underlying characteristics of the Big dimensional data well, exhibiting excellent generalization performances. The drawback of using SVD on the entire dataset, however, is the high computational complexity involved. To address this, a fast divide and conquer approximation scheme is introduced to maintain computational tractability on high volume data. The resultant algorithm proposed is labeled here as Fast Singular Value Decomposition-Hidden-nodes based Extreme Learning Machine or FSVD-H-ELM in short. In FSVD-H-ELM, instead of identifying the SVD hidden nodes directly from the entire dataset, SVD hidden nodes are derived from multiple random subsets of data sampled from the original dataset. Comprehensive experiments and comparisons are conducted to assess the FSVD-H-ELM against other state-of-the-art algorithms. The results obtained demonstrated the superior generalization performance and efficiency of the FSVD-H-ELM. Copyright © 2016 Elsevier Ltd. All rights reserved.
Batched QR and SVD Algorithms on GPUs with Applications in Hierarchical Matrix Compression
Halim Boukaram, Wajih
2017-09-14
We present high performance implementations of the QR and the singular value decomposition of a batch of small matrices hosted on the GPU with applications in the compression of hierarchical matrices. The one-sided Jacobi algorithm is used for its simplicity and inherent parallelism as a building block for the SVD of low rank blocks using randomized methods. We implement multiple kernels based on the level of the GPU memory hierarchy in which the matrices can reside and show substantial speedups against streamed cuSOLVER SVDs. The resulting batched routine is a key component of hierarchical matrix compression, opening up opportunities to perform H-matrix arithmetic efficiently on GPUs.
Batched QR and SVD Algorithms on GPUs with Applications in Hierarchical Matrix Compression
Halim Boukaram, Wajih; Turkiyyah, George; Ltaief, Hatem; Keyes, David E.
2017-01-01
We present high performance implementations of the QR and the singular value decomposition of a batch of small matrices hosted on the GPU with applications in the compression of hierarchical matrices. The one-sided Jacobi algorithm is used for its simplicity and inherent parallelism as a building block for the SVD of low rank blocks using randomized methods. We implement multiple kernels based on the level of the GPU memory hierarchy in which the matrices can reside and show substantial speedups against streamed cuSOLVER SVDs. The resulting batched routine is a key component of hierarchical matrix compression, opening up opportunities to perform H-matrix arithmetic efficiently on GPUs.
Singular-value demodulation of phase-shifted holograms.
Lopes, Fernando; Atlan, Michael
2015-06-01
We report on phase-shifted holographic interferogram demodulation by singular-value decomposition. Numerical processing of optically acquired interferograms over several modulation periods was performed in two steps: (1) rendering of off-axis complex-valued holograms by Fresnel transformation of the interferograms; and (2) eigenvalue spectrum assessment of the lag-covariance matrix of hologram pixels. Experimental results in low-light recording conditions were compared with demodulation by Fourier analysis, in the presence of random phase drifts.
Mauldin, F William; Lin, Dan; Hossack, John A
2011-11-01
A general filtering method, called the singular value filter (SVF), is presented as a framework for principal component analysis (PCA) based filter design in medical ultrasound imaging. The SVF approach operates by projecting the original data onto a new set of bases determined from PCA using singular value decomposition (SVD). The shape of the SVF weighting function, which relates the singular value spectrum of the input data to the filtering coefficients assigned to each basis function, is designed in accordance with a signal model and statistical assumptions regarding the underlying source signals. In this paper, we applied SVF for the specific application of clutter artifact rejection in diagnostic ultrasound imaging. SVF was compared to a conventional PCA-based filtering technique, which we refer to as the blind source separation (BSS) method, as well as a simple frequency-based finite impulse response (FIR) filter used as a baseline for comparison. The performance of each filter was quantified in simulated lesion images as well as experimental cardiac ultrasound data. SVF was demonstrated in both simulation and experimental results, over a wide range of imaging conditions, to outperform the BSS and FIR filtering methods in terms of contrast-to-noise ratio (CNR) and motion tracking performance. In experimental mouse heart data, SVF provided excellent artifact suppression with an average CNR improvement of 1.8 dB with over 40% reduction in displacement tracking error. It was further demonstrated from simulation and experimental results that SVF provided superior clutter rejection, as reflected in larger CNR values, when filtering was achieved using complex pulse-echo received data and non-binary filter coefficients.
The Research on Denoising of SAR Image Based on Improved K-SVD Algorithm
Tan, Linglong; Li, Changkai; Wang, Yueqin
2018-04-01
SAR images often receive noise interference in the process of acquisition and transmission, which can greatly reduce the quality of images and cause great difficulties for image processing. The existing complete DCT dictionary algorithm is fast in processing speed, but its denoising effect is poor. In this paper, the problem of poor denoising, proposed K-SVD (K-means and singular value decomposition) algorithm is applied to the image noise suppression. Firstly, the sparse dictionary structure is introduced in detail. The dictionary has a compact representation and can effectively train the image signal. Then, the sparse dictionary is trained by K-SVD algorithm according to the sparse representation of the dictionary. The algorithm has more advantages in high dimensional data processing. Experimental results show that the proposed algorithm can remove the speckle noise more effectively than the complete DCT dictionary and retain the edge details better.
A Fault Diagnosis Model Based on LCD-SVD-ANN-MIV and VPMCD for Rotating Machinery
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Songrong Luo
2016-01-01
Full Text Available The fault diagnosis process is essentially a class discrimination problem. However, traditional class discrimination methods such as SVM and ANN fail to capitalize the interactions among the feature variables. Variable predictive model-based class discrimination (VPMCD can adequately use the interactions. But the feature extraction and selection will greatly affect the accuracy and stability of VPMCD classifier. Aiming at the nonstationary characteristics of vibration signal from rotating machinery with local fault, singular value decomposition (SVD technique based local characteristic-scale decomposition (LCD was developed to extract the feature variables. Subsequently, combining artificial neural net (ANN and mean impact value (MIV, ANN-MIV as a kind of feature selection approach was proposed to select more suitable feature variables as input vector of VPMCD classifier. In the end of this paper, a novel fault diagnosis model based on LCD-SVD-ANN-MIV and VPMCD is proposed and proved by an experimental application for roller bearing fault diagnosis. The results show that the proposed method is effective and noise tolerant. And the comparative results demonstrate that the proposed method is superior to the other methods in diagnosis speed, diagnosis success rate, and diagnosis stability.
An R-peak detection method that uses an SVD filter and a search back system.
Jung, Woo-Hyuk; Lee, Sang-Goog
2012-12-01
In this paper, we present a method for detecting the R-peak of an ECG signal by using an singular value decomposition (SVD) filter and a search back system. The ECG signal was detected in two phases: the pre-processing phase and the decision phase. The pre-processing phase consisted of the stages for the SVD filter, Butterworth High Pass Filter (HPF), moving average (MA), and squaring, whereas the decision phase consisted of a single stage that detected the R-peak. In the pre-processing phase, the SVD filter removed noise while the Butterworth HPF eliminated baseline wander. The MA removed the remaining noise of the signal that had gone through the SVD filter to make the signal smooth, and squaring played a role in strengthening the signal. In the decision phase, the threshold was used to set the interval before detecting the R-peak. When the latest R-R interval (RRI), suggested by Hamilton et al., was greater than 150% of the previous RRI, the method of detecting the R-peak in such an interval was modified to be 150% or greater than the smallest interval of the two most latest RRIs. When the modified search back system was used, the error rate of the peak detection decreased to 0.29%, compared to 1.34% when the modified search back system was not used. Consequently, the sensitivity was 99.47%, the positive predictivity was 99.47%, and the detection error was 1.05%. Furthermore, the quality of the signal in data with a substantial amount of noise was improved, and thus, the R-peak was detected effectively. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.
A Note on Inclusion Intervals of Matrix Singular Values
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Shu-Yu Cui
2012-01-01
Full Text Available We establish an inclusion relation between two known inclusion intervals of matrix singular values in some special case. In addition, based on the use of positive scale vectors, a known inclusion interval of matrix singular values is also improved.
Generalized reduced rank tests using the singular value decomposition
Kleibergen, F.R.; Paap, R.
2002-01-01
We propose a novel statistic to test the rank of a matrix. The rank statistic overcomes deficiencies of existing rank statistics, like: necessity of a Kronecker covariance matrix for the canonical correlation rank statistic of Anderson (1951), sensitivity to the ordering of the variables for the LDU
International comparisons of road safety using Singular Value Decomposition.
Oppe, S.
2001-01-01
There is a general interest in the comparison of road safety developments in different countries. Comparisons have been made, based on absolute levels of accident or fatality risk or on the rate of change of functions regarding risk, the number of accidents, fatalities or injuries over time. Such
Generalized Reduced Rank Tests using the Singular Value Decomposition
F.R. Kleibergen (Frank); R. Paap (Richard)
2003-01-01
textabstractWe propose a novel statistic to test the rank of a matrix. The rank statistic overcomes deficiencies of existing rank statistics, like: necessity of a Kronecker covariance matrix for the canonical correlation rank statistic of Anderson (1951), sensitivity to the ordering of the variables
Gu, Rongbao; Shao, Yanmin
2016-07-01
In this paper, a new concept of multi-scales singular value decomposition entropy based on DCCA cross correlation analysis is proposed and its predictive power for the Dow Jones Industrial Average Index is studied. Using Granger causality analysis with different time scales, it is found that, the singular value decomposition entropy has predictive power for the Dow Jones Industrial Average Index for period less than one month, but not for more than one month. This shows how long the singular value decomposition entropy predicts the stock market that extends Caraiani's result obtained in Caraiani (2014). On the other hand, the result also shows an essential characteristic of stock market as a chaotic dynamic system.
High Performance Polar Decomposition on Distributed Memory Systems
Sukkari, Dalal E.
2016-08-08
The polar decomposition of a dense matrix is an important operation in linear algebra. It can be directly calculated through the singular value decomposition (SVD) or iteratively using the QR dynamically-weighted Halley algorithm (QDWH). The former is difficult to parallelize due to the preponderant number of memory-bound operations during the bidiagonal reduction. We investigate the latter scenario, which performs more floating-point operations but exposes at the same time more parallelism, and therefore, runs closer to the theoretical peak performance of the system, thanks to more compute-bound matrix operations. Profiling results show the performance scalability of QDWH for calculating the polar decomposition using around 9200 MPI processes on well and ill-conditioned matrices of 100K×100K problem size. We study then the performance impact of the QDWH-based polar decomposition as a pre-processing step toward calculating the SVD itself. The new distributed-memory implementation of the QDWH-SVD solver achieves up to five-fold speedup against current state-of-the-art vendor SVD implementations. © Springer International Publishing Switzerland 2016.
Bullinaria, John A; Levy, Joseph P
2012-09-01
In a previous article, we presented a systematic computational study of the extraction of semantic representations from the word-word co-occurrence statistics of large text corpora. The conclusion was that semantic vectors of pointwise mutual information values from very small co-occurrence windows, together with a cosine distance measure, consistently resulted in the best representations across a range of psychologically relevant semantic tasks. This article extends that study by investigating the use of three further factors--namely, the application of stop-lists, word stemming, and dimensionality reduction using singular value decomposition (SVD)--that have been used to provide improved performance elsewhere. It also introduces an additional semantic task and explores the advantages of using a much larger corpus. This leads to the discovery and analysis of improved SVD-based methods for generating semantic representations (that provide new state-of-the-art performance on a standard TOEFL task) and the identification and discussion of problems and misleading results that can arise without a full systematic study.
Precision PEP-II optics measurement with an SVD-enhanced Least-Square fitting
Yan, Y. T.; Cai, Y.
2006-03-01
A singular value decomposition (SVD)-enhanced Least-Square fitting technique is discussed. By automatic identifying, ordering, and selecting dominant SVD modes of the derivative matrix that responds to the variations of the variables, the converging process of the Least-Square fitting is significantly enhanced. Thus the fitting speed can be fast enough for a fairly large system. This technique has been successfully applied to precision PEP-II optics measurement in which we determine all quadrupole strengths (both normal and skew components) and sextupole feed-downs as well as all BPM gains and BPM cross-plane couplings through Least-Square fitting of the phase advances and the Local Green's functions as well as the coupling ellipses among BPMs. The local Green's functions are specified by 4 local transfer matrix components R12, R34, R32, R14. These measurable quantities (the Green's functions, the phase advances and the coupling ellipse tilt angles and axis ratios) are obtained by analyzing turn-by-turn Beam Position Monitor (BPM) data with a high-resolution model-independent analysis (MIA). Once all of the quadrupoles and sextupole feed-downs are determined, we obtain a computer virtual accelerator which matches the real accelerator in linear optics. Thus, beta functions, linear coupling parameters, and interaction point (IP) optics characteristics can be measured and displayed.
A singular value sensitivity approach to robust eigenstructure assignment
DEFF Research Database (Denmark)
Søgaard-Andersen, Per; Trostmann, Erik; Conrad, Finn
1986-01-01
A design technique for improving the feedback properties of multivariable state feedback systems designed using eigenstructure assignment is presented. Based on a singular value analysis of the feedback properties a design parameter adjustment procedure is outlined. This procedure allows...
Using dynamic mode decomposition for real-time background/foreground separation in video
Kutz, Jose Nathan; Grosek, Jacob; Brunton, Steven; Fu, Xing; Pendergrass, Seth
2017-06-06
The technique of dynamic mode decomposition (DMD) is disclosed herein for the purpose of robustly separating video frames into background (low-rank) and foreground (sparse) components in real-time. Foreground/background separation is achieved at the computational cost of just one singular value decomposition (SVD) and one linear equation solve, thus producing results orders of magnitude faster than robust principal component analysis (RPCA). Additional techniques, including techniques for analyzing the video for multi-resolution time-scale components, and techniques for reusing computations to allow processing of streaming video in real time, are also described herein.
Golafshan, Reza; Yuce Sanliturk, Kenan
2016-03-01
Ball bearings remain one of the most crucial components in industrial machines and due to their critical role, it is of great importance to monitor their conditions under operation. However, due to the background noise in acquired signals, it is not always possible to identify probable faults. This incapability in identifying the faults makes the de-noising process one of the most essential steps in the field of Condition Monitoring (CM) and fault detection. In the present study, Singular Value Decomposition (SVD) and Hankel matrix based de-noising process is successfully applied to the ball bearing time domain vibration signals as well as to their spectrums for the elimination of the background noise and the improvement the reliability of the fault detection process. The test cases conducted using experimental as well as the simulated vibration signals demonstrate the effectiveness of the proposed de-noising approach for the ball bearing fault detection.
International Nuclear Information System (INIS)
Uzdin, Raam
2014-01-01
Unambiguous (non-orthogonal) state discrimination (USD) has a fundamental importance in quantum information and quantum cryptography. Various aspects of two-state and multiple-state USD are studied here using singular value decomposition of the evolution operator that describes a given state discriminating system. In particular, we relate the minimal angle between states to the ratio of the minimal and maximal singular values. This is supported by a simple geometrical picture in two-state USD. Furthermore, by studying the singular vectors population we find that the minimal angle between input vectors in multiple-state USD is always larger than the minimal angle in two-state USD in the same system. As an example we study what pure states can be probabilistically transformed into maximally entangled pure states in a given system . (paper)
Singular value correlation functions for products of Wishart random matrices
International Nuclear Information System (INIS)
Akemann, Gernot; Kieburg, Mario; Wei, Lu
2013-01-01
We consider the product of M quadratic random matrices with complex elements and no further symmetry, where all matrix elements of each factor have a Gaussian distribution. This generalizes the classical Wishart–Laguerre Gaussian unitary ensemble with M = 1. In this paper, we first compute the joint probability distribution for the singular values of the product matrix when the matrix size N and the number M are fixed but arbitrary. This leads to a determinantal point process which can be realized in two different ways. First, it can be written as a one-matrix singular value model with a non-standard Jacobian, or second, for M ⩾ 2, as a two-matrix singular value model with a set of auxiliary singular values and a weight proportional to the Meijer G-function. For both formulations, we determine all singular value correlation functions in terms of the kernels of biorthogonal polynomials which we explicitly construct. They are given in terms of the hypergeometric and Meijer G-functions, generalizing the Laguerre polynomials for M = 1. Our investigation was motivated from applications in telecommunication of multi-layered scattering multiple-input and multiple-output channels. We present the ergodic mutual information for finite-N for such a channel model with M − 1 layers of scatterers as an example. (paper)
Directory of Open Access Journals (Sweden)
Stuart Gary W
2004-12-01
Full Text Available Abstract Background Eukaryotic whole genome sequences are accumulating at an impressive rate. Effective methods for comparing multiple whole eukaryotic genomes on a large scale are needed. Most attempted solutions involve the production of large scale alignments, and many of these require a high stringency pre-screen for putative orthologs in order to reduce the effective size of the dataset and provide a reasonably high but unknown fraction of correctly aligned homologous sites for comparison. As an alternative, highly efficient methods that do not require the pre-alignment of operationally defined orthologs are also being explored. Results A non-alignment method based on the Singular Value Decomposition (SVD was used to compare the predicted protein complement of nine whole eukaryotic genomes ranging from yeast to man. This analysis resulted in the simultaneous identification and definition of a large number of well conserved motifs and gene families, and produced a species tree supporting one of two conflicting hypotheses of metazoan relationships. Conclusions Our SVD-based analysis of the entire protein complement of nine whole eukaryotic genomes suggests that highly conserved motifs and gene families can be identified and effectively compared in a single coherent definition space for the easy extraction of gene and species trees. While this occurs without the explicit definition of orthologs or homologous sites, the analysis can provide a basis for these definitions.
Directory of Open Access Journals (Sweden)
Abhijeet Ravankar
2016-05-01
Full Text Available Line detection is an important problem in computer vision, graphics and autonomous robot navigation. Lines detected using a laser range sensor (LRS mounted on a robot can be used as features to build a map of the environment, and later to localize the robot in the map, in a process known as Simultaneous Localization and Mapping (SLAM. We propose an efficient algorithm for line detection from LRS data using a novel hopping-points Singular Value Decomposition (SVD and Hough transform-based algorithm, in which SVD is applied to intermittent LRS points to accelerate the algorithm. A reverse-hop mechanism ensures that the end points of the line segments are accurately extracted. Line segments extracted from the proposed algorithm are used to form a map and, subsequently, LRS data points are matched with the line segments to localize the robot. The proposed algorithm eliminates the drawbacks of point-based matching algorithms like the Iterative Closest Points (ICP algorithm, the performance of which degrades with an increasing number of points. We tested the proposed algorithm for mapping and localization in both simulated and real environments, and found it to detect lines accurately and build maps with good self-localization.
Multiscale singular value manifold for rotating machinery fault diagnosis
Energy Technology Data Exchange (ETDEWEB)
Feng, Yi; Lu, BaoChun; Zhang, Deng Feng [School of Mechanical Engineering, Nanjing University of Science and Technology,Nanjing (United States)
2017-01-15
Time-frequency distribution of vibration signal can be considered as an image that contains more information than signal in time domain. Manifold learning is a novel theory for image recognition that can be also applied to rotating machinery fault pattern recognition based on time-frequency distributions. However, the vibration signal of rotating machinery in fault condition contains cyclical transient impulses with different phrases which are detrimental to image recognition for time-frequency distribution. To eliminate the effects of phase differences and extract the inherent features of time-frequency distributions, a multiscale singular value manifold method is proposed. The obtained low-dimensional multiscale singular value manifold features can reveal the differences of different fault patterns and they are applicable to classification and diagnosis. Experimental verification proves that the performance of the proposed method is superior in rotating machinery fault diagnosis.
Pápai, Ferenc; Szűcs, István
2015-01-01
The singular value decomposition of the measured frequency response function matrix, as a very effective tool of experimental modal analysis is used over the last twenty-five years. The complex mode indication function has become a common numerical tool in processing experimental data. There are many references on the development of complex mode indication function including the enhanced mode indication function and its use together with the enhanced frequency response function to form spatia...
Pápai, Ferenc; Szűcs, István
2015-01-01
The singular value decomposition of the measured frequency response function matrix, as a very effective tool of experimental modal analysis is used over the last twenty-five years. The complex mode indication function has become a common numerical tool in processing experimental data. There are many references on the development of complex mode indication function including the enhanced mode indication function and its use together with the enhanced frequency response function to form spatia...
A SVD Based Image Complexity Measure
DEFF Research Database (Denmark)
Gustafsson, David Karl John; Pedersen, Kim Steenstrup; Nielsen, Mads
2009-01-01
Images are composed of geometric structures and texture, and different image processing tools - such as denoising, segmentation and registration - are suitable for different types of image contents. Characterization of the image content in terms of geometric structure and texture is an important...... problem that one is often faced with. We propose a patch based complexity measure, based on how well the patch can be approximated using singular value decomposition. As such the image complexity is determined by the complexity of the patches. The concept is demonstrated on sequences from the newly...... collected DIKU Multi-Scale image database....
Directory of Open Access Journals (Sweden)
Te Han
2016-01-01
Full Text Available Nowadays, the fault diagnosis of rolling bearing in aeroengines is based on the vibration signal measured on casing, instead of bearing block. However, the vibration signal of the bearing is often covered by a series of complex components caused by other structures (rotor, gears. Therefore, when bearings cause failure, it is still not certain that the fault feature can be extracted from the vibration signal on casing. In order to solve this problem, a novel fault feature extraction method for rolling bearing based on empirical mode decomposition (EMD and the difference spectrum of singular value is proposed in this paper. Firstly, the vibration signal is decomposed by EMD. Next, the difference spectrum of singular value method is applied. The study finds that each peak on the difference spectrum corresponds to each component in the original signal. According to the peaks on the difference spectrum, the component signal of the bearing fault can be reconstructed. To validate the proposed method, the bearing fault data collected on the casing are analyzed. The results indicate that the proposed rolling bearing diagnosis method can accurately extract the fault feature that is submerged in other component signals and noise.
Algorithms for large scale singular value analysis of spatially variant tomography systems
International Nuclear Information System (INIS)
Cao-Huu, Tuan; Brownell, G.; Lachiver, G.
1996-01-01
The problem of determining the eigenvalues of large matrices occurs often in the design and analysis of modem tomography systems. As there is an interest in solving systems containing an ever-increasing number of variables, current research effort is being made to create more robust solvers which do not depend on some special feature of the matrix for convergence (e.g. block circulant), and to improve the speed of already known and understood solvers so that solving even larger systems in a reasonable time becomes viable. Our standard techniques for singular value analysis are based on sparse matrix factorization and are not applicable when the input matrices are large because the algorithms cause too much fill. Fill refers to the increase of non-zero elements in the LU decomposition of the original matrix A (the system matrix). So we have developed iterative solutions that are based on sparse direct methods. Data motion and preconditioning techniques are critical for performance. This conference paper describes our algorithmic approaches for large scale singular value analysis of spatially variant imaging systems, and in particular of PCR2, a cylindrical three-dimensional PET imager 2 built at the Massachusetts General Hospital (MGH) in Boston. We recommend the desirable features and challenges for the next generation of parallel machines for optimal performance of our solver
Multi-linear sparse reconstruction for SAR imaging based on higher-order SVD
Gao, Yu-Fei; Gui, Guan; Cong, Xun-Chao; Yang, Yue; Zou, Yan-Bin; Wan, Qun
2017-12-01
This paper focuses on the spotlight synthetic aperture radar (SAR) imaging for point scattering targets based on tensor modeling. In a real-world scenario, scatterers usually distribute in the block sparse pattern. Such a distribution feature has been scarcely utilized by the previous studies of SAR imaging. Our work takes advantage of this structure property of the target scene, constructing a multi-linear sparse reconstruction algorithm for SAR imaging. The multi-linear block sparsity is introduced into higher-order singular value decomposition (SVD) with a dictionary constructing procedure by this research. The simulation experiments for ideal point targets show the robustness of the proposed algorithm to the noise and sidelobe disturbance which always influence the imaging quality of the conventional methods. The computational resources requirement is further investigated in this paper. As a consequence of the algorithm complexity analysis, the present method possesses the superiority on resource consumption compared with the classic matching pursuit method. The imaging implementations for practical measured data also demonstrate the effectiveness of the algorithm developed in this paper.
SVD-BASED TRANSMIT BEAMFORMING FOR VARIOUS MODULATIONS WITH CONVOLUTION ENCODING
Directory of Open Access Journals (Sweden)
M. Raja
2011-09-01
Full Text Available This paper present a new beamforming technique using singular value decomposition (SVD for closed loop Multiple-input, multiple-output (MIMO wireless systems with various modulation techniques such as BPSK, 16-QAM, 16-PSK, DPSK and PAM along with convolution encoder. The channel matrix is decomposed into a number of independent orthogonal modes of excitation, which refer to as eigenmodes of the channel. Transmit precoding is performed by multiplying the input symbols with unitary matrix to produce the transmit beamforming, and the precoded symbols are transmitted over Rayleigh fading channel. At the receiver, combining process is performed by using maximum ratio combiner (MRC, and the receiver shaping is performed to retrieve the original input symbols by multiplying the received signal with conjugate transpose of the unitary matrix. Furthermore, the expressions for average bit error rate (BER for M-PSK and average BER for M-QAM are derived. The superiority of the proposed work is proved by simulation results and the proposed work is compared to the other beamforming methods.
Comparison of DCT, SVD and BFOA based multimodal biometric watermarking system
Directory of Open Access Journals (Sweden)
S. Anu H. Nair
2015-12-01
Full Text Available Digital image watermarking is a major domain for hiding the biometric information, in which the watermark data are made to be concealed inside a host image imposing imperceptible change in the picture. Due to the advance in digital image watermarking, the majority of research aims to make a reliable improvement in robustness to prevent the attack. The reversible invisible watermarking scheme is used for fingerprint and iris multimodal biometric system. A novel approach is used for fusing different biometric modalities. Individual unique modalities of fingerprint and iris biometric are extracted and fused using different fusion techniques. The performance of different fusion techniques is evaluated and the Discrete Wavelet Transform fusion method is identified as the best. Then the best fused biometric template is watermarked into a cover image. The various watermarking techniques such as the Discrete Cosine Transform (DCT, Singular Value Decomposition (SVD and Bacterial Foraging Optimization Algorithm (BFOA are implemented to the fused biometric feature image. Performance of watermarking systems is compared using different metrics. It is found that the watermarked images are found robust over different attacks and they are able to reverse the biometric template for Bacterial Foraging Optimization Algorithm (BFOA watermarking technique.
A Reliable Image Watermarking Scheme Based on Redistributed Image Normalization and SVD
Directory of Open Access Journals (Sweden)
Musrrat Ali
2016-01-01
Full Text Available Digital image watermarking is the process of concealing secret information in a digital image for protecting its rightful ownership. Most of the existing block based singular value decomposition (SVD digital watermarking schemes are not robust to geometric distortions, such as rotation in an integer multiple of ninety degree and image flipping, which change the locations of the pixels but don’t make any changes to the pixel’s intensity of the image. Also, the schemes have used a constant scaling factor to give the same weightage to the coefficients of different magnitudes that results in visible distortion in some regions of the watermarked image. Therefore, to overcome the problems mentioned here, this paper proposes a novel image watermarking scheme by incorporating the concepts of redistributed image normalization and variable scaling factor depending on the coefficient’s magnitude to be embedded. Furthermore, to enhance the security and robustness the watermark is shuffled by using the piecewise linear chaotic map before the embedding. To investigate the robustness of the scheme several attacks are applied to seriously distort the watermarked image. Empirical analysis of the results has demonstrated the efficiency of the proposed scheme.
Characteristic gene selection via weighting principal components by singular values.
Directory of Open Access Journals (Sweden)
Jin-Xing Liu
Full Text Available Conventional gene selection methods based on principal component analysis (PCA use only the first principal component (PC of PCA or sparse PCA to select characteristic genes. These methods indeed assume that the first PC plays a dominant role in gene selection. However, in a number of cases this assumption is not satisfied, so the conventional PCA-based methods usually provide poor selection results. In order to improve the performance of the PCA-based gene selection method, we put forward the gene selection method via weighting PCs by singular values (WPCS. Because different PCs have different importance, the singular values are exploited as the weights to represent the influence on gene selection of different PCs. The ROC curves and AUC statistics on artificial data show that our method outperforms the state-of-the-art methods. Moreover, experimental results on real gene expression data sets show that our method can extract more characteristic genes in response to abiotic stresses than conventional gene selection methods.
Institute of Scientific and Technical Information of China (English)
MAO Wen-shu; WANG Qian-qian; PENG Jun; LI Yong-hua
2008-01-01
Based on the precipitation data of Meiyu at 37 stations in the valleys of Yangtze and Huaihe Rivers from 1954 to 2001, the temporal-spatial characteristics of Meiyu precipitation and their relationships with the sea surface temperature in northern Pacific are investigated using such methods as harmonic analysis, empirical orthogonal function (EOF), composite analysis and singular value decomposition (SVD). The results show that the temporal evolution and spatial distribution of Meiyu precipitation are not homogeneous in the Yangtze-Huaihe Rivers basins but with prominent inter-annual and inter-decadal variabilities. The key region between the anomalies of Meiyu precipitation and the monthly sea surface temperature anomalies (SSTA) lies in the west wind drift of North Pacific, which influences the precipitation anomaly of Meiyu precipitation over a key period of time from January to March in the same year. When the SST in the North Pacific west wind drift is warmer (colder) than average during these months, Meiyu precipitation anomalously increases (decreases) in the concurrent year. Results of SVD are consistent with those of composite analysis which pass the significance test of Monte-Carlo at 0.05.
Ground roll attenuation using a curvelet-SVD filter: a case study from the west of Iran
International Nuclear Information System (INIS)
Boustani, Bahareh; Javaherian, Abdorahim; Mortazavi, Seyed Ahmad; Torabi, Siyavash
2013-01-01
In reflection seismology, a ground roll is a low frequency, low velocity and high amplitude surface wave. It usually has stronger amplitude than reflections, and masks valuable information carried by signals. Many filters in different domains have been used for ground roll attenuation such as tau-p and f-k filters. Recently, in many studies, the curvelet transform has been used for ground roll attenuation. The curvelet transform creates a good separation between ground roll and reflections in dip and frequency, especially in high frequency subbands. In this paper, based on the adaptive curvelet filter, a new method is introduced through a combination of the adaptive curvelet and adaptive singular value decomposition (ASVD) filters and is called a curvelet-SVD filter. In this filter, the subbands in a curvelet domain are divided into three categories based on the ground roll energy in each subband. These categories are subbands (1) with high energy containing only ground roll, (2) with medium energy that contains both ground roll and reflections, and (3) with low energy containing only reflections. The category that contains only ground roll will be muted, as in common usage of the adaptive curvelet filter. If the category that contains both ground roll and reflections is unchanged, part of the ground roll will not be attenuated. If this category is muted, part of the reflections will be damaged. To overcome this problem, ASVD is applied to attenuate ground roll in the subbands of this category. The category that contains only reflections will not be touched. This filter was applied to a synthetic and to a real data set from the west of Iran. The synthetic data contained dispersed and aliased ground roll. A curvelet-SVD filter could attenuate dispersed ground roll but it could not completely attenuate aliased ground roll. Because of the damage to the reflections, the energy threshold for applying ASVD in the curvelet domain could not be selected any lower. In real
A singular-value method for reconstruction of nonradial and lossy objects.
Jiang, Wei; Astheimer, Jeffrey; Waag, Robert
2012-03-01
Efficient inverse scattering algorithms for nonradial lossy objects are presented using singular-value decomposition to form reduced-rank representations of the scattering operator. These algorithms extend eigenfunction methods that are not applicable to nonradial lossy scattering objects because the scattering operators for these objects do not have orthonormal eigenfunction decompositions. A method of local reconstruction by segregation of scattering contributions from different local regions is also presented. Scattering from each region is isolated by forming a reduced-rank representation of the scattering operator that has domain and range spaces comprised of far-field patterns with retransmitted fields that focus on the local region. Methods for the estimation of the boundary, average sound speed, and average attenuation slope of the scattering object are also given. These methods yielded approximations of scattering objects that were sufficiently accurate to allow residual variations to be reconstructed in a single iteration. Calculated scattering from a lossy elliptical object with a random background, internal features, and white noise is used to evaluate the proposed methods. Local reconstruction yielded images with spatial resolution that is finer than a half wavelength of the center frequency and reproduces sound speed and attenuation slope with relative root-mean-square errors of 1.09% and 11.45%, respectively.
Directory of Open Access Journals (Sweden)
Nicolas M Bertagnolli
Full Text Available To search for evolutionary forces that might act upon transcript length, we use the singular value decomposition (SVD to identify the length distribution functions of sets and subsets of human and yeast transcripts from profiles of mRNA abundance levels across gel electrophoresis migration distances that were previously measured by DNA microarrays. We show that the SVD identifies the transcript length distribution functions as "asymmetric generalized coherent states" from the DNA microarray data and with no a-priori assumptions. Comparing subsets of human and yeast transcripts of the same gene ontology annotations, we find that in both disparate eukaryotes, transcripts involved in protein synthesis or mitochondrial metabolism are significantly shorter than typical, and in particular, significantly shorter than those involved in glucose metabolism. Comparing the subsets of human transcripts that are overexpressed in glioblastoma multiforme (GBM or normal brain tissue samples from The Cancer Genome Atlas, we find that GBM maintains normal brain overexpression of significantly short transcripts, enriched in transcripts that are involved in protein synthesis or mitochondrial metabolism, but suppresses normal overexpression of significantly longer transcripts, enriched in transcripts that are involved in glucose metabolism and brain activity. These global relations among transcript length, cellular metabolism and tumor development suggest a previously unrecognized physical mode for tumor and normal cells to differentially regulate metabolism in a transcript length-dependent manner. The identified distribution functions support a previous hypothesis from mathematical modeling of evolutionary forces that act upon transcript length in the manner of the restoring force of the harmonic oscillator.
Directory of Open Access Journals (Sweden)
Akira R Kinjo
Full Text Available Position-specific scoring matrices (PSSMs are useful for detecting weak homology in protein sequence analysis, and they are thought to contain some essential signatures of the protein families. In order to elucidate what kind of ingredients constitute such family-specific signatures, we apply singular value decomposition to a set of PSSMs and examine the properties of dominant right and left singular vectors. The first right singular vectors were correlated with various amino acid indices including relative mutability, amino acid composition in protein interior, hydropathy, or turn propensity, depending on proteins. A significant correlation between the first left singular vector and a measure of site conservation was observed. It is shown that the contribution of the first singular component to the PSSMs act to disfavor potentially but falsely functionally important residues at conserved sites. The second right singular vectors were highly correlated with hydrophobicity scales, and the corresponding left singular vectors with contact numbers of protein structures. It is suggested that sequence alignment with a PSSM is essentially equivalent to threading supplemented with functional information. In addition, singular vectors may be useful for analyzing and annotating the characteristics of conserved sites in protein families.
Newsom, J. R.; Mukhopadhyay, V.
1983-01-01
A method for designing robust feedback controllers for multiloop systems is presented. Robustness is characterized in terms of the minimum singular value of the system return difference matrix at the plant input. Analytical gradients of the singular values with respect to design variables in the controller are derived. A cumulative measure of the singular values and their gradients with respect to the design variables is used with a numerical optimization technique to increase the system's robustness. Both unconstrained and constrained optimization techniques are evaluated. Numerical results are presented for a two output drone flight control system.
DEFF Research Database (Denmark)
Haldrup, Kristoffer
2014-01-01
The development of new X-ray light sources, XFELs, with unprecedented time and brilliance characteristics has led to the availability of very large datasets with high time resolution and superior signal strength. The chaotic nature of the emission processes in such sources as well as entirely novel...... detector demands has also led to significant challenges in terms of data analysis. This paper describes a heuristic approach to datasets where spurious background contributions of a magnitude similar to (or larger) than the signal of interest prevents conventional analysis approaches. The method relies...
Use of multiple singular value decompositions to analyze complex intracellular calcium ion signals
Martinez, Josue G.; Huang, Jianhua Z.; Burghardt, Robert C.; Barhoumi, Rola; Carroll, Raymond J.
2009-01-01
) to extract the signals from such movies, in a way that is semi-automatic and tuned closely to the actual data and their many complexities. These complexities include the following. First, the images themselves are of no interest: all interest focuses
DEFF Research Database (Denmark)
Christensen, Steen; Doherty, John
2008-01-01
super parameters), and that the structural errors caused by using pilot points and super parameters to parameterize the highly heterogeneous log-transmissivity field can be significant. For the test case much effort is put into studying how the calibrated model's ability to make accurate predictions...
Implicit-shifted Symmetric QR Singular Value Decomposition of 3x3 Matrices
2016-04-01
Graph 33, 4, 138:1– 138:11. TREFETHEN, L. N., AND BAU III, D. 1997. Numerical linear algebra , vol. 50. Siam. XU, H., SIN, F., ZHU, Y., AND BARBIČ, J...matrices with minimal branching and elementary floating point operations. Tech. rep., University of Wisconsin- Madison. SAITO, S., ZHOU, Z.-Y., AND
Radar Micro-Doppler Feature Extraction Using the Singular Value Decomposition
Wit, J.J.M. de; Harmanny, R.I.A.; Molchanov, P.
2014-01-01
Abstract—The micro-Doppler spectrogram depends on parts of a target moving and rotating in addition to the main body motion (e.g., spinning rotor blades) and is thus characteristic for the type of target. In this study, the micro-Doppler spectrogram is exploited to distinguish between birds and
The comparison between SVD-DCT and SVD-DWT digital image watermarking
Wira Handito, Kurniawan; Fauzi, Zulfikar; Aminy Ma’ruf, Firda; Widyaningrum, Tanti; Muslim Lhaksmana, Kemas
2018-03-01
With internet, anyone can publish their creation into digital data simply, inexpensively, and absolutely easy to be accessed by everyone. However, the problem appears when anyone else claims that the creation is their property or modifies some part of that creation. It causes necessary protection of copyrights; one of the examples is with watermarking method in digital image. The application of watermarking technique on digital data, especially on image, enables total invisibility if inserted in carrier image. Carrier image will not undergo any decrease of quality and also the inserted image will not be affected by attack. In this paper, watermarking will be implemented on digital image using Singular Value Decomposition based on Discrete Wavelet Transform (DWT) and Discrete Cosine Transform (DCT) by expectation in good performance of watermarking result. In this case, trade-off happen between invisibility and robustness of image watermarking. In embedding process, image watermarking has a good quality for scaling factor < 0.1. The quality of image watermarking in decomposition level 3 is better than level 2 and level 1. Embedding watermark in low-frequency is robust to Gaussian blur attack, rescale, and JPEG compression, but in high-frequency is robust to Gaussian noise.
Burns, Jack O.; Tauscher, Keith; Rapetti, David; Mirocha, Jordan; Switzer, Eric
2018-01-01
We have designed a complete data analysis pipeline for constraining Cosmic Dawn physics using sky-averaged spectra in the VHF range (40-200 MHz) obtained either from the ground (e.g., the Experiment to Detect Global Epoch of Reionization Signal, EDGES; and the Cosmic Twilight Polarimeter, CTP) or from orbit above the lunar farside (e.g., the Dark Ages Radio Explorer, DARE). In the case of DARE, we avoid Earth-based RFI, ionospheric effects, and radio solar emissions (when observing at night). To extract the 21-cm spectrum, we parametrize the cosmological signal and systematics with two separate sets of modes defined through Singular Value Decomposition (SVD) of training set curves. The training set for the 21-cm spin-flip brightness temperatures is composed of theoretical models of the first stars, galaxies and black holes created by varying physical parameters within the ares code. The systematics training set is created using sky and beam data to model the beam-weighted foregrounds (which are about four orders of magnitude larger than the signal) as well as expected lab data to model receiver systematics. To constrain physical parameters determining the 21-cm spectrum, we apply to the extracted signal a series of consecutive fitting techniques including two usages of a Markov Chain Monte Carlo (MCMC) algorithm. Importantly, our pipeline efficiently utilizes the significant differences between the foreground and the 21-cm signal in spatial and spectral variations. In addition, it incorporates for the first time polarization data, dramatically improving the constraining power. We are currently validating this end-to-end pipeline using detailed simulations of the signal, foregrounds and instruments. This work was directly supported by the NASA Solar System Exploration Research Virtual Institute cooperative agreement number 80ARC017M0006 and funding from the NASA Ames Research Center cooperative agreement NNX16AF59G.
A Jacobi-Davidson type method for the generalized singular value problem
Hochstenbach, M.E.
2009-01-01
We discuss a new method for the iterative computation of some of the generalized singular values and vectors of a large sparse matrix. Our starting point is the augmented matrix formulation of the GSVD. The subspace expansion is performed by (approximately) solving a Jacobi–Davidson type correction
A locally convergent Jacobi iteration for the tensor singular value problem
Shekhawat, Hanumant Singh; Weiland, Siep
2018-01-01
Multi-linear functionals or tensors are useful in study and analysis multi-dimensional signal and system. Tensor approximation, which has various applications in signal processing and system theory, can be achieved by generalizing the notion of singular values and singular vectors of matrices to
Wu, Sheng-Jhih; Chu, Moody T.
2017-08-01
An inverse eigenvalue problem usually entails two constraints, one conditioned upon the spectrum and the other on the structure. This paper investigates the problem where triple constraints of eigenvalues, singular values, and diagonal entries are imposed simultaneously. An approach combining an eclectic mix of skills from differential geometry, optimization theory, and analytic gradient flow is employed to prove the solvability of such a problem. The result generalizes the classical Mirsky, Sing-Thompson, and Weyl-Horn theorems concerning the respective majorization relationships between any two of the arrays of main diagonal entries, eigenvalues, and singular values. The existence theory fills a gap in the classical matrix theory. The problem might find applications in wireless communication and quantum information science. The technique employed can be implemented as a first-step numerical method for constructing the matrix. With slight modification, the approach might be used to explore similar types of inverse problems where the prescribed entries are at general locations.
International Nuclear Information System (INIS)
Wu, Sheng-Jhih; Chu, Moody T
2017-01-01
An inverse eigenvalue problem usually entails two constraints, one conditioned upon the spectrum and the other on the structure. This paper investigates the problem where triple constraints of eigenvalues, singular values, and diagonal entries are imposed simultaneously. An approach combining an eclectic mix of skills from differential geometry, optimization theory, and analytic gradient flow is employed to prove the solvability of such a problem. The result generalizes the classical Mirsky, Sing–Thompson, and Weyl-Horn theorems concerning the respective majorization relationships between any two of the arrays of main diagonal entries, eigenvalues, and singular values. The existence theory fills a gap in the classical matrix theory. The problem might find applications in wireless communication and quantum information science. The technique employed can be implemented as a first-step numerical method for constructing the matrix. With slight modification, the approach might be used to explore similar types of inverse problems where the prescribed entries are at general locations. (paper)
Sensitivity analysis of automatic flight control systems using singular value concepts
Herrera-Vaillard, A.; Paduano, J.; Downing, D.
1985-01-01
A sensitivity analysis is presented that can be used to judge the impact of vehicle dynamic model variations on the relative stability of multivariable continuous closed-loop control systems. The sensitivity analysis uses and extends the singular-value concept by developing expressions for the gradients of the singular value with respect to variations in the vehicle dynamic model and the controller design. Combined with a priori estimates of the accuracy of the model, the gradients are used to identify the elements in the vehicle dynamic model and controller that could severely impact the system's relative stability. The technique is demonstrated for a yaw/roll damper stability augmentation designed for a business jet.
Energy Technology Data Exchange (ETDEWEB)
Liang, Wei; Zhang, Laibin; Mingda, Wang; Jinqiu, Hu [College of Mechanical and Transportation Engineering, China University of Petroleum, Beijing, (China)
2010-07-01
The negative wave pressure method is one of the processes used to detect leaks on oil pipelines. The development of new leakage recognition processes is difficult because it is practically impossible to collect leakage pressure samples. The method of leakage feature extraction and the selection of the recognition model are also important in pipeline leakage detection. This study investigated a new feature extraction approach Singular Value Projection (SVP). It projects the singular value to a standard basis. A new pipeline recognition model based on the multi-class Support Vector Machines was also developed. It was found that SVP is a clear and concise recognition feature of the negative pressure wave. Field experiments proved that the model provided a high recognition accuracy rate. This approach to pipeline leakage detection based on the SVP and SVM has a high application value.
rCUR: an R package for CUR matrix decomposition
Directory of Open Access Journals (Sweden)
Bodor András
2012-05-01
Full Text Available Abstract Background Many methods for dimensionality reduction of large data sets such as those generated in microarray studies boil down to the Singular Value Decomposition (SVD. Although singular vectors associated with the largest singular values have strong optimality properties and can often be quite useful as a tool to summarize the data, they are linear combinations of up to all of the data points, and thus it is typically quite hard to interpret those vectors in terms of the application domain from which the data are drawn. Recently, an alternative dimensionality reduction paradigm, CUR matrix decompositions, has been proposed to address this problem and has been applied to genetic and internet data. CUR decompositions are low-rank matrix decompositions that are explicitly expressed in terms of a small number of actual columns and/or actual rows of the data matrix. Since they are constructed from actual data elements, CUR decompositions are interpretable by practitioners of the field from which the data are drawn. Results We present an implementation to perform CUR matrix decompositions, in the form of a freely available, open source R-package called rCUR. This package will help users to perform CUR-based analysis on large-scale data, such as those obtained from different high-throughput technologies, in an interactive and exploratory manner. We show two examples that illustrate how CUR-based techniques make it possible to reduce significantly the number of probes, while at the same time maintaining major trends in data and keeping the same classification accuracy. Conclusions The package rCUR provides functions for the users to perform CUR-based matrix decompositions in the R environment. In gene expression studies, it gives an additional way of analysis of differential expression and discriminant gene selection based on the use of statistical leverage scores. These scores, which have been used historically in diagnostic regression
SVD compression for magnetic resonance fingerprinting in the time domain.
McGivney, Debra F; Pierre, Eric; Ma, Dan; Jiang, Yun; Saybasili, Haris; Gulani, Vikas; Griswold, Mark A
2014-12-01
Magnetic resonance (MR) fingerprinting is a technique for acquiring and processing MR data that simultaneously provides quantitative maps of different tissue parameters through a pattern recognition algorithm. A predefined dictionary models the possible signal evolutions simulated using the Bloch equations with different combinations of various MR parameters and pattern recognition is completed by computing the inner product between the observed signal and each of the predicted signals within the dictionary. Though this matching algorithm has been shown to accurately predict the MR parameters of interest, one desires a more efficient method to obtain the quantitative images. We propose to compress the dictionary using the singular value decomposition, which will provide a low-rank approximation. By compressing the size of the dictionary in the time domain, we are able to speed up the pattern recognition algorithm, by a factor of between 3.4-4.8, without sacrificing the high signal-to-noise ratio of the original scheme presented previously.
A QDWH-Based SVD Software Framework on Distributed-Memory Manycore Systems
Sukkari, Dalal
2017-01-01
This paper presents a high performance software framework for computing a dense SVD on distributed- memory manycore systems. Originally introduced by Nakatsukasa et al. (Nakatsukasa et al. 2010; Nakatsukasa and Higham 2013), the SVD solver relies on the polar decomposition using the QR Dynamically-Weighted Halley algorithm (QDWH). Although the QDWH-based SVD algorithm performs a significant amount of extra floating-point operations compared to the traditional SVD with the one-stage bidiagonal reduction, the inherent high level of concurrency associated with Level 3 BLAS compute-bound kernels ultimately compensates for the arithmetic complexity overhead. Using the ScaLAPACK two-dimensional block cyclic data distribution with a rectangular processor topology, the resulting QDWH-SVD further reduces excessive communications during the panel factorization, while increasing the degree of parallelism during the update of the trailing submatrix, as opposed to relying to the default square processor grid. After detailing the algorithmic complexity and the memory footprint of the algorithm, we conduct a thorough performance analysis and study the impact of the grid topology on the performance by looking at the communication and computation profiling trade-offs. We report performance results against state-of-the-art existing QDWH software implementations (e.g., Elemental) and their SVD extensions on large-scale distributed-memory manycore systems based on commodity Intel x86 Haswell processors and Knights Landing (KNL) architecture. The QDWH-SVD framework achieves up to 3/8-fold on the Haswell/KNL-based platforms, respectively, against ScaLAPACK PDGESVD and turns out to be a competitive alternative for well and ill-conditioned matrices. We finally come up herein with a performance model based on these empirical results. Our QDWH-based polar decomposition and its SVD extension are freely available at https://github.com/ecrc/qdwh.git and https
Robust stability analysis of large power systems using the structured singular value theory
Energy Technology Data Exchange (ETDEWEB)
Castellanos, R.; Sarmiento, H. [Instituto de Investigaciones Electricas, Cuernavaca, Morelos (Mexico); Messina, A.R. [Cinvestav, Graduate Program in Electrical Engineering, Guadalajara, Jalisco (Mexico)
2005-07-01
This paper examines the application of structured singular value (SSV) theory to analyse robust stability of complex power systems with respect to a set of structured uncertainties. Based on SSV theory and the frequency sweep method, techniques for robust analysis of large-scale power systems are developed. The main interest is focused on determining robust stability for varying operating conditions and uncertainties in the structure of the power system. The applicability of the proposed techniques is verified through simulation studies on a large-scale power system. In particular, results for the system are considered for a wide range of uncertainties of operating conditions. Specifically, the developed technique is used to estimate the effect of variations in the parameters of a major system inter-tie on the nominal stability of a critical inter-area mode. (Author)
A High Performance QDWH-SVD Solver using Hardware Accelerators
Sukkari, Dalal E.; Ltaief, Hatem; Keyes, David E.
2015-01-01
few digits of accuracy, compared to the full double precision floating point arithmetic. We further leverage the single GPU QDWH-SVD implementation by introducing the first multi-GPU SVD solver to study the scalability of the QDWH-SVD framework.
Robust Stability Analysis of the Space Launch System Control Design: A Singular Value Approach
Pei, Jing; Newsome, Jerry R.
2015-01-01
Classical stability analysis consists of breaking the feedback loops one at a time and determining separately how much gain or phase variations would destabilize the stable nominal feedback system. For typical launch vehicle control design, classical control techniques are generally employed. In addition to stability margins, frequency domain Monte Carlo methods are used to evaluate the robustness of the design. However, such techniques were developed for Single-Input-Single-Output (SISO) systems and do not take into consideration the off-diagonal terms in the transfer function matrix of Multi-Input-Multi-Output (MIMO) systems. Robust stability analysis techniques such as H(sub infinity) and mu are applicable to MIMO systems but have not been adopted as standard practices within the launch vehicle controls community. This paper took advantage of a simple singular-value-based MIMO stability margin evaluation method based on work done by Mukhopadhyay and Newsom and applied it to the SLS high-fidelity dynamics model. The method computes a simultaneous multi-loop gain and phase margin that could be related back to classical margins. The results presented in this paper suggest that for the SLS system, traditional SISO stability margins are similar to the MIMO margins. This additional level of verification provides confidence in the robustness of the control design.
International Nuclear Information System (INIS)
Weinstein, M.
2012-01-01
I will talk about a new way of implementing Lanczos and contraction algorithms to diagonalize lattice Hamiltonians that dramatically reduces the memory required to do the computation, without restricting to variational ansatzes. (author)
Singular value decomposition analysis of a photoacoustic imaging system and 3D imaging at 0.7 FPS.
Roumeliotis, Michael B; Stodilka, Robert Z; Anastasio, Mark A; Ng, Eldon; Carson, Jeffrey J L
2011-07-04
Photoacoustic imaging is a non-ionizing imaging modality that provides contrast consistent with optical imaging techniques while the resolution and penetration depth is similar to ultrasound techniques. In a previous publication [Opt. Express 18, 11406 (2010)], a technique was introduced to experimentally acquire the imaging operator for a photoacoustic imaging system. While this was an important foundation for future work, we have recently improved the experimental procedure allowing for a more densely populated imaging operator to be acquired. Subsets of the imaging operator were produced by varying the transducer count as well as the measurement space temporal sampling rate. Examination of the matrix rank and the effect of contributing object space singular vectors to image reconstruction were performed. For a PAI system collecting only limited data projections, matrix rank increased linearly with transducer count and measurement space temporal sampling rate. Image reconstruction using a regularized pseudoinverse of the imaging operator was performed on photoacoustic signals from a point source, line source, and an array of point sources derived from the imaging operator. As expected, image quality increased for each object with increasing transducer count and measurement space temporal sampling rate. Using the same approach, but on experimentally sampled photoacoustic signals from a moving point-like source, acquisition, data transfer, reconstruction and image display took 1.4 s using one laser pulse per 3D frame. With relatively simple hardware improvements to data transfer and computation speed, our current imaging results imply that acquisition and display of 3D photoacoustic images at laser repetition rates of 10Hz is easily achieved.
Trigger Timing Module for SVD2 upgrade at Belle
International Nuclear Information System (INIS)
Chang, M.C.; Gao, Z.W.; Guo, Y.N.; Kawasaki, T.; Ueno, K.; Velikzhanin, Y.S.
2003-01-01
We have developed a Trigger Timing Module (TTM2) for the control and readout electronics (CORE) of the upgraded Silicon Vertex Detector (SVD2) for use in the BELLE experiment. Eleven Trigger Timing Modules located at one VME-crate provide timing and strobe signals for the SVD2 CORE electronics and make communication between SVD2 and Global Decision Logic of the BELLE data acquisition system. The main motivation to make a new TTM design is to avoid glitches
International Nuclear Information System (INIS)
Alcaraz, Raúl; Rieta, José Joaquín
2008-01-01
The proper analysis and characterization of atrial fibrillation (AF) from surface electrocardiographic (ECG) recordings requires to cancel out the ventricular activity (VA), which is composed of the QRS complex and the T wave. Historically, for single-lead ECGs, the averaged beat subtraction (ABS) has been the most widely used technique. However, this method is very sensitive to QRST wave variations and, moreover, high-quality cancelation templates may be difficult to obtain when only short length and single-lead recordings are available. In order to overcome these limitations, a new QRST cancelation method based on adaptive singular value cancelation (ASVC) applied to each single beat is proposed. In addition, an exhaustive study about the optimal set of complexes for better cancelation of every beat is also presented for the first time. The whole study has been carried out with both simulated and real AF signals. For simulated AF, the cancelation performance was evaluated making use of a cross-correlation index and the normalized mean square error (nmse) between the estimated and the original atrial activity (AA). For real AF signals, two additional new parameters were proposed. First, the ventricular residue (VR) index estimated the presence of ventricular activity in the extracted AA. Second, the similarity (S) evaluated how the algorithm preserved the AA segments out of the QRST interval. Results indicated that for simulated AF signals, mean correlation, nmse, VR and S values were 0.945 ± 0.024, 0.332 ± 0.073, 1.552 ± 0.386 and 0.986 ± 0.012, respectively, for the ASVC method and 0.866 ± 0.042, 0.424 ± 0.120, 2.161 ± 0.564 and 0.922 ± 0.051 for ABS. In the case of real signals, the mean VR and S values were 1.725 ± 0.826 and 0.983 ± 0.038, respectively, for ASVC and 3.159 ± 1.097 and 0.951 ± 0.049 for ABS. Thus, ASVC provides a more accurate beat-to-beat ventricular QRST representation than traditional techniques. As a consequence, VA cancelation
Water quality assessment using SVD-based principal component ...
African Journals Online (AJOL)
SVD) of hydrological data was tested for water quality assessment. Using two case studies of waste- and drinking water, PCA via SVD was able to find latent variables which explain 80.8% and 83.7% of the variance, respectively. By means of ...
Integrated ensemble noise-reconstructed empirical mode decomposition for mechanical fault detection
Yuan, Jing; Ji, Feng; Gao, Yuan; Zhu, Jun; Wei, Chenjun; Zhou, Yu
2018-05-01
A new branch of fault detection is utilizing the noise such as enhancing, adding or estimating the noise so as to improve the signal-to-noise ratio (SNR) and extract the fault signatures. Hereinto, ensemble noise-reconstructed empirical mode decomposition (ENEMD) is a novel noise utilization method to ameliorate the mode mixing and denoised the intrinsic mode functions (IMFs). Despite the possibility of superior performance in detecting weak and multiple faults, the method still suffers from the major problems of the user-defined parameter and the powerless capability for a high SNR case. Hence, integrated ensemble noise-reconstructed empirical mode decomposition is proposed to overcome the drawbacks, improved by two noise estimation techniques for different SNRs as well as the noise estimation strategy. Independent from the artificial setup, the noise estimation by the minimax thresholding is improved for a low SNR case, which especially shows an outstanding interpretation for signature enhancement. For approximating the weak noise precisely, the noise estimation by the local reconfiguration using singular value decomposition (SVD) is proposed for a high SNR case, which is particularly powerful for reducing the mode mixing. Thereinto, the sliding window for projecting the phase space is optimally designed by the correlation minimization. Meanwhile, the reasonable singular order for the local reconfiguration to estimate the noise is determined by the inflection point of the increment trend of normalized singular entropy. Furthermore, the noise estimation strategy, i.e. the selection approaches of the two estimation techniques along with the critical case, is developed and discussed for different SNRs by means of the possible noise-only IMF family. The method is validated by the repeatable simulations to demonstrate the synthetical performance and especially confirm the capability of noise estimation. Finally, the method is applied to detect the local wear fault
Mukhopadhyay, V.; Newsom, J. R.
1982-01-01
A stability margin evaluation method in terms of simultaneous gain and phase changes in all loops of a multiloop system is presented. A universal gain-phase margin evaluation diagram is constructed by generalizing an existing method using matrix singular value properties. Using this diagram and computing the minimum singular value of the system return difference matrix over the operating frequency range, regions of guaranteed stability margins can be obtained. Singular values are computed for a wing flutter suppression and a drone lateral attitude control problem. The numerical results indicate that this method predicts quite conservative stability margins. In the second example if the eigenvalue magnitude is used instead of the singular value, as a measure of nearness to singularity, more realistic stability margins are obtained. However, this relaxed measure generally cannot guarantee global stability.
The Belle II SVD data readout system
Energy Technology Data Exchange (ETDEWEB)
Thalmeier, R., E-mail: Richard.Thalmeier@oeaw.ac.at [Institute of High Energy Physics, Austrian Academy of Sciences, 1050 Vienna (Austria); Adamczyk, K. [H. Niewodniczanski Institute of Nuclear Physics, Krakow 31-342 (Poland); Aihara, H. [Department of Physics, University of Tokyo, Tokyo 113-0033 (Japan); Angelini, C. [Dipartimento di Fisica, Universita’ di Pisa, I-56127 Pisa (Italy); INFN Sezione di Pisa, I-56127 Pisa (Italy); Aziz, T.; Babu, V. [Tata Institute of Fundamental Research, Mumbai 400005 (India); Bacher, S. [H. Niewodniczanski Institute of Nuclear Physics, Krakow 31-342 (Poland); Bahinipati, S. [Indian Institute of Technology Bhubaneswar, Satya Nagar (India); Barberio, E.; Baroncelli, Ti.; Baroncelli, To. [School of Physics, University of Melbourne, Melbourne, Victoria 3010 (Australia); Basith, A.K. [Indian Institute of Technology Madras, Chennai 600036 (India); Batignani, G. [Dipartimento di Fisica, Universita’ di Pisa, I-56127 Pisa (Italy); INFN Sezione di Pisa, I-56127 Pisa (Italy); Bauer, A. [Institute of High Energy Physics, Austrian Academy of Sciences, 1050 Vienna (Austria); Behera, P.K. [Indian Institute of Technology Madras, Chennai 600036 (India); Bergauer, T. [Institute of High Energy Physics, Austrian Academy of Sciences, 1050 Vienna (Austria); Bettarini, S. [Dipartimento di Fisica, Universita’ di Pisa, I-56127 Pisa (Italy); INFN Sezione di Pisa, I-56127 Pisa (Italy); Bhuyan, B. [Indian Institute of Technolog y Guwahati, Assam 781039 (India); Bilka, T. [Faculty of Mathematics and Physics, Charles University, 12116 Prague (Czech Republic); Bosi, F. [INFN Sezione di Pisa, I-56127 Pisa (Italy); and others
2017-02-11
The Belle II Experiment at the High Energy Accelerator Research Organization (KEK) in Tsukuba, Japan, will explore the asymmetry between matter and antimatter and search for new physics beyond the standard model. 172 double-sided silicon strip detectors are arranged cylindrically in four layers around the collision point to be part of a system which measures the tracks of the collision products of electrons and positrons. A total of 1748 radiation-hard APV25 chips read out 128 silicon strips each and send the analog signals by time-division multiplexing out of the radiation zone to 48 Flash Analog Digital Converter Modules (FADC). Each of them applies processing to the data; for example, it uses a digital finite impulse response filter to compensate line signal distortions, and it extracts the peak timing and amplitude from a set of several data points for each hit, using a neural network. We present an overview of the SVD data readout system, along with front-end electronics, cabling, power supplies and data processing.
Directory of Open Access Journals (Sweden)
Didier G. Leibovici
2010-10-01
Full Text Available The purpose of this paper is to describe the R package {PTAk and how the spatio-temporal context can be taken into account in the analyses. Essentially PTAk( is a multiway multidimensional method to decompose a multi-entries data-array, seen mathematically as a tensor of any order. This PTAk-modes method proposes a way of generalizing SVD (singular value decomposition, as well as some other well known methods included in the R package, such as PARAFAC or CANDECOMP and the PCAn-modes or Tucker-n model. The example datasets cover different domains with various spatio-temporal characteristics and issues: (i~medical imaging in neuropsychology with a functional MRI (magnetic resonance imaging study, (ii~pharmaceutical research with a pharmacodynamic study with EEG (electro-encephaloegraphic data for a central nervous system (CNS drug, and (iii~geographical information system (GIS with a climatic dataset that characterizes arid and semi-arid variations. All the methods implemented in the R package PTAk also support non-identity metrics, as well as penalizations during the optimization process. As a result of these flexibilities, together with pre-processing facilities, PTAk constitutes a framework for devising extensions of multidimensional methods such ascorrespondence analysis, discriminant analysis, and multidimensional scaling, also enabling spatio-temporal constraints.
Wang, Chengwen; Quan, Long; Zhang, Shijie; Meng, Hongjun; Lan, Yuan
2017-03-01
Hydraulic servomechanism is the typical mechanical/hydraulic double-dynamics coupling system with the high stiffness control and mismatched uncertainties input problems, which hinder direct applications of many advanced control approaches in the hydraulic servo fields. In this paper, by introducing the singular value perturbation theory, the original double-dynamics coupling model of the hydraulic servomechanism was reduced to a integral chain system. So that, the popular ADRC (active disturbance rejection control) technology could be directly applied to the reduced system. In addition, the high stiffness control and mismatched uncertainties input problems are avoided. The validity of the simplified model is analyzed and proven theoretically. The standard linear ADRC algorithm is then developed based on the obtained reduced-order model. Extensive comparative co-simulations and experiments are carried out to illustrate the effectiveness of the proposed method. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.
Li, Dafa
2018-06-01
We construct ℓ -spin-flipping matrices from the coefficient matrices of pure states of n qubits and show that the ℓ -spin-flipping matrices are congruent and unitary congruent whenever two pure states of n qubits are SLOCC and LU equivalent, respectively. The congruence implies the invariance of ranks of the ℓ -spin-flipping matrices under SLOCC and then permits a reduction of SLOCC classification of n qubits to calculation of ranks of the ℓ -spin-flipping matrices. The unitary congruence implies the invariance of singular values of the ℓ -spin-flipping matrices under LU and then permits a reduction of LU classification of n qubits to calculation of singular values of the ℓ -spin-flipping matrices. Furthermore, we show that the invariance of singular values of the ℓ -spin-flipping matrices Ω 1^{(n)} implies the invariance of the concurrence for even n qubits and the invariance of the n-tangle for odd n qubits. Thus, the concurrence and the n-tangle can be used for LU classification and computing the concurrence and the n-tangle only performs additions and multiplications of coefficients of states.
FPGA-Based Online PQD Detection and Classification through DWT, Mathematical Morphology and SVD
Directory of Open Access Journals (Sweden)
Misael Lopez-Ramirez
2018-03-01
Full Text Available Power quality disturbances (PQD in electric distribution systems can be produced by the utilization of non-linear loads or environmental circumstances, causing electrical equipment malfunction and reduction of its useful life. Detecting and classifying different PQDs implies great efforts in planning and structuring the monitoring system. The main disadvantage of most works in the literature is that they treat a limited number of electrical disturbances through personal computer (PC-based computation techniques, which makes it difficult to perform an online PQD classification. In this work, the novel contribution is a methodology for PQD recognition and classification through discrete wavelet transform, mathematical morphology, decomposition of singular values, and statistical analysis. Furthermore, the timely and reliable classification of different disturbances is necessary; hence, a field programmable gate array (FPGA-based integrated circuit is developed to offer a portable hardware processing unit to perform fast, online PQD classification. The obtained numerical and experimental results demonstrate that the proposed method guarantees high effectiveness during online PQD detection and classification of real voltage/current signals.
Radar Measurements of Ocean Surface Waves using Proper Orthogonal Decomposition
2017-03-30
Golinval, 2002, Physical interpretation of the proper orthogonal modes using the singular value decomposition, Journal of Sound and Vibration, 249...complex and contain contributions from the environment (e.g., wind, waves, currents) as well as artifacts associated with electromagnetic (EM) (wave...Although there is no physical basis/ interpretation inherent to the method because it is purely a mathematical tool, there has been an increasing
SVD-Based Evaluation of Multiplexing in Multipinhole SPECT Systems
Directory of Open Access Journals (Sweden)
Aaron K. Jorgensen
2008-01-01
Full Text Available Multipinhole SPECT system design is largely a trial-and-error process. General principles can give system designers a general idea of how a system with certain characteristics will perform. However, the specific performance of any particular system is unknown before the system is tested. The development of an objective evaluation method that is not based on experimentation would facilitate the optimization of multipinhole systems. We derive a figure of merit for prediction of SPECT system performance based on the entire singular value spectrum of the system. This figure of merit contains significantly more information than the condition number of the system, and is therefore more revealing of system performance. This figure is then compared with simulated results of several SPECT systems and is shown to correlate well to the results of the simulations. The proposed figure of merit is useful for predicting system performance, but additional steps could be taken to improve its accuracy and applicability. The limits of the proposed method are discussed, and possible improvements to it are proposed.
Yu, Xuchao; Liang, Wei; Zhang, Laibin; Jin, Hao; Qiu, Jingwei
2016-05-01
During the last decades, leak detection for natural gas pipeline has become one of the paramount concerns of pipeline operators and researchers across the globe. However, acoustic wave method has been proved to be an effective way to identify and localize leakage for gas pipeline. Considering the fact that noises inevitably exist in the acoustic signals collected, noise reduction should be enforced on the signals for subsequent data mining and analysis. Thus, an integrated acoustic noise reduction method based on DTCWT and SVD is proposed in this study. The method is put forward based on the idea that noise reduction strategy should match the characteristics of the noisy signal. According to previous studies, it is known that the energy of acoustic signals collected under leaking condition is mainly concentrated in low-frequency portion (0-100 Hz). And ultralow-frequency component (0-5 Hz), which is taken as the characteristic frequency band in this study, can propagate a relatively longer distance and be captured by sensors. Therefore, in order to filter the noises and to reserve the characteristic frequency band, DTCWT is taken as the core to conduct multilevel decomposition and refining for acoustic signals and SVD is employed to eliminate noises in non-characteristic bands. Both simulation and field experiments show that DTCWT-SVD is an excellent method for acoustic noise reduction. At the end of this study, application in leakage localization shows that it becomes much easier and a little more accurate to estimate the location of leak hole after noise reduction by DTCWT-SVD.
An Implementation and Detailed Analysis of the K-SVD Image Denoising Algorithm
Directory of Open Access Journals (Sweden)
Marc Lebrun
2012-05-01
Full Text Available K-SVD is a signal representation method which, from a set of signals, can derive a dictionary able to approximate each signal with a sparse combination of the atoms. This paper focuses on the K-SVD-based image denoising algorithm. The implementation is described in detail and its parameters are analyzed and varied to come up with a reliable implementation.
Tensor decompositions for the analysis of atomic resolution electron energy loss spectra
Energy Technology Data Exchange (ETDEWEB)
Spiegelberg, Jakob; Rusz, Ján [Department of Physics and Astronomy, Uppsala University, Box 516, S-751 20 Uppsala (Sweden); Pelckmans, Kristiaan [Department of Information Technology, Uppsala University, Box 337, S-751 05 Uppsala (Sweden)
2017-04-15
A selection of tensor decomposition techniques is presented for the detection of weak signals in electron energy loss spectroscopy (EELS) data. The focus of the analysis lies on the correct representation of the simulated spatial structure. An analysis scheme for EEL spectra combining two-dimensional and n-way decomposition methods is proposed. In particular, the performance of robust principal component analysis (ROBPCA), Tucker Decompositions using orthogonality constraints (Multilinear Singular Value Decomposition (MLSVD)) and Tucker decomposition without imposed constraints, canonical polyadic decomposition (CPD) and block term decompositions (BTD) on synthetic as well as experimental data is examined. - Highlights: • A scheme for compression and analysis of EELS or EDX data is proposed. • Several tensor decomposition techniques are presented for BSS on hyperspectral data. • Robust PCA and MLSVD are discussed for denoising of raw data.
Zhang, Min; Zhu, Wusheng; Yun, Wenwei; Wang, Qizhang; Cheng, Maogang; Zhang, Zhizhong; Liu, Xinfeng; Zhou, Xianju; Xu, Gelin
2015-09-15
Maladjustment of matrix metalloproteinases (MMPs) results in cerebral vasculature and blood-brain barrier dysfunction, which is associated with small vessel disease (SVD). This study was to aim at evaluating correlations between matrix metalloproteinase-2 and 9 single nucleotide polymorphisms and the risk of SVD. A total of 178 patients with SVD were enrolled into this study via Nanjing Stroke Registry Program (NSRP) from January 2010 to November 2011. SVD patients were further subtyped as isolated lacunar infarction (ILI, absent or with mild leukoaraiosis) and ischemic leukoaraiosis (ILA, with moderate or severe leukoaraiosis) according to the Fazekas scale. 100 age- and gender-matched individuals from outpatient medical examination were recruited as the control group. The genotypes of MMP-2-1306 T/C and MMP-9-1562 C/T were determined by the TaqMan method. Of 178 SVD patients, 86 and 92 patients were classified as ILI and ILA, respectively. Comparison analysis between SVD patients and controls revealed a significant correlation between SVD and hypertension, as well as a prevalence of hypertension in ILA. Further genotype analysis showed that the frequency of MMP-2-1306 CC genotype was higher in ILA patients than in controls (P=0.009, χ(2) test; P=0.027, the multiple test with Bonferroni correction). Finally, logistic regression analysis with adjustment of age, sex and vascular risk factors showed that the MMP-2-1306 T/C polymorphism was an independent predictor for ILA (OR: 2.605; 95% confidence interval [CI], 1.067-6.364; P=0.036). Our findings suggest that the MMP-2-1306 T/C polymorphism is a direct risk factor for ILA. Copyright © 2015. Published by Elsevier B.V.
Subspace-Based Noise Reduction for Speech Signals via Diagonal and Triangular Matrix Decompositions
DEFF Research Database (Denmark)
Hansen, Per Christian; Jensen, Søren Holdt
We survey the definitions and use of rank-revealing matrix decompositions in single-channel noise reduction algorithms for speech signals. Our algorithms are based on the rank-reduction paradigm and, in particular, signal subspace techniques. The focus is on practical working algorithms, using both...... diagonal (eigenvalue and singular value) decompositions and rank-revealing triangular decompositions (ULV, URV, VSV, ULLV and ULLIV). In addition we show how the subspace-based algorithms can be evaluated and compared by means of simple FIR filter interpretations. The algorithms are illustrated...... with working Matlab code and applications in speech processing....
Decomposition of Space-Variant Blur in Image Deconvolution
Czech Academy of Sciences Publication Activity Database
Šroubek, Filip; Kamenický, Jan; Lu, Y. M.
2016-01-01
Roč. 23, č. 3 (2016), s. 346-350 ISSN 1070-9908 R&D Projects: GA ČR GA13-29225S; GA MŠk 7H14004 Grant - others:GA AV ČR(CZ) M100751201 Institutional support: RVO:67985556 Keywords : space-variant convolution * singular value decomposition * alternating direction method of multipliers Subject RIV: JD - Computer Applications, Robotics Impact factor: 2.528, year: 2016 http://library.utia.cas.cz/separaty/2016/ZOI/sroubek-0456182.pdf
Modeling multipulsing transition in ring cavity lasers with proper orthogonal decomposition
International Nuclear Information System (INIS)
Ding, Edwin; Shlizerman, Eli; Kutz, J. Nathan
2010-01-01
A low-dimensional model is constructed via the proper orthogonal decomposition (POD) to characterize the multipulsing phenomenon in a ring cavity laser mode locked by a saturable absorber. The onset of the multipulsing transition is characterized by an oscillatory state (created by a Hopf bifurcation) that is then itself destabilized to a double-pulse configuration (by a fold bifurcation). A four-mode POD analysis, which uses the principal components, or singular value decomposition modes, of the mode-locked laser, provides a simple analytic framework for a complete characterization of the entire transition process and its associated bifurcations. These findings are in good agreement with the full governing equation.
Cloud detection for MIPAS using singular vector decomposition
Directory of Open Access Journals (Sweden)
J. Hurley
2009-09-01
Full Text Available Satellite-borne high-spectral-resolution limb sounders, such as the Michelson Interferometer for Passive Atmospheric Sounding (MIPAS onboard ENVISAT, provide information on clouds, especially optically thin clouds, which have been difficult to observe in the past. The aim of this work is to develop, implement and test a reliable cloud detection method for infrared spectra measured by MIPAS.
Current MIPAS cloud detection methods used operationally have been developed to detect cloud effective filling more than 30% of the measurement field-of-view (FOV, under geometric and optical considerations – and hence are limited to detecting fairly thick cloud, or large physical extents of thin cloud. In order to resolve thin clouds, a new detection method using Singular Vector Decomposition (SVD is formulated and tested. This new SVD detection method has been applied to a year's worth of MIPAS data, and qualitatively appears to be more sensitive to thin cloud than the current operational method.
Constraint elimination in dynamical systems
Singh, R. P.; Likins, P. W.
1989-01-01
Large space structures (LSSs) and other dynamical systems of current interest are often extremely complex assemblies of rigid and flexible bodies subjected to kinematical constraints. A formulation is presented for the governing equations of constrained multibody systems via the application of singular value decomposition (SVD). The resulting equations of motion are shown to be of minimum dimension.
A multivariate calibration procedure for the tensammetric determination of detergents
Bos, M.
1989-01-01
A multivariate calibration procedure based on singular value decomposition (SVD) and the Ho-Kashyap algorithm is used for the tensammetric determination of the cationic detergents Hyamine 1622, benzalkonium chloride (BACl), N-cetyl-N,N,N-trimethylammonium bromide (CTABr) and mixtures of CTABr and
Tokamak Plasmas : Mirnov coil data analysis for tokamak ADITYA
Indian Academy of Sciences (India)
The spatial and temporal structures of magnetic signal in the tokamak ADITYA is analysed using recently developed singular value decomposition (SVD) technique. The analysis technique is ﬁrst tested with simulated data and then applied to the ADITYA Mirnov coil data to determine the structure of current peturbation as ...
Anti-plagiarism certification be an academic mandate
Digital Repository Service at National Institute of Oceanography (India)
Lakshminarayana, S.
-window of three to eight. Singular Value Decomposition (SVD) method is applied to compute the effects with different sets and keywords. An uniform gradient is noticed in the case of two documents similar in greater extent. In all other cases, it appeared to be a...
Journal of Earth System Science | Indian Academy of Sciences
Indian Academy of Sciences (India)
An attempt has been made to improve the accuracy of predicted rainfall using three different multi-model ensemble (MME) schemes, viz., simple arithmetic mean of models (EM), principal component regression (PCR) and singular value decomposition based multiple linear regressions (SVD). It is found out that among ...
Wavelength of ocean waves and surf beat at duck from array measurements
Digital Repository Service at National Institute of Oceanography (India)
Fernandes, A.A.; Menon, H.B.; Sarma, Y.V.B.; Jog, P.D.; Almeida, A.M.
, North Carolina, USA, has been used. The method used is an extension of the 3-gauge method of Esteva 1976 and 1977 for computing wave direction. Wavelength was also computed using the Singular Value Decomposition (SVD) method of Herbers et al. 1995 from...
Model-independent analysis with BPM correlation matrices
International Nuclear Information System (INIS)
Irwin, J.; Wang, C.X.; Yan, Y.T.; Bane, K.; Cai, Y.; Decker, F.; Minty, M.; Stupakov, G.; Zimmermann, F.
1998-06-01
The authors discuss techniques for Model-Independent Analysis (MIA) of a beamline using correlation matrices of physical variables and Singular Value Decomposition (SVD) of a beamline BPM matrix. The beamline matrix is formed from BPM readings for a large number of pulses. The method has been applied to the Linear Accelerator of the SLAC Linear Collider (SLC)
International Nuclear Information System (INIS)
Tanaka, Yuho; Uruma, Kazunori; Furukawa, Toshihiro; Nakao, Tomoki; Izumi, Kenya; Utsumi, Hiroaki
2017-01-01
This paper deals with an analysis problem for diffusion-ordered NMR spectroscopy (DOSY). DOSY is formulated as a matrix factorization problem of a given observed matrix. In order to solve this problem, a direct exponential curve resolution algorithm (DECRA) is well known. DECRA is based on singular value decomposition; the advantage of this algorithm is that the initial value is not required. However, DECRA requires a long calculating time, depending on the size of the given observed matrix due to the singular value decomposition, and this is a serious problem in practical use. Thus, this paper proposes a new analysis algorithm for DOSY to achieve a short calculating time. In order to solve matrix factorization for DOSY without using singular value decomposition, this paper focuses on the size of the given observed matrix. The observed matrix in DOSY is also a rectangular matrix with more columns than rows, due to limitation of the measuring time; thus, the proposed algorithm transforms the given observed matrix into a small observed matrix. The proposed algorithm applies the eigenvalue decomposition and the difference approximation to the small observed matrix, and the matrix factorization problem for DOSY is solved. The simulation and a data analysis show that the proposed algorithm achieves a lower calculating time than DECRA as well as similar analysis result results to DECRA. (author)
Directory of Open Access Journals (Sweden)
Batakliev Todor
2014-06-01
Full Text Available Catalytic ozone decomposition is of great significance because ozone is a toxic substance commonly found or generated in human environments (aircraft cabins, offices with photocopiers, laser printers, sterilizers. Considerable work has been done on ozone decomposition reported in the literature. This review provides a comprehensive summary of the literature, concentrating on analysis of the physico-chemical properties, synthesis and catalytic decomposition of ozone. This is supplemented by a review on kinetics and catalyst characterization which ties together the previously reported results. Noble metals and oxides of transition metals have been found to be the most active substances for ozone decomposition. The high price of precious metals stimulated the use of metal oxide catalysts and particularly the catalysts based on manganese oxide. It has been determined that the kinetics of ozone decomposition is of first order importance. A mechanism of the reaction of catalytic ozone decomposition is discussed, based on detailed spectroscopic investigations of the catalytic surface, showing the existence of peroxide and superoxide surface intermediates
A Hybrid DWT-SVD Image-Coding System (HDWTSVD for Color Images
Directory of Open Access Journals (Sweden)
Humberto Ochoa
2003-04-01
Full Text Available In this paper, we propose the HDWTSVD system to encode color images. Before encoding, the color components (RGB are transformed into YCbCr. Cb and Cr components are downsampled by a factor of two, both horizontally and vertically, before sending them through the encoder. A criterion based on the average standard deviation of 8x8 subblocks of the Y component is used to choose DWT or SVD for all the components. Standard test images are compressed based on the proposed algorithm.
Caffo, Brian S.; Crainiceanu, Ciprian M.; Verduzco, Guillermo; Joel, Suresh; Mostofsky, Stewart H.; Bassett, Susan Spear; Pekar, James J.
2010-01-01
Functional connectivity is the study of correlations in measured neurophysiological signals. Altered functional connectivity has been shown to be associated with a variety of cognitive and memory impairments and dysfunction, including Alzheimer’s disease. In this manuscript we use a two-stage application of the singular value decomposition to obtain data driven population-level measures of functional connectivity in functional magnetic resonance imaging (fMRI). The method is computationally s...
Chao, T.T.; Sanzolone, R.F.
1992-01-01
Sample decomposition is a fundamental and integral step in the procedure of geochemical analysis. It is often the limiting factor to sample throughput, especially with the recent application of the fast and modern multi-element measurement instrumentation. The complexity of geological materials makes it necessary to choose the sample decomposition technique that is compatible with the specific objective of the analysis. When selecting a decomposition technique, consideration should be given to the chemical and mineralogical characteristics of the sample, elements to be determined, precision and accuracy requirements, sample throughput, technical capability of personnel, and time constraints. This paper addresses these concerns and discusses the attributes and limitations of many techniques of sample decomposition along with examples of their application to geochemical analysis. The chemical properties of reagents as to their function as decomposition agents are also reviewed. The section on acid dissolution techniques addresses the various inorganic acids that are used individually or in combination in both open and closed systems. Fluxes used in sample fusion are discussed. The promising microwave-oven technology and the emerging field of automation are also examined. A section on applications highlights the use of decomposition techniques for the determination of Au, platinum group elements (PGEs), Hg, U, hydride-forming elements, rare earth elements (REEs), and multi-elements in geological materials. Partial dissolution techniques used for geochemical exploration which have been treated in detail elsewhere are not discussed here; nor are fire-assaying for noble metals and decomposition techniques for X-ray fluorescence or nuclear methods be discussed. ?? 1992.
Limited-memory adaptive snapshot selection for proper orthogonal decomposition
Energy Technology Data Exchange (ETDEWEB)
Oxberry, Geoffrey M. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Kostova-Vassilevska, Tanya [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Arrighi, Bill [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Chand, Kyle [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
2015-04-02
Reduced order models are useful for accelerating simulations in many-query contexts, such as optimization, uncertainty quantification, and sensitivity analysis. However, offline training of reduced order models can have prohibitively expensive memory and floating-point operation costs in high-performance computing applications, where memory per core is limited. To overcome this limitation for proper orthogonal decomposition, we propose a novel adaptive selection method for snapshots in time that limits offline training costs by selecting snapshots according an error control mechanism similar to that found in adaptive time-stepping ordinary differential equation solvers. The error estimator used in this work is related to theory bounding the approximation error in time of proper orthogonal decomposition-based reduced order models, and memory usage is minimized by computing the singular value decomposition using a single-pass incremental algorithm. Results for a viscous Burgers’ test problem demonstrate convergence in the limit as the algorithm error tolerances go to zero; in this limit, the full order model is recovered to within discretization error. The resulting method can be used on supercomputers to generate proper orthogonal decomposition-based reduced order models, or as a subroutine within hyperreduction algorithms that require taking snapshots in time, or within greedy algorithms for sampling parameter space.
New inclusion sets for singular values
Directory of Open Access Journals (Sweden)
Jun He
2017-03-01
Full Text Available Abstract In this paper, for a given matrix A = ( a i j ∈ C n × n $A=(a_{ij} \\in\\mathbb{C}^{n\\times n}$ , in terms of r i $r_{i}$ and c i $c_{i}$ , where r i = ∑ j = 1 , j ≠ i n | a i j | $r_{i} = \\sum _{j = 1,j \
Trace Norm Regularized CANDECOMP/PARAFAC Decomposition With Missing Data.
Liu, Yuanyuan; Shang, Fanhua; Jiao, Licheng; Cheng, James; Cheng, Hong
2015-11-01
In recent years, low-rank tensor completion (LRTC) problems have received a significant amount of attention in computer vision, data mining, and signal processing. The existing trace norm minimization algorithms for iteratively solving LRTC problems involve multiple singular value decompositions of very large matrices at each iteration. Therefore, they suffer from high computational cost. In this paper, we propose a novel trace norm regularized CANDECOMP/PARAFAC decomposition (TNCP) method for simultaneous tensor decomposition and completion. We first formulate a factor matrix rank minimization model by deducing the relation between the rank of each factor matrix and the mode- n rank of a tensor. Then, we introduce a tractable relaxation of our rank function, and then achieve a convex combination problem of much smaller-scale matrix trace norm minimization. Finally, we develop an efficient algorithm based on alternating direction method of multipliers to solve our problem. The promising experimental results on synthetic and real-world data validate the effectiveness of our TNCP method. Moreover, TNCP is significantly faster than the state-of-the-art methods and scales to larger problems.
Tomographic reconstruction of tokamak plasma light emission using wavelet-vaguelette decomposition
Schneider, Kai; Nguyen van Yen, Romain; Fedorczak, Nicolas; Brochard, Frederic; Bonhomme, Gerard; Farge, Marie; Monier-Garbet, Pascale
2012-10-01
Images acquired by cameras installed in tokamaks are difficult to interpret because the three-dimensional structure of the plasma is flattened in a non-trivial way. Nevertheless, taking advantage of the slow variation of the fluctuations along magnetic field lines, the optical transformation may be approximated by a generalized Abel transform, for which we proposed in Nguyen van yen et al., Nucl. Fus., 52 (2012) 013005, an inversion technique based on the wavelet-vaguelette decomposition. After validation of the new method using an academic test case and numerical data obtained with the Tokam 2D code, we present an application to an experimental movie obtained in the tokamak Tore Supra. A comparison with a classical regularization technique for ill-posed inverse problems, the singular value decomposition, allows us to assess the efficiency. The superiority of the wavelet-vaguelette technique is reflected in preserving local features, such as blobs and fronts, in the denoised emissivity map.
International Nuclear Information System (INIS)
Nguyen van yen, R.; Farge, M.; Fedorczak, N.; Monier-Garbet, P.; Brochard, F.; Bonhomme, G.; Schneider, K.
2012-01-01
Images acquired by cameras installed in tokamaks are difficult to interpret because the three-dimensional structure of the plasma is flattened in a non-trivial way. Nevertheless, taking advantage of the slow variation of the fluctuations along magnetic field lines, the optical transformation may be approximated by a generalized Abel transform, for which we propose an inversion technique based on the wavelet-vaguelette decomposition. After validation of the new method using an academic test case and numerical data obtained with the Tokam 2D code, we present an application to an experimental movie obtained in the tokamak Tore Supra. A comparison with a classical regularization technique for ill-posed inverse problems, the singular value decomposition, allows us to assess the efficiency. The superiority of the wavelet-vaguelette technique is reflected in preserving local features, such as blobs and fronts, in the denoised emissivity map.
A Robust Color Image Watermarking Scheme Using Entropy and QR Decomposition
Directory of Open Access Journals (Sweden)
L. Laur
2015-12-01
Full Text Available Internet has affected our everyday life drastically. Expansive volumes of information are exchanged over the Internet consistently which causes numerous security concerns. Issues like content identification, document and image security, audience measurement, ownership, copyrights and others can be settled by using digital watermarking. In this work, robust and imperceptible non-blind color image watermarking algorithm is proposed, which benefit from the fact that watermark can be hidden in different color channel which results into further robustness of the proposed technique to attacks. Given method uses some algorithms such as entropy, discrete wavelet transform, Chirp z-transform, orthogonal-triangular decomposition and Singular value decomposition in order to embed the watermark in a color image. Many experiments are performed using well-known signal processing attacks such as histogram equalization, adding noise and compression. Experimental results show that proposed scheme is imperceptible and robust against common signal processing attacks.
Mahrooghy, Majid; Ashraf, Ahmed B.; Daye, Dania; Mies, Carolyn; Rosen, Mark; Feldman, Michael; Kontos, Despina
2014-03-01
We evaluate the prognostic value of sparse representation-based features by applying the K-SVD algorithm on multiparametric kinetic, textural, and morphologic features in breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). K-SVD is an iterative dimensionality reduction method that optimally reduces the initial feature space by updating the dictionary columns jointly with the sparse representation coefficients. Therefore, by using K-SVD, we not only provide sparse representation of the features and condense the information in a few coefficients but also we reduce the dimensionality. The extracted K-SVD features are evaluated by a machine learning algorithm including a logistic regression classifier for the task of classifying high versus low breast cancer recurrence risk as determined by a validated gene expression assay. The features are evaluated using ROC curve analysis and leave one-out cross validation for different sparse representation and dimensionality reduction numbers. Optimal sparse representation is obtained when the number of dictionary elements is 4 (K=4) and maximum non-zero coefficients is 2 (L=2). We compare K-SVD with ANOVA based feature selection for the same prognostic features. The ROC results show that the AUC of the K-SVD based (K=4, L=2), the ANOVA based, and the original features (i.e., no dimensionality reduction) are 0.78, 0.71. and 0.68, respectively. From the results, it can be inferred that by using sparse representation of the originally extracted multi-parametric, high-dimensional data, we can condense the information on a few coefficients with the highest predictive value. In addition, the dimensionality reduction introduced by K-SVD can prevent models from over-fitting.
International Nuclear Information System (INIS)
Irmler, C.; Bauer, A.; Bergauer, T.; Adamczyk, K.; Bacher, S.; Aihara, H.; Angelini, C.; Batignani, G.; Bettarini, S.; Bosi, F.; Aziz, T.; Babu, V.; Bahinipati, S.; Barberio, E.; Baroncelli, Ti.; Baroncelli, To.; Basith, A.K.; Behera, P.K.; Bhuyan, B.; Bilka, T.
2016-01-01
The Belle II Silicon Vertex Detector comprises four layers of double-sided silicon strip detectors (DSSDs), consisting of ladders with two to five sensors each. All sensors are individually read out by APV25 chips with the Origami chip-on-sensor concept for the central DSSDs of the ladders. The chips sit on flexible circuits that are glued on the top of the sensors. This concept allows a low material budget and an efficient cooling of the chips by a single pipe per ladder. We present the construction of the first SVD ladders and results from precision measurements and electrical tests
Invariant object recognition based on the generalized discrete radon transform
Easley, Glenn R.; Colonna, Flavia
2004-04-01
We introduce a method for classifying objects based on special cases of the generalized discrete Radon transform. We adjust the transform and the corresponding ridgelet transform by means of circular shifting and a singular value decomposition (SVD) to obtain a translation, rotation and scaling invariant set of feature vectors. We then use a back-propagation neural network to classify the input feature vectors. We conclude with experimental results and compare these with other invariant recognition methods.
A hybrid numerical method for orbit correction
International Nuclear Information System (INIS)
White, G.; Himel, T.; Shoaee, H.
1997-09-01
The authors describe a simple hybrid numerical method for beam orbit correction in particle accelerators. The method overcomes both degeneracy in the linear system being solved and respects boundaries on the solution. It uses the Singular Value Decomposition (SVD) to find and remove the null-space in the system, followed by a bounded Linear Least Squares analysis of the remaining recast problem. It was developed for correcting orbit and dispersion in the B-factory rings
Optimum steering of photon beam lines in SPEAR
International Nuclear Information System (INIS)
Corbett, W.J.; Fong, B.; Lee, M.; Ziemann, V.
1993-04-01
A common operational requirement for many synchrotron light sources is to maintain steered photon beamlines with minimum corrector strength values. To solve this problem for SPEAR, we employed the Singular Value Decomposition (SVD) matrix-inversion technique to minimize corrector strengths while constraining the photon beamlines to remain on target. The result was a reduction in corrector strengths, yielding increased overhead for the photon-beam position feedback systems
Reweighted Low-Rank Tensor Completion and its Applications in Video Recovery
M., Baburaj; George, Sudhish N.
2016-01-01
This paper focus on recovering multi-dimensional data called tensor from randomly corrupted incomplete observation. Inspired by reweighted $l_1$ norm minimization for sparsity enhancement, this paper proposes a reweighted singular value enhancement scheme to improve tensor low tubular rank in the tensor completion process. An efficient iterative decomposition scheme based on t-SVD is proposed which improves low-rank signal recovery significantly. The effectiveness of the proposed method is es...
Analysis of a spectrum of a positron annihilation half life through inverse problem studies
International Nuclear Information System (INIS)
Monteiro, Roberto Pellacani G.; Viterbo, Vanessa C.; Braga, Joao Pedro; Magalhaes, Wellington F. de; Braga, A.P.
2002-01-01
Inversion of positron annihilation lifetime spectroscopy, based on a neural network Hopfield model and singular value decomposition (SVD) associated to Tikhonov regularization is presented in this work. From a previous reported density function for lysozyme in water a simulated spectrum, without spectrometer resolution effects, was generated. The precision of the inverted density function was analyzed taking into account the number of neurons and the learning time of the Hopfield network and the maximum position and areas for the spectral peaks in the SVD method considering noise and noiseless data. A fair agreement was obtained when comparing the inversion results with direct exact results. (author)
Directory of Open Access Journals (Sweden)
Hugo Lara
2014-12-01
Full Text Available The matrix completion problem (MC has been approximated by using the nuclear norm relaxation. Some algorithms based on this strategy require the computationally expensive singular value decomposition (SVD at each iteration. One way to avoid SVD calculations is to use alternating methods, which pursue the completion through matrix factorization with a low rank condition. In this work an augmented Lagrangean-type alternating algorithm is proposed. The new algorithm uses duality information to define the iterations, in contrast to the solely primal LMaFit algorithm, which employs a Successive Over Relaxation scheme. The convergence result is studied. Some numerical experiments are given to compare numerical performance of both proposals.
Spectral Decomposition Algorithm (SDA)
National Aeronautics and Space Administration — Spectral Decomposition Algorithm (SDA) is an unsupervised feature extraction technique similar to PCA that was developed to better distinguish spectral features in...
Thermal decomposition of pyrite
International Nuclear Information System (INIS)
Music, S.; Ristic, M.; Popovic, S.
1992-01-01
Thermal decomposition of natural pyrite (cubic, FeS 2 ) has been investigated using X-ray diffraction and 57 Fe Moessbauer spectroscopy. X-ray diffraction analysis of pyrite ore from different sources showed the presence of associated minerals, such as quartz, szomolnokite, stilbite or stellerite, micas and hematite. Hematite, maghemite and pyrrhotite were detected as thermal decomposition products of natural pyrite. The phase composition of the thermal decomposition products depends on the terature, time of heating and starting size of pyrite chrystals. Hematite is the end product of the thermal decomposition of natural pyrite. (author) 24 refs.; 6 figs.; 2 tabs
Multiresolution signal decomposition schemes
J. Goutsias (John); H.J.A.M. Heijmans (Henk)
1998-01-01
textabstract[PNA-R9810] Interest in multiresolution techniques for signal processing and analysis is increasing steadily. An important instance of such a technique is the so-called pyramid decomposition scheme. This report proposes a general axiomatic pyramid decomposition scheme for signal analysis
Decomposition of Sodium Tetraphenylborate
International Nuclear Information System (INIS)
Barnes, M.J.
1998-01-01
The chemical decomposition of aqueous alkaline solutions of sodium tetraphenylborate (NaTPB) has been investigated. The focus of the investigation is on the determination of additives and/or variables which influence NaTBP decomposition. This document describes work aimed at providing better understanding into the relationship of copper (II), solution temperature, and solution pH to NaTPB stability
Azimuthal decomposition of optical modes
CSIR Research Space (South Africa)
Dudley, Angela L
2012-07-01
Full Text Available This presentation analyses the azimuthal decomposition of optical modes. Decomposition of azimuthal modes need two steps, namely generation and decomposition. An azimuthally-varying phase (bounded by a ring-slit) placed in the spatial frequency...
Cellular decomposition in vikalloys
International Nuclear Information System (INIS)
Belyatskaya, I.S.; Vintajkin, E.Z.; Georgieva, I.Ya.; Golikov, V.A.; Udovenko, V.A.
1981-01-01
Austenite decomposition in Fe-Co-V and Fe-Co-V-Ni alloys at 475-600 deg C is investigated. The cellular decomposition in ternary alloys results in the formation of bcc (ordered) and fcc structures, and in quaternary alloys - bcc (ordered) and 12R structures. The cellular 12R structure results from the emergence of stacking faults in the fcc lattice with irregular spacing in four layers. The cellular decomposition results in a high-dispersion structure and magnetic properties approaching the level of well-known vikalloys [ru
Daverman, Robert J
2007-01-01
Decomposition theory studies decompositions, or partitions, of manifolds into simple pieces, usually cell-like sets. Since its inception in 1929, the subject has become an important tool in geometric topology. The main goal of the book is to help students interested in geometric topology to bridge the gap between entry-level graduate courses and research at the frontier as well as to demonstrate interrelations of decomposition theory with other parts of geometric topology. With numerous exercises and problems, many of them quite challenging, the book continues to be strongly recommended to eve
Sajjad, Muhammad; Mehmood, Irfan; Baik, Sung Wook
2015-01-01
Image super-resolution (SR) plays a vital role in medical imaging that allows a more efficient and effective diagnosis process. Usually, diagnosing is difficult and inaccurate from low-resolution (LR) and noisy images. Resolution enhancement through conventional interpolation methods strongly affects the precision of consequent processing steps, such as segmentation and registration. Therefore, we propose an efficient sparse coded image SR reconstruction technique using a trained dictionary. We apply a simple and efficient regularized version of orthogonal matching pursuit (ROMP) to seek the coefficients of sparse representation. ROMP has the transparency and greediness of OMP and the robustness of the L1-minization that enhance the dictionary learning process to capture feature descriptors such as oriented edges and contours from complex images like brain MRIs. The sparse coding part of the K-SVD dictionary training procedure is modified by substituting OMP with ROMP. The dictionary update stage allows simultaneously updating an arbitrary number of atoms and vectors of sparse coefficients. In SR reconstruction, ROMP is used to determine the vector of sparse coefficients for the underlying patch. The recovered representations are then applied to the trained dictionary, and finally, an optimization leads to high-resolution output of high-quality. Experimental results demonstrate that the super-resolution reconstruction quality of the proposed scheme is comparatively better than other state-of-the-art schemes.
Photochemical decomposition of catecholamines
International Nuclear Information System (INIS)
Mol, N.J. de; Henegouwen, G.M.J.B. van; Gerritsma, K.W.
1979-01-01
During photochemical decomposition (lambda=254 nm) adrenaline, isoprenaline and noradrenaline in aqueous solution were converted to the corresponding aminochrome for 65, 56 and 35% respectively. In determining this conversion, photochemical instability of the aminochromes was taken into account. Irradiations were performed in such dilute solutions that the neglect of the inner filter effect is permissible. Furthermore, quantum yields for the decomposition of the aminochromes in aqueous solution are given. (Author)
Institute of Scientific and Technical Information of China (English)
无
2011-01-01
[Objective] The aim was to study the relationship between spring precipitation anomaly in Northwest China and sea surface temperature anomaly (SSTA) in Key region in recent 50 years. [Method] Based on monthly average precipitation in Northwest China and global monthly sea surface temperature (SST) grid data, the effects of SSTA in equatorial central and eastern Pacific on spring precipitation in Northwest China were discussed by means of correlation and SVD analysis. [Result] For spring precipitation in Nor...
Linear analysis of rotationally invariant, radially variant tomographic imaging systems
International Nuclear Information System (INIS)
Huesmann, R.H.
1990-01-01
This paper describes a method to analyze the linear imaging characteristics of rotationally invariant, radially variant tomographic imaging systems using singular value decomposition (SVD). When the projection measurements from such a system are assumed to be samples from independent and identically distributed multi-normal random variables, the best estimate of the emission intensity is given by the unweighted least squares estimator. The noise amplification of this estimator is inversely proportional to the singular values of the normal matrix used to model projection and backprojection. After choosing an acceptable noise amplification, the new method can determine the number of parameters and hence the number of pixels that should be estimated from data acquired from an existing system with a fixed number of angles and projection bins. Conversely, for the design of a new system, the number of angles and projection bins necessary for a given number of pixels and noise amplification can be determined. In general, computing the SVD of the projection normal matrix has cubic computational complexity. However, the projection normal matrix for this class of rotationally invariant, radially variant systems has a block circulant form. A fast parallel algorithm to compute the SVD of this block circulant matrix makes the singular value analysis practical by asymptotically reducing the computation complexity of the method by a multiplicative factor equal to the number of angles squared
Decomposing Nekrasov decomposition
Energy Technology Data Exchange (ETDEWEB)
Morozov, A. [ITEP,25 Bolshaya Cheremushkinskaya, Moscow, 117218 (Russian Federation); Institute for Information Transmission Problems,19-1 Bolshoy Karetniy, Moscow, 127051 (Russian Federation); National Research Nuclear University MEPhI,31 Kashirskoe highway, Moscow, 115409 (Russian Federation); Zenkevich, Y. [ITEP,25 Bolshaya Cheremushkinskaya, Moscow, 117218 (Russian Federation); National Research Nuclear University MEPhI,31 Kashirskoe highway, Moscow, 115409 (Russian Federation); Institute for Nuclear Research of Russian Academy of Sciences,6a Prospekt 60-letiya Oktyabrya, Moscow, 117312 (Russian Federation)
2016-02-16
AGT relations imply that the four-point conformal block admits a decomposition into a sum over pairs of Young diagrams of essentially rational Nekrasov functions — this is immediately seen when conformal block is represented in the form of a matrix model. However, the q-deformation of the same block has a deeper decomposition — into a sum over a quadruple of Young diagrams of a product of four topological vertices. We analyze the interplay between these two decompositions, their properties and their generalization to multi-point conformal blocks. In the latter case we explain how Dotsenko-Fateev all-with-all (star) pair “interaction” is reduced to the quiver model nearest-neighbor (chain) one. We give new identities for q-Selberg averages of pairs of generalized Macdonald polynomials. We also translate the slicing invariance of refined topological strings into the language of conformal blocks and interpret it as abelianization of generalized Macdonald polynomials.
Decomposing Nekrasov decomposition
International Nuclear Information System (INIS)
Morozov, A.; Zenkevich, Y.
2016-01-01
AGT relations imply that the four-point conformal block admits a decomposition into a sum over pairs of Young diagrams of essentially rational Nekrasov functions — this is immediately seen when conformal block is represented in the form of a matrix model. However, the q-deformation of the same block has a deeper decomposition — into a sum over a quadruple of Young diagrams of a product of four topological vertices. We analyze the interplay between these two decompositions, their properties and their generalization to multi-point conformal blocks. In the latter case we explain how Dotsenko-Fateev all-with-all (star) pair “interaction” is reduced to the quiver model nearest-neighbor (chain) one. We give new identities for q-Selberg averages of pairs of generalized Macdonald polynomials. We also translate the slicing invariance of refined topological strings into the language of conformal blocks and interpret it as abelianization of generalized Macdonald polynomials.
Symmetric Tensor Decomposition
DEFF Research Database (Denmark)
Brachat, Jerome; Comon, Pierre; Mourrain, Bernard
2010-01-01
We present an algorithm for decomposing a symmetric tensor, of dimension n and order d, as a sum of rank-1 symmetric tensors, extending the algorithm of Sylvester devised in 1886 for binary forms. We recall the correspondence between the decomposition of a homogeneous polynomial in n variables...... of polynomial equations of small degree in non-generic cases. We propose a new algorithm for symmetric tensor decomposition, based on this characterization and on linear algebra computations with Hankel matrices. The impact of this contribution is two-fold. First it permits an efficient computation...... of the decomposition of any tensor of sub-generic rank, as opposed to widely used iterative algorithms with unproved global convergence (e.g. Alternate Least Squares or gradient descents). Second, it gives tools for understanding uniqueness conditions and for detecting the rank....
International Nuclear Information System (INIS)
Macasek, F.; Buriova, E.
2004-01-01
In this presentation authors present the results of analysis of decomposition products of [ 18 ]fluorodexyglucose. It is concluded that the coupling of liquid chromatography - mass spectrometry with electrospray ionisation is a suitable tool for quantitative analysis of FDG radiopharmaceutical, i.e. assay of basic components (FDG, glucose), impurities (Kryptofix) and decomposition products (gluconic and glucuronic acids etc.); 2-[ 18 F]fluoro-deoxyglucose (FDG) is sufficiently stable and resistant towards autoradiolysis; the content of radiochemical impurities (2-[ 18 F]fluoro-gluconic and 2-[ 18 F]fluoro-glucuronic acids in expired FDG did not exceed 1%
Extreme Computing for Extreme Adaptive Optics: the Key to Finding Life Outside our Solar System
Ltaief, Hatem; Sukkari, Dalal; Guyon, Olivier; Keyes, David E.
2018-01-01
The real-time correction of telescopic images in the search for exoplanets is highly sensitive to atmospheric aberrations. The pseudo- inverse algorithm is an efficient mathematical method to filter out these turbulences. We introduce a new partial singular value decomposition (SVD) algorithm based on QR-based Diagonally Weighted Halley (QDWH) iteration for the pseudo-inverse method of adaptive optics. The QDWH partial SVD algorithm selectively calculates the most significant singular values and their corresponding singular vectors. We develop a high performance implementation and demonstrate the numerical robustness of the QDWH-based partial SVD method. We also perform a benchmarking campaign on various generations of GPU hardware accelerators and compare against the state-of-the-art SVD implementation SGESDD from the MAGMA library. Numerical accuracy and performance results are reported using synthetic and real observational datasets from the Subaru telescope. Our implementation outperforms SGESDD by up to fivefold and fourfold performance speedups on ill-conditioned synthetic matrices and real observational datasets, respectively. The pseudo-inverse simulation code will be deployed on-sky for the Subaru telescope during observation nights scheduled early 2018.
Plutonium isotopic determination from gamma-ray spectra
International Nuclear Information System (INIS)
Skourikhine, A.N.; Strittmatter, R.B.; Zardecki, A.
1998-01-01
The use of low- and medium-resolution room-temperature detectors for the nondestructive assay of nuclear materials has widespread applications to the safeguarding of nuclear materials. The challenge to using these detectors is the inherent difficulty of the spectral analysis to determine the amount of specific nuclear materials in the measured samples. This is especially true for extracting plutonium isotopic content from low- and medium-resolution spectral lines that are not well resolved. In this paper, neural networks trained by stochastic and singular value decomposition algorithms are applied to retrieve the plutonium isotopic content from a simulated NaI spectra. The simulated sample consists of isotopes 238 Pu, 239 Pu, 240 Pu, 241 Pu, 242 Pu, and 241 Am. It is demonstrated that the neutral network optimized by singular value decomposition (SVD) and stochastic training algorithms is capable of estimating plutonium content consistently resulting in an average error much smaller than the error previously reported
Nuclear power plant sensor fault detection using singular value
Indian Academy of Sciences (India)
In a nuclear power plant, periodic sensor calibration is necessary to ensure the correctness of measurements. Those sensors which have gone out of calibration can lead to malfunction of the plant, possibly causing a loss in revenue or damage to equipment. Continuous sensor status monitoring is desirable to assure ...
Nuclear power plant sensor fault detection using singular value ...
Indian Academy of Sciences (India)
Shyamapada Mandal
2017-07-27
Jul 27, 2017 ... A nuclear power plant (NPP) has a large number of sensors to monitor different ... mathematical and systematic characteristics of the pro- cess. In these ..... avoid many problems associated with manual calibration of sensors.
Singular values of the Rogers-Ramanujan continued fraction
Gee, A.C.P.; Honsbeek, M
1999-01-01
Let $z\\in\\C$ be imaginary quadratic in the upper half plane.Then the Rogers-Ramanujan continued fraction evaluated at $q=e^{2\\pi i z}$ is contained in a class field of $\\Q(z)$. Ramanujan showed that for certain values of $z$, one can write these continued fractions as nested radicals. We use the
International Nuclear Information System (INIS)
Ikeda, H.; Matsuda, T.
1997-01-01
We explored wide-pitch ohmic-side structures for the BELLE SVD, where we proposed a field-plate structure combined with narrow p-barriers in between the readout electrodes of 90, 113, 180, and 226 μm-pitch detectors. The effect of the p-barriers was studied with a numerical model to trace the carrier trajectories. The charge collection and sharing properties were examined in practice for prototype small-size detectors with an IR pulse shining from either the junction side or the ohmic side. The channel separation capabilities were also shown to be appropriate under nominal operation conditions. (orig.)
Inverse scale space decomposition
DEFF Research Database (Denmark)
Schmidt, Marie Foged; Benning, Martin; Schönlieb, Carola-Bibiane
2018-01-01
We investigate the inverse scale space flow as a decomposition method for decomposing data into generalised singular vectors. We show that the inverse scale space flow, based on convex and even and positively one-homogeneous regularisation functionals, can decompose data represented...... by the application of a forward operator to a linear combination of generalised singular vectors into its individual singular vectors. We verify that for this decomposition to hold true, two additional conditions on the singular vectors are sufficient: orthogonality in the data space and inclusion of partial sums...... of the subgradients of the singular vectors in the subdifferential of the regularisation functional at zero. We also address the converse question of when the inverse scale space flow returns a generalised singular vector given that the initial data is arbitrary (and therefore not necessarily in the range...
Cacciatori, Sergio L; Marrani, Alessio
2013-01-01
By exploiting a "mixed" non-symmetric Freudenthal-Rozenfeld-Tits magic square, two types of coset decompositions are analyzed for the non-compact special K\\"ahler symmetric rank-3 coset E7(-25)/[(E6(-78) x U(1))/Z_3], occurring in supergravity as the vector multiplets' scalar manifold in N=2, D=4 exceptional Maxwell-Einstein theory. The first decomposition exhibits maximal manifest covariance, whereas the second (triality-symmetric) one is of Iwasawa type, with maximal SO(8) covariance. Generalizations to conformal non-compact, real forms of non-degenerate, simple groups "of type E7" are presented for both classes of coset parametrizations, and relations to rank-3 simple Euclidean Jordan algebras and normed trialities over division algebras are also discussed.
Directory of Open Access Journals (Sweden)
Ibgtc Bowala
2017-06-01
Full Text Available With the rapid growth of financial markets, analyzers are paying more attention on predictions. Stock data are time series data, with huge amounts. Feasible solution for handling the increasing amount of data is to use a cluster for parallel processing, and Hadoop parallel computing platform is a typical representative. There are various statistical models for forecasting time series data, but accurate clusters are a pre-requirement. Clustering analysis for time series data is one of the main methods for mining time series data for many other analysis processes. However, general clustering algorithms cannot perform clustering for time series data because series data has a special structure and a high dimensionality has highly co-related values due to high noise level. A novel model for time series clustering is presented using BIRCH, based on piecewise SVD, leading to a novel dimension reduction approach. Highly co-related features are handled using SVD with a novel approach for dimensionality reduction in order to keep co-related behavior optimal and then use BIRCH for clustering. The algorithm is a novel model that can handle massive time series data. Finally, this new model is successfully applied to real stock time series data of Yahoo finance with satisfactory results.
Energy Technology Data Exchange (ETDEWEB)
Peng, Bo [William R. Wiley Environmental; Kowalski, Karol [William R. Wiley Environmental
2017-08-11
The representation and storage of two-electron integral tensors are vital in large- scale applications of accurate electronic structure methods. Low-rank representation and efficient storage strategy of integral tensors can significantly reduce the numerical overhead and consequently time-to-solution of these methods. In this paper, by combining pivoted incomplete Cholesky decomposition (CD) with a follow-up truncated singular vector decomposition (SVD), we develop a decomposition strategy to approximately represent the two-electron integral tensor in terms of low-rank vectors. A systematic benchmark test on a series of 1-D, 2-D, and 3-D carbon-hydrogen systems demonstrates high efficiency and scalability of the compound two-step decomposition of the two-electron integral tensor in our implementation. For the size of atomic basis set N_b ranging from ~ 100 up to ~ 2, 000, the observed numerical scaling of our implementation shows O(N_b^{2.5~3}) versus O(N_b^{3~4}) of single CD in most of other implementations. More importantly, this decomposition strategy can significantly reduce the storage requirement of the atomic-orbital (AO) two-electron integral tensor from O(N_b^4) to O(N_b^2 log_{10}(N_b)) with moderate decomposition thresholds. The accuracy tests have been performed using ground- and excited-state formulations of coupled- cluster formalism employing single and double excitations (CCSD) on several bench- mark systems including the C_{60} molecule described by nearly 1,400 basis functions. The results show that the decomposition thresholds can be generally set to 10^{-4} to 10^{-3} to give acceptable compromise between efficiency and accuracy.
Clustering via Kernel Decomposition
DEFF Research Database (Denmark)
Have, Anna Szynkowiak; Girolami, Mark A.; Larsen, Jan
2006-01-01
Methods for spectral clustering have been proposed recently which rely on the eigenvalue decomposition of an affinity matrix. In this work it is proposed that the affinity matrix is created based on the elements of a non-parametric density estimator. This matrix is then decomposed to obtain...... posterior probabilities of class membership using an appropriate form of nonnegative matrix factorization. The troublesome selection of hyperparameters such as kernel width and number of clusters can be obtained using standard cross-validation methods as is demonstrated on a number of diverse data sets....
Danburite decomposition by sulfuric acid
International Nuclear Information System (INIS)
Mirsaidov, U.; Mamatov, E.D.; Ashurov, N.A.
2011-01-01
Present article is devoted to decomposition of danburite of Ak-Arkhar Deposit of Tajikistan by sulfuric acid. The process of decomposition of danburite concentrate by sulfuric acid was studied. The chemical nature of decomposition process of boron containing ore was determined. The influence of temperature on the rate of extraction of boron and iron oxides was defined. The dependence of decomposition of boron and iron oxides on process duration, dosage of H 2 SO 4 , acid concentration and size of danburite particles was determined. The kinetics of danburite decomposition by sulfuric acid was studied as well. The apparent activation energy of the process of danburite decomposition by sulfuric acid was calculated. The flowsheet of danburite processing by sulfuric acid was elaborated.
Thermal decomposition of lutetium propionate
DEFF Research Database (Denmark)
Grivel, Jean-Claude
2010-01-01
The thermal decomposition of lutetium(III) propionate monohydrate (Lu(C2H5CO2)3·H2O) in argon was studied by means of thermogravimetry, differential thermal analysis, IR-spectroscopy and X-ray diffraction. Dehydration takes place around 90 °C. It is followed by the decomposition of the anhydrous...... °C. Full conversion to Lu2O3 is achieved at about 1000 °C. Whereas the temperatures and solid reaction products of the first two decomposition steps are similar to those previously reported for the thermal decomposition of lanthanum(III) propionate monohydrate, the final decomposition...... of the oxycarbonate to the rare-earth oxide proceeds in a different way, which is here reminiscent of the thermal decomposition path of Lu(C3H5O2)·2CO(NH2)2·2H2O...
International Nuclear Information System (INIS)
LeBlanc, G.; Corbett, W.J.
1997-01-01
The response matrix, consisting of the closed orbit change at each beam position monitor (BPM) due to corrector magnet excitations, was measured and analyzed in order to calibrate a linear optics model of SPEAR. The model calibration was accomplished by varying model parameters to minimize the chi-square difference between the measured and the model response matrices. The singular value decomposition (SVD) matrix inversion method was used to solve the simultaneous equations. The calibrated model was then used to calculate corrections to the operational lattice. The results of the calibration and correction procedures are presented
[Surface electromyography signal classification using gray system theory].
Xie, Hongbo; Ma, Congbin; Wang, Zhizhong; Huang, Hai
2004-12-01
A new method based on gray correlation was introduced to improve the identification rate in artificial limb. The electromyography (EMG) signal was first transformed into time-frequency domain by wavelet transform. Singular value decomposition (SVD) was then used to extract feature vector from the wavelet coefficient for pattern recognition. The decision was made according to the maximum gray correlation coefficient. Compared with neural network recognition, this robust method has an almost equivalent recognition rate but much lower computation costs and less training samples.
Yang, Yi-Bo; Chen, Ying; Draper, Terrence; Liang, Jian; Liu, Keh-Fei
2018-03-01
We report the results on the proton mass decomposition and also on the related quark and glue momentum fractions. The results are based on overlap valence fermions on four ensembles of Nf = 2 + 1 DWF configurations with three lattice spacings and volumes, and several pion masses including the physical pion mass. With 1-loop pertur-bative calculation and proper normalization of the glue operator, we find that the u, d, and s quark masses contribute 9(2)% to the proton mass. The quark energy and glue field energy contribute 31(5)% and 37(5)% respectively in the MS scheme at µ = 2 GeV. The trace anomaly gives the remaining 23(1)% contribution. The u, d, s and glue momentum fractions in the MS scheme are consistent with the global analysis at µ = 2 GeV.
Erbium hydride decomposition kinetics.
Energy Technology Data Exchange (ETDEWEB)
Ferrizz, Robert Matthew
2006-11-01
Thermal desorption spectroscopy (TDS) is used to study the decomposition kinetics of erbium hydride thin films. The TDS results presented in this report are analyzed quantitatively using Redhead's method to yield kinetic parameters (E{sub A} {approx} 54.2 kcal/mol), which are then utilized to predict hydrogen outgassing in vacuum for a variety of thermal treatments. Interestingly, it was found that the activation energy for desorption can vary by more than 7 kcal/mol (0.30 eV) for seemingly similar samples. In addition, small amounts of less-stable hydrogen were observed for all erbium dihydride films. A detailed explanation of several approaches for analyzing thermal desorption spectra to obtain kinetic information is included as an appendix.
International Nuclear Information System (INIS)
Chen Xiangsong; Sun Weimin; Wang Fan; Goldman, T.
2011-01-01
We analyze the problem of spin decomposition for an interacting system from a natural perspective of constructing angular-momentum eigenstates. We split, from the total angular-momentum operator, a proper part which can be separately conserved for a stationary state. This part commutes with the total Hamiltonian and thus specifies the quantum angular momentum. We first show how this can be done in a gauge-dependent way, by seeking a specific gauge in which part of the total angular-momentum operator vanishes identically. We then construct a gauge-invariant operator with the desired property. Our analysis clarifies what is the most pertinent choice among the various proposals for decomposing the nucleon spin. A similar analysis is performed for extracting a proper part from the total Hamiltonian to construct energy eigenstates.
Luzanov, A V
2008-09-07
The Wigner function for the pure quantum states is used as an integral kernel of the non-Hermitian operator K, to which the standard singular value decomposition (SVD) is applied. It provides a set of the squared singular values treated as probabilities of the individual phase-space processes, the latter being described by eigenfunctions of KK(+) (for coordinate variables) and K(+)K (for momentum variables). Such a SVD representation is employed to obviate the well-known difficulties in the definition of the phase-space entropy measures in terms of the Wigner function that usually allows negative values. In particular, the new measures of nonclassicality are constructed in the form that automatically satisfies additivity for systems composed of noninteracting parts. Furthermore, the emphasis is given on the geometrical interpretation of the full entropy measure as the effective phase-space volume in the Wigner picture of quantum mechanics. The approach is exemplified by considering some generic vibrational systems. Specifically, for eigenstates of the harmonic oscillator and a superposition of coherent states, the singular value spectrum is evaluated analytically. Numerical computations are given for the nonlinear problems (the Morse and double well oscillators, and the Henon-Heiles system). We also discuss the difficulties in implementation of a similar technique for electronic problems.
Decomposition methods for unsupervised learning
DEFF Research Database (Denmark)
Mørup, Morten
2008-01-01
This thesis presents the application and development of decomposition methods for Unsupervised Learning. It covers topics from classical factor analysis based decomposition and its variants such as Independent Component Analysis, Non-negative Matrix Factorization and Sparse Coding...... methods and clustering problems is derived both in terms of classical point clustering but also in terms of community detection in complex networks. A guiding principle throughout this thesis is the principle of parsimony. Hence, the goal of Unsupervised Learning is here posed as striving for simplicity...... in the decompositions. Thus, it is demonstrated how a wide range of decomposition methods explicitly or implicitly strive to attain this goal. Applications of the derived decompositions are given ranging from multi-media analysis of image and sound data, analysis of biomedical data such as electroencephalography...
Allen, Robert C; John, Mallory G; Rutan, Sarah C; Filgueira, Marcelo R; Carr, Peter W
2012-09-07
A singular value decomposition-based background correction (SVD-BC) technique is proposed for the reduction of background contributions in online comprehensive two-dimensional liquid chromatography (LC×LC) data. The SVD-BC technique was compared to simply subtracting a blank chromatogram from a sample chromatogram and to a previously reported background correction technique for one dimensional chromatography, which uses an asymmetric weighted least squares (AWLS) approach. AWLS was the only background correction technique to completely remove the background artifacts from the samples as evaluated by visual inspection. However, the SVD-BC technique greatly reduced or eliminated the background artifacts as well and preserved the peak intensity better than AWLS. The loss in peak intensity by AWLS resulted in lower peak counts at the detection thresholds established using standards samples. However, the SVD-BC technique was found to introduce noise which led to detection of false peaks at the lower detection thresholds. As a result, the AWLS technique gave more precise peak counts than the SVD-BC technique, particularly at the lower detection thresholds. While the AWLS technique resulted in more consistent percent residual standard deviation values, a statistical improvement in peak quantification after background correction was not found regardless of the background correction technique used. Copyright © 2012 Elsevier B.V. All rights reserved.
Danburite decomposition by hydrochloric acid
International Nuclear Information System (INIS)
Mamatov, E.D.; Ashurov, N.A.; Mirsaidov, U.
2011-01-01
Present article is devoted to decomposition of danburite of Ak-Arkhar Deposit of Tajikistan by hydrochloric acid. The interaction of boron containing ores of Ak-Arkhar Deposit of Tajikistan with mineral acids, including hydrochloric acid was studied. The optimal conditions of extraction of valuable components from danburite composition were determined. The chemical composition of danburite of Ak-Arkhar Deposit was determined as well. The kinetics of decomposition of calcined danburite by hydrochloric acid was studied. The apparent activation energy of the process of danburite decomposition by hydrochloric acid was calculated.
AUTONOMOUS GAUSSIAN DECOMPOSITION
Energy Technology Data Exchange (ETDEWEB)
Lindner, Robert R.; Vera-Ciro, Carlos; Murray, Claire E.; Stanimirović, Snežana; Babler, Brian [Department of Astronomy, University of Wisconsin, 475 North Charter Street, Madison, WI 53706 (United States); Heiles, Carl [Radio Astronomy Lab, UC Berkeley, 601 Campbell Hall, Berkeley, CA 94720 (United States); Hennebelle, Patrick [Laboratoire AIM, Paris-Saclay, CEA/IRFU/SAp-CNRS-Université Paris Diderot, F-91191 Gif-sur Yvette Cedex (France); Goss, W. M. [National Radio Astronomy Observatory, P.O. Box O, 1003 Lopezville, Socorro, NM 87801 (United States); Dickey, John, E-mail: rlindner@astro.wisc.edu [University of Tasmania, School of Maths and Physics, Private Bag 37, Hobart, TAS 7001 (Australia)
2015-04-15
We present a new algorithm, named Autonomous Gaussian Decomposition (AGD), for automatically decomposing spectra into Gaussian components. AGD uses derivative spectroscopy and machine learning to provide optimized guesses for the number of Gaussian components in the data, and also their locations, widths, and amplitudes. We test AGD and find that it produces results comparable to human-derived solutions on 21 cm absorption spectra from the 21 cm SPectral line Observations of Neutral Gas with the EVLA (21-SPONGE) survey. We use AGD with Monte Carlo methods to derive the H i line completeness as a function of peak optical depth and velocity width for the 21-SPONGE data, and also show that the results of AGD are stable against varying observational noise intensity. The autonomy and computational efficiency of the method over traditional manual Gaussian fits allow for truly unbiased comparisons between observations and simulations, and for the ability to scale up and interpret the very large data volumes from the upcoming Square Kilometer Array and pathfinder telescopes.
AUTONOMOUS GAUSSIAN DECOMPOSITION
International Nuclear Information System (INIS)
Lindner, Robert R.; Vera-Ciro, Carlos; Murray, Claire E.; Stanimirović, Snežana; Babler, Brian; Heiles, Carl; Hennebelle, Patrick; Goss, W. M.; Dickey, John
2015-01-01
We present a new algorithm, named Autonomous Gaussian Decomposition (AGD), for automatically decomposing spectra into Gaussian components. AGD uses derivative spectroscopy and machine learning to provide optimized guesses for the number of Gaussian components in the data, and also their locations, widths, and amplitudes. We test AGD and find that it produces results comparable to human-derived solutions on 21 cm absorption spectra from the 21 cm SPectral line Observations of Neutral Gas with the EVLA (21-SPONGE) survey. We use AGD with Monte Carlo methods to derive the H i line completeness as a function of peak optical depth and velocity width for the 21-SPONGE data, and also show that the results of AGD are stable against varying observational noise intensity. The autonomy and computational efficiency of the method over traditional manual Gaussian fits allow for truly unbiased comparisons between observations and simulations, and for the ability to scale up and interpret the very large data volumes from the upcoming Square Kilometer Array and pathfinder telescopes
NRSA enzyme decomposition model data
U.S. Environmental Protection Agency — Microbial enzyme activities measured at more than 2000 US streams and rivers. These enzyme data were then used to predict organic matter decomposition and microbial...
Some nonlinear space decomposition algorithms
Energy Technology Data Exchange (ETDEWEB)
Tai, Xue-Cheng; Espedal, M. [Univ. of Bergen (Norway)
1996-12-31
Convergence of a space decomposition method is proved for a general convex programming problem. The space decomposition refers to methods that decompose a space into sums of subspaces, which could be a domain decomposition or a multigrid method for partial differential equations. Two algorithms are proposed. Both can be used for linear as well as nonlinear elliptic problems and they reduce to the standard additive and multiplicative Schwarz methods for linear elliptic problems. Two {open_quotes}hybrid{close_quotes} algorithms are also presented. They converge faster than the additive one and have better parallelism than the multiplicative method. Numerical tests with a two level domain decomposition for linear, nonlinear and interface elliptic problems are presented for the proposed algorithms.
Enhancing multilingual latent semantic analysis with term alignment information.
Energy Technology Data Exchange (ETDEWEB)
Chew, Peter A.; Bader, Brett William
2008-08-01
Latent Semantic Analysis (LSA) is based on the Singular Value Decomposition (SVD) of a term-by-document matrix for identifying relationships among terms and documents from co-occurrence patterns. Among the multiple ways of computing the SVD of a rectangular matrix X, one approach is to compute the eigenvalue decomposition (EVD) of a square 2 x 2 composite matrix consisting of four blocks with X and XT in the off-diagonal blocks and zero matrices in the diagonal blocks. We point out that significant value can be added to LSA by filling in some of the values in the diagonal blocks (corresponding to explicit term-to-term or document-to-document associations) and computing a term-by-concept matrix from the EVD. For the case of multilingual LSA, we incorporate information on cross-language term alignments of the same sort used in Statistical Machine Translation (SMT). Since all elements of the proposed EVD-based approach can rely entirely on lexical statistics, hardly any price is paid for the improved empirical results. In particular, the approach, like LSA or SMT, can still be generalized to virtually any language(s); computation of the EVD takes similar resources to that of the SVD since all the blocks are sparse; and the results of EVD are just as economical as those of SVD.
Reduced-rank approximations to the far-field transform in the gridded fast multipole method
Hesford, Andrew J.; Waag, Robert C.
2011-05-01
The fast multipole method (FMM) has been shown to have a reduced computational dependence on the size of finest-level groups of elements when the elements are positioned on a regular grid and FFT convolution is used to represent neighboring interactions. However, transformations between plane-wave expansions used for FMM interactions and pressure distributions used for neighboring interactions remain significant contributors to the cost of FMM computations when finest-level groups are large. The transformation operators, which are forward and inverse Fourier transforms with the wave space confined to the unit sphere, are smooth and well approximated using reduced-rank decompositions that further reduce the computational dependence of the FMM on finest-level group size. The adaptive cross approximation (ACA) is selected to represent the forward and adjoint far-field transformation operators required by the FMM. However, the actual error of the ACA is found to be greater than that predicted using traditional estimates, and the ACA generally performs worse than the approximation resulting from a truncated singular-value decomposition (SVD). To overcome these issues while avoiding the cost of a full-scale SVD, the ACA is employed with more stringent accuracy demands and recompressed using a reduced, truncated SVD. The results show a greatly reduced approximation error that performs comparably to the full-scale truncated SVD without degrading the asymptotic computational efficiency associated with ACA matrix assembly.
Directory of Open Access Journals (Sweden)
Xiaoyang Liu
2017-01-01
Full Text Available In order to analyze the channel estimation performance of near space high altitude platform station (HAPS in wireless communication system, the structure and formation of HAPS are studied in this paper. The traditional Least Squares (LS channel estimation method and Singular Value Decomposition-Linear Minimum Mean-Squared (SVD-LMMS channel estimation method are compared and investigated. A novel channel estimation method and model are proposed. The channel estimation performance of HAPS is studied deeply. The simulation and theoretical analysis results show that the performance of the proposed method is better than the traditional methods. The lower Bit Error Rate (BER and higher Signal Noise Ratio (SNR can be obtained by the proposed method compared with the LS and SVD-LMMS methods.
Energy Technology Data Exchange (ETDEWEB)
Zhang Pei [School of Physics and Astronomy, University of Manchester, Manchester M13 9PL (United Kingdom); Deutsches Elektronen-Synchrotron (DESY), 22607 Hamburg (Germany); Baboi, Nicoleta [Deutsches Elektronen-Synchrotron (DESY), 22607 Hamburg (Germany); Jones, Roger M.; Shinton, Ian R. R. [School of Physics and Astronomy, University of Manchester, Manchester M13 9PL (United Kingdom); Cockcroft Institute, Cheshire WA4 4AD (United Kingdom); Flisgen, Thomas; Glock, Hans-Walter [Institut fuer Allgemeine Elektrotechnik, Universitaet Rostock, 18051 Rostock (Germany)
2012-08-15
We investigate the feasibility of beam position diagnostics using higher order mode (HOM) signals excited by an electron beam in the third harmonic 3.9 GHz superconducting accelerating cavities at FLASH. After careful theoretical and experimental assessment of the HOM spectrum, three modal choices have been narrowed down to fulfill different diagnostics requirements. These are localized dipole beam-pipe modes, trapped cavity modes from the fifth dipole band, and propagating modes from the first two dipole bands. These modes are treated with various data analysis techniques: modal identification, direct linear regression (DLR), and singular value decomposition (SVD). Promising options for beam diagnostics are found from all three modal choices. This constitutes the first prediction, subsequently confirmed by experiments, of trapped HOMs in third harmonic cavities, and also the first direct comparison of DLR and SVD in the analysis of HOM-based beam diagnostics.
Multiview Trajectory Mapping Using Homography with Lens Distortion Correction
Directory of Open Access Journals (Sweden)
Andrea Cavallaro
2008-11-01
Full Text Available We present a trajectory mapping algorithm for a distributed camera setting that is based on statistical homography estimation accounting for the distortion introduced by camera lenses. Unlike traditional approaches based on the direct linear transformation (DLT algorithm and singular value decomposition (SVD, the planar homography estimation is derived from renormalization. In addition to this, the algorithm explicitly introduces a correction parameter to account for the nonlinear radial lens distortion, thus improving the accuracy of the transformation. We demonstrate the proposed algorithm by generating mosaics of the observed scenes and by registering the spatial locations of moving objects (trajectories from multiple cameras on the mosaics. Moreover, we objectively compare the transformed trajectories with those obtained by SVD and least mean square (LMS methods on standard datasets and demonstrate the advantages of the renormalization and the lens distortion correction.
Multiview Trajectory Mapping Using Homography with Lens Distortion Correction
Directory of Open Access Journals (Sweden)
Kayumbi Gabin
2008-01-01
Full Text Available Abstract We present a trajectory mapping algorithm for a distributed camera setting that is based on statistical homography estimation accounting for the distortion introduced by camera lenses. Unlike traditional approaches based on the direct linear transformation (DLT algorithm and singular value decomposition (SVD, the planar homography estimation is derived from renormalization. In addition to this, the algorithm explicitly introduces a correction parameter to account for the nonlinear radial lens distortion, thus improving the accuracy of the transformation. We demonstrate the proposed algorithm by generating mosaics of the observed scenes and by registering the spatial locations of moving objects (trajectories from multiple cameras on the mosaics. Moreover, we objectively compare the transformed trajectories with those obtained by SVD and least mean square (LMS methods on standard datasets and demonstrate the advantages of the renormalization and the lens distortion correction.
Downscaling atmospheric patterns to multi-site precipitation amounts in southern Scandinavia
DEFF Research Database (Denmark)
Gelati, Emiliano; Christensen, O.B.; Rasmussen, P.F.
2010-01-01
A non-homogeneous hidden Markov model (NHMM) is applied for downscaling atmospheric synoptic patterns to winter multi-site daily precipitation amounts. The implemented NHMM assumes precipitation to be conditional on a hidden weather state that follows a Markov chain, whose transition probabilities...... depend on current atmospheric information. The gridded atmospheric fields are summarized through the singular value decomposition (SVD) technique. SVD is applied to geopotential height and relative humidity at several pressure levels, to identify their principal spatial patterns co...... products of bivariate distributions. Conditional on the weather state, precipitation amounts are modelled separately at each gauge as independent gamma-distributed random variables. This modelling approach is applied to 51 precipitation gauges in Denmark and southern Sweden for the period 1981...
Shi, L; Jones., R M
2014-01-01
erating cavities at FLASH linac, DESY, are equipped with electronics for beam position monitoring, which are based on HOM signals from special couplers. These monitors provide the beam position without additional vacuum components and at low cost. Moreover, they can be used to align the beam in the cavities to reduce the HOM effects on the beam. However, the HOMBPM (Higher Order Mode based Beam Position Monitor) shows an instability problem over time. In this paper, we will present the status of studies on this issue. Several methods are utilized to calibrate the HOMBPMs. These methods include DLR (Direct Linear Regression), and SVD (Singular Value Decomposition). We found that SVD generally is more suitable for HOMBPM calibration. We focus on the HOMBPMs at 1.3 GHz cavities. Techniques developed here are applicable to 3.9 ...
Energy Technology Data Exchange (ETDEWEB)
Zhang, Pei, E-mail: pei.zhang@desy.de [School of Physics and Astronomy, The University of Manchester, Oxford Road, Manchester M13 9PL (United Kingdom); Deutsches Elektronen-Synchrotron (DESY), Notkestraße 85, D-22607 Hamburg (Germany); Cockcroft Institute of Science and Technology, Daresbury WA4 4AD (United Kingdom); Baboi, Nicoleta [Deutsches Elektronen-Synchrotron (DESY), Notkestraße 85, D-22607 Hamburg (Germany); Jones, Roger M. [School of Physics and Astronomy, The University of Manchester, Oxford Road, Manchester M13 9PL (United Kingdom); Cockcroft Institute of Science and Technology, Daresbury WA4 4AD (United Kingdom)
2014-01-11
Beam-excited higher order modes (HOMs) can be used to provide beam diagnostics. Here we focus on 3.9 GHz superconducting accelerating cavities. In particular we study dipole mode excitation and its application to beam position determinations. In order to extract beam position information, linear regression can be used. Due to a large number of sampling points in the waveforms, statistical methods are used to effectively reduce the dimension of the system, such as singular value decomposition (SVD) and k-means clustering. These are compared with the direct linear regression (DLR) on the entire waveforms. A cross-validation technique is used to study the sample independent precisions of the position predictions given by these three methods. A RMS prediction error in the beam position of approximately 50 μm can be achieved by DLR and SVD, while k-means clustering suggests 70 μm.
Zhang, P; Jones, R M
2014-01-01
Beam-excited higher order modes (HOM) can be used to provide beam diagnostics. Here we focus on 3.9 GHz superconducting accelerating cavities. In particular we study dipole mode excitation and its application to beam position determinations. In order to extract beam position information, linear regression can be used. Due to a large number of sampling points in the waveforms, statistical methods are used to effectively reduce the dimension of the system, such as singular value decomposition (SVD) and k-means clustering. These are compared with the direct linear regression (DLR) on the entire waveforms. A cross-validation technique is used to study the sample independent precisions of the position predictions given by these three methods. A RMS prediction error in the beam position of approximately 50 micron can be achieved by DLR and SVD, while k-means clustering suggests 70 micron.
Geladi, Paul; Nelson, Andrew; Lindholm-Sethson, Britta
2007-07-09
Electrical impedance gives multivariate complex number data as results. Two examples of multivariate electrical impedance data measured on lipid monolayers in different solutions give rise to matrices (16x50 and 38x50) of complex numbers. Multivariate data analysis by principal component analysis (PCA) or singular value decomposition (SVD) can be used for complex data and the necessary equations are given. The scores and loadings obtained are vectors of complex numbers. It is shown that the complex number PCA and SVD are better at concentrating information in a few components than the naïve juxtaposition method and that Argand diagrams can replace score and loading plots. Different concentrations of Magainin and Gramicidin A give different responses and also the role of the electrolyte medium can be studied. An interaction of Gramicidin A in the solution with the monolayer over time can be observed.
Zhang, P; Jones, R M; Shinton, I R R; Flisgen, T; Glock, H W
2012-01-01
We investigate the feasibility of beam position diagnostics using Higher Order Mode (HOM) signals excited by an electron beam in the third harmonic 3.9 GHz superconducting accelerating cavities at FLASH. After careful theoretical and experimental assessment of the HOM spectrum, three modal choices have been narrowed down to fulfill different diagnostics requirements. These are localized dipole beam-pipe modes, trapped cavity modes from the fifth dipole band and propagating modes from the first two dipole bands. These modes are treated with various data analysis techniques: modal identification, direct linear regression (DLR) and singular value decomposition (SVD). Promising options for beam diagnostics are found from all three modal choices. This constitutes the first prediction, subsequently confirmed by experiments, of trapped HOMs in third harmonic cavities, and also the first direct comparison of DLR and SVD in the analysis of HOM-based beam diagnostics.
a Unified Matrix Polynomial Approach to Modal Identification
Allemang, R. J.; Brown, D. L.
1998-04-01
One important current focus of modal identification is a reformulation of modal parameter estimation algorithms into a single, consistent mathematical formulation with a corresponding set of definitions and unifying concepts. Particularly, a matrix polynomial approach is used to unify the presentation with respect to current algorithms such as the least-squares complex exponential (LSCE), the polyreference time domain (PTD), Ibrahim time domain (ITD), eigensystem realization algorithm (ERA), rational fraction polynomial (RFP), polyreference frequency domain (PFD) and the complex mode indication function (CMIF) methods. Using this unified matrix polynomial approach (UMPA) allows a discussion of the similarities and differences of the commonly used methods. the use of least squares (LS), total least squares (TLS), double least squares (DLS) and singular value decomposition (SVD) methods is discussed in order to take advantage of redundant measurement data. Eigenvalue and SVD transformation methods are utilized to reduce the effective size of the resulting eigenvalue-eigenvector problem as well.
Determining extreme parameter correlation in ground water models
DEFF Research Database (Denmark)
Hill, Mary Cole; Østerby, Ole
2003-01-01
can go undetected even by experienced modelers. Extreme parameter correlation can be detected using parameter correlation coefficients, but their utility depends on the presence of sufficient, but not excessive, numerical imprecision of the sensitivities, such as round-off error. This work...... investigates the information that can be obtained from parameter correlation coefficients in the presence of different levels of numerical imprecision, and compares it to the information provided by an alternative method called the singular value decomposition (SVD). Results suggest that (1) calculated...... correlation coefficients with absolute values that round to 1.00 were good indicators of extreme parameter correlation, but smaller values were not necessarily good indicators of lack of correlation and resulting unique parameter estimates; (2) the SVD may be more difficult to interpret than parameter...
Real interest parity decomposition
Directory of Open Access Journals (Sweden)
Alex Luiz Ferreira
2009-09-01
Full Text Available The aim of this paper is to investigate the general causes of real interest rate differentials (rids for a sample of emerging markets for the period of January 1996 to August 2007. To this end, two methods are applied. The first consists of breaking the variance of rids down into relative purchasing power pariety and uncovered interest rate parity and shows that inflation differentials are the main source of rids variation; while the second method breaks down the rids and nominal interest rate differentials (nids into nominal and real shocks. Bivariate autoregressive models are estimated under particular identification conditions, having been adequately treated for the identified structural breaks. Impulse response functions and error variance decomposition result in real shocks as being the likely cause of rids.O objetivo deste artigo é investigar as causas gerais dos diferenciais da taxa de juros real (rids para um conjunto de países emergentes, para o período de janeiro de 1996 a agosto de 2007. Para tanto, duas metodologias são aplicadas. A primeira consiste em decompor a variância dos rids entre a paridade do poder de compra relativa e a paridade de juros a descoberto e mostra que os diferenciais de inflação são a fonte predominante da variabilidade dos rids; a segunda decompõe os rids e os diferenciais de juros nominais (nids em choques nominais e reais. Sob certas condições de identificação, modelos autorregressivos bivariados são estimados com tratamento adequado para as quebras estruturais identificadas e as funções de resposta ao impulso e a decomposição da variância dos erros de previsão são obtidas, resultando em evidências favoráveis a que os choques reais são a causa mais provável dos rids.
On the hadron mass decomposition
Lorcé, Cédric
2018-02-01
We argue that the standard decompositions of the hadron mass overlook pressure effects, and hence should be interpreted with great care. Based on the semiclassical picture, we propose a new decomposition that properly accounts for these pressure effects. Because of Lorentz covariance, we stress that the hadron mass decomposition automatically comes along with a stability constraint, which we discuss for the first time. We show also that if a hadron is seen as made of quarks and gluons, one cannot decompose its mass into more than two contributions without running into trouble with the consistency of the physical interpretation. In particular, the so-called quark mass and trace anomaly contributions appear to be purely conventional. Based on the current phenomenological values, we find that in average quarks exert a repulsive force inside nucleons, balanced exactly by the gluon attractive force.
On the hadron mass decomposition
Energy Technology Data Exchange (ETDEWEB)
Lorce, Cedric [Universite Paris-Saclay, Centre de Physique Theorique, Ecole Polytechnique, CNRS, Palaiseau (France)
2018-02-15
We argue that the standard decompositions of the hadron mass overlook pressure effects, and hence should be interpreted with great care. Based on the semiclassical picture, we propose a new decomposition that properly accounts for these pressure effects. Because of Lorentz covariance, we stress that the hadron mass decomposition automatically comes along with a stability constraint, which we discuss for the first time. We show also that if a hadron is seen as made of quarks and gluons, one cannot decompose its mass into more than two contributions without running into trouble with the consistency of the physical interpretation. In particular, the so-called quark mass and trace anomaly contributions appear to be purely conventional. Based on the current phenomenological values, we find that in average quarks exert a repulsive force inside nucleons, balanced exactly by the gluon attractive force. (orig.)
Abstract decomposition theorem and applications
Grossberg, R; Grossberg, Rami; Lessmann, Olivier
2005-01-01
Let K be an Abstract Elementary Class. Under the asusmptions that K has a nicely behaved forking-like notion, regular types and existence of some prime models we establish a decomposition theorem for such classes. The decomposition implies a main gap result for the class K. The setting is general enough to cover \\aleph_0-stable first-order theories (proved by Shelah in 1982), Excellent Classes of atomic models of a first order tehory (proved Grossberg and Hart 1987) and the class of submodels of a large sequentially homogenuus \\aleph_0-stable model (which is new).
Thermal decomposition of biphenyl (1963); Decomposition thermique du biphenyle (1963)
Energy Technology Data Exchange (ETDEWEB)
Clerc, M [Commissariat a l' Energie Atomique, Saclay (France). Centre d' Etudes Nucleaires
1962-06-15
The rates of formation of the decomposition products of biphenyl; hydrogen, methane, ethane, ethylene, as well as triphenyl have been measured in the vapour and liquid phases at 460 deg. C. The study of the decomposition products of biphenyl at different temperatures between 400 and 460 deg. C has provided values of the activation energies of the reactions yielding the main products of pyrolysis in the vapour phase. Product and Activation energy: Hydrogen 73 {+-} 2 kCal/Mole; Benzene 76 {+-} 2 kCal/Mole; Meta-triphenyl 53 {+-} 2 kCal/Mole; Biphenyl decomposition 64 {+-} 2 kCal/Mole; The rate of disappearance of biphenyl is only very approximately first order. These results show the major role played at the start of the decomposition by organic impurities which are not detectable by conventional physico-chemical analysis methods and the presence of which accelerates noticeably the decomposition rate. It was possible to eliminate these impurities by zone-melting carried out until the initial gradient of the formation curves for the products became constant. The composition of the high-molecular weight products (over 250) was deduced from the mean molecular weight and the dosage of the aromatic C - H bonds by infrared spectrophotometry. As a result the existence in tars of hydrogenated tetra, penta and hexaphenyl has been demonstrated. (author) [French] Les vitesses de formation des produits de decomposition du biphenyle: hydrogene, methane, ethane, ethylene, ainsi que des triphenyles, ont ete mesurees en phase vapeur et en phase liquide a 460 deg. C. L'etude des produits de decomposition du biphenyle a differentes temperatures comprises entre 400 et 460 deg. C, a fourni les valeurs des energies d'activation des reactions conduisant aux principaux produits de la pyrolyse en phase vapeur. Produit et Energie d'activation: Hydrogene 73 {+-} 2 kcal/Mole; Benzene 76 {+-} 2 kcal/Mole; Metatriphenyle, 53 {+-} 2 kcal/Mole; Decomposition du biphenyle 64 {+-} 2 kcal/Mole; La
Lie bialgebras with triangular decomposition
International Nuclear Information System (INIS)
Andruskiewitsch, N.; Levstein, F.
1992-06-01
Lie bialgebras originated in a triangular decomposition of the underlying Lie algebra are discussed. The explicit formulas for the quantization of the Heisenberg Lie algebra and some motion Lie algebras are given, as well as the algebra of rational functions on the quantum Heisenberg group and the formula for the universal R-matrix. (author). 17 refs
Decomposition of metal nitrate solutions
International Nuclear Information System (INIS)
Haas, P.A.; Stines, W.B.
1982-01-01
Oxides in powder form are obtained from aqueous solutions of one or more heavy metal nitrates (e.g. U, Pu, Th, Ce) by thermal decomposition at 300 to 800 deg C in the presence of about 50 to 500% molar concentration of ammonium nitrate to total metal. (author)
Probability inequalities for decomposition integrals
Czech Academy of Sciences Publication Activity Database
Agahi, H.; Mesiar, Radko
2017-01-01
Roč. 315, č. 1 (2017), s. 240-248 ISSN 0377-0427 Institutional support: RVO:67985556 Keywords : Decomposition integral * Superdecomposition integral * Probability inequalities Subject RIV: BA - General Mathematics OBOR OECD: Statistics and probability Impact factor: 1.357, year: 2016 http://library.utia.cas.cz/separaty/2017/E/mesiar-0470959.pdf
Thermal decomposition of ammonium hexachloroosmate
DEFF Research Database (Denmark)
Asanova, T I; Kantor, Innokenty; Asanov, I. P.
2016-01-01
Structural changes of (NH4)2[OsCl6] occurring during thermal decomposition in a reduction atmosphere have been studied in situ using combined energy-dispersive X-ray absorption spectroscopy (ED-XAFS) and powder X-ray diffraction (PXRD). According to PXRD, (NH4)2[OsCl6] transforms directly to meta...
A Tensor Decomposition-Based Approach for Detecting Dynamic Network States From EEG.
Mahyari, Arash Golibagh; Zoltowski, David M; Bernat, Edward M; Aviyente, Selin
2017-01-01
Functional connectivity (FC), defined as the statistical dependency between distinct brain regions, has been an important tool in understanding cognitive brain processes. Most of the current works in FC have focused on the assumption of temporally stationary networks. However, recent empirical work indicates that FC is dynamic due to cognitive functions. The purpose of this paper is to understand the dynamics of FC for understanding the formation and dissolution of networks of the brain. In this paper, we introduce a two-step approach to characterize the dynamics of functional connectivity networks (FCNs) by first identifying change points at which the network connectivity across subjects shows significant changes and then summarizing the FCNs between consecutive change points. The proposed approach is based on a tensor representation of FCNs across time and subjects yielding a four-mode tensor. The change points are identified using a subspace distance measure on low-rank approximations to the tensor at each time point. The network summarization is then obtained through tensor-matrix projections across the subject and time modes. The proposed framework is applied to electroencephalogram (EEG) data collected during a cognitive control task. The detected change-points are consistent with a priori known ERN interval. The results show significant connectivities in medial-frontal regions which are consistent with widely observed ERN amplitude measures. The tensor-based method outperforms conventional matrix-based methods such as singular value decomposition in terms of both change-point detection and state summarization. The proposed tensor-based method captures the topological structure of FCNs which provides more accurate change-point-detection and state summarization.
Investigating hydrogel dosimeter decomposition by chemical methods
International Nuclear Information System (INIS)
Jordan, Kevin
2015-01-01
The chemical oxidative decomposition of leucocrystal violet micelle hydrogel dosimeters was investigated using the reaction of ferrous ions with hydrogen peroxide or sodium bicarbonate with hydrogen peroxide. The second reaction is more effective at dye decomposition in gelatin hydrogels. Additional chemical analysis is required to determine the decomposition products
International Nuclear Information System (INIS)
Murase, Kenya; Yamazaki, Youichi; Shinohara, Masaaki
2003-01-01
The purpose of this study was to investigate the feasibility of the autoregressive moving average (ARMA) model for quantification of cerebral blood flow (CBF) with dynamic susceptibility contrast-enhanced magnetic resonance imaging (DSC-MRI) in comparison with deconvolution analysis based on singular value decomposition (DA-SVD). Using computer simulations, we generated a time-dependent concentration of the contrast agent in the volume of interest (VOI) from the arterial input function (AIF) modeled as a gamma-variate function under various CBFs, cerebral blood volumes and signal-to-noise ratios (SNRs) for three different types of residue function (exponential, triangular, and box-shaped). We also considered the effects of delay and dispersion in AIF. The ARMA model and DA-SVD were used to estimate CBF values from the simulated concentration-time curves in the VOI and AIFs, and the estimated values were compared with the assumed values. We found that the CBF value estimated by the ARMA model was more sensitive to the SNR and the delay in AIF than that obtained by DA-SVD. Although the ARMA model considerably overestimated CBF at low SNRs, it estimated the CBF more accurately than did DA-SVD at high SNRs for the exponential or triangular residue function. We believe this study will contribute to an understanding of the usefulness and limitations of the ARMA model when applied to quantification of CBF with DSC-MRI. (author)
A general approach to regularizing inverse problems with regional data using Slepian wavelets
Michel, Volker; Simons, Frederik J.
2017-12-01
Slepian functions are orthogonal function systems that live on subdomains (for example, geographical regions on the Earth’s surface, or bandlimited portions of the entire spectrum). They have been firmly established as a useful tool for the synthesis and analysis of localized (concentrated or confined) signals, and for the modeling and inversion of noise-contaminated data that are only regionally available or only of regional interest. In this paper, we consider a general abstract setup for inverse problems represented by a linear and compact operator between Hilbert spaces with a known singular-value decomposition (svd). In practice, such an svd is often only given for the case of a global expansion of the data (e.g. on the whole sphere) but not for regional data distributions. We show that, in either case, Slepian functions (associated to an arbitrarily prescribed region and the given compact operator) can be determined and applied to construct a regularization for the ill-posed regional inverse problem. Moreover, we describe an algorithm for constructing the Slepian basis via an algebraic eigenvalue problem. The obtained Slepian functions can be used to derive an svd for the combination of the regionalizing projection and the compact operator. As a result, standard regularization techniques relying on a known svd become applicable also to those inverse problems where the data are regionally given only. In particular, wavelet-based multiscale techniques can be used. An example for the latter case is elaborated theoretically and tested on two synthetic numerical examples.
Using DEDICOM for completely unsupervised part-of-speech tagging.
Energy Technology Data Exchange (ETDEWEB)
Chew, Peter A.; Bader, Brett William; Rozovskaya, Alla (University of Illinois, Urbana, IL)
2009-02-01
A standard and widespread approach to part-of-speech tagging is based on Hidden Markov Models (HMMs). An alternative approach, pioneered by Schuetze (1993), induces parts of speech from scratch using singular value decomposition (SVD). We introduce DEDICOM as an alternative to SVD for part-of-speech induction. DEDICOM retains the advantages of SVD in that it is completely unsupervised: no prior knowledge is required to induce either the tagset or the associations of terms with tags. However, unlike SVD, it is also fully compatible with the HMM framework, in that it can be used to estimate emission- and transition-probability matrices which can then be used as the input for an HMM. We apply the DEDICOM method to the CONLL corpus (CONLL 2000) and compare the output of DEDICOM to the part-of-speech tags given in the corpus, and find that the correlation (almost 0.5) is quite high. Using DEDICOM, we also estimate part-of-speech ambiguity for each term, and find that these estimates correlate highly with part-of-speech ambiguity as measured in the original corpus (around 0.88). Finally, we show how the output of DEDICOM can be evaluated and compared against the more familiar output of supervised HMM-based tagging.
Dictionary-Based Tensor Canonical Polyadic Decomposition
Cohen, Jeremy Emile; Gillis, Nicolas
2018-04-01
To ensure interpretability of extracted sources in tensor decomposition, we introduce in this paper a dictionary-based tensor canonical polyadic decomposition which enforces one factor to belong exactly to a known dictionary. A new formulation of sparse coding is proposed which enables high dimensional tensors dictionary-based canonical polyadic decomposition. The benefits of using a dictionary in tensor decomposition models are explored both in terms of parameter identifiability and estimation accuracy. Performances of the proposed algorithms are evaluated on the decomposition of simulated data and the unmixing of hyperspectral images.
Decomposition of diesel oil by various microorganisms
Energy Technology Data Exchange (ETDEWEB)
Suess, A; Netzsch-Lehner, A
1969-01-01
Previous experiments demonstrated the decomposition of diesel oil in different soils. In this experiment the decomposition of /sup 14/C-n-Hexadecane labelled diesel oil by special microorganisms was studied. The results were as follows: (1) In the experimental soils the microorganisms Mycoccus ruber, Mycobacterium luteum and Trichoderma hamatum are responsible for the diesel oil decomposition. (2) By adding microorganisms to the soil an increase of the decomposition rate was found only in the beginning of the experiments. (3) Maximum decomposition of diesel oil was reached 2-3 weeks after incubation.
Variance decomposition in stochastic simulators.
Le Maître, O P; Knio, O M; Moraes, A
2015-06-28
This work aims at the development of a mathematical and computational approach that enables quantification of the inherent sources of stochasticity and of the corresponding sensitivities in stochastic simulations of chemical reaction networks. The approach is based on reformulating the system dynamics as being generated by independent standardized Poisson processes. This reformulation affords a straightforward identification of individual realizations for the stochastic dynamics of each reaction channel, and consequently a quantitative characterization of the inherent sources of stochasticity in the system. By relying on the Sobol-Hoeffding decomposition, the reformulation enables us to perform an orthogonal decomposition of the solution variance. Thus, by judiciously exploiting the inherent stochasticity of the system, one is able to quantify the variance-based sensitivities associated with individual reaction channels, as well as the importance of channel interactions. Implementation of the algorithms is illustrated in light of simulations of simplified systems, including the birth-death, Schlögl, and Michaelis-Menten models.
Variance decomposition in stochastic simulators
Le Maître, O. P.; Knio, O. M.; Moraes, A.
2015-06-01
This work aims at the development of a mathematical and computational approach that enables quantification of the inherent sources of stochasticity and of the corresponding sensitivities in stochastic simulations of chemical reaction networks. The approach is based on reformulating the system dynamics as being generated by independent standardized Poisson processes. This reformulation affords a straightforward identification of individual realizations for the stochastic dynamics of each reaction channel, and consequently a quantitative characterization of the inherent sources of stochasticity in the system. By relying on the Sobol-Hoeffding decomposition, the reformulation enables us to perform an orthogonal decomposition of the solution variance. Thus, by judiciously exploiting the inherent stochasticity of the system, one is able to quantify the variance-based sensitivities associated with individual reaction channels, as well as the importance of channel interactions. Implementation of the algorithms is illustrated in light of simulations of simplified systems, including the birth-death, Schlögl, and Michaelis-Menten models.
Variance decomposition in stochastic simulators
Energy Technology Data Exchange (ETDEWEB)
Le Maître, O. P., E-mail: olm@limsi.fr [LIMSI-CNRS, UPR 3251, Orsay (France); Knio, O. M., E-mail: knio@duke.edu [Department of Mechanical Engineering and Materials Science, Duke University, Durham, North Carolina 27708 (United States); Moraes, A., E-mail: alvaro.moraesgutierrez@kaust.edu.sa [King Abdullah University of Science and Technology, Thuwal (Saudi Arabia)
2015-06-28
This work aims at the development of a mathematical and computational approach that enables quantification of the inherent sources of stochasticity and of the corresponding sensitivities in stochastic simulations of chemical reaction networks. The approach is based on reformulating the system dynamics as being generated by independent standardized Poisson processes. This reformulation affords a straightforward identification of individual realizations for the stochastic dynamics of each reaction channel, and consequently a quantitative characterization of the inherent sources of stochasticity in the system. By relying on the Sobol-Hoeffding decomposition, the reformulation enables us to perform an orthogonal decomposition of the solution variance. Thus, by judiciously exploiting the inherent stochasticity of the system, one is able to quantify the variance-based sensitivities associated with individual reaction channels, as well as the importance of channel interactions. Implementation of the algorithms is illustrated in light of simulations of simplified systems, including the birth-death, Schlögl, and Michaelis-Menten models.
Variance decomposition in stochastic simulators
Le Maî tre, O. P.; Knio, O. M.; Moraes, Alvaro
2015-01-01
This work aims at the development of a mathematical and computational approach that enables quantification of the inherent sources of stochasticity and of the corresponding sensitivities in stochastic simulations of chemical reaction networks. The approach is based on reformulating the system dynamics as being generated by independent standardized Poisson processes. This reformulation affords a straightforward identification of individual realizations for the stochastic dynamics of each reaction channel, and consequently a quantitative characterization of the inherent sources of stochasticity in the system. By relying on the Sobol-Hoeffding decomposition, the reformulation enables us to perform an orthogonal decomposition of the solution variance. Thus, by judiciously exploiting the inherent stochasticity of the system, one is able to quantify the variance-based sensitivities associated with individual reaction channels, as well as the importance of channel interactions. Implementation of the algorithms is illustrated in light of simulations of simplified systems, including the birth-death, Schlögl, and Michaelis-Menten models.
Excimer laser decomposition of silicone
International Nuclear Information System (INIS)
Laude, L.D.; Cochrane, C.; Dicara, Cl.; Dupas-Bruzek, C.; Kolev, K.
2003-01-01
Excimer laser irradiation of silicone foils is shown in this work to induce decomposition, ablation and activation of such materials. Thin (100 μm) laminated silicone foils are irradiated at 248 nm as a function of impacting laser fluence and number of pulsed irradiations at 1 s intervals. Above a threshold fluence of 0.7 J/cm 2 , material starts decomposing. At higher fluences, this decomposition develops and gives rise to (i) swelling of the irradiated surface and then (ii) emission of matter (ablation) at a rate that is not proportioned to the number of pulses. Taking into consideration the polymer structure and the foil lamination process, these results help defining the phenomenology of silicone ablation. The polymer decomposition results in two parts: one which is organic and volatile, and another part which is inorganic and remains, forming an ever thickening screen to light penetration as the number of light pulses increases. A mathematical model is developed that accounts successfully for this physical screening effect
Thermic decomposition of biphenyl; Decomposition thermique du biphenyle
Energy Technology Data Exchange (ETDEWEB)
Lutz, M [Commissariat a l' Energie Atomique, Saclay (France). Centre d' Etudes Nucleaires
1966-03-01
Liquid and vapour phase pyrolysis of very pure biphenyl obtained by methods described in the text was carried out at 400 C in sealed ampoules, the fraction transformed being always less than 0.1 per cent. The main products were hydrogen, benzene, terphenyls, and a deposit of polyphenyls strongly adhering to the walls. Small quantities of the lower aliphatic hydrocarbons were also found. The variation of the yields of these products with a) the pyrolysis time, b) the state (gas or liquid) of the biphenyl, and c) the pressure of the vapour was measured. Varying the area and nature of the walls showed that in the absence of a liquid phase, the pyrolytic decomposition takes place in the adsorbed layer, and that metallic walls promote the reaction more actively than do those of glass (pyrex or silica). A mechanism is proposed to explain the results pertaining to this decomposition in the adsorbed phase. The adsorption seems to obey a Langmuir isotherm, and the chemical act which determines the overall rate of decomposition is unimolecular. (author) [French] Du biphenyle tres pur, dont la purification est decrite, est pyrolyse a 400 C en phase vapeur et en phase liquide dans des ampoules scellees sous vide, a des taux de decomposition n'ayant jamais depasse 0,1 pour cent. Les produits provenant de la pyrolyse sont essentiellement: l' hydrogene, le benzene, les therphenyles, et un depot de polyphenyles adherant fortement aux parois. En plus il se forme de faibles quantites d'hydrocarbures aliphatiques gazeux. On indique la variation des rendements des differents produits avec la duree de pyrolyse, l'etat gazeux ou liquide du biphenyle, et la pression de la vapeur. Variant la superficie et la nature des parois, on montre qu'en absence de liquide la pyrolyse se fait en phase adsorbee. La pyrolyse est plus active au contact de parois metalliques que de celles de verres (pyrex ou silice). A partir des resultats experimentaux un mecanisme de degradation du biphenyle en phase
Dolomite decomposition under CO2
International Nuclear Information System (INIS)
Guerfa, F.; Bensouici, F.; Barama, S.E.; Harabi, A.; Achour, S.
2004-01-01
Full text.Dolomite (MgCa (CO 3 ) 2 is one of the most abundant mineral species on the surface of the planet, it occurs in sedimentary rocks. MgO, CaO and Doloma (Phase mixture of MgO and CaO, obtained from the mineral dolomite) based materials are attractive steel-making refractories because of their potential cost effectiveness and world wide abundance more recently, MgO is also used as protective layers in plasma screen manufacture ceel. The crystal structure of dolomite was determined as rhombohedral carbonates, they are layers of Mg +2 and layers of Ca +2 ions. It dissociates depending on the temperature variations according to the following reactions: MgCa (CO 3 ) 2 → MgO + CaO + 2CO 2 .....MgCa (CO 3 ) 2 → MgO + Ca + CaCO 3 + CO 2 .....This latter reaction may be considered as a first step for MgO production. Differential thermal analysis (DTA) are used to control dolomite decomposition and the X-Ray Diffraction (XRD) was used to elucidate thermal decomposition of dolomite according to the reaction. That required samples were heated to specific temperature and holding times. The average particle size of used dolomite powders is 0.3 mm, as where, the heating temperature was 700 degree celsius, using various holding times (90 and 120 minutes). Under CO 2 dolomite decomposed directly to CaCO 3 accompanied by the formation of MgO, no evidence was offered for the MgO formation of either CaO or MgCO 3 , under air, simultaneous formation of CaCO 3 , CaO and accompanied dolomite decomposition
Spectral Tensor-Train Decomposition
DEFF Research Database (Denmark)
Bigoni, Daniele; Engsig-Karup, Allan Peter; Marzouk, Youssef M.
2016-01-01
The accurate approximation of high-dimensional functions is an essential task in uncertainty quantification and many other fields. We propose a new function approximation scheme based on a spectral extension of the tensor-train (TT) decomposition. We first define a functional version of the TT...... adaptive Smolyak approach. The method is also used to approximate the solution of an elliptic PDE with random input data. The open source software and examples presented in this work are available online (http://pypi.python.org/pypi/TensorToolbox/)....
Robust Automatic Target Recognition via HRRP Sequence Based on Scatterer Matching
Directory of Open Access Journals (Sweden)
Yuan Jiang
2018-02-01
Full Text Available High resolution range profile (HRRP plays an important role in wideband radar automatic target recognition (ATR. In order to alleviate the sensitivity to clutter and target aspect, employing a sequence of HRRP is a promising approach to enhance the ATR performance. In this paper, a novel HRRP sequence-matching method based on singular value decomposition (SVD is proposed. First, the HRRP sequence is decoupled into the angle space and the range space via SVD, which correspond to the span of the left and the right singular vectors, respectively. Second, atomic norm minimization (ANM is utilized to estimate dominant scatterers in the range space and the Hausdorff distance is employed to measure the scatter similarity between the test and training data. Next, the angle space similarity between the test and training data is evaluated based on the left singular vector correlations. Finally, the range space matching result and the angle space correlation are fused with the singular values as weights. Simulation and outfield experimental results demonstrate that the proposed matching metric is a robust similarity measure for HRRP sequence recognition.
Decomposition of Multi-player Games
Zhao, Dengji; Schiffel, Stephan; Thielscher, Michael
Research in General Game Playing aims at building systems that learn to play unknown games without human intervention. We contribute to this endeavour by generalising the established technique of decomposition from AI Planning to multi-player games. To this end, we present a method for the automatic decomposition of previously unknown games into independent subgames, and we show how a general game player can exploit a successful decomposition for game tree search.
Constructive quantum Shannon decomposition from Cartan involutions
International Nuclear Information System (INIS)
Drury, Byron; Love, Peter
2008-01-01
The work presented here extends upon the best known universal quantum circuit, the quantum Shannon decomposition proposed by Shende et al (2006 IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst. 25 1000). We obtain the basis of the circuit's design in a pair of Cartan decompositions. This insight gives a simple constructive factoring algorithm in terms of the Cartan involutions corresponding to these decompositions
Constructive quantum Shannon decomposition from Cartan involutions
Energy Technology Data Exchange (ETDEWEB)
Drury, Byron; Love, Peter [Department of Physics, 370 Lancaster Ave., Haverford College, Haverford, PA 19041 (United States)], E-mail: plove@haverford.edu
2008-10-03
The work presented here extends upon the best known universal quantum circuit, the quantum Shannon decomposition proposed by Shende et al (2006 IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst. 25 1000). We obtain the basis of the circuit's design in a pair of Cartan decompositions. This insight gives a simple constructive factoring algorithm in terms of the Cartan involutions corresponding to these decompositions.
Mathematical modelling of the decomposition of explosives
International Nuclear Information System (INIS)
Smirnov, Lev P
2010-01-01
Studies on mathematical modelling of the molecular and supramolecular structures of explosives and the elementary steps and overall processes of their decomposition are analyzed. Investigations on the modelling of combustion and detonation taking into account the decomposition of explosives are also considered. It is shown that solution of problems related to the decomposition kinetics of explosives requires the use of a complex strategy based on the methods and concepts of chemical physics, solid state physics and theoretical chemistry instead of empirical approach.
Decomposition in pelagic marine ecosytems
International Nuclear Information System (INIS)
Lucas, M.I.
1986-01-01
During the decomposition of plant detritus, complex microbial successions develop which are dominated in the early stages by a number of distinct bacterial morphotypes. The microheterotrophic community rapidly becomes heterogenous and may include cyanobacteria, fungi, yeasts and bactivorous protozoans. Microheterotrophs in the marine environment may have a biomass comparable to that of all other heterotrophs and their significance as a resource to higher trophic orders, and in the regeneration of nutrients, particularly nitrogen, that support 'regenerated' primary production, has aroused both attention and controversy. Numerous methods have been employed to measure heterotrophic bacterial production and activity. The most widely used involve estimates of 14 C-glucose uptake; the frequency of dividing cells; the incorporation of 3 H-thymidine and exponential population growth in predator-reduced filtrates. Recent attempts to model decomposition processes and C and N fluxes in pelagic marine ecosystems are described. This review examines the most sensitive components and predictions of the models with particular reference to estimates of bacterial production, net growth yield and predictions of N cycling determined by 15 N methodology. Directed estimates of nitrogen (and phosphorus) flux through phytoplanktonic and bacterioplanktonic communities using 15 N (and 32 P) tracer methods are likely to provide more realistic measures of nitrogen flow through planktonic communities
Infrared multiphoton absorption and decomposition
International Nuclear Information System (INIS)
Evans, D.K.; McAlpine, R.D.
1984-01-01
The discovery of infrared laser induced multiphoton absorption (IRMPA) and decomposition (IRMPD) by Isenor and Richardson in 1971 generated a great deal of interest in these phenomena. This interest was increased with the discovery by Ambartzumian, Letokhov, Ryadbov and Chekalin that isotopically selective IRMPD was possible. One of the first speculations about these phenomena was that it might be possible to excite a particular mode of a molecule with the intense infrared laser beam and cause decomposition or chemical reaction by channels which do not predominate thermally, thus providing new synthetic routes for complex chemicals. The potential applications to isotope separation and novel chemistry stimulated efforts to understand the underlying physics and chemistry of these processes. At ICOMP I, in 1977 and at ICOMP II in 1980, several authors reviewed the current understandings of IRMPA and IRMPD as well as the particular aspect of isotope separation. There continues to be a great deal of effort into understanding IRMPA and IRMPD and we will briefly review some aspects of these efforts with particular emphasis on progress since ICOMP II. 31 references
Decomposition of Diethylstilboestrol in Soil
DEFF Research Database (Denmark)
Gregers-Hansen, Birte
1964-01-01
The rate of decomposition of DES-monoethyl-1-C14 in soil was followed by measurement of C14O2 released. From 1.6 to 16% of the added C14 was recovered as C14O2 during 3 months. After six months as much as 12 to 28 per cent was released as C14O2.Determination of C14 in the soil samples after the e...... not inhibit the CO2 production from the soil.Experiments with γ-sterilized soil indicated that enzymes present in the soil are able to attack DES.......The rate of decomposition of DES-monoethyl-1-C14 in soil was followed by measurement of C14O2 released. From 1.6 to 16% of the added C14 was recovered as C14O2 during 3 months. After six months as much as 12 to 28 per cent was released as C14O2.Determination of C14 in the soil samples after...
Spreading Sequence System for Full Connectivity Relay Network
Kwon, Hyuck M. (Inventor); Yang, Jie (Inventor); Pham, Khanh D. (Inventor)
2018-01-01
Fully connected uplink and downlink fully connected relay network systems using pseudo-noise spreading and despreading sequences subjected to maximizing the signal-to-interference-plus-noise ratio. The relay network systems comprise one or more transmitting units, relays, and receiving units connected via a communication network. The transmitting units, relays, and receiving units each may include a computer for performing the methods and steps described herein and transceivers for transmitting and/or receiving signals. The computer encodes and/or decodes communication signals via optimum adaptive PN sequences found by employing Cholesky decompositions and singular value decompositions (SVD). The PN sequences employ channel state information (CSI) to more effectively and more securely computing the optimal sequences.
Cyganek, Boguslaw; Smolka, Bogdan
2015-02-01
In this paper a system for real-time recognition of objects in multidimensional video signals is proposed. Object recognition is done by pattern projection into the tensor subspaces obtained from the factorization of the signal tensors representing the input signal. However, instead of taking only the intensity signal the novelty of this paper is first to build the Extended Structural Tensor representation from the intensity signal that conveys information on signal intensities, as well as on higher-order statistics of the input signals. This way the higher-order input pattern tensors are built from the training samples. Then, the tensor subspaces are built based on the Higher-Order Singular Value Decomposition of the prototype pattern tensors. Finally, recognition relies on measurements of the distance of a test pattern projected into the tensor subspaces obtained from the training tensors. Due to high-dimensionality of the input data, tensor based methods require high memory and computational resources. However, recent achievements in the technology of the multi-core microprocessors and graphic cards allows real-time operation of the multidimensional methods as is shown and analyzed in this paper based on real examples of object detection in digital images.
Decomposition kinetics of plutonium hydride
Energy Technology Data Exchange (ETDEWEB)
Haschke, J.M.; Stakebake, J.L.
1979-01-01
Kinetic data for decomposition of PuH/sub 1/ /sub 95/ provides insight into a possible mechanism for the hydriding and dehydriding reactions of plutonium. The fact that the rate of the hydriding reaction, K/sub H/, is proportional to P/sup 1/2/ and the rate of the dehydriding process, K/sub D/, is inversely proportional to P/sup 1/2/ suggests that the forward and reverse reactions proceed by opposite paths of the same mechanism. The P/sup 1/2/ dependence of hydrogen solubility in metals is characteristic of the dissociative absorption of hydrogen; i.e., the reactive species is atomic hydrogen. It is reasonable to assume that the rates of the forward and reverse reactions are controlled by the surface concentration of atomic hydrogen, (H/sub s/), that K/sub H/ = c'(H/sub s/), and that K/sub D/ = c/(H/sub s/), where c' and c are proportionality constants. For this surface model, the pressure dependence of K/sub D/ is related to (H/sub s/) by the reaction (H/sub s/) reversible 1/2H/sub 2/(g) and by its equilibrium constant K/sub e/ = (H/sub 2/)/sup 1/2//(H/sub s/). In the pressure range of ideal gas behavior, (H/sub s/) = K/sub e//sup -1/(RT)/sup -1/2/ and the decomposition rate is given by K/sub D/ = cK/sub e/(RT)/sup -1/2/P/sup 1/2/. For an analogous treatment of the hydriding process with this model, it can be readily shown that K/sub H/ = c'K/sub e//sup -1/(RT)/sup -1/2/P/sup 1/2/. The inverse pressure dependence and direct temperature dependence of the decomposition rate are correctly predicted by this mechanism which is most consistent with the observed behavior of the Pu--H system.
Gosses, Moritz; Nowak, Wolfgang; Wöhling, Thomas
2017-04-01
Physically-based modeling is a wide-spread tool in understanding and management of natural systems. With the high complexity of many such models and the huge amount of model runs necessary for parameter estimation and uncertainty analysis, overall run times can be prohibitively long even on modern computer systems. An encouraging strategy to tackle this problem are model reduction methods. In this contribution, we compare different proper orthogonal decomposition (POD, Siade et al. (2010)) methods and their potential applications to groundwater models. The POD method performs a singular value decomposition on system states as simulated by the complex (e.g., PDE-based) groundwater model taken at several time-steps, so-called snapshots. The singular vectors with the highest information content resulting from this decomposition are then used as a basis for projection of the system of model equations onto a subspace of much lower dimensionality than the original complex model, thereby greatly reducing complexity and accelerating run times. In its original form, this method is only applicable to linear problems. Many real-world groundwater models are non-linear, tough. These non-linearities are introduced either through model structure (unconfined aquifers) or boundary conditions (certain Cauchy boundaries, like rivers with variable connection to the groundwater table). To date, applications of POD focused on groundwater models simulating pumping tests in confined aquifers with constant head boundaries. In contrast, POD model reduction either greatly looses accuracy or does not significantly reduce model run time if the above-mentioned non-linearities are introduced. We have also found that variable Dirichlet boundaries are problematic for POD model reduction. An extension to the POD method, called POD-DEIM, has been developed for non-linear groundwater models by Stanko et al. (2016). This method uses spatial interpolation points to build the equation system in the
Spinodal decomposition in fluid mixtures
International Nuclear Information System (INIS)
Kawasaki, Kyozi; Koga, Tsuyoshi
1993-01-01
We study the late stage dynamics of spinodal decomposition in binary fluids by the computer simulation of the time-dependent Ginzburg-Landau equation. We obtain a temporary linear growth law of the characteristic length of domains in the late stage. This growth law has been observed in many real experiments of binary fluids and indicates that the domain growth proceeds by the flow caused by the surface tension of interfaces. We also find that the dynamical scaling law is satisfied in this hydrodynamic domain growth region. By comparing the scaling functions for fluids with that for the case without hydrodynamic effects, we find that the scaling functions for the two systems are different. (author)
Early stage litter decomposition across biomes
Ika Djukic; Sebastian Kepfer-Rojas; Inger Kappel Schmidt; Klaus Steenberg Larsen; Claus Beier; Björn Berg; Kris Verheyen; Adriano Caliman; Alain Paquette; Alba Gutiérrez-Girón; Alberto Humber; Alejandro Valdecantos; Alessandro Petraglia; Heather Alexander; Algirdas Augustaitis; Amélie Saillard; Ana Carolina Ruiz Fernández; Ana I. Sousa; Ana I. Lillebø; Anderson da Rocha Gripp; André-Jean Francez; Andrea Fischer; Andreas Bohner; Andrey Malyshev; Andrijana Andrić; Andy Smith; Angela Stanisci; Anikó Seres; Anja Schmidt; Anna Avila; Anne Probst; Annie Ouin; Anzar A. Khuroo; Arne Verstraeten; Arely N. Palabral-Aguilera; Artur Stefanski; Aurora Gaxiola; Bart Muys; Bernard Bosman; Bernd Ahrends; Bill Parker; Birgit Sattler; Bo Yang; Bohdan Juráni; Brigitta Erschbamer; Carmen Eugenia Rodriguez Ortiz; Casper T. Christiansen; E. Carol Adair; Céline Meredieu; Cendrine Mony; Charles A. Nock; Chi-Ling Chen; Chiao-Ping Wang; Christel Baum; Christian Rixen; Christine Delire; Christophe Piscart; Christopher Andrews; Corinna Rebmann; Cristina Branquinho; Dana Polyanskaya; David Fuentes Delgado; Dirk Wundram; Diyaa Radeideh; Eduardo Ordóñez-Regil; Edward Crawford; Elena Preda; Elena Tropina; Elli Groner; Eric Lucot; Erzsébet Hornung; Esperança Gacia; Esther Lévesque; Evanilde Benedito; Evgeny A. Davydov; Evy Ampoorter; Fabio Padilha Bolzan; Felipe Varela; Ferdinand Kristöfel; Fernando T. Maestre; Florence Maunoury-Danger; Florian Hofhansl; Florian Kitz; Flurin Sutter; Francisco Cuesta; Francisco de Almeida Lobo; Franco Leandro de Souza; Frank Berninger; Franz Zehetner; Georg Wohlfahrt; George Vourlitis; Geovana Carreño-Rocabado; Gina Arena; Gisele Daiane Pinha; Grizelle González; Guylaine Canut; Hanna Lee; Hans Verbeeck; Harald Auge; Harald Pauli; Hassan Bismarck Nacro; Héctor A. Bahamonde; Heike Feldhaar; Heinke Jäger; Helena C. Serrano; Hélène Verheyden; Helge Bruelheide; Henning Meesenburg; Hermann Jungkunst; Hervé Jactel; Hideaki Shibata; Hiroko Kurokawa; Hugo López Rosas; Hugo L. Rojas Villalobos; Ian Yesilonis; Inara Melece; Inge Van Halder; Inmaculada García Quirós; Isaac Makelele; Issaka Senou; István Fekete; Ivan Mihal; Ivika Ostonen; Jana Borovská; Javier Roales; Jawad Shoqeir; Jean-Christophe Lata; Jean-Paul Theurillat; Jean-Luc Probst; Jess Zimmerman; Jeyanny Vijayanathan; Jianwu Tang; Jill Thompson; Jiří Doležal; Joan-Albert Sanchez-Cabeza; Joël Merlet; Joh Henschel; Johan Neirynck; Johannes Knops; John Loehr; Jonathan von Oppen; Jónína Sigríður Þorláksdóttir; Jörg Löffler; José-Gilberto Cardoso-Mohedano; José-Luis Benito-Alonso; Jose Marcelo Torezan; Joseph C. Morina; Juan J. Jiménez; Juan Dario Quinde; Juha Alatalo; Julia Seeber; Jutta Stadler; Kaie Kriiska; Kalifa Coulibaly; Karibu Fukuzawa; Katalin Szlavecz; Katarína Gerhátová; Kate Lajtha; Kathrin Käppeler; Katie A. Jennings; Katja Tielbörger; Kazuhiko Hoshizaki; Ken Green; Lambiénou Yé; Laryssa Helena Ribeiro Pazianoto; Laura Dienstbach; Laura Williams; Laura Yahdjian; Laurel M. Brigham; Liesbeth van den Brink; Lindsey Rustad; al. et
2018-01-01
Through litter decomposition enormous amounts of carbon is emitted to the atmosphere. Numerous large-scale decomposition experiments have been conducted focusing on this fundamental soil process in order to understand the controls on the terrestrial carbon transfer to the atmosphere. However, previous studies were mostly based on site-specific litter and methodologies...
Nutrient Dynamics and Litter Decomposition in Leucaena ...
African Journals Online (AJOL)
Nutrient contents and rate of litter decomposition were investigated in Leucaena leucocephala plantation in the University of Agriculture, Abeokuta, Ogun State, Nigeria. Litter bag technique was used to study the pattern and rate of litter decomposition and nutrient release of Leucaena leucocephala. Fifty grams of oven-dried ...
Climate history shapes contemporary leaf litter decomposition
Michael S. Strickland; Ashley D. Keiser; Mark A. Bradford
2015-01-01
Litter decomposition is mediated by multiple variables, of which climate is expected to be a dominant factor at global scales. However, like other organisms, traits of decomposers and their communities are shaped not just by the contemporary climate but also their climate history. Whether or not this affects decomposition rates is underexplored. Here we source...
The decomposition of estuarine macrophytes under different ...
African Journals Online (AJOL)
The aim of this study was to determine the decomposition characteristics of the most dominant submerged macrophyte and macroalgal species in the Great Brak Estuary. Laboratory experiments were conducted to determine the effect of different temperature regimes on the rate of decomposition of 3 macrophyte species ...
Decomposition and flame structure of hydrazinium nitroformate
Louwers, J.; Parr, T.; Hanson-Parr, D.
1999-01-01
The decomposition of hydrazinium nitroformate (HNF) was studied in a hot quartz cell and by dropping small amounts of HNF on a hot plate. The species formed during the decomposition were identified by ultraviolet-visible absorption experiments. These experiments reveal that first HONO is formed. The
Multilevel index decomposition analysis: Approaches and application
International Nuclear Information System (INIS)
Xu, X.Y.; Ang, B.W.
2014-01-01
With the growing interest in using the technique of index decomposition analysis (IDA) in energy and energy-related emission studies, such as to analyze the impacts of activity structure change or to track economy-wide energy efficiency trends, the conventional single-level IDA may not be able to meet certain needs in policy analysis. In this paper, some limitations of single-level IDA studies which can be addressed through applying multilevel decomposition analysis are discussed. We then introduce and compare two multilevel decomposition procedures, which are referred to as the multilevel-parallel (M-P) model and the multilevel-hierarchical (M-H) model. The former uses a similar decomposition procedure as in the single-level IDA, while the latter uses a stepwise decomposition procedure. Since the stepwise decomposition procedure is new in the IDA literature, the applicability of the popular IDA methods in the M-H model is discussed and cases where modifications are needed are explained. Numerical examples and application studies using the energy consumption data of the US and China are presented. - Highlights: • We discuss the limitations of single-level decomposition in IDA applied to energy study. • We introduce two multilevel decomposition models, study their features and discuss how they can address the limitations. • To extend from single-level to multilevel analysis, necessary modifications to some popular IDA methods are discussed. • We further discuss the practical significance of the multilevel models and present examples and cases to illustrate
In situ study of glasses decomposition layer
International Nuclear Information System (INIS)
Zarembowitch-Deruelle, O.
1997-01-01
The aim of this work is to understand the involved mechanisms during the decomposition of glasses by water and the consequences on the morphology of the decomposition layer, in particular in the case of a nuclear glass: the R 7 T 7 . The chemical composition of this glass being very complicated, it is difficult to know the influence of the different elements on the decomposition kinetics and on the resulting morphology because several atoms have a same behaviour. Glasses with simplified composition (only 5 elements) have then been synthesized. The morphological and structural characteristics of these glasses have been given. They have then been decomposed by water. The leaching curves do not reflect the decomposition kinetics but the solubility of the different elements at every moment. The three steps of the leaching are: 1) de-alkalinization 2) lattice rearrangement 3) heavy elements solubilization. Two decomposition layer types have also been revealed according to the glass heavy elements rate. (O.M.)
Multilinear operators for higher-order decompositions.
Energy Technology Data Exchange (ETDEWEB)
Kolda, Tamara Gibson
2006-04-01
We propose two new multilinear operators for expressing the matrix compositions that are needed in the Tucker and PARAFAC (CANDECOMP) decompositions. The first operator, which we call the Tucker operator, is shorthand for performing an n-mode matrix multiplication for every mode of a given tensor and can be employed to concisely express the Tucker decomposition. The second operator, which we call the Kruskal operator, is shorthand for the sum of the outer-products of the columns of N matrices and allows a divorce from a matricized representation and a very concise expression of the PARAFAC decomposition. We explore the properties of the Tucker and Kruskal operators independently of the related decompositions. Additionally, we provide a review of the matrix and tensor operations that are frequently used in the context of tensor decompositions.
Management intensity alters decomposition via biological pathways
Wickings, Kyle; Grandy, A. Stuart; Reed, Sasha; Cleveland, Cory
2011-01-01
Current conceptual models predict that changes in plant litter chemistry during decomposition are primarily regulated by both initial litter chemistry and the stage-or extent-of mass loss. Far less is known about how variations in decomposer community structure (e.g., resulting from different ecosystem management types) could influence litter chemistry during decomposition. Given the recent agricultural intensification occurring globally and the importance of litter chemistry in regulating soil organic matter storage, our objectives were to determine the potential effects of agricultural management on plant litter chemistry and decomposition rates, and to investigate possible links between ecosystem management, litter chemistry and decomposition, and decomposer community composition and activity. We measured decomposition rates, changes in litter chemistry, extracellular enzyme activity, microarthropod communities, and bacterial versus fungal relative abundance in replicated conventional-till, no-till, and old field agricultural sites for both corn and grass litter. After one growing season, litter decomposition under conventional-till was 20% greater than in old field communities. However, decomposition rates in no-till were not significantly different from those in old field or conventional-till sites. After decomposition, grass residue in both conventional- and no-till systems was enriched in total polysaccharides relative to initial litter, while grass litter decomposed in old fields was enriched in nitrogen-bearing compounds and lipids. These differences corresponded with differences in decomposer communities, which also exhibited strong responses to both litter and management type. Overall, our results indicate that agricultural intensification can increase litter decomposition rates, alter decomposer communities, and influence litter chemistry in ways that could have important and long-term effects on soil organic matter dynamics. We suggest that future
LMDI decomposition approach: A guide for implementation
International Nuclear Information System (INIS)
Ang, B.W.
2015-01-01
Since it was first used by researchers to analyze industrial electricity consumption in the early 1980s, index decomposition analysis (IDA) has been widely adopted in energy and emission studies. Lately its use as the analytical component of accounting frameworks for tracking economy-wide energy efficiency trends has attracted considerable attention and interest among policy makers. The last comprehensive literature review of IDA was reported in 2000 which is some years back. After giving an update and presenting the key trends in the last 15 years, this study focuses on the implementation issues of the logarithmic mean Divisia index (LMDI) decomposition methods in view of their dominance in IDA in recent years. Eight LMDI models are presented and their origin, decomposition formulae, and strengths and weaknesses are summarized. Guidelines on the choice among these models are provided to assist users in implementation. - Highlights: • Guidelines for implementing LMDI decomposition approach are provided. • Eight LMDI decomposition models are summarized and compared. • The development of the LMDI decomposition approach is presented. • The latest developments of index decomposition analysis are briefly reviewed.
Thermal decomposition of beryllium perchlorate tetrahydrate
International Nuclear Information System (INIS)
Berezkina, L.G.; Borisova, S.I.; Tamm, N.S.; Novoselova, A.V.
1975-01-01
Thermal decomposition of Be(ClO 4 ) 2 x4H 2 O was studied by the differential flow technique in the helium stream. The kinetics was followed by an exchange reaction of the perchloric acid appearing by the decomposition with potassium carbonate. The rate of CO 2 liberation in this process was recorded by a heat conductivity detector. The exchange reaction yielding CO 2 is quantitative, it is not the limiting one and it does not distort the kinetics of the process of perchlorate decomposition. The solid products of decomposition were studied by infrared and NMR spectroscopy, roentgenography, thermography and chemical analysis. A mechanism suggested for the decomposition involves intermediate formation of hydroxyperchlorate: Be(ClO 4 ) 2 x4H 2 O → Be(OH)ClO 4 +HClO 4 +3H 2 O; Be(OH)ClO 4 → BeO+HClO 4 . Decomposition is accompained by melting of the sample. The mechanism of decomposition is hydrolytic. At room temperature the hydroxyperchlorate is a thick syrup-like compound crystallizing after long storing
Thermal decomposition of lanthanide and actinide tetrafluorides
International Nuclear Information System (INIS)
Gibson, J.K.; Haire, R.G.
1988-01-01
The thermal stabilities of several lanthanide/actinide tetrafluorides have been studied using mass spectrometry to monitor the gaseous decomposition products, and powder X-ray diffraction (XRD) to identify solid products. The tetrafluorides, TbF 4 , CmF 4 , and AmF 4 , have been found to thermally decompose to their respective solid trifluorides with accompanying release of fluorine, while cerium tetrafluoride has been found to be significantly more thermally stable and to congruently sublime as CeF 4 prior to appreciable decomposition. The results of these studies are discussed in relation to other relevant experimental studies and the thermodynamics of the decomposition processes. 9 refs., 3 figs
Decomposition of lake phytoplankton. 1
International Nuclear Information System (INIS)
Hansen, L.; Krog, G.F.; Soendergaard, M.
1986-01-01
Short-time (24 h) and long-time (4-6 d) decomposition of phytoplankton cells were investigasted under in situ conditions in four Danish lakes. Carbon-14-labelled, dead algae were exposed to sterile or natural lake water and the dynamics of cell lysis and bacterial utilization of the leached products were followed. The lysis process was dominated by an initial fast water extraction. Within 2 to 4 h from 4 to 34% of the labelled carbon leached from the algal cells. After 24 h from 11 to 43% of the initial particulate carbon was found as dissolved carbon in the experiments with sterile lake water; after 4 to 6 d the leaching was from 67 to 78% of the initial 14 C. The leached compounds were utilized by bacteria. A comparison of the incubations using sterile and natural water showed that a mean of 71% of the lysis products was metabolized by microorganisms within 24 h. In two experiments the uptake rate equalled the leaching rate. (author)
Decomposition of lake phytoplankton. 2
International Nuclear Information System (INIS)
Hansen, L.; Krog, G.F.; Soendergaard, M.
1986-01-01
The lysis process of phytoplankton was followed in 24 h incubations in three Danish lakes. By means of gel-chromatography it was shown that the dissolved carbon leaching from different algal groups differed in molecular weight composition. Three distinct molecular weight classes (>10,000; 700 to 10,000 and < 700 Daltons) leached from blue-green algae in almost equal proportion. The lysis products of spring-bloom diatoms included only the two smaller size classes, and the molecules between 700 and 10,000 Daltons dominated. Measurements of cell content during decomposition of the diatoms revealed polysaccharides and low molecular weight compounds to dominate the lysis products. No proteins were leached during the first 24 h after cell death. By incubating the dead algae in natural lake water, it was possible to detect a high bacterial affinity towards molecules between 700 and 10,000 Daltons, although the other size classes were also utilized. Bacterial transformation of small molecules to larger molecules could be demonstrated. (author)
A Decomposition Theorem for Finite Automata.
Santa Coloma, Teresa L.; Tucci, Ralph P.
1990-01-01
Described is automata theory which is a branch of theoretical computer science. A decomposition theorem is presented that is easier than the Krohn-Rhodes theorem. Included are the definitions, the theorem, and a proof. (KR)
Spatial domain decomposition for neutron transport problems
International Nuclear Information System (INIS)
Yavuz, M.; Larsen, E.W.
1989-01-01
A spatial Domain Decomposition method is proposed for modifying the Source Iteration (SI) and Diffusion Synthetic Acceleration (DSA) algorithms for solving discrete ordinates problems. The method, which consists of subdividing the spatial domain of the problem and performing the transport sweeps independently on each subdomain, has the advantage of being parallelizable because the calculations in each subdomain can be performed on separate processors. In this paper we describe the details of this spatial decomposition and study, by numerical experimentation, the effect of this decomposition on the SI and DSA algorithms. Our results show that the spatial decomposition has little effect on the convergence rates until the subdomains become optically thin (less than about a mean free path in thickness)
Johnston, P R; Walker, S J; Hyttinen, J A; Kilpatrick, D
1994-04-01
The inverse problem of electrocardiography, the computation of epicardial potentials from body surface potentials, is influenced by the desired resolution on the epicardium, the number of recording points on the body surface, and the method of limiting the inversion process. To examine the role of these variables in the computation of the inverse transform, Tikhonov's zero-order regularization and singular value decomposition (SVD) have been used to invert the forward transfer matrix. The inverses have been compared in a data-independent manner using the resolution and the noise amplification as endpoints. Sets of 32, 50, 192, and 384 leads were chosen as sets of body surface data, and 26, 50, 74, and 98 regions were chosen to represent the epicardium. The resolution and noise were both improved by using a greater number of electrodes on the body surface. When 60% of the singular values are retained, the results show a trade-off between noise and resolution, with typical maximal epicardial noise levels of less than 0.5% of maximum epicardial potentials for 26 epicardial regions, 2.5% for 50 epicardial regions, 7.5% for 74 epicardial regions, and 50% for 98 epicardial regions. As the number of epicardial regions is increased, the regularization technique effectively fixes the noise amplification but markedly decreases the resolution, whereas SVD results in an increase in noise and a moderate decrease in resolution. Overall the regularization technique performs slightly better than SVD in the noise-resolution relationship. There is a region at the posterior of the heart that was poorly resolved regardless of the number of regions chosen. The variance of the resolution was such as to suggest the use of variable-size epicardial regions based on the resolution.
Joint Matrices Decompositions and Blind Source Separation
Czech Academy of Sciences Publication Activity Database
Chabriel, G.; Kleinsteuber, M.; Moreau, E.; Shen, H.; Tichavský, Petr; Yeredor, A.
2014-01-01
Roč. 31, č. 3 (2014), s. 34-43 ISSN 1053-5888 R&D Projects: GA ČR GA102/09/1278 Institutional support: RVO:67985556 Keywords : joint matrices decomposition * tensor decomposition * blind source separation Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 5.852, year: 2014 http://library.utia.cas.cz/separaty/2014/SI/tichavsky-0427607.pdf
Review on Thermal Decomposition of Ammonium Nitrate
Chaturvedi, Shalini; Dave, Pragnesh N.
2013-01-01
In this review data from the literature on thermal decomposition of ammonium nitrate (AN) and the effect of additives to their thermal decomposition are summarized. The effect of additives like oxides, cations, inorganic acids, organic compounds, phase-stablized CuO, etc., is discussed. The effect of an additive mainly occurs at the exothermic peak of pure AN in a temperature range of 200°C to 140°C.
Microbiological decomposition of bagasse after radiation pasteurization
International Nuclear Information System (INIS)
Ito, Hitoshi; Ishigaki, Isao
1987-01-01
Microbiological decomposition of bagasse was studied for upgrading to animal feeds after radiation pasteurization. Solid-state culture media of bagasse were prepared with addition of some amount of inorganic salts for nitrogen source, and after irradiation, fungi were infected for cultivation. In this study, many kind of cellulosic fungi such as Pleurotus ostreatus, P. flavellatus, Verticillium sp., Coprinus cinereus, Lentinus edodes, Aspergillus niger, Trichoderma koningi, T. viride were used for comparison of decomposition of crude fibers. In alkali nontreated bagasse, P. ostreatus, P. flavellatus, C. cinereus and Verticillium sp. could decompose crude fibers from 25 to 34 % after one month of cultivation, whereas other fungi such as A. niger, T. koningi, T. viride, L. edodes decomposed below 10 %. On the contrary, alkali treatment enhanced the decomposition of crude fiber by A. niger, T. koningi and T. viride to be 29 to 47 % as well as Pleurotus species or C. cinereus. Other species of mushrooms such as L. edodes had a little ability of decomposition even after alkali treatment. Radiation treatment with 10 kGy could not enhance the decomposition of bagasse compared with steam treatment, whereas higher doses of radiation treatment enhanced a little of decomposition of crude fibers by microorganisms. (author)
Decomposition of tetrachloroethylene by ionizing radiation
International Nuclear Information System (INIS)
Hakoda, T.; Hirota, K.; Hashimoto, S.
1998-01-01
Decomposition of tetrachloroethylene and other chloroethenes by ionizing radiation were examined to get information on treatment of industrial off-gas. Model gases, airs containing chloroethenes, were confined in batch reactors and irradiated with electron beam and gamma ray. The G-values of decomposition were larger in the order of tetrachloro- > trichloro- > trans-dichloro- > cis-dichloro- > monochloroethylene in electron beam irradiation and tetrachloro-, trichloro-, trans-dichloro- > cis-dichloro- > monochloroethylene in gamma ray irradiation. For tetrachloro-, trichloro- and trans-dichloroethylene, G-values of decomposition in EB irradiation increased with increase of chlorine atom in a molecule, while those in gamma ray irradiation were almost kept constant. The G-value of decomposition for tetrachloroethylene in EB irradiation was the largest of those for all chloroethenes. In order to examine the effect of the initial concentration on G-value of decomposition, airs containing 300 to 1,800 ppm of tetrachloroethylene were irradiated with electron beam and gamma ray. The G-values of decomposition in both irradiation increased with the initial concentration. Those in electron beam irradiation were two times larger than those in gamma ray irradiation
Microbiological decomposition of bagasse after radiation pasteurization
Energy Technology Data Exchange (ETDEWEB)
Ito, Hitoshi; Ishigaki, Isao
1987-11-01
Microbiological decomposition of bagasse was studied for upgrading to animal feeds after radiation pasteurization. Solid-state culture media of bagasse were prepared with addition of some amount of inorganic salts for nitrogen source, and after irradiation, fungi were infected for cultivation. In this study, many kind of cellulosic fungi such as Pleurotus ostreatus, P. flavellatus, Verticillium sp., Coprinus cinereus, Lentinus edodes, Aspergillus niger, Trichoderma koningi, T. viride were used for comparison of decomposition of crude fibers. In alkali nontreated bagasse, P. ostreatus, P. flavellatus, C. cinereus and Verticillium sp. could decompose crude fibers from 25 to 34 % after one month of cultivation, whereas other fungi such as A. niger, T. koningi, T. viride, L. edodes decomposed below 10 %. On the contrary, alkali treatment enhanced the decomposition of crude fiber by A. niger, T. koningi and T. viride to be 29 to 47 % as well as Pleurotus species or C. cinereus. Other species of mushrooms such as L. edodes had a little ability of decomposition even after alkali treatment. Radiation treatment with 10 kGy could not enhance the decomposition of bagasse compared with steam treatment, whereas higher doses of radiation treatment enhanced a little of decomposition of crude fibers by microorganisms.
Monte-Carlo error analysis in x-ray spectral deconvolution
International Nuclear Information System (INIS)
Shirk, D.G.; Hoffman, N.M.
1985-01-01
The deconvolution of spectral information from sparse x-ray data is a widely encountered problem in data analysis. An often-neglected aspect of this problem is the propagation of random error in the deconvolution process. We have developed a Monte-Carlo approach that enables us to attach error bars to unfolded x-ray spectra. Our Monte-Carlo error analysis has been incorporated into two specific deconvolution techniques: the first is an iterative convergent weight method; the second is a singular-value-decomposition (SVD) method. These two methods were applied to an x-ray spectral deconvolution problem having m channels of observations with n points in energy space. When m is less than n, this problem has no unique solution. We discuss the systematics of nonunique solutions and energy-dependent error bars for both methods. The Monte-Carlo approach has a particular benefit in relation to the SVD method: It allows us to apply the constraint of spectral nonnegativity after the SVD deconvolution rather than before. Consequently, we can identify inconsistencies between different detector channels
A collaborative filtering-based approach to biomedical knowledge discovery.
Lever, Jake; Gakkhar, Sitanshu; Gottlieb, Michael; Rashnavadi, Tahereh; Lin, Santina; Siu, Celia; Smith, Maia; Jones, Martin R; Krzywinski, Martin; Jones, Steven J M; Wren, Jonathan
2018-02-15
The increase in publication rates makes it challenging for an individual researcher to stay abreast of all relevant research in order to find novel research hypotheses. Literature-based discovery methods make use of knowledge graphs built using text mining and can infer future associations between biomedical concepts that will likely occur in new publications. These predictions are a valuable resource for researchers to explore a research topic. Current methods for prediction are based on the local structure of the knowledge graph. A method that uses global knowledge from across the knowledge graph needs to be developed in order to make knowledge discovery a frequently used tool by researchers. We propose an approach based on the singular value decomposition (SVD) that is able to combine data from across the knowledge graph through a reduced representation. Using cooccurrence data extracted from published literature, we show that SVD performs better than the leading methods for scoring discoveries. We also show the diminishing predictive power of knowledge discovery as we compare our predictions with real associations that appear further into the future. Finally, we examine the strengths and weaknesses of the SVD approach against another well-performing system using several predicted associations. All code and results files for this analysis can be accessed at https://github.com/jakelever/knowledgediscovery. sjones@bcgsc.ca. Supplementary data are available at Bioinformatics online. © The Author (2017). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com
Directory of Open Access Journals (Sweden)
Lee Yun-Shien
2008-03-01
Full Text Available Abstract Background The hierarchical clustering tree (HCT with a dendrogram 1 and the singular value decomposition (SVD with a dimension-reduced representative map 2 are popular methods for two-way sorting the gene-by-array matrix map employed in gene expression profiling. While HCT dendrograms tend to optimize local coherent clustering patterns, SVD leading eigenvectors usually identify better global grouping and transitional structures. Results This study proposes a flipping mechanism for a conventional agglomerative HCT using a rank-two ellipse (R2E, an improved SVD algorithm for sorting purpose seriation by Chen 3 as an external reference. While HCTs always produce permutations with good local behaviour, the rank-two ellipse seriation gives the best global grouping patterns and smooth transitional trends. The resulting algorithm automatically integrates the desirable properties of each method so that users have access to a clustering and visualization environment for gene expression profiles that preserves coherent local clusters and identifies global grouping trends. Conclusion We demonstrate, through four examples, that the proposed method not only possesses better numerical and statistical properties, it also provides more meaningful biomedical insights than other sorting algorithms. We suggest that sorted proximity matrices for genes and arrays, in addition to the gene-by-array expression matrix, can greatly aid in the search for comprehensive understanding of gene expression structures. Software for the proposed methods can be obtained at http://gap.stat.sinica.edu.tw/Software/GAP.
A DDoS Attack Detection Method Based on Hybrid Heterogeneous Multiclassifier Ensemble Learning
Directory of Open Access Journals (Sweden)
Bin Jia
2017-01-01
Full Text Available The explosive growth of network traffic and its multitype on Internet have brought new and severe challenges to DDoS attack detection. To get the higher True Negative Rate (TNR, accuracy, and precision and to guarantee the robustness, stability, and universality of detection system, in this paper, we propose a DDoS attack detection method based on hybrid heterogeneous multiclassifier ensemble learning and design a heuristic detection algorithm based on Singular Value Decomposition (SVD to construct our detection system. Experimental results show that our detection method is excellent in TNR, accuracy, and precision. Therefore, our algorithm has good detective performance for DDoS attack. Through the comparisons with Random Forest, k-Nearest Neighbor (k-NN, and Bagging comprising the component classifiers when the three algorithms are used alone by SVD and by un-SVD, it is shown that our model is superior to the state-of-the-art attack detection techniques in system generalization ability, detection stability, and overall detection performance.
Closed orbit feedback with digital signal processing
International Nuclear Information System (INIS)
Chung, Y.; Kirchman, J.; Lenkszus, F.
1994-01-01
The closed orbit feedback experiment conducted on the SPEAR using the singular value decomposition (SVD) technique and digital signal processing (DSP) is presented. The beam response matrix, defined as beam motion at beam position monitor (BPM) locations per unit kick by corrector magnets, was measured and then analyzed using SVD. Ten BPMs, sixteen correctors, and the eight largest SVD eigenvalues were used for closed orbit correction. The maximum sampling frequency for the closed loop feedback was measured at 37 Hz. Using the proportional and integral (PI) control algorithm with the gains Kp = 3 and K I = 0.05 and the open-loop bandwidth corresponding to 1% of the sampling frequency, a correction bandwidth (-3 dB) of approximately 0.8 Hz was achieved. Time domain measurements showed that the response time of the closed loop feedback system for 1/e decay was approximately 0.25 second. This result implies ∼ 100 Hz correction bandwidth for the planned beam position feedback system for the Advanced Photon Source storage ring with the projected 4-kHz sampling frequency
Zhu, Lei; Guo, Ning; Li, Quanzheng; Ma, Ying; Jacboson, Orit; Lee, Seulki; Choi, Hak Soo; Mansfield, James R; Niu, Gang; Chen, Xiaoyuan
2012-01-01
The aim of this study is to determine if dynamic optical imaging could provide comparable kinetic parameters to that of dynamic PET imaging by a near-infrared dye/(64)Cu dual-labeled cyclic RGD peptide. The integrin α(v)β(3) binding RGD peptide was conjugated with a macrocyclic chelator 1,4,7,10-tetraazacyclododecane-1,4,7,10-tetraacetic acid (DOTA) for copper labeling and PET imaging and a near-infrared dye ZW-1 for optical imaging. The in vitro biological activity of RGD-C(DOTA)-ZW-1 was characterized by cell staining and receptor binding assay. Sixty-min dynamic PET and optical imaging were acquired on a MDA-MB-435 tumor model. Singular value decomposition (SVD) method was applied to compute the dynamic optical signal from the two-dimensional optical projection images. Compartment models were used to quantitatively analyze and compare the dynamic optical and PET data. The dual-labeled probe (64)Cu-RGD-C(DOTA)-ZW-1 showed integrin specific binding in vitro and in vivo. The binding potential (Bp) derived from dynamic optical imaging (1.762 ± 0.020) is comparable to that from dynamic PET (1.752 ± 0.026). The signal un-mixing process using SVD improved the accuracy of kinetic modeling of 2D dynamic optical data. Our results demonstrate that 2D dynamic optical imaging with SVD analysis could achieve comparable quantitative results as dynamic PET imaging in preclinical xenograft models.
Aridity and decomposition processes in complex landscapes
Ossola, Alessandro; Nyman, Petter
2015-04-01
Decomposition of organic matter is a key biogeochemical process contributing to nutrient cycles, carbon fluxes and soil development. The activity of decomposers depends on microclimate, with temperature and rainfall being major drivers. In complex terrain the fine-scale variation in microclimate (and hence water availability) as a result of slope orientation is caused by differences in incoming radiation and surface temperature. Aridity, measured as the long-term balance between net radiation and rainfall, is a metric that can be used to represent variations in water availability within the landscape. Since aridity metrics can be obtained at fine spatial scales, they could theoretically be used to investigate how decomposition processes vary across complex landscapes. In this study, four research sites were selected in tall open sclerophyll forest along a aridity gradient (Budyko dryness index ranging from 1.56 -2.22) where microclimate, litter moisture and soil moisture were monitored continuously for one year. Litter bags were packed to estimate decomposition rates (k) using leaves of a tree species not present in the study area (Eucalyptus globulus) in order to avoid home-field advantage effects. Litter mass loss was measured to assess the activity of macro-decomposers (6mm litter bag mesh size), meso-decomposers (1 mm mesh), microbes above-ground (0.2 mm mesh) and microbes below-ground (2 cm depth, 0.2 mm mesh). Four replicates for each set of bags were installed at each site and bags were collected at 1, 2, 4, 7 and 12 months since installation. We first tested whether differences in microclimate due to slope orientation have significant effects on decomposition processes. Then the dryness index was related to decomposition rates to evaluate if small-scale variation in decomposition can be predicted using readily available information on rainfall and radiation. Decomposition rates (k), calculated fitting single pool negative exponential models, generally
Decomposition of forest products buried in landfills
International Nuclear Information System (INIS)
Wang, Xiaoming; Padgett, Jennifer M.; Powell, John S.; Barlaz, Morton A.
2013-01-01
Highlights: • This study tracked chemical changes of wood and paper in landfills. • A decomposition index was developed to quantify carbohydrate biodegradation. • Newsprint biodegradation as measured here is greater than previous reports. • The field results correlate well with previous laboratory measurements. - Abstract: The objective of this study was to investigate the decomposition of selected wood and paper products in landfills. The decomposition of these products under anaerobic landfill conditions results in the generation of biogenic carbon dioxide and methane, while the un-decomposed portion represents a biogenic carbon sink. Information on the decomposition of these municipal waste components is used to estimate national methane emissions inventories, for attribution of carbon storage credits, and to assess the life-cycle greenhouse gas impacts of wood and paper products. Hardwood (HW), softwood (SW), plywood (PW), oriented strand board (OSB), particleboard (PB), medium-density fiberboard (MDF), newsprint (NP), corrugated container (CC) and copy paper (CP) were buried in landfills operated with leachate recirculation, and were excavated after approximately 1.5 and 2.5 yr. Samples were analyzed for cellulose (C), hemicellulose (H), lignin (L), volatile solids (VS), and organic carbon (OC). A holocellulose decomposition index (HOD) and carbon storage factor (CSF) were calculated to evaluate the extent of solids decomposition and carbon storage. Samples of OSB made from HW exhibited cellulose plus hemicellulose (C + H) loss of up to 38%, while loss for the other wood types was 0–10% in most samples. The C + H loss was up to 81%, 95% and 96% for NP, CP and CC, respectively. The CSFs for wood and paper samples ranged from 0.34 to 0.47 and 0.02 to 0.27 g OC g −1 dry material, respectively. These results, in general, correlated well with an earlier laboratory-scale study, though NP and CC decomposition measured in this study were higher than
Decomposition of forest products buried in landfills
Energy Technology Data Exchange (ETDEWEB)
Wang, Xiaoming, E-mail: xwang25@ncsu.edu [Department of Civil, Construction, and Environmental Engineering, Campus Box 7908, North Carolina State University, Raleigh, NC 27695-7908 (United States); Padgett, Jennifer M. [Department of Civil, Construction, and Environmental Engineering, Campus Box 7908, North Carolina State University, Raleigh, NC 27695-7908 (United States); Powell, John S. [Department of Chemical and Biomolecular Engineering, Campus Box 7905, North Carolina State University, Raleigh, NC 27695-7905 (United States); Barlaz, Morton A. [Department of Civil, Construction, and Environmental Engineering, Campus Box 7908, North Carolina State University, Raleigh, NC 27695-7908 (United States)
2013-11-15
Highlights: • This study tracked chemical changes of wood and paper in landfills. • A decomposition index was developed to quantify carbohydrate biodegradation. • Newsprint biodegradation as measured here is greater than previous reports. • The field results correlate well with previous laboratory measurements. - Abstract: The objective of this study was to investigate the decomposition of selected wood and paper products in landfills. The decomposition of these products under anaerobic landfill conditions results in the generation of biogenic carbon dioxide and methane, while the un-decomposed portion represents a biogenic carbon sink. Information on the decomposition of these municipal waste components is used to estimate national methane emissions inventories, for attribution of carbon storage credits, and to assess the life-cycle greenhouse gas impacts of wood and paper products. Hardwood (HW), softwood (SW), plywood (PW), oriented strand board (OSB), particleboard (PB), medium-density fiberboard (MDF), newsprint (NP), corrugated container (CC) and copy paper (CP) were buried in landfills operated with leachate recirculation, and were excavated after approximately 1.5 and 2.5 yr. Samples were analyzed for cellulose (C), hemicellulose (H), lignin (L), volatile solids (VS), and organic carbon (OC). A holocellulose decomposition index (HOD) and carbon storage factor (CSF) were calculated to evaluate the extent of solids decomposition and carbon storage. Samples of OSB made from HW exhibited cellulose plus hemicellulose (C + H) loss of up to 38%, while loss for the other wood types was 0–10% in most samples. The C + H loss was up to 81%, 95% and 96% for NP, CP and CC, respectively. The CSFs for wood and paper samples ranged from 0.34 to 0.47 and 0.02 to 0.27 g OC g{sup −1} dry material, respectively. These results, in general, correlated well with an earlier laboratory-scale study, though NP and CC decomposition measured in this study were higher than
Directory of Open Access Journals (Sweden)
Sheng-Ping Yan
2014-01-01
Full Text Available We perform a comparison between the local fractional Adomian decomposition and local fractional function decomposition methods applied to the Laplace equation. The operators are taken in the local sense. The results illustrate the significant features of the two methods which are both very effective and straightforward for solving the differential equations with local fractional derivative.
Global decomposition experiment shows soil animal impacts on decomposition are climate-dependent
Czech Academy of Sciences Publication Activity Database
Wall, D.H.; Bradford, M.A.; John, M.G.St.; Trofymow, J.A.; Behan-Pelletier, V.; Bignell, D.E.; Dangerfield, J.M.; Parton, W.J.; Rusek, Josef; Voigt, W.; Wolters, V.; Gardel, H.Z.; Ayuke, F. O.; Bashford, R.; Beljakova, O.I.; Bohlen, P.J.; Brauman, A.; Flemming, S.; Henschel, J.R.; Johnson, D.L.; Jones, T.H.; Kovářová, Marcela; Kranabetter, J.M.; Kutny, L.; Lin, K.-Ch.; Maryati, M.; Masse, D.; Pokarzhevskii, A.; Rahman, H.; Sabará, M.G.; Salamon, J.-A.; Swift, M.J.; Varela, A.; Vasconcelos, H.L.; White, D.; Zou, X.
2008-01-01
Roč. 14, č. 11 (2008), s. 2661-2677 ISSN 1354-1013 Institutional research plan: CEZ:AV0Z60660521; CEZ:AV0Z60050516 Keywords : climate decomposition index * decomposition * litter Subject RIV: EH - Ecology, Behaviour Impact factor: 5.876, year: 2008
Steganography based on pixel intensity value decomposition
Abdulla, Alan Anwar; Sellahewa, Harin; Jassim, Sabah A.
2014-05-01
This paper focuses on steganography based on pixel intensity value decomposition. A number of existing schemes such as binary, Fibonacci, Prime, Natural, Lucas, and Catalan-Fibonacci (CF) are evaluated in terms of payload capacity and stego quality. A new technique based on a specific representation is proposed to decompose pixel intensity values into 16 (virtual) bit-planes suitable for embedding purposes. The proposed decomposition has a desirable property whereby the sum of all bit-planes does not exceed the maximum pixel intensity value, i.e. 255. Experimental results demonstrate that the proposed technique offers an effective compromise between payload capacity and stego quality of existing embedding techniques based on pixel intensity value decomposition. Its capacity is equal to that of binary and Lucas, while it offers a higher capacity than Fibonacci, Prime, Natural, and CF when the secret bits are embedded in 1st Least Significant Bit (LSB). When the secret bits are embedded in higher bit-planes, i.e., 2nd LSB to 8th Most Significant Bit (MSB), the proposed scheme has more capacity than Natural numbers based embedding. However, from the 6th bit-plane onwards, the proposed scheme offers better stego quality. In general, the proposed decomposition scheme has less effect in terms of quality on pixel value when compared to most existing pixel intensity value decomposition techniques when embedding messages in higher bit-planes.
Microbial Signatures of Cadaver Gravesoil During Decomposition.
Finley, Sheree J; Pechal, Jennifer L; Benbow, M Eric; Robertson, B K; Javan, Gulnaz T
2016-04-01
Genomic studies have estimated there are approximately 10(3)-10(6) bacterial species per gram of soil. The microbial species found in soil associated with decomposing human remains (gravesoil) have been investigated and recognized as potential molecular determinants for estimates of time since death. The nascent era of high-throughput amplicon sequencing of the conserved 16S ribosomal RNA (rRNA) gene region of gravesoil microbes is allowing research to expand beyond more subjective empirical methods used in forensic microbiology. The goal of the present study was to evaluate microbial communities and identify taxonomic signatures associated with the gravesoil human cadavers. Using 16S rRNA gene amplicon-based sequencing, soil microbial communities were surveyed from 18 cadavers placed on the surface or buried that were allowed to decompose over a range of decomposition time periods (3-303 days). Surface soil microbial communities showed a decreasing trend in taxon richness, diversity, and evenness over decomposition, while buried cadaver-soil microbial communities demonstrated increasing taxon richness, consistent diversity, and decreasing evenness. The results show that ubiquitous Proteobacteria was confirmed as the most abundant phylum in all gravesoil samples. Surface cadaver-soil communities demonstrated a decrease in Acidobacteria and an increase in Firmicutes relative abundance over decomposition, while buried soil communities were consistent in their community composition throughout decomposition. Better understanding of microbial community structure and its shifts over time may be important for advancing general knowledge of decomposition soil ecology and its potential use during forensic investigations.
Thermal decomposition process of silver behenate
International Nuclear Information System (INIS)
Liu Xianhao; Lu Shuxia; Zhang Jingchang; Cao Weiliang
2006-01-01
The thermal decomposition processes of silver behenate have been studied by infrared spectroscopy (IR), X-ray diffraction (XRD), combined thermogravimetry-differential thermal analysis-mass spectrometry (TG-DTA-MS), transmission electron microscopy (TEM) and UV-vis spectroscopy. The TG-DTA and the higher temperature IR and XRD measurements indicated that complicated structural changes took place while heating silver behenate, but there were two distinct thermal transitions. During the first transition at 138 deg. C, the alkyl chains of silver behenate were transformed from an ordered into a disordered state. During the second transition at about 231 deg. C, a structural change took place for silver behenate, which was the decomposition of silver behenate. The major products of the thermal decomposition of silver behenate were metallic silver and behenic acid. Upon heating up to 500 deg. C, the final product of the thermal decomposition was metallic silver. The combined TG-MS analysis showed that the gas products of the thermal decomposition of silver behenate were carbon dioxide, water, hydrogen, acetylene and some small molecule alkenes. TEM and UV-vis spectroscopy were used to investigate the process of the formation and growth of metallic silver nanoparticles
Radar rainfall image repair techniques
Directory of Open Access Journals (Sweden)
Stephen M. Wesson
2004-01-01
Full Text Available There are various quality problems associated with radar rainfall data viewed in images that include ground clutter, beam blocking and anomalous propagation, to name a few. To obtain the best rainfall estimate possible, techniques for removing ground clutter (non-meteorological echoes that influence radar data quality on 2-D radar rainfall image data sets are presented here. These techniques concentrate on repairing the images in both a computationally fast and accurate manner, and are nearest neighbour techniques of two sub-types: Individual Target and Border Tracing. The contaminated data is estimated through Kriging, considered the optimal technique for the spatial interpolation of Gaussian data, where the 'screening effect' that occurs with the Kriging weighting distribution around target points is exploited to ensure computational efficiency. Matrix rank reduction techniques in combination with Singular Value Decomposition (SVD are also suggested for finding an efficient solution to the Kriging Equations which can cope with near singular systems. Rainfall estimation at ground level from radar rainfall volume scan data is of interest and importance in earth bound applications such as hydrology and agriculture. As an extension of the above, Ordinary Kriging is applied to three-dimensional radar rainfall data to estimate rainfall rate at ground level. Keywords: ground clutter, data infilling, Ordinary Kriging, nearest neighbours, Singular Value Decomposition, border tracing, computation time, ground level rainfall estimation
Radiolytic decomposition of 4-bromodiphenyl ether
International Nuclear Information System (INIS)
Tang Liang; Xu Gang; Wu Wenjing; Shi Wenyan; Liu Ning; Bai Yulei; Wu Minghong
2010-01-01
Polybrominated diphenyl ethers (PBDEs) spread widely in the environment are mainly removed by photochemical and anaerobic microbial degradation. In this paper, the decomposition of 4-bromodiphenyl ether (BDE -3), the PBDEs homologues, is investigated by electron beam irradiation of its ethanol/water solution (reduction system) and acetonitrile/water solution (oxidation system). The radiolytic products were determined by GC coupled with electron capture detector, and the reaction rate constant of e sol - in the reduction system was measured at 2.7 x 10 10 L · mol -1 · s -1 by pulsed radiolysis. The results show that the BDE-3 concentration affects strongly the decomposition ratio in the alkali solution, and the reduction system has a higher BDE-3 decomposition rate than the oxidation system. This indicates that the BDE-3 was reduced by effectively capturing e sol - in radiolytic process. (authors)
Parallel processing for pitch splitting decomposition
Barnes, Levi; Li, Yong; Wadkins, David; Biederman, Steve; Miloslavsky, Alex; Cork, Chris
2009-10-01
Decomposition of an input pattern in preparation for a double patterning process is an inherently global problem in which the influence of a local decomposition decision can be felt across an entire pattern. In spite of this, a large portion of the work can be massively distributed. Here, we discuss the advantages of geometric distribution for polygon operations with limited range of influence. Further, we have found that even the naturally global "coloring" step can, in large part, be handled in a geometrically local manner. In some practical cases, up to 70% of the work can be distributed geometrically. We also describe the methods for partitioning the problem into local pieces and present scaling data up to 100 CPUs. These techniques reduce DPT decomposition runtime by orders of magnitude.
Thermal Plasma decomposition of fluoriated greenhouse gases
Energy Technology Data Exchange (ETDEWEB)
Choi, Soo Seok; Watanabe, Takayuki [Tokyo Institute of Technology, Yokohama (Japan); Park, Dong Wha [Inha University, Incheon (Korea, Republic of)
2012-02-15
Fluorinated compounds mainly used in the semiconductor industry are potent greenhouse gases. Recently, thermal plasma gas scrubbers have been gradually replacing conventional burn-wet type gas scrubbers which are based on the combustion of fossil fuels because high conversion efficiency and control of byproduct generation are achievable in chemically reactive high temperature thermal plasma. Chemical equilibrium composition at high temperature and numerical analysis on a complex thermal flow in the thermal plasma decomposition system are used to predict the process of thermal decomposition of fluorinated gas. In order to increase economic feasibility of the thermal plasma decomposition process, increase of thermal efficiency of the plasma torch and enhancement of gas mixing between the thermal plasma jet and waste gas are discussed. In addition, noble thermal plasma systems to be applied in the thermal plasma gas treatment are introduced in the present paper.
Hydrogen peroxide decomposition kinetics in aquaculture water
DEFF Research Database (Denmark)
Arvin, Erik; Pedersen, Lars-Flemming
2015-01-01
during the HP decomposition. The model assumes that the enzyme decay is controlled by an inactivation stoichiometry related to the HP decomposition. In order to make the model easily applicable, it is furthermore assumed that the COD is a proxy of the active biomass concentration of the water and thereby......Hydrogen peroxide (HP) is used in aquaculture systems where preventive or curative water treatments occasionally are required. Use of chemical agents can be challenging in recirculating aquaculture systems (RAS) due to extended water retention time and because the agents must not damage the fish...... reared or the nitrifying bacteria in the biofilters at concentrations required to eliminating pathogens. This calls for quantitative insight into the fate of the disinfectant residuals during water treatment. This paper presents a kinetic model that describes the HP decomposition in aquaculture water...
Multilevel domain decomposition for electronic structure calculations
International Nuclear Information System (INIS)
Barrault, M.; Cances, E.; Hager, W.W.; Le Bris, C.
2007-01-01
We introduce a new multilevel domain decomposition method (MDD) for electronic structure calculations within semi-empirical and density functional theory (DFT) frameworks. This method iterates between local fine solvers and global coarse solvers, in the spirit of domain decomposition methods. Using this approach, calculations have been successfully performed on several linear polymer chains containing up to 40,000 atoms and 200,000 atomic orbitals. Both the computational cost and the memory requirement scale linearly with the number of atoms. Additional speed-up can easily be obtained by parallelization. We show that this domain decomposition method outperforms the density matrix minimization (DMM) method for poor initial guesses. Our method provides an efficient preconditioner for DMM and other linear scaling methods, variational in nature, such as the orbital minimization (OM) procedure
Feature selection for high-dimensional integrated data
Zheng, Charles; Schwartz, Scott; Chapkin, Robert S.; Carroll, Raymond J.; Ivanov, Ivan
2012-01-01
Motivated by the problem of identifying correlations between genes or features of two related biological systems, we propose a model of feature selection in which only a subset of the predictors Xt are dependent on the multidimensional variate Y, and the remainder of the predictors constitute a “noise set” Xu independent of Y. Using Monte Carlo simulations, we investigated the relative performance of two methods: thresholding and singular-value decomposition, in combination with stochastic optimization to determine “empirical bounds” on the small-sample accuracy of an asymptotic approximation. We demonstrate utility of the thresholding and SVD feature selection methods to with respect to a recent infant intestinal gene expression and metagenomics dataset.
Using linear algebra for protein structural comparison and classification.
Gomide, Janaína; Melo-Minardi, Raquel; Dos Santos, Marcos Augusto; Neshich, Goran; Meira, Wagner; Lopes, Júlio César; Santoro, Marcelo
2009-07-01
In this article, we describe a novel methodology to extract semantic characteristics from protein structures using linear algebra in order to compose structural signature vectors which may be used efficiently to compare and classify protein structures into fold families. These signatures are built from the pattern of hydrophobic intrachain interactions using Singular Value Decomposition (SVD) and Latent Semantic Indexing (LSI) techniques. Considering proteins as documents and contacts as terms, we have built a retrieval system which is able to find conserved contacts in samples of myoglobin fold family and to retrieve these proteins among proteins of varied folds with precision of up to 80%. The classifier is a web tool available at our laboratory website. Users can search for similar chains from a specific PDB, view and compare their contact maps and browse their structures using a JMol plug-in.
Using linear algebra for protein structural comparison and classification
Directory of Open Access Journals (Sweden)
Janaína Gomide
2009-01-01
Full Text Available In this article, we describe a novel methodology to extract semantic characteristics from protein structures using linear algebra in order to compose structural signature vectors which may be used efficiently to compare and classify protein structures into fold families. These signatures are built from the pattern of hydrophobic intrachain interactions using Singular Value Decomposition (SVD and Latent Semantic Indexing (LSI techniques. Considering proteins as documents and contacts as terms, we have built a retrieval system which is able to find conserved contacts in samples of myoglobin fold family and to retrieve these proteins among proteins of varied folds with precision of up to 80%. The classifier is a web tool available at our laboratory website. Users can search for similar chains from a specific PDB, view and compare their contact maps and browse their structures using a JMol plug-in.
DFT based spatial multiplexing and maximum ratio transmission for mm-wawe large MIMO
DEFF Research Database (Denmark)
Phan-Huy, D.-T.; Tölli, A.; Rajatheva, N.
2014-01-01
-SM-MRT). When the DFT-SM scheme alone is used, the data streams are either mapped onto different angles of departures in the case of aligned linear arrays, or mapped onto different orbital angular momentums in the case of aligned circular arrays. Maximum ratio transmission pre-equalizes the channel......By using large point-to-point multiple input multiple output (MIMO), spatial multiplexing of a large number of data streams in wireless communications using millimeter-waves (mm-waves) can be achieved. However, according to the antenna spacing and transmitter-receiver distance, the MIMO channel...... is likely to be ill-conditioned. In such conditions, highly complex schemes such as the singular value decomposition (SVD) are necessary. In this paper, we propose a new low complexity system called discrete Fourier transform based spatial multiplexing (DFT-SM) with maximum ratio transmission (DFT...
Computationally efficient near-field source localization using third-order moments
Chen, Jian; Liu, Guohong; Sun, Xiaoying
2014-12-01
In this paper, a third-order moment-based estimation of signal parameters via rotational invariance techniques (ESPRIT) algorithm is proposed for passive localization of near-field sources. By properly choosing sensor outputs of the symmetric uniform linear array, two special third-order moment matrices are constructed, in which the steering matrix is the function of electric angle γ, while the rotational factor is the function of electric angles γ and ϕ. With the singular value decomposition (SVD) operation, all direction-of-arrivals (DOAs) are estimated from a polynomial rooting version. After substituting the DOA information into the steering matrix, the rotational factor is determined via the total least squares (TLS) version, and the related range estimations are performed. Compared with the high-order ESPRIT method, the proposed algorithm requires a lower computational burden, and it avoids the parameter-match procedure. Computer simulations are carried out to demonstrate the performance of the proposed algorithm.
Sparse Bayesian Learning for DOA Estimation with Mutual Coupling
Directory of Open Access Journals (Sweden)
Jisheng Dai
2015-10-01
Full Text Available Sparse Bayesian learning (SBL has given renewed interest to the problem of direction-of-arrival (DOA estimation. It is generally assumed that the measurement matrix in SBL is precisely known. Unfortunately, this assumption may be invalid in practice due to the imperfect manifold caused by unknown or misspecified mutual coupling. This paper describes a modified SBL method for joint estimation of DOAs and mutual coupling coefficients with uniform linear arrays (ULAs. Unlike the existing method that only uses stationary priors, our new approach utilizes a hierarchical form of the Student t prior to enforce the sparsity of the unknown signal more heavily. We also provide a distinct Bayesian inference for the expectation-maximization (EM algorithm, which can update the mutual coupling coefficients more efficiently. Another difference is that our method uses an additional singular value decomposition (SVD to reduce the computational complexity of the signal reconstruction process and the sensitivity to the measurement noise.
Kong, Gang; Dai, Dao-Qing; Zou, Lu-Min
2008-07-01
In order to remove the artifacts of peripheral digital subtraction angiography (DSA), an affine transformation-based automatic image registration algorithm is introduced here. The whole process is described as follows: First, rectangle feature templates are constructed with their centers of the extracted Harris corners in the mask, and motion vectors of the central feature points are estimated using template matching technology with the similarity measure of maximum histogram energy. And then the optimal parameters of the affine transformation are calculated with the matrix singular value decomposition (SVD) method. Finally, bilinear intensity interpolation is taken to the mask according to the specific affine transformation. More than 30 peripheral DSA registrations are performed with the presented algorithm, and as the result, moving artifacts of the images are removed with sub-pixel precision, and the time consumption is less enough to satisfy the clinical requirements. Experimental results show the efficiency and robustness of the algorithm.
Feature selection for high-dimensional integrated data
Zheng, Charles
2012-04-26
Motivated by the problem of identifying correlations between genes or features of two related biological systems, we propose a model of feature selection in which only a subset of the predictors Xt are dependent on the multidimensional variate Y, and the remainder of the predictors constitute a “noise set” Xu independent of Y. Using Monte Carlo simulations, we investigated the relative performance of two methods: thresholding and singular-value decomposition, in combination with stochastic optimization to determine “empirical bounds” on the small-sample accuracy of an asymptotic approximation. We demonstrate utility of the thresholding and SVD feature selection methods to with respect to a recent infant intestinal gene expression and metagenomics dataset.
DEFF Research Database (Denmark)
Marstrand, J.R.; Rostrup, Egill; Garde, Ellen
2001-01-01
Changes in cerebral blood flow (CBF) induced by Acetazolamide (ACZ) were measured using dynamic susceptibility contrast MRI (DSC-MRI) with both spin echo (SE) EPI and gradient echo (GE) EPI, and related to changes in internal carotid artery (ICA) flow measured by phase-mapping. Also examined...... was the effect of repeated bolus injections. CBF, cerebral blood volume (CBV), and mean transit time (MTT) were calculated by singular value decomposition (SVD) and by deconvolution using an exponential function as kernel. The results showed no dependency on calculation method. GE-EPI measured a significant...... increase in CBF and CBV in response to ACZ, while SE-EPI measured a significant increase in CBV and MTT. CBV and MTT change measured by SE-EPI was sensitive to previous bolus injections. There was a significant linear relation between change in CBF measured by GE-EPI and change in ICA flow. In conclusion...
Fast determination of plasma parameters
International Nuclear Information System (INIS)
Wijnands, T.J.; Parlange, F.; Joffrin, E.
1995-01-01
Fast analysis of diagnostic signals of a tokamak discharge is demonstrated by using 4 fundamentally different techniques. A comparison between Function Parametrization (FP), Canonical Correlation Analysis (CCA) and a particular Neural Network (NN) configuration known as the Multi Layer Perceptron (MLP) is carried out, thereby taking a unique linear model based on a Singular Value Decomposition (SVD) as a reference. The various techniques provide all functional representations of characteristic plasma parameters in terms of the values of the measurements and are based on an analysis of a large, experimentally achieved database. A brief mathematical description of the various techniques is given, followed by two particular applications to Tore Supra diagnostic data. The first problem is concerned with the identification of the plasma boundary parameters using the poloidal field and differential poloidal flux measurements. A second application involves the interpretation of line integrated data from the multichannel interfero-polarimeter to obtain the central value of the safety factor. (author) 4 refs.; 3 figs
Predicting responses from Rasch measures.
Linacre, John M
2010-01-01
There is a growing family of Rasch models for polytomous observations. Selecting a suitable model for an existing dataset, estimating its parameters and evaluating its fit is now routine. Problems arise when the model parameters are to be estimated from the current data, but used to predict future data. In particular, ambiguities in the nature of the current data, or overfit of the model to the current dataset, may mean that better fit to the current data may lead to worse fit to future data. The predictive power of several Rasch and Rasch-related models are discussed in the context of the Netflix Prize. Rasch-related models are proposed based on Singular Value Decomposition (SVD) and Boltzmann Machines.
Using the modified sample entropy to detect determinism
Energy Technology Data Exchange (ETDEWEB)
Xie Hongbo, E-mail: xiehb@sjtu.or [Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hung Hom, Kowloon (Hong Kong); Department of Biomedical Engineering, Jiangsu University, Zhenjiang (China); Guo Jingyi [Department of Health Technology and Informatics, Hong Kong Polytechnic University, Hung Hom, Kowloon (Hong Kong); Zheng Yongping, E-mail: ypzheng@ieee.or [Department of Health Technology and Informatics, Hong Kong Polytechnic University, Hung Hom, Kowloon (Hong Kong); Reseach Institute of Innovative Products and Technologies, Hong Kong Polytechnic University (Hong Kong)
2010-08-23
A modified sample entropy (mSampEn), based on the nonlinear continuous and convex function, has been proposed and proven to be superior to the standard sample entropy (SampEn) in several aspects. In this Letter, we empirically investigate the ability of the mSampEn statistic combined with surrogate data method to detect determinism. The effects of the datasets length and noise on the proposed method to differentiate between deterministic and stochastic dynamics are tested on several benchmark time series. The noise performance of the mSampEn statistic is also compared with the singular value decomposition (SVD) and symplectic geometry spectrum (SGS) based methods. The results indicate that the mSampEn statistic is a robust index for detecting determinism in short and noisy time series.
Beginning of fish defrosting by using non-destructive ultrasonic technique
International Nuclear Information System (INIS)
Malainine, M; Faiz, B; Izbaim, D; Aboudaoud, I; Moudden, A; Maze, G
2012-01-01
During the experiments carried out on the monitoring and the study of fish defrosting by an ultrasonic technique, we have difficulties in detecting the beginning of the thawing which is an important criterion of fish quality control. To address this problem, we use the Singular Value Decomposition method (SVD) which is a mathematical tool that permits to separate the high and low energies of an histogram. The image representing low energy signals indicates the start of the thawing by showing an echo that was hidden in the original image for cod fish. Using transducers for central frequencies above 500 kHz the observed results are not very good. Therefore, this method is suitable for fish which fat content is medium or low.
The correction of linear lattice gradient errors using an AC dipole
Energy Technology Data Exchange (ETDEWEB)
Wang,G.; Bai, M.; Litvinenko, V.N.; Satogata, T.
2009-05-04
Precise measurement of optics from coherent betatron oscillations driven by ac dipoles have been demonstrated at RHIC and the Tevatron. For RHIC, the observed rms beta-beat is about 10%. Reduction of beta-beating is an essential component of performance optimization at high energy colliders. A scheme of optics correction was developed and tested in the RHIC 2008 run, using ac dipole optics for measurement and a few adjustable trim quadruples for correction. In this scheme, we first calculate the phase response matrix from the. measured phase advance, and then apply singular value decomposition (SVD) algorithm to the phase response matrix to find correction quadruple strengths. We present both simulation and some preliminary experimental results of this correction.
Skokos, C.; Bountis, T.; Antonopoulos, C.
2008-12-01
The recently introduced GALI method is used for rapidly detecting chaos, determining the dimensionality of regular motion and predicting slow diffusion in multi-dimensional Hamiltonian systems. We propose an efficient computation of the GALIk indices, which represent volume elements of k randomly chosen deviation vectors from a given orbit, based on the Singular Value Decomposition (SVD) algorithm. We obtain theoretically and verify numerically asymptotic estimates of GALIs long-time behavior in the case of regular orbits lying on low-dimensional tori. The GALIk indices are applied to rapidly detect chaotic oscillations, identify low-dimensional tori of Fermi-Pasta-Ulam (FPU) lattices at low energies and predict weak diffusion away from quasiperiodic motion, long before it is actually observed in the oscillations.
Wang, Wei; Chen, Xiyuan
2018-02-23
In view of the fact the accuracy of the third-degree Cubature Kalman Filter (CKF) used for initial alignment under large misalignment angle conditions is insufficient, an improved fifth-degree CKF algorithm is proposed in this paper. In order to make full use of the innovation on filtering, the innovation covariance matrix is calculated recursively by an innovative sequence with an exponent fading factor. Then a new adaptive error covariance matrix scaling algorithm is proposed. The Singular Value Decomposition (SVD) method is used for improving the numerical stability of the fifth-degree CKF in this paper. In order to avoid the overshoot caused by excessive scaling of error covariance matrix during the convergence stage, the scaling scheme is terminated when the gradient of azimuth reaches the maximum. The experimental results show that the improved algorithm has better alignment accuracy with large misalignment angles than the traditional algorithm.
Environmental Performance in Countries Worldwide: Determinant Factors and Multivariate Analysis
Directory of Open Access Journals (Sweden)
Isabel Gallego-Alvarez
2014-11-01
Full Text Available The aim of this study is to analyze the environmental performance of countries and the variables that can influence it. At the same time, we performed a multivariate analysis using the HJ-biplot, an exploratory method that looks for hidden patterns in the data, obtained from the usual singular value decomposition (SVD of the data matrix, to contextualize the countries grouped by geographical areas and the variables relating to environmental indicators included in the environmental performance index. The sample used comprises 149 countries of different geographic areas. The findings obtained from the empirical analysis emphasize that socioeconomic factors, such as economic wealth and education, as well as institutional factors represented by the style of public administration, in particular control of corruption, are determinant factors of environmental performance in the countries analyzed. In contrast, no effect on environmental performance was found for factors relating to the internal characteristics of a country or political factors.
Optimal PMU Placement with Uncertainty Using Pareto Method
Directory of Open Access Journals (Sweden)
A. Ketabi
2012-01-01
Full Text Available This paper proposes a method for optimal placement of Phasor Measurement Units (PMUs in state estimation considering uncertainty. State estimation has first been turned into an optimization exercise in which the objective function is selected to be the number of unobservable buses which is determined based on Singular Value Decomposition (SVD. For the normal condition, Differential Evolution (DE algorithm is used to find the optimal placement of PMUs. By considering uncertainty, a multiobjective optimization exercise is hence formulated. To achieve this, DE algorithm based on Pareto optimum method has been proposed here. The suggested strategy is applied on the IEEE 30-bus test system in several case studies to evaluate the optimal PMUs placement.
Eigenspace-based fuzzy c-means for sensing trending topics in Twitter
Muliawati, T.; Murfi, H.
2017-07-01
As the information and communication technology are developed, the fulfillment of information can be obtained through social media, like Twitter. The enormous number of internet users has triggered fast and large data flow, thus making the manual analysis is difficult or even impossible. An automated methods for data analysis is needed, one of which is the topic detection and tracking. An alternative method other than latent Dirichlet allocation (LDA) is a soft clustering approach using Fuzzy C-Means (FCM). FCM meets the assumption that a document may consist of several topics. However, FCM works well in low-dimensional data but fails in high-dimensional data. Therefore, we propose an approach where FCM works on low-dimensional data by reducing the data using singular value decomposition (SVD). Our simulations show that this approach gives better accuracies in term of topic recall than LDA for sensing trending topic in Twitter about an event.
Enhanced 2D-DOA Estimation for Large Spacing Three-Parallel Uniform Linear Arrays
Directory of Open Access Journals (Sweden)
Dong Zhang
2018-01-01
Full Text Available An enhanced two-dimensional direction of arrival (2D-DOA estimation algorithm for large spacing three-parallel uniform linear arrays (ULAs is proposed in this paper. Firstly, we use the propagator method (PM to get the highly accurate but ambiguous estimation of directional cosine. Then, we use the relationship between the directional cosine to eliminate the ambiguity. This algorithm not only can make use of the elements of the three-parallel ULAs but also can utilize the connection between directional cosine to improve the estimation accuracy. Besides, it has satisfied estimation performance when the elevation angle is between 70° and 90° and it can automatically pair the estimated azimuth and elevation angles. Furthermore, it has low complexity without using any eigen value decomposition (EVD or singular value decompostion (SVD to the covariance matrix. Simulation results demonstrate the effectiveness of our proposed algorithm.
A General Approach for Orthogonal 4-Tap Integer Multiwavelet Transforms
Directory of Open Access Journals (Sweden)
Mingli Jing
2010-01-01
Full Text Available An algorithm for orthogonal 4-tap integer multiwavelet transforms is proposed. We compute the singular value decomposition (SVD of block recursive matrices of transform matrix, and then transform matrix can be rewritten in a product of two block diagonal matrices and a permutation matrix. Furthermore, we factorize the block matrix of block diagonal matrices into triangular elementary reversible matrices (TERMs, which map integers to integers by rounding arithmetic. The cost of factorizing block matrix into TERMs does not increase with the increase of the dimension of transform matrix, and the proposed algorithm is in-place calculation and without allocating auxiliary memory. Examples of integer multiwavelet transform using DGHM and CL are given, which verify that the proposed algorithm is an executable algorithm and outperforms the existing algorithm for orthogonal 4-tap integer multiwavelet transform.
Radial Basis Function Networks for Conversion of Sound Spectra
Directory of Open Access Journals (Sweden)
Carlo Drioli
2001-03-01
Full Text Available In many advanced signal processing tasks, such as pitch shifting, voice conversion or sound synthesis, accurate spectral processing is required. Here, the use of Radial Basis Function Networks (RBFN is proposed for the modeling of the spectral changes (or conversions related to the control of important sound parameters, such as pitch or intensity. The identification of such conversion functions is based on a procedure which learns the shape of the conversion from few couples of target spectra from a data set. The generalization properties of RBFNs provides for interpolation with respect to the pitch range. In the construction of the training set, mel-cepstral encoding of the spectrum is used to catch the perceptually most relevant spectral changes. Moreover, a singular value decomposition (SVD approach is used to reduce the dimension of conversion functions. The RBFN conversion functions introduced are characterized by a perceptually-based fast training procedure, desirable interpolation properties and computational efficiency.
Phase Noise Effect on MIMO-OFDM Systems with Common and Independent Oscillators
Directory of Open Access Journals (Sweden)
Xiaoming Chen
2017-01-01
Full Text Available The effects of oscillator phase noises (PNs on multiple-input multiple-output (MIMO orthogonal frequency division multiplexing (OFDM systems are studied. It is shown that PNs of common oscillators at the transmitter and at the receiver have the same influence on the performance of (single-stream beamforming MIMO-OFDM systems, yet different influences on spatial multiplexing MIMO-OFDM systems with singular value decomposition (SVD based precoding/decoding. When each antenna is equipped with an independent oscillator, the PNs at the transmitter and at the receiver have different influences on beamforming MIMO-OFDM systems as well as spatial multiplexing MIMO-OFDM systems. Specifically, the PN effect on the transmitter (receiver can be alleviated by having more transmit (receive antennas for the case of independent oscillators. It is found that the independent oscillator case outperforms the common oscillator case in terms of error vector magnitude (EVM.
Adaptive PCA based fault diagnosis scheme in imperial smelting process.
Hu, Zhikun; Chen, Zhiwen; Gui, Weihua; Jiang, Bin
2014-09-01
In this paper, an adaptive fault detection scheme based on a recursive principal component analysis (PCA) is proposed to deal with the problem of false alarm due to normal process changes in real process. Our further study is also dedicated to develop a fault isolation approach based on Generalized Likelihood Ratio (GLR) test and Singular Value Decomposition (SVD) which is one of general techniques of PCA, on which the off-set and scaling fault can be easily isolated with explicit off-set fault direction and scaling fault classification. The identification of off-set and scaling fault is also applied. The complete scheme of PCA-based fault diagnosis procedure is proposed. The proposed scheme is first applied to Imperial Smelting Process, and the results show that the proposed strategies can be able to mitigate false alarms and isolate faults efficiently. Copyright © 2013 ISA. Published by Elsevier Ltd. All rights reserved.
LaRue, James P.; Luzanov, Yuriy
2013-05-01
A new extension to the way in which the Bidirectional Associative Memory (BAM) algorithms are implemented is presented here. We will show that by utilizing the singular value decomposition (SVD) and integrating principles of independent component analysis (ICA) into the nullspace (NS) we have created a novel approach to mitigating spurious attractors. We demonstrate this with two applications. The first application utilizes a one-layer association while the second application is modeled after the several hierarchal associations of ventral pathways. The first application will detail the way in which we manage the associations in terms of matrices. The second application will take what we have learned from the first example and apply it to a cascade of a convolutional neural network (CNN) and perceptron this being our signal processing model of the ventral pathways, i.e., visual systems.
Correction of failure in antenna array using matrix pencil technique
International Nuclear Information System (INIS)
Khan, SU; Rahim, MKA
2017-01-01
In this paper a non-iterative technique is developed for the correction of faulty antenna array based on matrix pencil technique (MPT). The failure of a sensor in antenna array can damage the radiation power pattern in terms of sidelobes level and nulls. In the developed technique, the radiation pattern of the array is sampled to form discrete power pattern information set. Then this information set can be arranged in the form of Hankel matrix (HM) and execute the singular value decomposition (SVD). By removing nonprincipal values, we obtain an optimum lower rank estimation of HM. This lower rank matrix corresponds to the corrected pattern. Then the proposed technique is employed to recover the weight excitation and position allocations from the estimated matrix. Numerical simulations confirm the efficiency of the proposed technique, which is compared with the available techniques in terms of sidelobes level and nulls. (paper)
Speech/Nonspeech Detection Using Minimal Walsh Basis Functions
Directory of Open Access Journals (Sweden)
Pwint Moe
2007-01-01
Full Text Available This paper presents a new method to detect speech/nonspeech components of a given noisy signal. Employing the combination of binary Walsh basis functions and an analysis-synthesis scheme, the original noisy speech signal is modified first. From the modified signals, the speech components are distinguished from the nonspeech components by using a simple decision scheme. Minimal number of Walsh basis functions to be applied is determined using singular value decomposition (SVD. The main advantages of the proposed method are low computational complexity, less parameters to be adjusted, and simple implementation. It is observed that the use of Walsh basis functions makes the proposed algorithm efficiently applicable in real-world situations where processing time is crucial. Simulation results indicate that the proposed algorithm achieves high-speech and nonspeech detection rates while maintaining a low error rate for different noisy conditions.
First Beta-Beating Measurement in the LHC
Aiba, M; Franchi, A; Giovannozzi, M; Kain, V; Morita, A; Tomás, R; Vanbavinckhove, G; Wenninger, J
2009-01-01
This note reports on the first LHC beta-beating and coupling measurements. Thanks to an excellent functioning of the BPM system and the related software, injection oscillations were recorded for the first 90 turns at all BPMs of Beam 2. Three different algorithms are used to measure the optics parameters from the BPM data. All algorithms show consistent measurements but feature different accuracy. The Singular Value Decomposition (SVD) approach shows a high resolution despite the limited number of turns. The vertical beta-beating is observed to be about a factor of two larger than in the horizontal plane. This asymetry is partly due to sextupoles misalignments but also suggests the possible existance of focusing errors at defocussing locations. Rather large coupling is observed since no skew quadrupole was excited at the time of the data acquisition. We also report a list of suspected malfunctioning BPMs identified through various analyses.
Fast approximate convex decomposition using relative concavity
Ghosh, Mukulika; Amato, Nancy M.; Lu, Yanyan; Lien, Jyh-Ming
2013-01-01
Approximate convex decomposition (ACD) is a technique that partitions an input object into approximately convex components. Decomposition into approximately convex pieces is both more efficient to compute than exact convex decomposition and can also generate a more manageable number of components. It can be used as a basis of divide-and-conquer algorithms for applications such as collision detection, skeleton extraction and mesh generation. In this paper, we propose a new method called Fast Approximate Convex Decomposition (FACD) that improves the quality of the decomposition and reduces the cost of computing it for both 2D and 3D models. In particular, we propose a new strategy for evaluating potential cuts that aims to reduce the relative concavity, rather than absolute concavity. As shown in our results, this leads to more natural and smaller decompositions that include components for small but important features such as toes or fingers while not decomposing larger components, such as the torso, that may have concavities due to surface texture. Second, instead of decomposing a component into two pieces at each step, as in the original ACD, we propose a new strategy that uses a dynamic programming approach to select a set of n c non-crossing (independent) cuts that can be simultaneously applied to decompose the component into n c+1 components. This reduces the depth of recursion and, together with a more efficient method for computing the concavity measure, leads to significant gains in efficiency. We provide comparative results for 2D and 3D models illustrating the improvements obtained by FACD over ACD and we compare with the segmentation methods in the Princeton Shape Benchmark by Chen et al. (2009) [31]. © 2012 Elsevier Ltd. All rights reserved.
Fast approximate convex decomposition using relative concavity
Ghosh, Mukulika
2013-02-01
Approximate convex decomposition (ACD) is a technique that partitions an input object into approximately convex components. Decomposition into approximately convex pieces is both more efficient to compute than exact convex decomposition and can also generate a more manageable number of components. It can be used as a basis of divide-and-conquer algorithms for applications such as collision detection, skeleton extraction and mesh generation. In this paper, we propose a new method called Fast Approximate Convex Decomposition (FACD) that improves the quality of the decomposition and reduces the cost of computing it for both 2D and 3D models. In particular, we propose a new strategy for evaluating potential cuts that aims to reduce the relative concavity, rather than absolute concavity. As shown in our results, this leads to more natural and smaller decompositions that include components for small but important features such as toes or fingers while not decomposing larger components, such as the torso, that may have concavities due to surface texture. Second, instead of decomposing a component into two pieces at each step, as in the original ACD, we propose a new strategy that uses a dynamic programming approach to select a set of n c non-crossing (independent) cuts that can be simultaneously applied to decompose the component into n c+1 components. This reduces the depth of recursion and, together with a more efficient method for computing the concavity measure, leads to significant gains in efficiency. We provide comparative results for 2D and 3D models illustrating the improvements obtained by FACD over ACD and we compare with the segmentation methods in the Princeton Shape Benchmark by Chen et al. (2009) [31]. © 2012 Elsevier Ltd. All rights reserved.
Denoising time-resolved microscopy image sequences with singular value thresholding
Energy Technology Data Exchange (ETDEWEB)
Furnival, Tom, E-mail: tjof2@cam.ac.uk; Leary, Rowan K., E-mail: rkl26@cam.ac.uk; Midgley, Paul A., E-mail: pam33@cam.ac.uk
2017-07-15
Time-resolved imaging in microscopy is important for the direct observation of a range of dynamic processes in both the physical and life sciences. However, the image sequences are often corrupted by noise, either as a result of high frame rates or a need to limit the radiation dose received by the sample. Here we exploit both spatial and temporal correlations using low-rank matrix recovery methods to denoise microscopy image sequences. We also make use of an unbiased risk estimator to address the issue of how much thresholding to apply in a robust and automated manner. The performance of the technique is demonstrated using simulated image sequences, as well as experimental scanning transmission electron microscopy data, where surface adatom motion and nanoparticle structural dynamics are recovered at rates of up to 32 frames per second. - Highlights: • Correlations in space and time are harnessed to denoise microscopy image sequences. • A robust estimator provides automated selection of the denoising parameter. • Motion tracking and automated noise estimation provides a versatile algorithm. • Application to time-resolved STEM enables study of atomic and nanoparticle dynamics.
Steinbuch, M.; Terlouw, J.C.; Bosgra, O.H.; Smit, S.G.
1992-01-01
The investigation of closed-loop systems subject to model perturbations is an important issue to assure stability robustness of a control design. A large variety of model perturbations can be described by norm-bounded uncertainty models. A general approach for modelling structured complex and
Separable decompositions of bipartite mixed states
Li, Jun-Li; Qiao, Cong-Feng
2018-04-01
We present a practical scheme for the decomposition of a bipartite mixed state into a sum of direct products of local density matrices, using the technique developed in Li and Qiao (Sci. Rep. 8:1442, 2018). In the scheme, the correlation matrix which characterizes the bipartite entanglement is first decomposed into two matrices composed of the Bloch vectors of local states. Then, we show that the symmetries of Bloch vectors are consistent with that of the correlation matrix, and the magnitudes of the local Bloch vectors are lower bounded by the correlation matrix. Concrete examples for the separable decompositions of bipartite mixed states are presented for illustration.
Vector domain decomposition schemes for parabolic equations
Vabishchevich, P. N.
2017-09-01
A new class of domain decomposition schemes for finding approximate solutions of timedependent problems for partial differential equations is proposed and studied. A boundary value problem for a second-order parabolic equation is used as a model problem. The general approach to the construction of domain decomposition schemes is based on partition of unity. Specifically, a vector problem is set up for solving problems in individual subdomains. Stability conditions for vector regionally additive schemes of first- and second-order accuracy are obtained.
Two Notes on Discrimination and Decomposition
DEFF Research Database (Denmark)
Nielsen, Helena Skyt
1998-01-01
1. It turns out that the Oaxaca-Blinder wage decomposition is inadequate when it comes to calculation of separate contributions for indicator variables. The contributions are not robust against a change of reference group. I extend the Oaxaca-Blinder decomposition to handle this problem. 2. The p....... The paper suggests how to use the logit model to decompose the gender difference in the probability of an occurrence. The technique is illustrated by an analysis of discrimination in child labor in rural Zambia....
Gamma ray induced decomposition of lanthanide nitrates
International Nuclear Information System (INIS)
Joshi, N.G.; Garg, A.N.
1992-01-01
Gamma ray induced decomposition of the lanthanide nitrates, Ln(NO 3 ) 3 .xH 2 O where Ln=La, Ce, Pr, Nd, Sm, Eu, Gd, Tb, Dy, Ho, Tm and Yb has been studied at different absorbed doses up to 600 kGy. G(NO 2 - ) values depend on the absorbed dose and the nature of the outer cation. It has been observed that those lanthanides which exhibit variable valency (Ce and Eu) show lower G-values. An attempt has been made to correlate thermal and radiolytic decomposition processes. (author). 20 refs., 3 figs., 1 tab
Excess Sodium Tetraphenylborate and Intermediates Decomposition Studies
Energy Technology Data Exchange (ETDEWEB)
Barnes, M.J.
1998-12-07
The stability of excess amounts of sodium tetraphenylborate (NaTPB) in the In-Tank Precipitation (ITP) facility depends on a number of variables. Concentration of palladium, initial benzene, and sodium ion as well as temperature provide the best opportunities for controlling the decomposition rate. This study examined the influence of these four variable on the reactivity of palladium-catalyzed sodium tetraphenylborate decomposition. Also, single effects tests investigated the reactivity of simulants with continuous stirring and nitrogen ventilation, with very high benzene concentrations, under washed sodium concentrations, with very high palladium concentrations, and with minimal quantities of excess NaTPB.
Multiresolution signal decomposition transforms, subbands, and wavelets
Akansu, Ali N; Haddad, Paul R
2001-01-01
The uniqueness of this book is that it covers such important aspects of modern signal processing as block transforms from subband filter banks and wavelet transforms from a common unifying standpoint, thus demonstrating the commonality among these decomposition techniques. In addition, it covers such ""hot"" areas as signal compression and coding, including particular decomposition techniques and tables listing coefficients of subband and wavelet filters and other important properties.The field of this book (Electrical Engineering/Computer Science) is currently booming, which is, of course
Basis of the biological decomposition of xenobiotica
International Nuclear Information System (INIS)
Mueller, R. von
1993-01-01
The ability of micro-organisms to decompose different molecules and to use them as a source of carbon, nitrogen, sulphur or energy is the basis for all biological processes for cleaning up contaminated soil. Therefore, the knowledge of these decomposition processes is an important precondition for judging which contamination can be treated biologically at all and which materials can be decomposed biologically. The decomposition schemes of the most important harmful material classes (aliphatic, aromatic and chlorinated hydrocarbons) are introduced and the consequences which arise for the practical application in biological cleaning up of contaminated soils are discussed. (orig.) [de
Eigenvalue Decomposition-Based Modified Newton Algorithm
Directory of Open Access Journals (Sweden)
Wen-jun Wang
2013-01-01
Full Text Available When the Hessian matrix is not positive, the Newton direction may not be the descending direction. A new method named eigenvalue decomposition-based modified Newton algorithm is presented, which first takes the eigenvalue decomposition of the Hessian matrix, then replaces the negative eigenvalues with their absolute values, and finally reconstructs the Hessian matrix and modifies the searching direction. The new searching direction is always the descending direction. The convergence of the algorithm is proven and the conclusion on convergence rate is presented qualitatively. Finally, a numerical experiment is given for comparing the convergence domains of the modified algorithm and the classical algorithm.
Regularised reconstruction of sound fields with a spherical microphone array
DEFF Research Database (Denmark)
Granados Corsellas, Alba; Jacobsen, Finn; Fernandez Grande, Efren
2013-01-01
implementation might lead to disastrous reconstructions. A large number of regularisation tools based on singular value decomposition are available, and it has been found that the acoustic holography problem for certain geometries can be formulated in such a way that similarities to singular value decomposition...... become apparent. Hence, a number of regularisation methods, including truncated singular value decomposition, standard Tikhonov, constrained Tikhonov, iterative Tikhonov, Landweber and Rutishauser, have been adapted for spherical near field acoustic holography. The accuracy of the methods is examined...
An investigation on thermal decomposition of DNTF-CMDB propellants
Energy Technology Data Exchange (ETDEWEB)
Zheng, Wei; Wang, Jiangning; Ren, Xiaoning; Zhang, Laying; Zhou, Yanshui [Xi' an Modern Chemistry Research Institute, Xi' an 710065 (China)
2007-12-15
The thermal decomposition of DNTF-CMDB propellants was investigated by pressure differential scanning calorimetry (PDSC) and thermogravimetry (TG). The results show that there is only one decomposition peak on DSC curves, because the decomposition peak of DNTF cannot be separated from that of the NC/NG binder. The decomposition of DNTF can be obviously accelerated by the decomposition products of the NC/NG binder. The kinetic parameters of thermal decompositions for four DNTF-CMDB propellants at 6 MPa were obtained by the Kissinger method. It is found that the reaction rate decreases with increasing content of DNTF. (Abstract Copyright [2007], Wiley Periodicals, Inc.)
Maadooliat, Mehdi; Gao, Xin; Huang, Jianhua Z.
2012-01-01
Despite considerable progress in the past decades, protein structure prediction remains one of the major unsolved problems in computational biology. Angular-sampling-based methods have been extensively studied recently due to their ability to capture the continuous conformational space of protein structures. The literature has focused on using a variety of parametric models of the sequential dependencies between angle pairs along the protein chains. In this article, we present a thorough review of angular-sampling-based methods by assessing three main questions: What is the best distribution type to model the protein angles? What is a reasonable number of components in a mixture model that should be considered to accurately parameterize the joint distribution of the angles? and What is the order of the local sequence-structure dependency that should be considered by a prediction method? We assess the model fits for different methods using bivariate lag-distributions of the dihedral/planar angles. Moreover, the main information across the lags can be extracted using a technique called Lag singular value decomposition (LagSVD), which considers the joint distribution of the dihedral/planar angles over different lags using a nonparametric approach and monitors the behavior of the lag-distribution of the angles using singular value decomposition. As a result, we developed graphical tools and numerical measurements to compare and evaluate the performance of different model fits. Furthermore, we developed a web-tool (http://www.stat.tamu. edu/~madoliat/LagSVD) that can be used to produce informative animations. © The Author 2012. Published by Oxford University Press.
Maadooliat, Mehdi
2012-08-27
Despite considerable progress in the past decades, protein structure prediction remains one of the major unsolved problems in computational biology. Angular-sampling-based methods have been extensively studied recently due to their ability to capture the continuous conformational space of protein structures. The literature has focused on using a variety of parametric models of the sequential dependencies between angle pairs along the protein chains. In this article, we present a thorough review of angular-sampling-based methods by assessing three main questions: What is the best distribution type to model the protein angles? What is a reasonable number of components in a mixture model that should be considered to accurately parameterize the joint distribution of the angles? and What is the order of the local sequence-structure dependency that should be considered by a prediction method? We assess the model fits for different methods using bivariate lag-distributions of the dihedral/planar angles. Moreover, the main information across the lags can be extracted using a technique called Lag singular value decomposition (LagSVD), which considers the joint distribution of the dihedral/planar angles over different lags using a nonparametric approach and monitors the behavior of the lag-distribution of the angles using singular value decomposition. As a result, we developed graphical tools and numerical measurements to compare and evaluate the performance of different model fits. Furthermore, we developed a web-tool (http://www.stat.tamu. edu/~madoliat/LagSVD) that can be used to produce informative animations. © The Author 2012. Published by Oxford University Press.
Decomposition of jellyfish carrion in situ
DEFF Research Database (Denmark)
Chelsky, Ariella; Pitt, Kylie A.; Ferguson, Angus J.P.
2016-01-01
Jellyfish often form blooms that persist for weeks to months before they collapse en masse, resulting in the sudden release of large amounts of organic matter to the environment. This study investigated the biogeochemical and ecological effects of the decomposition of jellyfish in a shallow coast...
Compactly supported frames for decomposition spaces
DEFF Research Database (Denmark)
Nielsen, Morten; Rasmussen, Kenneth Niemann
2012-01-01
In this article we study a construction of compactly supported frame expansions for decomposition spaces of Triebel-Lizorkin type and for the associated modulation spaces. This is done by showing that finite linear combinations of shifts and dilates of a single function with sufficient decay in b...
Thermal Decomposition of Aluminium Chloride Hexahydrate
Czech Academy of Sciences Publication Activity Database
Hartman, Miloslav; Trnka, Otakar; Šolcová, Olga
2005-01-01
Roč. 44, č. 17 (2005), s. 6591-6598 ISSN 0888-5885 R&D Projects: GA ČR(CZ) GA203/02/0002 Institutional research plan: CEZ:AV0Z40720504 Keywords : aluminum chloride hexahydrate * thermal decomposition * reaction kinetics Subject RIV: CI - Industrial Chemistry, Chemical Engineering Impact factor: 1.504, year: 2005
Preparation, Structure Characterization and Thermal Decomposition ...
African Journals Online (AJOL)
NJD
Decomposition Process of the Dysprosium(III) m-Methylbenzoate 1 ... A dinuclear complex [Dy(m-MBA)3phen]2·H2O was prepared by the reaction of DyCl3·6H2O, m-methylbenzoic acid and .... ing rate of 10 °C min–1 are illustrated in Fig. 4.
A decomposition of pairwise continuity via ideals
Directory of Open Access Journals (Sweden)
Mahes Wari
2016-02-01
Full Text Available In this paper, we introduce and study the notions of (i, j - regular - ℐ -closed sets, (i, j - Aℐ -sets, (i, j - ℐ -locally closed sets, p- Aℐ -continuous functions and p- ℐ -LC-continuous functions in ideal bitopological spaces and investigate some of their properties. Also, a new decomposition of pairwise continuity is obtained using these sets.
Nested grids ILU-decomposition (NGILU)
Ploeg, A. van der; Botta, E.F.F.; Wubs, F.W.
1996-01-01
A preconditioning technique is described which shows, in many cases, grid-independent convergence. This technique only requires an ordering of the unknowns based on the different levels of multigrid, and an incomplete LU-decomposition based on a drop tolerance. The method is demonstrated on a
A Martingale Decomposition of Discrete Markov Chains
DEFF Research Database (Denmark)
Hansen, Peter Reinhard
We consider a multivariate time series whose increments are given from a homogeneous Markov chain. We show that the martingale component of this process can be extracted by a filtering method and establish the corresponding martingale decomposition in closed-form. This representation is useful fo...
Triboluminescence and associated decomposition of solid methanol
International Nuclear Information System (INIS)
Trout, G.J.; Moore, D.E.; Hawke, J.G.
1975-01-01
The decomposition is initiated by the cooling of solid methanol through the β → α transiRon at 157.8K, producing the gases hydrogen, carbon monoxide, and methane. The passage through this lambda transition causes the breakup of large crystals of β-methanol into crystallites of α-methanol and is accompanied by light emission as well as decomposition. This triboluminescence is accompanied by, and apparently produced by, electrical discharges through methanol vapor in the vicinity of the solid. The potential differences needed to produce the electrical breakdown of the methanol vapor apparently arise from the disruption of the long hydrogen bonded chains of methanol molecules present in crystalline methanol. Charge separation following crystal deformation is a characteristic of substances which exhibit gas discharge triboluminescence; solid methanol has been found to emit such luminescence when mechanically deformed in the absence of the β → α transition The decomposition products are not produced directly by the breaking up of the solid methanol but from the vapor phase methanol by the electrical discharges. That gas phase decomposition does occur was confirmed by observing that the vapors of C 2 H 5 OH, CH 3 OD, and CD 3 OD decompose on being admitted to a vessel containing methanol undergoing the β → α phase transition. (U.S.)
On Orthogonal Decomposition of a Sobolev Space
Lakew, Dejenie A.
2016-01-01
The theme of this short article is to investigate an orthogonal decomposition of a Sobolev space and look at some properties of the inner product therein and the distance defined from the inner product. We also determine the dimension of the orthogonal difference space and show the expansion of spaces as their regularity increases.
TP89 - SIRZ Decomposition Spectral Estimation
Energy Technology Data Exchange (ETDEWEB)
Seetho, Isacc M. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Azevedo, Steve [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Smith, Jerel [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Brown, William D. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Martz, Jr., Harry E. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
2016-12-08
The primary objective of this test plan is to provide X-ray CT measurements of known materials for the purposes of generating and testing MicroCT and EDS spectral estimates. These estimates are to be used in subsequent Ze/RhoE decomposition analyses of acquired data.
Methodologies in forensic and decomposition microbiology
Culturable microorganisms represent only 0.1-1% of the total microbial diversity of the biosphere. This has severely restricted the ability of scientists to study the microbial biodiversity associated with the decomposition of ephemeral resources in the past. Innovations in technology are bringing...
Organic matter decomposition in simulated aquaculture ponds
Torres Beristain, B.
2005-01-01
Different kinds of organic and inorganic compounds (e.g. formulated food, manures, fertilizers) are added to aquaculture ponds to increase fish production. However, a large part of these inputs are not utilized by the fish and are decomposed inside the pond. The microbiological decomposition of the
Wood decomposition as influenced by invertebrates
Michael D. Ulyshen
2014-01-01
The diversity and habitat requirements of invertebrates associated with dead wood have been the subjects of hundreds of studies in recent years but we still know very little about the ecological or economic importance of these organisms. The purpose of this review is to examine whether, how and to what extent invertebrates affect wood decomposition in terrestrial...
Decomposition of variance for spatial Cox processes
DEFF Research Database (Denmark)
Jalilian, Abdollah; Guan, Yongtao; Waagepetersen, Rasmus
Spatial Cox point processes is a natural framework for quantifying the various sources of variation governing the spatial distribution of rain forest trees. We introduce a general criterion for variance decomposition for spatial Cox processes and apply it to specific Cox process models...
Decomposition of variance for spatial Cox processes
DEFF Research Database (Denmark)
Jalilian, Abdollah; Guan, Yongtao; Waagepetersen, Rasmus
2013-01-01
Spatial Cox point processes is a natural framework for quantifying the various sources of variation governing the spatial distribution of rain forest trees. We introduce a general criterion for variance decomposition for spatial Cox processes and apply it to specific Cox process models...
Decomposition of variance for spatial Cox processes
DEFF Research Database (Denmark)
Jalilian, Abdollah; Guan, Yongtao; Waagepetersen, Rasmus
Spatial Cox point processes is a natural framework for quantifying the various sources of variation governing the spatial distribution of rain forest trees. We introducea general criterion for variance decomposition for spatial Cox processes and apply it to specific Cox process models with additive...
Linear, Constant-rounds Bit-decomposition
DEFF Research Database (Denmark)
Reistad, Tord; Toft, Tomas
2010-01-01
When performing secure multiparty computation, tasks may often be simple or difficult depending on the representation chosen. Hence, being able to switch representation efficiently may allow more efficient protocols. We present a new protocol for bit-decomposition: converting a ring element x ∈ ℤ M...
Decomposition of oxalate precipitates by photochemical reaction
International Nuclear Information System (INIS)
Jae-Hyung Yoo; Eung-Ho Kim
1999-01-01
A photo-radiation method was applied to decompose oxalate precipitates so that it can be dissolved into dilute nitric acid. This work has been studied as a part of partitioning of minor actinides. Minor actinides can be recovered from high-level wastes as oxalate precipitates, but they tend to be coprecipitated together with lanthanide oxalates. This requires another partitioning step for mutual separation of actinide and lanthanide groups. In this study, therefore, some experimental work of photochemical decomposition of oxalate was carried out to prove its feasibility as a step of partitioning process. The decomposition of oxalic acid in the presence of nitric acid was performed in advance in order to understand the mechanistic behaviour of oxalate destruction, and then the decomposition of neodymium oxalate, which was chosen as a stand-in compound representing minor actinide and lanthanide oxalates, was examined. The decomposition rate of neodymium oxalate was found as 0.003 mole/hr at the conditions of 0.5 M HNO 3 and room temperature when a mercury lamp was used as a light source. (author)
Detailed Chemical Kinetic Modeling of Hydrazine Decomposition
Meagher, Nancy E.; Bates, Kami R.
2000-01-01
The purpose of this research project is to develop and validate a detailed chemical kinetic mechanism for gas-phase hydrazine decomposition. Hydrazine is used extensively in aerospace propulsion, and although liquid hydrazine is not considered detonable, many fuel handling systems create multiphase mixtures of fuels and fuel vapors during their operation. Therefore, a thorough knowledge of the decomposition chemistry of hydrazine under a variety of conditions can be of value in assessing potential operational hazards in hydrazine fuel systems. To gain such knowledge, a reasonable starting point is the development and validation of a detailed chemical kinetic mechanism for gas-phase hydrazine decomposition. A reasonably complete mechanism was published in 1996, however, many of the elementary steps included had outdated rate expressions and a thorough investigation of the behavior of the mechanism under a variety of conditions was not presented. The current work has included substantial revision of the previously published mechanism, along with a more extensive examination of the decomposition behavior of hydrazine. An attempt to validate the mechanism against the limited experimental data available has been made and was moderately successful. Further computational and experimental research into the chemistry of this fuel needs to be completed.
Decomposition approaches to integration without a measure
Czech Academy of Sciences Publication Activity Database
Greco, S.; Mesiar, Radko; Rindone, F.; Sipeky, L.
2016-01-01
Roč. 287, č. 1 (2016), s. 37-47 ISSN 0165-0114 Institutional support: RVO:67985556 Keywords : Choquet integral * Decision making * Decomposition integral Subject RIV: BA - General Mathematics Impact factor: 2.718, year: 2016 http://library.utia.cas.cz/separaty/2016/E/mesiar-0457408.pdf
Radiolytic decomposition of dioxins in liquid wastes
International Nuclear Information System (INIS)
Zhao Changli; Taguchi, M.; Hirota, K.; Takigami, M.; Kojima, T.
2006-01-01
The dioxins including polychlorinated dibenzo-p-dioxins (PCDDs) and polychlorinated dibenzofurans (PCDFs) are some of the most toxic persistent organic pollutants. These chemicals have widely contaminated the air, water, and soil. They would accumulate in the living body through the food chains, leading to a serious public health hazard. In the present study, radiolytic decomposition of dioxins has been investigated in liquid wastes, including organic waste and waste-water. Dioxin-containing organic wastes are commonly generated in nonane or toluene. However, it was found that high radiation doses are required to completely decompose dioxins in the two solvents. The decomposition was more efficient in ethanol than in nonane or toluene. The addition of ethanol to toluene or nonane could achieve >90% decomposition of dioxins at the dose of 100 kGy. Thus, dioxin-containing organic wastes can be treated as regular organic wastes after addition of ethanol and subsequent γ-ray irradiation. On the other hand, radiolytic decomposition of dioxins easily occurred in pure-water than in waste-water, because the reaction species is largely scavenged by the dominant organic materials in waste-water. Dechlorination was not a major reaction pathway for the radiolysis of dioxin in water. In addition, radiolytic mechanism and dechlorinated pathways in liquid wastes were also discussed. (authors)
Strongly \\'etale difference algebras and Babbitt's decomposition
Tomašić, Ivan; Wibmer, Michael
2015-01-01
We introduce a class of strongly \\'{e}tale difference algebras, whose role in the study of difference equations is analogous to the role of \\'{e}tale algebras in the study of algebraic equations. We deduce an improved version of Babbitt's decomposition theorem and we present applications to difference algebraic groups and the compatibility problem.
Thermal decomposition of barium valerate in argon
DEFF Research Database (Denmark)
Torres, P.; Norby, Poul; Grivel, Jean-Claude
2015-01-01
The thermal decomposition of barium valerate (Ba(C4H9CO2)(2)/Ba-pentanoate) was studied in argon by means of thermogravimetry, differential thermal analysis, IR-spectroscopy, X-ray diffraction and hot-stage optical microscopy. Melting takes place in two different steps, at 200 degrees C and 280...
A framework for bootstrapping morphological decomposition
CSIR Research Space (South Africa)
Joubert, LJ
2004-11-01
Full Text Available The need for a bootstrapping approach to the morphological decomposition of words in agglutinative languages such as isiZulu is motivated, and the complexities of such an approach are described. The authors then introduce a generic framework which...
Direct observation of nanowire growth and decomposition
DEFF Research Database (Denmark)
Rackauskas, Simas; Shandakov, Sergey D; Jiang, Hua
2017-01-01
knowledge, so far this has been only postulated, but never observed at the atomic level. By means of in situ environmental transmission electron microscopy we monitored and examined the atomic layer transformation at the conditions of the crystal growth and its decomposition using CuO nanowires selected...
Nash-Williams’ cycle-decomposition theorem
DEFF Research Database (Denmark)
Thomassen, Carsten
2016-01-01
We give an elementary proof of the theorem of Nash-Williams that a graph has an edge-decomposition into cycles if and only if it does not contain an odd cut. We also prove that every bridgeless graph has a collection of cycles covering each edge at least once and at most 7 times. The two results...
Distributed Model Predictive Control via Dual Decomposition
DEFF Research Database (Denmark)
Biegel, Benjamin; Stoustrup, Jakob; Andersen, Palle
2014-01-01
This chapter presents dual decomposition as a means to coordinate a number of subsystems coupled by state and input constraints. Each subsystem is equipped with a local model predictive controller while a centralized entity manages the subsystems via prices associated with the coupling constraints...
Wood decomposition as influenced by invertebrates.
Ulyshen, Michael D
2016-02-01
The diversity and habitat requirements of invertebrates associated with dead wood have been the subjects of hundreds of studies in recent years but we still know very little about the ecological or economic importance of these organisms. The purpose of this review is to examine whether, how and to what extent invertebrates affect wood decomposition in terrestrial ecosystems. Three broad conclusions can be reached from the available literature. First, wood decomposition is largely driven by microbial activity but invertebrates also play a significant role in both temperate and tropical environments. Primary mechanisms include enzymatic digestion (involving both endogenous enzymes and those produced by endo- and ectosymbionts), substrate alteration (tunnelling and fragmentation), biotic interactions and nitrogen fertilization (i.e. promoting nitrogen fixation by endosymbiotic and free-living bacteria). Second, the effects of individual invertebrate taxa or functional groups can be accelerative or inhibitory but the cumulative effect of the entire community is generally to accelerate wood decomposition, at least during the early stages of the process (most studies are limited to the first 2-3 years). Although methodological differences and design limitations preclude meta-analysis, studies aimed at quantifying the contributions of invertebrates to wood decomposition commonly attribute 10-20% of wood loss to these organisms. Finally, some taxa appear to be particularly influential with respect to promoting wood decomposition. These include large wood-boring beetles (Coleoptera) and termites (Termitoidae), especially fungus-farming macrotermitines. The presence or absence of these species may be more consequential than species richness and the influence of invertebrates is likely to vary biogeographically. Published 2014. This article is a U.S. Government work and is in the public domain in the USA.
The Slice Algorithm For Irreducible Decomposition of Monomial Ideals
DEFF Research Database (Denmark)
Roune, Bjarke Hammersholt
2009-01-01
Irreducible decomposition of monomial ideals has an increasing number of applications from biology to pure math. This paper presents the Slice Algorithm for computing irreducible decompositions, Alexander duals and socles of monomial ideals. The paper includes experiments showing good performance...
Thermal decomposition of γ-irradiated lead nitrate
International Nuclear Information System (INIS)
Nair, S.M.K.; Kumar, T.S.S.
1990-01-01
The thermal decomposition of unirradiated and γ-irradiated lead nitrate was studied by the gas evolution method. The decomposition proceeds through initial gas evolution, a short induction period, an acceleratory stage and a decay stage. The acceleratory and decay stages follow the Avrami-Erofeev equation. Irradiation enhances the decomposition but does not affect the shape of the decomposition curve. (author) 10 refs.; 7 figs.; 2 tabs
Laamiri, Imen; Khouaja, Anis; Messaoud, Hassani
2015-03-01
In this paper we provide a convergence analysis of the alternating RGLS (Recursive Generalized Least Square) algorithm used for the identification of the reduced complexity Volterra model describing stochastic non-linear systems. The reduced Volterra model used is the 3rd order SVD-PARAFC-Volterra model provided using the Singular Value Decomposition (SVD) and the Parallel Factor (PARAFAC) tensor decomposition of the quadratic and the cubic kernels respectively of the classical Volterra model. The Alternating RGLS (ARGLS) algorithm consists on the execution of the classical RGLS algorithm in alternating way. The ARGLS convergence was proved using the Ordinary Differential Equation (ODE) method. It is noted that the algorithm convergence canno׳t be ensured when the disturbance acting on the system to be identified has specific features. The ARGLS algorithm is tested in simulations on a numerical example by satisfying the determined convergence conditions. To raise the elegies of the proposed algorithm, we proceed to its comparison with the classical Alternating Recursive Least Squares (ARLS) presented in the literature. The comparison has been built on a non-linear satellite channel and a benchmark system CSTR (Continuous Stirred Tank Reactor). Moreover the efficiency of the proposed identification approach is proved on an experimental Communicating Two Tank system (CTTS). Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.
Implementation of domain decomposition and data decomposition algorithms in RMC code
International Nuclear Information System (INIS)
Liang, J.G.; Cai, Y.; Wang, K.; She, D.
2013-01-01
The applications of Monte Carlo method in reactor physics analysis is somewhat restricted due to the excessive memory demand in solving large-scale problems. Memory demand in MC simulation is analyzed firstly, it concerns geometry data, data of nuclear cross-sections, data of particles, and data of tallies. It appears that tally data is dominant in memory cost and should be focused on in solving the memory problem. Domain decomposition and tally data decomposition algorithms are separately designed and implemented in the reactor Monte Carlo code RMC. Basically, the domain decomposition algorithm is a strategy of 'divide and rule', which means problems are divided into different sub-domains to be dealt with separately and some rules are established to make sure the whole results are correct. Tally data decomposition consists in 2 parts: data partition and data communication. Two algorithms with differential communication synchronization mechanisms are proposed. Numerical tests have been executed to evaluate performance of the new algorithms. Domain decomposition algorithm shows potentials to speed up MC simulation as a space parallel method. As for tally data decomposition algorithms, memory size is greatly reduced
Decompositional equivalence: A fundamental symmetry underlying quantum theory
Fields, Chris
2014-01-01
Decompositional equivalence is the principle that there is no preferred decomposition of the universe into subsystems. It is shown here, by using simple thought experiments, that quantum theory follows from decompositional equivalence together with Landauer's principle. This demonstration raises within physics a question previously left to psychology: how do human - or any - observers agree about what constitutes a "system of interest"?
Climate fails to predict wood decomposition at regional scales
Mark A. Bradford; Robert J. Warren; Petr Baldrian; Thomas W. Crowther; Daniel S. Maynard; Emily E. Oldfield; William R. Wieder; Stephen A. Wood; Joshua R. King
2014-01-01
Decomposition of organic matter strongly influences ecosystem carbon storage1. In Earth-system models, climate is a predominant control on the decomposition rates of organic matter2, 3, 4, 5. This assumption is based on the mean response of decomposition to climate, yet there is a growing appreciation in other areas of global change science that projections based on...
In situ XAS of the solvothermal decomposition of dithiocarbamate complexes
Islam, H.-U.; Roffey, A.; Hollingsworth, N.; Catlow, R.; Wolthers, M.; de Leeuw, N.H.; Bras, W.; Sankar, G.; Hogarth, G.
2012-01-01
An in situ XAS study of the solvothermal decomposition of iron and nickel dithiocarbamate complexes was performed in order to gain understanding of the decomposition mechanisms. This work has given insight into the steps involved in the decomposition, showing variation in reaction pathways between
Advanced Oxidation: Oxalate Decomposition Testing With Ozone
International Nuclear Information System (INIS)
Ketusky, E.; Subramanian, K.
2012-01-01
At the Savannah River Site (SRS), oxalic acid is currently considered the preferred agent for chemically cleaning the large underground Liquid Radioactive Waste Tanks. It is applied only in the final stages of emptying a tank when generally less than 5,000 kg of waste solids remain, and slurrying based removal methods are no-longer effective. The use of oxalic acid is preferred because of its combined dissolution and chelating properties, as well as the fact that corrosion to the carbon steel tank walls can be controlled. Although oxalic acid is the preferred agent, there are significant potential downstream impacts. Impacts include: (1) Degraded evaporator operation; (2) Resultant oxalate precipitates taking away critically needed operating volume; and (3) Eventual creation of significant volumes of additional feed to salt processing. As an alternative to dealing with the downstream impacts, oxalate decomposition using variations of ozone based Advanced Oxidation Process (AOP) were investigated. In general AOPs use ozone or peroxide and a catalyst to create hydroxyl radicals. Hydroxyl radicals have among the highest oxidation potentials, and are commonly used to decompose organics. Although oxalate is considered among the most difficult organic to decompose, the ability of hydroxyl radicals to decompose oxalate is considered to be well demonstrated. In addition, as AOPs are considered to be 'green' their use enables any net chemical additions to the waste to be minimized. In order to test the ability to decompose the oxalate and determine the decomposition rates, a test rig was designed, where 10 vol% ozone would be educted into a spent oxalic acid decomposition loop, with the loop maintained at 70 C and recirculated at 40L/min. Each of the spent oxalic acid streams would be created from three oxalic acid strikes of an F-area simulant (i.e., Purex = high Fe/Al concentration) and H-area simulant (i.e., H area modified Purex = high Al/Fe concentration) after nearing
ADVANCED OXIDATION: OXALATE DECOMPOSITION TESTING WITH OZONE
Energy Technology Data Exchange (ETDEWEB)
Ketusky, E.; Subramanian, K.
2012-02-29
At the Savannah River Site (SRS), oxalic acid is currently considered the preferred agent for chemically cleaning the large underground Liquid Radioactive Waste Tanks. It is applied only in the final stages of emptying a tank when generally less than 5,000 kg of waste solids remain, and slurrying based removal methods are no-longer effective. The use of oxalic acid is preferred because of its combined dissolution and chelating properties, as well as the fact that corrosion to the carbon steel tank walls can be controlled. Although oxalic acid is the preferred agent, there are significant potential downstream impacts. Impacts include: (1) Degraded evaporator operation; (2) Resultant oxalate precipitates taking away critically needed operating volume; and (3) Eventual creation of significant volumes of additional feed to salt processing. As an alternative to dealing with the downstream impacts, oxalate decomposition using variations of ozone based Advanced Oxidation Process (AOP) were investigated. In general AOPs use ozone or peroxide and a catalyst to create hydroxyl radicals. Hydroxyl radicals have among the highest oxidation potentials, and are commonly used to decompose organics. Although oxalate is considered among the most difficult organic to decompose, the ability of hydroxyl radicals to decompose oxalate is considered to be well demonstrated. In addition, as AOPs are considered to be 'green' their use enables any net chemical additions to the waste to be minimized. In order to test the ability to decompose the oxalate and determine the decomposition rates, a test rig was designed, where 10 vol% ozone would be educted into a spent oxalic acid decomposition loop, with the loop maintained at 70 C and recirculated at 40L/min. Each of the spent oxalic acid streams would be created from three oxalic acid strikes of an F-area simulant (i.e., Purex = high Fe/Al concentration) and H-area simulant (i.e., H area modified Purex = high Al/Fe concentration
Directory of Open Access Journals (Sweden)
Vincenti Matthew P
2005-11-01
Full Text Available Abstract Background The responses to interleukin 1 (IL-1 in human chondrocytes constitute a complex regulatory mechanism, where multiple transcription factors interact combinatorially to transcription-factor binding motifs (TFBMs. In order to select a critical set of TFBMs from genomic DNA information and an array-derived data, an efficient algorithm to solve a combinatorial optimization problem is required. Although computational approaches based on evolutionary algorithms are commonly employed, an analytical algorithm would be useful to predict TFBMs at nearly no computational cost and evaluate varying modelling conditions. Singular value decomposition (SVD is a powerful method to derive primary components of a given matrix. Applying SVD to a promoter matrix defined from regulatory DNA sequences, we derived a novel method to predict the critical set of TFBMs. Results The promoter matrix was defined to establish a quantitative relationship between the IL-1-driven mRNA alteration and genomic DNA sequences of the IL-1 responsive genes. The matrix was decomposed with SVD, and the effects of 8 potential TFBMs (5'-CAGGC-3', 5'-CGCCC-3', 5'-CCGCC-3', 5'-ATGGG-3', 5'-GGGAA-3', 5'-CGTCC-3', 5'-AAAGG-3', and 5'-ACCCA-3' were predicted from a pool of 512 random DNA sequences. The prediction included matches to the core binding motifs of biologically known TFBMs such as AP2, SP1, EGR1, KROX, GC-BOX, ABI4, ETF, E2F, SRF, STAT, IK-1, PPARγ, STAF, ROAZ, and NFκB, and their significance was evaluated numerically using Monte Carlo simulation and genetic algorithm. Conclusion The described SVD-based prediction is an analytical method to provide a set of potential TFBMs involved in transcriptional regulation. The results would be useful to evaluate analytically a contribution of individual DNA sequences.
Identifying Talent in Youth Sport: A Novel Methodology Using Higher-Dimensional Analysis.
Directory of Open Access Journals (Sweden)
Kevin Till
Full Text Available Prediction of adult performance from early age talent identification in sport remains difficult. Talent identification research has generally been performed using univariate analysis, which ignores multivariate relationships. To address this issue, this study used a novel higher-dimensional model to orthogonalize multivariate anthropometric and fitness data from junior rugby league players, with the aim of differentiating future career attainment. Anthropometric and fitness data from 257 Under-15 rugby league players was collected. Players were grouped retrospectively according to their future career attainment (i.e., amateur, academy, professional. Players were blindly and randomly divided into an exploratory (n = 165 and validation dataset (n = 92. The exploratory dataset was used to develop and optimize a novel higher-dimensional model, which combined singular value decomposition (SVD with receiver operating characteristic analysis. Once optimized, the model was tested using the validation dataset. SVD analysis revealed 60 m sprint and agility 505 performance were the most influential characteristics in distinguishing future professional players from amateur and academy players. The exploratory dataset model was able to distinguish between future amateur and professional players with a high degree of accuracy (sensitivity = 85.7%, specificity = 71.1%; p<0.001, although it could not distinguish between future professional and academy players. The validation dataset model was able to distinguish future professionals from the rest with reasonable accuracy (sensitivity = 83.3%, specificity = 63.8%; p = 0.003. Through the use of SVD analysis it was possible to objectively identify criteria to distinguish future career attainment with a sensitivity over 80% using anthropometric and fitness data alone. As such, this suggests that SVD analysis may be a useful analysis tool for research and practice within talent identification.
Self-decomposition of radiochemicals. Principles, control, observations and effects
International Nuclear Information System (INIS)
Evans, E.A.
1976-01-01
The aim of the booklet is to remind the established user of radiochemicals of the problems of self-decomposition and to inform those investigators who are new to the applications of radiotracers. The section headings are: introduction; radionuclides; mechanisms of decomposition; effects of temperature; control of decomposition; observations of self-decomposition (sections for compounds labelled with (a) carbon-14, (b) tritium, (c) phosphorus-32, (d) sulphur-35, (e) gamma- or X-ray emitting radionuclides, decomposition of labelled macromolecules); effects of impurities in radiotracer investigations; stability of labelled compounds during radiotracer studies. (U.K.)
Pitfalls in VAR based return decompositions: A clarification
DEFF Research Database (Denmark)
Engsted, Tom; Pedersen, Thomas Quistgaard; Tanggaard, Carsten
in their analysis is not "cashflow news" but "inter- est rate news" which should not be zero. Consequently, in contrast to what Chen and Zhao claim, their decomposition does not serve as a valid caution against VAR based decompositions. Second, we point out that in order for VAR based decompositions to be valid......Based on Chen and Zhao's (2009) criticism of VAR based return de- compositions, we explain in detail the various limitations and pitfalls involved in such decompositions. First, we show that Chen and Zhao's interpretation of their excess bond return decomposition is wrong: the residual component...
hermal decomposition of irradiated casein molecules
International Nuclear Information System (INIS)
Ali, M.A.; Elsayed, A.A.
1998-01-01
NON-Isothermal studies were carried out using the derivatograph where thermogravimetry (TG) and differential thermogravimetry (DTG) measurements were used to obtain the activation energies of the first and second reactions for casein (glyco-phospho-protein) decomposition before and after exposure to 1 Gy γ-rays and up to 40 x 1 04 μg Gy fast neutrons. 25C f was used as a source of fast neutrons, associated with γ-rays. 137 Cs source was used as pure γ-source. The activation energies for the first and second reactions for casein decomposition were found to be smaller at 400 μGy than that at lower and higher fast neutron doses. However, no change in activation energies was observed after γ-irradiation. it is concluded from the present study that destruction of casein molecules by low level fast neutron doses may lead to changes of shelf storage period of milk
Investigation into kinetics of decomposition of nitrates
International Nuclear Information System (INIS)
Belov, B.A.; Gorozhankin, Eh.V.; Efremov, V.N.; Sal'nikova, N.S.; Suris, A.L.
1985-01-01
Using the method of thermogravimetry, the decomposition of nitrates, Cd(NO 3 ) 2 x4H 2 O, La(NO 3 ) 2 x6H 2 O, Sr(NO 3 ) 2 , ZrO(NO 3 ) 2 x2H 2 O, Y(NO 3 ) 3 x6H 2 O, in particular, is studied in the 20-1000 deg C range. It is shown, that gaseous pyrolysis, products, remaining in the material, hamper greatly the heat transfer required for the decomposition which reduces the reaction order. An effective activation energy of the process is in a satisfactory agreement with the characteristic temperature of the last endotherm. Kinetic parameters are calculated by the minimization method using a computer
Multiple Shooting and Time Domain Decomposition Methods
Geiger, Michael; Körkel, Stefan; Rannacher, Rolf
2015-01-01
This book offers a comprehensive collection of the most advanced numerical techniques for the efficient and effective solution of simulation and optimization problems governed by systems of time-dependent differential equations. The contributions present various approaches to time domain decomposition, focusing on multiple shooting and parareal algorithms. The range of topics covers theoretical analysis of the methods, as well as their algorithmic formulation and guidelines for practical implementation. Selected examples show that the discussed approaches are mandatory for the solution of challenging practical problems. The practicability and efficiency of the presented methods is illustrated by several case studies from fluid dynamics, data compression, image processing and computational biology, giving rise to possible new research topics. This volume, resulting from the workshop Multiple Shooting and Time Domain Decomposition Methods, held in Heidelberg in May 2013, will be of great interest to applied...
Mobility Modelling through Trajectory Decomposition and Prediction
Faghihi, Farbod
2017-01-01
The ubiquity of mobile devices with positioning sensors make it possible to derive user's location at any time. However, constantly sensing the position in order to track the user's movement is not feasible, either due to the unavailability of sensors, or computational and storage burdens. In this thesis, we present and evaluate a novel approach for efficiently tracking user's movement trajectories using decomposition and prediction of trajectories. We facilitate tracking by taking advantage ...
Thermal decomposition kinetics of ammonium uranyl carbonate
International Nuclear Information System (INIS)
Kim, E.H.; Park, J.J.; Park, J.H.; Chang, I.S.; Choi, C.S.; Kim, S.D.
1994-01-01
The thermal decomposition kinetics of AUC [ammonium uranyl carbonate; (NH 4 ) 4 UO 2 (CO 3 ) 3 [ in an isothermal thermogravimetric (TG) reactor under N 2 atmosphere has been determined. The kinetic data can be represented by the two-dimensional nucleation and growth model. The reaction rate increases and activation energy decreases with increasing particle size and precipitation time which appears in the particle size larger than 30 μm in the mechano-chemical phenomena. (orig.)
Radiation decomposition of technetium-99m radiopharmaceuticals
International Nuclear Information System (INIS)
Billinghurst, M.W.; Rempel, S.; Westendorf, B.A.
1979-01-01
Technetium-99m radiopharmaceuticals are shown to be subject to autoradiation-induced decomposition, which results in increasing abundance of pertechnetate in the preparation. This autodecomposition is catalyzed by the presence of oxygen, although the removal of oxygen does not prevent its occurrence. The initial appearance of pertechnetate in the radiopharmaceutical is shown to be a function of the amount of radioactivity, the quantity of stannous ion used, and the ratio of /sup 99m/Tc to total technetium in the preparation
Information decomposition method to analyze symbolical sequences
International Nuclear Information System (INIS)
Korotkov, E.V.; Korotkova, M.A.; Kudryashov, N.A.
2003-01-01
The information decomposition (ID) method to analyze symbolical sequences is presented. This method allows us to reveal a latent periodicity of any symbolical sequence. The ID method is shown to have advantages in comparison with application of the Fourier transformation, the wavelet transform and the dynamic programming method to look for latent periodicity. Examples of the latent periods for poetic texts, DNA sequences and amino acids are presented. Possible origin of a latent periodicity for different symbolical sequences is discussed
Domain decomposition methods for fluid dynamics
International Nuclear Information System (INIS)
Clerc, S.
1995-01-01
A domain decomposition method for steady-state, subsonic fluid dynamics calculations, is proposed. The method is derived from the Schwarz alternating method used for elliptic problems, extended to non-linear hyperbolic problems. Particular emphasis is given on the treatment of boundary conditions. Numerical results are shown for a realistic three-dimensional two-phase flow problem with the FLICA-4 code for PWR cores. (from author). 4 figs., 8 refs
Domain decomposition multigrid for unstructured grids
Energy Technology Data Exchange (ETDEWEB)
Shapira, Yair
1997-01-01
A two-level preconditioning method for the solution of elliptic boundary value problems using finite element schemes on possibly unstructured meshes is introduced. It is based on a domain decomposition and a Galerkin scheme for the coarse level vertex unknowns. For both the implementation and the analysis, it is not required that the curves of discontinuity in the coefficients of the PDE match the interfaces between subdomains. Generalizations to nonmatching or overlapping grids are made.
Decomposition of monolithic web application to microservices
Zaymus, Mikulas
2017-01-01
Solteq Oyj has an internal Wellbeing project for massage reservations. The task of this thesis was to transform the monolithic architecture of this application to microservices. The thesis starts with a detailed comparison between microservices and monolithic application. It points out the benefits and disadvantages microservice architecture can bring to the project. Next, it describes the theory and possible strategies that can be used in the process of decomposition of an existing monoli...
Numerical CP Decomposition of Some Difficult Tensors
Czech Academy of Sciences Publication Activity Database
Tichavský, Petr; Phan, A. H.; Cichocki, A.
2017-01-01
Roč. 317, č. 1 (2017), s. 362-370 ISSN 0377-0427 R&D Projects: GA ČR(CZ) GA14-13713S Institutional support: RVO:67985556 Keywords : Small matrix multiplication * Canonical polyadic tensor decomposition * Levenberg-Marquardt method Subject RIV: BB - Applied Statistics, Operational Research OBOR OECD: Applied mathematics Impact factor: 1.357, year: 2016 http://library.utia.cas.cz/separaty/2017/SI/tichavsky-0468385. pdf
Influence of Family Structure on Variance Decomposition
DEFF Research Database (Denmark)
Edwards, Stefan McKinnon; Sarup, Pernille Merete; Sørensen, Peter
Partitioning genetic variance by sets of randomly sampled genes for complex traits in D. melanogaster and B. taurus, has revealed that population structure can affect variance decomposition. In fruit flies, we found that a high likelihood ratio is correlated with a high proportion of explained ge...... capturing pure noise. Therefore it is necessary to use both criteria, high likelihood ratio in favor of a more complex genetic model and proportion of genetic variance explained, to identify biologically important gene groups...
Nonconformity problem in 3D Grid decomposition
Czech Academy of Sciences Publication Activity Database
Kolcun, Alexej
2002-01-01
Roč. 10, č. 1 (2002), s. 249-253 ISSN 1213-6972. [International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision 2002/10./. Plzeň, 04.02.2002-08.02.2002] R&D Projects: GA ČR GA105/99/1229; GA ČR GA105/01/1242 Institutional research plan: CEZ:AV0Z3086906 Keywords : structured mesh * decomposition * nonconformity Subject RIV: BA - General Mathematics
Domain decomposition methods for mortar finite elements
Energy Technology Data Exchange (ETDEWEB)
Widlund, O.
1996-12-31
In the last few years, domain decomposition methods, previously developed and tested for standard finite element methods and elliptic problems, have been extended and modified to work for mortar and other nonconforming finite element methods. A survey will be given of work carried out jointly with Yves Achdou, Mario Casarin, Maksymilian Dryja and Yvon Maday. Results on the p- and h-p-version finite elements will also be discussed.
Randomized interpolative decomposition of separated representations
Biagioni, David J.; Beylkin, Daniel; Beylkin, Gregory
2015-01-01
We introduce an algorithm to compute tensor interpolative decomposition (dubbed CTD-ID) for the reduction of the separation rank of Canonical Tensor Decompositions (CTDs). Tensor ID selects, for a user-defined accuracy ɛ, a near optimal subset of terms of a CTD to represent the remaining terms via a linear combination of the selected terms. CTD-ID can be used as an alternative to or in combination with the Alternating Least Squares (ALS) algorithm. We present examples of its use within a convergent iteration to compute inverse operators in high dimensions. We also briefly discuss the spectral norm as a computational alternative to the Frobenius norm in estimating approximation errors of tensor ID. We reduce the problem of finding tensor IDs to that of constructing interpolative decompositions of certain matrices. These matrices are generated via randomized projection of the terms of the given tensor. We provide cost estimates and several examples of the new approach to the reduction of separation rank.
Decomposition and reduction of AUC in hydrogen
International Nuclear Information System (INIS)
Ge Qingren; Kang Shifang; Zhou Meng
1987-01-01
AUC (Ammonium Uranyl Carbonate) conversion processes have been adopted extensively in nuclear fuel cycle. The kinetics investigation of these processes, however, has not yet been reported in detail at the published literatures. In the present work, the decomposition kinetics of AUC in hydrogen has been determined by non-isothermal method. DSC curves are solved with computer by Ge Qingren method. The results show that the kinetics obeys Avrami-Erofeev equation within 90% conversion. The apparent activation energy and preexponent are found to be 113.0 kJ/mol and 7.11 x 10 11 s -1 respectively. The reduction kinetics of AUC decomposition product in hydrogen at the range of 450 - 600 deg C has been determined by isothermal thermogravimetric method. The results show that good linear relationship can be obtained from the plot of conversion vs time, and that the apparent activation energy is found to be 113.9 kJ/mol. The effects of particle size and partial pressure of hydrogen are examined in reduction of AUC decomposition product. The reduction mechanism and the structure of particle are discussed according to the kinetics behaviour and SEM (scanning electron microscope) photograph
Decomposition of oxalate precipitates by photochemical reaction
International Nuclear Information System (INIS)
Yoo, J.H.; Kim, E.H.
1998-01-01
A photo-radiation method was applied to decompose oxalate precipitates so that it can be dissolved into dilute nitric acid. This work has been studied as a part of partitioning of minor actinides. Minor actinides can be recovered from high-level wastes as oxalate precipitates, but they tend to be coprecipitated together with lanthanide oxalates. This requires another partitioning step for mutual separation of actinide and lanthanide groups. In this study, therefore, the photochemical decomposition mechanism of oxalates in the presence of nitric acid was elucidated by experimental work. The decomposition of oxalates was proved to be dominated by the reaction with hydroxyl radical generated from the nitric acid, rather than with nitrite ion also formed from nitrate ion. The decomposition rate of neodymium oxalate, which was chosen as a stand-in compound representing minor actinide and lanthanide oxalates, was found to be 0.003 M/hr at the conditions of 0.5 M HNO 3 and room temperature when a mercury lamp was used as a light source. (author)
Tensor gauge condition and tensor field decomposition
Zhu, Ben-Chao; Chen, Xiang-Song
2015-10-01
We discuss various proposals of separating a tensor field into pure-gauge and gauge-invariant components. Such tensor field decomposition is intimately related to the effort of identifying the real gravitational degrees of freedom out of the metric tensor in Einstein’s general relativity. We show that as for a vector field, the tensor field decomposition has exact correspondence to and can be derived from the gauge-fixing approach. The complication for the tensor field, however, is that there are infinitely many complete gauge conditions in contrast to the uniqueness of Coulomb gauge for a vector field. The cause of such complication, as we reveal, is the emergence of a peculiar gauge-invariant pure-gauge construction for any gauge field of spin ≥ 2. We make an extensive exploration of the complete tensor gauge conditions and their corresponding tensor field decompositions, regarding mathematical structures, equations of motion for the fields and nonlinear properties. Apparently, no single choice is superior in all aspects, due to an awkward fact that no gauge-fixing can reduce a tensor field to be purely dynamical (i.e. transverse and traceless), as can the Coulomb gauge in a vector case.
Thermal decomposition and reaction of confined explosives
International Nuclear Information System (INIS)
Catalano, E.; McGuire, R.; Lee, E.; Wrenn, E.; Ornellas, D.; Walton, J.
1976-01-01
Some new experiments designed to accurately determine the time interval required to produce a reactive event in confined explosives subjected to temperatures which will cause decomposition are described. Geometry and boundary conditions were both well defined so that these experiments on the rapid thermal decomposition of HE are amenable to predictive modelling. Experiments have been carried out on TNT, TATB and on two plastic-bonded HMX-based high explosives, LX-04 and LX-10. When the results of these experiments are plotted as the logarithm of the time to explosion versus 1/T K (Arrhenius plot), the curves produced are remarkably linear. This is in contradiction to the results obtained by an iterative solution of the Laplace equation for a system with a first order rate heat source. Such calculations produce plots which display considerable curvature. The experiments have also shown that the time to explosion is strongly influenced by the void volume in the containment vessel. Results of the experiments with calculations based on the heat flow equations coupled with first-order models of chemical decomposition are compared. The comparisons demonstrate the need for a more realistic reaction model
Bregmanized Domain Decomposition for Image Restoration
Langer, Andreas
2012-05-22
Computational problems of large-scale data are gaining attention recently due to better hardware and hence, higher dimensionality of images and data sets acquired in applications. In the last couple of years non-smooth minimization problems such as total variation minimization became increasingly important for the solution of these tasks. While being favorable due to the improved enhancement of images compared to smooth imaging approaches, non-smooth minimization problems typically scale badly with the dimension of the data. Hence, for large imaging problems solved by total variation minimization domain decomposition algorithms have been proposed, aiming to split one large problem into N > 1 smaller problems which can be solved on parallel CPUs. The N subproblems constitute constrained minimization problems, where the constraint enforces the support of the minimizer to be the respective subdomain. In this paper we discuss a fast computational algorithm to solve domain decomposition for total variation minimization. In particular, we accelerate the computation of the subproblems by nested Bregman iterations. We propose a Bregmanized Operator Splitting-Split Bregman (BOS-SB) algorithm, which enforces the restriction onto the respective subdomain by a Bregman iteration that is subsequently solved by a Split Bregman strategy. The computational performance of this new approach is discussed for its application to image inpainting and image deblurring. It turns out that the proposed new solution technique is up to three times faster than the iterative algorithm currently used in domain decomposition methods for total variation minimization. © Springer Science+Business Media, LLC 2012.
Gas hydrates forming and decomposition conditions analysis
Directory of Open Access Journals (Sweden)
А. М. Павленко
2017-07-01
Full Text Available The concept of gas hydrates has been defined; their brief description has been given; factors that affect the formation and decomposition of the hydrates have been reported; their distribution, structure and thermodynamic conditions determining the gas hydrates formation disposition in gas pipelines have been considered. Advantages and disadvantages of the known methods for removing gas hydrate plugs in the pipeline have been analyzed, the necessity of their further studies has been proved. In addition to the negative impact on the process of gas extraction, the hydrates properties make it possible to outline the following possible fields of their industrial use: obtaining ultrahigh pressures in confined spaces at the hydrate decomposition; separating hydrocarbon mixtures by successive transfer of individual components through the hydrate given the mode; obtaining cold due to heat absorption at the hydrate decomposition; elimination of the open gas fountain by means of hydrate plugs in the bore hole of the gushing gasser; seawater desalination, based on the hydrate ability to only bind water molecules into the solid state; wastewater purification; gas storage in the hydrate state; dispersion of high temperature fog and clouds by means of hydrates; water-hydrates emulsion injection into the productive strata to raise the oil recovery factor; obtaining cold in the gas processing to cool the gas, etc.
Differential Decomposition Among Pig, Rabbit, and Human Remains.
Dautartas, Angela; Kenyhercz, Michael W; Vidoli, Giovanna M; Meadows Jantz, Lee; Mundorff, Amy; Steadman, Dawnie Wolfe
2018-03-30
While nonhuman animal remains are often utilized in forensic research to develop methods to estimate the postmortem interval, systematic studies that directly validate animals as proxies for human decomposition are lacking. The current project compared decomposition rates among pigs, rabbits, and humans at the University of Tennessee's Anthropology Research Facility across three seasonal trials that spanned nearly 2 years. The Total Body Score (TBS) method was applied to quantify decomposition changes and calculate the postmortem interval (PMI) in accumulated degree days (ADD). Decomposition trajectories were analyzed by comparing the estimated and actual ADD for each seasonal trial and by fuzzy cluster analysis. The cluster analysis demonstrated that the rabbits formed one group while pigs and humans, although more similar to each other than either to rabbits, still showed important differences in decomposition patterns. The decomposition trends show that neither nonhuman model captured the pattern, rate, and variability of human decomposition. © 2018 American Academy of Forensic Sciences.
Hu, Shujuan; Chou, Jifan; Cheng, Jianbo
2018-04-01
In order to study the interactions between the atmospheric circulations at the middle-high and low latitudes from the global perspective, the authors proposed the mathematical definition of three-pattern circulations, i.e., horizontal, meridional and zonal circulations with which the actual atmospheric circulation is expanded. This novel decomposition method is proved to accurately describe the actual atmospheric circulation dynamics. The authors used the NCEP/NCAR reanalysis data to calculate the climate characteristics of those three-pattern circulations, and found that the decomposition model agreed with the observed results. Further dynamical analysis indicates that the decomposition model is more accurate to capture the major features of global three dimensional atmospheric motions, compared to the traditional definitions of Rossby wave, Hadley circulation and Walker circulation. The decomposition model for the first time realized the decomposition of global atmospheric circulation using three orthogonal circulations within the horizontal, meridional and zonal planes, offering new opportunities to study the large-scale interactions between the middle-high latitudes and low latitudes circulations.
Energy Technology Data Exchange (ETDEWEB)
Usoltsev, Ilya; Eichler, Robert; Tuerler, Andreas [Paul Scherrer Institut (PSI), Villigen (Switzerland); Bern Univ. (Switzerland)
2016-11-01
The decomposition behavior of group 6 metal hexacarbonyl complexes (M(CO){sub 6}) in a tubular flow reactor is simulated. A microscopic Monte-Carlo based model is presented for assessing the first bond dissociation enthalpy of M(CO){sub 6} complexes. The suggested approach superimposes a microscopic model of gas adsorption chromatography with a first-order heterogeneous decomposition model. The experimental data on the decomposition of Mo(CO){sub 6} and W(CO){sub 6} are successfully simulated by introducing available thermodynamic data. Thermodynamic data predicted by relativistic density functional theory is used in our model to deduce the most probable experimental behavior of the corresponding Sg carbonyl complex. Thus, the design of a chemical experiment with Sg(CO){sub 6} is suggested, which is sensitive to benchmark our theoretical understanding of the bond stability in carbonyl compounds of the heaviest elements.
Ohmichi, Yuya
2017-07-01
In this letter, we propose a simple and efficient framework of dynamic mode decomposition (DMD) and mode selection for large datasets. The proposed framework explicitly introduces a preconditioning step using an incremental proper orthogonal decomposition (POD) to DMD and mode selection algorithms. By performing the preconditioning step, the DMD and mode selection can be performed with low memory consumption and therefore can be applied to large datasets. Additionally, we propose a simple mode selection algorithm based on a greedy method. The proposed framework is applied to the analysis of three-dimensional flow around a circular cylinder.
Complex mode indication function and its applications to spatial domain parameter estimation
Shih, C. Y.; Tsuei, Y. G.; Allemang, R. J.; Brown, D. L.
1988-10-01
This paper introduces the concept of the Complex Mode Indication Function (CMIF) and its application in spatial domain parameter estimation. The concept of CMIF is developed by performing singular value decomposition (SVD) of the Frequency Response Function (FRF) matrix at each spectral line. The CMIF is defined as the eigenvalues, which are the square of the singular values, solved from the normal matrix formed from the FRF matrix, [ H( jω)] H[ H( jω)], at each spectral line. The CMIF appears to be a simple and efficient method for identifying the modes of the complex system. The CMIF identifies modes by showing the physical magnitude of each mode and the damped natural frequency for each root. Since multiple reference data is applied in CMIF, repeated roots can be detected. The CMIF also gives global modal parameters, such as damped natural frequencies, mode shapes and modal participation vectors. Since CMIF works in the spatial domain, uneven frequency spacing data such as data from spatial sine testing can be used. A second-stage procedure for accurate damped natural frequency and damping estimation as well as mode shape scaling is also discussed in this paper.
Directory of Open Access Journals (Sweden)
Erna Apriliani
2011-01-01
Full Text Available Kalman filter is an algorithm to estimate the state variable of dynamical stochastic system. The square root ensemble Kalman filter is an modification of Kalman filter. The square root ensemble Kalman filter is proposed to keep the computational stability and reduce the computational time. In this paper we study the efficiency of the reduced rank ensemble Kalman filter. We apply this algorithm to the non isothermal continue stirred tank reactor problem. We decompose the covariance of the ensemble estimation by using the singular value decomposition (the SVD, and then we reduced the rank of the diagonal matrix of those singular values. We make a simulation by using Matlab program. We took some the number of ensemble such as 100, 200 and 500. We compared the computational time and the accuracy between the square root ensemble Kalman filter and the ensemble Kalman filter. The reduced rank ensemble Kalman filter can’t be applied in this problem because the dimension of state variable is too less.
Thermodynamic anomaly in magnesium hydroxide decomposition
International Nuclear Information System (INIS)
Reis, T.A.
1983-08-01
The Origin of the discrepancy in the equilibrium water vapor pressure measurements for the reaction Mg(OH) 2 (s) = MgO(s) + H 2 O(g) when determined by Knudsen effusion and static manometry at the same temperature was investigated. For this reaction undergoing continuous thermal decomposition in Knudsen cells, Kay and Gregory observed that by extrapolating the steady-state apparent equilibrium vapor pressure measurements to zero-orifice, the vapor pressure was approx. 10 -4 of that previously established by Giauque and Archibald as the true thermodynamic equilibrium vapor pressure using statistical mechanical entropy calculations for the entropy of water vapor. This large difference in vapor pressures suggests the possibility of the formation in a Knudsen cell of a higher energy MgO that is thermodynamically metastable by about 48 kJ / mole. It has been shown here that experimental results are qualitatively independent of the type of Mg(OH) 2 used as a starting material, which confirms the inferences of Kay and Gregory. Thus, most forms of Mg(OH) 2 are considered to be the stable thermodynamic equilibrium form. X-ray diffraction results show that during the course of the reaction only the equilibrium NaCl-type MgO is formed, and no different phases result from samples prepared in Knudsen cells. Surface area data indicate that the MgO molar surface area remains constant throughout the course of the reaction at low decomposition temperatures, and no significant annealing occurs at less than 400 0 C. Scanning electron microscope photographs show no change in particle size or particle surface morphology. Solution calorimetric measurements indicate no inherent hgher energy content in the MgO from the solid produced in Knudsen cells. The Knudsen cell vapor pressure discrepancy may reflect the formation of a transient metastable MgO or Mg(OH) 2 -MgO solid solution during continuous thermal decomposition in Knudsen cells
Efficient morse decompositions of vector fields.
Chen, Guoning; Mischaikow, Konstantin; Laramee, Robert S; Zhang, Eugene
2008-01-01
Existing topology-based vector field analysis techniques rely on the ability to extract the individual trajectories such as fixed points, periodic orbits, and separatrices that are sensitive to noise and errors introduced by simulation and interpolation. This can make such vector field analysis unsuitable for rigorous interpretations. We advocate the use of Morse decompositions, which are robust with respect to perturbations, to encode the topological structures of a vector field in the form of a directed graph, called a Morse connection graph (MCG). While an MCG exists for every vector field, it need not be unique. Previous techniques for computing MCG's, while fast, are overly conservative and usually results in MCG's that are too coarse to be useful for the applications. To address this issue, we present a new technique for performing Morse decomposition based on the concept of tau-maps, which typically provides finer MCG's than existing techniques. Furthermore, the choice of tau provides a natural tradeoff between the fineness of the MCG's and the computational costs. We provide efficient implementations of Morse decomposition based on tau-maps, which include the use of forward and backward mapping techniques and an adaptive approach in constructing better approximations of the images of the triangles in the meshes used for simulation.. Furthermore, we propose the use of spatial tau-maps in addition to the original temporal tau-maps. These techniques provide additional trade-offs between the quality of the MCGs and the speed of computation. We demonstrate the utility of our technique with various examples in the plane and on surfaces including engine simulation data sets.
Methyl Iodide Decomposition at BWR Conditions
International Nuclear Information System (INIS)
Pop, Mike; Bell, Merl
2012-09-01
Based on favourable results from short-term testing of methanol addition to an operating BWR plant, AREVA has performed numerous studies in support of necessary Engineering and Plant Safety Evaluations prior to extended injection of methanol. The current paper presents data from a study intended to provide further understanding of the decomposition of methyl iodide as it affects the assessment of methyl iodide formation with the application of methanol at BWR Plants. This paper describes the results of the decomposition testing under UV-C light at laboratory conditions and its effect on the subject methyl iodide production evaluation. The study as to the formation and decomposition of methyl iodide as it is effected by methanol addition is one phase of a larger AREVA effort to provide a generic plant Safety Evaluation prior to long-term methanol injection to an operating BWR. Other testing phases have investigated the compatibility of methanol with fuel construction materials, plant structural materials, plant consumable materials (i.e. elastomers and coatings), and ion exchange resins. Methyl iodide is known to be very unstable, typically preserved with copper metal or other stabilizing materials when produced and stored. It is even more unstable when exposed to light, heat, radiation, and water. Additionally, it is known that methyl iodide will decompose radiolytically, and that this effect may be simulated using ultra-violet radiation (UV-C) [2]. In the tests described in this paper, the use of a UV-C light source provides activation energy for the formation of methyl iodide. Thus is similar to the effect expected from Cherenkov radiation present in a reactor core after shutdown. Based on the testing described in this paper, it is concluded that injection of methanol at concentrations below 2.5 ppm in BWR applications to mitigate IGSCC of internals is inconsequential to the accident conditions postulated in the FSAR as they are related to methyl iodide formation
Task Decomposition Module For Telerobot Trajectory Generation
Wavering, Albert J.; Lumia, Ron
1988-10-01
A major consideration in the design of trajectory generation software for a Flight Telerobotic Servicer (FTS) is that the FTS will be called upon to perform tasks which require a diverse range of manipulator behaviors and capabilities. In a hierarchical control system where tasks are decomposed into simpler and simpler subtasks, the task decomposition module which performs trajectory planning and execution should therefore be able to accommodate a wide range of algorithms. In some cases, it will be desirable to plan a trajectory for an entire motion before manipulator motion commences, as when optimizing over the entire trajectory. Many FTS motions, however, will be highly sensory-interactive, such as moving to attain a desired position relative to a non-stationary object whose position is periodically updated by a vision system. In this case, the time-varying nature of the trajectory may be handled either by frequent replanning using updated sensor information, or by using an algorithm which creates a less specific state-dependent plan that determines the manipulator path as the trajectory is executed (rather than a priori). This paper discusses a number of trajectory generation techniques from these categories and how they may be implemented in a task decompo-sition module of a hierarchical control system. The structure, function, and interfaces of the proposed trajectory gener-ation module are briefly described, followed by several examples of how different algorithms may be performed by the module. The proposed task decomposition module provides a logical structure for trajectory planning and execution, and supports a large number of published trajectory generation techniques.
Decomposition of Variance for Spatial Cox Processes.
Jalilian, Abdollah; Guan, Yongtao; Waagepetersen, Rasmus
2013-03-01
Spatial Cox point processes is a natural framework for quantifying the various sources of variation governing the spatial distribution of rain forest trees. We introduce a general criterion for variance decomposition for spatial Cox processes and apply it to specific Cox process models with additive or log linear random intensity functions. We moreover consider a new and flexible class of pair correlation function models given in terms of normal variance mixture covariance functions. The proposed methodology is applied to point pattern data sets of locations of tropical rain forest trees.
Decomposition kinetics of aminoborane in aqueous solutions
International Nuclear Information System (INIS)
Shvets, I.B.; Erusalimchik, I.G.
1984-01-01
Kinetics of aminoborane hydrolysis has been studied using the method of polarization galvanostatical curves on a platinum electrode in buffer solutions at pH 3; 5; 7. The supposition that the reaction of aminoborane hydrolysis is the reaction of the first order by aminoborane is proved. The rate constant of aminoborane decomposition in the solution with pH 5 is equal to: K=2.5x10 -5 s -1 and with pH 3 it equals K=1.12x10 -4 s -1
Thermal decomposition of uranyl sulphate hydrate
International Nuclear Information System (INIS)
Sato, T.; Ozawa, F.; Ikoma, S.
1980-01-01
The thermal decomposition of uranyl sulphate hydrate (UO 2 SO 4 .3H 2 O) has been investigated by thermogravimetry, differential thermal analysis, X-ray diffraction and infrared spectrophotometry. As a result, it is concluded that uranyl sulphate hydrate decomposes thermally: UO 2 SO 4 .3H 2 O → UO 2 SO 4 .xH 2 O(2.5 = 2 SO 4 . 2H 2 O → UO 2 SO 4 .H 2 O → UO 2 SO 4 → α-UO 2 SO 4 → β-UO 2 SO 4 → U 3 O 8 . (author)
Cellulose decomposition in a 50 MVA transformer
International Nuclear Information System (INIS)
Piechalak, B.W.
1992-01-01
Dissolved gas-in-oil analysis for carbon monoxide and carbon dioxide has been used for years to predict cellulose decomposition in a transformer. However, the levels at which these gases become significant have not been widely agreed upon. This paper evaluates the gas analysis results from the nitrogen blanket and the oil of a 50 MVA unit auxiliary transformer in terms of whether accelerated thermal breakdown or normal aging of the paper is occurring. Furthermore, this paper presents additional data on carbon monoxide and carbon dioxide levels in unit and system auxiliary transformers at generating stations and explains why their levels differ
Observation of spinodal decomposition in nuclei?
International Nuclear Information System (INIS)
Guarnera, A.; Colonna, M.; Chomaz, Ph.
1996-01-01
In the framework of the recently developed stochastic one-body descriptions it has been shown that the occurrence of nuclear multifragmentation by spinodal decomposition is characterized by typical size and time scales; in particular, the formation of nearly equal mass fragments is expected around Z=10. A first preliminary comparison of our predictions with experimental data for the Xe + Cu at 45 MeV/A and for Xe + Sn at 50 MeV/A recently measured by the Indra collaboration is presented. The agreement of the results with the data seems finally to plead in favour of a possible occurrence of a first order phase transition. (K.A.)
Foreword - Acid decomposition of borosilicate ores
International Nuclear Information System (INIS)
Mirsaidov, U.M.; Kurbonov, A.S.; Mamatov, E.D.
2015-01-01
The elaboration and development of technology of processing of mineral raw materials have an important role for the industry of Tajikistan. The results of researches of the staff of Institute of Chemistry and Nuclear and Radiation Safety Agency of the Republic of Tajikistan were considered in present monograph. The physicochemical and technological aspects of processing of borosilicate raw materials of Ak-Arkhar Deposit of Tajikistan were considered. The necessary conditions of acid decomposition of raw materials were defined. The flowsheets for the processing of boron raw materials were proposed.
Multiresolution signal decomposition transforms, subbands, and wavelets
Akansu, Ali N
1992-01-01
This book provides an in-depth, integrated, and up-to-date exposition of the topic of signal decomposition techniques. Application areas of these techniques include speech and image processing, machine vision, information engineering, High-Definition Television, and telecommunications. The book will serve as the major reference for those entering the field, instructors teaching some or all of the topics in an advanced graduate course and researchers needing to consult an authoritative source.n The first book to give a unified and coherent exposition of multiresolutional signal decompos
Fringe pattern denoising via image decomposition.
Fu, Shujun; Zhang, Caiming
2012-02-01
Filtering off noise from a fringe pattern is one of the key tasks in optical interferometry. In this Letter, using some suitable function spaces to model different components of a fringe pattern, we propose a new fringe pattern denoising method based on image decomposition. In our method, a fringe image is divided into three parts: low-frequency fringe, high-frequency fringe, and noise, which are processed in different spaces. An adaptive threshold in wavelet shrinkage involved in this algorithm improves its denoising performance. Simulation and experimental results show that our algorithm obtains smooth and clean fringes with different frequencies while preserving fringe features effectively.
MADCam: The multispectral active decomposition camera
DEFF Research Database (Denmark)
Hilger, Klaus Baggesen; Stegmann, Mikkel Bille
2001-01-01
A real-time spectral decomposition of streaming three-band image data is obtained by applying linear transformations. The Principal Components (PC), the Maximum Autocorrelation Factors (MAF), and the Maximum Noise Fraction (MNF) transforms are applied. In the presented case study the PC transform...... that utilised information drawn from the temporal dimension instead of the traditional spatial approach. Using the CIF format (352x288) frame rates up to 30 Hz are obtained and in VGA mode (640x480) up to 15 Hz....
Memory effect and fast spinodal decomposition
International Nuclear Information System (INIS)
Koide, T.; Krein, G.; Ramos, Rudnei O.
2007-01-01
We consider the modification of the Cahn-Hilliard equation when a time delay process through a memory function is taken into account. We then study the process of spinodal decomposition in fast phase transitions associated with a conserved order parameter. The introduced memory effect plays an important role to obtain a finite group velocity. Then, we discuss the constraint for the parameters to satisfy causality. The memory effect is seen to affect the dynamics of phase transition at short times and have the effect of delaying, in a significant way, the process of rapid growth of the order parameter that follows a quench into the spinodal region. (author)
Partial Least Squares tutorial for analyzing neuroimaging data
Directory of Open Access Journals (Sweden)
Patricia Van Roon
2014-09-01
Full Text Available Partial least squares (PLS has become a respected and meaningful soft modeling analysis technique that can be applied to very large datasets where the number of factors or variables is greater than the number of observations. Current biometric studies (e.g., eye movements, EKG, body movements, EEG are often of this nature. PLS eliminates the multiple linear regression issues of over-fitting data by finding a few underlying or latent variables (factors that account for most of the variation in the data. In real-world applications, where linear models do not always apply, PLS can model the non-linear relationship well. This tutorial introduces two PLS methods, PLS Correlation (PLSC and PLS Regression (PLSR and their applications in data analysis which are illustrated with neuroimaging examples. Both methods provide straightforward and comprehensible techniques for determining and modeling relationships between two multivariate data blocks by finding latent variables that best describes the relationships. In the examples, the PLSC will analyze the relationship between neuroimaging data such as Event-Related Potential (ERP amplitude averages from different locations on the scalp with their corresponding behavioural data. Using the same data, the PLSR will be used to model the relationship between neuroimaging and behavioural data. This model will be able to predict future behaviour solely from available neuroimaging data. To find latent variables, Singular Value Decomposition (SVD for PLSC and Non-linear Iterative PArtial Least Squares (NIPALS for PLSR are implemented in this tutorial. SVD decomposes the large data block into three manageable matrices containing a diagonal set of singular values, as well as left and right singular vectors. For PLSR, NIPALS algorithms are used because it provides amore precise estimation of the latent variables. Mathematica notebooks are provided for each PLS method with clearly labeled sections and subsections. The
Beretta, Gian Paolo; Rivadossi, Luca; Janbozorgi, Mohammad
2018-04-01
Rate-Controlled Constrained-Equilibrium (RCCE) modeling of complex chemical kinetics provides acceptable accuracies with much fewer differential equations than for the fully Detailed Kinetic Model (DKM). Since its introduction by James C. Keck, a drawback of the RCCE scheme has been the absence of an automatable, systematic procedure to identify the constraints that most effectively warrant a desired level of approximation for a given range of initial, boundary, and thermodynamic conditions. An optimal constraint identification has been recently proposed. Given a DKM with S species, E elements, and R reactions, the procedure starts by running a probe DKM simulation to compute an S-vector that we call overall degree of disequilibrium (ODoD) because its scalar product with the S-vector formed by the stoichiometric coefficients of any reaction yields its degree of disequilibrium (DoD). The ODoD vector evolves in the same (S-E)-dimensional stoichiometric subspace spanned by the R stoichiometric S-vectors. Next we construct the rank-(S-E) matrix of ODoD traces obtained from the probe DKM numerical simulation and compute its singular value decomposition (SVD). By retaining only the first C largest singular values of the SVD and setting to zero all the others we obtain the best rank-C approximation of the matrix of ODoD traces whereby its columns span a C-dimensional subspace of the stoichiometric subspace. This in turn yields the best approximation of the evolution of the ODoD vector in terms of only C parameters that we call the constraint potentials. The resulting order-C RCCE approximate model reduces the number of independent differential equations related to species, mass, and energy balances from S+2 to C+E+2, with substantial computational savings when C ≪ S-E.
Thermoanalytical study of the decomposition of yttrium trifluoroacetate thin films
International Nuclear Information System (INIS)
Eloussifi, H.; Farjas, J.; Roura, P.; Ricart, S.; Puig, T.; Obradors, X.; Dammak, M.
2013-01-01
We present the use of the thermal analysis techniques to study yttrium trifluoroacetate thin films decomposition. In situ analysis was done by means of thermogravimetry, differential thermal analysis, and evolved gas analysis. Solid residues at different stages and the final product have been characterized by X-ray diffraction and scanning electron microscopy. The thermal decomposition of yttrium trifluoroacetate thin films results in the formation of yttria and presents the same succession of intermediates than powder's decomposition, however, yttria and all intermediates but YF 3 appear at significantly lower temperatures. We also observe a dependence on the water partial pressure that was not observed in the decomposition of yttrium trifluoroacetate powders. Finally, a dependence on the substrate chemical composition is discerned. - Highlights: • Thermal decomposition of yttrium trifluoroacetate films. • Very different behavior of films with respect to powders. • Decomposition is enhanced in films. • Application of thermal analysis to chemical solution deposition synthesis of films
Combinatorial geometry domain decomposition strategies for Monte Carlo simulations
Energy Technology Data Exchange (ETDEWEB)
Li, G.; Zhang, B.; Deng, L.; Mo, Z.; Liu, Z.; Shangguan, D.; Ma, Y.; Li, S.; Hu, Z. [Institute of Applied Physics and Computational Mathematics, Beijing, 100094 (China)
2013-07-01
Analysis and modeling of nuclear reactors can lead to memory overload for a single core processor when it comes to refined modeling. A method to solve this problem is called 'domain decomposition'. In the current work, domain decomposition algorithms for a combinatorial geometry Monte Carlo transport code are developed on the JCOGIN (J Combinatorial Geometry Monte Carlo transport INfrastructure). Tree-based decomposition and asynchronous communication of particle information between domains are described in the paper. Combination of domain decomposition and domain replication (particle parallelism) is demonstrated and compared with that of MERCURY code. A full-core reactor model is simulated to verify the domain decomposition algorithms using the Monte Carlo particle transport code JMCT (J Monte Carlo Transport Code), which has being developed on the JCOGIN infrastructure. Besides, influences of the domain decomposition algorithms to tally variances are discussed. (authors)
Combinatorial geometry domain decomposition strategies for Monte Carlo simulations
International Nuclear Information System (INIS)
Li, G.; Zhang, B.; Deng, L.; Mo, Z.; Liu, Z.; Shangguan, D.; Ma, Y.; Li, S.; Hu, Z.
2013-01-01
Analysis and modeling of nuclear reactors can lead to memory overload for a single core processor when it comes to refined modeling. A method to solve this problem is called 'domain decomposition'. In the current work, domain decomposition algorithms for a combinatorial geometry Monte Carlo transport code are developed on the JCOGIN (J Combinatorial Geometry Monte Carlo transport INfrastructure). Tree-based decomposition and asynchronous communication of particle information between domains are described in the paper. Combination of domain decomposition and domain replication (particle parallelism) is demonstrated and compared with that of MERCURY code. A full-core reactor model is simulated to verify the domain decomposition algorithms using the Monte Carlo particle transport code JMCT (J Monte Carlo Transport Code), which has being developed on the JCOGIN infrastructure. Besides, influences of the domain decomposition algorithms to tally variances are discussed. (authors)
Freeman-Durden Decomposition with Oriented Dihedral Scattering
Directory of Open Access Journals (Sweden)
Yan Jian
2014-10-01
Full Text Available In this paper, when the azimuth direction of polarimetric Synthetic Aperature Radars (SAR differs from the planting direction of crops, the double bounce of the incident electromagnetic waves from the terrain surface to the growing crops is investigated and compared with the normal double bounce. Oriented dihedral scattering model is developed to explain the investigated double bounce and is introduced into the Freeman-Durden decomposition. The decomposition algorithm corresponding to the improved decomposition is then proposed. The airborne polarimetric SAR data for agricultural land covering two flight tracks are chosen to validate the algorithm; the decomposition results show that for agricultural vegetated land, the improved Freeman-Durden decomposition has the advantage of increasing the decomposition coherency among the polarimetric SAR data along the different flight tracks.
A handbook of decomposition methods in analytical chemistry
International Nuclear Information System (INIS)
Bok, R.
1984-01-01
Decomposition methods of metals, alloys, fluxes, slags, calcine, inorganic salts, oxides, nitrides, carbides, borides, sulfides, ores, minerals, rocks, concentrates, glasses, ceramics, organic substances, polymers, phyto- and biological materials from the viewpoint of sample preparation for analysis have been described. The methods are systemitized according to decomposition principle: thermal with the use of electricity, irradiation, dissolution with participation of chemical reactions and without it. Special equipment for different decomposition methods is described. Bibliography contains 3420 references
Crop residue decomposition in Minnesota biochar amended plots
S. L. Weyers; K. A. Spokas
2014-01-01
Impacts of biochar application at laboratory scales are routinely studied, but impacts of biochar application on decomposition of crop residues at field scales have not been widely addressed. The priming or hindrance of crop residue decomposition could have a cascading impact on soil processes, particularly those influencing nutrient availability. Our objectives were to evaluate biochar effects on field decomposition of crop residue, using plots that were amended with ...
Universality of Schmidt decomposition and particle identity
Sciara, Stefania; Lo Franco, Rosario; Compagno, Giuseppe
2017-03-01
Schmidt decomposition is a widely employed tool of quantum theory which plays a key role for distinguishable particles in scenarios such as entanglement characterization, theory of measurement and state purification. Yet, its formulation for identical particles remains controversial, jeopardizing its application to analyze general many-body quantum systems. Here we prove, using a newly developed approach, a universal Schmidt decomposition which allows faithful quantification of the physical entanglement due to the identity of particles. We find that it is affected by single-particle measurement localization and state overlap. We study paradigmatic two-particle systems where identical qubits and qutrits are located in the same place or in separated places. For the case of two qutrits in the same place, we show that their entanglement behavior, whose physical interpretation is given, differs from that obtained before by different methods. Our results are generalizable to multiparticle systems and open the way for further developments in quantum information processing exploiting particle identity as a resource.
Excess Sodium Tetraphenylborate and Intermediates Decomposition Studies
International Nuclear Information System (INIS)
Barnes, M.J.; Peterson, R.A.
1998-04-01
The stability of excess amounts of sodium tetraphenylborate (NaTPB) in the In-Tank Precipitation (ITP) facility depends on a number of variables. Concentration of palladium, initial benzene, and sodium ion as well as temperature provide the best opportunities for controlling the decomposition rate. This study examined the influence of these four variables on the reactivity of palladium-catalyzed sodium tetraphenylborate decomposition. Also, single effects tests investigated the reactivity of simulants with continuous stirring and nitrogen ventilation, with very high benzene concentrations, under washed sodium concentrations, with very high palladium concentrations, and with minimal quantities of excess NaTPB. These tests showed the following.The testing demonstrates that current facility configuration does not provide assured safety of operations relative to the hazards of benzene (in particular to maintain the tank headspace below 60 percent of the lower flammability limit (lfl) for benzene generation rates of greater than 7 mg/(L.h)) from possible accelerated reaction of excess NaTPB. Current maximal operating temperatures of 40 degrees C and the lack of protection against palladium entering Tank 48H provide insufficient protection against the onset of the reaction. Similarly, control of the amount of excess NaTPB, purification of the organic, or limiting the benzene content of the slurry (via stirring) and ionic strength of the waste mixture prove inadequate to assure safe operation
Excess Sodium Tetraphenylborate and Intermediates Decomposition Studies
Energy Technology Data Exchange (ETDEWEB)
Barnes, M.J. [Westinghouse Savannah River Company, AIKEN, SC (United States); Peterson , R.A.
1998-04-01
The stability of excess amounts of sodium tetraphenylborate (NaTPB) in the In-Tank Precipitation (ITP) facility depends on a number of variables. Concentration of palladium, initial benzene, and sodium ion as well as temperature provide the best opportunities for controlling the decomposition rate. This study examined the influence of these four variables on the reactivity of palladium-catalyzed sodium tetraphenylborate decomposition. Also, single effects tests investigated the reactivity of simulants with continuous stirring and nitrogen ventilation, with very high benzene concentrations, under washed sodium concentrations, with very high palladium concentrations, and with minimal quantities of excess NaTPB. These tests showed the following.The testing demonstrates that current facility configuration does not provide assured safety of operations relative to the hazards of benzene (in particular to maintain the tank headspace below 60 percent of the lower flammability limit (lfl) for benzene generation rates of greater than 7 mg/(L.h)) from possible accelerated reaction of excess NaTPB. Current maximal operating temperatures of 40 degrees C and the lack of protection against palladium entering Tank 48H provide insufficient protection against the onset of the reaction. Similarly, control of the amount of excess NaTPB, purification of the organic, or limiting the benzene content of the slurry (via stirring) and ionic strength of the waste mixture prove inadequate to assure safe operation.
Comparing structural decomposition analysis and index
International Nuclear Information System (INIS)
Hoekstra, Rutger; Van den Bergh, Jeroen C.J.M.
2003-01-01
To analyze and understand historical changes in economic, environmental, employment or other socio-economic indicators, it is useful to assess the driving forces or determinants that underlie these changes. Two techniques for decomposing indicator changes at the sector level are structural decomposition analysis (SDA) and index decomposition analysis (IDA). For example, SDA and IDA have been used to analyze changes in indicators such as energy use, CO 2 -emissions, labor demand and value added. The changes in these variables are decomposed into determinants such as technological, demand, and structural effects. SDA uses information from input-output tables while IDA uses aggregate data at the sector-level. The two methods have developed quite independently, which has resulted in each method being characterized by specific, unique techniques and approaches. This paper has three aims. First, the similarities and differences between the two approaches are summarized. Second, the possibility of transferring specific techniques and indices is explored. Finally, a numerical example is used to illustrate differences between the two approaches
Global sensitivity analysis by polynomial dimensional decomposition
Energy Technology Data Exchange (ETDEWEB)
Rahman, Sharif, E-mail: rahman@engineering.uiowa.ed [College of Engineering, The University of Iowa, Iowa City, IA 52242 (United States)
2011-07-15
This paper presents a polynomial dimensional decomposition (PDD) method for global sensitivity analysis of stochastic systems subject to independent random input following arbitrary probability distributions. The method involves Fourier-polynomial expansions of lower-variate component functions of a stochastic response by measure-consistent orthonormal polynomial bases, analytical formulae for calculating the global sensitivity indices in terms of the expansion coefficients, and dimension-reduction integration for estimating the expansion coefficients. Due to identical dimensional structures of PDD and analysis-of-variance decomposition, the proposed method facilitates simple and direct calculation of the global sensitivity indices. Numerical results of the global sensitivity indices computed for smooth systems reveal significantly higher convergence rates of the PDD approximation than those from existing methods, including polynomial chaos expansion, random balance design, state-dependent parameter, improved Sobol's method, and sampling-based methods. However, for non-smooth functions, the convergence properties of the PDD solution deteriorate to a great extent, warranting further improvements. The computational complexity of the PDD method is polynomial, as opposed to exponential, thereby alleviating the curse of dimensionality to some extent.
Formation and decomposition of ammoniated ammonium ions
International Nuclear Information System (INIS)
Ikezoe, Yasumasa; Suzuki, Kazuya; Nakashima, Mikio; Yokoyama, Atsushi; Shiraishi, Hirotsugu; Ohno, Shin-ichi
1998-09-01
Structures, frequencies, and chemical reactions of ammoniated ammonium ions (NH 4 + .nNH 3 ) were investigated theoretically by ab initio molecular orbital calculations and experimentally by observing their formation and decomposition in a corona discharge-jet expansion process. The ab initio calculations were carried out using a Gaussian 94 program, which gave optimized structures, binding energies and harmonic vibrational frequencies of NH 4 + .nNH 3 . Effects of discharge current, the reactant gas and the diameter of the gas expanding pinhole were examined on the size n distribution of NH 4 + .nNH 3 . The results indicated that the cluster ion, in the jet expansion process, grew in size mostly equal to or less than one unit under experimental conditions employed. Effects of discharge current, pinhole diameter, flight time in vacuum and cluster size were examined on the decomposition rate of cluster ions formed. In our experimental conditions, the internal energies of cluster ions were mainly determined through exo- and/or endo-thermic reactions involved in the cluster formation process. (author)
Salient Object Detection via Structured Matrix Decomposition.
Peng, Houwen; Li, Bing; Ling, Haibin; Hu, Weiming; Xiong, Weihua; Maybank, Stephen J
2016-05-04
Low-rank recovery models have shown potential for salient object detection, where a matrix is decomposed into a low-rank matrix representing image background and a sparse matrix identifying salient objects. Two deficiencies, however, still exist. First, previous work typically assumes the elements in the sparse matrix are mutually independent, ignoring the spatial and pattern relations of image regions. Second, when the low-rank and sparse matrices are relatively coherent, e.g., when there are similarities between the salient objects and background or when the background is complicated, it is difficult for previous models to disentangle them. To address these problems, we propose a novel structured matrix decomposition model with two structural regularizations: (1) a tree-structured sparsity-inducing regularization that captures the image structure and enforces patches from the same object to have similar saliency values, and (2) a Laplacian regularization that enlarges the gaps between salient objects and the background in feature space. Furthermore, high-level priors are integrated to guide the matrix decomposition and boost the detection. We evaluate our model for salient object detection on five challenging datasets including single object, multiple objects and complex scene images, and show competitive results as compared with 24 state-of-the-art methods in terms of seven performance metrics.
Spectral decomposition of nonlinear systems with memory
Svenkeson, Adam; Glaz, Bryan; Stanton, Samuel; West, Bruce J.
2016-02-01
We present an alternative approach to the analysis of nonlinear systems with long-term memory that is based on the Koopman operator and a Lévy transformation in time. Memory effects are considered to be the result of interactions between a system and its surrounding environment. The analysis leads to the decomposition of a nonlinear system with memory into modes whose temporal behavior is anomalous and lacks a characteristic scale. On average, the time evolution of a mode follows a Mittag-Leffler function, and the system can be described using the fractional calculus. The general theory is demonstrated on the fractional linear harmonic oscillator and the fractional nonlinear logistic equation. When analyzing data from an ill-defined (black-box) system, the spectral decomposition in terms of Mittag-Leffler functions that we propose may uncover inherent memory effects through identification of a small set of dynamically relevant structures that would otherwise be obscured by conventional spectral methods. Consequently, the theoretical concepts we present may be useful for developing more general methods for numerical modeling that are able to determine whether observables of a dynamical system are better represented by memoryless operators, or operators with long-term memory in time, when model details are unknown.
DECOMPOSITION OF MANUFACTURING PROCESSES: A REVIEW
Directory of Open Access Journals (Sweden)
N.M.Z.N. Mohamed
2012-06-01
Full Text Available Manufacturing is a global activity that started during the industrial revolution in the late 19th century to cater for the large-scale production of products. Since then, manufacturing has changed tremendously through the innovations of technology, processes, materials, communication and transportation. The major challenge facing manufacturing is to produce more products using less material, less energy and less involvement of labour. To face these challenges, manufacturing companies must have a strategy and competitive priority in order for them to compete in a dynamic market. A review of the literature on the decomposition of manufacturing processes outlines three main processes, namely: high volume, medium volume and low volume. The decomposition shows that each sub process has its own characteristics and depends on the nature of the firm’s business. Two extreme processes are continuous line production (fast extreme and project shop (slow extreme. Other processes are in between these two extremes of the manufacturing spectrum. Process flow patterns become less complex with cellular, line and continuous flow compared with jobbing and project. The review also indicates that when the product is high variety and low volume, project or functional production is applied.
Finite Range Decomposition of Gaussian Processes
Brydges, C D; Mitter, P K
2003-01-01
Let $D$ be the finite difference Laplacian associated to the lattice $bZ^{d}$. For dimension $dge 3$, $age 0$ and $L$ a sufficiently large positive dyadic integer, we prove that the integral kernel of the resolvent $G^{a}:=(a-D)^{-1}$ can be decomposed as an infinite sum of positive semi-definite functions $ V_{n} $ of finite range, $ V_{n} (x-y) = 0$ for $|x-y|ge O(L)^{n}$. Equivalently, the Gaussian process on the lattice with covariance $G^{a}$ admits a decomposition into independent Gaussian processes with finite range covariances. For $a=0$, $ V_{n} $ has a limiting scaling form $L^{-n(d-2)}Gamma_{ c,ast }{bigl (frac{x-y}{ L^{n}}bigr )}$ as $nrightarrow infty$. As a corollary, such decompositions also exist for fractional powers $(-D)^{-alpha/2}$, $0
Primary decomposition of torsion R[X]-modules
Directory of Open Access Journals (Sweden)
William A. Adkins
1994-01-01
Full Text Available This paper is concerned with studying hereditary properties of primary decompositions of torsion R[X]-modules M which are torsion free as R-modules. Specifically, if an R[X]-submodule of M is pure as an R-submodule, then the primary decomposition of M determines a primary decomposition of the submodule. This is a generalization of the classical fact from linear algebra that a diagonalizable linear transformation on a vector space restricts to a diagonalizable linear transformation of any invariant subspace. Additionally, primary decompositions are considered under direct sums and tensor product.
Are litter decomposition and fire linked through plant species traits?
Cornelissen, Johannes H C; Grootemaat, Saskia; Verheijen, Lieneke M; Cornwell, William K; van Bodegom, Peter M; van der Wal, René; Aerts, Rien
2017-11-01
Contents 653 I. 654 II. 657 III. 659 IV. 661 V. 662 VI. 663 VII. 665 665 References 665 SUMMARY: Biological decomposition and wildfire are connected carbon release pathways for dead plant material: slower litter decomposition leads to fuel accumulation. Are decomposition and surface fires also connected through plant community composition, via the species' traits? Our central concept involves two axes of trait variation related to decomposition and fire. The 'plant economics spectrum' (PES) links biochemistry traits to the litter decomposability of different fine organs. The 'size and shape spectrum' (SSS) includes litter particle size and shape and their consequent effect on fuel bed structure, ventilation and flammability. Our literature synthesis revealed that PES-driven decomposability is largely decoupled from predominantly SSS-driven surface litter flammability across species; this finding needs empirical testing in various environmental settings. Under certain conditions, carbon release will be dominated by decomposition, while under other conditions litter fuel will accumulate and fire may dominate carbon release. Ecosystem-level feedbacks between decomposition and fire, for example via litter amounts, litter decomposition stage, community-level biotic interactions and altered environment, will influence the trait-driven effects on decomposition and fire. Yet, our conceptual framework, explicitly comparing the effects of two plant trait spectra on litter decomposition vs fire, provides a promising new research direction for better understanding and predicting Earth surface carbon dynamics. © 2017 The Authors. New Phytologist © 2017 New Phytologist Trust.